{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression with Hundred Hammers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we will explain how to use the HundredHammers library to perfom a basic model selection and hyperparameter optimization for a classification problem. \n",
    "\n",
    "To do this, we will use one of the example datasets available in the scikit-learn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:36:44.106350300Z",
     "start_time": "2023-09-22T19:36:42.905977300Z"
    }
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "import hundred_hammers as hh\n",
    "from hundred_hammers.model_zoo import (\n",
    "    DummyRegressor,\n",
    "    Ridge,\n",
    "    DecisionTreeRegressor,\n",
    "    KNeighborsRegressor,\n",
    ")\n",
    "\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we store the data in the X (input) and y (target) variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:36:44.122081400Z",
     "start_time": "2023-09-22T19:36:44.107350900Z"
    }
   },
   "outputs": [],
   "source": [
    "data = load_diabetes()\n",
    "X = data.data\n",
    "y = data.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to first train some models with their default configuration. If you don't specify the models that you want to use, some regression models will be chosen for you.\n",
    "\n",
    "To see which models are chosen by default, you can check the ```DEFAULT_REGRESSION_MODELS``` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:36:44.127474400Z",
     "start_time": "2023-09-22T19:36:44.123081800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Dummy Mean', DummyRegressor(), {}),\n",
       " ('Dummy Median', DummyRegressor(strategy='median'), {}),\n",
       " ('Linear Regression', LinearRegression(), {}),\n",
       " ('Decision Tree', DecisionTreeRegressor(), {}),\n",
       " ('SVR', SVR(), {}),\n",
       " ('Linear SVR', LinearSVR(), {}),\n",
       " ('Ridge', Ridge(), {}),\n",
       " ('Passive Aggressive', PassiveAggressiveRegressor(), {}),\n",
       " ('KNN', KNeighborsRegressor(), {}),\n",
       " ('Neural Network Regressor', MLPRegressor(), {}),\n",
       " ('Gaussian Process', GaussianProcessRegressor(), {}),\n",
       " ('Random Forest', RandomForestRegressor(), {}),\n",
       " ('AdaBoost', AdaBoostRegressor(), {}),\n",
       " ('Gradient Boosting', GradientBoostingRegressor(), {})]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hh.model_zoo.DEFAULT_REGRESSION_MODELS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that it is composed of a list of tuples. Each tuple contains the name we give to the regressors, an instance of the class that implements the regression model and a grid of hyperparameters (which now is empty, but will be explained later).\n",
    "\n",
    "Those are the models that we are going to use now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation with default models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First create the HundredHammersRegressor object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:36:44.130563800Z",
     "start_time": "2023-09-22T19:36:44.129062700Z"
    }
   },
   "outputs": [],
   "source": [
    "hh_models = hh.HundredHammersRegressor(show_progress_bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then evaluate the models. Apart from the actual data (the variables X and y), you can pass other parameters. ```optim_hyper``` checks whether we want to optimize the hyperparameters of the models and n_grid_points controls how many values from each hyperparameter to check in the optimization.\n",
    "\n",
    "Since we don't want to optimize the hyperparameters, optim_hyper will stay as false."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:09.814714700Z",
     "start_time": "2023-09-22T19:36:44.131564Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating models...: 100%|██████████| 14/14 [00:52<00:00,  3.77s/it]\n"
     ]
    }
   ],
   "source": [
    "# configure the logger\n",
    "hh.hh_logger.setLevel(logging.WARNING)\n",
    "\n",
    "# Evaluate the models and store the results in a variable\n",
    "df_results = hh_models.evaluate(X, y, optim_hyper=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the line above the evaluation of the models. This configures the logger to only show warnings (of which there should be none). The setting you most likely would want to use in an interactive enviroment would be ```logging.INFO```, since you get information about each model in \"real time\". \n",
    "\n",
    "If you want to see more detailed information, you can set the level to ```logging.DEBUG```. It outputs a lot of information, but it might be useful if you encounter a bug.\n",
    "\n",
    "For the purposes of this notebook, it will be kept to ```logging.WARNING``` but you are welcome to change it if you are running this notebook locally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now show the results of our execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:09.830188Z",
     "start_time": "2023-09-22T19:37:09.815715300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Avg R2 (Validation Train)</th>\n",
       "      <th>Std R2 (Validation Train)</th>\n",
       "      <th>Avg R2 (Validation Test)</th>\n",
       "      <th>Std R2 (Validation Test)</th>\n",
       "      <th>Avg R2 (Train)</th>\n",
       "      <th>Std R2 (Train)</th>\n",
       "      <th>Avg R2 (Test)</th>\n",
       "      <th>Std R2 (Test)</th>\n",
       "      <th>Avg MSE (Validation Train)</th>\n",
       "      <th>...</th>\n",
       "      <th>Avg MSE (Test)</th>\n",
       "      <th>Std MSE (Test)</th>\n",
       "      <th>Avg MAE (Validation Train)</th>\n",
       "      <th>Std MAE (Validation Train)</th>\n",
       "      <th>Avg MAE (Validation Test)</th>\n",
       "      <th>Std MAE (Validation Test)</th>\n",
       "      <th>Avg MAE (Train)</th>\n",
