def test_transform(self, feature_set_dataframe):
test_key = TimestampFeature()
df = test_key.transform(feature_set_dataframe)
assert df.schema[TIMESTAMP_COLUMN].dataType == DataType.TIMESTAMP.spark
def test_transform_ms(self, feature_set_dataframe_ms):
test_key = TimestampFeature(from_ms=True)
df = test_key.transform(feature_set_dataframe_ms).orderBy("timestamp")
df = df.withColumn("timestamp", df["timestamp"].cast(StringType())).collect()
assert df[0]["timestamp"] == "2020-02-12 21:18:31.112"
assert df[1]["timestamp"] == "2020-02-12 21:18:42.223"
def test_transform_mask(self, feature_set_dataframe_date):
test_key = TimestampFeature(mask="yyyy-MM-dd")
df = test_key.transform(feature_set_dataframe_date).orderBy("timestamp")
df = df.withColumn("timestamp", df["timestamp"].cast(StringType())).collect()
assert df[0]["timestamp"] == "2020-02-07 00:00:00"
assert df[1]["timestamp"] == "2020-02-08 00:00:00"
def test_transform_ms_from_column_small_time_diff(
self, feature_set_dataframe_small_time_diff
):
test_key = TimestampFeature(from_ms=True)
df = test_key.transform(feature_set_dataframe_small_time_diff).orderBy(
"timestamp"
)
df = df.withColumn("timestamp", df["timestamp"].cast(StringType())).collect()
assert df[0]["timestamp"] != df[1]["timestamp"]
def __init__(self):
super(FirstPipeline, self).__init__(
source=Source(
readers=[TableReader(id="t", database="db", table="table",)],
query=f"select * from t", # noqa
),
feature_set=FeatureSet(
name="first",
entity="entity",
description="description",
features=[
Feature(name="feature1", description="test", dtype=DataType.FLOAT,),
Feature(
name="feature2",
description="another test",
dtype=DataType.STRING,
),
],
keys=[
KeyFeature(
name="id", description="identifier", dtype=DataType.BIGINT,
)
],
timestamp=TimestampFeature(),
),
sink=Sink(
writers=[HistoricalFeatureStoreWriter(), OnlineFeatureStoreWriter()]
),
)
def test_get_schema(self):
expected_schema = [
{"column_name": "id", "type": LongType(), "primary_key": True},
{"column_name": "timestamp", "type": TimestampType(), "primary_key": False},
{
"column_name": "feature1__avg_over_2_minutes_fixed_windows",
"type": FloatType(),
"primary_key": False,
},
{
"column_name": "feature1__avg_over_15_minutes_fixed_windows",
"type": FloatType(),
"primary_key": False,
},
{
"column_name": "feature1__stddev_pop_over_2_minutes_fixed_windows",
"type": DoubleType(),
"primary_key": False,
},
{
"column_name": "feature1__stddev_pop_over_15_minutes_fixed_windows",
"type": DoubleType(),
"primary_key": False,
},
]
feature_set = FeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
transformation=SparkFunctionTransform(
functions=[
Function(F.avg, DataType.FLOAT),
Function(F.stddev_pop, DataType.DOUBLE),
]
).with_window(
partition_by="id",
order_by=TIMESTAMP_COLUMN,
mode="fixed_windows",
window_definition=["2 minutes", "15 minutes"],
),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.BIGINT,
)
],
timestamp=TimestampFeature(),
)
schema = feature_set.get_schema()
assert schema == expected_schema
def test_h3_feature_set(self, h3_input_df, h3_target_df):
spark_client = SparkClient()
feature_set = AggregatedFeatureSet(
name="h3_test",
entity="h3geolocation",
description="Test",
keys=[
KeyFeature(
name="h3_id",
description="The h3 hash ID",
dtype=DataType.DOUBLE,
transformation=H3HashTransform(
h3_resolutions=[6, 7, 8, 9, 10, 11, 12],
lat_column="lat",
lng_column="lng",
).with_stack(),
)
],
timestamp=TimestampFeature(),
features=[
Feature(
name="house_id",
description="Count of house ids over a day.",
transformation=AggregatedTransform(
functions=[Function(F.count, DataType.BIGINT)]),
),
],
).with_windows(definitions=["1 day"])
output_df = feature_set.construct(h3_input_df,
client=spark_client,
end_date="2016-04-14")
assert_dataframe_equality(output_df, h3_target_df)
def test_feature_set():
return AggregatedFeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
transformation=AggregatedTransform(functions=[
Function(functions.avg, DataType.DOUBLE),
