house-price-prediction-decision-forest.ipynb

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      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n",
      "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n",
      "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"
     ]
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    {
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     "text": [
      "/kaggle/input/house-prices-advanced-regression-techniques/sample_submission.csv\n",
      "/kaggle/input/house-prices-advanced-regression-techniques/data_description.txt\n",
      "/kaggle/input/house-prices-advanced-regression-techniques/train.csv\n",
      "/kaggle/input/house-prices-advanced-regression-techniques/test.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow_decision_forests as tfdf\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "train_file_path = \"../input/house-prices-advanced-regression-techniques/train.csv\"\n",
    "dataset_df = pd.read_csv(train_file_path)"
   ]
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    {
     "data": {
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       "\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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Lvl</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Lvl</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "\n",
       "[3 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "dataset_df.head(3)"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "count      1460.000000\n",
      "mean     180921.195890\n",
      "std       79442.502883\n",
      "min       34900.000000\n",
      "25%      129975.000000\n",
      "50%      163000.000000\n",
      "75%      214000.000000\n",
      "max      755000.000000\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dataset_df = dataset_df.drop('Id', axis=1)\n",
    "print(dataset_df['SalePrice'].describe())"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20/3097212826.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset_df['SalePrice'], color='g', bins=100, hist_kws={'alpha': 0.4});\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 8))\n",
    "sns.distplot(dataset_df['SalePrice'], color='g', bins=100, hist_kws={'alpha': 0.4});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eec260d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:32.038787Z",
     "iopub.status.busy": "2023-08-12T16:41:32.038355Z",
     "iopub.status.idle": "2023-08-12T16:41:32.046273Z",
     "shell.execute_reply": "2023-08-12T16:41:32.045161Z"
    },
    "papermill": {
     "duration": 0.020787,
     "end_time": "2023-08-12T16:41:32.048699",
     "exception": false,
     "start_time": "2023-08-12T16:41:32.027912",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[dtype('int64'), dtype('O'), dtype('float64')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(dataset_df.dtypes.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45f2434c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:32.069441Z",
     "iopub.status.busy": "2023-08-12T16:41:32.069040Z",
     "iopub.status.idle": "2023-08-12T16:41:32.092945Z",
     "shell.execute_reply": "2023-08-12T16:41:32.092099Z"
    },
    "papermill": {
     "duration": 0.036942,
     "end_time": "2023-08-12T16:41:32.095162",
     "exception": false,
     "start_time": "2023-08-12T16:41:32.058220",
     "status": "completed"
    },
    "tags": []
   },
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  WoodDeckSF  \\\n",
       "0          2003       196.0         706           0  ...           0   \n",
       "1          1976         0.0         978           0  ...         298   \n",
       "2          2002       162.0         486           0  ...           0   \n",
       "3          1970         0.0         216           0  ...           0   \n",
       "4          2000       350.0         655           0  ...         192   \n",
       "\n",
       "   OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  \\\n",
       "0           61              0          0            0         0        0   \n",
       "1            0              0          0            0         0        0   \n",
       "2           42              0          0            0         0        0   \n",
       "3           35            272          0            0         0        0   \n",
       "4           84              0          0            0         0        0   \n",
       "\n",
       "   MoSold  YrSold  SalePrice  \n",
       "0       2    2008     208500  \n",
       "1       5    2007     181500  \n",
       "2       9    2008     223500  \n",
       "3       2    2006     140000  \n",
       "4      12    2008     250000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num = dataset_df.select_dtypes(include = ['float64', 'int64'])\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95bd9234",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:32.116141Z",
     "iopub.status.busy": "2023-08-12T16:41:32.115309Z",
     "iopub.status.idle": "2023-08-12T16:41:43.008459Z",
     "shell.execute_reply": "2023-08-12T16:41:43.007540Z"
    },
    "papermill": {
     "duration": 10.907399,
     "end_time": "2023-08-12T16:41:43.011961",
     "exception": false,
     "start_time": "2023-08-12T16:41:32.104562",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hGMY//vEPQ5Lx6KOPGoZhGE8//bQhySgpKek2j/Hx8cY3vvENc7n9+cfliiuuMK644oouj3Ps2DHD6XQaS5YsMS688EK3de2PWVtb22U7qaqq6nQfV5udOXOm27E3bNhgSDJ27dplptFecTKNjY2GJOO73/1uh3Wu37LrP9dvprtzxKJFi4zY2NhuP9P196Dt+eqiiy4yvvnNb7pt9/DDDxuSjHfeecctffLkyUa/fv2MTz/91O14jz32mNt2P/7xjw1Jxr333uuWvmvXLkOS8bOf/cwt/aOPPjL69+9vrFy5stN8t7a2Gk6n06ivrzckGRUVFd2WE+Ghq3sWm81mPPzww+Z2y5YtMwYPHtyjY912221u6a77l4ceesgtPSMjw7jooovM5VO9t+pq367OM22vDV3tqa2urjPT0tKMd999t9u6OHbsmPGf//zHSE1NNe644w4zvbq62pBkrFu3rsM+rr89GzZscEufOXOmce6553b7eQge4dCmXP+NHDmyy7wdP37ccDqdxpNPPmn06dPH+Pe//22uW7RoUYd9r7jiCrfrUJeuPvuqq64yvvzyyy4/v7tz1oMPPtjhPOzS03vSYEcPxhCTkZGhvn37aunSpXriiSc6vFLSU08++aSqq6vd/ouKOvmQnNddd12HNJvNpj/+8Y/at2+ffv7zn+u73/2uDh8+rJ/+9Kc677zz3F4dk06MGdL+sx977DGPygH01D333KPf//73+vnPf66LL77YbV1GRoYSExPN5X79+iktLc1tJr6XXnpJV155peLj4820Pn366IYbbuj0884555wOv/P77ruvR3ntrJ25BvrfunWrfvSjH2n8+PF64YUXtHDhQl1zzTUdnuZdf/31HT7fNSsaIJ14lffvf/+7srOz3Ya46NOnjxYsWKCDBw+af7+vvPJKffXVV3r11VclneipOHXqVE2ZMkVVVVVmmnRifJreMAzD46E0nnnmGV1++eUaMGCAoqKiFB0drccee0zvvvtuj/b/wQ9+0KGdXHbZZd3uc80117gtjx07VpLc/l7QXnEqLr74YkVHR5v//exnP3Nb39k5wlM33XSTXn31VbdrtXXr1mncuHFKT08302pra/XSSy9pzpw55sD0c+fO1Wmnndbl61vt8/ncc8/JYrHoxhtvdOuNcsYZZ+iCCy5we03s0KFD+v73v6+EhASzbY8cOVKSety+ER7a3rP85S9/0aJFi5Sbm6tf//rXkqRLL71Un332mebNm6eKiopuX6NvP0PreeedJ+nEmKLt03s7G7On91aeanuduWvXLv3hD39Q//79deWVV2r//v3mdseOHdP999+vMWPGqG/fvoqKilLfvn21f//+XrUli8XSYXy+sWPHejxrNQInlNtUdXW1JkyY0GHbN998U9dcc43i4uLUp08fRUdHa+HChTp+/Lg++OCDXn1uW22vz1555RWtXbtWu3fv1owZM9yGB/DWOasn96TBjkleQsw555yj7du364EHHlBubq6OHDmi5ORkLV++XD/4wQ96fJzzzjvPo4GIu3uN57zzzjP/qBiGoV/84hdasWKF7rnnHrfXb6xWq18HQQaKior0k5/8RD/96U+1bNmyDuvj4uI6pNlsNrfX/ZuamnTGGWd02K6zNOnECcGT33lMTEyXM9dGR0dr+vTpmj59upmn7OxsPffcc/rLX/6imTNnmtsOHz6cdoZuffrppzIMo9O/664x35qamiRJ3/zmNxUTE6Pt27crISFBdXV1mjp1qg4ePKhf/epX+vLLL7V9+3YlJydr1KhRkmReINXW1naZhyNHjuhf//pXh6B/T2zatEnXX3+95s6dq7y8PJ1xxhmKiorSb37zmx6PVzNixIhet5P2fy9sNpskdRgehPaK7gwbNkz9+/fv9KbhD3/4g1paWvTPf/6zQ0C7u3OEJ+bPn6+77rpLpaWlKi4u1r59+1RdXa2HH37YbbvHH39chmEoOztbn332mZl+zTXX6Pe//73ee++9DjPotv/b8sknn8gwDLcHdW0lJydLOjF0wbRp0/Txxx/rnnvu0fnnn6/Y2Fi1trYqMzOzR0PxIHy0v2eZMWOG6uvrtXLlSt14441asGCBjh07pt/+9re67rrr1NraqnHjxuknP/mJpk6d6nastrPDSjLHPu0s/ejRo6eUT19rf52ZmZmpiRMn6uyzz9a9996r9evXS5JWrFghu92uH/7wh7riiis0ZMgQWa1W3Xzzzb1qSzExMR0mtLDZbL2uJwReqLepQYMGuY2n39DQoG9961s699xz9ctf/lJJSUnq16+fXn/9deXm5p7SOaP99dm3vvUtDR8+XPPmzVNpaaluueUWr56zenJPGuzowRiCvvWtb+nPf/6zPv/8c7322msaP368br/9dj399NM+/+ye9jKxWCy64447NHjwYO3du9fHuQK6VlRUpMLCQhUWFroNTtxbcXFxamxs7JDeWdqp6E1Prri4ON1+++2SRDtDr7luMv75z392WPfxxx9LOhEEkU5cGE6YMEHbt29XVVWVzjjjDJ1//vnKysqSdGKA+BdeeMGt9+LFF1+soUOHavPmzV2OJ7h582a1tra6zerer1+/TgeNb/8E/amnntKoUaNUXl6ua6+9VpmZmbrkkkuCdsB52iva6tOnjyZPnqzdu3d3aINjxozRJZdcovPPP7/Dft6eOG/IkCH69re/rSeffFLHjx/XunXr1K9fP82bN8/cprW1VaWlpZJOjKs4ZMgQ87/f//73kjqf7KV9XocNGyaLxaKdO3d22ivlT3/6k6QT7eOtt97Sgw8+qNtuu00TJ07UuHHjOr3xQmQaO3asvvrqK7Nnkqsn7ueff67nn39ehmFo9uzZQdPrx2azdXpucj3E85YzzzxTw4YN01tvvWWmPfXUU1q4cKHuv/9+TZ8+XZdeeqkuueQSJkyCm1BrU2396U9/0pEjR7Rp0ybdeOONmjBhgi655JIuJ087Va43V1ztjHOWOwKMIaxPnz667LLLZLfbJUlvvPGGpK57U/hKZzen0okb1ObmZrMnDOBv9913nwoLC3X33Xfrxz/+8Skda9KkSXrhhRfcBqE/fvy4ORGGLzmdzi4vQl3d7mln6K3Y2Fhddtll2rRpk9v5orW1VU899ZRGjBihtLQ0M33KlCn63//9X23cuNEMJMbGxiozM1O/+tWv9PHHH7sFGPv27au8vDy9++67bjNQuxw6dEgFBQUaPHiw28yBSUlJevvtt922/eCDDzoMt2GxWNS3b1+3IEZjY6PXZpH2FO0VPVVQUKDjx4/r+9//fkAnLrnpppv08ccfa8uWLXrqqaf0ne98x3wNWpK2bdumgwcPKjc3Vy+99FKH/77xjW/oySefPOmkFrNnz5ZhGPrHP/6hSy65pMN/roCqq027rmddHnnkEe8WHCFrz549kmTOROsSGxurq666Sv/93/+t//znP/q///s/r3zeqd5bdXZee/HFF7ucIMlTBw8e1L/+9S+dfvrpZprFYunQlp5//nn94x//cEvz9/0jgkuotam2OjtnGIah3/72t6d87M646srVznpzzoqEdsYr0kHmL3/5i44cOaIvvvhC0omZa5999llJ0syZM/Xkk0/qxRdf1KxZs5SYmKijR4+aT41dN3annXaaRo4cqYqKCl155ZUaOnSohg0bpqSkJJ/keenSpfrss8903XXXKT09XX369NF7772nn//857JarfrhD3/ok88FuvOzn/1M9957r2bMmKFZs2bptddec1ufmZnZq+Pdfffd2rx5syZPnqx7771XMTExstvtnc6o5m2ff/65kpKSNHfuXE2ZMkUJCQn68ssv9fLLL+uXv/ylzjvvPM2ZM8fn+UDoevHFF1VXV9chvbi4WFOnTtWkSZN01113qW/fvnr44Ye1d+9erV+/3i14d+WVV+r48eN64YUX9MQTT5jpU6ZM0Y9//GNZLBa3noiStHLlSu3Zs0c//OEP9dZbb+mGG27QoEGD9Pbbb+vBBx/UJ598oueee87sKSlJCxYs0I033qhbb71V1113nerr6/XAAw90uOidPXu2Nm3apFtvvVXZ2dn66KOPdN999+nMM890G3/K32iv6KnLL79cdrtdt912my666CItXbpU3/jGN8yexRs3bpSkHr8Sffz4cfOasS3XDWJXpk2bphEjRujWW29VY2OjbrrpJrf1jz32mKKiovSjH/2o0+D4LbfcouXLl+v555/Xt7/97W7Lu3TpUt10003avXu3srKyFBsbq3/+85/auXOnzj//fP2///f/NHr0aJ1zzjnKz8+XYRgaOnSo/vznP5vjvSKy7N271wxeNzU1adOmTaqqqtJ3vvMdjRo1Sv/1X/+l/v376/LLL9eZZ56pxsZGFRcXa9CgQRo3bpxX8nCq91YLFizQPffco3vvvVdXXHGF9u3bp1//+tcaNGiQx3n66quvzGvb48ePq7a2Vg888IAkmb3lpRPnytLSUo0ePVpjx47V//7v/+rBBx/UiBEj3I53zjnnqH///vr973+v8847TwMGDNBZZ53FA7EwFA5tqq2pU6eqb9++mjdvnlauXKmjR4/qN7/5jT799NNTzucnn3xitrOjR49qz549+slPfqLBgweb58renLNcD9J++ctfatGiRYqOjta5556r00477ZTzGjQCMbMMujZy5MguZyyqra01du3aZXznO98xRo4cadhsNiMuLs644oorjM2bN7sdZ/v27caFF15o2Gw2Q5I5Q9nJZhRtm4/OZjV75plnOmy7bds2Y/HixcaYMWOMQYMGGVFRUcaZZ55pzJkzx21WTcPo2SyHgDd0NcOe6z+XkSNHGrNmzep0//Yz1v7tb38zMjMzDZvNZpxxxhlGXl6e8eijj3Y6i3Rns5G11dUs0p21D4fDYaxZs8a46qqrjMTERMNmsxn9+vUzzjvvPGPlypVGU1OT2/aSjNzc3G4/H5HhZDPx1dbWGn/961+NyZMnG7GxsUb//v2NzMxM489//nOHY7W2thrDhg0zJBn/+Mc/zPS//e1vhiS32QHb71dWVmZcccUVxqBBg8zPPvfcczud7bK1tdV44IEHjOTkZKNfv37GJZdcYrz44oudtslVq1YZSUlJhs1mM8477zzjt7/9baczbnY1i/SDDz7YZd319DzoOpZr5k3aK3prz549xk033WSMGjXK/L2kpKQYCxcuNF544QVzu+6uoVyzvHb2n2vGzM5mkXb50Y9+ZEgyEhISjOPHj5vphw8fNvr27Wtce+21Xeb/008/Nfr3729cffXVhmF8Pevt4cOHO93+8ccfNy677DLzb84555xjLFy40Ni9e7e5zb59+4ypU6cap512mjFkyBBj7ty5RkNDQ5ezjiL8dHb+GjRokJGRkWE89NBDxtGjRw3DMIwnnnjCmDRpkhEfH2/07dvXOOuss4zrr7/eePvttzscq/39T1e/1c7a2qncWzkcDmPlypVGQkKC0b9/f+OKK64w9uzZ47VZpK1Wq3HWWWcZV111lfHyyy+7bfvpp58aS5YsMU4//XQjJibGmDBhgvHXv/6103Pq+vXrjdGjRxvR0dFuba2rvz2d5Q3BK1za1KxZszrMBP3nP//ZuOCCC4x+/foZZ599tpGXl2f85S9/6fRey9NZpKOjo43k5GTjpptuMmpqaty27c05q6CgwDjrrLMMq9Xqlr/e3JMGM4thdDEwEgAAgA/cfPPNeuKJJ7Rx48YOk1gAAAAACD0EGAEAgF8dP35c1157raqqqvTnP/+5w6yEAAAAAEILAUYAAAAAAAAAHmMWaQAAAAAAAAAeI8AIAAAAAAAAwGMEGAEAYWv58uVKSkqSxWLR3r17zfSJEycqOTlZGRkZysjI0M9//nNzXUtLi+bNm6eUlBSlpaVp06ZN5rrW1lbddtttOuecc5SSkqKHH37Yr+UBAAAAgGAUFegMAADgK9nZ2Vq5cqUmTJjQYd3atWs1e/bsDulr1qyRzWZTTU2NamtrNX78eE2aNElDhgzRU089pX379umDDz7Q559/rosuukiTJ0/W6NGj/VEcAAAAAAhKIR1gbG1t1ccff6zTTjtNFosl0NkBJEmGYeiLL77QWWedJas1NDsJ07YQbDxtV1lZWb3+rPLycpWWlkqSRo0apaysLFVUVCgnJ0fl5eX6/ve/rz59+mjo0KG6/vrr9fTTT6uwsLBHx6ZtIdiEwzlLom0h+NC2AN+gbQG+4Y22FdIBxo8//lgJCQmBzgbQqY8++kgjRowIdDY8QttCsPJmu8rLy1NBQYHGjBmj4uJiJScnS5IaGho0cuRIc7ukpCQ1NDR0uW737t1dfobD4ZDD4TCX//GPf2jMmDFeyT/gTaF8zpI4byF40bYA36BtAb5xKm0rpAOMp512mqQTFTBw4MAA5yYwnE6nKisrNW3aNEVHRwc6O0Eh0HXS3NyshIQE8/cZik7WtgJdx8GO+jm53taRt9tVWVmZEhISZBiG7Ha7Zs+erX379pnr2z5JNgzDbd/u1rVXXFysoqKiDum/+93vFBMT42n2Aa9paWnRzTffHNLnLCm8rwnD/ZwSruULh+tBqfu2Fa7fXSiLhO+EtoX2qK+e666uvNG2QjrA6LrJGzhwYNhdTPaU0+lUTEyMBg4cSGP6/wVLnYRyV/eTta1gqeNgRf2cnKd15K125XpibLFYtGzZMt11111qampSXFycEhMTVVdXp+HDh0uS6uvrNXPmTEky140bN85cl5iY2OXnFBQUaMWKFeay68R97bXXdtm2qqqqNHXq1Ij97VAHJ/irHpqbm3XzzTeH9DlLCu9rwnA/p4R7+cK5bYX7dxeKIuk7oW3BhfrquZ7U1am0rZAOMAIA0FvHjh1TU1OT4uPjJUkbN25UfHy84uLiJElz586V3W5XaWmpamtrtWPHDpWUlJjrHnnkEc2ZM0eff/65ysvLtXXr1i4/y2azyWazdUiPjo7u9gLoZOsjAXVwgq/rgToGAACANxBgBACErdzcXFVUVKixsVFTpkzRgAED9NZbb2nWrFlyOByyWq0aNmyYNm/ebO6Tl5enxYsXKyUlRVarVXa7XUOHDpUkLViwQNXV1UpLSzO3Pe+88wJSNgAAAAAIFgQYAQBhy263y263d0jvbmKW2NhYlZeXd7quT58+nR4PAAAAACJZ6M7rDgAAAAAAACDgCDACAAAAAAAA8BgBRgAAAAAAAAAeYwzGHkrKf978d92qWQHMCRA80gu3yXHcQpsAvIy2BYQ2rhsRSThnAb5B20KooQcjEAK2bdumiy++WBdeeKHS09P1xBNPSJIOHTqkGTNmKDU1Venp6dq5c6e5T0tLi+bNm6eUlBSlpaVp06ZNgco+ACCCLF++XElJSbJYLNq7d6+ZvnjxYp177rnKyMhQVlaW9uzZY67LycnRiBEjlJGRoYyMDOXl5QUg5/6VlP+8+R8AAECoowcjEOQMw9D3vvc9vfTSSxo7dqzq6uo0evRozZkzR/n5+crMzNTWrVtVXV2t7OxsHThwQFFRUVqzZo1sNptqampUW1ur8ePHa9KkSRoyZEigiwQACGPZ2dlauXKlJkyY4JZ+7bXX6tFHH1VUVJSee+45XX/99frggw/M9fn5+Vq2bJm/s+tz9GYEAACRgB6MQIj47LPPJEnNzc2Ki4uTzWbThg0blJubK0kaN26c4uPjzV6M5eXl5rpRo0YpKytLFRUVAck7ACByZGVlacSIER3Sr7nmGkVFnXi2nZmZqfr6erW2tvo7ewAAAPABejACQc5isWjDhg2aM2eOYmNj9emnn2rTpk364osv1NraquHDh5vbJiUlqaGhQZLU0NCgkSNHdrquPYfDIYfDYS43NzdLkpxOp5xOZ4ftXWk2q+G2jBNc9UG9dK23dURdAuHll7/8pWbOnCmr9etn3Q899JAeffRRJSYm6ic/+YkyMjK63L+35y1/s/UxOk1vm7e223SX53A/p4Rr+cKtPAAAnAwBRiDIHTt2TMXFxaqoqNDll1+u6upqXXvttXr77bdlsVjctjUM9xuatuvbr2uruLhYRUVFHdIrKysVExPT5X73XXKi58mWLVt6VJZIU1VVFegsBL2e1lFLS4uPcwLAX5566ilt2LBBf/3rX820n/70pzrzzDNltVr1xz/+UVdddZX279+vAQMGdHoMT89b/vLApZ2ntz1ftt2mJ+fRcD+nhFv5OG8BACINAUYgyO3Zs0cff/yxLr/8ckknXoU+66yz9Pbbb0uSDh8+bPZirK+vV2JioiQpMTFRdXV1butmzpzZ6WcUFBRoxYoV5nJzc7MSEhI0bdo0DRw4sMP2TqdTVVVVume3VY5Wi/YWTvdegcOAq36mTp2q6OjoQGcnKPW2jly9kwCEtvLychUVFemFF17Q6aefbqafffbZ5r+/853vKD8/X++//74uvvjiTo/T2/OWv6UXbus0ve35su023Z1Hw/2cEq7l47wFAIg0BBiBIJeQkKCDBw/q/fff17nnnquamhodOHBAaWlpmjt3rux2uwoLC1VdXa3GxkZzUH3XutLSUtXW1mrHjh0qKSnp9DNsNptsNluH9Ojo6G4v9h2tFjmOW8LqhsCbTlZ/6HkdUY9A6NuwYYPuvvtubd++3XwY5nLw4EFz3MbXXntNTU1NSklJ6fJYnp63/MVx3NJpetu8td2mp38Hg6FsvhJu5fNVWbZt26Yf/ehHam1tldPpVF5enhYtWqRDhw5p4cKFOnDggGw2m0pKSsxrwpaWFi1ZskTV1dWyWq1atWqV5syZ45P8AQAiFwFGIMjFx8frkUceUXZ2tqxWqwzD0MMPP6yzzz5bq1ev1oIFC5Samqq+ffuqrKzMHEA/Ly9PixcvVkpKiqxWq+x2u4YOHRrg0gAAwl1ubq4qKirU2NioKVOmaMCAAaqpqdH8+fN1xhln6Nvf/ra57QsvvKC4uDjl5OTok08+UZ8+fdS/f38988wzGjRoUABLAQQfwzD0ve99Ty+99JLGjh2ruro6jR49WnPmzFF+fr4yMzO1detWVVdXKzs7WwcOHFBUVJTWrFkjm82mmpoa1dbWavz48Zo0aZKGDBkS6CIBAMIIAUYgBMybN0/z5s3rkB4fH6/KyspO94mNjVV5ebmvswYAgBu73S673d4hvbtJL7Zv3+7LLAFh5bPPPpN04jXsuLg42Ww2bdiwQbW1tZJODKcTHx+vnTt3auLEiSovL1dpaakkadSoUcrKylJFRYVycnICUwAAQFgiwAgAAAAAQc5isWjDhg2aM2eOYmNj9emnn2rTpk364osv1Nraao67LUlJSUlqaGiQJDU0NGjkyJGdrmuvNzO0u5ZtVsNtGYETrrOytxXOZQNCHQFGAAAAAAhyx44dU3FxsSoqKnT55Zerurpa1157rd5++21ZLO7jfhqG4bbcdn37dW15MkP7fZe0SurZbOjwj3Cblb0tT2doX758uTZv3qz6+nq98847Sk9PlyQtXrxYf/vb39S/f38NHDhQa9euVUZGhiQpJydH27dv17BhwyRJU6dO1YMPPihJam1t1Q9+8ANt2bJFFotFK1as0K233nrqBQRCGAFGAAAAAAhye/bs0ccff6zLL79c0olXoc866yy9/fbbkqTDhw+bvRjr6+vNyZQSExNVV1fntm7mzJmdfkZvZmh3zQB+z26rHK2WbmdDh3+E66zsbXk6Q3t2drZWrlxpTn7kcu211+rRRx9VVFSUnnvuOV1//fX64IMPzPX5+flatmxZh+M99dRT2rdvnz744AN9/vnnuuiiizR58mSNHj3ao/wB4YAAIwAAAHCKkvKfD3QWEOYSEhJ08OBBvf/++zr33HNVU1OjAwcOKC0tTXPnzpXdbldhYaGqq6vV2NhoBlJc60pLS1VbW6sdO3aopKSk08/wZIZ2R6tFjuOWsA1ohaJwm5W9LU/LlZWV1Wn6NddcY/47MzNT9fX1am1tldVq7fZ45eX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",
      "text/plain": [
       "<Figure size 1600x2000 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_num.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "962105a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:43.038936Z",
     "iopub.status.busy": "2023-08-12T16:41:43.037708Z",
     "iopub.status.idle": "2023-08-12T16:41:43.048248Z",
     "shell.execute_reply": "2023-08-12T16:41:43.046735Z"
    },
    "papermill": {
     "duration": 0.026413,
     "end_time": "2023-08-12T16:41:43.050572",
     "exception": false,
     "start_time": "2023-08-12T16:41:43.024159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1008 examples in training, 452 examples in testing.\n"
     ]
    }
   ],
   "source": [
    "def split_dataset(dataset, test_ratio=0.30):\n",
    "  test_indices = np.random.rand(len(dataset)) < test_ratio\n",
    "  return dataset[~test_indices], dataset[test_indices]\n",
    "\n",
    "train_ds_pd, valid_ds_pd = split_dataset(dataset_df)\n",
    "print(\"{} examples in training, {} examples in testing.\".format(\n",
    "    len(train_ds_pd), len(valid_ds_pd)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ae5db81b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:43.077507Z",
     "iopub.status.busy": "2023-08-12T16:41:43.076609Z",
     "iopub.status.idle": "2023-08-12T16:41:43.448239Z",
     "shell.execute_reply": "2023-08-12T16:41:43.447084Z"
    },
    "papermill": {
     "duration": 0.388296,
     "end_time": "2023-08-12T16:41:43.451093",
     "exception": false,
     "start_time": "2023-08-12T16:41:43.062797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "label = 'SalePrice'\n",
    "train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label, task = tfdf.keras.Task.REGRESSION)\n",
    "valid_ds = tfdf.keras.pd_dataframe_to_tf_dataset(valid_ds_pd, label=label, task = tfdf.keras.Task.REGRESSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34868d58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:43.477522Z",
     "iopub.status.busy": "2023-08-12T16:41:43.476618Z",
     "iopub.status.idle": "2023-08-12T16:41:43.544488Z",
     "shell.execute_reply": "2023-08-12T16:41:43.543430Z"
    },
    "papermill": {
     "duration": 0.084245,
     "end_time": "2023-08-12T16:41:43.547433",
     "exception": false,
     "start_time": "2023-08-12T16:41:43.463188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use /tmp/tmp_1aeyae8 as temporary training directory\n"
     ]
    }
   ],
   "source": [
    "rf = tfdf.keras.RandomForestModel(task = tfdf.keras.Task.REGRESSION)\n",
    "rf.compile(metrics=[\"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d0d11c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:43.574055Z",
     "iopub.status.busy": "2023-08-12T16:41:43.573337Z",
     "iopub.status.idle": "2023-08-12T16:41:58.389156Z",
     "shell.execute_reply": "2023-08-12T16:41:58.387782Z"
    },
    "papermill": {
     "duration": 14.832561,
     "end_time": "2023-08-12T16:41:58.392189",
     "exception": false,
     "start_time": "2023-08-12T16:41:43.559628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading training dataset...\n",
      "Training dataset read in 0:00:08.236427. Found 1008 examples.\n",
      "Training model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO 23-08-12 16:41:53.5506 UTC kernel.cc:1243] Loading model from path /tmp/tmp_1aeyae8/model/ with prefix 185411e1a2144c46\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model trained in 0:00:02.188473\n",
