baseline_models_grid_search.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/30/c46f3dw51w749hbqzgst91r40000gn/T/ipykernel_87691/1988355783.py:42: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  players_stats = players_stats.applymap(str_to_int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 31860 entries, 0 to 31859\n",
      "Data columns (total 45 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   height                     31860 non-null  float64\n",
      " 1   weight                     31860 non-null  int64  \n",
      " 2   games_played               31860 non-null  int64  \n",
      " 3   games_started              31860 non-null  int64  \n",
      " 4   min_per                    31573 non-null  float64\n",
      " 5   field_goal                 31860 non-null  float64\n",
      " 6   field_attmps               31860 non-null  float64\n",
      " 7   field_pct                  31127 non-null  float64\n",
      " 8   two_pointer                31860 non-null  float64\n",
      " 9   two_pattamps               31860 non-null  float64\n",
      " 10  two_pct                    30592 non-null  float64\n",
      " 11  three_ptrs                 31860 non-null  float64\n",
      " 12  three_pattmp               31860 non-null  float64\n",
      " 13  three_pct                  31127 non-null  float64\n",
      " 14  free_throws                31860 non-null  float64\n",
      " 15  free_attmps                31860 non-null  float64\n",
      " 16  free_pct                   29103 non-null  float64\n",
      " 17  assists                    31860 non-null  float64\n",
      " 18  steals                     31860 non-null  float64\n",
      " 19  blocks                     31860 non-null  float64\n",
      " 20  points                     31860 non-null  float64\n",
      " 21  off_reb                    31860 non-null  float64\n",
      " 22  def_reb                    31860 non-null  float64\n",
      " 23  total_reb                  31860 non-null  float64\n",
      " 24  turnovers                  31860 non-null  float64\n",
      " 25  drafted                    31860 non-null  int64  \n",
      " 26  position_Center            31860 non-null  int64  \n",
      " 27  position_Center-Forward    31860 non-null  int64  \n",
      " 28  position_Forward           31860 non-null  int64  \n",
      " 29  position_Forward-Center    31860 non-null  int64  \n",
      " 30  position_Forward-Guard     31860 non-null  int64  \n",
      " 31  position_Forward-PF        31860 non-null  int64  \n",
      " 32  position_Forward-SF        31860 non-null  int64  \n",
      " 33  position_Forward-SF-Guard  31860 non-null  int64  \n",
      " 34  position_Guard             31860 non-null  int64  \n",
      " 35  position_Guard-Forward     31860 non-null  int64  \n",
      " 36  position_Guard-PG          31860 non-null  int64  \n",
      " 37  position_Guard-SG          31860 non-null  int64  \n",
      " 38  position_PForward          31860 non-null  int64  \n",
      " 39  position_PForward-F        31860 non-null  int64  \n",
      " 40  position_PGuard            31860 non-null  int64  \n",
      " 41  position_PGuard-G          31860 non-null  int64  \n",
      " 42  position_SForward          31860 non-null  int64  \n",
      " 43  position_SForward-F        31860 non-null  int64  \n",
      " 44  position_SGuard-G          31860 non-null  int64  \n",
      "dtypes: float64(22), int64(23)\n",
      "memory usage: 11.2 MB\n",
      "Non-numeric columns: Index([], dtype='object')\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>games_played</th>\n",
       "      <th>games_started</th>\n",
       "      <th>min_per</th>\n",
       "      <th>field_goal</th>\n",
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       "      <th>two_pattamps</th>\n",
       "      <th>two_pct</th>\n",
       "      <th>three_ptrs</th>\n",
       "      <th>three_pattmp</th>\n",
       "      <th>three_pct</th>\n",
       "      <th>free_throws</th>\n",
       "      <th>free_attmps</th>\n",
       "      <th>free_pct</th>\n",
       "      <th>assists</th>\n",
       "      <th>steals</th>\n",
       "      <th>blocks</th>\n",
       "      <th>points</th>\n",
       "      <th>off_reb</th>\n",
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       "      <th>total_reb</th>\n",
       "      <th>turnovers</th>\n",
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       "      <th>position_Forward</th>\n",
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       "      <th>position_Forward-SF</th>\n",
