From 06416cffbca9dc240e1c5c1cc56e2fd63de7c439 Mon Sep 17 00:00:00 2001
From: Achille Mbogol Touye <achille.mbogol-touye@univ-grenoble-alpes.fr>
Date: Tue, 26 Sep 2023 17:52:38 +0200
Subject: [PATCH] help function
---
WineQuality-DNN_Reg-lightning/data_load.py | 67 ++++++++++++++++++++++
1 file changed, 67 insertions(+)
create mode 100644 WineQuality-DNN_Reg-lightning/data_load.py
diff --git a/WineQuality-DNN_Reg-lightning/data_load.py b/WineQuality-DNN_Reg-lightning/data_load.py
new file mode 100644
index 0000000..9102923
--- /dev/null
+++ b/WineQuality-DNN_Reg-lightning/data_load.py
@@ -0,0 +1,67 @@
+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)
+ }
+
+
--
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