sklearn简单实现机器学习算法记录
需要引入最重要的库：Scikit-learn
一、KNN算法
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
iris = datasets.load_iris()
iris_x = iris.data
iris_y = iris.target
x_train, x_test, y_train, y_test = train_test_split(iris_x, iris_y, test_size=0.3)
knn = KNeighborsClassifier()
knn.fit(x_train, y_train)
print(knn.predict(x_test))
print(y_test)
二、朴素贝叶斯
from sklearn.naive_bayes import BernoulliNB
def loadDataSet():
'''
postingList: 进行词条切分后的文档集合
classVec:类别标签
使用伯努利模型的贝叶斯分类器只考虑单词出现与否（0，1）
'''
postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
['stop', 'posting', 'stupid', 'worthless', 'garbage'],
['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
classVec = [0, 1, 0, 1, 0, 1] # 1代表侮辱性文字，0代表正常言论
return postingList, classVec
def create_wordVec(dataset):
word_set = set([])
for doc in dataset:
word_set = word_set | set(doc) # 通过对两个集合取并，找出所有非重复的单词
return list(word_set)
def words2Vec(wordList, input_set):
'''
@wordList：为前一个函数的输出值（包含单词）
@input_set：输入需要分类的集合
函数输出：包含0，1的布尔型向量（对应Wordlist中的单词出现与否）
'''
return_vec = [0] * len(wordList)
# 创建与词汇表等长的列表向量
for word in input_set:
if word in wordList:
return_vec[wordList.index(word)] = 1 # 出现的单词赋1
else:
print("the word %s is not in list" % word)
return return_vec
if __name__ == '__main__':
p, c = loadDataSet()
vocab = create_wordVec(p)
vec = []
for pl in p:
vec.append(words2Vec(vocab, pl))
clf = BernoulliNB(alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None) # 伯努利模型
clf.fit(vec, c)
print("预测值：")
print(clf.predict(vec))
print("正确值：")
print(c)
三、Logistic回归
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
breast_cancer = load_breast_cancer()
# print(diabetes)
diabetes_x = breast_cancer.data
diabetes_y = breast_cancer.target
# print(diabetes_x)
# print(diabetes_y)
x_train, x_test, y_train, y_test = train_test_split(diabetes_x, diabetes_y, test_size=0.3)
log = LogisticRegression(solver='liblinear')
log.fit(x_train, y_train)
print(log.predict(x_test))
print(y_test)
# count = 0
# l = len(y_test)
# print(l)
# for i in range(l):
# if log.predict(x_test)[i] != y_test[i]:
# count += 1
# print(count)
#
# print(1 - count / l) # 输出准确率
四、支持向量机SVM
1. 线性 SVM 分类器
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs
from sklearn.svm import SVC
X, y = make_blobs(n_samples=50, centers=2,
random_state=0, cluster_std=0.60)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')
model = SVC(kernel='linear')
model.fit(X, y)
def plot_svc_decision_function(clf, ax=None, plot_support=True):
"""Plot the decision function for a 2D SVC"""
if ax is None:
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
x = np.linspace(xlim[0], xlim[1], 30)
y = np.linspace(ylim[0], ylim[1], 30)
Y, X = np.meshgrid(y, x)
xy = np.vstack([X.ravel(), Y.ravel()]).T
P = model.decision_function(xy).reshape(X.shape)
ax.contour(X, Y, P, colors='k',
levels=[-1, 0, 1], alpha=0.5,
linestyles=['--', '-', '--'])
if plot_support:
ax.scatter(clf.support_vectors_[:, 0],
clf.support_vectors_[:, 1],
s=300, linewidth=1, facecolors='none')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
plot_svc_decision_function(model) #显示分界线
plt.show()
print("分类值：")
print(model.predict(X))
print("正确值：")
print(y)
2. SVM 与 核函数
对于非线性可切分的数据集，要做分割，就要借助于核函数
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_circles
from sklearn.svm import SVC
from mpl_toolkits import mplot3d
X, y = make_circles(100, factor=0.1, noise=0.1)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')
# r = np.exp(-(X ** 2).sum(1))
# 画出3D图像
#
# def plot_3D(elev=30, azim=30, X=X, Y=y):
# ax = plt.subplot(projection='3d')
# ax.scatter3D(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
# ax.view_init(elev=elev, azim=azim)
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# ax.set_zlabel('z')
#
#
# plot_3D(elev=45, azim=45, X=X, Y=y)
# plt.show()
model = SVC(kernel='rbf', C=1E6)
model.fit(X, y)
def plot_svc_decision_function(clf, ax=None, plot_support=True):
"""Plot the decision function for a 2D SVC"""
if ax is None:
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
x = np.linspace(xlim[0], xlim[1], 30)
y = np.linspace(ylim[0], ylim[1], 30)
Y, X = np.meshgrid(y, x)
xy = np.vstack([X.ravel(), Y.ravel()]).T
P = model.decision_function(xy).reshape(X.shape)
ax.contour(X, Y, P, colors='k',
levels=[-1, 0, 1], alpha=0.5,
linestyles=['--', '-', '--'])
if plot_support:
ax.scatter(clf.support_vectors_[:, 0],
clf.support_vectors_[:, 1],
s=300, linewidth=1, facecolors='none')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
plot_svc_decision_function(model)
plt.show()
print("分类值：")
print(model.predict(X))
print("正确值：")
print(y)
3. 总结
- 非线性映射是SVM方法的理论基础，SVM利用内积核函数代替向高维空间的非线性映射；
- 对特征空间划分的最优超平面是SVM的目标，最大化分类边际的思想是SVM方法的核心；
- 支持向量是SVM的训练结果,在SVM分类决策中起决定作用的是支持向量。因此，模型需要存储空间小，算法鲁棒性强；
- 无任何前提假设，不涉及概率测度；
- SVM算法对大规模训练样本难以实施；
- 用SVM解决多分类问题存在困难，经典的支持向量机算法只给出了二类分类的算法，而在数据挖掘的实际应用中，一般要解决多类的分类问题。可以通过多个二类支持向量机的组合来解决。主要有一对多组合模式、一对一组合模式和SVM决策树；再就是通过构造多个分类器的组合来解决。主要原理是克服SVM固有的缺点，结合其他算法的优势，解决多类问题的分类精度。如：与粗集理论结合，形成一种优势互补的多类问题的组合分类器；
- SVM是O(n^3)的时间复杂度。在sklearn里，LinearSVC是可扩展的(也就是对海量数据也可以支持得不错), 对特别大的数据集SVC就略微有点尴尬了。不过对于特别大的数据集，你倒是可以试试采样一些样本出来，然后用rbf核的SVC来做做分类。