diff --git a/quantile_forest/tests/test_quantile_forest.py b/quantile_forest/tests/test_quantile_forest.py index 96b1076..1d51e58 100755 --- a/quantile_forest/tests/test_quantile_forest.py +++ b/quantile_forest/tests/test_quantile_forest.py @@ -58,17 +58,17 @@ def check_regression_toy(name, weighted_quantile): ForestRegressor = FOREST_REGRESSORS[name] - regr = ForestRegressor(n_estimators=10, max_samples_leaf=None, bootstrap=False, random_state=0) - regr.fit(X, y) + est = ForestRegressor(n_estimators=10, max_samples_leaf=None, bootstrap=False, random_state=0) + est.fit(X, y) # Check model and apply outputs shape. - leaf_indices = regr.apply(X) - assert leaf_indices.shape == (len(X), regr.n_estimators) - assert 10 == len(regr) + leaf_indices = est.apply(X) + assert leaf_indices.shape == (len(X), est.n_estimators) + assert 10 == len(est) # Check aggregated quantile predictions. y_true = [[0.0, 0.5, 1.0], [2.0, 2.0, 2.0], [2.0, 2.0, 2.0]] - y_pred = regr.predict( + y_pred = est.predict( y_test, quantiles=quantiles, weighted_quantile=weighted_quantile, @@ -78,7 +78,7 @@ def check_regression_toy(name, weighted_quantile): # Check unaggregated quantile predictions. y_true = [[0.25, 0.5, 0.75], [2.0, 2.0, 2.0], [2.0, 2.0, 2.0]] - y_pred = regr.predict( + y_pred = est.predict( y_test, quantiles=quantiles, weighted_quantile=weighted_quantile, @@ -86,7 +86,7 @@ def check_regression_toy(name, weighted_quantile): ) assert_allclose(y_pred, y_true) - assert regr._more_tags() + assert est._more_tags() @pytest.mark.parametrize("name", FOREST_REGRESSORS) @@ -96,25 +96,25 @@ def test_regression_toy(name, weighted_quantile): def check_california_criterion(name, criterion): - # Check for consistency on the California Housing Prices dataset. + """Check for consistency on the California Housing dataset.""" ForestRegressor = FOREST_REGRESSORS[name] - regr = ForestRegressor(n_estimators=5, criterion=criterion, max_features=None, random_state=0) - regr.fit(X_california, y_california) - score = regr.score(X_california, y_california, quantiles=0.5) + est = ForestRegressor(n_estimators=5, criterion=criterion, max_features=None, random_state=0) + est.fit(X_california, y_california) + score = est.score(X_california, y_california, quantiles=0.5) assert score > 0.9, f"Failed with max_features=None, criterion {criterion} and score={score}." # Test maximum features. - regr = ForestRegressor(n_estimators=5, criterion=criterion, max_features=6, random_state=0) - regr.fit(X_california, y_california) - score = regr.score(X_california, y_california, quantiles=0.5) + est = ForestRegressor(n_estimators=5, criterion=criterion, max_features=6, random_state=0) + est.fit(X_california, y_california) + score = est.score(X_california, y_california, quantiles=0.5) assert score > 0.9, f"Failed with max_features=6, criterion {criterion} and score={score}." # Test sample weights. - regr = ForestRegressor(n_estimators=5, criterion=criterion, random_state=0) + est = ForestRegressor(n_estimators=5, criterion=criterion, random_state=0) sample_weight = np.concatenate([np.zeros(1), np.ones(len(y_california) - 1)]) - regr.fit(X_california, y_california, sample_weight=sample_weight) - score = regr.score(X_california, y_california, quantiles=0.5) + est.fit(X_california, y_california, sample_weight=sample_weight) + score = est.score(X_california, y_california, quantiles=0.5) assert score > 0.9, f"Failed with criterion {criterion}, sample weight and score={score}." @@ -125,7 +125,7 @@ def test_california(name, criterion): def check_predict_quantiles_toy(name): - # Check quantile predictions on toy data. + """Check quantile predictions on toy data.""" quantiles = [0.25, 0.5, 0.75] ForestRegressor = FOREST_REGRESSORS[name] @@ -267,6 +267,7 @@ def check_predict_quantiles( weighted_quantile, aggregate_leaves_first, ): + """Check quantile predictions.""" ForestRegressor = FOREST_REGRESSORS[name] # Check predicted quantiles on (semi-)random data. @@ -577,7 +578,7 @@ def test_predict_quantiles( def check_quantile_ranks_toy(name): - # Check rank predictions on toy data. + """Check quantile ranks on toy data.""" ForestRegressor = FOREST_REGRESSORS[name] # Check predicted ranks on toy sample. @@ -650,7 +651,7 @@ def test_quantile_ranks_toy(name): def check_quantile_ranks(name): - # Check rank predictions. + """Check quantile ranks.""" ForestRegressor = FOREST_REGRESSORS[name] # Check predicted ranks on (semi-)random data. @@ -698,7 +699,7 @@ def test_quantile_ranks(name): def check_proximity_counts(name): - # Check proximity counts. + """Check proximity counts.""" ForestRegressor = FOREST_REGRESSORS[name] # Check proximity counts on toy sample. @@ -795,7 +796,7 @@ def test_proximity_counts(name): def check_max_samples_leaf(name): - # Check that the `max_samples_leaf` parameter correctly samples leaves. + """Check that the `max_samples_leaf` parameter correctly samples leaves.""" X = X_california y = y_california @@ -849,7 +850,7 @@ def test_max_samples_leaf(name): def check_oob_samples(name): - # Check OOB sample generation. + """Check OOB sample generation.""" X = X_california y = y_california @@ -874,7 +875,7 @@ def test_oob_samples(name): def check_oob_samples_duplicates(name): - # Check OOB sampling with duplicates. + """Check OOB sampling with duplicates.""" X = np.array( [ [1, 2, 3], @@ -915,7 +916,7 @@ def check_predict_oob( weighted_quantile, aggregate_leaves_first, ): - # Check OOB predictions. + """Check OOB predictions.""" X = X_california y = y_california @@ -1126,7 +1127,7 @@ def test_predict_oob( def check_quantile_ranks_oob(name): - # Check OOB quantile rank predictions. + """Check OOB quantile ranks.""" X = X_california y = y_california @@ -1183,7 +1184,7 @@ def test_quantile_ranks_oob(name): def check_proximity_counts_oob(name): - # Check OOB proximity counts. + """Check OOB proximity counts.""" X = X_california y = y_california @@ -1262,6 +1263,7 @@ def test_proximity_counts_oob(name): def check_monotonic_constraints(name, max_samples_leaf): + """Check monotonic constraints.""" ForestRegressor = FOREST_REGRESSORS[name] n_samples = 1000 @@ -1335,8 +1337,7 @@ def test_monotonic_constraints(name, max_samples_leaf): def check_serialization(name, sparse_pickle, monotonic_cst, multi_target): - # Check model serialization/deserialization. - + """Check model serialization/deserialization.""" X = X_california if multi_target: @@ -1370,7 +1371,7 @@ def test_serialization(name, sparse_pickle, monotonic_cst, multi_target): def test_calc_quantile(): - # Check quantile calculations. + """Check quantile calculations.""" quantiles = [0.0, 0.25, 0.5, 0.75, 1.0] interpolations = [b"linear", b"lower", b"higher", b"midpoint", b"nearest"] @@ -1440,7 +1441,7 @@ def test_calc_quantile(): def test_calc_weighted_quantile(): - # Check weighted quantile calculations. + """Check weighted quantile calculations.""" quantiles = [0.0, 0.25, 0.5, 0.75, 1.0] interpolations = [b"linear", b"lower", b"higher", b"midpoint", b"nearest"] @@ -1559,7 +1560,7 @@ def _dicts_to_input_pairs(input_dicts): def test_calc_quantile_rank(): - # Check quantile rank calculations. + """Check quantile rank calculations.""" kinds = [b"rank", b"weak", b"strict", b"mean"] inputs = [ diff --git a/quantile_forest/tests/test_utils.py b/quantile_forest/tests/test_utils.py index d93aa9c..8a6ff05 100755 --- a/quantile_forest/tests/test_utils.py +++ b/quantile_forest/tests/test_utils.py @@ -11,7 +11,7 @@ def test_generate_unsampled_indices(): - # Check unsampled indices generation. + """Check unsampled indices generation.""" max_index = 20 duplicates = [[1, 4], [19, 10], [2, 3, 5], [6, 13]] @@ -40,7 +40,7 @@ def _generate_unsampled_indices(sample_indices, n_total_samples): def test_group_indices_by_value(): - # Check grouping indices by value. + """Check grouping indices by value.""" inputs = np.array([1, 3, 2, 2, 5, 4, 5, 5], dtype=np.int64) actual_indices, actual_values = group_indices_by_value(inputs) @@ -58,7 +58,7 @@ def test_group_indices_by_value(): def test_map_indices_to_leaves(): - # Check mapping of indices to leaf nodes. + """Check mapping of indices to leaf nodes.""" y_train_leaves = np.zeros((3, 1, 3), dtype=np.int64) bootstrap_indices = np.array([[1], [2], [3], [4], [5]], dtype=np.int64) leaf_indices = np.array([1, 2])