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help.py
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help.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Oct 24 09:54:33 2018
@author: yaoyu
"""
import os
from time import time
from keras.models import Sequential
from keras.layers import Dense, Input, Dropout, Conv1D, Reshape, MaxPooling1D, ZeroPadding1D, AveragePooling1D
from keras.layers.normalization import BatchNormalization
from sklearn.metrics import roc_curve, auc, roc_auc_score,average_precision_score
import numpy as np
from keras.layers import Dense, Dropout, Merge
import utils.tools as utils
from keras.regularizers import l2
from gensim.models import Word2Vec
import copy
import psutil
import random
import h5py
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from keras import backend as K
import matplotlib.pyplot as plt
import tensorflow as tf
import pandas as pd
from sklearn.model_selection import train_test_split
from keras.optimizers import SGD
from keras.initializers import glorot_uniform
from keras.layers import Add, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
lr = 0.01
epochs = 50
batch_size = 64
dr = 0.4
l2c = 0.01
def identify_block(X, f, filters, stage, block):
"""
X - 输入的tensor类型数据，维度为（m, n_H_prev, n_W_prev, n_H_prev）
f - kernal大小
filters - 整数列表，定义每一层卷积层过滤器的数量
stage - 整数 定义层位置
block - 字符串 定义层位置
X - 恒等输出，tensor类型，维度（n_H, n_W, n_C）
"""
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
F1, F2, F3 = filters # 定义输出特征的个数
X_shortcut = X #(?,14,256)
X = Conv1D(filters=F1, kernel_size=1, strides=1, padding='valid', name=conv_name_base + '2a',
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=-1, name=bn_name_base + '2a')(X)
X = Activation('relu')(X)
X = Conv1D(filters=F2, kernel_size=f, strides=1, padding='same', name=conv_name_base + '2b',
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=-1, name=bn_name_base + '2b')(X)
X = Activation('relu')(X)
X = Conv1D(filters=F3, kernel_size=1, strides=1, padding='valid', name=conv_name_base + '2c',
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=-1, name=bn_name_base + '2c')(X)
# 没有激活
X = Add()([X, X_shortcut]) #(?,14,256)
X = Activation('relu')(X)
return X
def convolutional_block(X, f, filters, stage, block, s=2):
# 参数意义和上文相同
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
F1, F2, F3 = filters
X_shortcut = X
X = Conv1D(filters=F1, kernel_size=1, strides=s, padding='valid', name=conv_name_base + '2a',
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=-1, name=bn_name_base + '2a')(X)
X = Activation('relu')(X)#(?,14,64)
X = Conv1D(filters=F2, kernel_size=f, strides=1, padding='same', name=conv_name_base + '2b',
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=-1, name=bn_name_base + '2b')(X)
X = Activation('relu')(X)
X = Conv1D(filters=F3, kernel_size=1, strides=1, padding='valid', name=conv_name_base + '2c',
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=-1, name=bn_name_base + '2c')(X)
# shortcut
X_shortcut = Conv1D(filters=F3, kernel_size=1, strides=s, padding='valid', name=conv_name_base + '1',
kernel_initializer=glorot_uniform(seed=0))(X_shortcut)
X_shortcut = BatchNormalization(axis=-1, name=bn_name_base + '1')(X_shortcut)
X = Add()([X, X_shortcut])
X = Activation('relu')(X) #(?, 7, 512)
return X
def token(dataset):
token_dataset = []
for i in range(len(dataset)):
seq = []
for j in range(len(dataset[i])):
seq.append(dataset[i][j])
token_dataset.append(seq)
return token_dataset
def pandding_J(protein,maxlen):
padded_protein = copy.deepcopy(protein)
for i in range(len(padded_protein)):
if len(padded_protein[i])<maxlen:
for j in range(len(padded_protein[i]),maxlen):
padded_protein[i].append('J')
return padded_protein
def averagenum(num):
nsum = 0
for i in range(len(num)):
nsum += num[i]
return nsum / len(num)
def max_min_avg_length(pro_swissProt):
length = []
for i in range(len(pro_swissProt)):
length.append(len(pro_swissProt[i]))
maxNum = max(length) #maxNum = 5
minNum = min(length) #minNum = 1
index_max = length.index(maxNum)
index_min = length.index(minNum)
avg = averagenum(length)
return index_max,index_min,avg
def protein_representation(wv,tokened_seq_protein,maxlen,size):
represented_protein = []
for i in range(len(tokened_seq_protein)):
temp_sentence = []
for j in range(maxlen):
if tokened_seq_protein[i][j]=='J':
temp_sentence.extend(np.zeros(size))
else:
temp_sentence.extend(wv[tokened_seq_protein[i][j]])
represented_protein.append(np.array(temp_sentence))
return represented_protein
def read_traingingData(file_name):
# read sample from a file
seq = []
with open(file_name, 'r') as fp:
i = 0
for line in fp:
seq.append(line.split('\n')[0])
i = i+1
return seq
def mkdir(path):
folder = os.path.exists(path)
if not folder:
os.makedirs(path)
print("--- new folder... ---")
print("--- OK ---")
else:
print("--- There is this folder! ---")
def getMemorystate():
phymem = psutil.virtual_memory()
line = "Memory: %5s%% %6s/%s"%(phymem.percent,
str(int(phymem.used/1024/1024))+"M",
str(int(phymem.total/1024/1024))+"M")
return line