      "Compiling model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO 23-08-12 16:41:53.9684 UTC decision_forest.cc:660] Model loaded with 300 root(s), 95872 node(s), and 73 input feature(s).\n",
      "[INFO 23-08-12 16:41:53.9685 UTC abstract_model.cc:1311] Engine \"RandomForestOptPred\" built\n",
      "[INFO 23-08-12 16:41:53.9685 UTC kernel.cc:1075] Use fast generic engine\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7d30e2dfe320> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: could not get source code\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Model compiled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7d30ee8527d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x=train_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa3f9166",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:58.428513Z",
     "iopub.status.busy": "2023-08-12T16:41:58.427940Z",
     "iopub.status.idle": "2023-08-12T16:41:58.464355Z",
     "shell.execute_reply": "2023-08-12T16:41:58.462880Z"
    },
    "papermill": {
     "duration": 0.057383,
     "end_time": "2023-08-12T16:41:58.467378",
     "exception": false,
     "start_time": "2023-08-12T16:41:58.409995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script src=\"https://d3js.org/d3.v6.min.js\"></script>\n",
       "<div id=\"tree_plot_62f16f8ee9644bf394ec04f403397468\"></div>\n",
       "<script>\n",
       "/*\n",
       " * Copyright 2021 Google LLC.\n",
       " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       " * you may not use this file except in compliance with the License.\n",
       " * You may obtain a copy of the License at\n",
       " *\n",
       " *     https://www.apache.org/licenses/LICENSE-2.0\n",
       " *\n",
       " * Unless required by applicable law or agreed to in writing, software\n",
       " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       " * See the License for the specific language governing permissions and\n",
       " * limitations under the License.\n",
       " */\n",
       "\n",
       "/**\n",
       " *  Plotting of decision trees generated by TF-DF.\n",
       " *\n",
       " *  A tree is a recursive structure of node objects.\n",
       " *  A node contains one or more of the following components:\n",
       " *\n",
       " *    - A value: Representing the output of the node. If the node is not a leaf,\n",
       " *      the value is only present for analysis i.e. it is not used for\n",
       " *      predictions.\n",
       " *\n",
       " *    - A condition : For non-leaf nodes, the condition (also known as split)\n",
       " *      defines a binary test to branch to the positive or negative child.\n",
       " *\n",
       " *    - An explanation: Generally a plot showing the relation between the label\n",
       " *      and the condition to give insights about the effect of the condition.\n",
       " *\n",
       " *    - Two children : For non-leaf nodes, the children nodes. The first\n",
       " *      children (i.e. \"node.children[0]\") is the negative children (drawn in\n",
       " *      red). The second children is the positive one (drawn in green).\n",
       " *\n",
       " */\n",
       "\n",
       "/**\n",
       " * Plots a single decision tree into a DOM element.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!tree} raw_tree Recursive tree structure.\n",
       " * @param {string} canvas_id Id of the output dom element.\n",
       " */\n",
       "function display_tree(options, raw_tree, canvas_id) {\n",
       "  console.log(options);\n",
       "\n",
       "  // Determine the node placement.\n",
       "  const tree_struct = d3.tree().nodeSize(\n",
       "      [options.node_y_offset, options.node_x_offset])(d3.hierarchy(raw_tree));\n",
       "\n",
       "  // Boundaries of the node placement.\n",
       "  let x_min = Infinity;\n",
       "  let x_max = -x_min;\n",
       "  let y_min = Infinity;\n",
       "  let y_max = -x_min;\n",
       "\n",
       "  tree_struct.each(d => {\n",
       "    if (d.x > x_max) x_max = d.x;\n",
       "    if (d.x < x_min) x_min = d.x;\n",
       "    if (d.y > y_max) y_max = d.y;\n",
       "    if (d.y < y_min) y_min = d.y;\n",
       "  });\n",
       "\n",
       "  // Size of the plot.\n",
       "  const width = y_max - y_min + options.node_x_size + options.margin * 2;\n",
       "  const height = x_max - x_min + options.node_y_size + options.margin * 2 +\n",
       "      options.node_y_offset - options.node_y_size;\n",
       "\n",
       "  const plot = d3.select(canvas_id);\n",
       "\n",
       "  // Tool tip\n",
       "  options.tooltip = plot.append('div')\n",
       "                        .attr('width', 100)\n",
       "                        .attr('height', 100)\n",
       "                        .style('padding', '4px')\n",
       "                        .style('background', '#fff')\n",
       "                        .style('box-shadow', '4px 4px 0px rgba(0,0,0,0.1)')\n",
       "                        .style('border', '1px solid black')\n",
       "                        .style('font-family', 'sans-serif')\n",
       "                        .style('font-size', options.font_size)\n",
       "                        .style('position', 'absolute')\n",
       "                        .style('z-index', '10')\n",
       "                        .attr('pointer-events', 'none')\n",
       "                        .style('display', 'none');\n",
       "\n",
       "  // Create canvas\n",
       "  const svg = plot.append('svg').attr('width', width).attr('height', height);\n",
       "  const graph =\n",
       "      svg.style('overflow', 'visible')\n",
       "          .append('g')\n",
       "          .attr('font-family', 'sans-serif')\n",
       "          .attr('font-size', options.font_size)\n",
       "          .attr(\n",
       "              'transform',\n",
       "              () => `translate(${options.margin},${\n",
       "                  - x_min + options.node_y_offset / 2 + options.margin})`);\n",
       "\n",
       "  // Plot bounding box.\n",
       "  if (options.show_plot_bounding_box) {\n",
       "    svg.append('rect')\n",
       "        .attr('width', width)\n",
