{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SHAP explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "192b323c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "from trustyai.utils import TestModels\n",
    "from trustyai.model import feature, output, Model, Dataset\n",
    "from trustyai.model import simple_prediction\n",
    "from trustyai.explainers import SHAPExplainer\n",
    "\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "%pip install -r ../requirements-examples.txt --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f854540",
   "metadata": {},
   "source": [
    "# Python Models\n",
    "Now let's go over how to use a Python model with TrustyAI. First, let's grab a dataset, we'll use the California Housing dataset from `sklearn`, which tries\n",
    "to predict the median house value of various California housing districts given a number of different attributes of the district.\n",
    "\n",
    "After downloading the dataset, we then split it into train and test splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "629467eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Train: (16512, 8), X Test: (4128, 8), Y Train: (16512, 1), Y Test: (4128, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = datasets.fetch_california_housing(data_home=\"data\", return_X_y=True, as_frame=True)\n",
    "y = pd.DataFrame(y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.8)\n",
    "print(f\"X Train: {X_train.shape}, X Test: {X_test.shape}, Y Train: {y_train.shape}, Y Test: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ff2acc",
   "metadata": {},
   "source": [
    "Now let's grab our model, just a simple xgboost regressor. We'll then plot its test predictions against the the true test labels, to see how well it does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f6f4167",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rui/.virtualenvs/trustyai-explainability-python/lib/python3.10/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE 0.9280984757003108\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xgboost\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.style.use('../styles/material_rh.mplstyle')\n",
    "\n",
    "# uncomment to train from scratch\n",
    "# xgb_model = xgboost.XGBRegressor(objective='reg:squarederror', tree_method='gpu_hist')\n",
    "# xgb_model.fit(X_train, y_train)\n",
    "# print('Test MSE', xgb_model.score(X_test, y_test))\n",
    "# xgb_model.save_model(\"models/california_xgboost\")\n",
    " \n",
    "# load and score model\n",
    "xgb_model = xgboost.XGBRegressor(objective='reg:squarederror')\n",
    "xgb_model.load_model(\"models/california_xgboost\")\n",
    "print('Test MSE', xgb_model.score(X_test, y_test))\n",
    "\n",
    "# grab predictions and find largest error\n",
    "predictions = xgb_model.predict(X_test)\n",
    "worst = np.argmax(np.abs(predictions - y_test['MedHouseVal'].values))\n",
    "\n",
    "# plot predictions\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.scatter(y_test['MedHouseVal'].iloc[worst], predictions[worst], color='r')\n",
    "plt.plot([0,5], [0,5], color='k')\n",
    "plt.xlabel(\"True Value\")\n",
    "plt.ylabel(\"Predicted Value\")\n",
    "plt.title(\"XGBoost Predictions, California Housing\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26a96e2",
   "metadata": {},
   "source": [
    "That's pretty decent! Let's grab a point to explain; let's choose that really erroneous point marked in red in the above plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfc998f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc          3.729200\n",
       "HouseAge        6.000000\n",
       "AveRooms        4.583333\n",
       "AveBedrms       1.083333\n",
       "Population     69.000000\n",
       "AveOccup        2.875000\n",
       "Latitude       37.800000\n",
       "Longitude    -121.290000\n",
       "Name: 16556, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_to_explain = X_test.iloc[worst]\n",
    "point_to_explain"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a944ae2b",
   "metadata": {},
   "source": [
    "We'll need to convert it into a Prediction object in order to pass it to the SHAP Explainer. Notice we index our dataframes by `.iloc[worst:worst+1]`, this is because\n",
    "we need to pass single-row dataframes to preserve the type information of each column. If we used `.iloc[worst]` we'd retrieve a `pd.Series` object which removes the type information from individual values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a764a5a",
   "metadata": {},
   "source": [
    "Now we can wrap our model into a TrustyAI PredictionProvider. We do this via an `ArrowModel`, which rapidly speeds up the data transfer between Python and the TrustyAI Java library. \n",
