# -*- coding: utf-8 -*-

# Copyright 2022 Google LLC

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

#

import proto # type: ignore

from google.cloud.bigquery_v2.types import encryption_config

from google.cloud.bigquery_v2.types import model_reference as gcb_model_reference

from google.cloud.bigquery_v2.types import standard_sql

from google.cloud.bigquery_v2.types import table_reference

from google.protobuf import timestamp_pb2 # type: ignore

from google.protobuf import wrappers_pb2 # type: ignore

__protobuf__ = proto.module(

package="google.cloud.bigquery.v2",

manifest={

"Model",

"GetModelRequest",

"PatchModelRequest",

"DeleteModelRequest",

"ListModelsRequest",

"ListModelsResponse",

},

)

class Model(proto.Message):

r"""

Attributes:

etag (str):

Output only. A hash of this resource.

model_reference (google.cloud.bigquery_v2.types.ModelReference):

Required. Unique identifier for this model.

creation_time (int):

Output only. The time when this model was

created, in millisecs since the epoch.

last_modified_time (int):

Output only. The time when this model was

last modified, in millisecs since the epoch.

description (str):

Optional. A user-friendly description of this

model.

friendly_name (str):

Optional. A descriptive name for this model.

labels (Mapping[str, str]):

The labels associated with this model. You

can use these to organize and group your models.

Label keys and values can be no longer than 63

characters, can only contain lowercase letters,

numeric characters, underscores and dashes.

International characters are allowed. Label

values are optional. Label keys must start with

a letter and each label in the list must have a

different key.

expiration_time (int):

Optional. The time when this model expires,

in milliseconds since the epoch. If not present,

the model will persist indefinitely. Expired

models will be deleted and their storage

reclaimed. The defaultTableExpirationMs

property of the encapsulating dataset can be

used to set a default expirationTime on newly

created models.

location (str):

Output only. The geographic location where

the model resides. This value is inherited from

the dataset.

encryption_configuration (google.cloud.bigquery_v2.types.EncryptionConfiguration):

Custom encryption configuration (e.g., Cloud

KMS keys). This shows the encryption

configuration of the model data while stored in

BigQuery storage. This field can be used with

PatchModel to update encryption key for an

already encrypted model.

model_type (google.cloud.bigquery_v2.types.Model.ModelType):

Output only. Type of the model resource.

training_runs (Sequence[google.cloud.bigquery_v2.types.Model.TrainingRun]):

Output only. Information for all training runs in increasing

order of start_time.

feature_columns (Sequence[google.cloud.bigquery_v2.types.StandardSqlField]):

Output only. Input feature columns that were

used to train this model.

label_columns (Sequence[google.cloud.bigquery_v2.types.StandardSqlField]):

Output only. Label columns that were used to train this

model. The output of the model will have a `predicted_`

prefix to these columns.

best_trial_id (int):

The best trial_id across all training runs.

"""

class ModelType(proto.Enum):

r"""Indicates the type of the Model."""

MODEL_TYPE_UNSPECIFIED = 0

LINEAR_REGRESSION = 1

LOGISTIC_REGRESSION = 2

KMEANS = 3

MATRIX_FACTORIZATION = 4

DNN_CLASSIFIER = 5

TENSORFLOW = 6

DNN_REGRESSOR = 7

BOOSTED_TREE_REGRESSOR = 9

BOOSTED_TREE_CLASSIFIER = 10

ARIMA = 11

AUTOML_REGRESSOR = 12

AUTOML_CLASSIFIER = 13

ARIMA_PLUS = 19

class LossType(proto.Enum):

r"""Loss metric to evaluate model training performance."""

LOSS_TYPE_UNSPECIFIED = 0

MEAN_SQUARED_LOSS = 1

MEAN_LOG_LOSS = 2

class DistanceType(proto.Enum):

r"""Distance metric used to compute the distance between two

points.

"""

DISTANCE_TYPE_UNSPECIFIED = 0

EUCLIDEAN = 1

COSINE = 2

class DataSplitMethod(proto.Enum):

r"""Indicates the method to split input data into multiple

tables.