Function(functions.stddev_pop, DataType.DOUBLE),
]),
),
Feature(
name="feature2",
description="test",
transformation=AggregatedTransform(
functions=[Function(functions.count, DataType.INTEGER)]),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.BIGINT,
)
],
timestamp=TimestampFeature(),
).with_windows(definitions=["1 week", "2 days"])
def feature_set():
feature_set = FeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
transformation=SparkFunctionTransform(functions=[
Function(functions.avg, DataType.FLOAT),
Function(functions.stddev_pop, DataType.DOUBLE),
]).with_window(
partition_by="id",
order_by=TIMESTAMP_COLUMN,
mode="fixed_windows",
window_definition=["2 minutes", "15 minutes"],
),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.BIGINT,
)
],
timestamp=TimestampFeature(),
)
return feature_set
def test_construct(
self, feature_set_dataframe, fixed_windows_output_feature_set_dataframe
):
# given
spark_client = SparkClient()
# arrange
feature_set = FeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
transformation=SparkFunctionTransform(
functions=[
Function(F.avg, DataType.FLOAT),
Function(F.stddev_pop, DataType.FLOAT),
]
).with_window(
partition_by="id",
order_by=TIMESTAMP_COLUMN,
mode="fixed_windows",
window_definition=["2 minutes", "15 minutes"],
),
),
Feature(
name="divided_feature",
description="unit test",
dtype=DataType.FLOAT,
transformation=CustomTransform(
transformer=divide, column1="feature1", column2="feature2",
),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(),
)
output_df = (
feature_set.construct(feature_set_dataframe, client=spark_client)
.orderBy(feature_set.timestamp_column)
.select(feature_set.columns)
)
target_df = fixed_windows_output_feature_set_dataframe.orderBy(
feature_set.timestamp_column
).select(feature_set.columns)
# assert
assert_dataframe_equality(output_df, target_df)
def test_args_without_transformation(self):
test_key = TimestampFeature(from_column="ts")
assert test_key.name == TIMESTAMP_COLUMN
assert test_key.from_column == "ts"
assert test_key.dtype == DataType.TIMESTAMP
def transformer():
# primary key
keys = [
KeyFeature(
name="customer_id",
description="Unique identificator code for customer.",
from_column="customer_id",
dtype=DataType.STRING,
)
]
ts_feature = TimestampFeature(from_column="order_created_at")
# features transformations
features = [
#order_total_amount(),
count_items_in_order(),
avg_order_total_amount_from_last_1_month(),
ratio_order_amount_and_items(),
ratio_order_amount_and_average_ticket()
]
# joining all together
feature_set = FeatureSet(
name="orders_feature_master_table",
entity=
"orders_feature_master_table", # entity: to which "business context" this feature set belongs
description="Features describring events about ifood store.",
keys=keys,
timestamp=ts_feature,
features=features,
)
return feature_set
def agg_feature_set():
return AggregatedFeatureSet(
name="name",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
transformation=AggregatedTransform(
functions=[Function(functions.avg, DataType.DOUBLE)], ),
),
Feature(
name="feature2",
description="test",
transformation=AggregatedTransform(
functions=[Function(functions.avg, DataType.DOUBLE)]),
),
],
keys=[
KeyFeature(
name="id",
description="description",
dtype=DataType.BIGINT,
)
],
timestamp=TimestampFeature(),
)
def __init__(self):
super(UserChargebacksPipeline, self).__init__(
source=Source(
readers=[
FileReader(
id="chargeback_events",
path="data/order_events/input.csv",
format="csv",
format_options={"header": True},
)
],
query=("""
select
cpf,
timestamp(chargeback_timestamp) as timestamp,
order_id
from
chargeback_events
where
chargeback_timestamp is not null
"""),
),
feature_set=AggregatedFeatureSet(
name="user_chargebacks",
entity="user",
description="Aggregates the total of chargebacks from users in "
"different time windows.",
keys=[
KeyFeature(
name="cpf",
description="User unique identifier, entity key.",
dtype=DataType.STRING,
)
],
timestamp=TimestampFeature(),
features=[
