From 611c82b5beca9045d23d00607168e9054d99bb1f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 7 Jun 2017 09:32:28 -0700
Subject: [PATCH] Adds integration test for
DNNLinearCombined((Classifier)|(Regressor)).
PiperOrigin-RevId: 158278512
---
tensorflow/python/estimator/BUILD | 11 +
.../canned/dnn_linear_combined_test.py | 358 ++++++++++++++++++
2 files changed, 369 insertions(+)
diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 2549c925cfe..1d3f8de20ab 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -208,9 +208,20 @@ py_test(
deps = [
":dnn_linear_combined",
":dnn_testing_utils",
+ ":export_export",
":linear_testing_utils",
+ ":numpy_io",
+ ":pandas_io",
+ ":prediction_keys",
"//tensorflow/core:protos_all_py",
"//tensorflow/python:client_testlib",
+ "//tensorflow/python:dtypes",
+ "//tensorflow/python:framework_ops",
+ "//tensorflow/python:nn",
+ "//tensorflow/python:parsing_ops",
+ "//tensorflow/python:platform",
+ "//tensorflow/python:training",
+ "//tensorflow/python/feature_column",
],
)
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
index f8fc6fbc6ab..16b4be7b24a 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
@@ -18,11 +18,39 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
+import shutil
+import tempfile
+
+import numpy as np
+import six
+
+from tensorflow.core.example import example_pb2
+from tensorflow.core.example import feature_pb2
from tensorflow.python.estimator.canned import dnn_linear_combined
from tensorflow.python.estimator.canned import dnn_testing_utils
from tensorflow.python.estimator.canned import linear_testing_utils
+from tensorflow.python.estimator.canned import prediction_keys
+from tensorflow.python.estimator.export import export
+from tensorflow.python.estimator.inputs import numpy_io
+from tensorflow.python.estimator.inputs import pandas_io
+from tensorflow.python.feature_column import feature_column
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
from tensorflow.python.ops import nn
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.platform import gfile
from tensorflow.python.platform import test
+from tensorflow.python.training import input as input_lib
+
+try:
+ # pylint: disable=g-import-not-at-top
+ import pandas as pd
+ HAS_PANDAS = True
+except IOError:
+ # Pandas writes a temporary file during import. If it fails, don't use pandas.
+ HAS_PANDAS = False
+except ImportError:
+ HAS_PANDAS = False
class DNNOnlyModelFnTest(dnn_testing_utils.BaseDNNModelFnTest, test.TestCase):
@@ -122,5 +150,335 @@ class LinearOnlyRegressorTrainingTest(
self, _linear_regressor_fn)
+class DNNLinearCombinedRegressorIntegrationTest(test.TestCase):
+
+ def setUp(self):
+ self._model_dir = tempfile.mkdtemp()
+
+ def tearDown(self):
+ if self._model_dir:
+ shutil.rmtree(self._model_dir)
+
+ def _test_complete_flow(
+ self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
+ label_dimension, batch_size):
+ linear_feature_columns = [
+ feature_column.numeric_column('x', shape=(input_dimension,))]
+ dnn_feature_columns = [
+ feature_column.numeric_column('x', shape=(input_dimension,))]
+ feature_columns = linear_feature_columns + dnn_feature_columns
+ est = dnn_linear_combined.DNNLinearCombinedRegressor(
+ linear_feature_columns=linear_feature_columns,
+ dnn_hidden_units=(2, 2),
+ dnn_feature_columns=dnn_feature_columns,
+ label_dimension=label_dimension,
+ model_dir=self._model_dir)
+
+ # TRAIN
+ num_steps = 10
+ est.train(train_input_fn, steps=num_steps)
+
+ # EVALUTE
+ scores = est.evaluate(eval_input_fn)
+ self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
+ self.assertIn('loss', six.iterkeys(scores))
+
+ # PREDICT
+ predictions = np.array([
+ x[prediction_keys.PredictionKeys.PREDICTIONS]
+ for x in est.predict(predict_input_fn)
+ ])
+ self.assertAllEqual((batch_size, label_dimension), predictions.shape)
+
+ # EXPORT
+ feature_spec = feature_column.make_parse_example_spec(feature_columns)
+ serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+ feature_spec)
+ export_dir = est.export_savedmodel(tempfile.mkdtemp(),
+ serving_input_receiver_fn)
+ self.assertTrue(gfile.Exists(export_dir))
+
+ def test_numpy_input_fn(self):
+ """Tests complete flow with numpy_input_fn."""
