def create_and_fit_team_model(df, df_X, teams=CURRENT_TEAMS, n_attempts=3):
"""
Get the team-level stan model, which can give probabilities of
each potential scoreline in a given fixture.
"""
model_team = bpl.BPLModel(df, X=df_X)
for i in range(n_attempts):
print(f"attempt {i + 1} of {n_attempts}...", end=" ", flush=True)
try:
model_team.fit()
print("SUCCESS!")
break
except RuntimeError:
print("FAILED.")
if i + 1 == n_attempts:
raise
else:
continue
# check if each team is known to the model, and if not, add it using FIFA rankings
for team in teams:
if team not in model_team.team_indices.keys():
try:
strvals = df_X.loc[(df_X["team"] == team),
["att", "mid", "defn", "ovr"]].values
intvals = [int(v) for v in strvals[0]]
model_team.add_new_team(team, intvals)
print("Adding new team {} with covariates".format(team))
except Exception:
model_team.add_new_team(team)
print("Adding new team {} without covariates".format(team))
return model_team
def get_team_model(df, df_X):
"""
Get the team-level stan model, which can give probabilities of
each potential scoreline in a given fixture.
"""
## TEMP - GET NAN ERROR IF USE DF_X
# model_team = bpl.BPLModel(df, X=df_X)
model_team = bpl.BPLModel(df, X=None)
model_team.fit()
return model_team
def create_and_fit_team_model(df, df_X, teams=CURRENT_TEAMS):
"""
Get the team-level stan model, which can give probabilities of
each potential scoreline in a given fixture.
"""
model_team = bpl.BPLModel(df, X=df_X)
model_team.fit()
# check if each team is known to the model, and if not, add it using FIFA rankings
for team in teams:
if not team in model_team.team_indices.keys():
try:
strvals = df_X.loc[(df_X["team"] == team),
["att", "mid", "defn", "ovr"]].values
intvals = [int(v) for v in strvals[0]]
model_team.add_new_team(team, intvals)
print("Adding new team {} with covariates".format(team))
except:
model_team.add_new_team(team)
print("Adding new team {} without covariates".format(team))
return model_team