       "        .attr('height', height)\n",
       "        .attr('fill', 'none')\n",
       "        .attr('stroke-width', 1.0)\n",
       "        .attr('stroke', 'black');\n",
       "  }\n",
       "\n",
       "  // Draw the edges.\n",
       "  display_edges(options, graph, tree_struct);\n",
       "\n",
       "  // Draw the nodes.\n",
       "  display_nodes(options, graph, tree_struct);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Draw the nodes of the tree.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!graph} graph D3 search handle containing the graph.\n",
       " * @param {!tree_struct} tree_struct Structure of the tree (node placement,\n",
       " *     data, etc.).\n",
       " */\n",
       "function display_nodes(options, graph, tree_struct) {\n",
       "  const nodes = graph.append('g')\n",
       "                    .selectAll('g')\n",
       "                    .data(tree_struct.descendants())\n",
       "                    .join('g')\n",
       "                    .attr('transform', d => `translate(${d.y},${d.x})`);\n",
       "\n",
       "  nodes.append('rect')\n",
       "      .attr('x', 0.5)\n",
       "      .attr('y', 0.5)\n",
       "      .attr('width', options.node_x_size)\n",
       "      .attr('height', options.node_y_size)\n",
       "      .attr('stroke', 'lightgrey')\n",
       "      .attr('stroke-width', 1)\n",
       "      .attr('fill', 'white')\n",
       "      .attr('y', -options.node_y_size / 2);\n",
       "\n",
       "  // Brackets on the right of condition nodes without children.\n",
       "  non_leaf_node_without_children =\n",
       "      nodes.filter(node => node.data.condition != null && node.children == null)\n",
       "          .append('g')\n",
       "          .attr('transform', `translate(${options.node_x_size},0)`);\n",
       "\n",
       "  non_leaf_node_without_children.append('path')\n",
       "      .attr('d', 'M0,0 C 10,0 0,10 10,10')\n",
       "      .attr('fill', 'none')\n",
       "      .attr('stroke-width', 1.0)\n",
       "      .attr('stroke', '#F00');\n",
       "\n",
       "  non_leaf_node_without_children.append('path')\n",
       "      .attr('d', 'M0,0 C 10,0 0,-10 10,-10')\n",
       "      .attr('fill', 'none')\n",
       "      .attr('stroke-width', 1.0)\n",
       "      .attr('stroke', '#0F0');\n",
       "\n",
       "  const node_content = nodes.append('g').attr(\n",
       "      'transform',\n",
       "      `translate(0,${options.node_padding - options.node_y_size / 2})`);\n",
       "\n",
       "  node_content.append(node => create_node_element(options, node));\n",
       "}\n",
       "\n",
       "/**\n",
       " * Creates the D3 content for a single node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!node} node Node to draw.\n",
       " * @return {!d3} D3 content.\n",
       " */\n",
       "function create_node_element(options, node) {\n",
       "  // Output accumulator.\n",
       "  let output = {\n",
       "    // Content to draw.\n",
       "    content: d3.create('svg:g'),\n",
       "    // Vertical offset to the next element to draw.\n",
       "    vertical_offset: 0\n",
       "  };\n",
       "\n",
       "  // Conditions.\n",
       "  if (node.data.condition != null) {\n",
       "    display_condition(options, node.data.condition, output);\n",
       "  }\n",
       "\n",
       "  // Values.\n",
       "  if (node.data.value != null) {\n",
       "    display_value(options, node.data.value, output);\n",
       "  }\n",
       "\n",
       "  // Explanations.\n",
       "  if (node.data.explanation != null) {\n",
       "    display_explanation(options, node.data.explanation, output);\n",
       "  }\n",
       "\n",
       "  return output.content.node();\n",
       "}\n",
       "\n",
       "\n",
       "/**\n",
       " * Adds a single line of text inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {string} text Text to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_node_text(options, text, output) {\n",
       "  output.content.append('text')\n",
       "      .attr('x', options.node_padding)\n",
       "      .attr('y', output.vertical_offset)\n",
       "      .attr('alignment-baseline', 'hanging')\n",
       "      .text(text);\n",
       "  output.vertical_offset += 10;\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a single line of text inside of a node with a tooltip.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {string} text Text to display.\n",
       " * @param {string} tooltip Text in the Tooltip.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_node_text_with_tooltip(options, text, tooltip, output) {\n",
       "  const item = output.content.append('text')\n",
       "                   .attr('x', options.node_padding)\n",
       "                   .attr('alignment-baseline', 'hanging')\n",
       "                   .text(text);\n",
       "\n",
       "  add_tooltip(options, item, () => tooltip);\n",
       "  output.vertical_offset += 10;\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a tooltip to a dom element.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!dom} target Dom element to equip with a tooltip.\n",
       " * @param {!func} get_content Generates the html content of the tooltip.\n",
       " */\n",
       "function add_tooltip(options, target, get_content) {\n",
       "  function show(d) {\n",
       "    options.tooltip.style('display', 'block');\n",
       "    options.tooltip.html(get_content());\n",
       "  }\n",
       "\n",
       "  function hide(d) {\n",
       "    options.tooltip.style('display', 'none');\n",
       "  }\n",
       "\n",
       "  function move(d) {\n",
       "    options.tooltip.style('display', 'block');\n",
       "    options.tooltip.style('left', (d.pageX + 5) + 'px');\n",
       "    options.tooltip.style('top', d.pageY + 'px');\n",
       "  }\n",
       "\n",
       "  target.on('mouseover', show);\n",
       "  target.on('mouseout', hide);\n",
       "  target.on('mousemove', move);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a condition inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!condition} condition Condition to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_condition(options, condition, output) {\n",
