def test_register_feature_set(self, sqlite_store):
fs = FeatureSet("my-feature-set")
fs.add(Feature(name="my-feature-1", dtype=ValueType.INT64))
fs.add(Feature(name="my-feature-2", dtype=ValueType.INT64))
fs.add(Entity(name="my-entity-1", dtype=ValueType.INT64))
fs._version = 1
feature_set_spec_proto = fs.to_proto().spec
sqlite_store.register_feature_set(feature_set_spec_proto)
feature_row = FeatureRowProto.FeatureRow(
feature_set="feature_set_1",
event_timestamp=Timestamp(),
fields=[
FieldProto.Field(
name="feature_1", value=ValueProto.Value(float_val=1.2)
),
FieldProto.Field(
name="feature_2", value=ValueProto.Value(float_val=1.2)
),
FieldProto.Field(
name="feature_3", value=ValueProto.Value(float_val=1.2)
),
],
)
# sqlite_store.upsert_feature_row(feature_set_proto, feature_row)
assert True
def convert_dict_to_proto_values(row: dict,
df_datetime_dtype: pd.DataFrame.dtypes,
feature_set) -> FeatureRowProto.FeatureRow:
"""
Encode a dictionary describing a feature row into a FeatureRows object.
Args:
row: Dictionary describing a feature row.
df_datetime_dtype: Pandas dtype of datetime column.
feature_set: Feature set describing feature row.
Returns:
FeatureRow
"""
feature_row = FeatureRowProto.FeatureRow(
event_timestamp=_pd_datetime_to_timestamp_proto(
df_datetime_dtype, row[DATETIME_COLUMN]),
feature_set=feature_set.project + "/" + feature_set.name + ":" +
str(feature_set.version),
)
for field_name, field in feature_set.fields.items():
feature_row.fields.extend([
FieldProto.Field(
name=field.name,
value=_python_value_to_proto_value(field.dtype,
row[field.name]),
)
])
return feature_row
def convert_series_to_proto_values(row: pd.Series):
"""
Converts a Pandas Series to a Feast FeatureRow
Args:
row: pd.Series The row that should be converted
Returns:
Feast FeatureRow
"""
feature_row = FeatureRowProto.FeatureRow(
event_timestamp=_pd_datetime_to_timestamp_proto(
dataframe[DATETIME_COLUMN].dtype, row[DATETIME_COLUMN]),
feature_set=feature_set.name + ":" + str(feature_set.version),
)
for field_name, field in feature_set.fields.items():
feature_row.fields.extend([
FieldProto.Field(
name=field.name,
value=_python_value_to_proto_value(field.dtype,
row[field.name]),
)
])
return feature_row
def convert_series_to_proto_values(row: pd.Series):
feature_row = FeatureRowProto.FeatureRow(
event_timestamp=pd_datetime_to_timestamp_proto(
dataframe[DATETIME_COLUMN].dtype, row[DATETIME_COLUMN]),
feature_set=feature_set.name + ":" + str(feature_set.version),
)
for field_name, field in feature_set.fields.items():
feature_row.fields.extend([
FieldProto.Field(
name=field.name,
value=pd_value_to_proto_value(field.dtype,
row[field.name]),
)
])
return feature_row
def GetOnlineFeatures(self, request: GetOnlineFeaturesRequest, context):
response = GetOnlineFeaturesResponse(feature_data_sets=[
GetOnlineFeaturesResponse.FeatureDataSet(
name="feature_set_1",
feature_rows=[
FeatureRowProto.FeatureRow(
feature_set="feature_set_1",
event_timestamp=Timestamp(),
fields=[
FieldProto.Field(
name="feature_1",
value=ValueProto.Value(float_val=1.2),
),
FieldProto.Field(
name="feature_2",
value=ValueProto.Value(float_val=1.2),
),
FieldProto.Field(
name="feature_3",
value=ValueProto.Value(float_val=1.2),
),
],
),
FeatureRowProto.FeatureRow(
feature_set="feature_set_1",
event_timestamp=Timestamp(),
fields=[
FieldProto.Field(
name="feature_1",
value=ValueProto.Value(float_val=1.2),
),
FieldProto.Field(
name="feature_2",
value=ValueProto.Value(float_val=1.2),
),
FieldProto.Field(
name="feature_3",
value=ValueProto.Value(float_val=1.2),
),
],
),
FeatureRowProto.FeatureRow(
feature_set="feature_set_1",
event_timestamp=Timestamp(),
fields=[
FieldProto.Field(
name="feature_1",
value=ValueProto.Value(float_val=1.2),
),
FieldProto.Field(
name="feature_2",
value=ValueProto.Value(float_val=1.2),
),
FieldProto.Field(
name="feature_3",
value=ValueProto.Value(float_val=1.2),
),
],
),
],
)
])
return response