ideal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa2ee6e3-44e9-4782-ac26-96a48c7cac95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import seaborn as sn\n",
    "import xgboost as xgb\n",
    "from torch import nn\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, VotingClassifier\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c986ab09-836d-455d-be2a-712751a6d891",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ishaan/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (27) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    },
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP</th>\n",
       "      <th>Min_per</th>\n",
       "      <th>Ortg</th>\n",
       "      <th>usg</th>\n",
       "      <th>eFG</th>\n",
       "      <th>TS_per</th>\n",
       "      <th>ORB_per</th>\n",
       "      <th>DRB_per</th>\n",
       "      <th>AST_per</th>\n",
       "      <th>TO_per</th>\n",
       "      <th>...</th>\n",
       "      <th>mp</th>\n",
       "      <th>ogbpm</th>\n",
       "      <th>dgbpm</th>\n",
       "      <th>oreb</th>\n",
       "      <th>dreb</th>\n",
       "      <th>treb</th>\n",
       "      <th>ast</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49638</th>\n",
       "      <td>0.750</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.111271</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.358000</td>\n",
       "      <td>0.373533</td>\n",
       "      <td>0.002093</td>\n",
       "      <td>0.010181</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.270</td>\n",
       "      <td>...</td>\n",
       "      <td>0.231612</td>\n",
       "      <td>0.377152</td>\n",
       "      <td>0.561686</td>\n",
       "      <td>0.054371</td>\n",
       "      <td>0.125729</td>\n",
       "      <td>0.118280</td>\n",
       "      <td>0.04194</td>\n",
       "      <td>0.104850</td>\n",
       "      <td>0.049086</td>\n",
       "      <td>0.087907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38277</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0.153061</td>\n",
       "      <td>0.111871</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.325867</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>0.008448</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.177</td>\n",
       "      <td>...</td>\n",
       "      <td>0.183810</td>\n",
       "      <td>0.383931</td>\n",
       "      <td>0.551688</td>\n",
       "      <td>0.064214</td>\n",
       "      <td>0.074237</td>\n",
       "      <td>0.082540</td>\n",
       "      <td>0.05714</td>\n",
       "      <td>0.047625</td>\n",
       "      <td>0.009053</td>\n",
       "      <td>0.132931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36125</th>\n",
       "      <td>0.800</td>\n",
       "      <td>0.113265</td>\n",
       "      <td>0.116427</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.276667</td>\n",
       "      <td>0.279800</td>\n",
       "      <td>0.010212</td>\n",
       "      <td>0.012924</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107878</td>\n",
       "      <td>0.381813</td>\n",
       "      <td>0.561556</td>\n",
       "      <td>0.127686</td>\n",
       "      <td>0.078741</td>\n",
       "      <td>0.111113</td>\n",
       "      <td>0.02121</td>\n",
       "      <td>0.022725</td>\n",
       "      <td>0.017288</td>\n",
       "      <td>0.040281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59627</th>\n",
       "      <td>0.025</td>\n",
       "      <td>0.006122</td>\n",
       "      <td>0.093165</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.512800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024188</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.510</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.360488</td>\n",
       "      <td>0.539394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086614</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50918</th>\n",
       "      <td>0.675</td>\n",
       "      <td>0.478571</td>\n",
       "      <td>0.097962</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.272667</td>\n",
       "      <td>0.281867</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.008448</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.394194</td>\n",
       "      <td>0.370491</td>\n",
       "      <td>0.559080</td>\n",
       "      <td>0.163096</td>\n",
       "      <td>0.167641</td>\n",
       "      <td>0.193547</td>\n",
       "      <td>0.04839</td>\n",
       "      <td>0.169350</td>\n",
       "      <td>0.122706</td>\n",
       "      <td>0.105058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          GP   Min_per      Ortg    usg       eFG    TS_per   ORB_per  \\\n",
       "49638  0.750  0.285714  0.111271  0.264  0.358000  0.373533  0.002093   \n",
       "38277  0.500  0.153061  0.111871  0.508  0.320000  0.325867  0.002981   \n",
       "36125  0.800  0.113265  0.116427  0.330  0.276667  0.279800  0.010212   \n",
       "59627  0.025  0.006122  0.093165  0.672  0.666667  0.512800  0.000000   \n",
       "50918  0.675  0.478571  0.097962  0.228  0.272667  0.281867  0.003425   \n",
       "\n",
       "        DRB_per  AST_per  TO_per  ...        mp     ogbpm     dgbpm      oreb  \\\n",
