tutorials/extending/tutorial01_custom_method.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extending TemporAI Tutorial 01: Writing a Custom Method Plugin\n",
    "\n",
    "This tutorial shows how to extend TemporAI by wring a custom *method* (as in algorithm, model) plugin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Skip the below cell if you are not on Google Colab / already have TemporAI installed:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install temporai\n",
    "\n",
    "# Or from the repo, for the latest version:\n",
    "# %pip install git+https://github.com/vanderschaarlab/temporai.git"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing a Custom `Plugin` 101\n",
    "\n",
    "In order to write a custom plugin for TemporAI, you need to do the following:\n",
    "1. Inherit from the appropriate **base class** for the category of plugin you are writing.\n",
    "2. Implement the **methods** (as in, functions of the class) that the plugin needs.\n",
    "3. **Register** the plugin with TemporAI.\n",
    "\n",
    "We will go through an example in this tutorial."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Inherit from the appropriate **base class** for the category of the method plugin you are writing.\n",
    "\n",
    "You need to find which category of method plugin you are writing.\n",
    "\n",
    "A summary of different plugin categories is available in the \n",
    "[README](https://github.com/vanderschaarlab/temporai/blob/main/README.md#-methods).\n",
    "\n",
    "You can also view all the different plugin categories as so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['prediction.one_off.classification',\n",
       " 'prediction.one_off.regression',\n",
       " 'prediction.temporal.classification',\n",
       " 'prediction.temporal.regression',\n",
       " 'preprocessing.encoding.static',\n",
       " 'preprocessing.encoding.temporal',\n",
       " 'preprocessing.imputation.static',\n",
       " 'preprocessing.imputation.temporal',\n",
       " 'preprocessing.nop',\n",
       " 'preprocessing.scaling.static',\n",
       " 'preprocessing.scaling.temporal',\n",
       " 'time_to_event',\n",
       " 'treatments.one_off.regression',\n",
       " 'treatments.temporal.classification',\n",
       " 'treatments.temporal.regression']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tempor import plugin_loader\n",
    "\n",
    "plugin_categories = plugin_loader.list_categories(plugin_type=\"method\")\n",
    "\n",
    "list(plugin_categories.keys())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember you can also see the existing method plugins and how they correspond to different categories, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'prediction'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'one_off'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'classification'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'cde_classifier'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ode_classifier'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'nn_classifier'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'laplace_ode_classifier'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'laplace_ode_regressor'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'nn_regressor'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ode_regressor'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cde_regressor'</span><span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'classification'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'seq2seq_classifier'</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'seq2seq_regressor'</span><span style=\"font-weight: bold\">]}</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'encoding'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'static'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'static_onehot_encoder'</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ts_onehot_encoder'</span><span style=\"font-weight: bold\">]}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'imputation'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'static'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'static_tabular_imputer'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ffill'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ts_tabular_imputer'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'bfill'</span><span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'nop'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'nop_transformer'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'scaling'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'static'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'static_minmax_scaler'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'static_standard_scaler'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ts_minmax_scaler'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ts_standard_scaler'</span><span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'time_to_event'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ts_coxph'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ts_xgb'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'dynamic_deephit'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'treatments'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'one_off'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'synctwin_regressor'</span><span style=\"font-weight: bold\">]}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'classification'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'crn_classifier'</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'crn_regressor'</span><span style=\"font-weight: bold\">]}</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'prediction'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'one_off'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'classification'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'cde_classifier'\u001b[0m, \u001b[32m'ode_classifier'\u001b[0m, \u001b[32m'nn_classifier'\u001b[0m, \u001b[32m'laplace_ode_classifier'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'laplace_ode_regressor'\u001b[0m, \u001b[32m'nn_regressor'\u001b[0m, \u001b[32m'ode_regressor'\u001b[0m, \u001b[32m'cde_regressor'\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'classification'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'seq2seq_classifier'\u001b[0m\u001b[1m]\u001b[0m, \u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'seq2seq_regressor'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'preprocessing'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'encoding'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'static'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'static_onehot_encoder'\u001b[0m\u001b[1m]\u001b[0m, \u001b[32m'temporal'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ts_onehot_encoder'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'imputation'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'static'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'static_tabular_imputer'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ffill'\u001b[0m, \u001b[32m'ts_tabular_imputer'\u001b[0m, \u001b[32m'bfill'\u001b[0m\u001b[1m]\u001b[0m\n",
