From 309c6229e830d2dee571fbe84ac5706290075fe1 Mon Sep 17 00:00:00 2001 From: fmind Date: Fri, 23 Feb 2024 20:52:36 +0000 Subject: [PATCH 01/11] Initial gh-pages commit From 0ee50f4a2cdc5eb93ed22596df27a8a3296ca9d8 Mon Sep 17 00:00:00 2001 From: fmind Date: Fri, 23 Feb 2024 20:52:36 +0000 Subject: [PATCH 02/11] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20fm?= =?UTF-8?q?ind/mlops-python-package@c5454ea67896cb777a63ff088aeccd166138db?= =?UTF-8?q?f2=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitkeep | 0 bikes.html | 249 ++++++++ bikes/configs.html | 495 +++++++++++++++ bikes/datasets.html | 805 ++++++++++++++++++++++++ bikes/jobs.html | 1308 ++++++++++++++++++++++++++++++++++++++ bikes/metrics.html | 664 ++++++++++++++++++++ bikes/models.html | 975 ++++++++++++++++++++++++++++ bikes/registers.html | 1281 +++++++++++++++++++++++++++++++++++++ bikes/schemas.html | 1432 ++++++++++++++++++++++++++++++++++++++++++ bikes/scripts.html | 433 +++++++++++++ bikes/searchers.html | 681 ++++++++++++++++++++ bikes/services.html | 953 ++++++++++++++++++++++++++++ bikes/splitters.html | 901 ++++++++++++++++++++++++++ index.html | 7 + search.js | 46 ++ 15 files changed, 10230 insertions(+) create mode 100644 .gitkeep create mode 100644 bikes.html create mode 100644 bikes/configs.html create mode 100644 bikes/datasets.html create mode 100644 bikes/jobs.html create mode 100644 bikes/metrics.html create mode 100644 bikes/models.html create mode 100644 bikes/registers.html create mode 100644 bikes/schemas.html create mode 100644 bikes/scripts.html create mode 100644 bikes/searchers.html create mode 100644 bikes/services.html create mode 100644 bikes/splitters.html create mode 100644 index.html create mode 100644 search.js diff --git a/.gitkeep b/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/bikes.html b/bikes.html new file mode 100644 index 0000000..6e42f45 --- /dev/null +++ b/bikes.html @@ -0,0 +1,249 @@ + + + + + + + bikes API documentation + + + + + + + + + +
+
+

+bikes

+ +

Predict the number of bikes available.

+
+ + + + + + 
1"""Predict the number of bikes available."""
+
+ + +
+
+ + \ No newline at end of file diff --git a/bikes/configs.html b/bikes/configs.html new file mode 100644 index 0000000..0c68d4d --- /dev/null +++ b/bikes/configs.html @@ -0,0 +1,495 @@ + + + + + + + bikes.configs API documentation + + + + + + + + + +
+
+

+bikes.configs

+ +

Parse, merge, and convert YAML configs.

+
+ + + + + + 
 1"""Parse, merge, and convert YAML configs."""
+ 2
+ 3# %% IMPORTS
+ 4
+ 5import typing as T
+ 6
+ 7from cloudpathlib import AnyPath
+ 8from omegaconf import DictConfig, ListConfig, OmegaConf
+ 9
+10# %% TYPES
+11
+12Config = ListConfig | DictConfig
+13
+14# %% PARSERS
+15
+16
+17def parse_file(path: str) -> Config:
+18    """Parse a config file from a path.
+19
+20    Args:
+21        path (str): local or remote path.
+22
+23    Returns:
+24        Config: representation of the config file.
+25    """
+26    any_path = AnyPath(path)
+27    # pylint: disable=no-member
+28    text = any_path.read_text()  # type: ignore
+29    config = OmegaConf.create(text)
+30    return config
+31
+32
+33def parse_string(string: str) -> Config:
+34    """Parse the given config string.
+35
+36    Args:
+37        string (str): configuration string.
+38
+39    Returns:
+40        Config: representation of the config string.
+41    """
+42    return OmegaConf.create(string)
+43
+44
+45# %% MERGERS
+46
+47
+48def merge_configs(configs: T.Sequence[Config]) -> Config:
+49    """Merge a list of config objects into one.
+50
+51    Args:
+52        configs (list[Config]): list of config objects.
+53
+54    Returns:
+55        Config: representation of the merged config objects.
+56    """
+57    return OmegaConf.merge(*configs)
+58
+59
+60# %% CONVERTERS
+61
+62
+63def to_object(config: Config) -> object:
+64    """Convert a config object to a python object.
+65
+66    Args:
+67        config (Config): representation of the config.
+68
+69    Returns:
+70        object: conversion of the config to a python object.
+71    """
+72    return OmegaConf.to_container(config, resolve=True)
+
+ + +
+
+ +
+ + def + parse_file( path: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig: + + + +
+ + 
18def parse_file(path: str) -> Config:
+19    """Parse a config file from a path.
+20
+21    Args:
+22        path (str): local or remote path.
+23
+24    Returns:
+25        Config: representation of the config file.
+26    """
+27    any_path = AnyPath(path)
+28    # pylint: disable=no-member
+29    text = any_path.read_text()  # type: ignore
+30    config = OmegaConf.create(text)
+31    return config
+
+ + +

Parse a config file from a path.

+ +
Arguments:
+ +
    +
  • path (str): local or remote path.
    • +
+ +
Returns:
+ +
+

Config: representation of the config file.

+
+
+ + +
+
+ +
+ + def + parse_string( string: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig: + + + +
+ + 
34def parse_string(string: str) -> Config:
+35    """Parse the given config string.
+36
+37    Args:
+38        string (str): configuration string.
+39
+40    Returns:
+41        Config: representation of the config string.
+42    """
+43    return OmegaConf.create(string)
+
+ + +

Parse the given config string.

+ +
Arguments:
+ +
    +
  • string (str): configuration string.
    • +
+ +
Returns:
+ +
+

Config: representation of the config string.

+
+
+ + +
+
+ +
+ + def + merge_configs( configs: Sequence[omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig]) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig: + + + +
+ + 
49def merge_configs(configs: T.Sequence[Config]) -> Config:
+50    """Merge a list of config objects into one.
+51
+52    Args:
+53        configs (list[Config]): list of config objects.
+54
+55    Returns:
+56        Config: representation of the merged config objects.
+57    """
+58    return OmegaConf.merge(*configs)
+
+ + +

Merge a list of config objects into one.

+ +
Arguments:
+ +
    +
  • configs (list[Config]): list of config objects.
    • +
+ +
Returns:
+ +
+

Config: representation of the merged config objects.

+
+
+ + +
+
+ +
+ + def + to_object( config: omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig) -> object: + + + +
+ + 
64def to_object(config: Config) -> object:
+65    """Convert a config object to a python object.
+66
+67    Args:
+68        config (Config): representation of the config.
+69
+70    Returns:
+71        object: conversion of the config to a python object.
+72    """
+73    return OmegaConf.to_container(config, resolve=True)
+
+ + +

Convert a config object to a python object.

+ +
Arguments:
+ +
    +
  • config (Config): representation of the config.
    • +
+ +
Returns:
+ +
+

object: conversion of the config to a python object.

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/bikes/datasets.html b/bikes/datasets.html new file mode 100644 index 0000000..db9a39b --- /dev/null +++ b/bikes/datasets.html @@ -0,0 +1,805 @@ + + + + + + + bikes.datasets API documentation + + + + + + + + + +
+
+

+bikes.datasets

+ +

Read/Write datasets from/to external sources/destinations.

+
+ + + + + + 
 1"""Read/Write datasets from/to external sources/destinations."""
+ 2
+ 3# %% IMPORTS
+ 4
+ 5import abc
+ 6import typing as T
+ 7
+ 8import pandas as pd
+ 9import pydantic as pdt
+10
+11# %% READERS
+12
+13
+14class Reader(abc.ABC, pdt.BaseModel, strict=True):
+15    """Base class for a dataset reader.
+16
+17    Use a reader to load a dataset in memory.
+18    e.g., to read file, database, cloud storage, ...
+19
+20    Attributes:
+21        limit (int, optional): maximum number of rows to read from dataset.
+22    """
+23
+24    KIND: str
+25
+26    limit: int | None = None
+27
+28    @abc.abstractmethod
+29    def read(self) -> pd.DataFrame:
+30        """Read a dataframe from a dataset.
+31
+32        Returns:
+33            pd.DataFrame: dataframe representation.
+34        """
+35
+36
+37class ParquetReader(Reader):
+38    """Read a dataframe from a parquet file.
+39
+40    Attributes:
+41        path (str): local or remote path to a dataset.
+42    """
+43
+44    KIND: T.Literal["ParquetReader"] = "ParquetReader"
+45
+46    path: str
+47
+48    @T.override
+49    def read(self) -> pd.DataFrame:
+50        data = pd.read_parquet(self.path)
+51        if self.limit is not None:
+52            data = data.head(self.limit)
+53        return data
+54
+55
+56ReaderKind = ParquetReader
+57
+58# %% WRITERS
+59
+60
+61class Writer(abc.ABC, pdt.BaseModel, strict=True):
+62    """Base class for a dataset writer.
+63
+64    Use a writer to save a dataset from memory.
+65    e.g., to write file, database, cloud storage, ...
+66    """
+67
+68    KIND: str
+69
+70    @abc.abstractmethod
+71    def write(self, data: pd.DataFrame) -> None:
+72        """Write a dataframe to a dataset.
+73
+74        Args:
+75            data (pd.DataFrame): dataframe representation.
+76        """
+77
+78
+79class ParquetWriter(Writer):
+80    """Writer a dataframe to a parquet file.
+81
+82    Attributes:
+83        path (str): local or remote file to a dataset.
+84    """
+85
+86    KIND: T.Literal["ParquetWriter"] = "ParquetWriter"
+87
+88    path: str
+89
+90    @T.override
+91    def write(self, data: pd.DataFrame) -> None:
+92        pd.DataFrame.to_parquet(data, self.path)
+93
+94
+95WriterKind = ParquetWriter
+
+ + +
+
+ +
+ + class + Reader(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
15class Reader(abc.ABC, pdt.BaseModel, strict=True):
+16    """Base class for a dataset reader.
+17
+18    Use a reader to load a dataset in memory.
+19    e.g., to read file, database, cloud storage, ...
+20
+21    Attributes:
+22        limit (int, optional): maximum number of rows to read from dataset.
+23    """
+24
+25    KIND: str
+26
+27    limit: int | None = None
+28
+29    @abc.abstractmethod
+30    def read(self) -> pd.DataFrame:
+31        """Read a dataframe from a dataset.
+32
+33        Returns:
+34            pd.DataFrame: dataframe representation.
+35        """
+
+ + +

Base class for a dataset reader.

+ +

Use a reader to load a dataset in memory. +e.g., to read file, database, cloud storage, ...

+ +
Attributes:
+ +
    +
  • limit (int, optional): maximum number of rows to read from dataset.
    • +
+
+ + +
+ +
+
@abc.abstractmethod
+ + def + read(self) -> pandas.core.frame.DataFrame: + + + +
+ + 
29    @abc.abstractmethod
+30    def read(self) -> pd.DataFrame:
+31        """Read a dataframe from a dataset.
+32
+33        Returns:
+34            pd.DataFrame: dataframe representation.
+35        """
+
+ + +

Read a dataframe from a dataset.

+ +
Returns:
+ +
+

pd.DataFrame: dataframe representation.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + ParquetReader(Reader): + + + +
+ + 
38class ParquetReader(Reader):
+39    """Read a dataframe from a parquet file.
+40
+41    Attributes:
+42        path (str): local or remote path to a dataset.
+43    """
+44
+45    KIND: T.Literal["ParquetReader"] = "ParquetReader"
+46
+47    path: str
+48
+49    @T.override
+50    def read(self) -> pd.DataFrame:
+51        data = pd.read_parquet(self.path)
+52        if self.limit is not None:
+53            data = data.head(self.limit)
+54        return data
+
+ + +

Read a dataframe from a parquet file.

+ +
Attributes:
+ +
    +
  • path (str): local or remote path to a dataset.
    • +
+
+ + +
+ +
+
@T.override
+ + def + read(self) -> pandas.core.frame.DataFrame: + + + +
+ + 
49    @T.override
+50    def read(self) -> pd.DataFrame:
+51        data = pd.read_parquet(self.path)
+52        if self.limit is not None:
+53            data = data.head(self.limit)
+54        return data
+
+ + +

Read a dataframe from a dataset.

+ +
Returns:
+ +
+

pd.DataFrame: dataframe representation.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + Writer(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
62class Writer(abc.ABC, pdt.BaseModel, strict=True):
+63    """Base class for a dataset writer.
+64
+65    Use a writer to save a dataset from memory.
+66    e.g., to write file, database, cloud storage, ...
+67    """
+68
+69    KIND: str
+70
+71    @abc.abstractmethod
+72    def write(self, data: pd.DataFrame) -> None:
+73        """Write a dataframe to a dataset.
+74
+75        Args:
+76            data (pd.DataFrame): dataframe representation.
+77        """
+
+ + +

Base class for a dataset writer.

+ +

Use a writer to save a dataset from memory. +e.g., to write file, database, cloud storage, ...

+
+ + +
+ +
+
@abc.abstractmethod
+ + def + write(self, data: pandas.core.frame.DataFrame) -> None: + + + +
+ + 
71    @abc.abstractmethod
+72    def write(self, data: pd.DataFrame) -> None:
+73        """Write a dataframe to a dataset.
+74
+75        Args:
+76            data (pd.DataFrame): dataframe representation.
+77        """
+
+ + +

Write a dataframe to a dataset.

+ +
Arguments:
+ +
    +
  • data (pd.DataFrame): dataframe representation.
    • +
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + ParquetWriter(Writer): + + + +
+ + 
80class ParquetWriter(Writer):
+81    """Writer a dataframe to a parquet file.
+82
+83    Attributes:
+84        path (str): local or remote file to a dataset.
+85    """
+86
+87    KIND: T.Literal["ParquetWriter"] = "ParquetWriter"
+88
+89    path: str
+90
+91    @T.override
+92    def write(self, data: pd.DataFrame) -> None:
+93        pd.DataFrame.to_parquet(data, self.path)
+
+ + +

Writer a dataframe to a parquet file.

+ +
Attributes:
+ +
    +
  • path (str): local or remote file to a dataset.
    • +
+
+ + +
+ +
+
@T.override
+ + def + write(self, data: pandas.core.frame.DataFrame) -> None: + + + +
+ + 
91    @T.override
+92    def write(self, data: pd.DataFrame) -> None:
+93        pd.DataFrame.to_parquet(data, self.path)
+
+ + +

Write a dataframe to a dataset.

+ +
Arguments:
+ +
    +
  • data (pd.DataFrame): dataframe representation.
    • +
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/bikes/jobs.html b/bikes/jobs.html new file mode 100644 index 0000000..7c7f864 --- /dev/null +++ b/bikes/jobs.html @@ -0,0 +1,1308 @@ + + + + + + + bikes.jobs API documentation + + + + + + + + + +
+
+

+bikes.jobs

+ +

High-level jobs for the project.

+
+ + + + + + 
  1"""High-level jobs for the project."""
+  2
+  3# %% IMPORTS
+  4
+  5import abc
+  6import typing as T
+  7
+  8import mlflow
+  9import pydantic as pdt
+ 10from loguru import logger
+ 11
+ 12from bikes import datasets, metrics, models, registers, schemas, searchers, services, splitters
+ 13
+ 14# %% TYPES
+ 15
+ 16# local job variables
+ 17Locals = dict[str, T.Any]
+ 18
+ 19# %% JOBS
+ 20
+ 21
+ 22class Job(abc.ABC, pdt.BaseModel, strict=True):
+ 23    """Base class for a job.
+ 24
+ 25    use a job to execute runs in  context.
+ 26    e.g., to define common services like logger
+ 27
+ 28    Attributes:
+ 29        logger_service (services.LoggerService): manage the logging system.
+ 30        mlflow_service (services.MLflowService): manage the mlflow system.
+ 31    """
+ 32
+ 33    KIND: str
+ 34
+ 35    logger_service: services.LoggerService = services.LoggerService()
+ 36    mlflow_service: services.MLflowService = services.MLflowService()
+ 37
+ 38    def __enter__(self) -> T.Self:
+ 39        """Enter the job context.
+ 40
+ 41        Returns:
+ 42            T.Self: return the current object.
+ 43        """
+ 44        self.logger_service.start()
+ 45        logger.debug("[START] MLflow service: {}", self.mlflow_service)
+ 46        self.mlflow_service.start()
+ 47        return self
+ 48
+ 49    def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]:
+ 50        """Exit the job context.
+ 51
+ 52        Args:
+ 53            exc_type: ignored.
+ 54            exc_value: ignored.
+ 55            traceback: ignored.
+ 56
+ 57        Returns:
+ 58            T.Literal[False]: always propagate exceptions.
+ 59        """
+ 60        logger.debug("[STOP] MLflow service: {}", self.mlflow_service)
+ 61        self.mlflow_service.stop()
+ 62        self.logger_service.stop()
+ 63        return False
+ 64
+ 65    @abc.abstractmethod
+ 66    def run(self) -> Locals:
+ 67        """Run the job in context.
+ 68
+ 69        Returns:
+ 70            Locals: local job variables.
+ 71        """
+ 72
+ 73
+ 74class TuningJob(Job):
+ 75    """Find the best hyperparameters for a model.
+ 76
+ 77    Attributes:
+ 78        run_name (str): name of the MLflow experiment run.
+ 79        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+ 80        targets (datasets.ReaderKind): dataset reader with targets variables.
+ 81        results (datasets.WriterKind): dataset writer for searcher results.
+ 82        model (models.ModelKind): machine learning model to tune.
+ 83        metric (metrics.MetricKind): main metric for evaluation.
+ 84        splitter (splitters.SplitterKind): splitter for datasets.
+ 85        searcher (searchers.SearcherKind): searcher algorithm.
+ 86    """
+ 87
+ 88    KIND: T.Literal["TuningJob"] = "TuningJob"
+ 89
+ 90    # run
+ 91    run_name: str = "Tuning"
+ 92    # read
+ 93    inputs: datasets.ReaderKind
+ 94    targets: datasets.ReaderKind
+ 95    # write
+ 96    results: datasets.WriterKind
+ 97    # model
+ 98    model: models.ModelKind = models.BaselineSklearnModel()
+ 99    # metric
+100    metric: metrics.MetricKind = metrics.SklearnMetric()
+101    # splitter
+102    splitter: splitters.SplitterKind = splitters.TimeSeriesSplitter()
+103    # searcher
+104    searcher: searchers.SearcherKind = searchers.GridCVSearcher(
+105        param_grid={"max_depth": [3, 5, 7]},
+106    )
+107
+108    @T.override
+109    def run(self) -> Locals:
+110        # run
+111        logger.info("Start run: {} ", self.run_name)
+112        with mlflow.start_run(run_name=self.run_name) as run:
+113            logger.info("- Run ID: {}", run.info.run_id)
+114            # read
+115            # - inputs
+116            logger.info("Read inputs: {}", self.inputs)
+117            inputs = schemas.InputsSchema.check(self.inputs.read())
+118            logger.info("- Inputs shape: {}", inputs.shape)
+119            # - targets
+120            logger.info("Read targets: {}", self.targets)
+121            targets = schemas.TargetsSchema.check(self.targets.read())
+122            logger.info("- Targets shape: {}", targets.shape)
+123            # - asserts
+124            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+125            # model
+126            logger.info("With model: {}", self.model)
+127            # metric
+128            logger.info("With metric: {}", self.metric)
+129            # splitter
+130            logger.info("With splitter: {}", self.splitter)
+131            # searcher
+132            logger.info("Execute searcher: {}", self.searcher)
+133            results, best_score, best_params = self.searcher.search(
+134                model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets
+135            )
+136            logger.info("- # Results: {}", len(results))
+137            logger.info("- Best Score: {}", best_score)
+138            logger.info("- Best Params: {}", best_params)
+139            # write
+140            logger.info("Write results: {}", self.results)
+141            self.results.write(results)
+142        return locals()
+143
+144
+145class TrainingJob(Job):
+146    """Train and register a single AI/ML model
+147
+148    Attributes:
+149        run_name (str): name of the MLflow experiment run.
+150        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+151        targets (datasets.ReaderKind): dataset reader with targets variables.
+152        saver (registers.SaverKind): save the trained model in registry.
+153        model (models.ModelKind): machine learning model to tune.
+154        signer (registers.SignerKind): signer for the trained model.
+155        scorers (list[metrics.MetricKind]): metrics for the evaluation.
+156        splitter (splitters.SplitterKind): splitter for datasets.
+157        registry_alias (str): alias of model.
+158    """
+159
+160    KIND: T.Literal["TrainingJob"] = "TrainingJob"
+161
+162    # run
+163    run_name: str = "Training"
+164    # read
+165    inputs: datasets.ReaderKind
+166    targets: datasets.ReaderKind
+167    # write
+168    saver: registers.SaverKind = registers.CustomSaver()
+169    # model
+170    model: models.ModelKind = models.BaselineSklearnModel()
+171    # signer
+172    signer: registers.SignerKind = registers.InferSigner()
+173    # scorers
+174    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
+175    # splitter
+176    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
+177    # register
+178    registry_alias: str = "Champion"
+179
+180    @T.override
+181    def run(self) -> Locals:
+182        # run
+183        logger.info("Start run: {} ", self.run_name)
+184        with mlflow.start_run(run_name=self.run_name) as run:
+185            logger.info("- Run ID: {}", run.info.run_id)
+186            # read
+187            # - inputs
+188            logger.info("Read inputs: {}", self.inputs)
+189            inputs = schemas.InputsSchema.check(self.inputs.read())
+190            logger.info("- Inputs shape: {}", inputs.shape)
+191            # - targets
+192            logger.info("Read targets: {}", self.targets)
+193            targets = schemas.TargetsSchema.check(self.targets.read())
+194            logger.info("- Targets shape: {}", targets.shape)
+195            # - asserts
+196            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+197            # split
+198            logger.info("With splitter: {}", self.splitter)
+199            # - index
+200            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+201            # - inputs
+202            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+203            logger.info("- Inputs train shape: {}", inputs_train.shape)
+204            logger.info("- Inputs test shape: {}", inputs_test.shape)
+205            # - targets
+206            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+207            logger.info("- Targets train shape: {}", targets_train.shape)
+208            logger.info("- Targets test shape: {}", targets_test.shape)
+209            # - asserts
+210            assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!"
+211            assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!"
+212            # model
+213            logger.info("Fit model: {}", self.model)
+214            self.model.fit(inputs=inputs_train, targets=targets_train)
+215            # outputs
+216            logger.info("Predict outputs: {}", len(inputs_test))
+217            outputs_test = self.model.predict(inputs=inputs_test)
+218            logger.info("- Outputs test shape: {}", outputs_test.shape)
+219            assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!"
+220            # scorers
+221            for i, scorer in enumerate(self.scorers, start=1):
+222                logger.info("{}. Run scorer: {}", i, scorer)
+223                score = scorer.score(targets=targets_test, outputs=outputs_test)
+224                mlflow.log_metric(key=scorer.name, value=score)
+225                logger.info("- Metric score: {}", score)
+226            # sign
+227            logger.info("Sign model: {}", self.signer)
+228            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+229            logger.info("- Model signature: {}", signature.to_dict())
+230            # save
+231            logger.info("Save model: {}", self.saver)
+232            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+233            logger.info("- Model URI: {}", info.model_uri)
+234            # register
+235            logger.info("Register model: {}", self.registry_alias)
+236            version = self.mlflow_service.register(
+237                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+238            )
+239            logger.info("- Model version: {}", version.version)
+240        return locals()
+241
+242
+243class InferenceJob(Job):
+244    """Load a model and generate predictions.
+245
+246    Attributes:
+247        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+248        outputs (datasets.WriterKind): dataset writer for the model outputs.
+249        registry_alias (str): alias of the model to load.
+250        loader (registers.LoaderKind): load the model from registry.
+251    """
+252
+253    KIND: T.Literal["InferenceJob"] = "InferenceJob"
+254
+255    # data
+256    inputs: datasets.ReaderKind
+257    outputs: datasets.WriterKind
+258    # model
+259    registry_alias: str = "Champion"
+260    loader: registers.LoaderKind = registers.CustomLoader()
+261
+262    @T.override
+263    def run(self) -> Locals:
+264        # read
+265        logger.info("Read inputs: {}", self.inputs)
+266        inputs = self.inputs.read()
+267        inputs = schemas.InputsSchema.check(inputs)
+268        logger.info("- Inputs shape: {}", inputs.shape)
+269        # uri
+270        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+271        logger.info("With URI: {}", uri)
+272        # load
+273        logger.info("Load model: {}", self.loader)
+274        model = self.loader.load(uri=uri)
+275        logger.info("- Model: {}", model)
+276        # predict
+277        logger.info("Predict outputs: {}", len(inputs))
+278        outputs = model.predict(data=inputs)
+279        logger.info("- Outputs shape: {}", outputs.shape)
+280        # write
+281        logger.info("Write outputs: {}", self.outputs)
+282        self.outputs.write(data=outputs)
+283        return locals()
+284
+285
+286JobKind = TuningJob | TrainingJob | InferenceJob
+
+ + +
+
+ +
+ + class + Job(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
23class Job(abc.ABC, pdt.BaseModel, strict=True):
+24    """Base class for a job.
+25
+26    use a job to execute runs in  context.
+27    e.g., to define common services like logger
+28
+29    Attributes:
+30        logger_service (services.LoggerService): manage the logging system.
+31        mlflow_service (services.MLflowService): manage the mlflow system.
+32    """
+33
+34    KIND: str
+35
+36    logger_service: services.LoggerService = services.LoggerService()
+37    mlflow_service: services.MLflowService = services.MLflowService()
+38
+39    def __enter__(self) -> T.Self:
+40        """Enter the job context.
+41
+42        Returns:
+43            T.Self: return the current object.
+44        """
+45        self.logger_service.start()
+46        logger.debug("[START] MLflow service: {}", self.mlflow_service)
+47        self.mlflow_service.start()
+48        return self
+49
+50    def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]:
+51        """Exit the job context.
+52
+53        Args:
+54            exc_type: ignored.
+55            exc_value: ignored.
+56            traceback: ignored.
+57
+58        Returns:
+59            T.Literal[False]: always propagate exceptions.
+60        """
+61        logger.debug("[STOP] MLflow service: {}", self.mlflow_service)
+62        self.mlflow_service.stop()
+63        self.logger_service.stop()
+64        return False
+65
+66    @abc.abstractmethod
+67    def run(self) -> Locals:
+68        """Run the job in context.
+69
+70        Returns:
+71            Locals: local job variables.
+72        """
+
+ + +

Base class for a job.

+ +

use a job to execute runs in context. +e.g., to define common services like logger

+ +
Attributes:
+ +
    +
  • logger_service (services.LoggerService): manage the logging system.
    • +
  • mlflow_service (services.MLflowService): manage the mlflow system.
    • +
+
+ + +
+ +
+
@abc.abstractmethod
+ + def + run(self) -> dict[str, typing.Any]: + + + +
+ + 
66    @abc.abstractmethod
+67    def run(self) -> Locals:
+68        """Run the job in context.
+69
+70        Returns:
+71            Locals: local job variables.
+72        """
+
+ + +

Run the job in context.

+ +
Returns:
+ +
+

Locals: local job variables.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + TuningJob(Job): + + + +
+ + 
 75class TuningJob(Job):
+ 76    """Find the best hyperparameters for a model.
+ 77
+ 78    Attributes:
+ 79        run_name (str): name of the MLflow experiment run.
+ 80        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+ 81        targets (datasets.ReaderKind): dataset reader with targets variables.
+ 82        results (datasets.WriterKind): dataset writer for searcher results.
+ 83        model (models.ModelKind): machine learning model to tune.
+ 84        metric (metrics.MetricKind): main metric for evaluation.
+ 85        splitter (splitters.SplitterKind): splitter for datasets.
+ 86        searcher (searchers.SearcherKind): searcher algorithm.
+ 87    """
+ 88
+ 89    KIND: T.Literal["TuningJob"] = "TuningJob"
+ 90
+ 91    # run
+ 92    run_name: str = "Tuning"
+ 93    # read
+ 94    inputs: datasets.ReaderKind
+ 95    targets: datasets.ReaderKind
+ 96    # write
+ 97    results: datasets.WriterKind
+ 98    # model
+ 99    model: models.ModelKind = models.BaselineSklearnModel()
+100    # metric
+101    metric: metrics.MetricKind = metrics.SklearnMetric()
+102    # splitter
+103    splitter: splitters.SplitterKind = splitters.TimeSeriesSplitter()
+104    # searcher
+105    searcher: searchers.SearcherKind = searchers.GridCVSearcher(
+106        param_grid={"max_depth": [3, 5, 7]},
+107    )
+108
+109    @T.override
+110    def run(self) -> Locals:
+111        # run
+112        logger.info("Start run: {} ", self.run_name)
+113        with mlflow.start_run(run_name=self.run_name) as run:
+114            logger.info("- Run ID: {}", run.info.run_id)
+115            # read
+116            # - inputs
+117            logger.info("Read inputs: {}", self.inputs)
+118            inputs = schemas.InputsSchema.check(self.inputs.read())
+119            logger.info("- Inputs shape: {}", inputs.shape)
+120            # - targets
+121            logger.info("Read targets: {}", self.targets)
+122            targets = schemas.TargetsSchema.check(self.targets.read())
+123            logger.info("- Targets shape: {}", targets.shape)
+124            # - asserts
+125            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+126            # model
+127            logger.info("With model: {}", self.model)
+128            # metric
+129            logger.info("With metric: {}", self.metric)
+130            # splitter
+131            logger.info("With splitter: {}", self.splitter)
+132            # searcher
+133            logger.info("Execute searcher: {}", self.searcher)
+134            results, best_score, best_params = self.searcher.search(
+135                model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets
+136            )
+137            logger.info("- # Results: {}", len(results))
+138            logger.info("- Best Score: {}", best_score)
+139            logger.info("- Best Params: {}", best_params)
+140            # write
+141            logger.info("Write results: {}", self.results)
+142            self.results.write(results)
+143        return locals()
+
+ + +

Find the best hyperparameters for a model.

+ +
Attributes:
+ +
    +
  • run_name (str): name of the MLflow experiment run.
    • +
  • inputs (datasets.ReaderKind): dataset reader with inputs variables.
    • +
  • targets (datasets.ReaderKind): dataset reader with targets variables.
    • +
  • results (datasets.WriterKind): dataset writer for searcher results.
    • +
  • model (models.ModelKind): machine learning model to tune.
    • +
  • metric (metrics.MetricKind): main metric for evaluation.
    • +
  • splitter (splitters.SplitterKind): splitter for datasets.
    • +
  • searcher (searchers.SearcherKind): searcher algorithm.
    • +
+
+ + +
+ +
+
@T.override
+ + def + run(self) -> dict[str, typing.Any]: + + + +
+ + 
109    @T.override
+110    def run(self) -> Locals:
+111        # run
+112        logger.info("Start run: {} ", self.run_name)
+113        with mlflow.start_run(run_name=self.run_name) as run:
+114            logger.info("- Run ID: {}", run.info.run_id)
+115            # read
+116            # - inputs
+117            logger.info("Read inputs: {}", self.inputs)
+118            inputs = schemas.InputsSchema.check(self.inputs.read())
+119            logger.info("- Inputs shape: {}", inputs.shape)
+120            # - targets
+121            logger.info("Read targets: {}", self.targets)
+122            targets = schemas.TargetsSchema.check(self.targets.read())
+123            logger.info("- Targets shape: {}", targets.shape)
+124            # - asserts
+125            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+126            # model
+127            logger.info("With model: {}", self.model)
+128            # metric
+129            logger.info("With metric: {}", self.metric)
+130            # splitter
+131            logger.info("With splitter: {}", self.splitter)
+132            # searcher
+133            logger.info("Execute searcher: {}", self.searcher)
+134            results, best_score, best_params = self.searcher.search(
+135                model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets
+136            )
+137            logger.info("- # Results: {}", len(results))
+138            logger.info("- Best Score: {}", best_score)
+139            logger.info("- Best Params: {}", best_params)
+140            # write
+141            logger.info("Write results: {}", self.results)
+142            self.results.write(results)
+143        return locals()
+
+ + +

Run the job in context.

+ +
Returns:
+ +
+

Locals: local job variables.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + TrainingJob(Job): + + + +
+ + 
146class TrainingJob(Job):
+147    """Train and register a single AI/ML model
+148
+149    Attributes:
+150        run_name (str): name of the MLflow experiment run.
+151        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+152        targets (datasets.ReaderKind): dataset reader with targets variables.
+153        saver (registers.SaverKind): save the trained model in registry.
+154        model (models.ModelKind): machine learning model to tune.
+155        signer (registers.SignerKind): signer for the trained model.
+156        scorers (list[metrics.MetricKind]): metrics for the evaluation.
+157        splitter (splitters.SplitterKind): splitter for datasets.
+158        registry_alias (str): alias of model.
+159    """
+160
+161    KIND: T.Literal["TrainingJob"] = "TrainingJob"
+162
+163    # run
+164    run_name: str = "Training"
+165    # read
+166    inputs: datasets.ReaderKind
+167    targets: datasets.ReaderKind
+168    # write
+169    saver: registers.SaverKind = registers.CustomSaver()
+170    # model
+171    model: models.ModelKind = models.BaselineSklearnModel()
+172    # signer
+173    signer: registers.SignerKind = registers.InferSigner()
+174    # scorers
+175    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
+176    # splitter
+177    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
+178    # register
+179    registry_alias: str = "Champion"
+180
+181    @T.override
+182    def run(self) -> Locals:
+183        # run
+184        logger.info("Start run: {} ", self.run_name)
+185        with mlflow.start_run(run_name=self.run_name) as run:
+186            logger.info("- Run ID: {}", run.info.run_id)
+187            # read
+188            # - inputs
+189            logger.info("Read inputs: {}", self.inputs)
+190            inputs = schemas.InputsSchema.check(self.inputs.read())
+191            logger.info("- Inputs shape: {}", inputs.shape)
+192            # - targets
+193            logger.info("Read targets: {}", self.targets)
+194            targets = schemas.TargetsSchema.check(self.targets.read())
+195            logger.info("- Targets shape: {}", targets.shape)
+196            # - asserts
+197            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+198            # split
+199            logger.info("With splitter: {}", self.splitter)
+200            # - index
+201            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+202            # - inputs
+203            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+204            logger.info("- Inputs train shape: {}", inputs_train.shape)
+205            logger.info("- Inputs test shape: {}", inputs_test.shape)
+206            # - targets
+207            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+208            logger.info("- Targets train shape: {}", targets_train.shape)
+209            logger.info("- Targets test shape: {}", targets_test.shape)
+210            # - asserts
+211            assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!"
+212            assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!"
+213            # model
+214            logger.info("Fit model: {}", self.model)
+215            self.model.fit(inputs=inputs_train, targets=targets_train)
+216            # outputs
+217            logger.info("Predict outputs: {}", len(inputs_test))
+218            outputs_test = self.model.predict(inputs=inputs_test)
+219            logger.info("- Outputs test shape: {}", outputs_test.shape)
+220            assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!"
+221            # scorers
+222            for i, scorer in enumerate(self.scorers, start=1):
+223                logger.info("{}. Run scorer: {}", i, scorer)
+224                score = scorer.score(targets=targets_test, outputs=outputs_test)
+225                mlflow.log_metric(key=scorer.name, value=score)
+226                logger.info("- Metric score: {}", score)
+227            # sign
+228            logger.info("Sign model: {}", self.signer)
+229            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+230            logger.info("- Model signature: {}", signature.to_dict())
+231            # save
+232            logger.info("Save model: {}", self.saver)
+233            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+234            logger.info("- Model URI: {}", info.model_uri)
+235            # register
+236            logger.info("Register model: {}", self.registry_alias)
+237            version = self.mlflow_service.register(
+238                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+239            )
+240            logger.info("- Model version: {}", version.version)
+241        return locals()
+
+ + +

Train and register a single AI/ML model

+ +
Attributes:
+ +
    +
  • run_name (str): name of the MLflow experiment run.
    • +
  • inputs (datasets.ReaderKind): dataset reader with inputs variables.
    • +
  • targets (datasets.ReaderKind): dataset reader with targets variables.
    • +
  • saver (registers.SaverKind): save the trained model in registry.
    • +
  • model (models.ModelKind): machine learning model to tune.
    • +
  • signer (registers.SignerKind): signer for the trained model.
    • +
  • scorers (list[metrics.MetricKind]): metrics for the evaluation.
    • +
  • splitter (splitters.SplitterKind): splitter for datasets.
    • +
  • registry_alias (str): alias of model.
    • +
+
+ + +
+ +
+
@T.override
+ + def + run(self) -> dict[str, typing.Any]: + + + +
+ + 
181    @T.override
+182    def run(self) -> Locals:
+183        # run
+184        logger.info("Start run: {} ", self.run_name)
+185        with mlflow.start_run(run_name=self.run_name) as run:
+186            logger.info("- Run ID: {}", run.info.run_id)
+187            # read
+188            # - inputs
+189            logger.info("Read inputs: {}", self.inputs)
+190            inputs = schemas.InputsSchema.check(self.inputs.read())
+191            logger.info("- Inputs shape: {}", inputs.shape)
+192            # - targets
+193            logger.info("Read targets: {}", self.targets)
+194            targets = schemas.TargetsSchema.check(self.targets.read())
+195            logger.info("- Targets shape: {}", targets.shape)
+196            # - asserts
+197            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+198            # split
+199            logger.info("With splitter: {}", self.splitter)
+200            # - index
+201            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+202            # - inputs
+203            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+204            logger.info("- Inputs train shape: {}", inputs_train.shape)
+205            logger.info("- Inputs test shape: {}", inputs_test.shape)
+206            # - targets
+207            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+208            logger.info("- Targets train shape: {}", targets_train.shape)
+209            logger.info("- Targets test shape: {}", targets_test.shape)
+210            # - asserts
+211            assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!"
+212            assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!"
+213            # model
+214            logger.info("Fit model: {}", self.model)
+215            self.model.fit(inputs=inputs_train, targets=targets_train)
+216            # outputs
+217            logger.info("Predict outputs: {}", len(inputs_test))
+218            outputs_test = self.model.predict(inputs=inputs_test)
+219            logger.info("- Outputs test shape: {}", outputs_test.shape)
+220            assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!"
+221            # scorers
+222            for i, scorer in enumerate(self.scorers, start=1):
+223                logger.info("{}. Run scorer: {}", i, scorer)
+224                score = scorer.score(targets=targets_test, outputs=outputs_test)
+225                mlflow.log_metric(key=scorer.name, value=score)
+226                logger.info("- Metric score: {}", score)
+227            # sign
+228            logger.info("Sign model: {}", self.signer)
+229            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+230            logger.info("- Model signature: {}", signature.to_dict())
+231            # save
+232            logger.info("Save model: {}", self.saver)
+233            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+234            logger.info("- Model URI: {}", info.model_uri)
+235            # register
+236            logger.info("Register model: {}", self.registry_alias)
+237            version = self.mlflow_service.register(
+238                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+239            )
+240            logger.info("- Model version: {}", version.version)
+241        return locals()
+
+ + +

Run the job in context.

+ +
Returns:
+ +
+

Locals: local job variables.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + InferenceJob(Job): + + + +
+ + 
244class InferenceJob(Job):
+245    """Load a model and generate predictions.
+246
+247    Attributes:
+248        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+249        outputs (datasets.WriterKind): dataset writer for the model outputs.
+250        registry_alias (str): alias of the model to load.
+251        loader (registers.LoaderKind): load the model from registry.
+252    """
+253
+254    KIND: T.Literal["InferenceJob"] = "InferenceJob"
+255
+256    # data
+257    inputs: datasets.ReaderKind
+258    outputs: datasets.WriterKind
+259    # model
+260    registry_alias: str = "Champion"
+261    loader: registers.LoaderKind = registers.CustomLoader()
+262
+263    @T.override
+264    def run(self) -> Locals:
+265        # read
+266        logger.info("Read inputs: {}", self.inputs)
+267        inputs = self.inputs.read()
+268        inputs = schemas.InputsSchema.check(inputs)
+269        logger.info("- Inputs shape: {}", inputs.shape)
+270        # uri
+271        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+272        logger.info("With URI: {}", uri)
+273        # load
+274        logger.info("Load model: {}", self.loader)
+275        model = self.loader.load(uri=uri)
+276        logger.info("- Model: {}", model)
+277        # predict
+278        logger.info("Predict outputs: {}", len(inputs))
+279        outputs = model.predict(data=inputs)
+280        logger.info("- Outputs shape: {}", outputs.shape)
+281        # write
+282        logger.info("Write outputs: {}", self.outputs)
+283        self.outputs.write(data=outputs)
+284        return locals()
+
+ + +

Load a model and generate predictions.

+ +
Attributes:
+ +
    +
  • inputs (datasets.ReaderKind): dataset reader with inputs variables.
    • +
  • outputs (datasets.WriterKind): dataset writer for the model outputs.
    • +
  • registry_alias (str): alias of the model to load.
    • +
  • loader (registers.LoaderKind): load the model from registry.
    • +
+
+ + +
+ +
+
@T.override
+ + def + run(self) -> dict[str, typing.Any]: + + + +
+ + 
263    @T.override
+264    def run(self) -> Locals:
+265        # read
+266        logger.info("Read inputs: {}", self.inputs)
+267        inputs = self.inputs.read()
+268        inputs = schemas.InputsSchema.check(inputs)
+269        logger.info("- Inputs shape: {}", inputs.shape)
+270        # uri
+271        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+272        logger.info("With URI: {}", uri)
+273        # load
+274        logger.info("Load model: {}", self.loader)
+275        model = self.loader.load(uri=uri)
+276        logger.info("- Model: {}", model)
+277        # predict
+278        logger.info("Predict outputs: {}", len(inputs))
+279        outputs = model.predict(data=inputs)
+280        logger.info("- Outputs shape: {}", outputs.shape)
+281        # write
+282        logger.info("Write outputs: {}", self.outputs)
+283        self.outputs.write(data=outputs)
+284        return locals()
+
+ + +

Run the job in context.

+ +
Returns:
+ +
+

Locals: local job variables.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/bikes/metrics.html b/bikes/metrics.html new file mode 100644 index 0000000..93dd563 --- /dev/null +++ b/bikes/metrics.html @@ -0,0 +1,664 @@ + + + + + + + bikes.metrics API documentation + + + + + + + + + +
+
+

+bikes.metrics

+ +

Evaluate model performance with metrics.

+
+ + + + + + 
 1"""Evaluate model performance with metrics."""
+ 2
+ 3# %% IMPORTS
+ 4
+ 5import abc
+ 6import typing as T
+ 7
+ 8import pydantic as pdt
+ 9from sklearn import metrics
+10
+11from bikes import models, schemas
+12
+13# %% METRICS
+14
+15
+16class Metric(abc.ABC, pdt.BaseModel, strict=True):
+17    """Base class for a metric.
+18
+19    Use metrics to evaluate model performance.
+20    e.g., accuracy, precision, recall, mae, f1, ...
+21
+22    Attributes:
+23        name (str): name of the metric.
+24    """
+25
+26    KIND: str
+27
+28    name: str
+29
+30    @abc.abstractmethod
+31    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+32        """Score the outputs against the targets.
+33
+34        Args:
+35            targets (schemas.Targets): expected values.
+36            outputs (schemas.Outputs): predicted values.
+37
+38        Returns:
+39            float: metric result.
+40        """
+41
+42    def scorer(self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets) -> float:
+43        """Score the model outputs against the targets.
+44
+45        Args:
+46            model (models.Model): model to evaluate.
+47            inputs (schemas.Inputs): model inputs values.
+48            targets (schemas.Targets): model expected values.
+49
+50        Returns:
+51            float: metric result.
+52        """
+53        outputs = model.predict(inputs=inputs)  # prediction
+54        score = self.score(targets=targets, outputs=outputs)
+55        return score
+56
+57
+58class SklearnMetric(Metric):
+59    """Compute metrics with sklearn.
+60
+61    Attributes:
+62        name (str): name of the sklearn metric.
+63        greater_is_better (bool): maximize or minimize.
+64    """
+65
+66    KIND: T.Literal["SklearnMetric"] = "SklearnMetric"
+67
+68    name: str = "mean_squared_error"
+69    greater_is_better: bool = False
+70
+71    @T.override
+72    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+73        metric = getattr(metrics, self.name)
+74        sign = 1 if self.greater_is_better else -1
+75        y_true = targets[schemas.TargetsSchema.cnt]
+76        y_pred = outputs[schemas.OutputsSchema.prediction]
+77        score = metric(y_pred=y_pred, y_true=y_true) * sign
+78        return score
+79
+80
+81MetricKind = SklearnMetric
+
+ + +
+
+ +
+ + class + Metric(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
17class Metric(abc.ABC, pdt.BaseModel, strict=True):
+18    """Base class for a metric.
+19
+20    Use metrics to evaluate model performance.
+21    e.g., accuracy, precision, recall, mae, f1, ...
+22
+23    Attributes:
+24        name (str): name of the metric.
+25    """
+26
+27    KIND: str
+28
+29    name: str
+30
+31    @abc.abstractmethod
+32    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+33        """Score the outputs against the targets.
+34
+35        Args:
+36            targets (schemas.Targets): expected values.
+37            outputs (schemas.Outputs): predicted values.
+38
+39        Returns:
+40            float: metric result.
+41        """
+42
+43    def scorer(self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets) -> float:
+44        """Score the model outputs against the targets.
+45
+46        Args:
+47            model (models.Model): model to evaluate.
+48            inputs (schemas.Inputs): model inputs values.
+49            targets (schemas.Targets): model expected values.
+50
+51        Returns:
+52            float: metric result.
+53        """
+54        outputs = model.predict(inputs=inputs)  # prediction
+55        score = self.score(targets=targets, outputs=outputs)
+56        return score
+
+ + +

Base class for a metric.

+ +

Use metrics to evaluate model performance. +e.g., accuracy, precision, recall, mae, f1, ...

+ +
Attributes:
+ +
    +
  • name (str): name of the metric.
    • +
+
+ + +
+ +
+
@abc.abstractmethod
+ + def + score( self, targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], outputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float: + + + +
+ + 
31    @abc.abstractmethod
+32    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+33        """Score the outputs against the targets.
+34
+35        Args:
+36            targets (schemas.Targets): expected values.
+37            outputs (schemas.Outputs): predicted values.
+38
+39        Returns:
+40            float: metric result.
+41        """
+
+ + +

Score the outputs against the targets.

+ +
Arguments:
+ +
    +
  • targets (schemas.Targets): expected values.
    • +
  • outputs (schemas.Outputs): predicted values.
    • +
+ +
Returns:
+ +
+

float: metric result.

+
+
+ + +
+
+ +
+ + def + scorer( self, model: bikes.models.Model, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> float: + + + +
+ + 
43    def scorer(self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets) -> float:
+44        """Score the model outputs against the targets.
+45
+46        Args:
+47            model (models.Model): model to evaluate.
+48            inputs (schemas.Inputs): model inputs values.
+49            targets (schemas.Targets): model expected values.
+50
+51        Returns:
+52            float: metric result.
+53        """
+54        outputs = model.predict(inputs=inputs)  # prediction
+55        score = self.score(targets=targets, outputs=outputs)
+56        return score
+
+ + +

Score the model outputs against the targets.

+ +
Arguments:
+ +
    +
  • model (models.Model): model to evaluate.
    • +
  • inputs (schemas.Inputs): model inputs values.
    • +
  • targets (schemas.Targets): model expected values.
    • +
+ +
Returns:
+ +
+

float: metric result.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + SklearnMetric(Metric): + + + +
+ + 
59class SklearnMetric(Metric):
+60    """Compute metrics with sklearn.
+61
+62    Attributes:
+63        name (str): name of the sklearn metric.
+64        greater_is_better (bool): maximize or minimize.
+65    """
+66
+67    KIND: T.Literal["SklearnMetric"] = "SklearnMetric"
+68
+69    name: str = "mean_squared_error"
+70    greater_is_better: bool = False
+71
+72    @T.override
+73    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+74        metric = getattr(metrics, self.name)
+75        sign = 1 if self.greater_is_better else -1
+76        y_true = targets[schemas.TargetsSchema.cnt]
+77        y_pred = outputs[schemas.OutputsSchema.prediction]
+78        score = metric(y_pred=y_pred, y_true=y_true) * sign
+79        return score
+
+ + +

Compute metrics with sklearn.

+ +
Attributes:
+ +
    +
  • name (str): name of the sklearn metric.
    • +
  • greater_is_better (bool): maximize or minimize.
    • +
+
+ + +
+ +
+
@T.override
+ + def + score( self, targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], outputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float: + + + +
+ + 
72    @T.override
+73    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+74        metric = getattr(metrics, self.name)
+75        sign = 1 if self.greater_is_better else -1
+76        y_true = targets[schemas.TargetsSchema.cnt]
+77        y_pred = outputs[schemas.OutputsSchema.prediction]
+78        score = metric(y_pred=y_pred, y_true=y_true) * sign
+79        return score
+
+ + +

Score the outputs against the targets.

+ +
Arguments:
+ +
    +
  • targets (schemas.Targets): expected values.
    • +
  • outputs (schemas.Outputs): predicted values.
    • +
+ +
Returns:
+ +
+

float: metric result.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
Metric
+
scorer
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/bikes/models.html b/bikes/models.html new file mode 100644 index 0000000..74a9e65 --- /dev/null +++ b/bikes/models.html @@ -0,0 +1,975 @@ + + + + + + + bikes.models API documentation + + + + + + + + + +
+
+

+bikes.models

+ +

Define trainable machine learning models.

+
+ + + + + + 
  1"""Define trainable machine learning models."""
+  2
+  3# %% IMPORTS
+  4
+  5import abc
+  6import typing as T
+  7
+  8import pydantic as pdt
+  9from sklearn import compose, ensemble, pipeline, preprocessing
+ 10
+ 11from bikes import schemas
+ 12
+ 13# %% TYPES
+ 14
+ 15ParamKey = str
+ 16ParamValue = T.Any
+ 17Params = dict[ParamKey, ParamValue]
+ 18
+ 19# %% MODELS
+ 20
+ 21
+ 22class Model(abc.ABC, pdt.BaseModel, strict=True):
+ 23    """Base class for a model.
+ 24
+ 25    Use a model to adapt AI/ML frameworks.
+ 26    e.g., to swap easily one model with another.
+ 27    """
+ 28
+ 29    KIND: str
+ 30
+ 31    # pylint: disable=unused-argument
+ 32    def get_params(self, deep: bool = True) -> Params:
+ 33        """Get the model params.
+ 34
+ 35        Args:
+ 36            deep (bool, optional): ignored. Defaults to True.
+ 37
+ 38        Returns:
+ 39            Params: internal model parameters.
+ 40        """
+ 41        params: Params = {}
+ 42        for key, value in self.model_dump().items():
+ 43            if not key.startswith("_") and not key.isupper():
+ 44                params[key] = value
+ 45        return params
+ 46
+ 47    def set_params(self, **params: ParamValue) -> T.Self:
+ 48        """Set the model params in place.
+ 49
+ 50        Returns:
+ 51            T.Self: instance of the model.
+ 52        """
+ 53        for key, value in params.items():
+ 54            setattr(self, key, value)
+ 55        return self
+ 56
+ 57    @abc.abstractmethod
+ 58    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self:
+ 59        """Fit the model on the given inputs and targets.
+ 60
+ 61        Args:
+ 62            inputs (schemas.Inputs): model training inputs.
+ 63            targets (schemas.Targets): model training targets.
+ 64
+ 65        Returns:
+ 66            Model: instance of the model.
+ 67        """
+ 68
+ 69    @abc.abstractmethod
+ 70    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+ 71        """Generate outputs with the model for the given inputs.
+ 72
+ 73        Args:
+ 74            inputs (schemas.Inputs): model prediction inputs.
+ 75
+ 76        Returns:
+ 77            schemas.Outputs: model prediction outputs.
+ 78        """
+ 79
+ 80
+ 81class BaselineSklearnModel(Model):
+ 82    """Simple baseline model built on top of sklearn.
+ 83
+ 84    Attributes:
+ 85        max_depth (int): maximum depth of the random forest.
+ 86        n_estimators (int): number of estimators in the random forest.
+ 87        random_state (int, optional): random state of the machine learning pipeline.
+ 88    """
+ 89
+ 90    KIND: T.Literal["BaselineSklearnModel"] = "BaselineSklearnModel"
+ 91
+ 92    # params
+ 93    max_depth: int = 20
+ 94    n_estimators: int = 200
+ 95    random_state: int | None = 42
+ 96    # private
+ 97    _pipeline: pipeline.Pipeline | None = None
+ 98    _numericals: list[str] = [
+ 99        "yr",
+100        "mnth",
+101        "hr",
+102        "holiday",
+103        "weekday",
+104        "workingday",
+105        "temp",
+106        "atemp",
+107        "hum",
+108        "windspeed",
+109        "casual",
+110        # "registered", # too correlated with target
+111    ]
+112    _categoricals: list[str] = [
+113        "season",
+114        "weathersit",
+115    ]
+116
+117    @T.override
+118    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
+119        # subcomponents
+120        categoricals_transformer = preprocessing.OneHotEncoder(sparse_output=False, handle_unknown="ignore")
+121        # components
+122        transformer = compose.ColumnTransformer(
+123            [
+124                ("categoricals", categoricals_transformer, self._categoricals),
+125                ("numericals", "passthrough", self._numericals),
+126            ],
+127            remainder="drop",
+128        )
+129        regressor = ensemble.RandomForestRegressor(
+130            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
+131        )
+132        # pipeline
+133        self._pipeline = pipeline.Pipeline(
+134            steps=[
+135                ("transformer", transformer),
+136                ("regressor", regressor),
+137            ]
+138        )
+139        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
+140        return self
+141
+142    @T.override
+143    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+144        assert self._pipeline is not None, "Model should be fitted first!"
+145        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
+146        outputs = schemas.Outputs({schemas.OutputsSchema.prediction: prediction}, index=inputs.index)
+147        return outputs
+148
+149
+150ModelKind = BaselineSklearnModel
+
+ + +
+
+ +
+ + class + Model(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
23class Model(abc.ABC, pdt.BaseModel, strict=True):
+24    """Base class for a model.
+25
+26    Use a model to adapt AI/ML frameworks.
+27    e.g., to swap easily one model with another.
+28    """
+29
+30    KIND: str
+31
+32    # pylint: disable=unused-argument
+33    def get_params(self, deep: bool = True) -> Params:
+34        """Get the model params.
+35
+36        Args:
+37            deep (bool, optional): ignored. Defaults to True.
+38
+39        Returns:
+40            Params: internal model parameters.
+41        """
+42        params: Params = {}
+43        for key, value in self.model_dump().items():
+44            if not key.startswith("_") and not key.isupper():
+45                params[key] = value
+46        return params
+47
+48    def set_params(self, **params: ParamValue) -> T.Self:
+49        """Set the model params in place.
+50
+51        Returns:
+52            T.Self: instance of the model.
+53        """
+54        for key, value in params.items():
+55            setattr(self, key, value)
+56        return self
+57
+58    @abc.abstractmethod
+59    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self:
+60        """Fit the model on the given inputs and targets.
+61
+62        Args:
+63            inputs (schemas.Inputs): model training inputs.
+64            targets (schemas.Targets): model training targets.
+65
+66        Returns:
+67            Model: instance of the model.
+68        """
+69
+70    @abc.abstractmethod
+71    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+72        """Generate outputs with the model for the given inputs.
+73
+74        Args:
+75            inputs (schemas.Inputs): model prediction inputs.
+76
+77        Returns:
+78            schemas.Outputs: model prediction outputs.
+79        """
+
+ + +

Base class for a model.

+ +

Use a model to adapt AI/ML frameworks. +e.g., to swap easily one model with another.

+
+ + +
+ +
+ + def + get_params(self, deep: bool = True) -> dict[str, typing.Any]: + + + +
+ + 
33    def get_params(self, deep: bool = True) -> Params:
+34        """Get the model params.
+35
+36        Args:
+37            deep (bool, optional): ignored. Defaults to True.
+38
+39        Returns:
+40            Params: internal model parameters.
+41        """
+42        params: Params = {}
+43        for key, value in self.model_dump().items():
+44            if not key.startswith("_") and not key.isupper():
+45                params[key] = value
+46        return params
+
+ + +

Get the model params.

+ +
Arguments:
+ +
    +
  • deep (bool, optional): ignored. Defaults to True.
    • +
+ +
Returns:
+ +
+

Params: internal model parameters.

+
+
+ + +
+
+ +
+ + def + set_params(self, **params: Any) -> Self: + + + +
+ + 
48    def set_params(self, **params: ParamValue) -> T.Self:
+49        """Set the model params in place.
+50
+51        Returns:
+52            T.Self: instance of the model.
+53        """
+54        for key, value in params.items():
+55            setattr(self, key, value)
+56        return self
+
+ + +

Set the model params in place.

+ +
Returns:
+ +
+

T.Self: instance of the model.

+
+
+ + +
+
+ +
+
@abc.abstractmethod
+ + def + fit( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> Self: + + + +
+ + 
58    @abc.abstractmethod
+59    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self:
+60        """Fit the model on the given inputs and targets.
+61
+62        Args:
+63            inputs (schemas.Inputs): model training inputs.
+64            targets (schemas.Targets): model training targets.
+65
+66        Returns:
+67            Model: instance of the model.
+68        """
+
+ + +

Fit the model on the given inputs and targets.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): model training inputs.
    • +
  • targets (schemas.Targets): model training targets.
    • +
+ +
Returns:
+ +
+

Model: instance of the model.

+
+
+ + +
+
+ +
+
@abc.abstractmethod
+ + def + predict( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]: + + + +
+ + 
70    @abc.abstractmethod
+71    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+72        """Generate outputs with the model for the given inputs.
+73
+74        Args:
+75            inputs (schemas.Inputs): model prediction inputs.
+76
+77        Returns:
+78            schemas.Outputs: model prediction outputs.
+79        """
+
+ + +

Generate outputs with the model for the given inputs.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): model prediction inputs.
    • +
+ +
Returns:
+ +
+

schemas.Outputs: model prediction outputs.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + BaselineSklearnModel(Model): + + + +
+ + 
 82class BaselineSklearnModel(Model):
+ 83    """Simple baseline model built on top of sklearn.
+ 84
+ 85    Attributes:
+ 86        max_depth (int): maximum depth of the random forest.
+ 87        n_estimators (int): number of estimators in the random forest.
+ 88        random_state (int, optional): random state of the machine learning pipeline.
+ 89    """
+ 90
+ 91    KIND: T.Literal["BaselineSklearnModel"] = "BaselineSklearnModel"
+ 92
+ 93    # params
+ 94    max_depth: int = 20
+ 95    n_estimators: int = 200
+ 96    random_state: int | None = 42
+ 97    # private
+ 98    _pipeline: pipeline.Pipeline | None = None
+ 99    _numericals: list[str] = [
+100        "yr",
+101        "mnth",
+102        "hr",
+103        "holiday",
+104        "weekday",
+105        "workingday",
+106        "temp",
+107        "atemp",
+108        "hum",
+109        "windspeed",
+110        "casual",
+111        # "registered", # too correlated with target
+112    ]
+113    _categoricals: list[str] = [
+114        "season",
+115        "weathersit",
+116    ]
+117
+118    @T.override
+119    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
+120        # subcomponents
+121        categoricals_transformer = preprocessing.OneHotEncoder(sparse_output=False, handle_unknown="ignore")
+122        # components
+123        transformer = compose.ColumnTransformer(
+124            [
+125                ("categoricals", categoricals_transformer, self._categoricals),
+126                ("numericals", "passthrough", self._numericals),
+127            ],
+128            remainder="drop",
+129        )
+130        regressor = ensemble.RandomForestRegressor(
+131            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
+132        )
+133        # pipeline
+134        self._pipeline = pipeline.Pipeline(
+135            steps=[
+136                ("transformer", transformer),
+137                ("regressor", regressor),
+138            ]
+139        )
+140        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
+141        return self
+142
+143    @T.override
+144    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+145        assert self._pipeline is not None, "Model should be fitted first!"
+146        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
+147        outputs = schemas.Outputs({schemas.OutputsSchema.prediction: prediction}, index=inputs.index)
+148        return outputs
+
+ + +

Simple baseline model built on top of sklearn.

+ +
Attributes:
+ +
    +
  • max_depth (int): maximum depth of the random forest.
    • +
  • n_estimators (int): number of estimators in the random forest.
    • +
  • random_state (int, optional): random state of the machine learning pipeline.
    • +
+
+ + +
+ +
+
@T.override
+ + def + fit( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> bikes.models.BaselineSklearnModel: + + + +
+ + 
118    @T.override
+119    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
+120        # subcomponents
+121        categoricals_transformer = preprocessing.OneHotEncoder(sparse_output=False, handle_unknown="ignore")
+122        # components
+123        transformer = compose.ColumnTransformer(
+124            [
+125                ("categoricals", categoricals_transformer, self._categoricals),
+126                ("numericals", "passthrough", self._numericals),
+127            ],
+128            remainder="drop",
+129        )
+130        regressor = ensemble.RandomForestRegressor(
+131            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
+132        )
+133        # pipeline
+134        self._pipeline = pipeline.Pipeline(
+135            steps=[
+136                ("transformer", transformer),
+137                ("regressor", regressor),
+138            ]
+139        )
+140        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
+141        return self
+
+ + +

Fit the model on the given inputs and targets.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): model training inputs.
    • +
  • targets (schemas.Targets): model training targets.
    • +
+ +
Returns:
+ +
+

Model: instance of the model.

+
+
+ + +
+
+ +
+
@T.override
+ + def + predict( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]: + + + +
+ + 
143    @T.override
+144    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+145        assert self._pipeline is not None, "Model should be fitted first!"
+146        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
+147        outputs = schemas.Outputs({schemas.OutputsSchema.prediction: prediction}, index=inputs.index)
+148        return outputs
+
+ + +

Generate outputs with the model for the given inputs.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): model prediction inputs.
    • +
+ +
Returns:
+ +
+

schemas.Outputs: model prediction outputs.

+
+
+ + +
+
+ +
+ + def + model_post_init(self: pydantic.main.BaseModel, __context: Any) -> None: + + + +
+ + 
265def init_private_attributes(self: BaseModel, __context: Any) -> None:
+266    """This function is meant to behave like a BaseModel method to initialise private attributes.
+267
+268    It takes context as an argument since that's what pydantic-core passes when calling it.
+269
+270    Args:
+271        self: The BaseModel instance.
+272        __context: The context.
+273    """
+274    if getattr(self, '__pydantic_private__', None) is None:
+275        pydantic_private = {}
+276        for name, private_attr in self.__private_attributes__.items():
+277            default = private_attr.get_default()
+278            if default is not PydanticUndefined:
+279                pydantic_private[name] = default
+280        object_setattr(self, '__pydantic_private__', pydantic_private)
+
+ + +

This function is meant to behave like a BaseModel method to initialise private attributes.

+ +

It takes context as an argument since that's what pydantic-core passes when calling it.

+ +
Arguments:
+ +
    +
  • self: The BaseModel instance.
    • +
  • __context: The context.
    • +
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+ +
+
+
+
+ + \ No newline at end of file diff --git a/bikes/registers.html b/bikes/registers.html new file mode 100644 index 0000000..5ddc05b --- /dev/null +++ b/bikes/registers.html @@ -0,0 +1,1281 @@ + + + + + + + bikes.registers API documentation + + + + + + + + + +
+
+

+bikes.registers

+ +

Adapters, signers, savers, and loaders for model registries.

+
+ + + + + + 
  1"""Adapters, signers, savers, and loaders for model registries."""
+  2
+  3# %% IMPORTS
+  4
+  5import abc
+  6import typing as T
+  7
+  8import mlflow
+  9import pydantic as pdt
+ 10
+ 11from bikes import models, schemas
+ 12
+ 13# %% TYPES
+ 14
+ 15Info: T.TypeAlias = mlflow.models.model.ModelInfo
+ 16Version: T.TypeAlias = mlflow.entities.model_registry.ModelVersion
+ 17Signature: T.TypeAlias = mlflow.models.ModelSignature
+ 18CustomModel: T.TypeAlias = mlflow.pyfunc.PythonModel
+ 19
+ 20# %% ADAPTERS
+ 21
+ 22
+ 23class CustomAdapter(mlflow.pyfunc.PythonModel):
+ 24    """Adapt a custom model to the MLflow PyFunc flavor.
+ 25
+ 26    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+ 27    """
+ 28
+ 29    def __init__(self, model: models.Model):
+ 30        """Initialize the custom adapter.
+ 31
+ 32        Args:
+ 33            model (models.Model): project model.
+ 34        """
+ 35        self.model = model
+ 36
+ 37    # pylint: disable=arguments-differ, unused-argument
+ 38    def predict(self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs) -> schemas.Outputs:
+ 39        """Generate predictions from a custom model.
+ 40
+ 41        Args:
+ 42            context (mlflow.pyfunc.PythonModelContext): ignored.
+ 43            inputs (schemas.Inputs): inputs for the model.
+ 44
+ 45        Returns:
+ 46            schemas.Outputs: outputs of the model.
+ 47        """
+ 48        return self.model.predict(inputs=inputs)
+ 49
+ 50
+ 51# %% SIGNERS
+ 52
+ 53
+ 54class Signer(abc.ABC, pdt.BaseModel, strict=True):
+ 55    """Base class for making signatures.
+ 56
+ 57    Allow to switch between signing approaches.
+ 58    e.g., automatic inference vs manual signatures
+ 59    https://mlflow.org/docs/latest/models.html#model-signature-and-input-example
+ 60    """
+ 61
+ 62    KIND: str
+ 63
+ 64    @abc.abstractmethod
+ 65    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+ 66        """Make a model signature from inputs/outputs.
+ 67
+ 68        Args:
+ 69            inputs (schemas.Inputs): inputs of the model.
+ 70            outputs (schemas.Outputs): ouputs of the model.
+ 71
+ 72        Returns:
+ 73            ModelSignature: generated signature for the model.
+ 74        """
+ 75
+ 76
+ 77class InferSigner(Signer):
+ 78    """Generate model signatures from data inference."""
+ 79
+ 80    KIND: T.Literal["InferModelSigner"] = "InferModelSigner"
+ 81
+ 82    @T.override
+ 83    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+ 84        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
+ 85
+ 86
+ 87SignerKind = InferSigner
+ 88
+ 89
+ 90# %% SAVERS
+ 91
+ 92
+ 93class Saver(abc.ABC, pdt.BaseModel, strict=True):
+ 94    """Base class for saving models in registry.
+ 95
+ 96    Separate model definition from serialization.
+ 97    e.g., to switch between serialization flavors.
+ 98
+ 99    Attributes:
+100        path (str): model path inside the MLflow artifact store.
+101    """
+102
+103    KIND: str
+104
+105    path: str = "model"
+106
+107    @abc.abstractmethod
+108    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
+109        """Save a model in the model registry.
+110
+111        Args:
+112            model (models.Model): model to save.
+113            signature (Signature): model signature.
+114            input_example (schemas.Inputs): inputs sample.
+115
+116        Returns:
+117            Info: model saving information.
+118        """
+119
+120
+121class CustomSaver(Saver):
+122    """Saver for custom models using the MLflow PyFunc module.
+123
+124    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+125    """
+126
+127    KIND: T.Literal["CustomSaver"] = "CustomSaver"
+128
+129    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
+130        """Save a custom model to the MLflow Model Registry."""
+131        custom = CustomAdapter(model=model)  # adapt model
+132        return mlflow.pyfunc.log_model(
+133            artifact_path=self.path, python_model=custom, signature=signature, input_example=input_example
+134        )
+135
+136
+137SaverKind = CustomSaver
+138
+139
+140# %% LOADERS
+141
+142
+143class Loader(abc.ABC, pdt.BaseModel, strict=True):
+144    """Base class for loading models from registry.
+145
+146    Separate model definition from deserialization.
+147    e.g., to switch between deserialization flavors.
+148    """
+149
+150    KIND: str
+151
+152    @abc.abstractmethod
+153    def load(self, uri: str) -> T.Any:
+154        """Load a model from the model registry.
+155
+156        Args:
+157            uri (str): URI of the model to load.
+158
+159        Returns:
+160            T.Any: model loaded from registry.
+161        """
+162
+163
+164class CustomLoader(Loader):
+165    """Loader for custom models using the MLflow PyFunc module.
+166
+167    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+168    """
+169
+170    KIND: T.Literal["CustomLoader"] = "CustomLoader"
+171
+172    @T.override
+173    def load(self, uri: str) -> CustomModel:
+174        return mlflow.pyfunc.load_model(model_uri=uri)
+175
+176
+177LoaderKind = CustomLoader
+
+ + +
+
+ +
+ + class + CustomAdapter(mlflow.pyfunc.model.PythonModel): + + + +
+ + 
24class CustomAdapter(mlflow.pyfunc.PythonModel):
+25    """Adapt a custom model to the MLflow PyFunc flavor.
+26
+27    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+28    """
+29
+30    def __init__(self, model: models.Model):
+31        """Initialize the custom adapter.
+32
+33        Args:
+34            model (models.Model): project model.
+35        """
+36        self.model = model
+37
+38    # pylint: disable=arguments-differ, unused-argument
+39    def predict(self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs) -> schemas.Outputs:
+40        """Generate predictions from a custom model.
+41
+42        Args:
+43            context (mlflow.pyfunc.PythonModelContext): ignored.
+44            inputs (schemas.Inputs): inputs for the model.
+45
+46        Returns:
+47            schemas.Outputs: outputs of the model.
+48        """
+49        return self.model.predict(inputs=inputs)
+
+ + +

Adapt a custom model to the MLflow PyFunc flavor.

