03 - Linear Regression and Decision Tree predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2adb6c71",
   "metadata": {},
   "source": [
    "# Progetto di Data and Web mining\n",
    "\n",
    "Matteo Spanio, A.A. 2021/2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d330c826",
   "metadata": {},
   "source": [
    "# Linear model\n",
    "\n",
    "Let's verifity the assumption of linearity relation\n",
    "\n",
    "## Globally\n",
    "\n",
    "Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fc62495",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Zillow.data as zd\n",
    "from Zillow.transform import ZillowTransformer, ZillowEncoder\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "df_train = zd.get_train_dataset()\n",
    "df_validation = zd.get_validation_dataset()\n",
    "\n",
    "preprocessor = ZillowTransformer()\n",
    "preprocessor.fit(df_train)\n",
    "\n",
    "df_train = preprocessor.transform(df_train)\n",
    "df_validation = preprocessor.transform(df_validation)\n",
    "\n",
    "# one hot encoding and column type transform\n",
    "encoder = ZillowEncoder()\n",
    "encoder.fit(df_train)\n",
    "\n",
    "df_train = encoder.transform(df_train)\n",
    "df_validation = encoder.transform(df_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1014a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = zd.split_x_y(df_train)\n",
    "X_test, y_test = zd.split_x_y(df_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df72288a",
   "metadata": {},
   "source": [
    "The linear regression model implemented by sklearn provides two parameters to set, usually it isn't a model to tune, but since there are these parameters and linear regression training is really fast is worth looking for the best params."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2df0e06a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_intercept': False, 'positive': True}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from Zillow.metrics import print_measure\n",
    "import pandas as pd\n",
    "\n",
    "lr = LinearRegression()\n",
    "\n",
    "params = {\n",
    "    'fit_intercept': [True, False],\n",
    "    'positive': [False, True]\n",
    "}\n",
    "\n",
    "gscv = GridSearchCV(estimator=lr,\n",
    "                    scoring='neg_mean_absolute_error',\n",
    "                    param_grid=params,\n",
    "                    cv=5,\n",
    "                    n_jobs=-1\n",
    "                   )\n",
    "\n",
    "gscv.fit(X_train, y_train)\n",
    "\n",
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7888770",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.07094\n",
      " Mean squared error: 0.02999\n",
      "           R2 score: 0.00212\n"
     ]
    }
   ],
   "source": [
    "print_measure(y_true=y_test, y_pred=gscv.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddfea6e4",
   "metadata": {},
   "source": [
    "<div class='alert alert-info'>\n",
    "    From now on I'll assume that <code>fit_intercept = False</code> and <code>positive = True</code> are the best parameters for any linear regression model on this dataset\n",
    "</div>\n",
    "\n",
    "Then it's interesting to look if we can optimize this model, maybe not all features are useful. Let's get a feature ranking with recursive feature elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa92bb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RFE(estimator=LinearRegression(fit_intercept=False, positive=True),\n",
       "    n_features_to_select=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RFE</label><div class=\"sk-toggleable__content\"><pre>RFE(estimator=LinearRegression(fit_intercept=False, positive=True),\n",
       "    n_features_to_select=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(fit_intercept=False, positive=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(fit_intercept=False, positive=True)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RFE(estimator=LinearRegression(fit_intercept=False, positive=True),\n",
       "    n_features_to_select=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "lm = LinearRegression(fit_intercept=False, positive=True)\n",
    "rfe = RFE(estimator=lm, n_features_to_select=1)\n",
    "\n",
    "rfe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "846977d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "\n",
    "best_features = np.array(X_train.columns.to_list())[ np.argsort(rfe.ranking_)[::-1] ]\n",
    "\n",
    "mae = []\n",
    "\n",
    "for f in range(1,len(best_features)+1):\n",
    "    lm = LinearRegression(fit_intercept=False, positive=True)\n",
    "    scores = cross_val_score(lm, \n",
    "                             X_train.loc[:,best_features[:f]], y_train, \n",
    "                             cv=5, scoring='neg_mean_absolute_error')\n",
    "    mae += [-scores.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2842df24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full score: 0.06864720698038897\n",
      "Best score: 0.06851185330503948\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print (\"Full score:\", mae[-1])\n",
    "print (\"Best score:\", min(mae))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9,4))\n",
