mf_qe_nn_clf.py 文件源码

def make_mf_lr(X ,y, clf, X_test, n_round=3):
    n = X.shape[0]
    '''
    Fit metafeature by @clf and get prediction for test. Assumed that @clf -- regressor
    '''
    print clf
    mf_tr = np.zeros(X.shape[0])
    mf_te = np.zeros(X_test.shape[0])
    for i in range(n_round):
        skf = StratifiedKFold(y, n_folds=2, shuffle=True, random_state=42+i*1000)
        for ind_tr, ind_te in skf:
            X_tr = X[ind_tr]
            X_te = X[ind_te]

            # print('X_tr shape',X_tr.shape)
            # print('X_te shape',X_te.shape)

            y_tr = y[ind_tr]
            y_te = y[ind_te]

            clf.fit(X_tr, y_tr)
            mf_tr[ind_te] += clf.predict_proba(X_te)[:,1]
            mf_te += clf.predict_proba(X_test)[:,1]*0.5
            y_pred = clf.predict_proba(X_te)[:,1]
            score = roc_auc_score(y_te, y_pred)
            print 'pred[{}] score:{}'.format(i, score)
    return (mf_tr / n_round, mf_te / n_round)