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bi_diploid_testing.py
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bi_diploid_testing.py
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'''
GENOTYPE IMPUTATION ON DIPLOID DATA (contd...)
(Cleaned Version of the Code) - PART 2: Testing
Course Project for CM229: Machine Learning for Bio-informatics
A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library for
genotype imputation
Authors: Deepak Muralidharan, Manikandan Srinivasan
Last edited: 5/28/2016
'''
import tensorflow as tf
from tensorflow.python.ops.constant_op import constant
from tensorflow.models.rnn import rnn, rnn_cell
import numpy as np
import time
import sys
import math
from sklearn.metrics import f1_score
import random
import matplotlib.pyplot as plt
# Parameters
learning_rate = 0.01
training_iters = 100000
batch_size = 100
# Network Parameters
n_input = 3
n_steps = 50
n_hidden = 10
n_classes = 3
n_training = 1000
n_valid = 0
n_test = 92
data = np.loadtxt('data/geno_loc_new_diploid.txt',delimiter=',')
test_split = np.copy(data[n_training: n_training + n_test, 0:n_steps])
test_input = np.copy(test_split[:,0:n_steps])
test_label = np.copy(test_split[:,0:n_steps])
# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input]) # [batch size, number of steps, input dimension]
# Tensorflow LSTM cell requires 2x n_hidden length (state & cell)
istate_fw = tf.placeholder("float", [None, 2*n_hidden]) # [batch size, 2 * number of hidden units]
istate_bw = tf.placeholder("float", [None, 2*n_hidden]) # [batch size, 2 * number of hidden units]
y = tf.placeholder("float", [None, n_steps, n_classes]) # [batch size, number of steps, number of classes (same size as x)]
# Define weights
weights = {
# Hidden layer weights => 2*n_hidden because of foward + backward cells
'hidden': tf.Variable(tf.random_normal([n_input, 2*n_hidden])), # [input dimension, 2 * number of hidden units]
'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes])) # [2 * number of hidden units, number of classes]
}
biases = {
'hidden': tf.Variable(tf.random_normal([2*n_hidden])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
def BiRNN(_X, _istate_fw, _istate_bw, _weights, _biases):
# input shape: (batch_size, n_steps, n_input)
_X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size
# Reshape to prepare input to hidden activation
_X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input)
# Linear activation
_X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']
# Define lstm cells with tensorflow
# Forward direction cell
lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
# Backward direction cell
lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
# Split data because rnn cell needs a list of inputs for the RNN inner loop
_X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)
# Get lstm cell output
outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, _X,
initial_state_fw=_istate_fw,
initial_state_bw=_istate_bw)
# Linear activation
# Get inner loop last output
output = [tf.matmul(o, _weights['out']) + _biases['out'] for o in outputs]
return output
pred = BiRNN(x, istate_fw, istate_bw, weights, biases)
pred_arg = []
for i in xrange(0, len(pred)):
pred_arg.append(tf.argmax(pred[i],1))
pred_arg = tf.concat(0,pred_arg)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, './weights/diploid.bi.weights')
print "restored..."
mismatches = []
for pos in range(0,50):
truth_label = []
predicted_label = []
print pos
for i in range(0, n_test):
row_test_input = np.copy(test_input[i,:])
x_b = row_test_input.astype(int)
x_b = np.eye(n_input)[x_b]
x_b = x_b.astype(float)
x_b = np.reshape(x_b,[1, n_steps, n_classes])
y_pred = sess.run(pred_arg, feed_dict={x: x_b,
istate_fw: np.zeros((1, 2*n_hidden)),
istate_bw: np.zeros((1, 2*n_hidden))})
y_pred = np.asarray(y_pred)
truth_label.append(test_input[i,pos])
predicted_label.append(y_pred[pos])
truth_label1 = np.asarray(truth_label)
predicted_label1 = np.asarray(predicted_label)
print truth_label1
print predicted_label1
mismatches.append(sum(truth_label1 != np.around(predicted_label1)))
plt.stem(range(0,50),np.asarray(mismatches))
plt.title('SNP position vs Mismatches')
plt.xlabel('SNP position')
plt.ylabel('Number of Mismatches (out of 92)')
plt.savefig('./results/bi_rnn_diploid.png', bbox_inches='tight')
plt.show()