Feature(
name="cpf_chargebacks",
description=
"Total of chargebacks registered on user's CPF",
transformation=AggregatedTransform(functions=[
Function(functions.count, DataType.INTEGER)
]),
from_column="order_id",
),
],
).with_windows(
definitions=["3 days", "7 days", "30 days"]).add_post_hook(
ZeroFillHook()),
sink=Sink(writers=[
LocalHistoricalFSWriter(),
OnlineFeatureStoreWriter(
interval_mode=True,
check_schema_hook=NotCheckSchemaHook(),
debug_mode=True,
),
]),
)
def test_construct_rolling_windows_with_end_date(
self,
feature_set_dataframe,
rolling_windows_output_feature_set_dataframe_base_date,
):
# given
spark_client = SparkClient()
# arrange
feature_set = AggregatedFeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
transformation=AggregatedTransform(
functions=[
Function(F.avg, DataType.DOUBLE),
Function(F.stddev_pop, DataType.DOUBLE),
],
),
),
Feature(
name="feature2",
description="test",
transformation=AggregatedTransform(
functions=[
Function(F.avg, DataType.DOUBLE),
Function(F.stddev_pop, DataType.DOUBLE),
],
),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(),
).with_windows(definitions=["1 day", "1 week"])
# act
output_df = feature_set.construct(
feature_set_dataframe, client=spark_client, end_date="2016-04-18"
).orderBy("timestamp")
target_df = rolling_windows_output_feature_set_dataframe_base_date.orderBy(
feature_set.timestamp_column
).select(feature_set.columns)
# assert
assert_dataframe_equality(output_df, target_df)
def test_run_agg_with_end_date(self, spark_session):
test_pipeline = FeatureSetPipeline(
spark_client=SparkClient(),
source=Mock(
spec=Source,
readers=[
TableReader(
id="source_a",
database="db",
table="table",
)
],
query="select * from source_a",
),
feature_set=Mock(
spec=AggregatedFeatureSet,
name="feature_set",
entity="entity",
description="description",
keys=[
KeyFeature(
name="user_id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(from_column="ts"),
features=[
Feature(
name="listing_page_viewed__rent_per_month",
description="Average of something.",
transformation=AggregatedTransform(functions=[
Function(functions.avg, DataType.FLOAT),
Function(functions.stddev_pop, DataType.FLOAT),
], ),
),
],
),
sink=Mock(
spec=Sink,
writers=[HistoricalFeatureStoreWriter(db_config=None)],
),
)
# feature_set need to return a real df for streaming validation
sample_df = spark_session.createDataFrame([{
"a": "x",
"b": "y",
"c": "3"
}])
test_pipeline.feature_set.construct.return_value = sample_df
test_pipeline.run(end_date="2016-04-18")
test_pipeline.source.construct.assert_called_once()
test_pipeline.feature_set.construct.assert_called_once()
test_pipeline.sink.flush.assert_called_once()
test_pipeline.sink.validate.assert_called_once()
def test_pipeline_with_hooks(self, spark_session):
# arrange
hook1 = AddHook(value=1)
spark_session.sql(
"select 1 as id, timestamp('2020-01-01') as timestamp, 0 as feature"
).createOrReplaceTempView("test")
target_df = spark_session.sql(
"select 1 as id, timestamp('2020-01-01') as timestamp, 6 as feature, 2020 "
"as year, 1 as month, 1 as day")
historical_writer = HistoricalFeatureStoreWriter(debug_mode=True)
test_pipeline = FeatureSetPipeline(
source=Source(
readers=[
TableReader(
id="reader",
table="test",
).add_post_hook(hook1)
],
query="select * from reader",
).add_post_hook(hook1),
feature_set=FeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature",
description="test",
transformation=SQLExpressionTransform(
expression="feature + 1"),
dtype=DataType.INTEGER,
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(),
).add_pre_hook(hook1).add_post_hook(hook1),
sink=Sink(writers=[historical_writer], ).add_pre_hook(hook1),
)
# act
test_pipeline.run()
output_df = spark_session.table(
"historical_feature_store__feature_set")
# assert
output_df.show()
assert_dataframe_equality(output_df, target_df)
def timestamp(self, value: TimestampFeature):
if not isinstance(value, TimestampFeature):
raise ValueError("timestamp needs to be a TimestampFeature object.")
