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Extra_DNN_grid.py
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Extra_DNN_grid.py
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######################################################### 加载模块
import sys
from tensorflow import keras as K
import tensorflow as tf
from tensorflow.keras import regularizers
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
import numpy as np
from sklearn.model_selection import StratifiedShuffleSplit
import matplotlib.pyplot as plt
from tensorflow.keras.backend import clear_session
from sklearn.model_selection import PredefinedSplit
import math
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
########################################################################### 构建划分函数
# def load_data(CSV_FILE_PATH):
# IRIS = pd.read_csv(CSV_FILE_PATH)
# target_var = 'project_id' # 目标变量
# # 数据集的特征
# features = list(IRIS.columns)
# features.remove(target_var)
# # 目标变量的类别
# Class = IRIS[target_var].unique()
# # 目标变量的类别字典
# Class_dict = dict(zip(Class, range(len(Class))))
# # 增加一列target, 将目标变量进行编码
# IRIS['target'] = IRIS[target_var].apply(lambda x: Class_dict[x])
# # 对目标变量进行0-1编码(One-hot Encoding)
# lb = LabelBinarizer()
# lb.fit(list(Class_dict.values()))
# transformed_labels = lb.transform(IRIS['target'])
# y_bin_labels = [] # 对多分类进行0-1编码的变量
# for i in range(transformed_labels.shape[1]):
# y_bin_labels.append('y' + str(i))
# IRIS['y' + str(i)] = transformed_labels[:, i]
# # 将数据集分为训练集和测试集
# train_x, test_x, train_y, test_y = train_test_split(IRIS[features], IRIS[y_bin_labels], stratify=IRIS['project_id'],\
# train_size=0.8, test_size=0.2, random_state=1)
# return train_x, test_x, train_y, test_y
def FindLayerNodesLinear(n_layers, first_layer_nodes, last_layer_nodes):
layers = []
nodes_increment = (last_layer_nodes - first_layer_nodes)/ (n_layers-1)
nodes = first_layer_nodes
for i in range(1, n_layers+1):
layers.append(math.ceil(nodes))
nodes = nodes + nodes_increment
return layers
def FinddropoutLinear(n_layers, dropout):
layers = []
nodes_increment = round(dropout/(n_layers-1),2)
nodes = dropout
for i in range(1, n_layers+1):
layers.append(nodes)
nodes = round(nodes - nodes_increment,2)
if(nodes <= 0):
nodes = 0
return layers
def createmodel(n_layers, first_layer_nodes, last_layer_nodes, activation_func, loss_func,dropout):
model = Sequential()
n_nodes = FindLayerNodesLinear(n_layers, first_layer_nodes, last_layer_nodes)
n_dropout = FinddropoutLinear(n_layers,dropout)
for i in range(1, n_layers):
if i==1:
model.add(Dense(first_layer_nodes, input_dim=new_train_x.shape[1], activation=activation_func))
model.add(K.layers.Dropout(rate=n_dropout[0]))
else:
model.add(Dense(n_nodes[i-1], activation=activation_func))
model.add(K.layers.Dropout(rate=n_dropout[i-1]))
model.add(Dense(train_y.shape[1], activation='softmax'))
model.compile(optimizer='adam', loss=loss_func, metrics = ["accuracy"]) #note: metrics could also be 'mse'
return model
m = sys.argv[1]
print(m)
Extra_feature = pd.read_csv("/public/slst/home/ningwei/methylation/process_data/feature_importance_12_6/Extratree_feature45.csv")
m = int(m)
# train_x, test_x, train_y, test_y = load_data("/public/slst/home/ningwei/methylation/process_data/train_data/data_smote_11_22.csv")
# new_train_x = train_x[Extra_feature.iloc[0:m,0]]
# new_test_x = test_x[Extra_feature.iloc[0:m,0]]
data = pd.read_csv("/public/slst/home/ningwei/methylation/process_data/train_data/train_data_smote_12_6.csv")
Class = data['project_id'].unique()
Class_dict = dict(zip(Class, range(len(Class))))
data["target"] = data.iloc[:,0].apply(lambda x: Class_dict[x])
# 对目标变量进行0-1编码(One-hot Encoding)
lb = LabelBinarizer()
lb.fit(list(Class_dict.values()))
transformed_labels = lb.transform(data['target'])
train_y = transformed_labels
train_x = data.iloc[:,1:data.shape[1]]
