1. sklearn簡(jiǎn)介

sklearn圖標(biāo)

sklearn是基于python語(yǔ)言的機(jī)器學(xué)習(xí)工具包伐憾，是目前做機(jī)器學(xué)習(xí)項(xiàng)目當(dāng)之無(wú)愧的第一工具篙螟。
sklearn自帶了大量的數(shù)據(jù)集芹扭，可供我們練習(xí)各種機(jī)器學(xué)習(xí)算法吨娜。
sklearn集成了數(shù)據(jù)預(yù)處理脓匿、數(shù)據(jù)特征選擇、數(shù)據(jù)特征降維宦赠、分類\回歸\聚類模型陪毡、模型評(píng)估等非常全面算法米母。

2.sklearn數(shù)據(jù)類型

機(jī)器學(xué)習(xí)最終處理的數(shù)據(jù)都是數(shù)字，只不過(guò)這些數(shù)據(jù)可能以不同的形態(tài)被呈現(xiàn)出來(lái)毡琉，如矩陣铁瞒、文字、圖片桅滋、視頻慧耍、音頻等。

3.sklearn總覽

sklearn包含的模塊

數(shù)據(jù)集

image

• sklearn.datasets

1. 獲取小數(shù)據(jù)集(本地加載)：datasets.load_xxx( )
2. 獲取大數(shù)據(jù)集(在線下載)：datasets.fetch_xxx( )
3. 本地生成數(shù)據(jù)集(本地構(gòu)造)：datasets.make_xxx( )

數(shù)據(jù)集讀取的部分代碼：

from sklearn import datasets
import matplotlib.pyplot as plt

iris = datasets.load_iris()
features = iris.data
target = iris.target
print(features.shape,target.shape)
print(iris.feature_names)

boston = datasets.load_boston()
boston_features = boston.data
boston_target = boston.target
print(boston_features.shape,boston_target.shape)
print(boston.feature_names)

digits = datasets.load_digits()
digits_features = digits.data
digits_target = digits.target
print(digits_features.shape,digits_target.shape)

img = datasets.load_sample_image('flower.jpg')
print(img.shape)
plt.imshow(img)
plt.show()

data,target = datasets.make_blobs(n_samples=1000,n_features=2,centers=4,cluster_std=1)
plt.scatter(data[:,0],data[:,1],c=target)
plt.show()

data,target = datasets.make_classification(n_classes=4,n_samples=1000,n_features=2,n_informative=2,n_redundant=0,n_clusters_per_class=1)
print(data.shape)
plt.scatter(data[:,0],data[:,1],c=target)
plt.show()

x,y = datasets.make_regression(n_samples=10,n_features=1,n_targets=1,noise=1.5,random_state=1)
print(x.shape,y.shape)
plt.scatter(x,y)
plt.show()

數(shù)據(jù)預(yù)處理

image

• sklearn.preprocessing

數(shù)據(jù)預(yù)處理代碼

import numpy as np
from sklearn import preprocessing

#標(biāo)準(zhǔn)化：將數(shù)據(jù)轉(zhuǎn)換為均值為0许布，方差為1的數(shù)據(jù)兴革，即標(biāo)注正態(tài)分布的數(shù)據(jù)
x = np.array([[1,-1,2],[2,0,0],[0,1,-1]])
x_scale = preprocessing.scale(x)
print(x_scale.mean(axis=0),x_scale.std(axis=0))

std_scale = preprocessing.StandardScaler().fit(x)
x_std = std_scale.transform(x)
print(x_std.mean(axis=0),x_std.std(axis=0))

#將數(shù)據(jù)縮放至給定范圍（0-1）
mm_scale = preprocessing.MinMaxScaler()
x_mm = mm_scale.fit_transform(x)
print(x_mm.mean(axis=0),x_mm.std(axis=0))

#將數(shù)據(jù)縮放至給定范圍（-1-1）,適用于稀疏數(shù)據(jù)
mb_scale = preprocessing.MaxAbsScaler()
x_mb = mb_scale.fit_transform(x)
print(x_mb.mean(axis=0),x_mb.std(axis=0))