    "To create an ArrowModel, we need to pass it a function that accepts a Pandas DataFrame as input and outputs a Pandas DataFrame or Numpy Array. All sklearn models satisfy this with their\n",
    "`predict` or `predict_proba` functions, so this is really easy to do.\n",
    "\n",
    "We then call the `get_as_prediction_provider`\n",
    "function on the ArrowModel, to which we pass an example datapoint to use as a template for our data conversions. Make sure this template point has the same schema (i.e., feature names and types) as all the other points you plan on passing to the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca2c5c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.model import Model\n",
    "trustyai_model = Model(xgb_model.predict, dataframe_input=True, output_names=['MedHouseVal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2dffa0",
   "metadata": {},
   "source": [
    "With our model successfully wrapped, we can create our SHAP explainer. To do this we need to specify a *background dataset*, a small $(\\le100)$ set of representative examples of the model's input. We'll use the first 100 training points as our background dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a50ccfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.explainers import SHAPExplainer\n",
    "explainer = SHAPExplainer(background=X_train[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76f02fc",
   "metadata": {},
   "source": [
    "We can now produce our explanation: \n",
    "\n",
    "(this will produce some Java warnings, don't worry about these)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42b29e2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[main] INFO org.apache.arrow.memory.BaseAllocator - Debug mode disabled.\n",
      "[main] INFO org.apache.arrow.memory.DefaultAllocationManagerOption - allocation manager type not specified, using netty as the default type\n",
      "[main] INFO org.apache.arrow.memory.CheckAllocator - Using DefaultAllocationManager at memory/DefaultAllocationManagerFactory.class\n"
     ]
    }
   ],
   "source": [
    "explanations = explainer.explain(inputs=X_test.iloc[worst:worst+1], \n",
    "                                 outputs=y_test.iloc[worst:worst+1],\n",
    "                                 model=trustyai_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing SHAP Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the explanation, first as a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_82ae8_row1_col1, #T_82ae8_row2_col1, #T_82ae8_row3_col1, #T_82ae8_row4_col1, #T_82ae8_row5_col1, #T_82ae8_row8_col1 {\n",
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       "  background-color: #96e0a9;\n",
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       "  background-color: #6ad384;\n",
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       "#T_82ae8_row5_col3 {\n",
       "  background-color: #4ccb6b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_82ae8_row6_col1, #T_82ae8_row7_col1 {\n",
       "  background-color: #13ba3c;\n",
       "}\n",
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       "  background-color: #6ed587;\n",
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       "  background-color: #f34848;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #13ba3c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_82ae8\">\n",
       "  <caption>Explanation of MedHouseVal</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_82ae8_level0_col0\" class=\"col_heading level0 col0\" >Feature</th>\n",
       "      <th id=\"T_82ae8_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "      <th id=\"T_82ae8_level0_col2\" class=\"col_heading level0 col2\" >Mean Background Value</th>\n",
       "      <th id=\"T_82ae8_level0_col3\" class=\"col_heading level0 col3\" >SHAP Value</th>\n",
       "      <th id=\"T_82ae8_level0_col4\" class=\"col_heading level0 col4\" >Confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_82ae8_row0_col0\" class=\"data row0 col0\" >Background</td>\n",
       "      <td id=\"T_82ae8_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_82ae8_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_82ae8_row0_col3\" class=\"data row0 col3\" >2.156897</td>\n",
       "      <td id=\"T_82ae8_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_82ae8_row1_col0\" class=\"data row1 col0\" >MedInc</td>\n",
       "      <td id=\"T_82ae8_row1_col1\" class=\"data row1 col1\" >3.729200</td>\n",
       "      <td id=\"T_82ae8_row1_col2\" class=\"data row1 col2\" >4.085421</td>\n",
       "      <td id=\"T_82ae8_row1_col3\" class=\"data row1 col3\" >0.264785</td>\n",
