def testGetOutputAlternatives(self):
test_cases = (
(rnn_common.PredictionType.SINGLE_VALUE,
constants.ProblemType.CLASSIFICATION,
{prediction_key.PredictionKey.CLASSES: True,
prediction_key.PredictionKey.PROBABILITIES: True,
dynamic_rnn_estimator._get_state_name(0): True},
{'dynamic_rnn_output':
(constants.ProblemType.CLASSIFICATION,
{prediction_key.PredictionKey.CLASSES: True,
prediction_key.PredictionKey.PROBABILITIES: True})}),
(rnn_common.PredictionType.SINGLE_VALUE,
constants.ProblemType.LINEAR_REGRESSION,
{prediction_key.PredictionKey.SCORES: True,
dynamic_rnn_estimator._get_state_name(0): True,
dynamic_rnn_estimator._get_state_name(1): True},
{'dynamic_rnn_output':
(constants.ProblemType.LINEAR_REGRESSION,
{prediction_key.PredictionKey.SCORES: True})}),
(rnn_common.PredictionType.MULTIPLE_VALUE,
constants.ProblemType.CLASSIFICATION,
{prediction_key.PredictionKey.CLASSES: True,
prediction_key.PredictionKey.PROBABILITIES: True,
dynamic_rnn_estimator._get_state_name(0): True},
None))
for pred_type, prob_type, pred_dict, expected_alternatives in test_cases:
actual_alternatives = dynamic_rnn_estimator._get_output_alternatives(
pred_type, prob_type, pred_dict)
self.assertEqual(expected_alternatives, actual_alternatives)
def testGetOutputAlternatives(self):
test_cases = ((dynamic_rnn_estimator.PredictionType.SINGLE_VALUE,
constants.ProblemType.CLASSIFICATION, {
prediction_key.PredictionKey.CLASSES: True,
prediction_key.PredictionKey.PROBABILITIES: True,
dynamic_rnn_estimator._get_state_name(0): True
}, {
'dynamic_rnn_output':
(constants.ProblemType.CLASSIFICATION, {
prediction_key.PredictionKey.CLASSES: True,
prediction_key.PredictionKey.PROBABILITIES: True
})
}), (dynamic_rnn_estimator.PredictionType.SINGLE_VALUE,
constants.ProblemType.LINEAR_REGRESSION, {
prediction_key.PredictionKey.SCORES: True,
dynamic_rnn_estimator._get_state_name(0): True,
dynamic_rnn_estimator._get_state_name(1): True
}, {
'dynamic_rnn_output':
(constants.ProblemType.LINEAR_REGRESSION, {
prediction_key.PredictionKey.SCORES: True
})
}),
(dynamic_rnn_estimator.PredictionType.MULTIPLE_VALUE,
constants.ProblemType.CLASSIFICATION, {
prediction_key.PredictionKey.CLASSES: True,
prediction_key.PredictionKey.PROBABILITIES: True,
dynamic_rnn_estimator._get_state_name(0): True
}, None))
for pred_type, prob_type, pred_dict, expected_alternatives in test_cases:
actual_alternatives = dynamic_rnn_estimator._get_output_alternatives(
pred_type, prob_type, pred_dict)
self.assertEqual(expected_alternatives, actual_alternatives)