def test_classifier_sparse_input(self):
clf = RGFClassifier(prefix='clf', calc_prob='Softmax')
for sparse_format in (csr_matrix, csc_matrix, coo_matrix):
iris_sparse = sparse_format(self.iris.data)
clf.fit(iris_sparse, self.iris.target)
score = clf.score(iris_sparse, self.iris.target)
self.assertGreater(score, 0.8, "Failed with score = {0:.5f}".format(score))
def test_classifier_sparse_input(self):
clf = RGFClassifier(calc_prob='softmax')
for sparse_format in (sparse.bsr_matrix, sparse.coo_matrix, sparse.csc_matrix,
sparse.csr_matrix, sparse.dia_matrix, sparse.dok_matrix, sparse.lil_matrix):
iris_sparse = sparse_format(self.iris.data)
clf.fit(iris_sparse, self.iris.target)
score = clf.score(iris_sparse, self.iris.target)
self.assertGreater(score, 0.8, "Failed with score = {0:.5f}".format(score))
def test_softmax_classifier(self):
clf = RGFClassifier(prefix='clf', calc_prob='Softmax')
clf.fit(self.iris.data, self.iris.target)
proba_sum = clf.predict_proba(self.iris.data).sum(axis=1)
np.testing.assert_almost_equal(proba_sum, np.ones(self.iris.target.shape[0]))
score = clf.score(self.iris.data, self.iris.target)
print('Score: {0:.5f}'.format(score))
self.assertGreater(score, 0.8, "Failed with score = {0:.5f}".format(score))
def test_softmax_classifier(self):
clf = RGFClassifier(calc_prob='softmax')
clf.fit(self.iris.data, self.iris.target)
proba_sum = clf.predict_proba(self.iris.data).sum(axis=1)
np.testing.assert_almost_equal(proba_sum, np.ones(self.iris.target.shape[0]))
score = clf.score(self.iris.data, self.iris.target)
print('Score: {0:.5f}'.format(score))
self.assertGreater(score, 0.8, "Failed with score = {0:.5f}".format(score))
def test_bin_classifier(self):
clf = RGFClassifier(prefix='clf')
bin_target = (self.iris.target == 2).astype(int)
clf.fit(self.iris.data, bin_target)
proba_sum = clf.predict_proba(self.iris.data).sum(axis=1)
np.testing.assert_almost_equal(proba_sum, np.ones(bin_target.shape[0]))
score = clf.score(self.iris.data, bin_target)
print('Score: {0:.5f}'.format(score))
self.assertGreater(score, 0.8, "Failed with score = {0:.5f}".format(score))
def test_bin_classifier(self):
clf = RGFClassifier()
bin_target = (self.iris.target == 2).astype(int)
clf.fit(self.iris.data, bin_target)
proba_sum = clf.predict_proba(self.iris.data).sum(axis=1)
np.testing.assert_almost_equal(proba_sum, np.ones(bin_target.shape[0]))
score = clf.score(self.iris.data, bin_target)
print('Score: {0:.5f}'.format(score))
self.assertGreater(score, 0.8, "Failed with score = {0:.5f}".format(score))
from sklearn import datasets
from sklearn.utils.validation import check_random_state
from sklearn.ensemble import GradientBoostingClassifier
from rgf.sklearn import RGFClassifier, FastRGFClassifier
iris = datasets.load_iris()
rng = check_random_state(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
start = time.time()
clf = RGFClassifier()
clf.fit(iris.data, iris.target)
score = clf.score(iris.data, iris.target)
end = time.time()
print("RGF: {} sec".format(end - start))
print("score: {}".format(score))
start = time.time()
clf = FastRGFClassifier()
clf.fit(iris.data, iris.target)
score = clf.score(iris.data, iris.target)
end = time.time()
print("FastRGF: {} sec".format(end - start))
print("score: {}".format(score))
start = time.time()
clf = GradientBoostingClassifier()
clf.fit(iris.data, iris.target)