pizza_writeup1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1913758-5d55-4f48-accf-98c8fad7bfed",
   "metadata": {},
   "source": [
    "<b><h1 align='center'>Pizza Price Prediction </h1></b>\n",
    "<p style=\"text-align: center;\"> by Janice Liu </p>\n",
    "\n",
    "<h3>Problem Statement </h3>\n",
    "<p>The Pizza Price Prediction project is a Machine Learning models to predict the pizza prices of restaurants in the United States based off the median household income of the restaurants' zip codes. \n",
    "\n",
    "This problem can be applied to consumers who may use a price prediction model to compare pizza prices in different areas and make informed decisions on where to order from to save money. Another target audience could be pizza chain restaurants. They may use this price prediction model to determine how to optimally price their pizzas in different geographic locations to stay competitive and maximize profits.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5dd660-6235-48c8-9d38-af2aad8be317",
   "metadata": {},
   "source": [
    "<h3>Dataset </h3>\n",
    "<p>Datafiniti's pizza dataset is downloaded from <a href=\"https://www.kaggle.com/datasets/datafiniti/pizza-restaurants-and-the-pizza-they-sell\">here</a>. This dataset provides over 10,000 pizza entries with 24 different features from 2,278 unique pizza restaurants in the United States. The target variable would be the price of the pizzas given the zip code of the pizza restaurant. \n",
    "\n",
    "To determine the income bracket of each zip code, <a href=\"https://www.kaggle.com/datasets/goldenoakresearch/us-household-income-stats-geo-locations/versions/1?select=US_Income_Kaggle.csv\">this</a> dataset was used. It provides over 32,000 records of zip codes with over 11 features, two of them being the zip code and its median household income. By being able to correlate the zipcode to an income bracket, the pizza dataset was then merged with this one to create models that predicted the pizza prices given a certain zipcode.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2df1f0fa-1303-4058-b5d7-772fb41abb72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "\n",
    "import warnings\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.datasets import load_wine\n",
    "import optuna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbfbc4a8-d715-476b-96cd-085a75d4c3ae",
   "metadata": {},
   "source": [
    "<h4>Load in the Datasets </h4>\n",
    "<p>First, Datafiniti's pizza dataset is loaded in and then cleaned up. The zip code, or postal code, feature in the dataset was converted into strings and made uniformed by dropping all the nonexistent (\"nan\") entries and the suffixes of the postal code. To prevent extreme outliers from skewing the data, the menu.amountMax of each pizza was capped to $100. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bffa691-f1bf-4e72-a3c3-3c00be5197ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pizza dataset\n",
    "input_path = os.getcwd() + \"/input\"\n",
    "data_path = input_path + \"/pizza.csv\"\n",
    "pizza = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d040df9-027e-46b9-a437-041df002a3df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0f/glct3g514dd7ylpg_7nb05c80000gn/T/ipykernel_5311/3576796121.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pizza['postalCode'][i] = pizza['postalCode'][i].split(\"-\")[0]\n"
     ]
    }
   ],
   "source": [
    "# clean up postal code\n",
    "pizza['postalCode'] = pizza['postalCode'].astype(str)\n",
    "pizza = pizza[pizza.postalCode != \"nan\"]\n",
    "pizza = pizza[pizza[\"menus.amountMax\"] < 100.0]\n",
    "    \n",
    "for i,j in enumerate(pizza['postalCode']):    \n",
    "    # get rid of '-' postal code suffix\n",
    "    if \"-\" in j:\n",
    "        pizza['postalCode'][i] = pizza['postalCode'][i].split(\"-\")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c795750f-5b56-4336-b744-4424c9d85c19",
   "metadata": {},
   "source": [
    "<p>Then, the income dataset was loaded in. Similarly, the zip code feature was casted into string types. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c37c59b5-91f0-4967-ac63-358d18a58119",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading income data\n",
    "data_path = input_path + \"/income.csv\"\n",
    "income = pd.read_csv(data_path,engine='python',encoding='latin1')\n",
    "income['Zip_Code'] = income['Zip_Code'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba6d15a-9e43-4300-8fb4-1349c7f2d35e",
   "metadata": {},
   "source": [
    "<p>The pizza dataset and the income dataset is merged on their zip codes into one dataframe called complete_df, and then plotted. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b09d70fd-fc7a-47e4-a5a4-93f1b8b22931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11259 entries, 72120 to 89128\n",
