coco
2951 lines
154 KiB
Plaintext
2951 lines
154 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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"# 排卵试纸机器学习算法验证"
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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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"# 1. **import moudle**"
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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": 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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"# 2. **load data**"
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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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"scrolled": true
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},
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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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" data1 = pd.read_csv(\"ov2020-5-14/ovulation-4e-5-1.csv\")\n",
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" data2 = pd.read_csv(\"ov2020-5-14/ovulation-4e-10-1.csv\")\n",
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" data3 = pd.read_csv(\"ov2020-5-14/ovulation-4e-10-2.csv\")\n",
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" data4 = pd.read_csv(\"ov2020-5-14/ovulation-4e-15-1.csv\")\n",
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" data5 = pd.read_csv(\"ov2020-5-14/ovulation-4e-15-2.csv\")\n",
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" data6 = pd.read_csv(\"ov2020-5-14/ovulation-4e-25-1.csv\")\n",
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" data7 = pd.read_csv(\"ov2020-5-14/ovulation-4e-25-2.csv\")\n",
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" data8 = pd.read_csv(\"ov2020-5-14/ovulation-4e-40-1.csv\")\n",
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" data9 = pd.read_csv(\"ov2020-5-14/ovulation-4e-50-1.csv\")\n",
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" data10 = pd.read_csv(\"ov2020-5-14/ovulation-4e-50-2.csv\")\n",
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" data11 = pd.read_csv(\"ov2020-5-14/ovulation-4e-50-3.csv\")\n",
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" data12 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-1.csv\")\n",
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" data13 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-2.csv\")\n",
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" data14 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-3.csv\")\n",
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" data15 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-4.csv\")\n",
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" data16 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-5.csv\")\n",
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" data17 = pd.read_csv(\"ov2020-5-14/ovulation-5i-5-1.csv\")\n",
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" data18 = pd.read_csv(\"ov2020-5-14/ovulation-5i-10-1.csv\")\n",
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" data19 = pd.read_csv(\"ov2020-5-14/ovulation-5i-10-2.csv\")\n",
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" data20 = pd.read_csv(\"ov2020-5-14/ovulation-5i-15-1.csv\")\n",
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" data21 = pd.read_csv(\"ov2020-5-14/ovulation-5i-15-2.csv\")\n",
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" data22 = pd.read_csv(\"ov2020-5-14/ovulation-5i-25-1.csv\")\n",
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" data23 = pd.read_csv(\"ov2020-5-14/ovulation-5i-25-2.csv\")\n",
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" data24 = pd.read_csv(\"ov2020-5-14/ovulation-5i-40-1.csv\")\n",
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" data25 = pd.read_csv(\"ov2020-5-14/ovulation-5i-50-1.csv\")\n",
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" data26 = pd.read_csv(\"ov2020-5-14/ovulation-5i-50-2.csv\")\n",
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" data27 = pd.read_csv(\"ov2020-5-14/ovulation-5i-50-3.csv\")\n",
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" data28 = pd.read_csv(\"ov2020-5-14/ovulation-5i-75-1.csv\")\n",
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" data29 = pd.read_csv(\"ov2020-5-14/ovulation-5i-75-2.csv\")\n",
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" data30 = pd.read_csv(\"ov2020-5-14/ovulation-5i-75-3.csv\")\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": null,
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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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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n"
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]
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},
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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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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
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"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
||
" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
|
||
"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
||
" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
|
||
"C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_2204\\3906487309.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
||
" data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"data = data1.append(data2).append(data3).append(data4).append(data5).append(data6).append(data7).append(data8).append(data9).append(data10).append(data11).append(data12).append(data13).append(data14).append(data15).append(data16).append(data17).append(data18).append(data19).append(data20).append(data21).append(data22).append(data23).append(data24).append(data25).append(data26).append(data27).append(data28).append(data29).append(data30)\n",
|
||
"#data10_all['index'].replace(2,1,inplace=True)\n",
|
||
"data.describe()\n",
|
||
"data.to_excel('data_all_20230720.xlsx')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"try :\n",
|
||
"# data_iphone6p_75_10 = pd.read_csv(\"20170912.pm.csv\")\n",
|
||
"# data_iphone6p_1234 = pd.read_csv(\"20170920.pm.csv\")\n",
|
||
"# data_iphone6p_5 = pd.read_csv(\"20170922.pm.csv\")\n",
|
||
"# data_iphone6p_0 = pd.read_csv(\"20170925.am.csv\")\n",
|
||
"# data_iphone6p_0_0 = pd.read_csv(\"20170925.pm.csv\")\n",
|
||
"# data_iphone6p_246 = pd.read_csv(\"20171011.pm.csv\")\n",
|
||
" \n",
|
||
"# data1 = pd.read_csv(\"ovdata_reindex.csv\")\n",
|
||
"# data2 = pd.read_csv(\"ovdataMore_reindex.csv\")\n",
|
||
"# data3 = pd.read_csv(\"ov_data_2020_reindex.csv\")\n",
|
||
" ovdata = pd.read_csv(\"ovdata.csv\")\n",
|
||
" ovdataMore = pd.read_csv(\"ovdataMore.csv\")\n",
|
||
" ov_data_2020 = pd.read_csv(\"ov_data_2020.csv\")\n",
|
||
" data10more = pd.read_csv(\"data10more.csv\")\n",
|
||
"\n",
|
||
"# data4 = pd.read_csv(\"10_25_renew.csv\")\n",
|
||
"\n",
|
||
"# data_all = pd.read_csv(\"data_all_2019_2020_reindex.csv\")\n",
|
||
"# data_all = pd.read_csv(\"ov_data_2020_reindex.csv\")\n",
|
||
" \n",
|
||
"# data1 = pd.read_csv(\"ovdata.csv\")\n",
|
||
"# data2 = pd.read_csv(\"ovdataMore.csv\")\n",
|
||
"# data3 = pd.read_csv(\"ov_data_2020.csv\")\n",
|
||
"# data_test1 = pd.read_csv(\"./newData/test.csv\")\n",
|
||
"# data_test2 = pd.read_csv(\"./newData/nubia_test.csv\")\n",
|
||
" \n",
|
||
" print (\"load data successful !!!!!\")\n",
|
||
"except :\n",
|
||
" print (\"load data error !!!!!!!!!!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"data2019_2020_old = ovdata.append(ovdataMore).append(ov_data_2020).append(data10more)\n",
|
||
"data2019_2020_old['index'].replace(4,7,inplace=True)\n",
|
||
"data2019_2020_old['index'].replace(3,6,inplace=True)\n",
|
||
"data2019_2020_old['index'].replace(2,4,inplace=True)\n",
|
||
"data2019_2020_old['index'].replace(1,2,inplace=True)\n",
|
||
"\n",
|
||
"data2019_2020_old.describe()\n",
|
||
"data2019_2020_old.to_excel('data2019_2020_old.xlsx')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"load data successful !!!!!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"try :\n",
|
||
"# data_iphone6p_75_10 = pd.read_csv(\"20170912.pm.csv\")\n",
|
||
"# data_iphone6p_1234 = pd.read_csv(\"20170920.pm.csv\")\n",
|
||
"# data_iphone6p_5 = pd.read_csv(\"20170922.pm.csv\")\n",
|
||
"# data_iphone6p_0 = pd.read_csv(\"20170925.am.csv\")\n",
|
||
"# data_iphone6p_0_0 = pd.read_csv(\"20170925.pm.csv\")\n",
|
||
"# data_iphone6p_246 = pd.read_csv(\"20171011.pm.csv\")\n",
|
||
" \n",
|
||
"# data1 = pd.read_csv(\"ovdata_reindex.csv\")\n",
|
||
"# data2 = pd.read_csv(\"ovdataMore_reindex.csv\")\n",
|
||
"# data3 = pd.read_csv(\"ov_data_2020_reindex.csv\")\n",
|
||
" d1 = pd.read_excel(\"data_all_2020514.xlsx\")\n",
|
||
" d2 = pd.read_excel(\"data2019_2020_old.xlsx\")\n",
|
||
"# data4 = pd.read_csv(\"10_25_renew.csv\")\n",
|
||
"\n",
|
||
"# data_all = pd.read_csv(\"data_all_2019_2020_reindex.csv\")\n",
|
||
"# data_all = pd.read_csv(\"ov_data_2020_reindex.csv\")\n",
|
||
" \n",
|
||
"# data1 = pd.read_csv(\"ovdata.csv\")\n",
|
||
"# data2 = pd.read_csv(\"ovdataMore.csv\")\n",
|
||
"# data3 = pd.read_csv(\"ov_data_2020.csv\")\n",
|
||
"# data_test1 = pd.read_csv(\"./newData/test.csv\")\n",
|
||
"# data_test2 = pd.read_csv(\"./newData/nubia_test.csv\")\n",
|
||
" \n",
|
||
" print (\"load data successful !!!!!\")\n",
|
||
"except :\n",
|
||
" print (\"load data error !!!!!!!!!!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 3. **分析数据**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Unnamed: 0 left_block_R left_block_G left_block_B left_block_H \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 8668.489827 185.033086 156.928292 166.615296 198.278206 \n",
|
||
"std 9514.701850 24.755951 33.981587 31.208994 66.447509 \n",
|
||
"min 0.000000 43.000000 30.000000 33.000000 0.000000 \n",
|
||
"25% 1294.000000 165.000000 130.000000 142.000000 193.000000 \n",
|
||
"50% 3917.500000 187.000000 158.000000 169.000000 227.000000 \n",
|
||
"75% 14267.250000 204.000000 188.000000 193.000000 239.000000 \n",
|
||
"max 36231.000000 255.000000 255.000000 255.000000 250.000000 \n",
|
||
"\n",
|
||
" left_block_S left_block_V left_block_l left_block_a left_block_b \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 43.749002 185.842529 171.646495 139.822962 126.542602 \n",
|
||
"std 23.980098 24.836509 29.823830 6.400393 3.844306 \n",
|
||
"min 0.000000 43.000000 34.000000 121.000000 111.000000 \n",
|
||
"25% 24.000000 167.000000 150.000000 135.000000 125.000000 \n",
|
||
"50% 41.000000 187.000000 173.000000 140.000000 127.000000 \n",
|
||
"75% 63.000000 205.000000 198.000000 145.000000 129.000000 \n",
|
||
"max 148.000000 255.000000 255.000000 154.000000 144.000000 \n",
|
||
"\n",
|
||
" ... right_grayHist right_grayMax right_grayMin white_grayValue \\\n",
|
||
"count ... 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean ... 162.055135 197.940302 133.605890 205.953212 \n",
|
||
"std ... 25.113326 15.384508 23.201433 16.090321 \n",
|
||
"min ... 28.000000 91.000000 24.000000 102.000000 \n",
|
||
"25% ... 144.000000 190.000000 115.000000 197.000000 \n",
|
||
"50% ... 162.000000 199.000000 135.000000 205.000000 \n",
|
||
"75% ... 180.000000 208.000000 152.000000 218.000000 \n",
|
||
"max ... 251.000000 255.000000 242.000000 255.000000 \n",
|
||
"\n",
|
||
" white_grayStddevValue white_grayHist white_grayMax white_grayMin \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 0.185763 206.318885 207.747846 205.103663 \n",
|
||
"std 0.467831 16.578652 15.995001 16.190066 \n",
|
||
"min 0.000000 0.000000 103.000000 101.000000 \n",
|
||
"25% 0.000000 197.000000 199.000000 195.000000 \n",
|
||
"50% 0.000000 205.000000 206.000000 204.000000 \n",
|
||
"75% 0.000000 218.000000 219.000000 217.000000 \n",
|
||
"max 17.000000 254.000000 255.000000 255.000000 \n",
|
||
"\n",
|
||
" whiteBalance index \n",
|
||
"count 82180.0 82180.000000 \n",
|
||
"mean 0.0 4.072791 \n",
|
||
"std 0.0 2.285574 \n",
|
||
"min 0.0 0.000000 \n",
|
||
"25% 0.0 2.000000 \n",
|
||
"50% 0.0 4.000000 \n",
|
||
"75% 0.0 6.000000 \n",
|
||
"max 0.0 7.000000 \n",
|
||
"\n",
|
||
"[8 rows x 153 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# data4 = data_iphone6p_246[data_iphone6p_246[\"whiteBalance\"] == 0]\n",
|
||
"# data2= data_iphone6p_1234[data_iphone6p_1234[\"whiteBalance\"] == 0 ]\n",
|
||
"# data1 = data_iphone6p_75_10[data_iphone6p_75_10[\"whiteBalance\"] == 0 ]\n",
|
||
"# data3 = data_iphone6p_5[data_iphone6p_5[\"whiteBalance\"] == 0]\n",
|
||
