tecton.interactive.OnDemandFeatureView¶

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

OnDemandFeatureView 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.
`run`	Run the OnDemandFeatureView using mock inputs.
`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, timestamp_key=None, from_source=False, save=False, save_as=None)¶

Returns a Tecton tecton.DataFrame of historical values for this feature view.

Parameters

spine (Union[pyspark.sql.DataFrame, pandas.DataFrame, tecton.DataFrame]) – The spine to join against, as a dataframe. The returned data frame will contain rollups for all (join key, request data key) combinations that are required to compute a full frame from the spine.
timestamp_key (str) – Name of the time column in spine. This method will fetch the latest features computed before the specified timestamps in this column. If unspecified and this feature view has feature view dependencies, timestamp_key will default to the time column of the spine if there is only one present.
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 (Optional[str]) – Name to save the DataFrame as. If unspecified and save=True, a name will be generated.

Type

save_as: str

Examples

An OnDemandFeatureView fv that expects request time data for the key amount.

The request time data is defined in the feature definition as such:
request_schema = StructType()
request_schema.add(StructField(‘amount’, DoubleType()))
transaction_request = RequestDataSource(request_schema=request_schema)

1) fv.get_historical_features(spine) where spine=pandas.Dataframe({'amount': [30, 50, 10000]}) Fetch historical features from the offline store with request time data inputs 30, 50, and 10000 for key ‘amount’.

2) fv.get_historical_features(spine, save_as='my_dataset') where spine=pandas.Dataframe({'amount': [30, 50, 10000]}) Fetch historical features from the offline store request time data inputs 30, 50, and 10000 for key ‘amount’. Save the DataFrame as dataset with the name ‘my_dataset’.

An OnDemandFeatureView fv the expects request time data for the key amount and has a feature view dependency with join key user_id.

1) fv.get_historical_features(spine) where spine=pandas.Dataframe({'user_id': [1,2,3], 'date_1': [datetime(...), datetime(...), datetime(...)], 'amount': [30, 50, 10000]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps and values for amount in the spine.

Returns: A Tecton tecton.DataFrame.

get_online_features(join_keys=None, include_join_keys_in_response=False, request_data=None)¶

Returns a single Tecton tecton.FeatureVector from the Online Store. At least one of join_keys or request_data is required.

Parameters

join_keys (Optional[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.
request_data (Optional[Mapping[str, Union[int, int64, str, bytes, float]]]) –
Dictionary of request context values used for OnDemandFeatureViews.

Examples:
An OnDemandFeatureView fv that expects request time data for the key amount.

The request time data is defined in the feature definition as such:

request_schema = StructType()

request_schema.add(StructField(‘amount’, DoubleType()))

transaction_request = RequestDataSource(request_schema=request_schema)

1) fv.get_online_features(request_data={'amount': 50}) Fetch the latest features with input amount=50.

An OnDemandFeatureView fv that has a feature view dependency with join key user_id and expects request time data for the key amount.

1) fv.get_online_features(join_keys={'user_id': 1}, request_data={'amount': 50}, include_join_keys_in_respone=True) Fetch the latest features from the online store for user 1 with input amount=50. In the returned FeatureVector, nclude the join key information (user_id=1).

Returns

A tecton.FeatureVector of the results.

run(**mock_inputs)¶

Run the OnDemandFeatureView using mock inputs.

Parameters: mock_inputs (Union[Dict[str, Any], DataFrame, DataFrame]) – Required. Keyword args with the same expected keys as the OnDemandFeatureView’s inputs parameters. For the “python” mode, each input must be a Dictionary representing a single row. For the “pandas” mode, each input must be a DataFrame with all of them containing the same number of rows and matching row ordering.

Examples

A FeatureView ‘fv’ with inputs: ‘ds1’, ‘ds2’.

fv.run(ds1=mock_dataframe1, ds2=mock_dataframe2) Both ds1 and ds2 must be provided..

Returns: A Dict object for the “python” mode and a tecton DataFrame of the results for the “pandas” mode.

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

Attributes

`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
`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.)
`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.