coco
696 lines
24 KiB
Plaintext
696 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd \n",
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"import seaborn as sns\n",
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"from IPython.display import display\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits.mplot3d import Axes3D\n",
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"import sklearn\n",
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"%matplotlib inline\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": 2,
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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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"load data successful !!!!!\n"
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]
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}
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],
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"source": [
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"try :\n",
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" #PH值 - done\n",
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" #data = pd.read_csv(\"data-ph.csv\")\n",
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" #微蛋白 - done\n",
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" #data = pd.read_csv(\"data-mau.csv\") \n",
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" #蛋白质 - done\n",
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" #data = pd.read_csv(\"data-pro.csv\") \n",
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" #亚硝酸盐 - done\n",
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" #data = pd.read_csv(\"data-nit.csv\") \n",
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" \n",
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" #肌酐\n",
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" #data = pd.read_csv(\"data-cre.csv\") \n",
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" #葡萄糖\n",
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" #data = pd.read_csv(\"data-glu.csv\") \n",
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" \n",
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"\n",
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" #通体 数据不正确\n",
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" data = pd.read_csv(\"mix-mau-data.csv\") \n",
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" #data = pd.read_excel(\"data-ket.xlsx\") \n",
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"\n",
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" #比重\n",
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" #data = pd.read_csv(\"data-sg.csv\") \n",
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" #抗坏血酸\n",
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" #data = pd.read_csv(\"data-vc.csv\") \n",
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" \n",
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" #白细胞 - done\n",
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" #data = pd.read_csv(\"data-wbc.csv\") \n",
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" #尿胆原 - done\n",
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" #data = pd.read_csv(\"data-uro.csv\") \n",
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" #尿钙 -- done\n",
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" #data1 = pd.read_csv(\"data-uca.csv\")\n",
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" #data = pd.read_csv(\"data-uca2.csv\")\n",
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" #data = data1.append(data2);\n",
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" #胆红素 - done\n",
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" #data = pd.read_csv(\"data-bil.csv\") \n",
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" #潜血 - done\n",
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" #data = pd.read_csv(\"data-bld.csv\") \n",
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"\n",
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" \n",
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" \n",
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" print (\"load data successful !!!!!\")\n",
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"except :\n",
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" print (\"load data error !!!!!!!!!!\")"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>h</th>\n",
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" <th>s</th>\n",
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" <th>v</th>\n",
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" <th>l</th>\n",
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" <th>a</th>\n",
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" <th>b</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>19693.000000</td>\n",
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" <td>19693.000000</td>\n",
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" <td>19693.000000</td>\n",
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" <td>19693.000000</td>\n",
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" <td>19693.000000</td>\n",
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" <td>19693.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>54.418880</td>\n",
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" <td>81.691667</td>\n",
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" <td>163.198548</td>\n",
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" <td>142.412786</td>\n",
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" <td>141.673285</td>\n",
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" <td>139.583608</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>65.001826</td>\n",
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" <td>46.115913</td>\n",
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" <td>23.094477</td>\n",
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" <td>41.231902</td>\n",
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" <td>15.399092</td>\n",
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" <td>4.050424</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>76.000000</td>\n",
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" <td>46.000000</td>\n",
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" <td>118.000000</td>\n",
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" <td>121.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>5.000000</td>\n",
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" <td>35.000000</td>\n",