      "Data columns (total 24 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   id                  11259 non-null  object \n",
      " 1   dateAdded           11259 non-null  object \n",
      " 2   dateUpdated         11259 non-null  object \n",
      " 3   address             11259 non-null  object \n",
      " 4   categories          11259 non-null  object \n",
      " 5   primaryCategories   11259 non-null  object \n",
      " 6   city                11259 non-null  object \n",
      " 7   country             11259 non-null  object \n",
      " 8   keys                11259 non-null  object \n",
      " 9   latitude            11259 non-null  float64\n",
      " 10  longitude           11259 non-null  float64\n",
      " 11  menuPageURL         1782 non-null   object \n",
      " 12  menus.amountMax     11259 non-null  float64\n",
      " 13  menus.amountMin     11259 non-null  float64\n",
      " 14  menus.currency      11259 non-null  object \n",
      " 15  menus.dateSeen      11259 non-null  object \n",
      " 16  menus.description   4050 non-null   object \n",
      " 17  menus.name          11259 non-null  object \n",
      " 18  name                11259 non-null  object \n",
      " 19  priceRangeCurrency  11259 non-null  object \n",
      " 20  priceRangeMin       11259 non-null  int64  \n",
      " 21  priceRangeMax       11259 non-null  int64  \n",
      " 22  province            11259 non-null  object \n",
      " 23  Median              4395 non-null   float64\n",
      "dtypes: float64(5), int64(2), object(17)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# join both datasets\n",
    "pizza_df = pd.DataFrame(pizza)\n",
    "pizza_df = pizza_df.set_index(\"postalCode\")\n",
    "\n",
    "income_df = pd.DataFrame(income[[\"Zip_Code\", \"Median\"]])\n",
    "income_df = income_df.set_index(\"Zip_Code\")\n",
    "complete_df = pizza_df.join(income_df, on='postalCode')\n",
    "complete_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29bb91ea-549f-439d-a4cc-7b288d152c0d",
   "metadata": {},
   "source": [
    "<p>The dot graph  shows each entry of the maximum menu prices compared to the household median income of the restaurant zip code, both of which are in United States dollars. From this graph, it can be seen that as the median income increased, although it is not drastic, there are more pizzas that have higher menu maximum prices. \n",
    "\n",
    "As shown in this graph, one limitation of this data is that the income dataset does not encapsulate all the zip codes provided by the pizza dataset. Therefore, there is a portion of the data in the $0 median income range that is unusable.\n",
    "\n",
    "Despite removing some of the outliers previously, there are still some pizzas that are priced significantly higher than the rest. These data points most likely encapsulates the higher end restaurants within each neighborhood, which is more frequent in the higher median income groups. This was another limitation of the dataset, since it does not take into account the variations that determine pizza prices. The quality of the ingredients, the type of restaurant, etc. can all be factors that causes differing ranges of pizza prices. However, with this dataset, the general range of pizza prices can still be determined, which makes it a valid dataset. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f44e3f0-7f71-40bd-8350-989e7c846684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff3d2a3ee80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=complete_df, x=complete_df[\"Median\"], y=complete_df[\"menus.amountMax\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e2452f-8ff1-43e2-a352-84e1779a4ec3",
   "metadata": {},
   "source": [
    "<h4>Transform the Merged Dataset </h4>\n",
    "<p>The features of the dataset are then transformed for the machine learning models in a pipeline to ensure that multiple transformations occur in the accurate order. First, the SimpleImputer function replaces any missing values in the household median income feature with the mean of all the household median incomes for each pizza entry. Since province, primaryCategories, and city are all features that are object values, OrdinalEncoder is used to convert the strings into integers.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39509446-234f-4548-9824-44cf7be7343f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11259 entries, 72120 to 89128\n",
      "Data columns (total 10 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   imputer__Median             11259 non-null  float64\n",
      " 1   ord1__province              11259 non-null  float64\n",
      " 2   ord2__primaryCategories     11259 non-null  float64\n",
      " 3   ord3__city                  11259 non-null  float64\n",
      " 4   remainder__latitude         11259 non-null  float64\n",
      " 5   remainder__longitude        11259 non-null  float64\n",
      " 6   remainder__menus.amountMax  11259 non-null  float64\n",
