# -*- coding:utf-8 -*-
"""
@author: yq
@time: 2023/12/28
@desc:  特征工具类
"""
import re
from typing import List

import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder

FORMAT_DICT = {
    # 比例类 -1 - 1
    "bin_rate1": np.arange(-1, 1 + 0.1, 0.1),

    # 次数类1 0 -10
    "bin_cnt1": np.arange(0, 11, 1),
    # 次数类2 0 - 20
    "bin_cnt2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 17, 20],
    # 次数类3 0 - 50
    "bin_cnt3": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50],
    # 次数类4 0 - 100
    "bin_cnt4": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 80, 100],

    # 金额类1 0 - 1w
    "bin_amt1": np.arange(0, 1.1e4, 1e3),
    # 金额类2 0 - 5w
    "bin_amt2": np.arange(0, 5.5e4, 5e3),
    # 金额类3 0 - 10w
    "bin_amt3": np.arange(0, 11e4, 1e4),
    # 金额类4 0 - 20w
    "bin_amt4": [0, 1e4, 2e4, 3e4, 4e4, 5e4, 8e4, 10e4, 15e4, 20e4],
    # 金额类5 0 - 100w
    "bin_amt5": [0, 5e4, 10e4, 15e4, 20e4, 25e4, 30e4, 40e4, 50e4, 100e4],

    # 年龄类
    "bin_age": [20, 25, 30, 35, 40, 45, 50, 55, 60, 65],
}


# 粗分箱
def f_format_bin(data_describe: pd.Series):
    # 筛选最合适的标准化分箱节点
    percent10 = data_describe["10%"]
    percent90 = data_describe["90%"]
    cache = None
    for k, v_list in FORMAT_DICT.items():
        bin_min = min(v_list)
        bin_max = max(v_list)
        if bin_min <= percent10 and percent90 <= bin_max:
            if cache is None:
                cache = (k, bin_max)
            elif cache[1] > bin_max:
                cache = (k, bin_max)
    if cache is None:
        return None
    return FORMAT_DICT[cache[0]]


def f_format_value(points, raw_v):
    format_v = raw_v
    # 选择分箱内靠左的切分点
    for idx in range(1, len(points)):
        v_left = points[idx - 1]
        v_right = points[idx]
        # 靠左原则
        if v_left <= raw_v < v_right:
            format_v = v_left
        if raw_v > v_right:
            format_v = v_right

    return format_v


class OneHot():

    def __init__(self, ):
        self._one_hot_encoder = OneHotEncoder()

    def fit(self, data: pd.DataFrame, x_column: str):
        self._x_column = x_column
        self._one_hot_encoder.fit(data[x_column].to_numpy().reshape(-1, 1))
        self._columns_onehot = [re.sub(r"[\[\]<]", "", f"{x_column}({i})") for i in
                                self._one_hot_encoder.categories_[0]]

    def encoder(self, data: pd.DataFrame):
        one_hot_x = self._one_hot_encoder.transform(data[self._x_column].to_numpy().reshape(-1, 1))
        one_hot_x = one_hot_x.toarray()
        for idx, column_name in enumerate(self._columns_onehot):
            data[column_name] = one_hot_x[:, idx]

    @property
    def columns_onehot(self) -> List[str]:
        return self._columns_onehot