def setUp(self):
self.addCleanup(absltest.mock.patch.stopall)
super(FeatureVisualizerTest, self).setUp()
self.mock_bq_client = absltest.mock.create_autospec(
bigquery.client.Client)
self.features_table_path = 'project_id.dataset.features_table'
self.numerical_features = ('num_feature1', 'num_feature2')
self.categorical_features = ('cat_feature1', 'cat_feature2')
self.label_column = 'predictionLabel',
self.positive_class_label = True,
self.negative_class_label = False,
self.num_pos_instances = 10000,
self.num_neg_instances = 10000
self.feature_viz_obj = feature_visualizer.FeatureVisualizer(
bq_client=self.mock_bq_client,
features_table_path=self.features_table_path,
numerical_features=self.numerical_features,
categorical_features=self.categorical_features,
label_column=self.label_column,
positive_class_label=self.positive_class_label,
negative_class_label=self.negative_class_label,
num_pos_instances=self.num_pos_instances,
num_neg_instances=self.num_neg_instances)
self.mock_configure_sql = absltest.mock.patch.object(
utils, 'read_file', autospec=True).start()
self.mock_bq_client.query.return_value.to_dataframe.side_effect = [
NUMERICAL_FEATURES_STATS, NUMERICAL_FEATURES_SAMPLE,
CATEGORICAL_FEATURES_STATS
]
def test_plot_features_returns_correct_plots_for_numeric_label(self):
self.feature_viz_obj = feature_visualizer.FeatureVisualizer(
bq_client=self.mock_bq_client,
features_table_path=self.features_table_path,
numerical_features=self.numerical_features,
categorical_features=self.categorical_features,
label_column=self.label_column,
label_type='numerical',
num_instances=10000)
self.mock_configure_sql = absltest.mock.patch.object(
utils, 'read_file', autospec=True).start()
self.mock_bq_client.query.return_value.to_dataframe.side_effect = [
NUMERICAL_LABEL_NUMERICAL_FEATURES_STATS,
NUMERICAL_LABEL_NUMERICAL_FEATURES_SAMPLE,
NUMERICAL_LABEL_CATEGORICAL_FEATURES_STATS,
NUMERICAL_LABEL_STATS
]
num_feature_1_plots, cat_feature_1_plots = (
self.feature_viz_obj.plot_features())
with self.subTest(name='test the number of plots returned'):
self.assertLen(num_feature_1_plots, 2)
self.assertLen(cat_feature_1_plots, 2)
# Test elements in numerical fearture plots
num_snapshot_dates = sorted(set(
NUMERICAL_LABEL_NUMERICAL_FEATURES_STATS['snapshot_date']))
with self.subTest(name='test label vs num_feature1 scatter plot'):
self.assertEqual(
'Scatter plot of the Label vs [num_feature1] | correlation = 0.71',
num_feature_1_plots[0].get_title())
with self.subTest(
name='test snapshopt distribution of num_feature1'):
self.assertListEqual(
num_snapshot_dates,
sorted([
tick.get_text()
for tick in num_feature_1_plots[1].get_xticklabels()
]))
self.assertEqual(
'Snapshot-level distribution of [num_feature1]',
num_feature_1_plots[1].get_title())
# Test elements in categorical fearture plots
cat_feature1_values = list(
NUMERICAL_LABEL_STATS[
NUMERICAL_LABEL_STATS['feature'] == 'cat_feature1']['value'])
cat_snapshot_dates = sorted(set(
NUMERICAL_LABEL_CATEGORICAL_FEATURES_STATS[
NUMERICAL_LABEL_CATEGORICAL_FEATURES_STATS[
'feature'] == 'cat_feature1']['snapshot_date']))
with self.subTest(name='test the label distribution plot'):
self.assertListEqual(
cat_feature1_values,
sorted([
tick.get_text()
for tick in cat_feature_1_plots[0].get_xticklabels()
]))
self.assertEqual(
'Label distribution by [cat_feature1] categories',
cat_feature_1_plots[0].get_title())
with self.subTest(
name='test snapshopt distribution of cat_feature1'):
self.assertListEqual(
cat_snapshot_dates,
sorted([
tick.get_text()
for tick in cat_feature_1_plots[1].get_xticklabels()
]))
self.assertEqual(
'Snapshot-level distribution of [cat_feature1]',
cat_feature_1_plots[1].get_title())
def test_plot_features_returns_correct_plots_for_binary_label(self):