    "ax.plot(range(1,len(best_features)+1), mae, 'o-', label=\"MAE\")\n",
    "ax.set_title(\"MAE on varying features\")\n",
    "ax.set_xlabel(\"Number of Best features used\")\n",
    "ax.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2a6eee5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bathroomcnt', 'has_pool', 'has_fireplace',\n",
       "       'calculatedfinishedsquarefeet_was_nan', 'x3_Ventura',\n",
       "       'lotsizesquarefeet_was_nan', 'landtaxvaluedollarcnt',\n",
       "       'taxvaluedollarcnt', 'x1_Sunday', 'structuretaxvaluedollarcnt',\n",
       "       'x0_November', 'landtaxvaluedollarcnt_was_nan', 'taxamount',\n",
       "       'yearbuilt', 'calculatedfinishedsquarefeet', 'hashottuborspa',\n",
       "       'transaction_month_day', 'latitude', 'taxvaluedollarcnt_was_nan',\n",
       "       'structuretaxvaluedollarcnt_was_nan',\n",
       "       'x2_Commercial/Office/Residential mix',\n",
       "       'finishedsquarefeet12_was_nan', 'taxdelinquencyyear'], dtype='<U45')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array(X_train.columns.to_list())[ np.argsort(rfe.ranking_)[::-1] ])[:23]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6bed178",
   "metadata": {},
   "source": [
    "The $14^{th}$ features seems to be really significative, it turns out that the house dimension matters on prediction of errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb7c6245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'calculatedfinishedsquarefeet'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array(X_train.columns.to_list())[ np.argsort(rfe.ranking_)[::-1] ])[14]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336d47ef",
   "metadata": {},
   "source": [
    "Let's see how this model performs on the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "434a451f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.07081\n",
      " Mean squared error: 0.03000\n",
      "           R2 score: 0.00193\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression(fit_intercept=False, positive=True)\n",
    "\n",
    "lr.fit(X_train[best_features[:23]], y_train)\n",
    "\n",
    "print_measure(y_true=y_test, y_pred=lr.predict(X_test[best_features[:23]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b776bb",
   "metadata": {},
   "source": [
    "### Scaling data\n",
    "\n",
    "Usually the linear regressor is a model sensible to data distance, often scaling can be useful to change coefficient significativity. Since data are not normal I chose a `MinMaxScaler` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c686327b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RFE(estimator=LinearRegression(fit_intercept=False, positive=True),\n",
       "    n_features_to_select=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RFE</label><div class=\"sk-toggleable__content\"><pre>RFE(estimator=LinearRegression(fit_intercept=False, positive=True),\n",
       "    n_features_to_select=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(fit_intercept=False, positive=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(fit_intercept=False, positive=True)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RFE(estimator=LinearRegression(fit_intercept=False, positive=True),\n",
       "    n_features_to_select=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "\n",
    "lr = LinearRegression(fit_intercept=False, positive=True)\n",
    "\n",
    "rfe = RFE(estimator=lr, n_features_to_select=1)\n",
    "\n",
    "rfe.fit(scaler.transform( X_train ), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a608c72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_features = np.array(X_train.columns.to_list())[ np.argsort(rfe.ranking_)[::-1] ]\n",
    "\n",
    "mae = []\n",
    "\n",
    "for f in range(1,len(best_features)+1):\n",
    "    lm = LinearRegression(fit_intercept=False, positive=True)\n",
    "    scaler.fit(X_train.loc[:,best_features[:f]])\n",
    "    scores = cross_val_score(lm, \n",
    "                             scaler.transform( X_train.loc[:,best_features[:f]] ), y_train, \n",
    "                             cv=5, scoring='neg_mean_absolute_error')\n",
    "    mae += [-scores.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba68998e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full score: 0.06864333660300367\n",
      "Best score: 0.06851650852463502\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (\"Full score:\", mae[-1])\n",
    "print (\"Best score:\", min(mae))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9,4))\n",
    "ax.plot(range(1,len(best_features)+1), mae, 'o-', label=\"MAE\")\n",
    "ax.set_title(\"MAE on varying features\")\n",
    "ax.set_xlabel(\"Number of Best features used\")\n",
    "ax.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7842e0ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.07081\n",
      " Mean squared error: 0.03001\n",
      "           R2 score: 0.00170\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression(fit_intercept=False, positive=True)\n",
    "scaler.fit(X_train[best_features[:3]])\n",
    "lr.fit(scaler.transform(X_train[best_features[:3]]), y_train)\n",
