feat(Pytorch): added custom model to pytorch-forecasting (#8)

* feat: Refractored and created new model. Pipeline not ready yet.

* feat: Implemented and refactored a data pipeline.

* ref: Refractored to make more sense.

* feat: Training works now with models that you can change.

* feat: Added predict function but without working instructions.

* feat: gitignore.

lightning/create_dataset.py

@@ -0,0 +1,64 @@
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+
+from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer
+from pytorch_forecasting.data import GroupNormalizer
+from pytorch_forecasting.metrics import QuantileLoss
+
+import sys
+sys.path.insert(0, '..')
+
+from load_data import load_files
+
+print("success")
+
+def load_format_data(data_dir):
+    print("Data Starting    ===>", end="  ")
+    # load data
+    data = load_files(data_dir, add_features=True, log_returns=False, narrow_format=True)
+    # need to treat time as an independent column
+    data = data.reset_index().rename({'index':'time'}, axis = 'columns')
+    # we need a `time_idx` column for pytorch-forecasting, so we convert the time column to a time index by encoding the dates as consecutive days from the first date.
+    data['time_idx'] = (data['time']-data['time'].min()).astype('timedelta64[D]').astype(int)+1
+    # volume needs some love before we can use it
+    data.drop(columns=['volume'], inplace=True)
+
+    data['month'] = data['month'].astype(str)
+    data['day_month'] = data['day_month'].astype(str)
+    data['day_week'] = data['day_week'].astype(str)
+    
+    
+    print("<===  Data Loaded")
+    print(data.head(3))
+    print(data.describe())
+    print("")
+
+    
+    
+    
+    return data
+
+
+def create_dataloaders(data, kwargs):
+    print("DataLoader Starting ===>", end="  ")
+    # training_cutoff = "YYYY-MM-DD"  # day for cutoff
+    # training_cutoff = data["time_idx"].max() - max_prediction_length
+    batch_size = kwargs['batch_size']
+    del kwargs['batch_size']
+
+    training_dataset = TimeSeriesDataSet(
+        data, # data[lambda x: x.date < training_cutoff],
+        **kwargs
+    )
+
+    #%%
+    # create validation and training dataset
+    validation = TimeSeriesDataSet.from_dataset(training_dataset, data, predict=True, stop_randomization=True)
+    train_dataloader = training_dataset.to_dataloader(train=True, batch_size=batch_size, num_workers=0)
+    val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=0)
+
+
+    print("<===  DataLoader Created")
+    
+    
+    return training_dataset, train_dataloader, val_dataloader