exp_utils.py 文件源码

def prec_log(X_train, y_train, X_test, y_test):
    from sklearn.linear_model import LogisticRegression
    if not issparse(X_train):
        X_train = X_train.reshape((X_train.shape[0], -1))
    if not issparse(X_test):
        X_test = X_test.reshape((X_test.shape[0], -1))
    LOGGER.info('start predict: X_train.shape={},y_train.shape={},X_test.shape={},y_test.shape={}'.format(
        X_train.shape, y_train.shape, X_test.shape, y_test.shape))
    X_train = X_train.reshape((X_train.shape[0], -1))
    X_test = X_test.reshape((X_test.shape[0], -1))
    clf = LogisticRegression(solver='sag', n_jobs=-1, verbose=1)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    prec = float(np.sum(y_pred == y_test)) / len(y_test)
    LOGGER.info('prec_log={:.6f}%'.format(prec*100.0))
    return clf, y_pred