# -*- coding: utf-8 -*-
# @Time : 2022/3/17 17:36
# @Author : Leng Yang
# @FileName: categorical_process.py
# @Software: PyCharm


import pandas as pd
import numpy as np
from sklearn import metrics
from scipy.stats import chi2_contingency, chi2


def test():
    pass


class CategorySelfDescribe(object):
    """
    描述性统计量
    """

    def __init__(self):
        pass

    @staticmethod
    def category_describe(data: pd.Series) -> pd.DataFrame:
        """
        描述该列数据包含的分类名称和分类种类数量
        :param data: 输入数据，格式为pd.Series
        :return: pd.DataFrame, 返回dataframe形式包含分类名称列表和分类种类数量
        Examples
        --------
        >>> data1 = pd.DataFrame({'天气':['晴','晴','阴','雨'], '温度':['高','高','高','低']})
        >>> CategorySelfDescribe().category_describe('天气')
          categories  types
        0  [晴, 阴, 雨]    3.0
        """
        results = pd.DataFrame()
        results = results.append({'categories': data.unique(), 'types': len(data.unique())}, ignore_index=True)
        return results

    @staticmethod
    def category_frequency(data: pd.Series) -> pd.DataFrame:
        """
        频数表
        :param data: 输入数据，格式为pd.Series
        :return: pd.DataFrame, 返回频数表
        Examples
        --------
        >>> data1 = pd.DataFrame({'天气':['晴','晴','阴','雨','雨','雨','阴','晴','晴','雨','晴','阴','阴','雨'],
                     '温度':['高','高','高','低','低','低','低','低','低','低','低','低','高','低']})
        >>> CategorySelfDescribe().category_frequency('天气')
                unique_values  count  frequency
            0             晴      5    0.357143
            1             雨      5    0.357143
            2             阴      4    0.285714

        """
        df_freq = data.value_counts(ascending=False).rename_axis('unique_values').reset_index(name='count')
        df_freq['frequency'] = df_freq['count'] / len(data)
        return df_freq


class CategorySelfAnalyse(object):
    """
    对单列分类数据进行统计分析
    """

    def __init__(self):
        pass

    @staticmethod
    def entropy(data: pd.Series) -> float:
        """
        计算信息熵
        :param data: 输入数据，格式为pd.Series
        :return: float, 信息熵
        """
        prob = pd.value_counts(data) / len(data)
        return sum(np.log2(prob) * prob * (-1))


class CategoryMutualDescribe(object):
    """
    对两列不同的分类数据进行描述性统计
    """

    def __init__(self):
        pass

    @staticmethod
    def crosstab(row_data: pd.Series, col_data: pd.Series) -> pd.DataFrame:
        """
        对两列不同的分类数据进行列联表分析
        :param row_data: categorical数据1, 数据1分类作为列联表的行
        :param col_data: categorical数据2, 数据2分类作为列联表的列
        :return: pd.DataFrame, 列联表
        Examples
        --------
        >>> data1 = pd.DataFrame({'天气':['晴','晴','阴','雨'], '温度':['高','高','高','低']})
        >>> CategoryMutualDescribe().crosstab('天气','温度')
        温度   高 低
        天气
        晴     2  0
        阴     1  0
        雨     0  1
        """
        return pd.crosstab(row_data, col_data)


class MutualCategoricalAnalyse(object):
    """
    对两列分类数据进行统计分析
    """

    def __init__(self):
        pass

    @staticmethod
    def info_gain(df: pd.DataFrame, attr_col: str, data_col: str) -> float:
        """
        计算信息增益: Gain(D,A) = Ent(D) - Ent(D|A)
        使用某个特征A划分数据集D
        :param df_data: 输入数据，格式为dataframe
        :param attr_col: 特征数据列名
        :param data_col: 数据集列名
        :return: float, 信息增益
        """
        # e: 条件信息熵
        e1 = df.groupby(attr_col).apply(lambda x: CategorySelfAnalyse.entropy(x[data_col]))
        p1 = pd.value_counts(df[attr_col]) / len(df[attr_col])  # p(x)
        e2 = sum(e1 * p1)  # Ent(D|A)
        return CategorySelfAnalyse.entropy(df[data_col]) - e2

    @staticmethod
    def normalized_mutual_information(data1: pd.Series, data2: pd.Series) -> float:
        """
        Mutual Information between two clusterings. The Mutual Information is a measure of the similarity
        between two labels of the same data.
        Normalized Mutual Information (NMI) is a normalization of the Mutual
        Information (MI) score to scale the results between 0 (no mutual
        information) and 1 (perfect correlation).
        :param df_data: 输入数据，格式为dataframe
        :param data1: 分类数据1
        :param data2: 分类数据2
        :return: nmi : float, score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling
        """
        return metrics.normalized_mutual_info_score(data1, data2)

    @staticmethod
    def chi2_independence(data1: pd.Series, data2: pd.Series, alpha=0.05) -> pd.DataFrame:
        """
        卡方独立性检验
        :param alpha: 置信度，用来确定临界值
        :param data1: categroical数据1
        :param data2: categorical数据2
        :return: pd.DataFrame，内容如下：
            g: 卡方值，也就是统计量
            p: P值（统计学名词），与置信度对比，也可进行假设检验，P值小于置信度，即可拒绝原假设
            dof: 自由度
            re: 判读变量，1表示拒绝原假设，0表示接受原假设
            expctd: 原数据数组同维度的对应理论值
        """
        data = CategoryMutualDescribe.crosstab(data1, data2)
        result = pd.DataFrame(columns=['g', 'p', 'dof', 'expctd'])
        g, p, dof, expctd = chi2_contingency(data)
        result = result.append({'g': g, 'p': p, 'dof': dof, 'expctd': expctd}, ignore_index=True)
        if dof == 0:
            raise ValueError('自由度应该大于等于1')
        elif dof == 1:
            cv = chi2.isf(alpha * 0.5, dof)  # critical value
        else:
            cv = chi2.isf(alpha * 0.5, dof - 1)

        if g > cv:
            result.loc[0, 're'] = 1  # 表示拒绝原假设
        else:
            result.loc[0, 're'] = 0  # 表示接受原假设
        return result