787 lines
23 KiB
Python
787 lines
23 KiB
Python
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import pytest
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import numpy as np
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import scipy.sparse as sp
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from sklearn.base import clone
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.utils._testing import assert_almost_equal
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from sklearn.utils._testing import assert_raises
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from sklearn.utils._testing import assert_warns_message
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from sklearn.utils._testing import ignore_warnings
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from sklearn.utils.stats import _weighted_percentile
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from sklearn.dummy import DummyClassifier, DummyRegressor
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from sklearn.exceptions import NotFittedError
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@ignore_warnings
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def _check_predict_proba(clf, X, y):
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proba = clf.predict_proba(X)
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# We know that we can have division by zero
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log_proba = clf.predict_log_proba(X)
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y = np.atleast_1d(y)
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if y.ndim == 1:
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y = np.reshape(y, (-1, 1))
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n_outputs = y.shape[1]
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n_samples = len(X)
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if n_outputs == 1:
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proba = [proba]
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log_proba = [log_proba]
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for k in range(n_outputs):
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assert proba[k].shape[0] == n_samples
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assert proba[k].shape[1] == len(np.unique(y[:, k]))
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assert_array_almost_equal(proba[k].sum(axis=1), np.ones(len(X)))
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# We know that we can have division by zero
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assert_array_almost_equal(np.log(proba[k]), log_proba[k])
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def _check_behavior_2d(clf):
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# 1d case
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X = np.array([[0], [0], [0], [0]]) # ignored
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y = np.array([1, 2, 1, 1])
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est = clone(clf)
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est.fit(X, y)
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y_pred = est.predict(X)
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assert y.shape == y_pred.shape
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# 2d case
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y = np.array([[1, 0],
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[2, 0],
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[1, 0],
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[1, 3]])
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est = clone(clf)
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est.fit(X, y)
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y_pred = est.predict(X)
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assert y.shape == y_pred.shape
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def _check_behavior_2d_for_constant(clf):
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# 2d case only
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X = np.array([[0], [0], [0], [0]]) # ignored
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y = np.array([[1, 0, 5, 4, 3],
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[2, 0, 1, 2, 5],
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[1, 0, 4, 5, 2],
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[1, 3, 3, 2, 0]])
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est = clone(clf)
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est.fit(X, y)