self.feature_viz_obj = feature_visualizer.FeatureVisualizer(
bq_client=self.mock_bq_client,
features_table_path=self.features_table_path,
numerical_features=self.numerical_features,
categorical_features=self.categorical_features,
label_column=self.label_column,
label_type='binary',
positive_class_label='True',
negative_class_label='False',
num_pos_instances=10000,
num_neg_instances=10000)
self.mock_configure_sql = absltest.mock.patch.object(
utils, 'read_file', autospec=True).start()
self.mock_bq_client.query.return_value.to_dataframe.side_effect = [
BINARY_LABEL_NUMERICAL_FEATURES_STATS,
BINARY_LABEL_NUMERICAL_FEATURES_SAMPLE,
BINARY_LABEL_CATEGORICAL_FEATURES_STATS
]
label_values = ['False', 'True']
num_feature1_data = BINARY_LABEL_NUMERICAL_FEATURES_STATS[
BINARY_LABEL_NUMERICAL_FEATURES_STATS['feature'] == 'num_feature1']
snapshot_dates_num_true = sorted(set(
num_feature1_data[num_feature1_data['label'] == 'True']
['snapshot_date']))
snapshot_dates_num_false = sorted(set(
num_feature1_data[num_feature1_data['label'] == 'False']
['snapshot_date']))
cat_feature1_data = BINARY_LABEL_CATEGORICAL_FEATURES_STATS[
BINARY_LABEL_CATEGORICAL_FEATURES_STATS['feature'] == 'cat_feature1']
cat_feature1_category_values1 = sorted(set(cat_feature1_data['value']))
snapshot_dates_cat_true = sorted(set(
cat_feature1_data[cat_feature1_data['label'] == 'True']
['snapshot_date']))
snapshot_dates_cat_false = sorted(set(
cat_feature1_data[cat_feature1_data['label'] == 'False']
['snapshot_date']))
num_feature_1_plots, cat_feature_1_plots = (
self.feature_viz_obj.plot_features())
with self.subTest(name='test the number of plots returned'):
self.assertLen(num_feature_1_plots, 3)
self.assertLen(cat_feature_1_plots, 3)
# Test elements in numerical fearture plots
with self.subTest(name='test num_feature1 distribution box plot by label'):
self.assertListEqual(label_values, [
tick.get_text() for tick in num_feature_1_plots[0].get_yticklabels()
])
self.assertEqual('Distribution of [num_feature1]',
num_feature_1_plots[0].get_title())
with self.subTest(
name='test snapshopt distribution of num_feature1 when label=True'):
self.assertListEqual(
snapshot_dates_num_true,
sorted([
tick.get_text()
for tick in num_feature_1_plots[1].get_xticklabels()
]))
self.assertEqual(
'Snapshot-level distribution of [num_feature1] for label = True',
num_feature_1_plots[1].get_title())
with self.subTest(
name='test snapshopt distribution of [num_feature1] when label=False'):
self.assertListEqual(
snapshot_dates_num_false,
sorted([
tick.get_text()
for tick in num_feature_1_plots[2].get_xticklabels()
]))
self.assertEqual(
'Snapshot-level distribution of [num_feature1] for label = False',
num_feature_1_plots[2].get_title())
# Test elements in categorical fearture plots
with self.subTest(name='test the cat_feature1 distribution plot'):
self.assertListEqual(
cat_feature1_category_values1,
sorted([
tick.get_text()
for tick in cat_feature_1_plots[0].get_xticklabels()
]))
self.assertEqual('Distribution of [cat_feature1]',
cat_feature_1_plots[0].get_title())
with self.subTest(
name='test snapshopt distribution of cat_feature1 when label=True'):
self.assertListEqual(
snapshot_dates_cat_true,
sorted([
tick.get_text()
for tick in cat_feature_1_plots[1].get_xticklabels()
]))
self.assertEqual(
'Snapshot-level distribution of [cat_feature1] for label = True',
cat_feature_1_plots[1].get_title())
with self.subTest(
name='test snapshopt distribution of [cat_feature1] when label=False'):
self.assertListEqual(
snapshot_dates_cat_false,
sorted([
tick.get_text()
for tick in cat_feature_1_plots[2].get_xticklabels()
]))
self.assertEqual(
'Snapshot-level distribution of [cat_feature1] for label = False',
cat_feature_1_plots[2].get_title())