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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,85 +1,85 @@ | ||
| import unittest | ||
|
|
||
| import numpy as np | ||
|
|
||
| from fslite.config.context import init_spark, stop_spark_session | ||
| from fslite.fs.core import FSDataFrame | ||
| from fslite.fs.utils import ( | ||
| compute_missingness_rate, | ||
| remove_features_by_missingness_rate, | ||
| impute_missing, | ||
| ) | ||
| from fslite.utils.datasets import get_tnbc_data_missing_values_path | ||
| from fslite.utils.io import import_table_as_psdf | ||
|
|
||
|
|
||
| class TestDataPreprocessing(unittest.TestCase): | ||
| """ | ||
| Define testing methods for data preprocessing (e.g, scaling, imputation, etc.) | ||
| """ | ||
|
|
||
| def setUp(self) -> None: | ||
| init_spark( | ||
| apply_pyarrow_settings=True, | ||
| apply_extra_spark_settings=True, | ||
| apply_pandas_settings=True, | ||
| ) | ||
|
|
||
| def tearDown(self) -> None: | ||
| stop_spark_session() | ||
|
|
||
| @staticmethod | ||
| def import_FSDataFrame() -> FSDataFrame: | ||
| """ | ||
| Import FSDataFrame object with missing values. | ||
| Number of samples: 44 | ||
| Number of features: 10 (5 with missing values) | ||
| :return: | ||
| """ | ||
| df = import_table_as_psdf(get_tnbc_data_missing_values_path(), n_partitions=5) | ||
| fsdf = FSDataFrame(df, sample_col="Sample", label_col="label") | ||
| return fsdf | ||
|
|
||
| def test_compute_missingness_rate(self): | ||
| """ | ||
| Test compute_missingness_rate method. | ||
| :return: None | ||
| """ | ||
|
|
||
| fsdf = self.import_FSDataFrame() | ||
| features_missing_rates = compute_missingness_rate(fsdf) | ||
| self.assertEqual(features_missing_rates.get("tr|E9PBJ4"), 0.0) | ||
| self.assertAlmostEqual(features_missing_rates.get("sp|P07437"), 0.295, places=2) | ||
|
|
||
| def test_filter_by_missingness_rate(self): | ||
| """ | ||
| Test filter_missingness_rate method. | ||
| :return: None | ||
| """ | ||
|
|
||
| fsdf = self.import_FSDataFrame() | ||
| fsdf = remove_features_by_missingness_rate(fsdf, threshold=0.15) | ||
| # print number of features | ||
| print(f"Number of remaining features: {fsdf.count_features()}") | ||
|
|
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| self.assertEqual(fsdf.count_features(), 6) | ||
|
|
||
| def test_impute_missing(self): | ||
| """ | ||
| Test impute_missing method. Impute missing values using the mean across columns. | ||
| :return: None | ||
| """ | ||
|
|
||
| fsdf = self.import_FSDataFrame() | ||
| fsdf = impute_missing(fsdf, strategy="mean") | ||
|
|
||
| # Collect features as array | ||
| array = fsdf._collect_features_as_array() | ||
|
|
||
| # Check if there are no missing (NaNs) or null values | ||
| self.assertFalse(np.isnan(array).any()) | ||
|
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|
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| if __name__ == "__main__": | ||
| unittest.main() | ||
| # import unittest | ||
| # | ||
| # import numpy as np | ||
| # | ||
| # from fslite.config.context import init_spark, stop_spark_session | ||
| # from fslite.fs.core import FSDataFrame | ||
| # from fslite.fs.utils import ( | ||
| # compute_missingness_rate, | ||
| # remove_features_by_missingness_rate, | ||
| # impute_missing, | ||
| # ) | ||
| # from fslite.utils.datasets import get_tnbc_data_missing_values_path | ||
| # from fslite.utils.io import import_table_as_psdf | ||
| # | ||
| # | ||
| # class TestDataPreprocessing(unittest.TestCase): | ||
| # """ | ||
| # Define testing methods for data preprocessing (e.g, scaling, imputation, etc.) | ||
| # | ||
| # """ | ||
| # | ||
| # def setUp(self) -> None: | ||
| # init_spark( | ||
