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gridSearchChi2.py
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gridSearchChi2.py
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# coding: utf-8
import cPickle, json, pdb, pickle, theano, sys, numpy, time, os,datetime
import sklearn.decomposition
import os.path
import gc
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_regression
from experiment.PredictPrices import SdA_RNN
from experiment.PredictPrices import DBN_regression
from yoshihara.PredictPrices import RNNRBM_MLP
from yoshihara.PredictPrices import RNNRBM_DBN
from yoshihara.PredictPrices import DBN
from experiment.PredictPrices.RNN import train_RNN, train_RNN_hf, train_RNN_minibatch
import curses
import warnings
warnings.filterwarnings("ignore")
import locale
locale.setlocale(locale.LC_ALL, "")
default_model_dir = 'yoshihara/Model'
############################
### Setting parameters ###
############################
dataset_type = 'test' # ['all' / 'chi2_selected']
params = {
'experiment_type' : 'chi2_selected',#chi2_selected or baseline
'STEP1' : {
'beta' : 1.,
'model' : 'sae',
'n_hidden' : 5000,
'learning_rate' : 0.05
},
'STEP3' : {
'brandcode' : '0101'
},
'STEP4' : {
'model' : 'sda',
'corruption_levels' : [.3, .3, .3],
'hidden_recurrent' : 10,
'k' : 1,
'hidden_layers_sizes' : [10],
'pretrain' : {
'batch_size' : 50,
'learning_rate' : 0.05,
'epochs' : 1
},
'finetune' : {
'batch_size' : 50,
'learning_rate' : 0.0001,
'epochs' :1
}
}
}
#######################################
### grid searchのパラメータ格納 ###
#######################################
params['STEP3']['brandcode'] = ['0101']
params['STEP4']['hidden_layers_sizes'] = [
[2000]
]
params['STEP4']['hidden_recurrent'] = [1000]
params['STEP4']['pretrain'] = {
'batch_size' : [30],
'learning_rate' : [0.0005],
'epochs' : [200]
}
params['STEP4']['finetune'] = {
'batch_size' : [10,30,50,100],
'learning_rate' : [0.00001,0.0001,0.001,0.01],
'epochs' : [200]
}
model_dirs = {}
#######################################
#### config: message in cosole ####
#######################################
initial_msg = []
initial_msg.append('** どのステップから始めるかを入力して下さい。 **\n')
initial_msg.append('1: 圧縮モデル Sparse Auto-encoder / RBM の作成・訓練を行う')
initial_msg.append('2: 訓練された圧縮モデルを用い、複数記事を圧縮する')
initial_msg.append('3: 指定された銘柄の株価と記事データを組み合わせて銘柄の株価を予測する')
num_max = len(initial_msg)
labeltype_msg = []
labeltype_msg.append('以下から正解ラベルの形式を選択して下さい．\n')
labeltype_msg.append('1 : 回帰 : (終値 - 始値) / 終値')
labeltype_msg.append('2 : 回帰 : 翌日MACD - 当日MACD')
labeltype_msg.append('3 : 二値分類 : (終値 - 始値) <> 0')
labeltype_msg.append('4 : 二値分類 : 翌日MACD - 当日MACD <> 0')
x, y, z, i, l, m = '', '', '', '', '', ''
def msg_loop(stdscr):
global x, y, z, i, l, m
msg_head = ''
curses.echo()
while(1):
curses.flushinp()
stdscr.clear()
msg = '\n'.join(initial_msg) + '\n\n'
stdscr.addstr(msg)
x = int(stdscr.getstr())
if 0 < x < num_max:
# curses.flushinp()
# stdscr.clear()
# msg = '** ' + initial_msg[x] + '\n\n'
# msg += 'このステップから後のいくつのステップを実行しますか？\n'
# msg += '0: cancel, 1~: number of steps\n'
# stdscr.addstr(msg)
# y = int(stdscr.getstr())
# if y > 0:
curses.flushinp()
stdscr.clear()
msg = '** ' + initial_msg[x] + '\n\n'
msg = json.dumps(model_dirs, indent=2) + '\n'
msg += '以下のファイルが上書きされる可能性があります。実行しますか？ [ y / n ]\n'
for path in model_dirs.values():
if os.path.exists(path):
msg += path + '\n'
stdscr.addstr(msg)
z = stdscr.getstr()
if z == '' or z == 'y':
break
curses.flushinp()
stdscr.clear()
else:
curses.flushinp()
stdscr.clear()
msg = ' ** 注 ** ' + str(num_max) + 'までの値を入力して下さい。\n'
stdscr.addstr(msg)
curses.flushinp()
stdscr.clear()
if x == 1:
while True:
msg = '** ' + initial_msg[x] + '\n\n'
msg += '以下を選択して下さい。\n'
msg += '1: さいしょからはじめる、2: つづきからはじめる\n'
stdscr.addstr(msg)
i = int(stdscr.getstr())
curses.flushinp()
stdscr.clear()
if i <= 2:
break
else:
msg = ' ** 注 ** 1 ~ 2 までの値を入力して下さい。\n'
stdscr.addstr(msg)
if x == 3:
while True:
msg = '** ' + initial_msg[x] + '\n\n'
msg += '\n'.join(labeltype_msg) + '\n'
stdscr.addstr(msg)
l = int(stdscr.getstr())
stdscr.clear()
msg = '** ' + initial_msg[x] + '\n'
msg += '** ' + labeltype_msg[l] + '\n\n'
msg += '以下から予測モデルに利用するモデルを選択して下さい．\n'
msg += '1: SdA_regression, 2: DBN, 3: SdA_RNN, 4:RNNRBM_MLP, 5:RNNRBM_DBN\n'
stdscr.addstr(msg)
m = int(stdscr.getstr())
curses.flushinp()
stdscr.clear()
if l <= 4 and m <= 5:
break
else:
