Make boosted_trees Garden-official

official/boosted_trees/README.md

@@ -39,7 +39,7 @@ Note that the model_dir is cleaned up before every time training starts.
 
 Model parameters can be adjusted by flags, like `--n_trees`, `--max_depth`, `--learning_rate` and so on.  Check out the code for details.
 
-The final accuacy will be around 74% and loss will be around 0.516 over the eval set, when trained with the default parameters.
+The final accuracy will be around 74% and loss will be around 0.516 over the eval set, when trained with the default parameters.
 
 By default, the first 1 million examples among 11 millions are used for training, and the last 1 million examples are used for evaluation.
 The training/evaluation data can be selected as index ranges by flags `--train_start`, `--train_count`, `--eval_start`, `--eval_count`, etc.

official/boosted_trees/data_download.py

@@ -12,59 +12,54 @@
 from __future__ import division
 from __future__ import print_function
 
-import argparse
+import gzip
 import os
-import sys
 import tempfile
 
+# pylint: disable=g-bad-import-order
 import numpy as np
 import pandas as pd
 from six.moves import urllib
+from absl import app as absl_app
+from absl import flags
 import tensorflow as tf
 
-URL_ROOT = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00280'
-INPUT_FILE = 'HIGGS.csv.gz'
-NPZ_FILE = 'HIGGS.csv.gz.npz'  # numpy compressed file to contain 'data' array.
+from official.utils.flags import core as flags_core
 
-
-def parse_args():
-  """Parses arguments and returns a tuple (known_args, unparsed_args)."""
-  parser = argparse.ArgumentParser()
-  parser.add_argument(
-      '--data_dir', type=str, default='/tmp/higgs_data',
-      help='Directory to download higgs dataset and store training/eval data.')
-  return parser.parse_known_args()
+URL_ROOT = "https://archive.ics.uci.edu/ml/machine-learning-databases/00280"
+INPUT_FILE = "HIGGS.csv.gz"
+NPZ_FILE = "HIGGS.csv.gz.npz"  # numpy compressed file to contain "data" array.
 
 
 def _download_higgs_data_and_save_npz(data_dir):
   """Download higgs data and store as a numpy compressed file."""
   input_url = os.path.join(URL_ROOT, INPUT_FILE)
   np_filename = os.path.join(data_dir, NPZ_FILE)
   if tf.gfile.Exists(np_filename):
-    raise ValueError('data_dir already has the processed data file: {}'.format(
+    raise ValueError("data_dir already has the processed data file: {}".format(
         np_filename))
   if not tf.gfile.Exists(data_dir):
     tf.gfile.MkDir(data_dir)
   # 2.8 GB to download.
   try:
-    print('Data downloading..')
+    tf.logging.info("Data downloading...")
     temp_filename, _ = urllib.request.urlretrieve(input_url)
-
     # Reading and parsing 11 million csv lines takes 2~3 minutes.
-    print('Data processing.. taking multiple minutes..')
-    data = pd.read_csv(
-        temp_filename,
-        dtype=np.float32,
-        names=['c%02d' % i for i in range(29)]  # label + 28 features.
-    ).as_matrix()
+    tf.logging.info("Data processing... taking multiple minutes...")
+    with gzip.open(temp_filename, "rb") as csv_file:
+      data = pd.read_csv(
+          csv_file,
+          dtype=np.float32,
+          names=["c%02d" % i for i in range(29)]  # label + 28 features.
+      ).as_matrix()
   finally:
-    os.remove(temp_filename)
+    tf.gfile.Remove(temp_filename)
 
   # Writing to temporary location then copy to the data_dir (0.8 GB).
   f = tempfile.NamedTemporaryFile()
   np.savez_compressed(f, data=data)
   tf.gfile.Copy(f.name, np_filename)
-  print('Data saved to: {}'.format(np_filename))
+  tf.logging.info("Data saved to: {}".format(np_filename))
 
 
 def main(unused_argv):
@@ -73,6 +68,16 @@ def main(unused_argv):
   _download_higgs_data_and_save_npz(FLAGS.data_dir)
 
 
-if __name__ == '__main__':
-  FLAGS, unparsed = parse_args()
-  tf.app.run(argv=[sys.argv[0]] + unparsed)
+def define_data_download_flags():
+  """Add flags specifying data download arguments."""
+  flags.DEFINE_string(
+      name="data_dir", default="/tmp/higgs_data",
+      help=flags_core.help_wrap(
+          "Directory to download higgs dataset and store training/eval data."))
+
+
+if __name__ == "__main__":
+  tf.logging.set_verbosity(tf.logging.INFO)
+  define_data_download_flags()
+  FLAGS = flags.FLAGS
+  absl_app.run(main)

official/boosted_trees/train_higgs.py

@@ -29,64 +29,44 @@
 from __future__ import division
 from __future__ import print_function
 