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       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'nop'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'nop_transformer'\u001b[0m\u001b[1m]\u001b[0m,\n",
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       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'static'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'static_minmax_scaler'\u001b[0m, \u001b[32m'static_standard_scaler'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ts_minmax_scaler'\u001b[0m, \u001b[32m'ts_standard_scaler'\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'time_to_event'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ts_coxph'\u001b[0m, \u001b[32m'ts_xgb'\u001b[0m, \u001b[32m'dynamic_deephit'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'treatments'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'one_off'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'synctwin_regressor'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'classification'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'crn_classifier'\u001b[0m\u001b[1m]\u001b[0m, \u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'crn_regressor'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_plugins = plugin_loader.list(plugin_type=\"method\")\n",
    "\n",
    "from rich.pretty import pprint  # For prettifying the print output only.\n",
    "\n",
    "pprint(all_plugins, indent_guides=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say you would like to write a plugin of category `\"prediction.one_off.classification\"`.\n",
    "\n",
    "You can find which base class you need to inherit from as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base classes for all categories:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'prediction.one_off.classification'</span>: <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">class</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.prediction.one_off.classification.BaseOneOffClassifier'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'prediction.one_off.regression'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.prediction.one_off.regression.BaseOneOffRegressor'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'prediction.temporal.classification'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.prediction.temporal.classification.BaseTemporalClassifier'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'prediction.temporal.regression'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.prediction.temporal.regression.BaseTemporalRegressor'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing.encoding.static'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.preprocessing.encoding._base.BaseEncoder'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing.encoding.temporal'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.preprocessing.encoding._base.BaseEncoder'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing.imputation.static'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.preprocessing.imputation._base.BaseImputer'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing.imputation.temporal'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.preprocessing.imputation._base.BaseImputer'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing.nop'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.core._base_transformer.BaseTransformer'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing.scaling.static'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.preprocessing.scaling._base.BaseScaler'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing.scaling.temporal'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.preprocessing.scaling._base.BaseScaler'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'time_to_event'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.time_to_event.BaseTimeToEventAnalysis'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'treatments.one_off.regression'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.treatments.one_off._base.BaseOneOffTreatmentEffects'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'treatments.temporal.classification'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.treatments.temporal._base.BaseTemporalTreatmentEffects'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;,</span>\n",
       "<span style=\"color: #000000; text-decoration-color: #000000\">    </span><span style=\"color: #008000; text-decoration-color: #008000\">'treatments.temporal.regression'</span><span style=\"color: #000000; text-decoration-color: #000000\">: &lt;class </span><span style=\"color: #008000; text-decoration-color: #008000\">'tempor.methods.treatments.temporal._base.BaseTemporalTreatmentEffects'</span><span style=\"font-weight: bold\">&gt;</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'prediction.one_off.classification'\u001b[0m: \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'tempor.methods.prediction.one_off.classification.BaseOneOffClassifier'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'prediction.one_off.regression'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.prediction.one_off.regression.BaseOneOffRegressor'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'prediction.temporal.classification'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.prediction.temporal.classification.BaseTemporalClassifier'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'prediction.temporal.regression'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.prediction.temporal.regression.BaseTemporalRegressor'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'preprocessing.encoding.static'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.preprocessing.encoding._base.BaseEncoder'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'preprocessing.encoding.temporal'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.preprocessing.encoding._base.BaseEncoder'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'preprocessing.imputation.static'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.preprocessing.imputation._base.BaseImputer'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'preprocessing.imputation.temporal'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.preprocessing.imputation._base.BaseImputer'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'preprocessing.nop'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.core._base_transformer.BaseTransformer'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'preprocessing.scaling.static'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.preprocessing.scaling._base.BaseScaler'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'preprocessing.scaling.temporal'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.preprocessing.scaling._base.BaseScaler'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'time_to_event'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.time_to_event.BaseTimeToEventAnalysis'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'treatments.one_off.regression'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.treatments.one_off._base.BaseOneOffTreatmentEffects'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'treatments.temporal.classification'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.treatments.temporal._base.BaseTemporalTreatmentEffects'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[32m'treatments.temporal.regression'\u001b[0m\u001b[39m: <class \u001b[0m\u001b[32m'tempor.methods.treatments.temporal._base.BaseTemporalTreatmentEffects'\u001b[0m\u001b[1m>\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base class you need:\n",
      "<class 'tempor.methods.prediction.one_off.classification.BaseOneOffClassifier'>\n"
     ]
    }
   ],
   "source": [
    "plugin_categories = plugin_loader.list_categories(plugin_type=\"method\")\n",
    "\n",
    "print(\"Base classes for all categories:\")\n",
    "pprint(plugin_categories, indent_guides=False)\n",
    "\n",
    "print(\"Base class you need:\")\n",
    "print(plugin_categories[\"prediction.one_off.classification\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can then find the class in the TemporAI source code, to see its method signatures etc."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Implement the **methods** the plugin needs.\n",
    "\n",
    "Different category plugins have different methods (functions) that need to be implemented, but the key methods are:\n",
    "* `_fit()` where you provide your implementation of the fitting (training).\n",
    "* `_predict()` where you provide your implementation of the prediction (inference).\n",
    "* `_transform()` where you provide your implementation of data transformation (for preprocessing plugins).\n",
    "\n",
    "Classification-related plugins also have `_predict_proba()` and treatment effects plugins have `_predict_counterfactuals()`.\n",
    "\n",
    "Note that these methods have a preceding underscore `_`, and are different from the corresponding \"public\" methods\n",
    "without the underscore (e.g `fit()`). When extending, you need to implement the `_<...>` method,\n",
    "and the corresponding \"public\" method in TemporAI is what the user of your plugin will call.\n",
    "The \"public\" methods also do various necessary validation and other checks behind the scenes.\n",
    "\n",
    "If you haven't implemented some required method for the plugin, Python will notify you by raising an exception when you\n",
    "attempt to instantiate your plugin (see [Python `abc`](https://docs.python.org/3/library/abc.html)).\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our example case, you will need to implement the following methods for `BaseOneOffClassifier`:\n",
    "\n",
    "```python\n",
    "from tempor.methods.prediction.one_off.classification import BaseOneOffClassifier\n",
    "\n",
    "class MyPlugin(BaseOneOffClassifier):\n",
    "    # The initializer:\n",
    "    def __init__(self, **params: Any) -> None:\n",
    "        ...\n",
    "\n",
    "    # The _fit implementation.\n",
    "    def _fit(self, data: dataset.BaseDataset, *args, **kwargs):\n",
    "        ...\n",
    "\n",
    "    def _predict(self, data: dataset.PredictiveDataset, *args: Any, **kwargs: Any) -> samples.StaticSamplesBase:\n",
    "        ...\n",
    "\n",
    "    def _predict_proba(self, data: dataset.PredictiveDataset, *args: Any, **kwargs: Any) -> samples.StaticSamplesBase:\n",
    "        ...\n",
    "    \n",
    "    @staticmethod\n",
    "    def hyperparameter_space(*args: Any, **kwargs: Any) -> List[Params]:\n",
    "        # This method is not currently used in TemporAI (it will be used once AutoML component is implemented).\n",
    "        # For now, you may just return an empty list.\n",
    "        ...\n",
    "``` "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. **Register** the plugin with TemporAI.\n",
    "\n",
    "Registering your plugin with TemporAI is very simple, you need to use the `register_plugin` decorator,\n",
    "as shown in the example below.\n",
    "\n",
    "You will need to specify the `name` of your plugin and its `category` in the decorator.\n",
    "\n",
    "> **Note:** You may omit `plugin_type=\"method\"` below, as `\"method\"` is the default plugin type.\n",
    "\n",
    "```python\n",
    "from tempor.core.plugins import register_plugin\n",
    "\n",
    "@register_plugin(name=\"my_plugin\", category=\"prediction.one_off.classification\", plugin_type=\"method\")\n",
    "class MyPlugin(BaseOneOffClassifier):\n",
    "    ...\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note on `__init__` parameters (arguments)\n",
    "\n",
    "You will also need to define the input parameters (arguments) that will be passed into your plugin's `__init__` in the\n",
    "following way:\n",
    "\n",
    "```python\n",
    "import dataclasses\n",
    "\n",
    "# 1. Write dataclass with your __init__ parameters:\n",
    "@dataclasses.dataclass\n",