timestamp_columns = value.get_output_columns()
if len(timestamp_columns) > 1:
raise ValueError("TimestampFeature will produce multiple output columns.")
self.__timestamp = value
def test_source_raise(self):
with pytest.raises(ValueError,
match="source must be a Source instance"):
FeatureSetPipeline(
spark_client=SparkClient(),
source=Mock(
spark_client=SparkClient(),
readers=[
TableReader(
id="source_a",
database="db",
table="table",
),
],
query="select * from source_a",
),
feature_set=Mock(
spec=FeatureSet,
name="feature_set",
entity="entity",
description="description",
keys=[
KeyFeature(
name="user_id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(from_column="ts"),
features=[
Feature(
name="listing_page_viewed__rent_per_month",
description="Average of something.",
transformation=SparkFunctionTransform(functions=[
Function(functions.avg, DataType.FLOAT),
Function(functions.stddev_pop, DataType.FLOAT),
], ).with_window(
partition_by="user_id",
order_by=TIMESTAMP_COLUMN,
window_definition=["7 days", "2 weeks"],
mode="fixed_windows",
),
),
],
),
sink=Mock(
spec=Sink,
writers=[HistoricalFeatureStoreWriter(db_config=None)],
),
)
def feature_set():
key_features = [
KeyFeature(name="id", description="Description", dtype=DataType.INTEGER)
]
ts_feature = TimestampFeature(from_column=TIMESTAMP_COLUMN)
features = [
Feature(name="feature", description="Description", dtype=DataType.BIGINT,)
]
return FeatureSet(
"feature_set",
"entity",
"description",
keys=key_features,
timestamp=ts_feature,
features=features,
)
def test_construct_without_window(
self,
feature_set_dataframe,
target_df_without_window,
):
# given
spark_client = SparkClient()
# arrange
feature_set = AggregatedFeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
dtype=DataType.DOUBLE,
transformation=AggregatedTransform(
functions=[Function(F.avg, DataType.DOUBLE)]),
),
Feature(
name="feature2",
description="test",
dtype=DataType.FLOAT,
transformation=AggregatedTransform(
functions=[Function(F.count, DataType.BIGINT)]),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(from_column="fixed_ts"),
)
# act
output_df = feature_set.construct(feature_set_dataframe,
client=spark_client)
# assert
assert_dataframe_equality(output_df, target_df_without_window)
def feature_set_pipeline(
spark_context, spark_session,
):
feature_set_pipeline = FeatureSetPipeline(
source=Source(
readers=[
TableReader(id="b_source", table="b_table",).with_incremental_strategy(
incremental_strategy=IncrementalStrategy(column="timestamp")
),
],
query=f"select * from b_source ", # noqa
),
feature_set=FeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature",
description="test",
transformation=SparkFunctionTransform(
functions=[
Function(F.avg, DataType.FLOAT),
Function(F.stddev_pop, DataType.FLOAT),
],
).with_window(
partition_by="id",
order_by=TIMESTAMP_COLUMN,
mode="fixed_windows",
window_definition=["1 day"],
),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(),
),
sink=Sink(writers=[HistoricalFeatureStoreWriter(debug_mode=True)]),
)
return feature_set_pipeline
def test_construct_with_date_boundaries(
self, feature_set_dates_dataframe,
feature_set_dates_output_dataframe):
# given
spark_client = SparkClient()
# arrange
feature_set = FeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature",
description="test",
dtype=DataType.FLOAT,
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(),
)
output_df = (feature_set.construct(
feature_set_dates_dataframe,
client=spark_client,
start_date="2016-04-11",
end_date="2016-04-12",
).orderBy(feature_set.timestamp_column).select(feature_set.columns))
target_df = feature_set_dates_output_dataframe.orderBy(
feature_set.timestamp_column).select(feature_set.columns)
# assert
assert_dataframe_equality(output_df, target_df)
def test_construct_with_pivot(