"""

DATA_SPLIT_METHOD_UNSPECIFIED = 0

RANDOM = 1

CUSTOM = 2

SEQUENTIAL = 3

NO_SPLIT = 4

AUTO_SPLIT = 5

class DataFrequency(proto.Enum):

r"""Type of supported data frequency for time series forecasting

models.

"""

DATA_FREQUENCY_UNSPECIFIED = 0

AUTO_FREQUENCY = 1

YEARLY = 2

QUARTERLY = 3

MONTHLY = 4

WEEKLY = 5

DAILY = 6

HOURLY = 7

PER_MINUTE = 8

class HolidayRegion(proto.Enum):

r"""Type of supported holiday regions for time series forecasting

models.

"""

HOLIDAY_REGION_UNSPECIFIED = 0

GLOBAL = 1

NA = 2

JAPAC = 3

EMEA = 4

LAC = 5

AE = 6

AR = 7

AT = 8

AU = 9

BE = 10

BR = 11

CA = 12

CH = 13

CL = 14

CN = 15

CO = 16

CS = 17

CZ = 18

DE = 19

DK = 20

DZ = 21

EC = 22

EE = 23

EG = 24

ES = 25

FI = 26

FR = 27

GB = 28

GR = 29

HK = 30

HU = 31

ID = 32

IE = 33

IL = 34

IN = 35

IR = 36

IT = 37

JP = 38

KR = 39

LV = 40

MA = 41

MX = 42

MY = 43

NG = 44

NL = 45

NO = 46

NZ = 47

PE = 48

PH = 49

PK = 50

PL = 51

PT = 52

RO = 53

RS = 54

RU = 55

SA = 56

SE = 57

SG = 58

SI = 59

SK = 60

TH = 61

TR = 62

TW = 63

UA = 64

US = 65

VE = 66

VN = 67

ZA = 68

class LearnRateStrategy(proto.Enum):

r"""Indicates the learning rate optimization strategy to use."""

LEARN_RATE_STRATEGY_UNSPECIFIED = 0

LINE_SEARCH = 1

CONSTANT = 2

class OptimizationStrategy(proto.Enum):

r"""Indicates the optimization strategy used for training."""

OPTIMIZATION_STRATEGY_UNSPECIFIED = 0

BATCH_GRADIENT_DESCENT = 1

NORMAL_EQUATION = 2

class FeedbackType(proto.Enum):

r"""Indicates the training algorithm to use for matrix

factorization models.

"""

FEEDBACK_TYPE_UNSPECIFIED = 0

IMPLICIT = 1

EXPLICIT = 2

class SeasonalPeriod(proto.Message):

r""" """

class SeasonalPeriodType(proto.Enum):

r""""""

SEASONAL_PERIOD_TYPE_UNSPECIFIED = 0

NO_SEASONALITY = 1

DAILY = 2

WEEKLY = 3

MONTHLY = 4

QUARTERLY = 5

YEARLY = 6

class KmeansEnums(proto.Message):

r""" """

class KmeansInitializationMethod(proto.Enum):

r"""Indicates the method used to initialize the centroids for

KMeans clustering algorithm.

"""

KMEANS_INITIALIZATION_METHOD_UNSPECIFIED = 0

RANDOM = 1

CUSTOM = 2

KMEANS_PLUS_PLUS = 3

class RegressionMetrics(proto.Message):

r"""Evaluation metrics for regression and explicit feedback type

matrix factorization models.

Attributes:

mean_absolute_error (google.protobuf.wrappers_pb2.DoubleValue):

Mean absolute error.

mean_squared_error (google.protobuf.wrappers_pb2.DoubleValue):

Mean squared error.

mean_squared_log_error (google.protobuf.wrappers_pb2.DoubleValue):

Mean squared log error.

median_absolute_error (google.protobuf.wrappers_pb2.DoubleValue):

Median absolute error.

r_squared (google.protobuf.wrappers_pb2.DoubleValue):

R^2 score. This corresponds to r2_score in ML.EVALUATE.

"""

mean_absolute_error = proto.Field(

proto.MESSAGE,

number=1,

message=wrappers_pb2.DoubleValue,

)

mean_squared_error = proto.Field(

proto.MESSAGE,

number=2,

message=wrappers_pb2.DoubleValue,

)

mean_squared_log_error = proto.Field(

proto.MESSAGE,

number=3,

message=wrappers_pb2.DoubleValue,

)

median_absolute_error = proto.Field(

proto.MESSAGE,

number=4,

message=wrappers_pb2.DoubleValue,

)

r_squared = proto.Field(

proto.MESSAGE,

number=5,

message=wrappers_pb2.DoubleValue,

)

class AggregateClassificationMetrics(proto.Message):

r"""Aggregate metrics for classification/classifier models. For

multi-class models, the metrics are either macro-averaged or

micro-averaged. When macro-averaged, the metrics are calculated

for each label and then an unweighted average is taken of those

values. When micro-averaged, the metric is calculated globally

by counting the total number of correctly predicted rows.

Attributes:

precision (google.protobuf.wrappers_pb2.DoubleValue):

Precision is the fraction of actual positive

predictions that had positive actual labels. For

multiclass this is a macro-averaged metric

treating each class as a binary classifier.

recall (google.protobuf.wrappers_pb2.DoubleValue):

Recall is the fraction of actual positive

labels that were given a positive prediction.

For multiclass this is a macro-averaged metric.

accuracy (google.protobuf.wrappers_pb2.DoubleValue):

Accuracy is the fraction of predictions given

the correct label. For multiclass this is a

micro-averaged metric.

threshold (google.protobuf.wrappers_pb2.DoubleValue):

Threshold at which the metrics are computed.

For binary classification models this is the

positive class threshold. For multi-class

classfication models this is the confidence

threshold.

f1_score (google.protobuf.wrappers_pb2.DoubleValue):

The F1 score is an average of recall and

precision. For multiclass this is a

macro-averaged metric.

log_loss (google.protobuf.wrappers_pb2.DoubleValue):

Logarithmic Loss. For multiclass this is a

macro-averaged metric.

roc_auc (google.protobuf.wrappers_pb2.DoubleValue):

Area Under a ROC Curve. For multiclass this

is a macro-averaged metric.

"""

precision = proto.Field(

proto.MESSAGE,

number=1,

message=wrappers_pb2.DoubleValue,

)

recall = proto.Field(

proto.MESSAGE,

number=2,

message=wrappers_pb2.DoubleValue,

)

accuracy = proto.Field(

proto.MESSAGE,

number=3,

message=wrappers_pb2.DoubleValue,

)

threshold = proto.Field(

proto.MESSAGE,

number=4,

message=wrappers_pb2.DoubleValue,

)

f1_score = proto.Field(

proto.MESSAGE,

number=5,

message=wrappers_pb2.DoubleValue,

)

log_loss = proto.Field(

proto.MESSAGE,

number=6,

message=wrappers_pb2.DoubleValue,

)

roc_auc = proto.Field(

proto.MESSAGE,

number=7,

message=wrappers_pb2.DoubleValue,

)

class BinaryClassificationMetrics(proto.Message):

r"""Evaluation metrics for binary classification/classifier

models.

Attributes:

aggregate_classification_metrics (google.cloud.bigquery_v2.types.Model.AggregateClassificationMetrics):

Aggregate classification metrics.

binary_confusion_matrix_list (Sequence[google.cloud.bigquery_v2.types.Model.BinaryClassificationMetrics.BinaryConfusionMatrix]):

Binary confusion matrix at multiple

thresholds.

positive_label (str):

Label representing the positive class.

negative_label (str):

Label representing the negative class.

"""

class BinaryConfusionMatrix(proto.Message):

r"""Confusion matrix for binary classification models.

Attributes:

positive_class_threshold (google.protobuf.wrappers_pb2.DoubleValue):

Threshold value used when computing each of

the following metric.

true_positives (google.protobuf.wrappers_pb2.Int64Value):

Number of true samples predicted as true.

false_positives (google.protobuf.wrappers_pb2.Int64Value):

Number of false samples predicted as true.

true_negatives (google.protobuf.wrappers_pb2.Int64Value):

Number of true samples predicted as false.

false_negatives (google.protobuf.wrappers_pb2.Int64Value):

Number of false samples predicted as false.

precision (google.protobuf.wrappers_pb2.DoubleValue):

The fraction of actual positive predictions

that had positive actual labels.

recall (google.protobuf.wrappers_pb2.DoubleValue):

The fraction of actual positive labels that

were given a positive prediction.

f1_score (google.protobuf.wrappers_pb2.DoubleValue):

The equally weighted average of recall and

precision.

accuracy (google.protobuf.wrappers_pb2.DoubleValue):

The fraction of predictions given the correct

label.

"""

positive_class_threshold = proto.Field(

proto.MESSAGE,

number=1,

message=wrappers_pb2.DoubleValue,

)

true_positives = proto.Field(

proto.MESSAGE,

number=2,

message=wrappers_pb2.Int64Value,

)

false_positives = proto.Field(

proto.MESSAGE,

number=3,

message=wrappers_pb2.Int64Value,

)

true_negatives = proto.Field(

proto.MESSAGE,

number=4,

message=wrappers_pb2.Int64Value,

)

false_negatives = proto.Field(

proto.MESSAGE,

number=5,

message=wrappers_pb2.Int64Value,

)

precision = proto.Field(

proto.MESSAGE,

number=6,

message=wrappers_pb2.DoubleValue,

)

recall = proto.Field(

proto.MESSAGE,

number=7,

message=wrappers_pb2.DoubleValue,

)

f1_score = proto.Field(

proto.MESSAGE,

number=8,

message=wrappers_pb2.DoubleValue,

)

accuracy = proto.Field(

proto.MESSAGE,

number=9,

message=wrappers_pb2.DoubleValue,

)

aggregate_classification_metrics = proto.Field(

proto.MESSAGE,

number=1,

message="Model.AggregateClassificationMetrics",

)

binary_confusion_matrix_list = proto.RepeatedField(

proto.MESSAGE,

number=2,

message="Model.BinaryClassificationMetrics.BinaryConfusionMatrix",

)

positive_label = proto.Field(

proto.STRING,

number=3,

)

negative_label = proto.Field(

proto.STRING,

number=4,

)

class MultiClassClassificationMetrics(proto.Message):

r"""Evaluation metrics for multi-class classification/classifier

models.

Attributes:

aggregate_classification_metrics (google.cloud.bigquery_v2.types.Model.AggregateClassificationMetrics):

Aggregate classification metrics.

confusion_matrix_list (Sequence[google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics.ConfusionMatrix]):

Confusion matrix at different thresholds.

"""

class ConfusionMatrix(proto.Message):

r"""Confusion matrix for multi-class classification models.

Attributes:

confidence_threshold (google.protobuf.wrappers_pb2.DoubleValue):

Confidence threshold used when computing the

entries of the confusion matrix.

rows (Sequence[google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics.ConfusionMatrix.Row]):

One row per actual label.

"""

class Entry(proto.Message):

r"""A single entry in the confusion matrix.

Attributes:

predicted_label (str):

The predicted label. For confidence_threshold > 0, we will

also add an entry indicating the number of items under the

confidence threshold.

item_count (google.protobuf.wrappers_pb2.Int64Value):

Number of items being predicted as this

label.

"""

predicted_label = proto.Field(

proto.STRING,

number=1,

)

item_count = proto.Field(

proto.MESSAGE,

number=2,

message=wrappers_pb2.Int64Value,

)

class Row(proto.Message):

r"""A single row in the confusion matrix.

Attributes:

actual_label (str):

The original label of this row.

entries (Sequence[google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry]):

Info describing predicted label distribution.

