Convert dataset to Parquet (#1)

- Convert dataset to Parquet (6129f4ee0a2a8d07dc595171ed0e1583698a6601)
- Delete loading script (fd76f4ba4eaac7926d38acda4303b68423c16219)
- Delete data file (8e6f2247a3d140ea67d2caad94a869329693ed93)
- Delete data file (53444c520554488947c4e92c57bb36620e92e04d)
- Delete loading script auxiliary file (3cb87bc458705410bf77255a00f1cd617b09de48)
- Delete data file (b1a7d143d95ba59ea6d45c2f5fa8020dc43081a9)

.gitignore

@@ -1 +0,0 @@
-__pycache__/

README.md

@@ -1,7 +1,43 @@
 ---
+size_categories:
+- n<1K
 task_categories:
 - image-classification
 pretty_name: MNIST
-size_categories:
-- n<1K
----
+dataset_info:
+  features:
+  - name: image
+    dtype:
+      image:
+        mode: L
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+          '2': '2'
+          '3': '3'
+          '4': '4'
+          '5': '5'
+          '6': '6'
+          '7': '7'
+          '8': '8'
+          '9': '9'
+  splits:
+  - name: train
+    num_bytes: 17223300.0
+    num_examples: 60000
+  - name: test
+    num_bytes: 2875182.0
+    num_examples: 10000
+  download_size: 18157556
+  dataset_size: 20098482.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+---

data/{test.csv → test-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eff039c809f429c2de65bc69adc3f7a6ebd2ecd286a7e7e6ec39f114b64e7443
-size 18299664
+oid sha256:0ba50678b3c74a32dbda3e0b7aa2bc8ddd0c772a62e1533d4a540b1caa6dc999
+size 2595915

data/{train.csv → train-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:71f0e2ff351343e4d1217bc2e73c3127e219789f4df0fafa08ea19ad38057da4
-size 109636215
+oid sha256:c190ff735dd00eb84abf76313046e87998677797c4ae8e0c9628ee071687e875
+size 15561641

mnist.py

@@ -1,86 +0,0 @@
-import csv
-import datasets
-import numpy as np
-
-_HOMEPAGE = 'http://yann.lecun.com/exdb/mnist/'
-_LICENSE = '-'
-_DESCRIPTION = '''\
-The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples.
-It is a subset of a larger set available from NIST.
-The digits have been size-normalized and centered in a fixed-size image.
-'''
-_CITATION = '''-'''
-_NAMES = list('0123456789')
-
-
-class MNISTConfig(datasets.BuilderConfig):
-    '''Builder Config for MNIST'''
-
-    def __init__(
-        self, description, homepage, **kwargs
-    ):
-        super(MNISTConfig, self).__init__(
-            version=datasets.Version('1.0.0', ''),
-            **kwargs
-        )
-        self.description = description
-        self.homepage = homepage
-        self.train_image_url = 'data/train.csv'
-        self.test_image_url = 'data/test.csv'
-
-
-class MNIST(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        MNISTConfig(
-            description=_DESCRIPTION,
-            homepage=_HOMEPAGE
-        )
-    ]
-
-    def _info(self):
-        features = datasets.Features({
-            'image': datasets.Image(mode='L', decode=True, id=None),
-            'label': datasets.ClassLabel(names=_NAMES)
-        })
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager):
-        train_image_path = dl_manager.download(
-            self.config.train_image_url
-        )
-        test_image_path = dl_manager.download(
-            self.config.test_image_url
-        )
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    'data_path': f'{train_image_path}'
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    'data_path': f'{test_image_path}'
-                }
-            )
-        ]
-
-    def _generate_examples(self, data_path):
-        idx = 0
-        with open(data_path, newline='', encoding='utf-8') as csvfile:
-            csvreader = csv.reader(csvfile)
-            next(csvreader)
-            for row in csvreader:
-                example = {
-                    'image': np.array(row[1:], np.uint8).reshape(28, 28),
-                    'label': row[0]
-                }
-                yield idx, example
-                idx += 1