data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from soil_classifier.dataset import Landsat\n",
    "\n",
    "cwd = os.getcwd()\n",
    "DATA_FOLDER = cwd + '/data/'\n",
    "OUTPUT_FOLDER = cwd + '/outputs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples = 4435\n",
      "Test samples = 2000\n",
      "Number of classes = 6\n"
     ]
    }
   ],
   "source": [
    "dataset = Landsat()\n",
    "x_train, y_train, x_test, y_test = dataset.load()\n",
    "num_classes = dataset.num_classes\n",
    "\n",
    "print('Train samples = {}'.format(x_train.shape[0]))\n",
    "print('Test samples = {}'.format(x_test.shape[0]))\n",
    "print('Number of classes = {}'.format(num_classes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Samples amount per class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"pba0f5e37ec\">\n",
       "   <rect height=\"271.8\" width=\"558\" x=\"46.965625\" y=\"22.318125\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.hist(y_train,range=(1,num_classes+1),bins=num_classes,align='left',rwidth=0.8)\n",
    "plt.title('Number of class members of Train Set')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Amount')\n",
    "plt.show()\n",
    "\n",
    "fig.savefig(OUTPUT_FOLDER+'train_classes_num.png', dpi = 400)\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.hist(y_test,range=(1,num_classes+1),bins=num_classes,align='left',rwidth=0.8)\n",
    "plt.title('Number of class members Test Set')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Amount')\n",
    "plt.show()\n",
    "fig.savefig(OUTPUT_FOLDER+'test_classes_num.png', dpi = 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 12 Axes>"
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     "metadata": {},
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   "source": [
    "Nbands = dataset.num_bands\n",
    "Nshow = 3\n",
    "\n",
    "# idx_rnd = np.random.randint(np.size(x_train,0), size=(Nshow,1))\n",
    "idx_rnd = np.linspace(0,Nshow-1,Nshow,dtype=np.int)\n",
    "\n",
    "fig, splts = plt.subplots(Nshow,Nbands,figsize=(16,8))\n",
    "\n",
    "plt.setp([a.get_xticklabels() for a in fig.axes[:]], visible=False)\n",
    "plt.setp([a.get_yticklabels() for a in fig.axes[:]], visible=False)\n",
    "fig.subplots_adjust(hspace=0.5)\n",
    "fig.subplots_adjust(wspace=0.5)\n",
    "\n",
    "for i in range(Nshow):\n",
    "    for j in range(Nbands):\n",
    "        splts[i,j].imshow(np.reshape(x_train[idx_rnd[i],j*9:(j+1)*9],(3,3)),cmap=cm.gray)\n",
    "        splts[i,j].set_title('Class '+ str(int(y_train[idx_rnd[i]])) +' - Band ' + str(j))\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(OUTPUT_FOLDER+'somesamples.png', dpi = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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