Logistic.py

import numpy as np
import pandas as pd
from scipy.stats import norm

# モデルで使う関数定義
def logit(x):
    p = 1 / (1 + np.exp(-x))
    dp = p * (1 - p)
    return p, dp

def probit(x):
    p = norm.cdf(x)
    dp = norm.pdf(x)
    return p, dp

def bhood(func, param, y, z):
    xb = np.dot(z, param)
    p, dp = func(xb)
    p = np.clip(p, 1e-8, 1 - 1e-8)  # log(0)防止
    lhood = np.sum(y * np.log(p) + (1 - y) * np.log(1 - p))
    grad = np.dot(z.T, y - p)
    hess = -np.dot(z.T * dp.flatten(), z)
    return lhood, grad, hess

# メインクラス
class BinaryLogit:
    def __init__(self, mode="logit", tol=1e-4):
        self.mode = mode
        self.tol = tol
        self.coef = None
        self.se = None
        self.t = None
        self.APE = None
        self.PEA = None
        self.p = None
        self.likelihood = None
        self.n = None
        self.k = None

    def _get_func(self):
        if self.mode == "logit":
            return logit
        elif self.mode == "probit":
            return probit
        else:
            raise NotImplementedError(f"Unknown mode: {self.mode}")

    def _get_z_y(self, x, y=None):
        # Pandas から NumPy に変換
        if isinstance(x, pd.DataFrame):
            x_array = x.values
            x_names = ["const"] + x.columns.tolist()
        else:
            x_array = np.asarray(x)
            x_names = ["const"] + [f"x{i}" for i in range(x_array.shape[1])]

        self.n, self.k = x_array.shape
        const = np.ones((self.n, 1))
        z = np.hstack([const, x_array])
        self.x_names = x_names

        if y is None:
            return z

        if isinstance(y, (pd.Series, pd.DataFrame)):
            y_array = np.asarray(y).flatten()
            self.y_name = y.name if hasattr(y, 'name') else "y"
        else:
            y_array = np.asarray(y).flatten()
            self.y_name = "y"

        return z, y_array

    def fit(self, x, y):
        z, y = self._get_z_y(x, y)
        func = self._get_func()

        param = np.zeros(z.shape[1])
        f = bhood(func, param, y, z)
        r = np.max(np.abs(f[1]))

        while r > self.tol:
            param = param - np.dot(np.linalg.inv(f[2]), f[1])
            f = bhood(func, param, y, z)
            r = np.max(np.abs(f[1]))

        self.coef = param
        self.intercept_ = param[0]
        self.coef_ = param[1:]
        self.likelihood = f[0]
        self.se = np.sqrt(np.diag(np.linalg.inv(-f[2])))
        self.t = self.coef / self.se
        self.p = (1 - norm.cdf(np.abs(self.t))) * 2

        u = func(np.dot(z, param.reshape(-1, 1)))
        self.APE = np.mean(u[1]) * param
        u = func(np.dot(np.mean(z, axis=0), param))
        self.PEA = u[1] * param
        return self

    def predict(self, x):
        if isinstance(x, pd.DataFrame):
            x_array = x.values
        else:
            x_array = np.asarray(x)

        if x_array.shape[1] != self.k:
            raise ValueError(f"Expected {self.k} features, got {x_array.shape[1]}")

        const = np.ones((x_array.shape[0], 1))
        z = np.hstack([const, x_array])
        func = self._get_func()
        return func(np.dot(z, self.coef))[0]

    def summary(self):
        col_names = ["coef", "se", "t", "p", "PEA", "APE"]
        df = pd.DataFrame(np.c_[self.coef, self.se, self.t, self.p, self.PEA, self.APE],
                          index=self.x_names,
                          columns=col_names)
        return df