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y_pred = est.predict(X)
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assert y.shape == y_pred.shape
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def _check_equality_regressor(statistic, y_learn, y_pred_learn,
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y_test, y_pred_test):
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assert_array_almost_equal(np.tile(statistic, (y_learn.shape[0], 1)),
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y_pred_learn)
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assert_array_almost_equal(np.tile(statistic, (y_test.shape[0], 1)),
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y_pred_test)
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def test_most_frequent_and_prior_strategy():
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X = [[0], [0], [0], [0]] # ignored
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y = [1, 2, 1, 1]
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for strategy in ("most_frequent", "prior"):
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clf = DummyClassifier(strategy=strategy, random_state=0)
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clf.fit(X, y)
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assert_array_equal(clf.predict(X), np.ones(len(X)))
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_check_predict_proba(clf, X, y)
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if strategy == "prior":
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assert_array_almost_equal(clf.predict_proba([X[0]]),
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clf.class_prior_.reshape((1, -1)))
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else:
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assert_array_almost_equal(clf.predict_proba([X[0]]),
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clf.class_prior_.reshape((1, -1)) > 0.5)
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def test_most_frequent_and_prior_strategy_with_2d_column_y():
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# non-regression test added in
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# https://github.com/scikit-learn/scikit-learn/pull/13545
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X = [[0], [0], [0], [0]]
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y_1d = [1, 2, 1, 1]
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y_2d = [[1], [2], [1], [1]]
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for strategy in ("most_frequent", "prior"):
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clf_1d = DummyClassifier(strategy=strategy, random_state=0)
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clf_2d = DummyClassifier(strategy=strategy, random_state=0)
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clf_1d.fit(X, y_1d)
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clf_2d.fit(X, y_2d)
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assert_array_equal(clf_1d.predict(X), clf_2d.predict(X))
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def test_most_frequent_and_prior_strategy_multioutput():
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X = [[0], [0], [0], [0]] # ignored
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y = np.array([[1, 0],
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[2, 0],
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[1, 0],
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[1, 3]])
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n_samples = len(X)
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for strategy in ("prior", "most_frequent"):
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clf = DummyClassifier(strategy=strategy, random_state=0)
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clf.fit(X, y)
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assert_array_equal(clf.predict(X),
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np.hstack([np.ones((n_samples, 1)),
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np.zeros((n_samples, 1))]))
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_check_predict_proba(clf, X, y)
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_check_behavior_2d(clf)
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def test_stratified_strategy():
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X = [[0]] * 5 # ignored
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y = [1, 2, 1, 1, 2]
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clf = DummyClassifier(strategy="stratified", random_state=0)