       "      <td id=\"T_82ae8_row1_col4\" class=\"data row1 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_82ae8_row2_col0\" class=\"data row2 col0\" >HouseAge</td>\n",
       "      <td id=\"T_82ae8_row2_col1\" class=\"data row2 col1\" >6.000000</td>\n",
       "      <td id=\"T_82ae8_row2_col2\" class=\"data row2 col2\" >28.610000</td>\n",
       "      <td id=\"T_82ae8_row2_col3\" class=\"data row2 col3\" >0.445050</td>\n",
       "      <td id=\"T_82ae8_row2_col4\" class=\"data row2 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_82ae8_row3_col0\" class=\"data row3 col0\" >AveRooms</td>\n",
       "      <td id=\"T_82ae8_row3_col1\" class=\"data row3 col1\" >4.583333</td>\n",
       "      <td id=\"T_82ae8_row3_col2\" class=\"data row3 col2\" >5.542214</td>\n",
       "      <td id=\"T_82ae8_row3_col3\" class=\"data row3 col3\" >0.304172</td>\n",
       "      <td id=\"T_82ae8_row3_col4\" class=\"data row3 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_82ae8_row4_col0\" class=\"data row4 col0\" >AveBedrms</td>\n",
       "      <td id=\"T_82ae8_row4_col1\" class=\"data row4 col1\" >1.083333</td>\n",
       "      <td id=\"T_82ae8_row4_col2\" class=\"data row4 col2\" >1.115171</td>\n",
       "      <td id=\"T_82ae8_row4_col3\" class=\"data row4 col3\" >0.433965</td>\n",
       "      <td id=\"T_82ae8_row4_col4\" class=\"data row4 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_82ae8_row5_col0\" class=\"data row5 col0\" >Population</td>\n",
       "      <td id=\"T_82ae8_row5_col1\" class=\"data row5 col1\" >69.000000</td>\n",
       "      <td id=\"T_82ae8_row5_col2\" class=\"data row5 col2\" >1583.910000</td>\n",
       "      <td id=\"T_82ae8_row5_col3\" class=\"data row5 col3\" >0.522398</td>\n",
       "      <td id=\"T_82ae8_row5_col4\" class=\"data row5 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_82ae8_row6_col0\" class=\"data row6 col0\" >AveOccup</td>\n",
       "      <td id=\"T_82ae8_row6_col1\" class=\"data row6 col1\" >2.875000</td>\n",
       "      <td id=\"T_82ae8_row6_col2\" class=\"data row6 col2\" >2.805248</td>\n",
       "      <td id=\"T_82ae8_row6_col3\" class=\"data row6 col3\" >0.427528</td>\n",
       "      <td id=\"T_82ae8_row6_col4\" class=\"data row6 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_82ae8_row7_col0\" class=\"data row7 col0\" >Latitude</td>\n",
       "      <td id=\"T_82ae8_row7_col1\" class=\"data row7 col1\" >37.800000</td>\n",
       "      <td id=\"T_82ae8_row7_col2\" class=\"data row7 col2\" >35.448800</td>\n",
       "      <td id=\"T_82ae8_row7_col3\" class=\"data row7 col3\" >-0.497719</td>\n",
       "      <td id=\"T_82ae8_row7_col4\" class=\"data row7 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82ae8_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_82ae8_row8_col0\" class=\"data row8 col0\" >Longitude</td>\n",
       "      <td id=\"T_82ae8_row8_col1\" class=\"data row8 col1\" >-121.290000</td>\n",
       "      <td id=\"T_82ae8_row8_col2\" class=\"data row8 col2\" >-119.392500</td>\n",
       "      <td id=\"T_82ae8_row8_col3\" class=\"data row8 col3\" >0.692924</td>\n",
       "      <td id=\"T_82ae8_row8_col4\" class=\"data row8 col4\" >1.318919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x157253550>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explanations.as_html()['MedHouseVal']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature values in red/green indicate a lower/higher value than the average background value of that feature. SHAP values in red/green indicate a negative/positive contribution to the prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the explanation as a candlestick plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanations.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_26dff_row1_col1, #T_26dff_row2_col1, #T_26dff_row3_col1, #T_26dff_row4_col1, #T_26dff_row5_col1, #T_26dff_row8_col1 {\n",
       "  background-color: #ee0000;\n",
       "}\n",
       "#T_26dff_row1_col3 {\n",
       "  background-color: #a5e5b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_26dff_row2_col3 {\n",
       "  background-color: #66d281;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_26dff_row3_col3 {\n",
       "  background-color: #96e0a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_26dff_row4_col3 {\n",
       "  background-color: #6ad384;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_26dff_row5_col3 {\n",
       "  background-color: #4ccb6b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_26dff_row6_col1, #T_26dff_row7_col1 {\n",
       "  background-color: #13ba3c;\n",
       "}\n",
       "#T_26dff_row6_col3 {\n",
       "  background-color: #6ed587;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_26dff_row7_col3 {\n",
