| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # TODO: Address all TODOs and remove all explanatory comments | |
| """This is tracking data of the 2015-2016 NBA season""" | |
| import csv | |
| import json | |
| import os | |
| import py7zr | |
| import datasets | |
| import requests | |
| _CITATION = """\ | |
| @misc{Linou2016, | |
| title = {NBA-Player-Movements}, | |
| author={Kostya Linou}, | |
| publisher={SportVU}, | |
| year={2016} | |
| """ | |
| _DESCRIPTION = """\ | |
| This dataset is designed to give further easy access to tracking data. | |
| By merging all .7z files into one large .json file, access is easier to retrieve all information at once. | |
| """ | |
| _HOMEPAGE = "https://github.com/linouk23/NBA-Player-Movements/tree/master/" | |
| res = requests.get(_HOMEPAGE) | |
| items = res.json()['payload']['tree']['items'] | |
| _URL = "https://github.com/linouk23/NBA-Player-Movements/raw/master/data/2016.NBA.Raw.SportVU.Game.Logs" | |
| _URLS = {} | |
| for game in items: | |
| name = game['name'][:-3] | |
| _URLS[name] = _URL + "/" + name + ".7z" | |
| class NbaTracking(datasets.GeneratorBasedBuilder): | |
| """Tracking data for all games of 2015-2016 season in forms of coordinates for players and ball at each moment.""" | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "gameid": datasets.Value("string"), | |
| "gamedate": datasets.Value("string"), | |
| # question: how to indicate list of dictionaries? | |
| "events": datasets.Sequence( | |
| { | |
| "eventid": datasets.Value("string"), | |
| # "visitor": { | |
| # "name": datasets.Value("string"), | |
| # "teamid": datasets.Value("int64"), | |
| # "abbreviation": datasets.Value("string"), | |
| # "players": datasets.Sequence({ | |
| # "lastname": datasets.Value("string"), | |
| # "firstname": datasets.Value("string"), | |
| # "playerid": datasets.Value("int64"), | |
| # "jersey": datasets.Value("string"), | |
| # "position": datasets.Value("string") | |
| # }) | |
| # }, | |
| # "home": { | |
| # "name": datasets.Value("string"), | |
| # "teamid": datasets.Value("int64"), | |
| # "abbreviation": datasets.Value("string"), | |
| # "players": datasets.Sequence({ | |
| # "lastname": datasets.Value("string"), | |
| # "firstname": datasets.Value("string"), | |
| # "playerid": datasets.Value("int64"), | |
| # "jersey": datasets.Value("string"), | |
| # "position": datasets.Value("string") | |
| # }) | |
| # }, | |
| "moments": datasets.Sequence( | |
| # question, how to indicate lists of lists of different types | |
| { | |
| "quarter": datasets.Value("int64"), | |
| "game_clock": datasets.Value("float32"), | |
| "shot_clock": datasets.Value("float32"), | |
| "ball_coordinates": datasets.Sequence( | |
| datasets.Value("float32"), | |
| datasets.Value("float32"), | |
| datasets.Value("float32") | |
| ), | |
| "player_coordinates": datsets.Sequence( | |
| { | |
| "teamid": datasets.Value("int64"), | |
| "playerid": datasets.Value("int64"), | |
| "x": datasets.Value("float32"), | |
| "y": datasets.Value("float32"), | |
| "z": datasets.Value("float32") | |
| } | |
| ) | |
| } | |
| # datasets.Sequence( | |
| # datasets.Value("int64"), | |
| # datasets.Value("float32"), | |
| # datasets.Value("float32"), | |
| # datasets.Value("float32"), | |
| # datasets.Value("null"), | |
| # datasets.Sequence( | |
| # datasets.Sequence( | |
| # datasets.Value("int64"), | |
| # datasets.Value("int64"), | |
| # datasets.Value("float32"), | |
| # datasets.Value("float32"), | |
| # datasets.Value("float32") | |
| # ) | |
| # ) | |
| # ) | |
| ) | |
| } | |
| ) | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| urls = _URLS[self.config.name] | |
| data_dir = dl_manager.download_and_extract(urls) | |
| all_file_paths = {} | |
| for key, directory_path in data_dir.items(): | |
| all_file_paths[key] = os.path.join(directory_path, os.listdir(directory_path)) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepaths": all_file_paths, | |
| "split": "train", | |
| } | |
| ) | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
| for link in filepath: | |
| with open(link, encoding="utf-8") as fp: | |
| game_id = json.load(fp)["gameid"] | |
| game_date = json.load(fp)["gamedate"] | |
| for event in json.load(fp)["events"]: | |
| event_id = event["eventId"] | |
| for moment in event["moments"]: | |
| for element in moment: | |
| quarter = element[0] | |
| game_clock = element[2] | |
| shot_clock = element[3] | |
| ball_coords = element[5][0][2:] | |
| for position in element[5][1:]: | |
| team_id = position[0] | |
| player_id = position[1] | |
| x = position[2] | |
| y = position[3] | |
| z = position[4] | |
| yield game_id, { | |
| "gameid": game_id, | |
| "gamedate": game_date, | |
| "events": { | |
| "eventid": event_id, | |
| "moments": { | |
| "quarter": quarter, | |
| "game_clock": game_clock, | |
| "shot_clock": shot_clock, | |
| "ball_coordinates": ball_coords, | |
| "player_coordinates": { | |
| "teamid": team_id, | |
| "playerid": player_id, | |
| "x": x, | |
| "y": y, | |
| "z": z | |
| } | |
| } | |
| } | |
| } | |
| # for key, row in enumerate(fp): | |
| # data = json.load(row) | |
| # # Yields examples as (key, example) tuples | |
| # yield key, { | |
| # } | |