+ +

https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html

+
+ + +
+ +
+ + CustomAdapter(model: bikes.models.Model) + + + +
+ + 
30    def __init__(self, model: models.Model):
+31        """Initialize the custom adapter.
+32
+33        Args:
+34            model (models.Model): project model.
+35        """
+36        self.model = model
+
+ + +

Initialize the custom adapter.

+ +
Arguments:
+ +
    +
  • model (models.Model): project model.
    • +
+
+ + +
+
+ +
+ + def + predict( self, context: mlflow.pyfunc.model.PythonModelContext, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]: + + + +
+ + 
39    def predict(self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs) -> schemas.Outputs:
+40        """Generate predictions from a custom model.
+41
+42        Args:
+43            context (mlflow.pyfunc.PythonModelContext): ignored.
+44            inputs (schemas.Inputs): inputs for the model.
+45
+46        Returns:
+47            schemas.Outputs: outputs of the model.
+48        """
+49        return self.model.predict(inputs=inputs)
+
+ + +

Generate predictions from a custom model.

+ +
Arguments:
+ +
    +
  • context (mlflow.pyfunc.PythonModelContext): ignored.
    • +
  • inputs (schemas.Inputs): inputs for the model.
    • +
+ +
Returns:
+ +
+

schemas.Outputs: outputs of the model.

+
+
+ + +
+
+
Inherited Members
+
+
mlflow.pyfunc.model.PythonModel
+
load_context
+ +
+
+
+
+
+ +
+ + class + Signer(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
55class Signer(abc.ABC, pdt.BaseModel, strict=True):
+56    """Base class for making signatures.
+57
+58    Allow to switch between signing approaches.
+59    e.g., automatic inference vs manual signatures
+60    https://mlflow.org/docs/latest/models.html#model-signature-and-input-example
+61    """
+62
+63    KIND: str
+64
+65    @abc.abstractmethod
+66    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+67        """Make a model signature from inputs/outputs.
+68
+69        Args:
+70            inputs (schemas.Inputs): inputs of the model.
+71            outputs (schemas.Outputs): ouputs of the model.
+72
+73        Returns:
+74            ModelSignature: generated signature for the model.
+75        """
+
+ + +

Base class for making signatures.

+ +

Allow to switch between signing approaches. +e.g., automatic inference vs manual signatures +https://mlflow.org/docs/latest/models.html#model-signature-and-input-example

+
+ + +
+ +
+
@abc.abstractmethod
+ + def + sign( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], outputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature: + + + +
+ + 
65    @abc.abstractmethod
+66    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+67        """Make a model signature from inputs/outputs.
+68
+69        Args:
+70            inputs (schemas.Inputs): inputs of the model.
+71            outputs (schemas.Outputs): ouputs of the model.
+72
+73        Returns:
+74            ModelSignature: generated signature for the model.
+75        """
+
+ + +

Make a model signature from inputs/outputs.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): inputs of the model.
    • +
  • outputs (schemas.Outputs): ouputs of the model.
    • +
+ +
Returns:
+ +
+

ModelSignature: generated signature for the model.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + InferSigner(Signer): + + + +
+ + 
78class InferSigner(Signer):
+79    """Generate model signatures from data inference."""
+80
+81    KIND: T.Literal["InferModelSigner"] = "InferModelSigner"
+82
+83    @T.override
+84    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+85        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
+
+ + +

Generate model signatures from data inference.

+
+ + +
+ +
+
@T.override
+ + def + sign( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], outputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature: + + + +
+ + 
83    @T.override
+84    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+85        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
+
+ + +

Make a model signature from inputs/outputs.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): inputs of the model.
    • +
  • outputs (schemas.Outputs): ouputs of the model.
    • +
+ +
Returns:
+ +
+

ModelSignature: generated signature for the model.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + Saver(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
 94class Saver(abc.ABC, pdt.BaseModel, strict=True):
+ 95    """Base class for saving models in registry.
+ 96
+ 97    Separate model definition from serialization.
+ 98    e.g., to switch between serialization flavors.
+ 99
+100    Attributes:
+101        path (str): model path inside the MLflow artifact store.
+102    """
+103
+104    KIND: str
+105
+106    path: str = "model"
+107
+108    @abc.abstractmethod
+109    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
+110        """Save a model in the model registry.
+111
+112        Args:
+113            model (models.Model): model to save.
+114            signature (Signature): model signature.
+115            input_example (schemas.Inputs): inputs sample.
+116
+117        Returns:
+118            Info: model saving information.
+119        """
+
+ + +

Base class for saving models in registry.

+ +

Separate model definition from serialization. +e.g., to switch between serialization flavors.

+ +
Attributes:
+ +
    +
  • path (str): model path inside the MLflow artifact store.
    • +
+
+ + +
+ +
+
@abc.abstractmethod
+ + def + save( self, model: bikes.models.Model, signature: mlflow.models.signature.ModelSignature, input_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo: + + + +
+ + 
108    @abc.abstractmethod
+109    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
+110        """Save a model in the model registry.
+111
+112        Args:
+113            model (models.Model): model to save.
+114            signature (Signature): model signature.
+115            input_example (schemas.Inputs): inputs sample.
+116
+117        Returns:
+118            Info: model saving information.
+119        """
+
+ + +

Save a model in the model registry.

+ +
Arguments:
+ +
    +
  • model (models.Model): model to save.
    • +
  • signature (Signature): model signature.
    • +
  • input_example (schemas.Inputs): inputs sample.
    • +
+ +
Returns:
+ +
+

Info: model saving information.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + CustomSaver(Saver): + + + +
+ + 
122class CustomSaver(Saver):
+123    """Saver for custom models using the MLflow PyFunc module.
+124
+125    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+126    """
+127
+128    KIND: T.Literal["CustomSaver"] = "CustomSaver"
+129
+130    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
+131        """Save a custom model to the MLflow Model Registry."""
+132        custom = CustomAdapter(model=model)  # adapt model
+133        return mlflow.pyfunc.log_model(
+134            artifact_path=self.path, python_model=custom, signature=signature, input_example=input_example
+135        )
+
+ + +

Saver for custom models using the MLflow PyFunc module.

+ +

https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html

+
+ + +
+ +
+ + def + save( self, model: bikes.models.Model, signature: mlflow.models.signature.ModelSignature, input_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo: + + + +
+ + 
130    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
+131        """Save a custom model to the MLflow Model Registry."""
+132        custom = CustomAdapter(model=model)  # adapt model
+133        return mlflow.pyfunc.log_model(
+134            artifact_path=self.path, python_model=custom, signature=signature, input_example=input_example
+135        )
+
+ + +

Save a custom model to the MLflow Model Registry.

+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + Loader(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
144class Loader(abc.ABC, pdt.BaseModel, strict=True):
+145    """Base class for loading models from registry.
+146
+147    Separate model definition from deserialization.
+148    e.g., to switch between deserialization flavors.
+149    """
+150
+151    KIND: str
+152
+153    @abc.abstractmethod
+154    def load(self, uri: str) -> T.Any:
+155        """Load a model from the model registry.
+156
+157        Args:
+158            uri (str): URI of the model to load.
+159
+160        Returns:
+161            T.Any: model loaded from registry.
+162        """
+
+ + +

Base class for loading models from registry.

+ +

Separate model definition from deserialization. +e.g., to switch between deserialization flavors.

+
+ + +
+ +
+
@abc.abstractmethod
+ + def + load(self, uri: str) -> Any: + + + +
+ + 
153    @abc.abstractmethod
+154    def load(self, uri: str) -> T.Any:
+155        """Load a model from the model registry.
+156
+157        Args:
+158            uri (str): URI of the model to load.
+159
+160        Returns:
+161            T.Any: model loaded from registry.
+162        """
+
+ + +

Load a model from the model registry.

+ +
Arguments:
+ +
    +
  • uri (str): URI of the model to load.
    • +
+ +
Returns:
+ +
+

T.Any: model loaded from registry.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + CustomLoader(Loader): + + + +
+ + 
165class CustomLoader(Loader):
+166    """Loader for custom models using the MLflow PyFunc module.
+167
+168    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+169    """
+170
+171    KIND: T.Literal["CustomLoader"] = "CustomLoader"
+172
+173    @T.override
+174    def load(self, uri: str) -> CustomModel:
+175        return mlflow.pyfunc.load_model(model_uri=uri)
+
+ + +

Loader for custom models using the MLflow PyFunc module.

+ +

https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html

+
+ + +
+ +
+
@T.override
+ + def + load(self, uri: str) -> mlflow.pyfunc.model.PythonModel: + + + +
+ + 
173    @T.override
+174    def load(self, uri: str) -> CustomModel:
+175        return mlflow.pyfunc.load_model(model_uri=uri)
+
+ + +

Load a model from the model registry.

+ +
Arguments:
+ +
    +
  • uri (str): URI of the model to load.
    • +
+ +
Returns:
+ +
+

T.Any: model loaded from registry.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/bikes/schemas.html b/bikes/schemas.html new file mode 100644 index 0000000..73fcccf --- /dev/null +++ b/bikes/schemas.html @@ -0,0 +1,1432 @@ + + + + + + + bikes.schemas API documentation + + + + + + + + + +
+
+

+bikes.schemas

+ +

Define and validate dataframe schemas.

+
+ + + + + + 
 1"""Define and validate dataframe schemas."""
+ 2
+ 3# %% IMPORTS
+ 4
+ 5import pandas as pd
+ 6import pandera as pa
+ 7import pandera.typing as papd
+ 8
+ 9# %% SCHEMAS
+10
+11
+12class Schema(pa.DataFrameModel):
+13    """Base class for a dataframe schema.
+14
+15    Use a schema to type your dataframe object.
+16    e.g., to communicate and validate its fields.
+17    """
+18
+19    class Config:
+20        """Default configuration.
+21
+22        Attributes:
+23            coerce (bool): convert data type if possible.
+24            strict (bool): ensure the data type is correct.
+25        """
+26
+27        coerce: bool = True
+28        strict: bool = True
+29
+30    @classmethod
+31    def check(cls, data: pd.DataFrame, **kwargs):
+32        """Check the data with this schema.
+33
+34        Args:
+35            data (pd.DataFrame): dataframe to check.
+36
+37        Returns:
+38            pd.DataFrame: validated dataframe with schema.
+39        """
+40        return cls.validate(data, **kwargs)
+41
+42
+43class InputsSchema(Schema):
+44    """Schema for the project inputs."""
+45
+46    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+47    dteday: papd.Series[papd.DateTime] = pa.Field()
+48    season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4])
+49    yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1)
+50    mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12)
+51    hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23)
+52    holiday: papd.Series[papd.Bool] = pa.Field()
+53    weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6)
+54    workingday: papd.Series[papd.Bool] = pa.Field()
+55    weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4)
+56    temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+57    atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+58    hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+59    windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+60    casual: papd.Series[papd.UInt32] = pa.Field(ge=0)
+61    registered: papd.Series[papd.UInt32] = pa.Field(ge=0)
+62
+63
+64Inputs = papd.DataFrame[InputsSchema]
+65
+66
+67class TargetsSchema(Schema):
+68    """Schema for the project target."""
+69
+70    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+71    cnt: papd.Series[papd.UInt32] = pa.Field(ge=0)
+72
+73
+74Targets = papd.DataFrame[TargetsSchema]
+75
+76
+77class OutputsSchema(Schema):
+78    """Schema for the project output."""
+79
+80    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+81    prediction: papd.Series[papd.UInt32] = pa.Field(ge=0)
+82
+83
+84Outputs = papd.DataFrame[OutputsSchema]
+
+ + +
+
+ +
+ + class + Schema(pandera.api.pandas.model.DataFrameModel): + + + +
+ + 
13class Schema(pa.DataFrameModel):
+14    """Base class for a dataframe schema.
+15
+16    Use a schema to type your dataframe object.
+17    e.g., to communicate and validate its fields.
+18    """
+19
+20    class Config:
+21        """Default configuration.
+22
+23        Attributes:
+24            coerce (bool): convert data type if possible.
+25            strict (bool): ensure the data type is correct.
+26        """
+27
+28        coerce: bool = True
+29        strict: bool = True
+30
+31    @classmethod
+32    def check(cls, data: pd.DataFrame, **kwargs):
+33        """Check the data with this schema.
+34
+35        Args:
+36            data (pd.DataFrame): dataframe to check.
+37
+38        Returns:
+39            pd.DataFrame: validated dataframe with schema.
+40        """
+41        return cls.validate(data, **kwargs)
+
+ + +

Base class for a dataframe schema.

+ +

Use a schema to type your dataframe object. +e.g., to communicate and validate its fields.

+
+ + +
+ +
+
@docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+ + Schema(*args, **kwargs) + + + +
+ + 
152    @docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+153    def __new__(cls, *args, **kwargs) -> DataFrameBase[TDataFrameModel]:  # type: ignore [misc]
+154        """%(validate_doc)s"""
+155        return cast(
+156            DataFrameBase[TDataFrameModel], cls.validate(*args, **kwargs)
+157        )
+
+ + +

Check if all columns in a dataframe have a column in the Schema.

+ +
Parameters
+ +
    +
  • pd.DataFrame check_obj: the dataframe to be validated.
    • +
  • head: validate the first n rows. Rows overlapping with tail or +sample are de-duplicated.
    • +
  • tail: validate the last n rows. Rows overlapping with head or +sample are de-duplicated.
    • +
  • sample: validate a random sample of n rows. Rows overlapping +with head or tail are de-duplicated.
    • +
  • random_state: random seed for the sample argument.
    • +
  • lazy: if True, lazily evaluates dataframe against all validation +checks and raises a SchemaErrors. Otherwise, raise +SchemaError as soon as one occurs.
    • +
  • inplace: if True, applies coercion to the object of validation, +otherwise creates a copy of the data. +:returns: validated DataFrame
    • +
+ +
Raises
+ +
    +
  • SchemaError: when DataFrame violates built-in or custom +checks.
    • +
+ +

:example:

+ +

Calling schema.validate returns the dataframe.

+ +
+
>>> import pandas as pd
+>>> import pandera as pa
+>>>
+>>> df = pd.DataFrame({
+...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],
+...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]
+... })
+>>>
+>>> schema_withchecks = pa.DataFrameSchema({
+...     "probability": pa.Column(
+...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),
+...
+...     # check that the "category" column contains a few discrete
+...     # values, and the majority of the entries are dogs.
+...     "category": pa.Column(
+...         str, [
+...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),
+...             pa.Check(lambda s: (s == "dog").mean() > 0.5),
+...         ]),
+... })
+>>>
+>>> schema_withchecks.validate(df)[["probability", "category"]]
+   probability category
+0         0.10      dog
+1         0.40      dog
+2         0.52      cat
+3         0.23     duck
+4         0.80      dog
+5         0.76      dog
+
+
+
+ + +
+
+ +
+
@classmethod
+ + def + check(cls, data: pandas.core.frame.DataFrame, **kwargs): + + + +
+ + 
31    @classmethod
+32    def check(cls, data: pd.DataFrame, **kwargs):
+33        """Check the data with this schema.
+34
+35        Args:
+36            data (pd.DataFrame): dataframe to check.
+37
+38        Returns:
+39            pd.DataFrame: validated dataframe with schema.
+40        """
+41        return cls.validate(data, **kwargs)
+
+ + +

Check the data with this schema.

+ +
Arguments:
+ +
    +
  • data (pd.DataFrame): dataframe to check.
    • +
+ +
Returns:
+ +
+

pd.DataFrame: validated dataframe with schema.

+
+
+ + +
+
+
Inherited Members
+
+
pandera.api.pandas.model.DataFrameModel
+
to_schema
+
to_yaml
+
validate
+
strategy
+
example
+
get_metadata
+
pydantic_validate
+ +
+
+
+
+
+ +
+ + class + Schema.Config: + + + +
+ + 
20    class Config:
+21        """Default configuration.
+22
+23        Attributes:
+24            coerce (bool): convert data type if possible.
+25            strict (bool): ensure the data type is correct.
+26        """
+27
+28        coerce: bool = True
+29        strict: bool = True
+
+ + +

Default configuration.

+ +
Attributes:
+ +
    +
  • coerce (bool): convert data type if possible.
    • +
  • strict (bool): ensure the data type is correct.
    • +
+
+ + +
+
+ +
+ + class + InputsSchema(Schema): + + + +
+ + 
44class InputsSchema(Schema):
+45    """Schema for the project inputs."""
+46
+47    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+48    dteday: papd.Series[papd.DateTime] = pa.Field()
+49    season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4])
+50    yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1)
+51    mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12)
+52    hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23)
+53    holiday: papd.Series[papd.Bool] = pa.Field()
+54    weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6)
+55    workingday: papd.Series[papd.Bool] = pa.Field()
+56    weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4)
+57    temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+58    atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+59    hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+60    windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+61    casual: papd.Series[papd.UInt32] = pa.Field(ge=0)
+62    registered: papd.Series[papd.UInt32] = pa.Field(ge=0)
+
+ + +

Schema for the project inputs.

+
+ + +
+ +
+
@docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+ + InputsSchema(*args, **kwargs) + + + +
+ + 
152    @docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+153    def __new__(cls, *args, **kwargs) -> DataFrameBase[TDataFrameModel]:  # type: ignore [misc]
+154        """%(validate_doc)s"""
+155        return cast(
+156            DataFrameBase[TDataFrameModel], cls.validate(*args, **kwargs)
+157        )
+
+ + +

Check if all columns in a dataframe have a column in the Schema.

+ +
Parameters
+ +
    +
  • pd.DataFrame check_obj: the dataframe to be validated.
    • +
  • head: validate the first n rows. Rows overlapping with tail or +sample are de-duplicated.
    • +
  • tail: validate the last n rows. Rows overlapping with head or +sample are de-duplicated.
    • +
  • sample: validate a random sample of n rows. Rows overlapping +with head or tail are de-duplicated.
    • +
  • random_state: random seed for the sample argument.
    • +
  • lazy: if True, lazily evaluates dataframe against all validation +checks and raises a SchemaErrors. Otherwise, raise +SchemaError as soon as one occurs.
    • +
  • inplace: if True, applies coercion to the object of validation, +otherwise creates a copy of the data. +:returns: validated DataFrame
    • +
+ +
Raises
+ +
    +
  • SchemaError: when DataFrame violates built-in or custom +checks.
    • +
+ +

:example:

+ +

Calling schema.validate returns the dataframe.

+ +
+
>>> import pandas as pd
+>>> import pandera as pa
+>>>
+>>> df = pd.DataFrame({
+...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],
+...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]
+... })
+>>>
+>>> schema_withchecks = pa.DataFrameSchema({
+...     "probability": pa.Column(
+...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),
+...
+...     # check that the "category" column contains a few discrete
+...     # values, and the majority of the entries are dogs.
+...     "category": pa.Column(
+...         str, [
+...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),
+...             pa.Check(lambda s: (s == "dog").mean() > 0.5),
+...         ]),
+... })
+>>>
+>>> schema_withchecks.validate(df)[["probability", "category"]]
+   probability category
+0         0.10      dog
+1         0.40      dog
+2         0.52      cat
+3         0.23     duck
+4         0.80      dog
+5         0.76      dog
+
+
+
+ + +
+
+
+ instant: pandera.typing.pandas.Index[pandera.dtypes.UInt32] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ dteday: pandera.typing.pandas.Series[pandera.dtypes.Timestamp] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ season: pandera.typing.pandas.Series[pandera.dtypes.UInt8] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ yr: pandera.typing.pandas.Series[pandera.dtypes.UInt8] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ mnth: pandera.typing.pandas.Series[pandera.dtypes.UInt8] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ hr: pandera.typing.pandas.Series[pandera.dtypes.UInt8] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ holiday: pandera.typing.pandas.Series[pandera.dtypes.Bool] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ weekday: pandera.typing.pandas.Series[pandera.dtypes.UInt8] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ workingday: pandera.typing.pandas.Series[pandera.dtypes.Bool] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ weathersit: pandera.typing.pandas.Series[pandera.dtypes.UInt8] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ temp: pandera.typing.pandas.Series[pandera.dtypes.Float16] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ atemp: pandera.typing.pandas.Series[pandera.dtypes.Float16] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ hum: pandera.typing.pandas.Series[pandera.dtypes.Float16] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ windspeed: pandera.typing.pandas.Series[pandera.dtypes.Float16] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ casual: pandera.typing.pandas.Series[pandera.dtypes.UInt32] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ registered: pandera.typing.pandas.Series[pandera.dtypes.UInt32] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
Inherited Members
+
+
Schema
+
check
+ +
+
pandera.api.pandas.model.DataFrameModel
+
to_schema
+
to_yaml
+
validate
+
strategy
+
example
+
get_metadata
+
pydantic_validate
+ +
+
+
+
+
+
+ + class + InputsSchema.Config(pandera.api.pandas.model_config.BaseConfig): + + +
+ + +

Define DataFrameSchema-wide options.

+ +

new in 0.5.0

+
+ + +
+
+ +
+ + class + TargetsSchema(Schema): + + + +
+ + 
68class TargetsSchema(Schema):
+69    """Schema for the project target."""
+70
+71    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+72    cnt: papd.Series[papd.UInt32] = pa.Field(ge=0)
+
+ + +

Schema for the project target.

+
+ + +
+ +
+
@docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+ + TargetsSchema(*args, **kwargs) + + + +
+ + 
152    @docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+153    def __new__(cls, *args, **kwargs) -> DataFrameBase[TDataFrameModel]:  # type: ignore [misc]
+154        """%(validate_doc)s"""
+155        return cast(
+156            DataFrameBase[TDataFrameModel], cls.validate(*args, **kwargs)
+157        )
+
+ + +

Check if all columns in a dataframe have a column in the Schema.

+ +
Parameters
+ +
    +
  • pd.DataFrame check_obj: the dataframe to be validated.
    • +
  • head: validate the first n rows. Rows overlapping with tail or +sample are de-duplicated.
    • +
  • tail: validate the last n rows. Rows overlapping with head or +sample are de-duplicated.
    • +
  • sample: validate a random sample of n rows. Rows overlapping +with head or tail are de-duplicated.
    • +
  • random_state: random seed for the sample argument.
    • +
  • lazy: if True, lazily evaluates dataframe against all validation +checks and raises a SchemaErrors. Otherwise, raise +SchemaError as soon as one occurs.
    • +
  • inplace: if True, applies coercion to the object of validation, +otherwise creates a copy of the data. +:returns: validated DataFrame
    • +
+ +
Raises
+ +
    +
  • SchemaError: when DataFrame violates built-in or custom +checks.
    • +
+ +

:example:

+ +

Calling schema.validate returns the dataframe.

+ +
+
>>> import pandas as pd
+>>> import pandera as pa
+>>>
+>>> df = pd.DataFrame({
+...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],
+...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]
+... })
+>>>
+>>> schema_withchecks = pa.DataFrameSchema({
+...     "probability": pa.Column(
+...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),
+...
+...     # check that the "category" column contains a few discrete
+...     # values, and the majority of the entries are dogs.
+...     "category": pa.Column(
+...         str, [
+...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),
+...             pa.Check(lambda s: (s == "dog").mean() > 0.5),
+...         ]),
+... })
+>>>
+>>> schema_withchecks.validate(df)[["probability", "category"]]
+   probability category
+0         0.10      dog
+1         0.40      dog
+2         0.52      cat
+3         0.23     duck
+4         0.80      dog
+5         0.76      dog
+
+
+
+ + +
+
+
+ instant: pandera.typing.pandas.Index[pandera.dtypes.UInt32] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ cnt: pandera.typing.pandas.Series[pandera.dtypes.UInt32] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
Inherited Members
+
+
Schema
+
check
+ +
+
pandera.api.pandas.model.DataFrameModel
+
to_schema
+
to_yaml
+
validate
+
strategy
+
example
+
get_metadata
+
pydantic_validate
+ +
+
+
+
+
+
+ + class + TargetsSchema.Config(pandera.api.pandas.model_config.BaseConfig): + + +
+ + +

Define DataFrameSchema-wide options.

+ +

new in 0.5.0

+
+ + +
+
+ +
+ + class + OutputsSchema(Schema): + + + +
+ + 
78class OutputsSchema(Schema):
+79    """Schema for the project output."""
+80
+81    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+82    prediction: papd.Series[papd.UInt32] = pa.Field(ge=0)
+
+ + +

Schema for the project output.

+
+ + +
+ +
+
@docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+ + OutputsSchema(*args, **kwargs) + + + +
+ + 
152    @docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)
+153    def __new__(cls, *args, **kwargs) -> DataFrameBase[TDataFrameModel]:  # type: ignore [misc]
+154        """%(validate_doc)s"""
+155        return cast(
+156            DataFrameBase[TDataFrameModel], cls.validate(*args, **kwargs)
+157        )
+
+ + +

Check if all columns in a dataframe have a column in the Schema.

+ +
Parameters
+ +
    +
  • pd.DataFrame check_obj: the dataframe to be validated.
    • +
  • head: validate the first n rows. Rows overlapping with tail or +sample are de-duplicated.
    • +
  • tail: validate the last n rows. Rows overlapping with head or +sample are de-duplicated.
    • +
  • sample: validate a random sample of n rows. Rows overlapping +with head or tail are de-duplicated.
    • +
  • random_state: random seed for the sample argument.
    • +
  • lazy: if True, lazily evaluates dataframe against all validation +checks and raises a SchemaErrors. Otherwise, raise +SchemaError as soon as one occurs.
    • +
  • inplace: if True, applies coercion to the object of validation, +otherwise creates a copy of the data. +:returns: validated DataFrame
    • +
+ +
Raises
+ +
    +
  • SchemaError: when DataFrame violates built-in or custom +checks.
    • +
+ +

:example:

+ +

Calling schema.validate returns the dataframe.

+ +
+
>>> import pandas as pd
+>>> import pandera as pa
+>>>
+>>> df = pd.DataFrame({
+...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],
+...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]
+... })
+>>>
+>>> schema_withchecks = pa.DataFrameSchema({
+...     "probability": pa.Column(
+...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),
+...
+...     # check that the "category" column contains a few discrete
+...     # values, and the majority of the entries are dogs.
+...     "category": pa.Column(
+...         str, [
+...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),
+...             pa.Check(lambda s: (s == "dog").mean() > 0.5),
+...         ]),
+... })
+>>>
+>>> schema_withchecks.validate(df)[["probability", "category"]]
+   probability category
+0         0.10      dog
+1         0.40      dog
+2         0.52      cat
+3         0.23     duck
+4         0.80      dog
+5         0.76      dog
+
+
+
+ + +
+
+
+ instant: pandera.typing.pandas.Index[pandera.dtypes.UInt32] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
+ prediction: pandera.typing.pandas.Series[pandera.dtypes.UInt32] + + +
+ + +

Captures extra information about a field.

+ +

new in 0.5.0

+
+ + +
+
+
Inherited Members
+
+
Schema
+
check
+ +
+
pandera.api.pandas.model.DataFrameModel
+
to_schema
+
to_yaml
+
validate
+
strategy
+
example
+
get_metadata
+
pydantic_validate
+ +
+
+
+
+
+
+ + class + OutputsSchema.Config(pandera.api.pandas.model_config.BaseConfig): + + +
+ + +

Define DataFrameSchema-wide options.

+ +

new in 0.5.0

+
+ + +
+
+ + \ No newline at end of file diff --git a/bikes/scripts.html b/bikes/scripts.html new file mode 100644 index 0000000..ff132c3 --- /dev/null +++ b/bikes/scripts.html @@ -0,0 +1,433 @@ + + + + + + + bikes.scripts API documentation + + + + + + + + + +
+
+

+bikes.scripts

+ +

Entry point of the program.

+
+ + + + + + 
 1"""Entry point of the program."""
+ 2
+ 3# %% IMPORTS
+ 4
+ 5import argparse
+ 6import json
+ 7
+ 8import pydantic as pdt
+ 9import pydantic_settings as pdts
+10
+11from bikes import configs, jobs
+12
+13# %% SETTINGS
+14
+15
+16class Settings(pdts.BaseSettings, strict=True):
+17    """Settings for the program.
+18
+19    Attributes:
+20        job (jobs.JobKind): job associated with the settings.
+21    """
+22
+23    job: jobs.JobKind = pdt.Field(..., discriminator="KIND")
+24
+25
+26# %% PARSERS
+27
+28parser = argparse.ArgumentParser(description="Run a single job from external settings.")
+29parser.add_argument("configs", nargs="+", help="Config files for the job (local or remote).")
+30parser.add_argument("-e", "--extras", nargs="+", default=[], help="Config strings for the job.")
+31parser.add_argument("-s", "--schema", action="store_true", help="Print settings schema and exit.")
+32
+33# %% SCRIPTS
+34
+35
+36def main(argv: list[str] | None = None) -> int:
+37    """Main function of the program.
+38
+39    Args:
+40        argv (list[str] | None, optional): program arguments. Defaults to None for sys.argv.
+41
+42    Returns:
+43        int: status code of the program.
+44    """
+45    args = parser.parse_args(argv)
+46    if args.schema is True:
+47        schema = Settings.model_json_schema()
+48        print(json.dumps(schema, indent=2))
+49        return 0  # success
+50    files = map(configs.parse_file, args.configs)
+51    strings = map(configs.parse_string, args.extras)
+52    config = configs.merge_configs([*files, *strings])
+53    object_ = configs.to_object(config)  # convert to dict
+54    settings = Settings.model_validate(object_)  # to pydantic
+55    with settings.job as runner:
+56        runner.run()  # execute job
+57    return 0  # success
+
+ + +
+
+ +
+ + class + Settings(pydantic_settings.main.BaseSettings): + + + +
+ + 
17class Settings(pdts.BaseSettings, strict=True):
+18    """Settings for the program.
+19
+20    Attributes:
+21        job (jobs.JobKind): job associated with the settings.
+22    """
+23
+24    job: jobs.JobKind = pdt.Field(..., discriminator="KIND")
+
+ + +

Settings for the program.

+ +
Attributes:
+ +
    +
  • job (jobs.JobKind): job associated with the settings.
    • +
+
+ + +
+
Inherited Members
+
+
pydantic_settings.main.BaseSettings
+
BaseSettings
+
settings_customise_sources
+ +
+
pydantic.main.BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + def + main(argv: list[str] | None = None) -> int: + + + +
+ + 
37def main(argv: list[str] | None = None) -> int:
+38    """Main function of the program.
+39
+40    Args:
+41        argv (list[str] | None, optional): program arguments. Defaults to None for sys.argv.
+42
+43    Returns:
+44        int: status code of the program.
+45    """
+46    args = parser.parse_args(argv)
+47    if args.schema is True:
+48        schema = Settings.model_json_schema()
+49        print(json.dumps(schema, indent=2))
+50        return 0  # success
+51    files = map(configs.parse_file, args.configs)
+52    strings = map(configs.parse_string, args.extras)
+53    config = configs.merge_configs([*files, *strings])
+54    object_ = configs.to_object(config)  # convert to dict
+55    settings = Settings.model_validate(object_)  # to pydantic
+56    with settings.job as runner:
+57        runner.run()  # execute job
+58    return 0  # success
+
+ + +

Main function of the program.

+ +
Arguments:
+ +
    +
  • argv (list[str] | None, optional): program arguments. Defaults to None for sys.argv.
    • +
+ +
Returns:
+ +
+

int: status code of the program.

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/bikes/searchers.html b/bikes/searchers.html new file mode 100644 index 0000000..85fce09 --- /dev/null +++ b/bikes/searchers.html @@ -0,0 +1,681 @@ + + + + + + + bikes.searchers API documentation + + + + + + + + + +
+
+

+bikes.searchers

+ +

Find the best hyperparameters for a model.

+
+ + + + + + 
  1"""Find the best hyperparameters for a model."""
+  2
+  3# %% IMPORTS
+  4
+  5import abc
+  6import typing as T
+  7
+  8import pandas as pd
+  9import pydantic as pdt
+ 10from sklearn import model_selection
+ 11
+ 12from bikes import metrics, models, schemas, splitters
+ 13
+ 14# %% TYPES
+ 15
+ 16# Grid of model params
+ 17# {param name -> param values}
+ 18Grid = dict[str, list[T.Any]]
+ 19
+ 20# results of a model search
+ 21# (results, best score, best params)
+ 22Results = tuple[pd.DataFrame, float, models.Params]
+ 23
+ 24# cross-validation options for searchers
+ 25CrossValidation = int | splitters.Splits | splitters.Splitter
+ 26
+ 27# %% SEARCHERS
+ 28
+ 29
+ 30class Searcher(abc.ABC, pdt.BaseModel, strict=True):
+ 31    """Base class for a searcher.
+ 32
+ 33    note: use searcher to tune models.
+ 34    e.g., to find the best model params.
+ 35    """
+ 36
+ 37    KIND: str
+ 38
+ 39    @abc.abstractmethod
+ 40    def search(
+ 41        self,
+ 42        model: models.Model,
+ 43        metric: metrics.Metric,
+ 44        cv: CrossValidation,
+ 45        inputs: schemas.Inputs,
+ 46        targets: schemas.Targets,
+ 47    ) -> Results:
+ 48        """Search the best model for the given inputs and targets.
+ 49
+ 50        Args:
+ 51            model (models.Model): machine learning model to tune.
+ 52            metric (metrics.Metric): main metric to optimize.
+ 53            cv (CrossValidation): structure for cross-fold.
+ 54            inputs (schemas.Inputs): model inputs for tuning.
+ 55            targets (schemas.Targets): model targets for tuning.
+ 56
+ 57        Returns:
+ 58            Results: all the results of the tuning process.
+ 59        """
+ 60
+ 61
+ 62class GridCVSearcher(Searcher):
+ 63    """Grid searcher with cross-folds.
+ 64
+ 65    Attributes:
+ 66        param_grid (Grid): mapping of param key -> values.
+ 67        n_jobs (int, optional): number of jobs to run in parallel.
+ 68        refit (bool): refit the model after the tuning.
+ 69        verbose (int): set the search verbosity level.
+ 70        error_score (str | float): strategy or value on error.
+ 71        return_train_score (bool): include train scores.
+ 72    """
+ 73
+ 74    KIND: T.Literal["GridCVSearcher"] = "GridCVSearcher"
+ 75
+ 76    # public
+ 77    param_grid: Grid
+ 78    n_jobs: int | None = None
+ 79    refit: bool = False
+ 80    verbose: int = 3
+ 81    error_score: str | float = "raise"
+ 82    return_train_score: bool = False
+ 83
+ 84    @T.override
+ 85    def search(
+ 86        self,
+ 87        model: models.Model,
+ 88        metric: metrics.Metric,
+ 89        cv: CrossValidation,
+ 90        inputs: schemas.Inputs,
+ 91        targets: schemas.Targets,
+ 92    ) -> Results:
+ 93        searcher = model_selection.GridSearchCV(
+ 94            estimator=model,
+ 95            scoring=metric.scorer,
+ 96            cv=cv,
+ 97            param_grid=self.param_grid,
+ 98            n_jobs=self.n_jobs,
+ 99            refit=self.refit,
+100            verbose=self.verbose,
+101            error_score=self.error_score,
+102            return_train_score=self.return_train_score,
+103        )
+104        searcher.fit(inputs, targets)
+105        results = pd.DataFrame(searcher.cv_results_)
+106        return results, searcher.best_score_, searcher.best_params_
+107
+108
+109SearcherKind = GridCVSearcher
+
+ + +
+
+ +
+ + class + Searcher(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
31class Searcher(abc.ABC, pdt.BaseModel, strict=True):
+32    """Base class for a searcher.
+33
+34    note: use searcher to tune models.
+35    e.g., to find the best model params.
+36    """
+37
+38    KIND: str
+39
+40    @abc.abstractmethod
+41    def search(
+42        self,
+43        model: models.Model,
+44        metric: metrics.Metric,
+45        cv: CrossValidation,
+46        inputs: schemas.Inputs,
+47        targets: schemas.Targets,
+48    ) -> Results:
+49        """Search the best model for the given inputs and targets.
+50
+51        Args:
+52            model (models.Model): machine learning model to tune.
+53            metric (metrics.Metric): main metric to optimize.
+54            cv (CrossValidation): structure for cross-fold.
+55            inputs (schemas.Inputs): model inputs for tuning.
+56            targets (schemas.Targets): model targets for tuning.
+57
+58        Returns:
+59            Results: all the results of the tuning process.
+60        """
+
+ + +

Base class for a searcher.

+ +

note: use searcher to tune models. +e.g., to find the best model params.

+
+ + +
+ +
+
@abc.abstractmethod
+ + def + search( self, model: bikes.models.Model, metric: bikes.metrics.Metric, cv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter], inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]: + + + +
+ + 
40    @abc.abstractmethod
+41    def search(
+42        self,
+43        model: models.Model,
+44        metric: metrics.Metric,
+45        cv: CrossValidation,
+46        inputs: schemas.Inputs,
+47        targets: schemas.Targets,
+48    ) -> Results:
+49        """Search the best model for the given inputs and targets.
+50
+51        Args:
+52            model (models.Model): machine learning model to tune.
+53            metric (metrics.Metric): main metric to optimize.
+54            cv (CrossValidation): structure for cross-fold.
+55            inputs (schemas.Inputs): model inputs for tuning.
+56            targets (schemas.Targets): model targets for tuning.
+57
+58        Returns:
+59            Results: all the results of the tuning process.
+60        """
+
+ + +

Search the best model for the given inputs and targets.

+ +
Arguments:
+ +
    +
  • model (models.Model): machine learning model to tune.
    • +
  • metric (metrics.Metric): main metric to optimize.
    • +
  • cv (CrossValidation): structure for cross-fold.
    • +
  • inputs (schemas.Inputs): model inputs for tuning.
    • +
  • targets (schemas.Targets): model targets for tuning.
    • +
+ +
Returns:
+ +
+

Results: all the results of the tuning process.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + GridCVSearcher(Searcher): + + + +
+ + 
 63class GridCVSearcher(Searcher):
+ 64    """Grid searcher with cross-folds.
+ 65
+ 66    Attributes:
+ 67        param_grid (Grid): mapping of param key -> values.
+ 68        n_jobs (int, optional): number of jobs to run in parallel.
+ 69        refit (bool): refit the model after the tuning.
+ 70        verbose (int): set the search verbosity level.
+ 71        error_score (str | float): strategy or value on error.
+ 72        return_train_score (bool): include train scores.
+ 73    """
+ 74
+ 75    KIND: T.Literal["GridCVSearcher"] = "GridCVSearcher"
+ 76
+ 77    # public
+ 78    param_grid: Grid
+ 79    n_jobs: int | None = None
+ 80    refit: bool = False
+ 81    verbose: int = 3
+ 82    error_score: str | float = "raise"
+ 83    return_train_score: bool = False
+ 84
+ 85    @T.override
+ 86    def search(
+ 87        self,
+ 88        model: models.Model,
+ 89        metric: metrics.Metric,
+ 90        cv: CrossValidation,
+ 91        inputs: schemas.Inputs,
+ 92        targets: schemas.Targets,
+ 93    ) -> Results:
+ 94        searcher = model_selection.GridSearchCV(
+ 95            estimator=model,
+ 96            scoring=metric.scorer,
+ 97            cv=cv,
+ 98            param_grid=self.param_grid,
+ 99            n_jobs=self.n_jobs,
+100            refit=self.refit,
+101            verbose=self.verbose,
+102            error_score=self.error_score,
+103            return_train_score=self.return_train_score,
+104        )
+105        searcher.fit(inputs, targets)
+106        results = pd.DataFrame(searcher.cv_results_)
+107        return results, searcher.best_score_, searcher.best_params_
+
+ + +

Grid searcher with cross-folds.

+ +
Attributes:
+ +
    +
  • param_grid (Grid): mapping of param key -> values.
    • +
  • n_jobs (int, optional): number of jobs to run in parallel.
    • +
  • refit (bool): refit the model after the tuning.
    • +
  • verbose (int): set the search verbosity level.
    • +
  • error_score (str | float): strategy or value on error.
    • +
  • return_train_score (bool): include train scores.
    • +
+
+ + +
+ +
+
@T.override
+ + def + search( self, model: bikes.models.Model, metric: bikes.metrics.Metric, cv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter], inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]: + + + +
+ + 
 85    @T.override
+ 86    def search(
+ 87        self,
+ 88        model: models.Model,
+ 89        metric: metrics.Metric,
+ 90        cv: CrossValidation,
+ 91        inputs: schemas.Inputs,
+ 92        targets: schemas.Targets,
+ 93    ) -> Results:
+ 94        searcher = model_selection.GridSearchCV(
+ 95            estimator=model,
+ 96            scoring=metric.scorer,
+ 97            cv=cv,
+ 98            param_grid=self.param_grid,
+ 99            n_jobs=self.n_jobs,
+100            refit=self.refit,
+101            verbose=self.verbose,
+102            error_score=self.error_score,
+103            return_train_score=self.return_train_score,
+104        )
+105        searcher.fit(inputs, targets)
+106        results = pd.DataFrame(searcher.cv_results_)
+107        return results, searcher.best_score_, searcher.best_params_
+
+ + +

Search the best model for the given inputs and targets.

+ +
Arguments:
+ +
    +
  • model (models.Model): machine learning model to tune.
    • +
  • metric (metrics.Metric): main metric to optimize.
    • +
  • cv (CrossValidation): structure for cross-fold.
    • +
  • inputs (schemas.Inputs): model inputs for tuning.
    • +
  • targets (schemas.Targets): model targets for tuning.
    • +
+ +
Returns:
+ +
+

Results: all the results of the tuning process.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/bikes/services.html b/bikes/services.html new file mode 100644 index 0000000..804b16f --- /dev/null +++ b/bikes/services.html @@ -0,0 +1,953 @@ + + + + + + + bikes.services API documentation + + + + + + + + + +
+
+

+bikes.services

+ +

Manage global context during execution.

+
+ + + + + + 
  1"""Manage global context during execution."""
+  2
+  3# %% IMPORTS
+  4
+  5import abc
+  6import sys
+  7import typing as T
+  8
+  9import mlflow
+ 10import pydantic as pdt
+ 11from loguru import logger
+ 12from mlflow.tracking import MlflowClient
+ 13
+ 14# %% SERVICES
+ 15
+ 16
+ 17class Service(abc.ABC, pdt.BaseModel, strict=True):
+ 18    """Base class for a global service.
+ 19
+ 20    Use services to manage global contexts.
+ 21    e.g., logger object, mlflow client, spark context, ...
+ 22    """
+ 23
+ 24    @abc.abstractmethod
+ 25    def start(self) -> None:
+ 26        """Start the service."""
+ 27
+ 28    def stop(self) -> None:
+ 29        """Stop the service."""
+ 30        # does nothing by default
+ 31
+ 32
+ 33class LoggerService(Service):
+ 34    """Service for logging messages.
+ 35
+ 36    https://loguru.readthedocs.io/en/stable/api/logger.html
+ 37
+ 38    Attributes:
+ 39        sink (str): logging output.
+ 40        level (str): logging level.
+ 41        format (str): logging format.
+ 42        colorize (bool): colorize output.
+ 43        serialize (bool): convert to JSON.
+ 44        backtrace (bool): enable exception trace.
+ 45        diagnose (bool): enable variable display.
+ 46        catch (bool): catch errors during log handling.
+ 47    """
+ 48
+ 49    sink: str = "stderr"
+ 50    level: str = "INFO"
+ 51    format: str = (
+ 52        "<green>[{time:YYYY-MM-DD HH:mm:ss.SSS}]</green>"
+ 53        "<level>[{level}]</level>"
+ 54        "<cyan>[{name}:{function}:{line}]</cyan>"
+ 55        " <level>{message}</level>"
+ 56    )
+ 57    colorize: bool = True
+ 58    serialize: bool = False
+ 59    backtrace: bool = True
+ 60    diagnose: bool = False
+ 61    catch: bool = True
+ 62
+ 63    @T.override
+ 64    def start(self) -> None:
+ 65        # sinks
+ 66        sinks = {
+ 67            "stderr": sys.stderr,
+ 68            "stdout": sys.stdout,
+ 69        }
+ 70        # cleanup
+ 71        logger.remove()
+ 72        # convert
+ 73        config = self.model_dump()
+ 74        # replace
+ 75        # - use standard sinks or keep the original
+ 76        config["sink"] = sinks.get(config["sink"], config["sink"])
+ 77        # config
+ 78        logger.add(**config)
+ 79
+ 80
+ 81class MLflowService(Service):
+ 82    """Service for MLflow tracking and registry.
+ 83
+ 84    Attributes:
+ 85        autolog_disable (bool): disable autologging.
+ 86        autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
+ 87        autolog_exclusive (bool): If True, enables exclusive autologging.
+ 88        autolog_log_input_examples (bool): If True, logs input examples during autologging.
+ 89        autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
+ 90        autolog_log_models (bool): If True, enables logging of models during autologging.
+ 91        autolog_log_datasets (bool): If True, logs datasets used during autologging.
+ 92        autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
+ 93        tracking_uri (str): The URI for the MLflow tracking server.
+ 94        experiment_name (str): The name of the experiment to log runs under.
+ 95        registry_uri (str): The URI for the MLflow model registry.
+ 96        registry_name (str): The name of the registry.
+ 97    """
+ 98
+ 99    # autolog
+100    autolog_disable: bool = False
+101    autolog_disable_for_unsupported_versions: bool = False
+102    autolog_exclusive: bool = False
+103    autolog_log_input_examples: bool = True
+104    autolog_log_model_signatures: bool = True
+105    autolog_log_models: bool = False
+106    autolog_log_datasets: bool = True
+107    autolog_silent: bool = False
+108    # tracking
+109    tracking_uri: str = "http://localhost:5000"
+110    experiment_name: str = "bikes"
+111    # registry
+112    registry_uri: str = "http://localhost:5000"
+113    registry_name: str = "bikes"
+114
+115    def start(self):
+116        """Start the mlflow service."""
+117        # uri
+118        mlflow.set_tracking_uri(uri=self.tracking_uri)
+119        mlflow.set_registry_uri(uri=self.registry_uri)
+120        # experiment
+121        mlflow.set_experiment(experiment_name=self.experiment_name)
+122        # autologging
+123        mlflow.autolog(
+124            disable=self.autolog_disable,
+125            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
+126            exclusive=self.autolog_exclusive,
+127            log_input_examples=self.autolog_log_input_examples,
+128            log_model_signatures=self.autolog_log_model_signatures,
+129            log_models=self.autolog_log_models,
+130            silent=self.autolog_silent,
+131        )
+132
+133    def client(self) -> MlflowClient:
+134        """Get an instance of MLflow client."""
+135        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
+136
+137    def register(self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.ModelVersion:
+138        """Register a model to mlflow registry.
+139
+140        Args:
+141            run_id (str): id of mlflow run.
+142            path (str): path of artifact.
+143            alias (str): model alias.
+144
+145        Returns:
+146            mlflow.entities.model_registry.ModelVersion: registered version.
+147        """
+148        client = self.client()
+149        model_uri = f"runs:/{run_id}/{path}"
+150        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+151        client.set_registered_model_alias(name=self.registry_name, alias=alias, version=version.version)
+152        return version
+
+ + +
+
+ +
+ + class + Service(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
18class Service(abc.ABC, pdt.BaseModel, strict=True):
+19    """Base class for a global service.
+20
+21    Use services to manage global contexts.
+22    e.g., logger object, mlflow client, spark context, ...
+23    """
+24
+25    @abc.abstractmethod
+26    def start(self) -> None:
+27        """Start the service."""
+28
+29    def stop(self) -> None:
+30        """Stop the service."""
+31        # does nothing by default
+
+ + +

Base class for a global service.

+ +

Use services to manage global contexts. +e.g., logger object, mlflow client, spark context, ...

+
+ + +
+ +
+
@abc.abstractmethod
+ + def + start(self) -> None: + + + +
+ + 
25    @abc.abstractmethod
+26    def start(self) -> None:
+27        """Start the service."""
+
+ + +

Start the service.

+
+ + +
+
+ +
+ + def + stop(self) -> None: + + + +
+ + 
29    def stop(self) -> None:
+30        """Stop the service."""
+31        # does nothing by default
+
+ + +

Stop the service.

+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + LoggerService(Service): + + + +
+ + 
34class LoggerService(Service):
+35    """Service for logging messages.
+36
+37    https://loguru.readthedocs.io/en/stable/api/logger.html
+38
+39    Attributes:
+40        sink (str): logging output.
+41        level (str): logging level.
+42        format (str): logging format.
+43        colorize (bool): colorize output.
+44        serialize (bool): convert to JSON.
+45        backtrace (bool): enable exception trace.
+46        diagnose (bool): enable variable display.
+47        catch (bool): catch errors during log handling.
+48    """
+49
+50    sink: str = "stderr"
+51    level: str = "INFO"
+52    format: str = (
+53        "<green>[{time:YYYY-MM-DD HH:mm:ss.SSS}]</green>"
+54        "<level>[{level}]</level>"
+55        "<cyan>[{name}:{function}:{line}]</cyan>"
+56        " <level>{message}</level>"
+57    )
+58    colorize: bool = True
+59    serialize: bool = False
+60    backtrace: bool = True
+61    diagnose: bool = False
+62    catch: bool = True
+63
+64    @T.override
+65    def start(self) -> None:
+66        # sinks
+67        sinks = {
+68            "stderr": sys.stderr,
+69            "stdout": sys.stdout,
+70        }
+71        # cleanup
+72        logger.remove()
+73        # convert
+74        config = self.model_dump()
+75        # replace
+76        # - use standard sinks or keep the original
+77        config["sink"] = sinks.get(config["sink"], config["sink"])
+78        # config
+79        logger.add(**config)
+
+ + +

Service for logging messages.

+ +

https://loguru.readthedocs.io/en/stable/api/logger.html

+ +
Attributes:
+ +
    +
  • sink (str): logging output.
    • +
  • level (str): logging level.
    • +
  • format (str): logging format.
    • +
  • colorize (bool): colorize output.
    • +
  • serialize (bool): convert to JSON.
    • +
  • backtrace (bool): enable exception trace.
    • +
  • diagnose (bool): enable variable display.
    • +
  • catch (bool): catch errors during log handling.
    • +
+
+ + +
+ +
+
@T.override
+ + def + start(self) -> None: + + + +
+ + 
64    @T.override
+65    def start(self) -> None:
+66        # sinks
+67        sinks = {
+68            "stderr": sys.stderr,
+69            "stdout": sys.stdout,
+70        }
+71        # cleanup
+72        logger.remove()
+73        # convert
+74        config = self.model_dump()
+75        # replace
+76        # - use standard sinks or keep the original
+77        config["sink"] = sinks.get(config["sink"], config["sink"])
+78        # config
+79        logger.add(**config)
+
+ + +

Start the service.

+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
Service
+
stop
+ +
+
+
+
+
+ +
+ + class + MLflowService(Service): + + + +
+ + 
 82class MLflowService(Service):
+ 83    """Service for MLflow tracking and registry.
+ 84
+ 85    Attributes:
+ 86        autolog_disable (bool): disable autologging.
+ 87        autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
+ 88        autolog_exclusive (bool): If True, enables exclusive autologging.
+ 89        autolog_log_input_examples (bool): If True, logs input examples during autologging.
+ 90        autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
+ 91        autolog_log_models (bool): If True, enables logging of models during autologging.
+ 92        autolog_log_datasets (bool): If True, logs datasets used during autologging.
+ 93        autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
+ 94        tracking_uri (str): The URI for the MLflow tracking server.
+ 95        experiment_name (str): The name of the experiment to log runs under.
+ 96        registry_uri (str): The URI for the MLflow model registry.
+ 97        registry_name (str): The name of the registry.
+ 98    """
+ 99
+100    # autolog
+101    autolog_disable: bool = False
+102    autolog_disable_for_unsupported_versions: bool = False
+103    autolog_exclusive: bool = False
+104    autolog_log_input_examples: bool = True
+105    autolog_log_model_signatures: bool = True
+106    autolog_log_models: bool = False
+107    autolog_log_datasets: bool = True
+108    autolog_silent: bool = False
+109    # tracking
+110    tracking_uri: str = "http://localhost:5000"
+111    experiment_name: str = "bikes"
+112    # registry
+113    registry_uri: str = "http://localhost:5000"
+114    registry_name: str = "bikes"
+115
+116    def start(self):
+117        """Start the mlflow service."""
+118        # uri
+119        mlflow.set_tracking_uri(uri=self.tracking_uri)
+120        mlflow.set_registry_uri(uri=self.registry_uri)
+121        # experiment
+122        mlflow.set_experiment(experiment_name=self.experiment_name)
+123        # autologging
+124        mlflow.autolog(
+125            disable=self.autolog_disable,
+126            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
+127            exclusive=self.autolog_exclusive,
+128            log_input_examples=self.autolog_log_input_examples,
+129            log_model_signatures=self.autolog_log_model_signatures,
+130            log_models=self.autolog_log_models,
+131            silent=self.autolog_silent,
+132        )
+133
+134    def client(self) -> MlflowClient:
+135        """Get an instance of MLflow client."""
+136        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
+137
+138    def register(self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.ModelVersion:
+139        """Register a model to mlflow registry.
+140
+141        Args:
+142            run_id (str): id of mlflow run.
+143            path (str): path of artifact.
+144            alias (str): model alias.
+145
+146        Returns:
+147            mlflow.entities.model_registry.ModelVersion: registered version.
+148        """
+149        client = self.client()
+150        model_uri = f"runs:/{run_id}/{path}"
+151        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+152        client.set_registered_model_alias(name=self.registry_name, alias=alias, version=version.version)
+153        return version
+
+ + +

Service for MLflow tracking and registry.

+ +
Attributes:
+ +
    +
  • autolog_disable (bool): disable autologging.
    • +
  • autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
    • +
  • autolog_exclusive (bool): If True, enables exclusive autologging.
    • +
  • autolog_log_input_examples (bool): If True, logs input examples during autologging.
    • +
  • autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
    • +
  • autolog_log_models (bool): If True, enables logging of models during autologging.
    • +
  • autolog_log_datasets (bool): If True, logs datasets used during autologging.
    • +
  • autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
    • +
  • tracking_uri (str): The URI for the MLflow tracking server.
    • +
  • experiment_name (str): The name of the experiment to log runs under.
    • +
  • registry_uri (str): The URI for the MLflow model registry.
    • +
  • registry_name (str): The name of the registry.
    • +
+
+ + +
+ +
+ + def + start(self): + + + +
+ + 
116    def start(self):
+117        """Start the mlflow service."""
+118        # uri
+119        mlflow.set_tracking_uri(uri=self.tracking_uri)
+120        mlflow.set_registry_uri(uri=self.registry_uri)
+121        # experiment
+122        mlflow.set_experiment(experiment_name=self.experiment_name)
+123        # autologging
+124        mlflow.autolog(
+125            disable=self.autolog_disable,
+126            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
+127            exclusive=self.autolog_exclusive,
+128            log_input_examples=self.autolog_log_input_examples,
+129            log_model_signatures=self.autolog_log_model_signatures,
+130            log_models=self.autolog_log_models,
+131            silent=self.autolog_silent,
+132        )
+
+ + +

Start the mlflow service.

+
+ + +
+
+ +
+ + def + client(self) -> mlflow.tracking.client.MlflowClient: + + + +
+ + 
134    def client(self) -> MlflowClient:
+135        """Get an instance of MLflow client."""
+136        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
+
+ + +

Get an instance of MLflow client.

+
+ + +
+
+ +
+ + def + register( self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.model_version.ModelVersion: + + + +
+ + 
138    def register(self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.ModelVersion:
+139        """Register a model to mlflow registry.
+140
+141        Args:
+142            run_id (str): id of mlflow run.
+143            path (str): path of artifact.
+144            alias (str): model alias.
+145
+146        Returns:
+147            mlflow.entities.model_registry.ModelVersion: registered version.
+148        """
+149        client = self.client()
+150        model_uri = f"runs:/{run_id}/{path}"
+151        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+152        client.set_registered_model_alias(name=self.registry_name, alias=alias, version=version.version)
+153        return version
+
+ + +

Register a model to mlflow registry.

+ +
Arguments:
+ +
    +
  • run_id (str): id of mlflow run.
    • +
  • path (str): path of artifact.
    • +
  • alias (str): model alias.
    • +
+ +
Returns:
+ +
+

mlflow.entities.model_registry.ModelVersion: registered version.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
Service
+
stop
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/bikes/splitters.html b/bikes/splitters.html new file mode 100644 index 0000000..4e73df7 --- /dev/null +++ b/bikes/splitters.html @@ -0,0 +1,901 @@ + + + + + + + bikes.splitters API documentation + + + + + + + + + +
+
+

+bikes.splitters

+ +

Split dataframes into subsets (e.g., train/valid/test).

+
+ + + + + + 
  1"""Split dataframes into subsets (e.g., train/valid/test)."""
+  2
+  3# %% IMPORTS
+  4
+  5import abc
+  6import typing as T
+  7
+  8import numpy as np
+  9import pydantic as pdt
+ 10from sklearn import model_selection
+ 11
+ 12from bikes import schemas
+ 13
+ 14# %% TYPES
+ 15
+ 16Index = np.ndarray  # row index
+ 17TrainTest = tuple[Index, Index]
+ 18Splits = T.Iterator[TrainTest]
+ 19
+ 20# %% SPLITTERS
+ 21
+ 22
+ 23class Splitter(abc.ABC, pdt.BaseModel, strict=True):
+ 24    """Base class for a splitter.
+ 25
+ 26    Use splitters to split datasets.
+ 27    e.g., split between a train/test subsets.
+ 28    """
+ 29
+ 30    # https://scikit-learn.org/stable/glossary.html#term-CV-splitter
+ 31
+ 32    KIND: str
+ 33
+ 34    @abc.abstractmethod
+ 35    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+ 36        """Split a dataframe into subsets.
+ 37
+ 38        Args:
+ 39            inputs (schemas.Inputs): model inputs.
+ 40            targets (schemas.Targets): model targets.
+ 41            groups (list | None, optional): group labels. Defaults to None.
+ 42
+ 43        Returns:
+ 44            Splits: iterator over the dataframe splits.
+ 45        """
+ 46
+ 47    @abc.abstractmethod
+ 48    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+ 49        """Get the number of splits generated.
+ 50
+ 51        Args:
+ 52            inputs (schemas.Inputs): models inputs.
+ 53            targets (schemas.Targets): model targets.
+ 54            groups (list | None, optional): group labels. Defaults to None.
+ 55
+ 56        Returns:
+ 57            int: number of splits generated.
+ 58        """
+ 59
+ 60
+ 61class TrainTestSplitter(Splitter):
+ 62    """Split a dataframe into a train and test subsets.
+ 63
+ 64    Attributes:
+ 65        shuffle (bool): shuffle dataset before splitting.
+ 66        test_size (int | float): number or ratio for the test dataset.
+ 67        random_state (int): random state for the splitter object.
+ 68    """
+ 69
+ 70    KIND: T.Literal["TrainTestSplitter"] = "TrainTestSplitter"
+ 71
+ 72    shuffle: bool = False  # required (time sensitive)
+ 73    test_size: int | float = 24 * 30 * 2  # 2 months
+ 74    random_state: int = 42
+ 75
+ 76    @T.override
+ 77    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+ 78        index = np.arange(len(inputs))  # return integer position
+ 79        train_index, test_index = model_selection.train_test_split(
+ 80            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
+ 81        )
+ 82        yield train_index, test_index
+ 83
+ 84    @T.override
+ 85    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+ 86        return 1
+ 87
+ 88
+ 89class TimeSeriesSplitter(Splitter):
+ 90    """Split a dataframe into fixed time series subsets.
+ 91
+ 92    Attributes:
+ 93        gap (int): gap between splits.
+ 94        n_splits (int): number of split to generate.
+ 95        test_size (int | float): number or ratio for the test dataset.
+ 96    """
+ 97
+ 98    KIND: T.Literal["TimeSeriesSplitter"] = "TimeSeriesSplitter"
+ 99
+100    gap: int = 0
+101    n_splits: int = 4
+102    test_size: int | float = 24 * 30 * 2  # 2 months
+103
+104    @T.override
+105    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+106        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
+107        yield from splitter.split(inputs)
+108
+109    @T.override
+110    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+111        return self.n_splits
+112
+113
+114SplitterKind = TrainTestSplitter | TimeSeriesSplitter
+
+ + +
+
+ +
+ + class + Splitter(abc.ABC, pydantic.main.BaseModel): + + + +
+ + 
24class Splitter(abc.ABC, pdt.BaseModel, strict=True):
+25    """Base class for a splitter.
+26
+27    Use splitters to split datasets.
+28    e.g., split between a train/test subsets.
+29    """
+30
+31    # https://scikit-learn.org/stable/glossary.html#term-CV-splitter
+32
+33    KIND: str
+34
+35    @abc.abstractmethod
+36    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+37        """Split a dataframe into subsets.
+38
+39        Args:
+40            inputs (schemas.Inputs): model inputs.
+41            targets (schemas.Targets): model targets.
+42            groups (list | None, optional): group labels. Defaults to None.
+43
+44        Returns:
+45            Splits: iterator over the dataframe splits.
+46        """
+47
+48    @abc.abstractmethod
+49    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+50        """Get the number of splits generated.
+51
+52        Args:
+53            inputs (schemas.Inputs): models inputs.
+54            targets (schemas.Targets): model targets.
+55            groups (list | None, optional): group labels. Defaults to None.
+56
+57        Returns:
+58            int: number of splits generated.
+59        """
+
+ + +

Base class for a splitter.

+ +

Use splitters to split datasets. +e.g., split between a train/test subsets.

+
+ + +
+ +
+
@abc.abstractmethod
+ + def + split( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], groups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]: + + + +
+ + 
35    @abc.abstractmethod
+36    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+37        """Split a dataframe into subsets.
+38
+39        Args:
+40            inputs (schemas.Inputs): model inputs.
+41            targets (schemas.Targets): model targets.
+42            groups (list | None, optional): group labels. Defaults to None.
+43
+44        Returns:
+45            Splits: iterator over the dataframe splits.
+46        """
+
+ + +

Split a dataframe into subsets.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): model inputs.
    • +
  • targets (schemas.Targets): model targets.
    • +
  • groups (list | None, optional): group labels. Defaults to None.
    • +
+ +
Returns:
+ +
+

Splits: iterator over the dataframe splits.

+
+
+ + +
+
+ +
+
@abc.abstractmethod
+ + def + get_n_splits( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], groups: list | None = None) -> int: + + + +
+ + 
48    @abc.abstractmethod
+49    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+50        """Get the number of splits generated.
+51
+52        Args:
+53            inputs (schemas.Inputs): models inputs.
+54            targets (schemas.Targets): model targets.
+55            groups (list | None, optional): group labels. Defaults to None.
+56
+57        Returns:
+58            int: number of splits generated.
+59        """
+
+ + +

Get the number of splits generated.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): models inputs.
    • +
  • targets (schemas.Targets): model targets.
    • +
  • groups (list | None, optional): group labels. Defaults to None.
    • +
+ +
Returns:
+ +
+

int: number of splits generated.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + TrainTestSplitter(Splitter): + + + +
+ + 
62class TrainTestSplitter(Splitter):
+63    """Split a dataframe into a train and test subsets.
+64
+65    Attributes:
+66        shuffle (bool): shuffle dataset before splitting.
+67        test_size (int | float): number or ratio for the test dataset.
+68        random_state (int): random state for the splitter object.
+69    """
+70
+71    KIND: T.Literal["TrainTestSplitter"] = "TrainTestSplitter"
+72
+73    shuffle: bool = False  # required (time sensitive)
+74    test_size: int | float = 24 * 30 * 2  # 2 months
+75    random_state: int = 42
+76
+77    @T.override
+78    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+79        index = np.arange(len(inputs))  # return integer position
+80        train_index, test_index = model_selection.train_test_split(
+81            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
+82        )
+83        yield train_index, test_index
+84
+85    @T.override
+86    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+87        return 1
+
+ + +

Split a dataframe into a train and test subsets.

+ +
Attributes:
+ +
    +
  • shuffle (bool): shuffle dataset before splitting.
    • +
  • test_size (int | float): number or ratio for the test dataset.
    • +
  • random_state (int): random state for the splitter object.
    • +
+
+ + +
+ +
+
@T.override
+ + def + split( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], groups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]: + + + +
+ + 
77    @T.override
+78    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+79        index = np.arange(len(inputs))  # return integer position
+80        train_index, test_index = model_selection.train_test_split(
+81            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
+82        )
+83        yield train_index, test_index
+
+ + +

Split a dataframe into subsets.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): model inputs.
    • +
  • targets (schemas.Targets): model targets.
    • +
  • groups (list | None, optional): group labels. Defaults to None.
    • +
+ +
Returns:
+ +
+

Splits: iterator over the dataframe splits.

