01_d_SplitDataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import src.data.Dataset as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET_CSV:     /Users/carlos/WORKSPACE/MegaClassifier/data/interim/28570Images_filtered.csv\n",
      "OUTPUT_FOLDER:   /Users/carlos/WORKSPACE/MegaClassifier/data/processed\n"
     ]
    }
   ],
   "source": [
    "DATASET_CSV = os.path.abspath(\"./data/interim/28570Images_filtered.csv\")\n",
    "OUTPUT_FOLDER = os.path.abspath(\"./data/processed\")\n",
    "\n",
    "print(f\"DATASET_CSV:     {DATASET_CSV}\")\n",
    "print(f\"OUTPUT_FOLDER:   {OUTPUT_FOLDER}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file /Users/carlos/WORKSPACE/MegaClassifier/data/interim/28570Images_filtered.csv has been successfully opened.\n"
     ]
    },
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       "                                               file_name  label\n",
       "0             vacia/noanimal_zoo_1_4/37_20210115_85_.jpg      0\n",
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       "...                                                  ...    ...\n",
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       "\n",
       "[28570 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dt.load_from_csv(DATASET_CSV)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentaje_train = 0.70\n",
    "percentaje_val = 0.15\n",
    "percentaje_test = 0.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_file_name:   /Users/carlos/WORKSPACE/MegaClassifier/data/processed/train/28570Train.csv\n",
      "val_val_file_name: /Users/carlos/WORKSPACE/MegaClassifier/data/processed/validation/28570Validation.csv\n",
      "test_file_name:    /Users/carlos/WORKSPACE/MegaClassifier/data/processed/test/28570Test.csv\n"
     ]
    }
   ],
   "source": [
    "number_of_images = len(dataset[\"file_name\"])\n",
    "\n",
    "train_file_name = os.path.join(\n",
    "    OUTPUT_FOLDER, \"train\", str(number_of_images) + \"Train.csv\"\n",
    ")\n",
    "val_file_name = os.path.join(\n",
    "    OUTPUT_FOLDER, \"validation\", str(number_of_images) + \"Validation.csv\"\n",
    ")\n",
    "test_file_name = os.path.join(OUTPUT_FOLDER, \"test\", str(number_of_images) + \"Test.csv\")\n",
    "\n",
    "print(f\"train_file_name:   {train_file_name}\")\n",
    "print(f\"val_val_file_name: {val_file_name}\")\n",
    "print(f\"test_file_name:    {test_file_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset, val_dataset, test_dataset = dt.split_dataset(\n",
    "    dataset, percentaje_train, percentaje_val, percentaje_test\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "...                                                  ...    ...\n",
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     "execution_count": 7,
     "metadata": {},
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    "train_dataset"
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   "cell_type": "code",
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       "...                                                  ...    ...\n",
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     "metadata": {},
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    "val_dataset"
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       "...                                                  ...    ...\n",
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       "\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset has been successfully saved to /Users/carlos/WORKSPACE/MegaClassifier/data/processed/train/28570Train.csv\n",
      "The dataset has been successfully saved to /Users/carlos/WORKSPACE/MegaClassifier/data/processed/validation/28570Validation.csv\n",
      "The dataset has been successfully saved to /Users/carlos/WORKSPACE/MegaClassifier/data/processed/test/28570Test.csv\n"
     ]
    }
   ],
   "source": [
    "dt.dataset_to_csv(train_dataset, train_file_name)\n",
    "dt.dataset_to_csv(val_dataset, val_file_name)\n",
    "dt.dataset_to_csv(test_dataset, test_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================================================\n",
      "Calculos para TRAIN: 70.0%:\n",
      "==================================================================================\n",
      "- Valor teorico: 28570 x 70.0% = 19999.0\n",
      "- Valor generado: 19998\n",
      "\n",
      "- Valor teorico VACIA: 9665 x 70.0% = 6765.5\n",
      "- Valor generado VACIA: 6765\n",
      "\n",
      "- Valor teorico ANIMAL: 18905 x 70.0% = 13233.5\n",
      "- Valor generado ANIMAL: 13233\n",
