Upload eval for douban data

fluid/deep_attention_matching_net/douban/test.sh

 export CUDA_VISIBLE_DEVICES=0,1,2,3
-python -u test_and_evaluate.py --use_cuda \
+python -u ../test_and_evaluate.py --use_cuda \
+                --ext_eval \
                 --data_path ./data/data.pkl \
-                --save_path ./ \
+                --save_path ./eval_10000 \
                 --model_path models/step_10000 \
                 --batch_size 100 \
                 --vocab_size 172130 \

fluid/deep_attention_matching_net/douban/train.sh

 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python -u ../train_and_evaluate.py --use_cuda \
                 --data_path ./data/data.pkl \
+                --ext_eval \
+                --word_emb_init ./data/word_embedding.pkl \
                 --save_path ./models \
                 --batch_size 100 \
                 --vocab_size 172130 \

fluid/deep_attention_matching_net/model.py

@@ -131,6 +131,6 @@ class Net(object):
         sim = fluid.layers.concat(input=sim_turns, axis=2)
 
         # for douban
-        final_info = layers.cnn_3d(sim, 16, 16)
+        final_info = layers.cnn_3d(sim, 32, 16)
         loss, logits = layers.loss(final_info, label)
         return loss, logits

fluid/deep_attention_matching_net/test_and_evaluate.py

@@ -8,7 +8,6 @@ import paddle.fluid as fluid
 import utils.reader as reader
 import cPickle as pickle
 from utils.util import print_arguments
-import utils.evaluation as eva
 
 from model import Net
 
@@ -50,6 +49,10 @@ def parse_args():
         '--use_cuda',
         action='store_true',
         help='If set, use cuda for training.')
+    parser.add_argument(
+        '--ext_eval',
+        action='store_true',
+        help='If set, use MAP, MRR ect for evaluation.')
     parser.add_argument(
         '--max_turn_num',
         type=int,
@@ -147,6 +150,11 @@ def test(args):
     train_data, val_data, test_data = pickle.load(open(args.data_path, 'rb'))
     print("finish loading data ...")
 
+    if args.ext_eval:
+        import utils.douban_evaluation as eva
+    else:
+        import utils.evaluation as eva
+
     test_batches = reader.build_batches(test_data, data_conf)
 
     test_batch_num = len(test_batches["response"])

fluid/deep_attention_matching_net/train_and_evaluate.py

@@ -8,7 +8,6 @@ import paddle.fluid as fluid
 import utils.reader as reader
 import cPickle as pickle
 from utils.util import print_arguments
-import utils.evaluation as eva
 
 from model import Net
 
@@ -34,7 +33,7 @@ def parse_args():
     parser.add_argument(
         '--data_path',
         type=str,
-        default="data/ubuntu/data_small.pkl",
+        default="data/data_small.pkl",
         help='Path to training data. (default: %(default)s)')
     parser.add_argument(
         '--save_path',
@@ -45,6 +44,10 @@ def parse_args():
         '--use_cuda',
         action='store_true',
         help='If set, use cuda for training.')
+    parser.add_argument(
+        '--ext_eval',
+        action='store_true',
+        help='If set, use MAP, MRR ect for evaluation.')
     parser.add_argument(
         '--max_turn_num',
         type=int,
@@ -74,7 +77,7 @@ def parse_args():
         '--_EOS_',
         type=int,
         default=28270,
-        help='The id for end of sentence in vocabulary.')
+        help='The id for the end of sentence in vocabulary.')
     parser.add_argument(
         '--stack_num',
         type=int,
@@ -140,9 +143,15 @@ def train(args):
         main_program=test_program,
         share_vars_from=train_exe)
 
+    if args.ext_eval:
+        import utils.douban_evaluation as eva
+    else:
+        import utils.evaluation as eva
+
     if args.word_emb_init is not None:
         print("start loading word embedding init ...")
-        word_emb = pickle.load(open(args.word_emb_init, 'rb')).astype('float32')
+        word_emb = np.array(pickle.load(open(args.word_emb_init, 'rb'))).astype(
+            'float32')
         print("finish loading word embedding init  ...")
 
     print("start loading data ...")

fluid/deep_attention_matching_net/ubuntu/train.sh

 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python -u ../train_and_evaluate.py --use_cuda \
                 --data_path ./data/data.pkl \
+                --word_emb_init ./data/word_embedding.pkl \
                 --save_path ./models \
                 --batch_size 100 \
                 --vocab_size 434512 \

fluid/deep_attention_matching_net/utils/douban_evaluation.py

+import sys
+import numpy as np
+from sklearn.metrics import average_precision_score
+
+
+def mean_average_precision(sort_data):
+    #to do
+    count_1 = 0
+    sum_precision = 0
+    for index in range(len(sort_data)):
+        if sort_data[index][1] == 1:
+            count_1 += 1
+            sum_precision += 1.0 * count_1 / (index + 1)
+    return sum_precision / count_1
+
+
+def mean_reciprocal_rank(sort_data):
+    sort_lable = [s_d[1] for s_d in sort_data]
+    assert 1 in sort_lable
+    return 1.0 / (1 + sort_lable.index(1))
+
+
+def precision_at_position_1(sort_data):
+    if sort_data[0][1] == 1:
+        return 1
+    else:
+        return 0
+
+
+def recall_at_position_k_in_10(sort_data, k):
+    sort_lable = [s_d[1] for s_d in sort_data]
+    select_lable = sort_lable[:k]
+    return 1.0 * select_lable.count(1) / sort_lable.count(1)
+
+
+def evaluation_one_session(data):
+    sort_data = sorted(data, key=lambda x: x[0], reverse=True)
+    m_a_p = mean_average_precision(sort_data)
+    m_r_r = mean_reciprocal_rank(sort_data)
+    p_1 = precision_at_position_1(sort_data)
+    r_1 = recall_at_position_k_in_10(sort_data, 1)
+    r_2 = recall_at_position_k_in_10(sort_data, 2)
+    r_5 = recall_at_position_k_in_10(sort_data, 5)
+    return m_a_p, m_r_r, p_1, r_1, r_2, r_5
+
+
+def evaluate(file_path):
+    sum_m_a_p = 0
+    sum_m_r_r = 0
+    sum_p_1 = 0
+    sum_r_1 = 0
+    sum_r_2 = 0
+    sum_r_5 = 0
+    i = 0
+    total_num = 0
+    with open(file_path, 'r') as infile:
+        for line in infile:
+            if i % 10 == 0:
+                data = []
+
+            tokens = line.strip().split('\t')
+            data.append((float(tokens[0]), int(tokens[1])))
+            if i % 10 == 9:
+                total_num += 1
+                m_a_p, m_r_r, p_1, r_1, r_2, r_5 = evaluation_one_session(data)
+                sum_m_a_p += m_a_p
+                sum_m_r_r += m_r_r
+                sum_p_1 += p_1
+                sum_r_1 += r_1
+                sum_r_2 += r_2
+                sum_r_5 += r_5
+            i += 1
+    #print('total num: %s' %total_num)
+    #print('MAP: %s' %(1.0*sum_m_a_p/total_num))
+    #print('MRR: %s' %(1.0*sum_m_r_r/total_num))
+    #print('P@1: %s' %(1.0*sum_p_1/total_num))
+    return (1.0 * sum_m_a_p / total_num, 1.0 * sum_m_r_r / total_num,
+            1.0 * sum_p_1 / total_num, 1.0 * sum_r_1 / total_num,
+            1.0 * sum_r_2 / total_num, 1.0 * sum_r_5 / total_num)
+
+
+if __name__ == '__main__':
+    result = evaluate(sys.argv[1])
+    for r in result:
+        print(r)