104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
"""
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Testing for grid search module (sklearn.grid_search)
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"""
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from nose.tools import assert_equal, assert_raises
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from numpy.testing import assert_array_equal
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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 BaseEstimator
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from sklearn.grid_search import GridSearchCV
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from sklearn.datasets.samples_generator import make_classification
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from sklearn.svm import LinearSVC
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from sklearn.svm.sparse import LinearSVC as SparseLinearSVC
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from sklearn.metrics import f1_score
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class MockClassifier(BaseEstimator):
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"""Dummy classifier to test the cross-validation"""
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def __init__(self, foo_param=0):
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self.foo_param = foo_param
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def fit(self, X, Y):
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return self
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def predict(self, T):
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return T.shape[0]
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def score(self, X=None, Y=None):
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if self.foo_param > 1:
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score = 1.
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else:
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score = 0.
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return score
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X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
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y = np.array([1, 1, 2, 2])
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def test_grid_search():
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"""Test that the best estimator contains the right value for foo_param"""
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clf = MockClassifier()
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cross_validation = GridSearchCV(clf, {'foo_param': [1, 2, 3]})
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# make sure it selects the smallest parameter in case of ties
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assert_equal(cross_validation.fit(X, y).best_estimator.foo_param, 2)
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for i, foo_i in enumerate([1, 2, 3]):
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assert cross_validation.grid_scores_[i][0] == {'foo_param': foo_i}
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def test_grid_search_error():
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"""Test that grid search will capture errors on data with different
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length"""
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X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
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clf = LinearSVC()
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cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
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assert_raises(ValueError, cv.fit, X_[:180], y_)
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def test_grid_search_sparse():
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"""Test that grid search works with both dense and sparse matrices"""
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X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
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clf = LinearSVC()
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cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
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cv.fit(X_[:180], y_[:180])
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y_pred = cv.predict(X_[180:])
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C = cv.best_estimator.C
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X_ = sp.csr_matrix(X_)
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clf = SparseLinearSVC()
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cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
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cv.fit(X_[:180], y_[:180])
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y_pred2 = cv.predict(X_[180:])
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C2 = cv.best_estimator.C
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assert np.mean(y_pred == y_pred2) >= .9
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assert_equal(C, C2)
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def test_grid_search_sparse_score_func():
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X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
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clf = LinearSVC()
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cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
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# XXX: set refit to False due to a random bug when True (default)
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cv.set_params(refit=False).fit(X_[:180], y_[:180])
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y_pred = cv.predict(X_[180:])
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C = cv.best_estimator.C
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X_ = sp.csr_matrix(X_)
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clf = SparseLinearSVC()
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cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
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# XXX: set refit to False due to a random bug when True (default)
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cv.set_params(refit=False).fit(X_[:180], y_[:180])
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y_pred2 = cv.predict(X_[180:])
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C2 = cv.best_estimator.C
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assert_array_equal(y_pred, y_pred2)
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assert_equal(C, C2)
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