diff options
Diffstat (limited to 'tensorflow/contrib/tensor_forest/client/random_forest_test.py')
| -rw-r--r-- | tensorflow/contrib/tensor_forest/client/random_forest_test.py | 305 |
1 files changed, 296 insertions, 9 deletions
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest_test.py b/tensorflow/contrib/tensor_forest/client/random_forest_test.py index ac42364d25..aa0016b740 100644 --- a/tensorflow/contrib/tensor_forest/client/random_forest_test.py +++ b/tensorflow/contrib/tensor_forest/client/random_forest_test.py @@ -23,7 +23,39 @@ import numpy as np from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.contrib.tensor_forest.client import random_forest from tensorflow.contrib.tensor_forest.python import tensor_forest +from tensorflow.python.estimator.canned import head as head_lib +from tensorflow.python.estimator.inputs import numpy_io +from tensorflow.python.feature_column import feature_column_lib as core_feature_column +from tensorflow.python.framework import ops +from tensorflow.python.ops.losses import losses from tensorflow.python.platform import test +from tensorflow.python.training import checkpoint_utils + + +def _get_classification_input_fns(): + iris = base.load_iris() + data = iris.data.astype(np.float32) + labels = iris.target.astype(np.int32) + + train_input_fn = numpy_io.numpy_input_fn( + x=data, y=labels, batch_size=150, num_epochs=None, shuffle=False) + + predict_input_fn = numpy_io.numpy_input_fn( + x=data[:1,], y=None, batch_size=1, num_epochs=1, shuffle=False) + return train_input_fn, predict_input_fn + + +def _get_regression_input_fns(): + boston = base.load_boston() + data = boston.data.astype(np.float32) + labels = boston.target.astype(np.int32) + + train_input_fn = numpy_io.numpy_input_fn( + x=data, y=labels, batch_size=506, num_epochs=None, shuffle=False) + + predict_input_fn = numpy_io.numpy_input_fn( + x=data[:1,], y=None, batch_size=1, num_epochs=1, shuffle=False) + return train_input_fn, predict_input_fn class TensorForestTrainerTests(test.TestCase): @@ -39,32 +71,287 @@ class TensorForestTrainerTests(test.TestCase): inference_tree_paths=True) classifier = random_forest.TensorForestEstimator(hparams.fill()) + input_fn, predict_input_fn = _get_classification_input_fns() + classifier.fit(input_fn=input_fn, steps=100) + res = classifier.evaluate(input_fn=input_fn, steps=10) + + self.assertEqual(1.0, res['accuracy']) + self.assertAllClose(0.55144483, res['loss']) + + predictions = list(classifier.predict(input_fn=predict_input_fn)) + self.assertAllClose([[0.576117, 0.211942, 0.211942]], + [pred['probabilities'] for pred in predictions]) + + def testRegression(self): + """Tests regression using matrix data as input.""" + + hparams = tensor_forest.ForestHParams( + num_trees=5, + max_nodes=1000, + num_classes=1, + num_features=13, + regression=True, + split_after_samples=20) + + regressor = random_forest.TensorForestEstimator(hparams.fill()) + + input_fn, predict_input_fn = _get_regression_input_fns() + + regressor.fit(input_fn=input_fn, steps=100) + res = regressor.evaluate(input_fn=input_fn, steps=10) + self.assertGreaterEqual(0.1, res['loss']) + + predictions = list(regressor.predict(input_fn=predict_input_fn)) + self.assertAllClose([24.], [pred['scores'] for pred in predictions], atol=1) + + def testAdditionalOutputs(self): + """Tests multi-class classification using matrix data as input.""" + hparams = tensor_forest.ForestHParams( + num_trees=1, + max_nodes=100, + num_classes=3, + num_features=4, + split_after_samples=20, + inference_tree_paths=True) + classifier = random_forest.TensorForestEstimator( + hparams.fill(), keys_column='keys', include_all_in_serving=True) + iris = base.load_iris() data = iris.data.astype(np.float32) labels = iris.target.astype(np.int32) - classifier.fit(x=data, y=labels, steps=100, batch_size=50) - classifier.evaluate(x=data, y=labels, steps=10) + input_fn = numpy_io.numpy_input_fn( + x={ + 'x': data, + 'keys': np.arange(len(iris.data)).reshape(150, 1) + }, + y=labels, + batch_size=10, + num_epochs=1, + shuffle=False) - def testRegression(self): + classifier.fit(input_fn=input_fn, steps=100) + predictions = list(classifier.predict(input_fn=input_fn)) + # Check that there is a key column, tree paths and var. + for pred in predictions: + self.assertTrue('keys' in pred) + self.assertTrue('tree_paths' in pred) + self.assertTrue('prediction_variance' in pred) + + def _assert_checkpoint(self, model_dir, global_step): + reader = checkpoint_utils.load_checkpoint(model_dir) + self.assertLessEqual( + reader.get_tensor(ops.GraphKeys.GLOBAL_STEP), global_step) + + def testEarlyStopping(self): """Tests multi-class classification using matrix data as input.""" + hparams = tensor_forest.ForestHParams( + num_trees=100, + max_nodes=10000, + num_classes=3, + num_features=4, + split_after_samples=20, + inference_tree_paths=True) + classifier = random_forest.TensorForestEstimator( + hparams.fill(), + # Set a crazy threshold - 30% loss change. + early_stopping_loss_threshold=0.3, + early_stopping_rounds=2) + + input_fn, _ = _get_classification_input_fns() + classifier.fit(input_fn=input_fn, steps=100) + + # We stopped early. + self._assert_checkpoint(classifier.model_dir, global_step=5) + + +class CoreTensorForestTests(test.TestCase): + + def testTrainEvaluateInferDoesNotThrowErrorForClassifier(self): + head_fn = head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) hparams = tensor_forest.ForestHParams( num_trees=3, max_nodes=1000, + num_classes=3, + num_features=4, + split_after_samples=20, + inference_tree_paths=True) + + est = random_forest.CoreTensorForestEstimator(hparams.fill(), head=head_fn) + + input_fn, predict_input_fn = _get_classification_input_fns() + + est.train(input_fn=input_fn, steps=100) + res = est.evaluate(input_fn=input_fn, steps=1) + + self.assertEqual(1.0, res['accuracy']) + self.assertAllClose(0.55144483, res['loss']) + + predictions = list(est.predict(input_fn=predict_input_fn)) + self.assertAllClose([[0.576117, 0.211942, 0.211942]], + [pred['probabilities'] for pred in predictions]) + + def testRegression(self): + """Tests regression using matrix data as input.""" + head_fn = head_lib._regression_head( + label_dimension=1, + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + hparams = tensor_forest.ForestHParams( + num_trees=5, + max_nodes=1000, num_classes=1, num_features=13, regression=True, split_after_samples=20) - regressor = random_forest.TensorForestEstimator(hparams.fill()) + regressor = random_forest.CoreTensorForestEstimator( + hparams.fill(), head=head_fn) + + input_fn, predict_input_fn = _get_regression_input_fns() + + regressor.train(input_fn=input_fn, steps=100) + res = regressor.evaluate(input_fn=input_fn, steps=10) + self.assertGreaterEqual(0.1, res['loss']) + + predictions = list(regressor.predict(input_fn=predict_input_fn)) + self.assertAllClose( + [[24.]], [pred['predictions'] for pred in predictions], atol=1) + + def testWithFeatureColumns(self): + head_fn = head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + hparams = tensor_forest.ForestHParams( + num_trees=3, + max_nodes=1000, + num_classes=3, + num_features=4, + split_after_samples=20, + inference_tree_paths=True) + + est = random_forest.CoreTensorForestEstimator( + hparams.fill(), + head=head_fn, + feature_columns=[core_feature_column.numeric_column('x')]) + + iris = base.load_iris() + data = {'x': iris.data.astype(np.float32)} + labels = iris.target.astype(np.int32) + + input_fn = numpy_io.numpy_input_fn( + x=data, y=labels, batch_size=150, num_epochs=None, shuffle=False) + + est.train(input_fn=input_fn, steps=100) + res = est.evaluate(input_fn=input_fn, steps=1) + + self.assertEqual(1.0, res['accuracy']) + self.assertAllClose(0.55144483, res['loss']) + + def testAutofillsClassificationHead(self): + hparams = tensor_forest.ForestHParams( + num_trees=3, + max_nodes=1000, + num_classes=3, + num_features=4, + split_after_samples=20, + inference_tree_paths=True) + + est = random_forest.CoreTensorForestEstimator(hparams.fill()) + + input_fn, _ = _get_classification_input_fns() + + est.train(input_fn=input_fn, steps=100) + res = est.evaluate(input_fn=input_fn, steps=1) + + self.assertEqual(1.0, res['accuracy']) + self.assertAllClose(0.55144483, res['loss']) + + def testAutofillsRegressionHead(self): + hparams = tensor_forest.ForestHParams( + num_trees=5, + max_nodes=1000, + num_classes=1, + num_features=13, + regression=True, + split_after_samples=20) + + regressor = random_forest.CoreTensorForestEstimator(hparams.fill()) + + input_fn, predict_input_fn = _get_regression_input_fns() + + regressor.train(input_fn=input_fn, steps=100) + res = regressor.evaluate(input_fn=input_fn, steps=10) + self.assertGreaterEqual(0.1, res['loss']) + + predictions = list(regressor.predict(input_fn=predict_input_fn)) + self.assertAllClose( + [[24.]], [pred['predictions'] for pred in predictions], atol=1) + + def testAdditionalOutputs(self): + """Tests multi-class classification using matrix data as input.""" + hparams = tensor_forest.ForestHParams( + num_trees=1, + max_nodes=100, + num_classes=3, + num_features=4, + split_after_samples=20, + inference_tree_paths=True) + classifier = random_forest.CoreTensorForestEstimator( + hparams.fill(), keys_column='keys', include_all_in_serving=True) + + iris = base.load_iris() + data = iris.data.astype(np.float32) + labels = iris.target.astype(np.int32) + + input_fn = numpy_io.numpy_input_fn( + x={ + 'x': data, + 'keys': np.arange(len(iris.data)).reshape(150, 1) + }, + y=labels, + batch_size=10, + num_epochs=1, + shuffle=False) + + classifier.train(input_fn=input_fn, steps=100) + predictions = list(classifier.predict(input_fn=input_fn)) + # Check that there is a key column, tree paths and var. + for pred in predictions: + self.assertTrue('keys' in pred) + self.assertTrue('tree_paths' in pred) + self.assertTrue('prediction_variance' in pred) + + def _assert_checkpoint(self, model_dir, global_step): + reader = checkpoint_utils.load_checkpoint(model_dir) + self.assertLessEqual( + reader.get_tensor(ops.GraphKeys.GLOBAL_STEP), global_step) + + def testEarlyStopping(self): + head_fn = head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + hparams = tensor_forest.ForestHParams( + num_trees=3, + max_nodes=1000, + num_classes=3, + num_features=4, + split_after_samples=20, + inference_tree_paths=True) - boston = base.load_boston() - data = boston.data.astype(np.float32) - labels = boston.target.astype(np.int32) + est = random_forest.CoreTensorForestEstimator( + hparams.fill(), + head=head_fn, + # Set a crazy threshold - 30% loss change. + early_stopping_loss_threshold=0.3, + early_stopping_rounds=2) - regressor.fit(x=data, y=labels, steps=100, batch_size=50) - regressor.evaluate(x=data, y=labels, steps=10) + input_fn, _ = _get_classification_input_fns() + est.train(input_fn=input_fn, steps=100) + # We stopped early. + self._assert_checkpoint(est.model_dir, global_step=8) if __name__ == "__main__": |