6110T_class9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import f1_score, precision_recall_curve, precision_score, recall_score, accuracy_score, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes = pd.read_csv('https://raw.githubusercontent.com/delinai/schulich_ds1/main/Datasets/bikes_sharing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='count', ylabel='Count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# changing the output variable to a category > one is going to be BINARY, one will be multiclass\n",
    "sns.histplot(bikes['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10886.000000\n",
       "mean       191.574132\n",
       "std        181.144454\n",
       "min          1.000000\n",
       "25%         42.000000\n",
       "50%        145.000000\n",
       "75%        284.000000\n",
       "max        977.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for binary > use 145 bikes as the midpoint for High vs. Low\n",
    "bikes['count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# binary output variable > 1 = High # of rentals, 0 = Low # of rentals\n",
    "bikes['binary_output'] = bikes['count'].apply(lambda x: 1 if x >= 145 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes['multiclass_output'] = bikes['count'].apply(lambda x: 1 if x >= 0 and x <42 else 2 if x>= 42 and x <145 else 3 if x>=145 and x<284 else 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>binary_output</th>\n",
       "      <th>multiclass_output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  binary_output  \\\n",
       "0        81        0.0       3          13     16              0   \n",
       "1        80        0.0       8          32     40              0   \n",
       "2        80        0.0       5          27     32              0   \n",
       "3        75        0.0       3          10     13              0   \n",
       "4        75        0.0       0           1      1              0   \n",
       "\n",
       "   multiclass_output  \n",
       "0                  1  \n",
       "1                  1  \n",
       "2                  1  \n",
       "3                  1  \n",
       "4                  1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>multiclass_output</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binary_output</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "      <td>5431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "      <td>5455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime  season  holiday  workingday  weather  temp  atemp  \\\n",
       "binary_output                                                                \n",
       "0                  5431    5431     5431        5431     5431  5431   5431   \n",
       "1                  5455    5455     5455        5455     5455  5455   5455   \n",
       "\n",
       "               humidity  windspeed  casual  registered  count  \\\n",
       "binary_output                                                   \n",
       "0                  5431       5431    5431        5431   5431   \n",
       "1                  5455       5455    5455        5455   5455   \n",
       "\n",
       "               multiclass_output  \n",
       "binary_output                     \n",
       "0                           5431  \n",
       "1                           5455  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# binary problem > we can use Logistic Regression, KNN and Naive Bayes to make predictions\n",
    "# check for data balance\n",
    "bikes.groupby('binary_output').count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-processing - first split the data into training and testing\n",
    "X = bikes[['season','holiday','workingday','weather','temp','atemp','humidity','windspeed']]\n",
    "y = bikes['binary_output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reserve 30% for testing data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_columns = ['temp','atemp','humidity','windspeed']\n",
    "categorical_columns = ['season','holiday','workingday','weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a Column Transformer to SCALE the numeric columns and OneHotEncode the categoricals\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[('num',MinMaxScaler(),numeric_columns),\n",
    "                  ('cat',OneHotEncoder(),categorical_columns)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>binary_output</th>\n",
       "      <th>multiclass_output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  binary_output  \\\n",
       "0        81        0.0       3          13     16              0   \n",
       "1        80        0.0       8          32     40              0   \n",
       "2        80        0.0       5          27     32              0   \n",
       "3        75        0.0       3          10     13              0   \n",
       "4        75        0.0       0           1      1              0   \n",
       "\n",
       "   multiclass_output  \n",
       "0                  1  \n",
       "1                  1  \n",
       "2                  1  \n",
       "3                  1  \n",
       "4                  1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "logreg = LogisticRegression()\n",
    "nb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# cross validation to see in general which model performs best for the problem\n",
    "knn_scores = cross_val_score(knn, X_train, y_train, scoring='accuracy', cv=5 )\n",
    "logreg_scores = cross_val_score(logreg, X_train, y_train, scoring='accuracy', cv=5 )\n",
    "nb_scores = cross_val_score(nb, X_train, y_train, scoring='accuracy', cv=5 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7091863517060368\n",
      "0.7275590551181101\n",
      "0.6943569553805775\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(knn_scores))\n",
    "print(np.mean(logreg_scores))\n",
    "print(np.mean(nb_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# while LogReg is the best, we'll work with KNN so we can see how to tune hyperparameters\n",
    "# set up the pipeline\n",
    "pipeline = Pipeline(steps = [\n",
    "    ('preprocessor',preprocessor),\n",
    "    ('classifier', KNeighborsClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up a params grid\n",
    "param_grid = {\n",
    "    'classifier__n_neighbors':[5,25,55,105,375,575],\n",
    "    'classifier__weights':['uniform','distance'],\n",
    "    'classifier__metric':['euclidean','manhattan']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 457, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 763, in transform\n",
      "    Xs = self._fit_transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\", line 621, in _fit_transform\n",
      "    return Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1855, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/joblib/parallel.py\", line 1784, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/pipeline.py\", line 853, in _transform_one\n",
      "    res = transformer.transform(X)\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 882, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/homebrew/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 160, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [4] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                         MinMaxScaler(),\n",
       "                                                                         [&#x27;temp&#x27;,\n",
       "                                                                          &#x27;atemp&#x27;,\n",
       "                                                                          &#x27;humidity&#x27;,\n",
       "                                                                          &#x27;windspeed&#x27;]),\n",
       "                                                                        (&#x27;cat&#x27;,\n",
       "                                                                         OneHotEncoder(),\n",
       "                                                                         [&#x27;season&#x27;,\n",
       "                                                                          &#x27;holiday&#x27;,\n",
       "                                                                          &#x27;workingday&#x27;,\n",
       "                                                                          &#x27;weather&#x27;])])),\n",
       "                                       (&#x27;classifier&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;classifier__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;],\n",
       "                         &#x27;classifier__n_neighbors&#x27;: [5, 25, 55, 105, 375, 575],\n",
