Uploaded Test files
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from functools import partial
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import pytest
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import numpy as np
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from sklearn.metrics.cluster import adjusted_mutual_info_score
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from sklearn.metrics.cluster import adjusted_rand_score
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from sklearn.metrics.cluster import completeness_score
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from sklearn.metrics.cluster import fowlkes_mallows_score
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from sklearn.metrics.cluster import homogeneity_score
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from sklearn.metrics.cluster import mutual_info_score
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from sklearn.metrics.cluster import normalized_mutual_info_score
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from sklearn.metrics.cluster import v_measure_score
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from sklearn.metrics.cluster import silhouette_score
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from sklearn.metrics.cluster import calinski_harabasz_score
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from sklearn.metrics.cluster import davies_bouldin_score
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from sklearn.utils._testing import assert_allclose
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# Dictionaries of metrics
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# ------------------------
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# The goal of having those dictionaries is to have an easy way to call a
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# particular metric and associate a name to each function:
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# - SUPERVISED_METRICS: all supervised cluster metrics - (when given a
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# ground truth value)
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# - UNSUPERVISED_METRICS: all unsupervised cluster metrics
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#
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# Those dictionaries will be used to test systematically some invariance
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# properties, e.g. invariance toward several input layout.
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#
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SUPERVISED_METRICS = {
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"adjusted_mutual_info_score": adjusted_mutual_info_score,
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"adjusted_rand_score": adjusted_rand_score,
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"completeness_score": completeness_score,
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"homogeneity_score": homogeneity_score,
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"mutual_info_score": mutual_info_score,
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"normalized_mutual_info_score": normalized_mutual_info_score,
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"v_measure_score": v_measure_score,
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"fowlkes_mallows_score": fowlkes_mallows_score
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}
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UNSUPERVISED_METRICS = {
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"silhouette_score": silhouette_score,
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"silhouette_manhattan": partial(silhouette_score, metric='manhattan'),
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"calinski_harabasz_score": calinski_harabasz_score,
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"davies_bouldin_score": davies_bouldin_score
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}
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# Lists of metrics with common properties
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# ---------------------------------------
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# Lists of metrics with common properties are used to test systematically some
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# functionalities and invariance, e.g. SYMMETRIC_METRICS lists all metrics
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# that are symmetric with respect to their input argument y_true and y_pred.
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#
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# --------------------------------------------------------------------
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# Symmetric with respect to their input arguments y_true and y_pred.
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# Symmetric metrics only apply to supervised clusters.
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SYMMETRIC_METRICS = [
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"adjusted_rand_score", "v_measure_score",
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"mutual_info_score", "adjusted_mutual_info_score",
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"normalized_mutual_info_score", "fowlkes_mallows_score"
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]
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NON_SYMMETRIC_METRICS = ["homogeneity_score", "completeness_score"]
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# Metrics whose upper bound is 1
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NORMALIZED_METRICS = [
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"adjusted_rand_score", "homogeneity_score", "completeness_score",
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"v_measure_score", "adjusted_mutual_info_score", "fowlkes_mallows_score",
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"normalized_mutual_info_score"
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]
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rng = np.random.RandomState(0)
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y1 = rng.randint(3, size=30)
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y2 = rng.randint(3, size=30)
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def test_symmetric_non_symmetric_union():
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assert (sorted(SYMMETRIC_METRICS + NON_SYMMETRIC_METRICS) ==
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sorted(SUPERVISED_METRICS))
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# 0.22 AMI and NMI changes
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@pytest.mark.filterwarnings('ignore::FutureWarning')
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@pytest.mark.parametrize(
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'metric_name, y1, y2',
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[(name, y1, y2) for name in SYMMETRIC_METRICS]
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)
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def test_symmetry(metric_name, y1, y2):
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metric = SUPERVISED_METRICS[metric_name]
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assert metric(y1, y2) == pytest.approx(metric(y2, y1))
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@pytest.mark.parametrize(
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'metric_name, y1, y2',
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[(name, y1, y2) for name in NON_SYMMETRIC_METRICS]
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)
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def test_non_symmetry(metric_name, y1, y2):
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metric = SUPERVISED_METRICS[metric_name]
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assert metric(y1, y2) != pytest.approx(metric(y2, y1))
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# 0.22 AMI and NMI changes
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@pytest.mark.filterwarnings('ignore::FutureWarning')
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@pytest.mark.parametrize("metric_name", NORMALIZED_METRICS)
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def test_normalized_output(metric_name):
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upper_bound_1 = [0, 0, 0, 1, 1, 1]
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upper_bound_2 = [0, 0, 0, 1, 1, 1]
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metric = SUPERVISED_METRICS[metric_name]
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assert metric([0, 0, 0, 1, 1], [0, 0, 0, 1, 2]) > 0.0
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assert metric([0, 0, 1, 1, 2], [0, 0, 1, 1, 1]) > 0.0
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assert metric([0, 0, 0, 1, 2], [0, 1, 1, 1, 1]) < 1.0
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assert metric([0, 0, 0, 1, 2], [0, 1, 1, 1, 1]) < 1.0
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assert metric(upper_bound_1, upper_bound_2) == pytest.approx(1.0)
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lower_bound_1 = [0, 0, 0, 0, 0, 0]
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lower_bound_2 = [0, 1, 2, 3, 4, 5]
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score = np.array([metric(lower_bound_1, lower_bound_2),
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metric(lower_bound_2, lower_bound_1)])
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assert not (score < 0).any()
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# 0.22 AMI and NMI changes
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@pytest.mark.filterwarnings('ignore::FutureWarning')
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@pytest.mark.parametrize(
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"metric_name", dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
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)
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def test_permute_labels(metric_name):
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# All clustering metrics do not change score due to permutations of labels
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# that is when 0 and 1 exchanged.
