def update_element_history_fixtures_route():
logger.info("setting up client")
tasks_client = utils.set_up_tasks()
logger.info("purging queue")
utils.purge_cloud_queue("update-element-history-fixtures", tasks_client)
bootstrap_data = element_data.get_bootstrap()
elements = element_data.get_elements(bootstrap_data)
logger.info("setting up big query client")
big_query_client = utils.set_up_bigquery()
logger.info("deleting element gameweek history")
utils.run_query(
"DELETE FROM `footbot-001.fpl.element_gameweeks_2122` WHERE true",
big_query_client,
)
logger.info("deleting element fixtures")
utils.run_query(
"DELETE FROM `footbot-001.fpl.element_future_fixtures_2122` WHERE true",
big_query_client,
)
logger.info("queueing elements")
for element in elements:
create_update_element_history_fixtures_task(element, tasks_client)
logger.info("elements queued")
return "elements queued"
def update_element_history_fixtures_worker(element, delete=False):
logger.info(f"setting up client for element {element}")
client = utils.set_up_bigquery()
logger.info(f"getting element {element} gameweek history and fixtures")
(
element_history_df,
element_fixtures_df,
) = element_data.get_element_history_fixture_dfs(element)
if delete:
logger.info(f"deleting element {element} gameweek history")
utils.run_query(
f"DELETE FROM `footbot-001.fpl.element_gameweeks_2122` WHERE element = {element}",
client,
)
logger.info(f"writing element {element} gameweek history")
utils.write_to_table("fpl", "element_gameweeks_2122", element_history_df,
client)
logger.info(f"done writing element {element} gameweek history")
if delete:
logger.info(f"deleting element {element} fixtures")
utils.run_query(
f"DELETE FROM `footbot-001.fpl.element_future_fixtures_2122` WHERE element = {element}",
client,
)
logger.info(f"writing element {element} fixtures")
utils.write_to_table("fpl", "element_future_fixtures_2122",
element_fixtures_df, client)
logger.info(f"done writing element {element} fixtures")
def update_element_data_route():
logger.info("getting element data")
bootstrap_data = element_data.get_bootstrap()
element_df = element_data.get_element_df(bootstrap_data)
client = utils.set_up_bigquery()
logger.info("writing element data")
utils.write_to_table("fpl", "element_data_2122", element_df, client)
logger.info("done writing element data")
return "Updated element data, baby"
def update_predictions_route():
client = utils.set_up_bigquery()
predict_df = train_predict.get_predicted_points_df(
"./footbot/predictor/sql/train.sql",
"./footbot/predictor/sql/predict.sql",
client,
)
logger.info("writing predictions")
utils.write_to_table(
"fpl",
"element_gameweeks_predictions_2122_v01",
predict_df,
client,
write_disposition="WRITE_TRUNCATE",
)
logger.info("done writing predictions")
return "predictions updated"
def optimise_entry(
entry,
total_budget=1000,
first_team_factor=0.9,
bench_factor=0.1,
captain_factor=0.9,
vice_factor=0.1,
transfer_penalty=4,
transfer_limit=15,
start_event=1,
end_event=38,
login=None,
password=None,
):
"""
Select the optimal team and transfers for a specified entry.
If credentials are provided, use private entry data.
:param entry: Team identifier
:param total_budget: Total budget available, i.e. total team value + bank
:param first_team_factor: The probability a player on the first team will play
:param bench_factor: The probability a player on the bench will play
:param captain_factor: The probability the captain will play
:param vice_factor: The probability the captain will not play
:param transfer_penalty: The cost in points of transferring a player
:param transfer_limit: The limit to the number of transfers that can be made
:param start_event: Start of event range to optimiser over
:param end_event: End of event range to optimiser over
:param login: FPL login
:param password: FPL password
:return: Dictionary of team selection decisions
"""
logger.info("getting predictions")
client = set_up_bigquery()
players = run_templated_query(
"./footbot/optimiser/sql/optimiser.sql",
dict(start_event=start_event, end_event=end_event),
client,
).to_dict("records")
if login and password:
private_data = get_private_entry_data(entry, login, password)
existing_squad = [i["element"] for i in private_data["picks"]]
team_value = np.sum(
[i["selling_price"] for i in private_data["picks"]])
bank = private_data["transfers"]["bank"]
total_budget = team_value + bank
# update player values with selling prices for players in existing squad
for player in players:
for pick in private_data["picks"]:
if player["element"] == pick["element"]:
player["value"] = pick["selling_price"]
else:
public_data = get_public_entry_data(entry)
existing_squad = [i["element"] for i in public_data["picks"]]
logger.info("optimising team")
(
first_team,
bench,
captain,
vice,
transfers,
) = select_team(
players,
optimise_key="average_points",
existing_squad=existing_squad,
total_budget=total_budget,
first_team_factor=first_team_factor,
bench_factor=bench_factor,
captain_factor=captain_factor,
vice_factor=vice_factor,
transfer_penalty=transfer_penalty,
transfer_limit=transfer_limit,
)
first_team = get_sorted_safe_web_names(first_team, players)
bench = get_sorted_safe_web_names(bench, players, is_sorted=False)
captain = get_sorted_safe_web_names(captain, players)
vice = get_sorted_safe_web_names(vice, players)
transfers_in = get_sorted_safe_web_names(transfers["transfers_in"],
players)
transfers_out = get_sorted_safe_web_names(transfers["transfers_out"],
players)
return {
"first_team": first_team,
"bench": bench,
"captain": captain,
"vice": vice,
"transfers_in": transfers_in,
"transfers_out": transfers_out,
}