"# data0 = data_iphone6p_0[data_iphone6p_0[\"whiteBalance\"] == 0]\n",
|
||
"# data0_0 = data_iphone6p_0_0[data_iphone6p_0_0[\"whiteBalance\"] == 0]\n",
|
||
"\n",
|
||
"\n",
|
||
"#data_all = data2.append(data1[data1[\"index\"] == 5 ]).append(data3).append(data1[data1[\"index\"] == 7 ]).append(data1[data1[\"index\"] == 8 ]).append(data0).append(data0_0).append(data4)\n",
|
||
"#data1['index'].replace(4,6,inplace=True)\n",
|
||
"#data1['index'].replace(3,5,inplace=True)\n",
|
||
"#data1['index'].replace(2,4,inplace=True)\n",
|
||
"#data1['index'].replace(1,2,inplace=True)\n",
|
||
"\n",
|
||
"#data2['index'].replace(4,6,inplace=True)\n",
|
||
"#data2['index'].replace(3,5,inplace=True)\n",
|
||
"#data2['index'].replace(2,4,inplace=True)\n",
|
||
"#data2['index'].replace(1,2,inplace=True)\n",
|
||
"\n",
|
||
"#data3['index'].replace(4,6,inplace=True)\n",
|
||
"#data3['index'].replace(3,5,inplace=True)\n",
|
||
"#data3['index'].replace(2,4,inplace=True)\n",
|
||
"#data3['index'].replace(1,2,inplace=True)\n",
|
||
"\n",
|
||
"#data4['index'].replace(2,1,inplace=True)\n",
|
||
"#data4['index'].replace(4,2,inplace=True)\n",
|
||
"\n",
|
||
"#data1_0 = data1[data1[\"whiteBalance\"] == 0]\n",
|
||
"#data2_0 = data2[data2[\"whiteBalance\"] == 0]\n",
|
||
"#data3_0 = data3[data3[\"whiteBalance\"] == 0]\n",
|
||
"\n",
|
||
"#data_test_0 = data_test\n",
|
||
"\n",
|
||
"#data_all =data1_0.append(data2_0);\n",
|
||
"#data_all =data1.append(data2).append(data3);\n",
|
||
"\n",
|
||
"#data_all.to_csv('data_all_2019_2020_reindex.csv')\n",
|
||
"#data1.to_csv('ovdata_modifed.csv')\n",
|
||
"#data2.to_csv('ovdataMore_modifed.csv')\n",
|
||
"#data3.to_csv('ov_data_2020_modifed.csv')\n",
|
||
"#data4.to_csv('10_25_renew.csv')\n",
|
||
"\n",
|
||
"data =d1.append(d2)#.append(data3).append(data4)\n",
|
||
"data_all = data[data[\"whiteBalance\"] == 0]\n",
|
||
"print(data_all.describe())\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"whiteBlock_R_one =data_all[data_all[\"index\"] == 0 ][\"right_block_l_min\"]\n",
|
||
"whiteBlock_G_one = data_all[data_all[\"index\"] == 0 ][\"right_block_a_min\"]\n",
|
||
"whiteBlock_B_one = data_all[data_all[\"index\"] == 0 ][\"right_block_b_min\"]\n",
|
||
"\n",
|
||
"whiteBlock_R_two = data_all[data_all[\"index\"] == 1 ][\"right_block_l_min\"]\n",
|
||
"whiteBlock_G_two = data_all[data_all[\"index\"] == 1 ][\"right_block_a_min\"]\n",
|
||
"whiteBlock_B_two = data_all[data_all[\"index\"] == 1 ][\"right_block_b_min\"]\n",
|
||
"\n",
|
||
"whiteBlock_R_three = data_all[data_all[\"index\"] == 2 ][\"right_block_l_min\"]\n",
|
||
"whiteBlock_G_three = data_all[data_all[\"index\"] == 2 ][\"right_block_a_min\"]\n",
|
||
"whiteBlock_B_three = data_all[data_all[\"index\"] == 2 ][\"right_block_b_min\"]\n",
|
||
"\n",
|
||
"whiteBlock_R_four = data_all[data_all[\"index\"] == 4 ][\"right_block_l_min\"]\n",
|
||
"whiteBlock_G_four = data_all[data_all[\"index\"] == 4 ][\"right_block_a_min\"]\n",
|
||
"whiteBlock_B_four = data_all[data_all[\"index\"] == 4 ][\"right_block_b_min\"]\n",
|
||
"\n",
|
||
"\n",
|
||
"whiteBlock_R_five = data_all[data_all[\"index\"] == 6 ][\"right_block_l_min\"]\n",
|
||
"whiteBlock_G_five = data_all[data_all[\"index\"] == 6 ][\"right_block_a_min\"]\n",
|
||
"whiteBlock_B_five = data_all[data_all[\"index\"] == 6 ][\"right_block_b_min\"]\n",
|
||
"\n",
|
||
"whiteBlock_R_six = data_all[data_all[\"index\"] == 7 ][\"right_block_l_min\"]\n",
|
||
"whiteBlock_G_six = data_all[data_all[\"index\"] == 7 ][\"right_block_a_min\"]\n",
|
||
"whiteBlock_B_six = data_all[data_all[\"index\"] == 7 ][\"right_block_b_min\"]\n",
|
||
"\n",
|
||
"fig = plt.figure()\n",
|
||
"#plt.rcParams[\"figure.figsize\"] = 20,20\n",
|
||
"ax = Axes3D(fig)\n",
|
||
"\n",
|
||
"ax.set_xlim(0,255)\n",
|
||
"ax.set_ylim(0,255)\n",
|
||
"ax.set_zlim(0,255)\n",
|
||
"ax.set_xlabel('H')\n",
|
||
"ax.set_ylabel('S')\n",
|
||
"ax.set_zlabel('V')\n",
|
||
"ax.set_title('HSV colorspace OV right block max value')\n",
|
||
"# ax.scatter(whiteBlock_R_zero, whiteBlock_G_zero, whiteBlock_B_zero,s = 15,c='y')\n",
|
||
"ax.scatter(whiteBlock_R_one, whiteBlock_G_one, whiteBlock_B_one,s = 15,c='r')\n",
|
||
"\n",
|
||
"ax.scatter(whiteBlock_R_two, whiteBlock_G_two, whiteBlock_B_two,s = 15,c='g')\n",
|
||
"ax.scatter(whiteBlock_R_three, whiteBlock_G_three, whiteBlock_B_three,s = 15,c='b')\n",
|
||
"\n",
|
||
"ax.scatter(whiteBlock_R_four, whiteBlock_G_four, whiteBlock_B_four,s = 15,c='y')\n",
|
||
"ax.scatter(whiteBlock_R_five, whiteBlock_G_five, whiteBlock_B_five,s = 15,c='pink')\n",
|
||
"ax.scatter(whiteBlock_R_six, whiteBlock_G_six, whiteBlock_B_six,s = 15,c='c')\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data_all.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"hsv max min hist value h值要去掉"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 预处理数据"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Unnamed: 0</th>\n",
|
||
" <th>left_block_R</th>\n",
|
||
" <th>left_block_G</th>\n",
|
||
" <th>left_block_B</th>\n",
|
||
" <th>left_block_H</th>\n",
|
||
" <th>left_block_S</th>\n",
|
||
" <th>left_block_V</th>\n",
|
||
" <th>left_block_l</th>\n",
|
||
" <th>left_block_a</th>\n",
|
||
" <th>left_block_b</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>right_grayValue</th>\n",
|
||
" <th>right_grayStddevValue</th>\n",
|
||
" <th>right_grayHist</th>\n",
|
||
" <th>right_grayMax</th>\n",
|
||
" <th>right_grayMin</th>\n",
|
||
" <th>white_grayValue</th>\n",
|
||
" <th>white_grayStddevValue</th>\n",
|
||
" <th>white_grayHist</th>\n",
|
||
" <th>white_grayMax</th>\n",
|
||
" <th>white_grayMin</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>8668.489827</td>\n",
|
||
" <td>185.033086</td>\n",
|
||
" <td>156.928292</td>\n",
|
||
" <td>166.615296</td>\n",
|
||
" <td>198.278206</td>\n",
|
||
" <td>43.749002</td>\n",
|
||
" <td>185.842529</td>\n",
|
||
" <td>171.646495</td>\n",
|
||
" <td>139.822962</td>\n",
|
||
" <td>126.542602</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>164.572889</td>\n",
|
||
" <td>17.293563</td>\n",
|
||
" <td>162.055135</td>\n",
|
||
" <td>197.940302</td>\n",
|
||
" <td>133.605890</td>\n",
|
||
" <td>205.953212</td>\n",
|
||
" <td>0.185763</td>\n",
|
||
" <td>206.318885</td>\n",
|
||
" <td>207.747846</td>\n",
|
||
" <td>205.103663</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>9514.701850</td>\n",
|
||
" <td>24.755951</td>\n",
|
||
" <td>33.981587</td>\n",
|
||
" <td>31.208994</td>\n",
|
||
" <td>66.447509</td>\n",
|
||
" <td>23.980098</td>\n",
|
||
" <td>24.836509</td>\n",
|
||
" <td>29.823830</td>\n",
|
||
" <td>6.400393</td>\n",
|
||
" <td>3.844306</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>17.753885</td>\n",
|
||
" <td>4.441941</td>\n",
|
||
" <td>25.113326</td>\n",
|
||
" <td>15.384508</td>\n",
|
||
" <td>23.201433</td>\n",
|
||
" <td>16.090321</td>\n",
|
||
" <td>0.467831</td>\n",
|
||
" <td>16.578652</td>\n",
|
||
" <td>15.995001</td>\n",
|
||
" <td>16.190066</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>43.000000</td>\n",
|
||
" <td>30.000000</td>\n",
|
||
" <td>33.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>43.000000</td>\n",
|
||
" <td>34.000000</td>\n",
|
||
" <td>121.000000</td>\n",
|
||
" <td>111.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>49.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>28.000000</td>\n",
|
||
" <td>91.000000</td>\n",
|
||
" <td>24.000000</td>\n",
|
||
" <td>102.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>103.000000</td>\n",
|
||
" <td>101.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>1294.000000</td>\n",
|
||
" <td>165.000000</td>\n",
|
||
" <td>130.000000</td>\n",
|
||
" <td>142.000000</td>\n",
|
||
" <td>193.000000</td>\n",
|
||
" <td>24.000000</td>\n",
|
||
" <td>167.000000</td>\n",
|
||
" <td>150.000000</td>\n",
|
||
" <td>135.000000</td>\n",
|
||
" <td>125.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>152.000000</td>\n",
|
||
" <td>14.000000</td>\n",
|
||
" <td>144.000000</td>\n",
|
||
" <td>190.000000</td>\n",
|
||
" <td>115.000000</td>\n",
|
||
" <td>197.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>197.000000</td>\n",
|
||
" <td>199.000000</td>\n",
|
||
" <td>195.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>3917.500000</td>\n",
|
||
" <td>187.000000</td>\n",
|
||
" <td>158.000000</td>\n",
|
||
" <td>169.000000</td>\n",
|
||
" <td>227.000000</td>\n",
|
||
" <td>41.000000</td>\n",
|
||
" <td>187.000000</td>\n",
|
||
" <td>173.000000</td>\n",
|
||
" <td>140.000000</td>\n",
|
||
" <td>127.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>162.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>162.000000</td>\n",
|
||
" <td>199.000000</td>\n",
|
||
" <td>135.000000</td>\n",
|
||
" <td>205.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>205.000000</td>\n",
|
||
" <td>206.000000</td>\n",
|
||
" <td>204.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>14267.250000</td>\n",
|
||
" <td>204.000000</td>\n",
|
||
" <td>188.000000</td>\n",
|
||
" <td>193.000000</td>\n",
|
||
" <td>239.000000</td>\n",
|
||
" <td>63.000000</td>\n",
|
||
" <td>205.000000</td>\n",
|
||
" <td>198.000000</td>\n",
|
||
" <td>145.000000</td>\n",
|
||
" <td>129.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>179.000000</td>\n",
|
||
" <td>21.000000</td>\n",
|
||
" <td>180.000000</td>\n",
|
||
" <td>208.000000</td>\n",
|
||
" <td>152.000000</td>\n",
|
||
" <td>218.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>218.000000</td>\n",
|
||
" <td>219.000000</td>\n",
|
||
" <td>217.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>36231.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>250.000000</td>\n",
|
||
" <td>148.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>154.000000</td>\n",
|
||
" <td>144.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>248.000000</td>\n",
|
||
" <td>41.000000</td>\n",
|
||
" <td>251.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>242.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>254.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" <td>255.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>8 rows × 151 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Unnamed: 0 left_block_R left_block_G left_block_B left_block_H \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 8668.489827 185.033086 156.928292 166.615296 198.278206 \n",
|
||
"std 9514.701850 24.755951 33.981587 31.208994 66.447509 \n",
|
||
"min 0.000000 43.000000 30.000000 33.000000 0.000000 \n",
|
||
"25% 1294.000000 165.000000 130.000000 142.000000 193.000000 \n",
|
||
"50% 3917.500000 187.000000 158.000000 169.000000 227.000000 \n",
|
||
"75% 14267.250000 204.000000 188.000000 193.000000 239.000000 \n",
|
||
"max 36231.000000 255.000000 255.000000 255.000000 250.000000 \n",
|
||
"\n",
|
||
" left_block_S left_block_V left_block_l left_block_a left_block_b \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 43.749002 185.842529 171.646495 139.822962 126.542602 \n",
|
||
"std 23.980098 24.836509 29.823830 6.400393 3.844306 \n",
|
||
"min 0.000000 43.000000 34.000000 121.000000 111.000000 \n",
|
||
"25% 24.000000 167.000000 150.000000 135.000000 125.000000 \n",
|
||
"50% 41.000000 187.000000 173.000000 140.000000 127.000000 \n",
|
||
"75% 63.000000 205.000000 198.000000 145.000000 129.000000 \n",
|
||
"max 148.000000 255.000000 255.000000 154.000000 144.000000 \n",
|
||
"\n",
|
||
" ... right_grayValue right_grayStddevValue right_grayHist \\\n",
|
||
"count ... 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean ... 164.572889 17.293563 162.055135 \n",
|
||
"std ... 17.753885 4.441941 25.113326 \n",
|
||
"min ... 49.000000 1.000000 28.000000 \n",
|
||
"25% ... 152.000000 14.000000 144.000000 \n",
|
||
"50% ... 162.000000 17.000000 162.000000 \n",
|
||
"75% ... 179.000000 21.000000 180.000000 \n",
|
||
"max ... 248.000000 41.000000 251.000000 \n",
|
||
"\n",
|
||
" right_grayMax right_grayMin white_grayValue white_grayStddevValue \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 197.940302 133.605890 205.953212 0.185763 \n",
|
||
"std 15.384508 23.201433 16.090321 0.467831 \n",
|
||
"min 91.000000 24.000000 102.000000 0.000000 \n",
|
||
"25% 190.000000 115.000000 197.000000 0.000000 \n",
|
||
"50% 199.000000 135.000000 205.000000 0.000000 \n",
|
||
"75% 208.000000 152.000000 218.000000 0.000000 \n",
|
||
"max 255.000000 242.000000 255.000000 17.000000 \n",
|
||
"\n",
|
||
" white_grayHist white_grayMax white_grayMin \n",
|
||
"count 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 206.318885 207.747846 205.103663 \n",
|
||
"std 16.578652 15.995001 16.190066 \n",
|
||
"min 0.000000 103.000000 101.000000 \n",
|
||
"25% 197.000000 199.000000 195.000000 \n",
|
||
"50% 205.000000 206.000000 204.000000 \n",
|
||
"75% 218.000000 219.000000 217.000000 \n",
|
