95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
from Tools import getMMScoreType,WeightAndMatrix
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from sklearn.metrics import roc_auc_score
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from sklearn.model_selection import KFold,StratifiedKFold
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from keras import metrics
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from keras.models import Model
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import numpy as np
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from keras import backend as K
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from keras import optimizers
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from keras.models import Sequential,load_model
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from keras.layers import Dense, Dropout
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from keras.callbacks import EarlyStopping,ModelCheckpoint
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def final_dnn():
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length = 30
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nfold = 10
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f1 = open(r"POS.txt", "r")
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f2 = open(r"NEG.txt", "r")
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pos = set()
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neg = set()
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for line in f1.readlines():
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sp = line.strip().split("\t")
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pep = sp[0]
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site = sp[1] + "\t" + sp[2]
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pos.add(pep)
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for line in f2.readlines():
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sp = line.strip().split("\t")
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pep = sp[0]
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if pep not in pos:
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neg.add(pep)
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fw = open(r"AUCs.txt", "a")
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AAscores, l_aas, weight_coef, AAs = \
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WeightAndMatrix(r"traningout_best.txt")
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l_scores, l_type, peps = getMMScoreType(pos, neg, AAscores, weight_coef, l_aas, AAs, length)
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raw_scores = []
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for i in range(len(l_scores)):
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total = 0.0
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for j in range(len(l_scores[i])):
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total += l_scores[i][j]
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raw_scores.append(total)
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X = np.array(l_scores)
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Y = np.array(l_type)
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PEP = np.array(peps)
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parameter = [512, 0.2, 2, X.shape[1]]
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auc_all,best_model = dnn(X,Y,nfold,parameter,PEP)
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fw.write("Best AUC:" + "\t" + str(auc_all) + "\t" + str(best_model) + "\n")
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fw.flush()
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fw.close()
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def dnn(X,Y,nfold,parameter,PEP):
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skf = StratifiedKFold(n_splits=nfold)
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num = 0
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best_auc = 0.0
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best_model = 0
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Y_last = []
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Score_last = []
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for train_index, test_index in skf.split(X, Y):
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num += 1
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print("dnn_" + str(num))
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X_train, X_test = X[train_index], X[test_index]
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Y_train, Y_test = Y[train_index], Y[test_index]
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model = create_model(parameter)
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model.fit(X_train, Y_train, epochs=300, batch_size=100,validation_data=(X_test,Y_test),verbose=1,
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callbacks=[EarlyStopping(monitor="val_auc", mode="max", min_delta=0, patience=30),
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ModelCheckpoint(str(num) +'.model', monitor="val_auc", mode="max", save_best_only=True)])
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model = load_model(str(num) +".model")
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predict_x = model.predict(X_test)[:, 0]
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auc = roc_auc_score(Y_test, predict_x)
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if auc > best_auc:
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best_auc = auc
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best_model = num
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Y_last.extend(Y_test)
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Score_last.extend(predict_x)
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K.clear_session()
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auc_all = roc_auc_score(np.array(Y_last), np.array(Score_last))
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return auc_all,best_model
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def create_model(parameter):
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model = Sequential()
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model.add(Dense(parameter[0], activation='linear', input_dim=parameter[3]))
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model.add(Dropout(parameter[1]))
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for i in range(parameter[2]):
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fold = 2 ** (i+1)
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model.add(Dense(parameter[0]/ fold,activation='linear'))
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model.add(Dropout(parameter[1]))
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model.add(Dense(1, activation='sigmoid'))
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model.compile(optimizer=optimizers.Adam(lr=1e-3,decay=3e-5), loss='binary_crossentropy', metrics=[metrics.AUC(name="auc")])
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model.summary()
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return model
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final_dnn()
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