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])