print ' ** 注 ** 適切な値を入力して下さい。\n'
def load_model(model_type='sae', input=None, params_dir=None):
params = cPickle.load(open(params_dir))
if model_type == 'rbm':
model = RBM(input=input, params=params)
else:
model = SparseAutoencoder(input=input, params=params)
return model
############################################################
##### PHASE1: 複数記事の圧縮表現の獲得 #####
############################################################
### STEP 1: Sparse Auto-encoder / RBM のモデルの作成・訓練 ###
############################################################
def build_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['experiment_type'], brandcode=params['STEP3']['brandcode'])
# pdb.set_trace()
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = RBM(input=x, n_visible=dataset.phase1_input_size, n_hidden=params['STEP1']['n_hidden'], reg_weight=params['STEP1']['beta'])
train_rbm(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
else:
model = SparseAutoencoder(input=x, n_visible=dataset.phase1_input_size, n_hidden=params['STEP1']['n_hidden'], beta=params['STEP1']['beta'])
train_sae(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
def retrain_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['experiment_type'], brandcode=params['STEP3']['brandcode'])
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = load_model(model_type='rbm', input=x, params_dir=model_dirs['STEP1'])
train_rbm(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
else:
model = load_model(model_type='sae', input=x, params_dir=model_dirs['STEP1'])
train_sae(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
######################################################################
### STEP 2: 前のステップで訓練された圧縮モデルを用いた複数記事の圧縮表現の獲得 ###
######################################################################
def unify_kijis(dataset):
print 'STEP 2 start...'
if dataset == None:
print 'dataset load...'
dataset = Nikkei(dataset_type=params['experiment_type'], brandcode=params['STEP3']['brandcode'])
# model = load_model(model_dirs['STEP1'])
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
###########################################################
model = load_model(input=x, params_dir=model_dirs['STEP1'], model_type=params['STEP1']['model'])
# model = cPickle.load(open(model_dirs['STEP1']))
###########################################################
dataset.unify_kijis(model, params['STEP1']['model'], params['experiment_type'])
out = open(model_dirs['STEP2'], 'w')
out.write(cPickle.dumps(dataset))
return dataset
######################################################################
##### PHASE2: 各銘柄の株価の予測 #####
######################################################################
### STEP 3: 指定された銘柄の株価と記事データを組み合わせ，銘柄の株価を予測する ###
######################################################################
def reguralize_data(dataset, brandcodes):
for datatype in ['train', 'valid', 'test']:
# pdb.set_trace()
# dataset.phase2[datatype]['x'] = ((dataset.phase2[datatype]['x'] / dataset.phase2[datatype]['x'].max(axis=0)) ** 2)
dataset.phase2[datatype]['x'] = ((dataset.phase2[datatype]['x'] - dataset.phase2[datatype]['x'].min(axis=0)) ** 2) / ((dataset.phase2[datatype]['x'] - dataset.phase2[datatype]['x'].min(axis=0) + 0.001) ** 2).max(axis=0)
for brandcode in brandcodes:
dataset.phase2[datatype][brandcode] /= dataset.phase2[datatype][brandcode].max()
if theano.config.floatX == 'float32':
print 'cast to 32bit matrix'
dataset.phase2[datatype]['x'] = dataset.phase2[datatype]['x'].astype(numpy.float32)
for brandcode in brandcodes:
dataset.phase2[datatype][brandcode] = dataset.phase2[datatype][brandcode].astype(numpy.float32)
def change_brand(brandcode,brandcodes,label_type):
dataset = Nikkei(brandcode = brandcode)
dataset.unify_stockprices(dataset = dataset.raw_data[brandcode],brandcodes=brandcodes,label_type = label_type)
reguralize_data(dataset, brandcodes)
for datatype in ['train', 'valid', 'test']:
dataset.phase2[datatype]['y'] = dataset.phase2[datatype][brandcode]
return dataset
def get_model_params(model):
params = []
for param in model.params:
params.append(param.get_value())
return params
def set_model_params(model, params):
for i, param in enumerate(model.params):
model.params[i].set_value(params[i])
return model
def predict(dataset, model, brandcodes=['0101'], label_type=1, y_type=1, m=1):
print 'STEP 3 start...'
