标签:tor alpha 内存 ida import size ini scores lse
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
reduce_mem_usage 降低内存使用 绘制学习率曲线和验证曲线
标签:tor alpha 内存 ida import size ini scores lse
原文地址:https://www.cnblogs.com/654321cc/p/13192712.html