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chore: Fix typing (feast-dev#2534)
* Remove Array, Float32, etc. as top-level imports Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Fix type imports in docs Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Fix type imports in templates Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Fix type imports in tests Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Fix doctests Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Format Signed-off-by: Felix Wang <wangfelix98@gmail.com>
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‎docs/getting-started/concepts/feature-view.md‎

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@@ -7,7 +7,8 @@ A feature view is an object that represents a logical group of time-series featu
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{% tabs %}
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{% tab title="driver_trips_feature_view.py" %}
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```python
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from feast import BigQuerySource, FeatureView, Field, Float32, Int64
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from feast import BigQuerySource, FeatureView, Field
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from feast.types import Float32, Int64
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driver_stats_fv = FeatureView(
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name="driver_activity",
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{% tabs %}
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{% tab title="global_stats.py" %}
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```python
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from feast import BigQuerySource, FeatureView, Field, Int64
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from feast import BigQuerySource, FeatureView, Field
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from feast.types import Int64
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global_stats_fv = FeatureView(
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name="global_stats",
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{% tabs %}
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{% tab title="location_stats_feature_view.py" %}
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```python
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from feast import BigQuerySource, Entity, FeatureView, Field, Int32, ValueType
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from feast import BigQuerySource, Entity, FeatureView, Field, ValueType
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from feast.types import Int32
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location = Entity(name="location", join_key="location_id", value_type=ValueType.INT64)
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@@ -121,7 +124,8 @@ A feature is an individual measurable property. It is typically a property obser
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Features are defined as part of feature views. Since Feast does not transform data, a feature is essentially a schema that only contains a name and a type:
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```python
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from feast import Field, Float32
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from feast import Field
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from feast.types import Float32
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trips_today = Field(
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name="trips_today",
@@ -138,7 +142,8 @@ Feature names must be unique within a [feature view](feature-view.md#feature-vie
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On demand feature views allows users to use existing features and request time data (features only available at request time) to transform and create new features. Users define python transformation logic which is executed in both historical retrieval and online retrieval paths:
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```python
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from feast import Field, Float64, RequestSource
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from feast import Field, RequestSource
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from feast.types import Float64
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# Define a request data source which encodes features / information only
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# available at request time (e.g. part of the user initiated HTTP request)

‎docs/getting-started/concepts/point-in-time-joins.md‎

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@@ -7,7 +7,8 @@ Feature values in Feast are modeled as time-series records. Below is an example
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The above table can be registered with Feast through the following feature view:
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```python
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from feast import FeatureView, Field, FileSource, Float32, Int64
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from feast import FeatureView, Field, FileSource
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from feast.types import Float32, Int64
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driver_stats_fv = FeatureView(
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name="driver_hourly_stats",

‎docs/getting-started/quickstart.md‎

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@@ -82,7 +82,8 @@ online_store:
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from datetime import timedelta
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from feast import Entity, FeatureView, Field, FileSource, Float32, Int64, ValueType
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from feast import Entity, FeatureView, Field, FileSource, ValueType
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from feast.types import Float32, Int64
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# Read data from parquet files. Parquet is convenient for local development mode. For
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# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
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from datetime import timedelta
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from feast import Entity, FeatureView, Field, FileSource, Float32, Int64, ValueType
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from feast import Entity, FeatureView, Field, FileSource, ValueType
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from feast.types import Float32, Int64
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# Read data from parquet files. Parquet is convenient for local development mode. For
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# production, you can use your favorite DWH, such as BigQuery. See Feast documentation

‎docs/reference/alpha-on-demand-feature-view.md‎

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@@ -28,7 +28,8 @@ See [https://github.com/feast-dev/on-demand-feature-views-demo](https://github.c
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We register `RequestDataSource` inputs and the transform in `on_demand_feature_view`:
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```python
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from feast import Field, Float64, RequestSource
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from feast import Field, RequestSource
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from feast.types import Float64
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# Define a request data source which encodes features / information only
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# available at request time (e.g. part of the user initiated HTTP request)

‎docs/reference/data-sources/push.md‎

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@@ -14,7 +14,8 @@ When using a PushSource as a stream source in the definition of a feature view,
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### Defining a push source
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```python
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from feast import PushSource, ValueType, BigQuerySource, FeatureView, Feature, Field, Int64
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from feast import PushSource, ValueType, BigQuerySource, FeatureView, Feature, Field
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from feast.types import Int64
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push_source = PushSource(
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name="push_source",

‎docs/reference/feature-repository.md‎

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@@ -89,7 +89,8 @@ A feature repository can also contain one or more Python files that contain feat
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```python
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from datetime import timedelta
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field, Float32, String, ValueType
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field, ValueType
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from feast.types import Float32, String
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driver_locations_source = BigQuerySource(
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table_ref="rh_prod.ride_hailing_co.drivers",

‎docs/reference/feature-repository/README.md‎

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@@ -94,7 +94,8 @@ A feature repository can also contain one or more Python files that contain feat
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```python
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from datetime import timedelta
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field, Float32, String, ValueType
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field, ValueType
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from feast.types import Float32, String
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driver_locations_source = BigQuerySource(
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table_ref="rh_prod.ride_hailing_co.drivers",

‎docs/tutorials/validating-historical-features.md‎

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@@ -107,7 +107,8 @@ pyarrow.parquet.write_table(entities_2019_table, "entities.parquet")
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import pyarrow.parquet
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import pandas as pd
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from feast import FeatureView, Entity, FeatureStore, Field, Float64, Int64
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from feast import FeatureView, Entity, FeatureStore, Field
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from feast.types import Float64, Int64
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from feast.value_type import ValueType
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from feast.data_format import ParquetFormat
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from feast.on_demand_feature_view import on_demand_feature_view

‎sdk/python/feast/__init__.py‎

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from .on_demand_feature_view import OnDemandFeatureView
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from .repo_config import RepoConfig
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from .request_feature_view import RequestFeatureView
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from .types import (
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Array,
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Bool,
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Bytes,
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Float32,
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Float64,
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Int32,
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Int64,
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Invalid,
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String,
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UnixTimestamp,
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)
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from .value_type import ValueType
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logging.basicConfig(
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"RequestFeatureView",
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"SnowflakeSource",
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"PushSource",
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# Types
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"Array",
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"Invalid",
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"Bytes",
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"String",
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"Bool",
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"Int32",
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"Int64",
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"Float32",
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"Float64",
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"UnixTimestamp",
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]

‎sdk/python/feast/templates/aws/driver_repo.py‎

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from datetime import timedelta
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from feast import Entity, FeatureView, Field, Float32, Int64, RedshiftSource, ValueType
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from feast import Entity, FeatureView, Field, RedshiftSource, ValueType
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from feast.types import Float32, Int64
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# Define an entity for the driver. Entities can be thought of as primary keys used to
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# retrieve features. Entities are also used to join multiple tables/views during the

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