340 lines
10 KiB
Plaintext
340 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#机器学习100天——第十一天：K近邻法（K-NN）"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##第一步：导入相关库"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##第二步：导入数据集"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" User ID Gender Age EstimatedSalary Purchased\n",
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"0 15624510 Male 19 19000 0\n",
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"1 15810944 Male 35 20000 0\n",
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"2 15668575 Female 26 43000 0\n",
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"3 15603246 Female 27 57000 0\n",
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"4 15804002 Male 19 76000 0\n",
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"5 15728773 Male 27 58000 0\n",
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"6 15598044 Female 27 84000 0\n",
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"7 15694829 Female 32 150000 1\n",
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"8 15600575 Male 25 33000 0\n",
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"9 15727311 Female 35 65000 0\n",
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"10 15570769 Female 26 80000 0\n",
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"11 15606274 Female 26 52000 0\n",
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"12 15746139 Male 20 86000 0\n",
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"13 15704987 Male 32 18000 0\n",
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"14 15628972 Male 18 82000 0\n",
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"15 15697686 Male 29 80000 0\n",
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"16 15733883 Male 47 25000 1\n",
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"17 15617482 Male 45 26000 1\n",
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"18 15704583 Male 46 28000 1\n",
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"19 15621083 Female 48 29000 1\n",
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"20 15649487 Male 45 22000 1\n",
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"21 15736760 Female 47 49000 1\n",
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"22 15714658 Male 48 41000 1\n",
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"23 15599081 Female 45 22000 1\n",
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"24 15705113 Male 46 23000 1\n",
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"25 15631159 Male 47 20000 1\n",
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"26 15792818 Male 49 28000 1\n",
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"27 15633531 Female 47 30000 1\n",
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"28 15744529 Male 29 43000 0\n",
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"29 15669656 Male 31 18000 0\n",
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".. ... ... ... ... ...\n",
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"370 15611430 Female 60 46000 1\n",
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"371 15774744 Male 60 83000 1\n",
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"372 15629885 Female 39 73000 0\n",
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"373 15708791 Male 59 130000 1\n",
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"374 15793890 Female 37 80000 0\n",
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"375 15646091 Female 46 32000 1\n",
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"376 15596984 Female 46 74000 0\n",
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"377 15800215 Female 42 53000 0\n",
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"378 15577806 Male 41 87000 1\n",
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"379 15749381 Female 58 23000 1\n",
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"380 15683758 Male 42 64000 0\n",
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"381 15670615 Male 48 33000 1\n",
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"382 15715622 Female 44 139000 1\n",
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"383 15707634 Male 49 28000 1\n",
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"384 15806901 Female 57 33000 1\n",
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"385 15775335 Male 56 60000 1\n",
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"386 15724150 Female 49 39000 1\n",
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"387 15627220 Male 39 71000 0\n",
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"388 15672330 Male 47 34000 1\n",
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"389 15668521 Female 48 35000 1\n",
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"390 15807837 Male 48 33000 1\n",
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"391 15592570 Male 47 23000 1\n",
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"392 15748589 Female 45 45000 1\n",
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"393 15635893 Male 60 42000 1\n",
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"394 15757632 Female 39 59000 0\n",
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"395 15691863 Female 46 41000 1\n",
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"396 15706071 Male 51 23000 1\n",
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"397 15654296 Female 50 20000 1\n",
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"398 15755018 Male 36 33000 0\n",
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"399 15594041 Female 49 36000 1\n",
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"\n",
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"[400 rows x 5 columns]\n"
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]
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}
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],
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"source": [
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"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
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"print(dataset)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"为了方便理解，这里我们只取Age年龄和EstimatedSalary估计工资作为特征"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = dataset.iloc[:, [2, 3]].values\n",
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"y = dataset.iloc[:, 4].values"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##第三步：将数据划分成训练集和测试集"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##第四步：特征缩放"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
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" warnings.warn(msg, _DataConversionWarning)\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import StandardScaler\n",
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"sc = StandardScaler()\n",
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"X_train = sc.fit_transform(X_train)\n",
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"X_test = sc.transform(X_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"##第五步：使用K-NN对训练集数据进行训练"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"从sklearn的neighbors类中导入KNeighborsClassifier学习器"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.neighbors import KNeighborsClassifier"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"设置好相关的参数\n",
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"n_neighbors = 5(K值的选择，默认选择5)、\n",
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"metric = 'minkowski'(距离度量的选择，这里选择的是闵氏距离(默认参数))、\n",
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"p = 2 (距离度量metric的附属参数，只用于闵氏距离和带权重闵氏距离中p值的选择，p=1为曼哈顿距离， p=2为欧式距离。默认为2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
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" metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
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" weights='uniform')"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
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"classifier.fit(X_train, y_train)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##第六步：对测试集进行预测"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0\n",
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" 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
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" 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1]\n"
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]
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}
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],
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"source": [
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"y_pred = classifier.predict(X_test)\n",
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"print(y_pred)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##第七步：生成混淆矩阵"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"混淆矩阵可以对一个分类器性能进行分析，由此可以计算出许多指标，例如：ROC曲线、正确率等"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[64 4]\n",
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" [ 3 29]]\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import confusion_matrix\n",
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"cm = confusion_matrix(y_test, y_pred)\n",
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"print(cm)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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" 预测值\n",
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" 0 1\n",
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" 实0 64 4 \n",
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" 际1 3 29\n",
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" 值\n",
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"\n",
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"预测集中的0总共有68个，1总共有32个。\n",
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"在这个混淆矩阵中，实际有68个0，但K-NN预测出有67(64+3)个0，其中有3个实际上是1。\n",
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"同时K-NN预测出有33(4+29)个1，其中4个实际上是0。"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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