fix get_week

Source/Predict/predict.py

@@ -1,169 +1,156 @@
-import xgboost as xgb
-import numpy as np
-import pandas as pd
-import pickle as pkl
-import os
-import requests
-from bs4 import BeautifulSoup
-import warnings
-warnings.filterwarnings("ignore")
-from datetime import datetime
-
-# set dirs for other files
-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(data_directory, 'gbg_this_year.csv')
-gbg = pd.read_csv(file_path, low_memory=False)
-
-file_path = os.path.join(data_directory, 'results.csv')
-results = pd.read_csv(file_path, low_memory=False)
-
-# get team abbreviations
-file_path = os.path.join(pickle_directory, 'team_name_to_abbreviation.pkl')
-with open(file_path, 'rb') as f:
-    team_name_to_abbreviation = pkl.load(f)
-
-file_path = os.path.join(pickle_directory, 'team_abbreviation_to_name.pkl')
-with open(file_path, 'rb') as f:
-    team_abbreviation_to_name = pkl.load(f)
-
-# get schedule
-file_path = os.path.join(pickle_directory, 'schedule.pkl')
-with open(file_path, 'rb') as f:
-    schedule = pkl.load(f)
-
-# load models
-# moneyline
-model = 'xgboost_ML_no_odds_71.4%'
-file_path = os.path.join(model_directory, f'{model}.json')
-xgb_ml = xgb.Booster()
-xgb_ml.load_model(file_path)
-
-# over/under
-model = 'xgboost_OU_no_odds_59.8%'
-file_path = os.path.join(model_directory, f'{model}.json')
-xgb_ou = xgb.Booster()
-xgb_ou.load_model(file_path)
-
-
-def get_week():
-    headers = {
-    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
-    'Accept-Encoding': 'gzip, deflate',
-    'Accept-Language': 'en-US,en;q=0.9',
-    'Cache-Control': 'max-age=0',
-    'Connection': 'keep-alive',
-    'Dnt': '1',
-    'Upgrade-Insecure-Requests': '1',
-    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36'
-    }
-    url = 'https://www.nfl.com/schedules/'
-    resp = requests.get(url,headers=headers)
-    soup = BeautifulSoup(resp.text, 'html.parser')
-    h2_tags = soup.find_all('h2')
-    year = h2_tags[0].getText().split(' ')[0]
-    week = h2_tags[0].getText().split(' ')[-1]
-    return int(week), int(year)
-
-
-def get_games(week):
-    # pull from NBC
-    #url = 'https://www.nbcsports.com/nfl/schedule'
-    #df = pd.read_html(url)[week-1]
-    df = schedule[week-1]
-    df['Away Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Away TeamAway Team']]
-    df['Home Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Home TeamHome Team']]
-    df['Date'] = pd.to_datetime(df['Game TimeGame Time'])
-    df['Date'] = df['Date'].dt.strftime('%A %d/%m %I:%M %p')
-    df['Date'] = df['Date'].apply(lambda x: f"{x.split()[0]} {int(x.split()[1].split('/')[1])}/{int(x.split()[1].split('/')[0])} {x.split()[2]}".capitalize())
-
-    return df[['Away Team','Home Team','Date']]
-
-
-def get_one_week(home,away,season,week):
-    try:
-        max_GP_home = gbg.loc[((gbg['home_team'] == home) | (gbg['away_team'] == home)) & (gbg['GP'] < week)]['GP'].max()
-        max_GP_away = gbg.loc[((gbg['home_team'] == away) | (gbg['away_team'] == away)) & (gbg['GP'] < week)]['GP'].max()
-
-        home_df = gbg.loc[((gbg['away_team']==home) | (gbg['home_team']==home)) & (gbg['Season']==season) & (gbg['GP']==max_GP_home)]
-        gbg_home_team = home_df['home_team'].item()
-        home_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True)
-        home_df = home_df[[i for i in home_df.columns if '.Away' not in i] if gbg_home_team==home else [i for i in home_df.columns if '.Away' in i]]
-        home_df.columns = [i.replace('.Away','') for i in home_df.columns]
-
-        away_df = gbg.loc[((gbg['away_team']==away) | (gbg['home_team']==away)) & (gbg['Season']==season) & (gbg['GP']==max_GP_away)]
-        gbg_home_team = away_df['home_team'].item()
-        away_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True)
-        away_df = away_df[[i for i in away_df.columns if '.Away' not in i] if gbg_home_team==away else [i for i in away_df.columns if '.Away' in i]]
-        away_df.columns = [i.replace('.Away','') + '.Away' for i in away_df.columns]
-
