{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " *早早孕试纸机器学习算法验证*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**import moudle**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "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": "markdown", "metadata": {}, "source": [ "**load data**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "ename": "SyntaxError", "evalue": "Missing parentheses in call to 'print' (, line 14)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m14\u001b[0m\n\u001b[0;31m print \"load data successful !!!!!\"\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\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(\"light.csv\")\n", " data2 = pd.read_csv(\"nature_light.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": [ "**分析数据**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'data1' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m#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)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mdata1_0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"whiteBalance\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mdata2_0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"whiteBalance\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m#data_test_0 = data_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'data1' is not defined" ] } ], "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", "\n", "data1_0 = data1[data1[\"whiteBalance\"] == 0]\n", "data2_0 = data2[data2[\"whiteBalance\"] == 0]\n", "#data_test_0 = data_test\n", "\n", "data_all =data1_0.append(data2_0);\n", "#data_all =data1.append(data2);\n", "#data_all = data2\n", "whiteBlock_R_one = data_all[data_all[\"index\"] == 0 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_one = data_all[data_all[\"index\"] == 0 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_one = data_all[data_all[\"index\"] == 0 ][\"left_block_B_stddev\"]\n", "\n", "whiteBlock_R_two = data_all[data_all[\"index\"] == 1 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_two = data_all[data_all[\"index\"] == 1 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_two = data_all[data_all[\"index\"] == 1 ][\"left_block_B_stddev\"]\n", "\n", "whiteBlock_R_three = data_all[data_all[\"index\"] == 2 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_three = data_all[data_all[\"index\"] == 2 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_three = data_all[data_all[\"index\"] == 2 ][\"left_block_B_stddev\"]\n", "\n", "whiteBlock_R_four = data_all[data_all[\"index\"] == 3 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_four = data_all[data_all[\"index\"] == 3 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_four = data_all[data_all[\"index\"] == 3 ][\"left_block_B_stddev\"]\n", "\n", "\n", "whiteBlock_R_five = data_all[data_all[\"index\"] == 4 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_five = data_all[data_all[\"index\"] == 4 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_five = data_all[data_all[\"index\"] == 4 ][\"left_block_B_stddev\"]\n", "\n", "whiteBlock_R_six = data_all[data_all[\"index\"] == 5 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_six = data_all[data_all[\"index\"] == 5 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_six = data_all[data_all[\"index\"] == 5 ][\"left_block_B_stddev\"]\n", "\n", "whiteBlock_R_seven = data_all[data_all[\"index\"] == 6 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_seven = data_all[data_all[\"index\"] == 6 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_seven = data_all[data_all[\"index\"] == 6 ][\"left_block_B_stddev\"]\n", "\n", "whiteBlock_R_eghit = data_all[data_all[\"index\"] == 7 ][\"left_block_R_stddev\"]\n", "whiteBlock_G_eghit = data_all[data_all[\"index\"] == 7 ][\"left_block_G_stddev\"]\n", "whiteBlock_B_eghit = data_all[data_all[\"index\"] == 7 ][\"left_block_B_stddev\"]\n", "\n", "print(data_all.describe())\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index([u'dateTime', u'left_block_R', u'left_block_G', u'left_block_B', u'left_block_H', u'left_block_S', u'left_block_V', u'left_block_l', u'left_block_a', u'left_block_b', u'left_block_R_stddev', u'left_block_G_stddev', u'left_block_B_stddev', u'left_block_H_stddev', u'left_block_S_stddev', u'left_block_V_stddev', u'left_block_l_stddev', u'left_block_a_stddev', u'left_block_b_stddev', u'left_block_R_hist', u'left_block_G_hist', u'left_block_B_hist', u'left_block_H_hist', u'left_block_S_hist', u'left_block_V_hist', u'left_block_l_hist', u'left_block_a_hist', u'left_block_b_hist', u'left_block_R_max', u'left_block_G_max', u'left_block_B_max', u'left_block_H_max', u'left_block_S_max', u'left_block_V_max', u'left_block_l_max', u'left_block_a_max', u'left_block_b_max', u'left_block_R_min', u'left_block_G_min', u'left_block_B_min', u'left_block_H_min', u'left_block_S_min', u'left_block_V_min', u'left_block_l_min', u'left_block_a_min', u'left_block_b_min', u'right_block_R', u'right_block_G', u'right_block_B', u'right_block_H', u'right_block_S', u'right_block_V', u'right_block_l', u'right_block_a', u'right_block_b', u'right_block_R_stddev', u'right_block_G_stddev', u'right_block_B_stddev', u'right_block_H_stddev', u'right_block_S_stddev', u'right_block_V_stddev', u'right_block_l_stddev', u'right_block_a_stddev', u'right_block_b_stddev', u'right_block_R_hist', u'right_block_G_hist', u'right_block_B_hist', u'right_block_H_hist', u'right_block_S_hist', u'right_block_V_hist', u'right_block_l_hist', u'right_block_a_hist', u'right_block_b_hist', u'right_block_R_max', u'right_block_G_max', u'right_block_B_max', u'right_block_H_max', u'right_block_S_max', u'right_block_V_max', u'right_block_l_max', u'right_block_a_max', u'right_block_b_max', u'right_block_R_min', u'right_block_G_min', u'right_block_B_min', u'right_block_H_min', u'right_block_S_min', u'right_block_V_min', u'right_block_l_min', u'right_block_a_min', u'right_block_b_min', u'whiteBlock_R', u'whiteBlock_G', u'whiteBlock_B', u'whiteBlock_H', u'whiteBlock_S', u'whiteBlock_V', u'whiteBlock_l', u'whiteBlock_a', u'whiteBlock_b', ...], dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_all.columns" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "hsv max min hist value h值要去掉" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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left_block_Rleft_block_Gleft_block_Bleft_block_Hleft_block_Sleft_block_Vleft_block_lleft_block_aleft_block_bleft_block_R_stddevleft_block_G_stddevleft_block_B_stddevleft_block_H_stddevleft_block_S_stddevleft_block_V_stddevleft_block_l_stddevleft_block_a_stddevleft_block_b_stddevleft_block_R_histleft_block_G_hist
count 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000...
