tecton.interactive.BatchFeatureView¶

class tecton.interactive.BatchFeatureView(proto, fco_container)¶

BatchFeatureView class.

To get a FeatureView instance, call tecton.get_feature_view().

Methods

`delete_keys`	Deletes any materialized data that matches the specified join keys from the FeatureView.
`deletion_status`	Displays information for deletion jobs created with the delete_keys() method, which may include past jobs, scheduled jobs, and job failures.
`get_historical_features`	Returns a Tecton `tecton.DataFrame` of historical values for this feature view.
`get_online_features`	Returns a single Tecton `tecton.FeatureVector` from the Online Store.
`materialization_status`	Displays materialization information for the FeatureView, which may include past jobs, scheduled jobs, and job failures.
`run`	Run the FeatureView.
`summary`	Returns various information about this feature definition, including the most critical metadata such as the name, owner, features, etc.

delete_keys(keys, online=True, offline=True)¶

Deletes any materialized data that matches the specified join keys from the FeatureView. This method kicks off a job to delete the data in the offline and online stores. If a FeatureView has multiple entities, the full set of join keys must be specified. Only supports Delta offline store and Dynamo online store. (offline_config=DeltaConfig() and online_config left as default) Maximum 10000 keys can be deleted per request.

Parameters

keys (Union[DataFrame, DataFrame]) – The Dataframe to be deleted. Must conform to the FeatureView join keys.
online (bool) – (Optional, default=True) Whether or not to delete from the online store.
offline (bool) – (Optional, default=True) Whether or not to delete from the offline store.

Returns

None if deletion job was created successfully.

deletion_status(verbose=False, limit=1000, sort_columns=None, errors_only=False)¶

Displays information for deletion jobs created with the delete_keys() method, which may include past jobs, scheduled jobs, and job failures.

Parameters

verbose – If set to true, method will display additional low level deletion information, useful for debugging.
limit – Maximum number of jobs to return.
sort_columns – A comma-separated list of column names by which to sort the rows.

Param

errors_only: If set to true, method will only return jobs that failed with an error.

get_historical_features(spine=None, timestamp_key=None, start_time=None, end_time=None, entities=None, from_source=False, save=False, save_as=None)¶

Returns a Tecton tecton.DataFrame of historical values for this feature view. If no arguments are passed in, all feature values for this feature view will be returned in a Tecton DataFrame.

Note: The timestamp_key parameter is only applicable when a spine is passed in. Parameters start_time, end_time, and entities are only applicable when a spine is not passed in.

Parameters

spine (Union[pyspark.sql.DataFrame, pandas.DataFrame, tecton.DataFrame]) – The spine to join against, as a dataframe. If present, the returned DataFrame will contain rollups for all (join key, temporal key) combinations that are required to compute a full frame from the spine. To distinguish between spine columns and feature columns, feature columns are labeled as feature_view_name.feature_name in the returned DataFrame. If spine is not specified, it’ll return a DataFrame of feature values in the specified time range.
timestamp_key (str) – Name of the time column in the spine. This method will fetch the latest features computed before the specified timestamps in this column. If unspecified, will default to the time column of the spine if there is only one present. If more than one time column is present in the spine, you must specify which column you’d like to use.
start_time (datetime.datetime) – The interval start time from when we want to retrieve features. If no timezone is specified, will default to using UTC.
end_time (datetime.datetime) – The interval end time until when we want to retrieve features. If no timezone is specified, will default to using UTC.
entities (Union[pyspark.sql.DataFrame, pandas.DataFrame, tecton.DataFrame]) – Filter feature data returned to a set of entity IDs. If specified, this DataFrame should only contain join key columns.
from_source (bool) – Whether feature values should be recomputed from the original data source. If False, we will read the materialized values from the offline store.
save (bool) – Whether to persist the DataFrame as a Dataset object. Default is False.
save_as (str) – Name to save the DataFrame as. If unspecified and save=True, a name will be generated.

Examples

A FeatureView fv with join key user_id.

1) fv.get_historical_features(spine) where spine=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the spine.

2) fv.get_historical_features(spine, save_as='my_dataset) where spine=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the spine. Save the DataFrame as dataset with the name :py:mod`my_dataset`.

3) fv.get_historical_features(spine, timestamp_key='date_1') where spine=pandas.Dataframe({'user_id': [1,2,3], 'date_1': [datetime(...), datetime(...), datetime(...)], 'date_2': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the ‘date_1’ column in the spine.

4) fv.get_historical_features(start_time=datetime(...), end_time=datetime(...)) Fetch all historical features from the offline store in the time range specified by start_time and end_time.

Returns: A Tecton tecton.DataFrame.

get_online_features(join_keys, include_join_keys_in_response=False)¶

Returns a single Tecton tecton.FeatureVector from the Online Store.

Parameters

join_keys (Mapping[str, Union[int, int64, str, bytes]]) – Join keys of the enclosed FeatureViews.
include_join_keys_in_response (bool) – Whether to include join keys as part of the response FeatureVector.