       "  threshold_format = d3.format('r');\n",
       "\n",
       "  if (condition.type === 'IS_MISSING') {\n",
       "    display_node_text(options, `${condition.attribute} is missing`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'IS_TRUE') {\n",
       "    display_node_text(options, `${condition.attribute} is true`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'NUMERICAL_IS_HIGHER_THAN') {\n",
       "    format = d3.format('r');\n",
       "    display_node_text(\n",
       "        options,\n",
       "        `${condition.attribute} >= ${threshold_format(condition.threshold)}`,\n",
       "        output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'CATEGORICAL_IS_IN') {\n",
       "    display_node_text_with_tooltip(\n",
       "        options, `${condition.attribute} in [...]`,\n",
       "        `${condition.attribute} in [${condition.mask}]`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'CATEGORICAL_SET_CONTAINS') {\n",
       "    display_node_text_with_tooltip(\n",
       "        options, `${condition.attribute} intersect [...]`,\n",
       "        `${condition.attribute} intersect [${condition.mask}]`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'NUMERICAL_SPARSE_OBLIQUE') {\n",
       "    display_node_text_with_tooltip(\n",
       "        options, `Sparse oblique split...`,\n",
       "        `[${condition.attributes}]*[${condition.weights}]>=${\n",
       "            threshold_format(condition.threshold)}`,\n",
       "        output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  display_node_text(\n",
       "      options, `Non supported condition ${condition.type}`, output);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a value inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!value} value Value to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_value(options, value, output) {\n",
       "  if (value.type === 'PROBABILITY') {\n",
       "    const left_margin = 0;\n",
       "    const right_margin = 50;\n",
       "    const plot_width = options.node_x_size - options.node_padding * 2 -\n",
       "        left_margin - right_margin;\n",
       "\n",
       "    let cusum = Array.from(d3.cumsum(value.distribution));\n",
       "    cusum.unshift(0);\n",
       "    const distribution_plot = output.content.append('g').attr(\n",
       "        'transform', `translate(0,${output.vertical_offset + 0.5})`);\n",
       "\n",
       "    distribution_plot.selectAll('rect')\n",
       "        .data(value.distribution)\n",
       "        .join('rect')\n",
       "        .attr('height', 10)\n",
       "        .attr(\n",
       "            'x',\n",
       "            (d, i) =>\n",
       "                (cusum[i] * plot_width + left_margin + options.node_padding))\n",
       "        .attr('width', (d, i) => d * plot_width)\n",
       "        .style('fill', (d, i) => d3.schemeSet1[i]);\n",
       "\n",
       "    const num_examples =\n",
       "        output.content.append('g')\n",
       "            .attr('transform', `translate(0,${output.vertical_offset})`)\n",
       "            .append('text')\n",
       "            .attr('x', options.node_x_size - options.node_padding)\n",
       "            .attr('alignment-baseline', 'hanging')\n",
       "            .attr('text-anchor', 'end')\n",
       "            .text(`(${value.num_examples})`);\n",
       "\n",
       "    const distribution_details = d3.create('ul');\n",
       "    distribution_details.selectAll('li')\n",
       "        .data(value.distribution)\n",
       "        .join('li')\n",
       "        .append('span')\n",
       "        .text(\n",
       "            (d, i) =>\n",
       "                'class ' + i + ': ' + d3.format('.3%')(value.distribution[i]));\n",
       "\n",
       "    add_tooltip(options, distribution_plot, () => distribution_details.html());\n",
       "    add_tooltip(options, num_examples, () => 'Number of examples');\n",
       "\n",
       "    output.vertical_offset += 10;\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (value.type === 'REGRESSION') {\n",
       "    display_node_text(\n",
       "        options,\n",
       "        'value: ' + d3.format('r')(value.value) + ` (` +\n",
       "            d3.format('.6')(value.num_examples) + `)`,\n",
       "        output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (value.type === 'UPLIFT') {\n",
       "    display_node_text(\n",
       "        options,\n",
       "        'effect: ' + d3.format('r')(value.treatment_effect) + ` (` +\n",
       "            d3.format('.6')(value.num_examples) + `)`,\n",
       "        output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  display_node_text(options, `Non supported value ${value.type}`, output);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds an explanation inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!explanation} explanation Explanation to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_explanation(options, explanation, output) {\n",
       "  // Margin before the explanation.\n",
       "  output.vertical_offset += 10;\n",
       "\n",
       "  display_node_text(\n",
       "      options, `Non supported explanation ${explanation.type}`, output);\n",
       "}\n",
       "\n",
       "\n",
       "/**\n",
       " * Draw the edges of the tree.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!graph} graph D3 search handle containing the graph.\n",
       " * @param {!tree_struct} tree_struct Structure of the tree (node placement,\n",
       " *     data, etc.).\n",
       " */\n",
       "function display_edges(options, graph, tree_struct) {\n",
       "  // Draw an edge between a parent and a child node with a bezier.\n",
       "  function draw_single_edge(d) {\n",
       "    return 'M' + (d.source.y + options.node_x_size) + ',' + d.source.x + ' C' +\n",
       "        (d.source.y + options.node_x_size + options.edge_rounding) + ',' +\n",
       "        d.source.x + ' ' + (d.target.y - options.edge_rounding) + ',' +\n",
       "        d.target.x + ' ' + d.target.y + ',' + d.target.x;\n",
       "  }\n",
       "\n",
       "  graph.append('g')\n",
       "      .attr('fill', 'none')\n",
       "      .attr('stroke-width', 1.2)\n",
