def cluster_articles():
ms = MongoStore()
articles = [a for a in ms.get_pending_articles()]
if len(articles) > 0:
tfidf = TfidfVectorizer(tokenizer=preprocess)
good_articles = [article for article in articles
if article["text_content"].strip() != ""]
texts = [article["text_content"] for article in good_articles]
X_tfidf = tfidf.fit_transform(texts)
print X_tfidf
ap = AffinityPropagation(damping=0.95, max_iter=4000,
convergence_iter=400, copy=True, preference=-4,
affinity='euclidean', verbose=True)
C = ap.fit_predict(X_tfidf)
print X_tfidf.shape, C.shape
print C
centers = ap.cluster_centers_indices_
clusters = []
for c, center in enumerate(centers):
members = np.where(C == c)[0]
K = cosine_similarity(X_tfidf[members], X_tfidf[center])
member_sims = [(m, float(k)) for m, k in zip(members, K)]
member_sims.sort(key=lambda x: x[1], reverse=True)
cluster = {"articles": [], "date": datetime.now(), "summarized": False}
if len([member for member, sim in member_sims if sim > .55]) >= 3:
print texts[center][:75].replace("\n", " ")
for member, sim in member_sims:
print "\t{:3.3f} ".format(sim),
print good_articles[member]["title"][:60].replace("\n", " ")
cluster["articles"].append((good_articles[member]["_id"], sim))
else:
continue
clusters.append(cluster)
if len(clusters) > 0:
ms.insert_clusters(clusters)
ms.set_clustered_flag(articles)
from nltk import word_tokenize
from nltk.stem import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import AffinityPropagation
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# Import local modules
module_path = os.path.dirname(os.path.realpath(__file__))
datastore_module = os.path.join(module_path,
os.path.join('..' + os.sep + 'datastore'))
sys.path.append(datastore_module)
from mongo import MongoStore
ms = MongoStore()
articles = [a for a in ms.get_pending_articles()]
if len(articles) > 0:
stemmer = PorterStemmer()
stop = stopwords.words('english')
def preprocess(text):
tokens = [
tok for tok in word_tokenize(text.lower()) if tok not in stop
]
tokens_stemmed = [stemmer.stem(tok) for tok in tokens]
return tokens_stemmed
tfidf = TfidfVectorizer(tokenizer=preprocess)
def cluster_articles():
ms = MongoStore()
articles = [a for a in ms.get_pending_articles()]
if len(articles) > 0:
tfidf = TfidfVectorizer(tokenizer=preprocess)
good_articles = [
article for article in articles
if article["text_content"].strip() != ""
]
texts = [article["text_content"] for article in good_articles]
X_tfidf = tfidf.fit_transform(texts)
print X_tfidf
ap = AffinityPropagation(damping=0.95,
max_iter=4000,
convergence_iter=400,
copy=True,
preference=-4,
affinity='euclidean',
verbose=True)
C = ap.fit_predict(X_tfidf)
print X_tfidf.shape, C.shape
print C
centers = ap.cluster_centers_indices_
clusters = []
for c, center in enumerate(centers):
members = np.where(C == c)[0]
K = cosine_similarity(X_tfidf[members], X_tfidf[center])
member_sims = [(m, float(k)) for m, k in zip(members, K)]
member_sims.sort(key=lambda x: x[1], reverse=True)
cluster = {
"articles": [],
"date": datetime.now(),
"summarized": False
}
if len([member for member, sim in member_sims if sim > .55]) >= 3:
print texts[center][:75].replace("\n", " ")
for member, sim in member_sims:
print "\t{:3.3f} ".format(sim),
print good_articles[member]["title"][:60].replace(
"\n", " ")
cluster["articles"].append(
(good_articles[member]["_id"], sim))
else:
continue
clusters.append(cluster)
if len(clusters) > 0:
ms.insert_clusters(clusters)
ms.set_clustered_flag(articles)
from nltk import word_tokenize
from nltk.stem import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import AffinityPropagation
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# Import local modules
module_path = os.path.dirname(os.path.realpath(__file__))
datastore_module = os.path.join(module_path,os.path.join(
'..' + os.sep + 'datastore' ))
sys.path.append(datastore_module)
from mongo import MongoStore
ms = MongoStore()
articles = [a for a in ms.get_pending_articles()]
if len(articles) > 0:
stemmer = PorterStemmer()
stop = stopwords.words('english')
def preprocess(text):
tokens = [tok for tok in word_tokenize(text.lower())
if tok not in stop]
tokens_stemmed = [stemmer.stem(tok) for tok in tokens]
return tokens_stemmed
tfidf = TfidfVectorizer(tokenizer=preprocess)
