def test_quantile_regression_diff(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([1., 1.1, 1.2, 10, 1.4])
clr = MLPRegressor(hidden_layer_sizes=(3, ))
clr.fit(X, Y)
clq = QuantileMLPRegressor(hidden_layer_sizes=(3, ))
clq.fit(X, Y)
self.assertGreater(clr.n_iter_, 10)
self.assertGreater(clq.n_iter_, 10)
err1 = mean_absolute_error(Y, clr.predict(X))
err2 = mean_absolute_error(Y, clq.predict(X))
self.assertLesser(err1, 5)
self.assertLesser(err2, 5)
def test_quantile_regression_pandas(self):
X = pandas.DataFrame(numpy.array([[0.1, 0.2], [0.2, 0.3]]))
Y = numpy.array([1., 1.1])
clr = MLPRegressor(hidden_layer_sizes=(3, ))
clr.fit(X, Y)
clq = QuantileMLPRegressor(hidden_layer_sizes=(3, ))
clq.fit(X, Y)
self.assertGreater(clr.n_iter_, 10)
self.assertGreater(clq.n_iter_, 10)
err1 = mean_absolute_error(Y, clr.predict(X))
err2 = mean_absolute_error(Y, clq.predict(X))
self.assertLesser(err1, 3)
self.assertLesser(err2, 3)
def test_quantile_regression_grid_search(self):
X = numpy.random.random(100)
eps1 = (numpy.random.random(90) - 0.5) * 0.1
eps2 = numpy.random.random(10) * 2
eps = numpy.hstack([eps1, eps2])
X = X.reshape((100, 1)) # pylint: disable=E1101
Y = X.ravel() * 3.4 + 5.6 + eps
self.assertRaise(lambda: test_sklearn_grid_search_cv(
lambda: QuantileMLPRegressor(hidden_layer_sizes=(3,)), X, Y), ValueError)
res = test_sklearn_grid_search_cv(lambda: QuantileMLPRegressor(hidden_layer_sizes=(3,)),
X, Y, learning_rate_init=[0.001, 0.0001])
self.assertIn('model', res)
self.assertIn('score', res)
self.assertGreater(res['score'], 0)
self.assertLesser(res['score'], 10)
def test_quantile_regression_pickle(self):
X = random(100)
eps1 = (random(90) - 0.5) * 0.1
eps2 = random(10) * 2
eps = numpy.hstack([eps1, eps2])
X = X.reshape((100, 1)) # pylint: disable=E1101
Y = X.ravel() * 3.4 + 5.6 + eps
test_sklearn_pickle(lambda: MLPRegressor(hidden_layer_sizes=(3, )), X,
Y)
test_sklearn_pickle(
lambda: QuantileMLPRegressor(hidden_layer_sizes=(3, )), X, Y)
def test_quantile_regression_clone(self):
test_sklearn_clone(lambda: QuantileMLPRegressor())