+
+
+ + +
+
+ +
+
@T.override
+ + def + get_n_splits( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], groups: list | None = None) -> int: + + + +
+ + 
85    @T.override
+86    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+87        return 1
+
+ + +

Get the number of splits generated.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): models inputs.
    • +
  • targets (schemas.Targets): model targets.
    • +
  • groups (list | None, optional): group labels. Defaults to None.
    • +
+ +
Returns:
+ +
+

int: number of splits generated.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ +
+ + class + TimeSeriesSplitter(Splitter): + + + +
+ + 
 90class TimeSeriesSplitter(Splitter):
+ 91    """Split a dataframe into fixed time series subsets.
+ 92
+ 93    Attributes:
+ 94        gap (int): gap between splits.
+ 95        n_splits (int): number of split to generate.
+ 96        test_size (int | float): number or ratio for the test dataset.
+ 97    """
+ 98
+ 99    KIND: T.Literal["TimeSeriesSplitter"] = "TimeSeriesSplitter"
+100
+101    gap: int = 0
+102    n_splits: int = 4
+103    test_size: int | float = 24 * 30 * 2  # 2 months
+104
+105    @T.override
+106    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+107        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
+108        yield from splitter.split(inputs)
+109
+110    @T.override
+111    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+112        return self.n_splits
+
+ + +

Split a dataframe into fixed time series subsets.

+ +
Attributes:
+ +
    +
  • gap (int): gap between splits.
    • +
  • n_splits (int): number of split to generate.
    • +
  • test_size (int | float): number or ratio for the test dataset.
    • +
+
+ + +
+ +
+
@T.override
+ + def + split( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], groups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]: + + + +
+ + 
105    @T.override
+106    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
+107        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
+108        yield from splitter.split(inputs)
+
+ + +

Split a dataframe into subsets.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): model inputs.
    • +
  • targets (schemas.Targets): model targets.
    • +
  • groups (list | None, optional): group labels. Defaults to None.
    • +
+ +
Returns:
+ +
+

Splits: iterator over the dataframe splits.

+
+
+ + +
+
+ +
+
@T.override
+ + def + get_n_splits( self, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], groups: list | None = None) -> int: + + + +
+ + 
110    @T.override
+111    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
+112        return self.n_splits
+
+ + +

Get the number of splits generated.

+ +
Arguments:
+ +
    +
  • inputs (schemas.Inputs): models inputs.
    • +
  • targets (schemas.Targets): model targets.
    • +
  • groups (list | None, optional): group labels. Defaults to None.
    • +
+ +
Returns:
+ +
+

int: number of splits generated.

+
+
+ + +
+
+
Inherited Members
+
+
pydantic.main.BaseModel
+
BaseModel
+
model_extra
+
model_fields_set
+
model_construct
+
model_copy
+
model_dump
+
model_dump_json
+
model_json_schema
+
model_parametrized_name
+
model_post_init
+
model_rebuild
+
model_validate
+
model_validate_json
+
model_validate_strings
+
dict
+
json
+
parse_obj
+
parse_raw
+
parse_file
+
from_orm
+
construct
+
copy
+
schema
+
schema_json
+
validate
+
update_forward_refs
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..baf8f6e --- /dev/null +++ b/index.html @@ -0,0 +1,7 @@ + + + + + + + diff --git a/search.js b/search.js new file mode 100644 index 0000000..2eaf181 --- /dev/null +++ b/search.js @@ -0,0 +1,46 @@ +window.pdocSearch = (function(){ +/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oPredict the number of bikes available.

\n"}, "bikes.configs": {"fullname": "bikes.configs", "modulename": "bikes.configs", "kind": "module", "doc": "

Parse, merge, and convert YAML configs.

\n"}, "bikes.configs.parse_file": {"fullname": "bikes.configs.parse_file", "modulename": "bikes.configs", "qualname": "parse_file", "kind": "function", "doc": "

Parse a config file from a path.

\n\n
Arguments:
\n\n
    \n
  • path (str): local or remote path.
    • \n
\n\n
Returns:
\n\n
\n

Config: representation of the config file.

\n
\n", "signature": "(\tpath: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.parse_string": {"fullname": "bikes.configs.parse_string", "modulename": "bikes.configs", "qualname": "parse_string", "kind": "function", "doc": "

Parse the given config string.

\n\n
Arguments:
\n\n
    \n
  • string (str): configuration string.
    • \n
\n\n
Returns:
\n\n
\n

Config: representation of the config string.

\n
\n", "signature": "(\tstring: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.merge_configs": {"fullname": "bikes.configs.merge_configs", "modulename": "bikes.configs", "qualname": "merge_configs", "kind": "function", "doc": "

Merge a list of config objects into one.

\n\n
Arguments:
\n\n
    \n
  • configs (list[Config]): list of config objects.
    • \n
\n\n
Returns:
\n\n
\n

Config: representation of the merged config objects.

\n
\n", "signature": "(\tconfigs: Sequence[omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig]) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.to_object": {"fullname": "bikes.configs.to_object", "modulename": "bikes.configs", "qualname": "to_object", "kind": "function", "doc": "

Convert a config object to a python object.

\n\n
Arguments:
\n\n
    \n
  • config (Config): representation of the config.
    • \n
\n\n
Returns:
\n\n
\n

object: conversion of the config to a python object.

\n
\n", "signature": "(\tconfig: omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig) -> object:", "funcdef": "def"}, "bikes.datasets": {"fullname": "bikes.datasets", "modulename": "bikes.datasets", "kind": "module", "doc": "

Read/Write datasets from/to external sources/destinations.

\n"}, "bikes.datasets.Reader": {"fullname": "bikes.datasets.Reader", "modulename": "bikes.datasets", "qualname": "Reader", "kind": "class", "doc": "

Base class for a dataset reader.

\n\n

Use a reader to load a dataset in memory.\ne.g., to read file, database, cloud storage, ...

\n\n
Attributes:
\n\n
    \n
  • limit (int, optional): maximum number of rows to read from dataset.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Reader.read": {"fullname": "bikes.datasets.Reader.read", "modulename": "bikes.datasets", "qualname": "Reader.read", "kind": "function", "doc": "

Read a dataframe from a dataset.

\n\n
Returns:
\n\n
\n

pd.DataFrame: dataframe representation.

\n
\n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.ParquetReader": {"fullname": "bikes.datasets.ParquetReader", "modulename": "bikes.datasets", "qualname": "ParquetReader", "kind": "class", "doc": "

Read a dataframe from a parquet file.

\n\n
Attributes:
\n\n
    \n
  • path (str): local or remote path to a dataset.
    • \n
\n", "bases": "Reader"}, "bikes.datasets.ParquetReader.read": {"fullname": "bikes.datasets.ParquetReader.read", "modulename": "bikes.datasets", "qualname": "ParquetReader.read", "kind": "function", "doc": "

Read a dataframe from a dataset.

\n\n
Returns:
\n\n
\n

pd.DataFrame: dataframe representation.

\n
\n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.Writer": {"fullname": "bikes.datasets.Writer", "modulename": "bikes.datasets", "qualname": "Writer", "kind": "class", "doc": "

Base class for a dataset writer.

\n\n

Use a writer to save a dataset from memory.\ne.g., to write file, database, cloud storage, ...

\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Writer.write": {"fullname": "bikes.datasets.Writer.write", "modulename": "bikes.datasets", "qualname": "Writer.write", "kind": "function", "doc": "

Write a dataframe to a dataset.

\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame): dataframe representation.
    • \n
\n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.datasets.ParquetWriter": {"fullname": "bikes.datasets.ParquetWriter", "modulename": "bikes.datasets", "qualname": "ParquetWriter", "kind": "class", "doc": "

Writer a dataframe to a parquet file.

\n\n
Attributes:
\n\n
    \n
  • path (str): local or remote file to a dataset.
    • \n
\n", "bases": "Writer"}, "bikes.datasets.ParquetWriter.write": {"fullname": "bikes.datasets.ParquetWriter.write", "modulename": "bikes.datasets", "qualname": "ParquetWriter.write", "kind": "function", "doc": "

Write a dataframe to a dataset.

\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame): dataframe representation.
    • \n
\n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.jobs": {"fullname": "bikes.jobs", "modulename": "bikes.jobs", "kind": "module", "doc": "

High-level jobs for the project.

\n"}, "bikes.jobs.Job": {"fullname": "bikes.jobs.Job", "modulename": "bikes.jobs", "qualname": "Job", "kind": "class", "doc": "

Base class for a job.

\n\n

use a job to execute runs in context.\ne.g., to define common services like logger

\n\n
Attributes:
\n\n
    \n
  • logger_service (services.LoggerService): manage the logging system.
    • \n
  • mlflow_service (services.MLflowService): manage the mlflow system.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.jobs.Job.run": {"fullname": "bikes.jobs.Job.run", "modulename": "bikes.jobs", "qualname": "Job.run", "kind": "function", "doc": "

Run the job in context.

\n\n
Returns:
\n\n
\n

Locals: local job variables.

\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TuningJob": {"fullname": "bikes.jobs.TuningJob", "modulename": "bikes.jobs", "qualname": "TuningJob", "kind": "class", "doc": "

Find the best hyperparameters for a model.

\n\n
Attributes:
\n\n
    \n
  • run_name (str): name of the MLflow experiment run.
    • \n
  • inputs (datasets.ReaderKind): dataset reader with inputs variables.
    • \n
  • targets (datasets.ReaderKind): dataset reader with targets variables.
    • \n
  • results (datasets.WriterKind): dataset writer for searcher results.
    • \n
  • model (models.ModelKind): machine learning model to tune.
    • \n
  • metric (metrics.MetricKind): main metric for evaluation.
    • \n
  • splitter (splitters.SplitterKind): splitter for datasets.
    • \n
  • searcher (searchers.SearcherKind): searcher algorithm.
    • \n
\n", "bases": "Job"}, "bikes.jobs.TuningJob.run": {"fullname": "bikes.jobs.TuningJob.run", "modulename": "bikes.jobs", "qualname": "TuningJob.run", "kind": "function", "doc": "

Run the job in context.

\n\n
Returns:
\n\n
\n

Locals: local job variables.

\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TrainingJob": {"fullname": "bikes.jobs.TrainingJob", "modulename": "bikes.jobs", "qualname": "TrainingJob", "kind": "class", "doc": "

Train and register a single AI/ML model

\n\n
Attributes:
\n\n
    \n
  • run_name (str): name of the MLflow experiment run.
    • \n
  • inputs (datasets.ReaderKind): dataset reader with inputs variables.
    • \n
  • targets (datasets.ReaderKind): dataset reader with targets variables.
    • \n
  • saver (registers.SaverKind): save the trained model in registry.
    • \n
  • model (models.ModelKind): machine learning model to tune.
    • \n
  • signer (registers.SignerKind): signer for the trained model.
    • \n
  • scorers (list[metrics.MetricKind]): metrics for the evaluation.
    • \n
  • splitter (splitters.SplitterKind): splitter for datasets.
    • \n
  • registry_alias (str): alias of model.
    • \n
\n", "bases": "Job"}, "bikes.jobs.TrainingJob.run": {"fullname": "bikes.jobs.TrainingJob.run", "modulename": "bikes.jobs", "qualname": "TrainingJob.run", "kind": "function", "doc": "

Run the job in context.

\n\n
Returns:
\n\n
\n

Locals: local job variables.

\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.InferenceJob": {"fullname": "bikes.jobs.InferenceJob", "modulename": "bikes.jobs", "qualname": "InferenceJob", "kind": "class", "doc": "

Load a model and generate predictions.

\n\n
Attributes:
\n\n
    \n
  • inputs (datasets.ReaderKind): dataset reader with inputs variables.
    • \n
  • outputs (datasets.WriterKind): dataset writer for the model outputs.
    • \n
  • registry_alias (str): alias of the model to load.
    • \n
  • loader (registers.LoaderKind): load the model from registry.
    • \n
\n", "bases": "Job"}, "bikes.jobs.InferenceJob.run": {"fullname": "bikes.jobs.InferenceJob.run", "modulename": "bikes.jobs", "qualname": "InferenceJob.run", "kind": "function", "doc": "

Run the job in context.

\n\n
Returns:
\n\n
\n

Locals: local job variables.

\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.metrics": {"fullname": "bikes.metrics", "modulename": "bikes.metrics", "kind": "module", "doc": "

Evaluate model performance with metrics.

\n"}, "bikes.metrics.Metric": {"fullname": "bikes.metrics.Metric", "modulename": "bikes.metrics", "qualname": "Metric", "kind": "class", "doc": "

Base class for a metric.

\n\n

Use metrics to evaluate model performance.\ne.g., accuracy, precision, recall, mae, f1, ...

\n\n
Attributes:
\n\n
    \n
  • name (str): name of the metric.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.metrics.Metric.score": {"fullname": "bikes.metrics.Metric.score", "modulename": "bikes.metrics", "qualname": "Metric.score", "kind": "function", "doc": "

Score the outputs against the targets.

\n\n
Arguments:
\n\n
    \n
  • targets (schemas.Targets): expected values.
    • \n
  • outputs (schemas.Outputs): predicted values.
    • \n
\n\n
Returns:
\n\n
\n

float: metric result.

\n
\n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.Metric.scorer": {"fullname": "bikes.metrics.Metric.scorer", "modulename": "bikes.metrics", "qualname": "Metric.scorer", "kind": "function", "doc": "

Score the model outputs against the targets.

\n\n
Arguments:
\n\n
    \n
  • model (models.Model): model to evaluate.
    • \n
  • inputs (schemas.Inputs): model inputs values.
    • \n
  • targets (schemas.Targets): model expected values.
    • \n
\n\n
Returns:
\n\n
\n

float: metric result.

\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.SklearnMetric": {"fullname": "bikes.metrics.SklearnMetric", "modulename": "bikes.metrics", "qualname": "SklearnMetric", "kind": "class", "doc": "

Compute metrics with sklearn.

\n\n
Attributes:
\n\n
    \n
  • name (str): name of the sklearn metric.
    • \n
  • greater_is_better (bool): maximize or minimize.
    • \n
\n", "bases": "Metric"}, "bikes.metrics.SklearnMetric.score": {"fullname": "bikes.metrics.SklearnMetric.score", "modulename": "bikes.metrics", "qualname": "SklearnMetric.score", "kind": "function", "doc": "

Score the outputs against the targets.

\n\n
Arguments:
\n\n
    \n
  • targets (schemas.Targets): expected values.
    • \n
  • outputs (schemas.Outputs): predicted values.
    • \n
\n\n
Returns:
\n\n
\n

float: metric result.

\n
\n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.models": {"fullname": "bikes.models", "modulename": "bikes.models", "kind": "module", "doc": "

Define trainable machine learning models.

\n"}, "bikes.models.Model": {"fullname": "bikes.models.Model", "modulename": "bikes.models", "qualname": "Model", "kind": "class", "doc": "

Base class for a model.

\n\n

Use a model to adapt AI/ML frameworks.\ne.g., to swap easily one model with another.

\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.models.Model.get_params": {"fullname": "bikes.models.Model.get_params", "modulename": "bikes.models", "qualname": "Model.get_params", "kind": "function", "doc": "

Get the model params.

\n\n
Arguments:
\n\n
    \n
  • deep (bool, optional): ignored. Defaults to True.
    • \n
\n\n
Returns:
\n\n
\n

Params: internal model parameters.

\n
\n", "signature": "(self, deep: bool = True) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.models.Model.set_params": {"fullname": "bikes.models.Model.set_params", "modulename": "bikes.models", "qualname": "Model.set_params", "kind": "function", "doc": "

Set the model params in place.

\n\n
Returns:
\n\n
\n

T.Self: instance of the model.

\n
\n", "signature": "(self, **params: Any) -> Self:", "funcdef": "def"}, "bikes.models.Model.fit": {"fullname": "bikes.models.Model.fit", "modulename": "bikes.models", "qualname": "Model.fit", "kind": "function", "doc": "

Fit the model on the given inputs and targets.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): model training inputs.
    • \n
  • targets (schemas.Targets): model training targets.
    • \n
\n\n
Returns:
\n\n
\n

Model: instance of the model.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> Self:", "funcdef": "def"}, "bikes.models.Model.predict": {"fullname": "bikes.models.Model.predict", "modulename": "bikes.models", "qualname": "Model.predict", "kind": "function", "doc": "

Generate outputs with the model for the given inputs.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): model prediction inputs.
    • \n
\n\n
Returns:
\n\n
\n

schemas.Outputs: model prediction outputs.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel": {"fullname": "bikes.models.BaselineSklearnModel", "modulename": "bikes.models", "qualname": "BaselineSklearnModel", "kind": "class", "doc": "

Simple baseline model built on top of sklearn.

\n\n
Attributes:
\n\n
    \n
  • max_depth (int): maximum depth of the random forest.
    • \n
  • n_estimators (int): number of estimators in the random forest.
    • \n
  • random_state (int, optional): random state of the machine learning pipeline.
    • \n
\n", "bases": "Model"}, "bikes.models.BaselineSklearnModel.fit": {"fullname": "bikes.models.BaselineSklearnModel.fit", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.fit", "kind": "function", "doc": "

Fit the model on the given inputs and targets.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): model training inputs.
    • \n
  • targets (schemas.Targets): model training targets.
    • \n
\n\n
Returns:
\n\n
\n

Model: instance of the model.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> bikes.models.BaselineSklearnModel:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.predict": {"fullname": "bikes.models.BaselineSklearnModel.predict", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.predict", "kind": "function", "doc": "

Generate outputs with the model for the given inputs.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): model prediction inputs.
    • \n
\n\n
Returns:
\n\n
\n

schemas.Outputs: model prediction outputs.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.model_post_init": {"fullname": "bikes.models.BaselineSklearnModel.model_post_init", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.model_post_init", "kind": "function", "doc": "

This function is meant to behave like a BaseModel method to initialise private attributes.

\n\n

It takes context as an argument since that's what pydantic-core passes when calling it.

\n\n
Arguments:
\n\n
    \n
  • self: The BaseModel instance.
    • \n
  • __context: The context.
    • \n
\n", "signature": "(self: pydantic.main.BaseModel, __context: Any) -> None:", "funcdef": "def"}, "bikes.registers": {"fullname": "bikes.registers", "modulename": "bikes.registers", "kind": "module", "doc": "

Adapters, signers, savers, and loaders for model registries.

\n"}, "bikes.registers.CustomAdapter": {"fullname": "bikes.registers.CustomAdapter", "modulename": "bikes.registers", "qualname": "CustomAdapter", "kind": "class", "doc": "

Adapt a custom model to the MLflow PyFunc flavor.

\n\n

https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html

\n", "bases": "mlflow.pyfunc.model.PythonModel"}, "bikes.registers.CustomAdapter.__init__": {"fullname": "bikes.registers.CustomAdapter.__init__", "modulename": "bikes.registers", "qualname": "CustomAdapter.__init__", "kind": "function", "doc": "

Initialize the custom adapter.

\n\n
Arguments:
\n\n
    \n
  • model (models.Model): project model.
    • \n
\n", "signature": "(model: bikes.models.Model)"}, "bikes.registers.CustomAdapter.predict": {"fullname": "bikes.registers.CustomAdapter.predict", "modulename": "bikes.registers", "qualname": "CustomAdapter.predict", "kind": "function", "doc": "

Generate predictions from a custom model.

\n\n
Arguments:
\n\n
    \n
  • context (mlflow.pyfunc.PythonModelContext): ignored.
    • \n
  • inputs (schemas.Inputs): inputs for the model.
    • \n
\n\n
Returns:
\n\n
\n

schemas.Outputs: outputs of the model.

\n
\n", "signature": "(\tself,\tcontext: mlflow.pyfunc.model.PythonModelContext,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.registers.Signer": {"fullname": "bikes.registers.Signer", "modulename": "bikes.registers", "qualname": "Signer", "kind": "class", "doc": "

Base class for making signatures.

\n\n

Allow to switch between signing approaches.\ne.g., automatic inference vs manual signatures\nhttps://mlflow.org/docs/latest/models.html#model-signature-and-input-example

\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Signer.sign": {"fullname": "bikes.registers.Signer.sign", "modulename": "bikes.registers", "qualname": "Signer.sign", "kind": "function", "doc": "

Make a model signature from inputs/outputs.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): inputs of the model.
    • \n
  • outputs (schemas.Outputs): ouputs of the model.
    • \n
\n\n
Returns:
\n\n
\n

ModelSignature: generated signature for the model.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.InferSigner": {"fullname": "bikes.registers.InferSigner", "modulename": "bikes.registers", "qualname": "InferSigner", "kind": "class", "doc": "

Generate model signatures from data inference.

\n", "bases": "Signer"}, "bikes.registers.InferSigner.sign": {"fullname": "bikes.registers.InferSigner.sign", "modulename": "bikes.registers", "qualname": "InferSigner.sign", "kind": "function", "doc": "

Make a model signature from inputs/outputs.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): inputs of the model.
    • \n
  • outputs (schemas.Outputs): ouputs of the model.
    • \n
\n\n
Returns:
\n\n
\n

ModelSignature: generated signature for the model.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.Saver": {"fullname": "bikes.registers.Saver", "modulename": "bikes.registers", "qualname": "Saver", "kind": "class", "doc": "

Base class for saving models in registry.

\n\n

Separate model definition from serialization.\ne.g., to switch between serialization flavors.

\n\n
Attributes:
\n\n
    \n
  • path (str): model path inside the MLflow artifact store.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Saver.save": {"fullname": "bikes.registers.Saver.save", "modulename": "bikes.registers", "qualname": "Saver.save", "kind": "function", "doc": "

Save a model in the model registry.

\n\n
Arguments:
\n\n
    \n
  • model (models.Model): model to save.
    • \n
  • signature (Signature): model signature.
    • \n
  • input_example (schemas.Inputs): inputs sample.
    • \n
\n\n
Returns:
\n\n
\n

Info: model saving information.

\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.CustomSaver": {"fullname": "bikes.registers.CustomSaver", "modulename": "bikes.registers", "qualname": "CustomSaver", "kind": "class", "doc": "

Saver for custom models using the MLflow PyFunc module.

\n\n

https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html

\n", "bases": "Saver"}, "bikes.registers.CustomSaver.save": {"fullname": "bikes.registers.CustomSaver.save", "modulename": "bikes.registers", "qualname": "CustomSaver.save", "kind": "function", "doc": "

Save a custom model to the MLflow Model Registry.

\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.Loader": {"fullname": "bikes.registers.Loader", "modulename": "bikes.registers", "qualname": "Loader", "kind": "class", "doc": "

Base class for loading models from registry.

\n\n

Separate model definition from deserialization.\ne.g., to switch between deserialization flavors.

\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Loader.load": {"fullname": "bikes.registers.Loader.load", "modulename": "bikes.registers", "qualname": "Loader.load", "kind": "function", "doc": "

Load a model from the model registry.

\n\n
Arguments:
\n\n
    \n
  • uri (str): URI of the model to load.
    • \n
\n\n
Returns:
\n\n
\n

T.Any: model loaded from registry.

\n
\n", "signature": "(self, uri: str) -> Any:", "funcdef": "def"}, "bikes.registers.CustomLoader": {"fullname": "bikes.registers.CustomLoader", "modulename": "bikes.registers", "qualname": "CustomLoader", "kind": "class", "doc": "

Loader for custom models using the MLflow PyFunc module.

\n\n

https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html

\n", "bases": "Loader"}, "bikes.registers.CustomLoader.load": {"fullname": "bikes.registers.CustomLoader.load", "modulename": "bikes.registers", "qualname": "CustomLoader.load", "kind": "function", "doc": "

Load a model from the model registry.

\n\n
Arguments:
\n\n
    \n
  • uri (str): URI of the model to load.
    • \n
\n\n
Returns:
\n\n
\n

T.Any: model loaded from registry.

\n
\n", "signature": "(self, uri: str) -> mlflow.pyfunc.model.PythonModel:", "funcdef": "def"}, "bikes.schemas": {"fullname": "bikes.schemas", "modulename": "bikes.schemas", "kind": "module", "doc": "

Define and validate dataframe schemas.

\n"}, "bikes.schemas.Schema": {"fullname": "bikes.schemas.Schema", "modulename": "bikes.schemas", "qualname": "Schema", "kind": "class", "doc": "

Base class for a dataframe schema.

\n\n

Use a schema to type your dataframe object.\ne.g., to communicate and validate its fields.

\n", "bases": "pandera.api.pandas.model.DataFrameModel"}, "bikes.schemas.Schema.__init__": {"fullname": "bikes.schemas.Schema.__init__", "modulename": "bikes.schemas", "qualname": "Schema.__init__", "kind": "function", "doc": "

Check if all columns in a dataframe have a column in the Schema.

\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj: the dataframe to be validated.
    • \n
  • head: validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail: validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample: validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state: random seed for the sample argument.
    • \n
  • lazy: if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace: if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError: when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n

:example:

\n\n

Calling schema.validate returns the dataframe.

\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.Schema.Config": {"fullname": "bikes.schemas.Schema.Config", "modulename": "bikes.schemas", "qualname": "Schema.Config", "kind": "class", "doc": "

Default configuration.

\n\n
Attributes:
\n\n
    \n
  • coerce (bool): convert data type if possible.
    • \n
  • strict (bool): ensure the data type is correct.
    • \n
\n"}, "bikes.schemas.Schema.check": {"fullname": "bikes.schemas.Schema.check", "modulename": "bikes.schemas", "qualname": "Schema.check", "kind": "function", "doc": "

Check the data with this schema.

\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame): dataframe to check.
    • \n
\n\n
Returns:
\n\n
\n

pd.DataFrame: validated dataframe with schema.

\n
\n", "signature": "(cls, data: pandas.core.frame.DataFrame, **kwargs):", "funcdef": "def"}, "bikes.schemas.InputsSchema": {"fullname": "bikes.schemas.InputsSchema", "modulename": "bikes.schemas", "qualname": "InputsSchema", "kind": "class", "doc": "

Schema for the project inputs.

\n", "bases": "Schema"}, "bikes.schemas.InputsSchema.__init__": {"fullname": "bikes.schemas.InputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "InputsSchema.__init__", "kind": "function", "doc": "

Check if all columns in a dataframe have a column in the Schema.

\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj: the dataframe to be validated.
    • \n
  • head: validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail: validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample: validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state: random seed for the sample argument.
    • \n
  • lazy: if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace: if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError: when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n

:example:

\n\n

Calling schema.validate returns the dataframe.

\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.InputsSchema.instant": {"fullname": "bikes.schemas.InputsSchema.instant", "modulename": "bikes.schemas", "qualname": "InputsSchema.instant", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.dteday": {"fullname": "bikes.schemas.InputsSchema.dteday", "modulename": "bikes.schemas", "qualname": "InputsSchema.dteday", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Timestamp]"}, "bikes.schemas.InputsSchema.season": {"fullname": "bikes.schemas.InputsSchema.season", "modulename": "bikes.schemas", "qualname": "InputsSchema.season", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.yr": {"fullname": "bikes.schemas.InputsSchema.yr", "modulename": "bikes.schemas", "qualname": "InputsSchema.yr", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.mnth": {"fullname": "bikes.schemas.InputsSchema.mnth", "modulename": "bikes.schemas", "qualname": "InputsSchema.mnth", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.hr": {"fullname": "bikes.schemas.InputsSchema.hr", "modulename": "bikes.schemas", "qualname": "InputsSchema.hr", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.holiday": {"fullname": "bikes.schemas.InputsSchema.holiday", "modulename": "bikes.schemas", "qualname": "InputsSchema.holiday", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weekday": {"fullname": "bikes.schemas.InputsSchema.weekday", "modulename": "bikes.schemas", "qualname": "InputsSchema.weekday", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.workingday": {"fullname": "bikes.schemas.InputsSchema.workingday", "modulename": "bikes.schemas", "qualname": "InputsSchema.workingday", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weathersit": {"fullname": "bikes.schemas.InputsSchema.weathersit", "modulename": "bikes.schemas", "qualname": "InputsSchema.weathersit", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.temp": {"fullname": "bikes.schemas.InputsSchema.temp", "modulename": "bikes.schemas", "qualname": "InputsSchema.temp", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.atemp": {"fullname": "bikes.schemas.InputsSchema.atemp", "modulename": "bikes.schemas", "qualname": "InputsSchema.atemp", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.hum": {"fullname": "bikes.schemas.InputsSchema.hum", "modulename": "bikes.schemas", "qualname": "InputsSchema.hum", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.windspeed": {"fullname": "bikes.schemas.InputsSchema.windspeed", "modulename": "bikes.schemas", "qualname": "InputsSchema.windspeed", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.casual": {"fullname": "bikes.schemas.InputsSchema.casual", "modulename": "bikes.schemas", "qualname": "InputsSchema.casual", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.registered": {"fullname": "bikes.schemas.InputsSchema.registered", "modulename": "bikes.schemas", "qualname": "InputsSchema.registered", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.Config": {"fullname": "bikes.schemas.InputsSchema.Config", "modulename": "bikes.schemas", "qualname": "InputsSchema.Config", "kind": "class", "doc": "

Define DataFrameSchema-wide options.

\n\n

new in 0.5.0

\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.TargetsSchema": {"fullname": "bikes.schemas.TargetsSchema", "modulename": "bikes.schemas", "qualname": "TargetsSchema", "kind": "class", "doc": "

Schema for the project target.

\n", "bases": "Schema"}, "bikes.schemas.TargetsSchema.__init__": {"fullname": "bikes.schemas.TargetsSchema.__init__", "modulename": "bikes.schemas", "qualname": "TargetsSchema.__init__", "kind": "function", "doc": "

Check if all columns in a dataframe have a column in the Schema.

\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj: the dataframe to be validated.
    • \n
  • head: validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail: validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample: validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state: random seed for the sample argument.
    • \n
  • lazy: if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace: if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError: when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n

:example:

\n\n

Calling schema.validate returns the dataframe.

\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.TargetsSchema.instant": {"fullname": "bikes.schemas.TargetsSchema.instant", "modulename": "bikes.schemas", "qualname": "TargetsSchema.instant", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.cnt": {"fullname": "bikes.schemas.TargetsSchema.cnt", "modulename": "bikes.schemas", "qualname": "TargetsSchema.cnt", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.Config": {"fullname": "bikes.schemas.TargetsSchema.Config", "modulename": "bikes.schemas", "qualname": "TargetsSchema.Config", "kind": "class", "doc": "

Define DataFrameSchema-wide options.

\n\n

new in 0.5.0

\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.OutputsSchema": {"fullname": "bikes.schemas.OutputsSchema", "modulename": "bikes.schemas", "qualname": "OutputsSchema", "kind": "class", "doc": "

Schema for the project output.

\n", "bases": "Schema"}, "bikes.schemas.OutputsSchema.__init__": {"fullname": "bikes.schemas.OutputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "OutputsSchema.__init__", "kind": "function", "doc": "

Check if all columns in a dataframe have a column in the Schema.

\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj: the dataframe to be validated.
    • \n
  • head: validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail: validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample: validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state: random seed for the sample argument.
    • \n
  • lazy: if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace: if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError: when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n

:example:

\n\n

Calling schema.validate returns the dataframe.

\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.OutputsSchema.instant": {"fullname": "bikes.schemas.OutputsSchema.instant", "modulename": "bikes.schemas", "qualname": "OutputsSchema.instant", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.prediction": {"fullname": "bikes.schemas.OutputsSchema.prediction", "modulename": "bikes.schemas", "qualname": "OutputsSchema.prediction", "kind": "variable", "doc": "

Captures extra information about a field.

\n\n

new in 0.5.0

\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.Config": {"fullname": "bikes.schemas.OutputsSchema.Config", "modulename": "bikes.schemas", "qualname": "OutputsSchema.Config", "kind": "class", "doc": "

Define DataFrameSchema-wide options.

\n\n

new in 0.5.0

\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.scripts": {"fullname": "bikes.scripts", "modulename": "bikes.scripts", "kind": "module", "doc": "

Entry point of the program.

\n"}, "bikes.scripts.Settings": {"fullname": "bikes.scripts.Settings", "modulename": "bikes.scripts", "qualname": "Settings", "kind": "class", "doc": "

Settings for the program.

\n\n
Attributes:
\n\n
    \n
  • job (jobs.JobKind): job associated with the settings.
    • \n
\n", "bases": "pydantic_settings.main.BaseSettings"}, "bikes.scripts.main": {"fullname": "bikes.scripts.main", "modulename": "bikes.scripts", "qualname": "main", "kind": "function", "doc": "

Main function of the program.

\n\n
Arguments:
\n\n
    \n
  • argv (list[str] | None, optional): program arguments. Defaults to None for sys.argv.
    • \n
\n\n
Returns:
\n\n
\n

int: status code of the program.

\n
\n", "signature": "(argv: list[str] | None = None) -> int:", "funcdef": "def"}, "bikes.searchers": {"fullname": "bikes.searchers", "modulename": "bikes.searchers", "kind": "module", "doc": "

Find the best hyperparameters for a model.

\n"}, "bikes.searchers.Searcher": {"fullname": "bikes.searchers.Searcher", "modulename": "bikes.searchers", "qualname": "Searcher", "kind": "class", "doc": "

Base class for a searcher.

\n\n

note: use searcher to tune models.\ne.g., to find the best model params.

\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.searchers.Searcher.search": {"fullname": "bikes.searchers.Searcher.search", "modulename": "bikes.searchers", "qualname": "Searcher.search", "kind": "function", "doc": "

Search the best model for the given inputs and targets.

\n\n
Arguments:
\n\n
    \n
  • model (models.Model): machine learning model to tune.
    • \n
  • metric (metrics.Metric): main metric to optimize.
    • \n
  • cv (CrossValidation): structure for cross-fold.
    • \n
  • inputs (schemas.Inputs): model inputs for tuning.
    • \n
  • targets (schemas.Targets): model targets for tuning.
    • \n
\n\n
Returns:
\n\n
\n

Results: all the results of the tuning process.

\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.searchers.GridCVSearcher": {"fullname": "bikes.searchers.GridCVSearcher", "modulename": "bikes.searchers", "qualname": "GridCVSearcher", "kind": "class", "doc": "

Grid searcher with cross-folds.

\n\n
Attributes:
\n\n
    \n
  • param_grid (Grid): mapping of param key -> values.
    • \n
  • n_jobs (int, optional): number of jobs to run in parallel.
    • \n
  • refit (bool): refit the model after the tuning.
    • \n
  • verbose (int): set the search verbosity level.
    • \n
  • error_score (str | float): strategy or value on error.
    • \n
  • return_train_score (bool): include train scores.
    • \n
\n", "bases": "Searcher"}, "bikes.searchers.GridCVSearcher.search": {"fullname": "bikes.searchers.GridCVSearcher.search", "modulename": "bikes.searchers", "qualname": "GridCVSearcher.search", "kind": "function", "doc": "

Search the best model for the given inputs and targets.

\n\n
Arguments:
\n\n
    \n
  • model (models.Model): machine learning model to tune.
    • \n
  • metric (metrics.Metric): main metric to optimize.
    • \n
  • cv (CrossValidation): structure for cross-fold.
    • \n
  • inputs (schemas.Inputs): model inputs for tuning.
    • \n
  • targets (schemas.Targets): model targets for tuning.
    • \n
\n\n
Returns:
\n\n
\n

Results: all the results of the tuning process.

\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.services": {"fullname": "bikes.services", "modulename": "bikes.services", "kind": "module", "doc": "

Manage global context during execution.

\n"}, "bikes.services.Service": {"fullname": "bikes.services.Service", "modulename": "bikes.services", "qualname": "Service", "kind": "class", "doc": "

Base class for a global service.

\n\n

Use services to manage global contexts.\ne.g., logger object, mlflow client, spark context, ...

\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.services.Service.start": {"fullname": "bikes.services.Service.start", "modulename": "bikes.services", "qualname": "Service.start", "kind": "function", "doc": "

Start the service.

\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.Service.stop": {"fullname": "bikes.services.Service.stop", "modulename": "bikes.services", "qualname": "Service.stop", "kind": "function", "doc": "

Stop the service.

\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.LoggerService": {"fullname": "bikes.services.LoggerService", "modulename": "bikes.services", "qualname": "LoggerService", "kind": "class", "doc": "

Service for logging messages.

\n\n

https://loguru.readthedocs.io/en/stable/api/logger.html

\n\n
Attributes:
\n\n
    \n
  • sink (str): logging output.
    • \n
  • level (str): logging level.
    • \n
  • format (str): logging format.
    • \n
  • colorize (bool): colorize output.
    • \n
  • serialize (bool): convert to JSON.
    • \n
  • backtrace (bool): enable exception trace.
    • \n
  • diagnose (bool): enable variable display.
    • \n
  • catch (bool): catch errors during log handling.
    • \n
\n", "bases": "Service"}, "bikes.services.LoggerService.start": {"fullname": "bikes.services.LoggerService.start", "modulename": "bikes.services", "qualname": "LoggerService.start", "kind": "function", "doc": "

Start the service.

\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.MLflowService": {"fullname": "bikes.services.MLflowService", "modulename": "bikes.services", "qualname": "MLflowService", "kind": "class", "doc": "

Service for MLflow tracking and registry.

\n\n
Attributes:
\n\n
    \n
  • autolog_disable (bool): disable autologging.
    • \n
  • autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
    • \n
  • autolog_exclusive (bool): If True, enables exclusive autologging.
    • \n
  • autolog_log_input_examples (bool): If True, logs input examples during autologging.
    • \n
  • autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
    • \n
  • autolog_log_models (bool): If True, enables logging of models during autologging.
    • \n
  • autolog_log_datasets (bool): If True, logs datasets used during autologging.
    • \n
  • autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
    • \n
  • tracking_uri (str): The URI for the MLflow tracking server.
    • \n
  • experiment_name (str): The name of the experiment to log runs under.
    • \n
  • registry_uri (str): The URI for the MLflow model registry.
    • \n
  • registry_name (str): The name of the registry.
    • \n
\n", "bases": "Service"}, "bikes.services.MLflowService.start": {"fullname": "bikes.services.MLflowService.start", "modulename": "bikes.services", "qualname": "MLflowService.start", "kind": "function", "doc": "

Start the mlflow service.

\n", "signature": "(self):", "funcdef": "def"}, "bikes.services.MLflowService.client": {"fullname": "bikes.services.MLflowService.client", "modulename": "bikes.services", "qualname": "MLflowService.client", "kind": "function", "doc": "

Get an instance of MLflow client.

\n", "signature": "(self) -> mlflow.tracking.client.MlflowClient:", "funcdef": "def"}, "bikes.services.MLflowService.register": {"fullname": "bikes.services.MLflowService.register", "modulename": "bikes.services", "qualname": "MLflowService.register", "kind": "function", "doc": "

Register a model to mlflow registry.

\n\n
Arguments:
\n\n
    \n
  • run_id (str): id of mlflow run.
    • \n
  • path (str): path of artifact.
    • \n
  • alias (str): model alias.
    • \n
\n\n
Returns:
\n\n
\n

mlflow.entities.model_registry.ModelVersion: registered version.

\n
\n", "signature": "(\tself,\trun_id: str,\tpath: str,\talias: str) -> mlflow.entities.model_registry.model_version.ModelVersion:", "funcdef": "def"}, "bikes.splitters": {"fullname": "bikes.splitters", "modulename": "bikes.splitters", "kind": "module", "doc": "

Split dataframes into subsets (e.g., train/valid/test).

\n"}, "bikes.splitters.Splitter": {"fullname": "bikes.splitters.Splitter", "modulename": "bikes.splitters", "qualname": "Splitter", "kind": "class", "doc": "

Base class for a splitter.

\n\n

Use splitters to split datasets.\ne.g., split between a train/test subsets.

\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.splitters.Splitter.split": {"fullname": "bikes.splitters.Splitter.split", "modulename": "bikes.splitters", "qualname": "Splitter.split", "kind": "function", "doc": "

Split a dataframe into subsets.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): model inputs.
    • \n
  • targets (schemas.Targets): model targets.
    • \n
  • groups (list | None, optional): group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

Splits: iterator over the dataframe splits.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.Splitter.get_n_splits": {"fullname": "bikes.splitters.Splitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "Splitter.get_n_splits", "kind": "function", "doc": "

Get the number of splits generated.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): models inputs.
    • \n
  • targets (schemas.Targets): model targets.
    • \n
  • groups (list | None, optional): group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

int: number of splits generated.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter": {"fullname": "bikes.splitters.TrainTestSplitter", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter", "kind": "class", "doc": "

Split a dataframe into a train and test subsets.

\n\n
Attributes:
\n\n
    \n
  • shuffle (bool): shuffle dataset before splitting.
    • \n
  • test_size (int | float): number or ratio for the test dataset.
    • \n
  • random_state (int): random state for the splitter object.
    • \n
\n", "bases": "Splitter"}, "bikes.splitters.TrainTestSplitter.split": {"fullname": "bikes.splitters.TrainTestSplitter.split", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.split", "kind": "function", "doc": "

Split a dataframe into subsets.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): model inputs.
    • \n
  • targets (schemas.Targets): model targets.
    • \n
  • groups (list | None, optional): group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

Splits: iterator over the dataframe splits.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter.get_n_splits": {"fullname": "bikes.splitters.TrainTestSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.get_n_splits", "kind": "function", "doc": "

Get the number of splits generated.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): models inputs.
    • \n
  • targets (schemas.Targets): model targets.
    • \n
  • groups (list | None, optional): group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

int: number of splits generated.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter": {"fullname": "bikes.splitters.TimeSeriesSplitter", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter", "kind": "class", "doc": "

Split a dataframe into fixed time series subsets.

\n\n
Attributes:
\n\n
    \n
  • gap (int): gap between splits.
    • \n
  • n_splits (int): number of split to generate.
    • \n
  • test_size (int | float): number or ratio for the test dataset.
    • \n
\n", "bases": "Splitter"}, "bikes.splitters.TimeSeriesSplitter.split": {"fullname": "bikes.splitters.TimeSeriesSplitter.split", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.split", "kind": "function", "doc": "

Split a dataframe into subsets.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): model inputs.
    • \n
  • targets (schemas.Targets): model targets.
    • \n
  • groups (list | None, optional): group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

Splits: iterator over the dataframe splits.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter.get_n_splits": {"fullname": "bikes.splitters.TimeSeriesSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.get_n_splits", "kind": "function", "doc": "

Get the number of splits generated.

\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs): models inputs.
    • \n
  • targets (schemas.Targets): model targets.
    • \n
  • groups (list | None, optional): group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

int: number of splits generated.

\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}}, "docInfo": {"bikes": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "bikes.configs": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "bikes.configs.parse_file": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 45}, "bikes.configs.parse_string": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 41}, "bikes.configs.merge_configs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 78, "bases": 0, "doc": 47}, "bikes.configs.to_object": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, 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{"bikes.searchers.GridCVSearcher": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"bikes.services.MLflowService.register": {"tf": 1}}, "df": 1, "s": {"docs": {"bikes.services.MLflowService": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"bikes.schemas.Schema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.InputsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.TargetsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.OutputsSchema.__init__": {"tf": 5.830951894845301}}, "df": 4}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "y": {"docs": {"bikes.searchers.GridCVSearcher": {"tf": 1}}, "df": 1}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; + + // mirrored in build-search-index.js (part 1) + // Also split on html tags. this is a cheap heuristic, but good enough. + elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); + + let searchIndex; + if (docs._isPrebuiltIndex) { + console.info("using precompiled search index"); + searchIndex = elasticlunr.Index.load(docs); + } else { + console.time("building search index"); + // mirrored in build-search-index.js (part 2) + searchIndex = elasticlunr(function () { + this.pipeline.remove(elasticlunr.stemmer); + this.pipeline.remove(elasticlunr.stopWordFilter); + this.addField("qualname"); + this.addField("fullname"); + this.addField("annotation"); + this.addField("default_value"); + this.addField("signature"); + this.addField("bases"); + this.addField("doc"); + this.setRef("fullname"); + }); + for (let doc of docs) { + searchIndex.addDoc(doc); + } + console.timeEnd("building search index"); + } + + return (term) => searchIndex.search(term, { + fields: { + qualname: {boost: 4}, + fullname: {boost: 2}, + annotation: {boost: 2}, + default_value: {boost: 2}, + signature: {boost: 2}, + bases: {boost: 2}, + doc: {boost: 1}, + }, + expand: true + }); +})(); \ No newline at end of file From 73b9fe134c35d44ec876a608dc5c6df9103e7c1a Mon Sep 17 00:00:00 2001 From: fmind Date: Sun, 25 Feb 2024 21:02:05 +0000 Subject: [PATCH 03/11] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20fm?= =?UTF-8?q?ind/mlops-python-package@9a15ee5767d4eb1935b0e1a8c99d1c59fbc4e2?= =?UTF-8?q?5c=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- bikes/configs.html | 148 ++++---- bikes/jobs.html | 810 ++++++++++++++++++++++--------------------- bikes/metrics.html | 196 ++++++----- bikes/models.html | 626 +++++++++++++++++---------------- bikes/registers.html | 615 ++++++++++++++++---------------- bikes/schemas.html | 168 ++++----- bikes/scripts.html | 12 +- bikes/services.html | 108 +++--- bikes/splitters.html | 410 ++++++++++++---------- search.js | 2 +- 10 files changed, 1608 insertions(+), 1487 deletions(-) diff --git a/bikes/configs.html b/bikes/configs.html index 0c68d4d..dca8442 100644 --- a/bikes/configs.html +++ b/bikes/configs.html @@ -91,52 +91,51 @@

24 Config: representation of the config file. 25 """ 26 any_path = AnyPath(path) -27 # pylint: disable=no-member -28 text = any_path.read_text() # type: ignore -29 config = OmegaConf.create(text) -30 return config +27 text = any_path.read_text() # type: ignore +28 config = OmegaConf.create(text) +29 return config +30 31 -32 -33def parse_string(string: str) -> Config: -34 """Parse the given config string. -35 -36 Args: -37 string (str): configuration string. -38 -39 Returns: -40 Config: representation of the config string. -41 """ -42 return OmegaConf.create(string) +32def parse_string(string: str) -> Config: +33 """Parse the given config string. +34 +35 Args: +36 string (str): configuration string. +37 +38 Returns: +39 Config: representation of the config string. +40 """ +41 return OmegaConf.create(string) +42 43 -44 -45# %% MERGERS +44# %% MERGERS +45 46 -47 -48def merge_configs(configs: T.Sequence[Config]) -> Config: -49 """Merge a list of config objects into one. -50 -51 Args: -52 configs (list[Config]): list of config objects. -53 -54 Returns: -55 Config: representation of the merged config objects. -56 """ -57 return OmegaConf.merge(*configs) +47def merge_configs(configs: T.Sequence[Config]) -> Config: +48 """Merge a list of config objects into one. +49 +50 Args: +51 configs (list[Config]): list of config objects. +52 +53 Returns: +54 Config: representation of the merged config objects. +55 """ +56 return OmegaConf.merge(*configs) +57 58 -59 -60# %% CONVERTERS +59# %% CONVERTERS +60 61 -62 -63def to_object(config: Config) -> object: -64 """Convert a config object to a python object. -65 -66 Args: -67 config (Config): representation of the config. -68 -69 Returns: -70 object: conversion of the config to a python object. -71 """ -72 return OmegaConf.to_container(config, resolve=True) +62def to_object(config: Config) -> object: +63 """Convert a config object to a python object. +64 +65 Args: +66 config (Config): representation of the config. +67 +68 Returns: +69 object: conversion of the config to a python object. +70 """ +71 return OmegaConf.to_container(config, resolve=True) @@ -162,10 +161,9 @@

25 Config: representation of the config file. 26 """ 27 any_path = AnyPath(path) -28 # pylint: disable=no-member -29 text = any_path.read_text() # type: ignore -30 config = OmegaConf.create(text) -31 return config +28 text = any_path.read_text() # type: ignore +29 config = OmegaConf.create(text) +30 return config @@ -197,16 +195,16 @@

Returns:
- 
34def parse_string(string: str) -> Config:
-35    """Parse the given config string.
-36
-37    Args:
-38        string (str): configuration string.
-39
-40    Returns:
-41        Config: representation of the config string.
-42    """
-43    return OmegaConf.create(string)
+            
33def parse_string(string: str) -> Config:
+34    """Parse the given config string.
+35
+36    Args:
+37        string (str): configuration string.
+38
+39    Returns:
+40        Config: representation of the config string.
+41    """
+42    return OmegaConf.create(string)
 

 
 
@@ -238,16 +236,16 @@ 
Returns:
- 
49def merge_configs(configs: T.Sequence[Config]) -> Config:
-50    """Merge a list of config objects into one.
-51
-52    Args:
-53        configs (list[Config]): list of config objects.
-54
-55    Returns:
-56        Config: representation of the merged config objects.
-57    """
-58    return OmegaConf.merge(*configs)
+            
48def merge_configs(configs: T.Sequence[Config]) -> Config:
+49    """Merge a list of config objects into one.
+50
+51    Args:
+52        configs (list[Config]): list of config objects.
+53
+54    Returns:
+55        Config: representation of the merged config objects.
+56    """
+57    return OmegaConf.merge(*configs)
 

 
 
@@ -279,16 +277,16 @@ 
Returns:
- 
64def to_object(config: Config) -> object:
-65    """Convert a config object to a python object.
-66
-67    Args:
-68        config (Config): representation of the config.
-69
-70    Returns:
-71        object: conversion of the config to a python object.
-72    """
-73    return OmegaConf.to_container(config, resolve=True)
+            
63def to_object(config: Config) -> object:
+64    """Convert a config object to a python object.
+65
+66    Args:
+67        config (Config): representation of the config.
+68
+69    Returns:
+70        object: conversion of the config to a python object.
+71    """
+72    return OmegaConf.to_container(config, resolve=True)
 

 
 
diff --git a/bikes/jobs.html b/bikes/jobs.html
index 7c7f864..c059119 100644
--- a/bikes/jobs.html
+++ b/bikes/jobs.html
@@ -222,159 +222,169 @@ 

 131            # searcher
 132            logger.info("Execute searcher: {}", self.searcher)
 133            results, best_score, best_params = self.searcher.search(
-134                model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets
-135            )
-136            logger.info("- # Results: {}", len(results))
-137            logger.info("- Best Score: {}", best_score)
-138            logger.info("- Best Params: {}", best_params)
-139            # write
-140            logger.info("Write results: {}", self.results)
-141            self.results.write(results)
-142        return locals()
-143
-144
-145class TrainingJob(Job):
-146    """Train and register a single AI/ML model
+134                model=self.model,
+135                metric=self.metric,
+136                cv=self.splitter,
+137                inputs=inputs,
+138                targets=targets,
+139            )
+140            logger.info("- # Results: {}", len(results))
+141            logger.info("- Best Score: {}", best_score)
+142            logger.info("- Best Params: {}", best_params)
+143            # write
+144            logger.info("Write results: {}", self.results)
+145            self.results.write(results)
+146        return locals()
 147
-148    Attributes:
-149        run_name (str): name of the MLflow experiment run.
-150        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-151        targets (datasets.ReaderKind): dataset reader with targets variables.
-152        saver (registers.SaverKind): save the trained model in registry.
-153        model (models.ModelKind): machine learning model to tune.
-154        signer (registers.SignerKind): signer for the trained model.
-155        scorers (list[metrics.MetricKind]): metrics for the evaluation.
-156        splitter (splitters.SplitterKind): splitter for datasets.
-157        registry_alias (str): alias of model.
-158    """
-159
-160    KIND: T.Literal["TrainingJob"] = "TrainingJob"
-161
-162    # run
-163    run_name: str = "Training"
-164    # read
-165    inputs: datasets.ReaderKind
-166    targets: datasets.ReaderKind
-167    # write
-168    saver: registers.SaverKind = registers.CustomSaver()
-169    # model
-170    model: models.ModelKind = models.BaselineSklearnModel()
-171    # signer
-172    signer: registers.SignerKind = registers.InferSigner()
-173    # scorers
-174    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
-175    # splitter
-176    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
-177    # register
-178    registry_alias: str = "Champion"
-179
-180    @T.override
-181    def run(self) -> Locals:
-182        # run
-183        logger.info("Start run: {} ", self.run_name)
-184        with mlflow.start_run(run_name=self.run_name) as run:
-185            logger.info("- Run ID: {}", run.info.run_id)
-186            # read
-187            # - inputs
-188            logger.info("Read inputs: {}", self.inputs)
-189            inputs = schemas.InputsSchema.check(self.inputs.read())
-190            logger.info("- Inputs shape: {}", inputs.shape)
-191            # - targets
-192            logger.info("Read targets: {}", self.targets)
-193            targets = schemas.TargetsSchema.check(self.targets.read())
-194            logger.info("- Targets shape: {}", targets.shape)
-195            # - asserts
-196            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-197            # split
-198            logger.info("With splitter: {}", self.splitter)
-199            # - index
-200            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
-201            # - inputs
-202            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
-203            logger.info("- Inputs train shape: {}", inputs_train.shape)
-204            logger.info("- Inputs test shape: {}", inputs_test.shape)
-205            # - targets
-206            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
-207            logger.info("- Targets train shape: {}", targets_train.shape)
-208            logger.info("- Targets test shape: {}", targets_test.shape)
-209            # - asserts
-210            assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!"
-211            assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!"
-212            # model
-213            logger.info("Fit model: {}", self.model)
-214            self.model.fit(inputs=inputs_train, targets=targets_train)
-215            # outputs
-216            logger.info("Predict outputs: {}", len(inputs_test))
-217            outputs_test = self.model.predict(inputs=inputs_test)
-218            logger.info("- Outputs test shape: {}", outputs_test.shape)
-219            assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!"
-220            # scorers
-221            for i, scorer in enumerate(self.scorers, start=1):
-222                logger.info("{}. Run scorer: {}", i, scorer)
-223                score = scorer.score(targets=targets_test, outputs=outputs_test)
-224                mlflow.log_metric(key=scorer.name, value=score)
-225                logger.info("- Metric score: {}", score)
-226            # sign
-227            logger.info("Sign model: {}", self.signer)
-228            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
-229            logger.info("- Model signature: {}", signature.to_dict())
-230            # save
-231            logger.info("Save model: {}", self.saver)
-232            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
-233            logger.info("- Model URI: {}", info.model_uri)
-234            # register
-235            logger.info("Register model: {}", self.registry_alias)
-236            version = self.mlflow_service.register(
-237                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
-238            )
-239            logger.info("- Model version: {}", version.version)
-240        return locals()
-241
-242
-243class InferenceJob(Job):
-244    """Load a model and generate predictions.
-245
-246    Attributes:
-247        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-248        outputs (datasets.WriterKind): dataset writer for the model outputs.
-249        registry_alias (str): alias of the model to load.
-250        loader (registers.LoaderKind): load the model from registry.
-251    """
+148
+149class TrainingJob(Job):
+150    """Train and register a single AI/ML model.
+151
+152    Attributes:
+153        run_name (str): name of the MLflow experiment run.
+154        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+155        targets (datasets.ReaderKind): dataset reader with targets variables.
+156        saver (registers.SaverKind): save the trained model in registry.
+157        model (models.ModelKind): machine learning model to tune.
+158        signer (registers.SignerKind): signer for the trained model.
+159        scorers (list[metrics.MetricKind]): metrics for the evaluation.
+160        splitter (splitters.SplitterKind): splitter for datasets.
+161        registry_alias (str): alias of model.
+162    """
+163
+164    KIND: T.Literal["TrainingJob"] = "TrainingJob"
+165
+166    # run
+167    run_name: str = "Training"
+168    # read
+169    inputs: datasets.ReaderKind
+170    targets: datasets.ReaderKind
+171    # write
+172    saver: registers.SaverKind = registers.CustomSaver()
+173    # model
+174    model: models.ModelKind = models.BaselineSklearnModel()
+175    # signer
+176    signer: registers.SignerKind = registers.InferSigner()
+177    # scorers
+178    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
+179    # splitter
+180    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
+181    # register
+182    registry_alias: str = "Champion"
+183
+184    @T.override
+185    def run(self) -> Locals:
+186        # run
+187        logger.info("Start run: {} ", self.run_name)
+188        with mlflow.start_run(run_name=self.run_name) as run:
+189            logger.info("- Run ID: {}", run.info.run_id)
+190            # read
+191            # - inputs
+192            logger.info("Read inputs: {}", self.inputs)
+193            inputs = schemas.InputsSchema.check(self.inputs.read())
+194            logger.info("- Inputs shape: {}", inputs.shape)
+195            # - targets
+196            logger.info("Read targets: {}", self.targets)
+197            targets = schemas.TargetsSchema.check(self.targets.read())
+198            logger.info("- Targets shape: {}", targets.shape)
+199            # - asserts
+200            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+201            # split
+202            logger.info("With splitter: {}", self.splitter)
+203            # - index
+204            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+205            # - inputs
+206            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+207            logger.info("- Inputs train shape: {}", inputs_train.shape)
+208            logger.info("- Inputs test shape: {}", inputs_test.shape)
+209            # - targets
+210            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+211            logger.info("- Targets train shape: {}", targets_train.shape)
+212            logger.info("- Targets test shape: {}", targets_test.shape)
+213            # - asserts
+214            assert len(inputs_train) == len(
+215                targets_train
+216            ), "Inputs and targets train should have the same length!"
+217            assert len(inputs_test) == len(
+218                targets_test
+219            ), "Inputs and targets test should have the same length!"
+220            # model
+221            logger.info("Fit model: {}", self.model)
+222            self.model.fit(inputs=inputs_train, targets=targets_train)
+223            # outputs
+224            logger.info("Predict outputs: {}", len(inputs_test))
+225            outputs_test = self.model.predict(inputs=inputs_test)
+226            logger.info("- Outputs test shape: {}", outputs_test.shape)
+227            assert len(inputs_test) == len(
+228                outputs_test
+229            ), "Inputs and outputs test should have the same length!"
+230            # scorers
+231            for i, scorer in enumerate(self.scorers, start=1):
+232                logger.info("{}. Run scorer: {}", i, scorer)
+233                score = scorer.score(targets=targets_test, outputs=outputs_test)
+234                mlflow.log_metric(key=scorer.name, value=score)
+235                logger.info("- Metric score: {}", score)
+236            # sign
+237            logger.info("Sign model: {}", self.signer)
+238            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+239            logger.info("- Model signature: {}", signature.to_dict())
+240            # save
+241            logger.info("Save model: {}", self.saver)
+242            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+243            logger.info("- Model URI: {}", info.model_uri)
+244            # register
+245            logger.info("Register model: {}", self.registry_alias)
+246            version = self.mlflow_service.register(
+247                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+248            )
+249            logger.info("- Model version: {}", version.version)
+250        return locals()
+251
 252
-253    KIND: T.Literal["InferenceJob"] = "InferenceJob"
-254
-255    # data
-256    inputs: datasets.ReaderKind
-257    outputs: datasets.WriterKind
-258    # model
-259    registry_alias: str = "Champion"
-260    loader: registers.LoaderKind = registers.CustomLoader()
-261
-262    @T.override
-263    def run(self) -> Locals:
-264        # read
-265        logger.info("Read inputs: {}", self.inputs)
-266        inputs = self.inputs.read()
-267        inputs = schemas.InputsSchema.check(inputs)
-268        logger.info("- Inputs shape: {}", inputs.shape)
-269        # uri
-270        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
-271        logger.info("With URI: {}", uri)
-272        # load
-273        logger.info("Load model: {}", self.loader)
-274        model = self.loader.load(uri=uri)
-275        logger.info("- Model: {}", model)
-276        # predict
-277        logger.info("Predict outputs: {}", len(inputs))
-278        outputs = model.predict(data=inputs)
-279        logger.info("- Outputs shape: {}", outputs.shape)
-280        # write
-281        logger.info("Write outputs: {}", self.outputs)
-282        self.outputs.write(data=outputs)
-283        return locals()
-284
-285
-286JobKind = TuningJob | TrainingJob | InferenceJob
+253class InferenceJob(Job):
+254    """Load a model and generate predictions.
+255
+256    Attributes:
+257        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+258        outputs (datasets.WriterKind): dataset writer for the model outputs.
+259        registry_alias (str): alias of the model to load.
+260        loader (registers.LoaderKind): load the model from registry.
+261    """
+262
+263    KIND: T.Literal["InferenceJob"] = "InferenceJob"
+264
+265    # data
+266    inputs: datasets.ReaderKind
+267    outputs: datasets.WriterKind
+268    # model
+269    registry_alias: str = "Champion"
+270    loader: registers.LoaderKind = registers.CustomLoader()
+271
+272    @T.override
+273    def run(self) -> Locals:
+274        # read
+275        logger.info("Read inputs: {}", self.inputs)
+276        inputs = self.inputs.read()
+277        inputs = schemas.InputsSchema.check(inputs)
+278        logger.info("- Inputs shape: {}", inputs.shape)
+279        # uri
+280        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+281        logger.info("With URI: {}", uri)
+282        # load
+283        logger.info("Load model: {}", self.loader)
+284        model = self.loader.load(uri=uri)
+285        logger.info("- Model: {}", model)
+286        # predict
+287        logger.info("Predict outputs: {}", len(inputs))
+288        outputs = model.predict(data=inputs)
+289        logger.info("- Outputs shape: {}", outputs.shape)
+290        # write
+291        logger.info("Write outputs: {}", self.outputs)
+292        self.outputs.write(data=outputs)
+293        return locals()
+294
+295
+296JobKind = TuningJob | TrainingJob | InferenceJob
@@ -596,15 +606,19 @@
Inherited Members
132 # searcher 133 logger.info("Execute searcher: {}", self.searcher) 134 results, best_score, best_params = self.searcher.search( -135 model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets -136 ) -137 logger.info("- # Results: {}", len(results)) -138 logger.info("- Best Score: {}", best_score) -139 logger.info("- Best Params: {}", best_params) -140 # write -141 logger.info("Write results: {}", self.results) -142 self.results.write(results) -143 return locals() +135 model=self.model, +136 metric=self.metric, +137 cv=self.splitter, +138 inputs=inputs, +139 targets=targets, +140 ) +141 logger.info("- # Results: {}", len(results)) +142 logger.info("- Best Score: {}", best_score) +143 logger.info("- Best Params: {}", best_params) +144 # write +145 logger.info("Write results: {}", self.results) +146 self.results.write(results) +147 return locals() @@ -663,15 +677,19 @@
Attributes:
132 # searcher 133 logger.info("Execute searcher: {}", self.searcher) 134 results, best_score, best_params = self.searcher.search( -135 model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets -136 ) -137 logger.info("- # Results: {}", len(results)) -138 logger.info("- Best Score: {}", best_score) -139 logger.info("- Best Params: {}", best_params) -140 # write -141 logger.info("Write results: {}", self.results) -142 self.results.write(results) -143 return locals() +135 model=self.model, +136 metric=self.metric, +137 cv=self.splitter, +138 inputs=inputs, +139 targets=targets, +140 ) +141 logger.info("- # Results: {}", len(results)) +142 logger.info("- Best Score: {}", best_score) +143 logger.info("- Best Params: {}", best_params) +144 # write +145 logger.info("Write results: {}", self.results) +146 self.results.write(results) +147 return locals() @@ -732,106 +750,112 @@
Inherited Members
- 
146class TrainingJob(Job):
-147    """Train and register a single AI/ML model
-148
-149    Attributes:
-150        run_name (str): name of the MLflow experiment run.
-151        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-152        targets (datasets.ReaderKind): dataset reader with targets variables.
-153        saver (registers.SaverKind): save the trained model in registry.
-154        model (models.ModelKind): machine learning model to tune.
-155        signer (registers.SignerKind): signer for the trained model.
-156        scorers (list[metrics.MetricKind]): metrics for the evaluation.
-157        splitter (splitters.SplitterKind): splitter for datasets.
-158        registry_alias (str): alias of model.
-159    """
-160
-161    KIND: T.Literal["TrainingJob"] = "TrainingJob"
-162
-163    # run
-164    run_name: str = "Training"
-165    # read
-166    inputs: datasets.ReaderKind
-167    targets: datasets.ReaderKind
-168    # write
-169    saver: registers.SaverKind = registers.CustomSaver()
-170    # model
-171    model: models.ModelKind = models.BaselineSklearnModel()
-172    # signer
-173    signer: registers.SignerKind = registers.InferSigner()
-174    # scorers
-175    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
-176    # splitter
-177    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
-178    # register
-179    registry_alias: str = "Champion"
-180
-181    @T.override
-182    def run(self) -> Locals:
-183        # run
-184        logger.info("Start run: {} ", self.run_name)
-185        with mlflow.start_run(run_name=self.run_name) as run:
-186            logger.info("- Run ID: {}", run.info.run_id)
-187            # read
-188            # - inputs
-189            logger.info("Read inputs: {}", self.inputs)
-190            inputs = schemas.InputsSchema.check(self.inputs.read())
-191            logger.info("- Inputs shape: {}", inputs.shape)
-192            # - targets
-193            logger.info("Read targets: {}", self.targets)
-194            targets = schemas.TargetsSchema.check(self.targets.read())
-195            logger.info("- Targets shape: {}", targets.shape)
-196            # - asserts
-197            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-198            # split
-199            logger.info("With splitter: {}", self.splitter)
-200            # - index
-201            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
-202            # - inputs
-203            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
-204            logger.info("- Inputs train shape: {}", inputs_train.shape)
-205            logger.info("- Inputs test shape: {}", inputs_test.shape)
-206            # - targets
-207            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
-208            logger.info("- Targets train shape: {}", targets_train.shape)
-209            logger.info("- Targets test shape: {}", targets_test.shape)
-210            # - asserts
-211            assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!"
-212            assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!"
-213            # model
-214            logger.info("Fit model: {}", self.model)
-215            self.model.fit(inputs=inputs_train, targets=targets_train)
-216            # outputs
-217            logger.info("Predict outputs: {}", len(inputs_test))
-218            outputs_test = self.model.predict(inputs=inputs_test)
-219            logger.info("- Outputs test shape: {}", outputs_test.shape)
-220            assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!"
-221            # scorers
-222            for i, scorer in enumerate(self.scorers, start=1):
-223                logger.info("{}. Run scorer: {}", i, scorer)
-224                score = scorer.score(targets=targets_test, outputs=outputs_test)
-225                mlflow.log_metric(key=scorer.name, value=score)
-226                logger.info("- Metric score: {}", score)
-227            # sign
-228            logger.info("Sign model: {}", self.signer)
-229            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
-230            logger.info("- Model signature: {}", signature.to_dict())
-231            # save
-232            logger.info("Save model: {}", self.saver)
-233            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
-234            logger.info("- Model URI: {}", info.model_uri)
-235            # register
-236            logger.info("Register model: {}", self.registry_alias)
-237            version = self.mlflow_service.register(
-238                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
-239            )
-240            logger.info("- Model version: {}", version.version)
-241        return locals()
+            
150class TrainingJob(Job):
+151    """Train and register a single AI/ML model.
+152
+153    Attributes:
+154        run_name (str): name of the MLflow experiment run.
+155        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+156        targets (datasets.ReaderKind): dataset reader with targets variables.
+157        saver (registers.SaverKind): save the trained model in registry.
+158        model (models.ModelKind): machine learning model to tune.
+159        signer (registers.SignerKind): signer for the trained model.
+160        scorers (list[metrics.MetricKind]): metrics for the evaluation.
+161        splitter (splitters.SplitterKind): splitter for datasets.
+162        registry_alias (str): alias of model.
+163    """
+164
+165    KIND: T.Literal["TrainingJob"] = "TrainingJob"
+166
+167    # run
+168    run_name: str = "Training"
+169    # read
+170    inputs: datasets.ReaderKind
+171    targets: datasets.ReaderKind
+172    # write
+173    saver: registers.SaverKind = registers.CustomSaver()
+174    # model
+175    model: models.ModelKind = models.BaselineSklearnModel()
+176    # signer
+177    signer: registers.SignerKind = registers.InferSigner()
+178    # scorers
+179    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
+180    # splitter
+181    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
+182    # register
+183    registry_alias: str = "Champion"
+184
+185    @T.override
+186    def run(self) -> Locals:
+187        # run
+188        logger.info("Start run: {} ", self.run_name)
+189        with mlflow.start_run(run_name=self.run_name) as run:
+190            logger.info("- Run ID: {}", run.info.run_id)
+191            # read
+192            # - inputs
+193            logger.info("Read inputs: {}", self.inputs)
+194            inputs = schemas.InputsSchema.check(self.inputs.read())
+195            logger.info("- Inputs shape: {}", inputs.shape)
+196            # - targets
+197            logger.info("Read targets: {}", self.targets)
+198            targets = schemas.TargetsSchema.check(self.targets.read())
+199            logger.info("- Targets shape: {}", targets.shape)
+200            # - asserts
+201            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+202            # split
+203            logger.info("With splitter: {}", self.splitter)
+204            # - index
+205            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+206            # - inputs
+207            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+208            logger.info("- Inputs train shape: {}", inputs_train.shape)
+209            logger.info("- Inputs test shape: {}", inputs_test.shape)
+210            # - targets
+211            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+212            logger.info("- Targets train shape: {}", targets_train.shape)
+213            logger.info("- Targets test shape: {}", targets_test.shape)
+214            # - asserts
+215            assert len(inputs_train) == len(
+216                targets_train
+217            ), "Inputs and targets train should have the same length!"
+218            assert len(inputs_test) == len(
+219                targets_test
+220            ), "Inputs and targets test should have the same length!"
+221            # model
+222            logger.info("Fit model: {}", self.model)
+223            self.model.fit(inputs=inputs_train, targets=targets_train)
+224            # outputs
+225            logger.info("Predict outputs: {}", len(inputs_test))
+226            outputs_test = self.model.predict(inputs=inputs_test)
+227            logger.info("- Outputs test shape: {}", outputs_test.shape)
+228            assert len(inputs_test) == len(
+229                outputs_test
+230            ), "Inputs and outputs test should have the same length!"
+231            # scorers
+232            for i, scorer in enumerate(self.scorers, start=1):
+233                logger.info("{}. Run scorer: {}", i, scorer)
+234                score = scorer.score(targets=targets_test, outputs=outputs_test)
+235                mlflow.log_metric(key=scorer.name, value=score)
+236                logger.info("- Metric score: {}", score)
+237            # sign
+238            logger.info("Sign model: {}", self.signer)
+239            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+240            logger.info("- Model signature: {}", signature.to_dict())
+241            # save
+242            logger.info("Save model: {}", self.saver)
+243            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+244            logger.info("- Model URI: {}", info.model_uri)
+245            # register
+246            logger.info("Register model: {}", self.registry_alias)
+247            version = self.mlflow_service.register(
+248                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+249            )
+250            logger.info("- Model version: {}", version.version)
+251        return locals()
 

 
 
-            
Train and register a single AI/ML model

+            
Train and register a single AI/ML model.

