676 lines
338 KiB
Text
676 lines
338 KiB
Text
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{
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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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"run_control": {
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"marked": true
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}
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},
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"source": [
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"# 逻辑回归实验\n",
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"\n",
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"> 此次练习中，我们使用[Human Activity Recognition Using Smartphones](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones)数据集。它通过对参加测试者的智能手机上安装一个传感器而采集了参加测试者每天的日常活动（ADL）。目标是将日常活动分成六类（walking, walking upstairs, walking downstairs, sitting, standing, and laying）。\n",
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">\n",
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"> 该数据集也可以在Kaggle网站上获得：https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones/downloads/human-activity-recognition-with-smartphones.zip \n",
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"\n",
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"把训练文件重新命名为`e2.3_Human_Activity_Recognition_Using_Smartphones_Data.csv`"
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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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"run_control": {
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"marked": true
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}
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},
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"source": [
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"## 第一步：导入数据\n",
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"\n",
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"* 查看数据类型---因为有太多的列，所以最好使用value_counts\n",
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"* 判断其中的小数数值是否需要尺度缩放\n",
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"* 检查数据中各活动类型的划分\n",
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"* 把活动类型标签编码成一个整数"
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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": 37,
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"metadata": {
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"run_control": {
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"marked": true
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"filepath = 'e2.3_Human_Activity_Recognition_Using_Smartphones_Data.csv'\n",
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"data = pd.read_csv(filepath)"
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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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"run_control": {
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"marked": true
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}
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},
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"source": [
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"所有列的数据类型都是浮点数，除了活动标签列。"
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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": 38,
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"metadata": {
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"run_control": {
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"marked": true
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"float64 561\n",
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"int64 1\n",
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"object 1\n",
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"dtype: int64"
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]
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},
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"execution_count": 38,
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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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"data.dtypes.value_counts()"
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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": 39,
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"metadata": {
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"run_control": {
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"marked": true
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"angle(X,gravityMean) float64\n",
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"angle(Y,gravityMean) float64\n",
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"angle(Z,gravityMean) float64\n",
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"subject int64\n",
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"Activity object\n",
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"dtype: object"
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]
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},
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"execution_count": 39,
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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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"data.dtypes.tail()"
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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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"run_control": {
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"marked": true
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}
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},
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"source": [
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"数据都已经全部被缩放到-1到1之间了。"
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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": 40,
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"metadata": {
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"run_control": {
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"marked": true
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"-1.000000 466\n",
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"-0.995377 2\n",
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"-0.999996 2\n",
