166 lines
4.0 KiB
Python
166 lines
4.0 KiB
Python
"""Thin wrappers for a few scikit-learn datasets.
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URL:
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https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets
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"""
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from __future__ import annotations
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import warnings
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from typing import Sequence
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from sklearn.datasets import (
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load_iris,
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make_blobs,
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make_circles,
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make_classification,
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make_moons,
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)
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from prototorch.datasets.abstract import NumpyDataset
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class Iris(NumpyDataset):
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"""Iris Dataset by Ronald Fisher introduced in 1936.
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The dataset contains four measurements from flowers of three species of iris.
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.. list-table:: Iris
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:header-rows: 1
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* - dimensions
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- classes
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- training size
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- validation size
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- test size
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* - 4
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- 3
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- 150
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- 0
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- 0
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:param dims: select a subset of dimensions
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"""
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def __init__(self, dims: Sequence[int] | None = None):
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x, y = load_iris(return_X_y=True)
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if dims is not None:
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x = x[:, dims]
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super().__init__(x, y)
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class Blobs(NumpyDataset):
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"""Generate isotropic Gaussian blobs for clustering.
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Read more at
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https://scikit-learn.org/stable/datasets/sample_generators.html#sample-generators.
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"""
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def __init__(
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self,
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num_samples: int = 300,
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num_features: int = 2,
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seed: None | int = 0,
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):
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x, y = make_blobs(
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num_samples,
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num_features,
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centers=None,
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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class Random(NumpyDataset):
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"""Generate a random n-class classification problem.
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Read more at
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https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html.
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Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
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"""
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def __init__(
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self,
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num_samples: int = 300,
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num_features: int = 2,
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num_classes: int = 2,
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num_clusters: int = 2,
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num_informative: None | int = None,
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separation: float = 1.0,
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seed: None | int = 0,
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):
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if not num_informative:
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import math
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num_informative = math.ceil(math.log2(num_classes * num_clusters))
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if num_features < num_informative:
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warnings.warn("Generating more features than requested.")
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num_features = num_informative
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x, y = make_classification(
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num_samples,
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num_features,
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n_informative=num_informative,
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n_redundant=0,
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n_classes=num_classes,
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n_clusters_per_class=num_clusters,
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class_sep=separation,
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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class Circles(NumpyDataset):
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"""Make a large circle containing a smaller circle in 2D.
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A simple toy dataset to visualize clustering and classification algorithms.
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Read more at
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https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html
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"""
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def __init__(
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self,
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num_samples: int = 300,
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noise: float = 0.3,
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factor: float = 0.8,
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seed: None | int = 0,
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):
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x, y = make_circles(
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num_samples,
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noise=noise,
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factor=factor,
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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class Moons(NumpyDataset):
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"""Make two interleaving half circles.
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A simple toy dataset to visualize clustering and classification algorithms.
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Read more at
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https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
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"""
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def __init__(
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self,
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num_samples: int = 300,
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noise: float = 0.3,
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seed: None | int = 0,
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):
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x, y = make_moons(
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num_samples,
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noise=noise,
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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