)
plt.subplot(2, 2, 3)
plt.plot(self.train_acc_list, self.dev_acc_list)
plt.xlabel('train acc')
plt.ylabel('dev acc')
fig = plt.gcf()
plt.draw()
fig.savefig(file_name, dpi=100)
if __name__ == '__main__':
import os, sys
sys.path.append(os.pardir)
from common.utils import load_labeled_data, load_word_data
model = BinaryClassifier()
train_data = list(load_labeled_data('../../test/03-train-input.txt'))
test_data = list(load_word_data('../../test/03-train-answer.txt'))
trainer = Trainer(model, train_data, test_data)
trainer.train()
model.save_params('test.pkl')
model2 = BinaryClassifier()
model2.load_params('test.pkl')
print(model.params)
print(model2.params)
y = [model.predict(x) for model in models]
y = np.sign(sum(y))
print(f'{y}\t{" ".join(x)}')
elif arg.mode == 'diag':
from collections import defaultdict
from random import sample
model = BinaryClassifier()
model.load_params('model.pkl')
for name, value in sorted(model.params.items(), key=lambda i: -abs(i[1]))[:10]:
print(f'{name} : {value}')
mistakes = defaultdict(list)
gold = list(load_labeled_data('../../data/titles-en-test.labeled'))
for i, x in enumerate(load_word_data('../../data/titles-en-test.word')):
y = model.predict(x)
t = gold[i][1]
if y != t:
mistakes[f'{t} -> {y}'].append(' '.join(x))
for key, sentences in mistakes.items():
print(f'{key} : {len(sentences)}')
for line in sample(sentences, min(len(sentences), 20)):
print(line)
elif arg.mode == 'verbose':
model = BinaryClassifier()
model.load_params('model.pkl')
# python train_perceptron.py
if __name__ == '__main__':
import sys, os
sys.path.append(os.pardir)
from common.binary_classifier import BinaryClassifier, SimpleOptimizer, Trainer, NormalizingOptimizer
from common.utils import load_labeled_data, load_word_data
import matplotlib.pyplot as plt
model = BinaryClassifier()
optimizer = SimpleOptimizer()
# optimizer = NormalizingOptimizer()
train_data = list(load_labeled_data('../../data/titles-en-train.labeled'))
print(f'train data: {len(train_data)}')
trainer = Trainer(model, train_data, epochs=40, optimizer=optimizer)
trainer.train()
trainer.draw_accuracy('figure.png')
model.save_params('model.pkl')