    "\n",
    "print_measure(y_true=y_test, y_pred=lr.predict(scaler.transform(X_test[best_features[:3]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7949e7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bathroomcnt', 'bedroomcnt', 'calculatedfinishedsquarefeet'],\n",
       "      dtype='<U45')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_features[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e71cfc",
   "metadata": {},
   "source": [
    "This time we got the same error, but we needed just three features, getting a much more interpretable model. Inference here is really simple: size matters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4820905a",
   "metadata": {},
   "source": [
    "## Linear regression by county\n",
    "\n",
    "Let's try now to analyze features impact by county. I'll assume that scaling data will always be the best choiche for the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac1c966c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import Zillow.data as zd\n",
    "from Zillow.transform import ZillowTransformer, ZillowEncoder\n",
    "from Zillow.types import County, Features as ft\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# Orange\n",
    "o_train, o_validation, o_test = zd.get_preprocessed_data(County.ORANGE)\n",
    "\n",
    "# Ventura\n",
    "v_train, v_validation, v_test = zd.get_preprocessed_data(County.VENTURA)\n",
    "\n",
    "# Los Angeles\n",
    "la_train, la_validation, la_test = zd.get_preprocessed_data(County.LOS_ANGELES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfac51ef",
   "metadata": {},
   "source": [
    "### Orange\n",
    "\n",
    "I wrapped everything in a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "315aac25",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_o, y_train_o = zd.split_x_y(o_train)\n",
    "X_test_o, y_test_o = zd.split_x_y(o_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5f1ec1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Zillow.models import select_linear_regression_features\n",
    "\n",
    "lm = LinearRegression(fit_intercept=False, positive=True)\n",
    "\n",
    "best_params = select_linear_regression_features(X_train_o,\n",
    "                                                y_train_o,\n",
    "                                                lm,\n",
    "                                                scale=True,\n",
    "                                                plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b0494eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bathroomcnt'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3346dd89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.06148\n",
      " Mean squared error: 0.02399\n",
      "           R2 score: 0.00105\n"
     ]
    }
   ],
   "source": [
    "scaler.fit(X_train_o[best_params[:1]])\n",
    "lm.fit(scaler.transform(X_train_o[best_params[:1]]), y_train_o)\n",
    "\n",
    "print_measure(y_true=y_test_o, y_pred=lm.predict(scaler.transform(X_test_o[best_params[:1]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3a0cff",
   "metadata": {},
   "source": [
    "### Ventura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2cccc1e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_v, y_train_v = zd.split_x_y(v_train)\n",
    "X_test_v, y_test_v = zd.split_x_y(v_validation)\n",
    "\n",
    "best_features = select_linear_regression_features(X_train_v,\n",
    "                                                y_train_v,\n",
    "                                                lm,\n",
    "                                                scale=True,\n",
    "                                                plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2feaf48e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.06092\n",
      " Mean squared error: 0.02936\n",
      "           R2 score: 0.00228\n"
     ]
    }
   ],
   "source": [
    "scaler.fit(X_train_v[best_features[:1]])\n",
    "lm.fit(scaler.transform(X_train_v[best_features[:1]]), y_train_v)\n",
    "\n",
    "print_measure(y_true=y_test_v, y_pred=lm.predict(scaler.transform(X_test_v[best_features[:1]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d655448e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bathroomcnt'], dtype='<U36')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_features[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db921a3",
   "metadata": {},
   "source": [
    "### Los Angeles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfb22aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_la, y_train_la = zd.split_x_y(la_train)\n",
    "X_test_la, y_test_la = zd.split_x_y(la_test)\n",
    "\n",
    "best_features = select_linear_regression_features(X_train_la,\n",
    "                                                y_train_la,\n",
    "                                                lm,\n",
    "                                                scale=True,\n",
    "                                                plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "938ab493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.07449\n",
      " Mean squared error: 0.02914\n",
      "           R2 score: 0.00010\n"
