mirror of https://github.com/apache/superset.git
223 lines
6.5 KiB
Python
223 lines
6.5 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
|
|
# or more contributor license agreements. See the NOTICE file
|
|
# distributed with this work for additional information
|
|
# regarding copyright ownership. The ASF licenses this file
|
|
# to you 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
|
|
#
|
|
# http://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.
|
|
import pandas as pd
|
|
import pytest
|
|
|
|
from superset.exceptions import InvalidPostProcessingError
|
|
from superset.utils import pandas_postprocessing as pp
|
|
from superset.utils.pandas_postprocessing.utils import FLAT_COLUMN_SEPARATOR
|
|
from tests.unit_tests.fixtures.dataframes import (
|
|
multiple_metrics_df,
|
|
single_metric_df,
|
|
timeseries_df,
|
|
)
|
|
from tests.unit_tests.pandas_postprocessing.utils import series_to_list
|
|
|
|
|
|
def test_rolling_should_not_side_effect():
|
|
_timeseries_df = timeseries_df.copy()
|
|
pp.rolling(
|
|
df=timeseries_df,
|
|
columns={"y": "y"},
|
|
rolling_type="sum",
|
|
window=2,
|
|
min_periods=0,
|
|
)
|
|
assert _timeseries_df.equals(timeseries_df)
|
|
|
|
|
|
def test_rolling():
|
|
# sum rolling type
|
|
post_df = pp.rolling(
|
|
df=timeseries_df,
|
|
columns={"y": "y"},
|
|
rolling_type="sum",
|
|
window=2,
|
|
min_periods=0,
|
|
)
|
|
|
|
assert post_df.columns.tolist() == ["label", "y"]
|
|
assert series_to_list(post_df["y"]) == [1.0, 3.0, 5.0, 7.0]
|
|
|
|
# mean rolling type with alias
|
|
post_df = pp.rolling(
|
|
df=timeseries_df,
|
|
rolling_type="mean",
|
|
columns={"y": "y_mean"},
|
|
window=10,
|
|
min_periods=0,
|
|
)
|
|
assert post_df.columns.tolist() == ["label", "y", "y_mean"]
|
|
assert series_to_list(post_df["y_mean"]) == [1.0, 1.5, 2.0, 2.5]
|
|
|
|
# count rolling type
|
|
post_df = pp.rolling(
|
|
df=timeseries_df,
|
|
rolling_type="count",
|
|
columns={"y": "y"},
|
|
window=10,
|
|
min_periods=0,
|
|
)
|
|
assert post_df.columns.tolist() == ["label", "y"]
|
|
assert series_to_list(post_df["y"]) == [1.0, 2.0, 3.0, 4.0]
|
|
|
|
# quantile rolling type
|
|
post_df = pp.rolling(
|
|
df=timeseries_df,
|
|
columns={"y": "q1"},
|
|
rolling_type="quantile",
|
|
rolling_type_options={"quantile": 0.25},
|
|
window=10,
|
|
min_periods=0,
|
|
)
|
|
assert post_df.columns.tolist() == ["label", "y", "q1"]
|
|
assert series_to_list(post_df["q1"]) == [1.0, 1.25, 1.5, 1.75]
|
|
|
|
# incorrect rolling type
|
|
with pytest.raises(InvalidPostProcessingError):
|
|
pp.rolling(
|
|
df=timeseries_df,
|
|
columns={"y": "y"},
|
|
rolling_type="abc",
|
|
window=2,
|
|
)
|
|
|
|
# incorrect rolling type options
|
|
with pytest.raises(InvalidPostProcessingError):
|
|
pp.rolling(
|
|
df=timeseries_df,
|
|
columns={"y": "y"},
|
|
rolling_type="quantile",
|
|
rolling_type_options={"abc": 123},
|
|
window=2,
|
|
)
|
|
|
|
|
|
def test_rolling_should_empty_df():
|
|
pivot_df = pp.pivot(
|
|
df=single_metric_df,
|
|
index=["dttm"],
|
|
columns=["country"],
|
|
aggregates={"sum_metric": {"operator": "sum"}},
|
|
)
|
|
rolling_df = pp.rolling(
|
|
df=pivot_df,
|
|
rolling_type="sum",
|
|
window=2,
|
|
min_periods=2,
|
|
columns={"sum_metric": "sum_metric"},
|
|
)
|
|
assert rolling_df.empty is True
|
|
|
|
|
|
def test_rolling_after_pivot_with_single_metric():
|
|
pivot_df = pp.pivot(
|
|
df=single_metric_df,
|
|
index=["dttm"],
|
|
columns=["country"],
|
|
aggregates={"sum_metric": {"operator": "sum"}},
|
|
)
|
|
"""
|
|
sum_metric
|
|
country UK US
|
|
dttm
|
|
2019-01-01 5 6
|
|
2019-01-02 7 8
|
|
"""
|
|
rolling_df = pp.rolling(
|
|
df=pivot_df,
|
|
columns={"sum_metric": "sum_metric"},
|
|
rolling_type="sum",
|
|
window=2,
|
|
min_periods=0,
|
|
)
|
|
"""
|
|
sum_metric
|
|
country UK US
|
|
dttm
|
|
2019-01-01 5 6
|
|
2019-01-02 12 14
|
|
"""
|
|
flat_df = pp.flatten(rolling_df)
|
|
"""
|
|
dttm sum_metric, UK sum_metric, US
|
|
0 2019-01-01 5 6
|
|
1 2019-01-02 12 14
|
|
"""
|
|
assert flat_df.equals(
|
|
pd.DataFrame(
|
|
data={
|
|
"dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]),
|
|
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5, 12],
|
|
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6, 14],
|
|
}
|
|
)
|
|
)
|
|
|
|
|
|
def test_rolling_after_pivot_with_multiple_metrics():
|
|
pivot_df = pp.pivot(
|
|
df=multiple_metrics_df,
|
|
index=["dttm"],
|
|
columns=["country"],
|
|
aggregates={
|
|
"sum_metric": {"operator": "sum"},
|
|
"count_metric": {"operator": "sum"},
|
|
},
|
|
)
|
|
"""
|
|
count_metric sum_metric
|
|
country UK US UK US
|
|
dttm
|
|
2019-01-01 1 2 5 6
|
|
2019-01-02 3 4 7 8
|
|
"""
|
|
rolling_df = pp.rolling(
|
|
df=pivot_df,
|
|
columns={
|
|
"count_metric": "count_metric",
|
|
"sum_metric": "sum_metric",
|
|
},
|
|
rolling_type="sum",
|
|
window=2,
|
|
min_periods=0,
|
|
)
|
|
"""
|
|
count_metric sum_metric
|
|
country UK US UK US
|
|
dttm
|
|
2019-01-01 1 2 5 6
|
|
2019-01-02 4 6 12 14
|
|
"""
|
|
flat_df = pp.flatten(rolling_df)
|
|
"""
|
|
dttm count_metric, UK count_metric, US sum_metric, UK sum_metric, US
|
|
0 2019-01-01 1 2 5 6
|
|
1 2019-01-02 4 6 12 14
|
|
"""
|
|
assert flat_df.equals(
|
|
pd.DataFrame(
|
|
data={
|
|
"dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]),
|
|
FLAT_COLUMN_SEPARATOR.join(["count_metric", "UK"]): [1, 4],
|
|
FLAT_COLUMN_SEPARATOR.join(["count_metric", "US"]): [2, 6],
|
|
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5, 12],
|
|
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6, 14],
|
|
}
|
|
)
|
|
)
|