       "      .selectAll('path')\n",
       "      .data(tree_struct.links())\n",
       "      .join('path')\n",
       "      .attr('d', draw_single_edge)\n",
       "      .attr(\n",
       "          'stroke', d => (d.target === d.source.children[0]) ? '#0F0' : '#F00');\n",
       "}\n",
       "\n",
       "display_tree({\"margin\": 10, \"node_x_size\": 160, \"node_y_size\": 28, \"node_x_offset\": 180, \"node_y_offset\": 33, \"font_size\": 10, \"edge_rounding\": 20, \"node_padding\": 2, \"show_plot_bounding_box\": false}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 179695.203125, \"num_examples\": 1008.0, \"standard_deviation\": 70043.89415375568}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"GrLivArea\", \"threshold\": 1491.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 226949.3125, \"num_examples\": 463.0, \"standard_deviation\": 71262.16020265862}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"OverallQual\", \"threshold\": 7.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 303727.5625, \"num_examples\": 137.0, \"standard_deviation\": 65258.16327768912}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Neighborhood\", \"mask\": [\"NridgHt\", \"Gilbert\"]}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 372140.40625, \"num_examples\": 31.0, \"standard_deviation\": 66390.92907010618}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"BsmtExposure\", \"mask\": [\"Gd\"]}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 283720.03125, \"num_examples\": 106.0, \"standard_deviation\": 49456.456925479826}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"BsmtQual\", \"mask\": [\"TA\", \"Ex\"]}}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 194683.609375, \"num_examples\": 326.0, \"standard_deviation\": 43639.168766949704}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"KitchenQual\", \"mask\": [\"Gd\", \"Ex\"]}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 216370.421875, \"num_examples\": 182.0, \"standard_deviation\": 37435.43885903116}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"TotalBsmtSF\", \"threshold\": 805.5}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 167273.890625, \"num_examples\": 144.0, \"standard_deviation\": 34559.31936040297}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Fireplaces\", \"threshold\": 0.5}}]}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 139550.890625, \"num_examples\": 545.0, \"standard_deviation\": 35374.41834712831}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"YearBuilt\", \"threshold\": 1983.0}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 176475.921875, \"num_examples\": 139.0, \"standard_deviation\": 27675.472212446046}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"FullBath\", \"threshold\": 1.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 185102.453125, \"num_examples\": 103.0, \"standard_deviation\": 23363.40732783935}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"TotalBsmtSF\", \"threshold\": 1238.5}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 151794.46875, \"num_examples\": 36.0, \"standard_deviation\": 23948.36167208538}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"TotalBsmtSF\", \"threshold\": 1128.5}}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 126909.0625, \"num_examples\": 406.0, \"standard_deviation\": 28123.36365992214}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"GarageArea\", \"threshold\": 413.0}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 142382.5, \"num_examples\": 176.0, \"standard_deviation\": 25439.834918078614}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"GrLivArea\", \"threshold\": 1235.5}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 115068.515625, \"num_examples\": 230.0, \"standard_deviation\": 24031.274893849113}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"TotalBsmtSF\", \"threshold\": 969.5}}]}]}]}, \"#tree_plot_62f16f8ee9644bf394ec04f403397468\")\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfdf.model_plotter.plot_model_in_colab(rf, tree_idx=0, max_depth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "132cb155",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:58.506196Z",
     "iopub.status.busy": "2023-08-12T16:41:58.505694Z",
     "iopub.status.idle": "2023-08-12T16:41:58.708335Z",
     "shell.execute_reply": "2023-08-12T16:41:58.707284Z"
    },
    "papermill": {
     "duration": 0.222216,
     "end_time": "2023-08-12T16:41:58.711201",
     "exception": false,
     "start_time": "2023-08-12T16:41:58.488985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logs = rf.make_inspector().training_logs()\n",
    "plt.plot([log.num_trees for log in logs], [\n",
    "         log.evaluation.rmse for log in logs])\n",
    "plt.xlabel(\"Number of trees\")\n",
    "plt.ylabel(\"RMSE (out-of-bag)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54c9467c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:58.742546Z",
     "iopub.status.busy": "2023-08-12T16:41:58.742159Z",
     "iopub.status.idle": "2023-08-12T16:41:58.753335Z",
     "shell.execute_reply": "2023-08-12T16:41:58.751766Z"
    },
    "papermill": {
     "duration": 0.029907,
     "end_time": "2023-08-12T16:41:58.755922",
     "exception": false,
     "start_time": "2023-08-12T16:41:58.726015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Evaluation(num_examples=1008, accuracy=None, loss=None, rmse=27979.465446309183, ndcg=None, aucs=None, auuc=None, qini=None)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspector = rf.make_inspector()\n",
    "inspector.evaluation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dbea5172",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:58.787386Z",
     "iopub.status.busy": "2023-08-12T16:41:58.786944Z",
     "iopub.status.idle": "2023-08-12T16:41:59.656172Z",
     "shell.execute_reply": "2023-08-12T16:41:59.654825Z"
    },
    "papermill": {
     "duration": 0.887825,
     "end_time": "2023-08-12T16:41:59.658543",
     "exception": false,
     "start_time": "2023-08-12T16:41:58.770718",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 855ms/step - loss: 0.0000e+00 - mse: 1020760320.0000\n",
      "loss: 0.0000\n",
      "mse: 1020760320.0000\n"
     ]
    }
   ],
   "source": [