      " 7   remainder__menus.amountMin  11259 non-null  float64\n",
      " 8   remainder__priceRangeMin    11259 non-null  int64  \n",
      " 9   remainder__priceRangeMax    11259 non-null  int64  \n",
      "dtypes: float64(8), int64(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    },
    {
     "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>imputer__Median</th>\n",
       "      <th>ord1__province</th>\n",
       "      <th>ord2__primaryCategories</th>\n",
       "      <th>ord3__city</th>\n",
       "      <th>remainder__latitude</th>\n",
       "      <th>remainder__longitude</th>\n",
       "      <th>remainder__menus.amountMax</th>\n",
       "      <th>remainder__menus.amountMin</th>\n",
       "      <th>remainder__priceRangeMin</th>\n",
       "      <th>remainder__priceRangeMax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postalCode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72120</th>\n",
       "      <td>93909.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>34.832300</td>\n",
       "      <td>-92.183800</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.98</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85012</th>\n",
       "      <td>142758.854152</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>33.509266</td>\n",
       "      <td>-112.073044</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>142758.854152</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>39.144883</td>\n",
       "      <td>-84.432685</td>\n",
       "      <td>6.49</td>\n",
       "      <td>6.49</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48071</th>\n",
       "      <td>142758.854152</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>42.516669</td>\n",
       "      <td>-83.106630</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21224</th>\n",
       "      <td>142758.854152</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>39.286630</td>\n",
       "      <td>-76.566984</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.49</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            imputer__Median  ord1__province  ord2__primaryCategories  \\\n",
       "postalCode                                                             \n",
       "72120          93909.000000             2.0                      0.0   \n",
       "85012         142758.854152             3.0                      0.0   \n",
       "45209         142758.854152            30.0                      0.0   \n",
       "48071         142758.854152            19.0                      0.0   \n",
       "21224         142758.854152            18.0                      0.0   \n",
       "\n",
       "            ord3__city  remainder__latitude  remainder__longitude  \\\n",
       "postalCode                                                          \n",
       "72120            866.0            34.832300            -92.183800   \n",
       "85012            716.0            33.509266           -112.073044   \n",
       "45209            169.0            39.144883            -84.432685   \n",
       "48071            524.0            42.516669            -83.106630   \n",
       "21224             63.0            39.286630            -76.566984   \n",
       "\n",
       "            remainder__menus.amountMax  remainder__menus.amountMin  \\\n",
       "postalCode                                                           \n",
       "72120                             7.98                        7.98   \n",
       "85012                             6.00                        6.00   \n",
       "45209                             6.49                        6.49   \n",
       "48071                             5.99                        5.99   \n",
       "21224                             5.49                        5.49   \n",
       "\n",
       "            remainder__priceRangeMin  remainder__priceRangeMax  \n",
       "postalCode                                                      \n",
       "72120                              0                        25  \n",
       "85012                              0                        25  \n",
       "45209                              0                        25  \n",
       "48071                             25                        40  \n",
       "21224                              0                        25  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create master pipeline\n",
    "data_pipeline = ColumnTransformer(\n",
    "    transformers=[('imputer', SimpleImputer(strategy='mean'), ['Median']),\n",
    "                  ('ord1', OrdinalEncoder(), ['province']),\n",
    "                  ('ord2', OrdinalEncoder(), ['primaryCategories']),\n",
    "                  ('ord3', OrdinalEncoder(), ['city']),],\n",
    "    remainder='passthrough')\n",
    "data_pipeline.set_output(transform=\"pandas\")\n",
    "\n",
    "df_processed = data_pipeline.fit_transform(complete_df)\n",
