def co_review_rating_dev_dist(self, user_buz_rating):
'''
@summary: The distribution of the rating deviation of co-reviews for positive pairs and negative pairs
'''
score_pos, score_neg = [], []
for i in xrange(len(self.user_base) - 1):
print str(i)+'/'+str(len(self.user_base)), 'th', 'user'
u_i = self.user_base[i]
label_i = self.user_label[u_i]
for j in xrange(i+1, len(self.user_base)):
u_j = self.user_base[j]
label_j = self.user_label[u_j]
rating_dev_co_review = Yelp.pf_rating_deviation(u_i, u_j, (user_buz_rating,))
if rating_dev_co_review > 0:
if label_i * label_j == 1: score_pos.append(rating_dev_co_review)
else: score_neg.append(rating_dev_co_review)
print 'num of pos pairs:', len(score_pos)
print 'num of neg pairs:', len(score_neg)
print mquantiles(score_pos)
print mquantiles(score_neg)
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.boxplot([score_pos, score_neg],
labels=['pos', 'neg'])
ax2.hist([score_pos, score_neg],
color = ['r', 'b'],
cumulative=False,
normed=True,
label=['pos', 'neg'])
ax2.legend()
plt.show()
if __name__ == '__main__':
yc = ConfigParser.ConfigParser()
yc.read(CONFIG)
sfx = yc.get('Path', 'yep_data_class')
st = Stat(user_base = Utility.load_user_filtered(L_USER_F + sfx), # specify user base
user_label = Utility.load_user_label(R_USER_LABEL + sfx)) # specify user label
# st.co_review_num_dist(Utility.load_user_buz_rating(R_USER_BUZ_RATING + sfx))
# st.co_review_rating_dev_dist(Utility.load_user_buz_rating(R_USER_BUZ_RATING + sfx))
# st.knn_sim_distribution(np.iinfo(np.int64).max, Yelp.load_sim_mat(SM_COMMON_FRIEND + sfx))
# st.knn_sim_distribution(10, Yelp.load_sim_mat(SM_COMMON_FRIEND + sfx))
# st.knn_sim_distribution(5, Yelp.load_sim_mat(SM_COMMON_FRIEND + sfx))
# st.knn_sim_distribution(2, Yelp.load_sim_mat(SM_COMMON_FRIEND + sfx))
# st.knn_sim_distribution(1, Yelp.load_sim_mat(SM_COMMON_FRIEND + sfx))
# st.knn_sim_distribution(np.iinfo(np.int64).max, Yelp.load_sim_mat(SM_RATING_DEVIATION + sfx))
# st.knn_sim_distribution(np.iinfo(np.int64).max, Yelp.load_sim_mat(SM_COREVIEW_RATIO + sfx))
# st.knn_sim_distribution(1, Yelp.load_sim_mat(SM_COREVIEW_RATIO + sfx))
st.knn_sim_distribution(2, Yelp.load_sim_mat(SM_COREVIEW_RATIO + sfx))
# st.knn_sim_distribution(5, Yelp.load_sim_mat(SM_COREVIEW_RATIO + sfx))
# st.knn_sim_distribution(10, Yelp.load_sim_mat(SM_COREVIEW_RATIO + sfx))
