01_a_DatasetBasicStatistics.ipynb

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   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "from itables import init_notebook_mode\n",
    "\n",
    "import src.graphics as graphics\n",
    "\n",
    "init_notebook_mode(all_interactive=True)"
   ]
  },
  {
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   "execution_count": null,
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   "outputs": [],
   "source": [
    "RAW_DATASET = os.path.abspath(\"./data/raw/emptyNonEmptyDataset/10000Images.csv\")\n",
    "\n",
    "dataset_original = pd.read_csv(RAW_DATASET, sep=\";\")"
   ]
  },
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   "outputs": [],
   "source": [
    "num_images = len(dataset_original[\"file_name\"])\n",
    "num_classes = dataset_original[\"label\"].nunique()\n",
    "\n",
    "class_distribution = dataset_original[\"label\"].value_counts()\n",
    "\n",
    "print(f\"Number of images in dataset: {num_images}\")\n",
    "print(f\"Number of different classes: {num_classes}\")\n",
    "print(class_distribution)"
   ]
  },
  {
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     "start_time": "2025-02-16T12:08:57.626045Z"
    }
   },
   "outputs": [],
   "source": [
    "FASE_1a_FOLDER = os.path.abspath(\"./reports/fase_1/fase_1a\")\n",
    "os.makedirs(FASE_1a_FOLDER, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "fig_pie = graphics.create_pie_chart(\n",
    "    class_distribution.index,\n",
    "    class_distribution.values,\n",
    "    \"Class Distribution in Original Dataset\",\n",
    ")\n",
    "\n",
    "fig_pie.show()\n",
    "fig_pie.write_image(os.path.join(FASE_1a_FOLDER, \"class_distribution.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "fig_bar = graphics.create_bar_chart(\n",
    "    class_distribution.index,\n",
    "    class_distribution.values,\n",
    "    \"Class\",\n",
    "    \"Number of Images\",\n",
    "    \"Number of Images in Original Dataset by Class\",\n",
    "    show_values=True,\n",
    ")\n",
    "\n",
    "fig_bar.show()\n",
    "fig_bar.write_image(os.path.join(FASE_1a_FOLDER, \"class_barchart.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "start_time": "2025-02-16T12:08:57.778191Z"
    }
   },
   "outputs": [],
   "source": [
    "binary_distribution = class_distribution.copy()\n",
    "binary_distribution[\"animal\"] = binary_distribution.drop(\"vacia\").sum()\n",
    "binary_distribution = binary_distribution[[\"vacia\", \"animal\"]]\n",
    "\n",
    "fig_pie_binary = graphics.create_pie_chart(\n",
    "    binary_distribution.index,\n",
    "    binary_distribution.values,\n",
    "    \"Class Distribution (Empty VS Animal) in Original Dataset\",\n",
    "    show_values=True,\n",
    ")\n",
    "\n",
    "fig_pie_binary.show()\n",
    "fig_pie_binary.write_image(\n",
    "    os.path.join(FASE_1a_FOLDER, \"class_binary_distribution.png\")\n",
    ")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
  }
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