def test_historical_feature_retrieval_with_mapping(spark: SparkSession):
test_data_dir = path.join(pathlib.Path(__file__).parent.absolute(), "data")
entity_source = FileSource(
format="csv",
path=
f"file://{path.join(test_data_dir, 'column_mapping_test_entity.csv')}",
event_timestamp_column="event_timestamp",
field_mapping={"id": "customer_id"},
options={
"inferSchema": "true",
"header": "true"
},
)
booking_source = FileSource(
format="csv",
path=
f"file://{path.join(test_data_dir, 'column_mapping_test_feature.csv')}",
event_timestamp_column="datetime",
created_timestamp_column="created_datetime",
options={
"inferSchema": "true",
"header": "true"
},
)
booking_table = FeatureTable(
name="bookings",
entities=[Field("customer_id", "int32")],
features=[Field("total_bookings", "int32")],
)
joined_df = retrieve_historical_features(
spark,
entity_source,
[booking_source],
[booking_table],
)
expected_joined_schema = StructType([
StructField("customer_id", IntegerType()),
StructField("event_timestamp", TimestampType()),
StructField("bookings__total_bookings", IntegerType()),
])
expected_joined_data = [
(1001, datetime(year=2020, month=9, day=2), 200),
(1001, datetime(year=2020, month=9, day=3), 200),
(2001, datetime(year=2020, month=9, day=4), 600),
(2001, datetime(year=2020, month=9, day=4), 600),
(3001, datetime(year=2020, month=9, day=4), 700),
]
expected_joined_df = spark.createDataFrame(
spark.sparkContext.parallelize(expected_joined_data),
expected_joined_schema)
assert_dataframe_equal(joined_df, expected_joined_df)
def test_large_historical_feature_retrieval(spark: SparkSession,
large_entity_csv_file: str,
large_feature_csv_file: str):
nr_rows = 1000
start_datetime = datetime(year=2020, month=8, day=31)
expected_join_data = [(1000 + i, start_datetime + timedelta(days=i),
i * 10) for i in range(nr_rows)]
expected_join_data_schema = StructType([
StructField("customer_id", IntegerType()),
StructField("event_timestamp", TimestampType()),
StructField("feature__total_bookings", IntegerType()),
])
expected_join_data_df = spark.createDataFrame(
spark.sparkContext.parallelize(expected_join_data),
expected_join_data_schema)
entity_source = FileSource(
format="csv",
path=f"file://{large_entity_csv_file}",
event_timestamp_column="event_timestamp",
field_mapping={"id": "customer_id"},
options={
"inferSchema": "true",
"header": "true"
},
)
feature_source = FileSource(
format="csv",
path=f"file://{large_feature_csv_file}",
event_timestamp_column="event_timestamp",
created_timestamp_column="created_timestamp",
options={
"inferSchema": "true",
"header": "true"
},
)
feature_table = FeatureTable(
name="feature",
entities=[Field("customer_id", "int32")],
features=[Field("total_bookings", "int32")],
)
joined_df = retrieve_historical_features(spark, entity_source,
[feature_source], [feature_table])
assert_dataframe_equal(joined_df, expected_join_data_df)
def test_multiple_join(
spark: SparkSession,
composite_entity_schema: StructType,
customer_feature_schema: StructType,
driver_feature_schema: StructType,
):
entity_data = [
(1001, 8001, datetime(year=2020, month=9, day=2)),
(1001, 8002, datetime(year=2020, month=9, day=2)),
(2001, 8002, datetime(year=2020, month=9, day=3)),
]
entity_df = spark.createDataFrame(
spark.sparkContext.parallelize(entity_data), composite_entity_schema)
customer_table_data = [
(
1001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
100.0,
),
(
2001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
200.0,
),
]
customer_table_df = spark.createDataFrame(
spark.sparkContext.parallelize(customer_table_data),
customer_feature_schema)
customer_table = FeatureTable(
name="transactions",
features=[Field("daily_transactions", "double")],
entities=[Field("customer_id", "int32")],
max_age=86400,
)
driver_table_data = [
(
8001,
datetime(year=2020, month=8, day=31),
datetime(year=2020, month=8, day=31),
200,
),
(
8001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
300,
),
(
8002,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
600,
),
(
8002,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=2),
500,
),
]
driver_table_df = spark.createDataFrame(
spark.sparkContext.parallelize(driver_table_data),
driver_feature_schema)
driver_table = FeatureTable(
name="bookings",
features=[Field("completed_bookings", "int32")],
entities=[Field("driver_id", "int32")],
)
joined_df = join_entity_to_feature_tables(
entity_df,
"event_timestamp",
[customer_table_df, driver_table_df],
[customer_table, driver_table],
["event_timestamp"] * 2,
["created_timestamp"] * 2,
)
expected_joined_schema = StructType([
StructField("customer_id", IntegerType()),
StructField("driver_id", IntegerType()),
StructField("event_timestamp", TimestampType()),
StructField("transactions__daily_transactions", FloatType()),
StructField("bookings__completed_bookings", IntegerType()),
])
expected_joined_data = [
(
1001,
8001,
