def test_supervisedpcaRegressor_predict():
diabetes=datasets.load_diabetes()
X = diabetes.data
Y = diabetes.target
spca = SupervisedPCARegressor()
spca.fit(X, Y)
predictions = spca.predict(X)
mae=np.mean(abs(predictions-Y))
assert_almost_equal(mae,51.570682097)
def test_supervisedpcaRegressor_predict():
diabetes = datasets.load_diabetes()
X = diabetes.data
Y = diabetes.target
spca = SupervisedPCARegressor()
spca.fit(X, Y)
predictions = spca.predict(X)
mae = np.mean(abs(predictions - Y))
assert_almost_equal(mae, 51.570682097)
def test_supervisedpcaRegressor_fit():
# Test LinearRegression on a simple dataset.
# a simple dataset
diabetes=datasets.load_diabetes()
X = diabetes.data
Y = diabetes.target
spca = SupervisedPCARegressor()
spca.fit(X, Y,threshold=300,n_components=2)
assert_array_equal(spca._leavouts,[1,5])
assert_almost_equal(spca._model.coef_[0], [-537.7584256])
def test_supervisedpcaRegressor_fit():
# Test LinearRegression on a simple dataset.
# a simple dataset
diabetes = datasets.load_diabetes()
X = diabetes.data
Y = diabetes.target
spca = SupervisedPCARegressor()
spca.fit(X, Y, threshold=300, n_components=2)
assert_array_equal(spca._leavouts, [1, 5])
assert_almost_equal(spca._model.coef_[0], [-537.7584256])