||
"max 254.000000 255.000000 255.000000 \n",
|
||
"\n",
|
||
"[8 rows x 151 columns]"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"\n",
|
||
"train_labels = data_all[\"index\"]\n",
|
||
"train_features = data_all.drop(\"dateTime\",axis=1)\n",
|
||
"train_features = train_features.drop(\"index\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBalance\",axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features.describe()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"#这一节的代码，不要执行\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V\",axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_min\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_min\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_min\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_min\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_min\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_min\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_min\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_min\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_min\",axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features['lelf_right_R'] = train_features['left_block_R'] - train_features['right_block_R']\n",
|
||
"train_features['lelf_right_G'] = train_features['left_block_G'] - train_features['right_block_G']\n",
|
||
"train_features['lelf_right_B'] = train_features['left_block_B'] - train_features['right_block_B']\n",
|
||
"\n",
|
||
"# train_features['lelf_right_H'] = train_features['left_block_H'] - train_features['right_block_H']\n",
|
||
"# train_features['lelf_right_S'] = train_features['left_block_S'] - train_features['right_block_S']\n",
|
||
"# train_features['lelf_right_V'] = train_features['left_block_V'] - train_features['right_block_V']\n",
|
||
"\n",
|
||
"train_features['lelf_right_l'] = train_features['left_block_l'] - train_features['right_block_l']\n",
|
||
"train_features['lelf_right_a'] = train_features['left_block_a'] - train_features['right_block_a']\n",
|
||
"train_features['lelf_right_b'] = train_features['left_block_b'] - train_features['right_block_b']\n",
|
||
"\n",
|
||
"train_features['lelf_right_R_stddev'] = train_features['left_block_R_stddev'] - train_features['right_block_R_stddev']\n",
|
||
"train_features['lelf_right_G_stddev'] = train_features['left_block_G_stddev'] - train_features['right_block_G_stddev']\n",
|
||
"train_features['lelf_right_B_stddev'] = train_features['left_block_B_stddev'] - train_features['right_block_B_stddev']\n",
|
||
"\n",
|
||
"# train_features['lelf_right_H_stddev'] = train_features['left_block_H_stddev'] - train_features['right_block_H_stddev']\n",
|
||
"# train_features['lelf_right_S_stddev'] = train_features['left_block_S_stddev'] - train_features['right_block_S_stddev']\n",
|
||
"# train_features['lelf_right_V_stddev'] = train_features['left_block_V_stddev'] - train_features['right_block_V_stddev']\n",
|
||
"\n",
|
||
"train_features['lelf_right_l_stddev'] = train_features['left_block_l_stddev'] - train_features['right_block_l_stddev']\n",
|
||
"train_features['lelf_right_a_stddev'] = train_features['left_block_a_stddev'] - train_features['right_block_a_stddev']\n",
|
||
"train_features['lelf_right_b_stddev'] = train_features['left_block_b_stddev'] - train_features['right_block_b_stddev']\n",
|
||
"\n",
|
||
"train_features['lelf_right_R_hist'] = train_features['left_block_R_hist'] - train_features['right_block_R_hist']\n",
|
||
"train_features['lelf_right_G_hist'] = train_features['left_block_G_hist'] - train_features['right_block_G_hist']\n",
|
||
"train_features['lelf_right_B_hist'] = train_features['left_block_B_hist'] - train_features['right_block_B_hist']\n",
|
||
"\n",
|
||
"# train_features['lelf_right_H_hist'] = train_features['left_block_H_hist'] - train_features['right_block_H_hist']\n",
|
||
"# train_features['lelf_right_S_hist'] = train_features['left_block_S_hist'] - train_features['right_block_S_hist']\n",
|
||
"# train_features['lelf_right_V_hist'] = train_features['left_block_V_hist'] - train_features['right_block_V_hist']\n",
|
||
"\n",
|
||
"train_features['lelf_right_l_hist'] = train_features['left_block_l_hist'] - train_features['right_block_l_hist']\n",
|
||
"train_features['lelf_right_a_hist'] = train_features['left_block_a_hist'] - train_features['right_block_a_hist']\n",
|
||
"train_features['lelf_right_b_hist'] = train_features['left_block_b_hist'] - train_features['right_block_b_hist']\n",
|
||
"\n",
|
||
"train_features['lelf_right_R_max'] = train_features['left_block_R_max'] - train_features['right_block_R_max']\n",
|
||
"train_features['lelf_right_G_max'] = train_features['left_block_G_max'] - train_features['right_block_G_max']\n",
|
||
"train_features['lelf_right_B_max'] = train_features['left_block_B_max'] - train_features['right_block_B_max']\n",
|
||
"\n",
|
||
"# train_features['lelf_right_H_max'] = train_features['left_block_H_max'] - train_features['right_block_H_max']\n",
|
||
"# train_features['lelf_right_S_max'] = train_features['left_block_S_max'] - train_features['right_block_S_max']\n",
|
||
"# train_features['lelf_right_V_max'] = train_features['left_block_V_max'] - train_features['right_block_V_max']\n",
|
||
"\n",
|
||
"train_features['lelf_right_l_max'] = train_features['left_block_l_max'] - train_features['right_block_l_max']\n",
|
||
"train_features['lelf_right_a_max'] = train_features['left_block_a_max'] - train_features['right_block_a_max']\n",
|
||
"train_features['lelf_right_b_max'] = train_features['left_block_b_max'] - train_features['right_block_b_max']\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features['lelf_right_R_min'] = train_features['left_block_R_min'] - train_features['right_block_R_min']\n",
|
||
"train_features['lelf_right_G_min'] = train_features['left_block_G_min'] - train_features['right_block_G_min']\n",
|
||
"train_features['lelf_right_B_min'] = train_features['left_block_B_min'] - train_features['right_block_B_min']\n",
|
||
"\n",
|
||
"# train_features['lelf_right_H_min'] = train_features['left_block_H_min'] - train_features['right_block_H_min']\n",
|
||
"# train_features['lelf_right_S_min'] = train_features['left_block_S_min'] - train_features['right_block_S_min']\n",
|
||
"# train_features['lelf_right_V_min'] = train_features['left_block_V_min'] - train_features['right_block_V_min']\n",
|
||
"\n",
|
||
"train_features['lelf_right_l_min'] = train_features['left_block_l_min'] - train_features['right_block_l_min']\n",
|
||
"train_features['lelf_right_a_min'] = train_features['left_block_a_min'] - train_features['right_block_a_min']\n",
|
||
"train_features['lelf_right_b_min'] = train_features['left_block_b_min'] - train_features['right_block_b_min']\n",
|
||
"\n",
|
||
"train_features['lelf_right_gray_value'] = train_features['left_grayValue'] - train_features['right_grayValue']\n",
|
||
"train_features['lelf_right_gray_stddev'] = train_features['left_grayStddevValue'] - train_features['right_grayStddevValue']\n",
|
||
"train_features['lelf_right_gray_hist'] = train_features['left_grayHist'] - train_features['right_grayHist']\n",
|
||
"train_features['lelf_right_gray_max'] = train_features['left_grayMax'] - train_features['right_grayMax']\n",
|
||
"train_features['lelf_right_gray_min'] = train_features['left_grayMin'] - train_features['right_grayMin']\n",
|
||
"train_features.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# train_features_9是真正的训练数据"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>lelf_right_R</th>\n",
|
||
" <th>lelf_right_G</th>\n",
|
||
" <th>lelf_right_B</th>\n",
|
||
" <th>lelf_right_H</th>\n",
|
||
" <th>lelf_right_S</th>\n",
|
||
" <th>lelf_right_V</th>\n",
|
||
" <th>lelf_right_l</th>\n",
|
||
" <th>lelf_right_a</th>\n",
|
||
" <th>lelf_right_b</th>\n",
|
||
" <th>lelf_right_R_stddev</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>lelf_right_S_min</th>\n",
|
||
" <th>lelf_right_V_min</th>\n",
|
||
" <th>lelf_right_l_min</th>\n",
|
||
" <th>lelf_right_a_min</th>\n",
|
||
" <th>lelf_right_b_min</th>\n",
|
||
" <th>lelf_right_gray_value</th>\n",
|
||
" <th>lelf_right_gray_stddev</th>\n",
|
||
" <th>lelf_right_gray_hist</th>\n",
|
||
" <th>lelf_right_gray_max</th>\n",
|
||
" <th>lelf_right_gray_min</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" <td>82180.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>-1.048102</td>\n",
|
||
" <td>3.330993</td>\n",
|
||
" <td>1.959504</td>\n",
|
||
" <td>-18.906121</td>\n",
|
||
" <td>-2.293879</td>\n",
|
||
" <td>-0.708761</td>\n",
|
||
" <td>1.831492</td>\n",
|
||
" <td>-1.706668</td>\n",
|
||
" <td>0.187296</td>\n",
|
||
" <td>-0.147518</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-5.165551</td>\n",
|
||
" <td>-0.848077</td>\n",
|
||
" <td>6.700134</td>\n",
|
||
" <td>-0.805756</td>\n",
|
||
" <td>0.264091</td>\n",
|
||
" <td>1.860404</td>\n",
|
||
" <td>-0.936396</td>\n",
|
||
" <td>-6.518386</td>\n",
|
||
" <td>0.100158</td>\n",
|
||
" <td>6.950012</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>18.391457</td>\n",
|
||
" <td>30.145128</td>\n",
|
||
" <td>24.276399</td>\n",
|
||
" <td>56.810147</td>\n",
|
||
" <td>21.819350</td>\n",
|
||
" <td>19.022918</td>\n",
|
||
" <td>25.670779</td>\n",
|
||
" <td>6.057560</td>\n",
|
||
" <td>1.465808</td>\n",
|
||
" <td>8.414406</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>39.742770</td>\n",
|
||
" <td>6.110533</td>\n",
|
||
" <td>41.004732</td>\n",
|
||
" <td>2.175817</td>\n",
|
||
" <td>2.162132</td>\n",
|
||
" <td>25.907424</td>\n",
|
||
" <td>10.732107</td>\n",
|
||
" <td>43.350632</td>\n",
|
||
" <td>8.797537</td>\n",
|
||
" <td>40.825874</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>-59.000000</td>\n",
|
||
" <td>-73.000000</td>\n",
|
||
" <td>-66.000000</td>\n",
|
||
" <td>-242.000000</td>\n",
|
||
" <td>-82.000000</td>\n",
|
||
" <td>-59.000000</td>\n",
|
||
" <td>-68.000000</td>\n",
|
||
" <td>-23.000000</td>\n",
|
||
" <td>-4.000000</td>\n",
|
||
" <td>-29.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-149.000000</td>\n",
|
||
" <td>-24.000000</td>\n",
|
||
" <td>-89.000000</td>\n",
|
||
" <td>-10.000000</td>\n",
|
||
" <td>-6.000000</td>\n",
|
||
" <td>-67.000000</td>\n",
|
||
" <td>-38.000000</td>\n",
|
||
" <td>-251.000000</td>\n",
|
||
" <td>-28.000000</td>\n",
|
||
" <td>-85.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>-16.000000</td>\n",
|
||
" <td>-24.000000</td>\n",
|
||
" <td>-20.000000</td>\n",
|
||
" <td>-17.000000</td>\n",
|
||
" <td>-20.000000</td>\n",
|
||
" <td>-17.000000</td>\n",
|
||
" <td>-21.000000</td>\n",
|
||
" <td>-7.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>-6.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-38.000000</td>\n",
|
||
" <td>-5.000000</td>\n",
|
||
" <td>-28.000000</td>\n",
|
||
" <td>-2.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>-21.000000</td>\n",
|
||
" <td>-9.000000</td>\n",
|
||
" <td>-39.000000</td>\n",
|
||
" <td>-6.000000</td>\n",
|
||
" <td>-28.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>-2.000000</td>\n",
|
||
" <td>-3.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-7.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>8.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>-2.000000</td>\n",
|
||
" <td>-2.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>8.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>12.000000</td>\n",
|
||
" <td>27.000000</td>\n",
|
||
" <td>20.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>12.000000</td>\n",
|
||
" <td>21.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>7.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>27.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>40.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>22.000000</td>\n",
|
||
" <td>8.000000</td>\n",
|
||
" <td>27.000000</td>\n",
|
||
" <td>6.000000</td>\n",
|
||
" <td>41.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>65.000000</td>\n",
|
||
" <td>108.000000</td>\n",
|
||
" <td>83.000000</td>\n",
|
||
" <td>140.000000</td>\n",
|
||
" <td>69.000000</td>\n",
|
||
" <td>65.000000</td>\n",
|
||
" <td>91.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>8.000000</td>\n",
|
||
" <td>27.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>144.000000</td>\n",
|
||
" <td>29.000000</td>\n",
|
||
" <td>151.000000</td>\n",
|
||
" <td>7.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>93.000000</td>\n",
|
||
" <td>32.000000</td>\n",
|
||
" <td>141.000000</td>\n",
|
||
" <td>45.000000</td>\n",
|
||
" <td>150.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>8 rows × 50 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" lelf_right_R lelf_right_G lelf_right_B lelf_right_H lelf_right_S \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean -1.048102 3.330993 1.959504 -18.906121 -2.293879 \n",
