コード例 #1
0
                 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))
コード例 #2
0
ファイル: yelp.py プロジェクト: nuaaxc/ReputationManipulation
        return sm
    
    @staticmethod  
    def show_mat(sm):
#         print sm
        plt.spy(sm, marker='.', precision=0.1, markersize=5)
        plt.show()
    

if __name__ == '__main__':
    yc = ConfigParser.ConfigParser()
    yc.read(CONFIG)
    sfx = yc.get('Path', 'yep_data_class') # indicate which yelp dataset is used - _boston or _SanFrancisco
    
    y = Yelp(sfx = sfx,                                                     # specify dataset class
             user_base = Utility.load_user_filtered(L_USER_F + sfx))        # specify user base
    
#===============================================================================
# Generate Simialrity Matrix
#===============================================================================
    # 1) Common Friend
#     user_friend = Utility.load_user_friends(R_USER_FRIEND + sfx)
#     y.gen_sim_mat(Yelp.pf_common_friend, SM_COMMON_FRIEND + sfx , (user_friend,))

    # 2) Rating Deviation
#     user_buz_rating = Utility.load_user_buz_rating(R_USER_BUZ_RATING + sfx)
#     y.gen_sim_mat(Yelp.pf_rating_deviation, SM_RATING_DEVIATION + sfx , (user_buz_rating,))

    # 3) Co-review ratio
#     user_buz_rating = Utility.load_user_buz_rating(R_USER_BUZ_RATING + sfx)
#     y.gen_sim_mat(Yelp.pf_co_review_ratio, SM_COREVIEW_RATIO + sfx, (user_buz_rating,))