From 812e21f3c4c14b8cf215fb1221b74814b132f301 Mon Sep 17 00:00:00 2001
From: wen-bo-yang <ywb2545251@163.com>
Date: Mon, 27 Feb 2017 17:43:28 +0800
Subject: [PATCH] add cross reading sample files and fix bugs

---
 paddle/setup.py.in                            |  2 +-
 paddle/v2/dataset/config.py                   |  8 ----
 .../paddle}/v2/dataset/sentiment.py           | 42 +++++++++++++------
 3 files changed, 30 insertions(+), 22 deletions(-)
 delete mode 100644 paddle/v2/dataset/config.py
 rename {paddle => python/paddle}/v2/dataset/sentiment.py (73%)

diff --git a/paddle/setup.py.in b/paddle/setup.py.in
index d44f1145d..382d5be6e 100644
--- a/paddle/setup.py.in
+++ b/paddle/setup.py.in
@@ -72,7 +72,7 @@ setup(name="py_paddle",
   packages=['py_paddle'],
   include_dirs = include_dirs,
   install_requires = [
-    'nltk',
+    'nltk>=3.2.2',
     'numpy>=1.8.0',      # The numpy is required.
     'protobuf>=3.0.0'    # The paddle protobuf version
   ],
diff --git a/paddle/v2/dataset/config.py b/paddle/v2/dataset/config.py
deleted file mode 100644
index 304c4bc5c..000000000
--- a/paddle/v2/dataset/config.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import os
-
-__all__ = ['DATA_HOME']
-
-DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
-
-if not os.path.exists(DATA_HOME):
-    os.makedirs(DATA_HOME)
diff --git a/paddle/v2/dataset/sentiment.py b/python/paddle/v2/dataset/sentiment.py
similarity index 73%
rename from paddle/v2/dataset/sentiment.py
rename to python/paddle/v2/dataset/sentiment.py
index 83581eadf..9825d2ef9 100644
--- a/paddle/v2/dataset/sentiment.py
+++ b/python/paddle/v2/dataset/sentiment.py
@@ -20,9 +20,9 @@ The script fetch and preprocess movie_reviews data set
 that provided by NLTK
 """
 
-
 import nltk
 import numpy as np
+from itertools import chain
 from nltk.corpus import movie_reviews
 from config import DATA_HOME
 
@@ -50,9 +50,10 @@ def download_data_if_not_yet():
     except LookupError:
         print "Downloading movie_reviews data set, please wait....."
         nltk.download('movie_reviews', download_dir=DATA_HOME)
-        print "Download data set success......"
         # make sure that nltk can find the data
         nltk.data.path.append(DATA_HOME)
+        print "Download data set success....."
+        print "Path is " + nltk.data.find('corpora/movie_reviews').path
 
 
 def get_word_dict():
@@ -67,24 +68,39 @@ def get_word_dict():
     words_sort_list = words_freq.items()
     words_sort_list.sort(cmp=lambda a, b: b[1] - a[1])
     for index, word in enumerate(words_sort_list):
-        words_freq_sorted.append(word[0])
+        words_freq_sorted.append((word[0], index + 1))
     return words_freq_sorted
 
 
+def sort_files():
+    """
+    Sorted the sample for cross reading the sample
+    :return:
+        files_list
+    """
+    files_list = list()
+    download_data_if_not_yet()
+    neg_file_list = movie_reviews.fileids('neg')
+    pos_file_list = movie_reviews.fileids('pos')
+    files_list = list(chain.from_iterable(zip(neg_file_list, pos_file_list)))
+    return files_list
+
+
 def load_sentiment_data():
     """
     Load the data set
     :return:
         data_set
     """
-    label_dict = get_label_dict()
+    data_set = list()
     download_data_if_not_yet()
-    words_freq = nltk.FreqDist(w.lower() for w in movie_reviews.words())
-    data_set = [([words_freq[word.lower()]
-                  for word in movie_reviews.words(fileid)],
-                 label_dict[category])
-                for category in movie_reviews.categories()
-                for fileid in movie_reviews.fileids(category)]
+    words_ids = dict(get_word_dict())
+    for sample_file in sort_files():
+        words_list = list()
+        category = 0 if 'neg' in sample_file else 1
+        for word in movie_reviews.words(sample_file):
+            words_list.append(words_ids[word.lower()])
+        data_set.append((words_list, category))
     return data_set
 
 
@@ -98,9 +114,9 @@ def reader_creator(data):
         train data set or test data set
     """
     for each in data:
-        sentences = np.array(each[0], dtype=np.int32)
-        labels = np.array(each[1], dtype=np.int8)
-        yield sentences, labels
+        list_of_int = np.array(each[0], dtype=np.int32)
+        label = each[1]
+        yield list_of_int, label
 
 
 def train():
-- 
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