update readme

README.md

@@ -1 +1,56 @@
-# point-cloud-mnist-2D
+# Point CLoud MNIST
+
+A point cloud version of the original MNIST.
+
+![sample](docs/sample.png)
+
+## Getting Started
+
+```python
+from datasets.load import load_dataset
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# load dataset
+dataset = load_dataset("cgarciae/point-cloud-mnist")
+dataset.set_format("np")
+
+
+# get numpy arrays
+X_train = dataset["train"]["points"]
+y_train = dataset["train"]["label"]
+X_test = dataset["test"]["points"]
+y_test = dataset["test"]["label"]
+
+
+# plot some training samples
+figure = plt.figure(figsize=(10, 10))
+for i in range(3):
+    for j in range(3):
+        k = 3 * i + j
+        plt.subplot(3, 3, k + 1)
+        idx = np.random.randint(0, len(X_train))
+
+        plt.title(f"{y_train[idx]}")
+        plt.scatter(X_train[idx, :, 0], X_train[idx, :, 1])
+
+plt.show()
+```
+
+## Format
+
+* `points`: `(batch, point, 3)` array of uint8.
+* `label`: `(batch, 1)` array of uint8.
+
+Where `point` is the number of points in the point cloud. Points have no ordered and are shuffled when creating the data. Each point has the structure `[x, y, v]` where:
+
+* `x`: is the x coordinate of the point in the image.
+* `y`: is the y coordinate of the point in the image.
+* `v`: is the value of the pixel at the point in the image.
+
+Samples are padded with `0`s such that `point = 351` since its the largest number of non-zero pixels per image in the original dataset. You can tell apart padding point because they are the only ones where `v = 0`. 
+
+Here is the distribution of non-zero pixels in the MNIST:
+
+![distribution](docs/lengths.png)

poetry.lock

@@ -110,6 +110,14 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
+[[package]]
+name = "cycler"
+version = "0.11.0"
+description = "Composable style cycles"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "datasets"
 version = "1.14.0"
@@ -137,13 +145,13 @@ xxhash = "*"
 apache-beam = ["apache-beam (>=2.26.0)"]
 audio = ["librosa"]
 benchmarks = ["numpy (==1.18.5)", "tensorflow (==2.3.0)", "torch (==1.6.0)", "transformers (==3.0.2)"]
-dev = ["absl-py", "pytest", "pytest-datadir", "pytest-xdist", "apache-beam (>=2.26.0)", "elasticsearch", "aiobotocore", "boto3", "botocore", "fsspec", "moto[s3,server] (==2.0.4)", "rarfile (>=4.0)", "s3fs (==2021.08.1)", "tensorflow (>=2.3)", "torch", "torchaudio", "transformers", "bs4", "conllu", "langdetect", "lxml", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "tldextract", "zstandard", "bert-score (>=0.3.6)", "rouge-score", "sacrebleu", "scipy", "seqeval", "scikit-learn", "jiwer", "sentencepiece", "toml (>=0.10.1)", "requests-file (>=1.5.1)", "tldextract (>=3.1.0)", "texttable (>=1.6.3)", "Werkzeug (>=1.0.1)", "six (>=1.15.0,<1.16.0)", "black (==21.4b0)", "flake8 (==3.7.9)", "isort", "pyyaml (>=5.3.1)", "importlib-resources"]
+dev = ["absl-py", "pytest", "pytest-datadir", "pytest-xdist", "apache-beam (>=2.26.0)", "elasticsearch", "aiobotocore", "boto3", "botocore", "fsspec", "moto[server,s3] (==2.0.4)", "rarfile (>=4.0)", "s3fs (==2021.08.1)", "tensorflow (>=2.3)", "torch", "torchaudio", "transformers", "bs4", "conllu", "langdetect", "lxml", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "tldextract", "zstandard", "bert-score (>=0.3.6)", "rouge-score", "sacrebleu", "scipy", "seqeval", "scikit-learn", "jiwer", "sentencepiece", "toml (>=0.10.1)", "requests-file (>=1.5.1)", "tldextract (>=3.1.0)", "texttable (>=1.6.3)", "Werkzeug (>=1.0.1)", "six (>=1.15.0,<1.16.0)", "black (==21.4b0)", "flake8 (==3.7.9)", "isort", "pyyaml (>=5.3.1)", "importlib-resources"]
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 quality = ["black (==21.4b0)", "flake8 (==3.7.9)", "isort", "pyyaml (>=5.3.1)"]
 s3 = ["fsspec", "boto3", "botocore", "s3fs"]
 tensorflow = ["tensorflow (>=2.2.0)"]
 tensorflow_gpu = ["tensorflow-gpu (>=2.2.0)"]
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+tests = ["absl-py", "pytest", "pytest-datadir", "pytest-xdist", "apache-beam (>=2.26.0)", "elasticsearch", "aiobotocore", "boto3", "botocore", "fsspec", "moto[server,s3] (==2.0.4)", "rarfile (>=4.0)", "s3fs (==2021.08.1)", "tensorflow (>=2.3)", "torch", "torchaudio", "transformers", "bs4", "conllu", "langdetect", "lxml", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "tldextract", "zstandard", "bert-score (>=0.3.6)", "rouge-score", "sacrebleu", "scipy", "seqeval", "scikit-learn", "jiwer", "sentencepiece", "toml (>=0.10.1)", "requests-file (>=1.5.1)", "tldextract (>=3.1.0)", "texttable (>=1.6.3)", "Werkzeug (>=1.0.1)", "six (>=1.15.0,<1.16.0)", "importlib-resources"]
 torch = ["torch"]
 
