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| import subprocess, sys | |
| from multiprocessing import Pool | |
| import pandas as pd, json, os, math | |
| import numpy as np | |
| from tqdm import tqdm | |
| from sklearn.model_selection import train_test_split | |
| import matplotlib.pyplot as plt | |
| from cricksheet import get_all_matches | |
| # import ydata_profiling | |
| ## Reading IPL dataset | |
| total_wickets = 10 | |
| n_pools = 100 | |
| ## Feature selection/creation and ngram creation | |
| features = [ | |
| "matchid", | |
| "format", | |
| "inning", | |
| "batting_team", | |
| "bowling_team", | |
| "balls", | |
| "runs", | |
| "wickets", | |
| "wkt_last_5_overs", | |
| "runrate_last_5_overs", | |
| "runrate_last_5_overs-current_RR", | |
| "current_RR", | |
| # "average", | |
| "balls_left", | |
| "wkts_left", | |
| # "required_RR", | |
| # "projected_score_more", | |
| # "min_score_more", | |
| # "max_score_more", | |
| # "projected_avg_score_more", | |
| "final_score", | |
| "final_score_more", | |
| "deviation_from_projected", | |
| ] | |
| getformat = {"ODI": 1, "T20": 2} | |
| def extract_features(inning): | |
| data = [] | |
| # total_balls = ( | |
| # 120 if inning.format == "T20" else 300 if inning.format == "ODI" else None | |
| # ) | |
| total_balls = len(inning.df) | |
| df = inning.df | |
| # matchid = inning.matchid | |
| # batting_team = inning.battingteam | |
| for i in range(1, len(df)): | |
| min_RR = 0.5 | |
| max_RR = 2.5 | |
| runs = df.iloc[:i]["run"].sum() | |
| run_last_5_overs = df["run"].iloc[-30:].sum() | |
| runrate_last_5_overs = run_last_5_overs / 6 | |
| wickets = df.iloc[:i]["wicket"].sum() | |
| wkt_last_5_overs = df.iloc[:i]["wicket"].iloc[-30:].sum() | |
| balls = len(df.iloc[:i]) | |
| current_RR = (runs * 6) / balls | |
| rr_diff = runrate_last_5_overs - current_RR | |
| average = runs / (wickets + 1) | |
| balls_left = total_balls - balls | |
| wk_left = total_wickets - wickets | |
| required_RR = ( | |
| ((inning.target - runs) * 6) / balls if inning.inning == 2 else -9999 | |
| ) | |
| projected_score_more = current_RR * balls_left / 6 | |
| min_score_more = min_RR * balls_left / 6 | |
| max_score_more = max_RR * balls_left / 6 | |
| projected_avg_score_more = average * wk_left / 6 | |
| final_score_more = inning.final_score - runs | |
| format = getformat[inning.format] | |
| deviation_from_projected = final_score_more - projected_score_more | |
| data.append( | |
| ( | |
| inning.matchid, | |
| format, | |
| inning.inning, | |
| inning.battingteam, | |
| inning.bowlingteam, | |
| balls, | |
| runs, | |
| wickets, | |
| wkt_last_5_overs, | |
| round(runrate_last_5_overs, 2), | |
| round(rr_diff, 2), | |
| round(current_RR, 2), | |
| # average, | |
| balls_left, | |
| wk_left, | |
| # required_RR, | |
| # projected_score_more, | |
| # min_score_more, | |
| # max_score_more, | |
| # projected_avg_score_more, | |
| inning.final_score, | |
| final_score_more, | |
| round(deviation_from_projected), | |
| ) | |
| ) | |
| return data | |
| def save_features(innings, fname): | |
| print("Feature enggineering and ngram creation...") | |
| n_innings = len(innings) | |
| print(f"{n_innings=}") | |
| pool = Pool(processes=n_pools) | |
| Xy = pool.map(extract_features, innings) | |
| Xy = [xi for Xi in Xy for xi in Xi] | |
| print(f"{len(Xy)=}") | |
| featuresdf = pd.DataFrame(Xy, columns=features) | |
| # ydata_profiling.ProfileReport(featuresdf, title=fname).to_file(fname + ".html") | |
| featuresdf.to_feather(fname) | |
| featuresdf.to_csv(fname + ".csv") | |
| if __name__ == "__main__": | |
| print("Loading t20 data...") | |
| innings = get_all_matches(format="T20", since=2021) | |
| print("Saving t20 data") | |
| save_features(innings, "data/t20features.feather") | |
| print("Loading odi data...") | |
| innings = get_all_matches(format="ODI", since=2021) | |
| print("Saving odi data") | |
| save_features(innings, "data/odifeatures.feather") | |