Question

我的数据如下所示，并且我正在使用Facebook FbProphet进行预测。接下来，我想为数据框中的每个组计算SMAPE。我发现Kaggle用户here描述的功能，但是我不确定如何在当前代码中实现。这样SMAPE就可以为每个组计算。另外，我知道fbProphet具有验证功能，但是我想为每个组计算SMAPE。

注意：我是python的新手，请提供代码说明。

数据集

import pandas as pd
data = {'Date':['2017-01-01', '2017-01-01','2017-01-01','2017-01-01','2017-01-01','2017-01-01','2017-01-01','2017-01-01',
               '2017-02-01', '2017-02-01','2017-02-01','2017-02-01','2017-02-01','2017-02-01','2017-02-01','2017-02-01'],'Group':['A','A','B','B','C','C','D','D','A','A','B','B','C','C','D','D'],
       'Amount':['12.1','13.2','15.1','10.7','12.9','9.0','5.6','6.7','4.3','2.3','4.0','5.6','7.8','2.3','5.6','8.9']}
df = pd.DataFrame(data)
print (df)

到目前为止的代码...

def get_prediction(df):
    prediction = {}
    df = df.rename(columns={'Date': 'ds','Amount': 'y', 'Group': 'group'})
    df=df.groupby(['ds','group'])['y'].sum()
    df=pd.DataFrame(df).reset_index()
    list_articles = df.group.unique()

    for group in list_articles:
        article_df = df.loc[df['group'] == group]
        # set the uncertainty interval to 95% (the Prophet default is 80%)
        my_model = Prophet(weekly_seasonality= True, daily_seasonality=True,seasonality_prior_scale=1.0)
        my_model.fit(article_df)
        future_dates = my_model.make_future_dataframe(periods=6, freq='MS')
        forecast = my_model.predict(future_dates)
        prediction[group] = forecast
        my_model.plot(forecast)
    return prediction

Answer 1

您仍然可以使用fbprophet自己的cross_validation函数，但可以使用自己的得分。这是uber的一个不错的博客，介绍了他们如何进行回测（滑动窗口和展开窗口）：{{3}}

fbprophet的cv函数在滑动窗口上运行。如果可以的话，可以将其与自定义评分功能结合使用。我认为一种不错的方法是扩展Prophet并实现.score()方法。

这里是一个示例实现：

from fbprophet import Prophet
from fbprophet.diagnostics import cross_validation
import numpy as np

class ProphetEstimator(Prophet):
    """
        Wrapper with custom scoring
    """

    def __init__(self, *args, **kwargs):
        super(ProphetEstimator, self).__init__(*args, **kwargs)

    def score(self):
        # cross val score reusing prophets own cv implementation
        df_cv = cross_validation(self, horizon='6 days')
        # Here decide how you want to calculate SMAPE.
        # Here each sliding window is summed up, 
        # and the SMAPE is calculated over the sum of periods, for all windows.
        df_cv = df_cv.groupby('cutoff').agg({
            "yhat": "sum",
            'y': "sum"
        })
        smape = self.calc_smape(df_cv['yhat'], df_cv['y'])
        return smape

    def calc_smape(self, y_hat, y):
        return 100/len(y) * np.sum(2 * np.abs(y_hat - y) / (np.abs(y) + np.abs(y_hat)))


def get_prediction(df):
    prediction = {}
    df = df.rename(columns={'Date': 'ds','Amount': 'y', 'Group': 'group'})
    df=df.groupby(['ds','group'])['y'].sum()
    df=pd.DataFrame(df).reset_index()
    list_articles = df.group.unique()

    for group in list_articles:
        article_df = df.loc[df['group'] == group]
        # set the uncertainty interval to 95% (the Prophet default is 80%)
        my_model = ProphetEstimator(weekly_seasonality= True, daily_seasonality=True,seasonality_prior_scale=1.0)
        my_model.fit(article_df)
        smape = my_model.score() # store this somewhere
        future_dates = my_model.make_future_dataframe(periods=6, freq='MS')
        forecast = my_model.predict(future_dates)
        prediction[group] = (forecast, smape)
        my_model.plot(forecast)
    return prediction

如何在python中为时间序列数据中的组计算SMAPE？

1 个答案: