{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cfebebd9",
   "metadata": {},
   "source": [
    "![brainome logo](./images/brainome_logo.png)\n",
    "# 103 Model Type Selection\n",
    "Brainome™ creates predictors using these three model types:\n",
    "1. Random Forest\n",
    "2. Neural Network\n",
    "3. Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1474a59d",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "This notebook requires brainome as installed per notebook [brainome_101_Quick_Start](brainome_101_Quick_Start.ipynb)\n",
    "\n",
    "The training data set used is [titanic_train.csv](https://download.brainome.ai/data/public/titanic_train.csv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fb40c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python3 -m pip install brainome  --quiet\n",
    "!brainome --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f75440f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request as request\n",
    "print('Downloading titanic_train.csv')\n",
    "request.urlretrieve('https://download.brainome.ai/data/public/titanic_train.csv', 'titanic_train.csv')\n",
    "%ls -lh titanic_train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33177319",
   "metadata": {},
   "source": [
    "## 1. Automatic Model Selection\n",
    "Brainome can automatically select the most appropriate model type for your data's measurements.\n",
    "In titanic's case, brainome selects Random Forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4dc7f42",
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [],
   "source": [
    "!brainome titanic_train.csv -y -o predictor_103_automatic.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c74ca07",
   "metadata": {},
   "source": [
    "The predictor filename is `predictor_103_automatic.py`. The source code is approximately 39K bytes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b66cd4c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%ls -lh predictor_103_automatic.py\n",
    "%pycat predictor_103_automatic.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f889a13",
   "metadata": {},
   "source": [
    "## 2. Random Forest\n",
    "You can select the *Random Forest* model type by using the `-f RF` parameter.\n",
    "\n",
    "> Note: The `-modelonly` parameter bypasses the measurements phase which do not change from the previous runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de2aa21d",
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [],
   "source": [
    "!brainome titanic_train.csv -f RF -y -o predictor_103_RF.py -modelonly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a2691f",
   "metadata": {},
   "source": [
    "Open `predictor_103_RF.py` to view the Random Forest Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e55e9f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "%ls -lh predictor_103_RF.py\n",
    "%pycat predictor_103_RF.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72975533",
   "metadata": {},
   "source": [
    "## 3. Neural Network\n",
    "You can select the *Neural Network* model type by using the `-f NN` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53f7c9b4",
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [],
   "source": [
    "!brainome titanic_train.csv -f NN -y -o predictor_103_NN.py -modelonly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116ce265",
   "metadata": {},
   "source": [
    "Open `predictor_103_NN.py` to view the Neural Network Predictor. The source code is approximately 57K bytes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7495f8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%ls -lh predictor_103_NN.py\n",
    "%pycat predictor_103_NN.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715fee81",
   "metadata": {},
   "source": [
    "## 4. Decision Tree\n",
    "You can select the *Decision Tree* model type by using the `-f DT` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a727df3",
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [],
   "source": [
    "!brainome titanic_train.csv -f DT -y -o predictor_103_DT.py -modelonly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81455f1d",
   "metadata": {},
   "source": [
    "Open `predictor_103_DT.py` to view the Decision Tree Predictor. The source code is approximately 33K bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10318f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%ls -lh predictor_103_DT.py\n",
    "%pycat predictor_103_DT.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e36fb88",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "- Check out [104_Using_Predictor](./brainome_104_Using_Predictor.ipynb)\n",
    "- Check out [Using Measurement to Create Better Models](./brainome_200_Using_Measurement.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}