"""

actual_label = proto.Field(

proto.STRING,

number=1,

)

entries = proto.RepeatedField(

proto.MESSAGE,

number=2,

message="Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry",

)

confidence_threshold = proto.Field(

proto.MESSAGE,

number=1,

message=wrappers_pb2.DoubleValue,

)

rows = proto.RepeatedField(

proto.MESSAGE,

number=2,

message="Model.MultiClassClassificationMetrics.ConfusionMatrix.Row",

)

aggregate_classification_metrics = proto.Field(

proto.MESSAGE,

number=1,

message="Model.AggregateClassificationMetrics",

)

confusion_matrix_list = proto.RepeatedField(

proto.MESSAGE,

number=2,

message="Model.MultiClassClassificationMetrics.ConfusionMatrix",

)

class ClusteringMetrics(proto.Message):

r"""Evaluation metrics for clustering models.

Attributes:

davies_bouldin_index (google.protobuf.wrappers_pb2.DoubleValue):

Davies-Bouldin index.

mean_squared_distance (google.protobuf.wrappers_pb2.DoubleValue):

Mean of squared distances between each sample

to its cluster centroid.

clusters (Sequence[google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster]):

Information for all clusters.

"""

class Cluster(proto.Message):

r"""Message containing the information about one cluster.

Attributes:

centroid_id (int):

Centroid id.

feature_values (Sequence[google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster.FeatureValue]):

Values of highly variant features for this

cluster.

count (google.protobuf.wrappers_pb2.Int64Value):

Count of training data rows that were

assigned to this cluster.

"""

class FeatureValue(proto.Message):

r"""Representative value of a single feature within the cluster.

This message has `oneof`_ fields (mutually exclusive fields).

For each oneof, at most one member field can be set at the same time.

Setting any member of the oneof automatically clears all other

members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Attributes:

feature_column (str):

The feature column name.

numerical_value (google.protobuf.wrappers_pb2.DoubleValue):

The numerical feature value. This is the

centroid value for this feature.

This field is a member of `oneof`_ ``value``.

categorical_value (google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue):

The categorical feature value.

This field is a member of `oneof`_ ``value``.

"""

class CategoricalValue(proto.Message):

r"""Representative value of a categorical feature.

Attributes:

category_counts (Sequence[google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount]):

Counts of all categories for the categorical feature. If

there are more than ten categories, we return top ten (by

count) and return one more CategoryCount with category

"*OTHER*" and count as aggregate counts of remaining

categories.

"""

class CategoryCount(proto.Message):

r"""Represents the count of a single category within the cluster.

Attributes:

category (str):

The name of category.

count (google.protobuf.wrappers_pb2.Int64Value):

The count of training samples matching the

category within the cluster.

"""

category = proto.Field(

proto.STRING,

number=1,

)

count = proto.Field(

proto.MESSAGE,

number=2,

message=wrappers_pb2.Int64Value,

)

category_counts = proto.RepeatedField(

proto.MESSAGE,

number=1,

message="Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount",

)

feature_column = proto.Field(

proto.STRING,

number=1,

)

numerical_value = proto.Field(

proto.MESSAGE,

number=2,

oneof="value",

message=wrappers_pb2.DoubleValue,

)

categorical_value = proto.Field(

proto.MESSAGE,

number=3,

oneof="value",

message="Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue",

)

centroid_id = proto.Field(

proto.INT64,

number=1,

)

feature_values = proto.RepeatedField(

proto.MESSAGE,

number=2,

message="Model.ClusteringMetrics.Cluster.FeatureValue",

)

count = proto.Field(

proto.MESSAGE,

number=3,

message=wrappers_pb2.Int64Value,

)

davies_bouldin_index = proto.Field(

proto.MESSAGE,

number=1,

message=wrappers_pb2.DoubleValue,

)

mean_squared_distance = proto.Field(

proto.MESSAGE,

number=2,

message=wrappers_pb2.DoubleValue,

)

clusters = proto.RepeatedField(

proto.MESSAGE,

number=3,

message="Model.ClusteringMetrics.Cluster",

)

class RankingMetrics(proto.Message):

r"""Evaluation metrics used by weighted-ALS models specified by

feedback_type=implicit.

