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ModelsFinal.py
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import time
import numpy as np
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from matplotlib import pyplot
import matplotlib.pyplot as plt
from sklearn.metrics import auc, roc_curve
from sklearn import metrics, svm
from sklearn.metrics import precision_recall_fscore_support
import pandas as pd
from sklearn import preprocessing
import warnings
warnings.filterwarnings('ignore')
######################################################################################
######################### Models ###########################
def DLmodel1(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
tf.keras.layers.Dense(128,activation='relu'),
tf.keras.layers.Dense(64,activation='relu'),
tf.keras.layers.Dense(32,activation='relu'),
tf.keras.layers.Dense(16,activation='relu'),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.SGD(lr=0.0001),
metrics=['accuracy'])
return model
def DLmodel2(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
tf.keras.layers.Dense(128,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dense(64,activation='relu'),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(32,activation='relu'),
tf.keras.layers.Dense(16,activation='relu'),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),
metrics=['accuracy'])
return model
def DLmodel3(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
tf.keras.layers.Dense(128,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(32,activation='relu'),
tf.keras.layers.Dense(16,activation='relu'),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),
metrics=['accuracy'])
return model
def DLmodel4(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
tf.keras.layers.Dense(128,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(32,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dense(16,activation='relu'),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),
metrics=['accuracy'])
return model
def DLmodel5(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
tf.keras.layers.Dense(128,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(32,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dense(16,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),
metrics=['accuracy'])
return model
def DLmodel6(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
tf.keras.layers.Dense(128,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(32,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(16,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),
metrics=['accuracy'])
return model
def DLmodel7(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
tf.keras.layers.Dense(128,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(32,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(16,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),
metrics=['accuracy'])
return model
def DLmodel8(f):
model = keras.Sequential([
tf.keras.Input(shape=(f,)),
#tf.keras.layers.Dense(128,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
#tf.keras.layers.Dropout(0.4),
#tf.keras.layers.Dense(64,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
#tf.keras.layers.Dropout(0.4),
tf.keras.layers.Dense(32,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(16,activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
tf.keras.layers.Dense(1,activation='sigmoid'),
])
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),
metrics=['accuracy'])
return model