def testSentimentExampleAccuracy(self):
raw_data_dir = os.path.join(os.path.dirname(__file__),
'testdata/sentiment')
working_dir = self.get_temp_dir()
# Copy data from raw data directory to `working_dir`
for filename in [
'test_shuffled-00000-of-00001', 'train_shuffled-00000-of-00001'
]:
shutil.copy(os.path.join(raw_data_dir, filename), working_dir)
sentiment_example.transform_data(working_dir)
results = sentiment_example.train_and_evaluate(
working_dir, num_train_instances=1000, num_test_instances=1000)
self.assertGreaterEqual(results['accuracy'], 0.7)
# Delete temp directory and transform_fn directory. This ensures that the
# test of serving the model below will only pass if the SavedModel saved
# to sentiment_example.EXPORTED_MODEL_DIR is hermetic, i.e does not contain
# references to tft_temp and transform_fn.
shutil.rmtree(
os.path.join(working_dir, sentiment_example.TRANSFORM_TEMP_DIR))
shutil.rmtree(
os.path.join(working_dir, tft.TFTransformOutput.TRANSFORM_FN_DIR))
if local_model_server.local_model_server_supported():
model_name = 'my_model'
model_path = os.path.join(working_dir,
sentiment_example.EXPORTED_MODEL_DIR)
with local_model_server.start_server(model_name,
model_path) as address:
# Use made up data chosen to give high probability of negative
# sentiment.
ascii_classification_request = """model_spec { name: "my_model" }
input {
example_list {
examples {
features {
feature {
key: "review"
value: {
bytes_list {
value: "errible terrible terrible terrible terrible terrible terrible."
}
}
}
}
}
}
}"""
results = local_model_server.make_classification_request(
address, ascii_classification_request)
self.assertEqual(len(results), 1)
self.assertEqual(len(results[0].classes), 2)
self.assertEqual(results[0].classes[0].label, '0')
self.assertGreater(results[0].classes[0].score, 0.8)
self.assertEqual(results[0].classes[1].label, '1')
self.assertLess(results[0].classes[1].score, 0.2)
def testSentimentExampleAccuracy(self):
raw_data_dir = os.path.join(os.path.dirname(__file__),
'testdata/sentiment')
working_dir = self.get_temp_dir()
# Copy data from raw data directory to `working_dir`
for filename in [
'test_shuffled-00000-of-00001', 'train_shuffled-00000-of-00001'
]:
shutil.copy(os.path.join(raw_data_dir, filename), working_dir)
sentiment_example.transform_data(working_dir)
results = sentiment_example.train_and_evaluate(
working_dir, num_train_instances=1000, num_test_instances=1000)
self.assertGreaterEqual(results['accuracy'], 0.7)
if local_model_server.local_model_server_supported():
model_name = 'my_model'
model_path = os.path.join(working_dir,
sentiment_example.EXPORTED_MODEL_DIR)
with local_model_server.start_server(model_name,
model_path) as address:
# Use made up data chosen to give high probability of negative
# sentiment.
ascii_classification_request = """model_spec { name: "my_model" }
input {
example_list {
examples {
features {
feature {
key: "review"
value: {
bytes_list {
value: "errible terrible terrible terrible terrible terrible terrible."
}
}
}
}
}
}
}"""
results = local_model_server.make_classification_request(
address, ascii_classification_request)
self.assertEqual(len(results), 1)
self.assertEqual(len(results[0].classes), 2)
self.assertEqual(results[0].classes[0].label, '0')
self.assertGreater(results[0].classes[0].score, 0.8)
self.assertEqual(results[0].classes[1].label, '1')
self.assertLess(results[0].classes[1].score, 0.2)