       "      <th>position_Forward-SF-Guard</th>\n",
       "      <th>position_Guard</th>\n",
       "      <th>position_Guard-Forward</th>\n",
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       "      <th>position_PForward-F</th>\n",
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       "      <th>position_PGuard-G</th>\n",
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       "      <th>position_SForward-F</th>\n",
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       "      <td>6.416667</td>\n",
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       "      <td>0.3</td>\n",
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       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.750000</td>\n",
       "      <td>225</td>\n",
       "      <td>115</td>\n",
       "      <td>16</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.619</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>4.1</td>\n",
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       "      <td>1.4</td>\n",
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       "      <th>3</th>\n",
       "      <td>6.666667</td>\n",
       "      <td>210</td>\n",
       "      <td>107</td>\n",
       "      <td>55</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>0.473</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.473</td>\n",
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       "      <td>7.9</td>\n",
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       "      <td>6.750000</td>\n",
       "      <td>322</td>\n",
       "      <td>86</td>\n",
       "      <td>72</td>\n",
       "      <td>24.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.544</td>\n",
       "      <td>4.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.544</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "     height  weight  games_played  games_started  min_per  field_goal  \\\n",
       "0  6.416667     215            41             19     13.3         0.9   \n",
       "1  6.750000     225           115             16     11.2         1.5   \n",
       "2  6.416667     210            92             29     16.4         1.4   \n",
       "3  6.666667     210           107             55     19.0         3.1   \n",
       "4  6.750000     322            86             72     24.5         4.3   \n",
       "\n",
       "   field_attmps  field_pct  two_pointer  two_pattamps  two_pct  three_ptrs  \\\n",
       "0           2.1      0.414          0.9           2.0    0.429         0.0   \n",
       "1           2.3      0.619          1.5           2.3    0.619         0.0   \n",
       "2           4.0      0.346          0.4           1.2    0.360         0.9   \n",
       "3           6.5      0.473          3.1           6.5    0.474         0.0   \n",
       "4           7.8      0.544          4.3           7.8    0.544         0.0   \n",
       "\n",
       "   three_pattmp  three_pct  free_throws  free_attmps  free_pct  assists  \\\n",
       "0           0.1      0.414          0.7          1.2     0.529      0.1   \n",
       "1           0.0      0.619          0.6          0.7     0.788      0.2   \n",
       "2           2.8      0.346          0.4          0.6     0.611      0.6   \n",
       "3           0.0      0.473          1.8          2.8     0.641      0.4   \n",
       "4           0.0      0.544          2.0          4.1     0.482      0.3   \n",
       "\n",
       "   steals  blocks  points  off_reb  def_reb  total_reb  turnovers  drafted  \\\n",
       "0     0.4     0.3     2.4      1.3      1.7        3.0        1.0        0   \n",
       "1     0.2     0.8     3.5      0.7      1.6        2.3        0.6        0   \n",
       "2     0.4     0.2     4.1      0.6      1.4        2.0        0.8        0   \n",
       "3     0.5     0.7     7.9      2.5      3.2        5.7        1.2        0   \n",
       "4     0.6     0.6    10.5      3.1      4.3        7.3        1.5        0   \n",
       "\n",
       "   position_Center  position_Center-Forward  position_Forward  \\\n",
       "0                0                        0                 1   \n",
       "1                0                        0                 1   \n",
       "2                0                        0                 1   \n",
       "3                0                        0                 1   \n",
       "4                1                        0                 0   \n",
       "\n",
