Adds integration test for DNNLinearCombined((Classifier)|(Regressor)).
PiperOrigin-RevId: 158278512
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
cc6c91a9a9
commit
611c82b5be
@ -208,9 +208,20 @@ py_test(
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deps = [
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":dnn_linear_combined",
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":dnn_testing_utils",
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":export_export",
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":linear_testing_utils",
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":numpy_io",
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":pandas_io",
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":prediction_keys",
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"//tensorflow/core:protos_all_py",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:nn",
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"//tensorflow/python:parsing_ops",
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"//tensorflow/python:platform",
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"//tensorflow/python:training",
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"//tensorflow/python/feature_column",
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],
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)
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@ -18,11 +18,39 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import shutil
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import tempfile
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import numpy as np
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import six
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from tensorflow.core.example import example_pb2
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from tensorflow.core.example import feature_pb2
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from tensorflow.python.estimator.canned import dnn_linear_combined
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from tensorflow.python.estimator.canned import dnn_testing_utils
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from tensorflow.python.estimator.canned import linear_testing_utils
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from tensorflow.python.estimator.canned import prediction_keys
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from tensorflow.python.estimator.export import export
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from tensorflow.python.estimator.inputs import numpy_io
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from tensorflow.python.estimator.inputs import pandas_io
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from tensorflow.python.feature_column import feature_column
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import parsing_ops
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from tensorflow.python.platform import gfile
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from tensorflow.python.platform import test
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from tensorflow.python.training import input as input_lib
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try:
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# pylint: disable=g-import-not-at-top
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import pandas as pd
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HAS_PANDAS = True
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except IOError:
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# Pandas writes a temporary file during import. If it fails, don't use pandas.
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HAS_PANDAS = False
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except ImportError:
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HAS_PANDAS = False
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class DNNOnlyModelFnTest(dnn_testing_utils.BaseDNNModelFnTest, test.TestCase):
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@ -122,5 +150,335 @@ class LinearOnlyRegressorTrainingTest(
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self, _linear_regressor_fn)
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class DNNLinearCombinedRegressorIntegrationTest(test.TestCase):
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def setUp(self):
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self._model_dir = tempfile.mkdtemp()
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def tearDown(self):
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if self._model_dir:
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shutil.rmtree(self._model_dir)
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def _test_complete_flow(
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self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
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label_dimension, batch_size):
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linear_feature_columns = [
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feature_column.numeric_column('x', shape=(input_dimension,))]
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dnn_feature_columns = [
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feature_column.numeric_column('x', shape=(input_dimension,))]
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feature_columns = linear_feature_columns + dnn_feature_columns
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est = dnn_linear_combined.DNNLinearCombinedRegressor(
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linear_feature_columns=linear_feature_columns,
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dnn_hidden_units=(2, 2),
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dnn_feature_columns=dnn_feature_columns,
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label_dimension=label_dimension,
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model_dir=self._model_dir)
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# TRAIN
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num_steps = 10
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est.train(train_input_fn, steps=num_steps)
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# EVALUTE
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scores = est.evaluate(eval_input_fn)
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self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
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self.assertIn('loss', six.iterkeys(scores))
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# PREDICT
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predictions = np.array([
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x[prediction_keys.PredictionKeys.PREDICTIONS]
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for x in est.predict(predict_input_fn)
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])
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self.assertAllEqual((batch_size, label_dimension), predictions.shape)
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# EXPORT
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feature_spec = feature_column.make_parse_example_spec(feature_columns)
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serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
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feature_spec)
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export_dir = est.export_savedmodel(tempfile.mkdtemp(),
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serving_input_receiver_fn)
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self.assertTrue(gfile.Exists(export_dir))
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def test_numpy_input_fn(self):
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"""Tests complete flow with numpy_input_fn."""
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label_dimension = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
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data = data.reshape(batch_size, label_dimension)
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# learn y = x
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train_input_fn = numpy_io.numpy_input_fn(
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x={'x': data},
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y=data,
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batch_size=batch_size,
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num_epochs=None,
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shuffle=True)
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eval_input_fn = numpy_io.numpy_input_fn(
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x={'x': data},
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y=data,
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batch_size=batch_size,
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shuffle=False)
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predict_input_fn = numpy_io.numpy_input_fn(
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x={'x': data},
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batch_size=batch_size,
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shuffle=False)
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self._test_complete_flow(
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train_input_fn=train_input_fn,
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eval_input_fn=eval_input_fn,
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predict_input_fn=predict_input_fn,
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input_dimension=label_dimension,
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label_dimension=label_dimension,
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batch_size=batch_size)
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def test_pandas_input_fn(self):
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"""Tests complete flow with pandas_input_fn."""
