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55 lines
1.9 KiB
Python
55 lines
1.9 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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import numpy as np
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from superset.utils import pandas_postprocessing as pp
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from tests.unit_tests.fixtures.dataframes import categories_df
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def test_rank_should_rank():
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# Here we use np.isclose to avoid "false positives" in != tests
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# Plain
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_categories_df = categories_df.copy(deep=True)
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assert np.isclose(
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pp.rank(_categories_df, "asc_idx")["rank"],
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np.linspace(1.0 / 101.0, 1.0, 101),
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rtol=1e-8,
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).all()
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# Grouped
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gb = pp.rank(_categories_df, "asc_idx", "dept").groupby("dept")
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res = gb.apply(
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lambda x: np.isclose(
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x.sort_values("rank")["rank"],
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np.linspace(1.0 / len(x), 1.0, len(x)),
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rtol=1e-8,
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).all()
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)
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assert res.all()
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def test_rank_single_cat():
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# Check that reducing the category to one value still holds valid results
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_categories_df = categories_df.copy(deep=True)
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# This was raising up to 6.1.0, see https://github.com/apache/superset/issues/40709
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tmp_df = _categories_df[_categories_df["dept"] == "dept0"].reset_index(drop=True)
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pp.rank(tmp_df, "asc_idx", "dept")
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assert tmp_df["rank"].min() == 1.0 / len(tmp_df)
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assert tmp_df["rank"].max() == 1.0
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