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clf.fit(X, y)
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X = [[0]] * 500
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y_pred = clf.predict(X)
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p = np.bincount(y_pred) / float(len(X))
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assert_almost_equal(p[1], 3. / 5, decimal=1)
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assert_almost_equal(p[2], 2. / 5, decimal=1)
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_check_predict_proba(clf, X, y)
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def test_stratified_strategy_multioutput():
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X = [[0]] * 5 # ignored
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y = np.array([[2, 1],
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[2, 2],
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[1, 1],
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[1, 2],
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[1, 1]])
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clf = DummyClassifier(strategy="stratified", random_state=0)
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clf.fit(X, y)
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X = [[0]] * 500
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y_pred = clf.predict(X)
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for k in range(y.shape[1]):
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p = np.bincount(y_pred[:, k]) / float(len(X))
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assert_almost_equal(p[1], 3. / 5, decimal=1)
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assert_almost_equal(p[2], 2. / 5, decimal=1)
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_check_predict_proba(clf, X, y)
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_check_behavior_2d(clf)
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def test_uniform_strategy():
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X = [[0]] * 4 # ignored
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y = [1, 2, 1, 1]
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clf = DummyClassifier(strategy="uniform", random_state=0)
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clf.fit(X, y)
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X = [[0]] * 500
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y_pred = clf.predict(X)
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p = np.bincount(y_pred) / float(len(X))
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assert_almost_equal(p[1], 0.5, decimal=1)
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assert_almost_equal(p[2], 0.5, decimal=1)
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_check_predict_proba(clf, X, y)
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def test_uniform_strategy_multioutput():
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X = [[0]] * 4 # ignored
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y = np.array([[2, 1],
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[2, 2],
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[1, 2],
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[1, 1]])
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clf = DummyClassifier(strategy="uniform", random_state=0)
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clf.fit(X, y)
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X = [[0]] * 500
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y_pred = clf.predict(X)
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for k in range(y.shape[1]):
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p = np.bincount(y_pred[:, k]) / float(len(X))
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assert_almost_equal(p[1], 0.5, decimal=1)
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assert_almost_equal(p[2], 0.5, decimal=1)
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_check_predict_proba(clf, X, y)
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_check_behavior_2d(clf)
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def test_string_labels():
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X = [[0]] * 5
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y = ["paris", "paris", "tokyo", "amsterdam", "berlin"]
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clf = DummyClassifier(strategy="most_frequent")
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clf.fit(X, y)
