fix LTR example.

ltr/lambda_rank.py

@@ -3,10 +3,14 @@ import sys
 import gzip
 import functools
 import argparse
+import logging
 import numpy as np
 
 import paddle.v2 as paddle
 
+logger = logging.getLogger("paddle")
+logger.setLevel(logging.INFO)
+
 
 def lambda_rank(input_dim, is_infer):
     """
@@ -26,43 +30,39 @@ def lambda_rank(input_dim, is_infer):
     data = paddle.layer.data("data",
                              paddle.data_type.dense_vector_sequence(input_dim))
 
-    # Define hidden layer.
-    hd1 = paddle.layer.fc(
-        input=data,
-        size=128,
-        act=paddle.activation.Tanh(),
-        param_attr=paddle.attr.Param(initial_std=0.01))
-
-    hd2 = paddle.layer.fc(
-        input=hd1,
-        size=10,
-        act=paddle.activation.Tanh(),
-        param_attr=paddle.attr.Param(initial_std=0.01))
-    output = paddle.layer.fc(
-        input=hd2,
-        size=1,
-        act=paddle.activation.Linear(),
-        param_attr=paddle.attr.Param(initial_std=0.01))
+    # Define the hidden layer.
+    hd1 = paddle.layer.fc(input=data,
+                          size=128,
+                          act=paddle.activation.Tanh(),
+                          param_attr=paddle.attr.Param(initial_std=0.01))
+
+    hd2 = paddle.layer.fc(input=hd1,
+                          size=10,
+                          act=paddle.activation.Tanh(),
+                          param_attr=paddle.attr.Param(initial_std=0.01))
+    output = paddle.layer.fc(input=hd2,
+                             size=1,
+                             act=paddle.activation.Linear(),
+                             param_attr=paddle.attr.Param(initial_std=0.01))
 
     if not is_infer:
-        # Define evaluator.
-        evaluator = paddle.evaluator.auc(input=output, label=label)
-        # Define cost layer.
+        # Define the cost layer.
         cost = paddle.layer.lambda_cost(
             input=output, score=label, NDCG_num=6, max_sort_size=-1)
         return cost, output
     return output
 
 
-def train_lambda_rank(num_passes):
-    # The input for LambdaRank is a sequence.
+def lambda_rank_train(num_passes, model_save_dir):
+    # The input for LambdaRank must be a sequence.
     fill_default_train = functools.partial(
         paddle.dataset.mq2007.train, format="listwise")
     fill_default_test = functools.partial(
         paddle.dataset.mq2007.test, format="listwise")
 
     train_reader = paddle.batch(
-        paddle.reader.shuffle(fill_default_train, buf_size=100), batch_size=32)
+        paddle.reader.shuffle(
+            fill_default_train, buf_size=100), batch_size=32)
     test_reader = paddle.batch(fill_default_test, batch_size=32)
 
     # Training dataset: mq2007, input_dim = 46, dense format.
@@ -78,13 +78,15 @@ def train_lambda_rank(num_passes):
     #  Define end batch and end pass event handler.
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            print "Pass %d Batch %d Cost %.9f" % (event.pass_id, event.batch_id,
-                                                  event.cost)
+            logger.info("Pass %d Batch %d Cost %.9f" %
+                        (event.pass_id, event.batch_id, event.cost))
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(reader=test_reader, feeding=feeding)
-            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
-            with gzip.open("lambda_rank_params_%d.tar.gz" % (event.pass_id),
-                           "w") as f:
+            logger.info("\nTest with Pass %d, %s" %
+                        (event.pass_id, result.metrics))
+            with gzip.open(
+                    os.path.join(model_save_dir, "lambda_rank_params_%d.tar.gz"
+                                 % (event.pass_id)), "w") as f:
                 trainer.save_parameter_to_tar(f)
 
     feeding = {"label": 0, "data": 1}
@@ -95,17 +97,17 @@ def train_lambda_rank(num_passes):
         num_passes=num_passes)
 
 
-def lambda_rank_infer(pass_id):
+def lambda_rank_infer(test_model_path):
     """LambdaRank model inference interface.
 