datetime(year=2020, month=9, day=2),
100.0,
300,
),
(
1001,
8002,
datetime(year=2020, month=9, day=2),
100.0,
500,
),
(
2001,
8002,
datetime(year=2020, month=9, day=3),
None,
500,
),
]
expected_joined_df = spark.createDataFrame(
spark.sparkContext.parallelize(expected_joined_data),
expected_joined_schema)
assert_dataframe_equal(joined_df, expected_joined_df)
def test_select_subset_of_columns_as_entity_primary_keys(
spark: SparkSession,
composite_entity_schema: StructType,
customer_feature_schema: StructType,
):
entity_data = [
(1001, 8001, datetime(year=2020, month=9, day=2)),
(2001, 8002, datetime(year=2020, month=9, day=2)),
]
entity_df = spark.createDataFrame(
spark.sparkContext.parallelize(entity_data), composite_entity_schema)
feature_table_data = [
(
1001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=2),
100.0,
),
(
2001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
400.0,
),
]
feature_table_df = spark.createDataFrame(
spark.sparkContext.parallelize(feature_table_data),
customer_feature_schema)
feature_table = FeatureTable(
name="transactions",
features=[Field("daily_transactions", "double")],
entities=[Field("customer_id", "int32")],
)
joined_df = as_of_join(
entity_df,
"event_timestamp",
feature_table_df,
feature_table,
"event_timestamp",
"created_timestamp",
)
expected_joined_schema = StructType([
StructField("customer_id", IntegerType()),
StructField("driver_id", IntegerType()),
StructField("event_timestamp", TimestampType()),
StructField("transactions__daily_transactions", FloatType()),
])
expected_joined_data = [
(
1001,
8001,
datetime(year=2020, month=9, day=2),
100.0,
),
(
2001,
8002,
datetime(year=2020, month=9, day=2),
400.0,
),
]
expected_joined_df = spark.createDataFrame(
spark.sparkContext.parallelize(expected_joined_data),
expected_joined_schema)
assert_dataframe_equal(joined_df, expected_joined_df)
def test_join_with_composite_entity(
spark: SparkSession,
composite_entity_schema: StructType,
rating_feature_schema: StructType,
):
entity_data = [
(1001, 8001, datetime(year=2020, month=9, day=1)),
(1001, 8002, datetime(year=2020, month=9, day=3)),
(1001, 8003, datetime(year=2020, month=9, day=1)),
(2001, 8001, datetime(year=2020, month=9, day=2)),
]
entity_df = spark.createDataFrame(
spark.sparkContext.parallelize(entity_data), composite_entity_schema)
feature_table_data = [
(
1001,
8001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
3.0,
5.0,
),
(
1001,
8002,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
4.0,
3.0,
),
(
2001,
8001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
4.0,
4.5,
),
]
feature_table_df = spark.createDataFrame(
spark.sparkContext.parallelize(feature_table_data),
rating_feature_schema,
)
feature_table = FeatureTable(
name="ratings",
features=[
Field("customer_rating", "double"),
Field("driver_rating", "double")
],
entities=[Field("customer_id", "int32"),
Field("driver_id", "int32")],
max_age=86400,
)
joined_df = as_of_join(
entity_df,
"event_timestamp",
feature_table_df,
feature_table,
"event_timestamp",
"created_timestamp",
)
expected_joined_schema = StructType([
StructField("customer_id", IntegerType()),
StructField("driver_id", IntegerType()),
StructField("event_timestamp", TimestampType()),
StructField("ratings__customer_rating", FloatType()),
StructField("ratings__driver_rating", FloatType()),
])
expected_joined_data = [
(
1001,
8001,
datetime(year=2020, month=9, day=1),
3.0,
5.0,
),
(1001, 8002, datetime(year=2020, month=9, day=3), None, None),
(1001, 8003, datetime(year=2020, month=9, day=1), None, None),
(
2001,
8001,
datetime(year=2020, month=9, day=2),
4.0,
4.5,
),
]
expected_joined_df = spark.createDataFrame(
spark.sparkContext.parallelize(expected_joined_data),
expected_joined_schema)
assert_dataframe_equal(joined_df, expected_joined_df)
def test_join_with_max_age(
spark: SparkSession,
single_entity_schema: StructType,
customer_feature_schema: StructType,
):
entity_data = [
(1001, datetime(year=2020, month=9, day=1)),
(1001, datetime(year=2020, month=9, day=3)),
(2001, datetime(year=2020, month=9, day=2)),
]
entity_df = spark.createDataFrame(
spark.sparkContext.parallelize(entity_data), single_entity_schema)
feature_table_data = [
(
1001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
100.0,
),
(
2001,
datetime(year=2020, month=9, day=1),
datetime(year=2020, month=9, day=1),
200.0,
),
]
feature_table_df = spark.createDataFrame(
spark.sparkContext.parallelize(feature_table_data),
customer_feature_schema)
feature_table = FeatureTable(
name="transactions",
features=[Field("daily_transactions", "double")],
entities=[Field("customer_id", "int32")],
max_age=86400,
)
joined_df = as_of_join(
entity_df,
"event_timestamp",
feature_table_df,
feature_table,
"event_timestamp",
"created_timestamp",
)
expected_joined_schema = StructType([
StructField("customer_id", IntegerType()),
StructField("event_timestamp", TimestampType()),