mean 165.729696 134.358506 140.457706 178.764099 56.932838 166.771580 150.273458 140.877241 129.089342 11.584725 20.070492 15.906036 68.572622 18.027338 11.597200 17.160557 4.237259 0.840747 160.992618 123.221520...
std 30.728156 39.645554 35.580123 62.957946 28.703214 31.214224 35.806975 7.556304 2.173696 8.180077 11.086751 9.979590 37.974290 11.795136 8.177179 10.284627 1.908351 0.495328 35.854722 50.745105...
min 90.000000 51.000000 60.000000 7.000000 7.000000 90.000000 67.000000 124.000000 122.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 61.000000 23.000000...
25% 145.000000 103.000000 114.000000 143.000000 31.000000 146.000000 124.000000 135.000000 128.000000 4.000000 9.000000 6.000000 43.000000 7.000000 4.000000 7.000000 3.000000 1.000000 138.000000 80.000000...
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75% 186.000000 165.000000 167.000000 228.000000 83.000000 188.000000 177.000000 148.000000 131.000000 18.000000 28.000000 23.000000 100.000000 27.000000 18.000000 24.000000 6.000000 1.000000 186.000000 168.000000...
max 247.000000 230.000000 233.000000 248.000000 119.000000 247.000000 231.000000 154.000000 135.000000 36.000000 50.000000 43.000000 124.000000 51.000000 36.000000 44.000000 10.000000 3.000000 253.000000 236.000000...
\n", "

8 rows × 150 columns

\n", "
" ], "text/plain": [ " left_block_R left_block_G left_block_B left_block_H left_block_S \\\n", "count 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 \n", "mean 165.729696 134.358506 140.457706 178.764099 56.932838 \n", "std 30.728156 39.645554 35.580123 62.957946 28.703214 \n", "min 90.000000 51.000000 60.000000 7.000000 7.000000 \n", "25% 145.000000 103.000000 114.000000 143.000000 31.000000 \n", "50% 165.000000 135.000000 140.000000 202.000000 51.000000 \n", "75% 186.000000 165.000000 167.000000 228.000000 83.000000 \n", "max 247.000000 230.000000 233.000000 248.000000 119.000000 \n", "\n", " left_block_V left_block_l left_block_a left_block_b \\\n", "count 91301.000000 91301.000000 91301.000000 91301.000000 \n", "mean 166.771580 150.273458 140.877241 129.089342 \n", "std 31.214224 35.806975 7.556304 2.173696 \n", "min 90.000000 67.000000 124.000000 122.000000 \n", "25% 146.000000 124.000000 135.000000 128.000000 \n", "50% 166.000000 152.000000 141.000000 129.000000 \n", "75% 188.000000 177.000000 148.000000 131.000000 \n", "max 247.000000 231.000000 154.000000 135.000000 \n", "\n", " left_block_R_stddev left_block_G_stddev left_block_B_stddev \\\n", "count 91301.000000 91301.000000 91301.000000 \n", "mean 11.584725 20.070492 15.906036 \n", "std 8.180077 11.086751 9.979590 \n", "min 0.000000 0.000000 0.000000 \n", "25% 4.000000 9.000000 6.000000 \n", "50% 11.000000 21.000000 16.000000 \n", "75% 18.000000 28.000000 23.000000 \n", "max 36.000000 50.000000 43.000000 \n", "\n", " left_block_H_stddev left_block_S_stddev left_block_V_stddev \\\n", "count 91301.000000 91301.000000 91301.000000 \n", "mean 68.572622 18.027338 11.597200 \n", "std 