Adding a binary classification example

PiperOrigin-RevId: 171577979

tensorflow/contrib/boosted_trees/README.md

@@ -0,0 +1,11 @@
+# TF Boosted Trees (TFBT)
+
+TF Boosted trees is an implementation of a gradient boosting algorithm with
+trees used as week learners.
+
+## Examples
+Folder "examples" demonstrates how TFBT estimators can be used for various
+problems. Namely, it contains:
+* binary_mnist.py - an example on how to use TFBT for binary classification.
+* mnist.py - a multiclass example.
+* boston.py - a regression example.

tensorflow/contrib/boosted_trees/examples/binary_mnist.py

@@ -0,0 +1,169 @@
+# 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 multiclass MNIST TF Boosted trees example.
+
+  This example demonstrates how to run experiments with TF Boosted Trees on
+  a binary dataset. We use digits 4 and 9 from the original MNIST dataset.
+
+  Example Usage:
+  python tensorflow/contrib/boosted_trees/examples/binary_mnist.py \
+  --output_dir="/tmp/binary_mnist" --depth=4 --learning_rate=0.3 \
+  --batch_size=10761 --examples_per_layer=10761 --eval_batch_size=1030 \
+  --num_eval_steps=1 --num_trees=10 --l2=1 --vmodule=training_ops=1 \
+
+  When training is done, accuracy on eval data is reported. Point tensorboard
+  to the directory for the run to see how the training progresses:
+
+  tensorboard --logdir=/tmp/binary_mnist
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.learn import learn_runner
+
+
+def get_input_fn(data,
+                 batch_size,
+                 capacity=10000,
+                 min_after_dequeue=3000):
+  """Input function over MNIST data."""
+  # Keep only 4 and 9 digits.
+  ids = np.where((data.labels == 4) | (data.labels == 9))
+  images = data.images[ids]
+  labels = data.labels[ids]
+  # Make digit 4 label 0, 9 is 1.
+  labels = labels == 4
+
+  def _input_fn():
+    """Prepare features and labels."""
+    images_batch, labels_batch = tf.train.shuffle_batch(
+        tensors=[images,
+                 labels.astype(np.int32)],
+        batch_size=batch_size,
+        capacity=capacity,
+        min_after_dequeue=min_after_dequeue,
+        enqueue_many=True,
+        num_threads=4)
+    features_map = {"images": images_batch}
+    return features_map, labels_batch
+
+  return _input_fn
+
+
+# Main config - creates a TF Boosted Trees Estimator based on flags.
+def _get_tfbt(output_dir):
+  """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 / FLAGS.examples_per_layer
+  learner_config.constraints.max_tree_depth = FLAGS.depth
+
+  growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
+  learner_config.growing_mode = growing_mode
+  run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
+
+  # Create a TF Boosted trees estimator that can take in custom loss.
+  estimator = GradientBoostedDecisionTreeClassifier(
+      learner_config=learner_config,
+      examples_per_layer=FLAGS.examples_per_layer,
+      model_dir=output_dir,
+      num_trees=FLAGS.num_trees,
+      center_bias=False,
+      config=run_config)
+  return estimator
+
+
+def _make_experiment_fn(output_dir):
+  """Creates experiment for gradient boosted decision trees."""
+  data = tf.contrib.learn.datasets.mnist.load_mnist()
+  train_input_fn = get_input_fn(data.train, FLAGS.batch_size)
+  eval_input_fn = get_input_fn(data.validation, FLAGS.eval_batch_size)
+
+  return tf.contrib.learn.Experiment(
+      estimator=_get_tfbt(output_dir),
+      train_input_fn=train_input_fn,
+      eval_input_fn=eval_input_fn,
+      train_steps=None,
+      eval_steps=FLAGS.num_eval_steps,
+      eval_metrics=None)
+
+
+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(
+      "--output_dir",
+      type=str,
+      required=True,
+      help="Choose the dir for the output.")
+  parser.add_argument(
+      "--batch_size",
+      type=int,
+      default=1000,
+      help="The batch size for reading data.")
+  parser.add_argument(
+      "--eval_batch_size",
+      type=int,
+      default=1000,
+      help="Size of the batch for eval.")
+  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(
+      "--examples_per_layer",
+      type=int,
+      default=1000,
+      help="Number of examples to accumulate stats for per layer.")
+  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)

tensorflow/contrib/boosted_trees/examples/boston.py

@@ -44,8 +44,6 @@ 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
 
-_TEST_SPLIT_RATIO = 0.2
-_TEST_SPLIT_SEED = 42
 _BOSTON_NUM_FEATURES = 13