# ---------------------------------------------------------------------------- # Copyright (c) 2017-2023, QIIME 2 development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. # ---------------------------------------------------------------------------- import os import pandas as pd import pandas.testing as pdt import numpy as np import biom import qiime2 from qiime2.plugins import sample_classifier from q2_sample_classifier.tests.test_base_class import \ SampleClassifierTestPluginBase from q2_sample_classifier.tests.test_estimators import SampleEstimatorTestBase from q2_sample_classifier.classify import summarize class NowLetsTestTheActions(SampleClassifierTestPluginBase): def setUp(self): super().setUp() md = pd.Series(['a', 'a', 'b', 'b', 'b'], index=['a', 'b', 'c', 'd', 'e'], name='bugs') md.index.name = 'SampleID' self.md = qiime2.CategoricalMetadataColumn(md) tab = biom.Table( np.array([[3, 6, 7, 3, 6], [3, 4, 5, 6, 2], [8, 6, 4, 1, 0], [8, 6, 4, 1, 0], [8, 6, 4, 1, 0]]), observation_ids=['v', 'w', 'x', 'y', 'z'], sample_ids=['a', 'b', 'c', 'd', 'e']) self.tab = qiime2.Artifact.import_data('FeatureTable[Frequency]', tab) md2 = pd.DataFrame({'trash': ['a', 'a', 'b', 'b', 'b', 'junk'], 'floats': [0.1, 0.1, 1.3, 1.8, 1000.1, 0.1], 'ints': [0, 1, 2, 2, 2, 0], 'nans': [1, 1, 2, 2, np.nan, np.nan], 'negatives': [-7, -3, -1.2, -4, -9, -1]}, index=['a', 'b', 'c', 'd', 'e', 'peanut']) md2.index.name = 'SampleID' self.md2 = qiime2.Metadata(md2) # let's make sure the function runs w/o errors and that the correct # transformers are in place (see issue 114) def test_action_split_table(self): res = sample_classifier.actions.split_table( self.tab, self.md, test_size=0.5) y_train = res.training_targets.view(pd.Series) y_test = res.test_targets.view(pd.Series) # test whether extracted target is correct self.assertEqual(y_train.name, 'bugs') # test if complete target column is covered y_all = pd.concat([y_train, y_test]).sort_index() y_all.index.name = 'SampleID' pdt.assert_series_equal(y_all, self.md._series) def test_metatable(self): exp = biom.Table( np.array([[0.1, 0.1, 1.3, 1.8, 1000.1, 0.1], [0, 1, 2, 2, 2, 0]]), observation_ids=['floats', 'ints'], sample_ids=['a', 'b', 'c', 'd', 'e', 'peanut']) res, = sample_classifier.actions.metatable( self.md2, missing_values='drop_features') report = res.view(biom.Table).descriptive_equality(exp) self.assertIn('Tables appear equal', report, report) def test_metatable_missing_error(self): with self.assertRaisesRegex(ValueError, "missing values"): sample_classifier.actions.metatable( self.md2, missing_values='error') def test_metatable_drop_samples(self): exp = biom.Table( np.array([[3, 6, 7, 3], [3, 4, 5, 6], [8, 6, 4, 1], [8, 6, 4, 1], [8, 6, 4, 1], [0.1, 0.1, 1.3, 1.8], [0, 1, 2, 2], [1, 1, 2, 2]]), observation_ids=['v', 'w', 'x', 'y', 'z', 'floats', 'ints', 'nans'], sample_ids=['a', 'b', 'c', 'd']) res, = sample_classifier.actions.metatable( self.md2, self.tab, missing_values='drop_samples') report = res.view(biom.Table).descriptive_equality(exp) self.assertIn('Tables appear equal', report, report) def test_metatable_fill_na(self): exp = biom.Table( np.array([[3, 6, 7, 3, 6], [3, 4, 5, 6, 2], [8, 6, 4, 1, 0], [8, 6, 4, 1, 0], [8, 6, 4, 1, 0], [0.1, 0.1, 1.3, 1.8, 