    "class MyPluginParams:\n",
    "    # Specify the parameter, data type and default value as below:\n",
    "    lr: float = 0.001\n",
    "    batch_size: int = 100\n",
    "\n",
    "class MyPlugin(BaseOneOffClassifier):\n",
    "    # 2. Set the `ParamsDefinition` class variable in your plugin to this dataclass.\n",
    "    ParamsDefinition = MyPluginParams\n",
    "    \n",
    "    def __init__(self, **params: Any) -> None:\n",
    "        # 3. Call the parent __init__ as so.\n",
    "        super().__init__(**params)\n",
    "\n",
    "        # 4. You will now be able to access these in your class like so:\n",
    "        print(self.params.lr)\n",
    "        print(self.params.batch_size) \n",
    "\n",
    "\n",
    "# 5. The user will then be able to specify the arguments as necessary when initializing your plugin:\n",
    "model = MyPlugin(batch_size=22)\n",
    "```\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting it all together\n",
    "\n",
    "Now putting this together in an example of a one-off classifier plugin that always returns `1`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dataclasses\n",
    "from typing import Any, List\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from tempor.core.plugins import register_plugin\n",
    "from tempor.methods.core.params import Params\n",
    "from tempor.data import dataset, samples\n",
    "from tempor.methods.prediction.one_off.classification import BaseOneOffClassifier\n",
    "\n",
    "\n",
    "@dataclasses.dataclass\n",
    "class MyClassifierParams:\n",
    "    some_parameter: int = 1\n",
    "    other_parameter: float = 0.5\n",
    "\n",
    "\n",
    "@register_plugin(name=\"my_classifier\", category=\"prediction.one_off.classification\", plugin_type=\"method\")\n",
    "class MyClassifierClassifier(BaseOneOffClassifier):\n",
    "    ParamsDefinition = MyClassifierParams\n",
    "\n",
    "    def __init__(self, **param) -> None:\n",
    "        super().__init__(**param)\n",
    "\n",
    "    def _fit(self, data: dataset.BaseDataset, *args, **kwargs):\n",
    "        \"\"\"Does nothing.\"\"\"\n",
    "        return self  # Fit method needs to return `self`.\n",
    "\n",
    "    def _predict(self, data: dataset.PredictiveDataset, *args: Any, **kwargs: Any) -> samples.StaticSamplesBase:\n",
    "        \"\"\"Always returns 1\"\"\"\n",
    "\n",
    "        assert data.predictive.targets is not None\n",
    "        preds = np.ones_like(data.predictive.targets.numpy())\n",
    "\n",
    "        return samples.StaticSamples.from_numpy(preds, dtype=int)\n",
    "\n",
    "    def _predict_proba(self, data: dataset.PredictiveDataset, *args: Any, **kwargs: Any) -> samples.StaticSamplesBase:\n",
    "        \"\"\"Always returns 1.0\"\"\"\n",
    "\n",
    "        assert data.predictive.targets is not None\n",
    "        preds = np.ones_like(data.predictive.targets.numpy())\n",
    "\n",
    "        return samples.StaticSamples.from_numpy(preds, dtype=float)\n",
    "\n",
    "    @staticmethod\n",
    "    def hyperparameter_space(*args: Any, **kwargs: Any) -> List[Params]:\n",
    "        return []"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now see our plugin in TemporAI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'prediction'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'one_off'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'classification'</span>: <span style=\"font-weight: bold\">[</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'cde_classifier'</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'ode_classifier'</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'nn_classifier'</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'laplace_ode_classifier'</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'my_classifier'</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'laplace_ode_regressor'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'nn_regressor'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ode_regressor'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cde_regressor'</span><span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'classification'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'seq2seq_classifier'</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'seq2seq_regressor'</span><span style=\"font-weight: bold\">]}</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'preprocessing'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'encoding'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'static'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'static_onehot_encoder'</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ts_onehot_encoder'</span><span style=\"font-weight: bold\">]}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'imputation'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'static'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'static_tabular_imputer'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ffill'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ts_tabular_imputer'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'bfill'</span><span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'nop'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'nop_transformer'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'scaling'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'static'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'static_minmax_scaler'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'static_standard_scaler'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ts_minmax_scaler'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ts_standard_scaler'</span><span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'time_to_event'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'ts_coxph'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ts_xgb'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'dynamic_deephit'</span><span style=\"font-weight: bold\">]</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'treatments'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'one_off'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'synctwin_regressor'</span><span style=\"font-weight: bold\">]}</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'temporal'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'classification'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'crn_classifier'</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'regression'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'crn_regressor'</span><span style=\"font-weight: bold\">]}</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'prediction'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'one_off'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'classification'\u001b[0m: \u001b[1m[\u001b[0m\n",
