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[Modin] Add tests for modin (ray-project#16260)
Adds modin tests that run with and without ray client.
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,7 @@ | ||
| py_test( | ||
| name = "test_modin", | ||
| size = "small", | ||
| srcs = ["test_modin.py"], | ||
| deps = ["//:ray_lib"], | ||
| tags = ["exclusive"], | ||
| ) |
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| @@ -0,0 +1,134 @@ | ||
| # Licensed to Modin Development Team under one or more contributor license | ||
| # agreements. See the NOTICE file distributed with this work for additional | ||
| # information regarding copyright ownership. The Modin Development Team | ||
| # 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. | ||
| # | ||
| # This file is copied and adapted from | ||
| # http://github.com/modin-project/modin/master/modin/pandas/test/utils.py | ||
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||
| import pandas | ||
| import modin.pandas as pd | ||
| from modin.utils import to_pandas | ||
| from pandas.testing import (assert_series_equal, assert_frame_equal, | ||
| assert_extension_array_equal, assert_index_equal) | ||
| import numpy as np | ||
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| def categories_equals(left, right): | ||
| assert (left.ordered and right.ordered) or (not left.ordered | ||
| and not right.ordered) | ||
| assert_extension_array_equal(left, right) | ||
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| def df_categories_equals(df1, df2): | ||
| if not hasattr(df1, "select_dtypes"): | ||
| if isinstance(df1, pandas.CategoricalDtype): | ||
| return categories_equals(df1, df2) | ||
| elif isinstance(getattr(df1, "dtype"), | ||
| pandas.CategoricalDtype) and isinstance( | ||
| getattr(df1, "dtype"), pandas.CategoricalDtype): | ||
| return categories_equals(df1.dtype, df2.dtype) | ||
| else: | ||
| return True | ||
|
|
||
| categories_columns = df1.select_dtypes(include="category").columns | ||
| for column in categories_columns: | ||
| assert_extension_array_equal( | ||
| df1[column].values, | ||
| df2[column].values, | ||
| check_dtype=False, | ||
| ) | ||
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| def df_equals(df1, df2): | ||
| """Tests if df1 and df2 are equal. | ||
| Args: | ||
| df1: (pandas or modin DataFrame or series) dataframe to test if equal. | ||
| df2: (pandas or modin DataFrame or series) dataframe to test if equal. | ||
| Returns: | ||
| True if df1 is equal to df2. | ||
| """ | ||
| # Gets AttributError if modin's groupby object is not import like this | ||
| from modin.pandas.groupby import DataFrameGroupBy | ||
|
|
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| groupby_types = (pandas.core.groupby.DataFrameGroupBy, DataFrameGroupBy) | ||
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| # The typing behavior of how pandas treats its index is not consistent when | ||
| # the length of the DataFrame or Series is 0, so we just verify that the | ||
| # contents are the same. | ||
| if (hasattr(df1, "index") and hasattr(df2, "index") and len(df1) == 0 | ||
| and len(df2) == 0): | ||
| if type(df1).__name__ == type(df2).__name__: | ||
| if hasattr(df1, "name") and hasattr( | ||
| df2, "name") and df1.name == df2.name: | ||
| return | ||
| if (hasattr(df1, "columns") and hasattr(df2, "columns") | ||
| and df1.columns.equals(df2.columns)): | ||
| return | ||
| assert False | ||
|
|
||
| if isinstance(df1, (list, tuple)) and all( | ||
| isinstance(d, (pd.DataFrame, pd.Series, pandas.DataFrame, | ||
| pandas.Series)) for d in df1): | ||
| assert isinstance(df2, type(df1)), "Different type of collection" | ||
| assert len(df1) == len(df2), "Different length result" | ||
| return (df_equals(d1, d2) for d1, d2 in zip(df1, df2)) | ||
|
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||
| # Convert to pandas | ||
| if isinstance(df1, (pd.DataFrame, pd.Series)): | ||
| df1 = to_pandas(df1) | ||
| if isinstance(df2, (pd.DataFrame, pd.Series)): | ||
| df2 = to_pandas(df2) | ||
|
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||
| if isinstance(df1, pandas.DataFrame) and isinstance(df2, pandas.DataFrame): | ||
| if (df1.empty and not df2.empty) or (df2.empty and not df1.empty): | ||
| assert False, "One of the passed frames is empty, when other isn't" | ||
| elif df1.empty and df2.empty and type(df1) != type(df2): | ||
| assert ( | ||
| False | ||
| ), f"Empty frames have different types: {type(df1)} != {type(df2)}" | ||
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| if isinstance(df1, pandas.DataFrame) and isinstance(df2, pandas.DataFrame): | ||
| assert_frame_equal( | ||
| df1, | ||
| df2, | ||
| check_dtype=False, | ||
| check_datetimelike_compat=True, | ||
| check_index_type=False, | ||
| check_column_type=False, | ||
| check_categorical=False, | ||
| ) | ||
| df_categories_equals(df1, df2) | ||
| elif isinstance(df1, pandas.Index) and isinstance(df2, pandas.Index): | ||
| assert_index_equal(df1, df2) | ||
| elif isinstance(df1, pandas.Series) and isinstance(df2, pandas.Series): | ||
| assert_series_equal( | ||
| df1, df2, check_dtype=False, check_series_type=False) | ||
| elif isinstance(df1, groupby_types) and isinstance(df2, groupby_types): | ||
| for g1, g2 in zip(df1, df2): | ||
| assert g1[0] == g2[0] | ||
| df_equals(g1[1], g2[1]) | ||
| elif (isinstance(df1, pandas.Series) and isinstance(df2, pandas.Series) | ||
| and df1.empty and df2.empty): | ||
| assert all(df1.index == df2.index) | ||
| assert df1.dtypes == df2.dtypes | ||
| elif isinstance(df1, pandas.core.arrays.numpy_.PandasArray): | ||
| assert isinstance(df2, pandas.core.arrays.numpy_.PandasArray) | ||
| assert df1 == df2 | ||
| elif isinstance(df1, np.recarray) and isinstance(df2, np.recarray): | ||
| np.testing.assert_array_equal(df1, df2) | ||
| else: | ||
| if df1 != df2: | ||
| np.testing.assert_almost_equal(df1, df2) |
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