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assert_array_equal(clf.predict(X), ["paris"] * 5)
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@pytest.mark.parametrize("y,y_test", [
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([2, 1, 1, 1], [2, 2, 1, 1]),
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(np.array([[2, 2],
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[1, 1],
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[1, 1],
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[1, 1]]),
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np.array([[2, 2],
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[2, 2],
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[1, 1],
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[1, 1]]))
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])
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def test_classifier_score_with_None(y, y_test):
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clf = DummyClassifier(strategy="most_frequent")
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clf.fit(None, y)
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assert clf.score(None, y_test) == 0.5
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@pytest.mark.parametrize("strategy", [
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"stratified",
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"most_frequent",
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"prior",
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"uniform",
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"constant"
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])
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def test_classifier_prediction_independent_of_X(strategy):
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y = [0, 2, 1, 1]
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X1 = [[0]] * 4
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clf1 = DummyClassifier(strategy=strategy, random_state=0, constant=0)
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clf1.fit(X1, y)
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predictions1 = clf1.predict(X1)
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X2 = [[1]] * 4
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clf2 = DummyClassifier(strategy=strategy, random_state=0, constant=0)
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clf2.fit(X2, y)
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predictions2 = clf2.predict(X2)
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assert_array_equal(predictions1, predictions2)
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def test_classifier_exceptions():
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clf = DummyClassifier(strategy="unknown")
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assert_raises(ValueError, clf.fit, [], [])
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assert_raises(NotFittedError, clf.predict, [])
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assert_raises(NotFittedError, clf.predict_proba, [])
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def test_mean_strategy_regressor():
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random_state = np.random.RandomState(seed=1)
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X = [[0]] * 4 # ignored
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y = random_state.randn(4)
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reg = DummyRegressor()
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [np.mean(y)] * len(X))
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def test_mean_strategy_multioutput_regressor():
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random_state = np.random.RandomState(seed=1)
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X_learn = random_state.randn(10, 10)
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y_learn = random_state.randn(10, 5)
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mean = np.mean(y_learn, axis=0).reshape((1, -1))
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X_test = random_state.randn(20, 10)
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y_test = random_state.randn(20, 5)
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# Correctness oracle
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est = DummyRegressor()
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est.fit(X_learn, y_learn)
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y_pred_learn = est.predict(X_learn)