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"-0.999893 2\n",
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"-1.000000 2\n",
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" ... \n",
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"-0.999983 1\n",
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"-0.943439 1\n",
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"-0.998014 1\n",
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"-0.999915 1\n",
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" 1.000000 1\n",
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"Length: 93, dtype: int64"
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]
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},
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"execution_count": 40,
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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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"data.iloc[:, :-1].min().value_counts()"
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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": 41,
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"metadata": {
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"run_control": {
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"marked": true
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.000000 452\n",
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"0.994731 2\n",
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"0.805064 1\n",
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"0.908361 1\n",
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"0.891736 1\n",
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" ... \n",
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"0.990935 1\n",
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"0.979031 1\n",
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"0.928416 1\n",
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"0.848031 1\n",
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"30.000000 1\n",
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"Length: 110, dtype: int64"
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]
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},
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"execution_count": 41,
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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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"data.iloc[:, :-1].max().value_counts()"
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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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"检查数据中各活动类型的划分---已经比较平衡了。"
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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": 42,
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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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"LAYING 1407\n",
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"STANDING 1374\n",
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"SITTING 1286\n",
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"WALKING 1226\n",
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"WALKING_UPSTAIRS 1073\n",
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"WALKING_DOWNSTAIRS 986\n",
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"Name: Activity, dtype: int64"
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]
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},
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"execution_count": 42,
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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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"data.Activity.value_counts()"
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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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"Scikit learn的分类器不接受一个稀疏矩阵作为预测列。所以，可以使用`LabelEncoder`将活动标签编码为整数。"
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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": 43,
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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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"5593 3\n",
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"4163 4\n",
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"4519 5\n",
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"640 4\n",
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"6463 2\n",
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"Name: Activity, dtype: int64"
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]
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},
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"execution_count": 43,
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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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"from sklearn.preprocessing import LabelEncoder\n",
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"\n",
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"le = LabelEncoder()\n",
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"data['Activity'] = le.fit_transform(data.Activity)\n",
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"data['Activity'].sample(5)"
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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：划分训练数据和测试数据\n",
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"\n",
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"* 可以考虑使用Scikit-learn中的`StratifiedShuffleSplit`，以保证划分后的数据集中每个类别个案的比例与整个数据集相同。\n"
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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": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"feature_cols = data.columns[:-1]\n",
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"\n",
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"from sklearn.model_selection import StratifiedShuffleSplit\n",
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"\n",
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"# Get the split indexes\n",
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"strat_shuf_split = StratifiedShuffleSplit(n_splits=1,test_size=0.3, random_state=42)\n",
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"\n",