+ label_dimension = 2
+ batch_size = 10
+ data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
+ data = data.reshape(batch_size, label_dimension)
+ # learn y = x
+ train_input_fn = numpy_io.numpy_input_fn(
+ x={'x': data},
+ y=data,
+ batch_size=batch_size,
+ num_epochs=None,
+ shuffle=True)
+ eval_input_fn = numpy_io.numpy_input_fn(
+ x={'x': data},
+ y=data,
+ batch_size=batch_size,
+ shuffle=False)
+ predict_input_fn = numpy_io.numpy_input_fn(
+ x={'x': data},
+ batch_size=batch_size,
+ shuffle=False)
+
+ self._test_complete_flow(
+ train_input_fn=train_input_fn,
+ eval_input_fn=eval_input_fn,
+ predict_input_fn=predict_input_fn,
+ input_dimension=label_dimension,
+ label_dimension=label_dimension,
+ batch_size=batch_size)
+
+ def test_pandas_input_fn(self):
+ """Tests complete flow with pandas_input_fn."""
+ if not HAS_PANDAS:
+ return
+ label_dimension = 1
+ batch_size = 10
+ data = np.linspace(0., 2., batch_size, dtype=np.float32)
+ x = pd.DataFrame({'x': data})
+ y = pd.Series(data)
+ train_input_fn = pandas_io.pandas_input_fn(
+ x=x,
+ y=y,
+ batch_size=batch_size,
+ num_epochs=None,
+ shuffle=True)
+ eval_input_fn = pandas_io.pandas_input_fn(
+ x=x,
+ y=y,
+ batch_size=batch_size,
+ shuffle=False)
+ predict_input_fn = pandas_io.pandas_input_fn(
+ x=x,
+ batch_size=batch_size,
+ shuffle=False)
+
+ self._test_complete_flow(
+ train_input_fn=train_input_fn,
+ eval_input_fn=eval_input_fn,
+ predict_input_fn=predict_input_fn,
+ input_dimension=label_dimension,
+ label_dimension=label_dimension,
+ batch_size=batch_size)
+
+ def test_input_fn_from_parse_example(self):
+ """Tests complete flow with input_fn constructed from parse_example."""
+ label_dimension = 2
+ batch_size = 10
+ data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
+ data = data.reshape(batch_size, label_dimension)
+
+ serialized_examples = []
+ for datum in data:
+ example = example_pb2.Example(features=feature_pb2.Features(
+ feature={
+ 'x': feature_pb2.Feature(
+ float_list=feature_pb2.FloatList(value=datum)),
+ 'y': feature_pb2.Feature(
+ float_list=feature_pb2.FloatList(value=datum)),
+ }))
+ serialized_examples.append(example.SerializeToString())
+
+ feature_spec = {
+ 'x': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
+ 'y': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
+ }
+ def _train_input_fn():
+ feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
+ features = linear_testing_utils.queue_parsed_features(feature_map)
+ labels = features.pop('y')
+ return features, labels
+ def _eval_input_fn():
+ feature_map = parsing_ops.parse_example(
+ input_lib.limit_epochs(serialized_examples, num_epochs=1),
+ feature_spec)
+ features = linear_testing_utils.queue_parsed_features(feature_map)
+ labels = features.pop('y')
+ return features, labels
+ def _predict_input_fn():
+ feature_map = parsing_ops.parse_example(
+ input_lib.limit_epochs(serialized_examples, num_epochs=1),
+ feature_spec)
+ features = linear_testing_utils.queue_parsed_features(feature_map)
+ features.pop('y')
+ return features, None
+
+ self._test_complete_flow(
+ train_input_fn=_train_input_fn,
+ eval_input_fn=_eval_input_fn,
+ predict_input_fn=_predict_input_fn,
+ input_dimension=label_dimension,
+ label_dimension=label_dimension,
+ batch_size=batch_size)
+
+
+class DNNLinearCombinedClassifierIntegrationTest(test.TestCase):
+
+ def setUp(self):
+ self._model_dir = tempfile.mkdtemp()
+
+ def tearDown(self):
+ if self._model_dir:
+ shutil.rmtree(self._model_dir)
+
+ def _test_complete_flow(
+ self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
+ n_classes, batch_size):
+ linear_feature_columns = [
+ feature_column.numeric_column('x', shape=(input_dimension,))]
+ dnn_feature_columns = [
+ feature_column.numeric_column('x', shape=(input_dimension,))]
+ feature_columns = linear_feature_columns + dnn_feature_columns
+ est = dnn_linear_combined.DNNLinearCombinedClassifier(
+ linear_feature_columns=linear_feature_columns,
+ dnn_hidden_units=(2, 2),
+ dnn_feature_columns=dnn_feature_columns,
+ n_classes=n_classes,
+ model_dir=self._model_dir)
+
+ # TRAIN
+ num_steps = 10
+ est.train(train_input_fn, steps=num_steps)
+
+ # EVALUTE
+ scores = est.evaluate(eval_input_fn)
+ self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
+ self.assertIn('loss', six.iterkeys(scores))
+
+ # PREDICT
+ predicted_proba = np.array([
+ x[prediction_keys.PredictionKeys.PROBABILITIES]
+ for x in est.predict(predict_input_fn)
+ ])
+ self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)
+
+ # EXPORT
+ feature_spec = feature_column.make_parse_example_spec(feature_columns)
+ serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+ feature_spec)
+ export_dir = est.export_savedmodel(tempfile.mkdtemp(),
+ serving_input_receiver_fn)
+ self.assertTrue(gfile.Exists(export_dir))
+
+ def test_numpy_input_fn(self):
+ """Tests complete flow with numpy_input_fn."""