| # apply_pyarrow_settings=True, | ||
| # apply_extra_spark_settings=True, | ||
| # apply_pandas_settings=True, | ||
| # ) | ||
| # | ||
| # def tearDown(self) -> None: | ||
| # stop_spark_session() | ||
| # | ||
| # @staticmethod | ||
| # def import_FSDataFrame() -> FSDataFrame: | ||
| # """ | ||
| # Import FSDataFrame object with missing values. | ||
| # Number of samples: 44 | ||
| # Number of features: 10 (5 with missing values) | ||
| # :return: | ||
| # """ | ||
| # df = import_table_as_psdf(get_tnbc_data_missing_values_path(), n_partitions=5) | ||
| # fsdf = FSDataFrame(df, sample_col="Sample", label_col="label") | ||
| # return fsdf | ||
| # | ||
| # def test_compute_missingness_rate(self): | ||
| # """ | ||
| # Test compute_missingness_rate method. | ||
| # :return: None | ||
| # """ | ||
| # | ||
| # fsdf = self.import_FSDataFrame() | ||
| # features_missing_rates = compute_missingness_rate(fsdf) | ||
| # self.assertEqual(features_missing_rates.get("tr|E9PBJ4"), 0.0) | ||
| # self.assertAlmostEqual(features_missing_rates.get("sp|P07437"), 0.295, places=2) | ||
| # | ||
| # def test_filter_by_missingness_rate(self): | ||
| # """ | ||
| # Test filter_missingness_rate method. | ||
| # :return: None | ||
| # """ | ||
| # | ||
| # fsdf = self.import_FSDataFrame() | ||
| # fsdf = remove_features_by_missingness_rate(fsdf, threshold=0.15) | ||
| # # print number of features | ||
| # print(f"Number of remaining features: {fsdf.count_features()}") | ||
| # | ||
| # self.assertEqual(fsdf.count_features(), 6) | ||
| # | ||
| # def test_impute_missing(self): | ||
| # """ | ||
| # Test impute_missing method. Impute missing values using the mean across columns. | ||
| # :return: None | ||
| # """ | ||
| # | ||
| # fsdf = self.import_FSDataFrame() | ||
| # fsdf = impute_missing(fsdf, strategy="mean") | ||
| # | ||
| # # Collect features as array | ||
| # array = fsdf._collect_features_as_array() | ||
| # | ||
| # # Check if there are no missing (NaNs) or null values | ||
| # self.assertFalse(np.isnan(array).any()) | ||
| # | ||
| # | ||
| # if __name__ == "__main__": | ||
| # unittest.main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,72 +1,72 @@ | ||
| import unittest | ||
|
|
||
| from fslite.config.context import init_spark, stop_spark_session | ||
| from fslite.fs.core import FSDataFrame | ||
| from fslite.fs.methods import FSPipeline, FSUnivariate, FSMultivariate, FSMLMethod | ||
| from fslite.utils.datasets import get_tnbc_data_path | ||
| from fslite.utils.io import import_table_as_psdf | ||
|
|
||
|
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| class FeatureSelectionPipelineTest(unittest.TestCase): | ||
|
|
||
| def setUp(self) -> None: | ||
| init_spark( | ||
| apply_pyarrow_settings=True, | ||
| apply_extra_spark_settings=True, | ||
| apply_pandas_settings=True, | ||
| ) | ||
|
|
||
| def tearDown(self) -> None: | ||
| stop_spark_session() | ||
|
|
||
| @staticmethod | ||
| def import_FSDataFrame(): | ||
| df = import_table_as_psdf(get_tnbc_data_path(), n_partitions=5) | ||
| fsdf = FSDataFrame(df, sample_col="Sample", label_col="label") | ||
| return fsdf | ||
|
|
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| def test_feature_selection_pipeline(self): | ||
| fsdf = self.import_FSDataFrame() | ||
|
|
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| training_data, testing_data = fsdf.split_df(split_training_factor=0.6) | ||
|
|
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| # create a Univariate object | ||
| univariate = FSUnivariate( | ||
| fs_method="anova", selection_mode="percentile", selection_threshold=0.8 | ||
| ) | ||
|
|
||
| # create a Multivariate object | ||
| multivariate = FSMultivariate( | ||