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" <td>146.000000</td>\n",
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" <td>106.000000</td>\n",
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" <td>126.000000</td>\n",
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" <td>136.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>26.000000</td>\n",
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" <td>79.000000</td>\n",
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" <td>161.000000</td>\n",
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" <td>135.000000</td>\n",
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" <td>145.000000</td>\n",
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" <td>140.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>63.000000</td>\n",
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" <td>119.000000</td>\n",
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" <td>182.000000</td>\n",
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" <td>182.000000</td>\n",
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" <td>156.000000</td>\n",
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" <td>143.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>179.000000</td>\n",
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" <td>176.000000</td>\n",
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" <td>234.000000</td>\n",
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" <td>232.000000</td>\n",
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" <td>168.000000</td>\n",
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" <td>149.000000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" h s v l a \\\n",
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"count 19693.000000 19693.000000 19693.000000 19693.000000 19693.000000 \n",
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"mean 54.418880 81.691667 163.198548 142.412786 141.673285 \n",
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"std 65.001826 46.115913 23.094477 41.231902 15.399092 \n",
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"min 0.000000 0.000000 76.000000 46.000000 118.000000 \n",
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"25% 5.000000 35.000000 146.000000 106.000000 126.000000 \n",
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"50% 26.000000 79.000000 161.000000 135.000000 145.000000 \n",
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"75% 63.000000 119.000000 182.000000 182.000000 156.000000 \n",
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"max 179.000000 176.000000 234.000000 232.000000 168.000000 \n",
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"\n",
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" b \n",
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"count 19693.000000 \n",
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"mean 139.583608 \n",
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"std 4.050424 \n",
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"min 121.000000 \n",
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"25% 136.000000 \n",
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"50% 140.000000 \n",
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"75% 143.000000 \n",
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"max 149.000000 "
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]
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},
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"execution_count": 3,
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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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"train_labels = data[\"index\"]\n",
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"train_features = data.drop(\"index\",axis=1)\n",
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"\n",
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"train_features.describe()\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": 4,
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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:\\Anaconda3\\lib\\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",
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" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
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]
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}
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],
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"source": [
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"#from sklearn.model_selection import KFold\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import accuracy_score\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.metrics import f1_score\n",
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"from sklearn.metrics import precision_score\n",
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"from sklearn.metrics import recall_score\n",
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"\n",
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"\n",
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"from sklearn.ensemble import ExtraTreesClassifier\n",
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"from sklearn.ensemble import AdaBoostClassifier\n",
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"\n",
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"from sklearn.cross_validation import train_test_split\n",
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"X_train ,X_test,y_train,y_test = train_test_split(train_features,train_labels,test_size = 0.4, random_state = 0)\n",
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"#X_train ,X_test,y_train,y_test = train_test_split(train_features,train_labels,test_size = 0.2, random_state = 20)\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": 5,
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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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"-----------classification_report----\n",
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"\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 3478\n",
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" 1 0.99 0.99 0.99 1391\n",
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" 2 0.99 0.99 0.99 1594\n",
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" 3 0.99 0.99 0.99 1415\n",
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"\n",
