ClassRegressor-bins_equal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "831b3e2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6596bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3fb6cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from regression_classifier import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a17779cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/AB_NYC_2019_EDA.csv').drop(['Unnamed: 0'], axis=1)\n",
    "\n",
    "target_name = 'price'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "25343ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = df.drop(columns=[target_name]), df[target_name]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns)\n",
    "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac51652",
   "metadata": {},
   "source": [
    "# Test ClassRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13f4444e",
   "metadata": {},
   "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>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>n-hood_group_Brooklyn</th>\n",
       "      <th>n-hood_group_Manhattan</th>\n",
       "      <th>n-hood_group_Queens</th>\n",
       "      <th>n-hood_group_Staten Island</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>room_type_Shared room</th>\n",
       "      <th>Manh_dist</th>\n",
       "      <th>Broo_dist</th>\n",
       "      <th>log_minimum_nights</th>\n",
       "      <th>log_number_of_reviews</th>\n",
       "      <th>log_reviews_per_month</th>\n",
       "      <th>log_calculated_host_listings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92.669640</td>\n",
       "      <td>149</td>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.133675</td>\n",
       "      <td>0.025206</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-1.560648</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252.614598</td>\n",
       "      <td>225</td>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.032827</td>\n",
       "      <td>0.108917</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3.828641</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109.915169</td>\n",
       "      <td>150</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036737</td>\n",
       "      <td>0.158162</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.328504</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140.791735</td>\n",
       "      <td>89</td>\n",
       "      <td>194</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.096037</td>\n",
       "      <td>0.036351</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>5.602119</td>\n",
       "      <td>1.534714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121.010757</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027858</td>\n",
       "      <td>0.147596</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neighbourhood  price  availability_365  n-hood_group_Brooklyn  \\\n",
       "0      92.669640    149               365                      1   \n",
       "1     252.614598    225               355                      0   \n",
       "2     109.915169    150               365                      0   \n",
       "3     140.791735     89               194                      1   \n",
       "4     121.010757     80                 0                      0   \n",
