# 1. Construir Corpus texto categorizado
locPT = 'ch02/ES'
corpusPT = CategorizedPlaintextCorpusReader(locPT,
'.*\.txt',
cat_file="cat.txt")
print(corpusPT.fileids())
print(corpusPT.categories())
print(corpusPT.words(categories='ciencia'))
#print(corpusPT.raw())
vocab = set(w.lower() for w in corpusPT.words())
print('Tamanho Vocabulario:', len(vocab))
corpusCom = corpusPT.raw()
corpusComList = corpusCom.split()
print('Tamanho Total de palabras:', len(corpusComList))
# 2. Calcular medidas estadisticas simples
'''
Medidas: Tamanho médio das palavras, Tamanho médio das sentenças e Número de vezes que cada
item do vocabulário aparece no texto em média (escore de diversidade léxica)
'''
print(
'Tamanho médio das palavras/Tamanho médio das sentenças/Escore de diversidade léxica'
)
print('Medidas con StopWords de Corpus')
estadisticasSimples(corpusPT, 0, 0)
print('Medidas con StopWords x Categoria')
# "smiths","socar","texas","gpo","illinois","usc","virginia","nocoll"]
#colls = ["ia","getty","kentucky","minnesota","missouri","mwdl"]
#colls = ["nara","nocar","smiths","socar","texas","gpo","illinois","usc","virginia","nocoll"]
#data = {}
stats = {}
common = {}
for coll in colls:
print(reader.categories(coll+".txt"))
stats[coll] = {}
# 'kay. Can't pickle words. It's a stream reader.
# But maybe you can if you tokenize we regex
# Which also pulls out punctuation
print("prep & pickle words")
words = re.split(r'\W+', reader.raw(coll+'.txt'))
pickle.dump( words, open( "/media/storage/dpla-data/pickles/new/"+coll+"_words.p", "wb"))
#words = reader.words(coll+".txt")
#data[coll]["words"] = reader.words(coll+".txt")
print("getting count " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
stats[coll]["wc"] = len(words)
print(stats[coll]["wc"])
print("getting uniq " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
stats[coll]["uniq"] = len(set([w.lower() for w in words]))
print(stats[coll]["uniq"])
print("filtering & pickling " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
filtered = [w for w in words if w.lower() not in nltk.corpus.stopwords.words('english')]
pickle.dump( filtered, open( "/media/storage/dpla-data/pickles/new/"+coll+"_filtered.p", "wb"))
print("getting filter count " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
stats[coll]["fwc"] = len(filtered)
print(stats[coll]["fwc"])
import nltk
from nltk.corpus import wordnet as wn
import sys
Feature_Set={}
training_directory= "reviews"
Training_Corpus = CategorizedPlaintextCorpusReader(training_directory, r'pos|neg.*\.txt$', cat_pattern='(\w+)/*')
testing_directory= "reviews"
Testing_Corpus = CategorizedPlaintextCorpusReader(testing_directory, r'pos|neg.*\.txt$', cat_pattern='(\w+)/*')
Training_Corpus_Text=nltk.RegexpTokenizer('\w+').tokenize(Training_Corpus.raw())
Positive_Corpus_Text=nltk.RegexpTokenizer('\w+').tokenize(Training_Corpus.raw(categories="pos"))
Negative_Corpus_Text=nltk.RegexpTokenizer('\w+').tokenize(Training_Corpus.raw(categories="neg"))
Training_Vocabulary = nltk.FreqDist(w.lower() for w in Training_Corpus_Text)
Positive_Vocabulary = nltk.FreqDist(w.lower() for w in Positive_Corpus_Text)
Negative_Vocabulary = nltk.FreqDist(w.lower() for w in Negative_Corpus_Text)
pos_den=float(len(Positive_Corpus_Text))+float(len(Positive_Vocabulary.keys()))
neg_den=float(len(Negative_Corpus_Text))+float(len(Negative_Vocabulary.keys()))
for word, tag in nltk.pos_tag(Training_Vocabulary.keys()[:500]):
if tag=="JJ" or tag=="RB":
if word in Positive_Vocabulary.keys():
Feature_Set[word, "pos"]=(float((Positive_Vocabulary.freq(word)*len(Positive_Corpus_Text))+1)/float(pos_den))
"hathi","nypl"]
#colls = ["ia","getty","kentucky","minnesota","missouri","mwdl"]
#colls = ["nara","nocar","smiths","socar","texas","gpo","illinois","usc","virginia","nocoll"]
#data = {}
stats = {}
common = {}
for coll in colls:
print(reader.categories(coll+".txt"))
