--- title: Experiment Utils keywords: fastai sidebar: home_sidebar summary: "This notebook contains a set of functions to easily perform experiments on time series datasets. In this notebook you can see functions for:" description: "This notebook contains a set of functions to easily perform experiments on time series datasets. In this notebook you can see functions for:" nb_path: "nbs/experiments__utils.ipynb" ---
In the next two scetion you can see enviroment variables and imports that are used for set of functions shown in this notebook.
import torch as t
from neuralforecast.losses.numpy import mae, rmse
from neuralforecast.models.nhits.nhits import suggested_space as nhits_suggested_space
from neuralforecast.models.nbeats.nbeats import suggested_space as nbeats_suggested_space
from neuralforecast.data.datasets.epf import EPF
dataset = ['NP']
Y_df, X_df, S_df = EPF.load_groups(directory='data', groups=dataset)
X_df = X_df[['unique_id', 'ds', 'week_day']]
nhits_space = nhits_suggested_space(n_time_out=24, n_series=1, n_x=1, n_s=0, frequency='H')
nhits_space['max_steps'] = hp.choice('max_steps', [10]) # Override max_steps for faster example
trials = hyperopt_tunning(space=nhits_space, hyperopt_max_evals=2, loss_function_val=mae,
loss_functions_test={'mae': mae, 'rmse': rmse},
S_df=S_df, Y_df=Y_df, X_df=X_df, f_cols=[],
ds_in_val=7*24, ds_in_test=7*24,
return_forecasts=True, return_model=True, save_progress=False,
results_file=None, loss_kwargs={}, verbose=False)
best_model = trials.best_trial['result']['model']
nbeats_space = nbeats_suggested_space(n_time_out=24, n_series=1, n_x=1, n_s=0, frequency='H')
nbeats_space['max_steps'] = hp.choice('max_steps', [10]) # Override max_steps for faster example
trials = hyperopt_tunning(space=nbeats_space, hyperopt_max_evals=2, loss_function_val=mae,
loss_functions_test={'mae': mae, 'rmse': rmse},
S_df=S_df, Y_df=Y_df, X_df=X_df, f_cols=[],
ds_in_val=7*24, ds_in_test=7*24,
return_forecasts=True, return_model=True, save_progress=False,
results_file=None, loss_kwargs={}, verbose=False)
best_model = trials.best_trial['result']['model']