|
||
"std 18.391457 30.145128 24.276399 56.810147 21.819350 \n",
|
||
"min -59.000000 -73.000000 -66.000000 -242.000000 -82.000000 \n",
|
||
"25% -16.000000 -24.000000 -20.000000 -17.000000 -20.000000 \n",
|
||
"50% 0.000000 3.000000 3.000000 -2.000000 -3.000000 \n",
|
||
"75% 12.000000 27.000000 20.000000 3.000000 17.000000 \n",
|
||
"max 65.000000 108.000000 83.000000 140.000000 69.000000 \n",
|
||
"\n",
|
||
" lelf_right_V lelf_right_l lelf_right_a lelf_right_b \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean -0.708761 1.831492 -1.706668 0.187296 \n",
|
||
"std 19.022918 25.670779 6.057560 1.465808 \n",
|
||
"min -59.000000 -68.000000 -23.000000 -4.000000 \n",
|
||
"25% -17.000000 -21.000000 -7.000000 -1.000000 \n",
|
||
"50% 0.000000 2.000000 -1.000000 0.000000 \n",
|
||
"75% 12.000000 21.000000 4.000000 1.000000 \n",
|
||
"max 65.000000 91.000000 11.000000 8.000000 \n",
|
||
"\n",
|
||
" lelf_right_R_stddev ... lelf_right_S_min lelf_right_V_min \\\n",
|
||
"count 82180.000000 ... 82180.000000 82180.000000 \n",
|
||
"mean -0.147518 ... -5.165551 -0.848077 \n",
|
||
"std 8.414406 ... 39.742770 6.110533 \n",
|
||
"min -29.000000 ... -149.000000 -24.000000 \n",
|
||
"25% -6.000000 ... -38.000000 -5.000000 \n",
|
||
"50% -1.000000 ... -7.000000 -1.000000 \n",
|
||
"75% 7.000000 ... 27.000000 3.000000 \n",
|
||
"max 27.000000 ... 144.000000 29.000000 \n",
|
||
"\n",
|
||
" lelf_right_l_min lelf_right_a_min lelf_right_b_min \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 6.700134 -0.805756 0.264091 \n",
|
||
"std 41.004732 2.175817 2.162132 \n",
|
||
"min -89.000000 -10.000000 -6.000000 \n",
|
||
"25% -28.000000 -2.000000 -1.000000 \n",
|
||
"50% 8.000000 -1.000000 0.000000 \n",
|
||
"75% 40.000000 1.000000 2.000000 \n",
|
||
"max 151.000000 7.000000 11.000000 \n",
|
||
"\n",
|
||
" lelf_right_gray_value lelf_right_gray_stddev lelf_right_gray_hist \\\n",
|
||
"count 82180.000000 82180.000000 82180.000000 \n",
|
||
"mean 1.860404 -0.936396 -6.518386 \n",
|
||
"std 25.907424 10.732107 43.350632 \n",
|
||
"min -67.000000 -38.000000 -251.000000 \n",
|
||
"25% -21.000000 -9.000000 -39.000000 \n",
|
||
"50% 2.000000 -2.000000 -2.000000 \n",
|
||
"75% 22.000000 8.000000 27.000000 \n",
|
||
"max 93.000000 32.000000 141.000000 \n",
|
||
"\n",
|
||
" lelf_right_gray_max lelf_right_gray_min \n",
|
||
"count 82180.000000 82180.000000 \n",
|
||
"mean 0.100158 6.950012 \n",
|
||
"std 8.797537 40.825874 \n",
|
||
"min -28.000000 -85.000000 \n",
|
||
"25% -6.000000 -28.000000 \n",
|
||
"50% 0.000000 8.000000 \n",
|
||
"75% 6.000000 41.000000 \n",
|
||
"max 45.000000 150.000000 \n",
|
||
"\n",
|
||
"[8 rows x 50 columns]"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"train_features_9 = pd.DataFrame()\n",
|
||
"train_features_9['lelf_right_R'] = train_features['left_block_R'] - train_features['right_block_R']\n",
|
||
"train_features_9['lelf_right_G'] = train_features['left_block_G'] - train_features['right_block_G']\n",
|
||
"train_features_9['lelf_right_B'] = train_features['left_block_B'] - train_features['right_block_B']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_H'] = train_features['left_block_H'] - train_features['right_block_H']\n",
|
||
"train_features_9['lelf_right_S'] = train_features['left_block_S'] - train_features['right_block_S']\n",
|
||
"train_features_9['lelf_right_V'] = train_features['left_block_V'] - train_features['right_block_V']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_l'] = train_features['left_block_l'] - train_features['right_block_l']\n",
|
||
"train_features_9['lelf_right_a'] = train_features['left_block_a'] - train_features['right_block_a']\n",
|
||
"train_features_9['lelf_right_b'] = train_features['left_block_b'] - train_features['right_block_b']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_R_stddev'] = train_features['left_block_R_stddev'] - train_features['right_block_R_stddev']\n",
|
||
"train_features_9['lelf_right_G_stddev'] = train_features['left_block_G_stddev'] - train_features['right_block_G_stddev']\n",
|
||
"train_features_9['lelf_right_B_stddev'] = train_features['left_block_B_stddev'] - train_features['right_block_B_stddev']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_H_stddev'] = train_features['left_block_H_stddev'] - train_features['right_block_H_stddev']\n",
|
||
"train_features_9['lelf_right_S_stddev'] = train_features['left_block_S_stddev'] - train_features['right_block_S_stddev']\n",
|
||
"train_features_9['lelf_right_V_stddev'] = train_features['left_block_V_stddev'] - train_features['right_block_V_stddev']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_l_stddev'] = train_features['left_block_l_stddev'] - train_features['right_block_l_stddev']\n",
|
||
"train_features_9['lelf_right_a_stddev'] = train_features['left_block_a_stddev'] - train_features['right_block_a_stddev']\n",
|
||
"train_features_9['lelf_right_b_stddev'] = train_features['left_block_b_stddev'] - train_features['right_block_b_stddev']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_R_hist'] = train_features['left_block_R_hist'] - train_features['right_block_R_hist']\n",
|
||
"train_features_9['lelf_right_G_hist'] = train_features['left_block_G_hist'] - train_features['right_block_G_hist']\n",
|
||
"train_features_9['lelf_right_B_hist'] = train_features['left_block_B_hist'] - train_features['right_block_B_hist']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_H_hist'] = train_features['left_block_H_hist'] - train_features['right_block_H_hist']\n",
|
||
"train_features_9['lelf_right_S_hist'] = train_features['left_block_S_hist'] - train_features['right_block_S_hist']\n",
|
||
"train_features_9['lelf_right_V_hist'] = train_features['left_block_V_hist'] - train_features['right_block_V_hist']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_l_hist'] = train_features['left_block_l_hist'] - train_features['right_block_l_hist']\n",
|
||
"train_features_9['lelf_right_a_hist'] = train_features['left_block_a_hist'] - train_features['right_block_a_hist']\n",
|
||
"train_features_9['lelf_right_b_hist'] = train_features['left_block_b_hist'] - train_features['right_block_b_hist']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_R_max'] = train_features['left_block_R_max'] - train_features['right_block_R_max']\n",
|
||
"train_features_9['lelf_right_G_max'] = train_features['left_block_G_max'] - train_features['right_block_G_max']\n",
|
||
"train_features_9['lelf_right_B_max'] = train_features['left_block_B_max'] - train_features['right_block_B_max']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_H_max'] = train_features['left_block_H_max'] - train_features['right_block_H_max']\n",
|
||
"train_features_9['lelf_right_S_max'] = train_features['left_block_S_max'] - train_features['right_block_S_max']\n",
|
||
"train_features_9['lelf_right_V_max'] = train_features['left_block_V_max'] - train_features['right_block_V_max']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_l_max'] = train_features['left_block_l_max'] - train_features['right_block_l_max']\n",
|
||
"train_features_9['lelf_right_a_max'] = train_features['left_block_a_max'] - train_features['right_block_a_max']\n",
|
||
"train_features_9['lelf_right_b_max'] = train_features['left_block_b_max'] - train_features['right_block_b_max']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_R_min'] = train_features['left_block_R_min'] - train_features['right_block_R_min']\n",
|
||
"train_features_9['lelf_right_G_min'] = train_features['left_block_G_min'] - train_features['right_block_G_min']\n",
|
||
"train_features_9['lelf_right_B_min'] = train_features['left_block_B_min'] - train_features['right_block_B_min']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_H_min'] = train_features['left_block_H_min'] - train_features['right_block_H_min']\n",
|
||
"train_features_9['lelf_right_S_min'] = train_features['left_block_S_min'] - train_features['right_block_S_min']\n",
|
||
"train_features_9['lelf_right_V_min'] = train_features['left_block_V_min'] - train_features['right_block_V_min']\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_l_min'] = train_features['left_block_l_min'] - train_features['right_block_l_min']\n",
|
||
"train_features_9['lelf_right_a_min'] = train_features['left_block_a_min'] - train_features['right_block_a_min']\n",
|
||
"train_features_9['lelf_right_b_min'] = train_features['left_block_b_min'] - train_features['right_block_b_min']\n",
|
||
"\n",
|
||
"# train_features_9['left_grayValue']= train_features['left_grayValue'];\n",
|
||
"# train_features_9['left_grayStddevValue']= train_features['left_grayStddevValue'];\n",
|
||
"# train_features_9['left_grayHist']= train_features['left_grayHist'];\n",
|
||
"# train_features_9['left_grayMax']= train_features['left_grayMax'];\n",
|
||
"# train_features_9['left_grayMin']= train_features['left_grayMin'];\n",
|
||
"\n",
|
||
"# train_features_9['right_grayValue']= train_features['right_grayValue'];\n",
|
||
"# train_features_9['right_grayStddevValue']= train_features['right_grayStddevValue'];\n",
|
||
"# train_features_9['right_grayHist']= train_features['right_grayHist'];\n",
|
||
"# train_features_9['right_grayMax']= train_features['right_grayMax'];\n",
|
||
"# train_features_9['right_grayMin']= train_features['right_grayMin'];\n",
|
||
"\n",
|
||
"# train_features_9['lelf_R_stddev'] = train_features['left_block_R_stddev'] \n",
|
||
"# train_features_9['lelf_G_stddev'] = train_features['left_block_G_stddev'] \n",
|
||
"# train_features_9['lelf_B_stddev'] = train_features['left_block_B_stddev'] \n",
|
||
"\n",
|
||
"# train_features_9['left_block_R_min'] = train_features['left_block_R_min'] \n",
|
||
"# train_features_9['left_block_G_min'] = train_features['left_block_G_min'] \n",
|
||
"# train_features_9['left_block_B_min'] = train_features['left_block_B_min'] \n",
|
||
"\n",
|
||
"\n",
|
||
"train_features_9['lelf_right_gray_value'] = train_features['left_grayValue'] - train_features['right_grayValue']\n",
|
||
"train_features_9['lelf_right_gray_stddev'] = train_features['left_grayStddevValue'] - train_features['right_grayStddevValue']\n",
|
||
"train_features_9['lelf_right_gray_hist'] = train_features['left_grayHist'] - train_features['right_grayHist']\n",
|
||
"train_features_9['lelf_right_gray_max'] = train_features['left_grayMax'] - train_features['right_grayMax']\n",
|
||
"train_features_9['lelf_right_gray_min'] = train_features['left_grayMin'] - train_features['right_grayMin']\n",
|
||
"#train_features_9['index'] = train_labels\n",
|
||
"train_features_9.describe()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**去掉左边块的方差和白块和右边块的特征**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# train_features = train_features.drop(\"left_block_R\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_G\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_B\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"left_block_R_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_G_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_B_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"left_block_R_max\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_G_max\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_B_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_l\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_a\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_b\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_l_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_a_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_b_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_l_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_a_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_b_max\",axis=1)\n",
|
||
"##################################################################\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_R\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_G\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_B\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_R_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_G_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_B_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_R_max\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_G_max\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_B_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_l\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_a\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_b\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_l_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_a_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_b_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_l_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_a_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_b_max\",axis=1)\n",
