def test_same_coefs(self):
X, y, Z = self.generate_dataset(2, 10000)
local = LogisticRegression(tol=1e-4, C=10)
dist = SparkLogisticRegression(tol=1e-4, C=10)
local.fit(X, y)
dist.fit(Z, classes=np.unique(y))
assert_array_almost_equal(local.coef_, dist.coef_, decimal=1)
def test_same_coefs(self):
X, y, Z = self.make_classification(2, 10000)
local = LogisticRegression(tol=1e-4, C=10)
dist = SparkLogisticRegression(tol=1e-4, C=10)
local.fit(X, y)
dist.fit(Z, classes=np.unique(y))
assert_array_almost_equal(local.coef_, dist.coef_, decimal=1)
def test_same_prediction(self):
X, y, Z = self.make_classification(2, 100000)
local = LogisticRegression(tol=1e-4, C=10)
dist = SparkLogisticRegression(tol=1e-4, C=10)
y_local = local.fit(X, y).predict(X)
y_dist = dist.fit(Z, classes=local.classes_).predict(Z[:, 'X'])
y_converted = dist.to_scikit().predict(X)
assert (sum(y_local != y_dist.toarray()) < len(y_local) * 1. / 100.)
assert (sum(y_local != y_converted) < len(y_local) * 1. / 100.)
def test_same_coefs(self):
X, y, Z = self.make_classification(2, 10000)
local = LogisticRegression(tol=1e-4, C=10)
dist = SparkLogisticRegression(tol=1e-4, C=10)
local.fit(X, y)
dist.fit(Z, classes=np.unique(y))
converted = dist.to_scikit()
assert_array_almost_equal(local.coef_, dist.coef_, decimal=1)
assert_array_almost_equal(local.coef_, converted.coef_, decimal=1)
def test_same_prediction(self):
X, y, Z = self.make_classification(2, 100000)
local = LogisticRegression(tol=1e-4, C=10)
dist = SparkLogisticRegression(tol=1e-4, C=10)
y_local = local.fit(X, y).predict(X)
y_dist = dist.fit(Z, classes=local.classes_).predict(Z[:, 'X'])
y_converted = dist.to_scikit().predict(X)
assert (sum(y_local != y_dist.toarray()) < len(y_local) * 1. / 100.)
assert (sum(y_local != y_converted) < len(y_local) * 1. / 100.)
