def load_documents(self,path): docs = CategorizedPlaintextCorpusReader(path,r'.*/.*',cat_pattern=r'(.*)/.*') for cat in docs.categories(): self.cat_gram_freq[cat] = {} self.cat_word_freq[cat]={} return ((category,list(docs.words(fileid))) for category in docs.categories() for fileid in docs.fileids(category))
def load_documents(self,path): docs = CategorizedPlaintextCorpusReader(path,r'.*/.*',cat_pattern=r'(.*)/.*') print docs.categories() documents = [(list(docs.words(fileid)), category) for category in docs.categories() for fileid in docs.fileids(category) ] random.shuffle(documents) return documents
import nltk from nltk.corpus.reader.plaintext import CategorizedPlaintextCorpusReader DOC_PATTERN = r'[\w_\s]+/[\w\s\d\-]+\.TXT' CAT_PATTERN = r'([\w_\s]+)/.*' corpus = CategorizedPlaintextCorpusReader('ENGLISH', DOC_PATTERN, cat_pattern=CAT_PATTERN) print(corpus.categories()) print(corpus.fileids()[100:110]) print(corpus.words())
#Load Libraries import os import random from nltk.corpus.reader.plaintext import CategorizedPlaintextCorpusReader #Read the dataset into the categorized corpus # Directory of the corpus corpusdir = 'corpus/' review_corpus = CategorizedPlaintextCorpusReader(corpusdir, r'.*\.txt', cat_pattern=r'\d+_(\w+)\.txt') # list of documents(fileid) and category (pos/neg) documents = [(list(review_corpus.words(fileid)), category) for category in review_corpus.categories() for fileid in review_corpus.fileids(category)] random.shuffle(documents) for category in review_corpus.categories(): print(category) type(review_corpus) len(documents) #Compute word frequency import nltk all_words = nltk.FreqDist(w.lower() for w in review_corpus.words()) word_features = list(all_words)[:200]
print 'Loading corpus...', t = time() train_path = 'data/task1_train' cr = CategorizedPlaintextCorpusReader(train_path, '.*', cat_pattern='(\w*)') t = time() - t print str(t) + 's' # Test generation of CFD print 'Creating CFD...', sys.stdout.flush() t = time() cat = cr.categories()[0] n = 3 cfd = ConditionalFreqDist() prefix = ('',) * (n - 1) for ngram in ingrams(chain(prefix, cr.words(categories=[cat])), n): context = tuple(ngram[:-1]) token = ngram[-1] cfd[context].inc(token) t = time() - t print str(t) + 's'
label=target_name) plt.legend(loc='best', shadow=False, scatterpoints=1) plt.title('PCA of BULATS dataset') plt.show() return model if __name__ == "__main__": PATH = "model.pickle" # Loading speech features speech = pd.read_csv("/ExamplePath.csv") if not os.path.exists(PATH): nli = CategorizedPlaintextCorpusReader(CORPUS, DOC_PATTERN, cat_pattern=CAT_PATTERN) # since `nli` already has all the information (text and ids) # you don't need to iterate over it multiple times so # construct `X` and `y` in one go. X = [] y = [] for fileid in nli.fileids(): X.append({ 'text': nli.raw(fileid), 'id': fileid.split('/')[-1].split('.')[0] }) y.append(nli.categories(fileid)[0]) clf = PCA(n_components=2) model = build_and_evaluate(X, y, clf, speech)
return 1 in [c in str for c in set] def is_number(s): try: float(s) return True except ValueError: return False doc_lowercase = [w.lower() for w in doc] return lemma.lemmatize([w for w in doc_lowercase if not (is_number(w)) and len(w) > 1 and contains_any(w, wordchars) and not contains_any(w, exclude) and w not in stop]) doc_dict = {fid: clean(corpus.words(fid)) for cat in corpus.categories() for fid in corpus.fileids(cat)} # XXX docs = doc_dict.values() dictionary = gensim.corpora.Dictionary(docs) doc_ids = [k for k in doc_dict.keys()] doc_term_matrix = [dictionary.doc2bow(doc) for doc in docs] bow_array = np.array(doc_term_matrix) def find_best_lda_model(texts, bow, id2word, min_n=min_topics, max_n=max_topics): best_model = None max_coherence = -1 for n in range(min_n, max_n + 1): ctm = CtmModel( bow, id2word=id2word, num_topics=n)
from itertools import chain # from nltk import trigrams, word_tokenize, sent_tokenize, FreqDist from nltk.corpus.reader.plaintext import CategorizedPlaintextCorpusReader from nltk.util import ingrams n = 3 train_path = "data/task1_train" print "Loading categorized corpus in", train_path, "..." cr = CategorizedPlaintextCorpusReader(train_path, ".*", cat_pattern="(\w*)") # Get categories print "%d categories: %s" % (len(cr.categories()), ", ".join(cr.categories())) for c in [cr.categories()[0]]: print c + "..." sys.stdout.flush() ngrams = {} for i in range(n, 0, -1): print str(i) + "-grams..." ngrams[i] = {} prefix = ("",) * (i - 1) for ngram in ingrams(chain(prefix, cr.words(categories=[c])), n): if not ngram in ngrams[i]: ngrams[i][ngram] = 0 ngrams[i][ngram] += 1
r'.*\.txt', cat_file="../textcats.prn") """ fileid="nytimes-2017.txt" raw = corpus.raw(fileid) raw = raw.replace("N.H.S.", "NHS") words = word_tokenize(raw) words = corpus.words(fileid) clean0 = [word for word in words if word not in stoplist] """ bloblist = corpus.fileids() #bloblist = corpus.fileids(categories='2016') M=len(bloblist) # Look at the categories corpus.categories() # for each file in the corpus for fileid in bloblist: raw = corpus.raw(fileid) raw = raw.replace("N.H.S.", "NHS") raw = raw.replace("per cent", "%") raw = raw.replace("votes", "vote") raw = raw.replace("voted", "vote") words = word_tokenize(raw) # Bring in the default English NLTK stop words stoplist = stopwords.words('english') # Define additional stopwords in a string this will preserve the word image (without capital) mid sentence additional_stopwords = """also one The Media playback is unsupported on your device caption Image Images copyright Reuters AP Getty EPA said BBC"""