first add

DNN_final.py

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