37.974290 11.795136 8.177179 \n", "min 0.000000 0.000000 0.000000 \n", "25% 43.000000 7.000000 4.000000 \n", "50% 72.000000 17.000000 11.000000 \n", "75% 100.000000 27.000000 18.000000 \n", "max 124.000000 51.000000 36.000000 \n", "\n", " left_block_l_stddev left_block_a_stddev left_block_b_stddev \\\n", "count 91301.000000 91301.000000 91301.000000 \n", "mean 17.160557 4.237259 0.840747 \n", "std 10.284627 1.908351 0.495328 \n", "min 0.000000 0.000000 0.000000 \n", "25% 7.000000 3.000000 1.000000 \n", "50% 17.000000 5.000000 1.000000 \n", "75% 24.000000 6.000000 1.000000 \n", "max 44.000000 10.000000 3.000000 \n", "\n", " left_block_R_hist left_block_G_hist \n", "count 91301.000000 91301.000000 ... \n", "mean 160.992618 123.221520 ... \n", "std 35.854722 50.745105 ... \n", "min 61.000000 23.000000 ... \n", "25% 138.000000 80.000000 ... \n", "50% 163.000000 127.000000 ... \n", "75% 186.000000 168.000000 ... \n", "max 253.000000 236.000000 ... \n", "\n", "[8 rows x 150 columns]" ] }, "execution_count": 5, "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": { "collapsed": true }, "outputs": [], "source": [ "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": "code", "execution_count": 6, "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_stddevlelf_right_G_stddevlelf_right_B_stddevlelf_right_H_stddevlelf_right_S_stddevlelf_right_V_stddevlelf_right_l_stddevlelf_right_a_stddevlelf_right_b_stddevlelf_right_R_histlelf_right_G_hist
count 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000...
mean 1.262549 11.583499 8.142594 -30.762193 -11.383139 2.284028 8.190655 -4.545503 0.222155 -1.514967 -5.195452 -3.733223 -3.283907 -6.145836 -1.526018 -3.821097 -1.773880 -0.021993 2.052803 16.861217...
std 27.838211 44.035454 37.026650 64.432921 35.029158 29.075898 38.617100 8.653738 1.352848 9.775919 13.286977 11.913398 35.998510 15.019288 9.798269 12.313324 2.360502 0.581796 35.646931 59.786437...
min -52.000000 -63.000000 -54.000000 -222.000000 -84.000000 -52.000000 -56.000000 -25.000000 -4.000000 -18.000000 -37.000000 -31.000000 -108.000000 -35.000000 -19.000000 -30.000000 -9.000000 -2.000000 -110.000000 -134.000000...
25% -26.000000 -33.000000 -29.000000 -76.000000 -43.000000 -26.000000 -30.000000 -11.000000 -1.000000 -11.000000 -18.000000 -15.000000 -30.000000 -20.000000 -11.000000 -16.000000 -4.000000 0.000000 -33.000000 -43.000000...
50% 12.000000 22.000000 20.000000 -7.000000 -20.000000 12.000000 19.000000 -5.000000 0.000000 -4.000000 -6.000000 -5.000000 -9.000000 -8.000000 -4.000000 -5.000000 -2.000000 0.000000 12.000000 27.000000...
75% 26.000000 52.000000 42.000000 18.000000 22.000000 29.000000 44.000000 3.000000 1.000000 8.000000 8.000000 8.000000 24.000000 9.000000 8.000000 8.000000 0.000000 0.000000 30.000000 69.000000...
max 52.000000 99.000000 79.000000 152.000000 46.000000 60.000000 80.000000 13.000000 5.000000 19.000000 19.000000 19.000000 118.000000 24.000000 19.000000 20.000000 4.000000 2.000000 90.000000 153.000000...
