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kernel_classifier.py
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kernel_classifier.py
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import json
import csv
import numpy as np
import os
import pandas as pd
import random
import tensorflow as tf
from functions import quan_detector, most_repeared_promoter,dataset
from sklearn.metrics import confusion_matrix
from sklearn import datasets, linear_model,svm
from sklearn.metrics import mean_squared_error, r2_score
np.random.seed(42)
tf.set_random_seed(42)
random.seed(42)
def get_input_fn(dataset_split, batch_size, capacity=10000, min_after_dequeue=3000):
def _input_fn():
images_batch, labels_batch = tf.train.shuffle_batch(
tensors=dataset_split,
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
enqueue_many=True,
num_threads=4)
features_map = {'images': images_batch}
return features_map, labels_batch
return _input_fn
out_put_header = ['Promoter region','Posotive_zeros','Negative_zeros','Sum_zeros',
'Positive_freq', 'Negative_freq','Sum_freq',
'Sum_all','Percent_all', 'Vector_freq',
"AUC","Accuracy",
'>50%']
output_file_name = 'output_kernelC.csv'
# with open(output_file_name,'w') as f:
# writer = csv.writer(f)
# writer.writerow(out_put_header)
labels_file = 'labes.csv'
labels_df = pd.read_csv(labels_file, index_col=0)
ids_csv = labels_df.FID.tolist()
promoters_list = range(100)
for promoter_num in promoters_list:
print promoter_num
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()
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(),dtype=np.float32)
dataset_Y = np.array(dataset_.Pheno.tolist(),dtype=np.float32)
N = len(dataset_X)
# 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] != '0.0':
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)
y_train = np.argmax(y_train, axis=1)
y_test = np.argmax(y_test, axis=1)
data = {}
data['train'] = [x_train, y_train]
data['test'] = [x_test, y_test]
train_input_fn = get_input_fn(data['train'], batch_size=256)
eval_input_fn = get_input_fn(data['test'], batch_size=len(y_test))
image_column = tf.contrib.layers.real_valued_column('images', dimension=64)
optimizer = tf.train.FtrlOptimizer(
learning_rate=50.0, l2_regularization_strength=0.001)
kernel_mapper = tf.contrib.kernel_methods.RandomFourierFeatureMapper(
input_dim=64, output_dim=2000, stddev=5.0, name='rffm')
kernel_mappers = {image_column: [kernel_mapper]}
estimator = tf.contrib.kernel_methods.KernelLinearClassifier(
n_classes=2, optimizer=optimizer, kernel_mappers=kernel_mappers)
estimator.fit(input_fn=train_input_fn, steps=2000)
eval_metrics = estimator.evaluate(input_fn=eval_input_fn, steps=1)
# print(eval_metrics.items())
# # Make predictions using the testing set
# y_pred = estimator.predict(input_fn=eval_input_fn)
# # y_pred = np.argmax(y_pred,axis=1)
# y_test_num = y_test
# tn, fp, fn, tp = confusion_matrix(y_test_num, y_pred).ravel()
acc = eval_metrics['accuracy']
auc = eval_metrics['auc_precision_recall']
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,
auc, acc, acc>0.5]
with open(output_file_name,'a') as f:
writer = csv.writer(f)
writer.writerow(info)
print "Done"