 
 
Attributes:

 
@@ -861,67 +885,73 @@ 
Attributes:

 
     

     
-            
181    @T.override
-182    def run(self) -> Locals:
-183        # run
-184        logger.info("Start run: {} ", self.run_name)
-185        with mlflow.start_run(run_name=self.run_name) as run:
-186            logger.info("- Run ID: {}", run.info.run_id)
-187            # read
-188            # - inputs
-189            logger.info("Read inputs: {}", self.inputs)
-190            inputs = schemas.InputsSchema.check(self.inputs.read())
-191            logger.info("- Inputs shape: {}", inputs.shape)
-192            # - targets
-193            logger.info("Read targets: {}", self.targets)
-194            targets = schemas.TargetsSchema.check(self.targets.read())
-195            logger.info("- Targets shape: {}", targets.shape)
-196            # - asserts
-197            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-198            # split
-199            logger.info("With splitter: {}", self.splitter)
-200            # - index
-201            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
-202            # - inputs
-203            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
-204            logger.info("- Inputs train shape: {}", inputs_train.shape)
-205            logger.info("- Inputs test shape: {}", inputs_test.shape)
-206            # - targets
-207            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
-208            logger.info("- Targets train shape: {}", targets_train.shape)
-209            logger.info("- Targets test shape: {}", targets_test.shape)
-210            # - asserts
-211            assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!"
-212            assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!"
-213            # model
-214            logger.info("Fit model: {}", self.model)
-215            self.model.fit(inputs=inputs_train, targets=targets_train)
-216            # outputs
-217            logger.info("Predict outputs: {}", len(inputs_test))
-218            outputs_test = self.model.predict(inputs=inputs_test)
-219            logger.info("- Outputs test shape: {}", outputs_test.shape)
-220            assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!"
-221            # scorers
-222            for i, scorer in enumerate(self.scorers, start=1):
-223                logger.info("{}. Run scorer: {}", i, scorer)
-224                score = scorer.score(targets=targets_test, outputs=outputs_test)
-225                mlflow.log_metric(key=scorer.name, value=score)
-226                logger.info("- Metric score: {}", score)
-227            # sign
-228            logger.info("Sign model: {}", self.signer)
-229            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
-230            logger.info("- Model signature: {}", signature.to_dict())
-231            # save
-232            logger.info("Save model: {}", self.saver)
-233            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
-234            logger.info("- Model URI: {}", info.model_uri)
-235            # register
-236            logger.info("Register model: {}", self.registry_alias)
-237            version = self.mlflow_service.register(
-238                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
-239            )
-240            logger.info("- Model version: {}", version.version)
-241        return locals()
+            
185    @T.override
+186    def run(self) -> Locals:
+187        # run
+188        logger.info("Start run: {} ", self.run_name)
+189        with mlflow.start_run(run_name=self.run_name) as run:
+190            logger.info("- Run ID: {}", run.info.run_id)
+191            # read
+192            # - inputs
+193            logger.info("Read inputs: {}", self.inputs)
+194            inputs = schemas.InputsSchema.check(self.inputs.read())
+195            logger.info("- Inputs shape: {}", inputs.shape)
+196            # - targets
+197            logger.info("Read targets: {}", self.targets)
+198            targets = schemas.TargetsSchema.check(self.targets.read())
+199            logger.info("- Targets shape: {}", targets.shape)
+200            # - asserts
+201            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+202            # split
+203            logger.info("With splitter: {}", self.splitter)
+204            # - index
+205            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+206            # - inputs
+207            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+208            logger.info("- Inputs train shape: {}", inputs_train.shape)
+209            logger.info("- Inputs test shape: {}", inputs_test.shape)
+210            # - targets
+211            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+212            logger.info("- Targets train shape: {}", targets_train.shape)
+213            logger.info("- Targets test shape: {}", targets_test.shape)
+214            # - asserts
+215            assert len(inputs_train) == len(
+216                targets_train
+217            ), "Inputs and targets train should have the same length!"
+218            assert len(inputs_test) == len(
+219                targets_test
+220            ), "Inputs and targets test should have the same length!"
+221            # model
+222            logger.info("Fit model: {}", self.model)
+223            self.model.fit(inputs=inputs_train, targets=targets_train)
+224            # outputs
+225            logger.info("Predict outputs: {}", len(inputs_test))
+226            outputs_test = self.model.predict(inputs=inputs_test)
+227            logger.info("- Outputs test shape: {}", outputs_test.shape)
+228            assert len(inputs_test) == len(
+229                outputs_test
+230            ), "Inputs and outputs test should have the same length!"
+231            # scorers
+232            for i, scorer in enumerate(self.scorers, start=1):
+233                logger.info("{}. Run scorer: {}", i, scorer)
+234                score = scorer.score(targets=targets_test, outputs=outputs_test)
+235                mlflow.log_metric(key=scorer.name, value=score)
+236                logger.info("- Metric score: {}", score)
+237            # sign
+238            logger.info("Sign model: {}", self.signer)
+239            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+240            logger.info("- Model signature: {}", signature.to_dict())
+241            # save
+242            logger.info("Save model: {}", self.saver)
+243            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+244            logger.info("- Model URI: {}", info.model_uri)
+245            # register
+246            logger.info("Register model: {}", self.registry_alias)
+247            version = self.mlflow_service.register(
+248                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+249            )
+250            logger.info("- Model version: {}", version.version)
+251        return locals()
 

 
 
@@ -982,47 +1012,47 @@ 
Inherited Members

 
     

     
-            
244class InferenceJob(Job):
-245    """Load a model and generate predictions.
-246
-247    Attributes:
-248        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-249        outputs (datasets.WriterKind): dataset writer for the model outputs.
-250        registry_alias (str): alias of the model to load.
-251        loader (registers.LoaderKind): load the model from registry.
-252    """
-253
-254    KIND: T.Literal["InferenceJob"] = "InferenceJob"
-255
-256    # data
-257    inputs: datasets.ReaderKind
-258    outputs: datasets.WriterKind
-259    # model
-260    registry_alias: str = "Champion"
-261    loader: registers.LoaderKind = registers.CustomLoader()
-262
-263    @T.override
-264    def run(self) -> Locals:
-265        # read
-266        logger.info("Read inputs: {}", self.inputs)
-267        inputs = self.inputs.read()
-268        inputs = schemas.InputsSchema.check(inputs)
-269        logger.info("- Inputs shape: {}", inputs.shape)
-270        # uri
-271        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
-272        logger.info("With URI: {}", uri)
-273        # load
-274        logger.info("Load model: {}", self.loader)
-275        model = self.loader.load(uri=uri)
-276        logger.info("- Model: {}", model)
-277        # predict
-278        logger.info("Predict outputs: {}", len(inputs))
-279        outputs = model.predict(data=inputs)
-280        logger.info("- Outputs shape: {}", outputs.shape)
-281        # write
-282        logger.info("Write outputs: {}", self.outputs)
-283        self.outputs.write(data=outputs)
-284        return locals()
+            
254class InferenceJob(Job):
+255    """Load a model and generate predictions.
+256
+257    Attributes:
+258        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+259        outputs (datasets.WriterKind): dataset writer for the model outputs.
+260        registry_alias (str): alias of the model to load.
+261        loader (registers.LoaderKind): load the model from registry.
+262    """
+263
+264    KIND: T.Literal["InferenceJob"] = "InferenceJob"
+265
+266    # data
+267    inputs: datasets.ReaderKind
+268    outputs: datasets.WriterKind
+269    # model
+270    registry_alias: str = "Champion"
+271    loader: registers.LoaderKind = registers.CustomLoader()
+272
+273    @T.override
+274    def run(self) -> Locals:
+275        # read
+276        logger.info("Read inputs: {}", self.inputs)
+277        inputs = self.inputs.read()
+278        inputs = schemas.InputsSchema.check(inputs)
+279        logger.info("- Inputs shape: {}", inputs.shape)
+280        # uri
+281        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+282        logger.info("With URI: {}", uri)
+283        # load
+284        logger.info("Load model: {}", self.loader)
+285        model = self.loader.load(uri=uri)
+286        logger.info("- Model: {}", model)
+287        # predict
+288        logger.info("Predict outputs: {}", len(inputs))
+289        outputs = model.predict(data=inputs)
+290        logger.info("- Outputs shape: {}", outputs.shape)
+291        # write
+292        logger.info("Write outputs: {}", self.outputs)
+293        self.outputs.write(data=outputs)
+294        return locals()
 

 
 
@@ -1051,28 +1081,28 @@ 
Attributes:

 
     

     
-            
263    @T.override
-264    def run(self) -> Locals:
-265        # read
-266        logger.info("Read inputs: {}", self.inputs)
-267        inputs = self.inputs.read()
-268        inputs = schemas.InputsSchema.check(inputs)
-269        logger.info("- Inputs shape: {}", inputs.shape)
-270        # uri
-271        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
-272        logger.info("With URI: {}", uri)
-273        # load
-274        logger.info("Load model: {}", self.loader)
-275        model = self.loader.load(uri=uri)
-276        logger.info("- Model: {}", model)
-277        # predict
-278        logger.info("Predict outputs: {}", len(inputs))
-279        outputs = model.predict(data=inputs)
-280        logger.info("- Outputs shape: {}", outputs.shape)
-281        # write
-282        logger.info("Write outputs: {}", self.outputs)
-283        self.outputs.write(data=outputs)
-284        return locals()
+            
273    @T.override
+274    def run(self) -> Locals:
+275        # read
+276        logger.info("Read inputs: {}", self.inputs)
+277        inputs = self.inputs.read()
+278        inputs = schemas.InputsSchema.check(inputs)
+279        logger.info("- Inputs shape: {}", inputs.shape)
+280        # uri
+281        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+282        logger.info("With URI: {}", uri)
+283        # load
+284        logger.info("Load model: {}", self.loader)
+285        model = self.loader.load(uri=uri)
+286        logger.info("- Model: {}", model)
+287        # predict
+288        logger.info("Predict outputs: {}", len(inputs))
+289        outputs = model.predict(data=inputs)
+290        logger.info("- Outputs shape: {}", outputs.shape)
+291        # write
+292        logger.info("Write outputs: {}", self.outputs)
+293        self.outputs.write(data=outputs)
+294        return locals()
 

 
 
diff --git a/bikes/metrics.html b/bikes/metrics.html
index 93dd563..6994bca 100644
--- a/bikes/metrics.html
+++ b/bikes/metrics.html
@@ -115,46 +115,48 @@ 

 39            float: metric result.
 40        """
 41
-42    def scorer(self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets) -> float:
-43        """Score the model outputs against the targets.
-44
-45        Args:
-46            model (models.Model): model to evaluate.
-47            inputs (schemas.Inputs): model inputs values.
-48            targets (schemas.Targets): model expected values.
-49
-50        Returns:
-51            float: metric result.
-52        """
-53        outputs = model.predict(inputs=inputs)  # prediction
-54        score = self.score(targets=targets, outputs=outputs)
-55        return score
-56
-57
-58class SklearnMetric(Metric):
-59    """Compute metrics with sklearn.
-60
-61    Attributes:
-62        name (str): name of the sklearn metric.
-63        greater_is_better (bool): maximize or minimize.
-64    """
-65
-66    KIND: T.Literal["SklearnMetric"] = "SklearnMetric"
+42    def scorer(
+43        self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets
+44    ) -> float:
+45        """Score the model outputs against the targets.
+46
+47        Args:
+48            model (models.Model): model to evaluate.
+49            inputs (schemas.Inputs): model inputs values.
+50            targets (schemas.Targets): model expected values.
+51
+52        Returns:
+53            float: metric result.
+54        """
+55        outputs = model.predict(inputs=inputs)  # prediction
+56        score = self.score(targets=targets, outputs=outputs)
+57        return score
+58
+59
+60class SklearnMetric(Metric):
+61    """Compute metrics with sklearn.
+62
+63    Attributes:
+64        name (str): name of the sklearn metric.
+65        greater_is_better (bool): maximize or minimize.
+66    """
 67
-68    name: str = "mean_squared_error"
-69    greater_is_better: bool = False
-70
-71    @T.override
-72    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
-73        metric = getattr(metrics, self.name)
-74        sign = 1 if self.greater_is_better else -1
-75        y_true = targets[schemas.TargetsSchema.cnt]
-76        y_pred = outputs[schemas.OutputsSchema.prediction]
-77        score = metric(y_pred=y_pred, y_true=y_true) * sign
-78        return score
-79
-80
-81MetricKind = SklearnMetric
+68    KIND: T.Literal["SklearnMetric"] = "SklearnMetric"
+69
+70    name: str = "mean_squared_error"
+71    greater_is_better: bool = False
+72
+73    @T.override
+74    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+75        metric = getattr(metrics, self.name)
+76        sign = 1 if self.greater_is_better else -1
+77        y_true = targets[schemas.TargetsSchema.cnt]
+78        y_pred = outputs[schemas.OutputsSchema.prediction]
+79        score = metric(y_pred=y_pred, y_true=y_true) * sign
+80        return score
+81
+82
+83MetricKind = SklearnMetric
 

 
 
@@ -196,20 +198,22 @@ 

 40            float: metric result.
 41        """
 42
-43    def scorer(self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets) -> float:
-44        """Score the model outputs against the targets.
-45
-46        Args:
-47            model (models.Model): model to evaluate.
-48            inputs (schemas.Inputs): model inputs values.
-49            targets (schemas.Targets): model expected values.
-50
-51        Returns:
-52            float: metric result.
-53        """
-54        outputs = model.predict(inputs=inputs)  # prediction
-55        score = self.score(targets=targets, outputs=outputs)
-56        return score
+43    def scorer(
+44        self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets
+45    ) -> float:
+46        """Score the model outputs against the targets.
+47
+48        Args:
+49            model (models.Model): model to evaluate.
+50            inputs (schemas.Inputs): model inputs values.
+51            targets (schemas.Targets): model expected values.
+52
+53        Returns:
+54            float: metric result.
+55        """
+56        outputs = model.predict(inputs=inputs)  # prediction
+57        score = self.score(targets=targets, outputs=outputs)
+58        return score
@@ -281,20 +285,22 @@
Returns:
- 
43    def scorer(self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets) -> float:
-44        """Score the model outputs against the targets.
-45
-46        Args:
-47            model (models.Model): model to evaluate.
-48            inputs (schemas.Inputs): model inputs values.
-49            targets (schemas.Targets): model expected values.
-50
-51        Returns:
-52            float: metric result.
-53        """
-54        outputs = model.predict(inputs=inputs)  # prediction
-55        score = self.score(targets=targets, outputs=outputs)
-56        return score
+            
43    def scorer(
+44        self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets
+45    ) -> float:
+46        """Score the model outputs against the targets.
+47
+48        Args:
+49            model (models.Model): model to evaluate.
+50            inputs (schemas.Inputs): model inputs values.
+51            targets (schemas.Targets): model expected values.
+52
+53        Returns:
+54            float: metric result.
+55        """
+56        outputs = model.predict(inputs=inputs)  # prediction
+57        score = self.score(targets=targets, outputs=outputs)
+58        return score
 

 
 
@@ -363,27 +369,27 @@ 
Inherited Members
- 
59class SklearnMetric(Metric):
-60    """Compute metrics with sklearn.
-61
-62    Attributes:
-63        name (str): name of the sklearn metric.
-64        greater_is_better (bool): maximize or minimize.
-65    """
-66
-67    KIND: T.Literal["SklearnMetric"] = "SklearnMetric"
+            
61class SklearnMetric(Metric):
+62    """Compute metrics with sklearn.
+63
+64    Attributes:
+65        name (str): name of the sklearn metric.
+66        greater_is_better (bool): maximize or minimize.
+67    """
 68
-69    name: str = "mean_squared_error"
-70    greater_is_better: bool = False
-71
-72    @T.override
-73    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
-74        metric = getattr(metrics, self.name)
-75        sign = 1 if self.greater_is_better else -1
-76        y_true = targets[schemas.TargetsSchema.cnt]
-77        y_pred = outputs[schemas.OutputsSchema.prediction]
-78        score = metric(y_pred=y_pred, y_true=y_true) * sign
-79        return score
+69    KIND: T.Literal["SklearnMetric"] = "SklearnMetric"
+70
+71    name: str = "mean_squared_error"
+72    greater_is_better: bool = False
+73
+74    @T.override
+75    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+76        metric = getattr(metrics, self.name)
+77        sign = 1 if self.greater_is_better else -1
+78        y_true = targets[schemas.TargetsSchema.cnt]
+79        y_pred = outputs[schemas.OutputsSchema.prediction]
+80        score = metric(y_pred=y_pred, y_true=y_true) * sign
+81        return score
 

 
 
@@ -410,14 +416,14 @@ 
Attributes:
- 
72    @T.override
-73    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
-74        metric = getattr(metrics, self.name)
-75        sign = 1 if self.greater_is_better else -1
-76        y_true = targets[schemas.TargetsSchema.cnt]
-77        y_pred = outputs[schemas.OutputsSchema.prediction]
-78        score = metric(y_pred=y_pred, y_true=y_true) * sign
-79        return score
+            
74    @T.override
+75    def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float:
+76        metric = getattr(metrics, self.name)
+77        sign = 1 if self.greater_is_better else -1
+78        y_true = targets[schemas.TargetsSchema.cnt]
+79        y_pred = outputs[schemas.OutputsSchema.prediction]
+80        score = metric(y_pred=y_pred, y_true=y_true) * sign
+81        return score
 

 
 
diff --git a/bikes/models.html b/bikes/models.html
index 74a9e65..c9b0d06 100644
--- a/bikes/models.html
+++ b/bikes/models.html
@@ -116,126 +116,129 @@ 

  28
  29    KIND: str
  30
- 31    # pylint: disable=unused-argument
- 32    def get_params(self, deep: bool = True) -> Params:
- 33        """Get the model params.
- 34
- 35        Args:
- 36            deep (bool, optional): ignored. Defaults to True.
- 37
- 38        Returns:
- 39            Params: internal model parameters.
- 40        """
- 41        params: Params = {}
- 42        for key, value in self.model_dump().items():
- 43            if not key.startswith("_") and not key.isupper():
- 44                params[key] = value
- 45        return params
- 46
- 47    def set_params(self, **params: ParamValue) -> T.Self:
- 48        """Set the model params in place.
- 49
- 50        Returns:
- 51            T.Self: instance of the model.
- 52        """
- 53        for key, value in params.items():
- 54            setattr(self, key, value)
- 55        return self
- 56
- 57    @abc.abstractmethod
- 58    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self:
- 59        """Fit the model on the given inputs and targets.
- 60
- 61        Args:
- 62            inputs (schemas.Inputs): model training inputs.
- 63            targets (schemas.Targets): model training targets.
- 64
- 65        Returns:
- 66            Model: instance of the model.
- 67        """
- 68
- 69    @abc.abstractmethod
- 70    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
- 71        """Generate outputs with the model for the given inputs.
- 72
- 73        Args:
- 74            inputs (schemas.Inputs): model prediction inputs.
- 75
- 76        Returns:
- 77            schemas.Outputs: model prediction outputs.
- 78        """
+ 31    def get_params(self, deep: bool = True) -> Params:
+ 32        """Get the model params.
+ 33
+ 34        Args:
+ 35            deep (bool, optional): ignored. Defaults to True.
+ 36
+ 37        Returns:
+ 38            Params: internal model parameters.
+ 39        """
+ 40        params: Params = {}
+ 41        for key, value in self.model_dump().items():
+ 42            if not key.startswith("_") and not key.isupper():
+ 43                params[key] = value
+ 44        return params
+ 45
+ 46    def set_params(self, **params: ParamValue) -> T.Self:
+ 47        """Set the model params in place.
+ 48
+ 49        Returns:
+ 50            T.Self: instance of the model.
+ 51        """
+ 52        for key, value in params.items():
+ 53            setattr(self, key, value)
+ 54        return self
+ 55
+ 56    @abc.abstractmethod
+ 57    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self:
+ 58        """Fit the model on the given inputs and targets.
+ 59
+ 60        Args:
+ 61            inputs (schemas.Inputs): model training inputs.
+ 62            targets (schemas.Targets): model training targets.
+ 63
+ 64        Returns:
+ 65            Model: instance of the model.
+ 66        """
+ 67
+ 68    @abc.abstractmethod
+ 69    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+ 70        """Generate outputs with the model for the given inputs.
+ 71
+ 72        Args:
+ 73            inputs (schemas.Inputs): model prediction inputs.
+ 74
+ 75        Returns:
+ 76            schemas.Outputs: model prediction outputs.
+ 77        """
+ 78
  79
- 80
- 81class BaselineSklearnModel(Model):
- 82    """Simple baseline model built on top of sklearn.
- 83
- 84    Attributes:
- 85        max_depth (int): maximum depth of the random forest.
- 86        n_estimators (int): number of estimators in the random forest.
- 87        random_state (int, optional): random state of the machine learning pipeline.
- 88    """
- 89
- 90    KIND: T.Literal["BaselineSklearnModel"] = "BaselineSklearnModel"
- 91
- 92    # params
- 93    max_depth: int = 20
- 94    n_estimators: int = 200
- 95    random_state: int | None = 42
- 96    # private
- 97    _pipeline: pipeline.Pipeline | None = None
- 98    _numericals: list[str] = [
- 99        "yr",
-100        "mnth",
-101        "hr",
-102        "holiday",
-103        "weekday",
-104        "workingday",
-105        "temp",
-106        "atemp",
-107        "hum",
-108        "windspeed",
-109        "casual",
-110        # "registered", # too correlated with target
-111    ]
-112    _categoricals: list[str] = [
-113        "season",
-114        "weathersit",
-115    ]
-116
-117    @T.override
-118    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
-119        # subcomponents
-120        categoricals_transformer = preprocessing.OneHotEncoder(sparse_output=False, handle_unknown="ignore")
-121        # components
-122        transformer = compose.ColumnTransformer(
-123            [
-124                ("categoricals", categoricals_transformer, self._categoricals),
-125                ("numericals", "passthrough", self._numericals),
-126            ],
-127            remainder="drop",
-128        )
-129        regressor = ensemble.RandomForestRegressor(
-130            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
-131        )
-132        # pipeline
-133        self._pipeline = pipeline.Pipeline(
-134            steps=[
-135                ("transformer", transformer),
-136                ("regressor", regressor),
-137            ]
-138        )
-139        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
-140        return self
-141
-142    @T.override
-143    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
-144        assert self._pipeline is not None, "Model should be fitted first!"
-145        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
-146        outputs = schemas.Outputs({schemas.OutputsSchema.prediction: prediction}, index=inputs.index)
-147        return outputs
-148
-149
-150ModelKind = BaselineSklearnModel
+ 80class BaselineSklearnModel(Model):
+ 81    """Simple baseline model built on top of sklearn.
+ 82
+ 83    Attributes:
+ 84        max_depth (int): maximum depth of the random forest.
+ 85        n_estimators (int): number of estimators in the random forest.
+ 86        random_state (int, optional): random state of the machine learning pipeline.
+ 87    """
+ 88
+ 89    KIND: T.Literal["BaselineSklearnModel"] = "BaselineSklearnModel"
+ 90
+ 91    # params
+ 92    max_depth: int = 20
+ 93    n_estimators: int = 200
+ 94    random_state: int | None = 42
+ 95    # private
+ 96    _pipeline: pipeline.Pipeline | None = None
+ 97    _numericals: list[str] = [
+ 98        "yr",
+ 99        "mnth",
+100        "hr",
+101        "holiday",
+102        "weekday",
+103        "workingday",
+104        "temp",
+105        "atemp",
+106        "hum",
+107        "windspeed",
+108        "casual",
+109        # "registered", # too correlated with target
+110    ]
+111    _categoricals: list[str] = [
+112        "season",
+113        "weathersit",
+114    ]
+115
+116    @T.override
+117    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
+118        # subcomponents
+119        categoricals_transformer = preprocessing.OneHotEncoder(
+120            sparse_output=False, handle_unknown="ignore"
+121        )
+122        # components
+123        transformer = compose.ColumnTransformer(
+124            [
+125                ("categoricals", categoricals_transformer, self._categoricals),
+126                ("numericals", "passthrough", self._numericals),
+127            ],
+128            remainder="drop",
+129        )
+130        regressor = ensemble.RandomForestRegressor(
+131            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
+132        )
+133        # pipeline
+134        self._pipeline = pipeline.Pipeline(
+135            steps=[
+136                ("transformer", transformer),
+137                ("regressor", regressor),
+138            ]
+139        )
+140        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
+141        return self
+142
+143    @T.override
+144    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+145        assert self._pipeline is not None, "Model should be fitted first!"
+146        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
+147        outputs = schemas.Outputs(
+148            {schemas.OutputsSchema.prediction: prediction}, index=inputs.index
+149        )
+150        return outputs
+151
+152
+153ModelKind = BaselineSklearnModel
@@ -260,54 +263,53 @@

29 30 KIND: str 31 -32 # pylint: disable=unused-argument -33 def get_params(self, deep: bool = True) -> Params: -34 """Get the model params. -35 -36 Args: -37 deep (bool, optional): ignored. Defaults to True. -38 -39 Returns: -40 Params: internal model parameters. -41 """ -42 params: Params = {} -43 for key, value in self.model_dump().items(): -44 if not key.startswith("_") and not key.isupper(): -45 params[key] = value -46 return params -47 -48 def set_params(self, **params: ParamValue) -> T.Self: -49 """Set the model params in place. -50 -51 Returns: -52 T.Self: instance of the model. -53 """ -54 for key, value in params.items(): -55 setattr(self, key, value) -56 return self -57 -58 @abc.abstractmethod -59 def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self: -60 """Fit the model on the given inputs and targets. -61 -62 Args: -63 inputs (schemas.Inputs): model training inputs. -64 targets (schemas.Targets): model training targets. -65 -66 Returns: -67 Model: instance of the model. -68 """ -69 -70 @abc.abstractmethod -71 def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: -72 """Generate outputs with the model for the given inputs. -73 -74 Args: -75 inputs (schemas.Inputs): model prediction inputs. -76 -77 Returns: -78 schemas.Outputs: model prediction outputs. -79 """ +32 def get_params(self, deep: bool = True) -> Params: +33 """Get the model params. +34 +35 Args: +36 deep (bool, optional): ignored. Defaults to True. +37 +38 Returns: +39 Params: internal model parameters. +40 """ +41 params: Params = {} +42 for key, value in self.model_dump().items(): +43 if not key.startswith("_") and not key.isupper(): +44 params[key] = value +45 return params +46 +47 def set_params(self, **params: ParamValue) -> T.Self: +48 """Set the model params in place. +49 +50 Returns: +51 T.Self: instance of the model. +52 """ +53 for key, value in params.items(): +54 setattr(self, key, value) +55 return self +56 +57 @abc.abstractmethod +58 def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self: +59 """Fit the model on the given inputs and targets. +60 +61 Args: +62 inputs (schemas.Inputs): model training inputs. +63 targets (schemas.Targets): model training targets. +64 +65 Returns: +66 Model: instance of the model. +67 """ +68 +69 @abc.abstractmethod +70 def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: +71 """Generate outputs with the model for the given inputs. +72 +73 Args: +74 inputs (schemas.Inputs): model prediction inputs. +75 +76 Returns: +77 schemas.Outputs: model prediction outputs. +78 """ @@ -329,20 +331,20 @@

- 
33    def get_params(self, deep: bool = True) -> Params:
-34        """Get the model params.
-35
-36        Args:
-37            deep (bool, optional): ignored. Defaults to True.
-38
-39        Returns:
-40            Params: internal model parameters.
-41        """
-42        params: Params = {}
-43        for key, value in self.model_dump().items():
-44            if not key.startswith("_") and not key.isupper():
-45                params[key] = value
-46        return params
+            
32    def get_params(self, deep: bool = True) -> Params:
+33        """Get the model params.
+34
+35        Args:
+36            deep (bool, optional): ignored. Defaults to True.
+37
+38        Returns:
+39            Params: internal model parameters.
+40        """
+41        params: Params = {}
+42        for key, value in self.model_dump().items():
+43            if not key.startswith("_") and not key.isupper():
+44                params[key] = value
+45        return params
 

 
 
@@ -374,15 +376,15 @@ 
Returns:
- 
48    def set_params(self, **params: ParamValue) -> T.Self:
-49        """Set the model params in place.
-50
-51        Returns:
-52            T.Self: instance of the model.
-53        """
-54        for key, value in params.items():
-55            setattr(self, key, value)
-56        return self
+            
47    def set_params(self, **params: ParamValue) -> T.Self:
+48        """Set the model params in place.
+49
+50        Returns:
+51            T.Self: instance of the model.
+52        """
+53        for key, value in params.items():
+54            setattr(self, key, value)
+55        return self
 

 
 
@@ -409,17 +411,17 @@ 
Returns:
- 
58    @abc.abstractmethod
-59    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self:
-60        """Fit the model on the given inputs and targets.
-61
-62        Args:
-63            inputs (schemas.Inputs): model training inputs.
-64            targets (schemas.Targets): model training targets.
-65
-66        Returns:
-67            Model: instance of the model.
-68        """
+            
57    @abc.abstractmethod
+58    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self:
+59        """Fit the model on the given inputs and targets.
+60
+61        Args:
+62            inputs (schemas.Inputs): model training inputs.
+63            targets (schemas.Targets): model training targets.
+64
+65        Returns:
+66            Model: instance of the model.
+67        """
 

 
 
@@ -453,16 +455,16 @@ 
Returns:
- 
70    @abc.abstractmethod
-71    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
-72        """Generate outputs with the model for the given inputs.
-73
-74        Args:
-75            inputs (schemas.Inputs): model prediction inputs.
-76
-77        Returns:
-78            schemas.Outputs: model prediction outputs.
-79        """
+            
69    @abc.abstractmethod
+70    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+71        """Generate outputs with the model for the given inputs.
+72
+73        Args:
+74            inputs (schemas.Inputs): model prediction inputs.
+75
+76        Returns:
+77            schemas.Outputs: model prediction outputs.
+78        """
 

 
 
@@ -529,73 +531,77 @@ 
Inherited Members
- 
 82class BaselineSklearnModel(Model):
- 83    """Simple baseline model built on top of sklearn.
- 84
- 85    Attributes:
- 86        max_depth (int): maximum depth of the random forest.
- 87        n_estimators (int): number of estimators in the random forest.
- 88        random_state (int, optional): random state of the machine learning pipeline.
- 89    """
- 90
- 91    KIND: T.Literal["BaselineSklearnModel"] = "BaselineSklearnModel"
- 92
- 93    # params
- 94    max_depth: int = 20
- 95    n_estimators: int = 200
- 96    random_state: int | None = 42
- 97    # private
- 98    _pipeline: pipeline.Pipeline | None = None
- 99    _numericals: list[str] = [
-100        "yr",
-101        "mnth",
-102        "hr",
-103        "holiday",
-104        "weekday",
-105        "workingday",
-106        "temp",
-107        "atemp",
-108        "hum",
-109        "windspeed",
-110        "casual",
-111        # "registered", # too correlated with target
-112    ]
-113    _categoricals: list[str] = [
-114        "season",
-115        "weathersit",
-116    ]
-117
-118    @T.override
-119    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
-120        # subcomponents
-121        categoricals_transformer = preprocessing.OneHotEncoder(sparse_output=False, handle_unknown="ignore")
-122        # components
-123        transformer = compose.ColumnTransformer(
-124            [
-125                ("categoricals", categoricals_transformer, self._categoricals),
-126                ("numericals", "passthrough", self._numericals),
-127            ],
-128            remainder="drop",
-129        )
-130        regressor = ensemble.RandomForestRegressor(
-131            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
-132        )
-133        # pipeline
-134        self._pipeline = pipeline.Pipeline(
-135            steps=[
-136                ("transformer", transformer),
-137                ("regressor", regressor),
-138            ]
-139        )
-140        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
-141        return self
-142
-143    @T.override
-144    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
-145        assert self._pipeline is not None, "Model should be fitted first!"
-146        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
-147        outputs = schemas.Outputs({schemas.OutputsSchema.prediction: prediction}, index=inputs.index)
-148        return outputs
+            
 81class BaselineSklearnModel(Model):
+ 82    """Simple baseline model built on top of sklearn.
+ 83
+ 84    Attributes:
+ 85        max_depth (int): maximum depth of the random forest.
+ 86        n_estimators (int): number of estimators in the random forest.
+ 87        random_state (int, optional): random state of the machine learning pipeline.
+ 88    """
+ 89
+ 90    KIND: T.Literal["BaselineSklearnModel"] = "BaselineSklearnModel"
+ 91
+ 92    # params
+ 93    max_depth: int = 20
+ 94    n_estimators: int = 200
+ 95    random_state: int | None = 42
+ 96    # private
+ 97    _pipeline: pipeline.Pipeline | None = None
+ 98    _numericals: list[str] = [
+ 99        "yr",
+100        "mnth",
+101        "hr",
+102        "holiday",
+103        "weekday",
+104        "workingday",
+105        "temp",
+106        "atemp",
+107        "hum",
+108        "windspeed",
+109        "casual",
+110        # "registered", # too correlated with target
+111    ]
+112    _categoricals: list[str] = [
+113        "season",
+114        "weathersit",
+115    ]
+116
+117    @T.override
+118    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
+119        # subcomponents
+120        categoricals_transformer = preprocessing.OneHotEncoder(
+121            sparse_output=False, handle_unknown="ignore"
+122        )
+123        # components
+124        transformer = compose.ColumnTransformer(
+125            [
+126                ("categoricals", categoricals_transformer, self._categoricals),
+127                ("numericals", "passthrough", self._numericals),
+128            ],
+129            remainder="drop",
+130        )
+131        regressor = ensemble.RandomForestRegressor(
+132            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
+133        )
+134        # pipeline
+135        self._pipeline = pipeline.Pipeline(
+136            steps=[
+137                ("transformer", transformer),
+138                ("regressor", regressor),
+139            ]
+140        )
+141        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
+142        return self
+143
+144    @T.override
+145    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+146        assert self._pipeline is not None, "Model should be fitted first!"
+147        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
+148        outputs = schemas.Outputs(
+149            {schemas.OutputsSchema.prediction: prediction}, index=inputs.index
+150        )
+151        return outputs
 

 
 
@@ -623,30 +629,32 @@ 
Attributes:
- 
118    @T.override
-119    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
-120        # subcomponents
-121        categoricals_transformer = preprocessing.OneHotEncoder(sparse_output=False, handle_unknown="ignore")
-122        # components
-123        transformer = compose.ColumnTransformer(
-124            [
-125                ("categoricals", categoricals_transformer, self._categoricals),
-126                ("numericals", "passthrough", self._numericals),
-127            ],
-128            remainder="drop",
-129        )
-130        regressor = ensemble.RandomForestRegressor(
-131            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
-132        )
-133        # pipeline
-134        self._pipeline = pipeline.Pipeline(
-135            steps=[
-136                ("transformer", transformer),
-137                ("regressor", regressor),
-138            ]
-139        )
-140        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
-141        return self
+            
117    @T.override
+118    def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel":
+119        # subcomponents
+120        categoricals_transformer = preprocessing.OneHotEncoder(
+121            sparse_output=False, handle_unknown="ignore"
+122        )
+123        # components
+124        transformer = compose.ColumnTransformer(
+125            [
+126                ("categoricals", categoricals_transformer, self._categoricals),
+127                ("numericals", "passthrough", self._numericals),
+128            ],
+129            remainder="drop",
+130        )
+131        regressor = ensemble.RandomForestRegressor(
+132            max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state
+133        )
+134        # pipeline
+135        self._pipeline = pipeline.Pipeline(
+136            steps=[
+137                ("transformer", transformer),
+138                ("regressor", regressor),
+139            ]
+140        )
+141        self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt])
+142        return self
 

 
 
@@ -680,12 +688,14 @@ 
Returns:
- 
143    @T.override
-144    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
-145        assert self._pipeline is not None, "Model should be fitted first!"
-146        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
-147        outputs = schemas.Outputs({schemas.OutputsSchema.prediction: prediction}, index=inputs.index)
-148        return outputs
+            
144    @T.override
+145    def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
+146        assert self._pipeline is not None, "Model should be fitted first!"
+147        prediction = self._pipeline.predict(inputs)  # return an np.ndarray, not a dataframe!
+148        outputs = schemas.Outputs(
+149            {schemas.OutputsSchema.prediction: prediction}, index=inputs.index
+150        )
+151        return outputs
 

 
 
diff --git a/bikes/registers.html b/bikes/registers.html
index 5ddc05b..5d6d1d0 100644
--- a/bikes/registers.html
+++ b/bikes/registers.html
@@ -155,147 +155,155 @@ 

  34        """
  35        self.model = model
  36
- 37    # pylint: disable=arguments-differ, unused-argument
- 38    def predict(self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs) -> schemas.Outputs:
- 39        """Generate predictions from a custom model.
- 40
- 41        Args:
- 42            context (mlflow.pyfunc.PythonModelContext): ignored.
- 43            inputs (schemas.Inputs): inputs for the model.
- 44
- 45        Returns:
- 46            schemas.Outputs: outputs of the model.
- 47        """
- 48        return self.model.predict(inputs=inputs)
- 49
+ 37    def predict(
+ 38        self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs
+ 39    ) -> schemas.Outputs:
+ 40        """Generate predictions from a custom model.
+ 41
+ 42        Args:
+ 43            context (mlflow.pyfunc.PythonModelContext): ignored.
+ 44            inputs (schemas.Inputs): inputs for the model.
+ 45
+ 46        Returns:
+ 47            schemas.Outputs: outputs of the model.
+ 48        """
+ 49        return self.model.predict(inputs=inputs)
  50
- 51# %% SIGNERS
- 52
+ 51
+ 52# %% SIGNERS
  53
- 54class Signer(abc.ABC, pdt.BaseModel, strict=True):
- 55    """Base class for making signatures.
- 56
- 57    Allow to switch between signing approaches.
- 58    e.g., automatic inference vs manual signatures
- 59    https://mlflow.org/docs/latest/models.html#model-signature-and-input-example
- 60    """
- 61
- 62    KIND: str
- 63
- 64    @abc.abstractmethod
- 65    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
- 66        """Make a model signature from inputs/outputs.
- 67
- 68        Args:
- 69            inputs (schemas.Inputs): inputs of the model.
- 70            outputs (schemas.Outputs): ouputs of the model.
- 71
- 72        Returns:
- 73            ModelSignature: generated signature for the model.
- 74        """
- 75
+ 54
+ 55class Signer(abc.ABC, pdt.BaseModel, strict=True):
+ 56    """Base class for making signatures.
+ 57
+ 58    Allow to switch between signing approaches.
+ 59    e.g., automatic inference vs manual signatures
+ 60    https://mlflow.org/docs/latest/models.html#model-signature-and-input-example
+ 61    """
+ 62
+ 63    KIND: str
+ 64
+ 65    @abc.abstractmethod
+ 66    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+ 67        """Make a model signature from inputs/outputs.
+ 68
+ 69        Args:
+ 70            inputs (schemas.Inputs): inputs of the model.
+ 71            outputs (schemas.Outputs): ouputs of the model.
+ 72
+ 73        Returns:
+ 74            ModelSignature: generated signature for the model.
+ 75        """
  76
- 77class InferSigner(Signer):
- 78    """Generate model signatures from data inference."""
- 79
- 80    KIND: T.Literal["InferModelSigner"] = "InferModelSigner"
- 81
- 82    @T.override
- 83    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
- 84        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
- 85
+ 77
+ 78class InferSigner(Signer):
+ 79    """Generate model signatures from data inference."""
+ 80
+ 81    KIND: T.Literal["InferModelSigner"] = "InferModelSigner"
+ 82
+ 83    @T.override
+ 84    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+ 85        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
  86
- 87SignerKind = InferSigner
- 88
+ 87
+ 88SignerKind = InferSigner
  89
- 90# %% SAVERS
- 91
+ 90
+ 91# %% SAVERS
  92
- 93class Saver(abc.ABC, pdt.BaseModel, strict=True):
- 94    """Base class for saving models in registry.
- 95
- 96    Separate model definition from serialization.
- 97    e.g., to switch between serialization flavors.
- 98
- 99    Attributes:
-100        path (str): model path inside the MLflow artifact store.
-101    """
-102
-103    KIND: str
-104
-105    path: str = "model"
-106
-107    @abc.abstractmethod
-108    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
-109        """Save a model in the model registry.
-110
-111        Args:
-112            model (models.Model): model to save.
-113            signature (Signature): model signature.
-114            input_example (schemas.Inputs): inputs sample.
-115
-116        Returns:
-117            Info: model saving information.
-118        """
-119
-120
-121class CustomSaver(Saver):
-122    """Saver for custom models using the MLflow PyFunc module.
+ 93
+ 94class Saver(abc.ABC, pdt.BaseModel, strict=True):
+ 95    """Base class for saving models in registry.
+ 96
+ 97    Separate model definition from serialization.
+ 98    e.g., to switch between serialization flavors.
+ 99
+100    Attributes:
+101        path (str): model path inside the MLflow artifact store.
+102    """
+103
+104    KIND: str
+105
+106    path: str = "model"
+107
+108    @abc.abstractmethod
+109    def save(
+110        self, model: models.Model, signature: Signature, input_example: schemas.Inputs
+111    ) -> Info:
+112        """Save a model in the model registry.
+113
+114        Args:
+115            model (models.Model): model to save.
+116            signature (Signature): model signature.
+117            input_example (schemas.Inputs): inputs sample.
+118
+119        Returns:
+120            Info: model saving information.
+121        """
+122
 123
-124    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
-125    """
+124class CustomSaver(Saver):
+125    """Saver for custom models using the MLflow PyFunc module.
 126
-127    KIND: T.Literal["CustomSaver"] = "CustomSaver"
-128
-129    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
-130        """Save a custom model to the MLflow Model Registry."""
-131        custom = CustomAdapter(model=model)  # adapt model
-132        return mlflow.pyfunc.log_model(
-133            artifact_path=self.path, python_model=custom, signature=signature, input_example=input_example
-134        )
-135
-136
-137SaverKind = CustomSaver
-138
-139
-140# %% LOADERS
-141
-142
-143class Loader(abc.ABC, pdt.BaseModel, strict=True):
-144    """Base class for loading models from registry.
-145
-146    Separate model definition from deserialization.
-147    e.g., to switch between deserialization flavors.
-148    """
+127    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+128    """
+129
+130    KIND: T.Literal["CustomSaver"] = "CustomSaver"
+131
+132    def save(
+133        self, model: models.Model, signature: Signature, input_example: schemas.Inputs
+134    ) -> Info:
+135        """Save a custom model to the MLflow Model Registry."""
+136        custom = CustomAdapter(model=model)  # adapt model
+137        return mlflow.pyfunc.log_model(
+138            artifact_path=self.path,
+139            python_model=custom,
+140            signature=signature,
+141            input_example=input_example,
+142        )
+143
+144
+145SaverKind = CustomSaver
+146
+147
+148# %% LOADERS
 149
-150    KIND: str
-151
-152    @abc.abstractmethod
-153    def load(self, uri: str) -> T.Any:
-154        """Load a model from the model registry.
-155
-156        Args:
-157            uri (str): URI of the model to load.
-158
-159        Returns:
-160            T.Any: model loaded from registry.
-161        """
-162
+150
+151class Loader(abc.ABC, pdt.BaseModel, strict=True):
+152    """Base class for loading models from registry.
+153
+154    Separate model definition from deserialization.
+155    e.g., to switch between deserialization flavors.
+156    """
+157
+158    KIND: str
+159
+160    @abc.abstractmethod
+161    def load(self, uri: str) -> T.Any:
+162        """Load a model from the model registry.
 163
-164class CustomLoader(Loader):
-165    """Loader for custom models using the MLflow PyFunc module.
+164        Args:
+165            uri (str): URI of the model to load.
 166
-167    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
-168    """
-169
-170    KIND: T.Literal["CustomLoader"] = "CustomLoader"
+167        Returns:
+168            T.Any: model loaded from registry.
+169        """
+170
 171
-172    @T.override
-173    def load(self, uri: str) -> CustomModel:
-174        return mlflow.pyfunc.load_model(model_uri=uri)
-175
-176
-177LoaderKind = CustomLoader
+172class CustomLoader(Loader):
+173    """Loader for custom models using the MLflow PyFunc module.
+174
+175    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+176    """
+177
+178    KIND: T.Literal["CustomLoader"] = "CustomLoader"
+179
+180    @T.override
+181    def load(self, uri: str) -> CustomModel:
+182        return mlflow.pyfunc.load_model(model_uri=uri)
+183
+184
+185LoaderKind = CustomLoader
@@ -325,18 +333,19 @@

35 """ 36 self.model = model 37 -38 # pylint: disable=arguments-differ, unused-argument -39 def predict(self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs) -> schemas.Outputs: -40 """Generate predictions from a custom model. -41 -42 Args: -43 context (mlflow.pyfunc.PythonModelContext): ignored. -44 inputs (schemas.Inputs): inputs for the model. -45 -46 Returns: -47 schemas.Outputs: outputs of the model. -48 """ -49 return self.model.predict(inputs=inputs) +38 def predict( +39 self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs +40 ) -> schemas.Outputs: +41 """Generate predictions from a custom model. +42 +43 Args: +44 context (mlflow.pyfunc.PythonModelContext): ignored. +45 inputs (schemas.Inputs): inputs for the model. +46 +47 Returns: +48 schemas.Outputs: outputs of the model. +49 """ +50 return self.model.predict(inputs=inputs) @@ -388,17 +397,19 @@

Arguments:
- 
39    def predict(self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs) -> schemas.Outputs:
-40        """Generate predictions from a custom model.
-41
-42        Args:
-43            context (mlflow.pyfunc.PythonModelContext): ignored.
-44            inputs (schemas.Inputs): inputs for the model.
-45
-46        Returns:
-47            schemas.Outputs: outputs of the model.
-48        """
-49        return self.model.predict(inputs=inputs)
+            
38    def predict(
+39        self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs
+40    ) -> schemas.Outputs:
+41        """Generate predictions from a custom model.
+42
+43        Args:
+44            context (mlflow.pyfunc.PythonModelContext): ignored.
+45            inputs (schemas.Inputs): inputs for the model.
+46
+47        Returns:
+48            schemas.Outputs: outputs of the model.
+49        """
+50        return self.model.predict(inputs=inputs)
 

 
 
@@ -441,27 +452,27 @@ 
Inherited Members
- 
55class Signer(abc.ABC, pdt.BaseModel, strict=True):
-56    """Base class for making signatures.
-57
-58    Allow to switch between signing approaches.
-59    e.g., automatic inference vs manual signatures
-60    https://mlflow.org/docs/latest/models.html#model-signature-and-input-example
-61    """
-62
-63    KIND: str
-64
-65    @abc.abstractmethod
-66    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
-67        """Make a model signature from inputs/outputs.
-68
-69        Args:
-70            inputs (schemas.Inputs): inputs of the model.
-71            outputs (schemas.Outputs): ouputs of the model.
-72
-73        Returns:
-74            ModelSignature: generated signature for the model.
-75        """
+            
56class Signer(abc.ABC, pdt.BaseModel, strict=True):
+57    """Base class for making signatures.
+58
+59    Allow to switch between signing approaches.
+60    e.g., automatic inference vs manual signatures
+61    https://mlflow.org/docs/latest/models.html#model-signature-and-input-example
+62    """
+63
+64    KIND: str
+65
+66    @abc.abstractmethod
+67    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+68        """Make a model signature from inputs/outputs.
+69
+70        Args:
+71            inputs (schemas.Inputs): inputs of the model.
+72            outputs (schemas.Outputs): ouputs of the model.
+73
+74        Returns:
+75            ModelSignature: generated signature for the model.
+76        """
 

 
 
@@ -485,17 +496,17 @@ 
Inherited Members
- 
65    @abc.abstractmethod
-66    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
-67        """Make a model signature from inputs/outputs.
-68
-69        Args:
-70            inputs (schemas.Inputs): inputs of the model.
-71            outputs (schemas.Outputs): ouputs of the model.
-72
-73        Returns:
-74            ModelSignature: generated signature for the model.
-75        """
+            
66    @abc.abstractmethod
+67    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+68        """Make a model signature from inputs/outputs.
+69
+70        Args:
+71            inputs (schemas.Inputs): inputs of the model.
+72            outputs (schemas.Outputs): ouputs of the model.
+73
+74        Returns:
+75            ModelSignature: generated signature for the model.
+76        """
 

 
 
@@ -563,14 +574,14 @@ 
Inherited Members
- 
78class InferSigner(Signer):
-79    """Generate model signatures from data inference."""
-80
-81    KIND: T.Literal["InferModelSigner"] = "InferModelSigner"
-82
-83    @T.override
-84    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
-85        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
+            
79class InferSigner(Signer):
+80    """Generate model signatures from data inference."""
+81
+82    KIND: T.Literal["InferModelSigner"] = "InferModelSigner"
+83
+84    @T.override
+85    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+86        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
 

 
 
@@ -590,9 +601,9 @@ 
Inherited Members
- 
83    @T.override
-84    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
-85        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
+            
84    @T.override
+85    def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature:
+86        return mlflow.models.infer_signature(model_input=inputs, model_output=outputs)
 

 
 
@@ -660,32 +671,34 @@ 
Inherited Members
- 
 94class Saver(abc.ABC, pdt.BaseModel, strict=True):
- 95    """Base class for saving models in registry.
- 96
- 97    Separate model definition from serialization.
- 98    e.g., to switch between serialization flavors.
- 99
-100    Attributes:
-101        path (str): model path inside the MLflow artifact store.
-102    """
-103
-104    KIND: str
-105
-106    path: str = "model"
-107
-108    @abc.abstractmethod
-109    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
-110        """Save a model in the model registry.
-111
-112        Args:
-113            model (models.Model): model to save.
-114            signature (Signature): model signature.
-115            input_example (schemas.Inputs): inputs sample.
-116
-117        Returns:
-118            Info: model saving information.
-119        """
+            
 95class Saver(abc.ABC, pdt.BaseModel, strict=True):
+ 96    """Base class for saving models in registry.
+ 97
+ 98    Separate model definition from serialization.
+ 99    e.g., to switch between serialization flavors.
+100
+101    Attributes:
+102        path (str): model path inside the MLflow artifact store.
+103    """
+104
+105    KIND: str
+106
+107    path: str = "model"
+108
+109    @abc.abstractmethod
+110    def save(
+111        self, model: models.Model, signature: Signature, input_example: schemas.Inputs
+112    ) -> Info:
+113        """Save a model in the model registry.
+114
+115        Args:
+116            model (models.Model): model to save.
+117            signature (Signature): model signature.
+118            input_example (schemas.Inputs): inputs sample.
+119
+120        Returns:
+121            Info: model saving information.
+122        """
 

 
 
@@ -714,18 +727,20 @@ 
Attributes:
- 
108    @abc.abstractmethod
-109    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
-110        """Save a model in the model registry.
-111
-112        Args:
-113            model (models.Model): model to save.
-114            signature (Signature): model signature.
-115            input_example (schemas.Inputs): inputs sample.
-116
-117        Returns:
-118            Info: model saving information.
-119        """
+            
109    @abc.abstractmethod
+110    def save(
+111        self, model: models.Model, signature: Signature, input_example: schemas.Inputs
+112    ) -> Info:
+113        """Save a model in the model registry.
+114
+115        Args:
+116            model (models.Model): model to save.
+117            signature (Signature): model signature.
+118            input_example (schemas.Inputs): inputs sample.
+119
+120        Returns:
+121            Info: model saving information.
+122        """
 

 
 
@@ -794,20 +809,25 @@ 
Inherited Members
- 
122class CustomSaver(Saver):
-123    """Saver for custom models using the MLflow PyFunc module.
-124
-125    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
-126    """
+            
125class CustomSaver(Saver):
+126    """Saver for custom models using the MLflow PyFunc module.
 127
-128    KIND: T.Literal["CustomSaver"] = "CustomSaver"
-129
-130    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
-131        """Save a custom model to the MLflow Model Registry."""
-132        custom = CustomAdapter(model=model)  # adapt model
-133        return mlflow.pyfunc.log_model(
-134            artifact_path=self.path, python_model=custom, signature=signature, input_example=input_example
-135        )
+128    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+129    """
+130
+131    KIND: T.Literal["CustomSaver"] = "CustomSaver"
+132
+133    def save(
+134        self, model: models.Model, signature: Signature, input_example: schemas.Inputs
+135    ) -> Info:
+136        """Save a custom model to the MLflow Model Registry."""
+137        custom = CustomAdapter(model=model)  # adapt model
+138        return mlflow.pyfunc.log_model(
+139            artifact_path=self.path,
+140            python_model=custom,
+141            signature=signature,
+142            input_example=input_example,
+143        )
 

 
 
@@ -828,12 +848,17 @@ 
Inherited Members
- 
130    def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info:
-131        """Save a custom model to the MLflow Model Registry."""
-132        custom = CustomAdapter(model=model)  # adapt model
-133        return mlflow.pyfunc.log_model(
-134            artifact_path=self.path, python_model=custom, signature=signature, input_example=input_example
-135        )
+            
133    def save(
+134        self, model: models.Model, signature: Signature, input_example: schemas.Inputs
+135    ) -> Info:
+136        """Save a custom model to the MLflow Model Registry."""
+137        custom = CustomAdapter(model=model)  # adapt model
+138        return mlflow.pyfunc.log_model(
+139            artifact_path=self.path,
+140            python_model=custom,
+141            signature=signature,
+142            input_example=input_example,
+143        )
 

 
 
@@ -888,25 +913,25 @@ 
Inherited Members
- 
144class Loader(abc.ABC, pdt.BaseModel, strict=True):
-145    """Base class for loading models from registry.
-146
-147    Separate model definition from deserialization.
-148    e.g., to switch between deserialization flavors.
-149    """
-150
-151    KIND: str
-152
-153    @abc.abstractmethod
-154    def load(self, uri: str) -> T.Any:
-155        """Load a model from the model registry.
-156
-157        Args:
-158            uri (str): URI of the model to load.
-159
-160        Returns:
-161            T.Any: model loaded from registry.
-162        """
+            
152class Loader(abc.ABC, pdt.BaseModel, strict=True):
+153    """Base class for loading models from registry.
+154
+155    Separate model definition from deserialization.
+156    e.g., to switch between deserialization flavors.
+157    """
+158
+159    KIND: str
+160
+161    @abc.abstractmethod
+162    def load(self, uri: str) -> T.Any:
+163        """Load a model from the model registry.
+164
+165        Args:
+166            uri (str): URI of the model to load.
+167
+168        Returns:
+169            T.Any: model loaded from registry.
+170        """
 

 
 
@@ -929,16 +954,16 @@ 
Inherited Members
- 
153    @abc.abstractmethod
-154    def load(self, uri: str) -> T.Any:
-155        """Load a model from the model registry.
-156
-157        Args:
-158            uri (str): URI of the model to load.
-159
-160        Returns:
-161            T.Any: model loaded from registry.
-162        """
+            
161    @abc.abstractmethod
+162    def load(self, uri: str) -> T.Any:
+163        """Load a model from the model registry.
+164
+165        Args:
+166            uri (str): URI of the model to load.
+167
+168        Returns:
+169            T.Any: model loaded from registry.
+170        """
 

 
 
@@ -1005,17 +1030,17 @@ 
Inherited Members
- 
165class CustomLoader(Loader):
-166    """Loader for custom models using the MLflow PyFunc module.
-167
-168    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
-169    """
-170
-171    KIND: T.Literal["CustomLoader"] = "CustomLoader"
-172
-173    @T.override
-174    def load(self, uri: str) -> CustomModel:
-175        return mlflow.pyfunc.load_model(model_uri=uri)
+            
173class CustomLoader(Loader):
+174    """Loader for custom models using the MLflow PyFunc module.
+175
+176    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
+177    """
+178
+179    KIND: T.Literal["CustomLoader"] = "CustomLoader"
+180
+181    @T.override
+182    def load(self, uri: str) -> CustomModel:
+183        return mlflow.pyfunc.load_model(model_uri=uri)
 

 
 
@@ -1037,9 +1062,9 @@ 
Inherited Members
- 
173    @T.override
-174    def load(self, uri: str) -> CustomModel:
-175        return mlflow.pyfunc.load_model(model_uri=uri)
+            
181    @T.override
+182    def load(self, uri: str) -> CustomModel:
+183        return mlflow.pyfunc.load_model(model_uri=uri)
 

 
 
diff --git a/bikes/schemas.html b/bikes/schemas.html
index 73fcccf..235bf77 100644
--- a/bikes/schemas.html
+++ b/bikes/schemas.html
@@ -202,55 +202,56 @@ 

 33
 34        Args:
 35            data (pd.DataFrame): dataframe to check.
-36
-37        Returns:
-38            pd.DataFrame: validated dataframe with schema.
-39        """
-40        return cls.validate(data, **kwargs)
-41
+36            kwargs: additional arguments to validate().
+37
+38        Returns:
+39            pd.DataFrame: validated dataframe with schema.
+40        """
+41        return cls.validate(data, **kwargs)
 42
-43class InputsSchema(Schema):
-44    """Schema for the project inputs."""
-45
-46    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
-47    dteday: papd.Series[papd.DateTime] = pa.Field()
-48    season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4])
-49    yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1)
-50    mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12)
-51    hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23)
-52    holiday: papd.Series[papd.Bool] = pa.Field()
-53    weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6)
-54    workingday: papd.Series[papd.Bool] = pa.Field()
-55    weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4)
-56    temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-57    atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-58    hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-59    windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-60    casual: papd.Series[papd.UInt32] = pa.Field(ge=0)
-61    registered: papd.Series[papd.UInt32] = pa.Field(ge=0)
-62
+43
+44class InputsSchema(Schema):
+45    """Schema for the project inputs."""
+46
+47    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+48    dteday: papd.Series[papd.DateTime] = pa.Field()
+49    season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4])
+50    yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1)
+51    mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12)
+52    hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23)
+53    holiday: papd.Series[papd.Bool] = pa.Field()
+54    weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6)
+55    workingday: papd.Series[papd.Bool] = pa.Field()
+56    weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4)
+57    temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+58    atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+59    hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+60    windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+61    casual: papd.Series[papd.UInt32] = pa.Field(ge=0)
+62    registered: papd.Series[papd.UInt32] = pa.Field(ge=0)
 63
-64Inputs = papd.DataFrame[InputsSchema]
-65
+64
+65Inputs = papd.DataFrame[InputsSchema]
 66
-67class TargetsSchema(Schema):
-68    """Schema for the project target."""
-69
-70    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
-71    cnt: papd.Series[papd.UInt32] = pa.Field(ge=0)
-72
+67
+68class TargetsSchema(Schema):
+69    """Schema for the project target."""
+70
+71    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+72    cnt: papd.Series[papd.UInt32] = pa.Field(ge=0)
 73
-74Targets = papd.DataFrame[TargetsSchema]
-75
+74
+75Targets = papd.DataFrame[TargetsSchema]
 76
-77class OutputsSchema(Schema):
-78    """Schema for the project output."""
-79
-80    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
-81    prediction: papd.Series[papd.UInt32] = pa.Field(ge=0)
-82
+77
+78class OutputsSchema(Schema):
+79    """Schema for the project output."""
+80
+81    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+82    prediction: papd.Series[papd.UInt32] = pa.Field(ge=0)
 83
-84Outputs = papd.DataFrame[OutputsSchema]
+84
+85Outputs = papd.DataFrame[OutputsSchema]
@@ -290,11 +291,12 @@

34 35 Args: 36 data (pd.DataFrame): dataframe to check. -37 -38 Returns: -39 pd.DataFrame: validated dataframe with schema. -40 """ -41 return cls.validate(data, **kwargs) +37 kwargs: additional arguments to validate(). +38 +39 Returns: +40 pd.DataFrame: validated dataframe with schema. +41 """ +42 return cls.validate(data, **kwargs) @@ -411,11 +413,12 @@

Raises
34 35 Args: 36 data (pd.DataFrame): dataframe to check. -37 -38 Returns: -39 pd.DataFrame: validated dataframe with schema. -40 """ -41 return cls.validate(data, **kwargs) +37 kwargs: additional arguments to validate(). +38 +39 Returns: +40 pd.DataFrame: validated dataframe with schema. +41 """ +42 return cls.validate(data, **kwargs) @@ -425,6 +428,7 @@
Arguments:
  • data (pd.DataFrame): dataframe to check.
    • +
  • kwargs: additional arguments to validate().
Returns:
@@ -499,25 +503,25 @@
Attributes:
- 
44class InputsSchema(Schema):
-45    """Schema for the project inputs."""
-46
-47    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
-48    dteday: papd.Series[papd.DateTime] = pa.Field()
-49    season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4])
-50    yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1)
-51    mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12)
-52    hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23)
-53    holiday: papd.Series[papd.Bool] = pa.Field()
-54    weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6)
-55    workingday: papd.Series[papd.Bool] = pa.Field()
-56    weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4)
-57    temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-58    atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-59    hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-60    windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
-61    casual: papd.Series[papd.UInt32] = pa.Field(ge=0)
-62    registered: papd.Series[papd.UInt32] = pa.Field(ge=0)
+            
45class InputsSchema(Schema):
+46    """Schema for the project inputs."""
+47
+48    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+49    dteday: papd.Series[papd.DateTime] = pa.Field()
+50    season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4])
+51    yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1)
+52    mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12)
+53    hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23)
+54    holiday: papd.Series[papd.Bool] = pa.Field()
+55    weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6)
+56    workingday: papd.Series[papd.Bool] = pa.Field()
+57    weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4)
+58    temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+59    atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+60    hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+61    windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
+62    casual: papd.Series[papd.UInt32] = pa.Field(ge=0)
+63    registered: papd.Series[papd.UInt32] = pa.Field(ge=0)
 

 
 
@@ -901,11 +905,11 @@ 
Inherited Members
- 
68class TargetsSchema(Schema):
-69    """Schema for the project target."""
-70
-71    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
-72    cnt: papd.Series[papd.UInt32] = pa.Field(ge=0)
+            
69class TargetsSchema(Schema):
+70    """Schema for the project target."""
+71
+72    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+73    cnt: papd.Series[papd.UInt32] = pa.Field(ge=0)
 

 
 
@@ -1079,11 +1083,11 @@ 
Inherited Members
- 
78class OutputsSchema(Schema):
-79    """Schema for the project output."""
-80
-81    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
-82    prediction: papd.Series[papd.UInt32] = pa.Field(ge=0)
+            
79class OutputsSchema(Schema):
+80    """Schema for the project output."""
+81
+82    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
+83    prediction: papd.Series[papd.UInt32] = pa.Field(ge=0)
 

 
 
diff --git a/bikes/scripts.html b/bikes/scripts.html
index ff132c3..d3fae55 100644
--- a/bikes/scripts.html
+++ b/bikes/scripts.html
@@ -55,14 +55,14 @@ 
API Documentation

                     

 bikes.scripts    

 
-                        
Entry point of the program.

+                        
Command-line interface for the program.