      "\n",
      "==================================================================================\n",
      "Calculos para VALIDATION: 15.0%:\n",
      "==================================================================================\n",
      "- Valor teorico: 28570 x 15.0% = 4285.5\n",
      "- Valor generado: 4284\n",
      "\n",
      "- Valor teorico VACIA: 9665 x 15.0% = 1449.75\n",
      "- Valor generado VACIA: 1449\n",
      "\n",
      "- Valor teorico ANIMAL: 18905 x 15.0% = 2835.75\n",
      "- Valor generado ANIMAL: 2835\n",
      "\n",
      "==================================================================================\n",
      "Calculos para TEST: 15.0%:\n",
      "==================================================================================\n",
      "- Valor teorico: 28570 x 15.0% = 4285.5\n",
      "- Valor generado: 4288\n",
      "\n",
      "- Valor teorico VACIA: 9665 x 15.0% = 1449.75\n",
      "- Valor generado VACIA: 1451\n",
      "\n",
      "- Valor teorico ANIMAL: 18905 x 15.0% = 2835.75\n",
      "- Valor generado ANIMAL: 2837\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"==================================================================================\"\n",
    ")\n",
    "print(f\"Calculos para TRAIN: {percentaje_train*100}%:\")\n",
    "print(\n",
    "    \"==================================================================================\"\n",
    ")\n",
    "print(\n",
    "    f\"- Valor teorico: {len(dataset)} x {percentaje_train*100}% = {len(dataset)*percentaje_train}\"\n",
    ")\n",
    "print(f\"- Valor generado: {len(train_dataset)}\")\n",
    "print()\n",
    "print(\n",
    "    f\"- Valor teorico VACIA: {len(dataset[dataset['label'] == 0])} x {percentaje_train*100}% = {len(dataset[dataset['label'] == 0])*percentaje_train}\"\n",
    ")\n",
    "print(f\"- Valor generado VACIA: {len(train_dataset[train_dataset['label'] == 0])}\")\n",
    "print()\n",
    "print(\n",
    "    f\"- Valor teorico ANIMAL: {len(dataset[dataset['label'] == 1])} x {percentaje_train*100}% = {len(dataset[dataset['label'] == 1])*percentaje_train}\"\n",
    ")\n",
    "print(f\"- Valor generado ANIMAL: {len(train_dataset[train_dataset['label'] == 1])}\")\n",
    "print()\n",
    "print(\n",
    "    \"==================================================================================\"\n",
    ")\n",
    "print(f\"Calculos para VALIDATION: {percentaje_val*100}%:\")\n",
    "print(\n",
    "    \"==================================================================================\"\n",
    ")\n",
    "print(\n",
    "    f\"- Valor teorico: {len(dataset)} x {percentaje_val*100}% = {len(dataset)*percentaje_val}\"\n",
    ")\n",
    "print(f\"- Valor generado: {len(val_dataset)}\")\n",
    "print()\n",
    "print(\n",
    "    f\"- Valor teorico VACIA: {len(dataset[dataset['label'] == 0])} x {percentaje_val*100}% = {len(dataset[dataset['label'] == 0])*percentaje_val}\"\n",
    ")\n",
    "print(f\"- Valor generado VACIA: {len(val_dataset[val_dataset['label'] == 0])}\")\n",
    "print()\n",
    "print(\n",
    "    f\"- Valor teorico ANIMAL: {len(dataset[dataset['label'] == 1])} x {percentaje_val*100}% = {len(dataset[dataset['label'] == 1])*percentaje_val}\"\n",
    ")\n",
    "print(f\"- Valor generado ANIMAL: {len(val_dataset[val_dataset['label'] == 1])}\")\n",
    "print()\n",
    "print(\n",
    "    \"==================================================================================\"\n",
    ")\n",
    "print(f\"Calculos para TEST: {percentaje_test*100}%:\")\n",
    "print(\n",
    "    \"==================================================================================\"\n",
    ")\n",
    "print(\n",
    "    f\"- Valor teorico: {len(dataset)} x {percentaje_test*100}% = {len(dataset)*percentaje_test}\"\n",
    ")\n",
    "print(f\"- Valor generado: {len(test_dataset)}\")\n",
    "print()\n",
    "print(\n",
    "    f\"- Valor teorico VACIA: {len(dataset[dataset['label'] == 0])} x {percentaje_test*100}% = {len(dataset[dataset['label'] == 0])*percentaje_test}\"\n",
    ")\n",
    "print(f\"- Valor generado VACIA: {len(test_dataset[test_dataset['label'] == 0])}\")\n",
    "print()\n",
    "print(\n",
    "    f\"- Valor teorico ANIMAL: {len(dataset[dataset['label'] == 1])} x {percentaje_test*100}% = {len(dataset[dataset['label'] == 1])*percentaje_test}\"\n",
    ")\n",
    "print(f\"- Valor generado ANIMAL: {len(test_dataset[test_dataset['label'] == 1])}\")\n",
    "print()\n"
   ]
  }
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