一、特征选择可以减少过拟合代码实例
该实例来自机器学习实战第四章
#coding=utf-8
'''
We use KNN to show that feature selection maybe reduce overfitting
'''
from sklearn.base import clone
from itertools import combinations
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
class SBS():
def __init__(self, estimator, k_features, scoring=accuracy_score, test_size=0.25, random_state=1):
self.scoring = scoring
self.estimator = clone(estimator)
self.k_features = k_features
self.test_size = test_size
self.random_state = random_state
def fit(self, X, y):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = self.test_size, random_state=self.random_state)
dim = X_train.shape[1]
self.indices_ = tuple(range(dim))
self.subsets_ = [self.indices_]
score = self._calc_score(X_train, y_train, X_test, y_test, self.indices_)
self.scores_ = [score]
while dim > self.k_features:
scores = []
subsets = []
for p in combinations(self.indices_, r=dim-1):
score = self._calc_score(X_train, y_train, X_test, y_test, p)
scores.append(score)
subsets.append(p)
best = np.argmax(scores)
self.indices_ = subsets[best]
self.subsets_.append(self.indices_)
dim -= 1
self.scores_.append(scores[best])
self.k_score_ = self.scores_[-1]
return self
def transform(self, X):
return X[:, self.indices_]
def _calc_score(self, X_train, y_train, X_test, y_test, indices):
self.estimator.fit(X_train[:, indices], y_train)
y_pred = self.estimator.predict(X_test[:, indices])
score = self.scoring(y_test, y_pred)
return score
import pandas as pd
from sklearn.model_selection import train_test_split
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)
df_wine.columns = ['Class label', 'Alcohol',
'Malic acid',
'Ash',
'Alcalinity of ash',
'Magnesium',
'Total phenols',
'Flavanoids',
'Nonflavanoid phenols',
'Proanthocyanins',
'Color intensity',
'Hue',
'OD280/OD315 of diluted wines',
'Proline']
X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
knn = KNeighborsClassifier(n_neighbors=2)
sbs = SBS(knn, k_features=1)
sbs.fit(X_train_std, y_train)
k_feat = [len(k) for k in sbs.subsets_]
plt.plot(k_feat, sbs.scores_, marker='o')
plt.ylim([0.7, 1.1])
plt.ylabel('Accuracy')
plt.xlabel('Number of features')
plt.grid()
plt.show()
#Let's see what those five features are that yield such a good performance on validation dataset
#subsets_的第九个元素是选择了13个特征中的五个来进行训练
k5 = list(sbs.subsets_[8])
print(df_wine.columns[1:][k5])
'''
Index(['Alcohol', 'Malic acid', 'Alcalinity of ash', 'Hue', 'Proline'], dtype='object')
'''
#Let's evaluate the performance of the KNN classifer on the original test set
knn.fit(X_train_std, y_train)
print("Training Accuracy:", knn.score(X_train_std, y_train))
print("Test Accuracy:", knn.score(X_test_std, y_test))
'''
Training accuracy: 0.9838709677419355
Test Accuracy: 0.9444444444444444
'''
#We find a slight degree of overftting if we used all the 13 features on training
knn.fit(X_train_std[:, k5], y_train)
print("Training Accuracy:", knn.score(X_train_std[:, k5], y_train))
print("Test Accuracy:", knn.score(X_test_std[:, k5], y_test))
'''
Training Accuracy: 0.9596774193548387
Test Accuracy: 0.9629629629629629
'''
#We reduced overfitting and the prediction accuracy improved.