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error_analysis.py
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error_analysis.py
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# coding: utf-8
import cPickle, json, pdb, pickle, theano, sys, time, os, codecs
import numpy as np
from progressbar import ProgressBar
from sklearn.decomposition import PCA
from sklearn.svm import SVR
from sklearn.grid_search import GridSearchCV
import os.path
import copy
import theano.tensor as T
from dataset.Nikkei import Nikkei
from experiment.CompressSparseVector.SparseAutoencoder import SparseAutoencoder, train_sae
from experiment.CompressSparseVector.RBM import RBM, train_rbm
from experiment.PredictPrices import SdA
from experiment.PredictPrices import SdA_RNN
from experiment.PredictPrices import DBN
from experiment.PredictPrices.RNN import train_RNN, train_RNN_hf, train_RNN_minibatch
import curses
from run import *
def test_phase1(params, model_dirs):
model = load_model(input=x, params_dir=model_dirs['STEP1'], model_type=params['model'])
dic = json.load(codecs.open('dataset/dataset/chi2-result-unified_10000.rdic', 'r', 'utf-8'))
threshold = 0.2
max_len = 10
out = codecs.open(model_dirs['STEP1_analysis'], 'w', 'shift-jis')
# bar = ProgressBar(maxval=model.W.get_value().T.shape[0]).start()
args = model.W.get_value().T.argsort()
# pdb.set_trace()
w_t = model.W.get_value().T
words = []
for i in range(model.W.get_value().T.shape[0]):
# bar.update(i)
W_values = []
for n in range(-1, -11, -1):
if dic[str(args[i][n])] not in words:
words.append(dic[str(args[i][n])])
# W_values.append('%s - %.2f' % (dic[str(args[i][n])], w_t[i][args[i][n]]))
W_values.append('%s' % (dic[str(args[i][n])]))
out.write(','.join(W_values) + '\n')
out.close()
print model_dirs['STEP1_analysis'] + str(len(words))
def test_phase1_sda(params, model_dirs):
model = cPickle.load(open(model_dirs['STEP1']))
dic = json.load(codecs.open('dataset/dataset/chi2-result-unified.rdic', 'r', 'utf-8'))
threshold = 0.2
max_len = 10
out = codecs.open(model_dirs['STEP1_analysis'], 'w', 'shift-jis')
bar = ProgressBar(maxval=model.params[0].get_value().T.shape[0]).start()
args1 = model.params[0].get_value().T.argsort()
w_t1 = model.params[0].get_value().T
for i in range(model.params[0].get_value().T.shape[0]):
bar.update(i)
W_values = []
for n in range(-1, -11, -1):
W_values.append('%s - %.2f' % (dic[str(args1[i][n])], w_t1[i][args1[i][n]]))
# W_values.append('%s' % (dic[str(args1[i][n])]))
out.write(','.join(W_values) + '\n')
out.close()
out2 = codecs.open(model_dirs['STEP1_analysis_2'], 'w', 'shift-jis')
bar = ProgressBar(maxval=model.params[2].get_value().T.shape[0]).start()
args2 = model.params[2].get_value().T.argsort()
w_t2 = model.params[2].get_value().T
for i in range(model.params[2].get_value().T.shape[0]):
bar.update(i)
W_values = []
for n in range(-1, -4, -1):
index = args2[i][n]
for m in range(-1, -5, -1):
W_values.append('%s - %.2f' % (dic[str(args1[index][m])], w_t1[index][args1[index][m]]))
# W_values.append('%s' % (dic[str(args1[i][n])]))
W_values.append('')
out2.write(','.join(W_values) + '\n')
out2.close()
def main(params):
default_model_dir_prefix = 'experiment/Model/'
# default_model_dir_suffix = '/h%d_lr%s_b%s_c%s.%s' % (params['n_hidden'], str(params['learning_rate']), str(params['reg_weight']), str(params['corruption_level']), params['model'])
# default_model_dir_suffix_csv = '/h%d_lr%s_b%s_c%s_%s.csv' % (params['n_hidden'], str(params['learning_rate']), str(params['reg_weight']), str(params['corruption_level']), params['model'])
default_model_dir_suffix = '/h%d_lr%s_s%s_b%s_%s.%s' % (params['n_hidden'], str(params['learning_rate']), str(params['reg_weight']), str(params['batch_size']), params['dataset'], params['model'])
# default_model_dir_suffix_csv = '/h%d_lr%s_s%s_b%s_%s.csv' % (params['n_hidden'], str(params['learning_rate']), str(params['reg_weight']), str(params['batch_size']), params['model'])
default_model_dir_suffix_csv = '/h%d_lr%s_s%s_b%s_%s_%s.csv' % (params['n_hidden'], str(params['learning_rate']), str(params['reg_weight']), str(params['batch_size']), params['dataset'], params['model'])
default_model_dir_suffix_csv_2 = '/h%d_lr%s_s%s_b%s_%s_2.csv' % (params['n_hidden'], str(params['learning_rate']), str(params['reg_weight']), str(params['batch_size']), params['model'])
model_dirs = {
'STEP1' : default_model_dir_prefix + 'STEP1' + default_model_dir_suffix,
'STEP1_analysis' : default_model_dir_prefix + 'STEP1_analysis' + default_model_dir_suffix_csv,
'STEP1_analysis_2' : default_model_dir_prefix + 'STEP1_analysis' + default_model_dir_suffix_csv_2,
'STEP2' : default_model_dir_prefix + 'STEP2' + default_model_dir_suffix,
'STEP3' : default_model_dir_prefix + 'STEP3' + default_model_dir_suffix
}
if params['model'] == 'sda':
test_phase1_sda(params, model_dirs)
else:
test_phase1(params, model_dirs)
if __name__ == '__main__':
if len(sys.argv) <= 1:
params = {}
n_hiddens = [100, 1000, 2000]
learning_rates = [0.05]
reg_weights = [0., 0.1, 0.01, 0.02, 0.05, 0.001, 0.0001]
batch_sizes = [10, 20, 50, 100]
models = ['sae', 'rbm']
datasets = ['article', 'sentence']
for n_hidden in n_hiddens:
for learning_rate in learning_rates:
for reg_weight in reg_weights:
for batch_size in batch_sizes:
for model in models:
for dataset in datasets:
params['n_hidden'] = n_hidden
params['learning_rate'] = learning_rate
params['reg_weight'] = reg_weight
# params['corruption_level'] = corruption_level
params['batch_size'] = batch_size
params['model'] = model
params['dataset'] = dataset
try:
main(params)
except IOError:
pass
else:
params = {
'n_hidden' : int(sys.argv[1]),
'learning_rate' : float(sys.argv[2]),
'reg_weight' : float(sys.argv[3]),
'batch_size' : int(sys.argv[4]),
'model' : sys.argv[5]
}
main(params)