       "      <th>Std MAE (Train)</th>\n",
       "      <th>Avg MAE (Test)</th>\n",
       "      <th>Std MAE (Test)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dummy Mean</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.023206</td>\n",
       "      <td>0.033628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.001337</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6125.118931</td>\n",
       "      <td>...</td>\n",
       "      <td>5134.783503</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>67.301695</td>\n",
       "      <td>1.188564</td>\n",
       "      <td>67.615074</td>\n",
       "      <td>3.943941</td>\n",
       "      <td>67.339534</td>\n",
       "      <td>1.421085e-14</td>\n",
       "      <td>59.227456</td>\n",
       "      <td>7.105427e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dummy Median</td>\n",
       "      <td>-0.027476</td>\n",
       "      <td>0.007274</td>\n",
       "      <td>-0.050684</td>\n",
       "      <td>0.071611</td>\n",
       "      <td>-0.025922</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.045202</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6293.183322</td>\n",
       "      <td>...</td>\n",
       "      <td>5359.719101</td>\n",
       "      <td>9.094947e-13</td>\n",
       "      <td>66.517990</td>\n",
       "      <td>1.217067</td>\n",
       "      <td>66.993618</td>\n",
       "      <td>4.993507</td>\n",
       "      <td>66.566572</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>59.044944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.556522</td>\n",
       "      <td>0.015235</td>\n",
       "      <td>0.514558</td>\n",
       "      <td>0.067539</td>\n",
       "      <td>0.553925</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.332233</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2715.353061</td>\n",
       "      <td>...</td>\n",
       "      <td>3424.259334</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>42.404995</td>\n",
       "      <td>0.829168</td>\n",
       "      <td>43.952508</td>\n",
       "      <td>3.145022</td>\n",
       "      <td>42.593344</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>46.173585</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.046363</td>\n",
       "      <td>0.200661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.452581</td>\n",
       "      <td>7.741044e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7448.729213</td>\n",
       "      <td>3.969551e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.592362</td>\n",
       "      <td>5.524460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>72.787640</td>\n",
       "      <td>2.030020e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVR</td>\n",
       "      <td>0.159545</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.126855</td>\n",
       "      <td>0.062307</td>\n",
       "      <td>0.186951</td>\n",
       "      <td>2.775558e-17</td>\n",
       "      <td>0.128119</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5147.222487</td>\n",
       "      <td>...</td>\n",
       "      <td>4470.939683</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>59.987041</td>\n",
       "      <td>1.025494</td>\n",
       "      <td>61.080993</td>\n",
       "      <td>4.554385</td>\n",
       "      <td>58.932403</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>53.268617</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Linear SVR</td>\n",
       "      <td>-0.480290</td>\n",
       "      <td>0.021770</td>\n",
       "      <td>-0.499608</td>\n",
       "      <td>0.177843</td>\n",
       "      <td>-0.380897</td>\n",
       "      <td>2.104066e-03</td>\n",
       "      <td>-0.515761</td>\n",
       "      <td>2.670427e-03</td>\n",
       "      <td>9068.418326</td>\n",
       "      <td>...</td>\n",
       "      <td>7772.710116</td>\n",
       "      <td>1.369376e+01</td>\n",
       "      <td>72.936544</td>\n",
       "      <td>1.610525</td>\n",
       "      <td>73.117766</td>\n",
       "      <td>7.733969</td>\n",
       "      <td>71.098948</td>\n",
       "      <td>3.656171e-02</td>\n",
       "      <td>67.626756</td>\n",
       "      <td>6.017854e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.445599</td>\n",
       "      <td>0.013815</td>\n",
       "      <td>0.419461</td>\n",
       "      <td>0.046400</td>\n",
       "      <td>0.465084</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.340980</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>3394.642031</td>\n",
       "      <td>...</td>\n",
       "      <td>3379.406308</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>49.381703</td>\n",
       "      <td>0.756940</td>\n",
       "      <td>50.079365</td>\n",
       "      <td>3.114845</td>\n",
       "      <td>48.381085</td>\n",
       "      <td>7.105427e-15</td>\n",
       "      <td>46.566795</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Passive Aggressive</td>\n",
       "      <td>0.506681</td>\n",
       "      <td>0.026801</td>\n",
       "      <td>0.471776</td>\n",
       "      <td>0.060789</td>\n",
       "      <td>0.517667</td>\n",
       "      <td>9.373723e-03</td>\n",
       "      <td>0.358801</td>\n",
       "      <td>5.003282e-03</td>\n",
       "      <td>3020.352370</td>\n",
       "      <td>...</td>\n",
       "      <td>3288.020684</td>\n",
       "      <td>2.565646e+01</td>\n",
       "      <td>45.207007</td>\n",
       "      <td>1.403516</td>\n",
       "      <td>46.656725</td>\n",
       "      <td>3.233495</td>\n",
       "      <td>44.703236</td>\n",
       "      <td>6.186625e-01</td>\n",
       "      <td>45.272923</td>\n",
       "      <td>2.780478e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.615279</td>\n",
       "      <td>0.021038</td>\n",
       "      <td>0.410247</td>\n",
       "      <td>0.083013</td>\n",