 [[package]]
@@ -273,6 +281,30 @@ docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
 perf = ["ipython"]
 testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
 
+[[package]]
+name = "kiwisolver"
+version = "1.3.2"
+description = "A fast implementation of the Cassowary constraint solver"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "matplotlib"
+version = "3.4.3"
+description = "Python plotting package"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+cycler = ">=0.10"
+kiwisolver = ">=1.0.1"
+numpy = ">=1.16"
+pillow = ">=6.2.0"
+pyparsing = ">=2.2.1"
+python-dateutil = ">=2.7"
+
 [[package]]
 name = "multidict"
 version = "5.2.0"
@@ -343,6 +375,14 @@ category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 
+[[package]]
+name = "pillow"
+version = "8.4.0"
+description = "Python Imaging Library (Fork)"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "platformdirs"
 version = "2.4.0"
@@ -540,7 +580,7 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
 [metadata]
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 python-versions = "^3.7"
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 aiohttp = [
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+]
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@@ -808,6 +921,49 @@ pathspec = [
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 ]
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point-cloud-mnist.py

@@ -41,7 +41,7 @@ class MNIST(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
             name="point-cloud-mnist",
-            version=datasets.Version("1.0.0"),
+            version=datasets.Version(_VERSION),
             description=_DESCRIPTION,
         )
     ]

pyproject.toml

@@ -12,6 +12,7 @@ typer = "^0.0.8"
 idx2numpy = "^1.2.2"
 tqdm = "^4.42.1"
 datasets = "^1.14.0"
+matplotlib = "^3.4.3"
 
 [tool.poetry.dev-dependencies]
 debugpy = "^1.5.1"

test.py

@@ -1,6 +1,29 @@
-import datasets
 from datasets.load import load_dataset
+import matplotlib.pyplot as plt
+import numpy as np
 
+
+# load dataset
 dataset = load_dataset("cgarciae/point-cloud-mnist")
+dataset.set_format("np")
+
+
+# get numpy arrays
+X_train = dataset["train"]["points"]
+y_train = dataset["train"]["label"]
+X_test = dataset["test"]["points"]
+y_test = dataset["test"]["label"]
+
+
+# plot some training samples
+figure = plt.figure(figsize=(10, 10))
+for i in range(3):
+    for j in range(3):
+        k = 3 * i + j
+        plt.subplot(3, 3, k + 1)
+        idx = np.random.randint(0, len(X_train))
+
+        plt.title(f"{y_train[idx]}")
+        plt.scatter(X_train[idx, :, 0], X_train[idx, :, 1])
 
-dataset
+plt.show()