StructField("transactions__daily_transactions", FloatType()),
])
expected_joined_data = [
(
1001,
datetime(year=2020, month=9, day=1),
100.0,
),
(1001, datetime(year=2020, month=9, day=3), None),
(
2001,
datetime(year=2020, month=9, day=2),
200.0,
),
]
expected_joined_df = spark.createDataFrame(
spark.sparkContext.parallelize(expected_joined_data),
expected_joined_schema)
assert_dataframe_equal(joined_df, expected_joined_df)
def test_historical_feature_retrieval_with_schema_errors(spark: SparkSession):
test_data_dir = path.join(pathlib.Path(__file__).parent.absolute(), "data")
entity_source = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'customer_driver_pairs.csv')}",
event_timestamp_column="event_timestamp",
options={
"inferSchema": "true",
"header": "true"
},
)
entity_source_missing_timestamp = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'customer_driver_pairs.csv')}",
event_timestamp_column="datetime",
options={
"inferSchema": "true",
"header": "true"
},
)
entity_source_missing_entity = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'customers.csv')}",
event_timestamp_column="event_timestamp",
options={
"inferSchema": "true",
"header": "true"
},
)
booking_source = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'bookings.csv')}",
event_timestamp_column="event_timestamp",
created_timestamp_column="created_timestamp",
options={
"inferSchema": "true",
"header": "true"
},
)
booking_source_missing_timestamp = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'bookings.csv')}",
event_timestamp_column="datetime",
created_timestamp_column="created_datetime",
options={
"inferSchema": "true",
"header": "true"
},
)
booking_table = FeatureTable(
name="bookings",
entities=[Field("driver_id", "int32")],
features=[Field("completed_bookings", "int32")],
)
booking_table_missing_features = FeatureTable(
name="bookings",
entities=[Field("driver_id", "int32")],
features=[Field("nonexist_feature", "int32")],
)
booking_table_wrong_column_type = FeatureTable(
name="bookings",
entities=[Field("driver_id", "string")],
features=[Field("completed_bookings", "int32")],
)
with pytest.raises(SchemaError):
retrieve_historical_features(
spark,
entity_source_missing_timestamp,
[booking_source],
[booking_table],
)
with pytest.raises(SchemaError):
retrieve_historical_features(
spark,
entity_source,
[booking_source_missing_timestamp],
[booking_table],
)
with pytest.raises(SchemaError):
retrieve_historical_features(
spark,
entity_source,
[booking_source],
[booking_table_missing_features],
)
with pytest.raises(SchemaError):
retrieve_historical_features(
spark,
entity_source,
[booking_source],
[booking_table_wrong_column_type],
)
with pytest.raises(SchemaError):
retrieve_historical_features(
spark,
entity_source_missing_entity,
[booking_source],
[booking_table],
)
def test_historical_feature_retrieval(spark: SparkSession):
test_data_dir = path.join(pathlib.Path(__file__).parent.absolute(), "data")
entity_source = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'customer_driver_pairs.csv')}",
event_timestamp_column="event_timestamp",
options={
"inferSchema": "true",
"header": "true"
},
)
booking_source = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'bookings.csv')}",
event_timestamp_column="event_timestamp",
created_timestamp_column="created_timestamp",
options={
"inferSchema": "true",
"header": "true"
},
)
transaction_source = FileSource(
format="csv",
path=f"file://{path.join(test_data_dir, 'transactions.csv')}",
event_timestamp_column="event_timestamp",
created_timestamp_column="created_timestamp",
options={
"inferSchema": "true",
"header": "true"
},
)
booking_table = FeatureTable(
name="bookings",
entities=[Field("driver_id", "int32")],
features=[Field("completed_bookings", "int32")],
)
transaction_table = FeatureTable(
name="transactions",
entities=[Field("customer_id", "int32")],
features=[Field("daily_transactions", "double")],
max_age=86400,
)
joined_df = retrieve_historical_features(
spark,
entity_source,
[transaction_source, booking_source],
[transaction_table, booking_table],
)
expected_joined_schema = StructType([
StructField("customer_id", IntegerType()),
StructField("driver_id", IntegerType()),
StructField("event_timestamp", TimestampType()),
StructField("transactions__daily_transactions", FloatType()),
StructField("bookings__completed_bookings", IntegerType()),
])
expected_joined_data = [
(
1001,
8001,
datetime(year=2020, month=9, day=2),
100.0,
300,
),
(
1001,
8002,
datetime(year=2020, month=9, day=2),
100.0,
500,
),
(
1001,
8002,
datetime(year=2020, month=9, day=3),
None,
500,
),
(
2001,
8002,
datetime(year=2020, month=9, day=3),
None,
500,
),
(
2001,
8002,
datetime(year=2020, month=9, day=4),
None,
500,
),
]
expected_joined_df = spark.createDataFrame(
spark.sparkContext.parallelize(expected_joined_data),
expected_joined_schema)
assert_dataframe_equal(joined_df, expected_joined_df)