\n", "

8 rows × 50 columns

\n", "
" ], "text/plain": [ " lelf_right_R lelf_right_G lelf_right_B lelf_right_H lelf_right_S \\\n", "count 91301.000000 91301.000000 91301.000000 91301.000000 91301.000000 \n", "mean 1.262549 11.583499 8.142594 -30.762193 -11.383139 \n", "std 27.838211 44.035454 37.026650 64.432921 35.029158 \n", "min -52.000000 -63.000000 -54.000000 -222.000000 -84.000000 \n", "25% -26.000000 -33.000000 -29.000000 -76.000000 -43.000000 \n", "50% 12.000000 22.000000 20.000000 -7.000000 -20.000000 \n", "75% 26.000000 52.000000 42.000000 18.000000 22.000000 \n", "max 52.000000 99.000000 79.000000 152.000000 46.000000 \n", "\n", " lelf_right_V lelf_right_l lelf_right_a lelf_right_b \\\n", "count 91301.000000 91301.000000 91301.000000 91301.000000 \n", "mean 2.284028 8.190655 -4.545503 0.222155 \n", "std 29.075898 38.617100 8.653738 1.352848 \n", "min -52.000000 -56.000000 -25.000000 -4.000000 \n", "25% -26.000000 -30.000000 -11.000000 -1.000000 \n", "50% 12.000000 19.000000 -5.000000 0.000000 \n", "75% 29.000000 44.000000 3.000000 1.000000 \n", "max 60.000000 80.000000 13.000000 5.000000 \n", "\n", " lelf_right_R_stddev lelf_right_G_stddev lelf_right_B_stddev \\\n", "count 91301.000000 91301.000000 91301.000000 \n", "mean -1.514967 -5.195452 -3.733223 \n", "std 9.775919 13.286977 11.913398 \n", "min -18.000000 -37.000000 -31.000000 \n", "25% -11.000000 -18.000000 -15.000000 \n", "50% -4.000000 -6.000000 -5.000000 \n", "75% 8.000000 8.000000 8.000000 \n", "max 19.000000 19.000000 19.000000 \n", "\n", " lelf_right_H_stddev lelf_right_S_stddev lelf_right_V_stddev \\\n", "count 91301.000000 91301.000000 91301.000000 \n", "mean -3.283907 -6.145836 -1.526018 \n", "std 35.998510 15.019288 9.798269 \n", "min -108.000000 -35.000000 -19.000000 \n", "25% -30.000000 -20.000000 -11.000000 \n", "50% -9.000000 -8.000000 -4.000000 \n", "75% 24.000000 9.000000 8.000000 \n", "max 118.000000 24.000000 19.000000 \n", "\n", " lelf_right_l_stddev lelf_right_a_stddev lelf_right_b_stddev \\\n", "count 91301.000000 91301.000000 91301.000000 \n", "mean -3.821097 -1.773880 -0.021993 \n", "std 12.313324 2.360502 0.581796 \n", "min -30.000000 -9.000000 -2.000000 \n", "25% -16.000000 -4.000000 0.000000 \n", "50% -5.000000 -2.000000 0.000000 \n", "75% 8.000000 0.000000 0.000000 \n", "max 20.000000 4.000000 2.000000 \n", "\n", " lelf_right_R_hist lelf_right_G_hist \n", "count 91301.000000 91301.000000 ... \n", "mean 2.052803 16.861217 ... \n", "std 35.646931 59.786437 ... \n", "min -110.000000 -134.000000 ... \n", "25% -33.000000 -43.000000 ... \n", "50% 12.000000 27.000000 ... \n", "75% 30.000000 69.000000 ... \n", "max 90.000000 153.000000 ... \n", "\n", "[8 rows x 50 columns]" ] }, "execution_count": 6, "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": 24, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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left_block_Rleft_block_Gleft_block_Bleft_block_R_histleft_block_G_histleft_block_B_histleft_block_R_maxleft_block_G_maxleft_block_B_maxright_block_Rright_block_Gright_block_Bright_block_R_histright_block_G_histright_block_B_histright_block_R_maxright_block_G_maxright_block_B_max
count151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000
mean178.697470149.211462156.498504184.695405155.233132163.526025199.778027182.282382182.766180175.436182134.494487146.433846171.109646122.180043137.949731199.220391176.936189178.925115
std24.04650531.72936828.74144323.74023335.58419729.30519419.17407721.95309420.30443919.89415419.78433221.17715221.05993123.46596523.95993918.23906418.64065818.174246
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25%164.000000124.000000135.000000171.000000132.000000145.000000192.000000171.000000171.000000165.000000123.000000136.000000159.000000105.000000125.000000192.000000168.000000170.000000
50%180.000000147.000000158.000000189.000000161.000000168.000000201.000000182.000000184.000000178.000000136.000000149.000000173.000000123.000000140.000000199.000000176.000000180.000000
75%194.000000173.000000177.000000198.000000181.000000183.000000209.000000195.000000195.000000187.000000148.000000159.000000185.000000138.000000154.000000208.000000187.000000189.000000
max253.000000235.000000244.000000254.000000254.000000254.000000255.000000255.000000255.000000253.000000220.000000226.000000254.000000233.000000232.000000255.000000255.000000254.000000
