Merge pull request #173 from dzhwinter/fix_ltr2

"add start script"

ltr/lambda_rank.py

@@ -3,6 +3,7 @@ import gzip
 import paddle.v2 as paddle
 import numpy as np
 import functools
+import argparse
 
 
 def lambda_rank(input_dim):
@@ -117,6 +118,15 @@ 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.add_argument(
+        "--num_passes",
+        type=int,
+        help="num of passes in train| infer pass number of model")
+    args = parser.parse_args()
     paddle.init(use_gpu=False, trainer_count=1)
-    train_lambda_rank(2)
-    lambda_rank_infer(pass_id=1)
+    if args.run_type == "train":
+        train_lambda_rank(args.num_passes)
+    elif args.run_type == "infer":
+        lambda_rank_infer(pass_id=args.pass_num - 1)

ltr/ranknet.py

@@ -5,6 +5,7 @@ import functools
 import paddle.v2 as paddle
 import numpy as np
 from metrics import ndcg
+import argparse
 
 # ranknet is the classic pairwise learning to rank algorithm
 # http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf
@@ -104,7 +105,7 @@ def ranknet_infer(pass_id):
     # 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 - 1)))
+        gzip.open("ranknet_params_%d.tar.gz" % (pass_id)))
 
     # load data of same query and relevance documents, need ranknet to rank these candidates
     infer_query_id = []
@@ -118,18 +119,27 @@ def ranknet_infer(pass_id):
 
     for query_id, relevance_score, feature_vector in plain_txt_test():
         infer_query_id.append(query_id)
-        infer_data.append(feature_vector)
+        infer_data.append([feature_vector])
 
     # predict score of infer_data document. Re-sort the document base on predict score
     # in descending order. then we build the ranking documents
     scores = paddle.infer(
         output_layer=output, parameters=parameters, input=infer_data)
+    print scores
     for query_id, score in zip(infer_query_id, scores):
         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")
+    parser.add_argument(
+        "--num_passes",
+        type=int,
+        help="num of passes in train| infer pass number of model")
+    args = parser.parse_args()
     paddle.init(use_gpu=False, trainer_count=4)
-    pass_num = 2
-    train_ranknet(pass_num)
-    ranknet_infer(pass_id=pass_num - 1)
+    if args.run_type == "train":
+        train_ranknet(args.num_passes)
+    elif args.run_type == "infer":
+        ranknet_infer(pass_id=args.pass_num - 1)

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 rankenet_train.log
+
+python ranknet.py \
+       --run_type="infer" \
+       --num_passes=10 \
+       2>&1 | tee ranknet_infer.log