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"avg / total 1.00 1.00 1.00 7878\n",
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"\n",
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"-----------confusion_matrix---------\n",
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"\n",
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"[[3478 0 0 0]\n",
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" [ 0 1382 9 0]\n",
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" [ 0 10 1574 10]\n",
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" [ 0 0 8 1407]]\n",
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"------------------------------------\n",
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"\n",
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"Accuracy of prediction: 0.617\n",
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"-----------------------------------\n",
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"\n",
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"DecisionTree accuracy score: 0.9953033764914953\n",
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"f1 score: 0.9953033764914953\n",
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"precision_score: 0.9953033764914953\n",
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"recall_score: 0.9953033764914953\n"
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]
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}
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],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.metrics import classification_report,confusion_matrix\n",
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"\n",
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"dtree = DecisionTreeClassifier(criterion='gini',max_depth=None)\n",
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"dtree.fit(X_train,y_train)\n",
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"predictions = dtree.predict(X_test)\n",
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"\n",
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"print(\"-----------classification_report----\\n\")\n",
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"print(classification_report(y_test,predictions))\n",
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"print(\"-----------confusion_matrix---------\\n\")\n",
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"cm=confusion_matrix(y_test,predictions)\n",
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"print(cm)\n",
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"print(\"------------------------------------\\n\")\n",
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"print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))\n",
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"print(\"-----------------------------------\\n\")\n",
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"print (\"DecisionTree accuracy score:\" , accuracy_score(y_test,predictions))\n",
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"print (\"f1 score:\" , f1_score(y_test,predictions,average='micro'))\n",
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"print (\"precision_score:\" , precision_score(y_test,predictions,average='micro'))\n",
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"print (\"recall_score:\" , recall_score(y_test,predictions,average='micro'))"
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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": 6,
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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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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 3478\n",
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" 1 0.99 0.99 0.99 1391\n",
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" 2 0.99 0.99 0.99 1594\n",
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" 3 0.99 0.99 0.99 1415\n",
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"\n",
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"avg / total 1.00 1.00 1.00 7878\n",
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"\n",
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"[[3478 0 0 0]\n",
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" [ 0 1381 10 0]\n",
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" [ 0 8 1581 5]\n",
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" [ 0 0 7 1408]]\n",
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"---------------------------------\n",
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"\n",
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"Accuracy of prediction: 0.617\n",
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"RandomForest accuracy score: 0.9961919268849961\n",
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"---------------------------------\n",
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"\n",
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"f1 score: 0.9961919268849961\n",
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"precision_score: 0.9961919268849961\n",
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"recall_score: 0.9961919268849961\n"
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]
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}
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],
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"source": [
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"rfc = RandomForestClassifier(n_estimators=600)\n",
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"rfc.fit(X_train, y_train)\n",
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"rfc_pred = rfc.predict(X_test)\n",
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"cr = classification_report(y_test,predictions)\n",
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"print(cr)\n",
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"cm = confusion_matrix(y_test,rfc_pred)\n",
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"print(cm)\n",
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"\n",
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"print(\"---------------------------------\\n\")\n",
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"print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))\n",
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"print (\"RandomForest accuracy score:\" , accuracy_score(y_test,rfc_pred))\n",
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"print(\"---------------------------------\\n\")\n",
