def build_vectors(articles, weights):
"""
Build weighted vector representations for a list of articles.
"""
pub_vecs, bow_vecs, con_vecs = [], [], []
for a in articles:
pub_vecs.append(np.array([a.published]))
bow_vecs.append(vectorize(a.text))
con_vecs.append(concept_vectorize([c.slug for c in a.concepts]))
pub_vecs = normalize(csr_matrix(pub_vecs), copy=False)
bow_vecs = normalize(csr_matrix(bow_vecs), copy=False)
con_vecs = normalize(csr_matrix(con_vecs), copy=False)
# Merge vectors.
vecs = hstack([pub_vecs, bow_vecs, con_vecs])
# Convert to a scipy.sparse.lil_matrix because it is subscriptable.
vecs = vecs.tolil()
# Apply weights to the proper columns:
# col 0 = pub, cols 1-101 = bow, 102+ = concepts
# weights = [pub, bow, concept]
vecs[:,0] *= weights[0]
vecs[:,1:101] *= weights[1]
vecs[:,101:] *= weights[2]
return vecs.toarray()
'image': 'http://www.argos.la/image.jpg',
'name': name
}
return None
def faux_uri_for_name(name):
return "http://fauxpedia.org/resource/{0}".format(name)
def faux_commonness_for_name(name):
return 100
def faux_commonness_for_uri(name):
return 100
def faux_concepts(docs):
return ['Nautilus', 'Picard']
def faux_summarize(title, text):
return ['this', 'is', 'a', 'fake', 'summary']
def faux_multisummarize(docs):
return ['this', 'is', 'a', 'fake', 'summary']
from galaxy import vectorize
cached_vector = vectorize('foo bar')
def faux_vectorize(docs):
return cached_vector
def faux_save_from_url(url, filename):
return 'https://s3.amazon.com/fakeimage.jpg'
def vectors(self):
return vectorize(self.text)