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" ], "text/plain": [ " left_block_R left_block_G left_block_B left_block_R_hist \\\n", "count 151086.000000 151086.000000 151086.000000 151086.000000 \n", "mean 178.697470 149.211462 156.498504 184.695405 \n", "std 24.046505 31.729368 28.741443 23.740233 \n", "min 15.000000 67.000000 36.000000 13.000000 \n", "25% 164.000000 124.000000 135.000000 171.000000 \n", "50% 180.000000 147.000000 158.000000 189.000000 \n", "75% 194.000000 173.000000 177.000000 198.000000 \n", "max 253.000000 235.000000 244.000000 254.000000 \n", "\n", " left_block_G_hist left_block_B_hist left_block_R_max \\\n", "count 151086.000000 151086.000000 151086.000000 \n", "mean 155.233132 163.526025 199.778027 \n", "std 35.584197 29.305194 19.174077 \n", "min 30.000000 36.000000 31.000000 \n", "25% 132.000000 145.000000 192.000000 \n", "50% 161.000000 168.000000 201.000000 \n", "75% 181.000000 183.000000 209.000000 \n", "max 254.000000 254.000000 255.000000 \n", "\n", " left_block_G_max left_block_B_max right_block_R right_block_G \\\n", "count 151086.000000 151086.000000 151086.000000 151086.000000 \n", "mean 182.282382 182.766180 175.436182 134.494487 \n", "std 21.953094 20.304439 19.894154 19.784332 \n", "min 87.000000 45.000000 110.000000 76.000000 \n", "25% 171.000000 171.000000 165.000000 123.000000 \n", "50% 182.000000 184.000000 178.000000 136.000000 \n", "75% 195.000000 195.000000 187.000000 148.000000 \n", "max 255.000000 255.000000 253.000000 220.000000 \n", "\n", " right_block_B right_block_R_hist right_block_G_hist \\\n", "count 151086.000000 151086.000000 151086.000000 \n", "mean 146.433846 171.109646 122.180043 \n", "std 21.177152 21.059931 23.465965 \n", "min 78.000000 108.000000 65.000000 \n", "25% 136.000000 159.000000 105.000000 \n", "50% 149.000000 173.000000 123.000000 \n", "75% 159.000000 185.000000 138.000000 \n", "max 226.000000 254.000000 233.000000 \n", "\n", " right_block_B_hist right_block_R_max right_block_G_max \\\n", "count 151086.000000 151086.000000 151086.000000 \n", "mean 137.949731 199.220391 176.936189 \n", "std 23.959939 18.239064 18.640658 \n", "min 69.000000 129.000000 109.000000 \n", "25% 125.000000 192.000000 168.000000 \n", "50% 140.000000 199.000000 176.000000 \n", "75% 154.000000 208.000000 187.000000 \n", "max 232.000000 255.000000 255.000000 \n", "\n", " right_block_B_max \n", "count 151086.000000 \n", "mean 178.925115 \n", "std 18.174246 \n", "min 106.000000 \n", "25% 170.000000 \n", "50% 180.000000 \n", "75% 189.000000 \n", "max 254.000000 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "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": 14, "metadata": { "collapsed": true }, "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": 8, "metadata": { "collapsed": true }, "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": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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left_block_Rleft_block_Gleft_block_Bleft_block_Hleft_block_Sleft_block_Vleft_block_lleft_block_aleft_block_bleft_block_R_hist...whiteBlock_b_histwhiteBlock_R_maxwhiteBlock_G_maxwhiteBlock_B_maxwhiteBlock_H_maxwhiteBlock_S_maxwhiteBlock_V_maxwhiteBlock_l_maxwhiteBlock_a_maxwhiteBlock_b_max
count151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000...151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000151086.000000
mean178.697470149.211462156.498504179.57886248.304482180.058682164.499669140.003925127.971162184.695405...130.782197202.084376192.246787189.16585385.14000025.093086203.718994200.058430132.367804131.734747
std24.04650531.72936828.74144376.69621419.14897623.79665328.0734815.9342904.50578023.740233...4.66287419.94039921.28380321.17684698.77958611.14424018.42738319.2612192.7451614.569725
min15.00000067.00000036.0000006.0000002.00000078.00000073.00000097.000000105.00000013.000000...107.000000125.000000116.000000107.0000000.0000000.000000125.000000125.000000127.000000108.000000
25%164.000000124.000000135.000000149.00000035.000000165.000000143.000000136.000000127.000000171.000000...130.000000193.000000182.000000179.00000019.00000017.000000197.000000190.000000130.000000131.000000