-        df = home_df.reset_index(drop=True).merge(away_df.reset_index(drop=True), left_index=True, right_index=True)
-        return df
-    except ValueError:
-        return pd.DataFrame()
-
-
-def predict(home,away,season,week,total):
-    global results
-
-    # finish preparing data
-    if len(home)>4:
-        home_abbrev = team_name_to_abbreviation[home]
-    else:
-        home_abbrev = home
-
-    if len(away)>4:
-        away_abbrev = team_name_to_abbreviation[away]
-    else:
-        away_abbrev = away
-
-    data = get_one_week(home_abbrev,away_abbrev,season,week)
-    data['Total Score Close'] = total
-    matrix = xgb.DMatrix(data.astype(float).values)
-
-    # create game id 
-    if week < 10:
-        game_id = str(season) + '_0' + str(int(week)) + '_' + away_abbrev + '_' + home_abbrev
-    else:
-        game_id = str(season) + '_' + str(int(week)) + '_' + away_abbrev + '_' + home_abbrev
-
-    try:
-        moneyline_result = results.loc[results['game_id']==game_id, 'winner'].item()
-    except:
-        moneyline_result = 'N/A'
-
-    try:
-        ml_predicted_proba = xgb_ml.predict(matrix)[0][1]
-        winner_proba = max([ml_predicted_proba, 1-ml_predicted_proba]).item()
-        moneyline = {'Winner': [home if ml_predicted_proba>0.5 else away if ml_predicted_proba<0.5 else 'Toss-Up'],
-                     'Probabilities':[winner_proba],
-                     'Result': moneyline_result}
-    except:
-        moneyline = {'Winner': 'NA',
-                     'Probabilities':['N/A'],
-                     'Result': moneyline_result}
-    
-    try:
-        result = results.loc[results['game_id']==game_id, 'total'].item()
-        over_under_result = 'Over' if float(result)>float(total) else 'Push' if float(result)==float(total) else 'Under'
-
-    except:
-        over_under_result = 'N/A'
-    
-    try:
-        ou_predicted_proba = xgb_ou.predict(matrix)[0][1]
-        ou_proba = max([ou_predicted_proba, 1-ou_predicted_proba]).item()
-
-        over_under = {'Over/Under': ['Over' if ou_predicted_proba>0.5 else 'Under'],
-                      'Probability': [ou_proba],
-                      'Result': over_under_result}
-    except:
-        over_under = {'Over/Under': 'N/A',
-                      'Probability': ['N/A'],
-                      'Result': over_under_result}
-    
-    return game_id, moneyline, over_under
+import xgboost as xgb
+import numpy as np
+import pandas as pd
+import pickle as pkl
+import os
+import requests
+from bs4 import BeautifulSoup
+import warnings
+warnings.filterwarnings("ignore")
+from datetime import datetime
+
+# set dirs for other files
+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(data_directory, 'gbg_this_year.csv')
+gbg = pd.read_csv(file_path, low_memory=False)
+
+file_path = os.path.join(data_directory, 'results.csv')
+results = pd.read_csv(file_path, low_memory=False)
+
+# get team abbreviations
+file_path = os.path.join(pickle_directory, 'team_name_to_abbreviation.pkl')
+with open(file_path, 'rb') as f:
+    team_name_to_abbreviation = pkl.load(f)
+
+file_path = os.path.join(pickle_directory, 'team_abbreviation_to_name.pkl')
+with open(file_path, 'rb') as f:
+    team_abbreviation_to_name = pkl.load(f)
+
+# get schedule
+file_path = os.path.join(pickle_directory, 'schedule.pkl')
+with open(file_path, 'rb') as f:
+    schedule = pkl.load(f)
+
+# get current week
+file_path = os.path.join(pickle_directory, 'the_week.pkl')
+with open(file_path, 'rb') as f:
+    the_week = pkl.load(f)
+
+# load models
+# moneyline
+model = 'xgboost_ML_no_odds_71.4%'
+file_path = os.path.join(model_directory, f'{model}.json')
+xgb_ml = xgb.Booster()
+xgb_ml.load_model(file_path)
+
+# over/under
+model = 'xgboost_OU_no_odds_59.8%'
+file_path = os.path.join(model_directory, f'{model}.json')
+xgb_ou = xgb.Booster()
+xgb_ou.load_model(file_path)
+
+
+def get_week():
+    week = the_week['week']
+    year = the_week['year']
+    return int(week), int(year)
+
+
+def get_games(week):
+    df = schedule[week-1]
+    df['Away Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Away TeamAway Team']]