 

 
                         
 
                         
 
-                        
 1"""Entry point of the program."""
+                        
 1"""Command-line interface for the program."""
  2
  3# %% IMPORTS
  4
@@ -81,7 +81,7 @@ 

 17    """Settings for the program.
 18
 19    Attributes:
-20        job (jobs.JobKind): job associated with the settings.
+20        job (jobs.JobKind): job associated with settings.
 21    """
 22
 23    job: jobs.JobKind = pdt.Field(..., discriminator="KIND")
@@ -89,7 +89,7 @@ 

 25
 26# %% PARSERS
 27
-28parser = argparse.ArgumentParser(description="Run a single job from external settings.")
+28parser = argparse.ArgumentParser(prog="bikes", description="Run an ML job from configs.")
 29parser.add_argument("configs", nargs="+", help="Config files for the job (local or remote).")
 30parser.add_argument("-e", "--extras", nargs="+", default=[], help="Config strings for the job.")
 31parser.add_argument("-s", "--schema", action="store_true", help="Print settings schema and exit.")
@@ -138,7 +138,7 @@ 

 18    """Settings for the program.
 19
 20    Attributes:
-21        job (jobs.JobKind): job associated with the settings.
+21        job (jobs.JobKind): job associated with settings.
 22    """
 23
 24    job: jobs.JobKind = pdt.Field(..., discriminator="KIND")
@@ -150,7 +150,7 @@ 

 
Attributes:

 
 
    
-
  • job (jobs.JobKind):  job associated with the settings.
    • 
+
  • job (jobs.JobKind):  job associated with settings.
    • 
 

 

 
diff --git a/bikes/services.html b/bikes/services.html
index 804b16f..8e8c8d5 100644
--- a/bikes/services.html
+++ b/bikes/services.html
@@ -225,22 +225,26 @@ 

 134        """Get an instance of MLflow client."""
 135        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
 136
-137    def register(self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.ModelVersion:
-138        """Register a model to mlflow registry.
-139
-140        Args:
-141            run_id (str): id of mlflow run.
-142            path (str): path of artifact.
-143            alias (str): model alias.
-144
-145        Returns:
-146            mlflow.entities.model_registry.ModelVersion: registered version.
-147        """
-148        client = self.client()
-149        model_uri = f"runs:/{run_id}/{path}"
-150        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
-151        client.set_registered_model_alias(name=self.registry_name, alias=alias, version=version.version)
-152        return version
+137    def register(
+138        self, run_id: str, path: str, alias: str
+139    ) -> mlflow.entities.model_registry.ModelVersion:
+140        """Register a model to mlflow registry.
+141
+142        Args:
+143            run_id (str): id of mlflow run.
+144            path (str): path of artifact.
+145            alias (str): model alias.
+146
+147        Returns:
+148            mlflow.entities.model_registry.ModelVersion: registered version.
+149        """
+150        client = self.client()
+151        model_uri = f"runs:/{run_id}/{path}"
+152        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+153        client.set_registered_model_alias(
+154            name=self.registry_name, alias=alias, version=version.version
+155        )
+156        return version
 

 
 
@@ -581,22 +585,26 @@ 
Inherited Members

 135        """Get an instance of MLflow client."""
 136        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
 137
-138    def register(self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.ModelVersion:
-139        """Register a model to mlflow registry.
-140
-141        Args:
-142            run_id (str): id of mlflow run.
-143            path (str): path of artifact.
-144            alias (str): model alias.
-145
-146        Returns:
-147            mlflow.entities.model_registry.ModelVersion: registered version.
-148        """
-149        client = self.client()
-150        model_uri = f"runs:/{run_id}/{path}"
-151        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
-152        client.set_registered_model_alias(name=self.registry_name, alias=alias, version=version.version)
-153        return version
+138    def register(
+139        self, run_id: str, path: str, alias: str
+140    ) -> mlflow.entities.model_registry.ModelVersion:
+141        """Register a model to mlflow registry.
+142
+143        Args:
+144            run_id (str): id of mlflow run.
+145            path (str): path of artifact.
+146            alias (str): model alias.
+147
+148        Returns:
+149            mlflow.entities.model_registry.ModelVersion: registered version.
+150        """
+151        client = self.client()
+152        model_uri = f"runs:/{run_id}/{path}"
+153        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+154        client.set_registered_model_alias(
+155            name=self.registry_name, alias=alias, version=version.version
+156        )
+157        return version
@@ -690,22 +698,26 @@
Attributes:
- 
138    def register(self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.ModelVersion:
-139        """Register a model to mlflow registry.
-140
-141        Args:
-142            run_id (str): id of mlflow run.
-143            path (str): path of artifact.
-144            alias (str): model alias.
-145
-146        Returns:
-147            mlflow.entities.model_registry.ModelVersion: registered version.
-148        """
-149        client = self.client()
-150        model_uri = f"runs:/{run_id}/{path}"
-151        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
-152        client.set_registered_model_alias(name=self.registry_name, alias=alias, version=version.version)
-153        return version
+            
138    def register(
+139        self, run_id: str, path: str, alias: str
+140    ) -> mlflow.entities.model_registry.ModelVersion:
+141        """Register a model to mlflow registry.
+142
+143        Args:
+144            run_id (str): id of mlflow run.
+145            path (str): path of artifact.
+146            alias (str): model alias.
+147
+148        Returns:
+149            mlflow.entities.model_registry.ModelVersion: registered version.
+150        """
+151        client = self.client()
+152        model_uri = f"runs:/{run_id}/{path}"
+153        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+154        client.set_registered_model_alias(
+155            name=self.registry_name, alias=alias, version=version.version
+156        )
+157        return version
 

 
 
diff --git a/bikes/splitters.html b/bikes/splitters.html
index 4e73df7..fdf4ec4 100644
--- a/bikes/splitters.html
+++ b/bikes/splitters.html
@@ -123,86 +123,98 @@ 

  32    KIND: str
  33
  34    @abc.abstractmethod
- 35    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
- 36        """Split a dataframe into subsets.
- 37
- 38        Args:
- 39            inputs (schemas.Inputs): model inputs.
- 40            targets (schemas.Targets): model targets.
- 41            groups (list | None, optional): group labels. Defaults to None.
- 42
- 43        Returns:
- 44            Splits: iterator over the dataframe splits.
- 45        """
- 46
- 47    @abc.abstractmethod
- 48    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
- 49        """Get the number of splits generated.
- 50
- 51        Args:
- 52            inputs (schemas.Inputs): models inputs.
- 53            targets (schemas.Targets): model targets.
- 54            groups (list | None, optional): group labels. Defaults to None.
- 55
- 56        Returns:
- 57            int: number of splits generated.
- 58        """
+ 35    def split(
+ 36        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+ 37    ) -> Splits:
+ 38        """Split a dataframe into subsets.
+ 39
+ 40        Args:
+ 41            inputs (schemas.Inputs): model inputs.
+ 42            targets (schemas.Targets): model targets.
+ 43            groups (list | None, optional): group labels. Defaults to None.
+ 44
+ 45        Returns:
+ 46            Splits: iterator over the dataframe splits.
+ 47        """
+ 48
+ 49    @abc.abstractmethod
+ 50    def get_n_splits(
+ 51        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+ 52    ) -> int:
+ 53        """Get the number of splits generated.
+ 54
+ 55        Args:
+ 56            inputs (schemas.Inputs): models inputs.
+ 57            targets (schemas.Targets): model targets.
+ 58            groups (list | None, optional): group labels. Defaults to None.
  59
- 60
- 61class TrainTestSplitter(Splitter):
- 62    """Split a dataframe into a train and test subsets.
+ 60        Returns:
+ 61            int: number of splits generated.
+ 62        """
  63
- 64    Attributes:
- 65        shuffle (bool): shuffle dataset before splitting.
- 66        test_size (int | float): number or ratio for the test dataset.
- 67        random_state (int): random state for the splitter object.
- 68    """
- 69
- 70    KIND: T.Literal["TrainTestSplitter"] = "TrainTestSplitter"
- 71
- 72    shuffle: bool = False  # required (time sensitive)
- 73    test_size: int | float = 24 * 30 * 2  # 2 months
- 74    random_state: int = 42
+ 64
+ 65class TrainTestSplitter(Splitter):
+ 66    """Split a dataframe into a train and test subsets.
+ 67
+ 68    Attributes:
+ 69        shuffle (bool): shuffle dataset before splitting.
+ 70        test_size (int | float): number or ratio for the test dataset.
+ 71        random_state (int): random state for the splitter object.
+ 72    """
+ 73
+ 74    KIND: T.Literal["TrainTestSplitter"] = "TrainTestSplitter"
  75
- 76    @T.override
- 77    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
- 78        index = np.arange(len(inputs))  # return integer position
- 79        train_index, test_index = model_selection.train_test_split(
- 80            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
- 81        )
- 82        yield train_index, test_index
- 83
- 84    @T.override
- 85    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
- 86        return 1
- 87
- 88
- 89class TimeSeriesSplitter(Splitter):
- 90    """Split a dataframe into fixed time series subsets.
- 91
- 92    Attributes:
- 93        gap (int): gap between splits.
- 94        n_splits (int): number of split to generate.
- 95        test_size (int | float): number or ratio for the test dataset.
- 96    """
- 97
- 98    KIND: T.Literal["TimeSeriesSplitter"] = "TimeSeriesSplitter"
+ 76    shuffle: bool = False  # required (time sensitive)
+ 77    test_size: int | float = 24 * 30 * 2  # 2 months
+ 78    random_state: int = 42
+ 79
+ 80    @T.override
+ 81    def split(
+ 82        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+ 83    ) -> Splits:
+ 84        index = np.arange(len(inputs))  # return integer position
+ 85        train_index, test_index = model_selection.train_test_split(
+ 86            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
+ 87        )
+ 88        yield train_index, test_index
+ 89
+ 90    @T.override
+ 91    def get_n_splits(
+ 92        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+ 93    ) -> int:
+ 94        return 1
+ 95
+ 96
+ 97class TimeSeriesSplitter(Splitter):
+ 98    """Split a dataframe into fixed time series subsets.
  99
-100    gap: int = 0
-101    n_splits: int = 4
-102    test_size: int | float = 24 * 30 * 2  # 2 months
-103
-104    @T.override
-105    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
-106        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
-107        yield from splitter.split(inputs)
-108
-109    @T.override
-110    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
-111        return self.n_splits
-112
-113
-114SplitterKind = TrainTestSplitter | TimeSeriesSplitter
+100    Attributes:
+101        gap (int): gap between splits.
+102        n_splits (int): number of split to generate.
+103        test_size (int | float): number or ratio for the test dataset.
+104    """
+105
+106    KIND: T.Literal["TimeSeriesSplitter"] = "TimeSeriesSplitter"
+107
+108    gap: int = 0
+109    n_splits: int = 4
+110    test_size: int | float = 24 * 30 * 2  # 2 months
+111
+112    @T.override
+113    def split(
+114        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+115    ) -> Splits:
+116        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
+117        yield from splitter.split(inputs)
+118
+119    @T.override
+120    def get_n_splits(
+121        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+122    ) -> int:
+123        return self.n_splits
+124
+125
+126SplitterKind = TrainTestSplitter | TimeSeriesSplitter
@@ -230,30 +242,34 @@

33 KIND: str 34 35 @abc.abstractmethod -36 def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits: -37 """Split a dataframe into subsets. -38 -39 Args: -40 inputs (schemas.Inputs): model inputs. -41 targets (schemas.Targets): model targets. -42 groups (list | None, optional): group labels. Defaults to None. -43 -44 Returns: -45 Splits: iterator over the dataframe splits. -46 """ -47 -48 @abc.abstractmethod -49 def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int: -50 """Get the number of splits generated. -51 -52 Args: -53 inputs (schemas.Inputs): models inputs. -54 targets (schemas.Targets): model targets. -55 groups (list | None, optional): group labels. Defaults to None. -56 -57 Returns: -58 int: number of splits generated. -59 """ +36 def split( +37 self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None +38 ) -> Splits: +39 """Split a dataframe into subsets. +40 +41 Args: +42 inputs (schemas.Inputs): model inputs. +43 targets (schemas.Targets): model targets. +44 groups (list | None, optional): group labels. Defaults to None. +45 +46 Returns: +47 Splits: iterator over the dataframe splits. +48 """ +49 +50 @abc.abstractmethod +51 def get_n_splits( +52 self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None +53 ) -> int: +54 """Get the number of splits generated. +55 +56 Args: +57 inputs (schemas.Inputs): models inputs. +58 targets (schemas.Targets): model targets. +59 groups (list | None, optional): group labels. Defaults to None. +60 +61 Returns: +62 int: number of splits generated. +63 """ @@ -277,17 +293,19 @@ 

35    @abc.abstractmethod
-36    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
-37        """Split a dataframe into subsets.
-38
-39        Args:
-40            inputs (schemas.Inputs): model inputs.
-41            targets (schemas.Targets): model targets.
-42            groups (list | None, optional): group labels. Defaults to None.
-43
-44        Returns:
-45            Splits: iterator over the dataframe splits.
-46        """
+36    def split(
+37        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+38    ) -> Splits:
+39        """Split a dataframe into subsets.
+40
+41        Args:
+42            inputs (schemas.Inputs): model inputs.
+43            targets (schemas.Targets): model targets.
+44            groups (list | None, optional): group labels. Defaults to None.
+45
+46        Returns:
+47            Splits: iterator over the dataframe splits.
+48        """
@@ -322,18 +340,20 @@

Returns:
- 
48    @abc.abstractmethod
-49    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
-50        """Get the number of splits generated.
-51
-52        Args:
-53            inputs (schemas.Inputs): models inputs.
-54            targets (schemas.Targets): model targets.
-55            groups (list | None, optional): group labels. Defaults to None.
-56
-57        Returns:
-58            int: number of splits generated.
-59        """
+            
50    @abc.abstractmethod
+51    def get_n_splits(
+52        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+53    ) -> int:
+54        """Get the number of splits generated.
+55
+56        Args:
+57            inputs (schemas.Inputs): models inputs.
+58            targets (schemas.Targets): model targets.
+59            groups (list | None, optional): group labels. Defaults to None.
+60
+61        Returns:
+62            int: number of splits generated.
+63        """
 

 
 
@@ -402,32 +422,36 @@ 
Inherited Members
- 
62class TrainTestSplitter(Splitter):
-63    """Split a dataframe into a train and test subsets.
-64
-65    Attributes:
-66        shuffle (bool): shuffle dataset before splitting.
-67        test_size (int | float): number or ratio for the test dataset.
-68        random_state (int): random state for the splitter object.
-69    """
-70
-71    KIND: T.Literal["TrainTestSplitter"] = "TrainTestSplitter"
-72
-73    shuffle: bool = False  # required (time sensitive)
-74    test_size: int | float = 24 * 30 * 2  # 2 months
-75    random_state: int = 42
+            
66class TrainTestSplitter(Splitter):
+67    """Split a dataframe into a train and test subsets.
+68
+69    Attributes:
+70        shuffle (bool): shuffle dataset before splitting.
+71        test_size (int | float): number or ratio for the test dataset.
+72        random_state (int): random state for the splitter object.
+73    """
+74
+75    KIND: T.Literal["TrainTestSplitter"] = "TrainTestSplitter"
 76
-77    @T.override
-78    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
-79        index = np.arange(len(inputs))  # return integer position
-80        train_index, test_index = model_selection.train_test_split(
-81            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
-82        )
-83        yield train_index, test_index
-84
-85    @T.override
-86    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
-87        return 1
+77    shuffle: bool = False  # required (time sensitive)
+78    test_size: int | float = 24 * 30 * 2  # 2 months
+79    random_state: int = 42
+80
+81    @T.override
+82    def split(
+83        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+84    ) -> Splits:
+85        index = np.arange(len(inputs))  # return integer position
+86        train_index, test_index = model_selection.train_test_split(
+87            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
+88        )
+89        yield train_index, test_index
+90
+91    @T.override
+92    def get_n_splits(
+93        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+94    ) -> int:
+95        return 1
 

 
 
@@ -455,13 +479,15 @@ 
Attributes:
- 
77    @T.override
-78    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
-79        index = np.arange(len(inputs))  # return integer position
-80        train_index, test_index = model_selection.train_test_split(
-81            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
-82        )
-83        yield train_index, test_index
+            
81    @T.override
+82    def split(
+83        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+84    ) -> Splits:
+85        index = np.arange(len(inputs))  # return integer position
+86        train_index, test_index = model_selection.train_test_split(
+87            index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state
+88        )
+89        yield train_index, test_index
 

 
 
@@ -496,9 +522,11 @@ 
Returns:
- 
85    @T.override
-86    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
-87        return 1
+            
91    @T.override
+92    def get_n_splits(
+93        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+94    ) -> int:
+95        return 1
 

 
 
@@ -567,29 +595,33 @@ 
Inherited Members
- 
 90class TimeSeriesSplitter(Splitter):
- 91    """Split a dataframe into fixed time series subsets.
- 92
- 93    Attributes:
- 94        gap (int): gap between splits.
- 95        n_splits (int): number of split to generate.
- 96        test_size (int | float): number or ratio for the test dataset.
- 97    """
- 98
- 99    KIND: T.Literal["TimeSeriesSplitter"] = "TimeSeriesSplitter"
+            
 98class TimeSeriesSplitter(Splitter):
+ 99    """Split a dataframe into fixed time series subsets.
 100
-101    gap: int = 0
-102    n_splits: int = 4
-103    test_size: int | float = 24 * 30 * 2  # 2 months
-104
-105    @T.override
-106    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
-107        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
-108        yield from splitter.split(inputs)
-109
-110    @T.override
-111    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
-112        return self.n_splits
+101    Attributes:
+102        gap (int): gap between splits.
+103        n_splits (int): number of split to generate.
+104        test_size (int | float): number or ratio for the test dataset.
+105    """
+106
+107    KIND: T.Literal["TimeSeriesSplitter"] = "TimeSeriesSplitter"
+108
+109    gap: int = 0
+110    n_splits: int = 4
+111    test_size: int | float = 24 * 30 * 2  # 2 months
+112
+113    @T.override
+114    def split(
+115        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+116    ) -> Splits:
+117        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
+118        yield from splitter.split(inputs)
+119
+120    @T.override
+121    def get_n_splits(
+122        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+123    ) -> int:
+124        return self.n_splits
 

 
 
@@ -617,10 +649,12 @@ 
Attributes:
- 
105    @T.override
-106    def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits:
-107        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
-108        yield from splitter.split(inputs)
+            
113    @T.override
+114    def split(
+115        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+116    ) -> Splits:
+117        splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size)
+118        yield from splitter.split(inputs)
 

 
 
@@ -655,9 +689,11 @@ 
Returns:
- 
110    @T.override
-111    def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int:
-112        return self.n_splits
+            
120    @T.override
+121    def get_n_splits(
+122        self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None
+123    ) -> int:
+124        return self.n_splits
 

 
 
diff --git a/search.js b/search.js
index 2eaf181..e516848 100644
--- a/search.js
+++ b/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
 /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oPredict the number of bikes available.
\n"}, "bikes.configs": {"fullname": "bikes.configs", "modulename": "bikes.configs", "kind": "module", "doc": "
Parse, merge, and convert YAML configs.
\n"}, "bikes.configs.parse_file": {"fullname": "bikes.configs.parse_file", "modulename": "bikes.configs", "qualname": "parse_file", "kind": "function", "doc": "
Parse a config file from a path.
\n\n
Arguments:
\n\n
    \n
  • path (str):  local or remote path.
    • \n
\n\n
Returns:
\n\n
\n  
Config: representation of the config file.
\n
\n", "signature": "(\tpath: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.parse_string": {"fullname": "bikes.configs.parse_string", "modulename": "bikes.configs", "qualname": "parse_string", "kind": "function", "doc": "
Parse the given config string.
\n\n
Arguments:
\n\n
    \n
  • string (str):  configuration string.
    • \n
\n\n
Returns:
\n\n
\n  
Config: representation of the config string.
\n
\n", "signature": "(\tstring: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.merge_configs": {"fullname": "bikes.configs.merge_configs", "modulename": "bikes.configs", "qualname": "merge_configs", "kind": "function", "doc": "
Merge a list of config objects into one.
\n\n
Arguments:
\n\n
    \n
  • configs (list[Config]):  list of config objects.
    • \n
\n\n
Returns:
\n\n
\n  
Config: representation of the merged config objects.
\n
\n", "signature": "(\tconfigs: Sequence[omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig]) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.to_object": {"fullname": "bikes.configs.to_object", "modulename": "bikes.configs", "qualname": "to_object", "kind": "function", "doc": "
Convert a config object to a python object.
\n\n
Arguments:
\n\n
    \n
  • config (Config):  representation of the config.
    • \n
\n\n
Returns:
\n\n
\n  
object: conversion of the config to a python object.
\n
\n", "signature": "(\tconfig: omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig) -> object:", "funcdef": "def"}, "bikes.datasets": {"fullname": "bikes.datasets", "modulename": "bikes.datasets", "kind": "module", "doc": "
Read/Write datasets from/to external sources/destinations.
\n"}, "bikes.datasets.Reader": {"fullname": "bikes.datasets.Reader", "modulename": "bikes.datasets", "qualname": "Reader", "kind": "class", "doc": "
Base class for a dataset reader.
\n\n
Use a reader to load a dataset in memory.\ne.g., to read file, database, cloud storage, ...
\n\n
Attributes:
\n\n
    \n
  • limit (int, optional):  maximum number of rows to read from dataset.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Reader.read": {"fullname": "bikes.datasets.Reader.read", "modulename": "bikes.datasets", "qualname": "Reader.read", "kind": "function", "doc": "
Read a dataframe from a dataset.
\n\n
Returns:
\n\n
\n  
pd.DataFrame: dataframe representation.
\n
\n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.ParquetReader": {"fullname": "bikes.datasets.ParquetReader", "modulename": "bikes.datasets", "qualname": "ParquetReader", "kind": "class", "doc": "
Read a dataframe from a parquet file.
\n\n
Attributes:
\n\n
    \n
  • path (str):  local or remote path to a dataset.
    • \n
\n", "bases": "Reader"}, "bikes.datasets.ParquetReader.read": {"fullname": "bikes.datasets.ParquetReader.read", "modulename": "bikes.datasets", "qualname": "ParquetReader.read", "kind": "function", "doc": "
Read a dataframe from a dataset.
\n\n
Returns:
\n\n
\n  
pd.DataFrame: dataframe representation.
\n
\n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.Writer": {"fullname": "bikes.datasets.Writer", "modulename": "bikes.datasets", "qualname": "Writer", "kind": "class", "doc": "
Base class for a dataset writer.
\n\n
Use a writer to save a dataset from memory.\ne.g., to write file, database, cloud storage, ...
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Writer.write": {"fullname": "bikes.datasets.Writer.write", "modulename": "bikes.datasets", "qualname": "Writer.write", "kind": "function", "doc": "
Write a dataframe to a dataset.
\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame):  dataframe representation.
    • \n
\n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.datasets.ParquetWriter": {"fullname": "bikes.datasets.ParquetWriter", "modulename": "bikes.datasets", "qualname": "ParquetWriter", "kind": "class", "doc": "
Writer a dataframe to a parquet file.
\n\n
Attributes:
\n\n
    \n
  • path (str):  local or remote file to a dataset.
    • \n
\n", "bases": "Writer"}, "bikes.datasets.ParquetWriter.write": {"fullname": "bikes.datasets.ParquetWriter.write", "modulename": "bikes.datasets", "qualname": "ParquetWriter.write", "kind": "function", "doc": "
Write a dataframe to a dataset.
\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame):  dataframe representation.
    • \n
\n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.jobs": {"fullname": "bikes.jobs", "modulename": "bikes.jobs", "kind": "module", "doc": "
High-level jobs for the project.
\n"}, "bikes.jobs.Job": {"fullname": "bikes.jobs.Job", "modulename": "bikes.jobs", "qualname": "Job", "kind": "class", "doc": "
Base class for a job.
\n\n
use a job to execute runs in  context.\ne.g., to define common services like logger
\n\n
Attributes:
\n\n
    \n
  • logger_service (services.LoggerService):  manage the logging system.
    • \n
  • mlflow_service (services.MLflowService):  manage the mlflow system.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.jobs.Job.run": {"fullname": "bikes.jobs.Job.run", "modulename": "bikes.jobs", "qualname": "Job.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TuningJob": {"fullname": "bikes.jobs.TuningJob", "modulename": "bikes.jobs", "qualname": "TuningJob", "kind": "class", "doc": "
Find the best hyperparameters for a model.
\n\n
Attributes:
\n\n
    \n
  • run_name (str):  name of the MLflow experiment run.
    • \n
  • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
    • \n
  • targets (datasets.ReaderKind):  dataset reader with targets variables.
    • \n
  • results (datasets.WriterKind):  dataset writer for searcher results.
    • \n
  • model (models.ModelKind):  machine learning model to tune.
    • \n
  • metric (metrics.MetricKind):  main metric for evaluation.
    • \n
  • splitter (splitters.SplitterKind):  splitter for datasets.
    • \n
  • searcher (searchers.SearcherKind):  searcher algorithm.
    • \n
\n", "bases": "Job"}, "bikes.jobs.TuningJob.run": {"fullname": "bikes.jobs.TuningJob.run", "modulename": "bikes.jobs", "qualname": "TuningJob.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TrainingJob": {"fullname": "bikes.jobs.TrainingJob", "modulename": "bikes.jobs", "qualname": "TrainingJob", "kind": "class", "doc": "
Train and register a single AI/ML model
\n\n
Attributes:
\n\n
    \n
  • run_name (str):  name of the MLflow experiment run.
    • \n
  • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
    • \n
  • targets (datasets.ReaderKind):  dataset reader with targets variables.
    • \n
  • saver (registers.SaverKind):  save the trained model in registry.
    • \n
  • model (models.ModelKind):  machine learning model to tune.
    • \n
  • signer (registers.SignerKind):  signer for the trained model.
    • \n
  • scorers (list[metrics.MetricKind]):  metrics for the evaluation.
    • \n
  • splitter (splitters.SplitterKind):  splitter for datasets.
    • \n
  • registry_alias (str):  alias of model.
    • \n
\n", "bases": "Job"}, "bikes.jobs.TrainingJob.run": {"fullname": "bikes.jobs.TrainingJob.run", "modulename": "bikes.jobs", "qualname": "TrainingJob.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.InferenceJob": {"fullname": "bikes.jobs.InferenceJob", "modulename": "bikes.jobs", "qualname": "InferenceJob", "kind": "class", "doc": "
Load a model and generate predictions.
\n\n
Attributes:
\n\n
    \n
  • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
    • \n
  • outputs (datasets.WriterKind):  dataset writer for the model outputs.
    • \n
  • registry_alias (str):  alias of the model to load.
    • \n
  • loader (registers.LoaderKind):  load the model from registry.
    • \n
\n", "bases": "Job"}, "bikes.jobs.InferenceJob.run": {"fullname": "bikes.jobs.InferenceJob.run", "modulename": "bikes.jobs", "qualname": "InferenceJob.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.metrics": {"fullname": "bikes.metrics", "modulename": "bikes.metrics", "kind": "module", "doc": "
Evaluate model performance with metrics.
\n"}, "bikes.metrics.Metric": {"fullname": "bikes.metrics.Metric", "modulename": "bikes.metrics", "qualname": "Metric", "kind": "class", "doc": "
Base class for a metric.
\n\n
Use metrics to evaluate model performance.\ne.g., accuracy, precision, recall, mae, f1, ...
\n\n
Attributes:
\n\n
    \n
  • name (str):  name of the metric.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.metrics.Metric.score": {"fullname": "bikes.metrics.Metric.score", "modulename": "bikes.metrics", "qualname": "Metric.score", "kind": "function", "doc": "
Score the outputs against the targets.
\n\n
Arguments:
\n\n
    \n
  • targets (schemas.Targets):  expected values.
    • \n
  • outputs (schemas.Outputs):  predicted values.
    • \n
\n\n
Returns:
\n\n
\n  
float: metric result.
\n
\n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.Metric.scorer": {"fullname": "bikes.metrics.Metric.scorer", "modulename": "bikes.metrics", "qualname": "Metric.scorer", "kind": "function", "doc": "
Score the model outputs against the targets.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  model to evaluate.
    • \n
  • inputs (schemas.Inputs):  model inputs values.
    • \n
  • targets (schemas.Targets):  model expected values.
    • \n
\n\n
Returns:
\n\n
\n  
float: metric result.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.SklearnMetric": {"fullname": "bikes.metrics.SklearnMetric", "modulename": "bikes.metrics", "qualname": "SklearnMetric", "kind": "class", "doc": "
Compute metrics with sklearn.
\n\n
Attributes:
\n\n
    \n
  • name (str):  name of the sklearn metric.
    • \n
  • greater_is_better (bool):  maximize or minimize.
    • \n
\n", "bases": "Metric"}, "bikes.metrics.SklearnMetric.score": {"fullname": "bikes.metrics.SklearnMetric.score", "modulename": "bikes.metrics", "qualname": "SklearnMetric.score", "kind": "function", "doc": "
Score the outputs against the targets.
\n\n
Arguments:
\n\n
    \n
  • targets (schemas.Targets):  expected values.
    • \n
  • outputs (schemas.Outputs):  predicted values.
    • \n
\n\n
Returns:
\n\n
\n  
float: metric result.
\n
\n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.models": {"fullname": "bikes.models", "modulename": "bikes.models", "kind": "module", "doc": "
Define trainable machine learning models.
\n"}, "bikes.models.Model": {"fullname": "bikes.models.Model", "modulename": "bikes.models", "qualname": "Model", "kind": "class", "doc": "
Base class for a model.
\n\n
Use a model to adapt AI/ML frameworks.\ne.g., to swap easily one model with another.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.models.Model.get_params": {"fullname": "bikes.models.Model.get_params", "modulename": "bikes.models", "qualname": "Model.get_params", "kind": "function", "doc": "
Get the model params.
\n\n
Arguments:
\n\n
    \n
  • deep (bool, optional):  ignored. Defaults to True.
    • \n
\n\n
Returns:
\n\n
\n  
Params: internal model parameters.
\n
\n", "signature": "(self, deep: bool = True) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.models.Model.set_params": {"fullname": "bikes.models.Model.set_params", "modulename": "bikes.models", "qualname": "Model.set_params", "kind": "function", "doc": "
Set the model params in place.
\n\n
Returns:
\n\n
\n  
T.Self: instance of the model.
\n
\n", "signature": "(self, **params: Any) -> Self:", "funcdef": "def"}, "bikes.models.Model.fit": {"fullname": "bikes.models.Model.fit", "modulename": "bikes.models", "qualname": "Model.fit", "kind": "function", "doc": "
Fit the model on the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model training inputs.
    • \n
  • targets (schemas.Targets):  model training targets.
    • \n
\n\n
Returns:
\n\n
\n  
Model: instance of the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> Self:", "funcdef": "def"}, "bikes.models.Model.predict": {"fullname": "bikes.models.Model.predict", "modulename": "bikes.models", "qualname": "Model.predict", "kind": "function", "doc": "
Generate outputs with the model for the given inputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model prediction inputs.
    • \n
\n\n
Returns:
\n\n
\n  
schemas.Outputs: model prediction outputs.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel": {"fullname": "bikes.models.BaselineSklearnModel", "modulename": "bikes.models", "qualname": "BaselineSklearnModel", "kind": "class", "doc": "
Simple baseline model built on top of sklearn.
\n\n
Attributes:
\n\n
    \n
  • max_depth (int):  maximum depth of the random forest.
    • \n
  • n_estimators (int):  number of estimators in the random forest.
    • \n
  • random_state (int, optional):  random state of the machine learning pipeline.
    • \n
\n", "bases": "Model"}, "bikes.models.BaselineSklearnModel.fit": {"fullname": "bikes.models.BaselineSklearnModel.fit", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.fit", "kind": "function", "doc": "
Fit the model on the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model training inputs.
    • \n
  • targets (schemas.Targets):  model training targets.
    • \n
\n\n
Returns:
\n\n
\n  
Model: instance of the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> bikes.models.BaselineSklearnModel:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.predict": {"fullname": "bikes.models.BaselineSklearnModel.predict", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.predict", "kind": "function", "doc": "
Generate outputs with the model for the given inputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model prediction inputs.
    • \n
\n\n
Returns:
\n\n
\n  
schemas.Outputs: model prediction outputs.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.model_post_init": {"fullname": "bikes.models.BaselineSklearnModel.model_post_init", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.model_post_init", "kind": "function", "doc": "
This function is meant to behave like a BaseModel method to initialise private attributes.
\n\n
It takes context as an argument since that's what pydantic-core passes when calling it.
\n\n
Arguments:
\n\n
    \n
  • self:  The BaseModel instance.
    • \n
  • __context:  The context.
    • \n
\n", "signature": "(self: pydantic.main.BaseModel, __context: Any) -> None:", "funcdef": "def"}, "bikes.registers": {"fullname": "bikes.registers", "modulename": "bikes.registers", "kind": "module", "doc": "
Adapters, signers, savers, and loaders for model registries.
\n"}, "bikes.registers.CustomAdapter": {"fullname": "bikes.registers.CustomAdapter", "modulename": "bikes.registers", "qualname": "CustomAdapter", "kind": "class", "doc": "
Adapt a custom model to the MLflow PyFunc flavor.
\n\n
https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
\n", "bases": "mlflow.pyfunc.model.PythonModel"}, "bikes.registers.CustomAdapter.__init__": {"fullname": "bikes.registers.CustomAdapter.__init__", "modulename": "bikes.registers", "qualname": "CustomAdapter.__init__", "kind": "function", "doc": "
Initialize the custom adapter.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  project model.
    • \n
\n", "signature": "(model: bikes.models.Model)"}, "bikes.registers.CustomAdapter.predict": {"fullname": "bikes.registers.CustomAdapter.predict", "modulename": "bikes.registers", "qualname": "CustomAdapter.predict", "kind": "function", "doc": "
Generate predictions from a custom model.
\n\n
Arguments:
\n\n
    \n
  • context (mlflow.pyfunc.PythonModelContext):  ignored.
    • \n
  • inputs (schemas.Inputs):  inputs for the model.
    • \n
\n\n
Returns:
\n\n
\n  
schemas.Outputs: outputs of the model.
\n
\n", "signature": "(\tself,\tcontext: mlflow.pyfunc.model.PythonModelContext,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.registers.Signer": {"fullname": "bikes.registers.Signer", "modulename": "bikes.registers", "qualname": "Signer", "kind": "class", "doc": "
Base class for making signatures.
\n\n
Allow to switch between signing approaches.\ne.g., automatic inference vs manual signatures\nhttps://mlflow.org/docs/latest/models.html#model-signature-and-input-example
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Signer.sign": {"fullname": "bikes.registers.Signer.sign", "modulename": "bikes.registers", "qualname": "Signer.sign", "kind": "function", "doc": "
Make a model signature from inputs/outputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  inputs of the model.
    • \n
  • outputs (schemas.Outputs):  ouputs of the model.
    • \n
\n\n
Returns:
\n\n
\n  
ModelSignature: generated signature for the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.InferSigner": {"fullname": "bikes.registers.InferSigner", "modulename": "bikes.registers", "qualname": "InferSigner", "kind": "class", "doc": "
Generate model signatures from data inference.
\n", "bases": "Signer"}, "bikes.registers.InferSigner.sign": {"fullname": "bikes.registers.InferSigner.sign", "modulename": "bikes.registers", "qualname": "InferSigner.sign", "kind": "function", "doc": "
Make a model signature from inputs/outputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  inputs of the model.
    • \n
  • outputs (schemas.Outputs):  ouputs of the model.
    • \n
\n\n
Returns:
\n\n
\n  
ModelSignature: generated signature for the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.Saver": {"fullname": "bikes.registers.Saver", "modulename": "bikes.registers", "qualname": "Saver", "kind": "class", "doc": "
Base class for saving models in registry.
\n\n
Separate model definition from serialization.\ne.g., to switch between serialization flavors.
\n\n
Attributes:
\n\n
    \n
  • path (str):  model path inside the MLflow artifact store.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Saver.save": {"fullname": "bikes.registers.Saver.save", "modulename": "bikes.registers", "qualname": "Saver.save", "kind": "function", "doc": "
Save a model in the model registry.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  model to save.
    • \n
  • signature (Signature):  model signature.
    • \n
  • input_example (schemas.Inputs):  inputs sample.
    • \n
\n\n
Returns:
\n\n
\n  
Info: model saving information.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.CustomSaver": {"fullname": "bikes.registers.CustomSaver", "modulename": "bikes.registers", "qualname": "CustomSaver", "kind": "class", "doc": "
Saver for custom models using the MLflow PyFunc module.
\n\n
https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
\n", "bases": "Saver"}, "bikes.registers.CustomSaver.save": {"fullname": "bikes.registers.CustomSaver.save", "modulename": "bikes.registers", "qualname": "CustomSaver.save", "kind": "function", "doc": "
Save a custom model to the MLflow Model Registry.
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.Loader": {"fullname": "bikes.registers.Loader", "modulename": "bikes.registers", "qualname": "Loader", "kind": "class", "doc": "
Base class for loading models from registry.
\n\n
Separate model definition from deserialization.\ne.g., to switch between deserialization flavors.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Loader.load": {"fullname": "bikes.registers.Loader.load", "modulename": "bikes.registers", "qualname": "Loader.load", "kind": "function", "doc": "
Load a model from the model registry.
\n\n
Arguments:
\n\n
    \n
  • uri (str):  URI of the model to load.
    • \n
\n\n
Returns:
\n\n
\n  
T.Any: model loaded from registry.
\n
\n", "signature": "(self, uri: str) -> Any:", "funcdef": "def"}, "bikes.registers.CustomLoader": {"fullname": "bikes.registers.CustomLoader", "modulename": "bikes.registers", "qualname": "CustomLoader", "kind": "class", "doc": "
Loader for custom models using the MLflow PyFunc module.
\n\n
https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
\n", "bases": "Loader"}, "bikes.registers.CustomLoader.load": {"fullname": "bikes.registers.CustomLoader.load", "modulename": "bikes.registers", "qualname": "CustomLoader.load", "kind": "function", "doc": "
Load a model from the model registry.
\n\n
Arguments:
\n\n
    \n
  • uri (str):  URI of the model to load.
    • \n
\n\n
Returns:
\n\n
\n  
T.Any: model loaded from registry.
\n
\n", "signature": "(self, uri: str) -> mlflow.pyfunc.model.PythonModel:", "funcdef": "def"}, "bikes.schemas": {"fullname": "bikes.schemas", "modulename": "bikes.schemas", "kind": "module", "doc": "
Define and validate dataframe schemas.
\n"}, "bikes.schemas.Schema": {"fullname": "bikes.schemas.Schema", "modulename": "bikes.schemas", "qualname": "Schema", "kind": "class", "doc": "
Base class for a dataframe schema.
\n\n
Use a schema to type your dataframe object.\ne.g., to communicate and validate its fields.
\n", "bases": "pandera.api.pandas.model.DataFrameModel"}, "bikes.schemas.Schema.__init__": {"fullname": "bikes.schemas.Schema.__init__", "modulename": "bikes.schemas", "qualname": "Schema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.Schema.Config": {"fullname": "bikes.schemas.Schema.Config", "modulename": "bikes.schemas", "qualname": "Schema.Config", "kind": "class", "doc": "
Default configuration.
\n\n
Attributes:
\n\n
    \n
  • coerce (bool):  convert data type if possible.
    • \n
  • strict (bool):  ensure the data type is correct.
    • \n
\n"}, "bikes.schemas.Schema.check": {"fullname": "bikes.schemas.Schema.check", "modulename": "bikes.schemas", "qualname": "Schema.check", "kind": "function", "doc": "
Check the data with this schema.
\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame):  dataframe to check.
    • \n
\n\n
Returns:
\n\n
\n  
pd.DataFrame: validated dataframe with schema.
\n
\n", "signature": "(cls, data: pandas.core.frame.DataFrame, **kwargs):", "funcdef": "def"}, "bikes.schemas.InputsSchema": {"fullname": "bikes.schemas.InputsSchema", "modulename": "bikes.schemas", "qualname": "InputsSchema", "kind": "class", "doc": "
Schema for the project inputs.
\n", "bases": "Schema"}, "bikes.schemas.InputsSchema.__init__": {"fullname": "bikes.schemas.InputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "InputsSchema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.InputsSchema.instant": {"fullname": "bikes.schemas.InputsSchema.instant", "modulename": "bikes.schemas", "qualname": "InputsSchema.instant", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.dteday": {"fullname": "bikes.schemas.InputsSchema.dteday", "modulename": "bikes.schemas", "qualname": "InputsSchema.dteday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Timestamp]"}, "bikes.schemas.InputsSchema.season": {"fullname": "bikes.schemas.InputsSchema.season", "modulename": "bikes.schemas", "qualname": "InputsSchema.season", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.yr": {"fullname": "bikes.schemas.InputsSchema.yr", "modulename": "bikes.schemas", "qualname": "InputsSchema.yr", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.mnth": {"fullname": "bikes.schemas.InputsSchema.mnth", "modulename": "bikes.schemas", "qualname": "InputsSchema.mnth", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.hr": {"fullname": "bikes.schemas.InputsSchema.hr", "modulename": "bikes.schemas", "qualname": "InputsSchema.hr", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.holiday": {"fullname": "bikes.schemas.InputsSchema.holiday", "modulename": "bikes.schemas", "qualname": "InputsSchema.holiday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weekday": {"fullname": "bikes.schemas.InputsSchema.weekday", "modulename": "bikes.schemas", "qualname": "InputsSchema.weekday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.workingday": {"fullname": "bikes.schemas.InputsSchema.workingday", "modulename": "bikes.schemas", "qualname": "InputsSchema.workingday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weathersit": {"fullname": "bikes.schemas.InputsSchema.weathersit", "modulename": "bikes.schemas", "qualname": "InputsSchema.weathersit", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.temp": {"fullname": "bikes.schemas.InputsSchema.temp", "modulename": "bikes.schemas", "qualname": "InputsSchema.temp", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.atemp": {"fullname": "bikes.schemas.InputsSchema.atemp", "modulename": "bikes.schemas", "qualname": "InputsSchema.atemp", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.hum": {"fullname": "bikes.schemas.InputsSchema.hum", "modulename": "bikes.schemas", "qualname": "InputsSchema.hum", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.windspeed": {"fullname": "bikes.schemas.InputsSchema.windspeed", "modulename": "bikes.schemas", "qualname": "InputsSchema.windspeed", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.casual": {"fullname": "bikes.schemas.InputsSchema.casual", "modulename": "bikes.schemas", "qualname": "InputsSchema.casual", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.registered": {"fullname": "bikes.schemas.InputsSchema.registered", "modulename": "bikes.schemas", "qualname": "InputsSchema.registered", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.Config": {"fullname": "bikes.schemas.InputsSchema.Config", "modulename": "bikes.schemas", "qualname": "InputsSchema.Config", "kind": "class", "doc": "
Define DataFrameSchema-wide options.
\n\n
new in 0.5.0
\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.TargetsSchema": {"fullname": "bikes.schemas.TargetsSchema", "modulename": "bikes.schemas", "qualname": "TargetsSchema", "kind": "class", "doc": "
Schema for the project target.
\n", "bases": "Schema"}, "bikes.schemas.TargetsSchema.__init__": {"fullname": "bikes.schemas.TargetsSchema.__init__", "modulename": "bikes.schemas", "qualname": "TargetsSchema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.TargetsSchema.instant": {"fullname": "bikes.schemas.TargetsSchema.instant", "modulename": "bikes.schemas", "qualname": "TargetsSchema.instant", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.cnt": {"fullname": "bikes.schemas.TargetsSchema.cnt", "modulename": "bikes.schemas", "qualname": "TargetsSchema.cnt", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.Config": {"fullname": "bikes.schemas.TargetsSchema.Config", "modulename": "bikes.schemas", "qualname": "TargetsSchema.Config", "kind": "class", "doc": "
Define DataFrameSchema-wide options.
\n\n
new in 0.5.0
\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.OutputsSchema": {"fullname": "bikes.schemas.OutputsSchema", "modulename": "bikes.schemas", "qualname": "OutputsSchema", "kind": "class", "doc": "
Schema for the project output.
\n", "bases": "Schema"}, "bikes.schemas.OutputsSchema.__init__": {"fullname": "bikes.schemas.OutputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "OutputsSchema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.OutputsSchema.instant": {"fullname": "bikes.schemas.OutputsSchema.instant", "modulename": "bikes.schemas", "qualname": "OutputsSchema.instant", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.prediction": {"fullname": "bikes.schemas.OutputsSchema.prediction", "modulename": "bikes.schemas", "qualname": "OutputsSchema.prediction", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.Config": {"fullname": "bikes.schemas.OutputsSchema.Config", "modulename": "bikes.schemas", "qualname": "OutputsSchema.Config", "kind": "class", "doc": "
Define DataFrameSchema-wide options.
\n\n
new in 0.5.0
\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.scripts": {"fullname": "bikes.scripts", "modulename": "bikes.scripts", "kind": "module", "doc": "
Entry point of the program.
\n"}, "bikes.scripts.Settings": {"fullname": "bikes.scripts.Settings", "modulename": "bikes.scripts", "qualname": "Settings", "kind": "class", "doc": "
Settings for the program.
\n\n
Attributes:
\n\n
    \n
  • job (jobs.JobKind):  job associated with the settings.
    • \n
\n", "bases": "pydantic_settings.main.BaseSettings"}, "bikes.scripts.main": {"fullname": "bikes.scripts.main", "modulename": "bikes.scripts", "qualname": "main", "kind": "function", "doc": "
Main function of the program.
\n\n
Arguments:
\n\n
    \n
  • argv (list[str] | None, optional):  program arguments. Defaults to None for sys.argv.
    • \n
\n\n
Returns:
\n\n
\n  
int: status code of the program.
\n
\n", "signature": "(argv: list[str] | None = None) -> int:", "funcdef": "def"}, "bikes.searchers": {"fullname": "bikes.searchers", "modulename": "bikes.searchers", "kind": "module", "doc": "
Find the best hyperparameters for a model.
\n"}, "bikes.searchers.Searcher": {"fullname": "bikes.searchers.Searcher", "modulename": "bikes.searchers", "qualname": "Searcher", "kind": "class", "doc": "
Base class for a searcher.
\n\n
note: use searcher to tune models.\ne.g., to find the best model params.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.searchers.Searcher.search": {"fullname": "bikes.searchers.Searcher.search", "modulename": "bikes.searchers", "qualname": "Searcher.search", "kind": "function", "doc": "
Search the best model for the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  machine learning model to tune.
    • \n
  • metric (metrics.Metric):  main metric to optimize.
    • \n
  • cv (CrossValidation):  structure for cross-fold.
    • \n
  • inputs (schemas.Inputs):  model inputs for tuning.
    • \n
  • targets (schemas.Targets):  model targets for tuning.
    • \n
\n\n
Returns:
\n\n
\n  
Results: all the results of the tuning process.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.searchers.GridCVSearcher": {"fullname": "bikes.searchers.GridCVSearcher", "modulename": "bikes.searchers", "qualname": "GridCVSearcher", "kind": "class", "doc": "
Grid searcher with cross-folds.
\n\n
Attributes:
\n\n
    \n
  • param_grid (Grid):  mapping of param key -> values.
    • \n
  • n_jobs (int, optional):  number of jobs to run in parallel.
    • \n
  • refit (bool):  refit the model after the tuning.
    • \n
  • verbose (int):  set the search verbosity level.
    • \n
  • error_score (str | float):  strategy or value on error.
    • \n
  • return_train_score (bool):  include train scores.
    • \n
\n", "bases": "Searcher"}, "bikes.searchers.GridCVSearcher.search": {"fullname": "bikes.searchers.GridCVSearcher.search", "modulename": "bikes.searchers", "qualname": "GridCVSearcher.search", "kind": "function", "doc": "
Search the best model for the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  machine learning model to tune.
    • \n
  • metric (metrics.Metric):  main metric to optimize.
    • \n
  • cv (CrossValidation):  structure for cross-fold.
    • \n
  • inputs (schemas.Inputs):  model inputs for tuning.
    • \n
  • targets (schemas.Targets):  model targets for tuning.
    • \n
\n\n
Returns:
\n\n
\n  
Results: all the results of the tuning process.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.services": {"fullname": "bikes.services", "modulename": "bikes.services", "kind": "module", "doc": "
Manage global context during execution.
\n"}, "bikes.services.Service": {"fullname": "bikes.services.Service", "modulename": "bikes.services", "qualname": "Service", "kind": "class", "doc": "
Base class for a global service.
\n\n
Use services to manage global contexts.\ne.g., logger object, mlflow client, spark context, ...
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.services.Service.start": {"fullname": "bikes.services.Service.start", "modulename": "bikes.services", "qualname": "Service.start", "kind": "function", "doc": "
Start the service.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.Service.stop": {"fullname": "bikes.services.Service.stop", "modulename": "bikes.services", "qualname": "Service.stop", "kind": "function", "doc": "
Stop the service.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.LoggerService": {"fullname": "bikes.services.LoggerService", "modulename": "bikes.services", "qualname": "LoggerService", "kind": "class", "doc": "
Service for logging messages.
\n\n
https://loguru.readthedocs.io/en/stable/api/logger.html
\n\n
Attributes:
\n\n
    \n
  • sink (str):  logging output.
    • \n
  • level (str):  logging level.
    • \n
  • format (str):  logging format.
    • \n
  • colorize (bool):  colorize output.
    • \n
  • serialize (bool):  convert to JSON.
    • \n
  • backtrace (bool):  enable exception trace.
    • \n
  • diagnose (bool):  enable variable display.
    • \n
  • catch (bool):  catch errors during log handling.
    • \n
\n", "bases": "Service"}, "bikes.services.LoggerService.start": {"fullname": "bikes.services.LoggerService.start", "modulename": "bikes.services", "qualname": "LoggerService.start", "kind": "function", "doc": "
Start the service.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.MLflowService": {"fullname": "bikes.services.MLflowService", "modulename": "bikes.services", "qualname": "MLflowService", "kind": "class", "doc": "
Service for MLflow tracking and registry.
\n\n
Attributes:
\n\n
    \n
  • autolog_disable (bool):  disable autologging.
    • \n
  • autolog_disable_for_unsupported_versions (bool):  disable autologging for unsupported versions.
    • \n
  • autolog_exclusive (bool):  If True, enables exclusive autologging.
    • \n
  • autolog_log_input_examples (bool):  If True, logs input examples during autologging.
    • \n
  • autolog_log_model_signatures (bool):  If True, logs model signatures during autologging.
    • \n
  • autolog_log_models (bool):  If True, enables logging of models during autologging.
    • \n
  • autolog_log_datasets (bool):  If True, logs datasets used during autologging.
    • \n
  • autolog_silent (bool):  If True, suppresses all MLflow warnings during autologging.
    • \n
  • tracking_uri (str):  The URI for the MLflow tracking server.
    • \n
  • experiment_name (str):  The name of the experiment to log runs under.
    • \n
  • registry_uri (str):  The URI for the MLflow model registry.
    • \n
  • registry_name (str):  The name of the registry.
    • \n
\n", "bases": "Service"}, "bikes.services.MLflowService.start": {"fullname": "bikes.services.MLflowService.start", "modulename": "bikes.services", "qualname": "MLflowService.start", "kind": "function", "doc": "
Start the mlflow service.
\n", "signature": "(self):", "funcdef": "def"}, "bikes.services.MLflowService.client": {"fullname": "bikes.services.MLflowService.client", "modulename": "bikes.services", "qualname": "MLflowService.client", "kind": "function", "doc": "
Get an instance of MLflow client.
\n", "signature": "(self) -> mlflow.tracking.client.MlflowClient:", "funcdef": "def"}, "bikes.services.MLflowService.register": {"fullname": "bikes.services.MLflowService.register", "modulename": "bikes.services", "qualname": "MLflowService.register", "kind": "function", "doc": "
Register a model to mlflow registry.
\n\n
Arguments:
\n\n
    \n
  • run_id (str):  id of mlflow run.
    • \n
  • path (str):  path of artifact.
    • \n
  • alias (str):  model alias.
    • \n
\n\n
Returns:
\n\n
\n  
mlflow.entities.model_registry.ModelVersion: registered version.
\n
\n", "signature": "(\tself,\trun_id: str,\tpath: str,\talias: str) -> mlflow.entities.model_registry.model_version.ModelVersion:", "funcdef": "def"}, "bikes.splitters": {"fullname": "bikes.splitters", "modulename": "bikes.splitters", "kind": "module", "doc": "
Split dataframes into subsets (e.g., train/valid/test).
\n"}, "bikes.splitters.Splitter": {"fullname": "bikes.splitters.Splitter", "modulename": "bikes.splitters", "qualname": "Splitter", "kind": "class", "doc": "
Base class for a splitter.
\n\n
Use splitters to split datasets.\ne.g., split between a train/test subsets.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.splitters.Splitter.split": {"fullname": "bikes.splitters.Splitter.split", "modulename": "bikes.splitters", "qualname": "Splitter.split", "kind": "function", "doc": "
Split a dataframe into subsets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
Splits: iterator over the dataframe splits.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.Splitter.get_n_splits": {"fullname": "bikes.splitters.Splitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "Splitter.get_n_splits", "kind": "function", "doc": "
Get the number of splits generated.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  models inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
int: number of splits generated.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter": {"fullname": "bikes.splitters.TrainTestSplitter", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter", "kind": "class", "doc": "
Split a dataframe into a train and test subsets.
\n\n
Attributes:
\n\n
    \n
  • shuffle (bool):  shuffle dataset before splitting.
    • \n
  • test_size (int | float):  number or ratio for the test dataset.
    • \n
  • random_state (int):  random state for the splitter object.
    • \n
\n", "bases": "Splitter"}, "bikes.splitters.TrainTestSplitter.split": {"fullname": "bikes.splitters.TrainTestSplitter.split", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.split", "kind": "function", "doc": "
Split a dataframe into subsets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
Splits: iterator over the dataframe splits.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter.get_n_splits": {"fullname": "bikes.splitters.TrainTestSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.get_n_splits", "kind": "function", "doc": "
Get the number of splits generated.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  models inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
int: number of splits generated.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter": {"fullname": "bikes.splitters.TimeSeriesSplitter", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter", "kind": "class", "doc": "
Split a dataframe into fixed time series subsets.
\n\n
Attributes:
\n\n
    \n
  • gap (int):  gap between splits.
    • \n
  • n_splits (int):  number of split to generate.
    • \n
  • test_size (int | float):  number or ratio for the test dataset.
    • \n
\n", "bases": "Splitter"}, "bikes.splitters.TimeSeriesSplitter.split": {"fullname": "bikes.splitters.TimeSeriesSplitter.split", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.split", "kind": "function", "doc": "
Split a dataframe into subsets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
Splits: iterator over the dataframe splits.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter.get_n_splits": {"fullname": "bikes.splitters.TimeSeriesSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.get_n_splits", "kind": "function", "doc": "
Get the number of splits generated.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  models inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
int: number of splits generated.
\n
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"s": {"docs": {"bikes.jobs.TuningJob": {"tf": 1}, "bikes.searchers": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, ":": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"bikes.registers.CustomAdapter": {"tf": 1}, "bikes.registers.Signer": {"tf": 1}, "bikes.registers.CustomSaver": {"tf": 1}, "bikes.registers.CustomLoader": {"tf": 1}}, "df": 4}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {"bikes.services.LoggerService": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "l": {"docs": {"bikes.registers.CustomAdapter": {"tf": 1}, "bikes.registers.CustomSaver": {"tf": 1}, "bikes.registers.CustomLoader": 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{"bikes.searchers.GridCVSearcher": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"bikes.services.MLflowService.register": {"tf": 1}}, "df": 1, "s": {"docs": {"bikes.services.MLflowService": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"bikes.schemas.Schema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.InputsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.TargetsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.OutputsSchema.__init__": {"tf": 5.830951894845301}}, "df": 4}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "y": {"docs": {"bikes.searchers.GridCVSearcher": {"tf": 1}}, "df": 1}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"bikes": {"fullname": "bikes", "modulename": "bikes", "kind": "module", "doc": "
Predict the number of bikes available.
\n"}, "bikes.configs": {"fullname": "bikes.configs", "modulename": "bikes.configs", "kind": "module", "doc": "
Parse, merge, and convert YAML configs.
\n"}, "bikes.configs.parse_file": {"fullname": "bikes.configs.parse_file", "modulename": "bikes.configs", "qualname": "parse_file", "kind": "function", "doc": "
Parse a config file from a path.
\n\n
Arguments:
\n\n
    \n
  • path (str):  local or remote path.
    • \n
\n\n
Returns:
\n\n
\n  
Config: representation of the config file.
\n
\n", "signature": "(\tpath: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.parse_string": {"fullname": "bikes.configs.parse_string", "modulename": "bikes.configs", "qualname": "parse_string", "kind": "function", "doc": "
Parse the given config string.
\n\n
Arguments:
\n\n
    \n
  • string (str):  configuration string.
    • \n
\n\n
Returns:
\n\n
\n  
Config: representation of the config string.
\n
\n", "signature": "(\tstring: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.merge_configs": {"fullname": "bikes.configs.merge_configs", "modulename": "bikes.configs", "qualname": "merge_configs", "kind": "function", "doc": "
Merge a list of config objects into one.
\n\n
Arguments:
\n\n
    \n
  • configs (list[Config]):  list of config objects.
    • \n
\n\n
Returns:
\n\n
\n  
Config: representation of the merged config objects.
\n
\n", "signature": "(\tconfigs: Sequence[omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig]) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.to_object": {"fullname": "bikes.configs.to_object", "modulename": "bikes.configs", "qualname": "to_object", "kind": "function", "doc": "
Convert a config object to a python object.
\n\n
Arguments:
\n\n
    \n
  • config (Config):  representation of the config.
    • \n
\n\n
Returns:
\n\n
\n  
object: conversion of the config to a python object.
\n
\n", "signature": "(\tconfig: omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig) -> object:", "funcdef": "def"}, "bikes.datasets": {"fullname": "bikes.datasets", "modulename": "bikes.datasets", "kind": "module", "doc": "
Read/Write datasets from/to external sources/destinations.
\n"}, "bikes.datasets.Reader": {"fullname": "bikes.datasets.Reader", "modulename": "bikes.datasets", "qualname": "Reader", "kind": "class", "doc": "
Base class for a dataset reader.
\n\n
Use a reader to load a dataset in memory.\ne.g., to read file, database, cloud storage, ...
\n\n
Attributes:
\n\n
    \n
  • limit (int, optional):  maximum number of rows to read from dataset.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Reader.read": {"fullname": "bikes.datasets.Reader.read", "modulename": "bikes.datasets", "qualname": "Reader.read", "kind": "function", "doc": "
Read a dataframe from a dataset.
\n\n
Returns:
\n\n
\n  
pd.DataFrame: dataframe representation.
\n
\n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.ParquetReader": {"fullname": "bikes.datasets.ParquetReader", "modulename": "bikes.datasets", "qualname": "ParquetReader", "kind": "class", "doc": "
Read a dataframe from a parquet file.
\n\n
Attributes:
\n\n
    \n
  • path (str):  local or remote path to a dataset.
    • \n
\n", "bases": "Reader"}, "bikes.datasets.ParquetReader.read": {"fullname": "bikes.datasets.ParquetReader.read", "modulename": "bikes.datasets", "qualname": "ParquetReader.read", "kind": "function", "doc": "
Read a dataframe from a dataset.
\n\n
Returns:
\n\n
\n  
pd.DataFrame: dataframe representation.
\n
\n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.Writer": {"fullname": "bikes.datasets.Writer", "modulename": "bikes.datasets", "qualname": "Writer", "kind": "class", "doc": "
Base class for a dataset writer.
\n\n
Use a writer to save a dataset from memory.\ne.g., to write file, database, cloud storage, ...
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Writer.write": {"fullname": "bikes.datasets.Writer.write", "modulename": "bikes.datasets", "qualname": "Writer.write", "kind": "function", "doc": "
Write a dataframe to a dataset.
\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame):  dataframe representation.
    • \n
\n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.datasets.ParquetWriter": {"fullname": "bikes.datasets.ParquetWriter", "modulename": "bikes.datasets", "qualname": "ParquetWriter", "kind": "class", "doc": "
Writer a dataframe to a parquet file.
\n\n
Attributes:
\n\n
    \n
  • path (str):  local or remote file to a dataset.
    • \n
\n", "bases": "Writer"}, "bikes.datasets.ParquetWriter.write": {"fullname": "bikes.datasets.ParquetWriter.write", "modulename": "bikes.datasets", "qualname": "ParquetWriter.write", "kind": "function", "doc": "
Write a dataframe to a dataset.
\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame):  dataframe representation.
    • \n
\n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.jobs": {"fullname": "bikes.jobs", "modulename": "bikes.jobs", "kind": "module", "doc": "
High-level jobs for the project.
\n"}, "bikes.jobs.Job": {"fullname": "bikes.jobs.Job", "modulename": "bikes.jobs", "qualname": "Job", "kind": "class", "doc": "
Base class for a job.
\n\n
use a job to execute runs in  context.\ne.g., to define common services like logger
\n\n
Attributes:
\n\n
    \n
  • logger_service (services.LoggerService):  manage the logging system.
    • \n
  • mlflow_service (services.MLflowService):  manage the mlflow system.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.jobs.Job.run": {"fullname": "bikes.jobs.Job.run", "modulename": "bikes.jobs", "qualname": "Job.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TuningJob": {"fullname": "bikes.jobs.TuningJob", "modulename": "bikes.jobs", "qualname": "TuningJob", "kind": "class", "doc": "
Find the best hyperparameters for a model.
\n\n
Attributes:
\n\n
    \n
  • run_name (str):  name of the MLflow experiment run.
    • \n
  • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
    • \n
  • targets (datasets.ReaderKind):  dataset reader with targets variables.
    • \n
  • results (datasets.WriterKind):  dataset writer for searcher results.
    • \n
  • model (models.ModelKind):  machine learning model to tune.
    • \n
  • metric (metrics.MetricKind):  main metric for evaluation.
    • \n
  • splitter (splitters.SplitterKind):  splitter for datasets.
    • \n
  • searcher (searchers.SearcherKind):  searcher algorithm.
    • \n
\n", "bases": "Job"}, "bikes.jobs.TuningJob.run": {"fullname": "bikes.jobs.TuningJob.run", "modulename": "bikes.jobs", "qualname": "TuningJob.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TrainingJob": {"fullname": "bikes.jobs.TrainingJob", "modulename": "bikes.jobs", "qualname": "TrainingJob", "kind": "class", "doc": "
Train and register a single AI/ML model.
\n\n
Attributes:
\n\n
    \n
  • run_name (str):  name of the MLflow experiment run.
    • \n
  • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
    • \n
  • targets (datasets.ReaderKind):  dataset reader with targets variables.
    • \n
  • saver (registers.SaverKind):  save the trained model in registry.
    • \n
  • model (models.ModelKind):  machine learning model to tune.
    • \n
  • signer (registers.SignerKind):  signer for the trained model.
    • \n
  • scorers (list[metrics.MetricKind]):  metrics for the evaluation.
    • \n
  • splitter (splitters.SplitterKind):  splitter for datasets.
    • \n
  • registry_alias (str):  alias of model.
    • \n
\n", "bases": "Job"}, "bikes.jobs.TrainingJob.run": {"fullname": "bikes.jobs.TrainingJob.run", "modulename": "bikes.jobs", "qualname": "TrainingJob.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.InferenceJob": {"fullname": "bikes.jobs.InferenceJob", "modulename": "bikes.jobs", "qualname": "InferenceJob", "kind": "class", "doc": "
Load a model and generate predictions.
\n\n
Attributes:
\n\n
    \n
  • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
    • \n
  • outputs (datasets.WriterKind):  dataset writer for the model outputs.
    • \n
  • registry_alias (str):  alias of the model to load.
    • \n
  • loader (registers.LoaderKind):  load the model from registry.
    • \n
\n", "bases": "Job"}, "bikes.jobs.InferenceJob.run": {"fullname": "bikes.jobs.InferenceJob.run", "modulename": "bikes.jobs", "qualname": "InferenceJob.run", "kind": "function", "doc": "
Run the job in context.
\n\n
Returns:
\n\n
\n  
Locals: local job variables.
\n
\n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.metrics": {"fullname": "bikes.metrics", "modulename": "bikes.metrics", "kind": "module", "doc": "
Evaluate model performance with metrics.
\n"}, "bikes.metrics.Metric": {"fullname": "bikes.metrics.Metric", "modulename": "bikes.metrics", "qualname": "Metric", "kind": "class", "doc": "
Base class for a metric.
\n\n
Use metrics to evaluate model performance.\ne.g., accuracy, precision, recall, mae, f1, ...
\n\n
Attributes:
\n\n
    \n
  • name (str):  name of the metric.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.metrics.Metric.score": {"fullname": "bikes.metrics.Metric.score", "modulename": "bikes.metrics", "qualname": "Metric.score", "kind": "function", "doc": "
Score the outputs against the targets.
\n\n
Arguments:
\n\n
    \n
  • targets (schemas.Targets):  expected values.
    • \n
  • outputs (schemas.Outputs):  predicted values.
    • \n
\n\n
Returns:
\n\n
\n  
float: metric result.
\n
\n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.Metric.scorer": {"fullname": "bikes.metrics.Metric.scorer", "modulename": "bikes.metrics", "qualname": "Metric.scorer", "kind": "function", "doc": "
Score the model outputs against the targets.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  model to evaluate.
    • \n
  • inputs (schemas.Inputs):  model inputs values.
    • \n
  • targets (schemas.Targets):  model expected values.
    • \n
\n\n
Returns:
\n\n
\n  
float: metric result.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.SklearnMetric": {"fullname": "bikes.metrics.SklearnMetric", "modulename": "bikes.metrics", "qualname": "SklearnMetric", "kind": "class", "doc": "
Compute metrics with sklearn.
\n\n
Attributes:
\n\n
    \n
  • name (str):  name of the sklearn metric.
    • \n
  • greater_is_better (bool):  maximize or minimize.
    • \n
\n", "bases": "Metric"}, "bikes.metrics.SklearnMetric.score": {"fullname": "bikes.metrics.SklearnMetric.score", "modulename": "bikes.metrics", "qualname": "SklearnMetric.score", "kind": "function", "doc": "
Score the outputs against the targets.
\n\n
Arguments:
\n\n
    \n
  • targets (schemas.Targets):  expected values.
    • \n
  • outputs (schemas.Outputs):  predicted values.
    • \n
\n\n
Returns:
\n\n
\n  
float: metric result.
\n
\n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.models": {"fullname": "bikes.models", "modulename": "bikes.models", "kind": "module", "doc": "
Define trainable machine learning models.
\n"}, "bikes.models.Model": {"fullname": "bikes.models.Model", "modulename": "bikes.models", "qualname": "Model", "kind": "class", "doc": "
Base class for a model.
\n\n
Use a model to adapt AI/ML frameworks.\ne.g., to swap easily one model with another.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.models.Model.get_params": {"fullname": "bikes.models.Model.get_params", "modulename": "bikes.models", "qualname": "Model.get_params", "kind": "function", "doc": "
Get the model params.
\n\n
Arguments:
\n\n
    \n
  • deep (bool, optional):  ignored. Defaults to True.
    • \n
\n\n
Returns:
\n\n
\n  
Params: internal model parameters.
\n
\n", "signature": "(self, deep: bool = True) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.models.Model.set_params": {"fullname": "bikes.models.Model.set_params", "modulename": "bikes.models", "qualname": "Model.set_params", "kind": "function", "doc": "
Set the model params in place.
\n\n
Returns:
\n\n
\n  
T.Self: instance of the model.
\n
\n", "signature": "(self, **params: Any) -> Self:", "funcdef": "def"}, "bikes.models.Model.fit": {"fullname": "bikes.models.Model.fit", "modulename": "bikes.models", "qualname": "Model.fit", "kind": "function", "doc": "
Fit the model on the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model training inputs.
    • \n
  • targets (schemas.Targets):  model training targets.
    • \n
\n\n
Returns:
\n\n
\n  
Model: instance of the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> Self:", "funcdef": "def"}, "bikes.models.Model.predict": {"fullname": "bikes.models.Model.predict", "modulename": "bikes.models", "qualname": "Model.predict", "kind": "function", "doc": "
Generate outputs with the model for the given inputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model prediction inputs.
    • \n
\n\n
Returns:
\n\n
\n  
schemas.Outputs: model prediction outputs.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel": {"fullname": "bikes.models.BaselineSklearnModel", "modulename": "bikes.models", "qualname": "BaselineSklearnModel", "kind": "class", "doc": "
Simple baseline model built on top of sklearn.
\n\n
Attributes:
\n\n
    \n
  • max_depth (int):  maximum depth of the random forest.
    • \n
  • n_estimators (int):  number of estimators in the random forest.
    • \n
  • random_state (int, optional):  random state of the machine learning pipeline.
    • \n
\n", "bases": "Model"}, "bikes.models.BaselineSklearnModel.fit": {"fullname": "bikes.models.BaselineSklearnModel.fit", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.fit", "kind": "function", "doc": "
Fit the model on the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model training inputs.
    • \n
  • targets (schemas.Targets):  model training targets.
    • \n
\n\n
Returns:
\n\n
\n  
Model: instance of the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> bikes.models.BaselineSklearnModel:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.predict": {"fullname": "bikes.models.BaselineSklearnModel.predict", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.predict", "kind": "function", "doc": "
Generate outputs with the model for the given inputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model prediction inputs.
    • \n
\n\n
Returns:
\n\n
\n  
schemas.Outputs: model prediction outputs.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.model_post_init": {"fullname": "bikes.models.BaselineSklearnModel.model_post_init", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.model_post_init", "kind": "function", "doc": "
This function is meant to behave like a BaseModel method to initialise private attributes.
\n\n
It takes context as an argument since that's what pydantic-core passes when calling it.
\n\n
Arguments:
\n\n
    \n
  • self:  The BaseModel instance.
    • \n
  • __context:  The context.
    • \n
\n", "signature": "(self: pydantic.main.BaseModel, __context: Any) -> None:", "funcdef": "def"}, "bikes.registers": {"fullname": "bikes.registers", "modulename": "bikes.registers", "kind": "module", "doc": "
Adapters, signers, savers, and loaders for model registries.
\n"}, "bikes.registers.CustomAdapter": {"fullname": "bikes.registers.CustomAdapter", "modulename": "bikes.registers", "qualname": "CustomAdapter", "kind": "class", "doc": "
Adapt a custom model to the MLflow PyFunc flavor.
\n\n
https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
\n", "bases": "mlflow.pyfunc.model.PythonModel"}, "bikes.registers.CustomAdapter.__init__": {"fullname": "bikes.registers.CustomAdapter.__init__", "modulename": "bikes.registers", "qualname": "CustomAdapter.__init__", "kind": "function", "doc": "
Initialize the custom adapter.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  project model.
    • \n
\n", "signature": "(model: bikes.models.Model)"}, "bikes.registers.CustomAdapter.predict": {"fullname": "bikes.registers.CustomAdapter.predict", "modulename": "bikes.registers", "qualname": "CustomAdapter.predict", "kind": "function", "doc": "
Generate predictions from a custom model.
\n\n
Arguments:
\n\n
    \n
  • context (mlflow.pyfunc.PythonModelContext):  ignored.
    • \n
  • inputs (schemas.Inputs):  inputs for the model.
    • \n
\n\n
Returns:
\n\n
\n  
schemas.Outputs: outputs of the model.
\n
\n", "signature": "(\tself,\tcontext: mlflow.pyfunc.model.PythonModelContext,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.registers.Signer": {"fullname": "bikes.registers.Signer", "modulename": "bikes.registers", "qualname": "Signer", "kind": "class", "doc": "
Base class for making signatures.
\n\n
Allow to switch between signing approaches.\ne.g., automatic inference vs manual signatures\nhttps://mlflow.org/docs/latest/models.html#model-signature-and-input-example
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Signer.sign": {"fullname": "bikes.registers.Signer.sign", "modulename": "bikes.registers", "qualname": "Signer.sign", "kind": "function", "doc": "
Make a model signature from inputs/outputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  inputs of the model.
    • \n
  • outputs (schemas.Outputs):  ouputs of the model.
    • \n
\n\n
Returns:
\n\n
\n  
ModelSignature: generated signature for the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.InferSigner": {"fullname": "bikes.registers.InferSigner", "modulename": "bikes.registers", "qualname": "InferSigner", "kind": "class", "doc": "
Generate model signatures from data inference.
\n", "bases": "Signer"}, "bikes.registers.InferSigner.sign": {"fullname": "bikes.registers.InferSigner.sign", "modulename": "bikes.registers", "qualname": "InferSigner.sign", "kind": "function", "doc": "
Make a model signature from inputs/outputs.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  inputs of the model.
    • \n
  • outputs (schemas.Outputs):  ouputs of the model.
    • \n
\n\n
Returns:
\n\n
\n  
ModelSignature: generated signature for the model.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.Saver": {"fullname": "bikes.registers.Saver", "modulename": "bikes.registers", "qualname": "Saver", "kind": "class", "doc": "
Base class for saving models in registry.
\n\n
Separate model definition from serialization.\ne.g., to switch between serialization flavors.
\n\n
Attributes:
\n\n
    \n
  • path (str):  model path inside the MLflow artifact store.
    • \n
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Saver.save": {"fullname": "bikes.registers.Saver.save", "modulename": "bikes.registers", "qualname": "Saver.save", "kind": "function", "doc": "
Save a model in the model registry.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  model to save.
    • \n
  • signature (Signature):  model signature.
    • \n
  • input_example (schemas.Inputs):  inputs sample.
    • \n
\n\n
Returns:
\n\n
\n  
Info: model saving information.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.CustomSaver": {"fullname": "bikes.registers.CustomSaver", "modulename": "bikes.registers", "qualname": "CustomSaver", "kind": "class", "doc": "
Saver for custom models using the MLflow PyFunc module.
\n\n
https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
\n", "bases": "Saver"}, "bikes.registers.CustomSaver.save": {"fullname": "bikes.registers.CustomSaver.save", "modulename": "bikes.registers", "qualname": "CustomSaver.save", "kind": "function", "doc": "
Save a custom model to the MLflow Model Registry.
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.Loader": {"fullname": "bikes.registers.Loader", "modulename": "bikes.registers", "qualname": "Loader", "kind": "class", "doc": "
Base class for loading models from registry.
\n\n
Separate model definition from deserialization.\ne.g., to switch between deserialization flavors.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Loader.load": {"fullname": "bikes.registers.Loader.load", "modulename": "bikes.registers", "qualname": "Loader.load", "kind": "function", "doc": "
Load a model from the model registry.
\n\n
Arguments:
\n\n
    \n
  • uri (str):  URI of the model to load.
    • \n
\n\n
Returns:
\n\n
\n  
T.Any: model loaded from registry.
\n
\n", "signature": "(self, uri: str) -> Any:", "funcdef": "def"}, "bikes.registers.CustomLoader": {"fullname": "bikes.registers.CustomLoader", "modulename": "bikes.registers", "qualname": "CustomLoader", "kind": "class", "doc": "
Loader for custom models using the MLflow PyFunc module.
\n\n
https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
\n", "bases": "Loader"}, "bikes.registers.CustomLoader.load": {"fullname": "bikes.registers.CustomLoader.load", "modulename": "bikes.registers", "qualname": "CustomLoader.load", "kind": "function", "doc": "
Load a model from the model registry.
\n\n
Arguments:
\n\n
    \n
  • uri (str):  URI of the model to load.
    • \n
\n\n
Returns:
\n\n
\n  
T.Any: model loaded from registry.
\n
\n", "signature": "(self, uri: str) -> mlflow.pyfunc.model.PythonModel:", "funcdef": "def"}, "bikes.schemas": {"fullname": "bikes.schemas", "modulename": "bikes.schemas", "kind": "module", "doc": "
Define and validate dataframe schemas.
\n"}, "bikes.schemas.Schema": {"fullname": "bikes.schemas.Schema", "modulename": "bikes.schemas", "qualname": "Schema", "kind": "class", "doc": "
Base class for a dataframe schema.
\n\n
Use a schema to type your dataframe object.\ne.g., to communicate and validate its fields.
\n", "bases": "pandera.api.pandas.model.DataFrameModel"}, "bikes.schemas.Schema.__init__": {"fullname": "bikes.schemas.Schema.__init__", "modulename": "bikes.schemas", "qualname": "Schema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.Schema.Config": {"fullname": "bikes.schemas.Schema.Config", "modulename": "bikes.schemas", "qualname": "Schema.Config", "kind": "class", "doc": "
Default configuration.
\n\n
Attributes:
\n\n
    \n
  • coerce (bool):  convert data type if possible.
    • \n
  • strict (bool):  ensure the data type is correct.
    • \n
\n"}, "bikes.schemas.Schema.check": {"fullname": "bikes.schemas.Schema.check", "modulename": "bikes.schemas", "qualname": "Schema.check", "kind": "function", "doc": "
Check the data with this schema.
\n\n
Arguments:
\n\n
    \n
  • data (pd.DataFrame):  dataframe to check.
    • \n
  • kwargs:  additional arguments to validate().
    • \n
\n\n
Returns:
\n\n
\n  
pd.DataFrame: validated dataframe with schema.
\n
\n", "signature": "(cls, data: pandas.core.frame.DataFrame, **kwargs):", "funcdef": "def"}, "bikes.schemas.InputsSchema": {"fullname": "bikes.schemas.InputsSchema", "modulename": "bikes.schemas", "qualname": "InputsSchema", "kind": "class", "doc": "
Schema for the project inputs.
\n", "bases": "Schema"}, "bikes.schemas.InputsSchema.__init__": {"fullname": "bikes.schemas.InputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "InputsSchema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.InputsSchema.instant": {"fullname": "bikes.schemas.InputsSchema.instant", "modulename": "bikes.schemas", "qualname": "InputsSchema.instant", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.dteday": {"fullname": "bikes.schemas.InputsSchema.dteday", "modulename": "bikes.schemas", "qualname": "InputsSchema.dteday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Timestamp]"}, "bikes.schemas.InputsSchema.season": {"fullname": "bikes.schemas.InputsSchema.season", "modulename": "bikes.schemas", "qualname": "InputsSchema.season", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.yr": {"fullname": "bikes.schemas.InputsSchema.yr", "modulename": "bikes.schemas", "qualname": "InputsSchema.yr", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.mnth": {"fullname": "bikes.schemas.InputsSchema.mnth", "modulename": "bikes.schemas", "qualname": "InputsSchema.mnth", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.hr": {"fullname": "bikes.schemas.InputsSchema.hr", "modulename": "bikes.schemas", "qualname": "InputsSchema.hr", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.holiday": {"fullname": "bikes.schemas.InputsSchema.holiday", "modulename": "bikes.schemas", "qualname": "InputsSchema.holiday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weekday": {"fullname": "bikes.schemas.InputsSchema.weekday", "modulename": "bikes.schemas", "qualname": "InputsSchema.weekday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.workingday": {"fullname": "bikes.schemas.InputsSchema.workingday", "modulename": "bikes.schemas", "qualname": "InputsSchema.workingday", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weathersit": {"fullname": "bikes.schemas.InputsSchema.weathersit", "modulename": "bikes.schemas", "qualname": "InputsSchema.weathersit", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.temp": {"fullname": "bikes.schemas.InputsSchema.temp", "modulename": "bikes.schemas", "qualname": "InputsSchema.temp", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.atemp": {"fullname": "bikes.schemas.InputsSchema.atemp", "modulename": "bikes.schemas", "qualname": "InputsSchema.atemp", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.hum": {"fullname": "bikes.schemas.InputsSchema.hum", "modulename": "bikes.schemas", "qualname": "InputsSchema.hum", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.windspeed": {"fullname": "bikes.schemas.InputsSchema.windspeed", "modulename": "bikes.schemas", "qualname": "InputsSchema.windspeed", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.casual": {"fullname": "bikes.schemas.InputsSchema.casual", "modulename": "bikes.schemas", "qualname": "InputsSchema.casual", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.registered": {"fullname": "bikes.schemas.InputsSchema.registered", "modulename": "bikes.schemas", "qualname": "InputsSchema.registered", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.Config": {"fullname": "bikes.schemas.InputsSchema.Config", "modulename": "bikes.schemas", "qualname": "InputsSchema.Config", "kind": "class", "doc": "
Define DataFrameSchema-wide options.
\n\n
new in 0.5.0
\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.TargetsSchema": {"fullname": "bikes.schemas.TargetsSchema", "modulename": "bikes.schemas", "qualname": "TargetsSchema", "kind": "class", "doc": "
Schema for the project target.
\n", "bases": "Schema"}, "bikes.schemas.TargetsSchema.__init__": {"fullname": "bikes.schemas.TargetsSchema.__init__", "modulename": "bikes.schemas", "qualname": "TargetsSchema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.TargetsSchema.instant": {"fullname": "bikes.schemas.TargetsSchema.instant", "modulename": "bikes.schemas", "qualname": "TargetsSchema.instant", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.cnt": {"fullname": "bikes.schemas.TargetsSchema.cnt", "modulename": "bikes.schemas", "qualname": "TargetsSchema.cnt", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.Config": {"fullname": "bikes.schemas.TargetsSchema.Config", "modulename": "bikes.schemas", "qualname": "TargetsSchema.Config", "kind": "class", "doc": "
Define DataFrameSchema-wide options.
\n\n
new in 0.5.0
\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.OutputsSchema": {"fullname": "bikes.schemas.OutputsSchema", "modulename": "bikes.schemas", "qualname": "OutputsSchema", "kind": "class", "doc": "
Schema for the project output.
\n", "bases": "Schema"}, "bikes.schemas.OutputsSchema.__init__": {"fullname": "bikes.schemas.OutputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "OutputsSchema.__init__", "kind": "function", "doc": "
Check if all columns in a dataframe have a column in the Schema.
\n\n
Parameters
\n\n
    \n
  • pd.DataFrame check_obj:  the dataframe to be validated.
    • \n
  • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
    • \n
  • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
    • \n
  • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
    • \n
  • random_state:  random seed for the sample argument.
    • \n
  • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
    • \n
  • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
    • \n
\n\n
Raises
\n\n
    \n
  • SchemaError:  when DataFrame violates built-in or custom\nchecks.
    • \n
\n\n
:example:
\n\n
Calling schema.validate returns the dataframe.
\n\n
\n
>>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
\n
\n", "signature": "(*args, **kwargs)"}, "bikes.schemas.OutputsSchema.instant": {"fullname": "bikes.schemas.OutputsSchema.instant", "modulename": "bikes.schemas", "qualname": "OutputsSchema.instant", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.prediction": {"fullname": "bikes.schemas.OutputsSchema.prediction", "modulename": "bikes.schemas", "qualname": "OutputsSchema.prediction", "kind": "variable", "doc": "
Captures extra information about a field.
\n\n
new in 0.5.0
\n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.Config": {"fullname": "bikes.schemas.OutputsSchema.Config", "modulename": "bikes.schemas", "qualname": "OutputsSchema.Config", "kind": "class", "doc": "
Define DataFrameSchema-wide options.
\n\n
new in 0.5.0
\n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.scripts": {"fullname": "bikes.scripts", "modulename": "bikes.scripts", "kind": "module", "doc": "
Command-line interface for the program.
\n"}, "bikes.scripts.Settings": {"fullname": "bikes.scripts.Settings", "modulename": "bikes.scripts", "qualname": "Settings", "kind": "class", "doc": "
Settings for the program.
\n\n
Attributes:
\n\n
    \n
  • job (jobs.JobKind):  job associated with settings.
    • \n
\n", "bases": "pydantic_settings.main.BaseSettings"}, "bikes.scripts.main": {"fullname": "bikes.scripts.main", "modulename": "bikes.scripts", "qualname": "main", "kind": "function", "doc": "
Main function of the program.
\n\n
Arguments:
\n\n
    \n
  • argv (list[str] | None, optional):  program arguments. Defaults to None for sys.argv.
    • \n
\n\n
Returns:
\n\n
\n  
int: status code of the program.
\n
\n", "signature": "(argv: list[str] | None = None) -> int:", "funcdef": "def"}, "bikes.searchers": {"fullname": "bikes.searchers", "modulename": "bikes.searchers", "kind": "module", "doc": "
Find the best hyperparameters for a model.
\n"}, "bikes.searchers.Searcher": {"fullname": "bikes.searchers.Searcher", "modulename": "bikes.searchers", "qualname": "Searcher", "kind": "class", "doc": "
Base class for a searcher.
\n\n
note: use searcher to tune models.\ne.g., to find the best model params.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.searchers.Searcher.search": {"fullname": "bikes.searchers.Searcher.search", "modulename": "bikes.searchers", "qualname": "Searcher.search", "kind": "function", "doc": "
Search the best model for the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  machine learning model to tune.
    • \n
  • metric (metrics.Metric):  main metric to optimize.
    • \n
  • cv (CrossValidation):  structure for cross-fold.
    • \n
  • inputs (schemas.Inputs):  model inputs for tuning.
    • \n
  • targets (schemas.Targets):  model targets for tuning.
    • \n
\n\n
Returns:
\n\n
\n  
Results: all the results of the tuning process.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.searchers.GridCVSearcher": {"fullname": "bikes.searchers.GridCVSearcher", "modulename": "bikes.searchers", "qualname": "GridCVSearcher", "kind": "class", "doc": "
Grid searcher with cross-folds.
\n\n
Attributes:
\n\n
    \n
  • param_grid (Grid):  mapping of param key -> values.
    • \n
  • n_jobs (int, optional):  number of jobs to run in parallel.
    • \n
  • refit (bool):  refit the model after the tuning.
    • \n
  • verbose (int):  set the search verbosity level.
    • \n
  • error_score (str | float):  strategy or value on error.
    • \n
  • return_train_score (bool):  include train scores.
    • \n
\n", "bases": "Searcher"}, "bikes.searchers.GridCVSearcher.search": {"fullname": "bikes.searchers.GridCVSearcher.search", "modulename": "bikes.searchers", "qualname": "GridCVSearcher.search", "kind": "function", "doc": "
Search the best model for the given inputs and targets.
\n\n
Arguments:
\n\n
    \n
  • model (models.Model):  machine learning model to tune.
    • \n
  • metric (metrics.Metric):  main metric to optimize.
    • \n
  • cv (CrossValidation):  structure for cross-fold.
    • \n
  • inputs (schemas.Inputs):  model inputs for tuning.
    • \n
  • targets (schemas.Targets):  model targets for tuning.
    • \n
\n\n
Returns:
\n\n
\n  
Results: all the results of the tuning process.
\n
\n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.services": {"fullname": "bikes.services", "modulename": "bikes.services", "kind": "module", "doc": "
Manage global context during execution.
\n"}, "bikes.services.Service": {"fullname": "bikes.services.Service", "modulename": "bikes.services", "qualname": "Service", "kind": "class", "doc": "
Base class for a global service.
\n\n
Use services to manage global contexts.\ne.g., logger object, mlflow client, spark context, ...
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.services.Service.start": {"fullname": "bikes.services.Service.start", "modulename": "bikes.services", "qualname": "Service.start", "kind": "function", "doc": "
Start the service.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.Service.stop": {"fullname": "bikes.services.Service.stop", "modulename": "bikes.services", "qualname": "Service.stop", "kind": "function", "doc": "
Stop the service.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.LoggerService": {"fullname": "bikes.services.LoggerService", "modulename": "bikes.services", "qualname": "LoggerService", "kind": "class", "doc": "
Service for logging messages.
\n\n
https://loguru.readthedocs.io/en/stable/api/logger.html
\n\n
Attributes:
\n\n
    \n
  • sink (str):  logging output.
    • \n
  • level (str):  logging level.
    • \n
  • format (str):  logging format.
    • \n
  • colorize (bool):  colorize output.
    • \n
  • serialize (bool):  convert to JSON.
    • \n
  • backtrace (bool):  enable exception trace.
    • \n
  • diagnose (bool):  enable variable display.
    • \n
  • catch (bool):  catch errors during log handling.
    • \n
\n", "bases": "Service"}, "bikes.services.LoggerService.start": {"fullname": "bikes.services.LoggerService.start", "modulename": "bikes.services", "qualname": "LoggerService.start", "kind": "function", "doc": "
Start the service.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.MLflowService": {"fullname": "bikes.services.MLflowService", "modulename": "bikes.services", "qualname": "MLflowService", "kind": "class", "doc": "
Service for MLflow tracking and registry.
\n\n
Attributes:
\n\n
    \n
  • autolog_disable (bool):  disable autologging.
    • \n
  • autolog_disable_for_unsupported_versions (bool):  disable autologging for unsupported versions.
    • \n
  • autolog_exclusive (bool):  If True, enables exclusive autologging.
    • \n
  • autolog_log_input_examples (bool):  If True, logs input examples during autologging.
    • \n
  • autolog_log_model_signatures (bool):  If True, logs model signatures during autologging.
    • \n
  • autolog_log_models (bool):  If True, enables logging of models during autologging.
    • \n
  • autolog_log_datasets (bool):  If True, logs datasets used during autologging.
    • \n
  • autolog_silent (bool):  If True, suppresses all MLflow warnings during autologging.
    • \n
  • tracking_uri (str):  The URI for the MLflow tracking server.
    • \n
  • experiment_name (str):  The name of the experiment to log runs under.
    • \n
  • registry_uri (str):  The URI for the MLflow model registry.
    • \n
  • registry_name (str):  The name of the registry.
    • \n
\n", "bases": "Service"}, "bikes.services.MLflowService.start": {"fullname": "bikes.services.MLflowService.start", "modulename": "bikes.services", "qualname": "MLflowService.start", "kind": "function", "doc": "
Start the mlflow service.
\n", "signature": "(self):", "funcdef": "def"}, "bikes.services.MLflowService.client": {"fullname": "bikes.services.MLflowService.client", "modulename": "bikes.services", "qualname": "MLflowService.client", "kind": "function", "doc": "
Get an instance of MLflow client.
\n", "signature": "(self) -> mlflow.tracking.client.MlflowClient:", "funcdef": "def"}, "bikes.services.MLflowService.register": {"fullname": "bikes.services.MLflowService.register", "modulename": "bikes.services", "qualname": "MLflowService.register", "kind": "function", "doc": "
Register a model to mlflow registry.
\n\n
Arguments:
\n\n
    \n
  • run_id (str):  id of mlflow run.
    • \n
  • path (str):  path of artifact.
    • \n
  • alias (str):  model alias.
    • \n
\n\n
Returns:
\n\n
\n  
mlflow.entities.model_registry.ModelVersion: registered version.
\n
\n", "signature": "(\tself,\trun_id: str,\tpath: str,\talias: str) -> mlflow.entities.model_registry.model_version.ModelVersion:", "funcdef": "def"}, "bikes.splitters": {"fullname": "bikes.splitters", "modulename": "bikes.splitters", "kind": "module", "doc": "
Split dataframes into subsets (e.g., train/valid/test).
\n"}, "bikes.splitters.Splitter": {"fullname": "bikes.splitters.Splitter", "modulename": "bikes.splitters", "qualname": "Splitter", "kind": "class", "doc": "
Base class for a splitter.
\n\n
Use splitters to split datasets.\ne.g., split between a train/test subsets.
\n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.splitters.Splitter.split": {"fullname": "bikes.splitters.Splitter.split", "modulename": "bikes.splitters", "qualname": "Splitter.split", "kind": "function", "doc": "
Split a dataframe into subsets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
Splits: iterator over the dataframe splits.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.Splitter.get_n_splits": {"fullname": "bikes.splitters.Splitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "Splitter.get_n_splits", "kind": "function", "doc": "
Get the number of splits generated.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  models inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
int: number of splits generated.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter": {"fullname": "bikes.splitters.TrainTestSplitter", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter", "kind": "class", "doc": "
Split a dataframe into a train and test subsets.
\n\n
Attributes:
\n\n
    \n
  • shuffle (bool):  shuffle dataset before splitting.
    • \n
  • test_size (int | float):  number or ratio for the test dataset.
    • \n
  • random_state (int):  random state for the splitter object.
    • \n
\n", "bases": "Splitter"}, "bikes.splitters.TrainTestSplitter.split": {"fullname": "bikes.splitters.TrainTestSplitter.split", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.split", "kind": "function", "doc": "
Split a dataframe into subsets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
Splits: iterator over the dataframe splits.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter.get_n_splits": {"fullname": "bikes.splitters.TrainTestSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.get_n_splits", "kind": "function", "doc": "
Get the number of splits generated.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  models inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
int: number of splits generated.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter": {"fullname": "bikes.splitters.TimeSeriesSplitter", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter", "kind": "class", "doc": "
Split a dataframe into fixed time series subsets.
\n\n
Attributes:
\n\n
    \n
  • gap (int):  gap between splits.
    • \n
  • n_splits (int):  number of split to generate.
    • \n
  • test_size (int | float):  number or ratio for the test dataset.
    • \n
\n", "bases": "Splitter"}, "bikes.splitters.TimeSeriesSplitter.split": {"fullname": "bikes.splitters.TimeSeriesSplitter.split", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.split", "kind": "function", "doc": "
Split a dataframe into subsets.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  model inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
Splits: iterator over the dataframe splits.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter.get_n_splits": {"fullname": "bikes.splitters.TimeSeriesSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.get_n_splits", "kind": "function", "doc": "
Get the number of splits generated.
\n\n
Arguments:
\n\n
    \n
  • inputs (schemas.Inputs):  models inputs.
    • \n
  • targets (schemas.Targets):  model targets.
    • \n
  • groups (list | None, optional):  group labels. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n  
int: number of splits generated.
\n
\n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}}, "docInfo": {"bikes": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "bikes.configs": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "bikes.configs.parse_file": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 45}, "bikes.configs.parse_string": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 41}, "bikes.configs.merge_configs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 78, "bases": 0, "doc": 47}, "bikes.configs.to_object": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, 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"u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"bikes.schemas.Schema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.InputsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.TargetsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.OutputsSchema.__init__": {"tf": 5.830951894845301}}, "df": 4}}}}, "k": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"bikes.schemas.Schema.check": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "y": {"docs": {"bikes.searchers.GridCVSearcher": {"tf": 1}}, "df": 1}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
 
