def test_grid_search_precomputed_kernel(self):
# Test that grid search works when the input features are given in the
# form of a precomputed kernel matrix
X_, y_ = make_classification(n_samples=200,
n_features=100,
random_state=0)
# compute the training kernel matrix corresponding to the linear kernel
K_train = np.dot(X_[:180], X_[:180].T)
y_train = y_[:180]
clf = SVC(kernel='precomputed')
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
webserver_url=self.live_server_url)
wait(cv.fit(K_train, y_train))
assert_true(cv.best_score_ >= 0)
# compute the test kernel matrix
K_test = np.dot(X_[180:], X_[:180].T)
y_test = y_[180:]
y_pred = cv.predict(K_test)
assert_true(np.mean(y_pred == y_test) >= 0)
# test error is raised when the precomputed kernel is not array-like
# or sparse
assert_raises(ValueError, cv.fit, K_train.tolist(), y_train)
def test_grid_search_sparse_scoring(self):
X_, y_ = make_classification(n_samples=200,
n_features=100,
random_state=0)
clf = LinearSVC()
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
scoring="f1",
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180], y_[:180]))
y_pred = cv.predict(X_[180:])
C = cv.best_estimator_.C
X_ = sp.csr_matrix(X_)
clf = LinearSVC()
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
scoring="f1",
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180], y_[:180]))
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator_.C
assert_array_equal(y_pred, y_pred2)
assert_equal(C, C2)
# Smoke test the score
# np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]),
# cv.score(X_[:180], y[:180]))
# test loss where greater is worse
def f1_loss(y_true_, y_pred_):
return -f1_score(y_true_, y_pred_)
F1Loss = make_scorer(f1_loss, greater_is_better=False)
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
scoring=F1Loss,
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180], y_[:180]))
y_pred3 = cv.predict(X_[180:])
C3 = cv.best_estimator_.C
assert_equal(C, C3)
assert_array_equal(y_pred, y_pred3)