|
||
"\n",
|
||
"####################################################################\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_R\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_G\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_B\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_R_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_G_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_B_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_R_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_G_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_B_max\",axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_max\",axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_l\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_a\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_b\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_l_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_a_hist\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_b_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_l_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_a_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_b_max\",axis=1)\n",
|
||
"\n",
|
||
"##################################################################\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**去掉所有块的方差特征**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_b_stddev\",axis=1)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# train_features = train_features.drop(\"left_block_R\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_G\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_B\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"left_block_H\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_S\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_V\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"left_block_l\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_a\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_b\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_R\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_G\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_B\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_H\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_S\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_V\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_l\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_a\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_b\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_R\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_G\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_B\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_H\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_S\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_V\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_l\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_a\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_b\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"left_block_R_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_G_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_B_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"left_block_H_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_S_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"left_block_l_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_a_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"left_block_b_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_R_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_G_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_B_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_H_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_S_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"right_block_l_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_a_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"right_block_b_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_R_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_G_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_B_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_H_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_S_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_l_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_a_hist\",axis=1)\n",
|
||
"# train_features = train_features.drop(\"whiteBlock_b_hist\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_R_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_G_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_l_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_a_stddev\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_R_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_G_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_B_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"left_block_l_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_a_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"left_block_b_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_R_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_G_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_B_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"right_block_l_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_a_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"right_block_b_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_R_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_G_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_B_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_H_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_S_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_V_max\",axis=1)\n",
|
||
"\n",
|
||
"train_features = train_features.drop(\"whiteBlock_l_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_a_max\",axis=1)\n",
|
||
"train_features = train_features.drop(\"whiteBlock_b_max\",axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"train_features.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 测试一些算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#test all those Ensemble Methods\n",
|
||
"\n",
|
||
"from sklearn.ensemble import AdaBoostClassifier\n",
|
||
"from sklearn.ensemble import AdaBoostRegressor\n",
|
||
"\n",
|
||
"from sklearn.ensemble import BaggingClassifier\n",
|
||
"from sklearn.ensemble import BaggingRegressor\n",
|
||
"\n",
|
||
"from sklearn.ensemble import ExtraTreesClassifier\n",
|
||
"from sklearn.ensemble import ExtraTreesRegressor\n",
|
||
"\n",
|
||
"from sklearn.ensemble import GradientBoostingClassifier\n",
|
||
"from sklearn.ensemble import GradientBoostingRegressor\n",
|
||
"\n",
|
||
"from sklearn.ensemble import IsolationForest\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.ensemble import RandomForestRegressor\n",
|
||
"from sklearn.ensemble import RandomTreesEmbedding\n",
|
||
"\n",
|
||
"from sklearn.ensemble import StackingClassifier\n",
|
||
"from sklearn.ensemble import StackingRegressor\n",
|
||
"\n",
|
||
"from sklearn.ensemble import VotingClassifier\n",
|
||
"from sklearn.ensemble import VotingRegressor\n",
|
||
"\n",
|
||
"#from sklearn.ensemble import HistGradientBoostingRegressor\n",
|
||
"#from sklearn.ensemble import HistGradientBoostingClassifier\n",
|
||
"\n",
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"from sklearn.metrics import f1_score\n",
|
||
"from sklearn.metrics import precision_score\n",
|
||
"from sklearn.metrics import recall_score\n",
|
||
"\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"X_train ,X_test,y_train,y_test = train_test_split(train_features_9,train_labels,test_size = 0.3, random_state = 20)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"accuracy_score : 0.9800032449095482\n",
|
||
"f1_score : [0.99355631 0.96864764 0.98784441 0.96099719 0.98147301 0.90247074\n",
|
||
" 0.97109283 0.98481287]\n",
|
||
"precision_score: [0.99447174 0.97457627 0.98880597 0.95067621 0.97945675 0.99142857\n",
|
||
" 0.96137738 0.98784911]\n",
|
||
"recall_score : [0.99264255 0.9627907 0.98688472 0.97154472 0.98349759 0.82816229\n",
|
||
" 0.98100665 0.98179524]\n",
|
||
"runing time: 0:00:56.031340\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#case 1 AdaBoostClassifier \n",
|
||
"#https://www.cnblogs.com/pinard/p/6136914.html\n",
|
||
"from sklearn.tree import DecisionTreeClassifier\n",
|
||
"from datetime import datetime\n",
|
||
"bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=15, min_samples_split=20, min_samples_leaf=10),\n",
|
||
" n_estimators=50, learning_rate=0.8)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"bdt.fit(X_train, y_train)\n",
|
||
"pred = bdt.predict(X_test)\n",
|
||
"print (\"accuracy_score :\" , accuracy_score(y_test,pred))\n",
|
||
"print (\"f1_score :\" , f1_score(y_test,pred,average=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
||
"\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 2 AdaBoostRegressor\n",
|
||
"#https://www.programcreek.com/python/example/86712/sklearn.ensemble.AdaBoostRegressor\n",
|
||
"from sklearn.tree import DecisionTreeRegressor\n",
|
||
"from datetime import datetime\n",
|
||
"bdt = AdaBoostRegressor(DecisionTreeRegressor(max_depth=15, min_samples_split=20, min_samples_leaf=10),\n",
|
||
" n_estimators=50, learning_rate=0.8)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"bdt.fit(X_train, y_train)\n",
|
||
"pred = bdt.predict(X_test)\n",
|
||
"test_accuracy = bdt.score(X_test, y_test)\n",
|
||
"print(test_accuracy)\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 3 BaggingClassifier \n",
|
||
"from sklearn.tree import DecisionTreeClassifier\n",
|
||
"from datetime import datetime\n",
|
||
"bc = BaggingClassifier(DecisionTreeClassifier(max_depth=15, min_samples_split=20, min_samples_leaf=10),\n",
|
||
" max_samples=0.5,max_features=0.5)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"bc.fit(X_train, y_train)\n",
|
||
"pred = bc.predict(X_test)\n",
|
||
"print (\"accuracy_score :\" , accuracy_score(y_test,pred))\n",
|
||
"print (\"f1_score :\" , f1_score(y_test,pred,average=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
||
"\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 4 BaggingRegressor\n",
|
||
"from sklearn.tree import DecisionTreeRegressor\n",
|
||
"from datetime import datetime\n",
|
||
"br = BaggingRegressor(DecisionTreeRegressor(max_depth=15, min_samples_split=20, min_samples_leaf=10),\n",
|
||
" max_samples=0.5,max_features=0.5)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"br.fit(X_train, y_train)\n",
|
||
"pred = br.predict(X_test)\n",
|
||
"print(bdt.score(X_test, y_test))\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n",
|
||
"'''\n",
|
||
"Bagging通过降低基分类器的方差，改善了泛化误差\n",
|
||
"其性能依赖于基分类器的稳定性；如果基分类器不稳定，bagging有助于降低训练数据的随机波动导致的误差；如果稳定，则集成分类器的误差主要由基分类器的偏倚引起\n",
|
||
"由于每个样本被选中的概率相同，因此bagging并不侧重于训练数据集中的任何特定实例\n",
|
||
"'''"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 5 ExtraTreesClassifier \n",
|
||
"from datetime import datetime\n",
|
||
"ec = ExtraTreesClassifier(n_estimators=50,max_depth=20,min_samples_leaf=50)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"ec.fit(X_train, y_train)\n",
|
||
"pred = ec.predict(X_test)\n",
|
||
"print (\"accuracy_score :\" , accuracy_score(y_test,pred))\n",
|
||
"print (\"f1_score :\" , f1_score(y_test,pred,average=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
||
"\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 6 ExtraTreesRegressor \n",
|
||
"from datetime import datetime\n",
|
||
"er = ExtraTreesRegressor(n_estimators=50,max_depth=20,min_samples_leaf=50)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"er.fit(X_train, y_train)\n",
|
||
"pred = er.predict(X_test)\n",
|
||
"print (\"score :\" , er.score(X_test, y_test))\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 7 RandomForestClassifier \n",
|
||
"from datetime import datetime\n",
|
||
"rfc = RandomForestClassifier(n_estimators=50,max_depth=20,min_samples_leaf=50)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"rfc.fit(X_train, y_train)\n",
|
||
"pred = rfc.predict(X_test)\n",
|
||
"print (\"accuracy_score :\" , accuracy_score(y_test,pred))\n",
|
||
"print (\"f1_score :\" , f1_score(y_test,pred,average=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
||
"\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 8 RandomForestRegressor \n",
|
||
"from datetime import datetime\n",
|
||
"rfr = RandomForestRegressor(n_estimators=50,max_depth=20,min_samples_leaf=50)\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"rfr.fit(X_train, y_train)\n",
|
||