#適用于帶有異常值的數(shù)據(jù)
rob_scale = preprocessing.RobustScaler()
x_rob = rob_scale.fit_transform(x)
print(x_rob.mean(axis=0),x_rob.std(axis=0))

#正則化
nor_scale = preprocessing.Normalizer()
x_nor = nor_scale.fit_transform(x)
print(x_nor.mean(axis=0),x_nor.std(axis=0))

#特征二值化：將數(shù)值型特征轉(zhuǎn)換位布爾型的值
bin_scale = preprocessing.Binarizer()
x_bin = bin_scale.fit_transform(x)
print(x_bin)

#將分類特征或數(shù)據(jù)標(biāo)簽轉(zhuǎn)換位獨(dú)熱編碼
ohe = preprocessing.OneHotEncoder()
x1 = ([[0,0,3],[1,1,0],[1,0,2]])
x_ohe = ohe.fit(x1).transform([[0,1,3]])
print(x_ohe)

import numpy as np
from sklearn.preprocessing import PolynomialFeatures

x = np.arange(6).reshape(3,2)
poly = PolynomialFeatures(2)
x_poly = poly.fit_transform(x)
print(x)
print(x_poly)

import numpy as np
from sklearn.preprocessing import FunctionTransformer

#自定義的特征轉(zhuǎn)換函數(shù)
transformer = FunctionTransformer(np.log1p)

x = np.array([[0,1],[2,3]])
x_trans = transformer.transform(x)
print(x_trans)

import numpy as np
import sklearn.preprocessing

x = np.array([[-3,5,15],[0,6,14],[6,3,11]])
kbd = preprocessing.KBinsDiscretizer(n_bins=[3,2,2],encode='ordinal').fit(x)
x_kbd = kbd.transform(x)
print(x_kbd)

from sklearn.preprocessing import MultiLabelBinarizer

#多標(biāo)簽二值化
mlb = MultiLabelBinarizer()
x_mlb = mlb.fit_transform([(1,2),(3,4),(5,)])
print(x_mlb)

• sklearn.svm

上述preprocessing類函數(shù)的方法如下：

特征選擇

image

很多時(shí)候我們用于模型訓(xùn)練的數(shù)據(jù)集包含許多的特征，這些特征要么是有冗余蜜唾，要么是對(duì)結(jié)果的相關(guān)性很性忧；這時(shí)通過(guò)精心挑選一些"好"的特征來(lái)訓(xùn)練模型袁余，既能減小模型訓(xùn)練時(shí)間擎勘，也能夠提升模型性能。

例如一個(gè)數(shù)據(jù)集包含（鼻翼長(zhǎng)颖榜、眼角長(zhǎng)棚饵、額頭寬、血型）這四個(gè)特征掩完；我們用這些數(shù)據(jù)集進(jìn)行人臉識(shí)別噪漾，必定會(huì)去除（血型）這個(gè)特征后再進(jìn)行人臉識(shí)別；因?yàn)椋ㄑ停┻@個(gè)特征對(duì)于人臉識(shí)別這個(gè)目標(biāo)來(lái)說(shuō)是一個(gè)無(wú)用的特征且蓬。

• sklean.feature_selection

特征選擇實(shí)現(xiàn)代碼

from sklearn.datasets import load_digits
from sklearn.feature_selection import SelectKBest,chi2

digits = load_digits()
data = digits.data
target = digits.target
print(data.shape)
data_new = SelectKBest(chi2,k=20).fit_transform(data,target)
print(data_new.shape)

from sklearn.feature_selection import VarianceThreshold

x = [[0,0,1],[0,1,0],[1,0,0],[0,1,1],[0,1,0],[0,1,1]]
vt = VarianceThreshold(threshold=(0.8*(1-0.8)))
x_new = vt.fit_transform(x)
print(x)
print(x_new)

from sklearn.svm import LinearSVC
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectFromModel

iris = load_iris()
x,y = iris.data,iris.target

lsvc = LinearSVC(C=0.01,penalty='l1',dual=False).fit(x,y)
model = SelectFromModel(lsvc,prefit=True)
x_new = model.transform(x)

print(x.shape)
print(x_new.shape)

from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold,cross_val_score
from sklearn.feature_selection import RFECV
from sklearn.datasets import load_iris

iris = load_iris()
x,y = iris.data,iris.target

svc = SVC(kernel='linear')
rfecv = RFECV(estimator=svc,step=1,cv=StratifiedKFold(2),scoring='accuracy',verbose=1,n_jobs=1).fit(x,y)
x_rfe = rfecv.transform(x)
print(x_rfe.shape)

clf = SVC(gamma="auto", C=0.8)   
scores = (cross_val_score(clf, x_rfe, y, cv=5))
print(scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std()*2))