     Parameters:
-        pass_id : inference model in pass_id
+        test_model_path : The path of the trained model.
     """
-    print "Begin to Infer..."
+    logger.info("Begin to Infer...")
     input_dim = 46
     output = lambda_rank(input_dim, is_infer=True)
     parameters = paddle.parameters.Parameters.from_tar(
-        gzip.open("lambda_rank_params_%d.tar.gz" % (pass_id - 1)))
+        gzip.open(test_model_path))
 
     infer_query_id = None
     infer_data = []
@@ -128,15 +130,51 @@ def lambda_rank_infer(pass_id):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='LambdaRank demo')
-    parser.add_argument("--run_type", type=str, help="run type is train|infer")
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle LambdaRank example.")
+    parser.add_argument(
+        "--run_type",
+        type=str,
+        help=("A flag indicating to run the training or the inferring task. "
+              "Available options are: train or infer."),
+        default="train")
     parser.add_argument(
         "--num_passes",
         type=int,
-        help="The Num of passes in train| infer pass number of model.")
+        help="The number of passes to train the model.",
+        default=10)
+    parser.add_argument(
+        "--use_gpu",
+        type=bool,
+        help="A flag indicating whether to use the GPU device in training.",
+        default=False)
+    parser.add_argument(
+        "--trainer_count",
+        type=int,
+        help="The thread number used in training.",
+        default=1)
+    parser.add_argument(
+        "--model_save_dir",
+        type=str,
+        required=False,
+        help=("The path to save the trained models."),
+        default="models")
+    parser.add_argument(
+        "--test_model_path",
+        type=str,
+        required=False,
+        help=("This parameter works only in inferring task to "
+              "specify path of a trained model."),
+        default="")
+
     args = parser.parse_args()
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
     if args.run_type == "train":
-        train_lambda_rank(args.num_passes)
+        lambda_rank_train(args.num_passes, args.model_save_dir)
     elif args.run_type == "infer":
-        lambda_rank_infer(pass_id=args.num_passes - 1)
+        assert os.path.exists(args.test_model_path), (
+            "The trained model does not exit. Please set a correct path.")
+        lambda_rank_infer(args.test_model_path)
+    else:
+        logger.fatal(("A wrong value for parameter run type. "
+                      "Available options are: train or infer."))

ltr/metrics.py

@@ -10,7 +10,7 @@ def ndcg(score_list):
         score_list: np.array, shape=(sample_num,1)
 
     e.g. predict rank score list :
-    >>> scores =  [3, 2, 3, 0, 1, 2] 
+    >>> scores =  [3, 2, 3, 0, 1, 2]
     >>> ndcg_score = ndcg(scores)
     """
 

ltr/ranknet.py

@@ -2,15 +2,23 @@ import os
 import sys
 import gzip
 import functools
-import paddle.v2 as paddle
-import numpy as np
-from metrics import ndcg
 import argparse
+import logging
+import numpy as np
+
+import paddle.v2 as paddle
+
+logger = logging.getLogger("paddle")
+logger.setLevel(logging.INFO)
 
 # ranknet is the classic pairwise learning to rank algorithm
 # http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf
 
 
+def score_diff(right_score, left_score):
+    return np.average(np.abs(right_score - left_score))
+
+
 def half_ranknet(name_prefix, input_dim):
     """
     parameter in same name will be shared in paddle framework,
@@ -19,18 +27,21 @@ def half_ranknet(name_prefix, input_dim):
     https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/api.md
     """
     # data layer
-    data = paddle.layer.data(name_prefix + "/data",
+    data = paddle.layer.data(name_prefix + "_data",
                              paddle.data_type.dense_vector(input_dim))
 
     # hidden layer
     hd1 = paddle.layer.fc(
         input=data,
+        name=name_prefix + "_hidden",
         size=10,
         act=paddle.activation.Tanh(),
         param_attr=paddle.attr.Param(initial_std=0.01, name="hidden_w1"))
-    # fully connect layer/ output layer
+
+    # fully connected layer and output layer
     output = paddle.layer.fc(
         input=hd1,
+        name=name_prefix + "_score",
         size=1,
         act=paddle.activation.Linear(),
         param_attr=paddle.attr.Param(initial_std=0.01, name="output"))
@@ -45,14 +56,13 @@ def ranknet(input_dim):
     output_left = half_ranknet("left", input_dim)
     output_right = half_ranknet("right", input_dim)
 