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"train_idx, test_idx = next(strat_shuf_split.split(data[feature_cols], data.Activity))\n",
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"\n",
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"# Create the dataframes\n",
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"X_train = data.loc[train_idx, feature_cols]\n",
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"y_train = data.loc[train_idx, 'Activity']\n",
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"\n",
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"X_test = data.loc[test_idx, feature_cols]\n",
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"y_test = data.loc[test_idx, 'Activity']"
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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": 45,
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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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"0 0.191411\n",
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"2 0.186941\n",
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"1 0.174893\n",
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"3 0.166731\n",
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"5 0.145939\n",
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"4 0.134085\n",
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"Name: Activity, dtype: float64"
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]
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},
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"execution_count": 45,
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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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"y_train.value_counts(normalize=True)"
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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": 46,
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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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"0 0.191296\n",
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"2 0.186763\n",
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"1 0.174977\n",
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"3 0.166818\n",
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"5 0.145966\n",
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"4 0.134180\n",
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"Name: Activity, dtype: float64"
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]
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},
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"execution_count": 46,
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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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"y_test.value_counts(normalize=True)"
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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：训练模型\n",
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"\n",
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"* 用所有特征训练一个基本的使用缺省参数的逻辑回归模型。\n",
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"* 分别用L1和L2正则化来训练一个模型，使用交叉验证确定超参数的值。注意，正则化模型，尤其是L1模型可能需要一定训练时间。"
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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": 47,
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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": [
|
|||
|
|
"/home/ir/dev/justhomework/AIandML/.venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|||
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|||
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|||
|
|
" n_iter_i = _check_optimize_result(\n",
|
|||
|
|
"/home/ir/dev/justhomework/AIandML/.venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|||
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|||
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|||
|
|
" n_iter_i = _check_optimize_result(\n",
|
|||
|
|
"/home/ir/dev/justhomework/AIandML/.venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|||
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|||
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|||
|
|
" n_iter_i = _check_optimize_result(\n",
|
|||
|
|
"/home/ir/dev/justhomework/AIandML/.venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|||
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|||
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|||
|
|
" n_iter_i = _check_optimize_result(\n",
|
|||
|
|
"/home/ir/dev/justhomework/AIandML/.venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|||
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|||
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|||
|
|
" n_iter_i = _check_optimize_result(\n",
|
|||
|
|
"/home/ir/dev/justhomework/AIandML/.venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|||
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|||
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|||
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|||
|
|
" n_iter_i = _check_optimize_result(\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# 请在此处填写你的代码（训练一个基本的使用缺省参数的逻辑回归模型）\n",
|
|||
|
|
"from sklearn.linear_model import LogisticRegressionCV\n",
|
|||
|
|
"lr=LogisticRegressionCV().fit(X_train, y_train)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 48,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# L1 正则化的逻辑回归\n",
|
|||
|
|
"lr_l1 = LogisticRegressionCV(Cs=10, cv=4, penalty='l1', solver='liblinear').fit(X_train, y_train)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 49,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# 请在此处填写你的代码（L2 正则化的逻辑回归）\n",
|
|||
|
|
"lr_l2 = LogisticRegressionCV(Cs=10, cv=4, penalty='l2', solver='liblinear').fit(X_train, y_train)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## 第四步：\n",
|
|||
|
|
"\n",
|
|||
|
|
"* 输出上面训练出的三个模型中每个特征的系数；\n",
|
|||
|
|
"* 并绘制成图来比较它们的差异 （每个类别一张图）"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 50,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"[[-0.11183502 0.01204105 0.06963152 ... -0.55016141 -0.20539043\n",
|
|||
|
|
" -0.08843306]\n",
|
|||
|
|
" [-0.08548885 -0.11598664 -0.13913328 ... -1.26772337 -0.25360636\n",
|
|||
|
|
" -0.03148728]\n",
|
|||
|
|
" [ 0.0751782 0.12985791 0.11187319 ... 1.68572526 0.3474987\n",
|
|||
|
|
" -0.08177631]\n",
|
|||
|
|
