File size: 2,378 Bytes
e3af80f 1b07538 e3af80f 99591c6 e3af80f 99591c6 e3af80f 99591c6 e3af80f 99591c6 e3af80f 99591c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
#sk-learn風の線形回帰分析クラス
#SE, t-val,p-val, R2を出力
#x, yをpandas DataFrameで入力、self.olsは(coef, SE, t値, p値)のDataFrame
from scipy.stats import t
import numpy as np
import pandas as pd
class linear_regression():
def __init__(self, fit_intercept=True):
self.const = fit_intercept
def fit(self, x, y):
# pandas DataFrame → numpy配列に変換
if isinstance(x, pd.DataFrame):
x_np = x.values
x_cols = x.columns
else:
x_np = x
x_cols = [f"x{i}" for i in range(x.shape[1])]
# yがSeriesかDataFrameかに関係なく1列の配列にする
if isinstance(y, (pd.Series, pd.DataFrame)):
y_np = np.asarray(y).reshape(-1, 1)
else:
y_np = y.reshape(-1, 1)
if self.const:
ones = np.ones((x_np.shape[0], 1))
z = np.concatenate([ones, x_np], axis=1)
self.index = ['const'] + list(x_cols)
else:
z = x_np
self.index = list(x_cols)
self.phi = np.linalg.inv(z.T @ z) @ (z.T @ y_np)
if self.const:
self.intercept_ = self.phi[0, 0]
self.coef_ = self.phi[1:].flatten()
else:
self.intercept_ = 'NA'
self.coef_ = self.phi.flatten()
u = y_np - z @ self.phi
RSS = np.sum(u**2)
TSS = np.sum((y_np - np.mean(y_np))**2)
self.R2 = 1 - RSS / TSS
self.s2 = RSS / (z.shape[0] - z.shape[1])
self.SE = np.sqrt(self.s2 * np.diagonal(np.linalg.inv(z.T @ z)))
self.t = self.phi.flatten() / self.SE
self.p = (1 - t.cdf(np.abs(self.t), df=z.shape[0] - z.shape[1])) * 2
def predict(self, x):
if isinstance(x, pd.DataFrame):
x_np = x.values
else:
x_np = x
if self.const:
z = np.concatenate([np.ones((x_np.shape[0], 1)), x_np], axis=1)
else:
z = x_np
fcst = z @ self.phi
return fcst.squeeze()
def summary(self):
col_names = ["coef", "se", "t", "両側p値"]
output = pd.DataFrame(np.c_[self.phi.flatten(), self.SE, self.t, self.p],
index=self.index,
columns=col_names)
print(f'決定係数R^2: {self.R2}')
return output |