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James McCool
Refactor date handling in init_conn function of app.py. Updated date range logic to use the current date for filtering, setting min_date to one year ago and max_date to one day ago. This change enhances the accuracy and relevance of game log retrieval for analysis.
24a1053
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| import numpy as np | |
| import pandas as pd | |
| import pymongo | |
| import time | |
| from datetime import datetime, timedelta | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["League_of_Legends_Database"] | |
| current_date = datetime.now() | |
| collection = db["gamelogs"] | |
| max_date = current_date - timedelta(days=1) | |
| min_date = current_date - timedelta(days=365) | |
| team_names = collection.distinct("teamname") | |
| player_names = collection.distinct("playername") | |
| return db, team_names, player_names, min_date, max_date | |
| db, team_names, player_names, min_date, max_date = init_conn() | |
| display_formats = {'wKill%': '{:.2%}', 'wDeath%': '{:.2%}', 'wAssist%': '{:.2%}', 'lKill%': '{:.2%}', 'lDeath%': '{:.2%}', 'lAssist%': '{:.2%}'} | |
| # Create sidebar container for options | |
| with st.sidebar: | |
| st.header("Team Analysis Options") | |
| # Date filtering options | |
| st.subheader("Date Range") | |
| date_filter = st.radio( | |
| "Select Date Range", | |
| ["Last Year", "Custom Range"] | |
| ) | |
| if date_filter == "Last Year": | |
| end_date = max_date | |
| start_date = (end_date - timedelta(days=365)) | |
| else: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| start_date = st.date_input( | |
| "Start Date", | |
| value=max_date.date() - timedelta(days=30), | |
| min_value=min_date.date(), | |
| max_value=max_date.date() | |
| ) | |
| with col2: | |
| end_date = st.date_input( | |
| "End Date", | |
| value=max_date.date(), | |
| min_value=min_date.date(), | |
| max_value=max_date.date() | |
| ) | |
| selected_team = st.selectbox( | |
| "Select Team", | |
| options=team_names, | |
| index=team_names.index("T1") if "T1" in team_names else 0 | |
| ) | |
| st.subheader("Prediction Settings") | |
| win_loss = st.selectbox( | |
| "Select Win/Loss", | |
| options=["Win", "Loss"], | |
| index=0 | |
| ) | |
| kill_prediction = st.number_input( | |
| "Predicted Team Kills", | |
| min_value=0, | |
| max_value=100, | |
| value=20 | |
| ) | |
| death_prediction = st.number_input( | |
| "Predicted Team Deaths", | |
| min_value=0, | |
| max_value=100, | |
| value=5 | |
| ) | |
| def init_team_data(team, win_loss, kill_prediction, death_prediction, start_date, end_date): | |
| # Convert date objects to datetime strings in the correct format | |
| start_datetime = datetime.combine(start_date, datetime.min.time()).strftime("%Y-%m-%d %H:%M:%S") | |
| end_datetime = datetime.combine(end_date, datetime.max.time()).strftime("%Y-%m-%d %H:%M:%S") | |
| collection = db["gamelogs"] | |
| cursor = collection.find({"teamname": team, "date": {"$gte": start_datetime, "$lte": end_datetime}}) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| calc_columns = ['kills', 'deaths', 'assists', 'total_cs'] | |
| league_win_stats = {} | |
| league_loss_stats = {} | |
| league_pos_win_stats = {} | |
| league_pos_loss_stats = {} | |
| Opponent_win_allowed_stats = {} | |
| Opponent_loss_allowed_stats = {} | |
| Opponent_pos_win_allowed_stats = {} | |
| Opponent_pos_loss_allowed_stats = {} | |
| playername_win_stats = {} | |
| playername_loss_stats = {} | |
| teamname_win_stats = {} | |
| teamname_loss_stats = {} | |
| for stats in calc_columns: | |
| league_win_stats[stats] = raw_display[(raw_display['result'] == 1) & (raw_display['position'] != 'team')].groupby('league')[stats].mean().to_dict() | |
| league_loss_stats[stats] = raw_display[(raw_display['result'] == 0) & (raw_display['position'] != 'team')].groupby('league')[stats].mean().to_dict() | |
| Opponent_win_allowed_stats[stats] = raw_display[(raw_display['result'] == 1) & (raw_display['position'] != 'team')].groupby('Opponent')[stats].mean().to_dict() | |
| Opponent_loss_allowed_stats[stats] = raw_display[(raw_display['result'] == 0) & (raw_display['position'] != 'team')].groupby('Opponent')[stats].mean().to_dict() | |
| for stats in calc_columns: | |
| league_pos_win_stats[stats] = { | |
