KNN_neighbors 分类回归Kd树—代码演示

一、KNN分类模型 演示

1.1 kd树

import sys
import importlib
importlib.reload(sys)

# kd-tree每个结点中主要包含的数据结构如下 
class KdNode(object):
    def __init__(self, dom_elt, split, left, right):
        self.dom_elt = dom_elt  # k维向量节点(k维空间中的一个样本点)
        self.split = split      # 整数（进行分割维度的序号）
        self.left = left        # 该结点分割超平面左子空间构成的kd-tree
        self.right = right      # 该结点分割超平面右子空间构成的kd-tree

class KdTree(object):
    def __init__(self, data):
        k = len(data[0])                 # 数据维度

        def CreateNode(split, data_set): # 按第split维划分数据集exset创建KdNode
            if not data_set:
                return None

            # key参数的值为一个函数，此函数只有一个参数且返回一个值用来进行比较
            # operator模块提供的itemgetter函数用于获取对象的哪些维的数据，参数为需要获取的数据在对象中的序号
            # data_set.sort(key=itemgetter(split)) # 按要进行分割的那一维数据排序
            data_set.sort(key=lambda x: x[split])
            split_pos = len(data_set) // 2         # //为Python中的整数除法
            median = data_set[split_pos]           # 中位数分割点             
            split_next = (split + 1) % k           # cycle coordinates

            # 递归的创建kd树
            return KdNode(median, split, 
                          CreateNode(split_next, data_set[:split_pos]),     # 创建左子树
                          CreateNode(split_next, data_set[split_pos + 1:])) # 创建右子树

        self.root = CreateNode(0, data)         # 从第0维分量开始构建kd树,返回根节点

# KDTree的前序遍历
def preorder(root):  
    print (root.dom_elt)  
    if root.left:      # 节点不为空
        preorder(root.left)  
    if root.right:  
        preorder(root.right)  

if __name__ == "__main__":
    data = [[2,3],[5,4],[9,6],[4,7],[8,1],[7,2]]
    kd = KdTree(data)
    preorder(kd.root)

输出：
[7, 2]
[5, 4]
[2, 3]
[4, 7]
[9, 6]
[8, 1]

1.2 三分类 KNN

"""
================================
Nearest Neighbors Classification
================================

Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets

n_neighbors = 15             #设置K值：15个邻居
iris = datasets.load_iris()

# we only take the first two features. We could avoid this ugly
# slicing by using a two-dim dataset
X = iris.data[:, :2]
y = iris.target

h = .02  # step size in the mesh

# Create color maps 设置地图颜色
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

for weights in ['uniform', 'distance']:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X, y)

    # Plot the decision boundary. For that, we will assign a color to each
    # point in the mesh [x_min, x_max]x[y_min, y_max].
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # 绘制训练点
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
                edgecolor='k', s=20)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title("3-Class classification (k = %i, weights = '%s')"
              % (n_neighbors, weights))

plt.show()

二、KNN 回归模型 演示

import numpy as np
import matplotlib.pyplot as plt
from sklearn import neighbors

np.random.seed(0)
X = np.sort(5 * np.random.rand(40, 1), axis=0)
T = np.linspace(0, 5, 500)[:, np.newaxis]
y = np.sin(X).ravel()

y[::5] += 1 * (0.5 - np.random.rand(8))   # 添加噪音

#回归模型  Fit regression model
n_neighbors = 5                           # 设置5个邻居
plt.figure(figsize=((8,6)))
for i, weights in enumerate(['uniform','distance']):
    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
    y_ = knn.fit(X, y).predict(T)

    plt.subplot(2, 1, i+1)
    plt.scatter(X, y, c='k', label='data')
    plt.plot(T, y_, c='g', label='prediction')
    plt.axis('tight')
    plt.legend()
plt.title("KNeighborsRegressor(k=%i,weights='%s')"%(n_neighbors,weights))

三、KNN—数据归一化与参数优化_code

from sklearn import preprocessing           #数据标准化函数
from sklearn import model_selection         #拆分数据集
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report #分类模型评估报告

导入自带数据鸢尾花 —— 直接使用 KNN 方法训练

iris=load_iris()            #导入自带数据鸢尾花
x=iris.data
y=iris.target
x[:10]                      #查看数据
y[:10]

#数据集拆分
X_train,X_test,y_train,y_test = model_selection
                                .train_test_split(x,y,test_size=0.3,random_state=123456) 

#直接使用 KNN 分类方法 （稍微调整参数）
knn=KNeighborsClassifier(n_neighbors=10,
                         metric='minkowski',
                         p=1,weights='distance')
knn.fit(X_train,y_train)


pred=knn.predict(X_test)   #预测概率
print (classification_report(y_test,pred)) 

GridSearchCV 参数优化 ——使用最佳参数训练

# 使用 GridSearchCV 进行参数优化
parameters={
            'n_neighbors':[5,10,15,20,30],
            'weights':['uniform','distance'],
            'p':[1,2]
            }
knn=KNeighborsClassifier()
grid_search=GridSearchCV(knn,parameters,scoring='accuracy',cv=5)
grid_search.fit(x,y) 

grid_search.best_estimator_   #查看函数（最优评估）
grid_search.best_score_       #正确率 
grid_search.best_params_      #最佳组合（参数）

# 使用最优参数训练
knn=KNeighborsClassifier(n_neighbors=10,
                         metric='minkowski',
                         p=2,weights='distance')
knn.fit(X_train,y_train)  

pred=knn.predict(X_test)
print (classification_report(y_test,pred))

对数据标准化（归一化） 并预测

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

#标准化——（0-1标准化）
scaler1=preprocessing.StandardScaler()
scaler1.fit(x)
scaler1_x=scaler1.transform(x)
scaler1_x[:10] 

#标准化——（区间缩放法）
scaler2=preprocessing.MinMaxScaler()
scaler2.fit(x)
scaler2_x=scaler2.transform(x)
scaler2_x[:10]

x[:10]  #查看原始值 与标准化后的数据对比 

knn=KNeighborsClassifier(n_neighbors=10,metric='minkowski',
                         p=2,weights='distance')

knn.fit(scaler1_x,y)    #使用0-1标准化的数据
pred=knn.predict(scaler1_x)
print (classification_report(y,pred)) 

knn.fit(scaler2_x,y)    #使用区间缩放法的数据
pred=knn.predict(scaler2_x)
print (classification_report(y,pred))

最后

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