1000.1], [0, 1, 2, 2, 2], [1., 1., 2., 2., 0.]]), observation_ids=['v', 'w', 'x', 'y', 'z', 'floats', 'ints', 'nans'], sample_ids=['a', 'b', 'c', 'd', 'e']) res, = sample_classifier.actions.metatable( self.md2, self.tab, missing_values='fill') report = res.view(biom.Table).descriptive_equality(exp) self.assertIn('Tables appear equal', report, report) def test_metatable_with_merge(self): exp = biom.Table( np.array([[3, 6, 7, 3, 6], [3, 4, 5, 6, 2], [8, 6, 4, 1, 0], [8, 6, 4, 1, 0], [8, 6, 4, 1, 0], [0.1, 0.1, 1.3, 1.8, 1000.1], [0, 1, 2, 2, 2]]), observation_ids=['v', 'w', 'x', 'y', 'z', 'floats', 'ints'], sample_ids=['a', 'b', 'c', 'd', 'e']) res, = sample_classifier.actions.metatable( self.md2, self.tab, missing_values='drop_features') report = res.view(biom.Table).descriptive_equality(exp) self.assertIn('Tables appear equal', report, report) def test_metatable_with_merge_successful_inner_join(self): exp = biom.Table( np.array([[3, 6, 7, 3], [3, 4, 5, 6], [8, 6, 4, 1], [8, 6, 4, 1], [8, 6, 4, 1], [0.1, 0.1, 1.3, 1.8], [0, 1, 2, 2], [1., 1., 2., 2.]]), observation_ids=['v', 'w', 'x', 'y', 'z', 'floats', 'ints', 'nans'], sample_ids=['a', 'b', 'c', 'd']) res, = sample_classifier.actions.metatable( self.md2.filter_ids(['a', 'b', 'c', 'd']), self.tab, missing_values='error') report = res.view(biom.Table).descriptive_equality(exp) self.assertIn('Tables appear equal', report, report) def test_metatable_with_merge_error_inner_join(self): with self.assertRaisesRegex(ValueError, "Missing samples"): sample_classifier.actions.metatable( self.md2.filter_ids(['a', 'b', 'c', 'd']), self.tab, missing_samples='error', missing_values='drop_samples') def test_metatable_empty_metadata_after_drop_all_unique(self): with self.assertRaisesRegex( ValueError, "All metadata"): # are belong to us sample_classifier.actions.metatable( self.md2.filter_ids(['b', 'c']), self.tab, missing_values='drop_samples', drop_all_unique=True) def test_metatable_no_samples_after_filtering(self): junk_md = pd.DataFrame( {'trash': ['a', 'a', 'b', 'b', 'b', 'junk'], 'floats': [np.nan, np.nan, np.nan, 1.8, 1000.1, 0.1], 'ints': [0, 1, 2, np.nan, 2, 0], 'nans': [1, 1, 2, 2, np.nan, np.nan], 'negatives': [-7, -4, -1.2, -4, -9, -1]}, index=['a', 'b', 'c', 'd', 'e', 'peanut']) junk_md.index.name = 'SampleID' junk_md = qiime2.Metadata(junk_md) with self.assertRaisesRegex(ValueError, "All metadata samples"): sample_classifier.actions.metatable( junk_md, missing_values='drop_samples') # make sure summarize visualizer works and that rfe_scores are stored properly class TestSummarize(SampleEstimatorTestBase): def test_summary_with_rfecv(self): summarize(self.temp_dir.name, self.pipeline) self.assertTrue('rfe_plot.pdf' in os.listdir(self.temp_dir.name)) self.assertTrue('rfe_plot.png' in os.listdir(self.temp_dir.name)) self.assertTrue('rfe_scores.tsv' in os.listdir(self.temp_dir.name)) def test_summary_without_rfecv(self): # nuke the rfe_scores to test the other branch of _summarize_estimator del self.pipeline.rfe_scores summarize(self.temp_dir.name, self.pipeline) self.assertFalse('rfe_plot.pdf' in os.listdir(self.temp_dir.name)) self.assertFalse('rfe_plot.png' in os.listdir(self.temp_dir.name)) self.assertFalse('rfe_scores.tsv' in os.listdir(self.temp_dir.name))