50%180.000000147.000000158.000000217.00000049.000000181.000000165.000000142.000000128.000000189.000000...131.000000205.000000196.000000193.00000027.00000022.000000205.000000203.000000132.000000132.000000
75%194.000000173.000000177.000000239.00000065.000000196.000000184.000000145.000000130.000000198.000000...133.000000212.000000204.000000201.000000199.00000032.000000212.000000210.000000134.000000134.000000
max253.000000235.000000244.000000247.000000206.000000253.000000241.000000151.000000150.000000254.000000...150.000000255.000000255.000000255.000000255.000000123.000000255.000000255.000000158.000000153.000000
\n", "

8 rows × 81 columns

\n", "
" ], "text/plain": [ " left_block_R left_block_G left_block_B left_block_H \\\n", "count 151086.000000 151086.000000 151086.000000 151086.000000 \n", "mean 178.697470 149.211462 156.498504 179.578862 \n", "std 24.046505 31.729368 28.741443 76.696214 \n", "min 15.000000 67.000000 36.000000 6.000000 \n", "25% 164.000000 124.000000 135.000000 149.000000 \n", "50% 180.000000 147.000000 158.000000 217.000000 \n", "75% 194.000000 173.000000 177.000000 239.000000 \n", "max 253.000000 235.000000 244.000000 247.000000 \n", "\n", " left_block_S left_block_V left_block_l left_block_a \\\n", "count 151086.000000 151086.000000 151086.000000 151086.000000 \n", "mean 48.304482 180.058682 164.499669 140.003925 \n", "std 19.148976 23.796653 28.073481 5.934290 \n", "min 2.000000 78.000000 73.000000 97.000000 \n", "25% 35.000000 165.000000 143.000000 136.000000 \n", "50% 49.000000 181.000000 165.000000 142.000000 \n", "75% 65.000000 196.000000 184.000000 145.000000 \n", "max 206.000000 253.000000 241.000000 151.000000 \n", "\n", " left_block_b left_block_R_hist ... whiteBlock_b_hist \\\n", "count 151086.000000 151086.000000 ... 151086.000000 \n", "mean 127.971162 184.695405 ... 130.782197 \n", "std 4.505780 23.740233 ... 4.662874 \n", "min 105.000000 13.000000 ... 107.000000 \n", "25% 127.000000 171.000000 ... 130.000000 \n", "50% 128.000000 189.000000 ... 131.000000 \n", "75% 130.000000 198.000000 ... 133.000000 \n", "max 150.000000 254.000000 ... 150.000000 \n", "\n", " whiteBlock_R_max whiteBlock_G_max whiteBlock_B_max whiteBlock_H_max \\\n", "count 151086.000000 151086.000000 151086.000000 151086.000000 \n", "mean 202.084376 192.246787 189.165853 85.140000 \n", "std 19.940399 21.283803 21.176846 98.779586 \n", "min 125.000000 116.000000 107.000000 0.000000 \n", "25% 193.000000 182.000000 179.000000 19.000000 \n", "50% 205.000000 196.000000 193.000000 27.000000 \n", "75% 212.000000 204.000000 201.000000 199.000000 \n", "max 255.000000 255.000000 255.000000 255.000000 \n", "\n", " whiteBlock_S_max whiteBlock_V_max whiteBlock_l_max whiteBlock_a_max \\\n", "count 151086.000000 151086.000000 151086.000000 151086.000000 \n", "mean 25.093086 203.718994 200.058430 132.367804 \n", "std 11.144240 18.427383 19.261219 2.745161 \n", "min 0.000000 125.000000 125.000000 127.000000 \n", "25% 17.000000 197.000000 190.000000 130.000000 \n", "50% 22.000000 205.000000 203.000000 132.000000 \n", "75% 32.000000 212.000000 210.000000 134.000000 \n", "max 123.000000 255.000000 255.000000 158.000000 \n", "\n", " whiteBlock_b_max \n", "count 151086.000000 \n", "mean 131.734747 \n", "std 4.569725 \n", "min 108.000000 \n", "25% 131.000000 \n", "50% 132.000000 \n", "75% 134.000000 \n", "max 153.000000 \n", "\n", "[8 rows x 81 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_features.describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "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.cross_validation import train_test_split\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": 15, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold(n_splits=5, random_state=None, shuffle=False)\n", "('TRAIN:', array([ 21967, 21968, 21969, ..., 109831, 109832, 109833]), 'TEST:', array([ 0, 1, 2, ..., 21964, 21965, 21966]))\n", "svm linear accuracy score: 0.99995447717\n", "f1 score: 0.99995447717\n", "runing time: 0:00:09.607839\n", "('TRAIN:', array([ 0, 1, 2, ..., 109831, 109832, 109833]), 'TEST:', array([21967, 21968, 21969, ..., 43931, 43932, 43933]))\n", "svm linear accuracy score: 1.0\n", "f1 score: 1.0\n", "runing time: 0:00:08.782720\n", "('TRAIN:', array([ 0, 1, 2, ..., 109831, 109832, 109833]), 'TEST:', array([43934, 43935, 43936, ..., 65898, 65899, 65900]))\n", "svm linear accuracy score: 0.999817908681\n", "f1 