reduce_mem_usage 降低内存使用 绘制学习率曲线和验证曲线

def reduce_mem_usage(df):
    """ iterate through all the columns of a dataframe and modify the data type
    to reduce memory usage.
    """
    start_mem = df.memory_usage().sum()
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
    for col in df.columns:
        col_type = df[col].dtype
        if col_type != object:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == 'int':
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                    df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)
            else:
                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                    df[col] = df[col].astype(np.float16)
                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)
        else:
            df[col] = df[col].astype('category')
    end_mem = df.memory_usage().sum()
    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
    return df

绘制学习率曲线和验证曲线 

from sklearn.model_selection import learning_curve, validation_curve
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,n_jobs=1, train_size=np.linspac
    plt.figure()
    plt.title(title)
    if ylim is not None:
        plt.ylim(*ylim)
    plt.xlabel('Training example')
    plt.ylabel('score')
    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, t
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()#区域
    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
        train_scores_mean + train_scores_std, alpha=0.1,color="r")
    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
        test_scores_mean + test_scores_std, alpha=0.1,color="g")
    plt.plot(train_sizes, train_scores_mean, 'o-', color='r',label="Training score")
    plt.plot(train_sizes, test_scores_mean,'o-',color="g",label="Cross-validation score")
    plt.legend(loc="best")
    return plt