"print (\"score :\" , rfr.score(X_test, y_test))\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 9 C-Support Vector Classification.\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"svc_linear = SVC(kernel = 'linear',C=0.1)\n",
|
||
"#svm linear accuracy score: 0.974885004599816\n",
|
||
"svc_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",
|
||
"pred = svc_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=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
||
"#print(\"preds:\",pred[:10])\n",
|
||
"#print('trues:\\n',y_test[:10])\n",
|
||
"#print(\"\\n\")\n",
|
||
"\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n",
|
||
"\n",
|
||
"###针对同一份数据，\n",
|
||
"#svc_poly = SVC(kernel='poly',degree=3,gamma=0.001,C=0.1)\n",
|
||
"#clf_svc_poly = SVC(kernel='poly',degree=3,gamma=0.00001,C=0.1)\n",
|
||
"\n",
|
||
"##svm polynomial accuracy score: 0.37460901563937443\n",
|
||
"#svc_poly.fit(X_train, y_train)\n",
|
||
"#pred_poly = svc_poly.predict(X_test)\n",
|
||
"#print (\"svm polynomial accuracy score:\" , accuracy_score(y_test,pred_poly))\n",
|
||
"#print (\"f1 score :\" , f1_score(y_test,pred_poly,average=None))\n",
|
||
"#print (\"precision_score:\" , precision_score(y_test,pred_poly,average=None))\n",
|
||
"#print (\"recall_score :\" , recall_score(y_test,pred_poly,average=None))\n",
|
||
"\n",
|
||
"#svc_rbf = SVC(kernel='rbf', gamma=0.05,C=0.1)\n",
|
||
"##svm rbf accuracy score: 0.284360625574977\n",
|
||
"#svc_rbf.fit(X_train, y_train)\n",
|
||
"#pred_rbf = svc_rbf.predict(X_test)\n",
|
||
"#print (\"svm rbf accuracy score:\" , accuracy_score(y_test,pred_rbf))\n",
|
||
"#print (\"f1 score :\" , f1_score(y_test,pred_rbf,average=None))\n",
|
||
"#print (\"precision_score:\" , precision_score(y_test,pred_rbf,average=None))\n",
|
||
"#print (\"recall_score :\" , recall_score(y_test,pred_rbf,average=None))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#case 9 C-Support Vector Classification.\n",
|
||
"from sklearn.svm import SVR\n",
|
||
"\n",
|
||
"trarining_start_time = datetime.now()\n",
|
||
"\n",
|
||
"svr_linear = SVR(kernel = 'linear',C=0.1)\n",
|
||
"#svm linear accuracy score: 0.974885004599816\n",
|
||
"svr_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",
|
||
"pred = svr_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=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
||
"print(\"preds:\",pred[:10])\n",
|
||
"print('trues:\\n',y_test[:10])\n",
|
||
"print(\"\\n\")\n",
|
||
"\n",
|
||
"training_stop_time = datetime.now()\n",
|
||
"print (\"runing time:\",(training_stop_time - trarining_start_time))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# svc支持向量机算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#from sklearn.model_selection import KFold\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"from sklearn.metrics import f1_score\n",
|
||
"from sklearn.metrics import precision_score\n",
|
||
"from sklearn.metrics import recall_score\n",
|
||
"#集成学习(Ensemble Learning) \n",
|
||
"from sklearn.ensemble import ExtraTreesClassifier\n",
|
||
"from sklearn.ensemble import AdaBoostClassifier\n",
|
||
"\n",
|
||
"#报错：ModuleNotFoundError: No module named 'sklearn.cross_validation'\n",
|
||
"#原因：当前 sklearn 版本中 cross_validation 已经替换成了 model_selection，但其中的函数功能并没有变化\n",
|
||
"#from sklearn.cross_validation import train_test_split\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"X_train ,X_test,y_train,y_test = train_test_split(train_features_9,train_labels,test_size = 0.3, random_state = 20)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"##fit_transform,fit,transform区别和作用详解\n",
|
||
"###fit和transform没有任何关系，仅仅是数据处理的两个不同环节，之所以出来fit_transform这个函数名，仅仅是为了写代码方便，会高效一点。\n",
|
||
"###sklearn里的封装好的各种算法使用前都要fit，fit相对于整个代码而言，为后续API服务。fit之后，然后调用各种API方法,transform只是其中一个API方法，所以当你调用transform之外的方法，也必须要先fit。\n",
|
||
"###fit原义指的是安装、使适合的意思，其实有点train的含义，但是和train不同的是，它并不是一个训练的过程，而是一个适配的过程，过程都是确定的，最后得到一个可用于转换的有价值的信息。\n",
|
||
"###https://blog.csdn.net/weixin_38278334/article/details/82971752\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 逻辑回归算法测试"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"clf = LogisticRegression(random_state=0).fit(X_train, y_train)\n",
|
||
"clf.predict(X_test)\n",
|
||
"clf.score(X_test, y_test)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 特征/模型选择"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"##这个不需要了！\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=0.00001,C=0.1)\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",
|
||
"\n",
|
||
"\n",
|
||
" training_stop_time = datetime.now()\n",
|
||
"\n",
|
||
" print \"runing time:\",(training_stop_time - trarining_start_time)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import StratifiedKFold #交叉验证\n",
|
||
"from sklearn.model_selection import GridSearchCV #网格搜索\n",
|
||
"from sklearn.model_selection import train_test_split #将数据集分开成训练集和测试集\n",
|
||
"from xgboost import XGBClassifier #xgboost\n",
|
||
"\n",
|
||
"#这个不需要了！\n",
|
||
"model = XGBClassifier()\n",
|
||
"learning_rate = [0.0001,0.001,0.01,0.1,0.2,0.3] #学习率\n",
|
||
"gamma = [1, 0.1, 0.01, 0.001]\n",
|
||
"param_grid = dict(learning_rate = learning_rate,gamma = gamma)#转化为字典格式，网络搜索要求\n",
|
||
"kflod = StratifiedKFold(n_splits=10, shuffle = True,random_state=7)#将训练/测试数据集划分10个互斥子集，\n",
|
||
"grid_search = GridSearchCV(model,param_grid,scoring = 'neg_log_loss',n_jobs = -1,cv = kflod)\n",
|
||
"#scoring指定损失函数类型，n_jobs指定全部cpu跑，cv指定交叉验证\n",
|
||
"grid_result = grid_search.fit(X_train, y_train) #运行网格搜索\n",
|
||
"print(\"Best: %f using %s\" % (grid_result.best_score_,grid_search.best_params_))\n",
|
||
"#grid_scores_：给出不同参数情况下的评价结果。best_params_：描述了已取得最佳结果的参数的组合\n",
|
||
"#best_score_：成员提供优化过程期间观察到的最好的评分\n",
|
||
"#具有键作为列标题和值作为列的dict，可以导入到DataFrame中。\n",
|
||
"#注意，“params”键用于存储所有参数候选项的参数设置列表。\n",
|
||
"means = grid_result.cv_results_['mean_test_score']\n",
|
||
"params = grid_result.cv_results_['params']\n",
|
||
"for mean,param in zip(means,params):\n",
|
||
" print(\"%f with: %r\" % (mean,param))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import StratifiedKFold #交叉验证\n",
|
||
"from sklearn.model_selection import GridSearchCV #网格搜索\n",
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"from sklearn import metrics \n",
|
||
"#https://blog.csdn.net/WJWFighting/article/details/80983022\n",
|
||
"thresholds=np.linspace(0,0.1,20)#设置gamma参数列表,生成等差数列\n",
|
||
"thresholds\n",
|
||
"param_grid={'gamma':thresholds}\n",
|
||
"clf=GridSearchCV(SVC(kernel='rbf'),param_grid,cv=5)\n",
|
||
"clf.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"print(\"best param: {0}\\nbest score: {1}\".format(clf.best_params_, clf.best_score_))\n",
|
||
"\n",
|
||
"y_pred = clf.predict(X_test)\n",
|
||
"\n",
|
||
"print(\"查准率：\",metrics.precision_score(y_pred, y_test))\n",
|
||
"print(\"召回率：\",metrics.recall_score(y_pred, y_test))\n",
|
||
"print(\"F1：\",metrics.f1_score(y_pred, y_test))\n",
|
||
"\n",
|
||
"print(\"最佳效果：%0.3f\"% clf.best_score_)\n",
|
||
"print(\"最优参数组合：\")\n",
|
||
"best_parameters=clf.best_estimator_.get_params()\n",
|
||
"for param_name in sorted(param_grid.keys()):\n",
|
||
" print('\\t%s:%r' %(param_name,best_parameters[param_name]))\n",
|
||
"\n",
|
||
"#print(\"训练集评分：\",clf.score(x_train,y_train))\n",
|
||
"#print(\"测试集评分：\",clf.score(x_test,y_test))\n",
|
||
"\n",
|
||
"\"\"\"\n",
|
||
"SVC方法。常用的参数如下：\n",
|
||
"C：默认为1.0，是对于错误的惩罚项。\n",
|
||
"kernel：指定算法的核函数，默认为'rbf'，常用的有'linear'，'poly'，'rbf'，'sigmoid'，'precomputed'。\n",
|
||
"degree：多项式核函数的次数（'poly'），默认为3。 其他核函数会将其忽略。\n",
|
||
"gamma：'rbf'，'poly'和'sigmoid'的核系数。 如果gamma是'auto'，那么将使用1 / n_features。\n",
|
||
"\"\"\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#X_train = train_features_9\n",
|
||
"#y_train = train_labels\n",
|
||
"# X_test = test_features\n",
|
||
"# y_test = test_labels\n",
|
||
"#clf_svm_linear = SVC(kernel = 'linear',gamma=0.00001,C=0.01)\n",
|
||
"#clf_svm_linear = SVC(kernel = 'linear',gamma=0.01,C=0.01)\n",
|
||
"#svm linear accuracy score: 0.9746101835242169\n",
|
||
"clf_svm_linear = SVC(kernel = 'linear',C=0.1)\n",
|
||
"#svm linear accuracy score: 0.974885004599816\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",
|
||
"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=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
|
||
"print(\"preds:\",pred[:10])\n",
|
||
"print('trues:\\n',y_test[:10])\n",
|
||
"print(\"\\n\")\n",
|
||
"###针对同一份数据，\n",
|
||
"clf_svc_poly = SVC(kernel='poly',degree=3,gamma=0.001,C=0.1)\n",
|
||
"#clf_svc_poly = SVC(kernel='poly',degree=3,gamma=0.00001,C=0.1)\n",
|
||
"\n",
|
||
"##svm polynomial accuracy score: 0.37460901563937443\n",
|
||
"clf_svc_poly.fit(X_train, y_train)\n",
|
||
"pred_poly = clf_svc_poly.predict(X_test)\n",
|
||
"print (\"svm polynomial accuracy score:\" , accuracy_score(y_test,pred_poly))\n",
|
||
"print (\"f1 score :\" , f1_score(y_test,pred_poly,average=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred_poly,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred_poly,average=None))\n",
|
||
"\n",
|
||
"clf_svc_rbf = SVC(kernel='rbf', gamma=0.05,C=0.1)\n",
|
||
"##svm rbf accuracy score: 0.284360625574977\n",
|
||
"clf_svc_rbf.fit(X_train, y_train)\n",
|
||
"pred_rbf = clf_svc_rbf.predict(X_test)\n",
|
||
"print (\"svm rbf accuracy score:\" , accuracy_score(y_test,pred_rbf))\n",
|
||
"print (\"f1 score :\" , f1_score(y_test,pred_rbf,average=None))\n",
|
||
"print (\"precision_score:\" , precision_score(y_test,pred_rbf,average=None))\n",
|
||
"print (\"recall_score :\" , recall_score(y_test,pred_rbf,average=None))\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn_porter import Porter\n",
|
||
"\n",
|
||
"porter_clf_svm_linear = Porter(clf_svm_linear, language='c').export()\n",
|
||
"#porter_clf_svm_poly = Porter(clf_svm_poly, language='c').export()\n",
|
||
"# porter_clf_forest = Porter(clf_randomForest, language='c').export()\n",
|
||
"#porter_clf_extra_forest = Porter(clf_extra_forest, language='c').export()\n",
|
||
"\n",
|
||
"#print(porter_clf_svm_linear)\n",
|
||
"f = open(\"clf/clf_svm_linear_50features_2020.cpp\",'wb')\n",
|
||
"#f = open(\"clf/clf_svm_linear_50features_20171207.txt\",'wb')\n",
|
||
"#f = open(\"clf_svm_linear_125100_low_feature_data.txt\",'wb')\n",
|
||
"f.write(porter_clf_svm_linear.encode())\n",
|
||
"f.close()\n",
|
||
"#f = open(\"clf_svm_poly_2457100_data.txt\",'wb')\n",
|
||
"#f.write(porter_clf_svm_poly)\n",
|
||
"#f.close()\n",
|
||
"# f = open(\"clf/clf_randomForest_27features_stddev_c_0_01.txt\",'wb')\n",
|
||
"# f.write(porter_clf_forest)\n",
|
||
"# f.close()\n",
|
||
"# f = open(\"oclf_extra_forest_2457100_data_0824.txt\",'wb')\n",
|
||
"# f.write(porter_clf_extra_forest)\n",
|
||
"# f.close()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 随机森林算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#from sklearn.model_selection import KFold\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"from sklearn.metrics import f1_score\n",
|
||
"from sklearn.metrics import precision_score\n",
|
||
"from sklearn.metrics import recall_score\n",
|
||
"\n",
|
||
"\n",
|
||
"from sklearn.ensemble import ExtraTreesClassifier\n",
|
||
"from sklearn.ensemble import AdaBoostClassifier\n",
|
||
"\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"X_train ,X_test,y_train,y_test = train_test_split(train_features_9,train_labels,test_size = 0.3, random_state = 20)\n",
|
||
"#X_train ,X_test,y_train,y_test = train_test_split(train_features_9,train_labels,test_size = 0.2, random_state = 20)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.metrics import classification_report\n",
|
||
"from sklearn.metrics import classification_report,confusion_matrix\n",
|
||
"from sklearn.metrics import f1_score\n",
|
||
"from sklearn.metrics import precision_score\n",
|
||
"from sklearn.metrics import recall_score\n",
|
||
"\n",
|
||
"#rfc = RandomForestClassifier(n_estimators=600)\n",
|
||
"\n",
|
||
"#rfc = RandomForestClassifier(n_estimators=50)\n",
|
||
"#RandomForest accuracy score: 0.9955591300090916\n",
|
||
"\n",
|
||
"#rfc = RandomForestClassifier(n_estimators=50,min_samples_leaf=20)\n",
|
||
"#RandomForest accuracy score: 0.9772012028813204/0.9803133086229806/0.9811874956290649/0.9852786908175397\n",
|
||
"#50- 0.9648738541413158\n",
|
||
"\n",
|
||
"#rfc = RandomForestClassifier(n_estimators=50)\n",
|
||
"#---- 0.9857629593575079\n",
|
||
"#RandomForest accuracy score: 0.9955940974893349\n",
|
||
"\n",
|
||
"rfc = RandomForestClassifier(n_estimators=50,min_samples_leaf=50)\n",
|
||
"#30-0.9595197533868743 40 - 0.9553013709742841 50 - 0.9528271274438225\n",
|
||
"\n",
|
||
"#rfc = RandomForestClassifier(n_estimators=100,min_samples_leaf=50)\n",
|
||
"#RandomForest accuracy score: 0.97688649555913\n",
|
||
"\n",
|
||
"#rfc = RandomForestClassifier(n_estimators=50,min_samples_leaf=100)\n",
