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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 |