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data_helpers.py
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data_helpers.py
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# -*- coding: utf-8 -*-
from keras.preprocessing import text, sequence
from keras.utils.np_utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
import text_normalization as tn
import gensim
import numpy as np
import pandas as pd
import io
#################################
##########################
EmbeddingSize=300
vocab_size=50000
charset = "ا ب ت ث ج ح خ د ذ ر ز س ش ص ض ع غ ف ق ك ل م و ه ي ى ط ظ ن"
other_symbols = "0123456789-,;.!:/\\@#%^* -=()[] "
alphabet = (list(charset) list(other_symbols) ['\n'])
## Defining Data processing functions
def LoadData(Corpus,ClassesDict,Arabic=False): ## loading file
DF=pd.read_csv(Corpus,converters={'text': str})
labels=[ClassesDict[x] for x in DF['label'].tolist()]
sentences=DF['text'].tolist()
if Arabic:
sentences=[tn.NormForWord2Vec(line) for line in sentences]
TrueLabels=labels
labels=to_categorical(np.asarray(labels), num_classes=len(ClassesDict))
return sentences,labels,TrueLabels
def tokenizeData(X_train,X_valid,vocab_size,X_test=None, char_level=False): ## tokenization
"tokenize data"
#init tokenizer
tokenizer= Tokenizer(nb_words=vocab_size, filters='\t\n',split=" ",char_level=char_level)
#use tokenizer to split vocab and index them
tokenizer.fit_on_texts(X_train)
##txt to seq
X_train= tokenizer.texts_to_sequences(X_train)
X_valid = tokenizer.texts_to_sequences(X_valid)
if X_test != None:
X_test=tokenizer.texts_to_sequences(X_test)
reverse_word_map = dict(map(reversed, tokenizer.word_index.items()))
return X_train,X_valid,X_test,reverse_word_map
def paddingSequence(X_train,X_valid,maxLen,X_test=None): ## Sequence Padding
"make sure that a sequence is satisfied max_length condition"
#######equalize list of seq
X_train= pad_sequences(X_train, maxLen, padding='post', truncating='post')
X_valid= pad_sequences(X_valid, maxLen, padding='post', truncating='post')
if X_test != None:
X_test= pad_sequences(X_test, maxLen, padding='post',truncating='post')
return X_train,X_valid,X_test
def read_labels(categorical=False):
count=0
classes={}
for line in open("conf/label_list"):
if line.strip("\n") not in classes:
classes[line.strip("\n")] = count
count = 1
return classes
#######################
## Defining External Embedding Gensim functions
def GetEmbeddingWeights(embedding_dim,n_symbols,wordmap,vecDic):
embedding_weights = np.zeros((n_symbols, embedding_dim))
for index,word in wordmap.items():
if word in vecDic:
embedding_weights[index, :] = vecDic[word]
else:
## if doesn't exist initialize embedding vector from a random distribution
embedding_weights[index, :] = np.random.randn(embedding_dim)
return embedding_weights
def GetVecDicFromGensim(GensimFile):
Model=gensim.models.Word2Vec.load(GensimFile)
return Model.wv
def load_fasttext(FastTextFile):
fin = io.open(FastTextFile, 'r', encoding='utf-8', newline='\n', errors='ignore')
#n, d = map(int, fin.readline().split())
data = {}
for line in fin:
tokens = line.rstrip().split(' ')
data[tokens[0]] = np.array([list(map(float, tokens[1:]))])
return data
#####################################
def get_char2idx(x):
st=""
for line in x:
st =line
charset=set(st)
vocabsize=len(charset)
char2idx= dict(zip(list(charset),range(1,len(charset) 1)))
return char2idx,vocabsize
#####################################
def encode_all_data2chars(x,char2idx):
X_return=[]
for line in x:
idxs= [char2idx.get(c,1) for c in line]
X_return.append(idxs)
return X_return
#####################################
def encode_data2char(x):
char2idx = dict(zip(list(alphabet), range(2, len(alphabet) 2)))
print(len(char2idx))
X=[]
for line in x:
indices = [char2idx.get(c, 1) for c in line]
X.append(indices)
return X
#####################################
def get_word_map_num_symbols(corpus):
X_train,Y_train,Y_Train_true=LoadData(corpus,ClassesDict=get_classes())
X_test,Y_test,Y_test_true=LoadData(corpus,ClassesDict=get_classes())
print ('---- Tokenizing Training and Testing Data ------')
X_train,X_test,dummy,wordmap=tokenizeData(X_train,X_test,vocab_size=get_vocab_size())
n_symbols=len(wordmap) 1
return n_symbols,wordmap
#####################################
def set_corpus(Corpus):
corpus=Corpus
return corpus
#####################################
def get_classes():
return Classes
def get_corpus():
return Corpus
def get_testset():
return Testset
def get_vocab_size():
return vocab_size
def get_n_symbold():
return
###########
Classes=read_labels()
#############################################
Corpus="./data/train/MultiTrain.Shuffled.csv"
Testset="./data/dev/MultiDev.csv"