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127 lines
4.4 KiB
Python
127 lines
4.4 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from datetime import datetime
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import pytest
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from numpy import nan
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from numpy.testing import assert_array_equal
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from pandas import DataFrame
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from superset.exceptions import InvalidPostProcessingError
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from superset.utils.core import DTTM_ALIAS, PostProcessingContributionOrientation
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from superset.utils.pandas_postprocessing import contribution
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df_template = DataFrame(
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{
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DTTM_ALIAS: [
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datetime(2020, 7, 16, 14, 49),
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datetime(2020, 7, 16, 14, 50),
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datetime(2020, 7, 16, 14, 51),
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],
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"a": [1, 3, nan],
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"b": [1, 9, nan],
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"c": [nan, nan, nan],
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}
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)
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def test_non_numeric_columns():
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with pytest.raises(InvalidPostProcessingError, match="not numeric"):
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contribution(df_template.copy(), columns=[DTTM_ALIAS])
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def test_rename_should_have_same_length():
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with pytest.raises(InvalidPostProcessingError, match="same length"):
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contribution(df_template.copy(), columns=["a"], rename_columns=["aa", "bb"])
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def test_cell_contribution_across_row():
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processed_df = contribution(
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df_template.copy(),
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orientation=PostProcessingContributionOrientation.ROW,
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)
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assert processed_df.columns.tolist() == [DTTM_ALIAS, "a", "b", "c"]
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assert_array_equal(processed_df["a"].tolist(), [0.5, 0.25, nan])
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assert_array_equal(processed_df["b"].tolist(), [0.5, 0.75, nan])
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assert_array_equal(processed_df["c"].tolist(), [nan, nan, nan])
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def test_cell_contribution_across_column_without_temporal_column():
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df = df_template.copy()
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df.pop(DTTM_ALIAS)
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processed_df = contribution(
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df, orientation=PostProcessingContributionOrientation.COLUMN
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)
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assert processed_df.columns.tolist() == ["a", "b", "c"]
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assert_array_equal(processed_df["a"].tolist(), [0.25, 0.75, 0])
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assert_array_equal(processed_df["b"].tolist(), [0.1, 0.9, 0])
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assert_array_equal(processed_df["c"].tolist(), [nan, nan, nan])
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def test_contribution_on_selected_columns():
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df = df_template.copy()
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df.pop(DTTM_ALIAS)
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processed_df = contribution(
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df,
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orientation=PostProcessingContributionOrientation.COLUMN,
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columns=["a"],
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rename_columns=["pct_a"],
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)
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assert processed_df.columns.tolist() == ["a", "b", "c", "pct_a"]
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assert_array_equal(processed_df["a"].tolist(), [1, 3, nan])
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assert_array_equal(processed_df["b"].tolist(), [1, 9, nan])
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assert_array_equal(processed_df["c"].tolist(), [nan, nan, nan])
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assert processed_df["pct_a"].tolist() == [0.25, 0.75, 0]
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def test_contribution_with_time_shift_columns():
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df = DataFrame(
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{
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DTTM_ALIAS: [
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datetime(2020, 7, 16, 14, 49),
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datetime(2020, 7, 16, 14, 50),
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],
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"a": [3, 6],
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"b": [3, 3],
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"c": [6, 3],
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"a__1 week ago": [2, 2],
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"b__1 week ago": [1, 1],
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"c__1 week ago": [1, 1],
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}
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)
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processed_df = contribution(
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df,
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orientation=PostProcessingContributionOrientation.ROW,
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time_shifts=["1 week ago"],
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)
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assert processed_df.columns.tolist() == [
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DTTM_ALIAS,
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"a",
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"b",
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"c",
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"a__1 week ago",
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"b__1 week ago",
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"c__1 week ago",
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]
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assert_array_equal(processed_df["a"].tolist(), [0.25, 0.5])
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assert_array_equal(processed_df["b"].tolist(), [0.25, 0.25])
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assert_array_equal(processed_df["c"].tolist(), [0.50, 0.25])
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assert_array_equal(processed_df["a__1 week ago"].tolist(), [0.5, 0.5])
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assert_array_equal(processed_df["b__1 week ago"].tolist(), [0.25, 0.25])
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assert_array_equal(processed_df["c__1 week ago"].tolist(), [0.25, 0.25])
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