       "49638  0.010181    0.064   0.270  ...  0.231612  0.377152  0.561686  0.054371   \n",
       "38277  0.008448    0.142   0.177  ...  0.183810  0.383931  0.551688  0.064214   \n",
       "36125  0.012924    0.058   0.194  ...  0.107878  0.381813  0.561556  0.127686   \n",
       "59627  0.024188    0.308   0.510  ...  0.070000  0.360488  0.539394  0.000000   \n",
       "50918  0.008448    0.031   0.203  ...  0.394194  0.370491  0.559080  0.163096   \n",
       "\n",
       "           dreb      treb      ast       stl       blk       pts  \n",
       "49638  0.125729  0.118280  0.04194  0.104850  0.049086  0.087907  \n",
       "38277  0.074237  0.082540  0.05714  0.047625  0.009053  0.132931  \n",
       "36125  0.078741  0.111113  0.02121  0.022725  0.017288  0.040281  \n",
       "59627  0.086614  0.066667  0.05000  0.000000  0.000000  0.049849  \n",
       "50918  0.167641  0.193547  0.04839  0.169350  0.122706  0.105058  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use own model\n",
    "\n",
    "df = pd.read_csv(\"data.csv\")\n",
    "df[\"pick\"] = df[\"pick\"].notnull().astype('int')\n",
    "pick_df = df[\"pick\"]\n",
    "df = df.drop([\"yr\", \"ht\", \"num\", \"ast/tov\", \"rimmade\", \"rimmade+rimmiss\", \"midmade\", \"midmade+midmiss\", \n",
    "             \"rimmade/(rimmade+rimmiss)\", \"midmade/(midmade+midmiss)\", \"dunksmade\", \"dunksmiss+dunksmade\",\n",
    "             \"dunksmade/(dunksmade+dunksmiss)\", \"Unnamed: 64\", \"Unnamed: 65\", \"pick\", \"team\", \"conf\", \n",
    "              \"type\", \"year\", \"pid\", \"player_name\"], axis=1)\n",
    "df[\"Rec Rank\"] = df[\"Rec Rank\"].fillna(0)\n",
    "df = df.dropna()\n",
    "\n",
    "df = (df - df.min()) / (df.max() - df.min())\n",
    "# scaler = MinMaxScaler()\n",
    "# df[df.columns] = scaler.fit_transform(df[df.columns])\n",
    "\n",
    "df[\"pick\"] = pick_df\n",
    "train_df, valid_df = train_test_split(df, test_size=0.2)\n",
    "y_train_df = train_df[\"pick\"]\n",
    "y_valid_df = valid_df[\"pick\"]\n",
    "train_df = train_df.drop([\"pick\"], axis=1)\n",
    "valid_df = valid_df.drop([\"pick\"], axis=1)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "813629f2-03c3-495d-9f5c-0c05344d0962",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:37:54] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "----- XGB -----\n",
      "Train Accuracy: 0.9985659264115382\n",
      "Validation Accuracy: 0.981316069818897\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_train = train_df.to_numpy()\n",
    "y_train = y_train_df.to_numpy()\n",
    "x_valid = valid_df.to_numpy()\n",
    "y_valid = y_valid_df.to_numpy()\n",
    "\n",
    "algo_mapping = {\n",
    "        \"GNB\": GaussianNB(),\n",
    "        \"LR\": LogisticRegression(max_iter=1000),\n",
    "        \"KNN\": KNeighborsClassifier(),\n",
    "        \"LINEAR SVM\": SVC(kernel = \"linear\", C=0.001, probability=True),\n",
    "        \"RBF SVM\": SVC(gamma=2, C=1),\n",
    "        \"DT\": DecisionTreeClassifier(),\n",
    "        \"BG\": BaggingClassifier(DecisionTreeClassifier(), max_samples=0.5, n_estimators=100),\n",
    "        \"RF\": RandomForestClassifier(n_estimators=100, max_depth=10),\n",
    "        \"ADB\": AdaBoostClassifier(DecisionTreeClassifier(min_samples_split=10, max_depth=4), n_estimators=100, learning_rate=0.1),\n",
    "        \"GBC\": GradientBoostingClassifier(learning_rate=0.1, n_estimators=100, max_depth=6),\n",
    "        \"XGB\": xgb.XGBClassifier(colsample_bytree=0.2, learning_rate=0.3, max_depth=6, use_label_encoder=False),\n",
    "        \"EVC\": VotingClassifier(estimators=[\n",
    "            (\"adb\",AdaBoostClassifier(DecisionTreeClassifier(min_samples_split=10, max_depth=4), n_estimators=100, learning_rate=0.1),),\n",
    "            (\"gbc\",GradientBoostingClassifier(learning_rate=0.1, n_estimators=100, max_depth=6),),\n",
    "            (\"xgb\", xgb.XGBClassifier(colsample_bytree=0.2, learning_rate=0.3, max_depth=6, use_label_encoder=False))\n",
    "        ], voting=\"hard\"),\n",
    "        \"MLP\": MLPClassifier(alpha=1, max_iter=1000),\n",
    "    }\n",
    "\n",
    "def score(name, algo, x_train, y_train, x_valid, y_valid):\n",
    "    algo.fit(x_train, y_train.ravel())\n",
    "    prediction = algo.predict(x_valid)\n",
    "    print(\"-----\", name, \"-----\")\n",
    "    print(\"Train Accuracy:\", algo.score(x_train, y_train))\n",
    "    print(\"Validation Accuracy:\", algo.score(x_valid, y_valid))\n",
    "\n",
    "    cm = confusion_matrix(y_valid, prediction)\n",
    "    df_cm = pd.DataFrame(cm)\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(211)\n",
    "    sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}, ax=ax1, fmt='g').set_title(name + \" Validation Confusion Matrix\")\n",
    "    \n",
    "algos = [\"XGB\"]\n",
    "for i in algos:\n",
    "    score(i, algo_mapping[i], x_train, y_train, x_valid, y_valid)"
   ]
  },
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   "cell_type": "code",
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   "id": "8b703892-c3c2-450e-adae-9bd7518678e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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