first add

2024-11-27 08:48:24 +00:00
commit 253ccd12c6
27 changed files with 65307 additions and 0 deletions
--- a/MAML.py
+++ b/MAML.py
@@ -0,0 +1,119 @@
+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
+import copy
+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
+import random
+from Tools import getMMScoreType,WeightAndMatrix
+def fetshot():
+
+    length = 30
+    nfold = 5
+    f1 = open(r"functionsite.txt", "r")
+    f2 = open(r"POS.txt", "r")
+    funcinf = set()
+    for line in f1.readlines():
+        site = line.strip()
+        funcinf.add(site)
+    pos = []
+    neg = []
+    for line in f2.readlines():
+        sp = line.strip().split("\t")
+        pep = sp[0]
+        site = sp[1] + "\t" + sp[2]
+        if site in funcinf:
+            pos.append(pep)
+        else:
+            neg.append(pep)
+    print(len(pos))
+    fw = open("MAML_AUCs.txt", "a")
+    pos_size = len(pos)
+    for a in range(10000):
+        if len(neg) > pos_size*6:
+            new_neg = random.sample(neg,pos_size*5)
+            tem_neg = copy.deepcopy(neg)
+            for j in range(len(tem_neg)):
+                negpep = tem_neg[j]
+                if negpep in new_neg:
+                    neg.remove(negpep)
+            print(len(neg))
+
+            AAscores, l_aas, weight_coef, AAs = \
+                WeightAndMatrix("traningout_best.txt")
+            l_scores, l_type, peps = getMMScoreType(pos, new_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,a)
+            fw.write(str(a+1) + "\tBest:" + "\t" + str(auc_all) + "\t" + str(best_model) + "\n")
+            fw.flush()
+        else:
+            AAscores, l_aas, weight_coef, AAs = \
+                WeightAndMatrix("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, a)
+            fw.write(str(a + 1) + "\tBest:" + "\t" + str(auc_all) + "\t" + str(best_model) + "\n")
+            fw.flush()
+            break
+    fw.flush()
+    fw.close()
+
+
+def dnn(X,Y,nfold,parameter,PEP,a):
+    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 = load_model("original.model")
+        for i in range(6):
+            model.layers[i].trainable = False
+        model.compile(optimizer=optimizers.Adam(lr=1e-3, decay=3e-5), loss='binary_crossentropy',
+                      metrics=[metrics.AUC(name="auc")])
+        model.fit(X_train, Y_train, epochs=300, batch_size=8,validation_data=(X_test,Y_test),verbose=1,
+                  callbacks=[EarlyStopping(monitor="val_auc", mode="max", min_delta=0, patience=10),
+                             ModelCheckpoint(str(a+1) + "_" + str(num) +'.model', monitor="val_auc", mode="max", save_best_only=True)])
+        model = load_model(str(a+1) + "_" + 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
+
+
+fetshot()