【机器学习实战项目】10分钟学会Python怎么用K近邻KNN进行分类预测（四）

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.neighbors import KNeighborsClassifier
from sklearn import datasets
# 作图
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
import seaborn as sns

# 莺尾花数据集的前两维数据
iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target

k_list = [1, 3, 5, 8, 10, 15]
h = .02
# 创建不同颜色的画布
cmap_light = ListedColormap(['#a6cee3', '#1f78b4', '#b2df8a'])
cmap_bold = ListedColormap(['#e41a1c', '#377eb8', '#4daf4a'])
plt.figure(figsize=(15,14))
# 根据不同的k值进行可视化
for ind,k in enumerate(k_list):
    clf = KNeighborsClassifier(k)
    clf.fit(X, y)
    # 画出决策边界
    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()])
    # 根据边界填充颜色
    Z = Z.reshape(xx.shape)
    plt.subplot(321+ind)  
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
    # 数据点可视化到画布
    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)"% k)
plt.show()

### iris数据集
import numpy as np
# 加载莺尾花数据集
from sklearn import datasets
# 导入KNN分类器
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()
X = iris.data
y = iris.target
# 7/3分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# 训练模型
clf = KNeighborsClassifier(n_neighbors=5, p=2, metric="minkowski")
clf.fit(X_train, y_train)
# 预测
X_pred = clf.predict(X_test)
acc = sum(X_pred == y_test) / X_pred.shape[0]
print("预测的准确率ACC: %.3f" % acc)

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

np.random.seed(0)
# 随机生成40个(0, 1)之前的数，乘以5，再进行升序
X = np.sort(5 * np.random.rand(40, 1), axis=0)
# 创建[0, 5]之间的500个数的等差数列, 作为测试数据
T = np.linspace(0, 5, 500)[:, np.newaxis]
# 使用sin函数得到y值，并拉伸到一维
y = np.sin(X).ravel()
# Add noise to targets[y值增加噪声]
y[::5] += 1 * (0.5 - np.random.rand(8))

# 设置多个k近邻进行比较
n_neighbors = [1, 3, 5, 8, 10, 40]
# 设置图片大小
plt.figure(figsize=(20,14))
# 输出设为两列三行
rows = 3
cols = 2
for i, k in enumerate(n_neighbors):
    # 默认使用加权平均进行计算predictor
    clf = KNeighborsRegressor(n_neighbors=k, p=2, metric="minkowski")
    # 训练
    clf.fit(X, y)
    # 预测
    y_ = clf.predict(T)
    plt.subplot(rows, cols, i + 1)
    plt.scatter(X, y, color='red', label='data')
    plt.plot(T, y_, color='navy', label='prediction')
    plt.axis('tight')
    plt.legend()
    plt.title("KNeighborsRegressor (k = %i)" % (k))
plt.tight_layout()
plt.show()

# 下载需要用到的数据集
!wget https://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/3K/horse-colic.csv
# 下载数据集介绍
!wget https://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/3K/horse-colic.names

## 导入函数
import numpy as np
import pandas as pd
# kNN分类器
from sklearn.neighbors import KNeighborsClassifier
# kNN数据空值填充
from sklearn.impute import KNNImputer
# 计算带有空值的欧式距离
from sklearn.metrics.pairwise import nan_euclidean_distances
# 交叉验证
from sklearn.model_selection import cross_val_score
# KFlod的函数
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

## 空值填充
X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]]
imputer = KNNImputer(n_neighbors=2, metric='nan_euclidean')
imputer.fit_transform(X)

## 带空值的欧式距离计算公式
nan_euclidean_distances([[np.nan, 6, 5], [3, 4, 3]], [[3, 4, 3], [1, 2, np.nan], [8, 8, 7]])

# load dataset, 将?变成空值
input_file = './horse-colic.csv'
df_data = pd.read_csv(input_file, header=None, na_values='?')
# 得到训练数据和label, 第23列表示是否发生病变, 1: 表示Yes; 2: 表示No. 
data = df_data.values
ix = [i for i in range(data.shape[1]) if i != 23]
X, y = data[:, ix], data[:, 23]
# 查看所有特征的缺失值个数和缺失率
for i in range(df_data.shape[1]):
    n_miss = df_data[[i]].isnull().sum()
    perc = n_miss / df_data.shape[0] * 100
    if n_miss.values[0] > 0:
        print('>Feat: %d, Missing: %d, Missing ratio: (%.2f%%)' % (i, n_miss, perc))
# 查看总的空值个数
print('KNNImputer before Missing: %d' % sum(np.isnan(X).flatten()))
# 定义 knnimputer
imputer = KNNImputer()
# 填充数据集中的空值
imputer.fit(X)
# 转换数据集
Xtrans = imputer.transform(X)
# 打印转化后的数据集的空值
print('KNNImputer after Missing: %d' % sum(np.isnan(Xtrans).flatten()))

## 构建pipeline
results = list()
strategies = [str(i) for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 18, 20, 21]]
for s in strategies:
    # create the modeling pipeline
    pipe = Pipeline(steps=[('imputer', KNNImputer(n_neighbors=int(s))), ('model', KNeighborsClassifier())])
    # 数据多次随机划分取平均得分
    scores = []
    for k in range(20):
        # 得到训练集合和验证集合, 8: 2
        X_train, X_test, y_train, y_test = train_test_split(Xtrans, y, test_size=0.2)
        pipe.fit(X_train, y_train)
        # 验证model
        score = pipe.score(X_test, y_test)
        scores.append(score)
    # 保存results
    results.append(np.array(scores))
    print('>k: %s, Acc Mean: %.3f, Std: %.3f' % (s, np.mean(scores), np.std(scores)))
# print(results)
# plot model performance for comparison
plt.boxplot(results, labels=strategies, showmeans=True)
plt.show()