-
Notifications
You must be signed in to change notification settings - Fork 0
/
uni_haploid_testing.py
156 lines (117 loc) · 5 KB
/
uni_haploid_testing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
'''
GENOTYPE IMPUTATION ON HAPLOID DATA (Part 2: Testing)
A Reccurent Neural Network (LSTM) implementation example
using TensorFlow library for genotype imputation.
(Cleaned Version of the Code) - PART 2: Testing
Course Project for CM229: Machine Learning for Bio-informatics
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
display_step = 10
# Network Parameters
n_input = 1
n_steps = 49
n_hidden = 10
n_classes = 1
n_training = 2000
n_test = 184
data = np.loadtxt('data/geno_loc_new.txt',delimiter=',')
train_data = np.copy(data[0:n_training, 0:n_steps+1])
test_split = np.copy(data[n_training: n_training + n_test, 0:n_steps+1])
test_input = np.copy(test_split[:,0:n_steps])
test_label = np.copy(test_split[:,1:n_steps+1])
# 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 = tf.placeholder("float", [None, 2*n_hidden]) # [batch size, 2 * number of hidden units]# [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, n_hidden])), # [input dimension, 2 * number of hidden units]
'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) # [2 * number of hidden units, number of classes]
}
biases = {
'hidden': tf.Variable(tf.random_normal([n_hidden])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
def geno_iterator(raw_data, batch_size, num_steps):
"""
Assume that raw_data is a numpy matrix of rows -- number of individuals (2184)
and columns -- number of SNPs.
Here the number of SNPs = number of columns = number of time steps.
"""
col_iter = (raw_data.shape[0]) // batch_size # number of loops we would be needing
for i in range(col_iter):
x = np.copy(raw_data[i * batch_size: (i + 1) * batch_size, 0:num_steps]) # giving the entire range as time steps
y = np.copy(raw_data[i * batch_size: (i + 1) * batch_size, 1:(num_steps + 1)])
yield (x,y)
def RNN(_X, _istate, _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 a lstm cell with tensorflow
lstm_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, states = rnn.rnn(lstm_cell, _X, initial_state=_istate)
# Linear activation
# Get inner loop last output
output = [tf.matmul(o, _weights['out']) + _biases['out'] for o in outputs]
return output
pred = RNN(x, istate, weights, biases)
pred = tf.concat(1, pred)
_y = tf.squeeze(y,[2])
# Define loss function and optimizer
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(pred, _y)) # Softmax loss
# Initializing the variables
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, './weights/haploid.uni.weights')
print "restored..."
mismatches = []
for pos in range(1,49):
truth_label = []
predicted_label = []
print pos
for i in range(0, n_test):
row_test_input = np.copy(test_input[i,:])
row_test_input[pos]=0
row_test_input = np.reshape(row_test_input,[1, n_steps, n_input])
y_pred = sess.run([pred], feed_dict={x: row_test_input,
istate: np.zeros((1, 2*n_hidden))})
y_pred = np.asarray(y_pred)
y_pred = 1/(1+ np.exp(-y_pred))
truth_label.append(test_input[i,pos])
predicted_label.append(y_pred[0,0,pos-1])
truth_label1 = np.asarray(truth_label)
predicted_label1 = np.asarray(predicted_label)
#print(truth_label)
#print(predicted_label)
mismatches.append(sum(truth_label1 != np.around(predicted_label1)))
plt.stem(range(1,49),np.asarray(mismatches))
axes = plt.gca()
axes.set_ylim([0,100])
plt.title('SNP position vs Mismatches (Haploid Data) [Unidirectional RNN]')
plt.xlabel('SNP position')
plt.ylabel('Number of Mismatches (out of 184)')
plt.savefig('./results/uni_rnn_haploid.png', bbox_inches='tight')
plt.show()