       "\n",
       "   n-hood_group_Manhattan  n-hood_group_Queens  n-hood_group_Staten Island  \\\n",
       "0                       0                    0                           0   \n",
       "1                       1                    0                           0   \n",
       "2                       1                    0                           0   \n",
       "3                       0                    0                           0   \n",
       "4                       1                    0                           0   \n",
       "\n",
       "   room_type_Private room  room_type_Shared room  Manh_dist  Broo_dist  \\\n",
       "0                       1                      0   0.133675   0.025206   \n",
       "1                       0                      0   0.032827   0.108917   \n",
       "2                       1                      0   0.036737   0.158162   \n",
       "3                       0                      0   0.096037   0.036351   \n",
       "4                       0                      0   0.027858   0.147596   \n",
       "\n",
       "   log_minimum_nights  log_number_of_reviews  log_reviews_per_month  \\\n",
       "0            0.693147               2.302585              -1.560648   \n",
       "1            0.693147               3.828641              -0.967584   \n",
       "2            1.386294               0.000000              -0.328504   \n",
       "3            0.693147               5.602119               1.534714   \n",
       "4            2.397895               2.302585              -2.302585   \n",
       "\n",
       "   log_calculated_host_listings_count  \n",
       "0                            1.791759  \n",
       "1                            0.693147  \n",
       "2                            0.000000  \n",
       "3                            0.000000  \n",
       "4                            0.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69baef2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "y.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "581c2ca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48410.000000\n",
       "mean       137.575170\n",
       "std        103.780081\n",
       "min         10.000000\n",
       "25%         69.000000\n",
       "50%        105.000000\n",
       "75%        175.000000\n",
       "max        799.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[target_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d95e5a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 10. , 404.5],\n",
       "       [404.5, 799. ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_perc = ClassRegressor(n_bins=2, bins_calc_method='equal')\n",
    "clf_perc.fit(X, y)\n",
    "\n",
    "clf_perc.bin_borders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee0ff145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    47126\n",
       "1     1284\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(clf_perc.y_classes).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d709b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.464075    48400\n",
       "545.380062       10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(clf_perc.predict(X, regression=True)).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1e7e6bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "348d1b4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class regressor\n",
