def test_tee_None(self): import lale.datasets from lale.lib.lale import Tee pca = PCA() trainable = Tee() >> pca (train_X, train_y), (test_X, test_y) = lale.datasets.digits_df() trained = trainable.fit(train_X, train_y) _ = trained.transform(test_X)
def test_tee_def(self): import numpy as np import lale.datasets from lale.lib.lale import Tee def check_data(X, y): self.assertEqual(X.dtypes["x1"], np.float64) pca = PCA() trainable = Tee(listener=check_data) >> pca (train_X, train_y), (test_X, test_y) = lale.datasets.digits_df() trained = trainable.fit(train_X, train_y) _ = trained.transform(test_X)
def test_tee_obj(self): import numpy as np import lale.datasets from lale.lib.lale import Tee class check_data: def __init__(self, outerSelf): self._outerSelf = outerSelf def __call__(self, X, y): self._outerSelf.assertEqual(X.dtypes["x1"], np.float64) pca = PCA() trainable = Tee(listener=check_data(self)) >> pca (train_X, train_y), (test_X, test_y) = lale.datasets.digits_df() trained = trainable.fit(train_X, train_y) _ = trained.transform(test_X)