    "df_processed.drop(columns=['remainder__menuPageURL','remainder__menus.description', 'remainder__id', \n",
    "                           'remainder__address', 'remainder__country', 'remainder__keys', \n",
    "                           'remainder__menus.currency', 'remainder__priceRangeCurrency', 'remainder__name',\n",
    "                           'remainder__dateAdded', 'remainder__dateUpdated', 'remainder__menus.dateSeen'\n",
    "                          ], inplace=True)\n",
    "df_processed.drop(columns=['remainder__categories', 'remainder__menus.name',], inplace=True)\n",
    "df_processed.info()\n",
    "df_processed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15197dd3-23a6-4548-b97f-76b8845644d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split model\n",
    "X, y = df_processed[df_processed.columns.difference(['remainder__menus.amountMax'])], df_processed[['remainder__menus.amountMax']]\n",
    "\n",
    "X_train, X_rest, y_train, y_rest = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42)\n",
    "X_test, X_val, y_test, y_val = train_test_split(\n",
    "    X_rest, y_rest, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19ee1a23-8772-41ff-9c92-44649302274c",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [9007, 1126]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/0f/glct3g514dd7ylpg_7nb05c80000gn/T/ipykernel_5311/273798967.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# RandomForestRegressor model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mrf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         )\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    582\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    585\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1122\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmulti_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_numeric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    395\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    398\u001b[0m             \u001b[0;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m             \u001b[0;34m%\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [9007, 1126]"
     ]
    }
   ],
   "source": [
    "# RandomForestRegressor model\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train, y_val.values.ravel())\n",
    "\n",
    "prediction = rf.predict(X_test)\n",
    "r2_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a67d0833-1d20-44d7-92cf-e77e4fe3aad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter tuning\n",
    "param_dist = {\"max_depth\": [3, 5], \n",
    "    \"max_features\": sp_randint(1, 11), \n",
    "    \"min_samples_split\": sp_randint(2, 11), \n",
    "    \"bootstrap\": [True, False], \n",
    "    \"criterion\": ['friedman_mse', 'poisson', 'absolute_error', 'squared_error']} \n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Randomized search\n",
    "random_search = RandomizedSearchCV(rf, param_distributions=param_dist, \n",
    "    n_iter=2, cv=2) \n",
    "random_search.fit(X_train, y_train.values.ravel())\n",
    "hyperparams = random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3be5913-b0c7-40fa-992e-d798e08461d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train model\n",
    "rf = RandomForestRegressor(**hyperparams)\n",
    "rf.fit(X_val, y_val.values.ravel())\n",
    "\n",
    "prediction = rf.predict(X_test)\n",
    "r2_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ae4e7f9-63d4-40ca-8349-97038415ccf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf.predict(X_test)\n",
    "mse = mean_squared_error(y_test, prediction)\n",
    "rmse = mse**.5\n",
    "print(mse)\n",
    "print(rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1206fe83-e2b9-4050-ba63-3a83e3498319",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_processed['remainder__menus.amountMax'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95378236-69e5-4d78-a875-f9f4b29a5f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_mean = [np.mean(y_test)]*len(y_test)\n",
    "mse = mean_squared_error(y_test, y_test_mean)\n",
    "rmse = mse**.5\n",
    "print(mse)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd74f565-ef64-4de1-956e-3f27fb95a9e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build second model with xg_boost\n",
    "#hide warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "model = XGBRegressor()\n",
    "model.fit(X_val, y_val)\n",
    "\n",
    "# R^2 value for xg_boost model\n",
    "prediction = model.predict(X_test)\n",
    "r2_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "345f39e2-d469-41d2-b942-72c2380befae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameter tuning of XGBoost\n",
    "\n",
    "# optuna objective function\n",
    "def objective(trial):\n",
    "    \"\"\"Define the objective function\"\"\"\n",
    "\n",
    "    params = {\n",
    "        'max_depth': trial.suggest_int('max_depth', 1, 9),\n",