-import argparse
 import os
-import sys
 
+# pylint: disable=g-bad-import-order
+import numpy as np
 from absl import app as absl_app
 from absl import flags
-import numpy as np  # pylint: disable=wrong-import-order
-import tensorflow as tf  # pylint: disable=wrong-import-order
+import tensorflow as tf
+# pylint: enable=g-bad-import-order
 
 from official.utils.flags import core as flags_core
 from official.utils.flags._conventions import help_wrap
+from official.utils.logs import logger
 
+NPZ_FILE = "HIGGS.csv.gz.npz"  # numpy compressed file containing "data" array
 
-NPZ_FILE = 'HIGGS.csv.gz.npz'  # numpy compressed file containing 'data' array
-
-
-def define_train_higgs_flags():
-  """Add tree related flags as well as training/eval configuration."""
-  flags_core.define_base(stop_threshold=False, batch_size=False, num_gpu=False)
-  flags.adopt_module_key_flags(flags_core)
-
-  flags.DEFINE_integer(
-      name='train_start', default=0,
-      help=help_wrap('Start index of train examples within the data.'))
-  flags.DEFINE_integer(
-      name='train_count', default=1000000,
-      help=help_wrap('Number of train examples within the data.'))
-  flags.DEFINE_integer(
-      name='eval_start', default=10000000,
-      help=help_wrap('Start index of eval examples within the data.'))
-  flags.DEFINE_integer(
-      name='eval_count', default=1000000,
-      help=help_wrap('Number of eval examples within the data.'))
-
-  flags.DEFINE_integer(
-      'n_trees', default=100, help=help_wrap('Number of trees to build.'))
-  flags.DEFINE_integer(
-      'max_depth', default=6, help=help_wrap('Maximum depths of each tree.'))
-  flags.DEFINE_float(
-      'learning_rate', default=0.1,
-      help=help_wrap('Maximum depths of each tree.'))
-
-  flags_core.set_defaults(data_dir='/tmp/higgs_data',
-                          model_dir='/tmp/higgs_model')
 
+def read_higgs_data(data_dir, train_start, train_count, eval_start, eval_count):
+  """Reads higgs data from csv and returns train and eval data.
 
+  Args:
+    data_dir: A string, the directory of higgs dataset.
+    train_start: An integer, the start index of train examples within the data.
+    train_count: An integer, the number of train examples within the data.
+    eval_start: An integer, the start index of eval examples within the data.
+    eval_count: An integer, the number of eval examples within the data.
 
-def read_higgs_data(data_dir, train_start, train_count, eval_start, eval_count):
-  """Reads higgs data from csv and returns train and eval data."""
+  Returns:
+    Numpy array of train data and eval data.
+  """
   npz_filename = os.path.join(data_dir, NPZ_FILE)
   try:
     # gfile allows numpy to read data from network data sources as well.
-    with tf.gfile.Open(npz_filename, 'rb') as npz_file:
+    with tf.gfile.Open(npz_filename, "rb") as npz_file:
       with np.load(npz_file) as npz:
-        data = npz['data']
+        data = npz["data"]
   except Exception as e:
     raise RuntimeError(
-        'Error loading data; use data_download.py to prepare the data:\n{}: {}'
+        "Error loading data; use data_download.py to prepare the data:\n{}: {}"
         .format(type(e).__name__, e))
   return (data[train_start:train_start+train_count],
           data[eval_start:eval_start+eval_count])
@@ -105,18 +85,18 @@ def make_inputs_from_np_arrays(features_np, label_np):
   as a single tensor. Don't use batch.
 
   Args:
-    features_np: a numpy ndarray (shape=[batch_size, num_features]) for
+    features_np: A numpy ndarray (shape=[batch_size, num_features]) for
         float32 features.
-    label_np: a numpy ndarray (shape=[batch_size, 1]) for labels.
+    label_np: A numpy ndarray (shape=[batch_size, 1]) for labels.
 
   Returns:
-    input_fn: a function returning a Dataset of feature dict and label.
-    feature_column: a list of tf.feature_column.BucketizedColumn.
+    input_fn: A function returning a Dataset of feature dict and label.
+    feature_column: A list of tf.feature_column.BucketizedColumn.
   """
   num_features = features_np.shape[1]
   features_np_list = np.split(features_np, num_features, axis=1)
   # 1-based feature names.
-  feature_names = ['feature_%02d' % (i + 1) for i in range(num_features)]
+  feature_names = ["feature_%02d" % (i + 1) for i in range(num_features)]
 