       "   position_Forward-Center  position_Forward-Guard  position_Forward-PF  \\\n",
       "0                        0                       0                    0   \n",
       "1                        0                       0                    0   \n",
       "2                        0                       0                    0   \n",
       "3                        0                       0                    0   \n",
       "4                        0                       0                    0   \n",
       "\n",
       "   position_Forward-SF  position_Forward-SF-Guard  position_Guard  \\\n",
       "0                    0                          0               0   \n",
       "1                    0                          0               0   \n",
       "2                    0                          0               0   \n",
       "3                    0                          0               0   \n",
       "4                    0                          0               0   \n",
       "\n",
       "   position_Guard-Forward  position_Guard-PG  position_Guard-SG  \\\n",
       "0                       0                  0                  0   \n",
       "1                       0                  0                  0   \n",
       "2                       0                  0                  0   \n",
       "3                       0                  0                  0   \n",
       "4                       0                  0                  0   \n",
       "\n",
       "   position_PForward  position_PForward-F  position_PGuard  position_PGuard-G  \\\n",
       "0                  0                    0                0                  0   \n",
       "1                  0                    0                0                  0   \n",
       "2                  0                    0                0                  0   \n",
       "3                  0                    0                0                  0   \n",
       "4                  0                    0                0                  0   \n",
       "\n",
       "   position_SForward  position_SForward-F  position_SGuard-G  \n",
       "0                  0                    0                  0  \n",
       "1                  0                    0                  0  \n",
       "2                  0                    0                  0  \n",
       "3                  0                    0                  0  \n",
       "4                  0                    0                  0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "data_dir = \"data/\"\n",
    "\n",
    "college_players_stats = pd.read_csv(f'{data_dir}college_players_stats.csv', index_col=0)\n",
    "drafted_players = pd.read_csv(f'{data_dir}drafted_players.csv', index_col=0)\n",
    "not_drafted = pd.read_csv(f'{data_dir}not_drafted.csv', index_col=0)\n",
    "players_stats = pd.read_csv(f'{data_dir}players_stats.csv', index_col=0)\n",
    "\n",
    "# add label and normalize relevant values\n",
    "\n",
    "players_stats['drafted'] = players_stats['name'].isin(drafted_players['name'])\n",
    "players_stats['drafted'] = players_stats['drafted'].astype(int)\n",
    "\n",
    "\n",
    "# add label and normalize relevant values\n",
    "\n",
    "players_stats['drafted'] = players_stats['name'].isin(drafted_players['name'])\n",
    "players_stats['drafted'] = players_stats['drafted'].astype(int)\n",
    "players_stats = players_stats.drop(columns=['name'])\n",
    "\n",
    "# discretize height\n",
    "def convert_height(height_str):\n",
    "    feet, inches = map(int, height_str.split('-'))\n",
    "    return feet + inches / 12  \n",
    "\n",
    "# Apply the conversion function to the height column\n",
    "players_stats['height'] = players_stats['height'].apply(convert_height)\n",
    "\n",
    "# add binary columns for each position label\n",
    "def str_to_int(maybe_bool):\n",
    "    if isinstance(maybe_bool, bool):\n",
    "        return int(maybe_bool) \n",
    "    return maybe_bool\n",
    "\n",
    "def weight_to_int(input):\n",
    "    if(input == \"22l\"):\n",
    "        return 221\n",
    "    return(int(input)) \n",
    "players_stats['weight'] = players_stats['weight'].apply(weight_to_int)\n",
    "\n",
    "players_stats = pd.get_dummies(players_stats, columns=['position'])\n",
    "players_stats = players_stats.applymap(str_to_int)\n",
    "\n",
    "players_stats.info()\n",
    "non_numeric_columns = players_stats.select_dtypes(exclude=[\"number\"]).columns\n",
    "print(\"Non-numeric columns:\", non_numeric_columns)\n",
    "\n",
    "# TODO: add other metrics for training\n",
    "df = players_stats\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "pd.set_option('display.max_columns', None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([27611,  1003]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['drafted'], return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count drafted:\n",
      "408\n",
      "Count not drafted:\n",
      "5723\n",
      "Count downsample drafted:\n",
      "418\n",
      "Count downsample not drafted:\n",
      "1723\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([5516,  207]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 42\n",
    "epochs = 250\n",
    "lr = 0.01\n",
    "batch_size = 32\n",
    "\n",
    "# get data\n",
    "x = df.drop(columns=['drafted']).values\n",
    "y = df['drafted'].values\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=seed)\n",
    "\n",
    "# downsampling\n",
    "#separate drafted and non-drafted \n",
    "df_drafted = df[df['drafted'] == 1]\n",
    "df_non_drafted = df[df['drafted'] == 0]\n",
    "\n",
    "\n",
    "# Drop 20000 random samples from non-drafted\n",
    "df_non_drafted_sampled = df_non_drafted.sample(len(df_non_drafted) - 20000, random_state=seed)\n",
    "\n",
    "df_downsampled = pd.concat([df_drafted, df_non_drafted_sampled])\n",
    "x = df_downsampled.drop(columns=['drafted']).values\n",
    "y = df_downsampled['drafted'].values\n",
    "\n",
    "xd_train, xd_test, yd_train, yd_test = train_test_split(x, y, test_size=0.2, random_state=seed)\n",
    "\n",
    "\n",
    "\n",
    "count = 0\n",
    "count_tot = 0\n",
    "for y1,y2 in zip(y_train, y_test):\n",
    "    if(y1 == 1):\n",
    "        count+=1\n",
    "    if(y2 == 1):\n",
    "        count+=1\n",
    "    count_tot +=1\n",
    "\n",
    "print(\"Count drafted:\")\n",
    "print(count)\n",
    "print(\"Count not drafted:\")\n",
    "print(count_tot)\n",
    "\n",
    "np.unique(y_test, return_counts=True)\n",
    "\n",
    "count = 0\n",
    "count_tot = 0\n",
    "for y1,y2 in zip(yd_train, yd_test):\n",
    "    if(y1 == 1):\n",
    "        count+=1\n",
    "    if(y2 == 1):\n",
    "        count+=1\n",
    "    count_tot +=1\n",
    "\n",
    "print(\"Count downsample drafted:\")\n",
    "print(count)\n",
    "print(\"Count downsample not drafted:\")\n",
    "print(count_tot)\n",
    "\n",
    "np.unique(y_test, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = xd_train\n",
    "x_test = xd_test\n",
    "y_train = yd_train\n",
    "y_test = yd_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "\n",
    "def explain(y_test, y_pred, average='weighted'):\n",
    "    \"\"\"Look at precision, recall etc. for the model\"\"\"\n",
    "    precision = precision_score(y_test, y_pred, average=average, zero_division=0)\n",
    "    recall = recall_score(y_test, y_pred, average=average, zero_division=0)\n",
    "    f1 = f1_score(y_test, y_pred, average=average, zero_division=0)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "    # print(\"Precision: \", precision)\n",
    "    # print(\"Recall: \", recall)    \n",
    "    # print(\"F1: \", f1)\n",
    "    # print(\"Accuracy: \", accuracy)\n",
    "    # print(\"Distribution\", np.unique(y_pred, return_counts=True))\n",
    "\n",
    "    return {\n",
    "        \"precision\": precision,\n",
    "        \"recall\": recall,\n",
    "        \"f1\": f1,\n",
    "        \"accuracy\": accuracy\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 250 0.1 newton-cg\n",
      "Testing 250 0.1 lbfgs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srikanthsrinivasan/anaconda3/envs/csci360_lab1/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 250 1 newton-cg\n",
      "Testing 250 1 lbfgs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srikanthsrinivasan/anaconda3/envs/csci360_lab1/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 1000 0.1 newton-cg\n",
      "Testing 1000 0.1 lbfgs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srikanthsrinivasan/anaconda3/envs/csci360_lab1/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 1000 1 newton-cg\n",
      "Testing 1000 1 lbfgs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srikanthsrinivasan/anaconda3/envs/csci360_lab1/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: 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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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></th>\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">250</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.1</th>\n",
       "      <th>newton-cg</th>\n",
       "      <td>0.659574</td>\n",
       "      <td>0.305419</td>\n",
       "      <td>0.417508</td>\n",
       "      <td>0.899594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lbfgs</th>\n",
       "      <td>0.655914</td>\n",
       "      <td>0.300493</td>\n",
       "      <td>0.412162</td>\n",
       "      <td>0.899013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>newton-cg</th>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.349754</td>\n",
       "      <td>0.468647</td>\n",
       "      <td>0.906558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lbfgs</th>\n",
       "      <td>0.653061</td>\n",
       "      <td>0.315271</td>\n",
       "      <td>0.425249</td>\n",
       "      <td>0.899594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1000</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.1</th>\n",
       "      <th>newton-cg</th>\n",
       "      <td>0.659574</td>\n",
       "      <td>0.305419</td>\n",
       "      <td>0.417508</td>\n",
       "      <td>0.899594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lbfgs</th>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.320197</td>\n",
       "      <td>0.436242</td>\n",
       "      <td>0.902496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>newton-cg</th>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.349754</td>\n",
       "      <td>0.468647</td>\n",
       "      <td>0.906558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lbfgs</th>\n",
       "      <td>0.686869</td>\n",
       "      <td>0.334975</td>\n",
       "      <td>0.450331</td>\n",
       "      <td>0.903656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    precision    recall        f1  accuracy\n",
       "250  0.1 newton-cg   0.659574  0.305419  0.417508  0.899594\n",
       "         lbfgs       0.655914  0.300493  0.412162  0.899013\n",
       "     1.0 newton-cg   0.710000  0.349754  0.468647  0.906558\n",
       "         lbfgs       0.653061  0.315271  0.425249  0.899594\n",
       "1000 0.1 newton-cg   0.659574  0.305419  0.417508  0.899594\n",
       "         lbfgs       0.684211  0.320197  0.436242  0.902496\n",
       "     1.0 newton-cg   0.710000  0.349754  0.468647  0.906558\n",
       "         lbfgs       0.686869  0.334975  0.450331  0.903656"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg_results = {}\n",
    "\n",
    "for itrs in [250, 1000]:\n",
    "    for lr in [0.1, 1]:\n",
    "        for solver in ['newton-cg', 'lbfgs']:\n",
    "            print(\"Testing\", itrs, lr, solver)\n",
    "            log_reg = LogisticRegression(max_iter=itrs, C=lr, solver=solver)\n",
    "            log_reg.fit(x_train, y_train)\n",
    "            y_pred = log_reg.predict(x_test)\n",
    "            log_reg_results[(itrs, lr, solver)] = explain(y_test, y_pred, average='binary')\n",
    "\n",
    "pd.DataFrame(log_reg_results).T.head(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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></th>\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">5</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>gini</th>\n",
       "      <td>0.370192</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.374696</td>\n",
       "      <td>0.850842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entropy</th>\n",
       "      <td>0.367647</td>\n",
       "      <td>0.369458</td>\n",
       "      <td>0.368550</td>\n",
       "      <td>0.850842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>gini</th>\n",
       "      <td>0.350785</td>\n",
       "      <td>0.330049</td>\n",
       "      <td>0.340102</td>\n",
       "      <td>0.849100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entropy</th>\n",
       "      <td>0.369231</td>\n",
       "      <td>0.354680</td>\n",
       "      <td>0.361809</td>\n",
       "      <td>0.852583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>gini</th>\n",
       "      <td>0.351220</td>\n",
       "      <td>0.354680</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.846779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entropy</th>\n",
       "      <td>0.374429</td>\n",
       "      <td>0.403941</td>\n",
       "      <td>0.388626</td>\n",
       "      <td>0.850261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>gini</th>\n",
       "      <td>0.369458</td>\n",
       "      <td>0.369458</td>\n",
