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Test_DeepPPI_Sep_Con_On_Five_Subset.py
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Test_DeepPPI_Sep_Con_On_Five_Subset.py
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# -*- coding: utf-8 -*-
"""
Created on 2016/12/20 13:11
@author: sun
"""
import numpy as np
import pandas as pd
from scipy import interp
import matplotlib.pyplot as plt
from keras.layers import Dense, Input, Dropout
from keras.layers.merge import concatenate
from keras.optimizers import SGD
from keras.utils import np_utils
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_curve, auc
from utils.tools import calculate_performace, categorical_probas_to_classes
def get_neg(pos_num, neg_num, protein):
index = list(range(neg_num))
np.random.shuffle(index)
index = np.array(index)
neg_protein = protein.iloc[pos_num + index[0:pos_num]]
return neg_protein
def get_protein(pos_protein_1, neg_protein_1, profeat_feature):
pos_neg_protein = pd.concat([pos_protein_1, neg_protein_1], axis=0)
pos_neg_protein.index = np.arange(len(pos_protein_1) * 2)
labels = pos_neg_protein['interaction']
protein_a = profeat_feature.loc[pos_neg_protein.proteinA, :]
protein_b = profeat_feature.loc[pos_neg_protein.proteinB, :]
protein_a.index = np.arange(len(pos_protein_1) * 2)
protein_b.index = np.arange(len(pos_protein_1) * 2)
protein = pd.concat([protein_a, protein_b], axis=1)
#change data to numpy type
X = np.array(protein)
labels = np.array(labels)
#normalization
X = StandardScaler().fit_transform(X)
#shuffle data
np.random.seed(1)
index = list(range(len(labels)))
np.random.shuffle(index)
X = X[index]
labels = labels[index]
return X, labels
def get_sep_model():
input_1 = Input(shape=(1164, ), name='Protein_a')
protein_input1 = Dense(
512,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_proA_feature_1')(input_1)
protein_input1 = Dropout(0.2)(protein_input1)
protein_input1 = Dense(
256,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_proA_feature_2')(protein_input1)
protein_input1 = Dropout(0.2)(protein_input1)
protein_input1 = Dense(
128,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_proA_feature_3')(protein_input1)
protein_input1 = Dropout(0.2)(protein_input1)
input_2 = Input(shape=(1164, ), name='Protein_b')
protein_input2 = Dense(
512,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_proB_feature_1')(input_2)
protein_input2 = Dropout(0.2)(protein_input2)
protein_input2 = Dense(
256,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_proB_feature_2')(protein_input2)
protein_input2 = Dropout(0.2)(protein_input2)
protein_input2 = Dense(
128,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_proB_feature_3')(protein_input2)
protein_input2 = Dropout(0.2)(protein_input2)
merged_vector = concatenate([protein_input1, protein_input2], axis=1)
output = Dense(
128,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_feature_1')(merged_vector)
outputs = Dense(2, activation='softmax', name='output')(output)
model = Model(inputs=[input_1, input_2], outputs=outputs)
sgd = SGD(lr=0.01, momentum=0.9, decay=0.001)
model.compile(
loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# model.summary()
return model
def get_con_model():
input_1 = Input(shape=(2328, ), name='Protein')
protein_input1 = Dense(
512,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_feature_1')(input_1)
protein_input1 = Dropout(0.2)(protein_input1)
protein_input1 = Dense(
256,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_feature_2')(protein_input1)
protein_input1 = Dropout(0.2)(protein_input1)
protein_input1 = Dense(
128,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_feature_3')(protein_input1)
protein_input1 = Dropout(0.2)(protein_input1)
# protein_input1 = Dense(128, activation='relu', kernel_initializer='glorot_normal', name='High_dim_feature_4')(protein_input1)
# protein_input1=Dropout(0.2)(protein_input1)
output = Dense(
128,
activation='relu',
kernel_initializer='glorot_normal',
name='High_dim_feature')(protein_input1)
outputs = Dense(2, activation='softmax', name='output')(output)
model = Model(inputs=input_1, outputs=outputs)
sgd = SGD(lr=0.01, momentum=0.9, decay=0.001)
model.compile(
loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# model.summary()
return model
#%%
yeast_protein = pd.read_csv('dataset/yeast_protein_pair.csv')
yeast_feature_all = pd.read_csv('dataset/yeast_feature_all.csv', index_col=0)
# 1:1
posNum = 17257
negNum = 48594
pos_protein = yeast_protein[0:posNum]
sepscores = []
conscores = []
for j in range(5):
#Set the random number seed
np.random.seed(j)
neg_protein = get_neg(posNum, negNum, yeast_protein)
X, label = get_protein(pos_protein, neg_protein, yeast_feature_all)
X_train, X_test, y_train, y_test = train_test_split(
X, label, random_state=0)
X1_train = X_train[:, 0:1164]
X2_train = X_train[:, 1164:2328]
X1_test = X_test[:, 0:1164]
X2_test = X_test[:, 1164:2328]
del X, label, neg_protein
model = get_sep_model()
y_train = np_utils.to_categorical(y_train)
hist = model.fit(
[X1_train, X2_train], y_train, epochs=30, batch_size=64, verbose=0)
#prediction probability
y_score = model.predict([X1_test, X2_test])
y_test_tmp = np_utils.to_categorical(y_test)
fpr, tpr, _ = roc_curve(y_test_tmp[:, 0], y_score[:, 0])
roc_auc = auc(fpr, tpr)
y_class = categorical_probas_to_classes(y_score)
acc, precision, npv, sensitivity, specificity, mcc, f1 = calculate_performace(
len(y_class), y_class, y_test)
print((
'DeepPPI-sep:acc=%f,precision=%f,npv=%f,sensitivity=%f,specificity=%f,mcc=%f,roc_auc=%f'
% (acc, precision, npv, sensitivity, specificity, mcc, roc_auc)))
sepscores.append(
[acc, precision, npv, sensitivity, specificity, mcc, roc_auc])
model = get_con_model()
hist = model.fit(X_train, y_train, epochs=30, batch_size=64, verbose=0)
#prediction probability
y_score = model.predict(X_test)
y_test_tmp = np_utils.to_categorical(y_test)
fpr, tpr, _ = roc_curve(y_test_tmp[:, 0], y_score[:, 0])
roc_auc = auc(fpr, tpr)
y_class = categorical_probas_to_classes(y_score)
acc, precision, npv, sensitivity, specificity, mcc, f1 = calculate_performace(
len(y_class), y_class, y_test)
print((
'DeepPPI-con:acc=%f,precision=%f,npv=%f,sensitivity=%f,specificity=%f,mcc=%f,roc_auc=%f'
% (acc, precision, npv, sensitivity, specificity, mcc, roc_auc)))
conscores.append(
[acc, precision, npv, sensitivity, specificity, mcc, roc_auc])
sepscores = pd.DataFrame(sepscores)
sepscores.to_csv("sepscores.csv", index=None, header=None)
conscores = pd.DataFrame(conscores)
conscores.to_csv("conscores.csv", index=None, header=None)