data = pd.read_csv("/public/slst/home/ningwei/methylation/process_data/train_data/test_data_smote_12_7.csv")
data["target"] = data.iloc[:,0].apply(lambda x: Class_dict[x])
lb = LabelBinarizer()
lb.fit(list(Class_dict.values()))
transformed_labels = lb.transform(data['target'])
test_y = transformed_labels
test_x = data.iloc[:,1:data.shape[1]]
new_train_x = train_x[Extra_feature.iloc[0:m,0]]
new_test_x = test_x[Extra_feature.iloc[0:m,0]]
#train_x1, value_x, train_y1, value_y = train_test_split(new_train_x, train_y,train_size=0.8, test_size=0.2, random_state=1)
train_x1 = new_train_x
value_x = new_test_x
train_y1 = train_y
value_y = test_y
############################################################### 对数据进行标准化
# train_x1_zcore=(train_x1-new_train_x.mean(axis=0))/new_train_x.std(axis=0)
# value_x_zcore=(value_x-new_train_x.mean(axis=0))/new_train_x.std(axis=0)
########################################################################### 构建函数
train_val_features = np.concatenate((train_x1,value_x),axis=0)
train_val_labels = np.concatenate((train_y1,value_y ),axis=0)
test_fold = np.zeros(train_val_features.shape[0])
test_fold[:train_x1.shape[0]] = -1
ps = PredefinedSplit(test_fold=test_fold)
#####################################################################设置参数范围
model = KerasClassifier(build_fn=createmodel, verbose = False)
activation_funcs = ['relu']
#activation_funcs = ['relu']
loss_funcs = ['categorical_crossentropy']
if m ==5 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [50,40], last_layer_nodes = [40], dropout=[0,0.1], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m ==10 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [100,80,60], last_layer_nodes = [50,40], dropout=[0,0.3], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 15 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [150,130,110,90], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 20 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [200,180,160,140], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 25 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [250,230,200,180,160,130], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 30 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [300,280,250,230,200,180,150], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 35 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [350,330,310,280,250,230,200,180], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 40 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [400,380,360,340,310,280,250,230,200], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 45 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [450,430,410,400,380,360,340,310,280,250], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
if m == 50 :
#param_grid = dict(n_layers=[2,3,4,5,6,7], first_layer_nodes = [10000,8000,6000,5000,4000], last_layer_nodes = [60,90,120], dropout=[0,0.1,0.3,0.5,0.7,0.9], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50])
param_grid = dict(n_layers=[3,4,5,6], first_layer_nodes = [500,470,450,430,410,400,380,360,340,310,280,250], last_layer_nodes = [50,40], dropout=[0,0.3,0.5], activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [100,50], epochs = [50,100])
grid = GridSearchCV(estimator = model, param_grid = param_grid,cv=ps,n_jobs=1)
#grid = RandomizedSearchCV (estimator = model, param_grid = param_grid,cv=3,n_jobs=5)
################################################################ 进行训练
grid.fit(train_val_features, train_val_labels)
############################################################### 输出最高精确度及相应参数
print(grid.best_score_)
print(grid.best_params_)
f = open("/public/slst/home/ningwei/methylation/process_data/DNN_train_data_12_6/"+"Extra"+str(m)+".txt",'a') #若文件不存在，系统自动创建。'a'表示可连续写入到文件，保留原内容，在原内容之后写入。可修改该模式（'w+','w','wb'等）
f.write(str(grid.best_score_)) #将字符串写入文件中
f.write("\n") #换行
f.write(str(grid.best_params_)) #将字符串写入文件中
f.write("\n") #换行
f.close()