Resolving conflicts

Notebook Three.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "gbg_and_odds_this_year = pd.read_csv('Source/Data/gbg_and_odds_this_year.csv')\n",
+    "results = pd.read_csv('Source/Data/results.csv')\n",
+    "\n",
+    "from Source.Predict.predict import predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle as pkl\n",
+    "with open('Source/Pickles/team_abbreviation_to_name.pkl', 'rb') as f:\n",
+    "    team_abbreviation_to_name = pkl.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 32/32 [00:04<00:00,  6.79it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "predictions = {}\n",
+    "for game_id,home,away,season,week,total in tqdm(gbg_and_odds_this_year[['game_id','home_team','away_team','Season','GP','Total Score Close']].values):\n",
+    "    if week!=1:\n",
+    "        predictions[game_id] = predict(home,away,season,week,total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'winners_correct': '5', 'winners_incorrect': '11', 'over_unders_correct': '7', 'over_unders_incorrect': '9'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions_df = pd.DataFrame(predictions).T\n",
+    "predictions_df['predicted_winner'] = [i['Winner'][0] if type(i['Winner'])==list else None for i in predictions_df[1]]\n",
+    "predictions_df['predicted_winner'] = predictions_df['predicted_winner'].map(team_abbreviation_to_name)\n",
+    "predictions_df['predicted_over_under'] = [i['Over/Under'][0] if type(i['Over/Under'])==list else None for i in predictions_df[2]]\n",
+    "predictions_df = predictions_df.merge(results, left_index=True, right_on='game_id').merge(gbg_and_odds_this_year[['game_id','Total Score Close']]).dropna(subset=['predicted_winner'])\n",
+    "predictions_df['over_under'] = ['Over' if t>tsc else 'Under' if t<tsc else 'Push' for t,tsc in predictions_df[['total','Total Score Close']].values]\n",
+    "\n",
+    "predictions_df['winner_correct'] = (predictions_df['predicted_winner']==predictions_df['winner']).astype(int)\n",
+    "predictions_df['winner_incorrect'] = (predictions_df['predicted_winner']!=predictions_df['winner']).astype(int)\n",
+    "\n",
+    "predictions_df['over_under_correct'] = (predictions_df['predicted_over_under']==predictions_df['over_under']).astype(int)\n",
+    "predictions_df['over_under_incorrect'] = (predictions_df['predicted_over_under']!=predictions_df['over_under']).astype(int)\n",
+    "\n",
+    "winners_correct = predictions_df['winner_correct'].sum()\n",
+    "winners_incorrect = predictions_df['winner_incorrect'].sum()\n",
+    "\n",
+    "over_unders_correct = predictions_df['over_under_correct'].sum()\n",
+    "over_unders_incorrect = predictions_df['over_under_incorrect'].sum()\n",
+    "\n",
+    "record = {\"winners_correct\":str(winners_correct),\n",
+    "        \"winners_incorrect\":str(winners_incorrect),\n",
+    "        \"over_unders_correct\":str(over_unders_correct),\n",
+    "        \"over_unders_incorrect\":str(over_unders_incorrect)}\n",
+    "\n",
+    "import json\n",
+    "with open('Static/record.json', 'w') as f:\n",
+    "    json.dump(record,f)"
+   ]
+  }
+ ],
+ "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": "3.10.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Source/Data/gbg_and_odds_this_year.csv

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Source/Test/xgboost_ATS.py

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+from cgi import test
+import xgboost as xgb
+import pandas as pd
+import pickle as pkl
+import numpy as np
+import os
+
+model = 'xgboost_ATS_no_odds_57.3%'
+
+current_directory = os.path.dirname(os.path.abspath(__file__))
+parent_directory = os.path.dirname(current_directory)
+data_directory = os.path.join(parent_directory, 'Data')
+model_directory = os.path.join(parent_directory, 'Models')
+pickle_directory = os.path.join(parent_directory, 'Pickles')
+
+file_path = os.path.join(model_directory, f'{model}.json')
+xgb_ml = xgb.Booster()
+xgb_ml.load_model(file_path)
+
+file_path = os.path.join(pickle_directory, 'test_games_ATS_no_odds.pkl')
+with open(file_path,'rb') as f:
+    test_games = pkl.load(f).tolist()
+
+file_path = os.path.join(data_directory, 'gbg_and_odds.csv')
+gbg_and_odds = pd.read_csv(file_path)
+test_data = gbg_and_odds.loc[gbg_and_odds['game_id'].isin(test_games)]
+test_data_matrix = xgb.DMatrix(test_data.drop(columns=['game_id','Home-Team-Win','Home-Team-Cover','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings','Away Odds','Home Odds']).astype(float).values)
+
+predicted_probas = xgb_ml.predict(test_data_matrix)
+predictions = np.argmax(predicted_probas, axis=1)
+test_data['predicted_proba'] = [i[1] for i in predicted_probas]
+test_data['prediction'] = predictions
+test_data['correct'] = test_data['Home-Team-Cover']==test_data['prediction']
+print(test_data['predicted_proba'])
+print(test_data['correct'].mean())
+
+bets = test_data.loc[(test_data['predicted_proba']>0.5) | (test_data['predicted_proba']<0.5)]
+bets['winnings'] = [0.91 if c==1 else -1 for c in bets['correct']]
+
+print('Actual')
+print(bets.loc[bets['Home-Team-Cover']==1].shape)
+print(bets.loc[bets['Home-Team-Cover']==0].shape)
+print(bets.loc[bets['Home-Team-Cover']==2].shape)
+
+print('Predicted')
+print(bets.loc[bets['prediction']==1].shape)
+print(bets.loc[bets['prediction']==0].shape)
+print(bets.loc[bets['prediction']==2].shape)
+
+
+import matplotlib.pyplot as plt
+fig = plt.figure(facecolor='black')
+ax = fig.add_subplot(1, 1, 1, facecolor='black')
+
+# Plot data with line color as RGB(0, 128, 0)
+ax.plot(bets['winnings'].cumsum().values*100, linewidth=3, color=(0/255, 128/255, 0/255))
+
+# Set title and labels
+ax.set_title('MARCI 3.0 - Against the Spread', color='white')
+ax.set_xlabel('Games Bet On', color='white')
+ax.set_ylabel('Return (%)', color='white')
+
+# Change tick colors to white
+ax.tick_params(axis='x', colors='white')
+ax.tick_params(axis='y', colors='white')
+
+# Change axis edge colors
+ax.spines['bottom'].set_color('white')
+ax.spines['top'].set_color('white')
+ax.spines['left'].set_color('white')
+ax.spines['right'].set_color('white')
+
+plt.savefig(f'{model}_dark.png', facecolor='black')