     ]
    }
   ],
   "source": [
    "scaler.fit(X_train_la[best_features[:1]])\n",
    "lm.fit(scaler.transform(X_train_la[best_features[:1]]), y_train_la)\n",
    "\n",
    "print_measure(y_true=y_test_la, y_pred=lm.predict(scaler.transform(X_test_la[best_features[:1]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "111cdcaf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bathroomcnt'], dtype='<U36')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_features[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7195bf9",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Here we plot a summary of models performance measured on mean absolute error over the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90226352",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Zillow.metrics import measure_model\n",
    "\n",
    "scores = []\n",
    "tag = ['All', 'Orange', 'Ventura', 'LA']\n",
    "\n",
    "train, validation, test = zd.get_preprocessed_data()\n",
    "X_train, y_train = zd.split_x_y( pd.concat([train, validation], axis=0) )\n",
    "X_test, y_test = zd.split_x_y(test)\n",
    "    \n",
    "best_features = select_linear_regression_features(X_train,\n",
    "                                                  y_train,\n",
    "                                                  lm,\n",
    "                                                  scale=True)\n",
    "    \n",
    "scaler.fit(X_train[best_features[:3]])\n",
    "lm.fit(scaler.transform(X_train[best_features[:3]]), y_train)\n",
    "scores.append( measure_model(y_true=y_test,\n",
    "                             y_pred=lm.predict(scaler.transform( X_test[best_features[:3]] )))[0] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d0e7443",
   "metadata": {},
   "outputs": [],
   "source": [
    "for county in [County.ORANGE, County.VENTURA, County.LOS_ANGELES]:\n",
    "    train, validation, test = zd.get_preprocessed_data(county)\n",
    "    X_train, y_train = zd.split_x_y( pd.concat([train, validation], axis=0) )\n",
    "    X_test, y_test = zd.split_x_y(test)\n",
    "    \n",
    "    best_features = select_linear_regression_features(X_train,\n",
    "                                                      y_train,\n",
    "                                                      lm,\n",
    "                                                      scale=True)\n",
    "    \n",
    "    scaler.fit(X_train[best_features[:1]])\n",
    "    lm.fit(scaler.transform(X_train[best_features[:1]]), y_train)\n",
    "    scores.append( measure_model(y_true=y_test,\n",
    "                                 y_pred=lm.predict(scaler.transform( X_test[best_features[:1]] )))[0] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47aff5d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "sns.barplot(data=pd.DataFrame({'score': scores, 'area': tag}), x='area', y='score', ax=ax)\n",
    "ax.set_ylim(0.060,0.075);\n",
    "ax.set_ylabel('mean absolute error')\n",
    "ax.hlines(np.mean(scores[1:4]), xmin=-0.4, xmax=3.39, colors='coral', label='counties mean')\n",
    "ax.legend()\n",
    "ax.set_title('MAE per area with linear regressor');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "268cf184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06592797210793121"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(scores[1:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1f4f9a",
   "metadata": {},
   "source": [
    "# Decision trees\n",
    "\n",
    "Let us now study the predictive ability of a decision tree\n",
    "\n",
    "## Globally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad602b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Zillow.data as zd\n",
    "\n",
    "train, validation, test = zd.get_preprocessed_data()\n",
    "\n",
    "X_train, y_train = zd.split_x_y(train)\n",
    "X_validation, y_validation = zd.split_x_y(validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a29718",
   "metadata": {},
   "source": [
    "In this case you can try to find the optimal parameters for the decision tree, to save some calculation time the tuning will be done on the $40 \\%$ of the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83e4c46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42972, 42972)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_small, _, y_small, _ = train_test_split(X_train, y_train, train_size=0.4, random_state=42)\n",
    "\n",
    "len(X_small), len(y_small)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d63822",
   "metadata": {},
   "source": [
    "Even if we are evaluating the model with *mean absolute error* here we do cross validation on *mean squared error* criterion because it takes less time (more information about the sklearn implementation complexity can be found [here](https://github.com/scikit-learn/scikit-learn/issues/9626))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6d4087cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 4752 candidates, totalling 19008 fits\n",
      "CPU times: user 33.5 s, sys: 7.35 s, total: 40.9 s\n",
      "Wall time: 21min 20s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'squared_error',\n",
       " 'max_features': 1.0,\n",
