619 lines
18 KiB
Python
619 lines
18 KiB
Python
from __future__ import division
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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_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_true
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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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@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_equal(proba[k].shape[0], n_samples)
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assert_equal(proba[k].shape[1], len(np.unique(y[:, k])))
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assert_array_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_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_equal(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_equal(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_equal(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_equal(np.tile(statistic, (y_learn.shape[0], 1)),
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y_pred_learn)
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assert_array_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_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_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_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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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(ValueError, clf.predict, [])
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assert_raises(ValueError, 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(ValueError, 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))
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reg = DummyRegressor(strategy="quantile", quantile=0.3)
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [np.percentile(y, q=30)] * len(X))
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def test_quantile_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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quantile_values = np.percentile(y_learn, axis=0, q=80).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="quantile", quantile=0.5)
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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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# Correctness oracle
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est = DummyRegressor(strategy="quantile", quantile=0.8)
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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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quantile_values, 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_invalid():
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X = [[0]] * 5 # ignored
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y = [0] * 5 # ignored
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est = DummyRegressor(strategy="quantile")
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assert_raises(ValueError, est.fit, X, y)
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est = DummyRegressor(strategy="quantile", quantile=None)
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assert_raises(ValueError, est.fit, X, y)
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est = DummyRegressor(strategy="quantile", quantile=[0])
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assert_raises(ValueError, est.fit, X, y)
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est = DummyRegressor(strategy="quantile", quantile=-0.1)
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assert_raises(ValueError, est.fit, X, y)
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est = DummyRegressor(strategy="quantile", quantile=1.1)
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assert_raises(ValueError, est.fit, X, y)
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est = DummyRegressor(strategy="quantile", quantile='abc')
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assert_raises(TypeError, est.fit, X, y)
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def test_quantile_strategy_empty_train():
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est = DummyRegressor(strategy="quantile", quantile=0.4)
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assert_raises(ValueError, est.fit, [], [])
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def test_constant_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="constant", constant=[43])
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [43] * len(X))
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reg = DummyRegressor(strategy="constant", constant=43)
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reg.fit(X, y)
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assert_array_equal(reg.predict(X), [43] * len(X))
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def test_constant_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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# test with 2d array
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constants = random_state.randn(5)
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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="constant", constant=constants)
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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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constants, y_learn, y_pred_learn, y_test, y_pred_test)
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_check_behavior_2d_for_constant(est)
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def test_y_mean_attribute_regressor():
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X = [[0]] * 5
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y = [1, 2, 4, 6, 8]
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# when strategy = 'mean'
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est = DummyRegressor(strategy='mean')
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est.fit(X, y)
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assert_equal(est.constant_, np.mean(y))
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def test_unknown_strategey_regressor():
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X = [[0]] * 5
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y = [1, 2, 4, 6, 8]
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est = DummyRegressor(strategy='gona')
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assert_raises(ValueError, est.fit, X, y)
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def test_constants_not_specified_regressor():
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X = [[0]] * 5
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y = [1, 2, 4, 6, 8]
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est = DummyRegressor(strategy='constant')
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assert_raises(TypeError, est.fit, X, y)
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def test_constant_size_multioutput_regressor():
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random_state = np.random.RandomState(seed=1)
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X = random_state.randn(10, 10)
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y = random_state.randn(10, 5)
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est = DummyRegressor(strategy='constant', constant=[1, 2, 3, 4])
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assert_raises(ValueError, est.fit, X, y)
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def test_constant_strategy():
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X = [[0], [0], [0], [0]] # ignored
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y = [2, 1, 2, 2]
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clf = DummyClassifier(strategy="constant", random_state=0, constant=1)
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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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X = [[0], [0], [0], [0]] # ignored
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y = ['two', 'one', 'two', 'two']
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clf = DummyClassifier(strategy="constant", random_state=0, constant='one')
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clf.fit(X, y)
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assert_array_equal(clf.predict(X), np.array(['one'] * 4))
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_check_predict_proba(clf, X, y)
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def test_constant_strategy_multioutput():
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X = [[0], [0], [0], [0]] # ignored
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y = np.array([[2, 3],
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[1, 3],
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[2, 3],
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[2, 0]])
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n_samples = len(X)
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clf = DummyClassifier(strategy="constant", random_state=0,
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constant=[1, 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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def test_constant_strategy_exceptions():
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X = [[0], [0], [0], [0]] # ignored
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y = [2, 1, 2, 2]
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clf = DummyClassifier(strategy="constant", random_state=0)
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assert_raises(ValueError, clf.fit, X, y)
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clf = DummyClassifier(strategy="constant", random_state=0,
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constant=[2, 0])
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assert_raises(ValueError, clf.fit, X, y)
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|
|
|
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def test_classification_sample_weight():
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X = [[0], [0], [1]]
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y = [0, 1, 0]
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sample_weight = [0.1, 1., 0.1]
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|
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clf = DummyClassifier().fit(X, y, sample_weight)
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assert_array_almost_equal(clf.class_prior_, [0.2 / 1.2, 1. / 1.2])
|
|
|
|
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def test_constant_strategy_sparse_target():
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X = [[0]] * 5 # ignored
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y = sp.csc_matrix(np.array([[0, 1],
|
|
[4, 0],
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|
[1, 1],
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|
[1, 4],
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|
[1, 1]]))
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|
|
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n_samples = len(X)
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|
|
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clf = DummyClassifier(strategy="constant", random_state=0, constant=[1, 0])
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clf.fit(X, y)
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y_pred = clf.predict(X)
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|
assert_true(sp.issparse(y_pred))
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|
assert_array_equal(y_pred.toarray(), np.hstack([np.ones((n_samples, 1)),
|
|
np.zeros((n_samples, 1))]))
|
|
|
|
|
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def test_uniform_strategy_sparse_target_warning():
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X = [[0]] * 5 # ignored
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|
y = sp.csc_matrix(np.array([[2, 1],
|
|
[2, 2],
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|
[1, 4],
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|
[4, 2],
|
|
[1, 1]]))
|
|
|
|
clf = DummyClassifier(strategy="uniform", random_state=0)
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|
assert_warns_message(UserWarning,
|
|
"the uniform strategy would not save memory",
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|
clf.fit, X, y)
|
|
|
|
X = [[0]] * 500
|
|
y_pred = clf.predict(X)
|
|
|
|
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], 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_true(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_true(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_equal(est.constant_, np.average(y, weights=sample_weight))
|
|
|
|
est = DummyRegressor(strategy="median").fit(X, y, sample_weight)
|
|
assert_equal(est.constant_, _weighted_percentile(y, sample_weight, 50.))
|
|
|
|
est = DummyRegressor(strategy="quantile", quantile=.95).fit(X, y,
|
|
sample_weight)
|
|
assert_equal(est.constant_, _weighted_percentile(y, sample_weight, 95.))
|
|
|
|
|
|
def test_dummy_classifier_on_nan_value():
|
|
X = [[np.NaN]]
|
|
y = [1]
|
|
y_expected = [1]
|
|
clf = DummyClassifier()
|
|
clf.fit(X, y)
|
|
y_pred = clf.predict(X)
|
|
assert_array_equal(y_pred, y_expected)
|
|
|
|
|
|
def test_dummy_regressor_on_nan_value():
|
|
X = [[np.NaN]]
|
|
y = [1]
|
|
y_expected = [1]
|
|
clf = DummyRegressor()
|
|
clf.fit(X, y)
|
|
y_pred = clf.predict(X)
|
|
assert_array_equal(y_pred, y_expected)
|