Add word2vec

word2vec/.gitignore

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+data/train.list
+data/test.list
+data/simple-examples*

word2vec/calculate_dis.py

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+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Example:
+    python calculate_dis.py DICTIONARYTXT FEATURETXT
+
+Required arguments:
+    DICTIONARYTXT    the dictionary generated in dataprovider
+    FEATURETXT       the text format word feature, one line for one word
+"""
+
+import numpy as np
+from argparse import ArgumentParser
+
+
+def load_dict(fdict):
+    words = [line.strip() for line in fdict.readlines()]
+    dictionary = dict(zip(words, xrange(len(words))))
+    return dictionary
+
+
+def load_emb(femb):
+    feaBank = []
+    flag_firstline = True
+    for line in femb:
+        if flag_firstline:
+            flag_firstline = False
+            continue
+        fea = np.array([float(x) for x in line.strip().split(',')])
+        normfea = fea * 1.0 / np.linalg.norm(fea)
+        feaBank.append(normfea)
+    return feaBank
+
+
+def calcos(id1, id2, Fea):
+    f1 = Fea[id1]
+    f2 = Fea[id2]
+    return np.dot(f1.transpose(), f2)
+
+
+def get_wordidx(w, Dict):
+    if w not in Dict:
+        print 'ERROR: %s not in the dictionary' % w
+        return -1
+    return Dict[w]
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser()
+    parser.add_argument('dict', help='dictionary file')
+    parser.add_argument('fea', help='feature file')
+    args = parser.parse_args()
+
+    with open(args.dict) as fdict:
+        word_dict = load_dict(fdict)
+
+    with open(args.fea) as ffea:
+        word_fea = load_emb(ffea)
+
+    while True:
+        w1, w2 = raw_input("please input two words: ").split()
+        w1_id = get_wordidx(w1, word_dict)
+        w2_id = get_wordidx(w2, word_dict)
+        if w1_id == -1 or w2_id == -1:
+            continue
+        print 'similarity: %s' % (calcos(w1_id, w2_id, word_fea))

word2vec/data/getdata.sh

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+#!/bin/bash
+set -e
+
+wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
+tar -zxf simple-examples.tgz
+echo `pwd`/simple-examples/data/ptb.train.txt > train.list
+echo `pwd`/simple-examples/data/ptb.valid.txt > test.list

word2vec/dataprovider.py

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+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer.PyDataProvider2 import *
+import collections
+import logging
+import pdb
+
+logging.basicConfig(
+    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s', )
+logger = logging.getLogger('paddle')
+logger.setLevel(logging.INFO)
+
+N = 5  # Ngram
+cutoff = 50  # select words with frequency > cutoff to dictionary
+
+
+def build_dict(ftrain, fdict):
+    sentences = []
+    with open(ftrain) as fin:
+        for line in fin:
+            line = ['<s>'] + line.strip().split() + ['<e>']
+            sentences += line
+    wordfreq = collections.Counter(sentences)
+    wordfreq = filter(lambda x: x[1] > cutoff, wordfreq.items())
+    dictionary = sorted(wordfreq, key=lambda x: (-x[1], x[0]))
+    words, _ = list(zip(*dictionary))
+    for word in words:
+        print >> fdict, word
+    word_idx = dict(zip(words, xrange(len(words))))
+    logger.info("Dictionary size=%s" % len(words))
+    return word_idx
+
+
+def initializer(settings, srcText, dictfile, **xargs):
+    with open(dictfile, 'w') as fdict:
+        settings.dicts = build_dict(srcText, fdict)
+    input_types = []
+    for i in xrange(N):
+        input_types.append(integer_value(len(settings.dicts)))
+    settings.input_types = input_types
+
+
+@provider(init_hook=initializer)
+def process(settings, filename):
+    UNKID = settings.dicts['<unk>']
+    with open(filename) as fin:
+        for line in fin:
+            line = ['<s>'] * (N - 1) + line.strip().split() + ['<e>']
+            line = [settings.dicts.get(w, UNKID) for w in line]
+            for i in range(N, len(line) + 1):
+                yield line[i - N:i]

