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predict.py
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# You can write your own classification file to use the module
from attention.model import StructuredSelfAttention
from attention.train import train, get_activation_wts, evaluate, predict
from utils.pretrained_glove_embeddings import load_glove_embeddings
from visualization.attention_visualization import createHTML
import torch
import numpy as np
from torch.autograd import Variable
from keras.preprocessing.sequence import pad_sequences
import torch.nn.functional as F
import torch.utils.data as data_utils
import os
import sys
import json
import csv
from input.data_loader import load_data_set, load_label_data
import argparse
class Predict:
data_params = {}
model_params = {}
params_set = {}
def json_to_dict(self, json_set):
for k, v in json_set.items():
if v == 'False':
json_set[k] = False
elif v == 'True':
json_set[k] = True
else:
json_set[k] = v
return json_set
def init_model(self):
with open('config.json', 'r') as f:
self.params_set = json.load(f)
with open('model_params.json', 'r') as f:
self.model_params = json.load(f)
self.params_set = self.json_to_dict(self.params_set)
self.model_params = self.json_to_dict(self.model_params)
parser = argparse.ArgumentParser(
prog='train'
)
# parser.add_argument("square", type=int,
# help="display a square of a given number")
parser.add_argument("-i", "--data_csv", type=str,
help="data_csv")
parser.add_argument("-d", "--dict_txt", type=str,
help="dict_txt")
parser.add_argument("-s", "--syns_csv", type=str,
help="syns_csv")
parser.add_argument("-l", "--labels_csv", type=str,
help="labels_csv")
parser.add_argument('-v', '--verbose', action='store_true')
args = parser.parse_args()
if args.data_csv != None and len(args.data_csv) > 0:
self.data_params['data_csv'] = args.data_csv
else:
self.data_params['data_csv'] = 'data.csv'
if args.labels_csv != None and len(args.labels_csv) > 0:
self.data_params['labels_csv'] = args.labels_csv
else:
self.data_params['labels_csv'] = 'labels.csv'
if args.syns_csv != None and len(args.syns_csv) > 0:
self.data_params['syns_csv'] = args.syns_csv
else:
self.data_params['syns_csv'] = ''
if args.dict_txt != None and len(args.dict_txt) > 0:
self.data_params['dict_txt'] = args.dict_txt
else:
self.data_params['dict_txt'] = 'dict.txt'
self.params_set['verbose'] = args.verbose
print('\nLoading settings...')
print("data :", self.data_params)
print("param:", self.params_set)
print("model:", self.model_params)
def predict_attention(self, attention_model, wts, x_test_pad, word_to_id, word_to_word, count, filename):
labels = load_label_data(self.data_params['labels_csv'])
wts_add = torch.sum(wts, 1)
wts_add_np = wts_add.data.numpy()
wts_add_list = wts_add_np.tolist()
id_to_word = {v: k for k, v in word_to_id.items()}
result = []
text = []
correct = 0
correct2 = 0
n = 0
for test in x_test_pad:
attention_model.batch_size = 1
attention_model.hidden_state = attention_model.init_hidden()
x_test_var = Variable(torch.from_numpy(test).type(torch.LongTensor))
y_test_pred, _ = attention_model(x_test_var)
# 結果のリストを降順に並べる
m = 0
dic = {}
for x in y_test_pred[0]:
dic[x] = m
m += 1
yy = sorted(dic.items(), reverse=True)
m = 0
pred = []
for y in yy:
m += 1
l = str(y[1])
if len(labels) > 0:
l = labels[y[1]]
pred.append(l)
if m >= count:
break
result.append(pred)
n += 1
return result
def do_predict(self, text, count):
PATH = 'db/jc.model'
MAXLENGTH = self.model_params['timesteps']
# Load data
full_dataset = [['0', text]]
# data_path = data_params['data_csv']
# full_dataset = []
# with open(data_path, 'r', encoding="cp932") as f:
# reader = csv.reader(f)
# i = 0
# for line in reader:
# if len(line) > 0:
# full_dataset.append(line)
# i += 1
# break
# print(full_dataset)
train_loader, train_set, test_set, x_train_pad, x_test_pad, word_to_id, word_to_word = load_data_set(
full_dataset, self.data_params, 1, MAXLENGTH, self.model_params["vocab_size"], self.model_params['batch_size'], True)
# Using pretrained embeddings
if self.params_set["use_embeddings"]:
embeddings = load_glove_embeddings(
"glove/glove.6B.50d.txt", word_to_id, 50)
else:
embeddings = None
# Loadl model
attention_model = StructuredSelfAttention(batch_size=train_loader.batch_size, lstm_hid_dim=self.model_params['lstm_hidden_dimension'], d_a=self.model_params["d_a"], r=self.params_set["attention_hops"], vocab_size=len(
word_to_id), max_len=MAXLENGTH, type=1, n_classes=self.model_params["num_classes"], use_pretrained_embeddings=self.params_set["use_embeddings"], embeddings=embeddings)
attention_model.load_state_dict(torch.load(PATH))
# Predict
wts = get_activation_wts(attention_model, Variable(
torch.from_numpy(x_test_pad[:]).type(torch.LongTensor)))
print(wts.size())
res = self.predict_attention(
attention_model, wts, x_test_pad[:], word_to_id, word_to_word, count, filename='predict_attention')
return res
if __name__ == '__main__':
predict = Predict()
predict.init_model()
while True:
inputstr = input('> ')
if len(inputstr) > 0:
res = predict.do_predict(inputstr, 10)
print(res)