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y_pred_test = est.predict(X_test)
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_check_equality_regressor(mean, y_learn, y_pred_learn, y_test, y_pred_test)
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_check_behavior_2d(est)
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def test_regressor_exceptions():
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reg = DummyRegressor()
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assert_raises(NotFittedError, reg.predict, [])
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def test_median_strategy_regressor():
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random_state = np.random.RandomState(seed=1)
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X = [[0]] * 5 # ignored
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y = random_state.randn(5)
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reg = DummyRegressor(strategy="median")
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
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def test_median_strategy_multioutput_regressor():
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random_state = np.random.RandomState(seed=1)
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X_learn = random_state.randn(10, 10)
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y_learn = random_state.randn(10, 5)
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median = np.median(y_learn, axis=0).reshape((1, -1))
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X_test = random_state.randn(20, 10)
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y_test = random_state.randn(20, 5)
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# Correctness oracle
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est = DummyRegressor(strategy="median")
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est.fit(X_learn, y_learn)
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y_pred_learn = est.predict(X_learn)
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y_pred_test = est.predict(X_test)
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_check_equality_regressor(
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median, y_learn, y_pred_learn, y_test, y_pred_test)
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_check_behavior_2d(est)
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def test_quantile_strategy_regressor():
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random_state = np.random.RandomState(seed=1)
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X = [[0]] * 5 # ignored
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y = random_state.randn(5)
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reg = DummyRegressor(strategy="quantile", quantile=0.5)
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
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reg = DummyRegressor(strategy="quantile", quantile=0)
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [np.min(y)] * len(X))
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reg = DummyRegressor(strategy="quantile", quantile=1)
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [np.max(y)] * len(X))
|
||
|
|
|
||
|
|
reg = DummyRegressor(strategy="quantile", quantile=0.3)
|
||
|
|
reg.fit(X, y)
|
||
|
|
assert_array_equal(reg.predict(X), [np.percentile(y, q=30)] * len(X))
|
||
|
|
|
||
|
|
|
||
|
|
def test_quantile_strategy_multioutput_regressor():
|
||
|
|
|
||
|
|
random_state = np.random.RandomState(seed=1)
|
||
|
|
|
||
|
|
X_learn = random_state.randn(10, 10)
|
||
|
|
y_learn = random_state.randn(10, 5)
|
||
|
|
|
||
|
|
median = np.median(y_learn, axis=0).reshape((1, -1))
|
||
|
|
quantile_values = np.percentile(y_learn, axis=0, q=80).reshape((1, -1))
|
||
|
|
|
||
|
|
X_test = random_state.randn(20, 10)
|
||
|
|
y_test = random_state.randn(20, 5)
|
||
|
|
|
||
|
|
# Correctness oracle
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=0.5)
|
||
|
|
est.fit(X_learn, y_learn)
|
||
|
|
y_pred_learn = est.predict(X_learn)
|
||
|
|
y_pred_test = est.predict(X_test)
|
||
|
|
|
||
|
|
_check_equality_regressor(
|
||
|
|
median, y_learn, y_pred_learn, y_test, y_pred_test)
|
||
|
|
_check_behavior_2d(est)
|
||
|
|
|
||
|
|
# Correctness oracle
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=0.8)
|
||
|
|
est.fit(X_learn, y_learn)
|
||
|
|
y_pred_learn = est.predict(X_learn)
|
||
|
|
y_pred_test = est.predict(X_test)
|
||
|
|
|
||
|
|
_check_equality_regressor(
|
||
|
|
quantile_values, y_learn, y_pred_learn, y_test, y_pred_test)