if dataset == None:
if params['experiment_type'] == 'baseline':
print 'start to load baseline dataset...'
dataset = cPickle.load(open(default_model_dir + '/STEP2/baseline_original'))
elif params['experiment_type'] == 'chi2_selected':
print 'start to load chi2_selected dataset...'
else:
print 'start to load proposed dataset...'
dataset = cPickle.load(open(model_dirs['STEP2']))
print 'start to unify stockprice...'
# dataset.unify_stockprices(dataset=dataset.unified, brandcodes=brandcodes, dataset_type=params['experiment_type'], label_type=label_type)
"""
if params['experiment_type'] == 'chi2_selected':
#dataset = change_brand(brandcodes[0],brandcodes,label_type)
#dataset.unify_stockprices(dataset = dataset.raw_data[brandcodes[0]],brandcodes=brandcodes,label_type = label_type)
else:
dataset.unify_stockprices(dataset=dataset.baseline_original, brandcodes=brandcodes,
dataset_type=params['experiment_type'],label_type=label_type)
#change_brand(dataset, '0101')
"""
##################
#ファイル名の決定#
##################
if params['experiment_type'] == 'chi2_selected':
yoshihara_dir = 'chi2'
else:
yoshihara_dir = 'result'
if y_type ==0:
type = "regression"
else:
type = "classification"
if m == 1:
model_type = "sda"
elif m==2:
model_type = "dbn_only"
elif m==4:
model_type = "rnnrbm_mlp"
elif m==5:
model_type = "rnnrbm_dbn"
#model_dirs['STEP4_logs'] = '%s/%s/%s_%d_%s.csv' % (default_model_dir, 'result',type, label_type, model_type)
model_dirs['STEP4_logs'] = '%s/%s/%s_%d_%s.csv' % (default_model_dir,yoshihara_dir,type, label_type, model_type)
all_size = len(params['STEP4']['hidden_recurrent']) * len(params['STEP4']['hidden_layers_sizes']) * len(params['STEP4']['pretrain']['batch_size']) * len(params['STEP4']['pretrain']['learning_rate']) * len(params['STEP4']['pretrain']['epochs']) * len(params['STEP4']['finetune']['batch_size']) * len(params['STEP4']['finetune']['learning_rate']) * len(params['STEP4']['finetune']['epochs'])
i = 0
for brandcode in brandcodes:
dataset = ""
dataset = change_brand(brandcode,brandcodes,label_type)
for hidden_layers_sizes in params['STEP4']['hidden_layers_sizes']:
for batch_size_pretrain in params['STEP4']['pretrain']['batch_size']:
for learning_rate_pretrain in params['STEP4']['pretrain']['learning_rate']:
for epochs_pretrain in params['STEP4']['pretrain']['epochs']:
for hidden_recurrent in params['STEP4']['hidden_recurrent']:
pretrain_params = ""
pretrain_params = {
'dataset' : dataset,
'hidden_layers_sizes' : hidden_layers_sizes,
'pretrain_lr' : learning_rate_pretrain,
'pretrain_batch_size' : batch_size_pretrain,
'pretrain_epochs' : epochs_pretrain,
'corruption_levels' : params['STEP4']['corruption_levels'],
'k' : params['STEP4']['k'],
'hidden_recurrent': hidden_recurrent,
'n_outs' : (1 + y_type)
}
pretrain_model = ""
pretrain_model = model.pretrain(pretrain_params, y_type)
pretrain_params = ""
pretrain_params = get_model_params(pretrain_model)
for batch_size_finetune in params['STEP4']['finetune']['batch_size']:
for learning_rate_finetune in params['STEP4']['finetune']['learning_rate']:
for epochs_finetune in params['STEP4']['finetune']['epochs']:
set_model_params = (pretrain_model,pretrain_params)
finetune_params = ""
finetune_params = {
'dataset' : dataset,
'model' : pretrain_model,
'finetune_lr' : learning_rate_finetune,
'finetune_batch_size' : batch_size_finetune,
