data_prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# loading libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "# import seaborn as sns\n",
    "%matplotlib inline\n",
    "pd.options.display.max_colwidth = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# reading image labels file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Images 5000\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('../data/solution.csv')\n",
    "data.columns =  ['image_path', 'label']\n",
    "data.label = data.label -1\n",
    "new_str = '../data/train_images/'\n",
    "data.image_path = [new_str + str(x) + '.png' for x in data.image_path]\n",
    "\n",
    "unique_classes = data['label'].unique().tolist()\n",
    "\n",
    "label_count = pd.DataFrame(data['label'].value_counts()).reset_index()\n",
    "print ('Total Images {}'.format(label_count.label.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3    692.0\n",
       " 4    677.6\n",
       " 5    674.4\n",
       " 0    668.8\n",
       " 2    664.8\n",
       " 1    622.4\n",
       " Name: label, dtype: float64, 5000)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.label.value_counts() * 0.8, len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check if an image is invalid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [00:00<00:00, 11655.21it/s]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "total_count = 0\n",
    "deleted_styles = []\n",
    "valid_styles = []\n",
    "for path_to_image in tqdm(data.image_path):\n",
    "    total_count += 1    \n",
    "    try:\n",
    "        Image.open(path_to_image)\n",
    "        valid_styles.append(path_to_image)\n",
    "    except Exception as e:\n",
    "        print(\"deleting image :\" + str(path_to_image))\n",
    "        deleted_styles.append(path_to_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total images :  5000\n",
      "total deleted images :  0\n",
      "total valid images :  5000\n"
     ]
    }
   ],
   "source": [
    "print (\"total images : \" ,  len(data))\n",
    "print (\"total deleted images : \" ,   len(deleted_styles) )\n",
    "print (\"total valid images : \" ,   len(valid_styles) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# stratified train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape (3997, 2)\n",
      "valid shape (1003, 2)\n",
      "total shape (5000, 2)\n"
     ]
    }
   ],
   "source": [
    "df_valid = pd.DataFrame()\n",
    "df_train = pd.DataFrame()\n",
    "split = 0.8\n",
    "for cl in unique_classes:\n",
    "    temp = data[data.label == cl ]\n",
    "    np.random.seed(0)\n",
    "    temp.reset_index(drop = True, inplace = True)\n",
    "    train_rows = np.random.choice(temp.index, int(len(temp)* split), replace=False)\n",
    "    test_rows = [x for x in temp.index if x not in train_rows]\n",
    "    temp_train = temp.loc[train_rows]\n",
    "    temp_valid = temp.loc[test_rows]\n",
    "    df_valid = df_valid.append(temp_valid)\n",
    "    df_train = df_train.append(temp_train)\n",
    "    \n",
    "print ('train shape {}'.format(df_train.shape))\n",
    "print ('valid shape {}'.format(df_valid.shape))\n",
    "print ('total shape {}'.format(data.shape))\n",
    "\n",
    "df_train = df_train.sample(frac=1).reset_index(drop=True)\n",
    "df_valid = df_valid.sample(frac=1).reset_index(drop=True)\n",
    "df_train.to_csv('../data/train.txt', sep=' ', index = False, header=False)\n",
    "df_valid.to_csv('../data/valid.txt', sep=' ', index = False, header=False)\n",
    "# data.to_csv('../data/all_data_5.5l.txt', sep=' ', index = False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test_df preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40000, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  category\n",
       "0   1         2\n",
       "1   2         3\n",
       "2   3         1\n",
       "3   4         2\n",
       "4   5         1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('../submissions/sample.csv')\n",
    "print (submission.shape)\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40000, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>../data/test_images/1.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>../data/test_images/2.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>../data/test_images/3.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>../data/test_images/4.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>../data/test_images/5.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        path  label\n",
       "0  ../data/test_images/1.png      0\n",
       "1  ../data/test_images/2.png      0\n",
       "2  ../data/test_images/3.png      0\n",
       "3  ../data/test_images/4.png      0\n",
       "4  ../data/test_images/5.png      0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images_path = '../data/test_images/'\n",
    "test_df = submission.copy()\n",
    "test_df.columns = ['path', 'label']\n",
    "test_df.label = 0\n",
    "test_df.path = [test_images_path  + str(x) + '.png' for x in test_df.path]\n",
    "print (test_df.shape)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=472x553 at 0x7F90656C5D68>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = test_df.path.tolist()[75]\n",
    "Image.open(open(img, 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# saving test file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.to_csv('../data/test.txt', sep=' ', index = False, header=False)"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}