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"print (\"f1 score:\" , f1_score(y_test,rfc_pred,average='micro'))\n",
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"print (\"precision_score:\" , precision_score(y_test,rfc_pred,average='micro'))\n",
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"print (\"recall_score:\" , recall_score(y_test,rfc_pred,average='micro'))"
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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": 7,
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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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"KFold(n_splits=5, random_state=None, shuffle=False)\n",
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"TRAIN: [ 3939 3940 3941 ... 19690 19691 19692] TEST: [ 0 1 2 ... 3936 3937 3938]\n",
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"svm linear accuracy score: 0.9182533637979182\n",
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"f1 score: 0.9182533637979181\n",
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"precision_score: 0.9182533637979182\n",
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"recall_score: 0.9182533637979182\n",
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"runing time: 0:00:02.546188\n",
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"\n",
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"\n",
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"\n",
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"TRAIN: [ 0 1 2 ... 19690 19691 19692] TEST: [3939 3940 3941 ... 7875 7876 7877]\n",
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"svm linear accuracy score: 0.968012185833968\n",
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"f1 score: 0.968012185833968\n",
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"precision_score: 0.968012185833968\n",
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"recall_score: 0.968012185833968\n",
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"runing time: 0:00:02.914205\n",
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"\n",
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"\n",
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"\n",
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"TRAIN: [ 0 1 2 ... 19690 19691 19692] TEST: [ 7878 7879 7880 ... 11814 11815 11816]\n",
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"svm linear accuracy score: 0.6384869256156385\n",
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"f1 score: 0.6384869256156385\n",
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"precision_score: 0.6384869256156385\n",
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"recall_score: 0.6384869256156385\n",
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"runing time: 0:00:02.365734\n",
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"\n",
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"\n",
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"\n",
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"TRAIN: [ 0 1 2 ... 19690 19691 19692] TEST: [11817 11818 11819 ... 15752 15753 15754]\n",
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"svm linear accuracy score: 0.9850177755205688\n",
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"f1 score: 0.9850177755205688\n",
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"precision_score: 0.9850177755205688\n",
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"recall_score: 0.9850177755205688\n",
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"runing time: 0:00:02.819487\n",
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"\n",
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"\n",
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"\n",
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"TRAIN: [ 0 1 2 ... 15752 15753 15754] TEST: [15755 15756 15757 ... 19690 19691 19692]\n",
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"svm linear accuracy score: 1.0\n",
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"f1 score: 1.0\n",
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"precision_score: 1.0\n",
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"recall_score: 1.0\n",
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"runing time: 0:00:03.159911\n",
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"\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"from sklearn.model_selection import KFold\n",
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"\n",
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"X = train_features.values\n",
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"y = train_labels.values\n",
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"\n",
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"kf = KFold(n_splits=5)\n",
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"kf.get_n_splits(X)\n",
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"\n",
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"print(kf) \n",
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"\n",
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|
"for train_index, test_index in kf.split(X):\n",
|
|
" print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
|
|
" X_train, X_test = X[train_index], X[test_index]\n",
|
|
" y_train, y_test = y[train_index], y[test_index]\n",
|
|
" \n",
|
|
" \n",
|
|
" from datetime import datetime\n",
|
|
" trarining_start_time = datetime.now()\n",
|
|
"\n",
|
|
" rfc = RandomForestClassifier(n_estimators=600)\n",
|
|
" rfc.fit(X_train, y_train)\n",
|
|
" rfc_pred = rfc.predict(X_test) \n",
|
|
" print (\"svm linear accuracy score:\" , accuracy_score(y_test,rfc_pred))\n",
|
|
" print (\"f1 score:\" , f1_score(y_test,rfc_pred,average='micro'))\n",
|
|
" print (\"precision_score:\" , precision_score(y_test,rfc_pred,average='micro'))\n",
|
|
" print (\"recall_score:\" , recall_score(y_test,rfc_pred,average='micro'))\n",
|
|
"\n",
|
|
" training_stop_time = datetime.now()\n",
|
|
"\n",
|
|
" print (\"runing time:\",(training_stop_time - trarining_start_time))\n",
|
|
" print(\"\\n\\n\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"KFold(n_splits=5, random_state=None, shuffle=False)\n",
|
|
"TRAIN: [ 3939 3940 3941 ... 19690 19691 19692] TEST: [ 0 1 2 ... 3936 3937 3938]\n",
|
|
"svm linear accuracy score: 0.9301853262249302\n",
|
|
"f1 score: 0.9301853262249302\n",
|
|
"precision_score: 0.9301853262249302\n",
|
|
"recall_score: 0.9301853262249302\n",
|
|
