Python masked_mape_np示例

示例#1

文件： SOTABaselineMedian.py 项目： utopfish/autoencoders4imputation

def imputeMethodMedain(result,
                       originData,
                       missData,
                       missRate,
                       missPattern,
                       dataType='continuous'):
    imputationMethod = "median"
    try:
        imputedData = SimpleFill("median").fit_transform(missData)
        if dataType != 'continuous':

            mark = [
                temp[0] for temp in pd.DataFrame(np.unique(missData)).dropna(
                    axis=0).values
            ]
            imputedData = modifier(imputedData, mark)
        result = addResult(result, missRate, missPattern, imputationMethod,
                           evaluate.RMSE(originData, imputedData),
                           MAE(originData, imputedData),
                           masked_mape_np(originData, imputedData))
    except Exception as e:
        print(e)
        imputedData = 'none'
        result = addResult(result, missRate, missPattern, imputationMethod,
                           np.inf, np.inf, np.inf)
    return result, imputedData

示例#2

def imputeMethod2(result, originData, missData, missRate, missPattern):
    imputationMethod = "ycimpute KNN"
    try:
        imputedData = yKNN(k=int(math.sqrt(len(missData)))).complete(missData)
        result = addResult(result, missRate, missPattern, imputationMethod,
                           evaluate.RMSE(originData, imputedData),
                           MAE(originData, imputedData),
                           masked_mape_np(originData, imputedData))
    except Exception as e:
        print(e)
        result = addResult(result, missRate, missPattern, imputationMethod,
                           np.inf, np.inf, np.inf)
    return result

示例#3

文件： SOTABaselineRF.py 项目： utopfish/autoencoders4imputation

def imputeMethodMR(result,originData,missData,missRate,missPattern,dataType='continuous'):
    imputationMethod = "RandomForest"
    try:
        imputer = predictive_imputer.PredictiveImputer(f_model='RandomForest')
        imputedData = imputer.fit(missData).transform(missData.copy())
        if dataType!='continuous':
            mark = [temp[0] for temp in pd.DataFrame(np.unique(missData)).dropna(axis=0).values]
            imputedData = modifier(imputedData, mark)
        result = addResult(result, missRate, missPattern, imputationMethod,
                           evaluate.RMSE(originData, imputedData),
                           MAE(originData, imputedData),
                           masked_mape_np(originData, imputedData))
    except Exception as e:
        print(e)
        imputedData = 'none'
        result = addResult(result, missRate, missPattern, imputationMethod,
                           np.inf,
                           np.inf,
                           np.inf)
    return result,imputedData

示例#4

文件： SOTABaselineFixed.py 项目： utopfish/autoencoders4imputation

def imputeMethodFixed(result,
                      originData,
                      missData,
                      missRate,
                      missPattern,
                      dataType='continuous'):
    """
    默认值插补,插补为0
    :param result:
    :param originData:
    :param missData:
    :param missRate:
    :param missPattern:
    :param dataType:
    :return:
    """
    imputationMethod = "Fixed"

    try:
        imputedData = fixedImpute(missData)
        if dataType != 'continuous':

            mark = [
                temp[0] for temp in pd.DataFrame(np.unique(missData)).dropna(
                    axis=0).values
            ]
            imputedData = modifier(imputedData, mark)
        result = addResult(result, missRate, missPattern, imputationMethod,
                           evaluate.RMSE(originData, imputedData),
                           MAE(originData, imputedData),
                           masked_mape_np(originData, imputedData))
    except Exception as e:
        print(e)
        imputedData = 'none'
        result = addResult(result, missRate, missPattern, imputationMethod,
                           np.inf, np.inf, np.inf)
    return result, imputedData