def short_pass_inner_function(log, n_steps=60):
filtered_log = data.filter_log(
log,
type=(
"SHORT_PASS_ATTEMPT",
"NEW_POSSESSION",
"OPP_POSSESSION",
"KICK_RELEASE",
"GOAL_SCORED",
),
)
df = data.parse_log_to_df(filtered_log)
if len(df):
if "player" not in df.columns:
df["player"] = -1
df["target"] = (
(df.type == "SHORT_PASS_ATTEMPT")
& (
(df.type.shift(-1).isin(["NEW_POSSESSION", "KICK_RELEASE"]))
| (df.type.shift(-1) == "GOAL_SCORED")
| ((df.type.shift(-1) == "OPP_POSSESSION") & (df.player.shift(-1) == 0))
)
& (df.step.shift(-1) - df.step <= n_steps)
).astype(int)
df = df[df.type == "SHORT_PASS_ATTEMPT"]
return df
def position_score_inner_function(log):
filtered_log = data.filter_log(
log,
type=("ACTIVE_POS_SCORE", "GOAL_SCORED", "OPP_POSSESSION"),
)
df = data.parse_log_to_df(filtered_log)
if len(df):
df["reward"] = 0
goal_steps = df.step[df.type == "GOAL_SCORED"]
for step in goal_steps:
mask = (df.step < step) & (df.step >= (step - 20))
df.loc[mask, "reward"] = 1.0
opp_pos_steps = df.step[df.type == "OPP_POSSESSION"]
for step in opp_pos_steps:
mask = (df.step < step) & (df.step >= (step - 10))
df.loc[mask, "reward"] = -0.025
df = df[df.type == "ACTIVE_POS_SCORE"]
return df
def handle_inner_function(log, n_steps=10):
filtered_log = data.filter_log(
log,
type=(
"MOVE_WITH_BALL_ATTEMPT",
"LOST_POSSESSION",
"NEW_POSSESSION",
"OPP_POSSESSION",
"SHOT_ATTEMPT",
),
)
df = data.parse_log_to_df(filtered_log)
if len(df):
df["target"] = 1
bad_steps = df.step[df.type.isin(("LOST_POSSESSION", "OPP_POSSESSION"))]
for step in bad_steps:
mask = (df.step < step) & (df.step >= (step - n_steps))
df.loc[mask, "target"] = 0 # failed if lost possession
df = df[df.type == "MOVE_WITH_BALL_ATTEMPT"]
return df
def simulate_one_game(game_num):
agent_obj = agent.Agent(noise_sd=NOISE_SD)
def action(obs):
return agent_obj.action_wrapper(obs)
env = make(
environment="football",
configuration={
"save_video": False,
"scenario_name": "11_vs_11_kaggle",
"agentTimeout": 60,
"actTimeout": 60,
"runTimeout": 1200,
},
)
env.reset()
env.run([action, "cicada/simple_rules_based_agent.py"])
log_types_to_keep = [
"SHOT_ATTEMPT",
"GOAL_SCORED",
"OPP_GOAL_SCORED",
"MOVE_WITH_BALL_ATTEMPT",
"SHORT_PASS_ATTEMPT",
"LONG_PASS_ATTEMPT",
"LOST_POSSESSION",
"NEW_POSSESSION",
"OPP_POSSESSION",
"ACTIVE_POS_SCORE",
"KICK_WITH_NO_ATTEMPT_EVENT",
"KICK_RELEASE",
]
filtered_log = data.filter_log(agent_obj.state.log, type=log_types_to_keep)
data.add_to_log_file(filtered_log)
record_type_counts = data.count_record_types(filtered_log)
score = env.state[0]["observation"]["players_raw"][0]["score"]
data.add_to_score_file(score)
to_print = f"game_num: {str(game_num):>4}, score: {str(score):>8}, steps: {str(len(env.steps)):>4}, "
rec_count_strings = list()
for rec_type, short_name in [
("SHOT_ATTEMPT", "shot"),
("MOVE_WITH_BALL_ATTEMPT", "move"),
("SHORT_PASS_ATTEMPT", "short"),
("LONG_PASS_ATTEMPT", "long"),
("ACTIVE_POS_SCORE", "poss"),
]:
rec_count_strings.append(
f"{short_name}: {str(record_type_counts.get(rec_type, 0)):>4}"
)
to_print += ", ".join(rec_count_strings)
print(to_print)
def save_score_and_log(agent_obj, score):
log_types_to_keep = [
"SHOT_ATTEMPT",
"GOAL_SCORED",
"OPP_GOAL_SCORED",
"MOVE_WITH_BALL_ATTEMPT",
"SHORT_PASS_ATTEMPT",
"LONG_PASS_ATTEMPT",
"LOST_POSSESSION",
"NEW_POSSESSION",
"OPP_POSSESSION",
"ACTIVE_POS_SCORE",
"KICK_WITH_NO_ATTEMPT_EVENT",
"KICK_RELEASE",
]
filtered_log = data.filter_log(agent_obj.state.log, type=log_types_to_keep)
data.add_to_log_file(filtered_log)
data.add_to_score_file(score)
def shot_inner_function(log, n_steps=30):
filtered_log = data.filter_log(
log,
type=("SHOT_ATTEMPT", "GOAL_SCORED"),
)
df = data.parse_log_to_df(filtered_log)
if len(df):
df["target"] = (
(df.type == "SHOT_ATTEMPT")
& (df.type.shift(-1) == "GOAL_SCORED")
& (df.step.shift(-1) - df.step <= n_steps)
).astype(int)
df = df[df.type == "SHOT_ATTEMPT"]
if "eval_data.dist_to_goalie" in df.columns:
df = df[df["eval_data.dist_to_goalie"].isna()]
if "eval_data.pos" in df.columns:
df["posx"] = df["eval_data.pos"].str[0]
df["posy"] = df["eval_data.pos"].str[1]
return df