-    evaluator = paddle.evaluator.auc(input=output_left, label=label)
     # rankcost layer
     cost = paddle.layer.rank_cost(
         name="cost", left=output_left, right=output_right, label=label)
     return cost
 
 
-def train_ranknet(num_passes):
+def ranknet_train(num_passes, model_save_dir):
     train_reader = paddle.batch(
         paddle.reader.shuffle(paddle.dataset.mq2007.train, buf_size=100),
         batch_size=100)
@@ -70,22 +80,28 @@ def train_ranknet(num_passes):
         update_equation=paddle.optimizer.Adam(learning_rate=2e-4))
 
     # Define the input data order
-    feeding = {"label": 0, "left/data": 1, "right/data": 2}
+    feeding = {"label": 0, "left_data": 1, "right_data": 2}
 
     #  Define end batch and end pass event handler
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 100 == 0:
-                print "Pass %d Batch %d Cost %.9f" % (
-                    event.pass_id, event.batch_id, event.cost)
-            else:
-                sys.stdout.write(".")
-                sys.stdout.flush()
+            if event.batch_id % 25 == 0:
+                diff = score_diff(
+                    event.gm.getLayerOutputs("right_score")["right_score"][
+                        "value"],
+                    event.gm.getLayerOutputs("left_score")["left_score"][
+                        "value"])
+                logger.info(("Pass %d Batch %d : Cost %.6f, "
+                             "average absolute diff scores: %.6f") %
+                            (event.pass_id, event.batch_id, event.cost, diff))
+
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(reader=test_reader, feeding=feeding)
-            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
-            with gzip.open("ranknet_params_%d.tar.gz" % (event.pass_id),
-                           "w") as f:
+            logger.info("\nTest with Pass %d, %s" %
+                        (event.pass_id, result.metrics))
+            with gzip.open(
+                    os.path.join(model_save_dir, "ranknet_params_%d.tar.gz" %
+                                 (event.pass_id)), "w") as f:
                 trainer.save_parameter_to_tar(f)
 
     trainer.train(
@@ -95,18 +111,17 @@ def train_ranknet(num_passes):
         num_passes=num_passes)
 
 
-def ranknet_infer(pass_id):
+def ranknet_infer(model_path):
     """
-  load the trained model. And predict with plain txt input
-  """
-    print "Begin to Infer..."
+    load the trained model. And predict with plain txt input
+    """
+    logger.info("Begin to Infer...")
     feature_dim = 46
 
     # we just need half_ranknet to predict a rank score,
     # which can be used in sort documents
     output = half_ranknet("infer", feature_dim)
-    parameters = paddle.parameters.Parameters.from_tar(
-        gzip.open("ranknet_params_%d.tar.gz" % (pass_id)))
+    parameters = paddle.parameters.Parameters.from_tar(gzip.open(model_path))
 
     # load data of same query and relevance documents,
     # need ranknet to rank these candidates
@@ -133,16 +148,55 @@ def ranknet_infer(pass_id):
         print "query_id : ", query_id, " ranknet rank document order : ", score
 
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Ranknet demo')
-    parser.add_argument("--run_type", type=str, help="run type is train|infer")
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle RankNet example.")
+    parser.add_argument(
+        "--run_type",
+        type=str,
+        help=("A flag indicating to run the training or the inferring task. "
+              "Available options are: train or infer."),
+        default="train")
     parser.add_argument(
         "--num_passes",
         type=int,
-        help="num of passes in train| infer pass number of model")
+        help="The number of passes to train the model.",
+        default=10)
+    parser.add_argument(
+        "--use_gpu",
+        type=bool,
+        help="A flag indicating whether to use the GPU device in training.",
+        default=False)
+    parser.add_argument(
+        "--trainer_count",
+        type=int,
+        help="The thread number used in training.",
+        default=1)
+    parser.add_argument(
+        "--model_save_dir",
+        type=str,
+        required=False,
+        help=("The path to save the trained models."),
+        default="models")
+    parser.add_argument(
+        "--test_model_path",
+        type=str,
+        required=False,
+        help=("This parameter works only in inferring task to "
+              "specify path of a trained model."),
+        default="")
+
     args = parser.parse_args()
-    paddle.init(use_gpu=False, trainer_count=4)
+    if not os.path.exists(args.model_save_dir): os.mkdir(args.model_save_dir)
+
+    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
+
     if args.run_type == "train":
-        train_ranknet(args.num_passes)
+        ranknet_train(args.num_passes, args.model_save_dir)
     elif args.run_type == "infer":
-        ranknet_infer(pass_id=args.pass_num - 1)
+        assert os.path.exists(
+            args.test_model_path), "The trained model does not exit."
+        ranknet_infer(args.test_model_path)
+    else:
+        logger.fatal(("A wrong value for parameter run type. "
+                      "Available options are: train or infer."))