特征降維

特征降維

面對(duì)特征巨大的數(shù)據(jù)集欣硼，除了進(jìn)行特征選擇之外，我們還可以采取特征降維算法來(lái)減少特征數(shù)缅疟；特征降維于特征選擇的區(qū)別在于：特征選擇是從原始特征中挑選特征；而特征降維則是從原始特征中生成新的特征耘斩。

很多人會(huì)有比較特征選擇與特征降維優(yōu)劣的心理，其實(shí)這種脫離實(shí)際問(wèn)題的比較意義不大桅咆，我們要明白每一種算法都是有其擅長(zhǎng)的領(lǐng)域括授。

• sklearn.decomposition

特征降維代碼實(shí)現(xiàn)

"""數(shù)據(jù)降維"""

from sklearn.decomposition import PCA

x = np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])
pca1 = PCA(n_components=2)
pca2 = PCA(n_components='mle')
pca1.fit(x)
pca2.fit(x)
x_new1 = pca1.transform(x)
x_new2 = pca2.transform(x)
print(x_new1.shape)
print(x_new2.shape)

import numpy as np
from sklearn.decomposition import KernelPCA
import matplotlib.pyplot as plt
import math

#kernelPCA適用于對(duì)數(shù)據(jù)進(jìn)行非線性降維
x = []
y = []
N = 500

for i in range(N):
    deg = np.random.randint(0,360)
    if np.random.randint(0,2)%2 == 0:
        x.append([6*math.sin(deg),6*math.cos(deg)])
        y.append(1)
    else:
        x.append([15*math.sin(deg),15*math.cos(deg)])
        y.append(0)
        
y = np.array(y)
x = np.array(x)

kpca = KernelPCA(kernel='rbf',n_components=14)
x_kpca = kpca.fit_transform(x)
print(x_kpca.shape)

from sklearn.datasets import load_digits
from sklearn.decomposition import IncrementalPCA
from scipy import sparse
X, _ = load_digits(return_X_y=True)

#增量主成分分析：適用于大數(shù)據(jù)
transform = IncrementalPCA(n_components=7,batch_size=200)
transform.partial_fit(X[:100,:])

x_sparse = sparse.csr_matrix(X)
x_transformed = transform.fit_transform(x_sparse)
x_transformed.shape

import numpy as np
from sklearn.datasets import make_friedman1
from sklearn.decomposition import MiniBatchSparsePCA

x,_ = make_friedman1(n_samples=200,n_features=30,random_state=0)
transformer = MiniBatchSparsePCA(n_components=5,batch_size=50,random_state=0)
transformer.fit(x)
x_transformed = transformer.transform(x)
print(x_transformed.shape)

from sklearn.datasets import load_digits
from sklearn.decomposition import FactorAnalysis

x,_ = load_digits(return_X_y=True)
transformer = FactorAnalysis(n_components=7,random_state=0)
x_transformed = transformer.fit_transform(x)
print(x_transformed.shape)

• sklearn.manifold

分類模型

image

分類模型是能夠從數(shù)據(jù)集中學(xué)習(xí)知識(shí)荚虚，進(jìn)而提升自我認(rèn)知的一種模型，經(jīng)過(guò)學(xué)習(xí)后母债，它能夠區(qū)分出它所見(jiàn)過(guò)的事物漏隐；這種模型就非常類似一個(gè)識(shí)物的小朋友。

• sklearn.tree

決策樹(shù)分類

from sklearn.datasets import load_iris
from sklearn import tree

x,y = load_iris(return_X_y=True)
clf = tree.DecisionTreeClassifier()
clf = clf.fit(x,y)
tree.plot_tree(clf)

