Auto-MPG_REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "345951f7-0803-4301-870e-941ea93d0f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc31f645-e843-423a-a190-fc31ff8a4e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('auto-mpg.data', delimiter = r'\\s+', header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "36038695-86bd-40bd-9989-a8ae300bd25a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.00</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.00</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.00</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1      2      3       4     5   6  7                          8\n",
       "0    18.0  8  307.0  130.0  3504.0  12.0  70  1  chevrolet chevelle malibu\n",
       "1    15.0  8  350.0  165.0  3693.0  11.5  70  1          buick skylark 320\n",
       "2    18.0  8  318.0  150.0  3436.0  11.0  70  1         plymouth satellite\n",
       "3    16.0  8  304.0  150.0  3433.0  12.0  70  1              amc rebel sst\n",
       "4    17.0  8  302.0  140.0  3449.0  10.5  70  1                ford torino\n",
       "..    ... ..    ...    ...     ...   ...  .. ..                        ...\n",
       "393  27.0  4  140.0  86.00  2790.0  15.6  82  1            ford mustang gl\n",
       "394  44.0  4   97.0  52.00  2130.0  24.6  82  2                  vw pickup\n",
       "395  32.0  4  135.0  84.00  2295.0  11.6  82  1              dodge rampage\n",
       "396  28.0  4  120.0  79.00  2625.0  18.6  82  1                ford ranger\n",
       "397  31.0  4  119.0  82.00  2720.0  19.4  82  1                 chevy s-10\n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6afb98b4-c2f5-4835-a936-e88c81abdc89",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 1:7]\n",
    "T = data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd840f03-fbd5-45a9-b60c-47cfabab509a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      18.0\n",
       "1      15.0\n",
       "2      18.0\n",
       "3      16.0\n",
       "4      17.0\n",
       "       ... \n",
       "393    27.0\n",
       "394    44.0\n",
       "395    32.0\n",
       "396    28.0\n",
       "397    31.0\n",
       "Name: 0, Length: 398, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2fcaf5b8-7db5-4a1e-aeed-9f8801073d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = X.to_numpy()\n",
    "# T = T.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fcaae347-1b18-4556-b684-6b8837aa372f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xTrain, xTest, tTrain, tTest = train_test_split(X, T, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "964e1962-e551-4bbd-8f9d-f33f2e36412e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '?'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 7\u001b[0m\n\u001b[0;32m      1\u001b[0m net \u001b[38;5;241m=\u001b[39m MLPRegressor(solver \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlbfgs\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      2\u001b[0m                    alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e-5\u001b[39m, \n\u001b[0;32m      3\u001b[0m                    verbose \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m      4\u001b[0m                    hidden_layer_sizes \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m4\u001b[39m),\n\u001b[0;32m      5\u001b[0m                    random_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxTrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtTrain\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1351\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1344\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1346\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1347\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1348\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1349\u001b[0m     )\n\u001b[0;32m   1350\u001b[0m ):\n\u001b[1;32m-> 1351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:752\u001b[0m, in \u001b[0;36mBaseMultilayerPerceptron.fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    734\u001b[0m \u001b[38;5;129m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    735\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y):\n\u001b[0;32m    736\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model to data matrix X and target(s) y.\u001b[39;00m\n\u001b[0;32m    737\u001b[0m \n\u001b[0;32m    738\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    750\u001b[0m \u001b[38;5;124;03m        Returns a trained MLP model.\u001b[39;00m\n\u001b[0;32m    751\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 752\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mincremental\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:442\u001b[0m, in \u001b[0;36mBaseMultilayerPerceptron._fit\u001b[1;34m(self, X, y, incremental)\u001b[0m\n\u001b[0;32m    435\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    436\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhidden_layer_sizes must be > 0, got \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m hidden_layer_sizes\n\u001b[0;32m    437\u001b[0m     )\n\u001b[0;32m    438\u001b[0m first_pass \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoefs_\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m    439\u001b[0m     \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarm_start \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m incremental\n\u001b[0;32m    440\u001b[0m )\n\u001b[1;32m--> 442\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mincremental\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfirst_pass\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    444\u001b[0m n_samples, n_features \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m    446\u001b[0m \u001b[38;5;66;03m# Ensure y is 2D\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1614\u001b[0m, in \u001b[0;36mMLPRegressor._validate_input\u001b[1;34m(self, X, y, incremental, reset)\u001b[0m\n\u001b[0;32m   1613\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_input\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, incremental, reset):\n\u001b[1;32m-> 1614\u001b[0m     X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1615\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1616\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1617\u001b[0m \u001b[43m        \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1618\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1619\u001b[0m \u001b[43m        \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1620\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1621\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1622\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1623\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1624\u001b[0m         y \u001b[38;5;241m=\u001b[39m column_or_1d(y, warn\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:650\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    648\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[0;32m    649\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 650\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    651\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    653\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1192\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1187\u001b[0m         estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[0;32m   1188\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1189\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1190\u001b[0m     )\n\u001b[1;32m-> 1192\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1193\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1194\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1195\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1196\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1197\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1198\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1199\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1200\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1201\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1204\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1205\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1206\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1208\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[0;32m   1210\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:951\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    949\u001b[0m         array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    950\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 951\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    952\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m    953\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    954\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    955\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\_array_api.py:521\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m    519\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39marray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    520\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 521\u001b[0m     array \u001b[38;5;241m=\u001b[39m \u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    523\u001b[0m \u001b[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001b[39;00m\n\u001b[0;32m    524\u001b[0m \u001b[38;5;66;03m# container that is consistent with the input's namespace.\u001b[39;00m\n\u001b[0;32m    525\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array)\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '?'"
     ]
    }
   ],
   "source": [
    "net = MLPRegressor(solver = 'lbfgs',\n",
    "                   alpha = 1e-5, \n",
    "                   verbose = 1,\n",
    "                   hidden_layer_sizes = (4, 4),\n",
    "                   random_state = 1)\n",
    "\n",
    "net.fit(xTrain, tTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e61a2e80-d7cb-41b4-acd5-d891002c6e48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "This MLPRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m yTest \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxTest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marrange(\u001b[38;5;28mlen\u001b[39m(yTest))\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1597\u001b[0m, in \u001b[0;36mMLPRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Predict using the multi-layer perceptron model.\u001b[39;00m\n\u001b[0;32m   1586\u001b[0m \n\u001b[0;32m   1587\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1595\u001b[0m \u001b[38;5;124;03m        The predicted values.\u001b[39;00m\n\u001b[0;32m   1596\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1597\u001b[0m     \u001b[43mcheck_is_fitted\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1598\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_predict(X)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1544\u001b[0m, in \u001b[0;36mcheck_is_fitted\u001b[1;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[0;32m   1541\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not an estimator instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (estimator))\n\u001b[0;32m   1543\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_fitted(estimator, attributes, all_or_any):\n\u001b[1;32m-> 1544\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NotFittedError(msg \u001b[38;5;241m%\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m(estimator)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m})\n",
      "\u001b[1;31mNotFittedError\u001b[0m: This MLPRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
     ]
    }
   ],
   "source": [
    "yTest = net.predict(xTest)\n",
    "\n",
    "x = np.arrange(len(yTest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f94fde07-f22e-4fcb-9571-dd61c0eab8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()\n",
    "plt.plot(x, tTest, '.-b')\n",
    "plt.plot(x, yTest, '.-r')"
   ]
  }
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