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y_label = np.array([0, 0, 0, 1, 1, 0, 1])
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y_pred = np.array([1, 0, 1, 0, 1, 1, 0])
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if metric_name in SUPERVISED_METRICS:
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metric = SUPERVISED_METRICS[metric_name]
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score_1 = metric(y_pred, y_label)
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assert_allclose(score_1, metric(1 - y_pred, y_label))
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assert_allclose(score_1, metric(1 - y_pred, 1 - y_label))
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assert_allclose(score_1, metric(y_pred, 1 - y_label))
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else:
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metric = UNSUPERVISED_METRICS[metric_name]
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X = np.random.randint(10, size=(7, 10))
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score_1 = metric(X, y_pred)
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assert_allclose(score_1, metric(X, 1 - y_pred))
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# 0.22 AMI and NMI changes
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@pytest.mark.filterwarnings('ignore::FutureWarning')
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@pytest.mark.parametrize(
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"metric_name", dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
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)
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# For all clustering metrics Input parameters can be both
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# in the form of arrays lists, positive, negative or string
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def test_format_invariance(metric_name):
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y_true = [0, 0, 0, 0, 1, 1, 1, 1]
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y_pred = [0, 1, 2, 3, 4, 5, 6, 7]
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def generate_formats(y):
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y = np.array(y)
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yield y, 'array of ints'
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yield y.tolist(), 'list of ints'
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yield [str(x) + "-a" for x in y.tolist()], 'list of strs'
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yield (np.array([str(x) + "-a" for x in y.tolist()], dtype=object),
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'array of strs')
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yield y - 1, 'including negative ints'
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yield y + 1, 'strictly positive ints'
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if metric_name in SUPERVISED_METRICS:
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metric = SUPERVISED_METRICS[metric_name]
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score_1 = metric(y_true, y_pred)
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y_true_gen = generate_formats(y_true)
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y_pred_gen = generate_formats(y_pred)
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for (y_true_fmt, fmt_name), (y_pred_fmt, _) in zip(y_true_gen,
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y_pred_gen):
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assert score_1 == metric(y_true_fmt, y_pred_fmt)
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else:
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metric = UNSUPERVISED_METRICS[metric_name]
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X = np.random.randint(10, size=(8, 10))
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score_1 = metric(X, y_true)
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assert score_1 == metric(X.astype(float), y_true)
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y_true_gen = generate_formats(y_true)
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for (y_true_fmt, fmt_name) in y_true_gen:
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assert score_1 == metric(X, y_true_fmt)
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@pytest.mark.parametrize("metric", SUPERVISED_METRICS.values())
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def test_single_sample(metric):
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# only the supervised metrics support single sample
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for i, j in [(0, 0), (0, 1), (1, 0), (1, 1)]:
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metric([i], [j])
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@pytest.mark.parametrize(
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"metric_name, metric_func",
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dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS).items()
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)
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def test_inf_nan_input(metric_name, metric_func):
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if metric_name in SUPERVISED_METRICS:
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invalids = [([0, 1], [np.inf, np.inf]),
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([0, 1], [np.nan, np.nan]),
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([0, 1], [np.nan, np.inf])]
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else:
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X = np.random.randint(10, size=(2, 10))
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invalids = [(X, [np.inf, np.inf]),
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(X, [np.nan, np.nan]),
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(X, [np.nan, np.inf])]
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with pytest.raises(ValueError, match='contains NaN, infinity'):
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for args in invalids:
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metric_func(*args)
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