def test_toy_rf_classification_weighted(self, add_boolean_features):
with tf.Graph().as_default():
# Create toy model.
model_path = os.path.join(
tempfile.mkdtemp(dir=self.get_temp_dir()), "test_basic_rf_weighted")
test_utils.build_toy_random_forest(
model_path,
winner_take_all_inference=False,
add_boolean_features=add_boolean_features)
features = test_utils.build_toy_input_features()
# Prepare model.
model = inference.Model(model_path)
predictions = model.apply(features)
# Run model on toy dataset.
with self.session() as sess:
sess.run(model.init_op())
dense_predictions_values, dense_col_representation_values = sess.run([
predictions.dense_predictions, predictions.dense_col_representation
], test_utils.build_toy_input_feature_values(features))
logging.info("dense_predictions_values: %s", dense_predictions_values)
logging.info("dense_col_representation_values: %s",
dense_col_representation_values)
expected_proba, expected_classes = test_utils.expected_toy_predictions_rf_weighted(
add_boolean_features=add_boolean_features)
self.assertAllEqual(dense_col_representation_values, expected_classes)
self.assertAllClose(dense_predictions_values, expected_proba)
def test_real_gbdt(self):
"""Loads a real GBDT model."""
with tf.Graph().as_default():
model_path = os.path.join(test_data_path(), "model",
"adult_binary_class_gbdt")
features = {
"age": tf.placeholder(tf.float32, [None]),
"workclass": tf.placeholder(tf.string, [None]),
"fnlwgt": tf.placeholder(tf.float32, [None]),
"education": tf.placeholder(tf.string, [None]),
"education_num": tf.placeholder(tf.int32, [None]),
"marital_status": tf.placeholder(tf.string, [None]),
"occupation": tf.placeholder(tf.string, [None]),
"relationship": tf.placeholder(tf.string, [None]),
"race": tf.placeholder(tf.string, [None]),
"sex": tf.placeholder(tf.string, [None]),
"capital_gain": tf.placeholder(tf.float32, [None]),
"capital_loss": tf.placeholder(tf.float32, [None]),
"hours_per_week": tf.placeholder(tf.float32, [None]),
"native_country": tf.placeholder(tf.string, [None]),
}
model = inference.Model(model_path)
_ = model.apply(features)
with self.session() as sess:
sess.run(model.init_op())
def test_toy_gbdt_multiclass_classification(self):
with tf.Graph().as_default():
# Create toy model.
model_path = os.path.join(
tempfile.mkdtemp(dir=self.get_temp_dir()),
"test_basic_gbdt_multiclass")
test_utils.build_toy_gbdt(model_path, num_classes=3)
features = test_utils.build_toy_input_features()
# Prepare model.
model = inference.Model(model_path)
predictions = model.apply(features)
# Run model on toy dataset.
with self.session() as sess:
sess.run(model.init_op())
dense_predictions_values, dense_col_representation_values = sess.run([
predictions.dense_predictions, predictions.dense_col_representation
], test_utils.build_toy_input_feature_values(features))
logging.info("dense_predictions_values: %s", dense_predictions_values)
logging.info("dense_col_representation_values: %s",
dense_col_representation_values)
expected_proba, expected_classes = test_utils.expected_toy_predictions_gbdt_multiclass(
)
self.assertAllEqual(dense_col_representation_values, expected_classes)
self.assertAllClose(dense_predictions_values, expected_proba)
def test_toy_rf_classification_winner_takes_all(self, add_boolean_features,
has_catset):
# Create toy model.
model_path = os.path.join(
tempfile.mkdtemp(dir=self.get_temp_dir()), "test_basic_rf_wta")
test_utils.build_toy_random_forest(
model_path,
winner_take_all_inference=True,
add_boolean_features=add_boolean_features,
has_catset=has_catset)
features = test_utils.build_toy_input_feature_values(
features=None, has_catset=has_catset)
# Prepare model.
model = inference.Model(model_path)
@tf.function
def init_model():
tf.print("Loading model")
model.init_op()
@tf.function
def apply_model(features):
tf.print("Running model")
return model.apply(features)
init_model()
predictions = apply_model(features)
print("Predictions: %s", predictions)
logging.info("dense_predictions_values: %s", predictions.dense_predictions)
logging.info("dense_col_representation_values: %s",
predictions.dense_col_representation)
expected_proba, expected_classes = test_utils.expected_toy_predictions_rf_wta(
add_boolean_features=add_boolean_features, has_catset=has_catset)
self.assertAllEqual(predictions.dense_col_representation, expected_classes)
self.assertAllClose(predictions.dense_predictions, expected_proba)
def test_crash_op(self):
with tf.Graph().as_default():
# Create toy model.
model_path = os.path.join(
tempfile.mkdtemp(dir=self.get_temp_dir()), "test_basic_rf_wta")
test_utils.build_toy_random_forest(
model_path, winner_take_all_inference=True, has_catset=True)
features = test_utils.build_toy_input_features(has_catset=True)
# Prepare model.
model = inference.Model(model_path)
predictions = model.apply(features)
# Run model on toy dataset.
with self.session() as sess:
sess.run(model.init_op())
def good_feature_values():
return test_utils.build_toy_input_feature_values(
features, has_catset=True)
with self.assertRaises(tf.errors.InvalidArgumentError):
feature_values = good_feature_values()
feature_values = {features["a"]: feature_values[features["a"]]}
sess.run([
predictions.dense_predictions,
predictions.dense_col_representation
], feature_values)
with self.assertRaises(tf.errors.InvalidArgumentError):
feature_values = good_feature_values()
feature_values[features["a"]] = [2, 2, 0, 0, 6]
sess.run([
predictions.dense_predictions,
predictions.dense_col_representation
], feature_values)
with self.assertRaises(tf.errors.InvalidArgumentError):
feature_values = good_feature_values()
feature_values[features["b"]] = ["x", "z", "x", "z", "x"]
sess.run([
predictions.dense_predictions,
predictions.dense_col_representation
], feature_values)
with self.assertRaises(tf.errors.InvalidArgumentError):
feature_values = good_feature_values()
feature_values[features["e"]] = tf.ragged.constant_value(
[[], [], [], [], []], dtype=tf.int32)
sess.run([
predictions.dense_predictions,
predictions.dense_col_representation
], feature_values)
with self.assertRaises(tf.errors.InvalidArgumentError):
feature_values = good_feature_values()
feature_values[features["e"]] = tf.ragged.constant_value(
[[], []], dtype=tf.int32)
sess.run([
predictions.dense_predictions,
predictions.dense_col_representation
], feature_values)