       " 'max_leaf_nodes': 9,\n",
       " 'min_samples_leaf': 10,\n",
       " 'min_samples_split': 2,\n",
       " 'splitter': 'random'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "lr = DecisionTreeRegressor()\n",
    "\n",
    "params = {\n",
    "    'criterion': ['squared_error'],\n",
    "    'splitter': ['random', 'best'],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 5, 10],\n",
    "    'max_leaf_nodes': [x for x in range(2, 101)],\n",
    "    'max_features': ['sqrt', 1.0]\n",
    "}\n",
    "\n",
    "gscv = GridSearchCV(estimator=lr,\n",
    "                    return_train_score=True,\n",
    "                    scoring='neg_mean_absolute_error',\n",
    "                    param_grid=params,\n",
    "                    cv=4,\n",
    "                    verbose=1,\n",
    "                    n_jobs=-1,\n",
    "                   )\n",
    "\n",
    "gscv.fit(X_train, y_train)\n",
    "\n",
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c929a6c3",
   "metadata": {},
   "source": [
    "Obviously the model performs better with a low number of leaves: as the number of leaves increases, the model should become more and more specialized, going into overfitting. (anway here it doesn't seem to overfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1b005dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import Zillow.visualization as zv\n",
    "\n",
    "zv.plot_grid_search_validation_curve(gscv, 'max_leaf_nodes', ylim=(-.0715,-.068))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05330b82",
   "metadata": {},
   "source": [
    "We verify the performance by training the model on the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6481f837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.07002\n",
      " Mean squared error: 0.02856\n",
      "           R2 score: 0.00174\n"
     ]
    }
   ],
   "source": [
    "from Zillow.metrics import measure_model, print_measure\n",
    "\n",
    "train, validation, test = zd.get_preprocessed_data()\n",
    "train = pd.concat([train, validation], axis=0, ignore_index=True)\n",
    "X_train, y_train = zd.split_x_y(train)\n",
    "X_test, y_test = zd.split_x_y(test)\n",
    "\n",
    "errors = []\n",
    "\n",
    "regressor = DecisionTreeRegressor(splitter='random',\n",
    "                           min_samples_split=2,\n",
    "                           min_samples_leaf=10,\n",
    "                           max_leaf_nodes=9,\n",
    "                           max_features=1.,\n",
    "                           random_state=42)\n",
    "\n",
    "regressor.fit(X_train, y_train)\n",
    "errors.append(measure_model(y_true=y_test, y_pred=regressor.predict(X_test)))\n",
    "print_measure(y_true=y_test, y_pred=regressor.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866fe887",
   "metadata": {},
   "source": [
    "Here there isn't much to feature select since we used a tree with 9 leaf, so it doesn't look at all features to evaluate its decisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e9f18a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19447953, 0.        , 0.23301552, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.24697028, 0.        , 0.        , 0.07340074,\n",
       "       0.06869075, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.07461176, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.10883142, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9626780",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 67.8 ms, sys: 0 ns, total: 67.8 ms\n",
      "Wall time: 64.9 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.6923076923076923, 0.9375, 'bathroomcnt <= 3.5709377\\nsquared_error = 0.0271391\\nsamples = 134287\\nvalue = 0.0135875'),\n",
       " Text(0.5384615384615384, 0.8125, 'structuretaxvaluedollarcnt <= 2121076.6391448\\nsquared_error = 0.0254296\\nsamples = 124095\\nvalue = 0.0122914'),\n",
       " Text(0.46153846153846156, 0.6875, 'structuretaxvaluedollarcnt_was_nan <= 0.2024705\\nsquared_error = 0.0252001\\nsamples = 124077\\nvalue = 0.0122275'),\n",
       " Text(0.38461538461538464, 0.5625, 'taxdelinquencyflag <= 0.8063771\\nsquared_error = 0.0250518\\nsamples = 123706\\nvalue = 0.0123871'),\n",
       " Text(0.3076923076923077, 0.4375, 'calculatedfinishedsquarefeet <= 2648.6824657\\nsquared_error = 0.0245417\\nsamples = 120280\\nvalue = 0.0119145'),\n",
       " Text(0.23076923076923078, 0.3125, 'calculatedfinishedsquarefeet <= 407.1958592\\nsquared_error = 0.0235418\\nsamples = 111329\\nvalue = 0.010983'),\n",
       " Text(0.15384615384615385, 0.1875, 'x1_Thursday <= 0.952642\\nsquared_error = 0.3116536\\nsamples = 84\\nvalue = 0.1649495'),\n",
       " Text(0.07692307692307693, 0.0625, 'squared_error = 0.1684498\\nsamples = 71\\nvalue = 0.1070684'),\n",
       " Text(0.23076923076923078, 0.0625, 'squared_error = 0.9755373\\nsamples = 13\\nvalue = 0.4810692'),\n",
       " Text(0.3076923076923077, 0.1875, 'squared_error = 0.0233063\\nsamples = 111245\\nvalue = 0.0108668'),\n",
       " Text(0.38461538461538464, 0.3125, 'squared_error = 0.0368335\\nsamples = 8951\\nvalue = 0.0234998'),\n",
       " Text(0.46153846153846156, 0.4375, 'squared_error = 0.0426781\\nsamples = 3426\\nvalue = 0.0289797'),\n",