"runing time: 0:00:00.397934\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"TRAIN: [ 0 1 2 ... 19690 19691 19692] TEST: [3939 3940 3941 ... 7875 7876 7877]\n",
|
|
"svm linear accuracy score: 0.9692815435389693\n",
|
|
"f1 score: 0.9692815435389693\n",
|
|
"precision_score: 0.9692815435389693\n",
|
|
"recall_score: 0.9692815435389693\n",
|
|
"runing time: 0:00:00.981406\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"TRAIN: [ 0 1 2 ... 19690 19691 19692] TEST: [ 7878 7879 7880 ... 11814 11815 11816]\n",
|
|
"svm linear accuracy score: 0.6521959888296522\n",
|
|
"f1 score: 0.6521959888296522\n",
|
|
"precision_score: 0.6521959888296522\n",
|
|
"recall_score: 0.6521959888296522\n",
|
|
"runing time: 0:00:00.405946\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"TRAIN: [ 0 1 2 ... 19690 19691 19692] TEST: [11817 11818 11819 ... 15752 15753 15754]\n",
|
|
"svm linear accuracy score: 0.9951752158456069\n",
|
|
"f1 score: 0.9951752158456069\n",
|
|
"precision_score: 0.9951752158456069\n",
|
|
"recall_score: 0.9951752158456069\n",
|
|
"runing time: 0:00:01.247622\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"TRAIN: [ 0 1 2 ... 15752 15753 15754] TEST: [15755 15756 15757 ... 19690 19691 19692]\n",
|
|
"svm linear accuracy score: 1.0\n",
|
|
"f1 score: 1.0\n",
|
|
"precision_score: 1.0\n",
|
|
"recall_score: 1.0\n",
|
|
"runing time: 0:00:02.044532\n",
|
|
"\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.model_selection import KFold\n",
|
|
"\n",
|
|
"X = train_features.values\n",
|
|
"y = train_labels.values\n",
|
|
"\n",
|
|
"kf = KFold(n_splits=5)\n",
|
|
"kf.get_n_splits(X)\n",
|
|
"\n",
|
|
"print(kf) \n",
|
|
"\n",
|
|
"for train_index, test_index in kf.split(X):\n",
|
|
" print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
|
|
" X_train, X_test = X[train_index], X[test_index]\n",
|
|
" y_train, y_test = y[train_index], y[test_index]\n",
|
|
" \n",
|
|
" \n",
|
|
" from datetime import datetime\n",
|
|
" trarining_start_time = datetime.now()\n",
|
|
"\n",
|
|
" clf_svm_linear = SVC(kernel='linear', gamma=0.02, C=1)\n",
|
|
" clf_svm_linear = clf_svm_linear.fit(X_train, y_train)\n",
|
|
" #print(clf_svm_linear.predict(X_test))\n",
|
|
" pred = clf_svm_linear.predict(X_test)\n",
|
|
" print (\"svm linear accuracy score:\" , accuracy_score(y_test,pred))\n",
|
|
" print (\"f1 score:\" , f1_score(y_test,pred,average='micro'))\n",
|
|
" print (\"precision_score:\" , precision_score(y_test,pred,average='micro'))\n",
|
|
" print (\"recall_score:\" , recall_score(y_test,pred,average='micro'))\n",
|
|
"\n",
|
|
" training_stop_time = datetime.now()\n",
|
|
"\n",
|
|
" print (\"runing time:\",(training_stop_time - trarining_start_time))\n",
|
|
" print(\"\\n\\n\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"svm linear accuracy score: 0.9894643310484894\n",
|
|
"f1 score: 0.9894643310484894\n",
|
|
"precision_score: 0.9894643310484894\n",
|
|
"recall_score: 0.9894643310484894\n",
|
|
"runing clf_svm_linear time: 0:00:00.708116\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from datetime import datetime\n",
|
|
"trarining_start_time = datetime.now()\n",
|
|
"\n",
|
|
"X_train ,X_test,y_train,y_test = train_test_split(train_features,train_labels,test_size = 0.4, random_state = 0)\n",
|
|
"\n",
|
|
"#clf_svm_linear = SVC(kernel = 'linear',gamma=0.00001,C=0.01)\n",
|
|
"clf_svm_linear = SVC(kernel = 'linear',gamma=0.02,C=1)\n",
|
|
"clf_svm_linear = clf_svm_linear.fit(X_train, y_train)\n",
|
|
"pred = clf_svm_linear.predict(X_test)\n",
|
|
"#print (\"svm linear accuracy score:\" , accuracy_score(y_test,pred))\n",
|
|
"#print (\"f1 score:\" , f1_score(y_test,pred,average='micro'))\n",
|
|
"#print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
|
"#print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
|
"print (\"svm linear accuracy score:\" , accuracy_score(y_test,pred))\n",
|
|
"print (\"f1 score:\" , f1_score(y_test,pred,average='micro'))\n",
|
|
"print (\"precision_score:\" , precision_score(y_test,pred,average='micro'))\n",
|
|
"print (\"recall_score:\" , recall_score(y_test,pred,average='micro'))\n",
|
|
"\n",
|
|
"training_stop_time = datetime.now()\n",
|
|
"print (\"runing clf_svm_linear time:\",(training_stop_time - trarining_start_time))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "ModuleNotFoundError",
|
|
"evalue": "No module named 'sklearn_porter'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[1;32m<ipython-input-10-74e6bf229c70>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn_porter\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPorter\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mporter_clf_svm_liner_14items\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPorter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf_svm_linear\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlanguage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m#print(porter_clf_svm_liner_ph)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn_porter'"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn_porter import Porter\n",
|
|
"\n",
|
|
"porter_clf_svm_liner_14items = Porter(clf_svm_linear, language='c').export()\n",
|
|
"\n",
|
|
"#print(porter_clf_svm_liner_ph)\n",
|
|
"f = open(\"new14modal/svm_bil.c\",'wb')\n",
|
|
"f.write(porter_clf_svm_liner_14items.encode())\n",
|
|
"f.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn_porter import Porter\n",
|
|
"\n",
|
|
"porter_clf_svm_liner_14items = Porter(clf_svm_linear, language='js').export()\n",
|
|
"\n",
|
|
"#print(porter_clf_svm_linear)\n",
|
|
"f = open(\"new14modal/svm_bil.js\",'wb')\n",
|
|
"#f = open(\"clf_svm_linear_125100_low_feature_data.txt\",'wb')\n",
|
|
"f.write(porter_clf_svm_liner_14items.encode())\n",
|
|
"f.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn_porter import Porter\n",
|
|
"\n",
|
|
"porter_clf_svm_liner_14items = Porter(rfc, language='c').export()\n",
|
|
"\n",
|
|
"#print(porter_clf_svm_liner_ph)\n",
|
|
"f = open(\"new14modal/rfc_bil.c\",'wb')\n",
|
|
"f.write(porter_clf_svm_liner_14items.encode())\n",
|
|
"f.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn_porter import Porter\n",
|
|
"\n",
|
|
"porter_clf_svm_liner_14items = Porter(rfc, language='js').export()\n",
|
|
"\n",
|
|
"#print(porter_clf_svm_linear)\n",
|
|
"f = open(\"new14modal/rfc_bil.js\",'wb')\n",
|
|
"#f = open(\"clf_svm_linear_125100_low_feature_data.txt\",'wb')\n",
|
|
"f.write(porter_clf_svm_liner_14items.encode())\n",
|
|
"f.close()"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.6.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|