|
||
|
|
_check_behavior_2d(est)
|
||
|
|
|
||
|
|
|
||
|
|
def test_quantile_invalid():
|
||
|
|
|
||
|
|
X = [[0]] * 5 # ignored
|
||
|
|
y = [0] * 5 # ignored
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="quantile")
|
||
|
|
assert_raises(ValueError, est.fit, X, y)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=None)
|
||
|
|
assert_raises(ValueError, est.fit, X, y)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=[0])
|
||
|
|
assert_raises(ValueError, est.fit, X, y)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=-0.1)
|
||
|
|
assert_raises(ValueError, est.fit, X, y)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=1.1)
|
||
|
|
assert_raises(ValueError, est.fit, X, y)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile='abc')
|
||
|
|
assert_raises(TypeError, est.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_quantile_strategy_empty_train():
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=0.4)
|
||
|
|
assert_raises(ValueError, est.fit, [], [])
|
||
|
|
|
||
|
|
|
||
|
|
def test_constant_strategy_regressor():
|
||
|
|
|
||
|
|
random_state = np.random.RandomState(seed=1)
|
||
|
|
|
||
|
|
X = [[0]] * 5 # ignored
|
||
|
|
y = random_state.randn(5)
|
||
|
|
|
||
|
|
reg = DummyRegressor(strategy="constant", constant=[43])
|
||
|
|
reg.fit(X, y)
|
||
|
|
assert_array_equal(reg.predict(X), [43] * len(X))
|
||
|
|
|
||
|
|
reg = DummyRegressor(strategy="constant", constant=43)
|
||
|
|
reg.fit(X, y)
|
||
|
|
assert_array_equal(reg.predict(X), [43] * len(X))
|
||
|
|
|
||
|
|
|
||
|
|
def test_constant_strategy_multioutput_regressor():
|
||
|
|
|
||
|
|
random_state = np.random.RandomState(seed=1)
|
||
|
|
|
||
|
|
X_learn = random_state.randn(10, 10)
|
||
|
|
y_learn = random_state.randn(10, 5)
|
||
|
|
|
||
|
|
# test with 2d array
|
||
|
|
constants = random_state.randn(5)
|
||
|
|
|
||
|
|
X_test = random_state.randn(20, 10)
|
||
|
|
y_test = random_state.randn(20, 5)
|
||
|
|
|
||
|
|
# Correctness oracle
|
||
|
|
est = DummyRegressor(strategy="constant", constant=constants)
|
||
|
|
est.fit(X_learn, y_learn)
|
||
|
|
y_pred_learn = est.predict(X_learn)
|
||
|
|
y_pred_test = est.predict(X_test)
|
||
|
|
|
||
|
|
_check_equality_regressor(
|
||
|
|
constants, y_learn, y_pred_learn, y_test, y_pred_test)
|
||
|
|
_check_behavior_2d_for_constant(est)
|
||
|
|
|
||
|
|
|
||
|
|
def test_y_mean_attribute_regressor():
|
||
|
|
X = [[0]] * 5
|
||
|
|
y = [1, 2, 4, 6, 8]
|
||
|
|
# when strategy = 'mean'
|
||
|
|
est = DummyRegressor(strategy='mean')
|
||
|
|
est.fit(X, y)
|
||
|
|
|
||
|
|
assert est.constant_ == np.mean(y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_unknown_strategey_regressor():
|
||
|
|
X = [[0]] * 5
|
||
|
|
y = [1, 2, 4, 6, 8]
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy='gona')
|
||
|
|
assert_raises(ValueError, est.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_constants_not_specified_regressor():
|
||
|
|
X = [[0]] * 5
|
||
|
|
y = [1, 2, 4, 6, 8]
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy='constant')
|
||
|
|
assert_raises(TypeError, est.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_constant_size_multioutput_regressor():
|
||
|
|
random_state = np.random.RandomState(seed=1)
|
||
|
|
X = random_state.randn(10, 10)
|
||
|
|
y = random_state.randn(10, 5)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy='constant', constant=[1, 2, 3, 4])
|
||
|
|
assert_raises(ValueError, est.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_constant_strategy():
|
||
|
|
X = [[0], [0], [0], [0]] # ignored
|
||
|
|
y = [2, 1, 2, 2]
|
||
|
|
|
||
|
|
clf = DummyClassifier(strategy="constant", random_state=0, constant=1)
|
||
|
|
clf.fit(X, y)
|
||
|
|
assert_array_equal(clf.predict(X), np.ones(len(X)))
|
||
|
|
_check_predict_proba(clf, X, y)
|
||
|
|
|
||
|
|
X = [[0], [0], [0], [0]] # ignored
|
||
|
|
y = ['two', 'one', 'two', 'two']
|
||
|
|
clf = DummyClassifier(strategy="constant", random_state=0, constant='one')
|
||
|
|
clf.fit(X, y)
|
||
|
|
assert_array_equal(clf.predict(X), np.array(['one'] * 4))
|
||
|
|
_check_predict_proba(clf, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_constant_strategy_multioutput():
|
||
|
|
X = [[0], [0], [0], [0]] # ignored
|
||
|
|
y = np.array([[2, 3],
|
||
|
|
[1, 3],
|
||
|
|
[2, 3],
|
||
|
|
[2, 0]])
|
||
|
|
|
||
|
|
n_samples = len(X)
|
||
|
|
|
||
|
|
clf = DummyClassifier(strategy="constant", random_state=0,
|
||
|
|
constant=[1, 0])
|
||
|
|
clf.fit(X, y)
|
||
|
|
assert_array_equal(clf.predict(X),
|
||
|
|
np.hstack([np.ones((n_samples, 1)),
|
||
|
|
np.zeros((n_samples, 1))]))
|
||