• sklearn.ensemble

BaggingClassifier

#使用sklearn庫(kù)實(shí)現(xiàn)的決策樹(shù)裝袋法提升分類效果。其中X和Y分別是鳶尾花（iris）數(shù)據(jù)集中的自變量（花的特征）和因變量（花的類別）

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets

#加載iris數(shù)據(jù)集
iris=datasets.load_iris()
X=iris.data
Y=iris.target

#生成K折交叉驗(yàn)證數(shù)據(jù)
kfold=KFold(n_splits=9)

#決策樹(shù)及交叉驗(yàn)證
cart=DecisionTreeClassifier(criterion='gini',max_depth=2)
cart=cart.fit(X,Y)
result=cross_val_score(cart,X,Y,cv=kfold)  #采用K折交叉驗(yàn)證的方法來(lái)驗(yàn)證算法效果
print('CART數(shù)結(jié)果:',result.mean())

#裝袋法及交叉驗(yàn)證
model=BaggingClassifier(base_estimator=cart,n_estimators=100) #n_estimators=100為建立100個(gè)分類模型
result=cross_val_score(model,X,Y,cv=kfold)  #采用K折交叉驗(yàn)證的方法來(lái)驗(yàn)證算法效果
print('裝袋法提升后的結(jié)果:',result.mean())

AdaBoostClassifier

#基于sklearn庫(kù)中的提升法分類器對(duì)決策樹(shù)進(jìn)行優(yōu)化，提高分類準(zhǔn)確率兴猩，其中l(wèi)oad_breast_cancer()方法加載乳腺癌數(shù)據(jù)集悬襟，自變量（細(xì)胞核的特征）和因變量（良性帚桩、惡性）分別賦給X疼邀，Y變量

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets

#加載數(shù)據(jù)
dataset_all=datasets.load_breast_cancer()
X=dataset_all.data
Y=dataset_all.target

#初始化基本隨機(jī)數(shù)生成器
kfold=KFold(n_splits=10)

#決策樹(shù)及交叉驗(yàn)證
dtree=DecisionTreeClassifier(criterion='gini',max_depth=3)

#提升法及交叉驗(yàn)證
model=AdaBoostClassifier(base_estimator=dtree,n_estimators=100)
result=cross_val_score(model,X,Y,cv=kfold)
print("提升法改進(jìn)結(jié)果:",result.mean())

RandomForestClassifier 、ExtraTreesClassifier

#使用sklearn庫(kù)中的隨機(jī)森林算法和決策樹(shù)算法進(jìn)行效果比較丛塌，數(shù)據(jù)集由生成器隨機(jī)生成


from sklearn.model_selection import cross_val_score
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt

#make_blobs:sklearn中自帶的取類數(shù)據(jù)生成器隨機(jī)生成測(cè)試樣本，make_blobs方法中n_samples表示生成的隨機(jī)數(shù)樣本數(shù)量畜疾，n_features表示每個(gè)樣本的特征數(shù)量赴邻，centers表示類別數(shù)量，random_state表示隨機(jī)種子
x,y=make_blobs(n_samples=1000,n_features=6,centers=50,random_state=0)
plt.scatter(x[:,0],x[:,1],c=y)
plt.show()

#構(gòu)造隨機(jī)森林模型
clf=RandomForestClassifier(n_estimators=10,max_depth=None,min_samples_split=2,random_state=0)  #n_estimators表示弱學(xué)習(xí)器的最大迭代次數(shù)啡捶，或者說(shuō)最大的弱學(xué)習(xí)器的個(gè)數(shù)姥敛。如果設(shè)置值太小，模型容易欠擬合瞎暑；如果太大彤敛，計(jì)算量會(huì)較大，并且超過(guò)一定的數(shù)量后了赌，模型提升很小
scores=cross_val_score(clf,x,y)
print('RandomForestClassifier result:',scores.mean())

