Update breastmnist4.py

breastmnist4.py

@@ -1,17 +1,13 @@
+import pandas as pd
 import datasets
-import numpy as np
-import numpy as np
-import requests
-import datasets
-import os
 
 class BreastMNIST(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
-
+    
     def _info(self):
         return datasets.DatasetInfo(
             features=datasets.Features({
-                "image": datasets.Array3D(shape=(28, 28, 1), dtype="uint8"),  # Adjust shape if necessary
+                "image": datasets.Array3D(shape=(28, 28, 1), dtype="float32"),  # Use Array3D for image shapes
                 "label": datasets.ClassLabel(names=["benign", "malignant"])   # Adjust based on your labels
             }),
             description="BreastMNIST dataset containing medical imaging data",
@@ -19,60 +15,22 @@ class BreastMNIST(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # Here you would define how to split your dataset
+        train_data = "breastmnist_train.parquet"
+        test_data = "breastmnist_test.parquet"
+        val_data = "breastmnist_val.parquet"
+
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"split": "validation"},
-            ),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_data}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_data}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_data}),
         ]
 
-    def _download_data(self, url):
-        # Download the dataset and save it locally
-        response = requests.get(url)
-        if response.status_code == 200:
-            with open("breastmnist4.npz", "wb") as f:
-                f.write(response.content)
-        else:
-            raise ValueError(f"Failed to download the dataset from {url}")
-
-    def _generate_examples(self, filepath , split):
-        # Define the URL for the dataset
-        url =filepath
-
-        # Download the dataset
-        self._download_data(url)
-
-        # Load the dataset from the local file
-        data = np.load("breastmnist4.npz", allow_pickle=True)
-
-        # Yield examples based on the split
-        if split == "train":
-            for i in range(len(data['train_images'])):
-                yield i, {
-                    "image": data['train_images'][i],
-                    "label": data['train_labels'][i],
-                }
-        elif split == "test":
-            for i in range(len(data['test_images'])):
-                yield i, {
-                    "image": data['test_images'][i],
-                    "label": data['test_labels'][i],
-                }
-        elif split == "validation":
-            for i in range(len(data['val_images'])):
-                yield i, {
-                    "image": data['val_images'][i],
-                    "label": data['val_labels'][i],
-                }
-
-        # Optionally, remove the downloaded file after loading
-        os.remove("breastmnist4.npz")
+    def _generate_examples(self, filepath):
+        # Load the Parquet file
+        df = pd.read_parquet(filepath)
+        
+        for idx, row in df.iterrows():
+            yield idx, {
+                "image": row["image"],  # Ensure this matches the expected shape
+                "label": row["label"],
+            }