    "evaluation = rf.evaluate(x=valid_ds,return_dict=True)\n",
    "\n",
    "for name, value in evaluation.items():\n",
    "  print(f\"{name}: {value:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ce1f05f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:59.690924Z",
     "iopub.status.busy": "2023-08-12T16:41:59.690475Z",
     "iopub.status.idle": "2023-08-12T16:41:59.698841Z",
     "shell.execute_reply": "2023-08-12T16:41:59.697583Z"
    },
    "papermill": {
     "duration": 0.028352,
     "end_time": "2023-08-12T16:41:59.702343",
     "exception": false,
     "start_time": "2023-08-12T16:41:59.673991",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available variable importances:\n",
      "\t NUM_NODES\n",
      "\t INV_MEAN_MIN_DEPTH\n",
      "\t NUM_AS_ROOT\n",
      "\t SUM_SCORE\n"
     ]
    }
   ],
   "source": [
    "print(f\"Available variable importances:\")\n",
    "for importance in inspector.variable_importances().keys():\n",
    "  print(\"\\t\", importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3fc05013",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:59.734398Z",
     "iopub.status.busy": "2023-08-12T16:41:59.733974Z",
     "iopub.status.idle": "2023-08-12T16:41:59.743628Z",
     "shell.execute_reply": "2023-08-12T16:41:59.742383Z"
    },
    "papermill": {
     "duration": 0.028778,
     "end_time": "2023-08-12T16:41:59.746193",
     "exception": false,
     "start_time": "2023-08-12T16:41:59.717415",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(\"OverallQual\" (1; #62), 122.0),\n",
       " (\"GarageCars\" (1; #32), 51.0),\n",
       " (\"ExterQual\" (4; #22), 43.0),\n",
       " (\"Neighborhood\" (4; #59), 35.0),\n",
       " (\"GrLivArea\" (1; #38), 12.0),\n",
       " (\"YearBuilt\" (1; #76), 10.0),\n",
       " (\"KitchenQual\" (4; #44), 8.0),\n",
       " (\"GarageArea\" (1; #31), 5.0),\n",
       " (\"TotalBsmtSF\" (1; #73), 5.0),\n",
       " (\"BsmtQual\" (4; #14), 4.0),\n",
       " (\"1stFlrSF\" (1; #0), 3.0),\n",
       " (\"GarageYrBlt\" (1; #37), 2.0)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspector.variable_importances()[\"NUM_AS_ROOT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7fef410",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T16:41:59.779380Z",
     "iopub.status.busy": "2023-08-12T16:41:59.778558Z",
     "iopub.status.idle": "2023-08-12T16:42:00.166051Z",
     "shell.execute_reply": "2023-08-12T16:42:00.164789Z"
    },
    "papermill": {
     "duration": 0.407166,
     "end_time": "2023-08-12T16:42:00.168885",
     "exception": false,
     "start_time": "2023-08-12T16:41:59.761719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# Mean decrease in AUC of the class 1 vs the others.\n",
    "variable_importance_metric = \"NUM_AS_ROOT\"\n",
    "variable_importances = inspector.variable_importances()[\n",
    "    variable_importance_metric]\n",
    "\n",
    "# Extract the feature name and importance values.\n",
    "#\n",
    "# `variable_importances` is a list of <feature, importance> tuples.\n",
    "feature_names = [vi[0].name for vi in variable_importances]\n",
    "feature_importances = [vi[1] for vi in variable_importances]\n",
    "# The feature are ordered in decreasing importance value.\n",
    "feature_ranks = range(len(feature_names))\n",
    "bar = plt.barh(feature_ranks, feature_importances,\n",
    "               label=[str(x) for x in feature_ranks])\n",
    "plt.yticks(feature_ranks, feature_names)\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "# TODO: Replace with \"plt.bar_label()\" when available.\n",
    "# Label each bar with values\n",
    "for importance, patch in zip(feature_importances, bar.patches):\n",
    "    plt.text(patch.get_x() + patch.get_width(),\n",
    "             patch.get_y(), f\"{importance:.4f}\", va=\"top\")\n",
    "\n",
    "plt.xlabel(variable_importance_metric)\n",
    "plt.title(\"NUM AS ROOT of the class 1 vs the others\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00fbf9bb",
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    "execution": {
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     "start_time": "2023-08-12T16:42:00.185538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 23ms/step\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
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       "      <td>1464</td>\n",
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       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>197306.687500</td>\n",
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       "     Id      SalePrice\n",
       "0  1461  124121.203125\n",
       "1  1462  152227.062500\n",
       "2  1463  179542.484375\n",
       "3  1464  179915.156250\n",
       "4  1465  197306.687500"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_file_path = \"../input/house-prices-advanced-regression-techniques/test.csv\"\n",
    "test_data = pd.read_csv(test_file_path)\n",
    "ids = test_data.pop('Id')\n",
    "\n",
    "test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(\n",
    "    test_data,\n",
    "    task=tfdf.keras.Task.REGRESSION)\n",
    "\n",
    "preds = rf.predict(test_ds)\n",
    "output = pd.DataFrame({'Id': ids,\n",
    "                       'SalePrice': preds.squeeze()})\n",
    "\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dccee6a4",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 25ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>179542.484375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>179915.156250</td>\n",
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       "    <tr>\n",
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       "      <td>1465</td>\n",
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       "     Id      SalePrice\n",
       "0  1461  124121.203125\n",
       "1  1462  152227.062500\n",
       "2  1463  179542.484375\n",
       "3  1464  179915.156250\n",
       "4  1465  197306.687500"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')\n",
    "sample_submission_df['SalePrice'] = rf.predict(test_ds)\n",
    "sample_submission_df.to_csv('/kaggle/working/submission.csv', index=False)\n",
    "sample_submission_df.head()"
   ]
  }
 ],
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