Examples

A FeatureView fv with join key user_id.

1) fv.get_online_features(join_keys={'user_id': 1}) Fetch the latest features from the online store for user 1.

2) fv.get_online_features(join_keys={'user_id': 1}, include_join_keys_in_respone=True) Fetch the latest features from the online store for user 1 and include the join key information (user_id=1) in the returned FeatureVector.

Returns: A tecton.FeatureVector of the results.

materialization_status(verbose=False, limit=1000, sort_columns=None, errors_only=False)¶

Displays materialization information for the FeatureView, which may include past jobs, scheduled jobs, and job failures. This method returns different information depending on the type of FeatureView.

Parameters

verbose – If set to true, method will display additional low level materialization information, useful for debugging.
limit – Maximum number of jobs to return.
sort_columns – A comma-separated list of column names by which to sort the rows.

Param

errors_only: If set to true, method will only return jobs that failed with an error.

run(start_time=None, end_time=None, aggregation_level=None, **mock_sources)¶

Run the FeatureView. Supports transforming data directly from raw data sources or using mock data.

To run the feature view with data from raw data sources, the environment must have access to the data sources.

Parameters

start_time (Optional[datetime]) – The start time of the time window to materialize. If not set, defaults to end_time minus batch_schedule. If end_time is also not set, defaults to the start of the last complete materialization period.
end_time (Optional[datetime]) – The end time of the time window to materialize. If not set, defaults to start_time plus batch_schedule. If start_time is also not set, defaults to the end of the last complete materialization period.
aggregation_level (Optional[str]) –
For feature views with aggregations, aggregation_level configures which stage of the aggregation to run up to.

The query for Aggregate Feature Views operates in three steps:
1. The feature view query is run over the provided time range. The user defined transformations are applied over the data source.
2. The result of #1 is aggregated into tiles the size of the aggregation_interval.
3. The tiles from #2 are combined to form the final feature values. The number of tiles that are combined is based off of the time_window of the aggregation.
For testing and debugging purposes, to see the output of #1, use aggregation_level="disabled". For #2, use aggregation_level="partial". For #3, use aggregation_level="full".

aggregation_level="full" is the default behavior.
**mock_sources – kwargs for mock sources that should be used instead of fetching directly from raw data soruces. The keys should match the feature view’s function parameters. For feature views with multiple sources, mocking some data sources and using raw data for others is supported.

Returns

A tecton DataFrame of the results.

Example:

import tecton
import pandas
from datetime import datetime

# Example running a non-aggregate feature view with mock data.
feature_view = tecton.get_workspace("my_workspace").get_feature_view("my_feature_view")

mock_fraud_user_data = pandas.DataFrame({
    "user_id": ["user_1", "user_2", "user_3"],
    "timestamp": [datetime(2022, 5, 1, 0), datetime(2022, 5, 1, 2), datetime(2022, 5, 1, 5)],
    "credit_card_number": [1000, 4000, 5000],
})

result = feature_view.run(
  start_time=datetime(2022, 5, 1),
  end_time=datetime(2022, 5, 2),
  fraud_users_batch=mock_fraud_user_data)  # `fraud_users_batch` is the name of this FeatureView's data source parameter.

# Example running an aggregate feature view with real data.
aggregate_feature_view = tecton.get_workspace("my_workspace").get_feature_view("my_aggregate_feature_view")

result = aggregate_feature_view.run(
  start_time=datetime(2022, 5, 1),
  end_time=datetime(2022, 5, 2),
  aggregation_level="full")  # or "partial" or "disabled"

summary()¶: Returns various information about this feature definition, including the most critical metadata such as the name, owner, features, etc.

Attributes

`batch_schedule`	This represents how often we schedule batch materialization jobs.
`created_at`	Returns the creation date of this Tecton Object.
`defined_in`	Returns filename where this Tecton Object has been declared.
`description`	The description of this Tecton Object, set by user.
`entity_names`	Returns a list of entity names.
`family`	Deprecated.
`feature_start_time`	This represents the time at which features are first available.
`features`	Returns the names of the (output) features.
`id`	Returns the id of this object
`is_on_demand`	Deprecated.
`is_temporal`	Deprecated.
`is_temporal_aggregate`	Deprecated.
`join_keys`	Returns the join key column names
`max_data_delay`	The maximum data_delay for a data source input to this feature view.
`name`	The name of this Tecton Object.
`online_serving_index`	Returns Defines the set of join keys that will be indexed and queryable during online serving.
`owner`	The owner of this Tecton Object (typically the email of the primary maintainer.)
`tags`	Tags associated with this Tecton Object (key-value pairs of arbitrary metadata set by user.)
`timestamp_field`	Returns the timestamp_field of this FeatureView.
`url`	Returns a link to the Tecton Web UI.
`wildcard_join_key`	Returns a wildcard join key column name if it exists; Otherwise returns None.
`workspace`	Returns the workspace this Tecton Object was created in.