def do1DTest(self, trainable, train_X, train_y, test_X, test_y): #Test for 1-D array as input to the transformers train_X = train_X[:,0] test_X = test_X[:,0] trainable_pipeline = (trainable & NoOp()) >> ConcatFeatures() >> float32_transform() >> LR() trained_pipeline = trainable_pipeline.fit(train_X, train_y) trained_pipeline.predict(test_X) hyperopt = Hyperopt(estimator=trainable_pipeline, max_evals=1) trained_hyperopt = hyperopt.fit(train_X, train_y) trained_hyperopt.predict(test_X)
def doTest(self, trainable, train_X, train_y, test_X, test_y): trained = trainable.fit(train_X, train_y) transformed = trained.transform(test_X) with self.assertWarns(DeprecationWarning): trainable.transform(train_X) trainable.to_json() trainable_pipeline = trainable >> float32_transform() >> LR() trained_pipeline = trainable_pipeline.fit(train_X, train_y) trained_pipeline.predict(test_X) hyperopt = Hyperopt(estimator=trainable_pipeline, max_evals=1) trained_hyperopt = hyperopt.fit(train_X, train_y) trained_hyperopt.predict(test_X)