help function

WineQuality-DNN_Reg-lightning/data_load.py

+import torch
+import pandas as pd
+import lightning.pytorch as pl
+
+
+class WineQualityDataset(pl.LightningDataModule):
+    """Wine Quality dataset."""
+
+    def __init__(self, csv_file, transform=None):
+        
+        """
+        Args:
+            csv_file (string): Path to the csv file.
+            
+            transform (callable, optional): Optional transform to be applied on a sample.
+        """
+        super().__init__()
+        self.csv_file=csv_file
+        self.data = pd.read_csv(self.csv_file, header=0, sep=';')
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.data)
+ 
+    def __getitem__(self, idx):
+        features = self.data.iloc[idx, :-1].values.astype('float32')
+        target   = self.data.iloc[idx, -1:].values.astype('float32')
+        sample = {'features':features, 'quality':target}
+        
+        if self.transform:
+            sample = self.transform(sample)
+        
+        return sample
+        
+
+class Normalize(WineQualityDataset):
+    """normalize data"""
+    def __init__(self, csv_file):
+        mean,std=self.compute_mean_and_std(csv_file)
+        self.mean=mean
+        self.std=std
+
+    def compute_mean_and_std(self, csv_file):
+        """Compute the mean and std for each feature."""
+        dataset= WineQualityDataset(csv_file)
+        mean   = dataset.data.iloc[:,:-1].mean(axis=0).values.astype('float32')
+        std    = dataset.data.iloc[:,:-1].std(axis=0).values.astype('float32')
+        return mean,std    
+        
+    
+    def __call__(self, sample):
+        features, target = sample['features'],sample['quality']
+        norm_features = (features - self.mean) / self.std     # normalize features
+        return {'features':norm_features,
+                 'quality':target
+               }
+
+class ToTensor(object):
+    """Convert ndarrays in sample to Tensors."""
+
+    def __call__(self, sample):
+        features, target = sample['features'], sample['quality']        
+        return {'features': torch.from_numpy(features),
+                'quality' : torch.from_numpy(target)
+               }    
+
+