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test_get_features.py
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# Copyright 2022 The FeatHub Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC
from datetime import datetime
import pandas as pd
from feathub.common.exceptions import FeathubException
from feathub.feature_tables.sources.memory_store_source import MemoryStoreSource
from feathub.feature_views.derived_feature_view import DerivedFeatureView
from feathub.feature_views.feature import Feature
from feathub.tests.feathub_it_test_base import FeathubITTestBase
def _to_timestamp(datetime_str):
return datetime.strptime(datetime_str, "%Y-%m-%d %H:%M:%S")
class GetFeaturesITTest(ABC, FeathubITTestBase):
def test_get_table_from_file_source(self):
source = self.create_file_source(self.input_data.copy())
table = self.client.get_features(feature_descriptor=source)
df = table.to_pandas()
self.assertTrue(self.input_data.equals(df))
def test_get_table_with_single_key(self):
source = self.create_file_source(self.input_data.copy(), keys=["name"])
keys = pd.DataFrame(
[
["Alex"],
["Jack"],
["Dummy"],
],
columns=["name"],
)
result_df = (
self.client.get_features(feature_descriptor=source, keys=keys)
.to_pandas()
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
expected_result_df = (
pd.DataFrame(
[
["Alex", 100, 100, "2022-01-01 08:01:00"],
["Alex", 300, 200, "2022-01-02 08:03:00"],
["Jack", 500, 500, "2022-01-03 08:05:00"],
["Alex", 600, 800, "2022-01-03 08:06:00"],
],
columns=["name", "cost", "distance", "time"],
)
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
self.assertTrue(expected_result_df.equals(result_df))
def test_get_table_with_multiple_keys(self):
df = self.input_data.copy()
source = self.create_file_source(df, keys=["name"])
keys = pd.DataFrame(
[
["Alex", 100],
["Alex", 200],
["Jack", 500],
["Dummy", 300],
],
columns=["name", "cost"],
)
result_df = (
self.client.get_features(feature_descriptor=source, keys=keys)
.to_pandas()
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
expected_result_df = (
pd.DataFrame(
[
["Alex", 100, 100, "2022-01-01 08:01:00"],
["Jack", 500, 500, "2022-01-03 08:05:00"],
],
columns=["name", "cost", "distance", "time"],
)
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
self.assertTrue(expected_result_df.equals(result_df))
def test_get_table_with_non_exist_key(self):
df = self.input_data.copy()
source = self.create_file_source(df, keys=["name"])
keys = pd.DataFrame(
[
["Alex", 100],
["Alex", 200],
["Jack", 500],
["Dummy", 300],
],
columns=["name", "invalid_key"],
)
with self.assertRaises(FeathubException):
self.client.get_features(feature_descriptor=source, keys=keys).to_pandas()
def test_get_table_with_start_datetime(self):
df = self.input_data.copy()
source = self.create_file_source(df, keys=["name"])
start_datetime = _to_timestamp("2022-01-02 08:03:00")
result_df = (
self.client.get_features(
feature_descriptor=source, start_datetime=start_datetime
)
.to_pandas()
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
expected_result_df = (
pd.DataFrame(
[
["Alex", 300, 200, "2022-01-02 08:03:00"],
["Emma", 200, 250, "2022-01-02 08:04:00"],
["Jack", 500, 500, "2022-01-03 08:05:00"],
["Alex", 600, 800, "2022-01-03 08:06:00"],
],
columns=["name", "cost", "distance", "time"],
)
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
self.assertTrue(expected_result_df.equals(result_df))
def test_get_table_with_end_datetime(self):
df = self.input_data.copy()
source = self.create_file_source(df, keys=["name"])
end_datetime = _to_timestamp("2022-01-02 08:03:00")
result_df = (
self.client.get_features(
feature_descriptor=source, end_datetime=end_datetime
)
.to_pandas()
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
expected_result_df = (
pd.DataFrame(
[
["Alex", 100, 100, "2022-01-01 08:01:00"],
["Emma", 400, 250, "2022-01-01 08:02:00"],
],
columns=["name", "cost", "distance", "time"],
)
.sort_values(by=["name", "cost", "distance", "time"])
.reset_index(drop=True)
)
self.assertTrue(expected_result_df.equals(result_df))
def test_get_table_missing_timestamp(self):
df = self.input_data.copy()
df = df.drop(columns=["time"])
source = self.create_file_source(df, keys=["name"], timestamp_field=None)
_datetime = datetime.strptime("2022-01-02 08:03:00", "%Y-%m-%d %H:%M:%S")
with self.assertRaises(FeathubException):
self.client.get_features(
feature_descriptor=source, end_datetime=_datetime
).to_pandas()
with self.assertRaises(FeathubException):
self.client.get_features(
feature_descriptor=source, start_datetime=_datetime
).to_pandas()
def test_get_table_with_unsupported_feature_view(self):
with self.assertRaises(FeathubException):
self.client.get_features(
MemoryStoreSource("table", ["a"], "table")
).to_pandas()
def test_keep_source(self):
df = self.input_data.copy()
source = self.create_file_source(df)
f_cost_per_mile = Feature(
name="cost_per_mile",
transform="CAST(cost AS DOUBLE) / CAST(distance AS DOUBLE) + 10",
)
f_cost_per_mile2 = Feature(
name="cost_per_mile_2",
transform="CAST(cost AS DOUBLE) / CAST(distance AS DOUBLE) + 5",
)
feature_view = DerivedFeatureView(
name="feature_view",
source=source,
features=[f_cost_per_mile, f_cost_per_mile2],
keep_source_fields=True,
)
result_df = (
self.client.get_features(feature_view)
.to_pandas()
.sort_values(by=["name", "time"])
.reset_index(drop=True)
)
expected_result_df = df.sort_values(by=["name", "time"]).reset_index(drop=True)
expected_result_df["cost_per_mile"] = expected_result_df.apply(
lambda row: row["cost"] / row["distance"] + 10, axis=1
)
expected_result_df["cost_per_mile_2"] = expected_result_df.apply(
lambda row: row["cost"] / row["distance"] + 5, axis=1
)
self.assertTrue(expected_result_df.equals(result_df))
def test_deprecated_parameter(self):
source = self.create_file_source(self.input_data.copy())
table = self.client.get_features(features=source)
df = table.to_pandas()
self.assertTrue(self.input_data.equals(df))