       "      <td>0.618820</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.172488</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2354.986676</td>\n",
       "      <td>...</td>\n",
       "      <td>4243.422022</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>37.785156</td>\n",
       "      <td>1.130082</td>\n",
       "      <td>46.627299</td>\n",
       "      <td>3.687450</td>\n",
       "      <td>37.339377</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>49.492135</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Neural Network Regressor</td>\n",
       "      <td>-2.925185</td>\n",
       "      <td>0.119621</td>\n",
       "      <td>-2.988113</td>\n",
       "      <td>0.350351</td>\n",
       "      <td>-2.917495</td>\n",
       "      <td>9.059604e-02</td>\n",
       "      <td>-3.644418</td>\n",
       "      <td>1.081171e-01</td>\n",
       "      <td>24040.326288</td>\n",
       "      <td>...</td>\n",
       "      <td>23816.235196</td>\n",
       "      <td>5.544168e+02</td>\n",
       "      <td>134.976709</td>\n",
       "      <td>3.010464</td>\n",
       "      <td>135.014455</td>\n",
       "      <td>9.449213</td>\n",
       "      <td>134.932019</td>\n",
       "      <td>1.912264e+00</td>\n",
       "      <td>137.552821</td>\n",
       "      <td>1.910063e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gaussian Process</td>\n",
       "      <td>0.995085</td>\n",
       "      <td>0.001516</td>\n",
       "      <td>-13.683252</td>\n",
       "      <td>6.935421</td>\n",
       "      <td>0.984183</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>-9.864145</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>30.042210</td>\n",
       "      <td>...</td>\n",
       "      <td>55710.543741</td>\n",
       "      <td>7.275958e-12</td>\n",
       "      <td>2.979676</td>\n",
       "      <td>0.408915</td>\n",
       "      <td>182.608347</td>\n",
       "      <td>32.059452</td>\n",
       "      <td>5.759758</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>144.352684</td>\n",
       "      <td>2.842171e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.925400</td>\n",
       "      <td>0.003492</td>\n",
       "      <td>0.451311</td>\n",
       "      <td>0.081209</td>\n",
       "      <td>0.924440</td>\n",
       "      <td>1.996863e-03</td>\n",
       "      <td>0.261153</td>\n",
       "      <td>1.513057e-02</td>\n",
       "      <td>456.784823</td>\n",
       "      <td>...</td>\n",
       "      <td>3788.750941</td>\n",
       "      <td>7.758846e+01</td>\n",
       "      <td>17.203293</td>\n",
       "      <td>0.450834</td>\n",
       "      <td>46.492412</td>\n",
       "      <td>3.162357</td>\n",
       "      <td>17.225159</td>\n",
       "      <td>2.502492e-01</td>\n",
       "      <td>48.277247</td>\n",
       "      <td>5.898557e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.687707</td>\n",
       "      <td>0.017497</td>\n",
       "      <td>0.460084</td>\n",
       "      <td>0.072768</td>\n",
       "      <td>0.662306</td>\n",
       "      <td>5.843726e-03</td>\n",
       "      <td>0.279929</td>\n",
       "      <td>1.969705e-02</td>\n",
       "      <td>1911.433685</td>\n",
       "      <td>...</td>\n",
       "      <td>3692.470967</td>\n",
       "      <td>1.010051e+02</td>\n",
       "      <td>38.272124</td>\n",
       "      <td>1.042228</td>\n",
       "      <td>46.951305</td>\n",
       "      <td>3.079916</td>\n",
       "      <td>39.829220</td>\n",
       "      <td>4.565226e-01</td>\n",
       "      <td>47.444248</td>\n",
       "      <td>5.013379e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.889395</td>\n",
       "      <td>0.008559</td>\n",
       "      <td>0.441646</td>\n",
       "      <td>0.077073</td>\n",
       "      <td>0.857853</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.208258</td>\n",
       "      <td>1.787313e-03</td>\n",
       "      <td>677.065400</td>\n",
       "      <td>...</td>\n",
       "      <td>4059.994938</td>\n",
       "      <td>9.165209e+00</td>\n",
       "      <td>20.760164</td>\n",
       "      <td>0.796809</td>\n",
       "      <td>46.463974</td>\n",
       "      <td>3.126900</td>\n",
       "      <td>23.559587</td>\n",
       "      <td>3.552714e-15</td>\n",
       "      <td>49.229710</td>\n",
       "      <td>1.154214e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  Avg R2 (Validation Train)  \\\n",
       "0                 Dummy Mean                   0.000000   \n",
       "1               Dummy Median                  -0.027476   \n",
       "2          Linear Regression                   0.556522   \n",
       "3              Decision Tree                   1.000000   \n",
       "4                        SVR                   0.159545   \n",
       "5                 Linear SVR                  -0.480290   \n",
       "6                      Ridge                   0.445599   \n",
       "7         Passive Aggressive                   0.506681   \n",
       "8                        KNN                   0.615279   \n",
       "9   Neural Network Regressor                  -2.925185   \n",
       "10          Gaussian Process                   0.995085   \n",
       "11             Random Forest                   0.925400   \n",
       "12                  AdaBoost                   0.687707   \n",
       "13         Gradient Boosting                   0.889395   \n",
       "\n",
       "    Std R2 (Validation Train)  Avg R2 (Validation Test)  \\\n",
       "0                    0.000000                 -0.023206   \n",
       "1                    0.007274                 -0.050684   \n",
       "2                    0.015235                  0.514558   \n",
       "3                    0.000000                 -0.046363   \n",
       "4                    0.009264                  0.126855   \n",