     // mirrored in build-search-index.js (part 1)
     // Also split on html tags. this is a cheap heuristic, but good enough.

From 7c14427cdd2c7633a81073021e562f2d1117b20c Mon Sep 17 00:00:00 2001
From: fmind 
Date: Tue, 27 Feb 2024 20:43:58 +0000
Subject: [PATCH 04/11] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20fm?=
 =?UTF-8?q?ind/mlops-python-package@06b728abb65704902e5fca6dfedc0db4929131?=
 =?UTF-8?q?09=20=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 bikes/jobs.html     | 1329 ++++++++++++++++++++++---------------------
 bikes/services.html |  955 ++++++++++++++++++++-----------
 search.js           |    2 +-
 3 files changed, 1285 insertions(+), 1001 deletions(-)

diff --git a/bikes/jobs.html b/bikes/jobs.html
index c059119..600a8f6 100644
--- a/bikes/jobs.html
+++ b/bikes/jobs.html
@@ -118,273 +118,281 @@ 

  27
  28    Attributes:
  29        logger_service (services.LoggerService): manage the logging system.
- 30        mlflow_service (services.MLflowService): manage the mlflow system.
- 31    """
- 32
- 33    KIND: str
- 34
- 35    logger_service: services.LoggerService = services.LoggerService()
- 36    mlflow_service: services.MLflowService = services.MLflowService()
- 37
- 38    def __enter__(self) -> T.Self:
- 39        """Enter the job context.
- 40
- 41        Returns:
- 42            T.Self: return the current object.
- 43        """
- 44        self.logger_service.start()
- 45        logger.debug("[START] MLflow service: {}", self.mlflow_service)
- 46        self.mlflow_service.start()
- 47        return self
- 48
- 49    def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]:
- 50        """Exit the job context.
- 51
- 52        Args:
- 53            exc_type: ignored.
- 54            exc_value: ignored.
- 55            traceback: ignored.
+ 30        carbon_service (services.CarbonService): manage the carbon system.
+ 31        mlflow_service (services.MLflowService): manage the mlflow system.
+ 32    """
+ 33
+ 34    KIND: str
+ 35
+ 36    logger_service: services.LoggerService = services.LoggerService()
+ 37    carbon_service: services.CarbonService = services.CarbonService()
+ 38    mlflow_service: services.MLflowService = services.MLflowService()
+ 39
+ 40    def __enter__(self) -> T.Self:
+ 41        """Enter the job context.
+ 42
+ 43        Returns:
+ 44            T.Self: return the current object.
+ 45        """
+ 46        self.logger_service.start()  # start then log
+ 47        logger.debug("[START] Logger service: {}", self.logger_service)
+ 48        logger.debug("[START] Carbon service: {}", self.carbon_service)
+ 49        self.carbon_service.start()
+ 50        logger.debug("[START] MLflow service: {}", self.mlflow_service)
+ 51        self.mlflow_service.start()
+ 52        return self
+ 53
+ 54    def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]:
+ 55        """Exit the job context.
  56
- 57        Returns:
- 58            T.Literal[False]: always propagate exceptions.
- 59        """
- 60        logger.debug("[STOP] MLflow service: {}", self.mlflow_service)
- 61        self.mlflow_service.stop()
- 62        self.logger_service.stop()
- 63        return False
- 64
- 65    @abc.abstractmethod
- 66    def run(self) -> Locals:
- 67        """Run the job in context.
- 68
- 69        Returns:
- 70            Locals: local job variables.
- 71        """
+ 57        Args:
+ 58            exc_type: ignored.
+ 59            exc_value: ignored.
+ 60            traceback: ignored.
+ 61
+ 62        Returns:
+ 63            T.Literal[False]: always propagate exceptions.
+ 64        """
+ 65        logger.debug("[STOP] MLflow service: {}", self.mlflow_service)
+ 66        self.mlflow_service.stop()
+ 67        logger.debug("[STOP] Carbon service: {}", self.carbon_service)
+ 68        self.carbon_service.stop()
+ 69        logger.debug("[STOP] Logger service: {}", self.carbon_service)
+ 70        self.logger_service.stop()
+ 71        return False
  72
- 73
- 74class TuningJob(Job):
- 75    """Find the best hyperparameters for a model.
+ 73    @abc.abstractmethod
+ 74    def run(self) -> Locals:
+ 75        """Run the job in context.
  76
- 77    Attributes:
- 78        run_name (str): name of the MLflow experiment run.
- 79        inputs (datasets.ReaderKind): dataset reader with inputs variables.
- 80        targets (datasets.ReaderKind): dataset reader with targets variables.
- 81        results (datasets.WriterKind): dataset writer for searcher results.
- 82        model (models.ModelKind): machine learning model to tune.
- 83        metric (metrics.MetricKind): main metric for evaluation.
- 84        splitter (splitters.SplitterKind): splitter for datasets.
- 85        searcher (searchers.SearcherKind): searcher algorithm.
- 86    """
- 87
- 88    KIND: T.Literal["TuningJob"] = "TuningJob"
- 89
- 90    # run
- 91    run_name: str = "Tuning"
- 92    # read
- 93    inputs: datasets.ReaderKind
- 94    targets: datasets.ReaderKind
- 95    # write
- 96    results: datasets.WriterKind
- 97    # model
- 98    model: models.ModelKind = models.BaselineSklearnModel()
- 99    # metric
-100    metric: metrics.MetricKind = metrics.SklearnMetric()
-101    # splitter
-102    splitter: splitters.SplitterKind = splitters.TimeSeriesSplitter()
-103    # searcher
-104    searcher: searchers.SearcherKind = searchers.GridCVSearcher(
-105        param_grid={"max_depth": [3, 5, 7]},
-106    )
-107
-108    @T.override
-109    def run(self) -> Locals:
-110        # run
-111        logger.info("Start run: {} ", self.run_name)
-112        with mlflow.start_run(run_name=self.run_name) as run:
-113            logger.info("- Run ID: {}", run.info.run_id)
-114            # read
-115            # - inputs
-116            logger.info("Read inputs: {}", self.inputs)
-117            inputs = schemas.InputsSchema.check(self.inputs.read())
-118            logger.info("- Inputs shape: {}", inputs.shape)
-119            # - targets
-120            logger.info("Read targets: {}", self.targets)
-121            targets = schemas.TargetsSchema.check(self.targets.read())
-122            logger.info("- Targets shape: {}", targets.shape)
-123            # - asserts
-124            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-125            # model
-126            logger.info("With model: {}", self.model)
-127            # metric
-128            logger.info("With metric: {}", self.metric)
-129            # splitter
-130            logger.info("With splitter: {}", self.splitter)
-131            # searcher
-132            logger.info("Execute searcher: {}", self.searcher)
-133            results, best_score, best_params = self.searcher.search(
-134                model=self.model,
-135                metric=self.metric,
-136                cv=self.splitter,
-137                inputs=inputs,
-138                targets=targets,
-139            )
-140            logger.info("- # Results: {}", len(results))
-141            logger.info("- Best Score: {}", best_score)
-142            logger.info("- Best Params: {}", best_params)
-143            # write
-144            logger.info("Write results: {}", self.results)
-145            self.results.write(results)
-146        return locals()
-147
-148
-149class TrainingJob(Job):
-150    """Train and register a single AI/ML model.
-151
-152    Attributes:
-153        run_name (str): name of the MLflow experiment run.
-154        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-155        targets (datasets.ReaderKind): dataset reader with targets variables.
-156        saver (registers.SaverKind): save the trained model in registry.
-157        model (models.ModelKind): machine learning model to tune.
-158        signer (registers.SignerKind): signer for the trained model.
-159        scorers (list[metrics.MetricKind]): metrics for the evaluation.
-160        splitter (splitters.SplitterKind): splitter for datasets.
-161        registry_alias (str): alias of model.
-162    """
-163
-164    KIND: T.Literal["TrainingJob"] = "TrainingJob"
-165
-166    # run
-167    run_name: str = "Training"
-168    # read
-169    inputs: datasets.ReaderKind
-170    targets: datasets.ReaderKind
-171    # write
-172    saver: registers.SaverKind = registers.CustomSaver()
-173    # model
-174    model: models.ModelKind = models.BaselineSklearnModel()
-175    # signer
-176    signer: registers.SignerKind = registers.InferSigner()
-177    # scorers
-178    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
-179    # splitter
-180    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
-181    # register
-182    registry_alias: str = "Champion"
-183
-184    @T.override
-185    def run(self) -> Locals:
-186        # run
-187        logger.info("Start run: {} ", self.run_name)
-188        with mlflow.start_run(run_name=self.run_name) as run:
-189            logger.info("- Run ID: {}", run.info.run_id)
-190            # read
-191            # - inputs
-192            logger.info("Read inputs: {}", self.inputs)
-193            inputs = schemas.InputsSchema.check(self.inputs.read())
-194            logger.info("- Inputs shape: {}", inputs.shape)
-195            # - targets
-196            logger.info("Read targets: {}", self.targets)
-197            targets = schemas.TargetsSchema.check(self.targets.read())
-198            logger.info("- Targets shape: {}", targets.shape)
-199            # - asserts
-200            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-201            # split
-202            logger.info("With splitter: {}", self.splitter)
-203            # - index
-204            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
-205            # - inputs
-206            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
-207            logger.info("- Inputs train shape: {}", inputs_train.shape)
-208            logger.info("- Inputs test shape: {}", inputs_test.shape)
-209            # - targets
-210            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
-211            logger.info("- Targets train shape: {}", targets_train.shape)
-212            logger.info("- Targets test shape: {}", targets_test.shape)
-213            # - asserts
-214            assert len(inputs_train) == len(
-215                targets_train
-216            ), "Inputs and targets train should have the same length!"
-217            assert len(inputs_test) == len(
-218                targets_test
-219            ), "Inputs and targets test should have the same length!"
-220            # model
-221            logger.info("Fit model: {}", self.model)
-222            self.model.fit(inputs=inputs_train, targets=targets_train)
-223            # outputs
-224            logger.info("Predict outputs: {}", len(inputs_test))
-225            outputs_test = self.model.predict(inputs=inputs_test)
-226            logger.info("- Outputs test shape: {}", outputs_test.shape)
-227            assert len(inputs_test) == len(
-228                outputs_test
-229            ), "Inputs and outputs test should have the same length!"
-230            # scorers
-231            for i, scorer in enumerate(self.scorers, start=1):
-232                logger.info("{}. Run scorer: {}", i, scorer)
-233                score = scorer.score(targets=targets_test, outputs=outputs_test)
-234                mlflow.log_metric(key=scorer.name, value=score)
-235                logger.info("- Metric score: {}", score)
-236            # sign
-237            logger.info("Sign model: {}", self.signer)
-238            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
-239            logger.info("- Model signature: {}", signature.to_dict())
-240            # save
-241            logger.info("Save model: {}", self.saver)
-242            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
-243            logger.info("- Model URI: {}", info.model_uri)
-244            # register
-245            logger.info("Register model: {}", self.registry_alias)
-246            version = self.mlflow_service.register(
-247                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
-248            )
-249            logger.info("- Model version: {}", version.version)
-250        return locals()
-251
-252
-253class InferenceJob(Job):
-254    """Load a model and generate predictions.
-255
-256    Attributes:
-257        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-258        outputs (datasets.WriterKind): dataset writer for the model outputs.
-259        registry_alias (str): alias of the model to load.
-260        loader (registers.LoaderKind): load the model from registry.
-261    """
-262
-263    KIND: T.Literal["InferenceJob"] = "InferenceJob"
-264
-265    # data
-266    inputs: datasets.ReaderKind
-267    outputs: datasets.WriterKind
-268    # model
-269    registry_alias: str = "Champion"
-270    loader: registers.LoaderKind = registers.CustomLoader()
-271
-272    @T.override
-273    def run(self) -> Locals:
-274        # read
-275        logger.info("Read inputs: {}", self.inputs)
-276        inputs = self.inputs.read()
-277        inputs = schemas.InputsSchema.check(inputs)
-278        logger.info("- Inputs shape: {}", inputs.shape)
-279        # uri
-280        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
-281        logger.info("With URI: {}", uri)
-282        # load
-283        logger.info("Load model: {}", self.loader)
-284        model = self.loader.load(uri=uri)
-285        logger.info("- Model: {}", model)
-286        # predict
-287        logger.info("Predict outputs: {}", len(inputs))
-288        outputs = model.predict(data=inputs)
-289        logger.info("- Outputs shape: {}", outputs.shape)
-290        # write
-291        logger.info("Write outputs: {}", self.outputs)
-292        self.outputs.write(data=outputs)
-293        return locals()
-294
-295
-296JobKind = TuningJob | TrainingJob | InferenceJob
+ 77        Returns:
+ 78            Locals: local job variables.
+ 79        """
+ 80
+ 81
+ 82class TuningJob(Job):
+ 83    """Find the best hyperparameters for a model.
+ 84
+ 85    Attributes:
+ 86        run_name (str): name of the MLflow experiment run.
+ 87        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+ 88        targets (datasets.ReaderKind): dataset reader with targets variables.
+ 89        results (datasets.WriterKind): dataset writer for searcher results.
+ 90        model (models.ModelKind): machine learning model to tune.
+ 91        metric (metrics.MetricKind): main metric for evaluation.
+ 92        splitter (splitters.SplitterKind): splitter for datasets.
+ 93        searcher (searchers.SearcherKind): searcher algorithm.
+ 94    """
+ 95
+ 96    KIND: T.Literal["TuningJob"] = "TuningJob"
+ 97
+ 98    # run
+ 99    run_name: str = "Tuning"
+100    # read
+101    inputs: datasets.ReaderKind
+102    targets: datasets.ReaderKind
+103    # write
+104    results: datasets.WriterKind
+105    # model
+106    model: models.ModelKind = models.BaselineSklearnModel()
+107    # metric
+108    metric: metrics.MetricKind = metrics.SklearnMetric()
+109    # splitter
+110    splitter: splitters.SplitterKind = splitters.TimeSeriesSplitter()
+111    # searcher
+112    searcher: searchers.SearcherKind = searchers.GridCVSearcher(
+113        param_grid={"max_depth": [3, 5, 7]},
+114    )
+115
+116    @T.override
+117    def run(self) -> Locals:
+118        # run
+119        logger.info("Start run: {} ", self.run_name)
+120        with mlflow.start_run(run_name=self.run_name) as run:
+121            logger.info("- Run ID: {}", run.info.run_id)
+122            # read
+123            # - inputs
+124            logger.info("Read inputs: {}", self.inputs)
+125            inputs = schemas.InputsSchema.check(self.inputs.read())
+126            logger.info("- Inputs shape: {}", inputs.shape)
+127            # - targets
+128            logger.info("Read targets: {}", self.targets)
+129            targets = schemas.TargetsSchema.check(self.targets.read())
+130            logger.info("- Targets shape: {}", targets.shape)
+131            # - asserts
+132            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+133            # model
+134            logger.info("With model: {}", self.model)
+135            # metric
+136            logger.info("With metric: {}", self.metric)
+137            # splitter
+138            logger.info("With splitter: {}", self.splitter)
+139            # searcher
+140            logger.info("Execute searcher: {}", self.searcher)
+141            results, best_score, best_params = self.searcher.search(
+142                model=self.model,
+143                metric=self.metric,
+144                cv=self.splitter,
+145                inputs=inputs,
+146                targets=targets,
+147            )
+148            logger.info("- # Results: {}", len(results))
+149            logger.info("- Best Score: {}", best_score)
+150            logger.info("- Best Params: {}", best_params)
+151            # write
+152            logger.info("Write results: {}", self.results)
+153            self.results.write(results)
+154        return locals()
+155
+156
+157class TrainingJob(Job):
+158    """Train and register a single AI/ML model.
+159
+160    Attributes:
+161        run_name (str): name of the MLflow experiment run.
+162        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+163        targets (datasets.ReaderKind): dataset reader with targets variables.
+164        saver (registers.SaverKind): save the trained model in registry.
+165        model (models.ModelKind): machine learning model to tune.
+166        signer (registers.SignerKind): signer for the trained model.
+167        scorers (list[metrics.MetricKind]): metrics for the evaluation.
+168        splitter (splitters.SplitterKind): splitter for datasets.
+169        registry_alias (str): alias of model.
+170    """
+171
+172    KIND: T.Literal["TrainingJob"] = "TrainingJob"
+173
+174    # run
+175    run_name: str = "Training"
+176    # read
+177    inputs: datasets.ReaderKind
+178    targets: datasets.ReaderKind
+179    # write
+180    saver: registers.SaverKind = registers.CustomSaver()
+181    # model
+182    model: models.ModelKind = models.BaselineSklearnModel()
+183    # signer
+184    signer: registers.SignerKind = registers.InferSigner()
+185    # scorers
+186    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
+187    # splitter
+188    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
+189    # register
+190    registry_alias: str = "Champion"
+191
+192    @T.override
+193    def run(self) -> Locals:
+194        # run
+195        logger.info("Start run: {} ", self.run_name)
+196        with mlflow.start_run(run_name=self.run_name) as run:
+197            logger.info("- Run ID: {}", run.info.run_id)
+198            # read
+199            # - inputs
+200            logger.info("Read inputs: {}", self.inputs)
+201            inputs = schemas.InputsSchema.check(self.inputs.read())
+202            logger.info("- Inputs shape: {}", inputs.shape)
+203            # - targets
+204            logger.info("Read targets: {}", self.targets)
+205            targets = schemas.TargetsSchema.check(self.targets.read())
+206            logger.info("- Targets shape: {}", targets.shape)
+207            # - asserts
+208            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+209            # split
+210            logger.info("With splitter: {}", self.splitter)
+211            # - index
+212            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+213            # - inputs
+214            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+215            logger.info("- Inputs train shape: {}", inputs_train.shape)
+216            logger.info("- Inputs test shape: {}", inputs_test.shape)
+217            # - targets
+218            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+219            logger.info("- Targets train shape: {}", targets_train.shape)
+220            logger.info("- Targets test shape: {}", targets_test.shape)
+221            # - asserts
+222            assert len(inputs_train) == len(
+223                targets_train
+224            ), "Inputs and targets train should have the same length!"
+225            assert len(inputs_test) == len(
+226                targets_test
+227            ), "Inputs and targets test should have the same length!"
+228            # model
+229            logger.info("Fit model: {}", self.model)
+230            self.model.fit(inputs=inputs_train, targets=targets_train)
+231            # outputs
+232            logger.info("Predict outputs: {}", len(inputs_test))
+233            outputs_test = self.model.predict(inputs=inputs_test)
+234            logger.info("- Outputs test shape: {}", outputs_test.shape)
+235            assert len(inputs_test) == len(
+236                outputs_test
+237            ), "Inputs and outputs test should have the same length!"
+238            # scorers
+239            for i, scorer in enumerate(self.scorers, start=1):
+240                logger.info("{}. Run scorer: {}", i, scorer)
+241                score = scorer.score(targets=targets_test, outputs=outputs_test)
+242                mlflow.log_metric(key=scorer.name, value=score)
+243                logger.info("- Metric score: {}", score)
+244            # sign
+245            logger.info("Sign model: {}", self.signer)
+246            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+247            logger.info("- Model signature: {}", signature.to_dict())
+248            # save
+249            logger.info("Save model: {}", self.saver)
+250            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+251            logger.info("- Model URI: {}", info.model_uri)
+252            # register
+253            logger.info("Register model: {}", self.registry_alias)
+254            version = self.mlflow_service.register(
+255                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+256            )
+257            logger.info("- Model version: {}", version.version)
+258        return locals()
+259
+260
+261class InferenceJob(Job):
+262    """Load a model and generate predictions.
+263
+264    Attributes:
+265        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+266        outputs (datasets.WriterKind): dataset writer for the model outputs.
+267        registry_alias (str): alias of the model to load.
+268        loader (registers.LoaderKind): load the model from registry.
+269    """
+270
+271    KIND: T.Literal["InferenceJob"] = "InferenceJob"
+272
+273    # data
+274    inputs: datasets.ReaderKind
+275    outputs: datasets.WriterKind
+276    # model
+277    registry_alias: str = "Champion"
+278    loader: registers.LoaderKind = registers.CustomLoader()
+279
+280    @T.override
+281    def run(self) -> Locals:
+282        # read
+283        logger.info("Read inputs: {}", self.inputs)
+284        inputs = self.inputs.read()
+285        inputs = schemas.InputsSchema.check(inputs)
+286        logger.info("- Inputs shape: {}", inputs.shape)
+287        # uri
+288        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+289        logger.info("With URI: {}", uri)
+290        # load
+291        logger.info("Load model: {}", self.loader)
+292        model = self.loader.load(uri=uri)
+293        logger.info("- Model: {}", model)
+294        # predict
+295        logger.info("Predict outputs: {}", len(inputs))
+296        outputs = model.predict(data=inputs)
+297        logger.info("- Outputs shape: {}", outputs.shape)
+298        # write
+299        logger.info("Write outputs: {}", self.outputs)
+300        self.outputs.write(data=outputs)
+301        return locals()
+302
+303
+304JobKind = TuningJob | TrainingJob | InferenceJob
@@ -408,48 +416,56 @@

28 29 Attributes: 30 logger_service (services.LoggerService): manage the logging system. -31 mlflow_service (services.MLflowService): manage the mlflow system. -32 """ -33 -34 KIND: str -35 -36 logger_service: services.LoggerService = services.LoggerService() -37 mlflow_service: services.MLflowService = services.MLflowService() -38 -39 def __enter__(self) -> T.Self: -40 """Enter the job context. -41 -42 Returns: -43 T.Self: return the current object. -44 """ -45 self.logger_service.start() -46 logger.debug("[START] MLflow service: {}", self.mlflow_service) -47 self.mlflow_service.start() -48 return self -49 -50 def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]: -51 """Exit the job context. -52 -53 Args: -54 exc_type: ignored. -55 exc_value: ignored. -56 traceback: ignored. +31 carbon_service (services.CarbonService): manage the carbon system. +32 mlflow_service (services.MLflowService): manage the mlflow system. +33 """ +34 +35 KIND: str +36 +37 logger_service: services.LoggerService = services.LoggerService() +38 carbon_service: services.CarbonService = services.CarbonService() +39 mlflow_service: services.MLflowService = services.MLflowService() +40 +41 def __enter__(self) -> T.Self: +42 """Enter the job context. +43 +44 Returns: +45 T.Self: return the current object. +46 """ +47 self.logger_service.start() # start then log +48 logger.debug("[START] Logger service: {}", self.logger_service) +49 logger.debug("[START] Carbon service: {}", self.carbon_service) +50 self.carbon_service.start() +51 logger.debug("[START] MLflow service: {}", self.mlflow_service) +52 self.mlflow_service.start() +53 return self +54 +55 def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]: +56 """Exit the job context. 57 -58 Returns: -59 T.Literal[False]: always propagate exceptions. -60 """ -61 logger.debug("[STOP] MLflow service: {}", self.mlflow_service) -62 self.mlflow_service.stop() -63 self.logger_service.stop() -64 return False -65 -66 @abc.abstractmethod -67 def run(self) -> Locals: -68 """Run the job in context. -69 -70 Returns: -71 Locals: local job variables. -72 """ +58 Args: +59 exc_type: ignored. +60 exc_value: ignored. +61 traceback: ignored. +62 +63 Returns: +64 T.Literal[False]: always propagate exceptions. +65 """ +66 logger.debug("[STOP] MLflow service: {}", self.mlflow_service) +67 self.mlflow_service.stop() +68 logger.debug("[STOP] Carbon service: {}", self.carbon_service) +69 self.carbon_service.stop() +70 logger.debug("[STOP] Logger service: {}", self.carbon_service) +71 self.logger_service.stop() +72 return False +73 +74 @abc.abstractmethod +75 def run(self) -> Locals: +76 """Run the job in context. +77 +78 Returns: +79 Locals: local job variables. +80 """ @@ -462,6 +478,7 @@

Attributes:
  • logger_service (services.LoggerService): manage the logging system.
    • +
  • carbon_service (services.CarbonService): manage the carbon system.
  • mlflow_service (services.MLflowService): manage the mlflow system.
@@ -479,13 +496,13 @@
Attributes:
- 
66    @abc.abstractmethod
-67    def run(self) -> Locals:
-68        """Run the job in context.
-69
-70        Returns:
-71            Locals: local job variables.
-72        """
+            
74    @abc.abstractmethod
+75    def run(self) -> Locals:
+76        """Run the job in context.
+77
+78        Returns:
+79            Locals: local job variables.
+80        """
 

 
 
@@ -546,79 +563,79 @@ 
Inherited Members
- 
 75class TuningJob(Job):
- 76    """Find the best hyperparameters for a model.
- 77
- 78    Attributes:
- 79        run_name (str): name of the MLflow experiment run.
- 80        inputs (datasets.ReaderKind): dataset reader with inputs variables.
- 81        targets (datasets.ReaderKind): dataset reader with targets variables.
- 82        results (datasets.WriterKind): dataset writer for searcher results.
- 83        model (models.ModelKind): machine learning model to tune.
- 84        metric (metrics.MetricKind): main metric for evaluation.
- 85        splitter (splitters.SplitterKind): splitter for datasets.
- 86        searcher (searchers.SearcherKind): searcher algorithm.
- 87    """
- 88
- 89    KIND: T.Literal["TuningJob"] = "TuningJob"
- 90
- 91    # run
- 92    run_name: str = "Tuning"
- 93    # read
- 94    inputs: datasets.ReaderKind
- 95    targets: datasets.ReaderKind
- 96    # write
- 97    results: datasets.WriterKind
- 98    # model
- 99    model: models.ModelKind = models.BaselineSklearnModel()
-100    # metric
-101    metric: metrics.MetricKind = metrics.SklearnMetric()
-102    # splitter
-103    splitter: splitters.SplitterKind = splitters.TimeSeriesSplitter()
-104    # searcher
-105    searcher: searchers.SearcherKind = searchers.GridCVSearcher(
-106        param_grid={"max_depth": [3, 5, 7]},
-107    )
-108
-109    @T.override
-110    def run(self) -> Locals:
-111        # run
-112        logger.info("Start run: {} ", self.run_name)
-113        with mlflow.start_run(run_name=self.run_name) as run:
-114            logger.info("- Run ID: {}", run.info.run_id)
-115            # read
-116            # - inputs
-117            logger.info("Read inputs: {}", self.inputs)
-118            inputs = schemas.InputsSchema.check(self.inputs.read())
-119            logger.info("- Inputs shape: {}", inputs.shape)
-120            # - targets
-121            logger.info("Read targets: {}", self.targets)
-122            targets = schemas.TargetsSchema.check(self.targets.read())
-123            logger.info("- Targets shape: {}", targets.shape)
-124            # - asserts
-125            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-126            # model
-127            logger.info("With model: {}", self.model)
-128            # metric
-129            logger.info("With metric: {}", self.metric)
-130            # splitter
-131            logger.info("With splitter: {}", self.splitter)
-132            # searcher
-133            logger.info("Execute searcher: {}", self.searcher)
-134            results, best_score, best_params = self.searcher.search(
-135                model=self.model,
-136                metric=self.metric,
-137                cv=self.splitter,
-138                inputs=inputs,
-139                targets=targets,
-140            )
-141            logger.info("- # Results: {}", len(results))
-142            logger.info("- Best Score: {}", best_score)
-143            logger.info("- Best Params: {}", best_params)
-144            # write
-145            logger.info("Write results: {}", self.results)
-146            self.results.write(results)
-147        return locals()
+            
 83class TuningJob(Job):
+ 84    """Find the best hyperparameters for a model.
+ 85
+ 86    Attributes:
+ 87        run_name (str): name of the MLflow experiment run.
+ 88        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+ 89        targets (datasets.ReaderKind): dataset reader with targets variables.
+ 90        results (datasets.WriterKind): dataset writer for searcher results.
+ 91        model (models.ModelKind): machine learning model to tune.
+ 92        metric (metrics.MetricKind): main metric for evaluation.
+ 93        splitter (splitters.SplitterKind): splitter for datasets.
+ 94        searcher (searchers.SearcherKind): searcher algorithm.
+ 95    """
+ 96
+ 97    KIND: T.Literal["TuningJob"] = "TuningJob"
+ 98
+ 99    # run
+100    run_name: str = "Tuning"
+101    # read
+102    inputs: datasets.ReaderKind
+103    targets: datasets.ReaderKind
+104    # write
+105    results: datasets.WriterKind
+106    # model
+107    model: models.ModelKind = models.BaselineSklearnModel()
+108    # metric
+109    metric: metrics.MetricKind = metrics.SklearnMetric()
+110    # splitter
+111    splitter: splitters.SplitterKind = splitters.TimeSeriesSplitter()
+112    # searcher
+113    searcher: searchers.SearcherKind = searchers.GridCVSearcher(
+114        param_grid={"max_depth": [3, 5, 7]},
+115    )
+116
+117    @T.override
+118    def run(self) -> Locals:
+119        # run
+120        logger.info("Start run: {} ", self.run_name)
+121        with mlflow.start_run(run_name=self.run_name) as run:
+122            logger.info("- Run ID: {}", run.info.run_id)
+123            # read
+124            # - inputs
+125            logger.info("Read inputs: {}", self.inputs)
+126            inputs = schemas.InputsSchema.check(self.inputs.read())
+127            logger.info("- Inputs shape: {}", inputs.shape)
+128            # - targets
+129            logger.info("Read targets: {}", self.targets)
+130            targets = schemas.TargetsSchema.check(self.targets.read())
+131            logger.info("- Targets shape: {}", targets.shape)
+132            # - asserts
+133            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+134            # model
+135            logger.info("With model: {}", self.model)
+136            # metric
+137            logger.info("With metric: {}", self.metric)
+138            # splitter
+139            logger.info("With splitter: {}", self.splitter)
+140            # searcher
+141            logger.info("Execute searcher: {}", self.searcher)
+142            results, best_score, best_params = self.searcher.search(
+143                model=self.model,
+144                metric=self.metric,
+145                cv=self.splitter,
+146                inputs=inputs,
+147                targets=targets,
+148            )
+149            logger.info("- # Results: {}", len(results))
+150            logger.info("- Best Score: {}", best_score)
+151            logger.info("- Best Params: {}", best_params)
+152            # write
+153            logger.info("Write results: {}", self.results)
+154            self.results.write(results)
+155        return locals()
 

 
 
@@ -651,45 +668,45 @@ 
Attributes:
- 
109    @T.override
-110    def run(self) -> Locals:
-111        # run
-112        logger.info("Start run: {} ", self.run_name)
-113        with mlflow.start_run(run_name=self.run_name) as run:
-114            logger.info("- Run ID: {}", run.info.run_id)
-115            # read
-116            # - inputs
-117            logger.info("Read inputs: {}", self.inputs)
-118            inputs = schemas.InputsSchema.check(self.inputs.read())
-119            logger.info("- Inputs shape: {}", inputs.shape)
-120            # - targets
-121            logger.info("Read targets: {}", self.targets)
-122            targets = schemas.TargetsSchema.check(self.targets.read())
-123            logger.info("- Targets shape: {}", targets.shape)
-124            # - asserts
-125            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-126            # model
-127            logger.info("With model: {}", self.model)
-128            # metric
-129            logger.info("With metric: {}", self.metric)
-130            # splitter
-131            logger.info("With splitter: {}", self.splitter)
-132            # searcher
-133            logger.info("Execute searcher: {}", self.searcher)
-134            results, best_score, best_params = self.searcher.search(
-135                model=self.model,
-136                metric=self.metric,
-137                cv=self.splitter,
-138                inputs=inputs,
-139                targets=targets,
-140            )
-141            logger.info("- # Results: {}", len(results))
-142            logger.info("- Best Score: {}", best_score)
-143            logger.info("- Best Params: {}", best_params)
-144            # write
-145            logger.info("Write results: {}", self.results)
-146            self.results.write(results)
-147        return locals()
+            
117    @T.override
+118    def run(self) -> Locals:
+119        # run
+120        logger.info("Start run: {} ", self.run_name)
+121        with mlflow.start_run(run_name=self.run_name) as run:
+122            logger.info("- Run ID: {}", run.info.run_id)
+123            # read
+124            # - inputs
+125            logger.info("Read inputs: {}", self.inputs)
+126            inputs = schemas.InputsSchema.check(self.inputs.read())
+127            logger.info("- Inputs shape: {}", inputs.shape)
+128            # - targets
+129            logger.info("Read targets: {}", self.targets)
+130            targets = schemas.TargetsSchema.check(self.targets.read())
+131            logger.info("- Targets shape: {}", targets.shape)
+132            # - asserts
+133            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+134            # model
+135            logger.info("With model: {}", self.model)
+136            # metric
+137            logger.info("With metric: {}", self.metric)
+138            # splitter
+139            logger.info("With splitter: {}", self.splitter)
+140            # searcher
+141            logger.info("Execute searcher: {}", self.searcher)
+142            results, best_score, best_params = self.searcher.search(
+143                model=self.model,
+144                metric=self.metric,
+145                cv=self.splitter,
+146                inputs=inputs,
+147                targets=targets,
+148            )
+149            logger.info("- # Results: {}", len(results))
+150            logger.info("- Best Score: {}", best_score)
+151            logger.info("- Best Params: {}", best_params)
+152            # write
+153            logger.info("Write results: {}", self.results)
+154            self.results.write(results)
+155        return locals()
 

 
 
@@ -750,108 +767,108 @@ 
Inherited Members
- 
150class TrainingJob(Job):
-151    """Train and register a single AI/ML model.
-152
-153    Attributes:
-154        run_name (str): name of the MLflow experiment run.
-155        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-156        targets (datasets.ReaderKind): dataset reader with targets variables.
-157        saver (registers.SaverKind): save the trained model in registry.
-158        model (models.ModelKind): machine learning model to tune.
-159        signer (registers.SignerKind): signer for the trained model.
-160        scorers (list[metrics.MetricKind]): metrics for the evaluation.
-161        splitter (splitters.SplitterKind): splitter for datasets.
-162        registry_alias (str): alias of model.
-163    """
-164
-165    KIND: T.Literal["TrainingJob"] = "TrainingJob"
-166
-167    # run
-168    run_name: str = "Training"
-169    # read
-170    inputs: datasets.ReaderKind
-171    targets: datasets.ReaderKind
-172    # write
-173    saver: registers.SaverKind = registers.CustomSaver()
-174    # model
-175    model: models.ModelKind = models.BaselineSklearnModel()
-176    # signer
-177    signer: registers.SignerKind = registers.InferSigner()
-178    # scorers
-179    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
-180    # splitter
-181    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
-182    # register
-183    registry_alias: str = "Champion"
-184
-185    @T.override
-186    def run(self) -> Locals:
-187        # run
-188        logger.info("Start run: {} ", self.run_name)
-189        with mlflow.start_run(run_name=self.run_name) as run:
-190            logger.info("- Run ID: {}", run.info.run_id)
-191            # read
-192            # - inputs
-193            logger.info("Read inputs: {}", self.inputs)
-194            inputs = schemas.InputsSchema.check(self.inputs.read())
-195            logger.info("- Inputs shape: {}", inputs.shape)
-196            # - targets
-197            logger.info("Read targets: {}", self.targets)
-198            targets = schemas.TargetsSchema.check(self.targets.read())
-199            logger.info("- Targets shape: {}", targets.shape)
-200            # - asserts
-201            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-202            # split
-203            logger.info("With splitter: {}", self.splitter)
-204            # - index
-205            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
-206            # - inputs
-207            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
-208            logger.info("- Inputs train shape: {}", inputs_train.shape)
-209            logger.info("- Inputs test shape: {}", inputs_test.shape)
-210            # - targets
-211            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
-212            logger.info("- Targets train shape: {}", targets_train.shape)
-213            logger.info("- Targets test shape: {}", targets_test.shape)
-214            # - asserts
-215            assert len(inputs_train) == len(
-216                targets_train
-217            ), "Inputs and targets train should have the same length!"
-218            assert len(inputs_test) == len(
-219                targets_test
-220            ), "Inputs and targets test should have the same length!"
-221            # model
-222            logger.info("Fit model: {}", self.model)
-223            self.model.fit(inputs=inputs_train, targets=targets_train)
-224            # outputs
-225            logger.info("Predict outputs: {}", len(inputs_test))
-226            outputs_test = self.model.predict(inputs=inputs_test)
-227            logger.info("- Outputs test shape: {}", outputs_test.shape)
-228            assert len(inputs_test) == len(
-229                outputs_test
-230            ), "Inputs and outputs test should have the same length!"
-231            # scorers
-232            for i, scorer in enumerate(self.scorers, start=1):
-233                logger.info("{}. Run scorer: {}", i, scorer)
-234                score = scorer.score(targets=targets_test, outputs=outputs_test)
-235                mlflow.log_metric(key=scorer.name, value=score)
-236                logger.info("- Metric score: {}", score)
-237            # sign
-238            logger.info("Sign model: {}", self.signer)
-239            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
-240            logger.info("- Model signature: {}", signature.to_dict())
-241            # save
-242            logger.info("Save model: {}", self.saver)
-243            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
-244            logger.info("- Model URI: {}", info.model_uri)
-245            # register
-246            logger.info("Register model: {}", self.registry_alias)
-247            version = self.mlflow_service.register(
-248                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
-249            )
-250            logger.info("- Model version: {}", version.version)
-251        return locals()
+            
158class TrainingJob(Job):
+159    """Train and register a single AI/ML model.
+160
+161    Attributes:
+162        run_name (str): name of the MLflow experiment run.
+163        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+164        targets (datasets.ReaderKind): dataset reader with targets variables.
+165        saver (registers.SaverKind): save the trained model in registry.
+166        model (models.ModelKind): machine learning model to tune.
+167        signer (registers.SignerKind): signer for the trained model.
+168        scorers (list[metrics.MetricKind]): metrics for the evaluation.
+169        splitter (splitters.SplitterKind): splitter for datasets.
+170        registry_alias (str): alias of model.
+171    """
+172
+173    KIND: T.Literal["TrainingJob"] = "TrainingJob"
+174
+175    # run
+176    run_name: str = "Training"
+177    # read
+178    inputs: datasets.ReaderKind
+179    targets: datasets.ReaderKind
+180    # write
+181    saver: registers.SaverKind = registers.CustomSaver()
+182    # model
+183    model: models.ModelKind = models.BaselineSklearnModel()
+184    # signer
+185    signer: registers.SignerKind = registers.InferSigner()
+186    # scorers
+187    scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()]
+188    # splitter
+189    splitter: splitters.SplitterKind = splitters.TrainTestSplitter()
+190    # register
+191    registry_alias: str = "Champion"
+192
+193    @T.override
+194    def run(self) -> Locals:
+195        # run
+196        logger.info("Start run: {} ", self.run_name)
+197        with mlflow.start_run(run_name=self.run_name) as run:
+198            logger.info("- Run ID: {}", run.info.run_id)
+199            # read
+200            # - inputs
+201            logger.info("Read inputs: {}", self.inputs)
+202            inputs = schemas.InputsSchema.check(self.inputs.read())
+203            logger.info("- Inputs shape: {}", inputs.shape)
+204            # - targets
+205            logger.info("Read targets: {}", self.targets)
+206            targets = schemas.TargetsSchema.check(self.targets.read())
+207            logger.info("- Targets shape: {}", targets.shape)
+208            # - asserts
+209            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+210            # split
+211            logger.info("With splitter: {}", self.splitter)
+212            # - index
+213            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+214            # - inputs
+215            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+216            logger.info("- Inputs train shape: {}", inputs_train.shape)
+217            logger.info("- Inputs test shape: {}", inputs_test.shape)
+218            # - targets
+219            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+220            logger.info("- Targets train shape: {}", targets_train.shape)
+221            logger.info("- Targets test shape: {}", targets_test.shape)
+222            # - asserts
+223            assert len(inputs_train) == len(
+224                targets_train
+225            ), "Inputs and targets train should have the same length!"
+226            assert len(inputs_test) == len(
+227                targets_test
+228            ), "Inputs and targets test should have the same length!"
+229            # model
+230            logger.info("Fit model: {}", self.model)
+231            self.model.fit(inputs=inputs_train, targets=targets_train)
+232            # outputs
+233            logger.info("Predict outputs: {}", len(inputs_test))
+234            outputs_test = self.model.predict(inputs=inputs_test)
+235            logger.info("- Outputs test shape: {}", outputs_test.shape)
+236            assert len(inputs_test) == len(
+237                outputs_test
+238            ), "Inputs and outputs test should have the same length!"
+239            # scorers
+240            for i, scorer in enumerate(self.scorers, start=1):
+241                logger.info("{}. Run scorer: {}", i, scorer)
+242                score = scorer.score(targets=targets_test, outputs=outputs_test)
+243                mlflow.log_metric(key=scorer.name, value=score)
+244                logger.info("- Metric score: {}", score)
+245            # sign
+246            logger.info("Sign model: {}", self.signer)
+247            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+248            logger.info("- Model signature: {}", signature.to_dict())
+249            # save
+250            logger.info("Save model: {}", self.saver)
+251            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+252            logger.info("- Model URI: {}", info.model_uri)
+253            # register
+254            logger.info("Register model: {}", self.registry_alias)
+255            version = self.mlflow_service.register(
+256                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+257            )
+258            logger.info("- Model version: {}", version.version)
+259        return locals()
 

 
 