   # Create source feature_columns and bucketized_columns.
   def get_bucket_boundaries(feature):
@@ -155,16 +135,16 @@ def make_eval_inputs_from_np_arrays(features_np, label_np):
   num_features = features_np.shape[1]
   features_np_list = np.split(features_np, num_features, axis=1)
   # 1-based feature names.
-  feature_names = ['feature_%02d' % (i + 1) for i in range(num_features)]
+  feature_names = ["feature_%02d" % (i + 1) for i in range(num_features)]
 
   def input_fn():
     features = {
         feature_name: tf.constant(features_np_list[i])
         for i, feature_name in enumerate(feature_names)
     }
-    return tf.data.Dataset.zip(
-        (tf.data.Dataset.from_tensor_slices(features),
-         tf.data.Dataset.from_tensor_slices(label_np),)).batch(1000)
+    return tf.data.Dataset.zip((
+        tf.data.Dataset.from_tensor_slices(features),
+        tf.data.Dataset.from_tensor_slices(label_np),)).batch(1000)
 
   return input_fn
 
@@ -175,22 +155,37 @@ def train_boosted_trees(flags_obj):
   Args:
     flags_obj: An object containing parsed flag values.
   """
-
   # Clean up the model directory if present.
   if tf.gfile.Exists(flags_obj.model_dir):
     tf.gfile.DeleteRecursively(flags_obj.model_dir)
-  print('## data loading..')
+  tf.logging.info("## Data loading...")
   train_data, eval_data = read_higgs_data(
       flags_obj.data_dir, flags_obj.train_start, flags_obj.train_count,
       flags_obj.eval_start, flags_obj.eval_count)
-  print('## data loaded; train: {}{}, eval: {}{}'.format(
+  tf.logging.info("## Data loaded; train: {}{}, eval: {}{}".format(
       train_data.dtype, train_data.shape, eval_data.dtype, eval_data.shape))
-  # data consists of one label column and 28 feature columns following.
+
+  # Data consists of one label column followed by 28 feature columns.
   train_input_fn, feature_columns = make_inputs_from_np_arrays(
       features_np=train_data[:, 1:], label_np=train_data[:, 0:1])
   eval_input_fn = make_eval_inputs_from_np_arrays(
       features_np=eval_data[:, 1:], label_np=eval_data[:, 0:1])
-  print('## features prepared. training starts..')
+  tf.logging.info("## Features prepared. Training starts...")
+
+  # Create benchmark logger to log info about the training and metric values
+  run_params = {
+      "train_start": flags_obj.train_start,
+      "train_count": flags_obj.train_count,
+      "eval_start": flags_obj.eval_start,
+      "eval_count": flags_obj.eval_count,
+      "n_trees": flags_obj.n_trees,
+      "max_depth": flags_obj.max_depth,
+  }
+  benchmark_logger = logger.config_benchmark_logger(flags_obj)
+  benchmark_logger.log_run_info(
+      model_name="boosted_trees",
+      dataset_name="higgs",
+      run_params=run_params)
 
   # Though BoostedTreesClassifier is under tf.estimator, faster in-memory
   # training is yet provided as a contrib library.
@@ -203,7 +198,9 @@ def train_boosted_trees(flags_obj):
       learning_rate=flags_obj.learning_rate)
 
   # Evaluation.
-  eval_result = classifier.evaluate(eval_input_fn)
+  eval_results = classifier.evaluate(eval_input_fn)
+  # Benchmark the evaluation results
+  benchmark_logger.log_evaluation_result(eval_results)
 
   # Exporting the savedmodel.
   if flags_obj.export_dir is not None:
@@ -216,7 +213,37 @@ def main(_):
   train_boosted_trees(flags.FLAGS)
 
 
-if __name__ == '__main__':
+def define_train_higgs_flags():
+  """Add tree related flags as well as training/eval configuration."""
+  flags_core.define_base(stop_threshold=False, batch_size=False, num_gpu=False)
+  flags.adopt_module_key_flags(flags_core)
+
+  flags.DEFINE_integer(
+      name="train_start", default=0,
+      help=help_wrap("Start index of train examples within the data."))
+  flags.DEFINE_integer(
+      name="train_count", default=1000000,
+      help=help_wrap("Number of train examples within the data."))
+  flags.DEFINE_integer(
+      name="eval_start", default=10000000,
+      help=help_wrap("Start index of eval examples within the data."))
+  flags.DEFINE_integer(
+      name="eval_count", default=1000000,
+      help=help_wrap("Number of eval examples within the data."))
+
+  flags.DEFINE_integer(
+      "n_trees", default=100, help=help_wrap("Number of trees to build."))
+  flags.DEFINE_integer(
+      "max_depth", default=6, help=help_wrap("Maximum depths of each tree."))
+  flags.DEFINE_float(
+      "learning_rate", default=0.1,
+      help=help_wrap("The learning rate."))
+
+  flags_core.set_defaults(data_dir="/tmp/higgs_data",
+                          model_dir="/tmp/higgs_model")
+
+
+if __name__ == "__main__":
   # Training progress and eval results are shown as logging.INFO; so enables it.
   tf.logging.set_verbosity(tf.logging.INFO)
   define_train_higgs_flags()