def test_from_csv_staging_location_not_specified(self):
with pytest.raises(ValueError,
match="Specify staging_location for importing local file/dataframe"):
feature_columns = ["avg_distance_completed",
"avg_customer_distance_completed"]
csv_path = "tests/data/driver_features.csv"
Importer.from_csv(path=csv_path,
entity="driver",
granularity=Granularity.DAY,
owner="*****@*****.**",
feature_columns=feature_columns,
timestamp_column="ts")
with pytest.raises(ValueError,
match="Staging location must be in GCS") as e_info:
feature_columns = ["avg_distance_completed",
"avg_customer_distance_completed"]
csv_path = "tests/data/driver_features.csv"
Importer.from_csv(path=csv_path,
entity="driver",
granularity=Granularity.DAY,
owner="*****@*****.**",
staging_location="/home",
feature_columns=feature_columns,
timestamp_column="ts")
def test_from_csv_id_column_not_specified(self):
with pytest.raises(ValueError,
match="Column with name driver is not found"):
feature_columns = ["avg_distance_completed",
"avg_customer_distance_completed"]
csv_path = "tests/data/driver_features.csv"
Importer.from_csv(path=csv_path,
entity="driver",
granularity=Granularity.DAY,
owner="*****@*****.**",
staging_location="gs://test-bucket",
feature_columns=feature_columns,
timestamp_column="ts")
def test_from_csv_staging_location_not_valid(self):
with pytest.raises(ValueError,
match="Staging location must be in GCS") as e_info:
feature_columns = [
"avg_distance_completed", "avg_customer_distance_completed"
]
csv_path = "tests/data/driver_features.csv"
Importer.from_csv(path=csv_path,
entity="driver",
owner="*****@*****.**",
staging_location="/home",
feature_columns=feature_columns,
timestamp_column="ts")
def test_from_csv_timestamp_column_not_specified(self):
feature_columns = [
"avg_distance_completed", "avg_customer_distance_completed",
"avg_distance_cancelled"
]
csv_path = "tests/data/driver_features.csv"
entity_name = "driver"
granularity = Granularity.DAY
owner = "*****@*****.**"
staging_location = "gs://test-bucket"
id_column = "driver_id"
importer = Importer.from_csv(path=csv_path,
entity=entity_name,
granularity=granularity,
owner=owner,
staging_location=staging_location,
id_column=id_column,
feature_columns=feature_columns)
self._validate_csv_importer(importer,
csv_path,
entity_name,
granularity,
owner,
staging_location=staging_location,
id_column=id_column,
feature_columns=feature_columns)
def test_run_job_no_staging(self, client, mocker):
grpc_stub = jobs.JobServiceStub(grpc.insecure_channel(""))
mocker.patch.object(
grpc_stub,
'SubmitJob',
return_value=JobServiceTypes.SubmitImportJobResponse(
jobId="myjob12312"))
client._job_service_stub = grpc_stub
importer = Importer({"import": ImportSpec()}, None,
{"require_staging": False})
job_id = client.run(importer)
assert job_id == "myjob12312"
def test_stage_df_without_timestamp(self, mocker):
mocker.patch("feast.sdk.importer.df_to_gcs", return_value=True)
feature_columns = [
"avg_distance_completed", "avg_customer_distance_completed",
"avg_distance_cancelled"
]
csv_path = "tests/data/driver_features.csv"
entity_name = "driver"
owner = "*****@*****.**"
staging_location = "gs://test-bucket"
id_column = "driver_id"
importer = Importer.from_csv(path=csv_path,
entity=entity_name,
owner=owner,
staging_location=staging_location,
id_column=id_column,
feature_columns=feature_columns)
importer.stage(None)
def test_from_csv_feature_columns_not_specified(self):
csv_path = "tests/data/driver_features.csv"
entity_name = "driver"
granularity = Granularity.DAY
owner = "*****@*****.**"
staging_location = "gs://test-bucket"
id_column = "driver_id"
timestamp_column = "ts"
importer = Importer.from_csv(path=csv_path,
entity=entity_name,
granularity=granularity,
owner=owner,
staging_location=staging_location,
id_column=id_column,
timestamp_column=timestamp_column)
self._validate_csv_importer(importer, csv_path, entity_name,
granularity, owner,
staging_location=staging_location,
id_column=id_column,
timestamp_column=timestamp_column)
def test_from_df(self):
csv_path = "tests/data/driver_features.csv"
df = pd.read_csv(csv_path)
staging_location = "gs://test-bucket"
entity = "driver"
importer = Importer.from_df(df=df,
entity=entity,
granularity=Granularity.DAY,
owner="*****@*****.**",
staging_location=staging_location,
id_column="driver_id",
timestamp_column="ts")
assert importer.require_staging == True
assert ("{}/tmp_{}".format(staging_location, entity)
in importer.remote_path)
for feature in importer.features.values():
assert feature.name in df.columns
assert feature.id == "driver.day." + feature.name
import_spec = importer.spec
assert import_spec.type == "file"
assert import_spec.options == {
"format": "csv",
"path": importer.remote_path
}
assert import_spec.entities == ["driver"]
schema = import_spec.schema
assert schema.entityIdColumn == "driver_id"
assert schema.timestampValue is not None
feature_columns = [
"completed", "avg_distance_completed",
"avg_customer_distance_completed", "avg_distance_cancelled"
]
for col, field in zip(df.columns.values, schema.fields):
assert col == field.name
if col in feature_columns:
assert field.featureId == "driver.day." + col
def test_from_csv(self):
csv_path = "tests/data/driver_features.csv"
entity_name = "driver"
owner = "*****@*****.**"
staging_location = "gs://test-bucket"
id_column = "driver_id"
feature_columns = [
"avg_distance_completed", "avg_customer_distance_completed"
]
timestamp_column = "ts"
importer = Importer.from_csv(path=csv_path,
entity=entity_name,
owner=owner,
staging_location=staging_location,
id_column=id_column,
feature_columns=feature_columns,
timestamp_column=timestamp_column)
self._validate_csv_importer(importer, csv_path, entity_name, owner,
staging_location, id_column,
feature_columns, timestamp_column)