persona_eval.py

import json
import math


def docs(w, history_list):
    c = 0
    for i, h in enumerate(history_list):
        if w in h:
            c += 1
    return c


def gen_idf_dict(history_list):
    idf_dict = {}
    for i, h in enumerate(history_list):
        for w in h:
            if w not in idf_dict:
                idf = math.log(len(history_list) * 1.0 / docs(w, history_list))
                idf_dict[w] = idf
    return idf_dict


def cal_s_for_each_history(r, h, idf_dict):
    c = 0
    has_c = {}
    for w in r:
        if w in h and w not in has_c:
            c += idf_dict[w]
            has_c[w] = 1
    return c


def cal_p_cover(src_generate, all_personas):
    s_sum = 0
    line_cnt = 0
    for result, personas in zip(src_generate, all_personas):
        idf_dict = gen_idf_dict(personas)
        
        
        s_list = []
        for i, persona in enumerate(personas):
            s = cal_s_for_each_history(result, persona, idf_dict)
            s_list.append(s)
        s_max = max(s_list)
        s_sum += s_max
        line_cnt += 1
    return (s_sum + 0.0) / line_cnt


def cal_f1(result, personas):
    p_all = []
    for i, p in enumerate(personas):
        p_all += p
    h_set = set(p_all)
    r_set = set(result)
    if len(h_set) == 0 or len(r_set) == 0:
        p, r = 0, 0
    else:
        p = len(h_set & r_set) / len(r_set)
        r = len(h_set & r_set) / len(h_set)
    if p == r == 0:
        return 0
    return (2 * p * r) / (p + r)


def cal_p_f1(src_generate, all_personas):
    s_sum = 0
    line_cnt = 0
    for result, personas in zip(src_generate, all_personas):
        s_sum += cal_f1(result, personas)
        line_cnt += 1
    return (s_sum + 0.0) / line_cnt