" [-0.01769051 -0.03426794 0.09696276 ... -0.09957735 0.09537238\n",
|
|||
|
|
" 0.05246363]\n",
|
|||
|
|
" [ 0.29121888 0.10542645 0.10344476 ... -0.29944873 -0.12072361\n",
|
|||
|
|
" 0.08701147]\n",
|
|||
|
|
" [-0.15138271 -0.09707084 -0.24277896 ... 0.5311856 0.13684932\n",
|
|||
|
|
" 0.06222155]]\n",
|
|||
|
|
"[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... -1.36405254e+00\n",
|
|||
|
|
" 0.00000000e+00 -3.64077773e-02]\n",
|
|||
|
|
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
|
|||
|
|
" 0.00000000e+00 4.37527792e-02]\n",
|
|||
|
|
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
|
|||
|
|
" 0.00000000e+00 -5.12308232e-02]\n",
|
|||
|
|
" [-7.97455970e-01 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
|
|||
|
|
" 0.00000000e+00 -7.32824635e-03]\n",
|
|||
|
|
" [ 2.20777060e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
|
|||
|
|
" 0.00000000e+00 1.59171722e-03]\n",
|
|||
|
|
" [-1.79412930e+00 -8.70353323e+00 -3.63950699e+00 ... 3.68925247e+00\n",
|
|||
|
|
" 2.54478256e+00 9.44320760e-03]]\n",
|
|||
|
|
"[[-1.03725930e-01 5.50632499e-03 6.33482840e-02 ... -3.88543368e-01\n",
|
|||
|
|
" -1.92209172e-01 -5.84898375e-02]\n",
|
|||
|
|
" [ 5.25641157e-01 -5.49387275e-01 -1.43249433e+00 ... -3.28946016e+00\n",
|
|||
|
|
" -3.20149183e-01 6.20475591e-02]\n",
|
|||
|
|
" [ 8.37677925e-03 2.41687648e-01 1.26037480e-01 ... 2.54258164e+00\n",
|
|||
|
|
" 3.31285345e-01 -4.26362275e-02]\n",
|
|||
|
|
" [-3.09566944e-01 -1.46430241e-01 2.88616344e-01 ... -4.63003337e-01\n",
|
|||
|
|
" 1.34385341e-01 1.33288296e-03]\n",
|
|||
|
|
" [ 6.71720716e-01 1.43032994e-01 2.24426262e-01 ... -4.58979070e-01\n",
|
|||
|
|
" -1.02600783e-01 8.06090080e-03]\n",
|
|||
|
|
" [-4.62839540e-01 -7.29210412e-01 -8.47552456e-01 ... 1.85363537e+00\n",
|
|||
|
|
" 7.97945531e-01 1.60030165e-02]]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# 请在此处填写你的代码（输出各模型训练到的特征系数值）\n",
|
|||
|
|
"print(lr.coef_)\n",
|
|||
|
|
"print(lr_l1.coef_)\n",
|
|||
|
|
"print(lr_l2.coef_)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 51,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1500x1500 with 6 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# 请在此处填写你的代码（绘制6张图）\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"%matplotlib inline\n",
|
|||
|
|
"plt.figure(figsize=(30, 30), dpi=50)\n",
|
|||
|
|
"for i in range(6):\n",
|
|||
|
|
" plt.subplot(3, 2, i+1)\n",
|
|||
|
|
" data = np.vstack((lr.coef_[i, :], lr_l1.coef_[i, :], lr_l2.coef_[i, :]))\n",
|
|||
|
|
" plt.plot(np.arange(562), data.transpose(), '', label=['LR', 'L1', 'L2'])\n",
|
|||
|
|
" plt.legend()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## 第五步：预测数据\n",
|
|||
|
|
"\n",
|
|||
|
|
"* 将每个模型预测的类别和概率值都保存下来。"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 52,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# 将每个模型预测的类别和概率值都保存下来。\n",
|
|||
|
|
"y_pred = lr.predict(X_test)\n",
|
|||
|
|
"y_pred_l1 = lr_l1.predict(X_test)\n",
|
|||
|
|
"y_pred_l2 = lr_l2.predict(X_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
"y_pred_proba = lr.predict_proba(X_test)\n",
|
|||
|
|
"y_pred_proba_l1 = lr_l1.predict_proba(X_test)\n",
|
|||
|
|
"y_pred_proba_l2 = lr_l2.predict_proba(X_test)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## 第六步：评价模型\n",
|
|||
|
|
"\n",
|
|||
|
|
"对每个模型，分别计算下面的各评测指标值： \n",
|
|||
|
|
"\n",
|
|||
|
|
"* accuracy\n",
|
|||
|
|
"* precision\n",
|
|||
|
|
"* recall\n",
|
|||
|
|
"* fscore\n",
|
|||
|
|
"* confusion matrix"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 53,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"LR accuracy: 0.9873073436083409\n",
|
|||
|
|
"LR precision: 0.9883023003161968\n",
|
|||
|
|
"LR recall: 0.9881513487864519\n",
|
|||
|
|
"LR fscore: 0.9882215465046406\n",
|
|||
|
|
"LR confusion matrix: [[422 0 0 0 0 0]\n",
|
|||
|
|
" [ 0 371 15 0 0 0]\n",
|
|||
|
|
" [ 0 12 400 0 0 0]\n",
|
|||
|
|
" [ 0 0 0 368 0 0]\n",
|
|||
|
|
" [ 0 0 0 0 296 0]\n",
|
|||
|
|
" [ 0 0 0 1 0 321]]\n",
|
|||
|
|
"L1 accuracy: 0.9877606527651859\n",
|
|||
|
|
"L1 precision: 0.9887539714995753\n",
|
|||
|
|
"L1 recall: 0.9886689471301372\n",
|
|||
|
|
"L1 fscore: 0.9887068901807181\n",
|
|||
|
|
"L1 confusion matrix: [[422 0 0 0 0 0]\n",
|
|||
|
|
" [ 0 371 15 0 0 0]\n",
|
|||
|
|
" [ 0 12 400 0 0 0]\n",
|
|||
|
|
" [ 0 0 0 368 0 0]\n",
|
|||
|
|
" [ 0 0 0 0 296 0]\n",
|
|||
|
|
" [ 0 0 0 0 0 322]]\n",
|
|||
|
|
"L2 accuracy: 0.9868540344514959\n",
|
|||
|
|
"L2 precision: 0.9878399933872739\n",
|
|||
|
|
"L2 recall: 0.9877740662269671\n",
|
|||
|
|
"L2 fscore: 0.987806320894575\n",
|
|||
|
|
"L2 confusion matrix: [[422 0 0 0 0 0]\n",
|
|||
|
|
" [ 0 372 14 0 0 0]\n",
|
|||
|
|
" [ 0 14 398 0 0 0]\n",
|
|||
|
|
" [ 0 0 0 368 0 0]\n",
|
|||
|
|
" [ 0 0 0 0 296 0]\n",
|
|||
|
|
" [ 0 0 0 1 0 321]]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('LR accuracy: ', accuracy_score(y_test, y_pred))\n",
|
|||
|
|
"print('LR precision: ', precision_score(y_test, y_pred, average='macro'))\n",
|
|||
|
|
"print('LR recall: ', recall_score(y_test, y_pred, average='macro'))\n",
|
|||
|
|
"print('LR fscore: ', f1_score(y_test, y_pred, average='macro'))\n",
|
|||
|
|
"print('LR confusion matrix: ', confusion_matrix(y_test, y_pred))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('L1 accuracy: ', accuracy_score(y_test, y_pred_l1))\n",
|
|||
|
|
"print('L1 precision: ', precision_score(y_test, y_pred_l1, average='macro'))\n",
|
|||
|
|
"print('L1 recall: ', recall_score(y_test, y_pred_l1, average='macro'))\n",
|
|||
|
|
"print('L1 fscore: ', f1_score(y_test, y_pred_l1, average='macro'))\n",
|
|||
|
|
"print('L1 confusion matrix: ', confusion_matrix(y_test, y_pred_l1))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('L2 accuracy: ', accuracy_score(y_test, y_pred_l2))\n",
|
|||
|
|
"print('L2 precision: ', precision_score(y_test, y_pred_l2, average='macro'))\n",
|
|||
|
|
"print('L2 recall: ', recall_score(y_test, y_pred_l2, average='macro'))\n",
|
|||
|
|
"print('L2 fscore: ', f1_score(y_test, y_pred_l2, average='macro'))\n",
|
|||
|
|
"print('L2 confusion matrix: ', confusion_matrix(y_test, y_pred_l2))"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3.10.8 ('.venv': venv)",
|
|||
|
|
"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": "3.10.8"
|
|||
|
|
},
|
|||
|
|
"vscode": {
|
|||
|
|
"interpreter": {
|
|||
|
|
"hash": "1f0d395e06aa83586067b19165efc9b683889967164248deef4bbf1fa27cfb00"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|