[Python] Hyperparameter Optimisation

Im trying to forecast a time series using prophet model in python, for which I would like to find the optimal tuning parameters (like changepoint_range, changepoint_prior_scale, seasonality_prior_scale, holiday_prior_scale, holiday_mode) for the time series by minimising the error (MAPE) in test and train validation method and predict the future.

def mape(y_true, y_pred):
return round(np.mean(np.abs(((y_true-y_pred)/y_true)*100)),2)


Currently, I am using the optuna and tuner libraries but it only takes number (trials 50; 10 iterations and 50 samples) and variation in account, but I would like to consider all the parameter variations in account to provide the optimal tuning.

def objective(trial):
params={'changepoint_range': trial.suggest_loguniform('changepoint_range', 0.1, 1),
'changepoint_prior_scale' : trial.suggest_loguniform('changepoint_prior_scale', 0.01, 0.5),
'seasonality_prior_scale': trial.suggest_loguniform('seasonality_prior_scale', 0.01, 10),
'holidays_prior_scale': trial.suggest_loguniform('holidays_prior_scale', 0.01, 10),
'holidays_mode': trial.suggest_categorical('holidays_mode', ['additive', 'multiplicative'])
}
model = Prophet()
model.fit(Train)
future= model.make_future_dataframe(periods= tst)
forecast= model.predict(future)
prediction= forecast.tail(tst)
return mape(Test['y'], prediction['yhat'])
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=50)

def objective_function(args_list):
global Train, Test

params_evaluated= []
results= []

for params in args_list:
try:
model = Prophet()
model.fit(Train)
future= model.make_future_dataframe(periods= tst)
forecast= model.predict(future)
prediction= forecast.tail(tst)
error= mape(Test['y'], prediction['yhat'])

params_evaluated.append(params)
results.append(error)
except Exception as e:
print(f"Error for params {params}: {e}")
params_evaluated.append(params)
results.append(25)

return params_evaluated, results

from scipy.stats import uniform
params_space= dict(changepoint_range= uniform(0.5,0.5),
changepoint_prior_scale= uniform(0.001,0.5),
seasonality_prior_scale= uniform(0.01,10),
holidays_prior_scale= uniform(0.01, 10),
holidays_mode= ['additive', 'multiplicative']
)

conf_dict= dict()
conf_dict['initial_random']= 10
conf_dict['num_iteration']= 50
tuner= Tuner(params_space, objective_function, conf_dict)
results= tuner.minimize()

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