    "        'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 1.0),\n",
    "        'n_estimators': trial.suggest_int('n_estimators', 50, 500),\n",
    "        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),\n",
    "        'gamma': trial.suggest_loguniform('gamma', 1e-8, 1.0),\n",
    "        'subsample': trial.suggest_loguniform('subsample', 0.01, 1.0),\n",
    "        'colsample_bytree': trial.suggest_loguniform('colsample_bytree', 0.01, 1.0),\n",
    "        'reg_alpha': trial.suggest_loguniform('reg_alpha', 1e-8, 1.0),\n",
    "        'reg_lambda': trial.suggest_loguniform('reg_lambda', 1e-8, 1.0),\n",
    "        'eval_metric': 'mlogloss',\n",
    "        'use_label_encoder': False\n",
    "    }\n",
    "\n",
    "    # Fit the model\n",
    "    optuna_model = XGBRegressor(**params)\n",
    "    optuna_model.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions\n",
    "    prediction = model.predict(X_test)\n",
    "\n",
    "    # Evaluate predictions\n",
    "    accuracy = r2_score(y_test, prediction)\n",
    "    return accuracy\n",
    "\n",
    "# optimize the objective function\n",
    "study = optuna.create_study(direction='maximize')\n",
    "study.optimize(objective, n_trials=100)\n",
    "\n",
    "# print best parameters\n",
    "print('Number of finished trials: {}'.format(len(study.trials)))\n",
    "print('Best trial:')\n",
    "trial = study.best_trial\n",
    "\n",
    "print('  Value: {}'.format(trial.value))\n",
    "print('  Params: ')\n",
    "\n",
    "for key, value in trial.params.items():\n",
    "    print('    {}: {}'.format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceec2353-9b64-4ea0-a891-c6d37e2adf18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# refit model\n",
    "params = trial.params\n",
    "model = XGBRegressor(**params)\n",
    "model.fit(X_val, y_val)\n",
    "\n",
    "# R^2 value for xg_boost model\n",
    "prediction = model.predict(X_test)\n",
    "r2_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7601008-4f2c-479f-b30e-7111c2b94598",
   "metadata": {},
   "outputs": [],
   "source": [
    "# determining feature importance \n",
    "result = permutation_importance(\n",
    "    rf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=2\n",
    ")\n",
    "\n",
    "# feature_names = [f\"feature {i}\" for i in range(X.shape[1])]\n",
    "feature_names = X.columns\n",
    "importances = pd.Series(result.importances_mean, index=feature_names)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "importances.plot.bar(yerr=result.importances_std, ax=ax)\n",
    "ax.set_title(\"Feature importances using permutation on full model\")\n",
    "ax.set_ylabel(\"Mean accuracy decrease\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c4ee2d7-b473-4e3b-a9c4-95ff92ecf477",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot partial dependency plot \n",
    "features = [0, 6, (0,6)]\n",
    "PartialDependenceDisplay.from_estimator(rf, X, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdc4d0df-e37f-4f2f-8ecd-807c5a4b317c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train new model without remainder_menus.amountMin\n",
    "df_processed.drop(columns=['remainder__menus.amountMin',], inplace=True)\n",
    "\n",
    "# split model\n",
    "X, y = df_processed[df_processed.columns.difference(['remainder__menus.amountMax'])], df_processed[['remainder__menus.amountMax']]\n",
    "\n",
    "X_train, X_rest, y_train, y_rest = train_test_split(\n",
    "    X, y, test_size=0.8, random_state=42)\n",
    "X_test, X_val, y_test, y_val = train_test_split(\n",
    "    X_rest, y_rest, test_size=0.5, random_state=42)\n",
    "\n",
    "# train model\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "prediction = rf.predict(X_val)\n",
    "print(r2_score(y_val, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80cde9a3-e54d-4e4d-a4fe-e2185e3ba64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# determining feature importance w/o remainder__princeRangeMax\n",
    "result = permutation_importance(\n",
    "    rf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=2\n",
    ")\n",
    "\n",
    "# feature_names = [f\"feature {i}\" for i in range(X.shape[1])]\n",
    "feature_names = X.columns\n",
    "importances = pd.Series(result.importances_mean, index=feature_names)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "importances.plot.bar(yerr=result.importances_std, ax=ax)\n",
    "ax.set_title(\"Feature importances using permutation on full model\")\n",
    "ax.set_ylabel(\"Mean accuracy decrease\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# shows that the model needs menu amountMin to have accurate prediction, or else it's dependent on it's coordinate \n",
    "# but it wasn't that accurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af3aaa6a-26e9-45cc-818a-67ad1332fbfd",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}