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if not HAS_PANDAS:
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return
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label_dimension = 1
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batch_size = 10
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data = np.linspace(0., 2., batch_size, dtype=np.float32)
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x = pd.DataFrame({'x': data})
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y = pd.Series(data)
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train_input_fn = pandas_io.pandas_input_fn(
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x=x,
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y=y,
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batch_size=batch_size,
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num_epochs=None,
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shuffle=True)
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eval_input_fn = pandas_io.pandas_input_fn(
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x=x,
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y=y,
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batch_size=batch_size,
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shuffle=False)
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predict_input_fn = pandas_io.pandas_input_fn(
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x=x,
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batch_size=batch_size,
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shuffle=False)
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self._test_complete_flow(
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train_input_fn=train_input_fn,
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eval_input_fn=eval_input_fn,
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predict_input_fn=predict_input_fn,
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input_dimension=label_dimension,
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label_dimension=label_dimension,
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batch_size=batch_size)
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def test_input_fn_from_parse_example(self):
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"""Tests complete flow with input_fn constructed from parse_example."""
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label_dimension = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
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data = data.reshape(batch_size, label_dimension)
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serialized_examples = []
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for datum in data:
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example = example_pb2.Example(features=feature_pb2.Features(
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feature={
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'x': feature_pb2.Feature(
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float_list=feature_pb2.FloatList(value=datum)),
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'y': feature_pb2.Feature(
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float_list=feature_pb2.FloatList(value=datum)),
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}))
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serialized_examples.append(example.SerializeToString())
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feature_spec = {
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'x': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
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'y': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
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}
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def _train_input_fn():
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feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
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features = linear_testing_utils.queue_parsed_features(feature_map)
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labels = features.pop('y')
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return features, labels
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def _eval_input_fn():
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feature_map = parsing_ops.parse_example(
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input_lib.limit_epochs(serialized_examples, num_epochs=1),
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feature_spec)
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features = linear_testing_utils.queue_parsed_features(feature_map)
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labels = features.pop('y')
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return features, labels
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def _predict_input_fn():
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feature_map = parsing_ops.parse_example(
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input_lib.limit_epochs(serialized_examples, num_epochs=1),
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feature_spec)
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features = linear_testing_utils.queue_parsed_features(feature_map)
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features.pop('y')
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return features, None
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self._test_complete_flow(
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train_input_fn=_train_input_fn,
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eval_input_fn=_eval_input_fn,
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predict_input_fn=_predict_input_fn,
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input_dimension=label_dimension,
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label_dimension=label_dimension,
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batch_size=batch_size)
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class DNNLinearCombinedClassifierIntegrationTest(test.TestCase):
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def setUp(self):
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self._model_dir = tempfile.mkdtemp()
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def tearDown(self):
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if self._model_dir:
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shutil.rmtree(self._model_dir)
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def _test_complete_flow(
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self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
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n_classes, batch_size):
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linear_feature_columns = [
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feature_column.numeric_column('x', shape=(input_dimension,))]
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dnn_feature_columns = [
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feature_column.numeric_column('x', shape=(input_dimension,))]
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feature_columns = linear_feature_columns + dnn_feature_columns
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est = dnn_linear_combined.DNNLinearCombinedClassifier(
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linear_feature_columns=linear_feature_columns,
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dnn_hidden_units=(2, 2),
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dnn_feature_columns=dnn_feature_columns,
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n_classes=n_classes,
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model_dir=self._model_dir)
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# TRAIN
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num_steps = 10
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est.train(train_input_fn, steps=num_steps)
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# EVALUTE
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scores = est.evaluate(eval_input_fn)
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self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
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self.assertIn('loss', six.iterkeys(scores))
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# PREDICT
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predicted_proba = np.array([
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x[prediction_keys.PredictionKeys.PROBABILITIES]
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for x in est.predict(predict_input_fn)
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])
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self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)
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# EXPORT
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feature_spec = feature_column.make_parse_example_spec(feature_columns)
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serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
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feature_spec)
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export_dir = est.export_savedmodel(tempfile.mkdtemp(),
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serving_input_receiver_fn)
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self.assertTrue(gfile.Exists(export_dir))
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def test_numpy_input_fn(self):
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"""Tests complete flow with numpy_input_fn."""
