chore: Fix typing (#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>

docs/getting-started/concepts/feature-view.md

@@ -7,7 +7,8 @@ A feature view is an object that represents a logical group of time-series featu
 {% tabs %}
 {% tab title="driver_trips_feature_view.py" %}
 ```python
-from feast import BigQuerySource, FeatureView, Field, Float32, Int64
+from feast import BigQuerySource, FeatureView, Field
+from feast.types import Float32, Int64
 
 driver_stats_fv = FeatureView(
     name="driver_activity",
@@ -41,7 +42,8 @@ If a feature view contains features that are not related to a specific entity, t
 {% tabs %}
 {% tab title="global_stats.py" %}
 ```python
-from feast import BigQuerySource, FeatureView, Field, Int64
+from feast import BigQuerySource, FeatureView, Field
+from feast.types import Int64
 
 global_stats_fv = FeatureView(
     name="global_stats",
@@ -74,7 +76,8 @@ It is suggested that you dynamically specify the new FeatureView name using `.wi
 {% tabs %}
 {% tab title="location_stats_feature_view.py" %}
 ```python
-from feast import BigQuerySource, Entity, FeatureView, Field, Int32, ValueType
+from feast import BigQuerySource, Entity, FeatureView, Field, ValueType
+from feast.types import Int32
 
 location = Entity(name="location", join_key="location_id", value_type=ValueType.INT64)
 
@@ -121,7 +124,8 @@ A feature is an individual measurable property. It is typically a property obser
 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:
 
 ```python
-from feast import Field, Float32
+from feast import Field
+from feast.types import Float32
 