score: 0.999817908681\n", "runing time: 0:00:07.788273\n", "('TRAIN:', array([ 0, 1, 2, ..., 109831, 109832, 109833]), 'TEST:', array([65901, 65902, 65903, ..., 87865, 87866, 87867]))\n", "svm linear accuracy score: 0.999863431511\n", "f1 score: 0.999863431511\n", "runing time: 0:00:07.821979\n", "('TRAIN:', array([ 0, 1, 2, ..., 87865, 87866, 87867]), 'TEST:', array([ 87868, 87869, 87870, ..., 109831, 109832, 109833]))\n", "svm linear accuracy score: 0.999908950196\n", "f1 score: 0.999908950196\n", "runing time: 0:00:08.742652\n" ] } ], "source": [ "\n", "\n", "X = train_features.values\n", "y = train_labels.values\n", "\n", "kf = KFold(n_splits=5)\n", "kf.get_n_splits(X)\n", "\n", "print(kf) \n", "\n", "for train_index, test_index in kf.split(X):\n", " print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n", " X_train, X_test = X[train_index], X[test_index]\n", " y_train, y_test = y[train_index], y[test_index]\n", " \n", " \n", " from datetime import datetime\n", " trarining_start_time = datetime.now()\n", "\n", " clf_svm_linear = SVC(kernel = 'linear',gamma=0.00001,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": 8, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "#X_train = train_features_9\n", "#y_train = train_labels\n", "\n", "# X_test = test_features\n", "# y_test = test_labels\n", "\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", "\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')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "svm linear accuracy score: 0.999671430918\n", "f1 score : [ 1. 0.99931554 0.99927693 1. 1. 0.99964577\n", " 0.99963464]\n", "precision_score: [ 1. 0.99965765 0.99891579 1. 1. 0.99929178\n", " 1. ]\n", "recall_score : [ 1. 0.99897366 0.99963834 1. 1. 1.\n", " 0.99926954]\n", "('preds:', array([6, 6, 1, 4, 2, 3, 5, 1, 4, 1]))\n", "('trues:\\n', array([6, 6, 1, 4, 2, 3, 5, 1, 4, 1]))\n" ] } ], "source": [ "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", "\n", "print(\"preds:\",pred[:10])\n", "print('trues:\\n',y_test[:10])\n" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": true }, "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_20171207.txt\",'wb')\n", "#f = open(\"clf_svm_linear_125100_low_feature_data.txt\",'wb')\n", "f.write(porter_clf_svm_linear)\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": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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lelf_right_Rlelf_right_Glelf_right_Blelf_right_Hlelf_right_Vlelf_right_llelf_right_alelf_right_blelf_right_R_stddevlelf_right_G_stddev...lelf_right_H_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
count9472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.000000...9472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.0000009472.000000
mean4.99883914.26055710.544130-26.5350515.13682411.130912-4.2118880.506651-0.101668-2.332454...-24.4838470.6977418.245777-1.2251900.67778711.064189-1.53790125.8005702.9671668.171769
std12.45140922.42992118.34903554.74365112.61862618.7607795.0128311.0307075.7356428.606866...88.73896420.69937031.0934432.4201031.60154518.8880337.60592626.1956867.12998931.073313
min-18.000000-23.000000-22.000000-243.000000-18.000000-21.000000-16.000000-4.000000-15.000000-22.000000...-248.000000-68.000000-51.000000-11.000000-5.000000-21.000000-19.000000-61.000000-16.000000-50.000000
25%-6.000000-6.000000-7.000000-28.000000-6.000000-6.000000-7.0000000.000000-4.000000-9.000000...-3.000000-5.000000-20.000000-3.0000000.000000-6.000000-7.00000012.000000-3.000000-20.000000
50%7.00000016.00000013.000000-2.0000007.00000013.000000-4.0000000.000000-1.000000-3.000000...0.0000002.00000012.000000-1.0000000.00000013.000000-2.00000028.0000003.00000012.000000
75%14.00000027.00000022.0000003.00000014.00000023.0000001.0000001.0000005.0000006.000000...2.0000008.00000030.0000000.0000001.00000022.0000005.00000043.0000008.00000029.000000
max38.00000069.00000056.00000092.00000038.00000057.0000007.0000005.00000012.00000015.000000...241.00000053.00000073.0000005.0000007.00000058.00000014.00000093.00000026.00000073.000000
\n", "

8 rows × 45 columns

