clean_up_eval

Nixtla · Feb 3, 2025 · eddf9b3 · eddf9b3
1 parent 512e071
commit eddf9b3
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diff --git a/action_files/test_models/src/models.py b/action_files/test_models/src/models.py
@@ -1,30 +1,17 @@
-import os
 import time
 
 import fire
-# import numpy as np
 import pandas as pd
-# import pytorch_lightning as pl
-# import torch
 
-# import neuralforecast
 from neuralforecast.core import NeuralForecast
 
-# from neuralforecast.models.gru import GRU
 from neuralforecast.models.rnn import RNN
 from neuralforecast.models.tcn import TCN
-# from neuralforecast.models.lstm import LSTM
-# from neuralforecast.models.dilated_rnn import DilatedRNN
 from neuralforecast.models.deepar import DeepAR
-# from neuralforecast.models.mlp import MLP
 from neuralforecast.models.nhits import NHITS
 from neuralforecast.models.nbeats import NBEATS
-# from neuralforecast.models.nbeatsx import NBEATSx
 from neuralforecast.models.tft import TFT
 from neuralforecast.models.vanillatransformer import VanillaTransformer
-# from neuralforecast.models.informer import Informer
-# from neuralforecast.models.autoformer import Autoformer
-# from neuralforecast.models.patchtst import PatchTST
 from neuralforecast.models.dlinear import DLinear
 from neuralforecast.models.bitcn import BiTCN   
 from neuralforecast.models.tide import TiDE
@@ -33,20 +20,14 @@
 
 from neuralforecast.auto import (
     AutoMLP, 
-    # AutoNHITS, 
-    # AutoNBEATS, 
     AutoDilatedRNN, 
-    # AutoTFT
 )
 
 from neuralforecast.losses.pytorch import SMAPE, MAE, IQLoss
 from ray import tune
 
 from src.data import get_data
 
-os.environ['NIXTLA_ID_AS_COL'] = '1'
-
-
 def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
     train, horizon, freq, seasonality = get_data('data/', dataset, group)
     train['ds'] = pd.to_datetime(train['ds'])

diff --git a/action_files/test_models/src/models2.py b/action_files/test_models/src/models2.py
@@ -1,47 +1,25 @@
-import os
 import time
 
 import fire
-# import numpy as np
 import pandas as pd
-# import pytorch_lightning as pl
-# import torch
 
-# import neuralforecast
 from neuralforecast.core import NeuralForecast
 
 from neuralforecast.models.gru import GRU
-# from neuralforecast.models.rnn import RNN
-# from neuralforecast.models.tcn import TCN
 from neuralforecast.models.lstm import LSTM
 from neuralforecast.models.dilated_rnn import DilatedRNN
-# from neuralforecast.models.deepar import DeepAR
-# from neuralforecast.models.mlp import MLP
-# from neuralforecast.models.nhits import NHITS
-# from neuralforecast.models.nbeats import NBEATS
 from neuralforecast.models.nbeatsx import NBEATSx
-# from neuralforecast.models.tft import TFT
-# from neuralforecast.models.vanillatransformer import VanillaTransformer
-# from neuralforecast.models.informer import Informer
-# from neuralforecast.models.autoformer import Autoformer
-# from neuralforecast.models.patchtst import PatchTST
 
 from neuralforecast.auto import (
-    # AutoMLP, 
     AutoNHITS, 
     AutoNBEATS, 
-    # AutoDilatedRNN, 
-    # AutoTFT
 )
 
 from neuralforecast.losses.pytorch import MAE
 from ray import tune
 
 from src.data import get_data
 
-os.environ['NIXTLA_ID_AS_COL'] = '1'
-
-
 def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
     train, horizon, freq, seasonality = get_data('data/', dataset, group)
     train['ds'] = pd.to_datetime(train['ds'])
@@ -60,7 +38,6 @@ def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
         AutoNBEATS(h=horizon, loss=MAE(), config=config_nbeats, num_samples=2, cpus=1),
         AutoNHITS(h=horizon, loss=MAE(), config=config_nbeats, num_samples=2, cpus=1),
         NBEATSx(h=horizon, input_size=2 * horizon, loss=MAE(), max_steps=1000),
-        # PatchTST(h=horizon, input_size=2 * horizon, patch_len=4, stride=4, loss=MAE(), scaler_type='minmax1', windows_batch_size=512, max_steps=1000, val_check_steps=500),
     ]
 
     # Models

diff --git a/action_files/test_models/src/multivariate_evaluation.py b/action_files/test_models/src/multivariate_evaluation.py
@@ -61,6 +61,5 @@ def evaluate(model: str, dataset: str, group: str):
     df_evaluation.columns = ['dataset', 'model', 'metric', 'val']
     df_evaluation = df_evaluation.set_index(['dataset', 'metric', 'model']).unstack().round(3)
     df_evaluation = df_evaluation.droplevel(0, 1).reset_index()
-    # df_evaluation['AutoARIMA'] = [666.82, 15.35, 3.000]
     df_evaluation.to_csv('data/evaluation.csv')
     print(df_evaluation.T)
diff --git a/action_files/test_models/src/multivariate_models.py b/action_files/test_models/src/multivariate_models.py
@@ -10,7 +10,6 @@
 from neuralforecast.models.tsmixer import TSMixer
 from neuralforecast.models.tsmixerx import TSMixerx
 from neuralforecast.models.itransformer import iTransformer
-# # from neuralforecast.models.stemgnn import StemGNN
 from neuralforecast.models.mlpmultivariate import MLPMultivariate
 from neuralforecast.models.timemixer import TimeMixer
 
@@ -30,7 +29,6 @@ def main(dataset: str = 'multivariate', group: str = 'ETTm2') -> None:
         TSMixer(h=horizon, n_series=7, input_size=2 * horizon, loss=MAE(), dropout=0.0, max_steps=1000, val_check_steps=100, windows_batch_size=64, inference_windows_batch_size=64),
         TSMixerx(h=horizon, n_series=7, input_size=2*horizon, loss=MAE(), dropout=0.0, max_steps=1000, val_check_steps=100, windows_batch_size=64, inference_windows_batch_size=64),
         iTransformer(h=horizon, n_series=7, input_size=2 * horizon, loss=MAE(), dropout=0.0, max_steps=500, val_check_steps=100, windows_batch_size=64, inference_windows_batch_size=64),
-        # StemGNN(h=horizon, n_series=7, input_size=2*horizon, loss=MAE(), dropout_rate=0.0, max_steps=1000, val_check_steps=500, windows_batch_size=64, inference_windows_batch_size=64),
         MLPMultivariate(h=horizon, n_series=7, input_size=2*horizon, loss=MAE(), max_steps=1000, val_check_steps=100, windows_batch_size=64, inference_windows_batch_size=64),
         TimeMixer(h=horizon, n_series=7, input_size=2*horizon, loss=MAE(), dropout=0.0, max_steps=500, val_check_steps=100, windows_batch_size=64, inference_windows_batch_size=64)
     ]