[K-fold train function on Keras] #python #keras

k_fold_keras.py

from sklearn.model_selection import KFold
from sklearn.metrics import *

def kf_fit(model, x_train=X, y_train=y, test_data=test):
    kf = KFold(n_splits=10, shuffle=True, random_state=42069)
    preds = []
#     test_data = pad_sequences(test_data)
    fold = 0
    aucs = 0
    for train_idx, val_idx in kf.split(x_train):
        x_train_f = x_train[train_idx]
        y_train_f = y_train[train_idx]
        x_val_f = x_train[val_idx]
        y_val_f = y_train[val_idx]
        model.fit(x_train_f, y_train_f,
                  batch_size=256,
                  epochs=12,
                  verbose = 0,
                  validation_data=(x_val_f, y_val_f))
        # Get accuracy of model on validation data. It's not AUC but it's something at least!
        preds_val = model.predict([x_val_f], batch_size=512)
        preds.append(model.predict(test_data))
        fold+=1
        fpr, tpr, thresholds = roc_curve(y_val_f, preds_val, pos_label=1)
        aucs += auc(fpr,tpr)
        print('Fold {}, AUC = {}'.format(fold,auc(fpr, tpr)))
    print("Cross Validation AUC = {}".format(aucs/10))