--- title: NBEATS Ensemble for M4 dataset keywords: fastai sidebar: home_sidebar nb_path: "nbs/models_nbeats__nbeats_model_ensemble.ipynb" ---
# from google.colab import drive
# drive.mount('/content/drive')
# os.chdir('./drive/MyDrive/nixtlats')
# print(os.getcwd())
# !pip install torchinfo
# !pip install fastcore
# !pip install s3fs
# !pip install patool
# !pip install --upgrade pandas==1.2.4
# !pip install --upgrade requests==2.25.1
def show_tensorboard(logs_path, model_path):
logs_model_path = f'{logs_path}/{model_path}'
%load_ext tensorboard
%tensorboard --logdir $logs_model_path
LOGS_PATH = Path('lightning_logs')
val_freq_steps = 1
tensorboard_logs = True
NUM_WORKERS = 4
frequencies = [Yearly]
print_models_list(frequencies=frequencies, table_width=75)
ensemble = EnsembleNBEATSM4()
forecasts = ensemble.fit(frequencies=frequencies,
loader=TimeSeriesLoader,
val_freq_steps=val_freq_steps,
tensorboard_logs=tensorboard_logs,
logs_path=LOGS_PATH,
num_workers=NUM_WORKERS)
M4Evaluation.evaluate('data', 'Yearly', forecasts['Yearly'].drop('unique_id', axis=1).to_numpy())