 trips_today = Field(
     name="trips_today",
@@ -138,7 +142,8 @@ Feature names must be unique within a [feature view](feature-view.md#feature-vie
 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:
 
 ```python
-from feast import Field, Float64, RequestSource
+from feast import Field, RequestSource
+from feast.types import Float64
 
 # Define a request data source which encodes features / information only
 # available at request time (e.g. part of the user initiated HTTP request)

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

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

docs/getting-started/quickstart.md

@@ -82,7 +82,8 @@ online_store:
 
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, FileSource, Float32, Int64, ValueType
+from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast.types import Float32, Int64
 
 # Read data from parquet files. Parquet is convenient for local development mode. For
 # production, you can use your favorite DWH, such as BigQuery. See Feast documentation
@@ -151,7 +152,8 @@ feast apply
 
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, FileSource, Float32, Int64, ValueType
+from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast.types import Float32, Int64
 
 # Read data from parquet files. Parquet is convenient for local development mode. For
 # production, you can use your favorite DWH, such as BigQuery. See Feast documentation

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

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

docs/reference/data-sources/push.md

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

docs/reference/feature-repository.md

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

docs/reference/feature-repository/README.md

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

docs/tutorials/validating-historical-features.md

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

sdk/python/feast/__init__.py

@@ -17,18 +17,6 @@
 from .on_demand_feature_view import OnDemandFeatureView
 from .repo_config import RepoConfig
 from .request_feature_view import RequestFeatureView
-from .types import (
-    Array,
-    Bool,
-    Bytes,
-    Float32,
-    Float64,
-    Int32,
-    Int64,
-    Invalid,
-    String,
-    UnixTimestamp,
-)
 from .value_type import ValueType
 
 logging.basicConfig(
@@ -62,15 +50,4 @@
     "RequestFeatureView",
     "SnowflakeSource",
     "PushSource",
-    # Types
-    "Array",
-    "Invalid",
-    "Bytes",
-    "String",
-    "Bool",
-    "Int32",
-    "Int64",
-    "Float32",
-    "Float64",
-    "UnixTimestamp",
 ]

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

@@ -1,6 +1,7 @@
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, Float32, Int64, RedshiftSource, ValueType
+from feast import Entity, FeatureView, Field, RedshiftSource, ValueType
+from feast.types import Float32, Int64
 
 # Define an entity for the driver. Entities can be thought of as primary keys used to
 # retrieve features. Entities are also used to join multiple tables/views during the

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

@@ -1,6 +1,7 @@
 from datetime import timedelta
 
-from feast import BigQuerySource, Entity, FeatureView, Field, Float32, Int64, ValueType
+from feast import BigQuerySource, Entity, FeatureView, Field, ValueType
+from feast.types import Float32, Int64
 
 # Define an entity for the driver. Entities can be thought of as primary keys used to
 # retrieve features. Entities are also used to join multiple tables/views during the

sdk/python/feast/templates/local/example.py

@@ -2,7 +2,8 @@
 
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, FileSource, Float32, Int64, ValueType
+from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast.types import Float32, Int64
 
 # Read data from parquet files. Parquet is convenient for local development mode. For
 # production, you can use your favorite DWH, such as BigQuery. See Feast documentation

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

@@ -2,7 +2,8 @@
 
 import yaml
 
-from feast import Entity, FeatureView, Field, Float32, Int64, SnowflakeSource
+from feast import Entity, FeatureView, Field, SnowflakeSource
+from feast.types import Float32, Int64
 
 # Define an entity for the driver. Entities can be thought of as primary keys used to
 # retrieve features. Entities are also used to join multiple tables/views during the

sdk/python/feast/templates/spark/example.py

@@ -5,10 +5,11 @@
 from datetime import timedelta
 from pathlib import Path
 
-from feast import Entity, FeatureView, Field, Float32, Int64, ValueType
+from feast import Entity, FeatureView, Field, ValueType
 from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
     SparkSource,
 )
+from feast.types import Float32, Int64
 
 # Constants related to the generated data sets
 CURRENT_DIR = Path(__file__).parent

sdk/python/tests/doctest/test_all.py

@@ -11,17 +11,9 @@ def setup_feature_store():
     """Prepares the local environment for a FeatureStore docstring test."""
     from datetime import datetime, timedelta
 
-    from feast import (
-        Entity,
-        FeatureStore,
-        FeatureView,
-        Field,
-        FileSource,
-        Float32,
-        Int64,
-        ValueType,
-    )
+    from feast import Entity, FeatureStore, FeatureView, Field, FileSource, ValueType
     from feast.repo_operations import init_repo
+    from feast.types import Float32, Int64
 
     init_repo("feature_repo", "local")
     fs = FeatureStore(repo_path="feature_repo")

sdk/python/tests/example_repos/example_feature_repo_1.py

@@ -6,12 +6,10 @@
     FeatureService,
     FeatureView,
     Field,
-    Float32,
-    Int64,
     PushSource,
-    String,
     ValueType,
 )
+from feast.types import Float32, Int64, String
 
 driver_locations_source = BigQuerySource(
     table="feast-oss.public.drivers",

sdk/python/tests/example_repos/example_feature_repo_2.py

@@ -1,15 +1,7 @@
 from datetime import timedelta
 
-from feast import (
-    Entity,
-    FeatureView,
-    Field,
-    FileSource,
-    Float32,
-    Int32,
-    Int64,
-    ValueType,
-)
+from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast.types import Float32, Int32, Int64
 
 driver_hourly_stats = FileSource(
     path="%PARQUET_PATH%",  # placeholder to be replaced by the test

sdk/python/tests/example_repos/example_feature_repo_with_entity_join_key.py

@@ -1,6 +1,7 @@
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, FileSource, Float32, Int64, ValueType
+from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast.types import Float32, Int64
 
 driver_hourly_stats = FileSource(
     path="%PARQUET_PATH%",  # placeholder to be replaced by the test

sdk/python/tests/integration/feature_repos/universal/feature_views.py

@@ -5,19 +5,15 @@
 import pandas as pd
 
 from feast import (
-    Array,
     Feature,
     FeatureView,
     Field,
-    Float32,
-    Float64,
-    Int32,
     OnDemandFeatureView,
     PushSource,
     ValueType,
 )
 from feast.data_source import DataSource, RequestSource
-from feast.types import FeastType
+from feast.types import Array, FeastType, Float32, Float64, Int32
 from tests.integration.feature_repos.universal.entities import location
 
 

sdk/python/tests/integration/online_store/test_universal_online.py

@@ -13,12 +13,13 @@
 import requests
 from botocore.exceptions import BotoCoreError
 
-from feast import Entity, FeatureService, FeatureView, Field, String, ValueType
+from feast import Entity, FeatureService, FeatureView, Field, ValueType
 from feast.errors import (
     FeatureNameCollisionError,
     RequestDataNotFoundInEntityRowsException,
 )
 from feast.online_response import TIMESTAMP_POSTFIX
+from feast.types import String
 from feast.wait import wait_retry_backoff
 from tests.integration.feature_repos.repo_configuration import (
     Environment,

sdk/python/tests/integration/registration/test_universal_odfv_feature_inference.py

@@ -3,9 +3,10 @@
 import pandas as pd
 import pytest
 
-from feast import Field, Float64
+from feast import Field
 from feast.errors import SpecifiedFeaturesNotPresentError
 from feast.infra.offline_stores.file_source import FileSource
+from feast.types import Float64
 from tests.integration.feature_repos.universal.entities import customer, driver, item
 from tests.integration.feature_repos.universal.feature_views import (
     conv_rate_plus_100_feature_view,

sdk/python/tests/integration/scaffolding/test_partial_apply.py

@@ -2,7 +2,8 @@
 
 import pytest
 
-from feast import BigQuerySource, FeatureView, Field, Float32, String
+from feast import BigQuerySource, FeatureView, Field
+from feast.types import Float32, String
 from tests.utils.cli_utils import CliRunner, get_example_repo
 from tests.utils.online_read_write_test import basic_rw_test