#構(gòu)造極限森林模型
clf=ExtraTreesClassifier(n_estimators=10,max_depth=None,min_samples_split=2,random_state=0)
scores=cross_val_score(clf,x,y)
print('ExtraTreesClassifier result:',scores.mean())
#極限隨機(jī)數(shù)的效果好于隨機(jī)森林墨榄，原因在于計(jì)算分割點(diǎn)方法中的隨機(jī)性進(jìn)一步增強(qiáng)；相較于隨機(jī)森林勿她，其閾值是針對(duì)每個(gè)候選特征隨機(jī)生成的袄秩，并且選擇最佳閾值作為分割規(guī)則，這樣能夠減少一點(diǎn)模型的方程逢并，總體上效果更好

GradientBoostingClassifier

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import make_blobs


#make_blobs:sklearn中自帶的取類數(shù)據(jù)生成器隨機(jī)生成測(cè)試樣本之剧，make_blobs方法中n_samples表示生成的隨機(jī)數(shù)樣本數(shù)量，n_features表示每個(gè)樣本的特征數(shù)量砍聊，centers表示類別數(shù)量背稼，random_state表示隨機(jī)種子
x,y=make_blobs(n_samples=1000,n_features=6,centers=50,random_state=0)
plt.scatter(x[:,0],x[:,1],c=y)
plt.show()

x_train, x_test, y_train, y_test = train_test_split(x,y)

# 模型訓(xùn)練，使用GBDT算法
gbr = GradientBoostingClassifier(n_estimators=3000, max_depth=2, min_samples_split=2, learning_rate=0.1)
gbr.fit(x_train, y_train.ravel())

y_gbr = gbr.predict(x_train)
y_gbr1 = gbr.predict(x_test)
acc_train = gbr.score(x_train, y_train)
acc_test = gbr.score(x_test, y_test)
print(acc_train)
print(acc_test)

VotingClassifier

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.ensemble import VotingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

#VotingClassifier方法是一次使用多種分類模型進(jìn)行預(yù)測(cè)玻蝌，將多數(shù)預(yù)測(cè)結(jié)果作為最終結(jié)果
x,y = datasets.make_moons(n_samples=500,noise=0.3,random_state=42)

plt.scatter(x[y==0,0],x[y==0,1])
plt.scatter(x[y==1,0],x[y==1,1])
plt.show()

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

voting_hard = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()),
    ('svm_clf', SVC()),
    ('dt_clf', DecisionTreeClassifier(random_state=10)),], voting='hard')

voting_soft = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()),
    ('svm_clf', SVC(probability=True)),
    ('dt_clf', DecisionTreeClassifier(random_state=10)),
], voting='soft')

voting_hard.fit(x_train,y_train)
print(voting_hard.score(x_test,y_test))

voting_soft.fit(x_train,y_train)
print(voting_soft.score(x_test,y_test))

• sklearn.linear_model

LogisticRegression

import numpy as np
from sklearn import linear_model,datasets
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()
x = iris.data
y = iris.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

logreg = linear_model.LogisticRegression(C=1e5)
logreg.fit(x_train,y_train)

prepro = logreg.score(x_test,y_test)
print(prepro)

Perceptron

from sklearn.datasets import load_digits
from sklearn.linear_model import Perceptron

x,y = load_digits(return_X_y=True)
clf = Perceptron(tol=1e-3,random_state=0)
clf.fit(x,y)
clf.score(x,y)

SGDClassifier

import numpy as np
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline

x = np.array([[-1,-1],[-2,-1],[1,1],[2,1]])
y = np.array([1,1,2,2])

clf = make_pipeline(StandardScaler(),SGDClassifier(max_iter=1000,tol=1e-3))
clf.fit(x,y)
print(clf.score(x,y))
print(clf.predict([[-0.8,-1]]))

PassiveAggressiveClassifier

from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

x,y = make_classification(n_features=4,random_state=0)
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

clf = PassiveAggressiveClassifier(max_iter=1000,random_state=0,tol=1e-3)
clf.fit(x_train,y_train)
print(clf.score(x_test,y_test))

• sklearn.svm

SVC

import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC

x = [[2,0],[1,1],[2,3]]
y = [0,0,1]

clf = SVC(kernel='linear')
clf.fit(x,y)
print(clf.predict([[2,2]]))