       " Text(0.5384615384615384, 0.5625, 'squared_error = 0.0718113\\nsamples = 371\\nvalue = -0.0409861'),\n",
       " Text(0.6153846153846154, 0.6875, 'squared_error = 1.4132646\\nsamples = 18\\nvalue = 0.452452'),\n",
       " Text(0.8461538461538461, 0.8125, 'taxamount <= 100507.2865666\\nsquared_error = 0.0476845\\nsamples = 10192\\nvalue = 0.0293681'),\n",
       " Text(0.7692307692307693, 0.6875, 'squared_error = 0.0471549\\nsamples = 10119\\nvalue = 0.0302247'),\n",
       " Text(0.9230769230769231, 0.6875, 'squared_error = 0.1068929\\nsamples = 73\\nvalue = -0.0893685')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "plot_tree(regressor, ax=ax,\n",
    "          precision=7,\n",
    "          feature_names=X_train.columns.to_list(),\n",
    "          filled=True, rounded=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06b7bdc",
   "metadata": {},
   "source": [
    "## Decision tree by county\n",
    "\n",
    "It is assumed that the best params found with the GridSearch for the generic model is the same for the three counties (which actually have different features, it would therefore be useful to re-search for the optimal parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "13bc2fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Zillow.data as zd\n",
    "from Zillow.transform import ZillowTransformer, ZillowEncoder\n",
    "from Zillow.types import County, Features as ft\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# Orange\n",
    "o_train, o_validation, o_test = zd.get_preprocessed_data(County.ORANGE)\n",
    "\n",
    "# Ventura\n",
    "v_train, v_validation, v_test = zd.get_preprocessed_data(County.VENTURA)\n",
    "\n",
    "# Los Angeles\n",
    "la_train, la_validation, la_test = zd.get_preprocessed_data(County.LOS_ANGELES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eeb0c6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([o_train, o_validation], axis=0, ignore_index=True)\n",
    "X_train_o, y_train_o = zd.split_x_y(train)\n",
    "X_test_o, y_test_o = zd.split_x_y(o_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "70401a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orange\n",
      "Mean absolute error: 0.06262\n",
      " Mean squared error: 0.02425\n",
      "           R2 score: -0.00756\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from Zillow.metrics import measure_model\n",
    "\n",
    "regressor = DecisionTreeRegressor(splitter='random',\n",
    "                           min_samples_split=2,\n",
    "                           min_samples_leaf=10,\n",
    "                           max_leaf_nodes=9,\n",
    "                           max_features=1.,\n",
    "                           random_state=42)\n",
    "\n",
    "train = pd.concat([o_train, o_validation], axis=0, ignore_index=True)\n",
    "X_train_o, y_train_o = zd.split_x_y(train)\n",
    "\n",
    "regressor.fit( X_train_o, y_train_o)\n",
    "print('Orange')\n",
    "errors.append(measure_model(y_true=y_test_o, y_pred=regressor.predict(X_test_o)))\n",
    "print_measure(y_true=y_test_o, y_pred=regressor.predict(X_test_o))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "77b7b9e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ventura\n",
      "Mean absolute error: 0.06101\n",
      " Mean squared error: 0.02852\n",
      "           R2 score: 0.00333\n"
     ]
    }
   ],
   "source": [
    "# Ventura\n",
    "train = pd.concat([v_train, v_validation], axis=0, ignore_index=True)\n",
    "X_train_v, y_train_v = zd.split_x_y(train)\n",
    "X_test_v, y_test_v = zd.split_x_y(v_test)\n",
    "\n",
    "regressor.fit( X_train_v, y_train_v)\n",
    "print('Ventura')\n",
    "errors.append(measure_model(y_true=y_test_v, y_pred=regressor.predict(X_test_v)))\n",
    "print_measure(y_true=y_test_v, y_pred=regressor.predict(X_test_v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a028f5ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LA\n",
      "Mean absolute error: 0.07475\n",
      " Mean squared error: 0.02904\n",
      "           R2 score: 0.00361\n"
     ]
    }
   ],
   "source": [
    "# LA\n",
    "train = pd.concat([la_train, la_validation], axis=0, ignore_index=True)\n",
    "X_train_la, y_train_la = zd.split_x_y(train)\n",
    "X_test_la, y_test_la = zd.split_x_y(la_test)\n",
    "\n",
    "regressor.fit( X_train_la, y_train_la)\n",
    "print('LA')\n",
    "errors.append(measure_model(y_true=y_test_la, y_pred=regressor.predict(X_test_la)))\n",
