import json
import csv
import numpy as np
import os
import pandas as pd
import random
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.utils.generic_utils import get_custom_objects
from keras import backend as K
from keras.layers import Input, Convolution1D, \
GlobalAveragePooling2D, Dense, BatchNormalization, Activation,AveragePooling2D, \
GlobalMaxPooling2D, Flatten
from keras.layers.advanced_activations import LeakyReLU, PReLU
from functions import quan_detector, most_repeared_promoter,dataset
from sklearn.metrics import confusion_matrix
import argparse
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('start', metavar='N', type=int, nargs='+',
help='start position')
args = parser.parse_args()
np.random.seed(42)
tf.set_random_seed(42)
random.seed(42)
def swish(x):
return x * K.tanh(0.618* x)
get_custom_objects().update({'swish': Activation(swish)})
def architecture1(num_classes):
act = 'selu'
model = Sequential()
get_custom_objects().update({'swish': Activation(swish)})
# model.add(BatchNormalization(input_shape=(64,1), mode=0,epsilon=1e-05,
# beta_init=keras.initializers.Constant(value=0.05)))
# model.add(Activation('swish'))
model.add(Convolution1D(nb_filter=4, filter_length=1, input_shape=(64, 1)))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Convolution1D(nb_filter=32, filter_length=4))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Flatten())
model.add(Dense(148, activation='linear'))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Dense(16, activation='linear'))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
# 'adamax'
# return model
return model
def architecture(num_classes):
act = 'softplus'
model = Sequential()
get_custom_objects().update({'swish': Activation(swish)})
# model.add(BatchNormalization(input_shape=(64,1), mode=0,epsilon=1e-05,
# beta_init=keras.initializers.Constant(value=0.05)))
# model.add(Activation('swish'))
model.add(Convolution1D(nb_filter=4, filter_length=1, input_shape=(64, 1)))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Convolution1D(nb_filter=32, filter_length=4))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Flatten())
model.add(Dense(148, activation='linear'))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Dense(16, activation='linear'))
model.add(BatchNormalization(epsilon=1e-05))
model.add(Activation(act))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
# 'adamax'
# return model
return model
out_put_header = ['Promoter region','Posotive_zeros','Negative_zeros','Sum_zeros',
'Positive_freq', 'Negative_freq','Sum_freq',
'Sum_all','Percent_all', 'Vector_freq',
"True positive", "False positive", "True negative", "False negative", "Accuracy",
'>50%']
output_file_name = 'output_cnn_chr7.csv'
# with open(output_file_name,'w') as f:
# writer = csv.writer(f)
# writer.writerow(out_put_header)
labels_file = 'labels.csv'
labels_df = pd.read_csv(labels_file, index_col=0)
ids_csv = labels_df.FID.tolist()
with open('promoter1.csv', 'rb') as f:
reader = csv.reader(f)
promoter = list(reader)
print len(promoter)
promoters_list = range(args.start[0]*10,(args.start[0]+1)*10)#range(1,nn+1)
for promoter_num in promoters_list:
promoter_file = 'promoters/chr22_'+str(promoter_num)+'.json'
# # read files
with open(promoter_file) as json_data:
ind_var = json.load(json_data)
ids_json = ind_var.keys()
#print len(ids_json)
if len(ind_var[ids_json[0]]) == 64:
print promoter_num
var_num = []
for i in ids_csv:
id_name = str(i)
temp = ind_var[id_name]
var_seq = map(int, temp)
var_num.append(var_seq)
labels_df['vars'] = var_num
lab_num = {1: [1, 0], # positive
2: [0, 1]} # negative
pheno_new = []
for i in labels_df.Pheno.tolist():
pheno_new.append(lab_num[i])
d = {"Pheno": pheno_new, "Vars":labels_df.vars}
dataset_ = pd.DataFrame(d)
dataset_X = np.array(dataset_.Vars.tolist())
dataset_Y = np.array(dataset_.Pheno.tolist())
t_idx = [int(line.strip()) for line in open("train_id.txt", 'r')]
dataset_X= dataset_X[t_idx]
dataset_Y = dataset_Y[t_idx]
N = len(dataset_X)
print dataset_X.shape
# repeat information
per_zeros, p_zeros,n_zeros = quan_detector(dataset_X,dataset_Y)
count_zeros = p_zeros+n_zeros # sum of individuals without any variants
most_vector, max_count,count_vector = most_repeared_promoter(dataset_X,dataset_Y)
_, p_count,n_count = count_vector
vart_pos = []
for i in range(len(most_vector)):
if most_vector[i] != '0':
vart_pos.append(i)
np.random.seed(42)
tf.set_random_seed(42)
random.seed(42)
# network accuracy
x_train, y_train,x_test,y_test = dataset(dataset_X,dataset_Y,test_ratio=0.1)
#print x_train.shape
#print x_test.shape
num_classes = 2
x_train = x_train.astype('float32')#.reshape((len(x_train),8,8,1))
x_test = x_test.astype('float32')
x_train = x_train.astype('float32').reshape((len(x_train), 64, 1))
x_test = x_test.astype('float32').reshape((len(x_test), 64, 1))
cnn = architecture(num_classes)
history = cnn.fit(x_train, y_train,
batch_size=64,
epochs=50,
verbose=0,
validation_data=(x_test, y_test))
y_pred = cnn.predict_classes(x_test)
y_test_num = np.argmax(y_test,axis=1)
tn, fp, fn, tp = confusion_matrix(y_test_num, y_pred).ravel()
acc = (tp+tn)*1./(tp+fp+tn+fn)
info = ['promoter ' + str(promoter_num), p_zeros, n_zeros, count_zeros,
p_count, n_count, max_count,
max_count + count_zeros, (max_count + count_zeros) * 1. / N, vart_pos,
tp, fp, tn, fn, acc, acc > 0.5]
with open(output_file_name, 'a') as f:
writer = csv.writer(f)
writer.writerow(info)
else:
print "No "+ str(promoter_num) + "promoter file length is "+ str(len(ind_var[ids_json[0]]))
print "Done"
Datasets
Models
