362 lines
26 KiB
Plaintext
362 lines
26 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 机器学习100天——第二天：简单线性回归\n",
|
||
"## 第一步：数据预处理"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"这里导入我们需要的库，值得注意的是，这里比第一天多了一个matplotlib.pyploy:matplotlib是python上的一个2D绘图库,\n",
|
||
"matplotlib下的模块pyplott是一个有命令样式的函数集合，\n",
|
||
"matplotlib.pyploy是为我们对结果进行图像化作准备的。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"导入相关数据"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Hours Scores\n",
|
||
"0 2.5 21\n",
|
||
"1 5.1 47\n",
|
||
"2 3.2 27\n",
|
||
"3 8.5 75\n",
|
||
"4 3.5 30\n",
|
||
"5 1.5 20\n",
|
||
"6 9.2 88\n",
|
||
"7 5.5 60\n",
|
||
"8 8.3 81\n",
|
||
"9 2.7 25\n",
|
||
"10 7.7 85\n",
|
||
"11 5.9 62\n",
|
||
"12 4.5 41\n",
|
||
"13 3.3 42\n",
|
||
"14 1.1 17\n",
|
||
"15 8.9 95\n",
|
||
"16 2.5 30\n",
|
||
"17 1.9 24\n",
|
||
"18 6.1 67\n",
|
||
"19 7.4 69\n",
|
||
"20 2.7 30\n",
|
||
"21 4.8 54\n",
|
||
"22 3.8 35\n",
|
||
"23 6.9 76\n",
|
||
"24 7.8 86\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dataset = pd.read_csv('../datasets/studentscores.csv')\n",
|
||
"print(dataset)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"这里我们需要使用pandas的iloc(区分于loc根据index来索引，iloc利用行号来索引)方法来对数据进行处理，第一个参数为行号，:表示全部行，第二个参数 ：1表示截到第1列(也就是取第0列)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = dataset.iloc[ : , : 1 ].values\n",
|
||
"Y = dataset.iloc[ : , 1 ].values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"导入sklearn库的cross_validation类来对数据进行训练集、测试集划分"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/ymao/usr/miniconda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
|
||
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.cross_validation import train_test_split\n",
|
||
"X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 第二步：训练集使用简单线性回归模型来训练"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"从sklearn的线性模型类中调用线性回归模型"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.linear_model import LinearRegression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"创建一个线性回归对象regressor,并对训练集利用fit()方法进行训练"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"regressor = LinearRegression()\n",
|
||
"regressor = regressor.fit(X_train, Y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 第三步：预测结果"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"利用predict()方法对测试集进行预测"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"Y_pred = regressor.predict(X_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 可视化"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 训练集结果可视化"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"首先调用scatter方法，对训练集作散点图"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.collections.PathCollection at 0x7fe708af19b0>"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(X_train , Y_train, color = 'red')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"调用plot方法对训练集的预测作曲线图"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fe708b04550>]"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(X_train , regressor.predict(X_train), color ='blue')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"将结果可视化"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKNBFRFJCgS4ikhIKdBGRlPh/Rc+jTuQbrmQAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fe70c5abac8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 测试集结果可视化"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fe708b04dd8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(X_test , Y_test, color = 'red')\n",
|
||
"plt.plot(X_test , regressor.predict(X_test), color ='blue')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|