sklearn-str類型數(shù)據(jù)量化操作

OrdinalEncoder

import numpy as np import pandas as pd from sklearn.neighbors import KNeighborsClassifier from 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 np import pandas as pd from sklearn.neighbors import KNeighborsClassifier from 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"]) #一列轉(zhuǎn)換for col in salary.columns:salary[col] = labelEncode.fit_transform(salary[col]) salary

OnehotEncoder

import numpy as np import pandas as pd from sklearn.neighbors import KNeighborsClassifier from 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-hot onehot.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)

決策樹的熵原理

熵或者基尼系數(shù)分裂

?

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ID3——信息增益

C4.5——可以使用連續(xù)數(shù)據(jù)作為特征值

C5.0——準(zhǔn)確率更高了

CART——和C4.5相似，但是支持回歸

決策樹的使用

from sklearn import datasets import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn import tree iris = datasets.load_iris() X = iris["data"] y = iris["target"] feature_names = iris.feature_names X_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

決策樹屬性列分時(shí)計(jì)算

為什么一開始依靠petal_length<=2.6區(qū)分？

X_train.std(axis = 0) array([0.82300095, 0.42470578, 1.74587112, 0.75016619])

第三列波動(dòng)大選第三列

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])

屬性

決策樹gini系數(shù)

決策樹進(jìn)行數(shù)據(jù)處理的時(shí)候不需要對(duì)數(shù)據(jù)進(jìn)行去量綱化，規(guī)劃化，標(biāo)準(zhǔn)化

隨機(jī)森林原理?

100個(gè)原始樣本，抽取樣本可重復(fù)0，0，0，2，3，4......

import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier from sklearn import datasets import pandas as pd from sklearn.model_selection import train_test_split wine =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_score accuracy_score(y_test,y_) 1.0

總結(jié)

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