@@ -885,73 +902,73 @@ 
Attributes:
- 
185    @T.override
-186    def run(self) -> Locals:
-187        # run
-188        logger.info("Start run: {} ", self.run_name)
-189        with mlflow.start_run(run_name=self.run_name) as run:
-190            logger.info("- Run ID: {}", run.info.run_id)
-191            # read
-192            # - inputs
-193            logger.info("Read inputs: {}", self.inputs)
-194            inputs = schemas.InputsSchema.check(self.inputs.read())
-195            logger.info("- Inputs shape: {}", inputs.shape)
-196            # - targets
-197            logger.info("Read targets: {}", self.targets)
-198            targets = schemas.TargetsSchema.check(self.targets.read())
-199            logger.info("- Targets shape: {}", targets.shape)
-200            # - asserts
-201            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
-202            # split
-203            logger.info("With splitter: {}", self.splitter)
-204            # - index
-205            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
-206            # - inputs
-207            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
-208            logger.info("- Inputs train shape: {}", inputs_train.shape)
-209            logger.info("- Inputs test shape: {}", inputs_test.shape)
-210            # - targets
-211            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
-212            logger.info("- Targets train shape: {}", targets_train.shape)
-213            logger.info("- Targets test shape: {}", targets_test.shape)
-214            # - asserts
-215            assert len(inputs_train) == len(
-216                targets_train
-217            ), "Inputs and targets train should have the same length!"
-218            assert len(inputs_test) == len(
-219                targets_test
-220            ), "Inputs and targets test should have the same length!"
-221            # model
-222            logger.info("Fit model: {}", self.model)
-223            self.model.fit(inputs=inputs_train, targets=targets_train)
-224            # outputs
-225            logger.info("Predict outputs: {}", len(inputs_test))
-226            outputs_test = self.model.predict(inputs=inputs_test)
-227            logger.info("- Outputs test shape: {}", outputs_test.shape)
-228            assert len(inputs_test) == len(
-229                outputs_test
-230            ), "Inputs and outputs test should have the same length!"
-231            # scorers
-232            for i, scorer in enumerate(self.scorers, start=1):
-233                logger.info("{}. Run scorer: {}", i, scorer)
-234                score = scorer.score(targets=targets_test, outputs=outputs_test)
-235                mlflow.log_metric(key=scorer.name, value=score)
-236                logger.info("- Metric score: {}", score)
-237            # sign
-238            logger.info("Sign model: {}", self.signer)
-239            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
-240            logger.info("- Model signature: {}", signature.to_dict())
-241            # save
-242            logger.info("Save model: {}", self.saver)
-243            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
-244            logger.info("- Model URI: {}", info.model_uri)
-245            # register
-246            logger.info("Register model: {}", self.registry_alias)
-247            version = self.mlflow_service.register(
-248                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
-249            )
-250            logger.info("- Model version: {}", version.version)
-251        return locals()
+            
193    @T.override
+194    def run(self) -> Locals:
+195        # run
+196        logger.info("Start run: {} ", self.run_name)
+197        with mlflow.start_run(run_name=self.run_name) as run:
+198            logger.info("- Run ID: {}", run.info.run_id)
+199            # read
+200            # - inputs
+201            logger.info("Read inputs: {}", self.inputs)
+202            inputs = schemas.InputsSchema.check(self.inputs.read())
+203            logger.info("- Inputs shape: {}", inputs.shape)
+204            # - targets
+205            logger.info("Read targets: {}", self.targets)
+206            targets = schemas.TargetsSchema.check(self.targets.read())
+207            logger.info("- Targets shape: {}", targets.shape)
+208            # - asserts
+209            assert len(inputs) == len(targets), "Inputs and targets should have the same length!"
+210            # split
+211            logger.info("With splitter: {}", self.splitter)
+212            # - index
+213            train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets))
+214            # - inputs
+215            inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index]
+216            logger.info("- Inputs train shape: {}", inputs_train.shape)
+217            logger.info("- Inputs test shape: {}", inputs_test.shape)
+218            # - targets
+219            targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index]
+220            logger.info("- Targets train shape: {}", targets_train.shape)
+221            logger.info("- Targets test shape: {}", targets_test.shape)
+222            # - asserts
+223            assert len(inputs_train) == len(
+224                targets_train
+225            ), "Inputs and targets train should have the same length!"
+226            assert len(inputs_test) == len(
+227                targets_test
+228            ), "Inputs and targets test should have the same length!"
+229            # model
+230            logger.info("Fit model: {}", self.model)
+231            self.model.fit(inputs=inputs_train, targets=targets_train)
+232            # outputs
+233            logger.info("Predict outputs: {}", len(inputs_test))
+234            outputs_test = self.model.predict(inputs=inputs_test)
+235            logger.info("- Outputs test shape: {}", outputs_test.shape)
+236            assert len(inputs_test) == len(
+237                outputs_test
+238            ), "Inputs and outputs test should have the same length!"
+239            # scorers
+240            for i, scorer in enumerate(self.scorers, start=1):
+241                logger.info("{}. Run scorer: {}", i, scorer)
+242                score = scorer.score(targets=targets_test, outputs=outputs_test)
+243                mlflow.log_metric(key=scorer.name, value=score)
+244                logger.info("- Metric score: {}", score)
+245            # sign
+246            logger.info("Sign model: {}", self.signer)
+247            signature = self.signer.sign(inputs=inputs, outputs=outputs_test)
+248            logger.info("- Model signature: {}", signature.to_dict())
+249            # save
+250            logger.info("Save model: {}", self.saver)
+251            info = self.saver.save(model=self.model, signature=signature, input_example=inputs)
+252            logger.info("- Model URI: {}", info.model_uri)
+253            # register
+254            logger.info("Register model: {}", self.registry_alias)
+255            version = self.mlflow_service.register(
+256                run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias
+257            )
+258            logger.info("- Model version: {}", version.version)
+259        return locals()
 

 
 
@@ -1012,47 +1029,47 @@ 
Inherited Members
- 
254class InferenceJob(Job):
-255    """Load a model and generate predictions.
-256
-257    Attributes:
-258        inputs (datasets.ReaderKind): dataset reader with inputs variables.
-259        outputs (datasets.WriterKind): dataset writer for the model outputs.
-260        registry_alias (str): alias of the model to load.
-261        loader (registers.LoaderKind): load the model from registry.
-262    """
-263
-264    KIND: T.Literal["InferenceJob"] = "InferenceJob"
-265
-266    # data
-267    inputs: datasets.ReaderKind
-268    outputs: datasets.WriterKind
-269    # model
-270    registry_alias: str = "Champion"
-271    loader: registers.LoaderKind = registers.CustomLoader()
-272
-273    @T.override
-274    def run(self) -> Locals:
-275        # read
-276        logger.info("Read inputs: {}", self.inputs)
-277        inputs = self.inputs.read()
-278        inputs = schemas.InputsSchema.check(inputs)
-279        logger.info("- Inputs shape: {}", inputs.shape)
-280        # uri
-281        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
-282        logger.info("With URI: {}", uri)
-283        # load
-284        logger.info("Load model: {}", self.loader)
-285        model = self.loader.load(uri=uri)
-286        logger.info("- Model: {}", model)
-287        # predict
-288        logger.info("Predict outputs: {}", len(inputs))
-289        outputs = model.predict(data=inputs)
-290        logger.info("- Outputs shape: {}", outputs.shape)
-291        # write
-292        logger.info("Write outputs: {}", self.outputs)
-293        self.outputs.write(data=outputs)
-294        return locals()
+            
262class InferenceJob(Job):
+263    """Load a model and generate predictions.
+264
+265    Attributes:
+266        inputs (datasets.ReaderKind): dataset reader with inputs variables.
+267        outputs (datasets.WriterKind): dataset writer for the model outputs.
+268        registry_alias (str): alias of the model to load.
+269        loader (registers.LoaderKind): load the model from registry.
+270    """
+271
+272    KIND: T.Literal["InferenceJob"] = "InferenceJob"
+273
+274    # data
+275    inputs: datasets.ReaderKind
+276    outputs: datasets.WriterKind
+277    # model
+278    registry_alias: str = "Champion"
+279    loader: registers.LoaderKind = registers.CustomLoader()
+280
+281    @T.override
+282    def run(self) -> Locals:
+283        # read
+284        logger.info("Read inputs: {}", self.inputs)
+285        inputs = self.inputs.read()
+286        inputs = schemas.InputsSchema.check(inputs)
+287        logger.info("- Inputs shape: {}", inputs.shape)
+288        # uri
+289        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+290        logger.info("With URI: {}", uri)
+291        # load
+292        logger.info("Load model: {}", self.loader)
+293        model = self.loader.load(uri=uri)
+294        logger.info("- Model: {}", model)
+295        # predict
+296        logger.info("Predict outputs: {}", len(inputs))
+297        outputs = model.predict(data=inputs)
+298        logger.info("- Outputs shape: {}", outputs.shape)
+299        # write
+300        logger.info("Write outputs: {}", self.outputs)
+301        self.outputs.write(data=outputs)
+302        return locals()
 

 
 
@@ -1081,28 +1098,28 @@ 
Attributes:
- 
273    @T.override
-274    def run(self) -> Locals:
-275        # read
-276        logger.info("Read inputs: {}", self.inputs)
-277        inputs = self.inputs.read()
-278        inputs = schemas.InputsSchema.check(inputs)
-279        logger.info("- Inputs shape: {}", inputs.shape)
-280        # uri
-281        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
-282        logger.info("With URI: {}", uri)
-283        # load
-284        logger.info("Load model: {}", self.loader)
-285        model = self.loader.load(uri=uri)
-286        logger.info("- Model: {}", model)
-287        # predict
-288        logger.info("Predict outputs: {}", len(inputs))
-289        outputs = model.predict(data=inputs)
-290        logger.info("- Outputs shape: {}", outputs.shape)
-291        # write
-292        logger.info("Write outputs: {}", self.outputs)
-293        self.outputs.write(data=outputs)
-294        return locals()
+            
281    @T.override
+282    def run(self) -> Locals:
+283        # read
+284        logger.info("Read inputs: {}", self.inputs)
+285        inputs = self.inputs.read()
+286        inputs = schemas.InputsSchema.check(inputs)
+287        logger.info("- Inputs shape: {}", inputs.shape)
+288        # uri
+289        uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}"
+290        logger.info("With URI: {}", uri)
+291        # load
+292        logger.info("Load model: {}", self.loader)
+293        model = self.loader.load(uri=uri)
+294        logger.info("- Model: {}", model)
+295        # predict
+296        logger.info("Predict outputs: {}", len(inputs))
+297        outputs = model.predict(data=inputs)
+298        logger.info("- Outputs shape: {}", outputs.shape)
+299        # write
+300        logger.info("Write outputs: {}", self.outputs)
+301        self.outputs.write(data=outputs)
+302        return locals()
 

 
 
diff --git a/bikes/services.html b/bikes/services.html
index 8e8c8d5..0f2c243 100644
--- a/bikes/services.html
+++ b/bikes/services.html
@@ -50,6 +50,21 @@ 
API Documentation

                         
                 
 
+            
+            
  • 
+                    CarbonService
+                            
+
             
    • 
             
  • 
                     MLflowService
@@ -94,157 +109,205 @@ 
    
   3# %% IMPORTS
   4
   5import abc
-  6import sys
-  7import typing as T
-  8
-  9import mlflow
- 10import pydantic as pdt
- 11from loguru import logger
- 12from mlflow.tracking import MlflowClient
- 13
- 14# %% SERVICES
+  6import os
+  7import sys
+  8import typing as T
+  9
+ 10import codecarbon as cc
+ 11import mlflow
+ 12import pydantic as pdt
+ 13from loguru import logger
+ 14from mlflow.tracking import MlflowClient
  15
- 16
- 17class Service(abc.ABC, pdt.BaseModel, strict=True):
- 18    """Base class for a global service.
- 19
- 20    Use services to manage global contexts.
- 21    e.g., logger object, mlflow client, spark context, ...
- 22    """
- 23
- 24    @abc.abstractmethod
- 25    def start(self) -> None:
- 26        """Start the service."""
- 27
- 28    def stop(self) -> None:
- 29        """Stop the service."""
- 30        # does nothing by default
- 31
- 32
- 33class LoggerService(Service):
- 34    """Service for logging messages.
- 35
- 36    https://loguru.readthedocs.io/en/stable/api/logger.html
+ 16# %% SERVICES
+ 17
+ 18
+ 19class Service(abc.ABC, pdt.BaseModel, strict=True):
+ 20    """Base class for a global service.
+ 21
+ 22    Use services to manage global contexts.
+ 23    e.g., logger object, mlflow client, spark context, ...
+ 24    """
+ 25
+ 26    @abc.abstractmethod
+ 27    def start(self) -> None:
+ 28        """Start the service."""
+ 29
+ 30    def stop(self) -> None:
+ 31        """Stop the service."""
+ 32        # does nothing by default
+ 33
+ 34
+ 35class LoggerService(Service):
+ 36    """Service for logging messages.
  37
- 38    Attributes:
- 39        sink (str): logging output.
- 40        level (str): logging level.
- 41        format (str): logging format.
- 42        colorize (bool): colorize output.
- 43        serialize (bool): convert to JSON.
- 44        backtrace (bool): enable exception trace.
- 45        diagnose (bool): enable variable display.
- 46        catch (bool): catch errors during log handling.
- 47    """
- 48
- 49    sink: str = "stderr"
- 50    level: str = "INFO"
- 51    format: str = (
- 52        "<green>[{time:YYYY-MM-DD HH:mm:ss.SSS}]</green>"
- 53        "<level>[{level}]</level>"
- 54        "<cyan>[{name}:{function}:{line}]</cyan>"
- 55        " <level>{message}</level>"
- 56    )
- 57    colorize: bool = True
- 58    serialize: bool = False
- 59    backtrace: bool = True
- 60    diagnose: bool = False
- 61    catch: bool = True
- 62
- 63    @T.override
- 64    def start(self) -> None:
- 65        # sinks
- 66        sinks = {
- 67            "stderr": sys.stderr,
- 68            "stdout": sys.stdout,
- 69        }
- 70        # cleanup
- 71        logger.remove()
- 72        # convert
- 73        config = self.model_dump()
- 74        # replace
- 75        # - use standard sinks or keep the original
- 76        config["sink"] = sinks.get(config["sink"], config["sink"])
- 77        # config
- 78        logger.add(**config)
- 79
- 80
- 81class MLflowService(Service):
- 82    """Service for MLflow tracking and registry.
- 83
- 84    Attributes:
- 85        autolog_disable (bool): disable autologging.
- 86        autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
- 87        autolog_exclusive (bool): If True, enables exclusive autologging.
- 88        autolog_log_input_examples (bool): If True, logs input examples during autologging.
- 89        autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
- 90        autolog_log_models (bool): If True, enables logging of models during autologging.
- 91        autolog_log_datasets (bool): If True, logs datasets used during autologging.
- 92        autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
- 93        tracking_uri (str): The URI for the MLflow tracking server.
- 94        experiment_name (str): The name of the experiment to log runs under.
- 95        registry_uri (str): The URI for the MLflow model registry.
- 96        registry_name (str): The name of the registry.
- 97    """
- 98
- 99    # autolog
-100    autolog_disable: bool = False
-101    autolog_disable_for_unsupported_versions: bool = False
-102    autolog_exclusive: bool = False
-103    autolog_log_input_examples: bool = True
-104    autolog_log_model_signatures: bool = True
-105    autolog_log_models: bool = False
-106    autolog_log_datasets: bool = True
-107    autolog_silent: bool = False
-108    # tracking
-109    tracking_uri: str = "http://localhost:5000"
-110    experiment_name: str = "bikes"
-111    # registry
-112    registry_uri: str = "http://localhost:5000"
-113    registry_name: str = "bikes"
-114
-115    def start(self):
-116        """Start the mlflow service."""
-117        # uri
-118        mlflow.set_tracking_uri(uri=self.tracking_uri)
-119        mlflow.set_registry_uri(uri=self.registry_uri)
-120        # experiment
-121        mlflow.set_experiment(experiment_name=self.experiment_name)
-122        # autologging
-123        mlflow.autolog(
-124            disable=self.autolog_disable,
-125            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
-126            exclusive=self.autolog_exclusive,
-127            log_input_examples=self.autolog_log_input_examples,
-128            log_model_signatures=self.autolog_log_model_signatures,
-129            log_models=self.autolog_log_models,
-130            silent=self.autolog_silent,
-131        )
-132
-133    def client(self) -> MlflowClient:
-134        """Get an instance of MLflow client."""
-135        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
-136
-137    def register(
-138        self, run_id: str, path: str, alias: str
-139    ) -> mlflow.entities.model_registry.ModelVersion:
-140        """Register a model to mlflow registry.
+ 38    https://loguru.readthedocs.io/en/stable/api/logger.html
+ 39
+ 40    Attributes:
+ 41        sink (str): logging output.
+ 42        level (str): logging level.
+ 43        format (str): logging format.
+ 44        colorize (bool): colorize output.
+ 45        serialize (bool): convert to JSON.
+ 46        backtrace (bool): enable exception trace.
+ 47        diagnose (bool): enable variable display.
+ 48        catch (bool): catch errors during log handling.
+ 49    """
+ 50
+ 51    sink: str = "stderr"
+ 52    level: str = "INFO"
+ 53    format: str = (
+ 54        "<green>[{time:YYYY-MM-DD HH:mm:ss.SSS}]</green>"
+ 55        "<level>[{level}]</level>"
+ 56        "<cyan>[{name}:{function}:{line}]</cyan>"
+ 57        " <level>{message}</level>"
+ 58    )
+ 59    colorize: bool = True
+ 60    serialize: bool = False
+ 61    backtrace: bool = True
+ 62    diagnose: bool = False
+ 63    catch: bool = True
+ 64
+ 65    @T.override
+ 66    def start(self) -> None:
+ 67        # sinks
+ 68        sinks = {
+ 69            "stderr": sys.stderr,
+ 70            "stdout": sys.stdout,
+ 71        }
+ 72        # cleanup
+ 73        logger.remove()
+ 74        # convert
+ 75        config = self.model_dump()
+ 76        # replace
+ 77        # - use standard sinks or keep the original
+ 78        config["sink"] = sinks.get(config["sink"], config["sink"])
+ 79        # config
+ 80        logger.add(**config)
+ 81
+ 82
+ 83class CarbonService(Service):
+ 84    """Service for tracking carbon emissions.
+ 85
+ 86    Attributes:
+ 87        log_level (str): Level of logging to output.
+ 88        project_name (str): Name of the project to track.
+ 89        measure_power_secs (int): Interval for measuring in secs.
+ 90        output_dir (str): Directory where the output files are stored.
+ 91        output_file (str): Name of the output CSV file for emissions data.
+ 92        on_csv_write (str): Specifies the action on writing to CSV (append or overwrite).
+ 93        country_iso_code (str): ISO code of the country for tracking carbon emissions offline.
+ 94    """
+ 95
+ 96    # public
+ 97    # - inputs
+ 98    log_level: str = "ERROR"
+ 99    project_name: str = "bikes"
+100    measure_power_secs: int = 5
+101    # - outputs
+102    output_dir: str = "outputs"
+103    output_file: str = "emissions.csv"
+104    on_csv_write: str = "append"
+105    # - offline
+106    country_iso_code: str = "LUX"
+107    # private
+108    _tracker: cc.OfflineEmissionsTracker | None = None
+109
+110    def start(self):
+111        """Start the carbon service."""
+112        os.makedirs(self.output_dir, exist_ok=True)  # create output dir
+113        self._tracker = cc.OfflineEmissionsTracker(**self.model_dump())
+114        self._tracker.start()
+115
+116    def stop(self):
+117        """Stop the carbon service."""
+118        assert self._tracker, "Carbon tracker should be started!"
+119        self._tracker.flush()
+120        self._tracker.stop()
+121
+122
+123class MLflowService(Service):
+124    """Service for MLflow tracking and registry.
+125
+126    Attributes:
+127        autolog_disable (bool): disable autologging.
+128        autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
+129        autolog_exclusive (bool): If True, enables exclusive autologging.
+130        autolog_log_input_examples (bool): If True, logs input examples during autologging.
+131        autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
+132        autolog_log_models (bool): If True, enables logging of models during autologging.
+133        autolog_log_datasets (bool): If True, logs datasets used during autologging.
+134        autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
+135        enable_system_metrics (bool): enable system metrics logging.
+136        tracking_uri (str): The URI for the MLflow tracking server.
+137        experiment_name (str): The name of the experiment to log runs under.
+138        registry_uri (str): The URI for the MLflow model registry.
+139        registry_name (str): The name of the registry.
+140    """
 141
-142        Args:
-143            run_id (str): id of mlflow run.
-144            path (str): path of artifact.
-145            alias (str): model alias.
-146
-147        Returns:
-148            mlflow.entities.model_registry.ModelVersion: registered version.
-149        """
-150        client = self.client()
-151        model_uri = f"runs:/{run_id}/{path}"
-152        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
-153        client.set_registered_model_alias(
-154            name=self.registry_name, alias=alias, version=version.version
-155        )
-156        return version
+142    # autolog
+143    autolog_disable: bool = False
+144    autolog_disable_for_unsupported_versions: bool = False
+145    autolog_exclusive: bool = False
+146    autolog_log_input_examples: bool = True
+147    autolog_log_model_signatures: bool = True
+148    autolog_log_models: bool = False
+149    autolog_log_datasets: bool = True
+150    autolog_silent: bool = False
+151    # system
+152    enable_system_metrics: bool = True
+153    # tracking
+154    tracking_uri: str = "http://localhost:5000"
+155    experiment_name: str = "bikes"
+156    # registry
+157    registry_uri: str = "http://localhost:5000"
+158    registry_name: str = "bikes"
+159
+160    def start(self):
+161        """Start the mlflow service."""
+162        # uri
+163        mlflow.set_tracking_uri(uri=self.tracking_uri)
+164        mlflow.set_registry_uri(uri=self.registry_uri)
+165        # experiment
+166        mlflow.set_experiment(experiment_name=self.experiment_name)
+167        # autologging
+168        mlflow.autolog(
+169            disable=self.autolog_disable,
+170            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
+171            exclusive=self.autolog_exclusive,
+172            log_input_examples=self.autolog_log_input_examples,
+173            log_model_signatures=self.autolog_log_model_signatures,
+174            log_models=self.autolog_log_models,
+175            silent=self.autolog_silent,
+176        )
+177        # system metrics
+178        if self.enable_system_metrics:
+179            mlflow.enable_system_metrics_logging()
+180
+181    def client(self) -> MlflowClient:
+182        """Get an instance of MLflow client."""
+183        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
+184
+185    def register(
+186        self, run_id: str, path: str, alias: str
+187    ) -> mlflow.entities.model_registry.ModelVersion:
+188        """Register a model to mlflow registry.
+189
+190        Args:
+191            run_id (str): id of mlflow run.
+192            path (str): path of artifact.
+193            alias (str): model alias.
+194
+195        Returns:
+196            mlflow.entities.model_registry.ModelVersion: registered version.
+197        """
+198        client = self.client()
+199        model_uri = f"runs:/{run_id}/{path}"
+200        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+201        client.set_registered_model_alias(
+202            name=self.registry_name, alias=alias, version=version.version
+203        )
+204        return version
    • @@ -260,20 +323,20 @@

    - 
    18class Service(abc.ABC, pdt.BaseModel, strict=True):
-19    """Base class for a global service.
-20
-21    Use services to manage global contexts.
-22    e.g., logger object, mlflow client, spark context, ...
-23    """
-24
-25    @abc.abstractmethod
-26    def start(self) -> None:
-27        """Start the service."""
-28
-29    def stop(self) -> None:
-30        """Stop the service."""
-31        # does nothing by default
+            
    20class Service(abc.ABC, pdt.BaseModel, strict=True):
+21    """Base class for a global service.
+22
+23    Use services to manage global contexts.
+24    e.g., logger object, mlflow client, spark context, ...
+25    """
+26
+27    @abc.abstractmethod
+28    def start(self) -> None:
+29        """Start the service."""
+30
+31    def stop(self) -> None:
+32        """Stop the service."""
+33        # does nothing by default
 
    
 
 
@@ -296,9 +359,9 @@
    - 
    25    @abc.abstractmethod
-26    def start(self) -> None:
-27        """Start the service."""
+            
    27    @abc.abstractmethod
+28    def start(self) -> None:
+29        """Start the service."""
 
    
 
 
@@ -318,9 +381,9 @@
    - 
    29    def stop(self) -> None:
-30        """Stop the service."""
-31        # does nothing by default
+            
    31    def stop(self) -> None:
+32        """Stop the service."""
+33        # does nothing by default
 
    
 
 
@@ -375,52 +438,52 @@ 
    Inherited Members
    - 
    34class LoggerService(Service):
-35    """Service for logging messages.
-36
-37    https://loguru.readthedocs.io/en/stable/api/logger.html
+            
    36class LoggerService(Service):
+37    """Service for logging messages.
 38
-39    Attributes:
-40        sink (str): logging output.
-41        level (str): logging level.
-42        format (str): logging format.
-43        colorize (bool): colorize output.
-44        serialize (bool): convert to JSON.
-45        backtrace (bool): enable exception trace.
-46        diagnose (bool): enable variable display.
-47        catch (bool): catch errors during log handling.
-48    """
-49
-50    sink: str = "stderr"
-51    level: str = "INFO"
-52    format: str = (
-53        "<green>[{time:YYYY-MM-DD HH:mm:ss.SSS}]</green>"
-54        "<level>[{level}]</level>"
-55        "<cyan>[{name}:{function}:{line}]</cyan>"
-56        " <level>{message}</level>"
-57    )
-58    colorize: bool = True
-59    serialize: bool = False
-60    backtrace: bool = True
-61    diagnose: bool = False
-62    catch: bool = True
-63
-64    @T.override
-65    def start(self) -> None:
-66        # sinks
-67        sinks = {
-68            "stderr": sys.stderr,
-69            "stdout": sys.stdout,
-70        }
-71        # cleanup
-72        logger.remove()
-73        # convert
-74        config = self.model_dump()
-75        # replace
-76        # - use standard sinks or keep the original
-77        config["sink"] = sinks.get(config["sink"], config["sink"])
-78        # config
-79        logger.add(**config)
+39    https://loguru.readthedocs.io/en/stable/api/logger.html
+40
+41    Attributes:
+42        sink (str): logging output.
+43        level (str): logging level.
+44        format (str): logging format.
+45        colorize (bool): colorize output.
+46        serialize (bool): convert to JSON.
+47        backtrace (bool): enable exception trace.
+48        diagnose (bool): enable variable display.
+49        catch (bool): catch errors during log handling.
+50    """
+51
+52    sink: str = "stderr"
+53    level: str = "INFO"
+54    format: str = (
+55        "<green>[{time:YYYY-MM-DD HH:mm:ss.SSS}]</green>"
+56        "<level>[{level}]</level>"
+57        "<cyan>[{name}:{function}:{line}]</cyan>"
+58        " <level>{message}</level>"
+59    )
+60    colorize: bool = True
+61    serialize: bool = False
+62    backtrace: bool = True
+63    diagnose: bool = False
+64    catch: bool = True
+65
+66    @T.override
+67    def start(self) -> None:
+68        # sinks
+69        sinks = {
+70            "stderr": sys.stderr,
+71            "stdout": sys.stdout,
+72        }
+73        # cleanup
+74        logger.remove()
+75        # convert
+76        config = self.model_dump()
+77        # replace
+78        # - use standard sinks or keep the original
+79        config["sink"] = sinks.get(config["sink"], config["sink"])
+80        # config
+81        logger.add(**config)
 
    
 
 
@@ -455,22 +518,22 @@ 
    Attributes:
    - 
    64    @T.override
-65    def start(self) -> None:
-66        # sinks
-67        sinks = {
-68            "stderr": sys.stderr,
-69            "stdout": sys.stdout,
-70        }
-71        # cleanup
-72        logger.remove()
-73        # convert
-74        config = self.model_dump()
-75        # replace
-76        # - use standard sinks or keep the original
-77        config["sink"] = sinks.get(config["sink"], config["sink"])
-78        # config
-79        logger.add(**config)
+            
    66    @T.override
+67    def start(self) -> None:
+68        # sinks
+69        sinks = {
+70            "stderr": sys.stderr,
+71            "stdout": sys.stdout,
+72        }
+73        # cleanup
+74        logger.remove()
+75        # convert
+76        config = self.model_dump()
+77        # replace
+78        # - use standard sinks or keep the original
+79        config["sink"] = sinks.get(config["sink"], config["sink"])
+80        # config
+81        logger.add(**config)
 
    
 
 
@@ -514,6 +577,200 @@ 
    Inherited Members
    
             
+                                
+
    + +
    + +
    + + class + CarbonService(Service): + + + +
    + + 
     84class CarbonService(Service):
+ 85    """Service for tracking carbon emissions.
+ 86
+ 87    Attributes:
+ 88        log_level (str): Level of logging to output.
+ 89        project_name (str): Name of the project to track.
+ 90        measure_power_secs (int): Interval for measuring in secs.
+ 91        output_dir (str): Directory where the output files are stored.
+ 92        output_file (str): Name of the output CSV file for emissions data.
+ 93        on_csv_write (str): Specifies the action on writing to CSV (append or overwrite).
+ 94        country_iso_code (str): ISO code of the country for tracking carbon emissions offline.
+ 95    """
+ 96
+ 97    # public
+ 98    # - inputs
+ 99    log_level: str = "ERROR"
+100    project_name: str = "bikes"
+101    measure_power_secs: int = 5
+102    # - outputs
+103    output_dir: str = "outputs"
+104    output_file: str = "emissions.csv"
+105    on_csv_write: str = "append"
+106    # - offline
+107    country_iso_code: str = "LUX"
+108    # private
+109    _tracker: cc.OfflineEmissionsTracker | None = None
+110
+111    def start(self):
+112        """Start the carbon service."""
+113        os.makedirs(self.output_dir, exist_ok=True)  # create output dir
+114        self._tracker = cc.OfflineEmissionsTracker(**self.model_dump())
+115        self._tracker.start()
+116
+117    def stop(self):
+118        """Stop the carbon service."""
+119        assert self._tracker, "Carbon tracker should be started!"
+120        self._tracker.flush()
+121        self._tracker.stop()
+
    + + +

    Service for tracking carbon emissions.

    + +
    Attributes:
    + +
      +
    • log_level (str): Level of logging to output.
      • +
    • project_name (str): Name of the project to track.
      • +
    • measure_power_secs (int): Interval for measuring in secs.
      • +
    • output_dir (str): Directory where the output files are stored.
      • +
    • output_file (str): Name of the output CSV file for emissions data.
      • +
    • on_csv_write (str): Specifies the action on writing to CSV (append or overwrite).
      • +
    • country_iso_code (str): ISO code of the country for tracking carbon emissions offline.
      • +
    +
    + + +
    + +
    + + def + start(self): + + + +
    + + 
    111    def start(self):
+112        """Start the carbon service."""
+113        os.makedirs(self.output_dir, exist_ok=True)  # create output dir
+114        self._tracker = cc.OfflineEmissionsTracker(**self.model_dump())
+115        self._tracker.start()
+
    + + +

    Start the carbon service.

    +
    + + +
    +
    + +
    + + def + stop(self): + + + +
    + + 
    117    def stop(self):
+118        """Stop the carbon service."""
+119        assert self._tracker, "Carbon tracker should be started!"
+120        self._tracker.flush()
+121        self._tracker.stop()
+
    + + +

    Stop the carbon service.

    +
    + + +
    +
    + +
    + + def + model_post_init(self: pydantic.main.BaseModel, __context: Any) -> None: + + + +
    + + 
    265def init_private_attributes(self: BaseModel, __context: Any) -> None:
+266    """This function is meant to behave like a BaseModel method to initialise private attributes.
+267
+268    It takes context as an argument since that's what pydantic-core passes when calling it.
+269
+270    Args:
+271        self: The BaseModel instance.
+272        __context: The context.
+273    """
+274    if getattr(self, '__pydantic_private__', None) is None:
+275        pydantic_private = {}
+276        for name, private_attr in self.__private_attributes__.items():
+277            default = private_attr.get_default()
+278            if default is not PydanticUndefined:
+279                pydantic_private[name] = default
+280        object_setattr(self, '__pydantic_private__', pydantic_private)
+
    + + +

    This function is meant to behave like a BaseModel method to initialise private attributes.

    + +

    It takes context as an argument since that's what pydantic-core passes when calling it.

    + +
    Arguments:
    + +
      +
    • self: The BaseModel instance.
      • +
    • __context: The context.
      • +
    +
    + + +
    +
    +
    Inherited Members
    +
    +
    pydantic.main.BaseModel
    +
    BaseModel
    +
    model_extra
    +
    model_fields_set
    +
    model_construct
    +
    model_copy
    +
    model_dump
    +
    model_dump_json
    +
    model_json_schema
    +
    model_parametrized_name
    +
    model_rebuild
    +
    model_validate
    +
    model_validate_json
    +
    model_validate_strings
    +
    dict
    +
    json
    +
    parse_obj
    +
    parse_raw
    +
    parse_file
    +
    from_orm
    +
    construct
    +
    copy
    +
    schema
    +
    schema_json
    +
    validate
    +
    update_forward_refs
    +
    @@ -529,82 +786,88 @@
    Inherited Members
    - 
     82class MLflowService(Service):
- 83    """Service for MLflow tracking and registry.
- 84
- 85    Attributes:
- 86        autolog_disable (bool): disable autologging.
- 87        autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
- 88        autolog_exclusive (bool): If True, enables exclusive autologging.
- 89        autolog_log_input_examples (bool): If True, logs input examples during autologging.
- 90        autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
- 91        autolog_log_models (bool): If True, enables logging of models during autologging.
- 92        autolog_log_datasets (bool): If True, logs datasets used during autologging.
- 93        autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
- 94        tracking_uri (str): The URI for the MLflow tracking server.
- 95        experiment_name (str): The name of the experiment to log runs under.
- 96        registry_uri (str): The URI for the MLflow model registry.
- 97        registry_name (str): The name of the registry.
- 98    """
- 99
-100    # autolog
-101    autolog_disable: bool = False
-102    autolog_disable_for_unsupported_versions: bool = False
-103    autolog_exclusive: bool = False
-104    autolog_log_input_examples: bool = True
-105    autolog_log_model_signatures: bool = True
-106    autolog_log_models: bool = False
-107    autolog_log_datasets: bool = True
-108    autolog_silent: bool = False
-109    # tracking
-110    tracking_uri: str = "http://localhost:5000"
-111    experiment_name: str = "bikes"
-112    # registry
-113    registry_uri: str = "http://localhost:5000"
-114    registry_name: str = "bikes"
-115
-116    def start(self):
-117        """Start the mlflow service."""
-118        # uri
-119        mlflow.set_tracking_uri(uri=self.tracking_uri)
-120        mlflow.set_registry_uri(uri=self.registry_uri)
-121        # experiment
-122        mlflow.set_experiment(experiment_name=self.experiment_name)
-123        # autologging
-124        mlflow.autolog(
-125            disable=self.autolog_disable,
-126            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
-127            exclusive=self.autolog_exclusive,
-128            log_input_examples=self.autolog_log_input_examples,
-129            log_model_signatures=self.autolog_log_model_signatures,
-130            log_models=self.autolog_log_models,
-131            silent=self.autolog_silent,
-132        )
-133
-134    def client(self) -> MlflowClient:
-135        """Get an instance of MLflow client."""
-136        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
-137
-138    def register(
-139        self, run_id: str, path: str, alias: str
-140    ) -> mlflow.entities.model_registry.ModelVersion:
-141        """Register a model to mlflow registry.
+            
    124class MLflowService(Service):
+125    """Service for MLflow tracking and registry.
+126
+127    Attributes:
+128        autolog_disable (bool): disable autologging.
+129        autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions.
+130        autolog_exclusive (bool): If True, enables exclusive autologging.
+131        autolog_log_input_examples (bool): If True, logs input examples during autologging.
+132        autolog_log_model_signatures (bool): If True, logs model signatures during autologging.
+133        autolog_log_models (bool): If True, enables logging of models during autologging.
+134        autolog_log_datasets (bool): If True, logs datasets used during autologging.
+135        autolog_silent (bool): If True, suppresses all MLflow warnings during autologging.
+136        enable_system_metrics (bool): enable system metrics logging.
+137        tracking_uri (str): The URI for the MLflow tracking server.
+138        experiment_name (str): The name of the experiment to log runs under.
+139        registry_uri (str): The URI for the MLflow model registry.
+140        registry_name (str): The name of the registry.
+141    """
 142
-143        Args:
-144            run_id (str): id of mlflow run.
-145            path (str): path of artifact.
-146            alias (str): model alias.
-147
-148        Returns:
-149            mlflow.entities.model_registry.ModelVersion: registered version.
-150        """
-151        client = self.client()
-152        model_uri = f"runs:/{run_id}/{path}"
-153        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
-154        client.set_registered_model_alias(
-155            name=self.registry_name, alias=alias, version=version.version
-156        )
-157        return version
+143    # autolog
+144    autolog_disable: bool = False
+145    autolog_disable_for_unsupported_versions: bool = False
+146    autolog_exclusive: bool = False
+147    autolog_log_input_examples: bool = True
+148    autolog_log_model_signatures: bool = True
+149    autolog_log_models: bool = False
+150    autolog_log_datasets: bool = True
+151    autolog_silent: bool = False
+152    # system
+153    enable_system_metrics: bool = True
+154    # tracking
+155    tracking_uri: str = "http://localhost:5000"
+156    experiment_name: str = "bikes"
+157    # registry
+158    registry_uri: str = "http://localhost:5000"
+159    registry_name: str = "bikes"
+160
+161    def start(self):
+162        """Start the mlflow service."""
+163        # uri
+164        mlflow.set_tracking_uri(uri=self.tracking_uri)
+165        mlflow.set_registry_uri(uri=self.registry_uri)
+166        # experiment
+167        mlflow.set_experiment(experiment_name=self.experiment_name)
+168        # autologging
+169        mlflow.autolog(
+170            disable=self.autolog_disable,
+171            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
+172            exclusive=self.autolog_exclusive,
+173            log_input_examples=self.autolog_log_input_examples,
+174            log_model_signatures=self.autolog_log_model_signatures,
+175            log_models=self.autolog_log_models,
+176            silent=self.autolog_silent,
+177        )
+178        # system metrics
+179        if self.enable_system_metrics:
+180            mlflow.enable_system_metrics_logging()
+181
+182    def client(self) -> MlflowClient:
+183        """Get an instance of MLflow client."""
+184        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
+185
+186    def register(
+187        self, run_id: str, path: str, alias: str
+188    ) -> mlflow.entities.model_registry.ModelVersion:
+189        """Register a model to mlflow registry.
+190
+191        Args:
+192            run_id (str): id of mlflow run.
+193            path (str): path of artifact.
+194            alias (str): model alias.
+195
+196        Returns:
+197            mlflow.entities.model_registry.ModelVersion: registered version.
+198        """
+199        client = self.client()
+200        model_uri = f"runs:/{run_id}/{path}"
+201        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+202        client.set_registered_model_alias(
+203            name=self.registry_name, alias=alias, version=version.version
+204        )
+205        return version
 
    
 
 
@@ -621,6 +884,7 @@ 
    Attributes:
    
 
  • autolog_log_models (bool):  If True, enables logging of models during autologging.
    • 
 
  • autolog_log_datasets (bool):  If True, logs datasets used during autologging.
    • 
 
  • autolog_silent (bool):  If True, suppresses all MLflow warnings during autologging.
    • 
+
  • enable_system_metrics (bool):  enable system metrics logging.
    • 
 
  • tracking_uri (str):  The URI for the MLflow tracking server.
    • 
 
  • experiment_name (str):  The name of the experiment to log runs under.
    • 
 
  • registry_uri (str):  The URI for the MLflow model registry.
    • 
@@ -640,23 +904,26 @@ 
    Attributes:
    - 
    116    def start(self):
-117        """Start the mlflow service."""
-118        # uri
-119        mlflow.set_tracking_uri(uri=self.tracking_uri)
-120        mlflow.set_registry_uri(uri=self.registry_uri)
-121        # experiment
-122        mlflow.set_experiment(experiment_name=self.experiment_name)
-123        # autologging
-124        mlflow.autolog(
-125            disable=self.autolog_disable,
-126            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
-127            exclusive=self.autolog_exclusive,
-128            log_input_examples=self.autolog_log_input_examples,
-129            log_model_signatures=self.autolog_log_model_signatures,
-130            log_models=self.autolog_log_models,
-131            silent=self.autolog_silent,
-132        )
+            
    161    def start(self):
+162        """Start the mlflow service."""
+163        # uri
+164        mlflow.set_tracking_uri(uri=self.tracking_uri)
+165        mlflow.set_registry_uri(uri=self.registry_uri)
+166        # experiment
+167        mlflow.set_experiment(experiment_name=self.experiment_name)
+168        # autologging
+169        mlflow.autolog(
+170            disable=self.autolog_disable,
+171            disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions,
+172            exclusive=self.autolog_exclusive,
+173            log_input_examples=self.autolog_log_input_examples,
+174            log_model_signatures=self.autolog_log_model_signatures,
+175            log_models=self.autolog_log_models,
+176            silent=self.autolog_silent,
+177        )
+178        # system metrics
+179        if self.enable_system_metrics:
+180            mlflow.enable_system_metrics_logging()
 
    
 
 
@@ -676,9 +943,9 @@ 
    Attributes:
    - 
    134    def client(self) -> MlflowClient:
-135        """Get an instance of MLflow client."""
-136        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
+            
    182    def client(self) -> MlflowClient:
+183        """Get an instance of MLflow client."""
+184        return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri)
 
    
 
 
@@ -698,26 +965,26 @@ 
    Attributes:
    - 
    138    def register(
-139        self, run_id: str, path: str, alias: str
-140    ) -> mlflow.entities.model_registry.ModelVersion:
-141        """Register a model to mlflow registry.
-142
-143        Args:
-144            run_id (str): id of mlflow run.
-145            path (str): path of artifact.
-146            alias (str): model alias.
-147
-148        Returns:
-149            mlflow.entities.model_registry.ModelVersion: registered version.
-150        """
-151        client = self.client()
-152        model_uri = f"runs:/{run_id}/{path}"
-153        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
-154        client.set_registered_model_alias(
-155            name=self.registry_name, alias=alias, version=version.version
-156        )
-157        return version
+            
    186    def register(
+187        self, run_id: str, path: str, alias: str
+188    ) -> mlflow.entities.model_registry.ModelVersion:
+189        """Register a model to mlflow registry.
+190
+191        Args:
+192            run_id (str): id of mlflow run.
+193            path (str): path of artifact.
+194            alias (str): model alias.
+195
+196        Returns:
+197            mlflow.entities.model_registry.ModelVersion: registered version.
+198        """
+199        client = self.client()
+200        model_uri = f"runs:/{run_id}/{path}"
+201        version = mlflow.register_model(model_uri=model_uri, name=self.registry_name)
+202        client.set_registered_model_alias(
+203            name=self.registry_name, alias=alias, version=version.version
+204        )
+205        return version
 
    
 