| league: group.groupby('position')[stats].mean().to_dict() | |
| for league, group in raw_display[raw_display['result'] == 1].groupby('league') | |
| } | |
| league_pos_loss_stats[stats] = { | |
| league: group.groupby('position')[stats].mean().to_dict() | |
| for league, group in raw_display[raw_display['result'] == 0].groupby('league') | |
| } | |
| Opponent_pos_win_allowed_stats[stats] = { | |
| opponent: group.groupby('position')[stats].mean().to_dict() | |
| for opponent, group in raw_display[raw_display['result'] == 1].groupby('Opponent') | |
| } | |
| Opponent_pos_loss_allowed_stats[stats] = { | |
| opponent: group.groupby('position')[stats].mean().to_dict() | |
| for opponent, group in raw_display[raw_display['result'] == 0].groupby('Opponent') | |
| } | |
| for stats in calc_columns: | |
| playername_win_stats[stats] = raw_display[raw_display['result'] == 1].groupby(['playername'])[stats].mean().to_dict() | |
| playername_loss_stats[stats] = raw_display[raw_display['result'] == 0].groupby(['playername'])[stats].mean().to_dict() | |
| teamname_win_stats[stats] = raw_display[(raw_display['result'] == 1) & (raw_display['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() | |
| teamname_loss_stats[stats] = raw_display[(raw_display['result'] == 0) & (raw_display['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() | |
| for stat in calc_columns: | |
| column_name = f'league_avg_{stat}_win' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: league_win_stats[stat].get(row['league'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'league_avg_{stat}_loss' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: league_loss_stats[stat].get(row['league'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'Opponent_avg_{stat}_allowed_win' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: Opponent_win_allowed_stats[stat].get(row['Opponent'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'Opponent_avg_{stat}_allowed_loss' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: Opponent_loss_allowed_stats[stat].get(row['Opponent'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'league_pos_avg_{stat}_win' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: league_pos_win_stats[stat].get(row['league'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'league_pos_avg_{stat}_loss' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: league_pos_loss_stats[stat].get(row['league'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'Opponent_pos_avg_{stat}_allowed_win' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: Opponent_pos_win_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'Opponent_pos_avg_{stat}_allowed_loss' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: Opponent_pos_loss_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'playername_avg_{stat}_win' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: playername_win_stats[stat].get(row['playername'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'playername_avg_{stat}_loss' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: playername_loss_stats[stat].get(row['playername'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'teamname_avg_{stat}_win' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: teamname_win_stats[stat].get(row['teamname'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'teamname_avg_{stat}_loss' | |
| raw_display[column_name] = raw_display.apply( | |
| lambda row: teamname_loss_stats[stat].get(row['teamname'], 0), | |
| axis=1 | |
| ) | |
| raw_display['overall_win_kills_boost'] = raw_display['Opponent_avg_kills_allowed_win'] / raw_display['league_avg_kills_win'] | |
| raw_display['overall_win_deaths_boost'] = raw_display['Opponent_avg_deaths_allowed_win'] / raw_display['league_avg_deaths_win'] | |
| raw_display['overall_win_assists_boost'] = raw_display['Opponent_avg_assists_allowed_win'] / raw_display['league_avg_assists_win'] | |
| raw_display['overall_win_total_cs_boost'] = raw_display['Opponent_avg_total_cs_allowed_win'] / raw_display['league_avg_total_cs_win'] | |
| raw_display['overall_loss_kills_boost'] = raw_display['Opponent_avg_kills_allowed_loss'] / raw_display['league_avg_kills_loss'] | |
| raw_display['overall_loss_deaths_boost'] = raw_display['Opponent_avg_deaths_allowed_loss'] / raw_display['league_avg_deaths_loss'] | |
| raw_display['overall_loss_assists_boost'] = raw_display['Opponent_avg_assists_allowed_loss'] / raw_display['league_avg_assists_loss'] | |
| raw_display['overall_loss_total_cs_boost'] = raw_display['Opponent_avg_total_cs_allowed_loss'] / raw_display['league_avg_total_cs_loss'] | |
| raw_display['overall_win_kills_boost_pos'] = raw_display['Opponent_pos_avg_kills_allowed_win'] / raw_display['league_pos_avg_kills_win'] | |
| raw_display['overall_win_deaths_boost_pos'] = raw_display['Opponent_pos_avg_deaths_allowed_win'] / raw_display['league_pos_avg_deaths_win'] | |
| raw_display['overall_win_assists_boost_pos'] = raw_display['Opponent_pos_avg_assists_allowed_win'] / raw_display['league_pos_avg_assists_win'] | |
| raw_display['overall_win_total_cs_boost_pos'] = raw_display['Opponent_pos_avg_total_cs_allowed_win'] / raw_display['league_pos_avg_total_cs_win'] | |
| raw_display['overall_loss_kills_boost_pos'] = raw_display['Opponent_pos_avg_kills_allowed_loss'] / raw_display['league_pos_avg_kills_loss'] | |
| raw_display['overall_loss_deaths_boost_pos'] = raw_display['Opponent_pos_avg_deaths_allowed_loss'] / raw_display['league_pos_avg_deaths_loss'] | |
| raw_display['overall_loss_assists_boost_pos'] = raw_display['Opponent_pos_avg_assists_allowed_loss'] / raw_display['league_pos_avg_assists_loss'] | |
| raw_display['overall_loss_total_cs_boost_pos'] = raw_display['Opponent_pos_avg_total_cs_allowed_loss'] / raw_display['league_pos_avg_total_cs_loss'] | |
| raw_display['playername_avg_kill_share_win'] = raw_display['playername_avg_kills_win'] / raw_display['teamname_avg_kills_win'] | |
| raw_display['playername_avg_death_share_win'] = raw_display['playername_avg_deaths_win'] / raw_display['teamname_avg_deaths_win'] | |
| raw_display['playername_avg_assist_share_win'] = raw_display['playername_avg_assists_win'] / raw_display['teamname_avg_kills_win'] | |
| raw_display['playername_avg_cs_share_win'] = raw_display['playername_avg_total_cs_win'] / raw_display['teamname_avg_total_cs_win'] | |
| raw_display['playername_avg_kill_share_loss'] = raw_display['playername_avg_kills_loss'] / raw_display['teamname_avg_kills_loss'] | |
| raw_display['playername_avg_death_share_loss'] = raw_display['playername_avg_deaths_loss'] / raw_display['teamname_avg_deaths_loss'] | |
| raw_display['playername_avg_assist_share_loss'] = raw_display['playername_avg_assists_loss'] / raw_display['teamname_avg_kills_loss'] | |
| raw_display['playername_avg_cs_share_loss'] = raw_display['playername_avg_total_cs_loss'] / raw_display['teamname_avg_total_cs_loss'] | |
| raw_display = raw_display[['playername', 'teamname', 'playername_avg_kill_share_win', 'playername_avg_death_share_win', 'playername_avg_assist_share_win', 'playername_avg_total_cs_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_total_cs_loss']] | |
| raw_display = raw_display.rename(columns = {'playername_avg_kill_share_win': 'wKill%', 'playername_avg_death_share_win': 'wDeath%', 'playername_avg_assist_share_win': 'wAssist%', 'playername_avg_total_cs_win': 'wCS', 'playername_avg_kill_share_loss': 'lKill%', 'playername_avg_death_share_loss': 'lDeath%', 'playername_avg_assist_share_loss': 'lAssist%', 'playername_avg_total_cs_loss': 'lCS'}) | |
| team_data = raw_display.drop_duplicates(subset = ['playername']) | |
| if win_loss == "Win": | |
| team_data['Kill_Proj'] = team_data['wKill%'] * kill_prediction | |
| team_data['Death_Proj'] = team_data['wDeath%'] * death_prediction | |
| team_data['Assist_Proj'] = team_data['wAssist%'] * kill_prediction | |
| team_data = team_data[['playername', 'teamname', 'wKill%', 'wDeath%', 'wAssist%', 'wCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']] | |
| else: | |
| team_data['Kill_Proj'] = team_data['lKill%'] * kill_prediction | |
| team_data['Death_Proj'] = team_data['lDeath%'] * death_prediction | |
| team_data['Assist_Proj'] = team_data['lAssist%'] * kill_prediction | |
| team_data = team_data[['playername', 'teamname', 'lKill%', 'lDeath%', 'lAssist%', 'lCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']] | |
| return team_data.dropna().reset_index(drop=True) | |
| if st.button("Run"): | |
| st.dataframe(init_team_data(selected_team, win_loss, kill_prediction, death_prediction, start_date, end_date).style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True) |