       "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'cde_classifier'\u001b[0m,\n",
       "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'ode_classifier'\u001b[0m,\n",
       "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'nn_classifier'\u001b[0m,\n",
       "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'laplace_ode_classifier'\u001b[0m,\n",
       "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'my_classifier'\u001b[0m\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'laplace_ode_regressor'\u001b[0m, \u001b[32m'nn_regressor'\u001b[0m, \u001b[32m'ode_regressor'\u001b[0m, \u001b[32m'cde_regressor'\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'classification'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'seq2seq_classifier'\u001b[0m\u001b[1m]\u001b[0m, \u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'seq2seq_regressor'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'preprocessing'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'encoding'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'static'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'static_onehot_encoder'\u001b[0m\u001b[1m]\u001b[0m, \u001b[32m'temporal'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ts_onehot_encoder'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'imputation'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'static'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'static_tabular_imputer'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ffill'\u001b[0m, \u001b[32m'ts_tabular_imputer'\u001b[0m, \u001b[32m'bfill'\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'nop'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'nop_transformer'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'scaling'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'static'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'static_minmax_scaler'\u001b[0m, \u001b[32m'static_standard_scaler'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ts_minmax_scaler'\u001b[0m, \u001b[32m'ts_standard_scaler'\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'time_to_event'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'ts_coxph'\u001b[0m, \u001b[32m'ts_xgb'\u001b[0m, \u001b[32m'dynamic_deephit'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'treatments'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'one_off'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'synctwin_regressor'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m,\n",
       "\u001b[2;32m│   │   \u001b[0m\u001b[32m'temporal'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'classification'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'crn_classifier'\u001b[0m\u001b[1m]\u001b[0m, \u001b[32m'regression'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'crn_regressor'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`my_classifier` plugin found in the category 'prediction.one_off.classification': True\n"
     ]
    }
   ],
   "source": [
    "from tempor import plugin_loader\n",
    "\n",
    "all_plugins = plugin_loader.list(plugin_type=\"method\")\n",
    "\n",
    "pprint(all_plugins, indent_guides=True)\n",
    "\n",
    "my_classifier_found = \"my_classifier\" in all_plugins[\"prediction\"][\"one_off\"][\"classification\"]\n",
    "print(f\"`my_classifier` plugin found in the category 'prediction.one_off.classification': {my_classifier_found}\")\n",
    "assert my_classifier_found"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plugin can be used as normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyClassifierClassifier(\n",
      "    name='my_classifier',\n",
      "    category='prediction.one_off.classification',\n",
      "    plugin_type='method',\n",
      "    params={'some_parameter': 1, 'other_parameter': 0.5}\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Get the plugin.\n",
    "\n",
    "my_classifier = plugin_loader.get(\"prediction.one_off.classification.my_classifier\", plugin_type=\"method\")\n",
    "\n",
    "print(my_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<p><span style=\"font-family: monospace;\">StaticSamples</span> with data:</p><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>feat_0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_idx</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "StaticSamples with data:\n",
       "            feat_0\n",
       "sample_idx        \n",
       "0                1\n",
       "1                1\n",
       "2                1\n",
       "3                1\n",
       "4                1\n",
       "...            ...\n",
       "95               1\n",
       "96               1\n",
       "97               1\n",
       "98               1\n",
       "99               1\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit and predict on some data.\n",
    "\n",
    "dataset = plugin_loader.get(\"prediction.one_off.sine\", plugin_type=\"datasource\", random_state=42).load()\n",
    "\n",
    "my_classifier.fit(dataset)\n",
    "\n",
    "print(\"Prediction:\")\n",
    "my_classifier.predict(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎉 Congratulations!\n",
    "\n",
    "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the movement towards *Machine learning and AI for Medicine*, you can do so in the following ways!\n",
    "\n",
    "\n",
    "\n",
    "### ⭐ Star [TemporAI](https://github.com/vanderschaarlab/temporai) on GitHub\n",
    "\n",
    "- The easiest way to help our community is by just starring the repos! This helps raise awareness of the tools we're building.\n",
    "\n",
    "\n",
    "\n",
    "### Check out other projects from [vanderschaarlab](https://github.com/vanderschaarlab)\n",
    "- 📝 [HyperImpute](https://github.com/vanderschaarlab/hyperimpute)\n",
    "- 📊 [AutoPrognosis](https://github.com/vanderschaarlab/autoprognosis)\n",
    "- 🤖 [SynthCity](https://github.com/vanderschaarlab/synthcity)\n",
    " "
   ]
  }
 ],
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