|
||
"#RandomForest accuracy score: 0.9669906986502552\n",
|
||
"\n",
|
||
"\n",
|
||
"rfc.fit(X_train, y_train)\n",
|
||
"rfc_pred = rfc.predict(X_test)\n",
|
||
"cr = classification_report(y_test,rfc_pred)\n",
|
||
"print(cr)\n",
|
||
"cm = confusion_matrix(y_test,rfc_pred)\n",
|
||
"print(cm)\n",
|
||
"\n",
|
||
"print(\"---------------------------------\\n\")\n",
|
||
"print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))\n",
|
||
"print (\"RandomForest accuracy score:\" , accuracy_score(y_test,rfc_pred))\n",
|
||
"print(\"---------------------------------\\n\")\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'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn_porter import Porter\n",
|
||
"\n",
|
||
"porter_clf_rfc = Porter(rfc, language='c').export()\n",
|
||
"#porter_clf_svm_poly = Porter(clf_svm_poly, language='c').export()\n",
|
||
"# porter_clf_forest = Porter(clf_randomForest, language='c').export()\n",
|
||
"#porter_clf_extra_forest = Porter(clf_extra_forest, language='c').export()\n",
|
||
"\n",
|
||
"#print(porter_clf_svm_linear)\n",
|
||
"f = open(\"clf/ov_rtree50_f50_2020515.cpp\",'wb')\n",
|
||
"#f = open(\"clf/clf_svm_linear_50features_20171207.txt\",'wb')\n",
|
||
"#f = open(\"clf_svm_linear_125100_low_feature_data.txt\",'wb')\n",
|
||
"f.write(porter_clf_rfc.encode())\n",
|
||
"f.close()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import KFold\n",
|
||
"\n",
|
||
"X = train_features_9.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",
|
||
" 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": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nsimu = 21\n",
|
||
"accuracy=[0]*nsimu\n",
|
||
"ntree = [0]*nsimu\n",
|
||
"for i in range(1,nsimu):\n",
|
||
" rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=None,criterion='gini')\n",
|
||
" rfc.fit(X_train, y_train)\n",
|
||
" rfc_pred = rfc.predict(X_test)\n",
|
||
" cm = confusion_matrix(y_test,rfc_pred)\n",
|
||
" accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
|
||
" ntree[i]=i*5\n",
|
||
"\n",
|
||
" print (\"RandomForest accuracy score:\" , accuracy_score(y_test,rfc_pred))\n",
|
||
" print (\"f1 score:\" , f1_score(y_test,rfc_pred,average='micro')) \n",
|
||
" print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))\n",
|
||
"\n",
|
||
" \n",
|
||
"plt.figure(figsize=(10,6))\n",
|
||
"plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
|
||
"plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (criterion: 'gini')\", fontsize=18)\n",
|
||
"plt.xlabel(\"Number of trees\", fontsize=15)\n",
|
||
"plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.utils import shuffle\n",
|
||
"\n",
|
||
"\n",
|
||
"# data_shuffle1 = shuffle(data1)\n",
|
||
"# #data_shuffle = data_all;\n",
|
||
"# test_labels = data_shuffle1[\"index\"]\n",
|
||
"# test_features = data_shuffle1.drop(\"dateTime\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"index\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"whiteBalance\",axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"left_block_R_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"left_block_G_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"left_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"left_block_H_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"left_block_S_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"left_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"left_block_l_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"left_block_a_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"left_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"right_block_R_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"right_block_G_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"right_block_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"right_block_H_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"right_block_S_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"right_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"right_block_l_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"right_block_a_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"right_block_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_R_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_G_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_B_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_H_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_S_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_l_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_a_stddev\",axis=1)\n",
|
||
"# test_features = test_features.drop(\"whiteBlock_b_stddev\",axis=1)\n",
|
||
"\n",
|
||
"train_features_10 = pd.DataFrame()\n",
|
||
"train_features_10['lelf_right_R'] = test_features['left_block_R'] - test_features['right_block_R']\n",
|
||
"train_features_10['lelf_right_G'] = test_features['left_block_G'] - test_features['right_block_G']\n",
|
||
"train_features_10['lelf_right_B'] = test_features['left_block_B'] - test_features['right_block_B']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_H'] = test_features['left_block_H'] - test_features['right_block_H']\n",
|
||
"# train_features_10['lelf_right_S'] = test_features['left_block_S'] - test_features['right_block_S']\n",
|
||
"train_features_10['lelf_right_V'] = test_features['left_block_V'] - test_features['right_block_V']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_l'] = test_features['left_block_l'] - test_features['right_block_l']\n",
|
||
"train_features_10['lelf_right_a'] = test_features['left_block_a'] - test_features['right_block_a']\n",
|
||
"train_features_10['lelf_right_b'] = test_features['left_block_b'] - test_features['right_block_b']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_R_stddev'] = test_features['left_block_R_stddev'] - test_features['right_block_R_stddev']\n",
|
||
"train_features_10['lelf_right_G_stddev'] = test_features['left_block_G_stddev'] - test_features['right_block_G_stddev']\n",
|
||
"train_features_10['lelf_right_B_stddev'] = test_features['left_block_B_stddev'] - test_features['right_block_B_stddev']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_H_stddev'] = test_features['left_block_H_stddev'] - test_features['right_block_H_stddev']\n",
|
||
"# train_features_10['lelf_right_S_stddev'] = test_features['left_block_S_stddev'] - test_features['right_block_S_stddev']\n",
|
||
"train_features_10['lelf_right_V_stddev'] = test_features['left_block_V_stddev'] - test_features['right_block_V_stddev']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_l_stddev'] = test_features['left_block_l_stddev'] - test_features['right_block_l_stddev']\n",
|
||
"train_features_10['lelf_right_a_stddev'] = test_features['left_block_a_stddev'] - test_features['right_block_a_stddev']\n",
|
||
"train_features_10['lelf_right_b_stddev'] = test_features['left_block_b_stddev'] - test_features['right_block_b_stddev']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_R_hist'] = test_features['left_block_R_hist'] - test_features['right_block_R_hist']\n",
|
||
"train_features_10['lelf_right_G_hist'] = test_features['left_block_G_hist'] - test_features['right_block_G_hist']\n",
|
||
"train_features_10['lelf_right_B_hist'] = test_features['left_block_B_hist'] - test_features['right_block_B_hist']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_H_hist'] = test_features['left_block_H_hist'] - test_features['right_block_H_hist']\n",
|
||
"# train_features_10['lelf_right_S_hist'] = test_features['left_block_S_hist'] - test_features['right_block_S_hist']\n",
|
||
"train_features_10['lelf_right_V_hist'] = test_features['left_block_V_hist'] - test_features['right_block_V_hist']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_l_hist'] = test_features['left_block_l_hist'] - test_features['right_block_l_hist']\n",
|
||
"train_features_10['lelf_right_a_hist'] = test_features['left_block_a_hist'] - test_features['right_block_a_hist']\n",
|
||
"train_features_10['lelf_right_b_hist'] = test_features['left_block_b_hist'] - test_features['right_block_b_hist']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_R_max'] = test_features['left_block_R_max'] - test_features['right_block_R_max']\n",
|
||
"train_features_10['lelf_right_G_max'] = test_features['left_block_G_max'] - test_features['right_block_G_max']\n",
|
||
"train_features_10['lelf_right_B_max'] = test_features['left_block_B_max'] - test_features['right_block_B_max']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_H_max'] = test_features['left_block_H_max'] - test_features['right_block_H_max']\n",
|
||
"# train_features_10['lelf_right_S_max'] = test_features['left_block_S_max'] - test_features['right_block_S_max']\n",
|
||
"train_features_10['lelf_right_V_max'] = test_features['left_block_V_max'] - test_features['right_block_V_max']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_l_max'] = test_features['left_block_l_max'] - test_features['right_block_l_max']\n",
|
||
"train_features_10['lelf_right_a_max'] = test_features['left_block_a_max'] - test_features['right_block_a_max']\n",
|
||
"train_features_10['lelf_right_b_max'] = test_features['left_block_b_max'] - test_features['right_block_b_max']\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_R_min'] = test_features['left_block_R_min'] - test_features['right_block_R_min']\n",
|
||
"train_features_10['lelf_right_G_min'] = test_features['left_block_G_min'] - test_features['right_block_G_min']\n",
|
||
"train_features_10['lelf_right_B_min'] = test_features['left_block_B_min'] - test_features['right_block_B_min']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_H_min'] = test_features['left_block_H_min'] - test_features['right_block_H_min']\n",
|
||
"# train_features_10['lelf_right_S_min'] = test_features['left_block_S_min'] - test_features['right_block_S_min']\n",
|
||
"train_features_10['lelf_right_V_min'] = test_features['left_block_V_min'] - test_features['right_block_V_min']\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_l_min'] = test_features['left_block_l_min'] - test_features['right_block_l_min']\n",
|
||
"train_features_10['lelf_right_a_min'] = test_features['left_block_a_min'] - test_features['right_block_a_min']\n",
|
||
"train_features_10['lelf_right_b_min'] = test_features['left_block_b_min'] - test_features['right_block_b_min']\n",
|
||
"\n",
|
||
"# train_features_10['left_grayValue']= test_features['left_grayValue'];\n",
|
||
"# train_features_10['left_grayStddevValue']= test_features['left_grayStddevValue'];\n",
|
||
"# train_features_10['left_grayHist']= test_features['left_grayHist'];\n",
|
||
"# train_features_10['left_grayMax']= test_features['left_grayMax'];\n",
|
||
"# train_features_10['left_grayMin']= test_features['left_grayMin'];\n",
|
||
"\n",
|
||
"# train_features_10['right_grayValue']= test_features['right_grayValue'];\n",
|
||
"# train_features_10['right_grayStddevValue']= test_features['right_grayStddevValue'];\n",
|
||
"# train_features_10['right_grayHist']= test_features['right_grayHist'];\n",
|
||
"# train_features_10['right_grayMax']= test_features['right_grayMax'];\n",
|
||
"# train_features_10['right_grayMin']= test_features['right_grayMin'];\n",
|
||
"\n",
|
||
"# train_features_10['lelf_R_stddev'] = test_features['left_block_R_stddev'] \n",
|
||
"# train_features_10['lelf_G_stddev'] = test_features['left_block_G_stddev'] \n",
|
||
"# train_features_10['lelf_B_stddev'] = test_features['left_block_B_stddev'] \n",
|
||
"\n",
|
||
"# train_features_10['left_block_R_min'] = test_features['left_block_R_min'] \n",
|
||
"# train_features_10['left_block_G_min'] = test_features['left_block_G_min'] \n",
|
||
"# train_features_10['left_block_B_min'] = test_features['left_block_B_min'] \n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"train_features_10['lelf_right_gray_value'] = test_features['left_grayValue'] - test_features['right_grayValue']\n",
|
||
"train_features_10['lelf_right_gray_stddev'] = test_features['left_grayStddevValue'] - test_features['right_grayStddevValue']\n",
|
||
"train_features_10['lelf_right_gray_hist'] = test_features['left_grayHist'] - test_features['right_grayHist']\n",
|
||
"train_features_10['lelf_right_gray_max'] = test_features['left_grayMax'] - test_features['right_grayMax']\n",
|
||
"train_features_10['lelf_right_gray_min'] = test_features['left_grayMin'] - test_features['right_grayMin']\n",
|
||
"\n",
|
||
"train_features_10.describe()\n",
|
||
"\n",
|
||
"\n",
|
||
"# feature = feature.drop(\"left_block_H_hist\",axis=1)\n",
|
||
"# feature = feature.drop(\"right_block_H_hist\",axis=1)\n",
|
||
"# feature = feature.drop(\"whiteBlock_H_hist\",axis=1)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
" \n",
|
||
