"""Test the module SMOTENC.""" # Authors: Guillaume Lemaitre # Christos Aridas # Dzianis Dudnik # License: MIT from collections import Counter import numpy as np import pytest from scipy import sparse from sklearn.datasets import make_classification from sklearn.utils._testing import assert_allclose, assert_array_equal from imblearn.over_sampling import SMOTENC def data_heterogneous_ordered(): rng = np.random.RandomState(42) X = np.empty((30, 4), dtype=object) # create 2 random continuous feature X[:, :2] = rng.randn(30, 2) # create a categorical feature using some string X[:, 2] = rng.choice(["a", "b", "c"], size=30).astype(object) # create a categorical feature using some integer X[:, 3] = rng.randint(3, size=30) y = np.array([0] * 10 + [1] * 20) # return the categories return X, y, [2, 3] def data_heterogneous_unordered(): rng = np.random.RandomState(42) X = np.empty((30, 4), dtype=object) # create 2 random continuous feature X[:, [1, 2]] = rng.randn(30, 2) # create a categorical feature using some string X[:, 0] = rng.choice(["a", "b", "c"], size=30).astype(object) # create a categorical feature using some integer X[:, 3] = rng.randint(3, size=30) y = np.array([0] * 10 + [1] * 20) # return the categories return X, y, [0, 3] def data_heterogneous_masked(): rng = np.random.RandomState(42) X = np.empty((30, 4), dtype=object) # create 2 random continuous feature X[:, [1, 2]] = rng.randn(30, 2) # create a categorical feature using some string X[:, 0] = rng.choice(["a", "b", "c"], size=30).astype(object) # create a categorical feature using some integer X[:, 3] = rng.randint(3, size=30) y = np.array([0] * 10 + [1] * 20) # return the categories return X, y, [True, False, True] def data_heterogneous_unordered_multiclass(): rng = np.random.RandomState(42) X = np.empty((50, 4), dtype=object) # create 2 random continuous feature X[:, [1, 2]] = rng.randn(50, 2) # create a categorical feature using some string X[:, 0] = rng.choice(["a", "b", "c"], size=50).astype(object) # create a categorical feature using some integer X[:, 3] = rng.randint(3, size=50) y = np.array([0] * 10 + [1] * 15 + [2] * 25) # return the categories return X, y, [0, 3] def data_sparse(format): rng = np.random.RandomState(42) X = np.empty((30, 4), dtype=np.float64) # create 2 random continuous feature X[:, [1, 2]] = rng.randn(30, 2) # create a categorical feature using some string X[:, 0] = rng.randint(3, size=30) # create a categorical feature using some integer X[:, 3] = rng.randint(3, size=30) y = np.array([0] * 10 + [1] * 20) X = sparse.csr_matrix(X) if format == "csr" else sparse.csc_matrix(X) return X, y, [0, 3] def test_smotenc_error(): X, y, _ = data_heterogneous_unordered() categorical_features = [0, 10] smote = SMOTENC(random_state=0, categorical_features=categorical_features) with pytest.raises(ValueError, match="indices are out of range"): smote.fit_resample(X, y) @pytest.mark.parametrize( "data", [ data_heterogneous_ordered(), data_heterogneous_unordered(), data_heterogneous_masked(), data_sparse("csr"), data_sparse("csc"), ], ) def test_smotenc(data): X, y, categorical_features = data smote = SMOTENC(random_state=0, categorical_features=categorical_features) X_resampled, y_resampled = smote.fit_resample(X, y) assert X_resampled.dtype == X.dtype categorical_features = np.array(categorical_features) if categorical_features.dtype == bool: categorical_features = np.flatnonzero(categorical_features) for cat_idx in categorical_features: if sparse.issparse(X): assert set(X[:, cat_idx].data) == set(X_resampled[:, cat_idx].data) assert X[:, cat_idx].dtype == X_resampled[:, cat_idx].dtype else: assert set(X[:, cat_idx]) == set(X_resampled[:, cat_idx]) assert X[:, cat_idx].dtype == X_resampled[:, cat_idx].dtype # part of the common test which apply to SMOTE-NC even if it is not default # constructible def