word2vec/format_convert.py

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+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Example:
+    python format_convert.py --b2t -i INPUT -o OUTPUT -d DIM
+    python format_convert.py --t2b -i INPUT -o OUTPUT
+
+Options:
+    -h, --help  show this help message and exit
+    --b2t       convert parameter file of embedding model from binary to text
+    --t2b       convert parameter file of embedding model from text to binary
+    -i INPUT    input parameter file name
+    -o OUTPUT   output parameter file name
+    -d DIM      dimension of parameter
+"""
+from optparse import OptionParser
+import struct
+
+
+def binary2text(input, output, paraDim):
+    """
+    Convert a binary parameter file of embedding model to be a text file.  
+    input: the name of input binary parameter file, the format is:
+           1) the first 16 bytes is filehead:
+                version(4 bytes): version of paddle, default = 0
+                floatSize(4 bytes): sizeof(float) = 4
+                paraCount(8 bytes): total number of parameter
+           2) the next (paraCount * 4) bytes is parameters, each has 4 bytes 
+    output: the name of output text parameter file, for example:
+           0,4,32156096
+           -0.7845433,1.1937413,-0.1704215,...
+           0.0000909,0.0009465,-0.0008813,...
+           ...
+           the format is:
+           1) the first line is filehead: 
+              version=0, floatSize=4, paraCount=32156096
+           2) other lines print the paramters
+              a) each line prints paraDim paramters splitted by ','
+              b) there is paraCount/paraDim lines (embedding words)
+    paraDim: dimension of parameters 
+    """
+    fi = open(input, "rb")
+    fo = open(output, "w")
+    """
+    """
+    version, floatSize, paraCount = struct.unpack("iil", fi.read(16))
+    newHead = ','.join([str(version), str(floatSize), str(paraCount)])
+    print >> fo, newHead
+
+    bytes = 4 * int(paraDim)
+    format = "%df" % int(paraDim)
+    context = fi.read(bytes)
+    line = 0
+
+    while context:
+        numbers = struct.unpack(format, context)
+        lst = []
+        for i in numbers:
+            lst.append('%8.7f' % i)
+        print >> fo, ','.join(lst)
+        context = fi.read(bytes)
+        line += 1
+    fi.close()
+    fo.close()
+    print "binary2text finish, total", line, "lines"
+
+
+def get_para_count(input):
+    """
+    Compute the total number of embedding parameters in input text file. 
+    input: the name of input text file
+    """
+    numRows = 1
+    paraDim = 0
+    with open(input) as f:
+        line = f.readline()
+        paraDim = len(line.split(","))
+        for line in f:
+            numRows += 1
+    return numRows * paraDim
+
+
+def text2binary(input, output, paddle_head=True):
+    """
+    Convert a text parameter file of embedding model to be a binary file.
+    input: the name of input text parameter file, for example:
+           -0.7845433,1.1937413,-0.1704215,...
+           0.0000909,0.0009465,-0.0008813,... 
+           ...
+           the format is:
+           1) it doesn't have filehead
+           2) each line stores the same dimension of parameters, 
+              the separator is commas ','
+    output: the name of output binary parameter file, the format is:
+           1) the first 16 bytes is filehead: 
+             version(4 bytes), floatSize(4 bytes), paraCount(8 bytes)
+           2) the next (paraCount * 4) bytes is parameters, each has 4 bytes
+    """
+    fi = open(input, "r")
+    fo = open(output, "wb")
+
+    newHead = struct.pack("iil", 0, 4, get_para_count(input))
+    fo.write(newHead)
+
+    count = 0
+    for line in fi:
+        line = line.strip().split(",")
+        for i in range(0, len(line)):
+            binary_data = struct.pack("f", float(line[i]))
+            fo.write(binary_data)
+        count += 1
+    fi.close()
+    fo.close()
+    print "text2binary finish, total", count, "lines"
+
+
+def main():
+    """
+    Main entry for running format_convert.py 
+    """
+    usage = "usage: \n" \
+            "python %prog --b2t -i INPUT -o OUTPUT -d DIM \n" \
+            "python %prog --t2b -i INPUT -o OUTPUT"
+    parser = OptionParser(usage)
+    parser.add_option(
+        "--b2t",
+        action="store_true",
+        help="convert parameter file of embedding model from binary to text")
+    parser.add_option(
+        "--t2b",
+        action="store_true",
+        help="convert parameter file of embedding model from text to binary")
+    parser.add_option(
+        "-i", action="store", dest="input", help="input parameter file name")
+    parser.add_option(
+        "-o", action="store", dest="output", help="output parameter file name")
+    parser.add_option(
+        "-d", action="store", dest="dim", help="dimension of parameter")
+    (options, args) = parser.parse_args()
+    if options.b2t:
+        binary2text(options.input, options.output, options.dim)
+    if options.t2b:
+        text2binary(options.input, options.output)
+
+
+if __name__ == '__main__':
+    main()

word2vec/ngram.py

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+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+import math
+
+#################### Data Configure ####################
+args = {
+    'srcText': 'data/simple-examples/data/ptb.train.txt',
+    'dictfile': 'data/vocabulary.txt'
+}
+define_py_data_sources2(
+    train_list="data/train.list",
+    test_list="data/test.list",
+    module="dataprovider",
+    obj="process",
+    args=args)
+
+settings(
+    batch_size=100, regularization=L2Regularization(8e-4), learning_rate=3e-3)
+
+dictsize = 1953
+embsize = 32
+hiddensize = 256
+
+firstword = data_layer(name="firstw", size=dictsize)
+secondword = data_layer(name="secondw", size=dictsize)
+thirdword = data_layer(name="thirdw", size=dictsize)
+fourthword = data_layer(name="fourthw", size=dictsize)
+nextword = data_layer(name="fifthw", size=dictsize)
+
+
+# construct word embedding for each datalayer
+def wordemb(inlayer):
+    wordemb = table_projection(
+        input=inlayer,
+        size=embsize,
+        param_attr=ParamAttr(
+            name="_proj",
+            initial_std=0.001,
+            learning_rate=1,
+            l2_rate=0, ))
+    return wordemb
+
+
+Efirst = wordemb(firstword)
+Esecond = wordemb(secondword)
+Ethird = wordemb(thirdword)
+Efourth = wordemb(fourthword)
+
+# concatentate Ngram embeddings into context embedding
+contextemb = concat_layer(input=[Efirst, Esecond, Ethird, Efourth])
+hidden1 = fc_layer(
+    input=contextemb,
+    size=hiddensize,
+    act=SigmoidActivation(),
+    layer_attr=ExtraAttr(drop_rate=0.5),
+    bias_attr=ParamAttr(learning_rate=2),
+    param_attr=ParamAttr(
+        initial_std=1. / math.sqrt(embsize * 8), learning_rate=1))
+
+# use context embedding to predict nextword
+predictword = fc_layer(
+    input=hidden1,
+    size=dictsize,
+    bias_attr=ParamAttr(learning_rate=2),
+    act=SoftmaxActivation())
+
+cost = classification_cost(input=predictword, label=nextword)
+
+# network input and output
+outputs(cost)