NuSVC

from sklearn import svm
from numpy import *

x = array([[0],[1],[2],[3]])
y = array([0,1,2,3])

clf = svm.NuSVC()
clf.fit(x,y)
print(clf.predict([[4]]))

LinearSVC

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import LinearSVC

iris = datasets.load_iris()
X = iris.data
y = iris.target

plt.scatter(X[y==0, 0], X[y==0, 1], color='red')
plt.scatter(X[y==1, 0], X[y==1, 1], color='blue')
plt.show()

svc = LinearSVC(C=10**9)
svc.fit(X, y)
print(svc.score(X,y))

• sklearn.neighbors

NearestNeighbors

import numpy as np
from sklearn.neighbors import NearestNeighbors

samples = [[0,0,2],[1,0,0],[0,0,1]]
neigh = NearestNeighbors(n_neighbors=2,radius=0.4)
neigh.fit(samples)

print(neigh.kneighbors([[0,0,1.3]],2,return_distance=True))
print(neigh.radius_neighbors([[0,0,1.3]],0.4,return_distance=False))

NearestCentroid

from sklearn.neighbors import NearestCentroid
import numpy as np

x = np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])
y = np.array([1,1,1,2,2,2])

clf = NearestCentroid()
clf.fit(x,y)
print(clf.predict([[-0.8,-1]]))

KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier

x,y = [[0],[1],[2],[3]],[0,0,1,1]

neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(x,y)
print(neigh.predict([[1.1]]))

KDTree

import numpy as np
from sklearn.neighbors import KDTree
rng = np.random.RandomState(0)
x = rng.random_sample((10,3))
tree = KDTree(x,leaf_size=2)
dist,ind = tree.query(x[:1],k=3)
print(ind)

KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier
 
X = [[0], [1], [2], [3], [4], [5], [6], [7], [8]]
y = [0, 0, 0, 1, 1, 1, 2, 2, 2]
 
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y)
print(neigh.predict([[1.1]]))

• sklearn.discriminant_analysis

LDA

from sklearn import datasets
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

iris = datasets.load_iris()
X = iris.data[:-5]
pre_x = iris.data[-5:]
y = iris.target[:-5]
print ('first 10 raw samples:', X[:10])
clf = LDA()
clf.fit(X, y)
X_r = clf.transform(X)
pre_y = clf.predict(pre_x)
#降維結(jié)果
print ('first 10 transformed samples:', X_r[:10])
#預(yù)測(cè)目標(biāo)分類結(jié)果
print ('predict value:', pre_y)

QDA

from sklearn import datasets
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()

x = iris.data
y = iris.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

clf = QDA()
clf.fit(x_train,y_train)
print(clf.score(x_test,y_test))

• sklearn.gaussian_process

• sklearn.naive_bayes

GaussianNB

from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

iris = datasets.load_iris()
clf = GaussianNB()
clf = clf.fit(iris.data,iris.target)

y_pre = clf.predict(iris.data)

MultinomialNB

from sklearn import datasets
from sklearn.naive_bayes import MultinomialNB

iris = datasets.load_iris()
clf = MultinomialNB()
clf = clf.fit(iris.data, iris.target)
y_pred=clf.predict(iris.data)

BernoulliNB

from sklearn import datasets
from sklearn.naive_bayes import BernoulliNB

iris = datasets.load_iris()
clf = BernoulliNB()
clf = clf.fit(iris.data, iris.target)
y_pred=clf.predict(iris.data)

回歸模型

image

• sklearn.tree

DecisionTreeRegressor蟹肘、ExtraTreeRegressor

"""回歸"""

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor,ExtraTreeRegressor
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error
import numpy as np

boston = load_boston()
x = boston.data
y = boston.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

dtr = DecisionTreeRegressor()
dtr.fit(x_train,y_train)

etr = ExtraTreeRegressor()
etr.fit(x_train,y_train)

yetr_pred = etr.predict(x_test)
ydtr_pred = dtr.predict(x_test)

print(dtr.score(x_test,y_test))
print(r2_score(y_test,ydtr_pred))

print(etr.score(x_test,y_test))
print(r2_score(y_test,yetr_pred))

