Python 實現3種迴歸模型(Linear Regression,Lasso,Ridge)的示例
阿新 • • 發佈:2020-10-16
公共的抽象基類
import numpy as np from abc import ABCMeta,abstractmethod class LinearModel(metaclass=ABCMeta): """ Abstract base class of Linear Model. """ def __init__(self): # Before fit or predict,please transform samples' mean to 0,var to 1. self.scaler = StandardScaler() @abstractmethod def fit(self,X,y): """fit func""" def predict(self,X): # before predict,you must run fit func. if not hasattr(self,'coef_'): raise Exception('Please run `fit` before predict') X = self.scaler.transform(X) X = np.c_[np.ones(X.shape[0]),X] # `x @ y` == `np.dot(x,y)` return X @ self.coef_
Linear Regression
class LinearRegression(LinearModel): """ Linear Regression. """ def __init__(self): super().__init__() def fit(self,y): """ :param X_: shape = (n_samples + 1,n_features) :param y: shape = (n_samples]) :return: self """ self.scaler.fit(X) X = self.scaler.transform(X) X = np.c_[np.ones(X.shape[0]),X] self.coef_ = np.linalg.inv(X.T @ X) @ X.T @ y return self
Lasso
class Lasso(LinearModel): """ Lasso Regression,training by Coordinate Descent. cost = ||X @ coef_||^2 + alpha * ||coef_||_1 """ def __init__(self,alpha=1.0,n_iter=1000,e=0.1): self.alpha = alpha self.n_iter = n_iter self.e = e super().__init__() def fit(self,y): self.scaler.fit(X) X = self.scaler.transform(X) X = np.c_[np.ones(X.shape[0]),X] self.coef_ = np.zeros(X.shape[1]) for _ in range(self.n_iter): z = np.sum(X * X,axis=0) tmp = np.zeros(X.shape[1]) for k in range(X.shape[1]): wk = self.coef_[k] self.coef_[k] = 0 p_k = X[:,k] @ (y - X @ self.coef_) if p_k < -self.alpha / 2: w_k = (p_k + self.alpha / 2) / z[k] elif p_k > self.alpha / 2: w_k = (p_k - self.alpha / 2) / z[k] else: w_k = 0 tmp[k] = w_k self.coef_[k] = wk if np.linalg.norm(self.coef_ - tmp) < self.e: break self.coef_ = tmp return self
Ridge
class Ridge(LinearModel): """ Ridge Regression. """ def __init__(self,alpha=1.0): self.alpha = alpha super().__init__() def fit(self,X] self.coef_ = np.linalg.inv( X.T @ X + self.alpha * np.eye(X.shape[1])) @ X.T @ y return self
測試程式碼
import matplotlib.pyplot as plt import numpy as np def gen_reg_data(): X = np.arange(0,45,0.1) X = X + np.random.random(size=X.shape[0]) * 20 y = 2 * X + np.random.random(size=X.shape[0]) * 20 + 10 return X,y def test_linear_regression(): clf = LinearRegression() X,y = gen_reg_data() clf.fit(X,y) plt.plot(X,y,'.') X_axis = np.arange(-5,75,0.1) plt.plot(X_axis,clf.predict(X_axis)) plt.title("Linear Regression") plt.show() def test_lasso(): clf = Lasso() X,clf.predict(X_axis)) plt.title("Lasso") plt.show() def test_ridge(): clf = Ridge() X,clf.predict(X_axis)) plt.title("Ridge") plt.show()
測試效果
更多機器學習程式碼,請訪問 https://github.com/WiseDoge/plume
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