      "train mae 70.94529487146939\n",
      "test mae 70.98827324836698\n"
     ]
    }
   ],
   "source": [
    "clf = ClassRegressor(n_bins=2)\n",
    "clf.fit(X_train_scaled, y_train)\n",
    "\n",
    "pred_train = clf.predict(X_train_scaled, regression=True)\n",
    "pred_test = clf.predict(X_test_scaled, regression=True)\n",
    "\n",
    "print('Class regressor')\n",
    "print('train mae', mean_absolute_error(y_train, pred_train))\n",
    "print('test mae', mean_absolute_error(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "477b0935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "y_train.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24a551d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD5CAYAAAAndkJ4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVtElEQVR4nO3cf4jd9b3n8eeriWulvVqrdcgmYSOYhfqDpjhIwH/m1nLN3rtsLCh3ilsjDZsiKduCsMT+015KoMK2LsJVNl2L0e29NtiKwR/3Xld7KAV/NHa91WilwzVb5yaYbbXWKZjteN/7x/lMezJOzpw5mcmveT7gcL7n/f1+vvmcN62v+X6+35lUFZIkfeBkT0CSdGowECRJgIEgSWoMBEkSYCBIkhoDQZIEwMr5DkjyQeBHwNnt+Aer6qtJvgb8J+D/tkO/UlWPtTG3AVuB94D/XFV/3+pXAvcC5wCPAV+qqkpyNnAfcCXwa+Avq+pAv3ldeOGFtW7duoV81zPe7373Oz70oQ+d7GmckuxNf/anvzOpP88///yvqupjc+2bNxCAI8CnqmoqyVnAj5M83vbdUVX/tffgJJcC48BlwL8G/leSf1tV7wF3A9uAZ+gGwibgcbrh8VZVXZJkHLgd+Mt+k1q3bh379u0bYPrLR6fTYWxs7GRP45Rkb/qzP/2dSf1J8n+OtW/eJaPqmmofz2qvfr/Nthl4oKqOVNVrwARwVZJVwLlV9XR1fxvuPuC6njG72/aDwDVJMt/cJEmLZ6B7CElWJHkBOAw8UVXPtl1fTPKzJN9Jcn6rrQZe7xk+2Wqr2/bs+lFjqmoaeBu4YOFfR5I0rEGWjGjLPRuSfAR4KMnldJd/vk73auHrwDeBzwNz/WRfferMs+8Pkmyju+TEyMgInU5nkOkvG1NTU/bkGOxNf/anv+XSn4ECYUZV/SZJB9jUe+8gybeBR9rHSWBtz7A1wMFWXzNHvXfMZJKVwHnAm3P8+7uAXQCjo6N1pqzpLZYzaZ1zsdmb/uxPf8ulP/MuGSX5WLsyIMk5wKeBn7d7AjM+A7zUtvcC40nOTnIxsB54rqoOAe8k2djuD9wEPNwzZkvbvh54qvyre5J0Qg1yhbAK2J1kBd0A2VNVjyS5P8kGuks7B4AvAFTV/iR7gJeBaWB7W3ICuIU/Pnb6eHsB3APcn2SC7pXB+PF/NUnSQswbCFX1M+CTc9Q/12fMTmDnHPV9wOVz1N8FbphvLpKkpeNvKkuSAANBktQs6CmjM8W6HY+etH/7wDf+4qT925LUj1cIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJGCAQknwwyXNJ/jHJ/iR/1eofTfJEkl+09/N7xtyWZCLJq0mu7alfmeTFtu/OJGn1s5N8r9WfTbJuCb6rJKmPQa4QjgCfqqpPABuATUk2AjuAJ6tqPfBk+0ySS4Fx4DJgE3BXkhXtXHcD24D17bWp1bcCb1XVJcAdwO3H/9UkSQsxbyBU11T7eFZ7FbAZ2N3qu4Hr2vZm4IGqOlJVrwETwFVJVgHnVtXTVVXAfbPGzJzrQeCamasHSdKJsXKQg9pP+M8DlwB/XVXPJhmpqkMAVXUoyUXt8NXAMz3DJ1vt9217dn1mzOvtXNNJ3gYuAH41ax7b6F5hMDIyQqfTGfBrHu3WK6aHGrcYhp3zIKamppb0/Kcze9Of/elvufRnoECoqveADUk+AjyU5PI+h8/1k331qfcbM3seu4BdAKOjozU2NtZnGsd2845Hhxq3GA7cOLZk5+50OgzbkzOdvenP/vS3XPqzoKeMquo3QIfu2v8bbRmI9n64HTYJrO0ZtgY42Opr5qgfNSbJSuA84M2FzE2SdHwGecroY+3KgCTnAJ8Gfg7sBba0w7YAD7ftvcB4e3LoYro3j59ry0vvJNnY7g/cNGvMzLmuB55q9xkkSSfIIEtGq4Dd7T7CB4A9VfVIkqeBPUm2Ar8EbgCoqv1J9gAvA9PA9rbkBHALcC9wDvB4ewHcA9yfZILulcH4Ynw5SdLg5g2EqvoZ8Mk56r8GrjnGmJ3Azjnq+4D33X+oqndpgSJJOjn8TWVJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEDBEKStUl+mOSVJPuTfKnVv5bkn5O80F5/3jPmtiQTSV5Ncm1P/cokL7Z9dyZJq5+d5Hut/mySdUvwXSVJfQxyhTAN3FpVHwc2AtuTXNr23VFVG9rrMYC2bxy4DNgE3JVkRTv+bmAbsL69NrX6VuCtqroEuAO4/fi/miRpIeYNhKo6VFU/bdvvAK8Aq/sM2Qw8UFVHquo1YAK4Kskq4NyqerqqCrgPuK5nzO62/SBwzczVgyTpxFjQPYS2lPNJ4NlW+mKSnyX5TpLzW2018HrPsMlWW922Z9ePGlNV08DbwAULmZsk6fisHPTAJB8Gvg98uap+m+Ru4OtAtfdvAp8H5vrJvvrUmWdf7xy20V1yYmRkhE6nM+j0j3LrFdNDjVsMw855EFNTU0t6/tOZvenP/vS3XPozUCAkOYtuGHy3qn4AUFVv9Oz/NvBI+zgJrO0ZvgY42Opr5qj3jplMshI4D3hz9jyqahewC2B0dLTGxsYGmf773Lzj0aHGLYYDN44t2bk7nQ7D9uRMZ2/6sz/9LZf+DPKUUYB7gFeq6ls99VU9h30GeKlt7wXG25NDF9O9efxcVR0C3kmysZ3zJuDhnjFb2vb1wFPtPoMk6QQZ5ArhauBzwItJXmi1rwCfTbKB7tLOAeALAFW1P8ke4GW6Tyhtr6r32rhbgHuBc4DH2wu6gXN/kgm6Vwbjx/OlJEkLN28gVNWPmXuN/7E+Y3YCO+eo7wMun6P+LnDDfHORJC0df1NZkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJauYNhCRrk/wwyStJ9if5Uqt/NMkTSX7R3s/vGXNbkokkrya5tqd+ZZIX2747k6TVz07yvVZ/Nsm6JfiukqQ+BrlCmAZuraqPAxuB7UkuBXYAT1bVeuDJ9pm2bxy4DNgE3JVkRTvX3cA2YH17bWr1rcBbVXUJcAdw+yJ8N0nSAswbCFV1qKp+2rbfAV4BVgObgd3tsN3AdW17M/BAVR2pqteACeCqJKuAc6vq6aoq4L5ZY2bO9SBwzczVgyTpxFi5kIPbUs4ngWeBkao6BN3QSHJRO2w18EzPsMlW+33bnl2fGfN6O9d0kreBC4Bfzfr3t9G9wmBkZIROp7OQ6f/BrVdMDzVuMQw750FMTU0t6flPZ/amP/vT33Lpz8CBkOTDwPeBL1fVb/v8AD/XjupT7zfm6ELVLmAXwOjoaI2Njc0z67ndvOPRocYthgM3ji3ZuTudDsP25Exnb/qzP/0tl/4M9JRRkrPohsF3q+oHrfxGWwaivR9u9Ulgbc/wNcDBVl8zR/2oMUlWAucBby70y0iShjfIU0YB7gFeqapv9ezaC2xp21uAh3vq4+3JoYvp3jx+ri0vvZNkYzvnTbPGzJzreuCpdp9BknSCDLJkdDXwOeDFJC+02leAbwB7kmwFfgncAFBV+5PsAV6m+4TS9qp6r427BbgXOAd4vL2gGzj3J5mge2UwfnxfS5K0UPMGQlX9mLnX+AGuOcaYncDOOer7gMvnqL9LCxRJ0snhbypLkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJzbyBkOQ7SQ4neamn9rUk/5zkhfb68559tyWZSPJqkmt76lcmebHtuzNJWv3sJN9r9WeTrFvk7yhJGsAgVwj3ApvmqN9RVRva6zGAJJcC48BlbcxdSVa04+8GtgHr22vmnFuBt6rqEuAO4PYhv4sk6TjMGwhV9SPgzQHPtxl4oKqOVNVrwARwVZJVwLlV9XRVFXAfcF3PmN1t+0HgmpmrB0nSibPyOMZ+MclNwD7g1qp6C1gNPNNzzGSr/b5tz67T3l8HqKrpJG8DFwC/mv0PJtlG9yqDkZEROp3OUBO/9YrpocYthmHnPIipqaklPf/pzN70Z3/6Wy79GTYQ7ga+DlR7/ybweWCun+yrT5159h1drNoF7AIYHR2tsbGxBU16xs07Hh1q3GI4cOPYkp270+kwbE/OdPamP/vT33Lpz1BPGVXVG1X1XlX9C/Bt4Kq2axJY23PoGuBgq6+Zo37UmCQrgfMYfIlKkrRIhgqEdk9gxmeAmSeQ9gLj7cmhi+nePH6uqg4B7yTZ2O4P3AQ83DNmS9u+Hniq3WeQJJ1A8y4ZJflbYAy4MMkk8FVgLMkGuks7B4AvAFTV/iR7gJeBaWB7Vb3XTnUL3SeWzgEeby+Ae4D7k0zQvTIYX4TvJUlaoHkDoao+O0f5nj7H7wR2zlHfB1w+R/1d4Ib55iFJWlr+prIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkoABAiHJd5IcTvJST+2jSZ5I8ov2fn7PvtuSTCR5Ncm1PfUrk7zY9t2ZJK1+dpLvtfqzSdYt8neUJA1gkCuEe4FNs2o7gCeraj3wZPtMkkuBceCyNuauJCvamLuBbcD69po551bgraq6BLgDuH3YLyNJGt68gVBVPwLenFXeDOxu27uB63rqD1TVkap6DZgArkqyCji3qp6uqgLumzVm5lwPAtfMXD1Ikk6cYe8hjFTVIYD2flGrrwZe7zlustVWt+3Z9aPGVNU08DZwwZDzkiQNaeUin2+un+yrT73fmPefPNlGd9mJkZEROp3OEFOEW6+YHmrcYhh2zoOYmppa0vOfzuxNf/anv+XSn2ED4Y0kq6rqUFsOOtzqk8DanuPWAAdbfc0c9d4xk0lWAufx/iUqAKpqF7ALYHR0tMbGxoaa/M07Hh1q3GI4cOPYkp270+kwbE/OdPamP/vT33Lpz7BLRnuBLW17C/BwT328PTl0Md2bx8+1ZaV3kmxs9wdumjVm5lzXA0+1+wySpBNo3iuEJH8LjAEXJpkEvgp8A9iTZCvwS+AGgKran2QP8DIwDWyvqvfaqW6h+8TSOcDj7QVwD3B/kgm6Vwbji/LNJEkLMm8gVNVnj7HrmmMcvxPYOUd9H3D5HPV3aYEiSTp5/E1lSRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTmuQEhyIMmLSV5Isq/VPprkiSS/aO/n9xx/W5KJJK8mubanfmU7z0SSO5PkeOYlSVq4xbhC+NOq2lBVo+3zDuDJqloPPNk+k+RSYBy4DNgE3JVkRRtzN7ANWN9emxZhXpKkBViKJaPNwO62vRu4rqf+QFUdqarXgAngqiSrgHOr6umqKuC+njGSpBNk5XGOL+AfkhTw36tqFzBSVYcAqupQkovasauBZ3rGTrba79v27Pr7JNlG90qCkZEROp3OUJO+9YrpocYthmHnPIipqaklPf/pzN70Z3/6Wy79Od5AuLqqDrb/6D+R5Od9jp3rvkD1qb+/2A2cXQCjo6M1Nja2wOl23bzj0aHGLYYDN44t2bk7nQ7D9uRMZ2/6sz/9LZf+HNeSUVUdbO+HgYeAq4A32jIQ7f1wO3wSWNszfA1wsNXXzFGXJJ1AQwdCkg8l+ZOZbeDPgJeAvcCWdtgW4OG2vRcYT3J2kovp3jx+ri0vvZNkY3u66KaeMZKkE+R4loxGgIfaE6Irgb+pqr9L8hNgT5KtwC+BGwCqan+SPcDLwDSwvarea+e6BbgXOAd4vL0kSSfQ0IFQVf8EfGKO+q+Ba44xZiewc476PuDyYeciSTp+/qayJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJKAUygQkmxK8mqSiSQ7TvZ8JGm5OSUCIckK4K+BfwdcCnw2yaUnd1aStLycEoEAXAVMVNU/VdX/Ax4ANp/kOUnSsnKqBMJq4PWez5OtJkk6QVae7Ak0maNW7zso2QZsax+nkry6pLNaArl9SU9/IfCrJf0XTl/2pj/709+Z1J9/c6wdp0ogTAJrez6vAQ7OPqiqdgG7TtSkTjdJ9lXV6Mmex6nI3vRnf/pbLv05VZaMfgKsT3Jxkn8FjAN7T/KcJGlZOSWuEKpqOskXgb8HVgDfqar9J3lakrSsnBKBAFBVjwGPnex5nOZcTjs2e9Of/elvWfQnVe+7dytJWoZOlXsIkqSTzEA4TSRZm+SHSV5Jsj/Jl1r9o0meSPKL9n5+z5jb2p8CeTXJtSdv9ksvyQeTPJfkH1t//qrV7U+TZEWS/53kkfbZ3vRIciDJi0leSLKv1ZZVjwyE08c0cGtVfRzYCGxvf95jB/BkVa0HnmyfafvGgcuATcBd7U+EnKmOAJ+qqk8AG4BNSTZif3p9CXil57O9eb8/raoNPY+YLqseGQiniao6VFU/bdvv0P0/9mq6f+JjdztsN3Bd294MPFBVR6rqNWCC7p8IOSNV11T7eFZ7FfYHgCRrgL8A/kdP2d7Mb1n1yEA4DSVZB3wSeBYYqapD0A0N4KJ22LL7cyBtSeQF4DDwRFXZnz/6b8B/Af6lp2ZvjlbAPyR5vv1VBFhmPTplHjvVYJJ8GPg+8OWq+m0y11/96B46R+2MfqSsqt4DNiT5CPBQksv7HL5s+pPk3wOHq+r5JGODDJmjdkb2Zparq+pgkouAJ5L8vM+xZ2SPvEI4jSQ5i24YfLeqftDKbyRZ1favovvTMQz450DORFX1G6BDd23X/sDVwH9IcoDuXxL+VJL/ib05SlUdbO+HgYfoLgEtqx4ZCKeJdC8F7gFeqapv9ezaC2xp21uAh3vq40nOTnIxsB547kTN90RL8rF2ZUCSc4BPAz/H/lBVt1XVmqpaR/dG6FNV9R+xN3+Q5ENJ/mRmG/gz4CWWWY9cMjp9XA18DnixrZMDfAX4BrAnyVbgl8ANAFW1P8ke4GW6Tyhtb0sqZ6pVwO72pMcHgD1V9UiSp7E/x+L/dv5ohO4yI3T/u/g3VfV3SX7CMuqRv6ksSQJcMpIkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJAD+P66GrmGOQ2eFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(pred_train).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4ffd9bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dummy regressor\n",
      "train mae 73.40003703765083\n",
      "test mae 73.39060713427676\n"
     ]
    }
   ],
   "source": [
    "dummy_regr = DummyRegressor(strategy=\"mean\")\n",
    "dummy_regr.fit(X_train_scaled, y_train)\n",
    "\n",
    "\n",
    "pred_train = dummy_regr.predict(X_train_scaled)\n",
    "pred_test = dummy_regr.predict(X_test_scaled)\n",
    "\n",
    "print('Dummy regressor')\n",
    "print('train mae', mean_absolute_error(y_train, pred_train))\n",
    "print('test mae', mean_absolute_error(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852b93f9",
   "metadata": {},
   "source": [
    "# Test ClassRegressorEnsemble (with LinearRegression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "92b7de05",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_BINS = 2\n",
    "N_LEVELS = 1\n",
    "\n",
    "class_reg = ClassRegressorEnsemble(n_bins=N_BINS, n_levels=N_LEVELS, leaf_model=LinearRegression)\n",
    "\n",
    "class_reg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ca74bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0,\n",
       "  (0,)): <regression_classifier.class_regressor.ClassRegressor at 0x1b62f401700>}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_reg.models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "629074c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 10. , 404.5],\n",
       "       [404.5, 799. ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_reg.models[(0, (0,))].bin_borders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f8310fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#class_reg.models[(1, (0, 0))].bin_borders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8866bf52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#class_reg.models[(1, (0, 1))].bin_borders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc895148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(neighbourhood                         2.390667\n",