stats[coll] = {}
# 'kay. Can't pickle words. It's a stream reader.
# But maybe you can if you tokenize we regex
# Which also pulls out punctuation
print("prep & pickle words")
words = re.split(r'\W+', reader.raw(coll+'.txt'))
pickle.dump( words, open( "/media/storage/dpla-data/words/colls.oct/pickles/"+coll+"_words.p", "wb"))
#words = reader.words(coll+".txt")
#data[coll]["words"] = reader.words(coll+".txt")
print("getting count " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
stats[coll]["wc"] = len(words)
print(stats[coll]["wc"])
print("getting uniq " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
stats[coll]["uniq"] = len(set([w.lower() for w in words]))
print(stats[coll]["uniq"])
print("filtering & pickling " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
filtered = [w for w in words if w.lower() not in nltk.corpus.stopwords.words('english')]
pickle.dump( filtered, open( "/media/storage/dpla-data/words/colls.oct/pickles/"+coll+"_filtered.p", "wb"))
print("getting filter count " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
stats[coll]["fwc"] = len(filtered)
print(stats[coll]["fwc"])
art_i = []
class_i = []
#Conversion of Train Data into Single Input File
corpus_root = 'Train_set'
newcorpus = CategorizedPlaintextCorpusReader(corpus_root,
r'.*\.txt',
cat_pattern=r'(\w+)/*')
myfile = open('Input_Article_Data.csv', 'wb')
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL, lineterminator="\n")
for category in newcorpus.categories():
for fileid in newcorpus.fileids(category):
#print fileid,category
data1 = (newcorpus.raw(fileid).encode('utf-8')).replace(",", " ")
data_list = [data1, category]
wr.writerow(data_list)
myfile.close()
#Reading of Train Data as Lists
with open('Input_Article_Data.csv', 'r') as f:
for line in f.readlines():
l, name = line.strip().split(',')
l = (re.sub('[^A-Za-z0-9.]+', ' ', l)).lower()
# l=porter_stemmer.stem(l) #Reduces Accuracy From 50% To 37%
if (name != "Category"):
art_i.append([l])
class_i.append(name)
f.close()
"""
# Recurso de emociones CANADA
emotions_dict = pd.read_csv("emolex.csv")
emotions_dict = emotions_dict.set_index('Spanish (es)')
# Recurso de emociones SEL
sel_emotions_dict = pd.read_csv("SEL_full.txt", sep='\t', encoding = "ISO-8859-1")
sel_emotions_dict = sel_emotions_dict.set_index('Palabra')
"""
# Lee corpus de tweets
reader = CategorizedPlaintextCorpusReader('./',
r'mex.*\.txt',
cat_pattern=r'(\w+)/*')
tweets_train = reader.raw('mex_train.txt').split('\n')[:-1]
labels_train = reader.raw('mex_train_labels.txt').split('\n')[:-1]
labels_train = list(map(int, labels_train))
tweets_val = reader.raw('mex_val.txt').split('\n')[:-1]
labels_val = reader.raw('mex_val_labels.txt').split('\n')[:-1]
labels_val = list(map(int, labels_val))
tweets_test = reader.raw('mex_test.txt').split('\n')[:-1]
"""
corpus_palabras = []
for doc in tweets_train:
corpus_palabras += doc.split()
fdist = nltk.FreqDist(corpus_palabras)
V = sortFreqDict(fdist)