Applied Machine Learning in Python 8 - Lab: Linear Regression

假設有一個dataset定義如下:

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
 
 
np.random.seed(0)
n = 15
x = np.linspace(0,10,n) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10
 
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)

使用polynomial regression來fit data

這個dataset很明顯是non-linear separable，嘗試先用polynomial feature transformation來增加維度。我們測試不同polynomial degree對一百個input點預測:

def polynomial_regression():
    from sklearn.linear_model import LinearRegression
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.pipeline import make_pipeline
 
    # generate points used for prediction
    x_prediction = np.linspace(0, 10, 100)
    x_prediction = x_prediction[:, np.newaxis]
 
    # create matrix version
    X_poly = X_train[:, np.newaxis]
 
    result = []
    for count, degree in enumerate([1, 3, 6, 9]):
        model = make_pipeline(PolynomialFeatures(degree), LinearRegression())
        model.fit(X_poly, y_train)
        y_predicted = model.predict(x_prediction)
        result.append(y_predicted)
 
    result = np.array(result)
    return result

這四個model train出來，利用100個點預測如下:

找出不同degree的R-squared score

我們現在把degree 0 ~ 9的polynomial regression R^2 score列出來，包含training set 和 test set:

def ploynomial_r2_score():
 
    from sklearn.linear_model import LinearRegression
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.pipeline import make_pipeline
 
    # create matrix version
    X_poly = X_train[:, np.newaxis]
 
    train_scores = []
    test_scores = []
    for count, degree in enumerate(range(0,10)):
        model = make_pipeline(PolynomialFeatures(degree), LinearRegression())
        model.fit(X_poly, y_train)
        train_r2_score = model.score(X_poly, y_train)
        test_r2_score = model.score(X_test[:, np.newaxis], y_test)
        train_scores.append(train_r2_score)
        test_scores.append(test_r2_score)
 
    result = (np.array(train_scores), np.array(test_scores))
    return result

可以看到結果如下:

degree 0 training score = 0.0, test score = -0.4780864173714179
degree 1 training score = 0.4292457781234663, test score = -0.45237104233936676
degree 2 training score = 0.4510998044408247, test score = -0.06856984149915935
degree 3 training score = 0.587199536877985, test score = 0.005331052945771075
degree 4 training score = 0.9194194471769332, test score = 0.7300494281868128
degree 5 training score = 0.9757864143068216, test score = 0.8770830091535732
degree 6 training score = 0.9901823324795085, test score = 0.9214093981312127
degree 7 training score = 0.9935250927840401, test score = 0.9202150411626775
degree 8 training score = 0.9963754538774482, test score = 0.6324794961087974
degree 9 training score = 0.9980370625648909, test score = -0.6452460241564286

所以最好的model 是degree = 6

Lasso regression

polynomial regression在高維度的時候明顯 (degree >= 8) 造成overfitting，試著用lasso regression來regularize。

def lasso_regression():
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.linear_model import Lasso, LinearRegression
    from sklearn.pipeline import make_pipeline
 
    # create matrix version
    X_train_2D = X_train[:, np.newaxis]
    X_test_2D = X_test[:, np.newaxis]
 
    model_lr = make_pipeline(PolynomialFeatures(12), LinearRegression())
    model_lr.fit(X_train_2D, y_train)
 
    model_lasso = make_pipeline(PolynomialFeatures(12), Lasso(alpha=0.01, max_iter=10000))
    model_lasso.fit(X_train_2D, y_train)
 
    lr_test_r2_score = model_lr.score(X_test_2D, y_test)
    lasso_test_r2_score = model_lasso.score(X_test_2D, y_test)
 
    return (lr_test_r2_score, lasso_test_r2_score)

degree = 12的時候，lasso regression得到的test set R^2 score = 0.84，而ordinary linear regression則得到一個 -4.31的值，為何有負的?