写在开始之前,由于本人水平有限,代码还有许多不完善或设计漏洞,请指正交流,共同进步.

连续变量

对于连续变量类检验：平均误差（ME）、平均绝对误差（MAE）、相关系数（R）、均方根误差（RMSE）。

• ME
• MAE
• RMSE
  -R

离散变量(等级)

2）对于等级变量统计检验：TS评分、漏报率(PO)、空报率(FAR)、预报偏差(BS)

image.png

• TS
• PO
• FAR
• BS

介绍完毕,下面直接贴代码

import os
import datetime
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix


class Verify(object):
    def __init__(self):
        super(Verify, self).__init__()
        self.confusion_matrix = None
        self.NA = None
        self.NB = None
        self.NC = None

    def _get_level_verify(self, real_value, predict_value, levels):
        self.__init__()
        real_level, predict_level = self._check_levels(
            real_value, predict_value, levels)
        self.confusion_matrix = self._get_confusion_matric(
            real_level, predict_level)
        if self.NA is None or self.NB is None or self.NC is None:
            self.NA = {}
            self.NB = {}
            self.NC = {}
        for ivalue, vvalue in enumerate(self.confusion_matrix.columns):
            self.NA[vvalue] = self.confusion_matrix.ix[vvalue, vvalue]
            self.NB[vvalue] = self.confusion_matrix.ix[slice(
                None), vvalue].sum()
            self.NC[vvalue] = self.confusion_matrix.ix[vvalue,
                                                       slice(None)].sum()

    def _check_and_remove_nan(self, real_value, predict_value):
        logical_nan = ~np.logical_or(
            np.isnan(predict_value), np.isnan(real_value))
        predict_value = predict_value[logical_nan]
        real_value = real_value[logical_nan]
        return real_value, predict_value

    def _get_confusion_matric(self, real_level, predict_level):
        return pd.DataFrame(confusion_matrix(real_level, predict_level),
                            index=np.unique([real_level, predict_level]), columns=np.unique([real_level, predict_level]))

    def _check_levels(self, real_value, predict_value, levels):
        '''
        引用https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.digitize.html
        使用numpy.digitize 函数
        '''
        real_value, predict_value = self._check_and_remove_nan(
            real_value, predict_value)
        predict_level = np.digitize(predict_value, levels)
        real_level = np.digitize(real_value, levels)

        return real_level, predict_level

    def _calc_RMSE(self, real_value, predict_value):
        real_value, predict_value = self._check_and_remove_nan(
            real_value, predict_value)
        return np.sqrt(np.mean((predict_value - real_value) ** 2))

    def _calc_MAE(self, real_value, predict_value):
        real_value, predict_value = self._check_and_remove_nan(
            real_value, predict_value)
        return np.mean(np.abs(predict_value - real_value))

    def _calc_ME(self, real_value, predict_value):
        real_value, predict_value = self._check_and_remove_nan(
            real_value, predict_value)
        return np.mean(predict_value - real_value)

    def _calc_R(self, real_value, predict_value):
        real_value, predict_value = self._check_and_remove_nan(
            real_value, predict_value)
        return np.corrcoef(real_value, predict_value)[1, 0]

    def _calc_TS(self, real_value, predict_value, levels):
        '''
        根据等级计算TS评分,即等级预报的准确率
        TS = True / True + Flase Alarm + True negetive
        '''
        self._get_level_verify(real_value, predict_value, levels)
        return (pd.Series(self.NA) / (pd.Series(self.NB) + pd.Series(self.NA) + pd.Series(self.NC))).to_json()

    def _calc_PO(self, real_value, predict_value, levels):
        '''
        PO = True/ True + True negetive
        '''
        self._get_level_verify(real_value, predict_value, levels)
        return (pd.Series(self.NC) / (pd.Series(self.NA) + pd.Series(self.NC))).to_json()

    def _calc_FAR(self, real_value, predict_value, levels):
        '''
        FAR = False Alarm / True + True negetive
        '''
        self._get_level_verify(real_value, predict_value, levels)
        return (pd.Series(self.NB) / (pd.Series(self.NB) + pd.Series(self.NA))).to_json()

    def _calc_BS(self, real_value, predict_value, levels):
        '''
        BS = True + False Alarm / True + True negetive
        '''
        self._get_level_verify(real_value, predict_value, levels)
        return ((pd.Series(self.NA) + pd.Series(self.NB)) / (pd.Series(self.NA) + pd.Series(self.NC))).to_json()

'''
# 方法调试示例
verify_obj = Verify()
real_value = np.random.randint(0, 10, 100)
predict_value = np.random.randint(0, 10, 100)
print(verify_obj._calc_RMSE(real_value, predict_value))
print(verify_obj._calc_MAE(real_value, predict_value))
print(verify_obj._calc_ME(real_value, predict_value))
print(verify_obj._calc_R(real_value, predict_value))
print(verify_obj._calc_TS(real_value, predict_value, [2, 5, 7, 9]))
print(verify_obj._calc_PO(real_value, predict_value, [3, 5, 7, 9]))
print(verify_obj._calc_FAR(real_value, predict_value, [2, 5, 7, 9]))
print(verify_obj._calc_BS(real_value, predict_value, [2, 5, 7, 9]))
'''