self,
feature_set_df_pivot,
target_df_pivot_agg,
):
# given
spark_client = SparkClient()
# arrange
feature_set = AggregatedFeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature",
description="unit test",
transformation=AggregatedTransform(functions=[
Function(F.avg, DataType.FLOAT),
Function(F.stddev_pop, DataType.DOUBLE),
], ),
from_column="feature1",
)
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(from_column="fixed_ts"),
).with_pivot("pivot_col", ["S", "N"])
# act
output_df = feature_set.construct(feature_set_df_pivot,
client=spark_client)
# assert
assert_dataframe_equality(output_df, target_df_pivot_agg)
def feature_set():
key_features = [
KeyFeature(name="id",
description="Description",
dtype=DataType.INTEGER)
]
ts_feature = TimestampFeature(from_column="timestamp")
features = [
Feature(name="feature",
description="Description",
dtype=DataType.FLOAT),
]
return FeatureSet(
"test_sink_feature_set",
"test_sink_entity",
"description",
keys=key_features,
timestamp=ts_feature,
features=features,
)
def feature_set_incremental():
key_features = [
KeyFeature(name="id", description="Description", dtype=DataType.INTEGER)
]
ts_feature = TimestampFeature(from_column=TIMESTAMP_COLUMN)
features = [
Feature(
name="feature",
description="test",
transformation=AggregatedTransform(
functions=[Function(functions.sum, DataType.INTEGER)]
),
),
]
return AggregatedFeatureSet(
"feature_set",
"entity",
"description",
keys=key_features,
timestamp=ts_feature,
features=features,
)
def test_construct_rolling_windows_without_end_date(
self, feature_set_dataframe, rolling_windows_output_feature_set_dataframe
):
# given
spark_client = SparkClient()
# arrange
feature_set = AggregatedFeatureSet(
name="feature_set",
entity="entity",
description="description",
features=[
Feature(
name="feature1",
description="test",
transformation=AggregatedTransform(
functions=[
Function(F.avg, DataType.DOUBLE),
Function(F.stddev_pop, DataType.DOUBLE),
],
),
),
],
keys=[
KeyFeature(
name="id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(),
).with_windows(definitions=["1 day", "1 week"],)
# act & assert
with pytest.raises(ValueError):
_ = feature_set.construct(feature_set_dataframe, client=spark_client)
def test_feature_transform_with_data_type_array(self, spark_context,
spark_session):
# arrange
input_data = [
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 10
},
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 20
},
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 30
},
{
"id": 2,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 10
},
]
target_data = [
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature__collect_set": [30.0, 20.0, 10.0],
},
{
"id": 2,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature__collect_set": [10.0],
},
]
input_df = create_df_from_collection(
input_data, spark_context, spark_session).withColumn(
"timestamp",
functions.to_timestamp(functions.col("timestamp")))
target_df = create_df_from_collection(
target_data, spark_context, spark_session).withColumn(
"timestamp",
functions.to_timestamp(functions.col("timestamp")))
fs = AggregatedFeatureSet(
name="name",
entity="entity",
description="description",
keys=[
KeyFeature(name="id",
description="test",
dtype=DataType.INTEGER)
],
timestamp=TimestampFeature(),
features=[
Feature(
name="feature",
description="aggregations with ",
dtype=DataType.BIGINT,
transformation=AggregatedTransform(functions=[
Function(functions.collect_set, DataType.ARRAY_FLOAT),
], ),
from_column="feature",
),
],
)
# act
output_df = fs.construct(input_df, SparkClient())
# assert
assert_dataframe_equality(target_df, output_df)
def test_feature_transform_with_filter_expression(self, spark_context,
spark_session):
# arrange
input_data = [
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 10,
"type": "a",
},
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 20,
"type": "a",
},
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 30,
"type": "b",
},
{
"id": 2,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature": 10,
"type": "a",
},
]
target_data = [
{
"id": 1,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature_only_type_a__avg": 15.0,
"feature_only_type_a__min": 10,
"feature_only_type_a__max": 20,
},
{
"id": 2,
"timestamp": "2020-04-22T00:00:00+00:00",
"feature_only_type_a__avg": 10.0,
"feature_only_type_a__min": 10,
"feature_only_type_a__max": 10,
},
]
input_df = create_df_from_collection(
input_data, spark_context, spark_session).withColumn(
"timestamp",
functions.to_timestamp(functions.col("timestamp")))
target_df = create_df_from_collection(
target_data, spark_context, spark_session).withColumn(
"timestamp",
functions.to_timestamp(functions.col("timestamp")))
fs = AggregatedFeatureSet(
name="name",
entity="entity",
description="description",
keys=[
KeyFeature(name="id",
description="test",
dtype=DataType.INTEGER)
],
timestamp=TimestampFeature(),
features=[
Feature(
name="feature_only_type_a",
description="aggregations only when type = a",
dtype=DataType.BIGINT,
transformation=AggregatedTransform(
functions=[
Function(functions.avg, DataType.FLOAT),
Function(functions.min, DataType.FLOAT),
Function(functions.max, DataType.FLOAT),
],
filter_expression="type = 'a'",
),
from_column="feature",
),
],
)
# act
output_df = fs.construct(input_df, SparkClient())
# assert
assert_dataframe_equality(target_df, output_df)
def test_feature_set_args(self):
# arrange and act
out_columns = [
"user_id",
"timestamp",
"listing_page_viewed__rent_per_month__avg_over_7_days_fixed_windows",
"listing_page_viewed__rent_per_month__avg_over_2_weeks_fixed_windows",
"listing_page_viewed__rent_per_month__stddev_pop_over_7_days_fixed_windows",
"listing_page_viewed__rent_per_month__"
"stddev_pop_over_2_weeks_fixed_windows",
# noqa
]
pipeline = FeatureSetPipeline(
source=Source(
readers=[
TableReader(
id="source_a",
database="db",
table="table",
),
FileReader(
id="source_b",
path="path",
format="parquet",
),
],
query="select a.*, b.specific_feature "
"from source_a left join source_b on a.id=b.id",
),
feature_set=FeatureSet(
name="feature_set",
entity="entity",
description="description",
keys=[
KeyFeature(
name="user_id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(from_column="ts"),
features=[
Feature(
name="listing_page_viewed__rent_per_month",
description="Average of something.",
transformation=SparkFunctionTransform(functions=[
Function(functions.avg, DataType.FLOAT),
Function(functions.stddev_pop, DataType.FLOAT),
], ).with_window(
partition_by="user_id",
order_by=TIMESTAMP_COLUMN,
window_definition=["7 days", "2 weeks"],
mode="fixed_windows",
),
),
],
),
sink=Sink(writers=[
HistoricalFeatureStoreWriter(db_config=None),
OnlineFeatureStoreWriter(db_config=None),
], ),
)
assert isinstance(pipeline.spark_client, SparkClient)
assert len(pipeline.source.readers) == 2
assert all(
isinstance(reader, Reader) for reader in pipeline.source.readers)
assert isinstance(pipeline.source.query, str)
assert pipeline.feature_set.name == "feature_set"
assert pipeline.feature_set.entity == "entity"
assert pipeline.feature_set.description == "description"
assert isinstance(pipeline.feature_set.timestamp, TimestampFeature)
assert len(pipeline.feature_set.keys) == 1
assert all(
isinstance(k, KeyFeature) for k in pipeline.feature_set.keys)
assert len(pipeline.feature_set.features) == 1
assert all(
isinstance(feature, Feature)
for feature in pipeline.feature_set.features)
assert pipeline.feature_set.columns == out_columns
assert len(pipeline.sink.writers) == 2
assert all(
isinstance(writer, Writer) for writer in pipeline.sink.writers)