       "                         &#x27;classifier__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=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\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                         MinMaxScaler(),\n",
       "                                                                         [&#x27;temp&#x27;,\n",
       "                                                                          &#x27;atemp&#x27;,\n",
       "                                                                          &#x27;humidity&#x27;,\n",
       "                                                                          &#x27;windspeed&#x27;]),\n",
       "                                                                        (&#x27;cat&#x27;,\n",
       "                                                                         OneHotEncoder(),\n",
       "                                                                         [&#x27;season&#x27;,\n",
       "                                                                          &#x27;holiday&#x27;,\n",
       "                                                                          &#x27;workingday&#x27;,\n",
       "                                                                          &#x27;weather&#x27;])])),\n",
       "                                       (&#x27;classifier&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;classifier__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;],\n",
       "                         &#x27;classifier__n_neighbors&#x27;: [5, 25, 55, 105, 375, 575],\n",
       "                         &#x27;classifier__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=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: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                                  [&#x27;temp&#x27;, &#x27;atemp&#x27;, &#x27;humidity&#x27;,\n",
       "                                                   &#x27;windspeed&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;season&#x27;, &#x27;holiday&#x27;,\n",
       "                                                   &#x27;workingday&#x27;,\n",
       "                                                   &#x27;weather&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                 [&#x27;temp&#x27;, &#x27;atemp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;season&#x27;, &#x27;holiday&#x27;, &#x27;workingday&#x27;,\n",
       "                                  &#x27;weather&#x27;])])</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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;temp&#x27;, &#x27;atemp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div><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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;season&#x27;, &#x27;holiday&#x27;, &#x27;workingday&#x27;, &#x27;weather&#x27;]</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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(transformers=[('num',\n",
       "                                                                         MinMaxScaler(),\n",
       "                                                                         ['temp',\n",
       "                                                                          'atemp',\n",
       "                                                                          'humidity',\n",
       "                                                                          'windspeed']),\n",
       "                                                                        ('cat',\n",
       "                                                                         OneHotEncoder(),\n",
       "                                                                         ['season',\n",
       "                                                                          'holiday',\n",
       "                                                                          'workingday',\n",
       "                                                                          'weather'])])),\n",
       "                                       ('classifier', KNeighborsClassifier())]),\n",
       "             param_grid={'classifier__metric': ['euclidean', 'manhattan'],\n",
       "                         'classifier__n_neighbors': [5, 25, 55, 105, 375, 575],\n",
       "                         'classifier__weights': ['uniform', 'distance']},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid Search\n",
    "grid_search = GridSearchCV(pipeline, param_grid, cv=5, verbose=1, scoring='accuracy')\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'classifier__metric': 'euclidean', 'classifier__n_neighbors': 5, 'classifier__weights': 'uniform'}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to set up our final pipeline, we can set up first our final model\n",
    "final_classifier = KNeighborsClassifier(n_neighbors=grid_search.best_params_['classifier__n_neighbors'],\n",
    "                                        metric=grid_search.best_params_['classifier__metric'],\n",
    "                                        weights=grid_search.best_params_['classifier__weights'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create final pipeline with BEST model\n",
    "final_pipeline = Pipeline(steps = [\n",
    "    ('preprocessor',preprocessor),\n",
    "    ('classifier',final_classifier)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                                  [&#x27;temp&#x27;, &#x27;atemp&#x27;, &#x27;humidity&#x27;,\n",
       "                                                   &#x27;windspeed&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;season&#x27;, &#x27;holiday&#x27;,\n",
       "                                                   &#x27;workingday&#x27;,\n",
       "                                                   &#x27;weather&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, KNeighborsClassifier(metric=&#x27;euclidean&#x27;))])</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                                  [&#x27;temp&#x27;, &#x27;atemp&#x27;, &#x27;humidity&#x27;,\n",
       "                                                   &#x27;windspeed&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;season&#x27;, &#x27;holiday&#x27;,\n",
       "                                                   &#x27;workingday&#x27;,\n",
       "                                                   &#x27;weather&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, KNeighborsClassifier(metric=&#x27;euclidean&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                 [&#x27;temp&#x27;, &#x27;atemp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;season&#x27;, &#x27;holiday&#x27;, &#x27;workingday&#x27;,\n",
       "                                  &#x27;weather&#x27;])])</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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;temp&#x27;, &#x27;atemp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div><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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;season&#x27;, &#x27;holiday&#x27;, &#x27;workingday&#x27;, &#x27;weather&#x27;]</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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num', MinMaxScaler(),\n",
       "                                                  ['temp', 'atemp', 'humidity',\n",
       "                                                   'windspeed']),\n",
       "                                                 ('cat', OneHotEncoder(),\n",
       "                                                  ['season', 'holiday',\n",
       "                                                   'workingday',\n",
       "                                                   'weather'])])),\n",
       "                ('classifier', KNeighborsClassifier(metric='euclidean'))])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit pipeline on X_train and y_train\n",
    "final_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = final_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KNN is not a probabilistic model, but it does offer a predict_proba() function if we want to use it for threshold adjustment\n",
    "probabilities = final_pipeline.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7348438456827924"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after tuning the model with hyperparameters, accuracy score improved 3%\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.754863813229572"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7046004842615012"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7288666249217283"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}