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Tutorial on how to achieve a full control of the configure_optimizers() behavior of NeuralForecast models
NeuralForecast models allow us to customize the default optimizer and learning rate scheduler behaviors via optimizer, optimizer_kwargs, lr_scheduler, lr_scheduler_kwargs. However this is not sufficient to support the use of ReduceLROnPlateau, for instance, as it requires the specification of monitor parameter. This tutorial provides an example of how to support the use of ReduceLROnPlateau.

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Load libraries

import numpy as np
import pandas as pd
import torch
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import NHITS
from neuralforecast.utils import AirPassengersPanel

from utilsforecast.plotting import plot_series

/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
2025-02-25 15:57:21,708 INFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.
2025-02-25 15:57:21,760 INFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.

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Data

We use the AirPassengers dataset for the demonstration of conformal prediction. 
AirPassengersPanel_train = AirPassengersPanel[AirPassengersPanel['ds'] < AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)
AirPassengersPanel_test = AirPassengersPanel[AirPassengersPanel['ds'] >= AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)
AirPassengersPanel_test['y'] = np.nan
AirPassengersPanel_test['y_[lag12]'] = np.nan

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Model training

We now train a NHITS model on the above dataset. We consider two different predictions: 1. Training using the default configure_optimizers(). 2. Training by overwriting the configure_optimizers() of the subclass of NHITS model. 
horizon = 12
input_size = 24

class CustomNHITS(NHITS):
    def configure_optimizers(self):
        optimizer = torch.optim.Adadelta(params=self.parameters(), rho=0.75)
        scheduler=torch.optim.lr_scheduler.ReduceLROnPlateau(
            optimizer=optimizer, mode='min',factor=0.5, patience=2,
        )
        scheduler_config = {
            'scheduler': scheduler,
            'interval': 'step',
            'frequency': 1,
            'monitor': 'train_loss',
            'strict': True,
            'name': None,
        }
        return {'optimizer': optimizer, 'lr_scheduler': scheduler_config}

models = [
    NHITS(h=horizon, input_size=input_size, max_steps=100, alias='NHITS-default-scheduler'),
    CustomNHITS(h=horizon, input_size=input_size, max_steps=100, alias='NHITS-ReduceLROnPlateau-scheduler'),
]
nf = NeuralForecast(models=models, freq='ME')
nf.fit(AirPassengersPanel_train)
preds = nf.predict(futr_df=AirPassengersPanel_test)

Seed set to 1
Seed set to 1
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs

  | Name         | Type          | Params | Mode 
-------------------------------------------------------
0 | loss         | MAE           | 0      | train
1 | padder_train | ConstantPad1d | 0      | train
2 | scaler       | TemporalNorm  | 0      | train
3 | blocks       | ModuleList    | 2.4 M  | train
-------------------------------------------------------
2.4 M     Trainable params
0         Non-trainable params
2.4 M     Total params
9.751     Total estimated model params size (MB)
34        Modules in train mode
0         Modules in eval mode
`Trainer.fit` stopped: `max_steps=100` reached.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs

  | Name         | Type          | Params | Mode 
-------------------------------------------------------
0 | loss         | MAE           | 0      | train
1 | padder_train | ConstantPad1d | 0      | train
2 | scaler       | TemporalNorm  | 0      | train
3 | blocks       | ModuleList    | 2.4 M  | train
-------------------------------------------------------
2.4 M     Trainable params
0         Non-trainable params
2.4 M     Total params
9.751     Total estimated model params size (MB)
34        Modules in train mode
0         Modules in eval mode
`Trainer.fit` stopped: `max_steps=100` reached.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs

Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.50it/s, v_num=85, train_loss_step=14.20, train_loss_epoch=14.20]Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.49it/s, v_num=85, train_loss_step=14.20, train_loss_epoch=14.20]
Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.78it/s, v_num=86, train_loss_step=24.10, train_loss_epoch=24.10]Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.77it/s, v_num=86, train_loss_step=24.10, train_loss_epoch=24.10]
Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 33.39it/s]
Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 246.29it/s]

plot_series(AirPassengersPanel_train, preds)
We can clearly notice the prediction outputs are different due to the change in configure_optimizers().