• sklearn.ensemble

GradientBoostingRegressor

import numpy as np
from sklearn.ensemble import GradientBoostingRegressor as GBR
from sklearn.datasets import make_regression

X, y = make_regression(1000, 2, noise=10)

gbr = GBR()
gbr.fit(X, y)
gbr_preds = gbr.predict(X)

AdaBoostRegressor

from sklearn.ensemble import AdaBoostRegressor
from sklearn.datasets import make_regression

x,y = make_regression(n_features=4,n_informative=2,random_state=0,shuffle=False)
regr = AdaBoostRegressor(random_state=0,n_estimators=100)
regr.fit(x,y)
regr.predict([[0,0,0,0]])

BaggingRegressor

from sklearn.ensemble import BaggingRegressor
from sklearn.datasets import make_regression
from sklearn.svm import SVR

x,y = make_regression(n_samples=100,n_features=4,n_informative=2,n_targets=1,random_state=0,shuffle=False)
br = BaggingRegressor(base_estimator=SVR(),n_estimators=10,random_state=0).fit(x,y)
br.predict([[0,0,0,0]])

ExtraTreesRegressor

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.ensemble import ExtraTreesRegressor

x,y = load_diabetes(return_X_y=True)
x_train,x_test,y_train,y_test = train_test_split(X,y,random_state=0)

etr = ExtraTreesRegressor(n_estimators=100,random_state=0).fit(x_train,y_train)
print(etr.score(x_test,y_test))

RandomForestRegressor

from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression

x,y = make_regression(n_features=4,n_informative=2,random_state=0,shuffle=False)

rfr = RandomForestRegressor(max_depth=2,random_state=0)
rfr.fit(x,y)
print(rfr.predict([[0,0,0,0]]))

• sklearn.linear_model

import numpy as np
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso

np.random.seed(0)
x = np.random.randn(10,5)
y = np.random.randn(10)
clf1 = Ridge(alpha=1.0)
clf2 = Lasso()
clf2.fit(x,y)
clf1.fit(x,y)
print(clf1.predict(x))
print(clf2.predict(x))

• sklearn.svm

• sklearn.neighbors

• sklearn.kernel_ridge

• sklearn.gaussian_process

GaussianProcessRegressor

from sklearn.datasets import make_friedman2
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import DotProduct,WhiteKernel

x,y = make_friedman2(n_samples=500,noise=0,random_state=0)

kernel = DotProduct()+WhiteKernel()
gpr = GaussianProcessRegressor(kernel=kernel,random_state=0).fit(x,y)
print(gpr.score(x,y))

• sklearn.cross_decomposition

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import train_test_split

boston = datasets.load_boston()

x = boston.data
y = boston.target

x_df = pd.DataFrame(x,columns=boston.feature_names)
y_df = pd.DataFrame(y)

pls = PLSRegression(n_components=2)

x_train,x_test,y_train,y_test = train_test_split(x_df,y_df,test_size=0.3,random_state=1)

pls.fit(x_train,y_train)
print(pls.predict(x_test))

聚類模型

聚類模型

• sklearn.cluster

• 模型方法

模型評(píng)估

模型評(píng)估

• 分類模型評(píng)估

• 回歸模型評(píng)估

• 聚類模型評(píng)估

模型優(yōu)化

寫在最后

本文所涉及的分類/回歸/聚類算法都將在我的個(gè)眾【人類之奴】中一一進(jìn)行詳細(xì)講解疆前，歡迎大家一起學(xué)習(xí)交流。

人類之奴

這篇文章的創(chuàng)作花了整整一周的時(shí)間聘萨，希望可以為大家的學(xué)習(xí)之路披荊斬棘竹椒！

后續(xù)將為大家?guī)?lái)更多更優(yōu)質(zhì)的文章！

優(yōu)秀參考

sklearn提供的自帶的數(shù)據(jù)集(make_blobs)

sklearn.datasets常用功能詳解

Sklearn-cluster聚類方法

Sklearn官方文檔中文整理5——高斯過(guò)程篇