       "5                    0.021770                 -0.499608   \n",
       "6                    0.013815                  0.419461   \n",
       "7                    0.026801                  0.471776   \n",
       "8                    0.021038                  0.410247   \n",
       "9                    0.119621                 -2.988113   \n",
       "10                   0.001516                -13.683252   \n",
       "11                   0.003492                  0.451311   \n",
       "12                   0.017497                  0.460084   \n",
       "13                   0.008559                  0.441646   \n",
       "\n",
       "    Std R2 (Validation Test)  Avg R2 (Train)  Std R2 (Train)  Avg R2 (Test)  \\\n",
       "0                   0.033628        0.000000    0.000000e+00      -0.001337   \n",
       "1                   0.071611       -0.025922    0.000000e+00      -0.045202   \n",
       "2                   0.067539        0.553925    0.000000e+00       0.332233   \n",
       "3                   0.200661        1.000000    0.000000e+00      -0.452581   \n",
       "4                   0.062307        0.186951    2.775558e-17       0.128119   \n",
       "5                   0.177843       -0.380897    2.104066e-03      -0.515761   \n",
       "6                   0.046400        0.465084    0.000000e+00       0.340980   \n",
       "7                   0.060789        0.517667    9.373723e-03       0.358801   \n",
       "8                   0.083013        0.618820    0.000000e+00       0.172488   \n",
       "9                   0.350351       -2.917495    9.059604e-02      -3.644418   \n",
       "10                  6.935421        0.984183    1.110223e-16      -9.864145   \n",
       "11                  0.081209        0.924440    1.996863e-03       0.261153   \n",
       "12                  0.072768        0.662306    5.843726e-03       0.279929   \n",
       "13                  0.077073        0.857853    1.110223e-16       0.208258   \n",
       "\n",
       "    Std R2 (Test)  Avg MSE (Validation Train)  ...  Avg MSE (Test)  \\\n",
       "0    0.000000e+00                 6125.118931  ...     5134.783503   \n",
       "1    0.000000e+00                 6293.183322  ...     5359.719101   \n",
       "2    0.000000e+00                 2715.353061  ...     3424.259334   \n",
       "3    7.741044e-02                    0.000000  ...     7448.729213   \n",
       "4    0.000000e+00                 5147.222487  ...     4470.939683   \n",
       "5    2.670427e-03                 9068.418326  ...     7772.710116   \n",
       "6    5.551115e-17                 3394.642031  ...     3379.406308   \n",
       "7    5.003282e-03                 3020.352370  ...     3288.020684   \n",
       "8    0.000000e+00                 2354.986676  ...     4243.422022   \n",
       "9    1.081171e-01                24040.326288  ...    23816.235196   \n",
       "10   0.000000e+00                   30.042210  ...    55710.543741   \n",
       "11   1.513057e-02                  456.784823  ...     3788.750941   \n",
       "12   1.969705e-02                 1911.433685  ...     3692.470967   \n",
       "13   1.787313e-03                  677.065400  ...     4059.994938   \n",
       "\n",
       "    Std MSE (Test)  Avg MAE (Validation Train)  Std MAE (Validation Train)  \\\n",
       "0     0.000000e+00                   67.301695                    1.188564   \n",
       "1     9.094947e-13                   66.517990                    1.217067   \n",
       "2     0.000000e+00                   42.404995                    0.829168   \n",
       "3     3.969551e+02                    0.000000                    0.000000   \n",
       "4     0.000000e+00                   59.987041                    1.025494   \n",
       "5     1.369376e+01                   72.936544                    1.610525   \n",
       "6     0.000000e+00                   49.381703                    0.756940   \n",
       "7     2.565646e+01                   45.207007                    1.403516   \n",
       "8     0.000000e+00                   37.785156                    1.130082   \n",
       "9     5.544168e+02                  134.976709                    3.010464   \n",
       "10    7.275958e-12                    2.979676                    0.408915   \n",
       "11    7.758846e+01                   17.203293                    0.450834   \n",
       "12    1.010051e+02                   38.272124                    1.042228   \n",
       "13    9.165209e+00                   20.760164                    0.796809   \n",
       "\n",
       "    Avg MAE (Validation Test)  Std MAE (Validation Test)  Avg MAE (Train)  \\\n",
       "0                   67.615074                   3.943941        67.339534   \n",
       "1                   66.993618                   4.993507        66.566572   \n",
       "2                   43.952508                   3.145022        42.593344   \n",
       "3                   61.592362                   5.524460         0.000000   \n",
       "4                   61.080993                   4.554385        58.932403   \n",
       "5                   73.117766                   7.733969        71.098948   \n",
       "6                   50.079365                   3.114845        48.381085   \n",
       "7                   46.656725                   3.233495        44.703236   \n",
       "8                   46.627299                   3.687450        37.339377   \n",
       "9                  135.014455                   9.449213       134.932019   \n",
       "10                 182.608347                  32.059452         5.759758   \n",
       "11                  46.492412                   3.162357        17.225159   \n",
       "12                  46.951305                   3.079916        39.829220   \n",
       "13                  46.463974                   3.126900        23.559587   \n",