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" ], "text/plain": [ " lelf_right_R lelf_right_G lelf_right_B lelf_right_H lelf_right_V \\\n", "count 9472.000000 9472.000000 9472.000000 9472.000000 9472.000000 \n", "mean 4.998839 14.260557 10.544130 -26.535051 5.136824 \n", "std 12.451409 22.429921 18.349035 54.743651 12.618626 \n", "min -18.000000 -23.000000 -22.000000 -243.000000 -18.000000 \n", "25% -6.000000 -6.000000 -7.000000 -28.000000 -6.000000 \n", "50% 7.000000 16.000000 13.000000 -2.000000 7.000000 \n", "75% 14.000000 27.000000 22.000000 3.000000 14.000000 \n", "max 38.000000 69.000000 56.000000 92.000000 38.000000 \n", "\n", " lelf_right_l lelf_right_a lelf_right_b lelf_right_R_stddev \\\n", "count 9472.000000 9472.000000 9472.000000 9472.000000 \n", "mean 11.130912 -4.211888 0.506651 -0.101668 \n", "std 18.760779 5.012831 1.030707 5.735642 \n", "min -21.000000 -16.000000 -4.000000 -15.000000 \n", "25% -6.000000 -7.000000 0.000000 -4.000000 \n", "50% 13.000000 -4.000000 0.000000 -1.000000 \n", "75% 23.000000 1.000000 1.000000 5.000000 \n", "max 57.000000 7.000000 5.000000 12.000000 \n", "\n", " lelf_right_G_stddev ... lelf_right_H_min \\\n", "count 9472.000000 ... 9472.000000 \n", "mean -2.332454 ... -24.483847 \n", "std 8.606866 ... 88.738964 \n", "min -22.000000 ... -248.000000 \n", "25% -9.000000 ... -3.000000 \n", "50% -3.000000 ... 0.000000 \n", "75% 6.000000 ... 2.000000 \n", "max 15.000000 ... 241.000000 \n", "\n", " lelf_right_V_min lelf_right_l_min lelf_right_a_min lelf_right_b_min \\\n", "count 9472.000000 9472.000000 9472.000000 9472.000000 \n", "mean 0.697741 8.245777 -1.225190 0.677787 \n", "std 20.699370 31.093443 2.420103 1.601545 \n", "min -68.000000 -51.000000 -11.000000 -5.000000 \n", "25% -5.000000 -20.000000 -3.000000 0.000000 \n", "50% 2.000000 12.000000 -1.000000 0.000000 \n", "75% 8.000000 30.000000 0.000000 1.000000 \n", "max 53.000000 73.000000 5.000000 7.000000 \n", "\n", " lelf_right_gray_value lelf_right_gray_stddev lelf_right_gray_hist \\\n", "count 9472.000000 9472.000000 9472.000000 \n", "mean 11.064189 -1.537901 25.800570 \n", "std 18.888033 7.605926 26.195686 \n", "min -21.000000 -19.000000 -61.000000 \n", "25% -6.000000 -7.000000 12.000000 \n", "50% 13.000000 -2.000000 28.000000 \n", "75% 22.000000 5.000000 43.000000 \n", "max 58.000000 14.000000 93.000000 \n", "\n", " lelf_right_gray_max lelf_right_gray_min \n", "count 9472.000000 9472.000000 \n", "mean 2.967166 8.171769 \n", "std 7.129989 31.073313 \n", "min -16.000000 -50.000000 \n", "25% -3.000000 -20.000000 \n", "50% 3.000000 12.000000 \n", "75% 8.000000 29.000000 \n", "max 26.000000 73.000000 \n", "\n", "[8 rows x 45 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "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": 111, "metadata": { "collapsed": true }, "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": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "svm linear accuracy score: 0.868243243243\n", "f1 score: 0.868243243243\n", "recall_score : 0.868243243243\n", "precision_score : 0.868243243243\n", "svm linear accuracy score: 0.907728040541\n", "f1 score: 0.907728040541\n", "recall_score : 0.907728040541\n", "precision_score : 0.907728040541\n", "('preds:', array([6, 1, 1, 6, 7, 7, 4, 4, 4, 4]))\n", "('trues:\\n', 2720 6\n", "3210 1\n", "3118 1\n", "2969 6\n", "1026 7\n", "1020 7\n", "2258 4\n", "2493 4\n", "3424 4\n", "2034 4\n", "Name: index, dtype: int64)\n", "7\n", "(2, 4)\n", "(4, 2)\n", "(4, 2)\n", "(2, 4)\n", "(6, 4)\n", "(2, 4)\n", "(2, 4)\n", "(2, 4)\n", "(2, 4)\n", "(4, 2)\n", "(4, 2)\n", "(2, 4)\n", "(2, 4)\n", "(4, 2)\n", "(4, 2)\n", "(2, 4)\n", "(2, 4)\n", "(2, 4)\n", "(2, 4)\n", 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6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(2, 4)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(4, 2)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(6, 7)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(2, 4)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(2, 4)\n", "(0, 1)\n", "(7, 6)\n", "(4, 2)\n", "(7, 6)\n", "(7, 6)\n", "(2, 1)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(4, 2)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(4, 2)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(7, 6)\n", "(9472, 0, 874)\n" ] } ], "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": 38, "metadata": { "collapsed": true }, "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()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }