Started adding files and folders we need. Initial structure, example of numpydoc docstring style, and some other in-progress work

Author: Seilmast

.gitignore

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-Data/
-Results/
-Experiments/
+Data/*
+Results/*
+Experiments/*
+env/*

main.py

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+import torch as th
+import torch.nn as nn 
+
+import argparse
+from utils import load_metric
+
+
+
+
+
+
+
+
+
+
+
+
+def main():
+    
+    parser = argparse.ArgumentParser(
+        prog='',
+        description='',
+        epilog='',
+    )
+    
+    parser.add_argument('--epoch', type=int, default=20, help='Amount of training epochs the model will do')
+    
+    args = parser.parse_args()
+
+
+
+
+    for epoch in range(args.epoch):
+        
+        #Training loop start
+        
+        #Eval loop start
+    
+
+
+
+if __name__ == '__main__':
+    main()

utils/__init__.py

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+from .load_metric import load_metric

utils/load_metric.py

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+import torch.nn as nn 
+from metrics import EntropyPrediction
+
+def load_metric(metricname:str) -> nn.Module:
+    '''
+    This function returns an instance of a class inhereting from nn.Module. 
+    This class returns the given metric given a set of label - prediction pairs. 
+    
+    Parameters
+    ----------
+    metricname: string
+        string naming the metric to return. 
+    
+    Returns
+    -------
+    Class
+        Returns an instance of a class inhereting from nn.Module.
+    
+    Raises
+    ------
+    ValueError
+        When the metricname parameter does not match any implemented metric, raise this error along with a descriptive message. 
+    '''
+    if metricname == 'EntropyPrediction':
+        return EntropyPrediction()
+    else:
+        raise ValueError(f'Metric: {metricname} has not been implemented. \nCheck the documentation for implemented metrics, or check your spelling')

utils/load_model.py

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+import torch.nn as nn 
+
+def load_model(modelname:str) -> nn.Module:
+    
+    raise ValueError(f'Metric: {modelname} has not been implemented. \nCheck the documentation for implemented metrics, or check your spelling')

utils/metrics/EntropyPred.py

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+import torch.nn as nn 
+
+
+class EntropyPrediction(nn.Module):
+    def __init__(self):
+        super().__init__()
+        
+    def __call__(self, y_true, y_false):
+        
+        return

utils/metrics/__init__.py

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+from .EntropyPred import EntropyPrediction