mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-29 03:37:45 +00:00
Mark Aron Szulyovszky
442915f847
* feat(Data): create DataSource, DataCollection, added hourly crypto data * fix(Data): hourly data format, loading & config
67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import pytorch_lightning as pl
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from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
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import torch
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from torch import nn
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import warnings
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warnings.filterwarnings("ignore")
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import sys
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sys.path.insert(0, '..')
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from data_loader.load_data import load_files
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def train_model(model, train_dataloader, val_dataloader, kwargs):
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print()
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print("Creating Trainer ===>", end=" ")
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# define trainer with early stopping
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early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min")
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lr_logger = LearningRateMonitor()
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trainer = pl.Trainer(
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**kwargs,
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callbacks=[lr_logger, early_stop_callback],
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)
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print("<=== Trainer Created")
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print("Finding Optimal LR ===>", end=" ")
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# find optimal learning rate (set limit_train_batches to 1.0 and log_interval = -1)
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res = trainer.tuner.lr_find(
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model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,
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)
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print(f"<=== suggested learning rate: {res.suggestion()}")
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fig = res.plot(show=True, suggest=True)
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fig.show()
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print("Training the model ===>", end=" ")
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# fit the model
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trainer.fit(
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model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
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)
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print("<=== Training Finished")
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return trainer
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def predict(trainer, model, val_dataloader):
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pass
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# best_model_path = trainer.checkpoint_callback.best_model_path
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# best_model = model.load_from_checkpoint(best_model_path)
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# # calcualte mean absolute error on validation set
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# actuals = torch.cat([y[0] for x, y in iter(val_dataloader)])
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# predictions = best_model.predict(val_dataloader)
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# (actuals - predictions).abs().mean()
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# raw_predictions, x = best_model.predict(val_dataloader, mode="raw", return_x=True)
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# for idx in range(10): # plot 10 examples
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# best_model.plot_prediction(x, raw_predictions, idx=idx, add_loss_to_title=True)
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# predictions, x = best_model.predict(val_dataloader, return_x=True)
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# predictions_vs_actuals = best_model.calculate_prediction_actual_by_variable(x, predictions)
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# best_model.plot_prediction_actual_by_variable(predictions_vs_actuals)
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