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"""Implements the label tranformers of the Atomic-VAEP framework."""
import pandas as pd
from pandera.typing import DataFrame
import socceraction.atomic.spadl.config as atomicspadl
from socceraction.atomic.spadl import AtomicSPADLSchema
def scores(actions: DataFrame[AtomicSPADLSchema], nr_actions: int = 10) -> pd.DataFrame:
"""Determine whether the team possessing the ball scored a goal within the next x actions.
Parameters
----------
actions : pd.DataFrame
The actions of a game.
nr_actions : int, default=10 # noqa: DAR103
Number of actions after the current action to consider.
Returns
-------
pd.DataFrame
A dataframe with a column 'scores' and a row for each action set to
True if a goal was scored by the team possessing the ball within the
next x actions; otherwise False.
"""
# merging goals, owngoals and team_ids
goals = actions["type_id"] == atomicspadl.actiontypes.index("goal")
owngoals = actions["type_id"] == atomicspadl.actiontypes.index("owngoal")
y = pd.concat([goals, owngoals, actions["team_id"]], axis=1)
y.columns = ["goal", "owngoal", "team_id"]
# adding future results
for i in range(1, nr_actions):
for c in ["team_id", "goal", "owngoal"]:
shifted = y[c].shift(-i)
shifted[-i:] = y[c].iloc[len(y) - 1]
y["%s+%d" % (c, i)] = shifted
res = y["goal"]
for i in range(1, nr_actions):
gi = y["goal+%d" % i] & (y["team_id+%d" % i] == y["team_id"])
ogi = y["owngoal+%d" % i] & (y["team_id+%d" % i] != y["team_id"])
res = res | gi | ogi
return pd.DataFrame(res, columns=["scores"])
def concedes(actions: DataFrame[AtomicSPADLSchema], nr_actions: int = 10) -> pd.DataFrame:
"""Determine whether the team possessing the ball conceded a goal within the next x actions.
Parameters
----------
actions : pd.DataFrame
The actions of a game.
nr_actions : int, default=10 # noqa: DAR103
Number of actions after the current action to consider.
Returns
-------
pd.DataFrame
A dataframe with a column 'concedes' and a row for each action set to
True if a goal was conceded by the team possessing the ball within the
next x actions; otherwise False.
"""
# merging goals, owngoals and team_ids
goals = actions["type_id"] == atomicspadl.actiontypes.index("goal")
owngoals = actions["type_id"] == atomicspadl.actiontypes.index("owngoal")
y = pd.concat([goals, owngoals, actions["team_id"]], axis=1)
y.columns = ["goal", "owngoal", "team_id"]
# adding future results
for i in range(1, nr_actions):
for c in ["team_id", "goal", "owngoal"]:
shifted = y[c].shift(-i)
shifted[-i:] = y[c].iloc[len(y) - 1]
y["%s+%d" % (c, i)] = shifted
res = y["owngoal"]
for i in range(1, nr_actions):
gi = y["goal+%d" % i] & (y["team_id+%d" % i] != y["team_id"])
ogi = y["owngoal+%d" % i] & (y["team_id+%d" % i] == y["team_id"])
res = res | gi | ogi
return pd.DataFrame(res, columns=["concedes"])
def goal_from_shot(actions: DataFrame[AtomicSPADLSchema]) -> pd.DataFrame:
"""Determine whether a goal was scored from the current action.
This label can be use to train an xG model.
Parameters
----------
actions : pd.DataFrame
The actions of a game.
Returns
-------
pd.DataFrame
A dataframe with a column 'goal' and a row for each action set to
True if a goal was scored from the current action; otherwise False.
"""
goals = (actions["type_id"] == atomicspadl.actiontypes.index("shot")) & (
actions["type_id"].shift(-1) == atomicspadl.actiontypes.index("goal")
)
return pd.DataFrame(goals.rename("goal"))
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