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segmentation.py
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segmentation.py
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# coding: utf-8
from __future__ import print_function, division
from munkres import Munkres # for Hungarian algorithm
import codecs, re
import sys, os, glob
import tqdm
import time, copy
import numpy as np
import pandas as pd
from numpy.linalg import norm
from scipy import stats
from scipy.misc import comb
from math import floor, ceil, log
import matplotlib.pyplot as plt
from itertools import groupby
#from document_helper import get_orig_labels, get_docnum, calc_doc_ptdw, read_file_data
from document_helper import calc_doc_ptdw, read_plaintext_and_labels
from document_helper import debug
#debug = True
#debug = not True
def calc_cost_matrix(topics, role_nums, f,
phi_val, phi_cols, phi_rows,
theta_val, theta_cols, theta_rows):
labeling_time = 0
word = ''
data = ''
original_topic_num = 0
known_words = phi_rows
sum_p_tdw = np.zeros((len(role_nums), len(topics)))
hits_num = np.zeros((len(role_nums), len(topics)))
t_start = time.time()
time_ptdw = 0
time_cycle = 0
time_magic = 0
phi_sort = np.argsort(phi_rows)
for i, line in enumerate(f):
if debug:
if i % 100:
continue
doc_num, data, original_topic_labels = read_plaintext_and_labels(line)
t0 = time.time()
doc_ptdw = calc_doc_ptdw(data, doc_num,
phi_val=phi_val, phi_rows=phi_rows, phi_sort=phi_sort,
theta_val=theta_val, theta_cols=theta_cols
)
time_ptdw += time.time() - t0
t0 = time.time()
for i, original_topic_num in enumerate(original_topic_labels):
sum_p_tdw[:, original_topic_num] += doc_ptdw[i]
time_cycle += time.time() - t0
t0 = time.time()
argmax_indices = np.argmax(doc_ptdw, axis=1)
np.add.at(hits_num, [argmax_indices, original_topic_labels], 1)
time_magic += time.time() - t0
print("time_ptdw: {} seconds, time_cycle: {} seconds, time_magic: {} seconds".format(time_ptdw, time_cycle, time_magic))
return {'soft': sum_p_tdw, 'harsh': hits_num}
def calc_solution_cost(indexes, cost_matrix):
res_s = 0
for row, column in indexes['soft']:
value = cost_matrix['soft'][row][column]
res_s += value
res_h = 0
for row, column in indexes['harsh']:
value = cost_matrix['harsh'][row][column]
res_h += value
return {'soft': res_s, 'harsh': res_h}
def segmentation_evaluation(topics, f,
phi_val, phi_cols, phi_rows,
theta_val, theta_cols, theta_rows,
indexes=None):
t_mnkr, t_cost_matrix = 0, 0
mnkr = Munkres()
res = {'soft': 0, 'harsh': 0}
res_list = []
#return res, {'soft': [], 'harsh': []}
topics_number = len(topics)
t_start = time.time()
# role playing
top_role_play = (
calc_cost_matrix(
topics=topics, role_nums=range(1, len(topics)+1),
f=f,
phi_val=phi_val, phi_cols=phi_cols, phi_rows=phi_rows,
theta_val=theta_val, theta_cols=theta_cols, theta_rows=theta_rows)
)
t_cost_matrix += (time.time() - t_start)
indexes = {'soft': [], 'harsh': []}
t0 = time.time()
for s in ['soft', 'harsh']:
matrix = top_role_play[s]
#cost_matrix2 = mnkr.make_cost_matrix(matrix,
# lambda cost: sys.maxsize - cost)
cost_matrix = []
for row in matrix:
cost_row = [(sys.maxsize - col) for col in row]
cost_matrix += [cost_row]
indexes[s] = mnkr.compute(cost_matrix)
t_mnkr += (time.time() - t0)
# segmentation evaluation
res = calc_solution_cost(indexes=indexes, cost_matrix=top_role_play)
t_end = time.time()
print("segmentation_evaluation: {} seconds".format(t_end - t_start))
print("mnkr: {} seconds, cost_matrix: {} seconds".format(t_mnkr, t_cost_matrix))
return res, indexes
def output_detailed_cost(topics, f,
phi_val, phi_cols, phi_rows,
theta_val, theta_cols, theta_rows,
indexes, filename):
sum_p_tdw = np.zeros((len(topics), len(topics)))
data_model = {t: [] for t, t2 in indexes["soft"]}
data_model["doc_len"] = []
details = {"soft": pd.DataFrame(data_model), "harsh": pd.DataFrame(data_model)}
details["soft"].index.name = "doc_num"
details["harsh"].index.name = "doc_num"
phi_sort = np.argsort(phi_rows)
for i, line in enumerate(f):
if debug:
if i % 100:
continue
doc_num, data, original_topic_labels = read_plaintext_and_labels(line)
doc_ptdw = calc_doc_ptdw(data, doc_num,
phi_val=phi_val, phi_rows=phi_rows, phi_sort=phi_sort,
theta_val=theta_val, theta_cols=theta_cols
)
def prepare(mode):
local_res = pd.Series({t: 0.0 for t, n in enumerate(topics)})
tmp = np.zeros(len(indexes[mode]), dtype=int)
for row, column in indexes[mode]:
tmp[column] = row
return local_res, tmp
local_res_s, tmp = prepare('soft')
for i, column in enumerate(original_topic_labels):
local_res_s[column] += doc_ptdw[i, tmp[column]]
local_res_s["doc_len"] = len(data)
details['soft'].loc[doc_num] = local_res_s
hits_num = np.zeros((len(topics), len(topics)))
argmax_indices = np.argmax(doc_ptdw, axis=1)
np.add.at(hits_num, [argmax_indices, original_topic_labels], 1)
local_res_h = pd.Series({t: 0 for t, n in enumerate(topics)})
for row, column in indexes["harsh"]:
local_res_h[column] = hits_num[row, column]
local_res_h["doc_len"] = len(data)
details['harsh'].loc[doc_num] = local_res_h
for m in ("soft", "harsh"):
details[m].to_csv(filename.format(m), sep=";", encoding='utf-8')
'''
def calc_solution_cost(indexes, cost_matrix):
res_s = 0
for row, column in indexes['soft']:
value = cost_matrix['soft'][row][column]
res_s += value
res_h = 0
for row, column in indexes['harsh']:
value = cost_matrix['harsh'][row][column]
res_h += value
return {'soft': res_s, 'harsh': res_h}
'''