       "\n",
       "    Std MAE (Train)  Avg MAE (Test)  Std MAE (Test)  \n",
       "0      1.421085e-14       59.227456    7.105427e-15  \n",
       "1      0.000000e+00       59.044944    0.000000e+00  \n",
       "2      0.000000e+00       46.173585    0.000000e+00  \n",
       "3      0.000000e+00       72.787640    2.030020e+00  \n",
       "4      0.000000e+00       53.268617    0.000000e+00  \n",
       "5      3.656171e-02       67.626756    6.017854e-02  \n",
       "6      7.105427e-15       46.566795    0.000000e+00  \n",
       "7      6.186625e-01       45.272923    2.780478e-01  \n",
       "8      0.000000e+00       49.492135    0.000000e+00  \n",
       "9      1.912264e+00      137.552821    1.910063e+00  \n",
       "10     0.000000e+00      144.352684    2.842171e-14  \n",
       "11     2.502492e-01       48.277247    5.898557e-01  \n",
       "12     4.565226e-01       47.444248    5.013379e-01  \n",
       "13     3.552714e-15       49.229710    1.154214e-01  \n",
       "\n",
       "[14 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's an ok way of displaying the result, but tables can sometimes be hard to read, this is why we also implement a couple of functions to display the information of the table in a more readable format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:10.309884200Z",
     "start_time": "2023-09-22T19:37:09.829186800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hh.plot_batch_results(df_results, metric_name=\"MSE\", title=\"Iris Dataset\", display=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:10.459463900Z",
     "start_time": "2023-09-22T19:37:10.309884200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take the models in positions 1, 2, 3 and 5\n",
    "models = [i for _, i, _ in hh_models.trained_models[1:3] + hh_models.trained_models[4:5]]\n",
    "\n",
    "# Plot the predictions\n",
    "hh.plot_regression_pred(\n",
    "    X,\n",
    "    y,\n",
    "    models=models,\n",
    "    metric=mean_squared_error,\n",
    "    title=\"Diabetes\",\n",
    "    y_label=\"Diabetes (Value)\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case we needed to use one of the trained models, we can take it from the ```trained_models``` attribute from the ```HundredHammersRegressor``` class. This value will consist on a list with tuples containing the name of the model and the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:10.464995500Z",
     "start_time": "2023-09-22T19:37:10.458464100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Dummy Mean', DummyRegressor(), {}),\n",
       " ('Dummy Median', DummyRegressor(strategy='median'), {}),\n",
       " ('Linear Regression', LinearRegression(), {}),\n",
       " ('Decision Tree', DecisionTreeRegressor(random_state=9), {}),\n",
       " ('SVR', SVR(), {}),\n",
       " ('Linear SVR', LinearSVR(random_state=9), {}),\n",
       " ('Ridge', Ridge(random_state=9), {}),\n",
       " ('Passive Aggressive', PassiveAggressiveRegressor(random_state=9), {}),\n",
       " ('KNN', KNeighborsRegressor(), {}),\n",
       " ('Neural Network Regressor', MLPRegressor(random_state=9), {}),\n",
       " ('Gaussian Process', GaussianProcessRegressor(random_state=9), {}),\n",
       " ('Random Forest', RandomForestRegressor(random_state=9), {}),\n",
       " ('AdaBoost', AdaBoostRegressor(random_state=9), {}),\n",
       " ('Gradient Boosting', GradientBoostingRegressor(random_state=9), {})]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hh_models.trained_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatic optimization of hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case we want to choose the models we want to evaluate, we must indicate them to the ```HundredHammersRegressor``` class.\n",
    "\n",
    "For this example, we will use four simple regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:10.466757800Z",
     "start_time": "2023-09-22T19:37:10.463994700Z"
    }
   },
   "outputs": [],
   "source": [
    "models_to_check = [\n",
    "    (\"Dummy\", DummyRegressor(), None),\n",
    "    (\"Ridge\", Ridge(random_state=0), None),\n",
    "    (\"Decision Tree\", DecisionTreeRegressor(random_state=0), None),\n",
    "    (\"KNN\", KNeighborsRegressor(), None),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each model has a name and an object that implements it. The third position in the tuple represents the user-specified grid of hyperparameters, however, we will let them be automatically generated.\n",
    "\n",
    "This will only happen for already configured models, if you want automatic generation of hyperparameters for a model that is not already added, check the \"example_add_model.ipynb\" notebook.\n",
    "\n",
    "We can now proceed passing these models to the ```HundredHammersRegressor``` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:10.472347300Z",
     "start_time": "2023-09-22T19:37:10.467748200Z"
    }
   },
   "outputs": [],
   "source": [
    "hh_models = hh.HundredHammersRegressor(models=models_to_check, show_progress_bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, since we want to optimize the hyperparameters of our models, we set the appropriate parameter to ```True```.\n",
    "\n",
    "We can configure how many parameters to check in the GridSearch step, ```n_grid_points``` will indicate how many values each of the hyperparameters will take. In this case, we will take 8 values for each one. \n",
    "In the case of categorical values, if there are less than 8 values, only those will be taken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:14.996323900Z",
     "start_time": "2023-09-22T19:37:10.471347Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating models...: 100%|██████████| 4/4 [00:01<00:00,  3.37it/s]         \n"
     ]
    }
   ],
   "source": [
