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H3_0-4760.py
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H3_0-4760.py
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#!/usr/bin/env python
import sys
import os
import pickle as pkl
import time
import random
import numpy as np
import theano.tensor as T
import theano
import pylearn2.train
import pylearn2.models.mlp as p2_md_mlp
import pylearn2.datasets.dense_design_matrix as p2_dt_dd
import pylearn2.training_algorithms.sgd as p2_alg_sgd
import pylearn2.training_algorithms.learning_rule as p2_alg_lr
import pylearn2.costs.mlp.dropout as p2_ct_mlp_dropout
import pylearn2.termination_criteria as p2_termcri
from numpy import dtype
def main():
base_name = sys.argv[1]
n_epoch = int(sys.argv[2])
n_hidden = int(sys.argv[3])
include_rate = float(sys.argv[4])
in_size = 943
out_size = 4760
b_size = 200
l_rate = 3e-4
l_rate_min = 1e-5
decay_factor = 0.9
lr_scale = 3.0
momentum = 0.5
init_vals = np.sqrt(6.0/(np.array([in_size, n_hidden, n_hidden, n_hidden])+np.array([n_hidden, n_hidden, n_hidden, out_size])))
print 'loading data...'
X_tr = np.load('bgedv2_X_tr_float64.npy')
Y_tr = np.load('bgedv2_Y_tr_0-4760_float64.npy')
Y_tr_target = np.array(Y_tr)
X_va = np.load('bgedv2_X_va_float64.npy')
Y_va = np.load('bgedv2_Y_va_0-4760_float64.npy')
Y_va_target = np.array(Y_va)
X_te = np.load('bgedv2_X_te_float64.npy')
Y_te = np.load('bgedv2_Y_te_0-4760_float64.npy')
Y_te_target = np.array(Y_te)
X_1000G = np.load('1000G_X_float64.npy')
Y_1000G = np.load('1000G_Y_0-4760_float64.npy')
Y_1000G_target = np.array(Y_1000G)
X_GTEx = np.load('GTEx_X_float64.npy')
Y_GTEx = np.load('GTEx_Y_0-4760_float64.npy')
Y_GTEx_target = np.array(Y_GTEx)
random.seed(0)
monitor_idx_tr = random.sample(range(88807), 5000)
data_tr = p2_dt_dd.DenseDesignMatrix(X=X_tr.astype('float32'), y=Y_tr.astype('float32'))
X_tr_monitor, Y_tr_monitor_target = X_tr[monitor_idx_tr, :], Y_tr_target[monitor_idx_tr, :]
h1_layer = p2_md_mlp.Tanh(layer_name='h1', dim=n_hidden, irange=init_vals[0], W_lr_scale=1.0, b_lr_scale=1.0)
h2_layer = p2_md_mlp.Tanh(layer_name='h2', dim=n_hidden, irange=init_vals[1], W_lr_scale=lr_scale, b_lr_scale=1.0)
h3_layer = p2_md_mlp.Tanh(layer_name='h3', dim=n_hidden, irange=init_vals[2], W_lr_scale=lr_scale, b_lr_scale=1.0)
o_layer = p2_md_mlp.Linear(layer_name='y', dim=out_size, irange=0.0001, W_lr_scale=lr_scale, b_lr_scale=1.0)
model = p2_md_mlp.MLP(nvis=in_size, layers=[h1_layer, h2_layer, h3_layer, o_layer], seed=1)
dropout_cost = p2_ct_mlp_dropout.Dropout(input_include_probs={'h1':1.0, 'h2':include_rate, 'h3':include_rate,
'y':include_rate},
input_scales={'h1':1.0, 'h2':np.float32(1.0/include_rate),
'h3':np.float32(1.0/include_rate),
'y':np.float32(1.0/include_rate)})
algorithm = p2_alg_sgd.SGD(batch_size=b_size, learning_rate=l_rate,
learning_rule = p2_alg_lr.Momentum(momentum),
termination_criterion=p2_termcri.EpochCounter(max_epochs=1000),
cost=dropout_cost)