       " availability_365                      0.804064\n",
       " n-hood_group_Brooklyn                -0.834955\n",
       " n-hood_group_Manhattan                1.123723\n",
       " n-hood_group_Queens                  -0.364281\n",
       " n-hood_group_Staten Island           -0.088610\n",
       " room_type_Private room               -0.925042\n",
       " room_type_Shared room                -0.156487\n",
       " Manh_dist                            -1.076243\n",
       " Broo_dist                             0.198089\n",
       " log_minimum_nights                    0.128277\n",
       " log_number_of_reviews                -0.838499\n",
       " log_reviews_per_month                -0.036852\n",
       " log_calculated_host_listings_count    1.449238\n",
       " Name: 0, dtype: float64,\n",
       " 419)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.iloc[0], y_train.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b45efd44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_reg.models[(0, (0,))].predict(X_train_scaled.iloc[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5263776f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([248.29169184])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_reg.predict(X_train_scaled.iloc[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2af4ce24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4787c953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "y_train.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e1a1ff3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pred_train = class_reg.predict(X_train_scaled)\n",
    "pd.Series(pred_train).hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa76bf97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2635da1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simple ClassRegressor ensemble\n",
      "train mae 47.43186437041619\n",
      "test mae 47.21950775279553\n"
     ]
    }
   ],
   "source": [
    "class_reg = ClassRegressorEnsemble(n_bins=N_BINS, n_levels=N_LEVELS, leaf_model=LinearRegression)\n",
    "class_reg.fit(X_train_scaled, y_train)\n",
    "\n",
    "pred_train = class_reg.predict(X_train_scaled)\n",
    "pred_test = class_reg.predict(X_test_scaled)\n",
    "\n",
    "print('Simple ClassRegressor ensemble')\n",
    "print('train mae', mean_absolute_error(y_train, pred_train))\n",
    "print('test mae', mean_absolute_error(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f458753d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd373b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simple ClassRegressor ensemble\n",
      "train mae 47.856500491811396\n",
      "test mae 48.27892035160739\n"
     ]
    }
   ],
   "source": [
    "class_reg = ClassRegressorEnsemble(n_bins=100, n_levels=1, leaf_model=None)\n",
    "class_reg.fit(X_train_scaled, y_train)\n",
    "\n",
    "pred_train = class_reg.predict(X_train_scaled)\n",
    "pred_test = class_reg.predict(X_test_scaled)\n",
    "\n",
    "print('Simple ClassRegressor ensemble')\n",
    "print('train mae', mean_absolute_error(y_train, pred_train))\n",
    "print('test mae', mean_absolute_error(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfcf79e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}