def __init__(self, featurestore_id):
self._feature_group_base_engine = (
feature_group_base_engine.FeatureGroupBaseEngine(featurestore_id))
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE)
self._expectations_api = expectations_api.ExpectationsApi(
featurestore_id, "featuregroups")
def __init__(self, feature_store_id):
self._feature_store_id = feature_store_id
self._feature_view_api = feature_view_api.FeatureViewApi(
feature_store_id)
self._tags_api = tags_api.TagsApi(feature_store_id, self.ENTITY_TYPE)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id)
self._transformation_function_engine = (
transformation_function_engine.TransformationFunctionEngine(
feature_store_id))
self._td_code_engine = code_engine.CodeEngine(
feature_store_id, self._TRAINING_DATA_API_PATH)
self._statistics_engine = statistics_engine.StatisticsEngine(
feature_store_id, self._TRAINING_DATA_API_PATH)
self._training_dataset_engine = training_dataset_engine.TrainingDatasetEngine(
feature_store_id)
self._query_constructor_api = query_constructor_api.QueryConstructorApi(
)
def __init__(
self,
name,
version,
data_format,
featurestore_id,
location="",
event_start_time=None,
event_end_time=None,
coalesce=False,
description=None,
storage_connector=None,
splits=None,
validation_size=None,
test_size=None,
train_start=None,
train_end=None,
validation_start=None,
validation_end=None,
test_start=None,
test_end=None,
seed=None,
created=None,
creator=None,
features=None,
statistics_config=None,
featurestore_name=None,
id=None,
inode_id=None,
training_dataset_type=None,
from_query=None,
querydto=None,
label=None,
transformation_functions=None,
train_split=None,
):
self._id = id
self._name = name
self._version = version
self._description = description
self._data_format = data_format
self._start_time = self._convert_event_time_to_timestamp(
event_start_time)
self._end_time = self._convert_event_time_to_timestamp(event_end_time)
self._validation_size = validation_size
self._test_size = test_size
self._train_start = train_start
self._train_end = train_end
self._validation_start = validation_start
self._validation_end = validation_end
self._test_start = test_start
self._test_end = test_end
self._coalesce = coalesce
self._seed = seed
self._location = location
self._from_query = from_query
self._querydto = querydto
self._feature_store_id = featurestore_id
self._transformation_functions = transformation_functions
self._train_split = train_split
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
featurestore_id)
self._training_dataset_engine = training_dataset_engine.TrainingDatasetEngine(
featurestore_id)
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE)
self._code_engine = code_engine.CodeEngine(featurestore_id,
self.ENTITY_TYPE)
self._transformation_function_engine = (
transformation_function_engine.TransformationFunctionEngine(
featurestore_id))
if training_dataset_type:
self.training_dataset_type = training_dataset_type
else:
self._training_dataset_type = None
# set up depending on user initialized or coming from backend response
if created is None:
# no type -> user init
self._features = features
self.storage_connector = storage_connector
self.splits = splits
self.statistics_config = statistics_config
self._label = label
if validation_size or test_size:
self._train_split = TrainingDatasetSplit.TRAIN
self.splits = {
TrainingDatasetSplit.TRAIN:
1 - (validation_size or 0) - (test_size or 0),
TrainingDatasetSplit.VALIDATION:
validation_size,
TrainingDatasetSplit.TEST:
test_size,
}
self._set_time_splits(
train_start,
train_end,
validation_start,
validation_end,
test_start,
test_end,
)
else:
# type available -> init from backend response
# make rest call to get all connector information, description etc.
self._storage_connector = StorageConnector.from_response_json(
storage_connector)
if features is None:
features = []
self._features = [
training_dataset_feature.TrainingDatasetFeature.
from_response_json(feat) for feat in features
]
self._splits = [
TrainingDatasetSplit.from_response_json(split)
for split in splits
]
self._statistics_config = StatisticsConfig.from_response_json(
statistics_config)
self._label = [
feat.name.lower() for feat in self._features if feat.label
]
self._vector_server = vector_server.VectorServer(
featurestore_id, features=self._features)
def __init__(
self,
name,
version,
data_format,
location,
featurestore_id,
coalesce=False,
description=None,
storage_connector=None,
splits=None,
seed=None,
created=None,
creator=None,
features=None,
statistics_config=None,
featurestore_name=None,
id=None,
inode_id=None,
training_dataset_type=None,
from_query=None,
querydto=None,
label=None,
):
self._id = id
self._name = name
self._version = version
self._description = description
self._data_format = data_format
self._coalesce = coalesce
self._seed = seed
self._location = location
self._from_query = from_query
self._querydto = querydto
self._feature_store_id = featurestore_id
self._prepared_statement_connection = None
self._prepared_statements = None
self._serving_keys = None
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
featurestore_id)
self._training_dataset_engine = training_dataset_engine.TrainingDatasetEngine(
featurestore_id)
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE)
# set up depending on user initialized or coming from backend response
if training_dataset_type is None:
# no type -> user init
self._features = features
self.storage_connector = storage_connector
self.splits = splits
self.statistics_config = statistics_config
self._label = label
else:
# type available -> init from backend response
# make rest call to get all connector information, description etc.
self._storage_connector = StorageConnector.from_response_json(
storage_connector)
self._features = [
training_dataset_feature.TrainingDatasetFeature.
