tecton.interactive.FeatureView¶

class tecton.interactive.FeatureView(proto)¶

FeatureView class.

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

Methods

`get_feature_dataframe`	Returns a Tecton `DataFrame` that contains the output Feature Transformation of the Feature View.
`get_feature_vector`	Returns a single Tecton `FeatureVector` from the Online Store.
`get_features`	Returns all the feature values that are defined by this Feature View in the specified time range.
`get_historical_features`	Returns a Tecton `DataFrame` of historical values for this feature view.
`get_online_features`	Returns a single Tecton `FeatureVector` from the Online Store.
`materialization_status`	Displays materialization information for the FeatureView, which may include past jobs, scheduled jobs, and job failures.
`preview`	Shows a preview of the FeatureView’s features.
`run`	Runs the feature view against passed-in mock data rather than the actual data sources.
`summary`	Returns various information about this feature definition, including the most critical metadata such as the name, owner, features, etc.

get_feature_dataframe(spine=None, spine_time_key=None, use_materialized_data=True, save=None, save_as=None)¶

Returns a Tecton DataFrame that contains the output Feature Transformation of the Feature View.

Parameters

spine (Union[DataFrame, DataFrame, None]) – (Optional) The spine to join against, as a dataframe. If present, the returned data frame will contain rollups for all (join key, temporal key) combinations that are required to compute a full frame from the spine. If spine is not specified, it’ll return a dataframe with sample feature vectors.
spine_time_key (Optional[str]) – (Optional) Name of the time column in spine. If unspecified, will default to the time column of the spine if there is only one present.
use_materialized_data (bool) – (Optional) Use materialized data if materialization is enabled
save (Optional[bool]) – (Optional) set to True to persist DataFrame as a Dataset object
save_as (Optional[str]) – (Optional) name to save the DataFrame as. Not applicable when save=False. If unspecified and save=True, a name will be generated.

Returns

A Tecton DataFrame.

get_feature_vector(join_keys=None, include_join_keys_in_response=False, request_context_map=None)¶

Returns a single Tecton FeatureVector from the Online Store. At least one of join_keys or request_context_map 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_context_map (Optional[Mapping[str, Union[int, int64, str, bytes, float]]]) – Dictionary of request context values.

Returns

A FeatureVector of the results.

get_features(entities=None, start_time=None, end_time=None, from_source=False)¶

Returns all the feature values that are defined by this Feature View in the specified time range.

Parameters

entities (Union[DataFrame, DataFrame, None]) – (Optional) Filter feature data to a set of entity IDs. If specified, this DataFrame should only contain join key columns.
start_time (Union[DateTime, datetime, None]) – (Optional) The interval start time from when we want to retrieve features.
end_time (Union[DateTime, datetime, None]) – (Optional) The interval end time until when we want to retrieve features.
from_source (bool) – Whether feature values should be recomputed from the original data source. If False, we will attempt to read the values from the materialized store.

Returns

A Tecton DataFrame with features values.

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 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[DataFrame, DataFrame, DataFrame, None]) –

(Optional) 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 (Optional[str]) – (Optional) Name of the time column in spine. If unspecified, will default to the time column of the spine if there is only one present.
start_time (Union[DateTime, datetime, None]) – (Optional) The interval start time from when we want to retrieve features. If no timezone is specified, will default to using UTC.
end_time (Union[DateTime, datetime, None]) – (Optional) The interval end time until when we want to retrieve features. If no timezone is specified, will default to using UTC.
entities (Union[DataFrame, DataFrame, DataFrame, None]) – (Optional) Filter feature data returned to a set of entity IDs. If specified, this DataFrame should only contain join key columns.
from_source (bool) – (Optional) Whether feature values should be recomputed from the original data source. If False, we will read the values from the materialized store.
save (bool) – (Optional) set to True to persist DataFrame as a Dataset object. Default is False.
save_as (Optional[str]) – (Optional) name to save the DataFrame as. If unspecified and save=True, a name will be generated.

Returns

A Tecton DataFrame.

get_online_features(join_keys, include_join_keys_in_response=False)¶

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

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.
request_data – Dictionary of request context values.

Returns

A 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. It is not currently supported by OnDemandFeatureView, as it does not involve materialization.

Parameters

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

preview(limit=10, time_range=None, use_materialized_data=True)¶

Shows a preview of the FeatureView’s features. Random, unique join_keys are chosen to showcase the features.

Parameters

limit – (Optional, default=10) The number of rows to preview
time_range (Optional[Period]) – (Optional) Time range to collect features from. Will default to recent data (past 2 days).
use_materialized_data (bool) – (Optional) Use materialized data if materialization is enabled.

Returns

A Tecton DataFrame.

run(**mock_inputs)¶

Runs the feature view against passed-in mock data rather than the actual data sources.

Parameters: mock_inputs (Union[DataFrame, DataFrame]) – Dictionary with the same expected keys as the FeatureView’s inputs parameter. Each input name maps to a Pandas DataFrame that should be evaluated for that node in the pipeline.

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

Attributes

`batch_materialization_schedule`	This represents how often we schedule batch materialization jobs.
`created_at`	Returns the creation date of this Tecton Primitive.
`defined_in`	Returns filename where this Tecton Primitive has been declared.
`description`	The description of this Tecton Primitive, set by user.
`entity_names`	Returns a list of entity names.
`family`	The family of this Tecton Primitive, used to group Primitives.
`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_online`	Returns whether or not this FeatureView is of type OnDemandFeatureView.
`is_temporal`	Returns whether or not this FeatureView is of type TemporalFeatureView.
`is_temporal_aggregate`	Returns whether or not this FeatureView is of type TemporalAggregateFeatureView.
`join_keys`	Returns the join key column names
`name`	The name of this Tecton Primitive.
`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 Primitive (typically the email of the primary maintainer.)
`schedule_offset`	If this attribute is non-empty, Tecton will schedule materialization jobs at an offset equal to this.
`tags`	Tags associated with this Tecton Primitive (key-value pairs of arbitrary metadata set by user.)
`timestamp_key`	Returns the timestamp_key column name of this FeatureView.
`type`	Returns the FeatureView type.
`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 Primitive was created in.