       "  background-color: #f34848;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_26dff_row8_col3 {\n",
       "  background-color: #13ba3c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_26dff\">\n",
       "  <caption>Explanation of MedHouseVal</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_26dff_level0_col0\" class=\"col_heading level0 col0\" >Feature</th>\n",
       "      <th id=\"T_26dff_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "      <th id=\"T_26dff_level0_col2\" class=\"col_heading level0 col2\" >Mean Background Value</th>\n",
       "      <th id=\"T_26dff_level0_col3\" class=\"col_heading level0 col3\" >SHAP Value</th>\n",
       "      <th id=\"T_26dff_level0_col4\" class=\"col_heading level0 col4\" >Confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_26dff_row0_col0\" class=\"data row0 col0\" >Background</td>\n",
       "      <td id=\"T_26dff_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_26dff_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_26dff_row0_col3\" class=\"data row0 col3\" >2.156897</td>\n",
       "      <td id=\"T_26dff_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_26dff_row1_col0\" class=\"data row1 col0\" >MedInc</td>\n",
       "      <td id=\"T_26dff_row1_col1\" class=\"data row1 col1\" >3.729200</td>\n",
       "      <td id=\"T_26dff_row1_col2\" class=\"data row1 col2\" >4.085421</td>\n",
       "      <td id=\"T_26dff_row1_col3\" class=\"data row1 col3\" >0.264785</td>\n",
       "      <td id=\"T_26dff_row1_col4\" class=\"data row1 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_26dff_row2_col0\" class=\"data row2 col0\" >HouseAge</td>\n",
       "      <td id=\"T_26dff_row2_col1\" class=\"data row2 col1\" >6.000000</td>\n",
       "      <td id=\"T_26dff_row2_col2\" class=\"data row2 col2\" >28.610000</td>\n",
       "      <td id=\"T_26dff_row2_col3\" class=\"data row2 col3\" >0.445050</td>\n",
       "      <td id=\"T_26dff_row2_col4\" class=\"data row2 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_26dff_row3_col0\" class=\"data row3 col0\" >AveRooms</td>\n",
       "      <td id=\"T_26dff_row3_col1\" class=\"data row3 col1\" >4.583333</td>\n",
       "      <td id=\"T_26dff_row3_col2\" class=\"data row3 col2\" >5.542214</td>\n",
       "      <td id=\"T_26dff_row3_col3\" class=\"data row3 col3\" >0.304172</td>\n",
       "      <td id=\"T_26dff_row3_col4\" class=\"data row3 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_26dff_row4_col0\" class=\"data row4 col0\" >AveBedrms</td>\n",
       "      <td id=\"T_26dff_row4_col1\" class=\"data row4 col1\" >1.083333</td>\n",
       "      <td id=\"T_26dff_row4_col2\" class=\"data row4 col2\" >1.115171</td>\n",
       "      <td id=\"T_26dff_row4_col3\" class=\"data row4 col3\" >0.433965</td>\n",
       "      <td id=\"T_26dff_row4_col4\" class=\"data row4 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_26dff_row5_col0\" class=\"data row5 col0\" >Population</td>\n",
       "      <td id=\"T_26dff_row5_col1\" class=\"data row5 col1\" >69.000000</td>\n",
       "      <td id=\"T_26dff_row5_col2\" class=\"data row5 col2\" >1583.910000</td>\n",
       "      <td id=\"T_26dff_row5_col3\" class=\"data row5 col3\" >0.522398</td>\n",
       "      <td id=\"T_26dff_row5_col4\" class=\"data row5 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_26dff_row6_col0\" class=\"data row6 col0\" >AveOccup</td>\n",
       "      <td id=\"T_26dff_row6_col1\" class=\"data row6 col1\" >2.875000</td>\n",
       "      <td id=\"T_26dff_row6_col2\" class=\"data row6 col2\" >2.805248</td>\n",
       "      <td id=\"T_26dff_row6_col3\" class=\"data row6 col3\" >0.427528</td>\n",
       "      <td id=\"T_26dff_row6_col4\" class=\"data row6 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_26dff_row7_col0\" class=\"data row7 col0\" >Latitude</td>\n",
       "      <td id=\"T_26dff_row7_col1\" class=\"data row7 col1\" >37.800000</td>\n",
       "      <td id=\"T_26dff_row7_col2\" class=\"data row7 col2\" >35.448800</td>\n",
       "      <td id=\"T_26dff_row7_col3\" class=\"data row7 col3\" >-0.497719</td>\n",
       "      <td id=\"T_26dff_row7_col4\" class=\"data row7 col4\" >0.498505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26dff_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_26dff_row8_col0\" class=\"data row8 col0\" >Longitude</td>\n",
       "      <td id=\"T_26dff_row8_col1\" class=\"data row8 col1\" >-121.290000</td>\n",
       "      <td id=\"T_26dff_row8_col2\" class=\"data row8 col2\" >-119.392500</td>\n",
       "      <td id=\"T_26dff_row8_col3\" class=\"data row8 col3\" >0.692924</td>\n",
       "      <td id=\"T_26dff_row8_col4\" class=\"data row8 col4\" >1.318919</td>\n",
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