from_response_json(feat) for feat in features
]
self._splits = splits
self._training_dataset_type = training_dataset_type
self._statistics_config = StatisticsConfig.from_response_json(
statistics_config)
self._label = [
feat.name.lower() for feat in self._features if feat.label
]
def __init__(
self,
name,
version,
data_format,
location,
featurestore_id,
description=None,
storage_connector=None,
splits=None,
seed=None,
created=None,
creator=None,
features=None,
statistics_config=None,
featurestore_name=None,
id=None,
jobs=None,
inode_id=None,
storage_connector_name=None,
storage_connector_id=None,
storage_connector_type=None,
training_dataset_type=None,
from_query=None,
querydto=None,
):
self._id = id
self._name = name
self._version = version
self._description = description
self._data_format = data_format
self._seed = seed
self._location = location
self._from_query = from_query
self._querydto = querydto
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
featurestore_id)
self._training_dataset_engine = training_dataset_engine.TrainingDatasetEngine(
featurestore_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
featurestore_id)
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE)
# set up depending on user initialized or coming from backend response
if training_dataset_type is None:
# no type -> user init
self._features = features
self.storage_connector = storage_connector
self.splits = splits
self.statistics_config = statistics_config
else:
# type available -> init from backend response
# make rest call to get all connector information, description etc.
self._storage_connector = self._storage_connector_api.get_by_id(
storage_connector_id, storage_connector_type)
self._features = [
training_dataset_feature.TrainingDatasetFeature.
from_response_json(feat) for feat in features
]
self._splits = splits
self._training_dataset_type = training_dataset_type
self.statistics_config = None
def __init__(self, featurestore_id):
self._feature_group_base_engine = (
feature_group_base_engine.FeatureGroupBaseEngine(featurestore_id))
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE)
def __init__(
self,
name,
version,
featurestore_id,
description="",
partition_key=None,
primary_key=None,
featurestore_name=None,
created=None,
creator=None,
id=None,
features=None,
location=None,
jobs=None,
desc_stats_enabled=None,
feat_corr_enabled=None,
feat_hist_enabled=None,
statistic_columns=None,
online_enabled=False,
time_travel_format=None,
hudi_enabled=False,
statistics_config=None,
):
super().__init__(featurestore_id)
self._feature_store_id = featurestore_id
self._feature_store_name = featurestore_name
self._description = description
self._created = created
self._creator = creator
self._version = version
self._name = name
self._id = id
self._features = [
feature.Feature.from_response_json(feat) if isinstance(feat, dict) else feat
for feat in features
]
self._location = location
self._jobs = jobs
self._online_enabled = online_enabled
self._time_travel_format = (
time_travel_format.upper() if time_travel_format is not None else None
)
self._hudi_enabled = hudi_enabled
if id is not None:
# initialized by backend
self.statistics_config = StatisticsConfig(
desc_stats_enabled,
feat_corr_enabled,
feat_hist_enabled,
statistic_columns,
)
self._primary_key = [
feat.name for feat in self._features if feat.primary is True
]
self._partition_key = [
feat.name for feat in self._features if feat.partition is True
]
else:
# initialized by user
self.statistics_config = statistics_config
self._primary_key = primary_key
self._partition_key = partition_key
self._feature_group_engine = feature_group_engine.FeatureGroupEngine(
featurestore_id
)
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE
)
def __init__(
self,
name,
version,
description,
featurestore_id,
partition_key=None,
primary_key=None,
featurestore_name=None,
created=None,
creator=None,
id=None,
features=None,
location=None,
jobs=None,
desc_stats_enabled=None,
feat_corr_enabled=None,
feat_hist_enabled=None,
statistic_columns=None,
online_enabled=False,
hudi_enabled=False,
default_storage="offline",
statistics_config=None,
):
self._feature_store_id = featurestore_id
self._feature_store_name = featurestore_name
self._description = description
self._created = created
self._creator = creator
self._version = version
self._name = name
self._id = id
self._features = [
feature.Feature.from_response_json(feat) for feat in features
]
self._location = location
self._jobs = jobs
self._online_enabled = online_enabled
self._default_storage = default_storage
self._hudi_enabled = hudi_enabled
if id is None:
# Initialized from the API
self._primary_key = primary_key
self._partition_key = partition_key
else:
# Initialized from the backend
self._primary_key = [f.name for f in self._features if f.primary]
self._partition_key = [
f.name for f in self._features if f.partition
]
if id is not None:
# initialized by backend
self.statistics_config = StatisticsConfig(
desc_stats_enabled,
feat_corr_enabled,
feat_hist_enabled,
statistic_columns,
)
self._primary_key = [
feat.name for feat in self._features if feat.primary is True
]
self._partition_key = [
feat.name for feat in self._features if feat.partition is True
]
else:
# initialized by user
self.statistics_config = statistics_config
self._primary_key = primary_key
self._partition_key = partition_key
self._feature_group_engine = feature_group_engine.FeatureGroupEngine(
featurestore_id)
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE)