    "df_results = hh_models.evaluate(X, y, optim_hyper=True, n_grid_points=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.004917300Z",
     "start_time": "2023-09-22T19:37:14.995317800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Model</th>\n",
       "      <th>Avg R2 (Validation Train)</th>\n",
       "      <th>Std R2 (Validation Train)</th>\n",
       "      <th>Avg R2 (Validation Test)</th>\n",
       "      <th>Std R2 (Validation Test)</th>\n",
       "      <th>Avg R2 (Train)</th>\n",
       "      <th>Std R2 (Train)</th>\n",
       "      <th>Avg R2 (Test)</th>\n",
       "      <th>Std R2 (Test)</th>\n",
       "      <th>Avg MSE (Validation Train)</th>\n",
       "      <th>...</th>\n",
       "      <th>Avg MSE (Test)</th>\n",
       "      <th>Std MSE (Test)</th>\n",
       "      <th>Avg MAE (Validation Train)</th>\n",
       "      <th>Std MAE (Validation Train)</th>\n",
       "      <th>Avg MAE (Validation Test)</th>\n",
       "      <th>Std MAE (Validation Test)</th>\n",
       "      <th>Avg MAE (Train)</th>\n",
       "      <th>Std MAE (Train)</th>\n",
       "      <th>Avg MAE (Test)</th>\n",
       "      <th>Std MAE (Test)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.023206</td>\n",
       "      <td>0.033628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.001337</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6125.118931</td>\n",
       "      <td>...</td>\n",
       "      <td>5134.783503</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>67.301695</td>\n",
       "      <td>1.188564</td>\n",
       "      <td>67.615074</td>\n",
       "      <td>3.943941</td>\n",
       "      <td>67.339534</td>\n",
       "      <td>1.421085e-14</td>\n",
       "      <td>59.227456</td>\n",
       "      <td>7.105427e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.553573</td>\n",
       "      <td>0.015243</td>\n",
       "      <td>0.516564</td>\n",
       "      <td>0.064871</td>\n",
       "      <td>0.551601</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.333235</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2733.434135</td>\n",
       "      <td>...</td>\n",
       "      <td>3419.120423</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>42.664323</td>\n",
       "      <td>0.836894</td>\n",
       "      <td>43.975160</td>\n",
       "      <td>3.176279</td>\n",
       "      <td>42.767356</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>46.036471</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.705049</td>\n",
       "      <td>0.025423</td>\n",
       "      <td>0.227255</td>\n",
       "      <td>0.138329</td>\n",
       "      <td>0.663131</td>\n",
       "      <td>2.495672e-03</td>\n",
       "      <td>-0.061039</td>\n",
       "      <td>1.694207e-02</td>\n",
       "      <td>1806.214920</td>\n",
       "      <td>...</td>\n",
       "      <td>5440.928933</td>\n",
       "      <td>8.687768e+01</td>\n",
       "      <td>29.342648</td>\n",
       "      <td>1.290889</td>\n",
       "      <td>52.845684</td>\n",
       "      <td>4.971056</td>\n",
       "      <td>31.059490</td>\n",
       "      <td>3.552714e-15</td>\n",
       "      <td>57.152247</td>\n",
       "      <td>7.021638e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.465824</td>\n",
       "      <td>0.017405</td>\n",
       "      <td>0.436415</td>\n",
       "      <td>0.061569</td>\n",
       "      <td>0.485286</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>0.315831</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>3270.483891</td>\n",
       "      <td>...</td>\n",
       "      <td>3508.367072</td>\n",
       "      <td>4.547474e-13</td>\n",
       "      <td>48.434238</td>\n",
       "      <td>0.888525</td>\n",
       "      <td>49.248325</td>\n",
       "      <td>2.949155</td>\n",
       "      <td>47.282775</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>47.117041</td>\n",
       "      <td>7.105427e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Model  Avg R2 (Validation Train)  Std R2 (Validation Train)  \\\n",
       "0          Dummy                   0.000000                   0.000000   \n",
       "1          Ridge                   0.553573                   0.015243   \n",
       "2  Decision Tree                   0.705049                   0.025423   \n",
       "3            KNN                   0.465824                   0.017405   \n",
       "\n",
       "   Avg R2 (Validation Test)  Std R2 (Validation Test)  Avg R2 (Train)  \\\n",
       "0                 -0.023206                  0.033628        0.000000   \n",
       "1                  0.516564                  0.064871        0.551601   \n",
       "2                  0.227255                  0.138329        0.663131   \n",
       "3                  0.436415                  0.061569        0.485286   \n",
       "\n",
       "   Std R2 (Train)  Avg R2 (Test)  Std R2 (Test)  Avg MSE (Validation Train)  \\\n",
       "0    0.000000e+00      -0.001337   0.000000e+00                 6125.118931   \n",
       "1    1.110223e-16       0.333235   0.000000e+00                 2733.434135   \n",
       "2    2.495672e-03      -0.061039   1.694207e-02                 1806.214920   \n",
       "3    5.551115e-17       0.315831   5.551115e-17                 3270.483891   \n",
       "\n",
       "   ...  Avg MSE (Test)  Std MSE (Test)  Avg MAE (Validation Train)  \\\n",
       "0  ...     5134.783503    0.000000e+00                   67.301695   \n",
       "1  ...     3419.120423    0.000000e+00                   42.664323   \n",
       "2  ...     5440.928933    8.687768e+01                   29.342648   \n",
       "3  ...     3508.367072    4.547474e-13                   48.434238   \n",
       "\n",
       "   Std MAE (Validation Train)  Avg MAE (Validation Test)  \\\n",
       "0                    1.188564                  67.615074   \n",
       "1                    0.836894                  43.975160   \n",
