- sklearn 把所有机器学习的模式整合统一起来了,学会了一个模式就可以通吃其他不同类型的学习模式
- 步骤一般分为导入模块-创建数据-建立模型-训练-预测
特征工程:
在机器学习中,很重要的一步是对特征的处理,我们参考下文,先给出一些常用的特征处理方法在sklearn中的用法
- 标准化(需要使用距离来度量相似性或用PCA降维时):
from sklearn.preprocessing import StandardScaler data_train = StandardScaler().fit_transform(data_train) data_test = StandardScaler().fit_transform(data_test)
- 区间缩放:
from sklearn.preprocessing import MinMaxScaler data = MinMaxScaler().fit_transform(data)
- 归一化(利于计算梯度下降,消除量纲):
from sklearn.preprocessing import Normalizer data = Normalizer().fit_transform(data)
- 定量特征二值化(大于epsilon为1,小于等于epsilon为0):
from sklearn.preprocessing import Binarizer data = Binarizer(threshold = epsilon).fit_transform(data)
- 类别型特征转换为数值型特征:
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实际上就是保留数值型特征,并将不同的类别转换为哑变量(独热编码),可参考:python中DictVectorizer的使用
from sklearn.feature_extraction import DictVectorizer vec = DictVectorizer(sparse = False) X_train = vec.fit_transform(X_train.to_dict(orient = 'recoed'))
- 卡方检验:
from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 #选择K个最好的特征,返回选择特征后的数据 skb = SelectKBest(chi2, k = 10).fit(X_train, y_train) X_train = skb.transform(X_train) X_test = skb.transform(X_test)
- 互信息法:
from sklearn.feature_selection import SelectKBest from minepy import MINE #由于MINE的设计不是函数式的,定义mic方法将其为函数式的,返回一个二元组,二元组的第2项设置成固定的P值0.5 def mic(x, y): m = MINE() m.compute_score(x, y) return (m.mic(), 0.5) #选择K个最好的特征,返回特征选择后的数据 SelectKBest(lambda X, Y: array(map(lambda x:mic(x, Y), X.T)).T, k=2).fit_transform(iris.data, iris.target)
- 主成分分析(PCA):
from sklearn.decomposition import PCA estimator = PCA(n_components=2)#几个主成分 X_pca = estimator.fit_transform(X_data)
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学习算法:
划分训练集和测试集:
from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 33)
训练:
from sklearn import LearnAlgorithm#导入对应的学习算法包 la = LearnAlgorithm() la.fit(X_train, y_train) y_predict = la.predict(x_test)
随机梯度下降法(SGD):
from sklearn.linear_model import SGDClassifier sgd = SGDClassifier() from sklearn.linear_model import SGDRegressor sgd = SGDRegressor(loss='squared_loss', penalty=None, random_state=42)
支持向量机(SVM):
支持向量分类(SVC):
from sklearn.svm import SVC svc_linear = SVC(kernel='linear')#线性核,可以选用不同的核
支持向量回归(SVR):
from sklearn.svm import SVR svr_linear = SVR(kernel='linear')#线性核,可以选用不同的核如poly,rbf
朴素贝叶斯(NaiveBayes):
from sklearn.naive_bayes import MultinomialNB mnb = MultinomialNB()
决策树(DecisionTreeClassifier):
from sklearn.tree import DecisionTreeClassifier dtc = DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=5)#最大深度和最小样本数,用于防止过拟合
随机森林(RandomForestClassifier):
from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier(max_depth=3, min_samples_leaf=5)
梯度提升树(GBDT):
from sklearn.ensemble import GradientBoostingClassifier gbc = GradientBoostingClassifier(max_depth=3, min_samples_leaf=5)
极限回归森林(ExtraTreesRegressor):
from sklearn.ensemble import ExtraTreesRegressor() etr = ExtraTreesRegressor()
评估:
from sklearn import metrics accuracy_rate = metrics.accuracy_score(y_test, y_predict) metrics.classification_report(y_test, y_predict, target_names = data.target_names)#可以获取准确率,召回率等数据
K折交叉检验:
from sklearn.cross_validation import cross_val_score,KFold cv = KFold(len(y), K, shuffle=True, random_state = 0) scores = cross_val_score(clf, X, y, cv = cv)
或
from sklearn.cross_validation import cross_val_score scores = cross_val_score(dt, X_train, y_train, cv = K)
注意这里的X,y需要为ndarray类型,如果是DataFrame则需要用df.values和df.values.flatten()转化
Pipeline机制:
pipeline机制实现了对全部步骤的流式化封装和管理,应用于参数集在数据集上的重复使用.Pipeline对象接受二元tuple构成的list,第一个元素为自定义名称,第二个元素为sklearn中的transformer或estimator,即处理特征和用于学习的方法.以朴素贝叶斯为例,根据处理特征的不同方法有以下代码:
clf_1 = Pipeline([('count_vec', CountVectorizer()), ('mnb', MultinomialNB())]) clf_2 = Pipeline([('hash_vec', HashingVectorizer(non_negative=True)), ('mnb', MultinomialNB())]) clf_3 = Pipeline([('tfidf_vec', TfidfVectorizer()), ('mnb', MultinomialNB())])
特征选择:
from sklearn import feature_selection fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=per) X_train_fs = fs.fit_transform(X_train, y_train)
我们以特征选择和5折交叉检验为例,实现一个完整的参数选择过程:
from sklearn import feature_selection from sklearn.cross_validation import cross_val_score percentiles = range(1,100) results= [] for i in percentiles: fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=i) X_train_fs = fs.fit_transform(X_train, y_train) scores = cross_val_score(dt, X_train_fs, y_train, cv = 5) results = np.append(results, scores.mean()) opt = np.where(results == results.max())[0] fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=opt) X_train_fs = fs.fit_transform(X_train, y_train) dt.fit(X_train_fs, y_train) y_predict = dt.predict(x_test)
超参数:
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超参数指机器学习模型里的框架参数,在竞赛和工程中都非常重要
集成学习(Ensemble Learning):
通过对多个模型融合以提升整体性能,如随机森林,XGBoost,参考下文:
Ensemble Learning-模型融合-Python实现
多线程网格搜索:
用于寻找最优参数,可参考下文:
from sklearn.cross_validation import train_test_split from sklearn.grid_search import GridSearchCV X_train, X_test, y_train, y_test = train_test_split(news.data[:3000], news.target[:3000], test_size=0.25, random_state=33) from sklearn.svm import SVC from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.pipeline import Pipeline clf = Pipeline([('vect', TfidfVectorizer(stop_words='english', analyzer='word')), ('svc', SVC())]) parameters = {'svc__gamma': np.logspace(-2, 1, 4), 'svc__C': np.logspace(-1, 1, 3)} gs = GridSearchCV(clf, parameters, verbose=2, refit=True, cv=3, n_jobs=-1) %time _=gs.fit(X_train, y_train) gs.best_params_, gs.best_score_ print gs.score(X_test, y_test)
转自:https://www.cnblogs.com/limitlessun/p/8489749.html