Airplane&cars_Compare_different_dataset_inOneGraph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "# -*- coding: utf-8 -*-\n",
    "from __future__ import print_function\n",
    "import os\n",
    "import time\n",
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "from scipy import stats\n",
    "from pylab import rcParams\n",
    "from sklearn.utils import check_random_state\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import pairwise_distances_argmin_min\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, \\\n",
    "    GradientBoostingClassifier\n",
    "from keras import backend as K\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "from keras import applications\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Model\n",
    "from keras.utils.vis_utils import plot_model\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "from sklearn import svm\n",
    "import numpy as np\n",
    "import os\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras import applications\n",
    "import glob\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "import glob\n",
    "import platform\n",
    "import time\n",
    "from PIL import Image\n",
    "from skimage.feature import hog\n",
    "import numpy as np\n",
    "import os\n",
    "import joblib\n",
    "from sklearn.svm import LinearSVC\n",
    "import shutil\n",
    "import sys\n",
    "\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "import cv2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def areas(lengths,choice,model):\n",
    "    sum = 0.\n",
    "    i = 0\n",
    "    k=10\n",
    "    #print(lengths,ks)\n",
    "    for length in lengths:\n",
    "        lengthhere = lengths[i]\n",
    "        if i == 0:\n",
    "            sum = sum + lengthhere*k/2\n",
    "        else:\n",
    "            sum = sum + (lengthhere+lengthhere)*k/2\n",
    "        i += 1\n",
    "    #print(\"The whole area is : 50,000\")\n",
    "    print(\"Airplane & car \"+choice+\" \"+model+\": The area below this line is: %.2f\"%sum)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'500'\n",
    "max_queried = 500\n",
    "\n",
    "#'Raw''HOG''Pretrained'\n",
    "choice_str=['HOG']\n",
    "\n",
    "#'RfModel''LogModel''SvmModel'\n",
    "models_str = ['RfModel', 'SvmModel','LogModel']\n",
    "\n",
    "#'RandomSelection''MarginSamplingSelection''EntropySelection'\n",
    "selection_functions_str = ['RandomSelection', 'MarginSamplingSelection','EntropySelection']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airplane & car HOG RfModel: The area below this line is: 45480.00\n",
      "Airplane & car HOG RfModel: The area below this line is: 47643.75\n",
      "Airplane & car HOG RfModel: The area below this line is: 47943.75\n",
      "Airplane & car HOG SvmModel: The area below this line is: 48622.50\n",
      "Airplane & car HOG SvmModel: The area below this line is: 47930.00\n",
      "Airplane & car HOG SvmModel: The area below this line is: 48578.75\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (50,) and (2,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-74ae9860b404>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'-'\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mselection_function\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"10\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.npy'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mselection_function\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"10-\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m             \u001b[0mareas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ForFYP/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m~/anaconda3/envs/ForFYP/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1609\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1611\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1612\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1613\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ForFYP/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ForFYP/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    368\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ForFYP/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 231\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    232\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (50,) and (2,)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "x = np.arange(float(10), 500 + float(10), float(10))\n",
    "\n",
    "for choice in choice_str:\n",
    "    for model in models_str:\n",
    "        for selection_function in selection_functions_str:\n",
    "            filename =choice+'-'+ model + '-' +selection_function + '-' + \"10\"+'.npy'\n",
    "            result = np.load(filename)\n",
    "            ax.plot(x, result ,label = model + '-' +selection_function + '-' + \"10-\"+choice)\n",
    "            areas(result,choice,model)\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlim([0,500])\n",
    "ax.set_ylim([0,100])\n",
    "ax.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}