baseline_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/2x/719ln_714y74dz825_4kskt40000gn/T/ipykernel_36038/1988355783.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>field_pct</th>\n",
       "      <th>two_pointer</th>\n",
       "      <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",
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       "      <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",
       "      <th>def_reb</th>\n",
       "      <th>total_reb</th>\n",
       "      <th>turnovers</th>\n",
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       "      <th>position_Center-Forward</th>\n",
       "      <th>position_Forward</th>\n",
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       "      <th>position_Forward-Guard</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",
       "      <td>215</td>\n",
       "      <td>41</td>\n",
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       "      <td>13.3</td>\n",
       "      <td>0.9</td>\n",
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       "      <td>0.9</td>\n",
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       "      <td>0.1</td>\n",
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       "      <td>0.1</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>2.4</td>\n",
       "      <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>6.416667</td>\n",
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       "      <td>0.4</td>\n",
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       "      <td>0.9</td>\n",
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       "      <td>0.346</td>\n",
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       "      <td>0.6</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.4</td>\n",
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       "    <tr>\n",
       "      <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",
       "      <td>3.1</td>\n",
       "      <td>6.5</td>\n",
       "      <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",
       "      <td>1.8</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>0.5</td>\n",
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       "      <td>7.9</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <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": 2,
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([27611,  1003]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['drafted'], return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "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": 5,
   "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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/hwenv/lib/python3.9/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": [
      "Precision:  0.6672360320325881\n",
      "Recall:  0.5316409356356389\n",
      "F1:  0.547456222020993\n",
      "Accuracy:  0.9620828236938669\n",
      "Distribution (array([0, 1]), array([5685,   38]))\n",
      "\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "Precision:  0.5902461683590912\n",
      "Recall:  0.6159582313025261\n",
      "F1:  0.6010563569092177\n",
      "Accuracy:  0.9363969945832605\n",
      "Distribution (array([0, 1]), array([5454,  269]))\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "Precision:  0.6966819307859771\n",
      "Recall:  0.513767590461477\n",
      "F1:  0.5178397444295518\n",
      "Accuracy:  0.9634806919447843\n",
      "Distribution (array([0, 1]), array([5709,   14]))\n",
      "\n",
      "\n",
      "\n",
      "Naive Bayes\n",
      "Precision:  0.5200932897398106\n",
      "Recall:  0.5941591085047275\n",
      "F1:  0.2176077961440067\n",
      "Accuracy:  0.24008387209505505\n",
      "Distribution (array([0, 1]), array([1177, 4546]))\n",
      "\n",
      "\n",
      "\n",
      "SVM\n",
      "Precision:  0.4819150795037568\n",
      "Recall:  0.5\n",
      "F1:  0.4907909956401815\n",
      "Accuracy:  0.9638301590075136\n",
      "Distribution (array([0]), array([5723]))\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WITH DOWNSAMPLED DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/hwenv/lib/python3.9/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": [
      "Precision:  0.7851010295021512\n",
      "Recall:  0.6443171506352087\n",
      "F1:  0.6832158460624495\n",
      "Accuracy:  0.8995937318630296\n",
      "Distribution (array([0, 1]), array([1627,   96]))\n",
      "\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "Precision:  0.6326967200782366\n",
      "Recall:  0.6372002203785325\n",
      "F1:  0.6348785259964564\n",
      "Accuracy:  0.8456181079512478\n",
      "Distribution (array([0, 1]), array([1512,  211]))\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "Precision:  0.8164328203801888\n",
      "Recall:  0.6586061057816956\n",
      "F1:  0.7024572968455522\n",
      "Accuracy:  0.905977945443993\n",
      "Distribution (array([0, 1]), array([1628,   95]))\n",
      "\n",
      "\n",
      "\n",
      "Naive Bayes\n",
      "Precision:  0.5654718910272267\n",
      "Recall:  0.6286670339642209\n",
      "F1:  0.3722336563904751\n",
      "Accuracy:  0.3900174114915844\n",
      "Distribution (array([0, 1]), array([ 493, 1230]))\n",
      "\n",
      "\n",
      "\n",
      "SVM\n",
      "Precision:  0.4410911201392919\n",
      "Recall:  0.5\n",
      "F1:  0.4687018193031144\n",
      "Accuracy:  0.8821822402785838\n",
      "Distribution (array([0]), array([1723]))\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "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))"
   ]
  }
 ],
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