sklearn-str类型数据量化操作
OrdinalEncoder
import numpy as npimport pandas as pdfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.preprocessing import OrdinalEncoder,OneHotEncoder,LabelEncodersalary = pd.read_csv("/Users/zhucan/Desktop/salary.txt")salary.drop(labels=["final_weight","education_num","capital_gain","capital_loss"],axis = 1,inplace=True)ordinalEncoder = OrdinalEncoder()data = ordinalEncoder.fit_transform(salary)u = salary["education"].unique()u
array(['Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college','Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school','5th-6th', '10th', '1st-4th', 'Preschool', '12th'], dtype=object)
u.sort()u
array(['10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th','Assoc-acdm', 'Assoc-voc', 'Bachelors', 'Doctorate', 'HS-grad','Masters', 'Preschool', 'Prof-school', 'Some-college'],dtype=object)
salary_ordinal = pd.DataFrame(data,columns=salary.columns)salary_ordinal.head()
LabelEncoder
import numpy as npimport pandas as pdfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.preprocessing import OrdinalEncoder,OneHotEncoder,LabelEncodersalary = pd.read_csv("/Users/zhucan/Desktop/salary.txt")salary.drop(labels=["final_weight","education_num","capital_gain","capital_loss"],axis = 1,inplace=True)labelEncode = LabelEncoder()salary_label = labelEncode.fit_transform(salary["education"]) #一列转换for col in salary.columns:salary[col] = labelEncode.fit_transform(salary[col])salary
OnehotEncoder
import numpy as npimport pandas as pdfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.preprocessing import OrdinalEncoder,OneHotEncoder,LabelEncodersalary = pd.read_csv("/Users/zhucan/Desktop/salary.txt")salary.drop(labels=["final_weight","education_num","capital_gain","capital_loss"],axis = 1,inplace=True)edu = salary[["education"]]onehotEncoder = OneHotEncoder()onehot = onehotEncoder.fit_transform(edu)# one-hotonehot.toarray()[:10]#一共16列
nd1.argmax(axis = 1)
array([ 9, 9, 11, 1, 9, 12, 6, 11, 12, 9])
np.sort(edu.education.unique())
array(['10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th','Assoc-acdm', 'Assoc-voc', 'Bachelors', 'Doctorate', 'HS-grad','Masters', 'Preschool', 'Prof-school', 'Some-college'],dtype=object)
决策树的熵原理
熵或者基尼系数分裂
ID3——信息增益
C4.5——可以使用连续数据作为特征值
C5.0——准确率更高了
CART——和C4.5相似，但是支持回归
决策树的使用
from sklearn import datasetsimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn import treeiris = datasets.load_iris()X = iris["data"]y = iris["target"]feature_names = iris.feature_namesX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1024)clf = DecisionTreeClassifier(criterion="entropy")clf.fit(X_train,y_train)y_ = clf.predict(X_test)print(accuracy_score(y_test,y_))plt.figure(figsize=(12,9),dpi=80)tree.plot_tree(clf,filled = True,feature_names = feature_names)
39/120*np.log2(120/39)+42/120*np.log2(120/42)+39/120*np.log2(120/39)1.5840680553754911
决策树属性列分时计算
为什么一开始依靠petal_length<=2.6区分？
X_train.std(axis = 0)
array([0.82300095, 0.42470578, 1.74587112, 0.75016619])
第三列波动大选第三列
np.sort(X_train[:,2])
array([1. , 1.1, 1.2, 1.2, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.4, 1.4, 1.4,1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,1.5, 1.5, 1.5, 1.5, 1.5, 1.6, 1.6, 1.6, 1.7, 1.7, 1.7, 1.7, 1.9,3.3, 3.5, 3.5, 3.6, 3.7, 3.8, 3.9, 3.9, 3.9, 4. , 4. , 4. , 4. ,4.1, 4.1, 4.1, 4.2, 4.2, 4.2, 4.2, 4.3, 4.4, 4.4, 4.4, 4.5, 4.5,4.5, 4.5, 4.5, 4.5, 4.5, 4.6, 4.6, 4.6, 4.7, 4.7, 4.7, 4.7, 4.8,4.8, 4.8, 4.8, 4.9, 4.9, 4.9, 5. , 5. , 5. , 5.1, 5.1, 5.1, 5.1,5.1, 5.1, 5.1, 5.2, 5.2, 5.3, 5.3, 5.4, 5.4, 5.5, 5.5, 5.5, 5.6,5.6, 5.6, 5.7, 5.8, 5.9, 5.9, 6. , 6. , 6.1, 6.1, 6.1, 6.3, 6.4,6.7, 6.7, 6.9])
(1.9+3.3)/2=2.6
X_train[y_train != 0].std(axis = 0)
array([0.66470906, 0.32769349, 0.80409522, 0.4143847 ])
X_train2 = X_train[y_train != 0]np.sort(X_train2[:,3])
array([1. , 1. , 1. , 1. , 1. , 1. , 1.1, 1.1, 1.2, 1.2, 1.2, 1.2, 1.2,1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.4, 1.4, 1.4,1.4, 1.4, 1.4, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,1.6, 1.6, 1.6, 1.6, 1.7, 1.7, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8,1.8, 1.8, 1.9, 1.9, 1.9, 1.9, 1.9, 2. , 2. , 2. , 2. , 2. , 2.1,2.1, 2.1, 2.1, 2.2, 2.2, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3,2.4, 2.5, 2.5])
X_train2 = X_train[y_train != 0]y_train2 = y_train[y_train!=0]index = np.argsort(X_train2[:,3])display(X_train[:,1][index])y_train2[index]
array([3. , 3.2, 3.2, 3.1, 3.5, 3.1, 3.7, 3. , 2.6, 3.6, 2.9, 3.8, 2.6,3.4, 2.9, 2.8, 3.3, 3.1, 2.9, 2.8, 3.4, 3. , 3. , 2.4, 2.2, 3. ,3.2, 3. , 3.1, 2.9, 3. , 2.9, 3.2, 3.4, 3.2, 2. , 3. , 2.5, 2.8,3.3, 2.7, 2.9, 3.2, 2.7, 2.4, 3. , 3.5, 3.2, 2.8, 3. , 4. , 2.5,3.8, 2.8, 2.7, 2.2, 3.7, 2.4, 2.7, 3.1, 2.8, 2.7, 2.8, 2.2, 3.6,3.8, 3.3, 3.2, 3. , 3.8, 3. , 3. , 3.1, 3. , 2.8, 3.9, 2.8, 2.8,3.2, 2.8, 3.4])array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2,1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
属性
决策树gini系数
决策树进行数据处理的时候不需要对数据进行去量纲化，规划化，标准化
随机森林原理
100个原始样本，抽取样本可重复0，0，0，2，3，4......
import numpy as npimport matplotlib.pyplot as pltfrom sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifierfrom sklearn import datasetsimport pandas as pdfrom sklearn.model_selection import train_test_splitwine =datasets.load_wine()X = wine["data"]y = wine["target"]X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)clf = RandomForestClassifier()clf.fit(X_train,y_train)y_ = clf.predict(X_test)from sklearn.metrics import accuracy_scoreaccuracy_score(y_test,y_)
1.0
