Merge pull request #573 from qingqing01/quick_start

Update the data of quick start.

demo/quick_start/data/README.md

@@ -0,0 +1,9 @@
+This dataset consists of electronics product reviews associated with
+binary labels (positive/negative) for sentiment classification.
+
+The preprocessed data can be downloaded by script `get_data.sh`.
+The data was derived from reviews_Electronics_5.json.gz at
+
+http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
+
+If you want to process the raw data, you can use the script `proc_from_raw_data/get_data.sh`.

demo/quick_start/data/get_data.sh

@@ -17,14 +17,11 @@ set -e
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
 cd $DIR
 
-echo "Downloading Amazon Electronics reviews data..."
-# http://jmcauley.ucsd.edu/data/amazon/
-wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
+# Download the preprocessed data
+wget http://paddlepaddle.bj.bcebos.com/demo/quick_start_preprocessed_data/preprocessed_data.tar.gz
 
-echo "Downloading mosesdecoder..."
-#https://github.com/moses-smt/mosesdecoder
-wget https://github.com/moses-smt/mosesdecoder/archive/master.zip
+# Extract package
+tar zxvf preprocessed_data.tar.gz
 
-unzip master.zip
-rm master.zip
-echo "Done."
+# Remove compressed package
+rm preprocessed_data.tar.gz

demo/quick_start/data/pred.list

@@ -1 +0,0 @@
-./data/pred.txt

demo/quick_start/data/pred.txt

@@ -1,2 +0,0 @@
-the device is cute , but that 's just about all that 's good. the specs are what you 'd expect : it 's a wifi mic , with some noise filter options. the app has the option to upload your baby 's name and photo , which is a cutesy touch. but the app is otherwise unstable and useless unless you upgrade for $ 60 / year.set up involves downloading the app , turning on the mic , switching your phone to the wifi network of the mic , telling the app your wifi settings , switching your wifi back to your home router. the app is then directly connected to your mic.the app is adware ! the main screen says " cry notifications on / off : upgrade to evoz premium and receive a text message of email when your baby is crying " .but the adware points out an important limitation , this monitor is only intended to be used from your home network. if you want to access it remotely , get a webcam. this app would make a lot more sense of the premium features were included with the hardware .
-don 't be fooled by my one star rating. if there was a zero , i would have selected it. this product was a waste of my money.it has never worked like the company said it supposed to. i only have one device , an iphone 4gs. after charging the the iphone mid way , the i.sound portable power max 16,000 mah is completely drained. the led light no longer lit up. when plugging the isound portable power max into a wall outlet to charge , it would charge for about 20-30 minutes and then all four battery led indicator lit up showing a full charge. i would leave it on to charge for the full 8 hours or more but each time with the same result upon using. don 't buy this thing. put your money to good use elsewhere .

demo/quick_start/data/proc_from_raw_data/get_data.sh

@@ -16,10 +16,26 @@
 # 1. size of pos : neg = 1:1.
 # 2. size of testing set = min(25k, len(all_data) * 0.1), others is traning set.
 # 3. distinct train set and test set.
-# 4. build dict
 
 set -e
 
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+cd $DIR
+
+# Download data
+echo "Downloading Amazon Electronics reviews data..."
+# http://jmcauley.ucsd.edu/data/amazon/
+wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
+echo "Downloading mosesdecoder..."
+# https://github.com/moses-smt/mosesdecoder
+wget https://github.com/moses-smt/mosesdecoder/archive/master.zip
+
+unzip master.zip
+rm master.zip
+
+##################
+# Preprocess data 
+echo "Preprocess data..."
 export LC_ALL=C
 UNAME_STR=`uname`
 
@@ -29,11 +45,11 @@ else
   SHUF_PROG='gshuf'
 fi
 
-mkdir -p data/tmp
-python preprocess.py -i data/reviews_Electronics_5.json.gz
+mkdir -p tmp
+python preprocess.py -i reviews_Electronics_5.json.gz
 # uniq and shuffle
-cd data/tmp
-echo 'uniq and shuffle...'
+cd tmp
+echo 'Uniq and shuffle...'
 cat pos_*|sort|uniq|${SHUF_PROG}> pos.shuffed
 cat neg_*|sort|uniq|${SHUF_PROG}> neg.shuffed
 
