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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
797791d2f4
* feat: Started implementing pytorch-forecasting. * feat(Forecasting): pytorch-forecasting scaffolding is now working, added narrow data format, fixed missing `time` column index name Co-authored-by: Daniel Szemerey <szemy2@gmail.com>
48 lines
1.0 KiB
Python
48 lines
1.0 KiB
Python
from load_data import load_files
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import pandas as pd
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# from tensorflow import keras
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from utils.normalize import normalize
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# import tensorflow as tf
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from utils.visualize import visualize_loss
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from torch.utils.data import DataLoader, random_split
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from model_lightning import LitManualAutoEncoder
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import pytorch_lightning as pl
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#%%
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data = load_files('data/', False)
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data.reset_index(drop=True, inplace=True)
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data = data[[column for column in data.columns if not column.endswith('volume')]]
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data.head()
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#%%
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ticker_to_predict = 'ETH_returns'
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learning_rate = 0.002
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batch_size = 64
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epochs = 100
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split_fraction = 0.715
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train_split = int(split_fraction * int(data.shape[0]))
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past = 10
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future = 1
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start = past + future
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end = start + train_split
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# train = DataLoader(train, batch_size=32)
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# test = DataLoader(test, batch_size=32)
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# val = DataLoader(val, batch_size=32)
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# init model
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ae = LitManualAutoEncoder()
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# Initialize a trainer
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trainer = pl.Trainer(gpus=1, max_epochs=3, progress_bar_refresh_rate=20)
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# Train the model ⚡
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# trainer.fit(ae, train, val) |