+    df['Home Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Home TeamHome Team']]
+    df['Date'] = pd.to_datetime(df['Game TimeGame Time'])
+    df['Date'] = df['Date'].dt.strftime('%A %d/%m %I:%M %p')
+    df['Date'] = df['Date'].apply(lambda x: f"{x.split()[0]} {int(x.split()[1].split('/')[1])}/{int(x.split()[1].split('/')[0])} {x.split()[2]}".capitalize())
+    return df[['Away Team','Home Team','Date']]
+
+
+def get_one_week(home,away,season,week):
+    try:
+        max_GP_home = gbg.loc[((gbg['home_team'] == home) | (gbg['away_team'] == home)) & (gbg['GP'] < week)]['GP'].max()
+        max_GP_away = gbg.loc[((gbg['home_team'] == away) | (gbg['away_team'] == away)) & (gbg['GP'] < week)]['GP'].max()
+
+        home_df = gbg.loc[((gbg['away_team']==home) | (gbg['home_team']==home)) & (gbg['Season']==season) & (gbg['GP']==max_GP_home)]
+        gbg_home_team = home_df['home_team'].item()
+        home_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True)
+        home_df = home_df[[i for i in home_df.columns if '.Away' not in i] if gbg_home_team==home else [i for i in home_df.columns if '.Away' in i]]
+        home_df.columns = [i.replace('.Away','') for i in home_df.columns]
+
+        away_df = gbg.loc[((gbg['away_team']==away) | (gbg['home_team']==away)) & (gbg['Season']==season) & (gbg['GP']==max_GP_away)]
+        gbg_home_team = away_df['home_team'].item()
+        away_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True)
+        away_df = away_df[[i for i in away_df.columns if '.Away' not in i] if gbg_home_team==away else [i for i in away_df.columns if '.Away' in i]]
+        away_df.columns = [i.replace('.Away','') + '.Away' for i in away_df.columns]
+
+        df = home_df.reset_index(drop=True).merge(away_df.reset_index(drop=True), left_index=True, right_index=True)
+        return df
+    except ValueError:
+        return pd.DataFrame()
+
+
+def predict(home,away,season,week,total):
+    global results
+
+    # finish preparing data
+    if len(home)>4:
+        home_abbrev = team_name_to_abbreviation[home]
+    else:
+        home_abbrev = home
+
+    if len(away)>4:
+        away_abbrev = team_name_to_abbreviation[away]
+    else:
+        away_abbrev = away
+
+    data = get_one_week(home_abbrev,away_abbrev,season,week)
+    data['Total Score Close'] = total
+    matrix = xgb.DMatrix(data.astype(float).values)
+
+    # create game id 
+    if week < 10:
+        game_id = str(season) + '_0' + str(int(week)) + '_' + away_abbrev + '_' + home_abbrev
+    else:
+        game_id = str(season) + '_' + str(int(week)) + '_' + away_abbrev + '_' + home_abbrev
+
+    try:
+        moneyline_result = results.loc[results['game_id']==game_id, 'winner'].item()
+    except:
+        moneyline_result = 'N/A'
+
+    try:
+        ml_predicted_proba = xgb_ml.predict(matrix)[0][1]
+        winner_proba = max([ml_predicted_proba, 1-ml_predicted_proba]).item()
+        moneyline = {'Winner': [home if ml_predicted_proba>0.5 else away if ml_predicted_proba<0.5 else 'Toss-Up'],
+                     'Probabilities':[winner_proba],
+                     'Result': moneyline_result}
+    except:
+        moneyline = {'Winner': 'NA',
+                     'Probabilities':['N/A'],
+                     'Result': moneyline_result}
+    
+    try:
+        result = results.loc[results['game_id']==game_id, 'total'].item()
+        over_under_result = 'Over' if float(result)>float(total) else 'Push' if float(result)==float(total) else 'Under'
+
+    except:
+        over_under_result = 'N/A'
+    
+    try:
+        ou_predicted_proba = xgb_ou.predict(matrix)[0][1]
+        ou_proba = max([ou_predicted_proba, 1-ou_predicted_proba]).item()
+
+        over_under = {'Over/Under': ['Over' if ou_predicted_proba>0.5 else 'Under'],
+                      'Probability': [ou_proba],
+                      'Result': over_under_result}
+    except:
+        over_under = {'Over/Under': 'N/A',
+                      'Probability': ['N/A'],
+                      'Result': over_under_result}
+    
+    return game_id, moneyline, over_under