ltr/run_lambdarank.sh

-#!/bin/sh
-
-python lambda_rank.py \
-       --run_type="train" \
-       --num_passes=10 \
-       2>&1 | tee lambdarank_train.log
-
-python lambda_rank.py \
-       --run_type="infer" \
-       --num_passes=10 \
-       2>&1 | tee lambdarank_infer.log

ltr/run_ranknet.sh

-#!/bin/sh
-
-python ranknet.py \
-       --run_type="train" \
-       --num_passes=10 \
-       2>&1 | tee ranknet_train.log
-
-python ranknet.py \
-       --run_type="infer" \
-       --num_passes=10 \
-       2>&1 | tee ranknet_infer.log

text_classification/utils.py

@@ -9,60 +9,60 @@ logger.setLevel(logging.INFO)
 
 def parse_train_cmd():
     parser = argparse.ArgumentParser(
-        description="PaddlePaddle text classification demo")
+        description="PaddlePaddle text classification example.")
     parser.add_argument(
         "--nn_type",
         type=str,
-        help="define which type of network to use, available: [dnn, cnn]",
+        help=("A flag that defines which type of network to use, "
+              "available: [dnn, cnn]."),
         default="dnn")
     parser.add_argument(
         "--train_data_dir",
         type=str,
         required=False,
-        help=("path of training dataset (default: None). "
-              "if this parameter is not set, "
-              "paddle.dataset.imdb will be used."),
+        help=("The path of training dataset (default: None). If this parameter "
+              "is not set, paddle.dataset.imdb will be used."),
         default=None)
     parser.add_argument(
         "--test_data_dir",
         type=str,
         required=False,
-        help=("path of testing dataset (default: None). "
-              "if this parameter is not set, "
-              "paddle.dataset.imdb will be used."),
+        help=("The path of testing dataset (default: None). If this parameter "
+              "is not set, paddle.dataset.imdb will be used."),
         default=None)
     parser.add_argument(
         "--word_dict",
         type=str,
         required=False,
-        help=("path of word dictionary (default: None)."
-              "if this parameter is not set, paddle.dataset.imdb will be used."
-              "if this parameter is set, but the file does not exist, "
-              "word dictionay will be built from "
-              "the training data automatically."),
+        help=("The path of word dictionary (default: None). If this parameter "
+              "is not set, paddle.dataset.imdb will be used. If this parameter "
+              "is set, but the file does not exist, word dictionay "
+              "will be built from the training data automatically."),
         default=None)
     parser.add_argument(
         "--label_dict",
         type=str,
         required=False,
-        help=("path of label dictionay (default: None)."
-              "if this parameter is not set, paddle.dataset.imdb will be used."
-              "if this parameter is set, but the file does not exist, "
-              "word dictionay will be built from "
-              "the training data automatically."),
+        help=("The path of label dictionay (default: None).If this parameter "
+              "is not set, paddle.dataset.imdb will be used. If this parameter "
+              "is set, but the file does not exist, word dictionay "
+              "will be built from the training data automatically."),
         default=None)
     parser.add_argument(
         "--batch_size",
         type=int,
         default=32,
-        help="the number of training examples in one forward/backward pass")
+        help="The number of training examples in one forward/backward pass.")
     parser.add_argument(
-        "--num_passes", type=int, default=10, help="number of passes to train")
+        "--num_passes",
+        type=int,
+        default=10,
+        help="The number of passes to train the model.")
     parser.add_argument(
         "--model_save_dir",
         type=str,
         required=False,
-        help=("path to save the trained models."),
+        help=("The path to save the trained models."),
         default="models")
 
     return parser.parse_args()