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n_classes = 2
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input_dimension = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
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x_data = data.reshape(batch_size, input_dimension)
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y_data = np.reshape(data[:batch_size], (batch_size, 1))
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# learn y = x
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train_input_fn = numpy_io.numpy_input_fn(
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x={'x': x_data},
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y=y_data,
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batch_size=batch_size,
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num_epochs=None,
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shuffle=True)
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eval_input_fn = numpy_io.numpy_input_fn(
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x={'x': x_data},
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y=y_data,
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batch_size=batch_size,
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shuffle=False)
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predict_input_fn = numpy_io.numpy_input_fn(
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x={'x': x_data},
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batch_size=batch_size,
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shuffle=False)
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self._test_complete_flow(
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train_input_fn=train_input_fn,
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eval_input_fn=eval_input_fn,
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predict_input_fn=predict_input_fn,
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input_dimension=input_dimension,
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n_classes=n_classes,
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batch_size=batch_size)
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def test_pandas_input_fn(self):
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"""Tests complete flow with pandas_input_fn."""
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if not HAS_PANDAS:
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return
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input_dimension = 1
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n_classes = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size, dtype=np.float32)
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x = pd.DataFrame({'x': data})
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y = pd.Series(data)
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train_input_fn = pandas_io.pandas_input_fn(
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x=x,
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y=y,
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batch_size=batch_size,
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num_epochs=None,
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shuffle=True)
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eval_input_fn = pandas_io.pandas_input_fn(
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x=x,
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y=y,
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batch_size=batch_size,
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shuffle=False)
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predict_input_fn = pandas_io.pandas_input_fn(
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x=x,
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batch_size=batch_size,
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shuffle=False)
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self._test_complete_flow(
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train_input_fn=train_input_fn,
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eval_input_fn=eval_input_fn,
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predict_input_fn=predict_input_fn,
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input_dimension=input_dimension,
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n_classes=n_classes,
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batch_size=batch_size)
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def test_input_fn_from_parse_example(self):
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"""Tests complete flow with input_fn constructed from parse_example."""
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input_dimension = 2
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n_classes = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
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data = data.reshape(batch_size, input_dimension)
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serialized_examples = []
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for datum in data:
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example = example_pb2.Example(features=feature_pb2.Features(
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feature={
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'x': feature_pb2.Feature(
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float_list=feature_pb2.FloatList(value=datum)),
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'y': feature_pb2.Feature(
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float_list=feature_pb2.FloatList(value=datum[:1])),
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}))
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serialized_examples.append(example.SerializeToString())
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feature_spec = {
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'x': parsing_ops.FixedLenFeature([input_dimension], dtypes.float32),
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'y': parsing_ops.FixedLenFeature([1], dtypes.float32),
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}
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def _train_input_fn():
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feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
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features = linear_testing_utils.queue_parsed_features(feature_map)
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labels = features.pop('y')
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return features, labels
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def _eval_input_fn():
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feature_map = parsing_ops.parse_example(
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input_lib.limit_epochs(serialized_examples, num_epochs=1),
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feature_spec)
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features = linear_testing_utils.queue_parsed_features(feature_map)
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labels = features.pop('y')
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return features, labels
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def _predict_input_fn():
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feature_map = parsing_ops.parse_example(
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input_lib.limit_epochs(serialized_examples, num_epochs=1),
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feature_spec)
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features = linear_testing_utils.queue_parsed_features(feature_map)
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features.pop('y')
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return features, None
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self._test_complete_flow(
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train_input_fn=_train_input_fn,
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eval_input_fn=_eval_input_fn,
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predict_input_fn=_predict_input_fn,
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input_dimension=input_dimension,
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n_classes=n_classes,
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batch_size=batch_size)
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if __name__ == '__main__':
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test.main()
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