"test_features = test_features.drop(\"left_block_H\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_S\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_V\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"right_block_H\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_S\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_V\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"whiteBlock_H\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_S\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_V\",axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"left_block_H_stddev\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_S_stddev\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"right_block_H_stddev\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_S_stddev\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"whiteBlock_H_stddev\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_S_stddev\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_V_stddev\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"left_block_H_hist\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_S_hist\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"right_block_H_hist\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_S_hist\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"whiteBlock_H_hist\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_S_hist\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_V_hist\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"left_block_H_max\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_S_max\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"right_block_H_max\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_S_max\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_V_max\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"whiteBlock_H_max\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_S_max\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_V_max\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"left_block_H_min\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_S_min\",axis=1)\n",
|
||
"test_features = test_features.drop(\"left_block_V_min\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"right_block_H_min\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_S_min\",axis=1)\n",
|
||
"test_features = test_features.drop(\"right_block_V_min\",axis=1)\n",
|
||
"\n",
|
||
"test_features = test_features.drop(\"whiteBlock_H_min\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_S_min\",axis=1)\n",
|
||
"test_features = test_features.drop(\"whiteBlock_V_min\",axis=1)\n",
|
||
" \n",
|
||
" \n",
|
||
"test_features['lelf_right_R'] = test_features['left_block_R'] - test_features['right_block_R']\n",
|
||
"test_features['lelf_right_G'] = test_features['left_block_G'] - test_features['right_block_G']\n",
|
||
"test_features['lelf_right_B'] = test_features['left_block_B'] - test_features['right_block_B']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_H'] = test_features['left_block_H'] - test_features['right_block_H']\n",
|
||
"# test_features['lelf_right_S'] = test_features['left_block_S'] - test_features['right_block_S']\n",
|
||
"# test_features['lelf_right_V'] = test_features['left_block_V'] - test_features['right_block_V']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_l'] = test_features['left_block_l'] - test_features['right_block_l']\n",
|
||
"# test_features['lelf_right_a'] = test_features['left_block_a'] - test_features['right_block_a']\n",
|
||
"# test_features['lelf_right_b'] = test_features['left_block_b'] - test_features['right_block_b']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_R_stddev'] = test_features['left_block_R_stddev'] - test_features['right_block_R_stddev']\n",
|
||
"# test_features['lelf_right_G_stddev'] = test_features['left_block_G_stddev'] - test_features['right_block_G_stddev']\n",
|
||
"# test_features['lelf_right_B_stddev'] = test_features['left_block_B_stddev'] - test_features['right_block_B_stddev']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_H_stddev'] = test_features['left_block_H_stddev'] - test_features['right_block_H_stddev']\n",
|
||
"# test_features['lelf_right_S_stddev'] = test_features['left_block_S_stddev'] - test_features['right_block_S_stddev']\n",
|
||
"# test_features['lelf_right_V_stddev'] = test_features['left_block_V_stddev'] - test_features['right_block_V_stddev']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_l_stddev'] = test_features['left_block_l_stddev'] - test_features['right_block_l_stddev']\n",
|
||
"# test_features['lelf_right_a_stddev'] = test_features['left_block_a_stddev'] - test_features['right_block_a_stddev']\n",
|
||
"# test_features['lelf_right_b_stddev'] = test_features['left_block_b_stddev'] - test_features['right_block_b_stddev']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_R_hist'] = test_features['left_block_R_hist'] - test_features['right_block_R_hist']\n",
|
||
"# test_features['lelf_right_G_hist'] = test_features['left_block_G_hist'] - test_features['right_block_G_hist']\n",
|
||
"# test_features['lelf_right_B_hist'] = test_features['left_block_B_hist'] - test_features['right_block_B_hist']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_H_hist'] = test_features['left_block_H_hist'] - test_features['right_block_H_hist']\n",
|
||
"# test_features['lelf_right_S_hist'] = test_features['left_block_S_hist'] - test_features['right_block_S_hist']\n",
|
||
"# test_features['lelf_right_V_hist'] = test_features['left_block_V_hist'] - test_features['right_block_V_hist']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_l_hist'] = test_features['left_block_l_hist'] - test_features['right_block_l_hist']\n",
|
||
"# test_features['lelf_right_a_hist'] = test_features['left_block_a_hist'] - test_features['right_block_a_hist']\n",
|
||
"# test_features['lelf_right_b_hist'] = test_features['left_block_b_hist'] - test_features['right_block_b_hist']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_R_max'] = test_features['left_block_R_max'] - test_features['right_block_R_max']\n",
|
||
"# test_features['lelf_right_G_max'] = test_features['left_block_G_max'] - test_features['right_block_G_max']\n",
|
||
"# test_features['lelf_right_B_max'] = test_features['left_block_B_max'] - test_features['right_block_B_max']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_H_max'] = test_features['left_block_H_max'] - test_features['right_block_H_max']\n",
|
||
"# test_features['lelf_right_S_max'] = test_features['left_block_S_max'] - test_features['right_block_S_max']\n",
|
||
"# test_features['lelf_right_V_max'] = test_features['left_block_V_max'] - test_features['right_block_V_max']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_l_max'] = test_features['left_block_l_max'] - test_features['right_block_l_max']\n",
|
||
"# test_features['lelf_right_a_max'] = test_features['left_block_a_max'] - test_features['right_block_a_max']\n",
|
||
"# test_features['lelf_right_b_max'] = test_features['left_block_b_max'] - test_features['right_block_b_max']\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"# test_features['lelf_right_R_min'] = test_features['left_block_R_min'] - test_features['right_block_R_min']\n",
|
||
"# test_features['lelf_right_G_min'] = test_features['left_block_G_min'] - test_features['right_block_G_min']\n",
|
||
"# test_features['lelf_right_B_min'] = test_features['left_block_B_min'] - test_features['right_block_B_min']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_H_min'] = test_features['left_block_H_min'] - test_features['right_block_H_min']\n",
|
||
"# test_features['lelf_right_S_min'] = test_features['left_block_S_min'] - test_features['right_block_S_min']\n",
|
||
"# test_features['lelf_right_V_min'] = test_features['left_block_V_min'] - test_features['right_block_V_min']\n",
|
||
"\n",
|
||
"# test_features['lelf_right_l_min'] = test_features['left_block_l_min'] - test_features['right_block_l_min']\n",
|
||
"# test_features['lelf_right_a_min'] = test_features['left_block_a_min'] - test_features['right_block_a_min']\n",
|
||
"# test_features['lelf_right_b_min'] = test_features['left_block_b_min'] - test_features['right_block_b_min']\n",
|
||
"\n",
|
||
"test_features['lelf_right_gray_value'] = test_features['left_grayValue'] - test_features['right_grayValue']\n",
|
||
"test_features['lelf_right_gray_stddev'] = test_features['left_grayStddevValue'] - test_features['right_grayStddevValue']\n",
|
||
"test_features['lelf_right_gray_hist'] = test_features['left_grayHist'] - test_features['right_grayHist']\n",
|
||
"test_features['lelf_right_gray_max'] = test_features['left_grayMax'] - test_features['right_grayMax']\n",
|
||
"test_features['lelf_right_gray_min'] = test_features['left_grayMin'] - test_features['right_grayMin']\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"pred = clf_svm_linear.predict(train_features_10)\n",
|
||
"test_features_gray_stddev = test_features['left_grayStddevValue']\n",
|
||
"test_features_np = np.ndarray(test_features_gray_stddev.shape,dtype = np.float32)\n",
|
||
"\n",
|
||
"test_features_np = test_features_gray_stddev.values\n",
|
||
"print \"svm linear accuracy score:\" , accuracy_score(test_labels,pred)\n",
|
||
"print \"f1 score:\" , f1_score(test_labels,pred,average='micro')\n",
|
||
"print \"recall_score :\" , recall_score(test_labels,pred,average='micro')\n",
|
||
"print \"precision_score :\" , precision_score(test_labels,pred,average='micro')\n",
|
||
"\n",
|
||
"for i in range(0, len(test_features_np)):\n",
|
||
" if test_features_np[i] < 3:\n",
|
||
" pred[i] =0\n",
|
||
"print \"svm linear accuracy score:\" , accuracy_score(test_labels,pred)\n",
|
||
"print \"f1 score:\" , f1_score(test_labels,pred,average='micro')\n",
|
||
"print \"recall_score :\" , recall_score(test_labels,pred,average='micro')\n",
|
||
"print \"precision_score :\" , precision_score(test_labels,pred,average='micro')\n",
|
||
"\n",
|
||
"\n",
|
||
"print(\"preds:\",pred[120:130])\n",
|
||
"print('trues:\\n',test_labels[120:130])\n",
|
||
"test_labels_np = np.ndarray(test_labels.shape,dtype= np.int32)\n",
|
||
"test_labels_np = test_labels.values\n",
|
||
"print(test_labels_np[0])\n",
|
||
"all_counter = 0\n",
|
||
"counter = 0\n",
|
||
"for i in range(0 ,len(pred) ):\n",
|
||
" if (pred[i] == 4 or (pred[i] == 4 and test_labels_np[i] ==4 )or test_labels_np[i] ==4 ) :\n",
|
||
" all_counter = all_counter + 1\n",
|
||
" if pred[i] != test_labels_np[i] :\n",
|
||
" counter = counter+1\n",
|
||
" print(pred[i] , test_labels_np[i])\n",
|
||
"print(len(pred),all_counter, counter) \n",
|
||
"all_counter = 0\n",
|
||
"counter = 0\n",
|
||
"for i in range(0 ,len(pred) ):\n",
|
||
" if pred[i] != test_labels_np[i] :\n",
|
||
" counter = counter+1\n",
|
||
" print(pred[i] , test_labels_np[i])\n",
|
||
"print(len(pred),all_counter, counter) \n",
|
||
"\n",
|
||
"# print \"svm linear accuracy score:\" , accuracy_score(test_labels,pred)\n",
|
||
"# print \"f1 score:\" , f1_score(test_labels,pred,average='micro')\n",
|
||
"# print \"recall_score :\" , recall_score(test_labels,pred,average='micro')\n",
|
||
"# print \"precision_score :\" , precision_score(test_labels,pred,average='micro')\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## from sklearn.metrics import recall_score\n",
|
||
"from sklearn.metrics import precision_score\n",
|
||
"print \"accuracy score:\" , accuracy_score(y_test,pred)\n",
|
||
"print \"recall_score :\" , recall_score(y_test,pred,average='macro')\n",
|
||
"print \"precision_score :\" , precision_score(y_test,pred,average='macro')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn_porter import Porter\n",
|
||
"\n",
|
||
"porter_java = Porter(clf_svm, language='java').export()\n",
|
||
"porter_c = Porter(clf_svm, language='c').export()\n",
|
||
"\n",
|
||
"f = open(\"Protein_c.txt\",'wb')\n",
|
||
"f.write(porter_c)\n",
|
||
"f.close()\n",
|
||
"\n",
|
||
"f = open(\"Protein_svm_java.txt\",'wb')\n",
|
||
"f.write(porter_java)\n",
|
||
"f.close()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"try :\n",
|
||
" data = pd.read_csv(\"data.csv\")\n",
|
||
" print (\"load data successful !!!!!\")\n",
|
||
" data0 = data[data[\"index\"] == 0]\n",
|
||
" data1 = data[data[\"index\"] == 1]\n",
|
||
" data2 = data[data[\"index\"] == 2]\n",
|
||
" data3 = data[data[\"index\"] == 3]\n",
|
||
" data4 = data[data[\"index\"] == 4]\n",
|
||
" data0.to_csv('data0.csv')\n",
|
||
" data1.to_csv('data1.csv')\n",
|
||
" data2.to_csv('data2.csv')\n",
|
||
" data3.to_csv('data3.csv')\n",
|
||
" data4.to_csv('data4.csv') \n",
|
||
"except :\n",
|
||
" print (\"load data error !!!!!!!!!!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.11.4"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|