Attributes:

mean_average_precision (google.protobuf.wrappers_pb2.DoubleValue):

Calculates a precision per user for all the

items by ranking them and then averages all the

precisions across all the users.

mean_squared_error (google.protobuf.wrappers_pb2.DoubleValue):

Similar to the mean squared error computed in

regression and explicit recommendation models

except instead of computing the rating directly,

the output from evaluate is computed against a

preference which is 1 or 0 depending on if the

rating exists or not.

normalized_discounted_cumulative_gain (google.protobuf.wrappers_pb2.DoubleValue):

A metric to determine the goodness of a

ranking calculated from the predicted confidence

by comparing it to an ideal rank measured by the

original ratings.

average_rank (google.protobuf.wrappers_pb2.DoubleValue):

Determines the goodness of a ranking by

computing the percentile rank from the predicted

confidence and dividing it by the original rank.

"""

mean_average_precision = proto.Field(

proto.MESSAGE,

number=1,

message=wrappers_pb2.DoubleValue,

)

mean_squared_error = proto.Field(

proto.MESSAGE,

number=2,

message=wrappers_pb2.DoubleValue,

)

normalized_discounted_cumulative_gain = proto.Field(

proto.MESSAGE,

number=3,

message=wrappers_pb2.DoubleValue,

)

average_rank = proto.Field(

proto.MESSAGE,

number=4,

message=wrappers_pb2.DoubleValue,

)

class ArimaForecastingMetrics(proto.Message):

r"""Model evaluation metrics for ARIMA forecasting models.

Attributes:

non_seasonal_order (Sequence[google.cloud.bigquery_v2.types.Model.ArimaOrder]):

Non-seasonal order.

arima_fitting_metrics (Sequence[google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics]):

Arima model fitting metrics.

seasonal_periods (Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType]):

Seasonal periods. Repeated because multiple

periods are supported for one time series.

has_drift (Sequence[bool]):

Whether Arima model fitted with drift or not.

It is always false when d is not 1.

time_series_id (Sequence[str]):

Id to differentiate different time series for

the large-scale case.

arima_single_model_forecasting_metrics (Sequence[google.cloud.bigquery_v2.types.Model.ArimaForecastingMetrics.ArimaSingleModelForecastingMetrics]):

Repeated as there can be many metric sets

(one for each model) in auto-arima and the

large-scale case.

"""

class ArimaSingleModelForecastingMetrics(proto.Message):

r"""Model evaluation metrics for a single ARIMA forecasting

model.

Attributes:

non_seasonal_order (google.cloud.bigquery_v2.types.Model.ArimaOrder):

Non-seasonal order.

arima_fitting_metrics (google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics):

Arima fitting metrics.

has_drift (bool):

Is arima model fitted with drift or not. It

is always false when d is not 1.

time_series_id (str):

The time_series_id value for this time series. It will be

one of the unique values from the time_series_id_column

specified during ARIMA model training. Only present when

time_series_id_column training option was used.

time_series_ids (Sequence[str]):

The tuple of time_series_ids identifying this time series.

It will be one of the unique tuples of values present in the

time_series_id_columns specified during ARIMA model

training. Only present when time_series_id_columns training

option was used and the order of values here are same as the

order of time_series_id_columns.

seasonal_periods (Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType]):

Seasonal periods. Repeated because multiple

periods are supported for one time series.

has_holiday_effect (google.protobuf.wrappers_pb2.BoolValue):

If true, holiday_effect is a part of time series

decomposition result.

has_spikes_and_dips (google.protobuf.wrappers_pb2.BoolValue):

If true, spikes_and_dips is a part of time series

decomposition result.

has_step_changes (google.protobuf.wrappers_pb2.BoolValue):

If true, step_changes is a part of time series decomposition

result.

