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Running on Zero
Running on Zero
| # Game sequence level microdata processing | |
| import os | |
| import pandas as pd | |
| import numpy as np | |
| import glob | |
| from datetime import datetime | |
| #import seaborn as sns | |
| # get available processor count | |
| import multiprocessing as mp | |
| num_worker = int(mp.cpu_count()/5*4) | |
| from joblib import Parallel, delayed | |
| def process_sequence(iter_game, df): | |
| ''' | |
| Generates dataframe of sequence | |
| ''' | |
| # For each game, process each data | |
| # For each game of the dataframe | |
| df_iter = df[df.gameIdx == iter_game] | |
| # Files to be saved | |
| df_agent = [] | |
| # Pull faceoffs | |
| list_faceoff = df_iter.query( | |
| 'eventTypeId == "FACEOFF"' | |
| ) | |
| # Create offense-based metrics | |
| # Home team is marked wtih the event code | |
| idx_home = df_iter.eventCode.iloc[0][0:3] | |
| # Away team code get | |
| teamcode = [iter_game[0:3], iter_game[4:7]] # Get teh list of team names | |
| [idx_away] = [iter for iter in teamcode if iter != idx_home] | |
| # Incorrect way | |
| #idx_home = iter_game[0:3] | |
| #idx_away = iter_game[4:7] | |
| # For each faceoff | |
| for iter_faceoff, idx_faceoff in enumerate(list_faceoff.index): | |
| # Get index of the following faceoff | |
| try: | |
| idx_faceoff_next = list_faceoff.index[iter_faceoff + 1] | |
| idx_faceoff_sequence = np.arange(idx_faceoff, idx_faceoff_next) | |
| except: # If the last faceoff of the game, go all the way to the last entry | |
| idx_faceoff_sequence = np.arange(idx_faceoff, df_iter.index.max()) | |
| # -------------------------------------------------------------------------------------------------------- | |
| # Get the measurements | |
| # Selecting the series based on the faceoff | |
| iter_decision = df.loc[(df.gameIdx == iter_game) & (df.index.isin(idx_faceoff_sequence)), :] | |
| # Series settings | |
| agent_period = iter_decision.period.iloc[0] # Period number | |
| # Time - series lengh | |
| agent_serieslength = np.max(iter_decision.periodTime) - np.min(iter_decision.periodTime) | |
| # Zone start location | |
| # python returns as list | |
| [agent_zonestartloc] = iter_decision[iter_decision.event == 'Faceoff'].rinkSide_play_relative | |
| # Create elemnts | |
| # Based on the home team | |
| # Faceoff win: 1 if home team won, 0 if visiting team won | |
| agent_faceoff = 1 if iter_decision.iloc[0].team_staff == 'home' else 0 | |
| # Home-agent type when faceoff: defense or offense | |
| # 1 if opponent (defense) 0 () | |
| agent_faceoff_posture = 'offense' if iter_decision.iloc[0].rinkSide_play_relative == 'opponent' else 'defense' | |
| # How many zones used for the action | |
| # If more than one, it means that the zone exit happened | |
| # 0 if only one zone, 1 otherwise (zone changed) | |
| agent_zonestay = 0 if len(np.unique(iter_decision[iter_decision.eventTypeId != 'STOP'].rinkSide_play)) == 1 else 1 | |
| # Sequence where goal happened | |
| agent_goal = 0 | |
| # 1 if goal exist, 0 otherwise | |
| # Sequence where goal made by home (1) or away (-1) or no goal exist | |
| # Tracking goal information | |
| agent_goal_type = "none" | |
| agent_goal_fin = 0 | |
| # non-even goal (6-5 with goalie pulled) | |
| # powerplay goal or Empty net goal (maybe its coded out) | |
| # strength_name: [nan, 'Even', 'Power Play', 'Short Handed'] | |
| if ("GOAL" in list(iter_decision.eventTypeId)): # If a goal happened in the sequence | |
| # Identify event record of goal: There should be one only | |
| iter_goalseries = iter_decision.loc[iter_decision.eventTypeId == 'GOAL', :] | |
| # (_) Get information on who made the goal | |
| if (iter_goalseries['team_staff'].iloc[0] == 'home'): | |
| agent_goal = 1 | |
| else: | |
| agent_goal = -1 | |
| # (_) Get the goal type | |
| agent_goal_type = iter_goalseries['strength_name'].iloc[0] | |
| # (_) Information if the goal is game-winning | |
| agent_goal_fin = 1 if (iter_goalseries['gameWinningGoal'].iloc[0] == True) else 0 | |
| else: #no action needed | |
| None | |
| ## Team - offense perspective | |
| # Team identity of the offense | |
| # Default is NEUT given they are starting at neutral location | |
| agent_offense_id = "NEUT" | |
| if (agent_zonestartloc == "own"): | |
| agent_offense_id = idx_away | |
| elif (agent_zonestartloc == "opponent"): | |
| agent_offense_id = idx_home | |
| # Has offense won the faceoff, default is 0 (i.e. neurtral zone start) | |
| agent_offense_faceoff_won = 0 | |
| if agent_offense_id == idx_home: | |
| if agent_faceoff == 1: | |
| agent_offense_faceoff_won = 1 | |
| else: | |
| None | |
| else: #(df_agent.idx_offense[iter] == df_agent.idx_away[iter]): | |
| # Else if the away team is the offensive zone team | |
| if agent_faceoff == 0: | |
| agent_offense_faceoff_won = 1 | |
| else: | |
| None | |
| # SOmething to go along with the possession duration: | |
| # Shot count for the period (subtract the cumulative count) | |
| # -------------------------------------------------------------------------------------------------------- | |
| # Create dataframe to save | |
| tempdf_agent = pd.DataFrame( | |
| data = { | |
| 'gameIdx':iter_decision.gameIdx.iloc[0], | |
| 'team_home' : idx_home, | |
| 'team_opponent' : idx_away, | |
| 'period': agent_period, | |
| 'time_length': agent_serieslength, | |
| 'zone_faceoff_home': agent_zonestartloc, | |
| 'zone_faceoff_offense':agent_offense_id, | |
| 'faceoff_won_home': agent_faceoff, | |
| 'faceoff_won_offense':agent_offense_faceoff_won, | |
| 'posture': agent_faceoff_posture, | |
| 'zone_change': agent_zonestay, | |
| 'goal': agent_goal, | |
| 'goal_strengh':agent_goal_type, | |
| 'goal_finalwin':agent_goal_fin | |
| }, index = [idx_faceoff] | |
| ) | |
| # Save the agent work | |
| if len(df_agent) == 0: # The first element | |
| df_agent = tempdf_agent | |
| else: # append the existing | |
| df_agent = pd.concat([df_agent, tempdf_agent]) | |
| return df_agent |