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# 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, {
# }
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