       "2                    1.290889                  52.845684   \n",
       "3                    0.888525                  49.248325   \n",
       "\n",
       "   Std MAE (Validation Test)  Avg MAE (Train)  Std MAE (Train)  \\\n",
       "0                   3.943941        67.339534     1.421085e-14   \n",
       "1                   3.176279        42.767356     0.000000e+00   \n",
       "2                   4.971056        31.059490     3.552714e-15   \n",
       "3                   2.949155        47.282775     0.000000e+00   \n",
       "\n",
       "   Avg MAE (Test)  Std MAE (Test)  \n",
       "0       59.227456    7.105427e-15  \n",
       "1       46.036471    0.000000e+00  \n",
       "2       57.152247    7.021638e-01  \n",
       "3       47.117041    7.105427e-15  \n",
       "\n",
       "[4 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have optimized the hyperparameters of the models, we can check which hyperparameters were chosen for each. This is done by checking the ```best_params``` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.012156200Z",
     "start_time": "2023-09-22T19:37:15.005918200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Dummy', {'strategy': 'mean'}),\n",
       " ('Ridge', {'alpha': 0.03727593720314938}),\n",
       " ('Decision Tree', {'criterion': 'absolute_error', 'max_depth': 5}),\n",
       " ('KNN', {'metric': 'cosine', 'n_neighbors': 72})]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hh_models.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.186457600Z",
     "start_time": "2023-09-22T19:37:15.009155600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hh.plot_batch_results(df_results, metric_name=\"MSE\", title=\"Iris Dataset\", display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization of hyperparameters with custom parameter grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, we will use four simple classifier models with grids of hyperparameters. \n",
    "\n",
    "These grid will contain all the paramaters that the gridsearch optimization will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.192816Z",
     "start_time": "2023-09-22T19:37:15.186457600Z"
    }
   },
   "outputs": [],
   "source": [
    "models_to_check = [\n",
    "    (\"Dummy\", DummyRegressor(), {\"strategy\": [\"median\"]}),\n",
    "    (\"Ridge\", Ridge(random_state=0), {\"alpha\": [1e-4, 1e-3, 1e-2, 0.1, 1, 10]}),\n",
    "    (\n",
    "        \"Decision Tree\",\n",
    "        DecisionTreeRegressor(random_state=0),\n",
    "        {\n",
    "            \"criterion\": [\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"],\n",
    "            \"max_depth\": [1, 2, 3, 4, 5, 6, 7],\n",
    "        },\n",
    "    ),\n",
    "    (\n",
    "        \"KNN\",\n",
    "        KNeighborsRegressor(),\n",
    "        {\"n_neighbors\": [1, 3, 5, 7, 9, 11], \"metric\": [\"manhattan\", \"euclidean\"]},\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now proceed passing these models to the ```HundredHammersRegressor``` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.193808300Z",
     "start_time": "2023-09-22T19:37:15.190320800Z"
    }
   },
   "outputs": [],
   "source": [
    "hh_models = hh.HundredHammersRegressor(models=models_to_check, show_progress_bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we want to optimize the hyperparameters of our models, we set the appropriate parameter to ```True```. \n",
    "\n",
    "We don't need to set the ```n_grid_points``` parameter since we have already chosen which parameters to take in the GridSearch step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.674320Z",
     "start_time": "2023-09-22T19:37:15.193808300Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating models...: 100%|██████████| 4/4 [00:00<00:00,  6.63it/s]         \n"
     ]
    }
   ],
   "source": [
    "df_results = hh_models.evaluate(X, y, optim_hyper=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.684181200Z",
     "start_time": "2023-09-22T19:37:15.674320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Model</th>\n",
       "      <th>Avg R2 (Validation Train)</th>\n",
       "      <th>Std R2 (Validation Train)</th>\n",
       "      <th>Avg R2 (Validation Test)</th>\n",
       "      <th>Std R2 (Validation Test)</th>\n",
       "      <th>Avg R2 (Train)</th>\n",
       "      <th>Std R2 (Train)</th>\n",
       "      <th>Avg R2 (Test)</th>\n",
       "      <th>Std R2 (Test)</th>\n",
       "      <th>Avg MSE (Validation Train)</th>\n",
       "      <th>...</th>\n",
       "      <th>Avg MSE (Test)</th>\n",
       "      <th>Std MSE (Test)</th>\n",
       "      <th>Avg MAE (Validation Train)</th>\n",
       "      <th>Std MAE (Validation Train)</th>\n",
       "      <th>Avg MAE (Validation Test)</th>\n",
       "      <th>Std MAE (Validation Test)</th>\n",
       "      <th>Avg MAE (Train)</th>\n",
       "      <th>Std MAE (Train)</th>\n",
       "      <th>Avg MAE (Test)</th>\n",
       "      <th>Std MAE (Test)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>-0.027476</td>\n",
       "      <td>0.007274</td>\n",
       "      <td>-0.050684</td>\n",
       "      <td>0.071611</td>\n",
       "      <td>-0.025922</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.045202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6293.183322</td>\n",
       "      <td>...</td>\n",
       "      <td>5359.719101</td>\n",
       "      <td>9.094947e-13</td>\n",
       "      <td>66.517990</td>\n",
       "      <td>1.217067</td>\n",
       "      <td>66.993618</td>\n",
       "      <td>4.993507</td>\n",
       "      <td>66.566572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.044944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.555347</td>\n",
       "      <td>0.015218</td>\n",
       "      <td>0.515900</td>\n",
       "      <td>0.066570</td>\n",
