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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Demonstrates a regression on Boston housing data.
This example demonstrates how to run experiments with TF Boosted Trees on
a regression dataset. We split all the data into 20% test and 80% train,
and are using l2 loss and l2 regularization.
Example Usage:
python tensorflow/contrib/boosted_trees/examples/boston.py \
--batch_size=404 --output_dir="/tmp/boston" --depth=4 --learning_rate=0.1 \
--num_eval_steps=1 --num_trees=500 --l2=0.001 \
--vmodule=training_ops=1
When training is done, mean squared error on eval data is reported.
Point tensorboard to the directory for the run to see how the training
progresses:
tensorboard --logdir=/tmp/boston
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import tensorflow as tf
from tensorflow.contrib.boosted_trees.estimator_batch import custom_export_strategy
from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeRegressor
from tensorflow.contrib.boosted_trees.proto import learner_pb2
from tensorflow.contrib.layers.python.layers import feature_column
from tensorflow.contrib.learn import learn_runner
_BOSTON_NUM_FEATURES = 13
# Main config - creates a TF Boosted Trees Estimator based on flags.
def _get_tfbt(output_dir, feature_cols):
"""Configures TF Boosted Trees estimator based on flags."""
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = FLAGS.learning_rate
learner_config.regularization.l1 = 0.0
learner_config.regularization.l2 = FLAGS.l2
learner_config.constraints.max_tree_depth = FLAGS.depth
run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
# Create a TF Boosted trees regression estimator.
estimator = GradientBoostedDecisionTreeRegressor(
learner_config=learner_config,
# This should be the number of examples. For large datasets it can be
# larger than the batch_size.
examples_per_layer=FLAGS.batch_size,
feature_columns=feature_cols,
label_dimension=1,
model_dir=output_dir,
num_trees=FLAGS.num_trees,
center_bias=False,
config=run_config)
return estimator
def _convert_fn(dtec, sorted_feature_names, num_dense, num_sparse_float,
num_sparse_int, export_dir, unused_eval_result):
universal_format = custom_export_strategy.convert_to_universal_format(
dtec, sorted_feature_names, num_dense, num_sparse_float, num_sparse_int)
with tf.gfile.GFile(os.path.join(export_dir, "tree_proto"), "w") as f:
f.write(str(universal_format))
def _make_experiment_fn(output_dir):
"""Creates experiment for gradient boosted decision trees."""
(x_train, y_train), (x_test,
y_test) = tf.keras.datasets.boston_housing.load_data()
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": x_train},
y=y_train,
batch_size=FLAGS.batch_size,
num_epochs=None,
shuffle=True)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": x_test}, y=y_test, num_epochs=1, shuffle=False)
feature_columns = [
feature_column.real_valued_column("x", dimension=_BOSTON_NUM_FEATURES)
]
feature_spec = tf.contrib.layers.create_feature_spec_for_parsing(
feature_columns)
serving_input_fn = tf.contrib.learn.utils.build_parsing_serving_input_fn(
feature_spec)
# An export strategy that outputs the feature importance and also exports
# the internal tree representation in another format.
export_strategy = custom_export_strategy.make_custom_export_strategy(
"exports",
convert_fn=_convert_fn,
feature_columns=feature_columns,
export_input_fn=serving_input_fn)
return tf.contrib.learn.Experiment(
estimator=_get_tfbt(output_dir, feature_columns),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=None,
eval_steps=FLAGS.num_eval_steps,
eval_metrics=None,
export_strategies=[export_strategy])
def main(unused_argv):
learn_runner.run(
experiment_fn=_make_experiment_fn,
output_dir=FLAGS.output_dir,
schedule="train_and_evaluate")
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
parser = argparse.ArgumentParser()
# Define the list of flags that users can change.
parser.add_argument(
"--batch_size",
type=int,
default=1000,
help="The batch size for reading data.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Choose the dir for the output.")
parser.add_argument(
"--num_eval_steps",
type=int,
default=1,
help="The number of steps to run evaluation for.")
# Flags for gradient boosted trees config.
parser.add_argument(
"--depth", type=int, default=4, help="Maximum depth of weak learners.")
parser.add_argument(
"--l2", type=float, default=1.0, help="l2 regularization per batch.")
parser.add_argument(
"--learning_rate",
type=float,
default=0.1,
help="Learning rate (shrinkage weight) with which each new tree is added."
)
parser.add_argument(
"--num_trees",
type=int,
default=None,
required=True,
help="Number of trees to grow before stopping.")
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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