"""

non_seasonal_order = proto.Field(

proto.MESSAGE,

number=1,

message="Model.ArimaOrder",

)

arima_fitting_metrics = proto.Field(

proto.MESSAGE,

number=2,

message="Model.ArimaFittingMetrics",

)

has_drift = proto.Field(

proto.BOOL,

number=3,

)

time_series_id = proto.Field(

proto.STRING,

number=4,

)

time_series_ids = proto.RepeatedField(

proto.STRING,

number=9,

)

seasonal_periods = proto.RepeatedField(

proto.ENUM,

number=5,

enum="Model.SeasonalPeriod.SeasonalPeriodType",

)

has_holiday_effect = proto.Field(

proto.MESSAGE,

number=6,

message=wrappers_pb2.BoolValue,

)

has_spikes_and_dips = proto.Field(

proto.MESSAGE,

number=7,

message=wrappers_pb2.BoolValue,

)

has_step_changes = proto.Field(

proto.MESSAGE,

number=8,

message=wrappers_pb2.BoolValue,

)

non_seasonal_order = proto.RepeatedField(

proto.MESSAGE,

number=1,

message="Model.ArimaOrder",

)

arima_fitting_metrics = proto.RepeatedField(

proto.MESSAGE,

number=2,

message="Model.ArimaFittingMetrics",

)

seasonal_periods = proto.RepeatedField(

proto.ENUM,

number=3,

enum="Model.SeasonalPeriod.SeasonalPeriodType",

)

has_drift = proto.RepeatedField(

proto.BOOL,

number=4,

)

time_series_id = proto.RepeatedField(

proto.STRING,

number=5,

)

arima_single_model_forecasting_metrics = proto.RepeatedField(

proto.MESSAGE,

number=6,

message="Model.ArimaForecastingMetrics.ArimaSingleModelForecastingMetrics",

)

class EvaluationMetrics(proto.Message):

r"""Evaluation metrics of a model. These are either computed on

all training data or just the eval data based on whether eval

data was used during training. These are not present for

imported models.

This message has `oneof`_ fields (mutually exclusive fields).

For each oneof, at most one member field can be set at the same time.

Setting any member of the oneof automatically clears all other

members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Attributes:

regression_metrics (google.cloud.bigquery_v2.types.Model.RegressionMetrics):

Populated for regression models and explicit

feedback type matrix factorization models.

This field is a member of `oneof`_ ``metrics``.

binary_classification_metrics (google.cloud.bigquery_v2.types.Model.BinaryClassificationMetrics):

Populated for binary

classification/classifier models.

This field is a member of `oneof`_ ``metrics``.

multi_class_classification_metrics (google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics):

Populated for multi-class

classification/classifier models.

This field is a member of `oneof`_ ``metrics``.

clustering_metrics (google.cloud.bigquery_v2.types.Model.ClusteringMetrics):

Populated for clustering models.

This field is a member of `oneof`_ ``metrics``.

ranking_metrics (google.cloud.bigquery_v2.types.Model.RankingMetrics):

Populated for implicit feedback type matrix

factorization models.

This field is a member of `oneof`_ ``metrics``.

arima_forecasting_metrics (google.cloud.bigquery_v2.types.Model.ArimaForecastingMetrics):

Populated for ARIMA models.

This field is a member of `oneof`_ ``metrics``.

"""

regression_metrics = proto.Field(

proto.MESSAGE,

number=1,

oneof="metrics",

message="Model.RegressionMetrics",

)

binary_classification_metrics = proto.Field(

proto.MESSAGE,

number=2,

oneof="metrics",

message="Model.BinaryClassificationMetrics",

)

multi_class_classification_metrics = proto.Field(

proto.MESSAGE,

number=3,

oneof="metrics",

message="Model.MultiClassClassificationMetrics",

)

clustering_metrics = proto.Field(

proto.MESSAGE,

number=4,

oneof="metrics",

message="Model.ClusteringMetrics",

)

ranking_metrics = proto.Field(

proto.MESSAGE,

number=5,

oneof="metrics",

message="Model.RankingMetrics",

)

arima_forecasting_metrics = proto.Field(

proto.MESSAGE,

number=6,

oneof="metrics",

message="Model.ArimaForecastingMetrics",

)

class DataSplitResult(proto.Message):

r"""Data split result. This contains references to the training

and evaluation data tables that were used to train the model.

Attributes:

training_table (google.cloud.bigquery_v2.types.TableReference):