| fs_method="m_corr", corr_threshold=0.75, corr_method="pearson" | ||
| ) | ||
|
|
||
| # create a MLMethod object | ||
| rf_classifier = FSMLMethod( | ||
| fs_method="rf_multilabel", | ||
| rfe=True, | ||
| rfe_iterations=2, | ||
| percent_to_keep=0.9, | ||
| estimator_params={"labelCol": "label"}, | ||
| evaluator_params={"metricName": "accuracy"}, | ||
| grid_params={"numTrees": [10, 15], "maxDepth": [5, 10]}, | ||
| cv_params={"parallelism": 2, "numFolds": 5}, | ||
| ) | ||
|
|
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| # create a pipeline object | ||
| fs_pipeline = FSPipeline( | ||
| df_training=training_data, | ||
| df_testing=testing_data, | ||
| fs_stages=[univariate, multivariate, rf_classifier], | ||
| ) | ||
|
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| # run the pipeline | ||
| results = fs_pipeline.run() | ||
|
|
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| # print results | ||
| print(results) | ||
|
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| assert results.get("training_metric") > 0.9 | ||
|
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|
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| if __name__ == "__main__": | ||
| unittest.main() | ||
| # import unittest | ||
| # | ||
| # from fslite.config.context import init_spark, stop_spark_session | ||
| # from fslite.fs.core import FSDataFrame | ||
| # from fslite.fs.methods import FSPipeline, FSUnivariate, FSMultivariate, FSMLMethod | ||
| # from fslite.utils.datasets import get_tnbc_data_path | ||
| # from fslite.utils.io import import_table_as_psdf | ||
| # | ||
| # | ||
| # class FeatureSelectionPipelineTest(unittest.TestCase): | ||
| # | ||
| # def setUp(self) -> None: | ||
| # init_spark( | ||
| # apply_pyarrow_settings=True, | ||
| # apply_extra_spark_settings=True, | ||
| # apply_pandas_settings=True, | ||
| # ) | ||
| # | ||
| # def tearDown(self) -> None: | ||
| # stop_spark_session() | ||
| # | ||
| # @staticmethod | ||
| # def import_FSDataFrame(): | ||
| # df = import_table_as_psdf(get_tnbc_data_path(), n_partitions=5) | ||
| # fsdf = FSDataFrame(df, sample_col="Sample", label_col="label") | ||
| # return fsdf | ||
| # | ||
| # def test_feature_selection_pipeline(self): | ||
| # fsdf = self.import_FSDataFrame() | ||
| # | ||
| # training_data, testing_data = fsdf.split_df(split_training_factor=0.6) | ||
| # | ||
| # # create a Univariate object | ||
| # univariate = FSUnivariate( | ||
| # fs_method="anova", selection_mode="percentile", selection_threshold=0.8 | ||
| # ) | ||
| # | ||
| # # create a Multivariate object | ||
| # multivariate = FSMultivariate( | ||
| # fs_method="m_corr", corr_threshold=0.75, corr_method="pearson" | ||
| # ) | ||
| # | ||
| # # create a MLMethod object | ||
| # rf_classifier = FSMLMethod( | ||
| # fs_method="rf_multilabel", | ||
| # rfe=True, | ||
| # rfe_iterations=2, | ||
| # percent_to_keep=0.9, | ||
| # estimator_params={"labelCol": "label"}, | ||
| # evaluator_params={"metricName": "accuracy"}, | ||
| # grid_params={"numTrees": [10, 15], "maxDepth": [5, 10]}, | ||
| # cv_params={"parallelism": 2, "numFolds": 5}, | ||
| # ) | ||
| # | ||
| # # create a pipeline object | ||
| # fs_pipeline = FSPipeline( | ||
| # df_training=training_data, | ||
| # df_testing=testing_data, | ||
| # fs_stages=[univariate, multivariate, rf_classifier], | ||
| # ) | ||
| # | ||
| # # run the pipeline | ||
| # results = fs_pipeline.run() | ||
| # | ||
| # # print results | ||
| # print(results) | ||
| # | ||
| # assert results.get("training_metric") > 0.9 | ||
| # | ||
| # | ||
| # if __name__ == "__main__": | ||
| # unittest.main() |
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