def test_maxabs_scaler_zero_variance_features(): """Check MaxAbsScaler on toy data with zero variance features""" X = [[0., 1., +0.5], [0., 1., -0.3], [0., 1., +1.5], [0., 0., +0.0]] scaler = MaxAbsScaler() X_trans = scaler.fit_transform(X) X_expected = [[0., 1., 1.0 / 3.0], [0., 1., -0.2], [0., 1., 1.0], [0., 0., 0.0]] assert_array_almost_equal(X_trans, X_expected) X_trans_inv = scaler.inverse_transform(X_trans) assert_array_almost_equal(X, X_trans_inv) # make sure new data gets transformed correctly X_new = [[+0., 2., 0.5], [-1., 1., 0.0], [+0., 1., 1.5]] X_trans_new = scaler.transform(X_new) X_expected_new = [[+0., 2.0, 1.0 / 3.0], [-1., 1.0, 0.0], [+0., 1.0, 1.0]] assert_array_almost_equal(X_trans_new, X_expected_new, decimal=2) # sparse data X_csr = sparse.csr_matrix(X) X_trans = scaler.fit_transform(X_csr) X_expected = [[0., 1., 1.0 / 3.0], [0., 1., -0.2], [0., 1., 1.0], [0., 0., 0.0]] assert_array_almost_equal(X_trans.A, X_expected) X_trans_inv = scaler.inverse_transform(X_trans) assert_array_almost_equal(X, X_trans_inv.A)
class MaxAbsScalerImpl(): def __init__(self, copy=True): self._hyperparams = {'copy': copy} self._wrapped_model = SKLModel(**self._hyperparams) def fit(self, X, y=None): if (y is not None): self._wrapped_model.fit(X, y) else: self._wrapped_model.fit(X) return self def transform(self, X): return self._wrapped_model.transform(X)
class CreateMaxAbsScaler(CreateModel): def fit(self, data, args): self.model = MaxAbsScaler() with Timer() as t: self.model.fit(data.X_train, data.y_train) return t.interval def test(self, data): assert self.model is not None return self.model.transform(data.X_test) def predict(self, data): with Timer() as t: self.predictions = self.test(data) data.learning_task = LearningTask.REGRESSION return t.interval