 
diff --git a/search.js b/search.js
index e516848..bccbe8d 100644
--- a/search.js
+++ b/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
 /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oPredict the number of bikes available.
    \n"}, "bikes.configs": {"fullname": "bikes.configs", "modulename": "bikes.configs", "kind": "module", "doc": "
    Parse, merge, and convert YAML configs.
    \n"}, "bikes.configs.parse_file": {"fullname": "bikes.configs.parse_file", "modulename": "bikes.configs", "qualname": "parse_file", "kind": "function", "doc": "
    Parse a config file from a path.
    \n\n
    Arguments:
    \n\n
      \n
    • path (str):  local or remote path.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Config: representation of the config file.
    \n
    \n", "signature": "(\tpath: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.parse_string": {"fullname": "bikes.configs.parse_string", "modulename": "bikes.configs", "qualname": "parse_string", "kind": "function", "doc": "
    Parse the given config string.
    \n\n
    Arguments:
    \n\n
      \n
    • string (str):  configuration string.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Config: representation of the config string.
    \n
    \n", "signature": "(\tstring: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.merge_configs": {"fullname": "bikes.configs.merge_configs", "modulename": "bikes.configs", "qualname": "merge_configs", "kind": "function", "doc": "
    Merge a list of config objects into one.
    \n\n
    Arguments:
    \n\n
      \n
    • configs (list[Config]):  list of config objects.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Config: representation of the merged config objects.
    \n
    \n", "signature": "(\tconfigs: Sequence[omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig]) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.to_object": {"fullname": "bikes.configs.to_object", "modulename": "bikes.configs", "qualname": "to_object", "kind": "function", "doc": "
    Convert a config object to a python object.
    \n\n
    Arguments:
    \n\n
      \n
    • config (Config):  representation of the config.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    object: conversion of the config to a python object.
    \n
    \n", "signature": "(\tconfig: omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig) -> object:", "funcdef": "def"}, "bikes.datasets": {"fullname": "bikes.datasets", "modulename": "bikes.datasets", "kind": "module", "doc": "
    Read/Write datasets from/to external sources/destinations.
    \n"}, "bikes.datasets.Reader": {"fullname": "bikes.datasets.Reader", "modulename": "bikes.datasets", "qualname": "Reader", "kind": "class", "doc": "
    Base class for a dataset reader.
    \n\n
    Use a reader to load a dataset in memory.\ne.g., to read file, database, cloud storage, ...
    \n\n
    Attributes:
    \n\n
      \n
    • limit (int, optional):  maximum number of rows to read from dataset.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Reader.read": {"fullname": "bikes.datasets.Reader.read", "modulename": "bikes.datasets", "qualname": "Reader.read", "kind": "function", "doc": "
    Read a dataframe from a dataset.
    \n\n
    Returns:
    \n\n
    \n  
    pd.DataFrame: dataframe representation.
    \n
    \n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.ParquetReader": {"fullname": "bikes.datasets.ParquetReader", "modulename": "bikes.datasets", "qualname": "ParquetReader", "kind": "class", "doc": "
    Read a dataframe from a parquet file.
    \n\n
    Attributes:
    \n\n
      \n
    • path (str):  local or remote path to a dataset.
      • \n
    \n", "bases": "Reader"}, "bikes.datasets.ParquetReader.read": {"fullname": "bikes.datasets.ParquetReader.read", "modulename": "bikes.datasets", "qualname": "ParquetReader.read", "kind": "function", "doc": "
    Read a dataframe from a dataset.
    \n\n
    Returns:
    \n\n
    \n  
    pd.DataFrame: dataframe representation.
    \n
    \n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.Writer": {"fullname": "bikes.datasets.Writer", "modulename": "bikes.datasets", "qualname": "Writer", "kind": "class", "doc": "
    Base class for a dataset writer.
    \n\n
    Use a writer to save a dataset from memory.\ne.g., to write file, database, cloud storage, ...
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Writer.write": {"fullname": "bikes.datasets.Writer.write", "modulename": "bikes.datasets", "qualname": "Writer.write", "kind": "function", "doc": "
    Write a dataframe to a dataset.
    \n\n
    Arguments:
    \n\n
      \n
    • data (pd.DataFrame):  dataframe representation.
      • \n
    \n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.datasets.ParquetWriter": {"fullname": "bikes.datasets.ParquetWriter", "modulename": "bikes.datasets", "qualname": "ParquetWriter", "kind": "class", "doc": "
    Writer a dataframe to a parquet file.
    \n\n
    Attributes:
    \n\n
      \n
    • path (str):  local or remote file to a dataset.
      • \n
    \n", "bases": "Writer"}, "bikes.datasets.ParquetWriter.write": {"fullname": "bikes.datasets.ParquetWriter.write", "modulename": "bikes.datasets", "qualname": "ParquetWriter.write", "kind": "function", "doc": "
    Write a dataframe to a dataset.
    \n\n
    Arguments:
    \n\n
      \n
    • data (pd.DataFrame):  dataframe representation.
      • \n
    \n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.jobs": {"fullname": "bikes.jobs", "modulename": "bikes.jobs", "kind": "module", "doc": "
    High-level jobs for the project.
    \n"}, "bikes.jobs.Job": {"fullname": "bikes.jobs.Job", "modulename": "bikes.jobs", "qualname": "Job", "kind": "class", "doc": "
    Base class for a job.
    \n\n
    use a job to execute runs in  context.\ne.g., to define common services like logger
    \n\n
    Attributes:
    \n\n
      \n
    • logger_service (services.LoggerService):  manage the logging system.
      • \n
    • mlflow_service (services.MLflowService):  manage the mlflow system.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.jobs.Job.run": {"fullname": "bikes.jobs.Job.run", "modulename": "bikes.jobs", "qualname": "Job.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TuningJob": {"fullname": "bikes.jobs.TuningJob", "modulename": "bikes.jobs", "qualname": "TuningJob", "kind": "class", "doc": "
    Find the best hyperparameters for a model.
    \n\n
    Attributes:
    \n\n
      \n
    • run_name (str):  name of the MLflow experiment run.
      • \n
    • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
      • \n
    • targets (datasets.ReaderKind):  dataset reader with targets variables.
      • \n
    • results (datasets.WriterKind):  dataset writer for searcher results.
      • \n
    • model (models.ModelKind):  machine learning model to tune.
      • \n
    • metric (metrics.MetricKind):  main metric for evaluation.
      • \n
    • splitter (splitters.SplitterKind):  splitter for datasets.
      • \n
    • searcher (searchers.SearcherKind):  searcher algorithm.
      • \n
    \n", "bases": "Job"}, "bikes.jobs.TuningJob.run": {"fullname": "bikes.jobs.TuningJob.run", "modulename": "bikes.jobs", "qualname": "TuningJob.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TrainingJob": {"fullname": "bikes.jobs.TrainingJob", "modulename": "bikes.jobs", "qualname": "TrainingJob", "kind": "class", "doc": "
    Train and register a single AI/ML model.
    \n\n
    Attributes:
    \n\n
      \n
    • run_name (str):  name of the MLflow experiment run.
      • \n
    • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
      • \n
    • targets (datasets.ReaderKind):  dataset reader with targets variables.
      • \n
    • saver (registers.SaverKind):  save the trained model in registry.
      • \n
    • model (models.ModelKind):  machine learning model to tune.
      • \n
    • signer (registers.SignerKind):  signer for the trained model.
      • \n
    • scorers (list[metrics.MetricKind]):  metrics for the evaluation.
      • \n
    • splitter (splitters.SplitterKind):  splitter for datasets.
      • \n
    • registry_alias (str):  alias of model.
      • \n
    \n", "bases": "Job"}, "bikes.jobs.TrainingJob.run": {"fullname": "bikes.jobs.TrainingJob.run", "modulename": "bikes.jobs", "qualname": "TrainingJob.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.InferenceJob": {"fullname": "bikes.jobs.InferenceJob", "modulename": "bikes.jobs", "qualname": "InferenceJob", "kind": "class", "doc": "
    Load a model and generate predictions.
    \n\n
    Attributes:
    \n\n
      \n
    • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
      • \n
    • outputs (datasets.WriterKind):  dataset writer for the model outputs.
      • \n
    • registry_alias (str):  alias of the model to load.
      • \n
    • loader (registers.LoaderKind):  load the model from registry.
      • \n
    \n", "bases": "Job"}, "bikes.jobs.InferenceJob.run": {"fullname": "bikes.jobs.InferenceJob.run", "modulename": "bikes.jobs", "qualname": "InferenceJob.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.metrics": {"fullname": "bikes.metrics", "modulename": "bikes.metrics", "kind": "module", "doc": "
    Evaluate model performance with metrics.
    \n"}, "bikes.metrics.Metric": {"fullname": "bikes.metrics.Metric", "modulename": "bikes.metrics", "qualname": "Metric", "kind": "class", "doc": "
    Base class for a metric.
    \n\n
    Use metrics to evaluate model performance.\ne.g., accuracy, precision, recall, mae, f1, ...
    \n\n
    Attributes:
    \n\n
      \n
    • name (str):  name of the metric.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.metrics.Metric.score": {"fullname": "bikes.metrics.Metric.score", "modulename": "bikes.metrics", "qualname": "Metric.score", "kind": "function", "doc": "
    Score the outputs against the targets.
    \n\n
    Arguments:
    \n\n
      \n
    • targets (schemas.Targets):  expected values.
      • \n
    • outputs (schemas.Outputs):  predicted values.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    float: metric result.
    \n
    \n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.Metric.scorer": {"fullname": "bikes.metrics.Metric.scorer", "modulename": "bikes.metrics", "qualname": "Metric.scorer", "kind": "function", "doc": "
    Score the model outputs against the targets.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  model to evaluate.
      • \n
    • inputs (schemas.Inputs):  model inputs values.
      • \n
    • targets (schemas.Targets):  model expected values.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    float: metric result.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.SklearnMetric": {"fullname": "bikes.metrics.SklearnMetric", "modulename": "bikes.metrics", "qualname": "SklearnMetric", "kind": "class", "doc": "
    Compute metrics with sklearn.
    \n\n
    Attributes:
    \n\n
      \n
    • name (str):  name of the sklearn metric.
      • \n
    • greater_is_better (bool):  maximize or minimize.
      • \n
    \n", "bases": "Metric"}, "bikes.metrics.SklearnMetric.score": {"fullname": "bikes.metrics.SklearnMetric.score", "modulename": "bikes.metrics", "qualname": "SklearnMetric.score", "kind": "function", "doc": "
    Score the outputs against the targets.
    \n\n
    Arguments:
    \n\n
      \n
    • targets (schemas.Targets):  expected values.
      • \n
    • outputs (schemas.Outputs):  predicted values.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    float: metric result.
    \n
    \n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.models": {"fullname": "bikes.models", "modulename": "bikes.models", "kind": "module", "doc": "
    Define trainable machine learning models.
    \n"}, "bikes.models.Model": {"fullname": "bikes.models.Model", "modulename": "bikes.models", "qualname": "Model", "kind": "class", "doc": "
    Base class for a model.
    \n\n
    Use a model to adapt AI/ML frameworks.\ne.g., to swap easily one model with another.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.models.Model.get_params": {"fullname": "bikes.models.Model.get_params", "modulename": "bikes.models", "qualname": "Model.get_params", "kind": "function", "doc": "
    Get the model params.
    \n\n
    Arguments:
    \n\n
      \n
    • deep (bool, optional):  ignored. Defaults to True.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Params: internal model parameters.
    \n
    \n", "signature": "(self, deep: bool = True) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.models.Model.set_params": {"fullname": "bikes.models.Model.set_params", "modulename": "bikes.models", "qualname": "Model.set_params", "kind": "function", "doc": "
    Set the model params in place.
    \n\n
    Returns:
    \n\n
    \n  
    T.Self: instance of the model.
    \n
    \n", "signature": "(self, **params: Any) -> Self:", "funcdef": "def"}, "bikes.models.Model.fit": {"fullname": "bikes.models.Model.fit", "modulename": "bikes.models", "qualname": "Model.fit", "kind": "function", "doc": "
    Fit the model on the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model training inputs.
      • \n
    • targets (schemas.Targets):  model training targets.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Model: instance of the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> Self:", "funcdef": "def"}, "bikes.models.Model.predict": {"fullname": "bikes.models.Model.predict", "modulename": "bikes.models", "qualname": "Model.predict", "kind": "function", "doc": "
    Generate outputs with the model for the given inputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model prediction inputs.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    schemas.Outputs: model prediction outputs.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel": {"fullname": "bikes.models.BaselineSklearnModel", "modulename": "bikes.models", "qualname": "BaselineSklearnModel", "kind": "class", "doc": "
    Simple baseline model built on top of sklearn.
    \n\n
    Attributes:
    \n\n
      \n
    • max_depth (int):  maximum depth of the random forest.
      • \n
    • n_estimators (int):  number of estimators in the random forest.
      • \n
    • random_state (int, optional):  random state of the machine learning pipeline.
      • \n
    \n", "bases": "Model"}, "bikes.models.BaselineSklearnModel.fit": {"fullname": "bikes.models.BaselineSklearnModel.fit", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.fit", "kind": "function", "doc": "
    Fit the model on the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model training inputs.
      • \n
    • targets (schemas.Targets):  model training targets.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Model: instance of the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> bikes.models.BaselineSklearnModel:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.predict": {"fullname": "bikes.models.BaselineSklearnModel.predict", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.predict", "kind": "function", "doc": "
    Generate outputs with the model for the given inputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model prediction inputs.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    schemas.Outputs: model prediction outputs.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.model_post_init": {"fullname": "bikes.models.BaselineSklearnModel.model_post_init", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.model_post_init", "kind": "function", "doc": "
    This function is meant to behave like a BaseModel method to initialise private attributes.
    \n\n
    It takes context as an argument since that's what pydantic-core passes when calling it.
    \n\n
    Arguments:
    \n\n
      \n
    • self:  The BaseModel instance.
      • \n
    • __context:  The context.
      • \n
    \n", "signature": "(self: pydantic.main.BaseModel, __context: Any) -> None:", "funcdef": "def"}, "bikes.registers": {"fullname": "bikes.registers", "modulename": "bikes.registers", "kind": "module", "doc": "
    Adapters, signers, savers, and loaders for model registries.
    \n"}, "bikes.registers.CustomAdapter": {"fullname": "bikes.registers.CustomAdapter", "modulename": "bikes.registers", "qualname": "CustomAdapter", "kind": "class", "doc": "
    Adapt a custom model to the MLflow PyFunc flavor.
    \n\n
    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
    \n", "bases": "mlflow.pyfunc.model.PythonModel"}, "bikes.registers.CustomAdapter.__init__": {"fullname": "bikes.registers.CustomAdapter.__init__", "modulename": "bikes.registers", "qualname": "CustomAdapter.__init__", "kind": "function", "doc": "
    Initialize the custom adapter.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  project model.
      • \n
    \n", "signature": "(model: bikes.models.Model)"}, "bikes.registers.CustomAdapter.predict": {"fullname": "bikes.registers.CustomAdapter.predict", "modulename": "bikes.registers", "qualname": "CustomAdapter.predict", "kind": "function", "doc": "
    Generate predictions from a custom model.
    \n\n
    Arguments:
    \n\n
      \n
    • context (mlflow.pyfunc.PythonModelContext):  ignored.
      • \n
    • inputs (schemas.Inputs):  inputs for the model.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    schemas.Outputs: outputs of the model.
    \n
    \n", "signature": "(\tself,\tcontext: mlflow.pyfunc.model.PythonModelContext,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.registers.Signer": {"fullname": "bikes.registers.Signer", "modulename": "bikes.registers", "qualname": "Signer", "kind": "class", "doc": "
    Base class for making signatures.
    \n\n
    Allow to switch between signing approaches.\ne.g., automatic inference vs manual signatures\nhttps://mlflow.org/docs/latest/models.html#model-signature-and-input-example
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Signer.sign": {"fullname": "bikes.registers.Signer.sign", "modulename": "bikes.registers", "qualname": "Signer.sign", "kind": "function", "doc": "
    Make a model signature from inputs/outputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  inputs of the model.
      • \n
    • outputs (schemas.Outputs):  ouputs of the model.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    ModelSignature: generated signature for the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.InferSigner": {"fullname": "bikes.registers.InferSigner", "modulename": "bikes.registers", "qualname": "InferSigner", "kind": "class", "doc": "
    Generate model signatures from data inference.
    \n", "bases": "Signer"}, "bikes.registers.InferSigner.sign": {"fullname": "bikes.registers.InferSigner.sign", "modulename": "bikes.registers", "qualname": "InferSigner.sign", "kind": "function", "doc": "
    Make a model signature from inputs/outputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  inputs of the model.
      • \n
    • outputs (schemas.Outputs):  ouputs of the model.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    ModelSignature: generated signature for the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.Saver": {"fullname": "bikes.registers.Saver", "modulename": "bikes.registers", "qualname": "Saver", "kind": "class", "doc": "
    Base class for saving models in registry.
    \n\n
    Separate model definition from serialization.\ne.g., to switch between serialization flavors.
    \n\n
    Attributes:
    \n\n
      \n
    • path (str):  model path inside the MLflow artifact store.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Saver.save": {"fullname": "bikes.registers.Saver.save", "modulename": "bikes.registers", "qualname": "Saver.save", "kind": "function", "doc": "
    Save a model in the model registry.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  model to save.
      • \n
    • signature (Signature):  model signature.
      • \n
    • input_example (schemas.Inputs):  inputs sample.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Info: model saving information.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.CustomSaver": {"fullname": "bikes.registers.CustomSaver", "modulename": "bikes.registers", "qualname": "CustomSaver", "kind": "class", "doc": "
    Saver for custom models using the MLflow PyFunc module.
    \n\n
    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
    \n", "bases": "Saver"}, "bikes.registers.CustomSaver.save": {"fullname": "bikes.registers.CustomSaver.save", "modulename": "bikes.registers", "qualname": "CustomSaver.save", "kind": "function", "doc": "
    Save a custom model to the MLflow Model Registry.
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.Loader": {"fullname": "bikes.registers.Loader", "modulename": "bikes.registers", "qualname": "Loader", "kind": "class", "doc": "
    Base class for loading models from registry.
    \n\n
    Separate model definition from deserialization.\ne.g., to switch between deserialization flavors.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Loader.load": {"fullname": "bikes.registers.Loader.load", "modulename": "bikes.registers", "qualname": "Loader.load", "kind": "function", "doc": "
    Load a model from the model registry.
    \n\n
    Arguments:
    \n\n
      \n
    • uri (str):  URI of the model to load.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    T.Any: model loaded from registry.
    \n
    \n", "signature": "(self, uri: str) -> Any:", "funcdef": "def"}, "bikes.registers.CustomLoader": {"fullname": "bikes.registers.CustomLoader", "modulename": "bikes.registers", "qualname": "CustomLoader", "kind": "class", "doc": "
    Loader for custom models using the MLflow PyFunc module.
    \n\n
    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
    \n", "bases": "Loader"}, "bikes.registers.CustomLoader.load": {"fullname": "bikes.registers.CustomLoader.load", "modulename": "bikes.registers", "qualname": "CustomLoader.load", "kind": "function", "doc": "
    Load a model from the model registry.
    \n\n
    Arguments:
    \n\n
      \n
    • uri (str):  URI of the model to load.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    T.Any: model loaded from registry.
    \n
    \n", "signature": "(self, uri: str) -> mlflow.pyfunc.model.PythonModel:", "funcdef": "def"}, "bikes.schemas": {"fullname": "bikes.schemas", "modulename": "bikes.schemas", "kind": "module", "doc": "
    Define and validate dataframe schemas.
    \n"}, "bikes.schemas.Schema": {"fullname": "bikes.schemas.Schema", "modulename": "bikes.schemas", "qualname": "Schema", "kind": "class", "doc": "
    Base class for a dataframe schema.
    \n\n
    Use a schema to type your dataframe object.\ne.g., to communicate and validate its fields.
    \n", "bases": "pandera.api.pandas.model.DataFrameModel"}, "bikes.schemas.Schema.__init__": {"fullname": "bikes.schemas.Schema.__init__", "modulename": "bikes.schemas", "qualname": "Schema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.Schema.Config": {"fullname": "bikes.schemas.Schema.Config", "modulename": "bikes.schemas", "qualname": "Schema.Config", "kind": "class", "doc": "
    Default configuration.
    \n\n
    Attributes:
    \n\n
      \n
    • coerce (bool):  convert data type if possible.
      • \n
    • strict (bool):  ensure the data type is correct.
      • \n
    \n"}, "bikes.schemas.Schema.check": {"fullname": "bikes.schemas.Schema.check", "modulename": "bikes.schemas", "qualname": "Schema.check", "kind": "function", "doc": "
    Check the data with this schema.
    \n\n
    Arguments:
    \n\n
      \n
    • data (pd.DataFrame):  dataframe to check.
      • \n
    • kwargs:  additional arguments to validate().
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    pd.DataFrame: validated dataframe with schema.
    \n
    \n", "signature": "(cls, data: pandas.core.frame.DataFrame, **kwargs):", "funcdef": "def"}, "bikes.schemas.InputsSchema": {"fullname": "bikes.schemas.InputsSchema", "modulename": "bikes.schemas", "qualname": "InputsSchema", "kind": "class", "doc": "
    Schema for the project inputs.
    \n", "bases": "Schema"}, "bikes.schemas.InputsSchema.__init__": {"fullname": "bikes.schemas.InputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "InputsSchema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.InputsSchema.instant": {"fullname": "bikes.schemas.InputsSchema.instant", "modulename": "bikes.schemas", "qualname": "InputsSchema.instant", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.dteday": {"fullname": "bikes.schemas.InputsSchema.dteday", "modulename": "bikes.schemas", "qualname": "InputsSchema.dteday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Timestamp]"}, "bikes.schemas.InputsSchema.season": {"fullname": "bikes.schemas.InputsSchema.season", "modulename": "bikes.schemas", "qualname": "InputsSchema.season", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.yr": {"fullname": "bikes.schemas.InputsSchema.yr", "modulename": "bikes.schemas", "qualname": "InputsSchema.yr", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.mnth": {"fullname": "bikes.schemas.InputsSchema.mnth", "modulename": "bikes.schemas", "qualname": "InputsSchema.mnth", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.hr": {"fullname": "bikes.schemas.InputsSchema.hr", "modulename": "bikes.schemas", "qualname": "InputsSchema.hr", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.holiday": {"fullname": "bikes.schemas.InputsSchema.holiday", "modulename": "bikes.schemas", "qualname": "InputsSchema.holiday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weekday": {"fullname": "bikes.schemas.InputsSchema.weekday", "modulename": "bikes.schemas", "qualname": "InputsSchema.weekday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.workingday": {"fullname": "bikes.schemas.InputsSchema.workingday", "modulename": "bikes.schemas", "qualname": "InputsSchema.workingday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weathersit": {"fullname": "bikes.schemas.InputsSchema.weathersit", "modulename": "bikes.schemas", "qualname": "InputsSchema.weathersit", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.temp": {"fullname": "bikes.schemas.InputsSchema.temp", "modulename": "bikes.schemas", "qualname": "InputsSchema.temp", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.atemp": {"fullname": "bikes.schemas.InputsSchema.atemp", "modulename": "bikes.schemas", "qualname": "InputsSchema.atemp", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.hum": {"fullname": "bikes.schemas.InputsSchema.hum", "modulename": "bikes.schemas", "qualname": "InputsSchema.hum", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.windspeed": {"fullname": "bikes.schemas.InputsSchema.windspeed", "modulename": "bikes.schemas", "qualname": "InputsSchema.windspeed", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.casual": {"fullname": "bikes.schemas.InputsSchema.casual", "modulename": "bikes.schemas", "qualname": "InputsSchema.casual", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.registered": {"fullname": "bikes.schemas.InputsSchema.registered", "modulename": "bikes.schemas", "qualname": "InputsSchema.registered", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.Config": {"fullname": "bikes.schemas.InputsSchema.Config", "modulename": "bikes.schemas", "qualname": "InputsSchema.Config", "kind": "class", "doc": "
    Define DataFrameSchema-wide options.
    \n\n
    new in 0.5.0
    \n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.TargetsSchema": {"fullname": "bikes.schemas.TargetsSchema", "modulename": "bikes.schemas", "qualname": "TargetsSchema", "kind": "class", "doc": "
    Schema for the project target.
    \n", "bases": "Schema"}, "bikes.schemas.TargetsSchema.__init__": {"fullname": "bikes.schemas.TargetsSchema.__init__", "modulename": "bikes.schemas", "qualname": "TargetsSchema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.TargetsSchema.instant": {"fullname": "bikes.schemas.TargetsSchema.instant", "modulename": "bikes.schemas", "qualname": "TargetsSchema.instant", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.cnt": {"fullname": "bikes.schemas.TargetsSchema.cnt", "modulename": "bikes.schemas", "qualname": "TargetsSchema.cnt", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.Config": {"fullname": "bikes.schemas.TargetsSchema.Config", "modulename": "bikes.schemas", "qualname": "TargetsSchema.Config", "kind": "class", "doc": "
    Define DataFrameSchema-wide options.
    \n\n
    new in 0.5.0
    \n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.OutputsSchema": {"fullname": "bikes.schemas.OutputsSchema", "modulename": "bikes.schemas", "qualname": "OutputsSchema", "kind": "class", "doc": "
    Schema for the project output.
    \n", "bases": "Schema"}, "bikes.schemas.OutputsSchema.__init__": {"fullname": "bikes.schemas.OutputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "OutputsSchema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.OutputsSchema.instant": {"fullname": "bikes.schemas.OutputsSchema.instant", "modulename": "bikes.schemas", "qualname": "OutputsSchema.instant", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.prediction": {"fullname": "bikes.schemas.OutputsSchema.prediction", "modulename": "bikes.schemas", "qualname": "OutputsSchema.prediction", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.Config": {"fullname": "bikes.schemas.OutputsSchema.Config", "modulename": "bikes.schemas", "qualname": "OutputsSchema.Config", "kind": "class", "doc": "
    Define DataFrameSchema-wide options.
    \n\n
    new in 0.5.0
    \n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.scripts": {"fullname": "bikes.scripts", "modulename": "bikes.scripts", "kind": "module", "doc": "
    Command-line interface for the program.
    \n"}, "bikes.scripts.Settings": {"fullname": "bikes.scripts.Settings", "modulename": "bikes.scripts", "qualname": "Settings", "kind": "class", "doc": "
    Settings for the program.
    \n\n
    Attributes:
    \n\n
      \n
    • job (jobs.JobKind):  job associated with settings.
      • \n
    \n", "bases": "pydantic_settings.main.BaseSettings"}, "bikes.scripts.main": {"fullname": "bikes.scripts.main", "modulename": "bikes.scripts", "qualname": "main", "kind": "function", "doc": "
    Main function of the program.
    \n\n
    Arguments:
    \n\n
      \n
    • argv (list[str] | None, optional):  program arguments. Defaults to None for sys.argv.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: status code of the program.
    \n
    \n", "signature": "(argv: list[str] | None = None) -> int:", "funcdef": "def"}, "bikes.searchers": {"fullname": "bikes.searchers", "modulename": "bikes.searchers", "kind": "module", "doc": "
    Find the best hyperparameters for a model.
    \n"}, "bikes.searchers.Searcher": {"fullname": "bikes.searchers.Searcher", "modulename": "bikes.searchers", "qualname": "Searcher", "kind": "class", "doc": "
    Base class for a searcher.
    \n\n
    note: use searcher to tune models.\ne.g., to find the best model params.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.searchers.Searcher.search": {"fullname": "bikes.searchers.Searcher.search", "modulename": "bikes.searchers", "qualname": "Searcher.search", "kind": "function", "doc": "
    Search the best model for the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  machine learning model to tune.
      • \n
    • metric (metrics.Metric):  main metric to optimize.
      • \n
    • cv (CrossValidation):  structure for cross-fold.
      • \n
    • inputs (schemas.Inputs):  model inputs for tuning.
      • \n
    • targets (schemas.Targets):  model targets for tuning.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Results: all the results of the tuning process.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.searchers.GridCVSearcher": {"fullname": "bikes.searchers.GridCVSearcher", "modulename": "bikes.searchers", "qualname": "GridCVSearcher", "kind": "class", "doc": "
    Grid searcher with cross-folds.
    \n\n
    Attributes:
    \n\n
      \n
    • param_grid (Grid):  mapping of param key -> values.
      • \n
    • n_jobs (int, optional):  number of jobs to run in parallel.
      • \n
    • refit (bool):  refit the model after the tuning.
      • \n
    • verbose (int):  set the search verbosity level.
      • \n
    • error_score (str | float):  strategy or value on error.
      • \n
    • return_train_score (bool):  include train scores.
      • \n
    \n", "bases": "Searcher"}, "bikes.searchers.GridCVSearcher.search": {"fullname": "bikes.searchers.GridCVSearcher.search", "modulename": "bikes.searchers", "qualname": "GridCVSearcher.search", "kind": "function", "doc": "
    Search the best model for the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  machine learning model to tune.
      • \n
    • metric (metrics.Metric):  main metric to optimize.
      • \n
    • cv (CrossValidation):  structure for cross-fold.
      • \n
    • inputs (schemas.Inputs):  model inputs for tuning.
      • \n
    • targets (schemas.Targets):  model targets for tuning.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Results: all the results of the tuning process.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.services": {"fullname": "bikes.services", "modulename": "bikes.services", "kind": "module", "doc": "
    Manage global context during execution.
    \n"}, "bikes.services.Service": {"fullname": "bikes.services.Service", "modulename": "bikes.services", "qualname": "Service", "kind": "class", "doc": "
    Base class for a global service.
    \n\n
    Use services to manage global contexts.\ne.g., logger object, mlflow client, spark context, ...
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.services.Service.start": {"fullname": "bikes.services.Service.start", "modulename": "bikes.services", "qualname": "Service.start", "kind": "function", "doc": "
    Start the service.
    \n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.Service.stop": {"fullname": "bikes.services.Service.stop", "modulename": "bikes.services", "qualname": "Service.stop", "kind": "function", "doc": "
    Stop the service.
    \n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.LoggerService": {"fullname": "bikes.services.LoggerService", "modulename": "bikes.services", "qualname": "LoggerService", "kind": "class", "doc": "
    Service for logging messages.
    \n\n
    https://loguru.readthedocs.io/en/stable/api/logger.html
    \n\n
    Attributes:
    \n\n
      \n
    • sink (str):  logging output.
      • \n
    • level (str):  logging level.
      • \n
    • format (str):  logging format.
      • \n
    • colorize (bool):  colorize output.
      • \n
    • serialize (bool):  convert to JSON.
      • \n
    • backtrace (bool):  enable exception trace.
      • \n
    • diagnose (bool):  enable variable display.
      • \n
    • catch (bool):  catch errors during log handling.
      • \n
    \n", "bases": "Service"}, "bikes.services.LoggerService.start": {"fullname": "bikes.services.LoggerService.start", "modulename": "bikes.services", "qualname": "LoggerService.start", "kind": "function", "doc": "
    Start the service.
    \n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.MLflowService": {"fullname": "bikes.services.MLflowService", "modulename": "bikes.services", "qualname": "MLflowService", "kind": "class", "doc": "
    Service for MLflow tracking and registry.
    \n\n
    Attributes:
    \n\n
      \n
    • autolog_disable (bool):  disable autologging.
      • \n
    • autolog_disable_for_unsupported_versions (bool):  disable autologging for unsupported versions.
      • \n
    • autolog_exclusive (bool):  If True, enables exclusive autologging.
      • \n
    • autolog_log_input_examples (bool):  If True, logs input examples during autologging.
      • \n
    • autolog_log_model_signatures (bool):  If True, logs model signatures during autologging.
      • \n
    • autolog_log_models (bool):  If True, enables logging of models during autologging.
      • \n
    • autolog_log_datasets (bool):  If True, logs datasets used during autologging.
      • \n
    • autolog_silent (bool):  If True, suppresses all MLflow warnings during autologging.
      • \n
    • tracking_uri (str):  The URI for the MLflow tracking server.
      • \n
    • experiment_name (str):  The name of the experiment to log runs under.
      • \n
    • registry_uri (str):  The URI for the MLflow model registry.
      • \n
    • registry_name (str):  The name of the registry.
      • \n
    \n", "bases": "Service"}, "bikes.services.MLflowService.start": {"fullname": "bikes.services.MLflowService.start", "modulename": "bikes.services", "qualname": "MLflowService.start", "kind": "function", "doc": "
    Start the mlflow service.
    \n", "signature": "(self):", "funcdef": "def"}, "bikes.services.MLflowService.client": {"fullname": "bikes.services.MLflowService.client", "modulename": "bikes.services", "qualname": "MLflowService.client", "kind": "function", "doc": "
    Get an instance of MLflow client.
    \n", "signature": "(self) -> mlflow.tracking.client.MlflowClient:", "funcdef": "def"}, "bikes.services.MLflowService.register": {"fullname": "bikes.services.MLflowService.register", "modulename": "bikes.services", "qualname": "MLflowService.register", "kind": "function", "doc": "
    Register a model to mlflow registry.
    \n\n
    Arguments:
    \n\n
      \n
    • run_id (str):  id of mlflow run.
      • \n
    • path (str):  path of artifact.
      • \n
    • alias (str):  model alias.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    mlflow.entities.model_registry.ModelVersion: registered version.
    \n
    \n", "signature": "(\tself,\trun_id: str,\tpath: str,\talias: str) -> mlflow.entities.model_registry.model_version.ModelVersion:", "funcdef": "def"}, "bikes.splitters": {"fullname": "bikes.splitters", "modulename": "bikes.splitters", "kind": "module", "doc": "
    Split dataframes into subsets (e.g., train/valid/test).
    \n"}, "bikes.splitters.Splitter": {"fullname": "bikes.splitters.Splitter", "modulename": "bikes.splitters", "qualname": "Splitter", "kind": "class", "doc": "
    Base class for a splitter.
    \n\n
    Use splitters to split datasets.\ne.g., split between a train/test subsets.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.splitters.Splitter.split": {"fullname": "bikes.splitters.Splitter.split", "modulename": "bikes.splitters", "qualname": "Splitter.split", "kind": "function", "doc": "
    Split a dataframe into subsets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Splits: iterator over the dataframe splits.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.Splitter.get_n_splits": {"fullname": "bikes.splitters.Splitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "Splitter.get_n_splits", "kind": "function", "doc": "
    Get the number of splits generated.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  models inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: number of splits generated.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter": {"fullname": "bikes.splitters.TrainTestSplitter", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter", "kind": "class", "doc": "
    Split a dataframe into a train and test subsets.
    \n\n
    Attributes:
    \n\n
      \n
    • shuffle (bool):  shuffle dataset before splitting.
      • \n
    • test_size (int | float):  number or ratio for the test dataset.
      • \n
    • random_state (int):  random state for the splitter object.
      • \n
    \n", "bases": "Splitter"}, "bikes.splitters.TrainTestSplitter.split": {"fullname": "bikes.splitters.TrainTestSplitter.split", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.split", "kind": "function", "doc": "
    Split a dataframe into subsets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Splits: iterator over the dataframe splits.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter.get_n_splits": {"fullname": "bikes.splitters.TrainTestSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.get_n_splits", "kind": "function", "doc": "
    Get the number of splits generated.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  models inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: number of splits generated.
    \n
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    Split a dataframe into fixed time series subsets.
    \n\n
    Attributes:
    \n\n
      \n
    • gap (int):  gap between splits.
      • \n
    • n_splits (int):  number of split to generate.
      • \n
    • test_size (int | float):  number or ratio for the test dataset.
      • \n
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    Split a dataframe into subsets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Splits: iterator over the dataframe splits.
    \n
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    Get the number of splits generated.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  models inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: number of splits generated.
    \n
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"u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"bikes.schemas.Schema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.InputsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.TargetsSchema.__init__": {"tf": 5.830951894845301}, "bikes.schemas.OutputsSchema.__init__": {"tf": 5.830951894845301}}, "df": 4}}}}, "k": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"bikes.schemas.Schema.check": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "y": {"docs": {"bikes.searchers.GridCVSearcher": {"tf": 1}}, "df": 1}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"bikes": {"fullname": "bikes", "modulename": "bikes", "kind": "module", "doc": "
    Predict the number of bikes available.
    \n"}, "bikes.configs": {"fullname": "bikes.configs", "modulename": "bikes.configs", "kind": "module", "doc": "
    Parse, merge, and convert YAML configs.
    \n"}, "bikes.configs.parse_file": {"fullname": "bikes.configs.parse_file", "modulename": "bikes.configs", "qualname": "parse_file", "kind": "function", "doc": "
    Parse a config file from a path.
    \n\n
    Arguments:
    \n\n
      \n
    • path (str):  local or remote path.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Config: representation of the config file.
    \n
    \n", "signature": "(\tpath: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.parse_string": {"fullname": "bikes.configs.parse_string", "modulename": "bikes.configs", "qualname": "parse_string", "kind": "function", "doc": "
    Parse the given config string.
    \n\n
    Arguments:
    \n\n
      \n
    • string (str):  configuration string.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Config: representation of the config string.
    \n
    \n", "signature": "(\tstring: str) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.merge_configs": {"fullname": "bikes.configs.merge_configs", "modulename": "bikes.configs", "qualname": "merge_configs", "kind": "function", "doc": "
    Merge a list of config objects into one.
    \n\n
    Arguments:
    \n\n
      \n
    • configs (list[Config]):  list of config objects.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Config: representation of the merged config objects.
    \n
    \n", "signature": "(\tconfigs: Sequence[omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig]) -> omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig:", "funcdef": "def"}, "bikes.configs.to_object": {"fullname": "bikes.configs.to_object", "modulename": "bikes.configs", "qualname": "to_object", "kind": "function", "doc": "
    Convert a config object to a python object.
    \n\n
    Arguments:
    \n\n
      \n
    • config (Config):  representation of the config.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    object: conversion of the config to a python object.
    \n
    \n", "signature": "(\tconfig: omegaconf.listconfig.ListConfig | omegaconf.dictconfig.DictConfig) -> object:", "funcdef": "def"}, "bikes.datasets": {"fullname": "bikes.datasets", "modulename": "bikes.datasets", "kind": "module", "doc": "
    Read/Write datasets from/to external sources/destinations.
    \n"}, "bikes.datasets.Reader": {"fullname": "bikes.datasets.Reader", "modulename": "bikes.datasets", "qualname": "Reader", "kind": "class", "doc": "
    Base class for a dataset reader.
    \n\n
    Use a reader to load a dataset in memory.\ne.g., to read file, database, cloud storage, ...
    \n\n
    Attributes:
    \n\n
      \n
    • limit (int, optional):  maximum number of rows to read from dataset.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Reader.read": {"fullname": "bikes.datasets.Reader.read", "modulename": "bikes.datasets", "qualname": "Reader.read", "kind": "function", "doc": "
    Read a dataframe from a dataset.
    \n\n
    Returns:
    \n\n
    \n  
    pd.DataFrame: dataframe representation.
    \n
    \n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.ParquetReader": {"fullname": "bikes.datasets.ParquetReader", "modulename": "bikes.datasets", "qualname": "ParquetReader", "kind": "class", "doc": "
    Read a dataframe from a parquet file.
    \n\n
    Attributes:
    \n\n
      \n
    • path (str):  local or remote path to a dataset.
      • \n
    \n", "bases": "Reader"}, "bikes.datasets.ParquetReader.read": {"fullname": "bikes.datasets.ParquetReader.read", "modulename": "bikes.datasets", "qualname": "ParquetReader.read", "kind": "function", "doc": "
    Read a dataframe from a dataset.
    \n\n
    Returns:
    \n\n
    \n  
    pd.DataFrame: dataframe representation.
    \n
    \n", "signature": "(self) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "bikes.datasets.Writer": {"fullname": "bikes.datasets.Writer", "modulename": "bikes.datasets", "qualname": "Writer", "kind": "class", "doc": "
    Base class for a dataset writer.
    \n\n
    Use a writer to save a dataset from memory.\ne.g., to write file, database, cloud storage, ...
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.datasets.Writer.write": {"fullname": "bikes.datasets.Writer.write", "modulename": "bikes.datasets", "qualname": "Writer.write", "kind": "function", "doc": "
    Write a dataframe to a dataset.
    \n\n
    Arguments:
    \n\n
      \n
    • data (pd.DataFrame):  dataframe representation.
      • \n
    \n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.datasets.ParquetWriter": {"fullname": "bikes.datasets.ParquetWriter", "modulename": "bikes.datasets", "qualname": "ParquetWriter", "kind": "class", "doc": "
    Writer a dataframe to a parquet file.
    \n\n
    Attributes:
    \n\n
      \n
    • path (str):  local or remote file to a dataset.
      • \n
    \n", "bases": "Writer"}, "bikes.datasets.ParquetWriter.write": {"fullname": "bikes.datasets.ParquetWriter.write", "modulename": "bikes.datasets", "qualname": "ParquetWriter.write", "kind": "function", "doc": "
    Write a dataframe to a dataset.
    \n\n
    Arguments:
    \n\n
      \n
    • data (pd.DataFrame):  dataframe representation.
      • \n
    \n", "signature": "(self, data: pandas.core.frame.DataFrame) -> None:", "funcdef": "def"}, "bikes.jobs": {"fullname": "bikes.jobs", "modulename": "bikes.jobs", "kind": "module", "doc": "
    High-level jobs for the project.
    \n"}, "bikes.jobs.Job": {"fullname": "bikes.jobs.Job", "modulename": "bikes.jobs", "qualname": "Job", "kind": "class", "doc": "
    Base class for a job.
    \n\n
    use a job to execute runs in  context.\ne.g., to define common services like logger
    \n\n
    Attributes:
    \n\n
      \n
    • logger_service (services.LoggerService):  manage the logging system.
      • \n
    • carbon_service (services.CarbonService):  manage the carbon system.
      • \n
    • mlflow_service (services.MLflowService):  manage the mlflow system.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.jobs.Job.run": {"fullname": "bikes.jobs.Job.run", "modulename": "bikes.jobs", "qualname": "Job.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TuningJob": {"fullname": "bikes.jobs.TuningJob", "modulename": "bikes.jobs", "qualname": "TuningJob", "kind": "class", "doc": "
    Find the best hyperparameters for a model.
    \n\n
    Attributes:
    \n\n
      \n
    • run_name (str):  name of the MLflow experiment run.
      • \n
    • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
      • \n
    • targets (datasets.ReaderKind):  dataset reader with targets variables.
      • \n
    • results (datasets.WriterKind):  dataset writer for searcher results.
      • \n
    • model (models.ModelKind):  machine learning model to tune.
      • \n
    • metric (metrics.MetricKind):  main metric for evaluation.
      • \n
    • splitter (splitters.SplitterKind):  splitter for datasets.
      • \n
    • searcher (searchers.SearcherKind):  searcher algorithm.
      • \n
    \n", "bases": "Job"}, "bikes.jobs.TuningJob.run": {"fullname": "bikes.jobs.TuningJob.run", "modulename": "bikes.jobs", "qualname": "TuningJob.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.TrainingJob": {"fullname": "bikes.jobs.TrainingJob", "modulename": "bikes.jobs", "qualname": "TrainingJob", "kind": "class", "doc": "
    Train and register a single AI/ML model.
    \n\n
    Attributes:
    \n\n
      \n
    • run_name (str):  name of the MLflow experiment run.
      • \n
    • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
      • \n
    • targets (datasets.ReaderKind):  dataset reader with targets variables.
      • \n
    • saver (registers.SaverKind):  save the trained model in registry.
      • \n
    • model (models.ModelKind):  machine learning model to tune.
      • \n
    • signer (registers.SignerKind):  signer for the trained model.
      • \n
    • scorers (list[metrics.MetricKind]):  metrics for the evaluation.
      • \n
    • splitter (splitters.SplitterKind):  splitter for datasets.
      • \n
    • registry_alias (str):  alias of model.
      • \n
    \n", "bases": "Job"}, "bikes.jobs.TrainingJob.run": {"fullname": "bikes.jobs.TrainingJob.run", "modulename": "bikes.jobs", "qualname": "TrainingJob.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.jobs.InferenceJob": {"fullname": "bikes.jobs.InferenceJob", "modulename": "bikes.jobs", "qualname": "InferenceJob", "kind": "class", "doc": "
    Load a model and generate predictions.
    \n\n
    Attributes:
    \n\n
      \n
    • inputs (datasets.ReaderKind):  dataset reader with inputs variables.
      • \n
    • outputs (datasets.WriterKind):  dataset writer for the model outputs.
      • \n
    • registry_alias (str):  alias of the model to load.
      • \n
    • loader (registers.LoaderKind):  load the model from registry.
      • \n
    \n", "bases": "Job"}, "bikes.jobs.InferenceJob.run": {"fullname": "bikes.jobs.InferenceJob.run", "modulename": "bikes.jobs", "qualname": "InferenceJob.run", "kind": "function", "doc": "
    Run the job in context.
    \n\n
    Returns:
    \n\n
    \n  
    Locals: local job variables.
    \n
    \n", "signature": "(self) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.metrics": {"fullname": "bikes.metrics", "modulename": "bikes.metrics", "kind": "module", "doc": "
    Evaluate model performance with metrics.
    \n"}, "bikes.metrics.Metric": {"fullname": "bikes.metrics.Metric", "modulename": "bikes.metrics", "qualname": "Metric", "kind": "class", "doc": "
    Base class for a metric.
    \n\n
    Use metrics to evaluate model performance.\ne.g., accuracy, precision, recall, mae, f1, ...
    \n\n
    Attributes:
    \n\n
      \n
    • name (str):  name of the metric.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.metrics.Metric.score": {"fullname": "bikes.metrics.Metric.score", "modulename": "bikes.metrics", "qualname": "Metric.score", "kind": "function", "doc": "
    Score the outputs against the targets.
    \n\n
    Arguments:
    \n\n
      \n
    • targets (schemas.Targets):  expected values.
      • \n
    • outputs (schemas.Outputs):  predicted values.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    float: metric result.
    \n
    \n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.Metric.scorer": {"fullname": "bikes.metrics.Metric.scorer", "modulename": "bikes.metrics", "qualname": "Metric.scorer", "kind": "function", "doc": "
    Score the model outputs against the targets.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  model to evaluate.
      • \n
    • inputs (schemas.Inputs):  model inputs values.
      • \n
    • targets (schemas.Targets):  model expected values.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    float: metric result.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> float:", "funcdef": "def"}, "bikes.metrics.SklearnMetric": {"fullname": "bikes.metrics.SklearnMetric", "modulename": "bikes.metrics", "qualname": "SklearnMetric", "kind": "class", "doc": "
    Compute metrics with sklearn.
    \n\n
    Attributes:
    \n\n
      \n
    • name (str):  name of the sklearn metric.
      • \n
    • greater_is_better (bool):  maximize or minimize.
      • \n
    \n", "bases": "Metric"}, "bikes.metrics.SklearnMetric.score": {"fullname": "bikes.metrics.SklearnMetric.score", "modulename": "bikes.metrics", "qualname": "SklearnMetric.score", "kind": "function", "doc": "
    Score the outputs against the targets.
    \n\n
    Arguments:
    \n\n
      \n
    • targets (schemas.Targets):  expected values.
      • \n
    • outputs (schemas.Outputs):  predicted values.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    float: metric result.
    \n
    \n", "signature": "(\tself,\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:", "funcdef": "def"}, "bikes.models": {"fullname": "bikes.models", "modulename": "bikes.models", "kind": "module", "doc": "
    Define trainable machine learning models.
    \n"}, "bikes.models.Model": {"fullname": "bikes.models.Model", "modulename": "bikes.models", "qualname": "Model", "kind": "class", "doc": "
    Base class for a model.
    \n\n
    Use a model to adapt AI/ML frameworks.\ne.g., to swap easily one model with another.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.models.Model.get_params": {"fullname": "bikes.models.Model.get_params", "modulename": "bikes.models", "qualname": "Model.get_params", "kind": "function", "doc": "
    Get the model params.
    \n\n
    Arguments:
    \n\n
      \n
    • deep (bool, optional):  ignored. Defaults to True.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Params: internal model parameters.
    \n
    \n", "signature": "(self, deep: bool = True) -> dict[str, typing.Any]:", "funcdef": "def"}, "bikes.models.Model.set_params": {"fullname": "bikes.models.Model.set_params", "modulename": "bikes.models", "qualname": "Model.set_params", "kind": "function", "doc": "
    Set the model params in place.
    \n\n
    Returns:
    \n\n
    \n  
    T.Self: instance of the model.
    \n
    \n", "signature": "(self, **params: Any) -> Self:", "funcdef": "def"}, "bikes.models.Model.fit": {"fullname": "bikes.models.Model.fit", "modulename": "bikes.models", "qualname": "Model.fit", "kind": "function", "doc": "
    Fit the model on the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model training inputs.
      • \n
    • targets (schemas.Targets):  model training targets.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Model: instance of the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> Self:", "funcdef": "def"}, "bikes.models.Model.predict": {"fullname": "bikes.models.Model.predict", "modulename": "bikes.models", "qualname": "Model.predict", "kind": "function", "doc": "
    Generate outputs with the model for the given inputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model prediction inputs.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    schemas.Outputs: model prediction outputs.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel": {"fullname": "bikes.models.BaselineSklearnModel", "modulename": "bikes.models", "qualname": "BaselineSklearnModel", "kind": "class", "doc": "
    Simple baseline model built on top of sklearn.
    \n\n
    Attributes:
    \n\n
      \n
    • max_depth (int):  maximum depth of the random forest.
      • \n
    • n_estimators (int):  number of estimators in the random forest.
      • \n
    • random_state (int, optional):  random state of the machine learning pipeline.
      • \n
    \n", "bases": "Model"}, "bikes.models.BaselineSklearnModel.fit": {"fullname": "bikes.models.BaselineSklearnModel.fit", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.fit", "kind": "function", "doc": "
    Fit the model on the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model training inputs.
      • \n
    • targets (schemas.Targets):  model training targets.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Model: instance of the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> bikes.models.BaselineSklearnModel:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.predict": {"fullname": "bikes.models.BaselineSklearnModel.predict", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.predict", "kind": "function", "doc": "
    Generate outputs with the model for the given inputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model prediction inputs.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    schemas.Outputs: model prediction outputs.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.models.BaselineSklearnModel.model_post_init": {"fullname": "bikes.models.BaselineSklearnModel.model_post_init", "modulename": "bikes.models", "qualname": "BaselineSklearnModel.model_post_init", "kind": "function", "doc": "
    This function is meant to behave like a BaseModel method to initialise private attributes.
    \n\n
    It takes context as an argument since that's what pydantic-core passes when calling it.
    \n\n
    Arguments:
    \n\n
      \n
    • self:  The BaseModel instance.
      • \n
    • __context:  The context.
      • \n
    \n", "signature": "(self: pydantic.main.BaseModel, __context: Any) -> None:", "funcdef": "def"}, "bikes.registers": {"fullname": "bikes.registers", "modulename": "bikes.registers", "kind": "module", "doc": "
    Adapters, signers, savers, and loaders for model registries.
    \n"}, "bikes.registers.CustomAdapter": {"fullname": "bikes.registers.CustomAdapter", "modulename": "bikes.registers", "qualname": "CustomAdapter", "kind": "class", "doc": "
    Adapt a custom model to the MLflow PyFunc flavor.
    \n\n
    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
    \n", "bases": "mlflow.pyfunc.model.PythonModel"}, "bikes.registers.CustomAdapter.__init__": {"fullname": "bikes.registers.CustomAdapter.__init__", "modulename": "bikes.registers", "qualname": "CustomAdapter.__init__", "kind": "function", "doc": "
    Initialize the custom adapter.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  project model.
      • \n
    \n", "signature": "(model: bikes.models.Model)"}, "bikes.registers.CustomAdapter.predict": {"fullname": "bikes.registers.CustomAdapter.predict", "modulename": "bikes.registers", "qualname": "CustomAdapter.predict", "kind": "function", "doc": "
    Generate predictions from a custom model.
    \n\n
    Arguments:
    \n\n
      \n
    • context (mlflow.pyfunc.PythonModelContext):  ignored.
      • \n
    • inputs (schemas.Inputs):  inputs for the model.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    schemas.Outputs: outputs of the model.
    \n
    \n", "signature": "(\tself,\tcontext: mlflow.pyfunc.model.PythonModelContext,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]:", "funcdef": "def"}, "bikes.registers.Signer": {"fullname": "bikes.registers.Signer", "modulename": "bikes.registers", "qualname": "Signer", "kind": "class", "doc": "
    Base class for making signatures.
    \n\n
    Allow to switch between signing approaches.\ne.g., automatic inference vs manual signatures\nhttps://mlflow.org/docs/latest/models.html#model-signature-and-input-example
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Signer.sign": {"fullname": "bikes.registers.Signer.sign", "modulename": "bikes.registers", "qualname": "Signer.sign", "kind": "function", "doc": "
    Make a model signature from inputs/outputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  inputs of the model.
      • \n
    • outputs (schemas.Outputs):  ouputs of the model.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    ModelSignature: generated signature for the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.InferSigner": {"fullname": "bikes.registers.InferSigner", "modulename": "bikes.registers", "qualname": "InferSigner", "kind": "class", "doc": "
    Generate model signatures from data inference.
    \n", "bases": "Signer"}, "bikes.registers.InferSigner.sign": {"fullname": "bikes.registers.InferSigner.sign", "modulename": "bikes.registers", "qualname": "InferSigner.sign", "kind": "function", "doc": "
    Make a model signature from inputs/outputs.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  inputs of the model.
      • \n
    • outputs (schemas.Outputs):  ouputs of the model.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    ModelSignature: generated signature for the model.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\toutputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> mlflow.models.signature.ModelSignature:", "funcdef": "def"}, "bikes.registers.Saver": {"fullname": "bikes.registers.Saver", "modulename": "bikes.registers", "qualname": "Saver", "kind": "class", "doc": "
    Base class for saving models in registry.
    \n\n
    Separate model definition from serialization.\ne.g., to switch between serialization flavors.
    \n\n
    Attributes:
    \n\n
      \n
    • path (str):  model path inside the MLflow artifact store.
      • \n
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Saver.save": {"fullname": "bikes.registers.Saver.save", "modulename": "bikes.registers", "qualname": "Saver.save", "kind": "function", "doc": "
    Save a model in the model registry.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  model to save.
      • \n
    • signature (Signature):  model signature.
      • \n
    • input_example (schemas.Inputs):  inputs sample.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Info: model saving information.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.CustomSaver": {"fullname": "bikes.registers.CustomSaver", "modulename": "bikes.registers", "qualname": "CustomSaver", "kind": "class", "doc": "
    Saver for custom models using the MLflow PyFunc module.
    \n\n
    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
    \n", "bases": "Saver"}, "bikes.registers.CustomSaver.save": {"fullname": "bikes.registers.CustomSaver.save", "modulename": "bikes.registers", "qualname": "CustomSaver.save", "kind": "function", "doc": "
    Save a custom model to the MLflow Model Registry.
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tsignature: mlflow.models.signature.ModelSignature,\tinput_example: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema]) -> mlflow.models.model.ModelInfo:", "funcdef": "def"}, "bikes.registers.Loader": {"fullname": "bikes.registers.Loader", "modulename": "bikes.registers", "qualname": "Loader", "kind": "class", "doc": "
    Base class for loading models from registry.
    \n\n
    Separate model definition from deserialization.\ne.g., to switch between deserialization flavors.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.registers.Loader.load": {"fullname": "bikes.registers.Loader.load", "modulename": "bikes.registers", "qualname": "Loader.load", "kind": "function", "doc": "
    Load a model from the model registry.
    \n\n
    Arguments:
    \n\n
      \n
    • uri (str):  URI of the model to load.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    T.Any: model loaded from registry.
    \n
    \n", "signature": "(self, uri: str) -> Any:", "funcdef": "def"}, "bikes.registers.CustomLoader": {"fullname": "bikes.registers.CustomLoader", "modulename": "bikes.registers", "qualname": "CustomLoader", "kind": "class", "doc": "
    Loader for custom models using the MLflow PyFunc module.
    \n\n
    https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html
    \n", "bases": "Loader"}, "bikes.registers.CustomLoader.load": {"fullname": "bikes.registers.CustomLoader.load", "modulename": "bikes.registers", "qualname": "CustomLoader.load", "kind": "function", "doc": "
    Load a model from the model registry.
    \n\n
    Arguments:
    \n\n
      \n
    • uri (str):  URI of the model to load.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    T.Any: model loaded from registry.
    \n
    \n", "signature": "(self, uri: str) -> mlflow.pyfunc.model.PythonModel:", "funcdef": "def"}, "bikes.schemas": {"fullname": "bikes.schemas", "modulename": "bikes.schemas", "kind": "module", "doc": "
    Define and validate dataframe schemas.
    \n"}, "bikes.schemas.Schema": {"fullname": "bikes.schemas.Schema", "modulename": "bikes.schemas", "qualname": "Schema", "kind": "class", "doc": "
    Base class for a dataframe schema.
    \n\n
    Use a schema to type your dataframe object.\ne.g., to communicate and validate its fields.
    \n", "bases": "pandera.api.pandas.model.DataFrameModel"}, "bikes.schemas.Schema.__init__": {"fullname": "bikes.schemas.Schema.__init__", "modulename": "bikes.schemas", "qualname": "Schema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.Schema.Config": {"fullname": "bikes.schemas.Schema.Config", "modulename": "bikes.schemas", "qualname": "Schema.Config", "kind": "class", "doc": "
    Default configuration.
    \n\n
    Attributes:
    \n\n
      \n
    • coerce (bool):  convert data type if possible.
      • \n
    • strict (bool):  ensure the data type is correct.
      • \n
    \n"}, "bikes.schemas.Schema.check": {"fullname": "bikes.schemas.Schema.check", "modulename": "bikes.schemas", "qualname": "Schema.check", "kind": "function", "doc": "
    Check the data with this schema.
    \n\n
    Arguments:
    \n\n
      \n
    • data (pd.DataFrame):  dataframe to check.
      • \n
    • kwargs:  additional arguments to validate().
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    pd.DataFrame: validated dataframe with schema.
    \n
    \n", "signature": "(cls, data: pandas.core.frame.DataFrame, **kwargs):", "funcdef": "def"}, "bikes.schemas.InputsSchema": {"fullname": "bikes.schemas.InputsSchema", "modulename": "bikes.schemas", "qualname": "InputsSchema", "kind": "class", "doc": "
    Schema for the project inputs.
    \n", "bases": "Schema"}, "bikes.schemas.InputsSchema.__init__": {"fullname": "bikes.schemas.InputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "InputsSchema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.InputsSchema.instant": {"fullname": "bikes.schemas.InputsSchema.instant", "modulename": "bikes.schemas", "qualname": "InputsSchema.instant", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.dteday": {"fullname": "bikes.schemas.InputsSchema.dteday", "modulename": "bikes.schemas", "qualname": "InputsSchema.dteday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Timestamp]"}, "bikes.schemas.InputsSchema.season": {"fullname": "bikes.schemas.InputsSchema.season", "modulename": "bikes.schemas", "qualname": "InputsSchema.season", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.yr": {"fullname": "bikes.schemas.InputsSchema.yr", "modulename": "bikes.schemas", "qualname": "InputsSchema.yr", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.mnth": {"fullname": "bikes.schemas.InputsSchema.mnth", "modulename": "bikes.schemas", "qualname": "InputsSchema.mnth", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.hr": {"fullname": "bikes.schemas.InputsSchema.hr", "modulename": "bikes.schemas", "qualname": "InputsSchema.hr", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.holiday": {"fullname": "bikes.schemas.InputsSchema.holiday", "modulename": "bikes.schemas", "qualname": "InputsSchema.holiday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weekday": {"fullname": "bikes.schemas.InputsSchema.weekday", "modulename": "bikes.schemas", "qualname": "InputsSchema.weekday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.workingday": {"fullname": "bikes.schemas.InputsSchema.workingday", "modulename": "bikes.schemas", "qualname": "InputsSchema.workingday", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Bool]"}, "bikes.schemas.InputsSchema.weathersit": {"fullname": "bikes.schemas.InputsSchema.weathersit", "modulename": "bikes.schemas", "qualname": "InputsSchema.weathersit", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt8]"}, "bikes.schemas.InputsSchema.temp": {"fullname": "bikes.schemas.InputsSchema.temp", "modulename": "bikes.schemas", "qualname": "InputsSchema.temp", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.atemp": {"fullname": "bikes.schemas.InputsSchema.atemp", "modulename": "bikes.schemas", "qualname": "InputsSchema.atemp", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.hum": {"fullname": "bikes.schemas.InputsSchema.hum", "modulename": "bikes.schemas", "qualname": "InputsSchema.hum", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.windspeed": {"fullname": "bikes.schemas.InputsSchema.windspeed", "modulename": "bikes.schemas", "qualname": "InputsSchema.windspeed", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.Float16]"}, "bikes.schemas.InputsSchema.casual": {"fullname": "bikes.schemas.InputsSchema.casual", "modulename": "bikes.schemas", "qualname": "InputsSchema.casual", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.registered": {"fullname": "bikes.schemas.InputsSchema.registered", "modulename": "bikes.schemas", "qualname": "InputsSchema.registered", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.InputsSchema.Config": {"fullname": "bikes.schemas.InputsSchema.Config", "modulename": "bikes.schemas", "qualname": "InputsSchema.Config", "kind": "class", "doc": "
    Define DataFrameSchema-wide options.
    \n\n
    new in 0.5.0
    \n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.TargetsSchema": {"fullname": "bikes.schemas.TargetsSchema", "modulename": "bikes.schemas", "qualname": "TargetsSchema", "kind": "class", "doc": "
    Schema for the project target.
    \n", "bases": "Schema"}, "bikes.schemas.TargetsSchema.__init__": {"fullname": "bikes.schemas.TargetsSchema.__init__", "modulename": "bikes.schemas", "qualname": "TargetsSchema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.TargetsSchema.instant": {"fullname": "bikes.schemas.TargetsSchema.instant", "modulename": "bikes.schemas", "qualname": "TargetsSchema.instant", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.cnt": {"fullname": "bikes.schemas.TargetsSchema.cnt", "modulename": "bikes.schemas", "qualname": "TargetsSchema.cnt", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.TargetsSchema.Config": {"fullname": "bikes.schemas.TargetsSchema.Config", "modulename": "bikes.schemas", "qualname": "TargetsSchema.Config", "kind": "class", "doc": "
    Define DataFrameSchema-wide options.
    \n\n
    new in 0.5.0
    \n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.schemas.OutputsSchema": {"fullname": "bikes.schemas.OutputsSchema", "modulename": "bikes.schemas", "qualname": "OutputsSchema", "kind": "class", "doc": "
    Schema for the project output.
    \n", "bases": "Schema"}, "bikes.schemas.OutputsSchema.__init__": {"fullname": "bikes.schemas.OutputsSchema.__init__", "modulename": "bikes.schemas", "qualname": "OutputsSchema.__init__", "kind": "function", "doc": "
    Check if all columns in a dataframe have a column in the Schema.
    \n\n
    Parameters
    \n\n
      \n
    • pd.DataFrame check_obj:  the dataframe to be validated.
      • \n
    • head:  validate the first n rows. Rows overlapping with tail or\nsample are de-duplicated.
      • \n
    • tail:  validate the last n rows. Rows overlapping with head or\nsample are de-duplicated.
      • \n
    • sample:  validate a random sample of n rows. Rows overlapping\nwith head or tail are de-duplicated.
      • \n
    • random_state:  random seed for the sample argument.
      • \n
    • lazy:  if True, lazily evaluates dataframe against all validation\nchecks and raises a SchemaErrors. Otherwise, raise\nSchemaError as soon as one occurs.
      • \n
    • inplace:  if True, applies coercion to the object of validation,\notherwise creates a copy of the data.\n:returns: validated DataFrame
      • \n
    \n\n
    Raises
    \n\n
      \n
    • SchemaError:  when DataFrame violates built-in or custom\nchecks.
      • \n
    \n\n
    :example:
    \n\n
    Calling schema.validate returns the dataframe.
    \n\n
    \n
    >>> import pandas as pd\n>>> import pandera as pa\n>>>\n>>> df = pd.DataFrame({\n...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],\n...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]\n... })\n>>>\n>>> schema_withchecks = pa.DataFrameSchema({\n...     "probability": pa.Column(\n...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),\n...\n...     # check that the "category" column contains a few discrete\n...     # values, and the majority of the entries are dogs.\n...     "category": pa.Column(\n...         str, [\n...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),\n...             pa.Check(lambda s: (s == "dog").mean() > 0.5),\n...         ]),\n... })\n>>>\n>>> schema_withchecks.validate(df)[["probability", "category"]]\n   probability category\n0         0.10      dog\n1         0.40      dog\n2         0.52      cat\n3         0.23     duck\n4         0.80      dog\n5         0.76      dog\n
    \n
    \n", "signature": "(*args, **kwargs)"}, "bikes.schemas.OutputsSchema.instant": {"fullname": "bikes.schemas.OutputsSchema.instant", "modulename": "bikes.schemas", "qualname": "OutputsSchema.instant", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Index[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.prediction": {"fullname": "bikes.schemas.OutputsSchema.prediction", "modulename": "bikes.schemas", "qualname": "OutputsSchema.prediction", "kind": "variable", "doc": "
    Captures extra information about a field.
    \n\n
    new in 0.5.0
    \n", "annotation": ": pandera.typing.pandas.Series[pandera.dtypes.UInt32]"}, "bikes.schemas.OutputsSchema.Config": {"fullname": "bikes.schemas.OutputsSchema.Config", "modulename": "bikes.schemas", "qualname": "OutputsSchema.Config", "kind": "class", "doc": "
    Define DataFrameSchema-wide options.
    \n\n
    new in 0.5.0
    \n", "bases": "pandera.api.pandas.model_config.BaseConfig"}, "bikes.scripts": {"fullname": "bikes.scripts", "modulename": "bikes.scripts", "kind": "module", "doc": "
    Command-line interface for the program.
    \n"}, "bikes.scripts.Settings": {"fullname": "bikes.scripts.Settings", "modulename": "bikes.scripts", "qualname": "Settings", "kind": "class", "doc": "
    Settings for the program.
    \n\n
    Attributes:
    \n\n
      \n
    • job (jobs.JobKind):  job associated with settings.
      • \n
    \n", "bases": "pydantic_settings.main.BaseSettings"}, "bikes.scripts.main": {"fullname": "bikes.scripts.main", "modulename": "bikes.scripts", "qualname": "main", "kind": "function", "doc": "
    Main function of the program.
    \n\n
    Arguments:
    \n\n
      \n
    • argv (list[str] | None, optional):  program arguments. Defaults to None for sys.argv.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: status code of the program.
    \n
    \n", "signature": "(argv: list[str] | None = None) -> int:", "funcdef": "def"}, "bikes.searchers": {"fullname": "bikes.searchers", "modulename": "bikes.searchers", "kind": "module", "doc": "
    Find the best hyperparameters for a model.
    \n"}, "bikes.searchers.Searcher": {"fullname": "bikes.searchers.Searcher", "modulename": "bikes.searchers", "qualname": "Searcher", "kind": "class", "doc": "
    Base class for a searcher.
    \n\n
    note: use searcher to tune models.\ne.g., to find the best model params.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.searchers.Searcher.search": {"fullname": "bikes.searchers.Searcher.search", "modulename": "bikes.searchers", "qualname": "Searcher.search", "kind": "function", "doc": "
    Search the best model for the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  machine learning model to tune.
      • \n
    • metric (metrics.Metric):  main metric to optimize.
      • \n
    • cv (CrossValidation):  structure for cross-fold.
      • \n
    • inputs (schemas.Inputs):  model inputs for tuning.
      • \n
    • targets (schemas.Targets):  model targets for tuning.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Results: all the results of the tuning process.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.searchers.GridCVSearcher": {"fullname": "bikes.searchers.GridCVSearcher", "modulename": "bikes.searchers", "qualname": "GridCVSearcher", "kind": "class", "doc": "
    Grid searcher with cross-folds.
    \n\n
    Attributes:
    \n\n
      \n
    • param_grid (Grid):  mapping of param key -> values.
      • \n
    • n_jobs (int, optional):  number of jobs to run in parallel.
      • \n
    • refit (bool):  refit the model after the tuning.
      • \n
    • verbose (int):  set the search verbosity level.
      • \n
    • error_score (str | float):  strategy or value on error.
      • \n
    • return_train_score (bool):  include train scores.
      • \n
    \n", "bases": "Searcher"}, "bikes.searchers.GridCVSearcher.search": {"fullname": "bikes.searchers.GridCVSearcher.search", "modulename": "bikes.searchers", "qualname": "GridCVSearcher.search", "kind": "function", "doc": "
    Search the best model for the given inputs and targets.
    \n\n
    Arguments:
    \n\n
      \n
    • model (models.Model):  machine learning model to tune.
      • \n
    • metric (metrics.Metric):  main metric to optimize.
      • \n
    • cv (CrossValidation):  structure for cross-fold.
      • \n
    • inputs (schemas.Inputs):  model inputs for tuning.
      • \n
    • targets (schemas.Targets):  model targets for tuning.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Results: all the results of the tuning process.
    \n
    \n", "signature": "(\tself,\tmodel: bikes.models.Model,\tmetric: bikes.metrics.Metric,\tcv: Union[int, Iterator[tuple[numpy.ndarray, numpy.ndarray]], bikes.splitters.Splitter],\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> tuple[pandas.core.frame.DataFrame, float, dict[str, typing.Any]]:", "funcdef": "def"}, "bikes.services": {"fullname": "bikes.services", "modulename": "bikes.services", "kind": "module", "doc": "
    Manage global context during execution.
    \n"}, "bikes.services.Service": {"fullname": "bikes.services.Service", "modulename": "bikes.services", "qualname": "Service", "kind": "class", "doc": "
    Base class for a global service.
    \n\n
    Use services to manage global contexts.\ne.g., logger object, mlflow client, spark context, ...
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.services.Service.start": {"fullname": "bikes.services.Service.start", "modulename": "bikes.services", "qualname": "Service.start", "kind": "function", "doc": "
    Start the service.
    \n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.Service.stop": {"fullname": "bikes.services.Service.stop", "modulename": "bikes.services", "qualname": "Service.stop", "kind": "function", "doc": "
    Stop the service.
    \n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.LoggerService": {"fullname": "bikes.services.LoggerService", "modulename": "bikes.services", "qualname": "LoggerService", "kind": "class", "doc": "
    Service for logging messages.
    \n\n
    https://loguru.readthedocs.io/en/stable/api/logger.html
    \n\n
    Attributes:
    \n\n
      \n
    • sink (str):  logging output.
      • \n
    • level (str):  logging level.
      • \n
    • format (str):  logging format.
      • \n
    • colorize (bool):  colorize output.
      • \n
    • serialize (bool):  convert to JSON.
      • \n
    • backtrace (bool):  enable exception trace.
      • \n
    • diagnose (bool):  enable variable display.
      • \n
    • catch (bool):  catch errors during log handling.
      • \n
    \n", "bases": "Service"}, "bikes.services.LoggerService.start": {"fullname": "bikes.services.LoggerService.start", "modulename": "bikes.services", "qualname": "LoggerService.start", "kind": "function", "doc": "
    Start the service.
    \n", "signature": "(self) -> None:", "funcdef": "def"}, "bikes.services.CarbonService": {"fullname": "bikes.services.CarbonService", "modulename": "bikes.services", "qualname": "CarbonService", "kind": "class", "doc": "
    Service for tracking carbon emissions.
    \n\n
    Attributes:
    \n\n
      \n
    • log_level (str):  Level of logging to output.
      • \n
    • project_name (str):  Name of the project to track.
      • \n
    • measure_power_secs (int):  Interval for measuring in secs.
      • \n
    • output_dir (str):  Directory where the output files are stored.
      • \n
    • output_file (str):  Name of the output CSV file for emissions data.
      • \n
    • on_csv_write (str):  Specifies the action on writing to CSV (append or overwrite).
      • \n
    • country_iso_code (str):  ISO code of the country for tracking carbon emissions offline.
      • \n
    \n", "bases": "Service"}, "bikes.services.CarbonService.start": {"fullname": "bikes.services.CarbonService.start", "modulename": "bikes.services", "qualname": "CarbonService.start", "kind": "function", "doc": "
    Start the carbon service.
    \n", "signature": "(self):", "funcdef": "def"}, "bikes.services.CarbonService.stop": {"fullname": "bikes.services.CarbonService.stop", "modulename": "bikes.services", "qualname": "CarbonService.stop", "kind": "function", "doc": "
    Stop the carbon service.
    \n", "signature": "(self):", "funcdef": "def"}, "bikes.services.CarbonService.model_post_init": {"fullname": "bikes.services.CarbonService.model_post_init", "modulename": "bikes.services", "qualname": "CarbonService.model_post_init", "kind": "function", "doc": "
    This function is meant to behave like a BaseModel method to initialise private attributes.
    \n\n
    It takes context as an argument since that's what pydantic-core passes when calling it.
    \n\n
    Arguments:
    \n\n
      \n
    • self:  The BaseModel instance.
      • \n
    • __context:  The context.
      • \n
    \n", "signature": "(self: pydantic.main.BaseModel, __context: Any) -> None:", "funcdef": "def"}, "bikes.services.MLflowService": {"fullname": "bikes.services.MLflowService", "modulename": "bikes.services", "qualname": "MLflowService", "kind": "class", "doc": "
    Service for MLflow tracking and registry.
    \n\n
    Attributes:
    \n\n
      \n
    • autolog_disable (bool):  disable autologging.
      • \n
    • autolog_disable_for_unsupported_versions (bool):  disable autologging for unsupported versions.
      • \n
    • autolog_exclusive (bool):  If True, enables exclusive autologging.
      • \n
    • autolog_log_input_examples (bool):  If True, logs input examples during autologging.
      • \n
    • autolog_log_model_signatures (bool):  If True, logs model signatures during autologging.
      • \n
    • autolog_log_models (bool):  If True, enables logging of models during autologging.
      • \n
    • autolog_log_datasets (bool):  If True, logs datasets used during autologging.
      • \n
    • autolog_silent (bool):  If True, suppresses all MLflow warnings during autologging.
      • \n
    • enable_system_metrics (bool):  enable system metrics logging.
      • \n
    • tracking_uri (str):  The URI for the MLflow tracking server.
      • \n
    • experiment_name (str):  The name of the experiment to log runs under.
      • \n
    • registry_uri (str):  The URI for the MLflow model registry.
      • \n
    • registry_name (str):  The name of the registry.
      • \n
    \n", "bases": "Service"}, "bikes.services.MLflowService.start": {"fullname": "bikes.services.MLflowService.start", "modulename": "bikes.services", "qualname": "MLflowService.start", "kind": "function", "doc": "
    Start the mlflow service.
    \n", "signature": "(self):", "funcdef": "def"}, "bikes.services.MLflowService.client": {"fullname": "bikes.services.MLflowService.client", "modulename": "bikes.services", "qualname": "MLflowService.client", "kind": "function", "doc": "
    Get an instance of MLflow client.
    \n", "signature": "(self) -> mlflow.tracking.client.MlflowClient:", "funcdef": "def"}, "bikes.services.MLflowService.register": {"fullname": "bikes.services.MLflowService.register", "modulename": "bikes.services", "qualname": "MLflowService.register", "kind": "function", "doc": "
    Register a model to mlflow registry.
    \n\n
    Arguments:
    \n\n
      \n
    • run_id (str):  id of mlflow run.
      • \n
    • path (str):  path of artifact.
      • \n
    • alias (str):  model alias.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    mlflow.entities.model_registry.ModelVersion: registered version.
    \n
    \n", "signature": "(\tself,\trun_id: str,\tpath: str,\talias: str) -> mlflow.entities.model_registry.model_version.ModelVersion:", "funcdef": "def"}, "bikes.splitters": {"fullname": "bikes.splitters", "modulename": "bikes.splitters", "kind": "module", "doc": "
    Split dataframes into subsets (e.g., train/valid/test).
    \n"}, "bikes.splitters.Splitter": {"fullname": "bikes.splitters.Splitter", "modulename": "bikes.splitters", "qualname": "Splitter", "kind": "class", "doc": "
    Base class for a splitter.
    \n\n
    Use splitters to split datasets.\ne.g., split between a train/test subsets.
    \n", "bases": "abc.ABC, pydantic.main.BaseModel"}, "bikes.splitters.Splitter.split": {"fullname": "bikes.splitters.Splitter.split", "modulename": "bikes.splitters", "qualname": "Splitter.split", "kind": "function", "doc": "
    Split a dataframe into subsets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Splits: iterator over the dataframe splits.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.Splitter.get_n_splits": {"fullname": "bikes.splitters.Splitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "Splitter.get_n_splits", "kind": "function", "doc": "
    Get the number of splits generated.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  models inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: number of splits generated.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter": {"fullname": "bikes.splitters.TrainTestSplitter", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter", "kind": "class", "doc": "
    Split a dataframe into a train and test subsets.
    \n\n
    Attributes:
    \n\n
      \n
    • shuffle (bool):  shuffle dataset before splitting.
      • \n
    • test_size (int | float):  number or ratio for the test dataset.
      • \n
    • random_state (int):  random state for the splitter object.
      • \n
    \n", "bases": "Splitter"}, "bikes.splitters.TrainTestSplitter.split": {"fullname": "bikes.splitters.TrainTestSplitter.split", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.split", "kind": "function", "doc": "
    Split a dataframe into subsets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Splits: iterator over the dataframe splits.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TrainTestSplitter.get_n_splits": {"fullname": "bikes.splitters.TrainTestSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TrainTestSplitter.get_n_splits", "kind": "function", "doc": "
    Get the number of splits generated.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  models inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: number of splits generated.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter": {"fullname": "bikes.splitters.TimeSeriesSplitter", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter", "kind": "class", "doc": "
    Split a dataframe into fixed time series subsets.
    \n\n
    Attributes:
    \n\n
      \n
    • gap (int):  gap between splits.
      • \n
    • n_splits (int):  number of split to generate.
      • \n
    • test_size (int | float):  number or ratio for the test dataset.
      • \n
    \n", "bases": "Splitter"}, "bikes.splitters.TimeSeriesSplitter.split": {"fullname": "bikes.splitters.TimeSeriesSplitter.split", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.split", "kind": "function", "doc": "
    Split a dataframe into subsets.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  model inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    Splits: iterator over the dataframe splits.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> Iterator[tuple[numpy.ndarray, numpy.ndarray]]:", "funcdef": "def"}, "bikes.splitters.TimeSeriesSplitter.get_n_splits": {"fullname": "bikes.splitters.TimeSeriesSplitter.get_n_splits", "modulename": "bikes.splitters", "qualname": "TimeSeriesSplitter.get_n_splits", "kind": "function", "doc": "
    Get the number of splits generated.
    \n\n
    Arguments:
    \n\n
      \n
    • inputs (schemas.Inputs):  models inputs.
      • \n
    • targets (schemas.Targets):  model targets.
      • \n
    • groups (list | None, optional):  group labels. Defaults to None.
      • \n
    \n\n
    Returns:
    \n\n
    \n  
    int: number of splits generated.
    \n
    \n", "signature": "(\tself,\tinputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema],\ttargets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],\tgroups: list | None = None) -> int:", "funcdef": "def"}}, "docInfo": {"bikes": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "bikes.configs": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "bikes.configs.parse_file": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 45}, "bikes.configs.parse_string": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 41}, "bikes.configs.merge_configs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 78, "bases": 0, "doc": 47}, "bikes.configs.to_object": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, 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"bikes.schemas.OutputsSchema.__init__": {"tf": 5.830951894845301}}, "df": 4}}}}, "k": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"bikes.schemas.Schema.check": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "y": {"docs": {"bikes.searchers.GridCVSearcher": {"tf": 1}}, "df": 1}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
 
     // mirrored in build-search-index.js (part 1)
     // Also split on html tags. this is a cheap heuristic, but good enough.

From 03e6eb946170521c88f5926252b9461cfaeb72ba Mon Sep 17 00:00:00 2001
From: fmind 
Date: Sat, 16 Mar 2024 18:24:31 +0000
Subject: [PATCH 05/11] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20fm?=
 =?UTF-8?q?ind/mlops-python-package@1b4343bbf186525910b702f69b74c95f9b67a3?=
 =?UTF-8?q?c7=20=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 bikes.html                       |   19 +-
 bikes/core.html                  |  246 +++
 bikes/{ => core}/metrics.html    |  278 ++-
 bikes/{ => core}/models.html     | 1063 +++++++----
 bikes/{ => core}/schemas.html    |  522 ++++--
 bikes/io.html                    |  247 +++
 bikes/{ => io}/configs.html      |  243 +--
 bikes/{ => io}/datasets.html     |  548 ++++--
 bikes/io/registries.html         | 2884 ++++++++++++++++++++++++++++++
 bikes/io/services.html           | 1637 +++++++++++++++++
 bikes/jobs.html                  | 1980 +++++++++++---------
 bikes/registers.html             | 1306 --------------
 bikes/scripts.html               |  221 +--
 bikes/services.html              | 1232 -------------
 bikes/settings.html              |  527 ++++++
 bikes/utils.html                 |  246 +++
 bikes/{ => utils}/searchers.html |  756 +++++---
 bikes/utils/signers.html         |  685 +++++++
 bikes/{ => utils}/splitters.html |  912 +++++++---
 search.js                        |    2 +-
 20 files changed, 10673 insertions(+), 4881 deletions(-)
 create mode 100644 bikes/core.html
 rename bikes/{ => core}/metrics.html (80%)
 rename bikes/{ => core}/models.html (67%)
 rename bikes/{ => core}/schemas.html (79%)
 create mode 100644 bikes/io.html
 rename bikes/{ => io}/configs.html (76%)
 rename bikes/{ => io}/datasets.html (76%)
 create mode 100644 bikes/io/registries.html
 create mode 100644 bikes/io/services.html
 delete mode 100644 bikes/registers.html
 delete mode 100644 bikes/services.html
 create mode 100644 bikes/settings.html
 create mode 100644 bikes/utils.html
 rename bikes/{ => utils}/searchers.html (62%)
 create mode 100644 bikes/utils/signers.html
 rename bikes/{ => utils}/splitters.html (68%)

diff --git a/bikes.html b/bikes.html
index 6e42f45..6cd5768 100644
--- a/bikes.html
+++ b/bikes.html
@@ -3,14 +3,14 @@
 
     
     
-    
+    
     bikes API documentation
 
     
     
     
-    
-    
+    
+

  • Metric
  • SklearnMetric
    • +
  • + MetricKind +
    • @@ -65,16 +101,16 @@

    API Documentation

    -bikes.metrics

    +bikes.core.metrics -

    Evaluate model performance with metrics.

    +

    Evaluate model performances with metrics.

    - 
     1"""Evaluate model performance with metrics."""
+                        
     1"""Evaluate model performances with metrics."""
  2
  3# %% IMPORTS
  4
@@ -84,19 +120,19 @@ 
    
  8import pydantic as pdt
  9from sklearn import metrics
 10
-11from bikes import models, schemas
+11from bikes.core import models, schemas
 12
 13# %% METRICS
 14
 15
-16class Metric(abc.ABC, pdt.BaseModel, strict=True):
-17    """Base class for a metric.
+16class Metric(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"):
+17    """Base class for a project metric.
 18
 19    Use metrics to evaluate model performance.
-20    e.g., accuracy, precision, recall, mae, f1, ...
+20    e.g., accuracy, precision, recall, MAE, F1, ...
 21
-22    Attributes:
-23        name (str): name of the metric.
+22    Parameters:
+23        name (str): name of the metric for the reporting.
 24    """
 25
 26    KIND: str
@@ -112,13 +148,13 @@ 
    
 36            outputs (schemas.Outputs): predicted values.
 37
 38        Returns:
-39            float: metric result.
+39            float: single result from the metric computation.
 40        """
 41
 42    def scorer(
 43        self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets
 44    ) -> float:
-45        """Score the model outputs against the targets.
+45        """Score the model outputs against targets.
 46
 47        Args:
 48            model (models.Model): model to evaluate.
@@ -126,9 +162,9 @@ 
    
 50            targets (schemas.Targets): model expected values.
 51
 52        Returns:
-53            float: metric result.
+53            float: single result from the metric computation.
 54        """
-55        outputs = model.predict(inputs=inputs)  # prediction
+55        outputs = model.predict(inputs=inputs)
 56        score = self.score(targets=targets, outputs=outputs)
 57        return score
 58
@@ -136,7 +172,7 @@ 
    
 60class SklearnMetric(Metric):
 61    """Compute metrics with sklearn.
 62
-63    Attributes:
+63    Parameters:
 64        name (str): name of the sklearn metric.
 65        greater_is_better (bool): maximize or minimize.
 66    """
@@ -153,7 +189,7 @@ 
    
 77        y_true = targets[schemas.TargetsSchema.cnt]
 78        y_pred = outputs[schemas.OutputsSchema.prediction]
 79        score = metric(y_pred=y_pred, y_true=y_true) * sign
-80        return score
+80        return float(score)
 81
 82
 83MetricKind = SklearnMetric
@@ -172,14 +208,14 @@ 
    
 
     
    
     
-            
    17class Metric(abc.ABC, pdt.BaseModel, strict=True):
-18    """Base class for a metric.
+            
    17class Metric(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"):
+18    """Base class for a project metric.
 19
 20    Use metrics to evaluate model performance.
-21    e.g., accuracy, precision, recall, mae, f1, ...
+21    e.g., accuracy, precision, recall, MAE, F1, ...
 22
-23    Attributes:
-24        name (str): name of the metric.
+23    Parameters:
+24        name (str): name of the metric for the reporting.
 25    """
 26
 27    KIND: str
@@ -195,13 +231,13 @@ 
    
 37            outputs (schemas.Outputs): predicted values.
 38
 39        Returns:
-40            float: metric result.
+40            float: single result from the metric computation.
 41        """
 42
 43    def scorer(
 44        self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets
 45    ) -> float:
-46        """Score the model outputs against the targets.
+46        """Score the model outputs against targets.
 47
 48        Args:
 49            model (models.Model): model to evaluate.
@@ -209,34 +245,56 @@ 
    
 51            targets (schemas.Targets): model expected values.
 52
 53        Returns:
-54            float: metric result.
+54            float: single result from the metric computation.
 55        """
-56        outputs = model.predict(inputs=inputs)  # prediction
+56        outputs = model.predict(inputs=inputs)
 57        score = self.score(targets=targets, outputs=outputs)
 58        return score
 
    
 
 
-            
    Base class for a metric.
    
+            
    Base class for a project metric.
    
 
 
    Use metrics to evaluate model performance.
-e.g., accuracy, precision, recall, mae, f1, ...
    
+e.g., accuracy, precision, recall, MAE, F1, ...
    
 
-
    Attributes:
    
+
    Arguments:
    
 
 
      
-
    • name (str):  name of the metric.
      • 
+
    • name (str):  name of the metric for the reporting.
      • 
 
    
 
    
 
 
+                            
    
+                                
    
+            KIND: str
+
+        
+    
    
+    
+    
+    
+
+                            
    
+                            
    
+                                
    
+            name: str
+
+        
+    
    
+    
+    
+    
+
+                            
    
                             
    
                                         
 
    
                     
    @abc.abstractmethod
    
 
         def
-        score(	self,	targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema],	outputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float:
+        score(	self,	targets: pandera.typing.pandas.DataFrame[bikes.core.schemas.TargetsSchema],	outputs: pandera.typing.pandas.DataFrame[bikes.core.schemas.OutputsSchema]) -> float:
 
                 
 
@@ -251,7 +309,7 @@ 
    Attributes:
    
 37            outputs (schemas.Outputs): predicted values.
 38
 39        Returns:
-40            float: metric result.
+40            float: single result from the metric computation.
 41        """
 
    
 
@@ -268,7 +326,7 @@ 
    Arguments:
    
 
    Returns:
    
 
 
    
-  
    float: metric result.
    
+  
    float: single result from the metric computation.
    @@ -279,7 +337,7 @@
    Returns:
    def - scorer( self, model: bikes.models.Model, inputs: pandera.typing.pandas.DataFrame[bikes.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema]) -> float: + scorer( self, model: bikes.core.models.Model, inputs: pandera.typing.pandas.DataFrame[bikes.core.schemas.InputsSchema], targets: pandera.typing.pandas.DataFrame[bikes.core.schemas.TargetsSchema]) -> float: @@ -288,7 +346,7 @@
    Returns:
    43    def scorer(
 44        self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets
 45    ) -> float:
-46        """Score the model outputs against the targets.
+46        """Score the model outputs against targets.
 47
 48        Args:
 49            model (models.Model): model to evaluate.
@@ -296,15 +354,15 @@ 
    Returns:
    
     51            targets (schemas.Targets): model expected values.
 52
 53        Returns:
-54            float: metric result.
+54            float: single result from the metric computation.
 55        """
-56        outputs = model.predict(inputs=inputs)  # prediction
+56        outputs = model.predict(inputs=inputs)
 57        score = self.score(targets=targets, outputs=outputs)
 58        return score
    -

    Score the model outputs against the targets.

    +

    Score the model outputs against targets.

    Arguments:
    @@ -317,11 +375,47 @@
    Arguments:
    Returns:
    -

    float: metric result.

    +

    float: single result from the metric computation.

    +
    +
    +
    + model_config = +{'strict': True, 'frozen': True, 'extra': 'forbid'} + + +
    + + + + +
    +
    +
    + model_fields = +{'KIND': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True)} + + +
    + + + + +
    +
    +
    + model_computed_fields = +{} + + +
    + + + +
    Inherited Members
    @@ -372,7 +466,7 @@
    Inherited Members
    61class SklearnMetric(Metric):
 62    """Compute metrics with sklearn.
 63
-64    Attributes:
+64    Parameters:
 65        name (str): name of the sklearn metric.
 66        greater_is_better (bool): maximize or minimize.
 67    """
@@ -389,13 +483,13 @@ 
    Inherited Members
    
     78        y_true = targets[schemas.TargetsSchema.cnt]
 79        y_pred = outputs[schemas.OutputsSchema.prediction]
 80        score = metric(y_pred=y_pred, y_true=y_true) * sign
-81        return score
+81        return float(score)

    Compute metrics with sklearn.

    -
    Attributes:
    +
    Arguments:
    • name (str): name of the sklearn metric.
      • @@ -404,13 +498,46 @@
      Attributes:
    +
    +
    + KIND: Literal['SklearnMetric'] + + +
    + + + + +
    +
    +
    + name: str + + +
    + + + + +
    +
    +
    + greater_is_better: bool + + +
    + + + + +
    @T.override
    def - score( self, targets: pandera.typing.pandas.DataFrame[bikes.schemas.TargetsSchema], outputs: pandera.typing.pandas.DataFrame[bikes.schemas.OutputsSchema]) -> float: + score( self, targets: pandera.typing.pandas.DataFrame[bikes.core.schemas.TargetsSchema], outputs: pandera.typing.pandas.DataFrame[bikes.core.schemas.OutputsSchema]) -> float: @@ -423,7 +550,7 @@
    Attributes:
    78 y_true = targets[schemas.TargetsSchema.cnt] 79 y_pred = outputs[schemas.OutputsSchema.prediction] 80 score = metric(y_pred=y_pred, y_true=y_true) * sign -81 return score +81 return float(score)
    @@ -439,11 +566,48 @@
    Arguments:
    Returns:
    -

    float: metric result.

    +

    float: single result from the metric computation.

    +
    +
    +
    + model_config = +{'strict': True, 'frozen': True, 'extra': 'forbid'} + + +
    + + + + +
    +
    +
    + model_fields = + + {'KIND': FieldInfo(annotation=Literal['SklearnMetric'], required=False, default='SklearnMetric'), 'name': FieldInfo(annotation=str, required=False, default='mean_squared_error'), 'greater_is_better': FieldInfo(annotation=bool, required=False, default=False)} + + +
    + + + + +
    +
    +
    + model_computed_fields = +{} + + +
    + + + +
    Inherited Members
    @@ -483,6 +647,18 @@
    Inherited Members
    +
    +
    +
    + MetricKind = +<class 'SklearnMetric'> + + +
    + + + +
    + \ No newline at end of file diff --git a/bikes/configs.html b/bikes/io/configs.html similarity index 76% rename from bikes/configs.html rename to bikes/io/configs.html index dca8442..efe4942 100644 --- a/bikes/configs.html +++ b/bikes/io/configs.html @@ -3,24 +3,24 @@ - - bikes.configs API documentation + + bikes.io.configs API documentation - - + +