'finetune_epochs' : epochs_finetune
}
finetune_model = ""
best_validation_loss = ""
test_score = ""
best_epoch = ""
finetune_model, best_validation_loss, test_score, best_epoch = model.finetune(finetune_params, y_type)
i += 1
print '%d / %d is done...' % (i , all_size)
out = open(model_dirs['STEP4_logs'], 'a')
out.write('%f,%f,%s,%s,%d,%f,%d,%d,%d,%f,%d,%d,%s\n' % (best_validation_loss, test_score, brandcode, str(hidden_layers_sizes).replace(',', ' '), batch_size_pretrain, learning_rate_pretrain, hidden_recurrent, epochs_pretrain, batch_size_finetune, learning_rate_finetune, epochs_finetune, best_epoch, str(datetime.datetime.now())))
out.close()
##############
### Main ###
##############
if __name__ == '__main__':
if len(sys.argv) > 1:
params['STEP1']['n_hidden'] = int(sys.argv[1])
params['STEP1']['learning_rate'] = float(sys.argv[2])
params['STEP1']['beta'] = float(sys.argv[3])
params['STEP1']['model'] = sys.argv[4]
else:
print sys.argv
print '引数が足りません．'
print '引数: n_hidden learning_rate beta model'
sys.exit()
model_dirs = {
'STEP1' : '%s/%s/h%d_lr%s_b%s.%s.params' % (default_model_dir, 'STEP1', params['STEP1']['n_hidden'], str(params['STEP1']['learning_rate']), str(params['STEP1']['beta']), params['STEP1']['model']),
'STEP2' : '%s/%s/h%d_lr%s_b%s.%s' % (default_model_dir, 'STEP2', params['STEP1']['n_hidden'], str(params['STEP1']['learning_rate']), str(params['STEP1']['beta']), params['STEP1']['model']),
'STEP3' : '%s/%s/h%d_lr%s_b%s.%s' % (default_model_dir, 'STEP3', params['STEP1']['n_hidden'], str(params['STEP1']['learning_rate']), str(params['STEP1']['beta']), params['STEP1']['model']),
# 'STEP4' : '%s/%s/%sh%d_lr%.2f_b%s.%s' % (default_model_dir, 'STEP4', params['STEP3']['brandcode'], params['STEP1']['n_hidden'], params['STEP1']['learning_rate'], str(params['STEP1']['beta']), params['STEP1']['model']),
}
# print params
curses.wrapper(msg_loop)
sys.stdout = os.fdopen(0, 'w', 0)
print initial_msg[x]
############################################################
##### PHASE1: 複数記事の圧縮表現の獲得 #####
############################################################
### STEP 1: Sparse Auto-encoder / RBM のモデルの作成・訓練 ###
############################################################
dataset = None
if x == 1:
if i == 1:
build_CompressModel()
elif i == 2:
retrain_CompressModel()
######################################################################
### STEP 2: 前のステップで訓練された圧縮モデルを用いた複数記事の圧縮表現の獲得 ###
######################################################################
if x == 2:
dataset = unify_kijis(dataset)
######################################################################
##### PHASE2: 各銘柄の株価の予測 #####
######################################################################
### STEP 3: 指定された銘柄の株価と記事データを組み合わせ，銘柄の株価を予測する ###
######################################################################
if x == 3:
model = ''
print labeltype_msg[l]
if m == 1:
print 'start SdA_regression'
model = SdA_regression
elif m == 2:
print 'start DBN'
model = DBN
elif m == 4:
print 'start RNNRBM_MLP'
model = RNNRBM_MLP
elif m == 5:
print 'start RNNRBM_DBN'
model = RNNRBM_DBN
brandcodes = ['0101', '7203', '6758', '6502', '7201', '6501', '6702', '6753', '8058', '8031', '7751']
#brandcodes = ['6502', '7201', '6501', '6702', '6753', '8058', '8031', '7751']
predict(dataset, model, brandcodes=brandcodes, label_type=l, y_type=int(l > 2),m=m)