       "      <td>0.369458</td>\n",
       "      <td>0.851422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entropy</th>\n",
       "      <td>0.383420</td>\n",
       "      <td>0.364532</td>\n",
       "      <td>0.373737</td>\n",
       "      <td>0.856065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               precision    recall        f1  accuracy\n",
       "5  10 gini      0.370192  0.379310  0.374696  0.850842\n",
       "      entropy   0.367647  0.369458  0.368550  0.850842\n",
       "   5  gini      0.350785  0.330049  0.340102  0.849100\n",
       "      entropy   0.369231  0.354680  0.361809  0.852583\n",
       "10 10 gini      0.351220  0.354680  0.352941  0.846779\n",
       "      entropy   0.374429  0.403941  0.388626  0.850261\n",
       "   5  gini      0.369458  0.369458  0.369458  0.851422\n",
       "      entropy   0.383420  0.364532  0.373737  0.856065"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt_results = {}\n",
    "\n",
    "for depth in [5, 10]: \n",
    "    for leaf in [10, 5]:\n",
    "        for criterion in ['gini', 'entropy']:\n",
    "            model = DecisionTreeClassifier()\n",
    "            model.fit(x_train, y_train)\n",
    "            y_pred = model.predict(x_test)\n",
    "\n",
    "            dt_results[(depth, leaf, criterion)] = explain(y_test, y_pred, average='binary')\n",
    "\n",
    "pd.DataFrame(dt_results).T.head(1016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NaN</th>\n",
       "      <th>1</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.683761</td>\n",
       "      <td>0.394089</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.907139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.675439</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.485804</td>\n",
       "      <td>0.905398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>1</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.369458</td>\n",
       "      <td>0.479233</td>\n",
       "      <td>0.905398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.697248</td>\n",
       "      <td>0.374384</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.907139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           precision    recall        f1  accuracy\n",
       "NaN 1 NaN   0.683761  0.394089  0.500000  0.907139\n",
       "    5 NaN   0.675439  0.379310  0.485804  0.905398\n",
       "10  1 NaN   0.681818  0.369458  0.479233  0.905398\n",
       "    5 NaN   0.697248  0.374384  0.487179  0.907139"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_results = {}\n",
    "\n",
    "for depth in [None, 10]:\n",
    "    for leaf in [1, 5]:\n",
    "        for feature in [None]:\n",
    "            rf = RandomForestClassifier(random_state=seed, max_depth=depth, min_samples_leaf=leaf, max_features=feature) # max_depth=depth, min_samples_leaf=leaf, max_features=feature)\n",
    "            rf.fit(x_train, y_train)\n",
    "            y_pred = rf.predict(x_test)\n",
    "\n",
    "            rf_results[(depth, leaf, feature)] = explain(y_test, y_pred, average='binary')\n",
    "\n",
    "pd.DataFrame(rf_results).T.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var smoothing:  0.1\n",
      "Var smoothing:  0.01\n",
      "Var smoothing:  0.001\n",
      "Var smoothing:  0.0001\n",
      "Var smoothing:  1e-07\n",
      "Var smoothing:  1e-08\n",
      "Var smoothing:  1e-09\n",
      "Var smoothing:  1e-10\n",
      "Var smoothing:  1e-11\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.000000e-01</th>\n",
       "      <th>1.000000e-02</th>\n",
       "      <th>1.000000e-03</th>\n",
       "      <th>1.000000e-04</th>\n",
       "      <th>1.000000e-07</th>\n",
       "      <th>1.000000e-08</th>\n",
       "      <th>1.000000e-09</th>\n",
       "      <th>1.000000e-10</th>\n",
       "      <th>1.000000e-11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.387640</td>\n",
       "      <td>0.405099</td>\n",
       "      <td>0.385787</td>\n",
       "      <td>0.369048</td>\n",
       "      <td>0.265957</td>\n",
       "      <td>0.198901</td>\n",
       "      <td>0.155285</td>\n",
       "      <td>0.132755</td>\n",
       "      <td>0.124922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.339901</td>\n",
       "      <td>0.704433</td>\n",
       "      <td>0.748768</td>\n",
       "      <td>0.763547</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.891626</td>\n",
       "      <td>0.940887</td>\n",
       "      <td>0.980296</td>\n",
       "      <td>0.980296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.362205</td>\n",
       "      <td>0.514388</td>\n",
       "      <td>0.509213</td>\n",
       "      <td>0.497592</td>\n",
       "      <td>0.406504</td>\n",
       "      <td>0.325247</td>\n",
       "      <td>0.266574</td>\n",
       "      <td>0.233843</td>\n",
       "      <td>0.221604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.858967</td>\n",
       "      <td>0.843297</td>\n",
       "      <td>0.829948</td>\n",
       "      <td>0.818340</td>\n",
       "      <td>0.703424</td>\n",
       "      <td>0.564132</td>\n",
       "      <td>0.390017</td>\n",
       "      <td>0.243180</td>\n",
       "      <td>0.188624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1.000000e-01  1.000000e-02  1.000000e-03  1.000000e-04  \\\n",
       "precision      0.387640      0.405099      0.385787      0.369048   \n",
       "recall         0.339901      0.704433      0.748768      0.763547   \n",
       "f1             0.362205      0.514388      0.509213      0.497592   \n",
       "accuracy       0.858967      0.843297      0.829948      0.818340   \n",
       "\n",
       "           1.000000e-07  1.000000e-08  1.000000e-09  1.000000e-10  \\\n",
       "precision      0.265957      0.198901      0.155285      0.132755   \n",
       "recall         0.862069      0.891626      0.940887      0.980296   \n",
       "f1             0.406504      0.325247      0.266574      0.233843   \n",
       "accuracy       0.703424      0.564132      0.390017      0.243180   \n",
       "\n",
       "           1.000000e-11  \n",
       "precision      0.124922  \n",
       "recall         0.980296  \n",
       "f1             0.221604  \n",
       "accuracy       0.188624  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb_results = {}\n",
    "\n",
    "for var_smoothing in [1e-1, 1e-2, 1e-3, 1e-4, 1e-7, 1e-8, 1e-9, 1e-10, 1e-11]:\n",
    "    print(\"Var smoothing: \", var_smoothing)\n",
    "    model = GaussianNB(var_smoothing=var_smoothing)\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "\n",
    "    nb_results[var_smoothing] = explain(y_test, y_pred, average='binary')\n",
    "\n",
    "pd.DataFrame(nb_results).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srikanthsrinivasan/anaconda3/envs/csci360_lab1/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "\n",
      "\n",
      "\n",
      "Naive Bayes\n",
      "\n",
      "\n",
      "\n",
      "SVM\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scikit learn logistic regression (NOT DOWNSAMPLED)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(random_state=seed, max_iter=250),\n",
    "    \"Decision Tree\": DecisionTreeClassifier(random_state=seed),\n",
    "    \"Random Forest\": RandomForestClassifier(random_state=seed),\n",
    "    \"Naive Bayes\": GaussianNB(),\n",
    "    \"SVM\": SVC(random_state=seed)\n",
    "}\n",
    "\n",
    "results = {}\n",
    "for name, model in models.items():\n",
    "    print(name)\n",
    "\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    \n",
    "    results[name] = explain(y_test, y_pred, average='macro')\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "# plot the results as a hist\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df.plot(kind='bar', figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WITH DOWNSAMPLED DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srikanthsrinivasan/anaconda3/envs/csci360_lab1/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "\n",
      "\n",
      "\n",
      "Naive Bayes\n",
      "\n",
      "\n",
      "\n",
      "SVM\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(random_state=seed, max_iter=250),\n",
    "    \"Decision Tree\": DecisionTreeClassifier(random_state=seed),\n",
    "    \"Random Forest\": RandomForestClassifier(random_state=seed),\n",
    "    \"Naive Bayes\": GaussianNB(),\n",
    "    \"SVM\": SVC(random_state=seed)\n",
    "}\n",
    "\n",
    "results = {}\n",
    "for name, model in models.items():\n",
    "    print(name)\n",
    "\n",
    "    model.fit(xd_train, yd_train)\n",
    "    yd_pred = model.predict(xd_test)\n",
    "    \n",
    "    results[name] = explain(yd_test, yd_pred, average='macro')\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "# plot the results as a hist\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df.plot(kind='bar', figsize=(10, 6))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "csci360_lab1",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}