Notebooks/3DSELD-Local.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Notebook\n",
    "This is a small notebook to execute our project. Please consider that you will have to download a 9 GB data set that, after the extraction process, will grows up to 200 GB or even more. If you don't want to use your own machine you should switch to the other notebook (/Notebooks/3DSELD-Colab.ipynb) and execute it on Google Colab which provides you also a GPU.\n",
    "\n",
    "Little legend for notebook execution:\n",
    "- [M] = mandatory\n",
    "- [OO] = only once"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [M] Move to Code folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.chdir('../Code/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [OO] Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [OO] Download the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "!pip install googledrivedownloader\n",
    "\n",
    "!python dataset_dwnldr.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [OO] Extraction of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python batch_feature_extraction.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training of the network\n",
    "All the parameters for the training are handled in __parameter.py__. You can set training, validation, test splits and many other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"models/\"):\n",
    "    os.makedirs(\"models/\")\n",
    "\n",
    "!python seld.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training outcomes plotting\n",
    "Plot the results of a certain trained model.\n",
    "\n",
    "Method usage: \n",
    "- simple_plotter.py \"models/foa_train[TRAIN_SPLITS]_validation[VAL_SPLIT]_seq[SEQ_LENGTH]_plot.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"plots/\"):\n",
    "    os.makedirs(\"plots/\")\n",
    "\n",
    "!python simple_plotter.py \"models/foa_train[3, 4]_validation2_seq128_plot.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model\n",
    "Test the pre-trained model and plot its results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"plots/\"):\n",
    "    os.makedirs(\"plots/\")\n",
    "\n",
    "!python seld_test.py"
   ]
  }
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