       "      <td>0.553033</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.329983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2722.561310</td>\n",
       "      <td>...</td>\n",
       "      <td>3435.796416</td>\n",
       "      <td>4.547474e-13</td>\n",
       "      <td>42.494128</td>\n",
       "      <td>0.829984</td>\n",
       "      <td>43.926355</td>\n",
       "      <td>3.180674</td>\n",
       "      <td>42.664912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.170390</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.473673</td>\n",
       "      <td>0.020150</td>\n",
       "      <td>0.373744</td>\n",
       "      <td>0.120035</td>\n",
       "      <td>0.477605</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>0.020330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3222.626891</td>\n",
       "      <td>...</td>\n",
       "      <td>5023.676966</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>43.848744</td>\n",
       "      <td>0.862175</td>\n",
       "      <td>47.866137</td>\n",
       "      <td>4.431692</td>\n",
       "      <td>44.014164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.353933</td>\n",
       "      <td>7.105427e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.554573</td>\n",
       "      <td>0.020198</td>\n",
       "      <td>0.459955</td>\n",
       "      <td>0.074056</td>\n",
       "      <td>0.548160</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.284378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2727.046694</td>\n",
       "      <td>...</td>\n",
       "      <td>3669.657350</td>\n",
       "      <td>9.094947e-13</td>\n",
       "      <td>41.663537</td>\n",
       "      <td>1.146837</td>\n",
       "      <td>45.521591</td>\n",
       "      <td>3.630353</td>\n",
       "      <td>41.737832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.806946</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Model  Avg R2 (Validation Train)  Std R2 (Validation Train)  \\\n",
       "0          Dummy                  -0.027476                   0.007274   \n",
       "1          Ridge                   0.555347                   0.015218   \n",
       "2  Decision Tree                   0.473673                   0.020150   \n",
       "3            KNN                   0.554573                   0.020198   \n",
       "\n",
       "   Avg R2 (Validation Test)  Std R2 (Validation Test)  Avg R2 (Train)  \\\n",
       "0                 -0.050684                  0.071611       -0.025922   \n",
       "1                  0.515900                  0.066570        0.553033   \n",
       "2                  0.373744                  0.120035        0.477605   \n",
       "3                  0.459955                  0.074056        0.548160   \n",
       "\n",
       "   Std R2 (Train)  Avg R2 (Test)  Std R2 (Test)  Avg MSE (Validation Train)  \\\n",
       "0    0.000000e+00      -0.045202            0.0                 6293.183322   \n",
       "1    1.110223e-16       0.329983            0.0                 2722.561310   \n",
       "2    5.551115e-17       0.020330            0.0                 3222.626891   \n",
       "3    0.000000e+00       0.284378            0.0                 2727.046694   \n",
       "\n",
       "   ...  Avg MSE (Test)  Std MSE (Test)  Avg MAE (Validation Train)  \\\n",
       "0  ...     5359.719101    9.094947e-13                   66.517990   \n",
       "1  ...     3435.796416    4.547474e-13                   42.494128   \n",
       "2  ...     5023.676966    0.000000e+00                   43.848744   \n",
       "3  ...     3669.657350    9.094947e-13                   41.663537   \n",
       "\n",
       "   Std MAE (Validation Train)  Avg MAE (Validation Test)  \\\n",
       "0                    1.217067                  66.993618   \n",
       "1                    0.829984                  43.926355   \n",
       "2                    0.862175                  47.866137   \n",
       "3                    1.146837                  45.521591   \n",
       "\n",
       "   Std MAE (Validation Test)  Avg MAE (Train)  Std MAE (Train)  \\\n",
       "0                   4.993507        66.566572              0.0   \n",
       "1                   3.180674        42.664912              0.0   \n",
       "2                   4.431692        44.014164              0.0   \n",
       "3                   3.630353        41.737832              0.0   \n",
       "\n",
       "   Avg MAE (Test)  Std MAE (Test)  \n",
       "0       59.044944    0.000000e+00  \n",
       "1       46.170390    0.000000e+00  \n",
       "2       54.353933    7.105427e-15  \n",
       "3       46.806946    0.000000e+00  \n",
       "\n",
       "[4 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have optimized the hyperparameters of the models, we can check which hyperparameters were chosen for each. This is done by checking the ```best_params``` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.689668900Z",
     "start_time": "2023-09-22T19:37:15.683181400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Dummy', {'strategy': 'median'}),\n",
       " ('Ridge', {'alpha': 0.01}),\n",
       " ('Decision Tree', {'criterion': 'absolute_error', 'max_depth': 2}),\n",
       " ('KNN', {'metric': 'euclidean', 'n_neighbors': 11})]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hh_models.best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also show the plots like last time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T19:37:15.870805100Z",
     "start_time": "2023-09-22T19:37:15.686668600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hh.plot_batch_results(df_results, metric_name=\"MSE\", title=\"Iris Dataset\", display=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "e_DL",
   "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": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}