def context_users_similarity(self):
"""Compute the similarity between users using context features"""
filename = os.path.join(OUTPUT_PATH, "pickle",
"context_users_features.pickle")
if os.path.isfile(filename):
with open(filename) as f:
features = pickle.load(f)
else:
self._processor = ContextProcessor()
features = []
# get all the features for each user
for user, docs in self._processor.iterate():
features.append(self._processor.get_features(docs, user))
with open(filename, "w+") as f:
pickle.dump(features, f)
reduced_features = []
for doc in features:
reduced_features.append(np.mean(doc, axis=1))
from ipdb import set_trace
set_trace()
# it is possible to cluster each user's documents
#
# for alexis, let's print the similarity matrix of his documents
draw_matrix(euclidean_distances(features[0], features[0]),
"context_alexis", OUTPUT_PATH)
def run_processors(self):
args = {'draw_2d': True, 'draw_pie': True}
processors = (TextProcessor(**args), ContextProcessor(**args))
for processor in processors:
processor.run()
