{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 排卵试纸机器学习算法验证" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. **import moudle**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd \n", "import seaborn as sns\n", "from IPython.display import display\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import sklearn\n", "%matplotlib inline\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. **load data**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "load data successful !!!!!\n" ] } ], "source": [ "try :\n", " data1 = pd.read_csv(\"ov2020-5-14/ovulation-4e-5-1.csv\")\n", " data2 = pd.read_csv(\"ov2020-5-14/ovulation-4e-10-1.csv\")\n", " data3 = pd.read_csv(\"ov2020-5-14/ovulation-4e-10-2.csv\")\n", " data4 = pd.read_csv(\"ov2020-5-14/ovulation-4e-15-1.csv\")\n", " data5 = pd.read_csv(\"ov2020-5-14/ovulation-4e-15-2.csv\")\n", " data6 = pd.read_csv(\"ov2020-5-14/ovulation-4e-25-1.csv\")\n", " data7 = pd.read_csv(\"ov2020-5-14/ovulation-4e-25-2.csv\")\n", " data8 = pd.read_csv(\"ov2020-5-14/ovulation-4e-40-1.csv\")\n", " data9 = pd.read_csv(\"ov2020-5-14/ovulation-4e-50-1.csv\")\n", " data10 = pd.read_csv(\"ov2020-5-14/ovulation-4e-50-2.csv\")\n", " data11 = pd.read_csv(\"ov2020-5-14/ovulation-4e-50-3.csv\")\n", " data12 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-1.csv\")\n", " data13 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-2.csv\")\n", " data14 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-3.csv\")\n", " data15 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-4.csv\")\n", " data16 = pd.read_csv(\"ov2020-5-14/ovulation-4e-75-5.csv\")\n", " data17 = pd.read_csv(\"ov2020-5-14/ovulation-5i-5-1.csv\")\n", " data18 = pd.read_csv(\"ov2020-5-14/ovulation-5i-10-1.csv\")\n", " data19 = pd.read_csv(\"ov2020-5-14/ovulation-5i-10-2.csv\")\n", " data20 = pd.read_csv(\"ov2020-5-14/ovulation-5i-15-1.csv\")\n", " data21 = pd.read_csv(\"ov2020-5-14/ovulation-5i-15-2.csv\")\n", " data22 = pd.read_csv(\"ov2020-5-14/ovulation-5i-25-1.csv\")\n", " data23 = pd.read_csv(\"ov2020-5-14/ovulation-5i-25-2.csv\")\n", " data24 = pd.read_csv(\"ov2020-5-14/ovulation-5i-40-1.csv\")\n", " data25 = pd.read_csv(\"ov2020-5-14/ovulation-5i-50-1.csv\")\n", " data26 = pd.read_csv(\"ov2020-5-14/ovulation-5i-50-2.csv\")\n", " data27 = pd.read_csv(\"ov2020-5-14/ovulation-5i-50-3.csv\")\n", " data28 = pd.read_csv(\"ov2020-5-14/ovulation-5i-75-1.csv\")\n", " data29 = pd.read_csv(\"ov2020-5-14/ovulation-5i-75-2.csv\")\n", " data30 = pd.read_csv(\"ov2020-5-14/ovulation-5i-75-3.csv\")\n", " \n", " print (\"load data successful !!!!!\")\n", "except :\n", " print (\"load data error !!!!!!!!!!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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": 2, "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", " 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": [ 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Unnamed: 0left_block_Rleft_block_Gleft_block_Bleft_block_Hleft_block_Sleft_block_Vleft_block_lleft_block_aleft_block_b...right_grayValueright_grayStddevValueright_grayHistright_grayMaxright_grayMinwhite_grayValuewhite_grayStddevValuewhite_grayHistwhite_grayMaxwhite_grayMin
count82180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.000000...82180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.000000
mean8668.489827185.033086156.928292166.615296198.27820643.749002185.842529171.646495139.822962126.542602...164.57288917.293563162.055135197.940302133.605890205.9532120.185763206.318885207.747846205.103663
std9514.70185024.75595133.98158731.20899466.44750923.98009824.83650929.8238306.4003933.844306...17.7538854.44194125.11332615.38450823.20143316.0903210.46783116.57865215.99500116.190066
min0.00000043.00000030.00000033.0000000.0000000.00000043.00000034.000000121.000000111.000000...49.0000001.00000028.00000091.00000024.000000102.0000000.0000000.000000103.000000101.000000
25%1294.000000165.000000130.000000142.000000193.00000024.000000167.000000150.000000135.000000125.000000...152.00000014.000000144.000000190.000000115.000000197.0000000.000000197.000000199.000000195.000000
50%3917.500000187.000000158.000000169.000000227.00000041.000000187.000000173.000000140.000000127.000000...162.00000017.000000162.000000199.000000135.000000205.0000000.000000205.000000206.000000204.000000
75%14267.250000204.000000188.000000193.000000239.00000063.000000205.000000198.000000145.000000129.000000...179.00000021.000000180.000000208.000000152.000000218.0000000.000000218.000000219.000000217.000000
max36231.000000255.000000255.000000255.000000250.000000148.000000255.000000255.000000154.000000144.000000...248.00000041.000000251.000000255.000000242.000000255.00000017.000000254.000000255.000000255.000000
\n", "

8 rows × 151 columns

\n", "
" ], "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": [ "
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lelf_right_Rlelf_right_Glelf_right_Blelf_right_Hlelf_right_Slelf_right_Vlelf_right_llelf_right_alelf_right_blelf_right_R_stddev...lelf_right_S_minlelf_right_V_minlelf_right_l_minlelf_right_a_minlelf_right_b_minlelf_right_gray_valuelelf_right_gray_stddevlelf_right_gray_histlelf_right_gray_maxlelf_right_gray_min
count82180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.000000...82180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.00000082180.000000
mean-1.0481023.3309931.959504-18.906121-2.293879-0.7087611.831492-1.7066680.187296-0.147518...-5.165551-0.8480776.700134-0.8057560.2640911.860404-0.936396-6.5183860.1001586.950012
std18.39145730.14512824.27639956.81014721.81935019.02291825.6707796.0575601.4658088.414406...39.7427706.11053341.0047322.1758172.16213225.90742410.73210743.3506328.79753740.825874
min-59.000000-73.000000-66.000000-242.000000-82.000000-59.000000-68.000000-23.000000-4.000000-29.000000...-149.000000-24.000000-89.000000-10.000000-6.000000-67.000000-38.000000-251.000000-28.000000-85.000000
25%-16.000000-24.000000-20.000000-17.000000-20.000000-17.000000-21.000000-7.000000-1.000000-6.000000...-38.000000-5.000000-28.000000-2.000000-1.000000-21.000000-9.000000-39.000000-6.000000-28.000000
50%0.0000003.0000003.000000-2.000000-3.0000000.0000002.000000-1.0000000.000000-1.000000...-7.000000-1.0000008.000000-1.0000000.0000002.000000-2.000000-2.0000000.0000008.000000
75%12.00000027.00000020.0000003.00000017.00000012.00000021.0000004.0000001.0000007.000000...27.0000003.00000040.0000001.0000002.00000022.0000008.00000027.0000006.00000041.000000
max65.000000108.00000083.000000140.00000069.00000065.00000091.00000011.0000008.00000027.000000...144.00000029.000000151.0000007.00000011.00000093.00000032.000000141.00000045.000000150.000000
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8 rows × 50 columns

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" ], "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 }