再论sklearn分类器

Posted 2020-12-26 jukan

https://www.cnblogs.com/hhh5460/p/5132203.html

这几天在看 sklearn 的文档，发现他的分类器有很多，这里做一些简略的记录。

大致可以将这些分类器分成两类： 1）单一分类器，2）集成分类器

 

一、单一分类器

下面这个例子对一些单一分类器效果做了比较

按 Ctrl+C 复制代码

按 Ctrl+C 复制代码

下图是效果图：

 

二、集成分类器

集成分类器有四种：Bagging, Voting, GridSearch, PipeLine。最后一个PipeLine其实是管道技术

1.Bagging

from sklearn.ensemble import BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier

meta_clf = KNeighborsClassifier() 
bg_clf = BaggingClassifier(meta_clf, max_samples=0.5, max_features=0.5)

 

2.Voting

from sklearn import datasets
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier

iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target

clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()

eclf = VotingClassifier(estimators=[(‘lr‘, clf1), (‘rf‘, clf2), (‘gnb‘, clf3)], voting=‘hard‘, weights=[2,1,2])

for clf, label in zip([clf1, clf2, clf3, eclf], [‘Logistic Regression‘, ‘Random Forest‘, ‘naive Bayes‘, ‘Ensemble‘]):
    scores = cross_validation.cross_val_score(clf, X, y, cv=5, scoring=‘accuracy‘)
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))

 

3.GridSearch

import numpy as np

from sklearn.datasets import load_digits

from sklearn.ensemble import RandomForestClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.grid_search import RandomizedSearchCV

# 生成数据
digits = load_digits()
X, y = digits.data, digits.target

# 元分类器
meta_clf = RandomForestClassifier(n_estimators=20)

# =================================================================
# 设置参数
param_dist = {"max_depth": [3, None],
              "max_features": sp_randint(1, 11),
              "min_samples_split": sp_randint(1, 11),
              "min_samples_leaf": sp_randint(1, 11),
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}

# 运行随机搜索 RandomizedSearch
n_iter_search = 20
rs_clf = RandomizedSearchCV(meta_clf, param_distributions=param_dist,
                                   n_iter=n_iter_search)

start = time()
rs_clf.fit(X, y)
print("RandomizedSearchCV took %.2f seconds for %d candidates"
      " parameter settings." % ((time() - start), n_iter_search))
print(rs_clf.grid_scores_)

# =================================================================
# 设置参数
param_grid = {"max_depth": [3, None],
              "max_features": [1, 3, 10],
              "min_samples_split": [1, 3, 10],
              "min_samples_leaf": [1, 3, 10],
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}

# 运行网格搜索 GridSearch
gs_clf = GridSearchCV(meta_clf, param_grid=param_grid)
start = time()
gs_clf.fit(X, y)

print("GridSearchCV took %.2f seconds for %d candidate parameter settings."
      % (time() - start, len(gs_clf.grid_scores_)))
print(gs_clf.grid_scores_)

 

4.PipeLine

第一个例子

from sklearn import svm
from sklearn.datasets import samples_generator
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression
from sklearn.pipeline import Pipeline

# 生成数据
X, y = samples_generator.make_classification(n_informative=5, n_redundant=0, random_state=42)

# 定义Pipeline，先方差分析，再SVM
anova_filter = SelectKBest(f_regression, k=5)
clf = svm.SVC(kernel=‘linear‘)
pipe = Pipeline([(‘anova‘, anova_filter), (‘svc‘, clf)])

# 设置anova的参数k=10，svc的参数C=0.1（用双下划线"__"连接！）
pipe.set_params(anova__k=10, svc__C=.1)
pipe.fit(X, y)

prediction = pipe.predict(X)

pipe.score(X, y)                        

# 得到 anova_filter 选出来的特征
s = pipe.named_steps[‘anova‘].get_support()
print(s)

第二个例子

import numpy as np

from sklearn import linear_model, decomposition, datasets
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV


digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target

# 定义管道，先降维(pca)，再逻辑回归
pca = decomposition.PCA()
logistic = linear_model.LogisticRegression()
pipe = Pipeline(steps=[(‘pca‘, pca), (‘logistic‘, logistic)])

# 把管道再作为grid_search的estimator
n_components = [20, 40, 64]
Cs = np.logspace(-4, 4, 3)
estimator = GridSearchCV(pipe, dict(pca__n_components=n_components, logistic__C=Cs))

estimator.fit(X_digits, y_digits)