+ n_classes = 2
+ input_dimension = 2
+ batch_size = 10
+ data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
+ x_data = data.reshape(batch_size, input_dimension)
+ y_data = np.reshape(data[:batch_size], (batch_size, 1))
+ # learn y = x
+ train_input_fn = numpy_io.numpy_input_fn(
+ x={'x': x_data},
+ y=y_data,
+ batch_size=batch_size,
+ num_epochs=None,
+ shuffle=True)
+ eval_input_fn = numpy_io.numpy_input_fn(
+ x={'x': x_data},
+ y=y_data,
+ batch_size=batch_size,
+ shuffle=False)
+ predict_input_fn = numpy_io.numpy_input_fn(
+ x={'x': x_data},
+ batch_size=batch_size,
+ shuffle=False)
+
+ self._test_complete_flow(
+ train_input_fn=train_input_fn,
+ eval_input_fn=eval_input_fn,
+ predict_input_fn=predict_input_fn,
+ input_dimension=input_dimension,
+ n_classes=n_classes,
+ batch_size=batch_size)
+
+ def test_pandas_input_fn(self):
+ """Tests complete flow with pandas_input_fn."""
+ if not HAS_PANDAS:
+ return
+ input_dimension = 1
+ n_classes = 2
+ batch_size = 10
+ data = np.linspace(0., 2., batch_size, dtype=np.float32)
+ x = pd.DataFrame({'x': data})
+ y = pd.Series(data)
+ train_input_fn = pandas_io.pandas_input_fn(
+ x=x,
+ y=y,
+ batch_size=batch_size,
+ num_epochs=None,
+ shuffle=True)
+ eval_input_fn = pandas_io.pandas_input_fn(
+ x=x,
+ y=y,
+ batch_size=batch_size,
+ shuffle=False)
+ predict_input_fn = pandas_io.pandas_input_fn(
+ x=x,
+ batch_size=batch_size,
+ shuffle=False)
+
+ self._test_complete_flow(
+ train_input_fn=train_input_fn,
+ eval_input_fn=eval_input_fn,
+ predict_input_fn=predict_input_fn,
+ input_dimension=input_dimension,
+ n_classes=n_classes,
+ batch_size=batch_size)
+
+ def test_input_fn_from_parse_example(self):
+ """Tests complete flow with input_fn constructed from parse_example."""
+ input_dimension = 2
+ n_classes = 2
+ batch_size = 10
+ data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
+ data = data.reshape(batch_size, input_dimension)
+
+ serialized_examples = []
+ for datum in data:
+ example = example_pb2.Example(features=feature_pb2.Features(
+ feature={
+ 'x': feature_pb2.Feature(
+ float_list=feature_pb2.FloatList(value=datum)),
+ 'y': feature_pb2.Feature(
+ float_list=feature_pb2.FloatList(value=datum[:1])),
+ }))
+ serialized_examples.append(example.SerializeToString())
+
+ feature_spec = {
+ 'x': parsing_ops.FixedLenFeature([input_dimension], dtypes.float32),
+ 'y': parsing_ops.FixedLenFeature([1], dtypes.float32),
+ }
+ def _train_input_fn():
+ feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
+ features = linear_testing_utils.queue_parsed_features(feature_map)
+ labels = features.pop('y')
+ return features, labels
+ def _eval_input_fn():
+ feature_map = parsing_ops.parse_example(
+ input_lib.limit_epochs(serialized_examples, num_epochs=1),
+ feature_spec)
+ features = linear_testing_utils.queue_parsed_features(feature_map)
+ labels = features.pop('y')
+ return features, labels
+ def _predict_input_fn():
+ feature_map = parsing_ops.parse_example(
+ input_lib.limit_epochs(serialized_examples, num_epochs=1),
+ feature_spec)
+ features = linear_testing_utils.queue_parsed_features(feature_map)
+ features.pop('y')
+ return features, None
+
+ self._test_complete_flow(
+ train_input_fn=_train_input_fn,
+ eval_input_fn=_eval_input_fn,
+ predict_input_fn=_predict_input_fn,
+ input_dimension=input_dimension,
+ n_classes=n_classes,
+ batch_size=batch_size)
+
+
if __name__ == '__main__':
test.main()