|
|
_check_predict_proba(clf, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('y, params, err_msg', [
|
||
|
|
([2, 1, 2, 2],
|
||
|
|
{'random_state': 0},
|
||
|
|
"Constant.*has to be specified"),
|
||
|
|
([2, 1, 2, 2],
|
||
|
|
{'constant': [2, 0]},
|
||
|
|
"Constant.*should have shape"),
|
||
|
|
(np.transpose([[2, 1, 2, 2], [2, 1, 2, 2]]),
|
||
|
|
{'constant': 2},
|
||
|
|
"Constant.*should have shape"),
|
||
|
|
([2, 1, 2, 2],
|
||
|
|
{'constant': 'my-constant'},
|
||
|
|
"constant=my-constant.*Possible values.*\\[1, 2]"),
|
||
|
|
(np.transpose([[2, 1, 2, 2], [2, 1, 2, 2]]),
|
||
|
|
{'constant': [2, 'unknown']},
|
||
|
|
"constant=\\[2, 'unknown'].*Possible values.*\\[1, 2]")],
|
||
|
|
ids=["no-constant", "too-many-constant", "not-enough-output",
|
||
|
|
"single-output", "multi-output"]
|
||
|
|
)
|
||
|
|
def test_constant_strategy_exceptions(y, params, err_msg):
|
||
|
|
X = [[0], [0], [0], [0]]
|
||
|
|
|
||
|
|
clf = DummyClassifier(strategy="constant", **params)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classification_sample_weight():
|
||
|
|
X = [[0], [0], [1]]
|
||
|
|
y = [0, 1, 0]
|
||
|
|
sample_weight = [0.1, 1., 0.1]
|
||
|
|
|
||
|
|
clf = DummyClassifier(strategy="stratified").fit(X, y, sample_weight)
|
||
|
|
assert_array_almost_equal(clf.class_prior_, [0.2 / 1.2, 1. / 1.2])
|
||
|
|
|
||
|
|
|
||
|
|
def test_constant_strategy_sparse_target():
|
||
|
|
X = [[0]] * 5 # ignored
|
||
|
|
y = sp.csc_matrix(np.array([[0, 1],
|
||
|
|
[4, 0],
|
||
|
|
[1, 1],
|
||
|
|
[1, 4],
|
||
|
|
[1, 1]]))
|
||
|
|
|
||
|
|
n_samples = len(X)
|
||
|
|
|
||
|
|
clf = DummyClassifier(strategy="constant", random_state=0, constant=[1, 0])
|
||
|
|
clf.fit(X, y)
|
||
|
|
y_pred = clf.predict(X)
|
||
|
|
assert sp.issparse(y_pred)
|
||
|
|
assert_array_equal(y_pred.toarray(), np.hstack([np.ones((n_samples, 1)),
|
||
|
|
np.zeros((n_samples, 1))]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_uniform_strategy_sparse_target_warning():
|
||
|
|
X = [[0]] * 5 # ignored
|
||
|
|
y = sp.csc_matrix(np.array([[2, 1],
|
||
|
|
[2, 2],
|
||
|
|
[1, 4],
|
||
|
|
[4, 2],
|
||
|
|
[1, 1]]))
|
||
|
|
|
||
|
|
clf = DummyClassifier(strategy="uniform", random_state=0)
|
||
|
|
assert_warns_message(UserWarning,
|
||
|
|
"the uniform strategy would not save memory",
|
||
|
|
clf.fit, X, y)
|
||
|
|
|
||
|
|
X = [[0]] * 500
|
||
|
|
y_pred = clf.predict(X)
|
||
|
|
|
||
|
|
for k in range(y.shape[1]):
|
||
|
|
p = np.bincount(y_pred[:, k]) / float(len(X))
|
||
|
|
assert_almost_equal(p[1], 1 / 3, decimal=1)
|
||
|
|
assert_almost_equal(p[2], 1 / 3, decimal=1)
|
||
|
|
assert_almost_equal(p[4], 1 / 3, decimal=1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_stratified_strategy_sparse_target():
|
||
|
|
X = [[0]] * 5 # ignored
|
||
|
|
y = sp.csc_matrix(np.array([[4, 1],
|
||
|
|
[0, 0],
|
||
|
|
[1, 1],
|
||
|
|
[1, 4],
|
||
|
|
[1, 1]]))
|
||
|
|
|
||
|
|
clf = DummyClassifier(strategy="stratified", random_state=0)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
X = [[0]] * 500
|
||
|
|
y_pred = clf.predict(X)
|
||
|
|
assert sp.issparse(y_pred)
|
||
|
|
y_pred = y_pred.toarray()
|
||
|
|
|
||
|
|
for k in range(y.shape[1]):
|
||
|
|
p = np.bincount(y_pred[:, k]) / float(len(X))
|
||
|
|
assert_almost_equal(p[1], 3. / 5, decimal=1)
|
||
|
|
assert_almost_equal(p[0], 1. / 5, decimal=1)
|
||
|
|
assert_almost_equal(p[4], 1. / 5, decimal=1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_most_frequent_and_prior_strategy_sparse_target():
|
||
|
|
X = [[0]] * 5 # ignored
|
||
|
|
y = sp.csc_matrix(np.array([[1, 0],
|
||
|
|
[1, 3],
|
||
|
|
[4, 0],
|
||
|
|
[0, 1],
|
||
|
|
[1, 0]]))
|
||
|
|
|
||
|
|
n_samples = len(X)
|
||
|
|
y_expected = np.hstack([np.ones((n_samples, 1)), np.zeros((n_samples, 1))])
|
||
|
|
for strategy in ("most_frequent", "prior"):
|
||
|
|
clf = DummyClassifier(strategy=strategy, random_state=0)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
y_pred = clf.predict(X)
|
||
|
|
assert sp.issparse(y_pred)
|
||
|
|
assert_array_equal(y_pred.toarray(), y_expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_dummy_regressor_sample_weight(n_samples=10):
|
||
|
|
random_state = np.random.RandomState(seed=1)
|
||
|
|
|
||
|
|
X = [[0]] * n_samples
|
||
|
|
y = random_state.rand(n_samples)
|
||
|
|
sample_weight = random_state.rand(n_samples)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="mean").fit(X, y, sample_weight)
|
||
|
|
assert est.constant_ == np.average(y, weights=sample_weight)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="median").fit(X, y, sample_weight)
|
||
|
|
