RANSACRegressor 在构造后更改 base_estimator 属性

Question

根据问题的公认答案，我正在尝试使用 RANSAC 来实现多项式回归量以拟合 5 阶多项式。

让数据调整为

x = [0.02965717 0.10966089 0.17002236 0.19015372 0.27044443 0.33011883
 0.40844298 0.4659353  0.54051902 0.61236153 0.68116213 0.74673223
 0.82403296 0.88216575 0.96342659]

y = [3.96001134e-03 6.81505094e-04 0.00000000e+00 1.13660854e-04
 2.26741003e-03 5.64587625e-03 1.24338500e-02 1.91707798e-02
 3.02265331e-02 4.34929443e-02 5.87863985e-02 7.59236901e-02
 9.96780928e-02 1.20366687e-01 1.53936744e-01]

和使用的估算器

from sklearn.linear_model import RANSACRegressor
from sklearn.metrics import mean_squared_error

class PolynomialRegression(object):
    def __init__(self, degree=3, coeffs=None):
        print(f"Degree: {degree}")
        self.degree = degree
        self.coeffs = coeffs

    def fit(self, X, y):
        self.coeffs = np.polyfit(X.ravel(), y, self.degree)

    def get_params(self, deep=False):
        return {'coeffs': self.coeffs}

    def set_params(self, coeffs=None, random_state=None):
        self.coeffs = coeffs

    def predict(self, X):
        poly_eqn = np.poly1d(self.coeffs)
        y_hat = poly_eqn(X.ravel())
        return y_hat

    def score(self, X, y):
        return mean_squared_error(y, self.predict(X))

拟合在以下代码段中完成：

import numpy as np
ransac = RANSACRegressor(base_estimator=PolynomialRegression(degree=5),
                          residual_threshold=np.std(y),
                          random_state=0,
                          min_samples=2)
ransac.fit(np.expand_dims(x, axis=1), y)
w = np.array(ransac.estimator_.coeffs)
print(w)

如您所见，我将 degree=5 的 PolynomialRegression 传递给 RANSACRegressor，我希望 w 有 6 个分量。然而，在执行代码时，PolynomialRegression 的阶数在某个时刻更改为 3，并且使用该默认值完成拟合，而不是我正在构建它的值。

输出：

Degree: 5
Degree: 3
[ 0.07331904  0.14501533 -0.05369491  0.00492718]

如何正确定义适合度？

Answer 1

问题出在 get_params 方法中，因为这预计 return 估计器的超参数，即它应该 return 多项式回归的 degree ，而不是估计的回归系数。见 documentation.

import warnings
import numpy as np
from sklearn.linear_model import RANSACRegressor
from sklearn.metrics import mean_squared_error
from sklearn.datasets import make_regression
warnings.filterwarnings('ignore')

class PolynomialRegression(object):
    def __init__(self, degree=3):
        print(f"Degree: {degree}")
        self.degree = degree

    def fit(self, X, y):
        self.coeffs = np.polyfit(X.ravel(), y, self.degree)

    def get_params(self, deep=False):
        return {'degree': self.degree}

    def set_params(self, **parameters):
        for parameter, value in parameters.items():
            setattr(self, parameter, value)
        return self

    def predict(self, X):
        poly_eqn = np.poly1d(self.coeffs)
        y_hat = poly_eqn(X.ravel())
        return y_hat

    def score(self, X, y):
        return mean_squared_error(y, self.predict(X))

x, y = make_regression(n_features=1, random_state=42)

ransac = RANSACRegressor(
    base_estimator=PolynomialRegression(degree=5),
    residual_threshold=np.std(y),
    random_state=0,
    min_samples=2
)

ransac.fit(x, y)

print(ransac.estimator_.coeffs)
# Degree: 5
# Degree: 5
# [ 2.15861169e-14  1.51841316e-14 -5.09828681e-14  2.71301269e-15
#   4.17411003e+01 -5.11272743e-15]

RANSACRegressor 在构造后更改 base_estimator 属性

RANSACRegressor changing base_estimator properties after construction

python

machine-learning

scikit-learn

ransac