| from datasets import DatasetBuilder, DatasetInfo, Features, Value, SplitGenerator | |
| import pandas as pd | |
| class Euro2020Dataset(DatasetBuilder): | |
| VERSION = "1.0.0" | |
| def _info(self): | |
| return DatasetInfo( | |
| description="Your dataset description here.", | |
| features=Features({ | |
| "HomeTeamName": Value("string"), | |
| "AwayTeamName": Value("string"), | |
| "DateandTimeCET": Value("string"), | |
| "MatchID": Value("int64"), | |
| "RoundName": Value("string"), | |
| "Stage": Value("string"), | |
| "MatchDay": Value("int64"), | |
| "Session": Value("int64"), | |
| "MatchMinute": Value("int64"), | |
| "InjuryTime": Value("int64"), | |
| "NumberofPhases": Value("int64"), | |
| "Phase": Value("int64"), | |
| "ScoreHome": Value("int64"), | |
| "ScoreAway": Value("int64"), | |
| "MatchStatus": Value("string"), | |
| "StadiumID": Value("int64"), | |
| "NumberofMatchesRefereedPostMatch": Value("int64"), | |
| "TotalNumberofMatchesRefereed": Value("int64"), | |
| "NumberofMatchesRefereedinGroupStage": Value("int64"), | |
| "NumberofMatchesRefereedinKnockoutStage": Value("int64"), | |
| "AssistantRefereeWebName": Value("string"), | |
| "Humidity": Value("int64"), | |
| "Temperature": Value("int64"), | |
| "WindSpeed": Value("int64"), | |
| "MatchEvent": Features({ | |
| "1-First Half": Features({ | |
| "Event": Value("string"), | |
| "Minute": Value("int"), | |
| "Phase": Value("int"), | |
| "InjuryMinute": Value("int"), | |
| "TeamFromID": Value("float"), | |
| "TeamToID": Value("float"), | |
| "PlayerFromID": Value("float"), | |
| "PlayerToID": Value("float"), | |
| "Time": Value("string"), | |
| "MatchEventAttribute": Value("float"), | |
| }), | |
| "2-Second Half": Features({ | |
| "Event": Value("string"), | |
| "Minute": Value("int"), | |
| "Phase": Value("int"), | |
| "InjuryMinute": Value("int"), | |
| "TeamFromID": Value("float"), | |
| "TeamToID": Value("float"), | |
| "PlayerFromID": Value("float"), | |
| "PlayerToID": Value("float"), | |
| "Time": Value("string"), | |
| "MatchEventAttribute": Value("float"), | |
| }), | |
| "3-Extra Time First Half": Features({ | |
| "Event": Value("string"), | |
| "Minute": Value("int"), | |
| "Phase": Value("int"), | |
| "InjuryMinute": Value("int"), | |
| "TeamFromID": Value("float"), | |
| "TeamToID": Value("float"), | |
| "PlayerFromID": Value("float"), | |
| "PlayerToID": Value("float"), | |
| "Time": Value("string"), | |
| "MatchEventAttribute": Value("float"), | |
| }), | |
| "4-Extra Time Second Half": Features({ | |
| "Event": Value("string"), | |
| "Minute": Value("int"), | |
| "Phase": Value("int"), | |
| "InjuryMinute": Value("int"), | |
| "TeamFromID": Value("float"), | |
| "TeamToID": Value("float"), | |
| "PlayerFromID": Value("float"), | |
| "PlayerToID": Value("float"), | |
| "Time": Value("string"), | |
| "MatchEventAttribute": Value("float"), | |
| }), | |
| "5-Penalty Shootout": Features({ | |
| "Event": Value("string"), | |
| "Minute": Value("int"), | |
| "Phase": Value("int"), | |
| "InjuryMinute": Value("int"), | |
| "TeamFromID": Value("float"), | |
| "TeamToID": Value("float"), | |
| "PlayerFromID": Value("float"), | |
| "PlayerToID": Value("float"), | |
| "Time": Value("string"), | |
| "MatchEventAttribute": Value("float"), | |
| }), | |
| }), | |
| "TeamLineUps": Value("dict"), | |
| "TeamStats": Value("dict"), | |
| "PlayerStats": Value("dict"), | |
| "PlayerPreMatchInfo": Value("dict"), | |
| }), | |
| supervised_keys=None, | |
| homepage="Optional dataset homepage", | |
| license= "", | |
| citation="Optional citation" | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # Since the final processed dataset is a single CSV file that combines the content from numerous rows to only 51 rows, | |
| # with each row representing the information of each game, I wil just use a single 'train' split and no need for test / validation. | |
| Euro2020_df = pd.read_csv("/Users/chuhanguo/Desktop/Winter 2024/STA 663/Euro2020.csv") | |
| train_path = "ForzaJuve1/UEFA_Euro_2020_Data/Euro2020.csv" | |
| Euro2020_df.to_csv(train_path, index = False) | |
| return [ | |
| SplitGenerator( | |
| name="train", | |
| gen_kwargs={"filepath": train_path} | |
| ), | |
| ] | |
| #def _generate_examples(self, filepath): | |
| #Here we read the CSV file and yield examples | |
| #data = pd.read_csv(filepath) | |
| #for idx, row in data.iterrows(): | |
| #yield idx, { | |
| #"PlayerID": row["PlayerID"], | |
| #"PlayerName": row["PlayerName"], | |
| #"PlayerSurname": row["PlayerSurname"], | |
| #"IsGoalkeeper": row["IsGoalkeeper"], | |
| #"PlayedTime": row["PlayedTime"], | |
| #"StatsID": row["StatsID"], | |
| #"StatsName": row["StatsName"], | |
| #"Value": row["Value"], | |
| #"Rank": row["Rank"], | |
| # Make sure to include all fields defined in _info | |
| #} | |