Improve dataset documentation.

docs/source/api.rst

@@ -5,9 +5,20 @@ ProtoTorch API Reference
 
 Datasets
 --------------------------------------
+
+Common Datasets
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: prototorch.datasets
    :members:
-   :undoc-members:
+
+
+Abstract Datasets
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Abstract Datasets are used to build your own datasets.
+
+.. autoclass:: prototorch.datasets.abstract.NumpyDataset
+   :members:
 
 Functions
 --------------------------------------

docs/source/conf.py

@@ -46,6 +46,7 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinx_rtd_theme",
     "sphinxcontrib.katex",
+    'sphinx_autodoc_typehints',
 ]
 
 # katex_prerender = True

prototorch/datasets/__init__.py

@@ -4,3 +4,5 @@ from .abstract import NumpyDataset
 from .iris import Iris
 from .spiral import Spiral
 from .tecator import Tecator
+
+__all__ = ['Iris', 'Spiral', 'Tecator']

prototorch/datasets/iris.py

@@ -5,12 +5,35 @@ URL:
 
 """
 
+from typing import Sequence
+
 from prototorch.datasets.abstract import NumpyDataset
 from sklearn.datasets import load_iris
 
 
 class Iris(NumpyDataset):
-    def __init__(self, dims=None):
+    """
+    Iris Dataset by Ronald Fisher introduced in 1936.
+
+    The dataset contains four measurements from flowers of three species of iris.
+
+    .. list-table:: Iris
+        :header-rows: 1
+
+        * - dimensions
+          - classes
+          - training size
+          - validation size
+          - test size
+        * - 4
+          - 2
+          - 150
+          - 0
+          - 0
+
+    :param dims: select a subset of dimensions
+    """
+    def __init__(self, dims: Sequence[int] = None):
         x, y = load_iris(return_X_y=True)
         if dims:
             x = x[:, dims]

prototorch/datasets/spiral.py

@@ -27,7 +27,27 @@ def make_spiral(n_samples=500, noise=0.3):
 
 
 class Spiral(torch.utils.data.TensorDataset):
-    """Spiral dataset for binary classification."""
+    """Spiral dataset for binary classification.
-    def __init__(self, n_samples=500, noise=0.3):
+
+    This datasets consists of two spirals of two different classes.
+
+    .. list-table:: Spiral
+        :header-rows: 1
+
+        * - dimensions
+          - classes
+          - training size
+          - validation size
+          - test size
+        * - 2
+          - 2
+          - n_samples
+          - 0
+          - 0
+
+    :param n_samples: number of random samples
+    :param noise: noise added to the spirals
+    """
+    def __init__(self, n_samples: int = 500, noise: float = 0.3):
         x, y = make_spiral(n_samples, noise)
         super().__init__(torch.Tensor(x), torch.LongTensor(y))

prototorch/datasets/tecator.py

@@ -40,15 +40,29 @@ import os
 
 import numpy as np
 import torch
-from torchvision.datasets.utils import download_file_from_google_drive
-
 from prototorch.datasets.abstract import ProtoDataset
+from torchvision.datasets.utils import download_file_from_google_drive
 
 
 class Tecator(ProtoDataset):
     """
-    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__
+    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__ for classification.
-    for classification.
+
+    The dataset contains wavelength measurements of meat.
+
+    .. list-table:: Tecator
+        :header-rows: 1
+
+        * - dimensions
+          - classes
+          - training size
+          - validation size
+          - test size
+        * - 100
+          - 2
+          - 129
+          - 43
+          - 43
     """
 
     _resources = [

prototorch/functions/losses.py

@@ -55,4 +55,5 @@ def lvq21_loss(distances, target_labels, prototype_labels):
     """
     dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
     mu = dp - dm
+
     return mu

setup.py

@@ -32,6 +32,7 @@ DOCS = [
     "sphinx",
     "sphinx_rtd_theme",
     "sphinxcontrib-katex",
+    "sphinx-autodoc-typehints",
 ]
 EXAMPLES = [
     "sklearn",