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fix(atomic): use iloc to select last value in the dataframe
This commit fixes a bug in the scores and concedes functions in atomic vaep, where the intention is to grab the last element in the dataframe and assign that value to the end of the shifted dataframe. See also #718 Fixes #749
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,108 +1,108 @@ | ||
| """Implements the label tranformers of the Atomic-VAEP framework.""" | ||
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| import pandas as pd | ||
| from pandera.typing import DataFrame | ||
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| import socceraction.atomic.spadl.config as atomicspadl | ||
| from socceraction.atomic.spadl import AtomicSPADLSchema | ||
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| 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"] | ||
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| # 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][len(y) - 1] | ||
| y["%s+%d" % (c, i)] = shifted | ||
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| 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 | ||
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| return pd.DataFrame(res, columns=["scores"]) | ||
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| 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"] | ||
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| # 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][len(y) - 1] | ||
| y["%s+%d" % (c, i)] = shifted | ||
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| 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 | ||
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| return pd.DataFrame(res, columns=["concedes"]) | ||
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| 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") | ||
| ) | ||
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| return pd.DataFrame(goals.rename("goal")) | ||
| """Implements the label tranformers of the Atomic-VAEP framework.""" | ||
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| import pandas as pd | ||
| from pandera.typing import DataFrame | ||
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| import socceraction.atomic.spadl.config as atomicspadl | ||
| from socceraction.atomic.spadl import AtomicSPADLSchema | ||
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| 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"] | ||
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| # 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 | ||
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| 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 | ||
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| return pd.DataFrame(res, columns=["scores"]) | ||
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| 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"] | ||
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| # 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 | ||
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| 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 | ||
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| return pd.DataFrame(res, columns=["concedes"]) | ||
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| 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") | ||
| ) | ||
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| return pd.DataFrame(goals.rename("goal")) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,18 @@ | ||
| import socceraction.atomic.spadl.utils as spu | ||
| import socceraction.atomic.vaep.labels as lab | ||
| from pandera.typing import DataFrame | ||
| from socceraction.atomic.spadl import AtomicSPADLSchema | ||
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| def test_scores(atomic_spadl_actions: DataFrame[AtomicSPADLSchema]) -> None: | ||
| nr_actions = 10 | ||
| atomic_spadl_actions = spu.add_names(atomic_spadl_actions) | ||
| scores = lab.scores(atomic_spadl_actions, nr_actions) | ||
| assert len(scores) == len(atomic_spadl_actions) | ||
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| def test_conceds(atomic_spadl_actions: DataFrame[AtomicSPADLSchema]) -> None: | ||
| nr_actions = 10 | ||
| atomic_spadl_actions = spu.add_names(atomic_spadl_actions) | ||
| concedes = lab.concedes(atomic_spadl_actions, nr_actions) | ||
| assert len(concedes) == len(atomic_spadl_actions) |