test_smotenc_check_target_type(): X, _, categorical_features = data_heterogneous_unordered() y = np.linspace(0, 1, 30) smote = SMOTENC(categorical_features=categorical_features, random_state=0) with pytest.raises(ValueError, match="Unknown label type: 'continuous'"): smote.fit_resample(X, y) rng = np.random.RandomState(42) y = rng.randint(2, size=(20, 3)) msg = "Multilabel and multioutput targets are not supported." with pytest.raises(ValueError, match=msg): smote.fit_resample(X, y) def test_smotenc_samplers_one_label(): X, _, categorical_features = data_heterogneous_unordered() y = np.zeros(30) smote = SMOTENC(categorical_features=categorical_features, random_state=0) with pytest.raises(ValueError, match="needs to have more than 1 class"): smote.fit(X, y) def test_smotenc_fit(): X, y, categorical_features = data_heterogneous_unordered() smote = SMOTENC(categorical_features=categorical_features, random_state=0) smote.fit_resample(X, y) assert hasattr( smote, "sampling_strategy_" ), "No fitted attribute sampling_strategy_" def test_smotenc_fit_resample(): X, y, categorical_features = data_heterogneous_unordered() target_stats = Counter(y) smote = SMOTENC(categorical_features=categorical_features, random_state=0) _, y_res = smote.fit_resample(X, y) _ = Counter(y_res) n_samples = max(target_stats.values()) assert all(value >= n_samples for value in Counter(y_res).values()) def test_smotenc_fit_resample_sampling_strategy(): X, y, categorical_features = data_heterogneous_unordered_multiclass() expected_stat = Counter(y)[1] smote = SMOTENC(categorical_features=categorical_features, random_state=0) sampling_strategy = {2: 25, 0: 25} smote.set_params(sampling_strategy=sampling_strategy) X_res, y_res = smote.fit_resample(X, y) assert Counter(y_res)[1] == expected_stat def test_smotenc_pandas(): pd = pytest.importorskip("pandas") # Check that the samplers handle pandas dataframe and pandas series X, y, categorical_features = data_heterogneous_unordered_multiclass() X_pd = pd.DataFrame(X) smote = SMOTENC(categorical_features=categorical_features, random_state=0) X_res_pd, y_res_pd = smote.fit_resample(X_pd, y) X_res, y_res = smote.fit_resample(X, y) # FIXME: we should use to_numpy with pandas >= 0.25 assert_array_equal(X_res_pd.values, X_res) assert_allclose(y_res_pd, y_res) def test_smotenc_preserve_dtype(): X, y = make_classification( n_samples=50, n_classes=3, n_informative=4, weights=[0.2, 0.3, 0.5], random_state=0, ) # Cast X and y to not default dtype X = X.astype(np.float32) y = y.astype(np.int32) smote = SMOTENC(categorical_features=[1], random_state=0) X_res, y_res = smote.fit_resample(X, y) assert X.dtype == X_res.dtype, "X dtype is not preserved" assert y.dtype == y_res.dtype, "y dtype is not preserved" @pytest.mark.parametrize("categorical_features", [[True, True, True], [0, 1, 2]]) def test_smotenc_raising_error_all_categorical(categorical_features): X, y = make_classification( n_features=3, n_informative=1, n_redundant=1, n_repeated=0, n_clusters_per_class=1, ) smote = SMOTENC(categorical_features=categorical_features) err_msg = "SMOTE-NC is not designed to work only with categorical features" with pytest.raises(ValueError, match=err_msg): smote.fit_resample(X, y) def test_smote_nc_with_null_median_std(): # Non-regression test for #662 # https://github.com/scikit-learn-contrib/imbalanced-learn/issues/662 data = np.array( [ [1, 2, 1, "A"], [2, 1, 2, "A"], [1, 2, 3, "B"], [1, 2, 4, "C"], [1, 2, 5, "C"], ], dtype="object", ) labels = np.array( ["class_1", "class_1", "class_1", "class_2", "class_2"], dtype=object ) smote = SMOTENC(categorical_features=[3], k_neighbors=1, random_state=0) X_res, y_res = smote.fit_resample(data, labels) # check that the categorical feature is not random but correspond to the # categories seen in the minority class samples assert X_res[-1, -1] == "C"