def test_fit_predict_from_numpy(self): import tensorflow as tf from omegaml.backends.tensorflow.tfestimatormodel import TFEstimatorModel om = self.om # note we use a custom input_fn estmdl = TFEstimatorModel(estimator_fn=make_estimator_fn(), input_fn=make_input_fn()) train_x, train_y, test_x, test_y = make_data() # create a feature dict from a numpy array train_x = train_x.values # numpy train_y = train_y.values test_x = test_x.values classifier = estmdl.fit(train_x, train_y) self.assertIsInstance(classifier, tf.estimator.LinearClassifier) # score score = estmdl.score(test_x, test_y) self.assertIsInstance(score, dict) self.assertIn('accuracy', score) self.assertIn('loss', score) # predict predict = [v for v in estmdl.predict(test_x)] self.assertIsInstance(predict, list) self.assertIn('logits', predict[0]) self.assertIn('probabilities', predict[0]) self.assertIn('classes', predict[0])
def test_fit_predict(self): import tensorflow as tf om = self.om # create classifier estmdl = TFEstimatorModel(estimator_fn=make_estimator_fn()) train_x, train_y, test_x, test_y = make_data() classifier = estmdl.fit(train_x, train_y) self.assertIsInstance(classifier, tf.estimator.LinearClassifier) # score score = estmdl.score(test_x, test_y) self.assertIsInstance(score, dict) self.assertIn('accuracy', score) self.assertIn('loss', score) # predict predict = [v for v in estmdl.predict(test_x)] self.assertIsInstance(predict, list) self.assertIn('logits', predict[0]) self.assertIn('probabilities', predict[0]) self.assertIn('classes', predict[0])