train = pylearn2.train.Train(dataset=data_tr, model=model, algorithm=algorithm)
train.setup()
x = T.matrix()
y = model.fprop(x)
f = theano.function([x], y)
MAE_va_old = 10.0
MAE_va_best = 10.0
MAE_tr_old = 10.0
MAE_te_old = 10.0
MAE_1000G_old = 10.0
MAE_1000G_best = 10.0
MAE_GTEx_old = 10.0
outlog = open(base_name + '.log', 'w')
log_str = '\t'.join(map(str, ['epoch', 'MAE_va', 'MAE_va_change', 'MAE_te', 'MAE_te_change',
'MAE_1000G', 'MAE_1000G_change', 'MAE_GTEx', 'MAE_GTEx_change',
'MAE_tr', 'MAE_tr_change', 'learing_rate', 'time(sec)']))
print log_str
outlog.write(log_str + '\n')
sys.stdout.flush()
for epoch in range(0, n_epoch):
t_old = time.time()
train.algorithm.train(train.dataset)
Y_va_hat = f(X_va.astype('float32')).astype('float64')
Y_te_hat = f(X_te.astype('float32')).astype('float64')
Y_tr_hat_monitor = f(X_tr_monitor.astype('float32')).astype('float64')
Y_1000G_hat = f(X_1000G.astype('float32')).astype('float64')
Y_GTEx_hat = f(X_GTEx.astype('float32')).astype('float64')
MAE_va = np.abs(Y_va_target - Y_va_hat).mean()
MAE_te = np.abs(Y_te_target - Y_te_hat).mean()
MAE_tr = np.abs(Y_tr_monitor_target - Y_tr_hat_monitor).mean()
MAE_1000G = np.abs(Y_1000G_target - Y_1000G_hat).mean()
MAE_GTEx = np.abs(Y_GTEx_target - Y_GTEx_hat).mean()
MAE_va_change = (MAE_va - MAE_va_old)/MAE_va_old
MAE_te_change = (MAE_te - MAE_te_old)/MAE_te_old
MAE_tr_change = (MAE_tr - MAE_tr_old)/MAE_tr_old
MAE_1000G_change = (MAE_1000G - MAE_1000G_old)/MAE_1000G_old
MAE_GTEx_change = (MAE_GTEx - MAE_GTEx_old)/MAE_GTEx_old
MAE_va_old = MAE_va
MAE_te_old = MAE_te
MAE_tr_old = MAE_tr
MAE_1000G_old = MAE_1000G
MAE_GTEx_old = MAE_GTEx
t_new = time.time()
l_rate = train.algorithm.learning_rate.get_value()
log_str = '\t'.join(map(str, [epoch+1, '%.6f'%MAE_va, '%.6f'%MAE_va_change, '%.6f'%MAE_te, '%.6f'%MAE_te_change,
'%.6f'%MAE_1000G, '%.6f'%MAE_1000G_change, '%.6f'%MAE_GTEx, '%.6f'%MAE_GTEx_change,
'%.6f'%MAE_tr, '%.6f'%MAE_tr_change, '%.5f'%l_rate, int(t_new-t_old)]))
print log_str
outlog.write(log_str + '\n')
sys.stdout.flush()
if MAE_tr_change > 0:
l_rate = l_rate*decay_factor
if l_rate < l_rate_min:
l_rate = l_rate_min
train.algorithm.learning_rate.set_value(np.float32(l_rate))
if MAE_va < MAE_va_best:
MAE_va_best = MAE_va
outmodel = open(base_name + '_bestva_model.pkl', 'wb')
pkl.dump(model, outmodel)
outmodel.close()
np.save(base_name + '_bestva_Y_te_hat.npy', Y_te_hat)
np.save(base_name + '_bestva_Y_va_hat.npy', Y_va_hat)
if MAE_1000G < MAE_1000G_best:
MAE_1000G_best = MAE_1000G
outmodel = open(base_name + '_best1000G_model.pkl', 'wb')
pkl.dump(model, outmodel)
outmodel.close()
np.save(base_name + '_best1000G_Y_1000G_hat.npy', Y_1000G_hat)
np.save(base_name + '_best1000G_Y_GTEx_hat.npy', Y_GTEx_hat)
print 'MAE_va_best : %.6f' % (MAE_va_best)
print 'MAE_1000G_best : %.6f' % (MAE_1000G_best)
outlog.write('MAE_va_best : %.6f' % (MAE_va_best) + '\n')
outlog.write('MAE_1000G_best : %.6f' % (MAE_1000G_best) + '\n')
outlog.close()
if __name__ == '__main__':
main()