assert est.constant_ == _weighted_percentile(y, sample_weight, 50.)
|
||
|
|
|
||
|
|
est = DummyRegressor(strategy="quantile", quantile=.95).fit(X, y,
|
||
|
|
sample_weight)
|
||
|
|
assert est.constant_ == _weighted_percentile(y, sample_weight, 95.)
|
||
|
|
|
||
|
|
|
||
|
|
def test_dummy_regressor_on_3D_array():
|
||
|
|
X = np.array([[['foo']], [['bar']], [['baz']]])
|
||
|
|
y = np.array([2, 2, 2])
|
||
|
|
y_expected = np.array([2, 2, 2])
|
||
|
|
cls = DummyRegressor()
|
||
|
|
cls.fit(X, y)
|
||
|
|
y_pred = cls.predict(X)
|
||
|
|
assert_array_equal(y_pred, y_expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_dummy_classifier_on_3D_array():
|
||
|
|
X = np.array([[['foo']], [['bar']], [['baz']]])
|
||
|
|
y = [2, 2, 2]
|
||
|
|
y_expected = [2, 2, 2]
|
||
|
|
y_proba_expected = [[1], [1], [1]]
|
||
|
|
cls = DummyClassifier(strategy="stratified")
|
||
|
|
cls.fit(X, y)
|
||
|
|
y_pred = cls.predict(X)
|
||
|
|
y_pred_proba = cls.predict_proba(X)
|
||
|
|
assert_array_equal(y_pred, y_expected)
|
||
|
|
assert_array_equal(y_pred_proba, y_proba_expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_dummy_regressor_return_std():
|
||
|
|
X = [[0]] * 3 # ignored
|
||
|
|
y = np.array([2, 2, 2])
|
||
|
|
y_std_expected = np.array([0, 0, 0])
|
||
|
|
cls = DummyRegressor()
|
||
|
|
cls.fit(X, y)
|
||
|
|
y_pred_list = cls.predict(X, return_std=True)
|
||
|
|
# there should be two elements when return_std is True
|
||
|
|
assert len(y_pred_list) == 2
|
||
|
|
# the second element should be all zeros
|
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assert_array_equal(y_pred_list[1], y_std_expected)
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|
|
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||
|
|
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||
|
|
@pytest.mark.parametrize("y,y_test", [
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||
|
|
([1, 1, 1, 2], [1.25] * 4),
|
||
|
|
(np.array([[2, 2],
|
||
|
|
[1, 1],
|
||
|
|
[1, 1],
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||
|
|
[1, 1]]),
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||
|
|
[[1.25, 1.25]] * 4)
|
||
|
|
|
||
|
|
])
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||
|
|
def test_regressor_score_with_None(y, y_test):
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||
|
|
reg = DummyRegressor()
|
||
|
|
reg.fit(None, y)
|
||
|
|
assert reg.score(None, y_test) == 1.0
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("strategy", [
|
||
|
|
"mean",
|
||
|
|
"median",
|
||
|
|
"quantile",
|
||
|
|
"constant"
|
||
|
|
])
|
||
|
|
def test_regressor_prediction_independent_of_X(strategy):
|
||
|
|
y = [0, 2, 1, 1]
|
||
|
|
X1 = [[0]] * 4
|
||
|
|
reg1 = DummyRegressor(strategy=strategy, constant=0, quantile=0.7)
|
||
|
|
reg1.fit(X1, y)
|
||
|
|
predictions1 = reg1.predict(X1)
|
||
|
|
|
||
|
|
X2 = [[1]] * 4
|
||
|
|
reg2 = DummyRegressor(strategy=strategy, constant=0, quantile=0.7)
|
||
|
|
reg2.fit(X2, y)
|
||
|
|
predictions2 = reg2.predict(X2)
|
||
|
|
|
||
|
|
assert_array_equal(predictions1, predictions2)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"strategy", ["stratified", "most_frequent", "prior", "uniform", "constant"]
|
||
|
|
)
|
||
|
|
def test_dtype_of_classifier_probas(strategy):
|
||
|
|
y = [0, 2, 1, 1]
|
||
|
|
X = np.zeros(4)
|
||
|
|
model = DummyClassifier(strategy=strategy, random_state=0, constant=0)
|
||
|
|
probas = model.fit(X, y).predict_proba(X)
|
||
|
|
|
||
|
|
assert probas.dtype == np.float64
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:The default value of strategy.*") # 0.24
|
||
|
|
@pytest.mark.parametrize('Dummy', (DummyRegressor, DummyClassifier))
|
||
|
|
def test_n_features_in_(Dummy):
|
||
|
|
X = [[1, 2]]
|
||
|
|
y = [0]
|
||
|
|
d = Dummy()
|
||
|
|
assert not hasattr(d, 'n_features_in_')
|
||
|
|
d.fit(X, y)
|
||
|
|
assert d.n_features_in_ is None
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("Dummy", (DummyRegressor, DummyClassifier))
|
||
|
|
def test_outputs_2d_deprecation(Dummy):
|
||
|
|
X = [[1, 2]]
|
||
|
|
y = [0]
|
||
|
|
with pytest.warns(FutureWarning,
|
||
|
|
match="will be removed in version 0.24"):
|
||
|
|
Dummy().fit(X, y).outputs_2d_
|
||
|
|
|
||
|
|
|
||
|
|
# TODO: Remove in 0.24 when DummyClassifier's `strategy` default updates
|
||
|
|
def test_strategy_stratified_deprecated_for_prior():
|
||
|
|
X, y = [[1, 2]], [0]
|
||
|
|
|
||
|
|
msg = ("The default value of strategy will change from "
|
||
|
|
"stratified to prior in 0.24")
|
||
|
|
with pytest.warns(FutureWarning, match=msg):
|
||
|
|
DummyClassifier().fit(X, y)
|