def predict(modelname, datasourcetype, datefrom, dateto):
date_from = date_utils.try_parse(datefrom)
date_to = date_utils.try_parse(dateto)
if not date_from:
logger.error('Error parse date.')
raise BadRequest
if not date_to:
logger.error('Error parse date.')
raise BadRequest
try:
ds = DataService(data_source_factory=DataSourceFactory(app.config))
X, Y, metadata = \
ds.get_data(
data_source_type=datasourcetype,
filter={"game_date": {"$gte": date_from, "$lt": date_to}})
if not X:
return jsonify(data=None)
ps = PredictionService(
model_storage=AzureBlobStorage(
app.config['AZURE_STORAGE_NAME'],
app.config['AZURE_STORAGE_KEY'],
'deepsportmodels'))
predictions = ps.predict(X=X, model_name=modelname)
return jsonify(data=[{'gameDate': str(pd[1]['game_date'].date()),
'team1Name': pd[1]['team1_name'],
'team2Name': pd[1]['team2_name'],
'winProba': pd[0][1] * 100}
for pd in zip(predictions, metadata)])
except:
logger.error("%s: Unexpected error: %s" % (__name__, sys.exc_info()))
raise InternalServerError
}
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--df')
parser.add_argument('--dt')
parser.add_argument('--st')
parser.add_argument('--bu')
parser.add_argument('--tss')
parser.add_argument('--gs')
parser.add_argument('--f')
args = parser.parse_args()
date_from = date_utils.try_parse(args.df)
date_to = date_utils.try_parse(args.dt)
source_type = args.st
team_stat_season = args.tss
games_season = args.gs
csv_filename = args.f
base_url = args.bu
ds = SportReferenceDataSource(
base_url=base_url,
team_stat_season=team_stat_season,
games_season=games_season,
game_type=source_type,
row_parse_strategy=__source_type_map[source_type],
cache_team_stats=True)
X, Y, metadata = ds.load(dict(date_from=date_from, date_to=date_to))