@@ -53,11 +69,11 @@ cat train.pos train.neg | ${SHUF_PROG} >../train.txt
 cat test.pos test.neg | ${SHUF_PROG} >../test.txt
 
 cd -
-echo 'data/train.txt' > data/train.list
-echo 'data/test.txt' > data/test.list
+echo 'train.txt' > train.list
+echo 'test.txt' > test.list
 
 # use 30k dict
-rm -rf data/tmp
-mv data/dict.txt data/dict_all.txt
-cat data/dict_all.txt | head -n 30001 > data/dict.txt
-echo 'preprocess finished'
+rm -rf tmp
+mv dict.txt dict_all.txt
+cat dict_all.txt | head -n 30001 > dict.txt
+echo 'Done.'

demo/quick_start/data/proc_from_raw_data/preprocess.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-1. (remove HTML before or not)tokensizing
+1. Tokenize the words and punctuation 
 2. pos sample : rating score 5; neg sample: rating score 1-2.
 
 Usage:
@@ -76,7 +76,11 @@ def tokenize(sentences):
     sentences : a list of input sentences.
     return: a list of processed text.
     """
-    dir = './data/mosesdecoder-master/scripts/tokenizer/tokenizer.perl'
+    dir = './mosesdecoder-master/scripts/tokenizer/tokenizer.perl'
+    if not os.path.exists(dir):
+        sys.exit(
+            "The ./mosesdecoder-master/scripts/tokenizer/tokenizer.perl does not exists."
+        )
     tokenizer_cmd = [dir, '-l', 'en', '-q', '-']
     assert isinstance(sentences, list)
     text = "\n".join(sentences)
@@ -104,7 +108,7 @@ def tokenize_batch(id):
         num_batch, instance, pre_fix = parse_queue.get()
         if num_batch == -1:  ### parse_queue finished
             tokenize_queue.put((-1, None, None))
-            sys.stderr.write("tokenize theread %s finish\n" % (id))
+            sys.stderr.write("Thread %s finish\n" % (id))
             break
         tokenize_instance = tokenize(instance)
         tokenize_queue.put((num_batch, tokenize_instance, pre_fix))

doc/demo/quick_start/index_en.md

@@ -59,12 +59,11 @@ To build your text classification system, your code will need to perform five st
 ## Preprocess data into standardized format
 In this example, you are going to use [Amazon electronic product review dataset](http://jmcauley.ucsd.edu/data/amazon/) to build a bunch of deep neural network models for text classification. Each text in this dataset is a product review. This dataset has two categories: “positive” and “negative”. Positive means the reviewer likes the product, while negative means the reviewer does not like the product.
 
-`demo/quick_start` in the [source code](https://github.com/baidu/Paddle) provides scripts for downloading data and preprocessing data as shown below. The data process takes several minutes (about 3 minutes in our machine).
+`demo/quick_start` in the [source code](https://github.com/PaddlePaddle/Paddle) provides script for downloading the preprocessed data as shown below. (If you want to process the raw data, you can use the script `demo/quick_start/data/proc_from_raw_data/get_data.sh`).
 
 ```bash
 cd demo/quick_start
 ./data/get_data.sh
-./preprocess.sh
 ```
 
 ## Transfer Data to Model

doc_cn/demo/quick_start/index.md

@@ -32,13 +32,11 @@
 
 ## 数据格式准备(Data Preparation)
 在本问题中，我们使用[Amazon电子产品评论数据](http://jmcauley.ucsd.edu/data/amazon/)，
-将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了数据下载脚本
-和预处理脚本。
+将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/PaddlePaddle/Paddle)的`demo/quick_start`里提供了下载已经预处理数据的脚本（如果想从最原始的数据处理，可以使用脚本 `./demo/quick_start/data/proc_from_raw_data/get_data.sh`）。
 
 ```bash
 cd demo/quick_start
 ./data/get_data.sh
-./preprocess.sh
 ```
 
 ## 数据向模型传送(Transfer Data to Model)
@@ -143,7 +141,7 @@ PyDataProvider2</a>。
 
 我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置
 连接请参考<a href = "../../../doc/layer.html">Layer文档</a>。
-所有配置在[源码](https://github.com/baidu/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。
+所有配置在[源码](https://github.com/PaddlePaddle/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。
 
 ### 逻辑回归模型(Logistic Regression)