    "print_measure(y_true=y_test_la, y_pred=regressor.predict(X_test_la))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b81d4f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q+KeWVh4RV0fEsIgY1rNnz42md+3alSVLlvgX6Fao8XmM+bWdZmZl1fIC/wagTzbcG3izlW1GA09ExO/S8N5AP+CpdLF4b+AJScMj4q3WFNe7d28aGhpoqjdp7VvXrl3p3bt3vcsws3aolsE4G+gvqR/FyTNjgc9XtJkBnC3pJoqTb5ZFxKJs+jiy3agR8Vtg98ZhSa8CwyLi7dYWt8022/jp7mZmtpGaBWNErJF0NnAX0BmYEhHzJE1K0ycDM4HjgPnASmDdvb3SbtNjgDNrVaOZmVmlmt4rNSJmUoRfPm5y9j6As5qYdyWwWwvL7/v+qzQzM1vPt4QzMzPLOBjNzMwyDkYzM7OMg9HMzCzjYDQzM8s4GM3MzDIORjMzs4yD0czMLONgNDMzyzgYzczMMg5GMzOzjIPRzMws42A0MzPLOBjNzMwyDkYzM7OMg9HMzCzjYDQzM8s4GM3MzDIORjMzs4yD0czMLONgNDMzyzgYzczMMg5GMzOzjIPRzMws42A0MzPLOBjNzMwyDkYzM7OMg9HMzCzjYDQzM8s4GM3MzDIORjMzs4yD0czMLONgNDMzyzgYzczMMjUNRknHSnpB0nxJF1SZLkmXp+lPSxqaxu8raW72Wi7pvDTtu5KeT+1vk9S9lttgZmYdS82CUVJn4ApgNLAfME7SfhXNRgP902sicCVARLwQEUMiYghwILASuC3Ncw8wMCIGAy8CX6/VNpiZWcdTyx7jcGB+RCyIiFXATcCYijZjgGlRmAV0l9Sros0o4OWIeA0gIu6OiDVp2iygd+02wczMOppaBuNewMJsuCGNa22bscBPmljH6cCv30eNZmZmG6hlMKrKuGhNG0nbAicAt2y0cOlCYA0wverKpYmS5kias3jx4tJFm5lZx1bLYGwA+mTDvYE3W9lmNPBERPwun0nSBOB44OSIqAxbACLi6ogYFhHDevbsuYmbYGZmHU0tg3E20F9Sv9TzGwvMqGgzAxifzk49BFgWEYuy6eOo2I0q6VjgH4ATImJl7co3M7OOqEutFhwRaySdDdwFdAamRMQ8SZPS9MnATOA4YD7FmaenNc4vaXvgGODMikX/CPgAcI8kgFkRMalW22FmZh1LzYIRICJmUoRfPm5y9j6As5qYdyWwW5XxH9nMZZqZma3jO9+YmZllHIxmZmYZB6OZmVnGwWhmZpZxMJqZmWUcjGZmZhkHo5mZWcbBaGZmlnEwmpmZZRyMZmZmGQejmZlZxsFoZmaWcTCamZllHIxmZmYZB6OZmVnGwWhmZpZxMJqZmWUcjGZmZhkHo5mZWcbBaGZmlnEwmpmZZRyMZmZmGQejmZlZxsFoZmaWcTCamZllHIxmZmYZB6OZmVnGwWhmZpZxMJqZmWUcjGZmZhkHo5mZWcbBaGZmlnEwmpmZZRyMZmZmGQejmZlZpqbBKOlYSS9Imi/pgirTJenyNP1pSUPT+H0lzc1eyyWdl6btKukeSS+lf3ep5TaYmVnHUrNglNQZuAIYDewHjJO0X0Wz0UD/9JoIXAkQES9ExJCIGAIcCKwEbkvzXADcGxH9gXvTsJmZ2WZRyx7jcGB+RCyIiFXATcCYijZjgGlRmAV0l9Sros0o4OWIeC2b5/r0/nrgMzWp3szMOqQuNVz2XsDCbLgBOLhEm72ARdm4scBPsuE9ImIRQEQskrR7tZVLmkjRC+WDH/zgptS/kQP/ftpmWc7W7vHvjq93CWZmm6yWPUZVGRetaSNpW+AE4JbWrjwiro6IYRExrGfPnq2d3czMOqhaBmMD0Ccb7g282co2o4EnIuJ32bjfNe5uTf/+frNVbGZmHV4tg3E20F9Sv9TzGwvMqGgzAxifzk49BFjWuJs0GceGu1Eb55mQ3k8A7tj8pZuZWUdVs2OMEbFG0tnAXUBnYEpEzJM0KU2fDMwEjgPmU5x5elrj/JK2B44BzqxY9KXAzZK+ALwOnFSrbTAzs46n2WBMl1ycGxH/vikLj4iZFOGXj5ucvQ/grCbmXQnsVmX8EoozVc3MzDa7ZnelRsRaNr7EwszMbKtVZlfqQ5J+BPwUWNE4MiKeqFlVZmZmdVImGA9L/34rGxfA0Zu/HDMzs/pqMRgj4qi2KMTMzGxL0OLlGpK6SfqBpDnp9X1J3dqiODMzs7ZW5jrGKcA7wOfSazkwtZZFmZmZ1UuZY4x7R8RfZcPflDS3RvWYmZnVVZke4/9K+njjgKQRwP/WriQzM7P6KdNjnARMy44r/pH1t2QzMzPbqpS5883fRMQBknYGiIjlbVKZmZlZHTQbjBGxVtKB6b0D0czMtnpldqU+KWkGxTMR8zvf/LxmVZmZmdVJmWDcFVjChne6CcDBaGZmW50yxxjfjoi/b6N6zMzM6qrM0zWGtlEtZmZmdVdmV+pcH2M0M7OOwscYzczMMmWernFaWxRiZma2JSjzdI19JN0r6Zk0PFjSN2pfmpmZWdsrc6/UHwNfB1YDRMTTwNhaFmVmZlYvZYJx+4h4rGLcmloUY2ZmVm9lgvFtSXtTnHCDpBOBRTWtyszMrE7KnJV6FnA1MEDSG8ArwMk1rcrMzKxOypyVugD4hKQdgE4R8U7tyzIzM6uPMj1GACJiRcutzMzM2rcyxxjNzMw6DAejmZlZpswF/ttLukjSj9Nwf0nH1740MzOztlemxzgV+DNwaBpuAP61ZhWZmZnVUZlg3DsivsP6O9/8L6CaVmVmZlYnZYJxlaTtWH+B/94UPUgzM7OtTpnLNS4G7gT6SJoOjAD8xA0zM9sqlbnA/25JjwOHUOxC/XJEvF3zyszMzOqgzFmp90bEkoj4VUT8MiLelnRvWxRnZmbW1poMRkldJe0K9JC0i6Rd06svsGeZhUs6VtILkuZLuqDKdEm6PE1/WtLQbFp3SbdKel7Sc5IOTeOHSJolaa6kOZKGt3qrzczMmtDcrtQzgfMoQvCJbPxy4IqWFiypc2p3DMUlHrMlzYiIZ7Nmo4H+6XUwcGX6F+Ay4M6IOFHStsD2afx3gG9GxK8lHZeGR7ZUj5mZWRlNBmNEXAZcJumciPjhJix7ODA/3YQcSTcBY4A8GMcA0yIigFmpl9gLWAEcAZyaalkFrGosDdg5ve8GvLkJtZmZmVVV5qzUZZLGV46MiGktzLcXsDAbbmB9b7C5NntRPAh5MTBV0gHA4xQn/ayg6MXeJel7FLuCDyuxDWZmZqWUuY7xoOx1OMXlGyeUmK/aTQCiZJsuwFDgyoj4GEUPsvEY5d8C50dEH+B84NqqK5cmpmOQcxYvXlyiXDMzs3KXa5yTD0vqBtxQYtkNQJ9suDcb7/Zsqk0ADRHxaBp/K+uDcQLw5fT+FuCaJuq+muIBywwbNqwykM3MzKralKdrrKQ4WaYls4H+kvqlk2fGAjMq2swAxqezUw8BlkXEooh4C1goad/UbhTrj02+CRyZ3h8NvLQJ22BmZlZViz1GSb9g/S7QTsB+wM0tzRcRaySdDdwFdAamRMQ8SZPS9MnATOA4YD5F4OZ31DkHmJ5CdUE27YsUJwV1Ad4FJrZUi5mZWVllTr75XvZ+DfBaRDSUWXhEzKQIv3zc5Ox9AGc1Me9cYFiV8Q8CB5ZZv5mZWWuVOcZ4f1sUYmZmtiVoMhglvcPGZ5FCcSZpRMTOVaaZmZm1a81d4L9TWxZiZma2JShzjJF0kf3hafCBiHi6diWZmZnVT5mna3wZmA7snl7TJZ3T/FxmZmbtU5ke4xeAg9Pt2JD0b8AjwKbcP9XMzGyLVuYCfwFrs+G1VL+Vm5mZWbtXpsc4FXhU0m0UgTiGJu5PamZm1t6VuY7xB5LuAz5OEYynRcSTtS7MzMysHsrcEm5vYF5EPCFpJHC4pFciYmmNazMzM2tzZY4x/gxYK+kjFE+y6AfcWNOqzMzM6qRMML4XEWuAvwQui4jzgV61LcvMzKw+ygTjaknjgPHAL9O4bWpXkpmZWf2UCcbTgEOBb0fEK5L6Af9Z27LMzMzqo8VgjIhnga8C8yQNAt6IiEtrXpmZmVkdlDkr9S+AycDLFJdr9JN0ZkT8utbFmZmZtbUyF/h/HzgqIubDuss3fgU4GM3MbKtT5hjj7xtDMVkA/L5G9ZiZmdVVcw8q/sv0dp6kmcDNFA8uPgmY3Qa1mZmZtbnmdqV+Onv/O+DI9H4xsEvNKjIzM6ujJoMxIk5ry0LMzMy2BGXOSu1K8UzG/YGujeMj4vQa1mVmZlYXZU6+uQH4P8CngPuB3sA7tSzKzMysXsoE40ci4iJgRURcD/wFMKi2ZZmZmdVHqXulpn+XShoIdAP61qwiMzOzOipzgf/VknYBvgHMAHYELqppVWZmZnXSYjBGxDXp7QPAh2tbjpmZWX2V2ZVqZmbWYTgYzczMMg5GMzOzTJmTb5B0GMWZqOvaR8S0GtVkZmZWN2XufHMDsDcwF1ibRgfgYDQzs61OmR7jMGC/iIhaF2NmZlZvZY4xPkNxSzgzM7OtXpkeYw/gWUmPAX9uHBkRJ9SsKjMzszopE4wXb+rCJR0LXAZ0Bq6JiEsrpitNPw5YCZwaEU+kad2Ba4CBFMc0T4+IR9K0c4CzgTXAryLia5taY2tc1fV/2mI17d/UF+tdgVm7c8DHdqp3Ce3CU0/W/hkWZe58c/+mLFhSZ+AK4BigAZgtaUZEPJs1Gw30T6+DgSvTv1AE5p0RcaKkbYHt03KPAsYAgyPiz5J235T6zMzMqilzVuohwA+BjwLbUvT+VkTEzi3MOhyYHxEL0nJuogi0PBjHANPSiT2zJHWX1AtYARwBnAoQEauAVWmevwUujYg/p2m/L7Gdm8WZ7x7eVqtq1x4/bXy9SzBrd5464sh6l2BJmZNvfgSMA14CtgPOSONashewMBtuSOPKtPkwsBiYKulJSddI2iG12Qc4XNKjku6XdFC1lUuaKGmOpDmLFy8uUa6ZmVnJO99ExHygc0SsjYipwMgSs6naokq26QIMBa6MiI9R9CAvSNO7ALsAhwB/D9ycjlVW1nx1RAyLiGE9e/YsUa6ZmVm5k29WpmN8cyV9B1gE7NDCPFD0/vpkw72BN0u2CaAhIh5N429lfTA2AD9Pu18fk/QexZmz7haamdn7VqbHeEpqdzZFz60P8Fcl5psN9JfULwXrWIrnOeZmAONVOARYFhGLIuItYKGkfVO7Uaw/Nnk7cDSApH0ojnu+XaIeMzOzFpU5K/U1SdsBvSLim2UXHBFrJJ0N3EVxws6UiJgnaVKaPhmYSXGpxnyKyzVOyxZxDjA9heqCbNoUYIqkZyhOyJngu/KYmdnmUuas1E8D36PomfWTNAT4VpkL/CNiJkX45eMmZ+8DOKuJeedS3I6ucvwq4G9aWreZmdmmKLMr9WKKSy+WwrrA6lurgszMzOqpTDCuiYhlNa/EzMxsC1DmrNRnJH0e6CypP3Au8HBtyzIzM6uPMj3Gc4D9KW4g/hNgOXBeDWsyMzOrmzJnpa4ELkwvMzOzrVqZs1KHAf9IccLNuvYRMbh2ZZmZmdVHmWOM0yluvfZb4L3almNmZlZfZYJxcURU3rHGzMxsq1QmGP9Z0jXAvRQn4AAQET+vWVVmZmZ1UiYYTwMGANuwfldqAA5GMzPb6pQJxgMiYlDNKzEzM9sClLmOcZak/WpeiZmZ2RagTI/x48AESa9QHGMUxf2/fbmGmZltdcoE47E1r8LMzGwLUep5jG1RiJmZ2ZagzDFGMzOzDsPBaGZmlnEwmpmZZRyMZmZmGQejmZlZxsFoZmaWcTCamZllHIxmZmYZB6OZmVnGwWhmZpZxMJqZmWUcjGZmZhkHo5mZWcbBaGZmlnEwmpmZZRyMZmZmGQejmZlZxsFoZmaWcTCamZllahqMko6V9IKk+ZIuqDJdki5P05+WNDSb1l3SrZKel/ScpEMr5v2qpJDUo5bbYGZmHUvNglFSZ+AKYDSwHzBO0n4VzUYD/dNrInBlNu0y4M6IGAAcADyXLbsPcAzweq3qNzOzjqmWPcbhwPyIWBARq4CbgDEVbcYA06IwC+guqZeknYEjgGsBImJVRCzN5vt34GtA1LB+MzPrgGoZjHsBC7PhhjSuTJsPA4uBqZKelHSNpB0AJJ0AvBERT9WscjMz67BqGYyqMq6yh9dUmy7AUODKiPgYsAK4QNL2wIXAP7W4cmmipDmS5ixevLh1lZuZWYdVy2BsAPpkw72BN0u2aQAaIuLRNP5WiqDcG+gHPCXp1dT+CUn/p3LlEXF1RAyLiGE9e/bcDJtjZmYdQS2DcTbQX1I/SdsCY4EZFW1mAOPT2amHAMsiYlFEvAUslLRvajcKeDYifhsRu0dE34joSxGgQ1N7MzOz961LrRYcEWsknQ3cBXQGpkTEPEmT0vTJwEzgOGA+sBI4LVvEOcD0FKoLKqaZmZnVRM2CESAiZlKEXz5ucvY+gLOamHcuMKyF5fd930WamZllfOcbMzOzjIPRzMws42A0MzPLOBjNzMwyDkYzM7OMg9HMzCzjYDQzM8s4GM3MzDIORjMzs4yD0czMLONgNDMzyzgYzczMMg5GMzOzjIPRzMws42A0MzPLOBjNzMwyDkYzM7OMg9HMzCzjYDQzM8s4GM3MzDIORjMzs4yD0czMLONgNDMzyzgYzczMMg5GMzOzTJd6F2DWnNe/NajeJbQbH/yn39a7BLOtgnuMZmZmGQejmZlZxsFoZmaWcTCamZllHIxmZmYZB6OZmVnGwWhmZpZxMJqZmWUcjGZmZpmaBqOkYyW9IGm+pAuqTJeky9P0pyUNzaZ1l3SrpOclPSfp0DT+u2nc05Juk9S9lttgZmYdS82CUVJn4ApgNLAfME7SfhXNRgP902sicGU27TLgzogYABwAPJfG3wMMjIjBwIvA12u1DWZm1vHUssc4HJgfEQsiYhVwEzCmos0YYFoUZgHdJfWStDNwBHAtQESsioil6f3dEbEmzT8L6F3DbTAzsw6mlsG4F7AwG25I48q0+TCwGJgq6UlJ10jaoco6Tgd+XW3lkiZKmiNpzuLFizd1G8zMrIOpZTCqyrgo2aYLMBS4MiI+BqwANjhGKelCYA0wvdrKI+LqiBgWEcN69uzZ2trNzKyDqmUwNgB9suHewJsl2zQADRHxaBp/K0VQAiBpAnA8cHJEVIatmZnZJqtlMM4G+kvqJ2lbYCwwo6LNDGB8Ojv1EGBZRCyKiLeAhZL2Te1GAc9CcaYr8A/ACRGxsob1m5lZB1SzBxVHxBpJZwN3AZ2BKRExT9KkNH0yMBM4DpgPrAROyxZxDjA9heqCbNqPgA8A90gCmBURk2q1HWZm1rHULBgBImImRfjl4yZn7wM4q4l55wLDqoz/yOat0szMbD3f+cbMzCzjYDQzM8s4GM3MzDIORjMzs4yD0czMLONgNDMzyzgYzczMMg5GMzOzjIPRzMws42A0MzPLOBjNzMwyDkYzM7OMg9HMzCzjYDQzM8s4GM3MzDIORjMzs0xNH1RsZu3TiB+OqHcJ7cJD5zxU7xKsBtxjNDMzyzgYzczMMg5GMzOzjIPRzMws42A0MzPLOBjNzMwyDkYzM7OMg9HMzCzjYDQzM8s4GM3MzDIORjMzs4yD0czMLONgNDMzyzgYzczMMg5GMzOzjIPRzMws42A0MzPL1DQYJR0r6QVJ8yVdUGW6JF2epj8taWg2rbukWyU9L+k5SYem8btKukfSS+nfXWq5DWZm1rHULBgldQauAEYD+wHjJO1X0Ww00D+9JgJXZtMuA+6MiAHAAcBzafwFwL0R0R+4Nw2bmZltFrXsMQ4H5kfEgohYBdwEjKloMwaYFoVZQHdJvSTtDBwBXAsQEasiYmk2z/Xp/fXAZ2q4DWZm1sHUMhj3AhZmww1pXJk2HwYWA1MlPSnpGkk7pDZ7RMQigPTv7rUo3szMOqYuNVy2qoyLkm26AEOBcyLiUUmXUewyvaj0yqWJFLtnAf4k6YWy87YzPYC3611ETt+bUO8S2sIW97nzz9X+O21VtrjPXOf6M68LbbbP/UPVRtYyGBuAPtlwb+DNkm0CaIiIR9P4W1l/LPF3knpFxCJJvYDfV1t5RFwNXP3+NmHLJ2lORAyrdx0djT/3tufPvO111M+8lrtSZwP9JfWTtC0wFphR0WYGMD6dnXoIsCwiFkXEW8BCSfumdqOAZ7N5GrskE4A7argNZmbWwdSsxxgRaySdDdwFdAamRMQ8SZPS9MnATOA4YD6wEjgtW8Q5wPQUqguyaZcCN0v6AvA6cFKttsHMzDoeRVQe9rP2RNLEtNvY2pA/97bnz7ztddTP3MFoZmaW8S3hzMzMMg7GdkbSZyWFpAFpuK+kZ9L7kZJ+Wd8Kt3ySeku6I91W8GVJl6Vj2bYZSbpP0qcqxp0n6f+1cjkjJR22eauzSpL+1My0yyS9IalDZEaH2MitzDjgQYqzfK2VJAn4OXB7uq3gPsCOwLcr2tXyUqaO4ids/HM6No1vjZFAq4Ix3ZLSNoMUhp+luBnLEXUup004GNsRSTsCI4Av4GDcVEcD70bEVICIWAucD5wu6UuSbpH0C+BuSTtKulfSE5J+K2kMrOulPyfpx5LmSbpb0nZp2kHphviPSPpu1pvvnIZnp+ln1mfz29StwPGSPgDF5wbsCWyfPp8n0ue9Y5r+qqRvZp/3gDTPJOB8SXMlHS7pOkknNq6ksaeTepa/kXQj8Ns07nZJj6fvaSK2KY4CnqG4l/W4OtfSJhyM7ctnKG6s/iLwh/xpJFba/sDj+YiIWE5x6U8X4FBgQkQcDbwLfDYihlL8cvh+6nFCceP7KyJif2Ap8Fdp/FRgUkQcCqzNVvMFiut0DwIOAr4oqV8Ntm+LERFLgMeAY9OosRQ3/r8Q+ET6XOcAX8lmezuNvxL4akS8CkwG/j0ihkTE/7Sw2uHAhRHR+MCC0yPiQGAYcK6k3TbDpnU04yh6+bdR/KGzTZ3rqTkHY/syjuJm7KR/O8Rfb5uZ2PjWhPn4eyLiD9m4SyQ9DfwXxX1890jTXomIuen940BfSd2BnSLi4TT+xmz5n6S4mcVc4FFgN4pw3drlu1PHAq9QPG3nofRZTGDD23L9PP37ONB3E9b3WES8kg2fK+kpYBbFXbY6wme+2aRj78dRHHpYTvGz+8n6VlV7Po7STqS/dI8GBkoKipsmBNCqExmMeazv3QGQnubSh6KHtyKbdDLQEzgwIlZLehXomqb9OWu3FtiO6vf+Xbcainv/3vW+qm9/bgd+kPZubAc8SfHHR1N/1DV+rmtp+vfTGtIf9akHn584te77kzQS+ARwaESslHQf678/K+dYoBvw27SzZHuKm7H8qp5F1Zp7jO3HiRSP6PpQRPSNiD4Uf333rnNd7c29FMe4xsO6kzS+D1xH8R8+1w34fQrFo2jihsONIuKPwDvp9oaw4XHgu4C/bdwNJWkfrX9izFYrIv4E3AdMoeg9zgJGSPoIgKTtJe3TwmLeAXbKhl8FDkzvxwBN7drrBvwxheIA4JAm2lnTxgFnpN85fYF+wCclbV/fsmrLwdh+jKPYx5/7GfCPdail3YrijhafBU6S9BLwIsWxxGqf43RgmKQ5FL3H50us4gvA1ZIeoeglLkvjr6G43+8T6YScq+g4e2x+QvGw8ZsiYjFwKvCTtIt6FjCghfl/AXy28eQb4MfAkZIeAw5mw15+7k6gS1rPv6R1WdO2l9SQvf4R+BRZ7zAiVlCcFf/pehXZFnznG7PNSNKOqZeEpAuAXhHx5TqXZWat0FH+YjVrK38h6esU/7deo+gdmVk74h6jmZlZxscYzczMMg5GMzOzjIPRzMws42A0MzPLOBjNOgg/ccKsHAej2Vai2pMkJP1J0rckPQocKulvJD2WLpa/qjEsJV0paU6a95t13RCzOnMwmm09qj1JYgfgmYg4GFgC/DUwIiKGUNyP9OQ074URMQwYTHFXmcFtXr3ZFsIX+JttPc6V9Nn0vvFJEmspbh0IMIriHqOz0w2htwN+n6Z9LvUyuwC9KJ6A8XQb1W22RXEwmm0FmnmSxLvpYcxQ3Lv1+oj4esW8/YCvAgdFxB8lXYefQmEdmHelmm0dyjxJ4l7gREm7A0jaVdKHgJ0pbsS9TNIewOi2KtpsS+Qeo9nW4U5gUnqSxAtUeZJERDwr6RvA3ZI6AauBsyJilqQnKZ5VuQB4qA3rNtvi+F6pZmZmGe9KNTMzyzgYzczMMg5GMzOzjIPRzMws42A0MzPLOBjNzMwyDkYzM7OMg9HMzCzz/wHIPv3BE3f6mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "data = pd.DataFrame({'score': [e[0] for e in errors], 'area': ['All', 'Orange', 'Ventura', 'LA']})\n",
    "sns.barplot(data=data, x='area', y='score', ax=ax)\n",
    "ax.set_ylim(0.060,0.075)\n",
    "ax.set_ylabel('mean absolute error')\n",
    "ax.hlines(np.mean([e[0] for e in errors[1:4]]), xmin=-0.4, xmax=3.39, colors='coral', label='mean per county');\n",
    "ax.legend()\n",
    "ax.set_title('MAE per area with dicision tree');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ff42d184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.066125996681552"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([e[0] for e in errors[1:4]])"
   ]
  }
 ],
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