"""Test for the metrics that perform pairwise distance computation.""" # Authors: Guillaume Lemaitre # License: MIT import numpy as np import pytest from sklearn.exceptions import NotFittedError from sklearn.preprocessing import LabelEncoder, OrdinalEncoder from sklearn.utils._testing import _convert_container from imblearn.metrics.pairwise import ValueDifferenceMetric @pytest.fixture def data(): rng = np.random.RandomState(0) feature_1 = ["A"] * 10 + ["B"] * 20 + ["C"] * 30 feature_2 = ["A"] * 40 + ["B"] * 20 feature_3 = ["A"] * 20 + ["B"] * 20 + ["C"] * 10 + ["D"] * 10 X = np.array([feature_1, feature_2, feature_3], dtype=object).T rng.shuffle(X) y = rng.randint(low=0, high=2, size=X.shape[0]) y_labels = np.array(["not apple", "apple"], dtype=object) y = y_labels[y] return X, y @pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64]) @pytest.mark.parametrize("k, r", [(1, 1), (1, 2), (2, 1), (2, 2)]) @pytest.mark.parametrize("y_type", ["list", "array"]) @pytest.mark.parametrize("encode_label", [True, False]) def test_value_difference_metric(data, dtype, k, r, y_type, encode_label): # Check basic feature of the metric: # * the shape of the distance matrix is (n_samples, n_samples) # * computing pairwise distance of X is the same than explicitely between # X and X. X, y = data y = _convert_container(y, y_type) if encode_label: y = LabelEncoder().fit_transform(y) encoder = OrdinalEncoder(dtype=dtype) X_encoded = encoder.fit_transform(X) vdm = ValueDifferenceMetric(k=k, r=r) vdm.fit(X_encoded, y) dist_1 = vdm.pairwise(X_encoded) dist_2 = vdm.pairwise(X_encoded, X_encoded) np.testing.assert_allclose(dist_1, dist_2) assert dist_1.shape == (X.shape[0], X.shape[0]) assert dist_2.shape == (X.shape[0], X.shape[0]) @pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64]) @pytest.mark.parametrize("k, r", [(1, 1), (1, 2), (2, 1), (2, 2)]) @pytest.mark.parametrize("y_type", ["list", "array"]) @pytest.mark.parametrize("encode_label", [True, False]) def test_value_difference_metric_property(dtype, k, r, y_type, encode_label): # Check the property of the vdm distance. Let's check the property # described in "Improved Heterogeneous Distance Functions", D.R. Wilson and # T.R. Martinez, Journal of Artificial Intelligence Research 6 (1997) 1-34 # https://arxiv.org/pdf/cs/9701101.pdf # # "if an attribute color has three values red, green and blue, and the # application is to identify whether or not an object is an apple, red and # green would be considered closer than red and blue because the former two # both have similar correlations with the output class apple." # defined our feature X = np.array(["green"] * 10 + ["red"] * 10 + ["blue"] * 10).reshape(-1, 1) # 0 - not an apple / 1 - an apple y = np.array([1] * 8 + [0] * 5 + [1] * 7 + [0] * 9 + [1]) y_labels = np.array(["not apple", "apple"], dtype=object) y = y_labels[y] y = _convert_container(y, y_type) if encode_label: y = LabelEncoder().fit_transform(y) encoder = OrdinalEncoder(dtype=dtype) X_encoded = encoder.fit_transform(X) vdm = ValueDifferenceMetric(k=k, r=r) vdm.fit(X_encoded, y) sample_green = encoder.transform([["green"]]) sample_red = encoder.transform([["red"]]) sample_blue = encoder.transform([["blue"]]) for sample in (sample_green, sample_red, sample_blue): # computing the distance between a sample of the same category should # give a null distance dist = vdm.pairwise(sample).squeeze() assert dist == pytest.approx(0) # check the property explained in the introduction example dist_1 = vdm.pairwise(sample_green, sample_red).squeeze() dist_2 = vdm.pairwise(sample_blue, sample_red).squeeze() dist_3 = vdm.pairwise(sample_blue, sample_green).squeeze() # green and red are very close # blue is closer to red than green assert dist_1 < dist_2 assert dist_1 < dist_3 assert dist_2 < dist_3 def test_value_difference_metric_categories(data): # Check that "auto" is equivalent to provide the number categories # beforehand X, y = data encoder = OrdinalEncoder(dtype=np.int32) X_encoded = encoder.fit_transform(X) n_categories = np.array([len(cat) for cat in encoder.categories_]) vdm_auto = ValueDifferenceMetric().fit(X_encoded, y) vdm_categories = ValueDifferenceMetric(n_categories=n_categories) vdm_categories.fit(X_encoded, y) np.testing.assert_array_equal(vdm_auto.n_categories_, n_categories) np.testing.assert_array_equal(vdm_auto.n_categories_, vdm_categories.n_categories_) def test_value_difference_metric_categories_error(data): # Check that we raise an error if n_categories is inconsistent with the # number of features in X X, y = data encoder = OrdinalEncoder(dtype=np.int32) X_encoded = encoder.fit_transform(X) n_categories = [1, 2] vdm = ValueDifferenceMetric(n_categories=n_categories) err_msg = "The length of n_categories is not consistent with the number" with pytest.raises(ValueError, match=err_msg): vdm.fit(X_encoded, y) def test_value_difference_metric_missing_categories(data): # Check that we don't get issue when a category is missing between 0 # n_categories - 1 X, y = data encoder = OrdinalEncoder(dtype=np.int32) X_encoded = encoder.fit_transform(X) n_categories = np.array([len(cat) for cat in encoder.categories_]) # remove a categories that could be between 0 and n_categories X_encoded[X_encoded[:, -1] == 1] = 0 np.testing.assert_array_equal(np.unique(X_encoded[:, -1]), [0, 2, 3]) vdm = ValueDifferenceMetric(n_categories=n_categories) vdm.fit(X_encoded, y) for n_cats, proba in zip(n_categories, vdm.proba_per_class_): assert proba.shape == (n_cats, len(np.unique(y))) def test_value_difference_value_unfitted(data): # Check that we raise a NotFittedError when `fit` is not not called before # pairwise. X, y = data encoder = OrdinalEncoder(dtype=np.int32) X_encoded = encoder.fit_transform(X) with pytest.raises(NotFittedError): ValueDifferenceMetric().pairwise(X_encoded)