tomaster 0.0.5

Topological Mode Analysis on Steroids

tomaster: Topological Mode Analysis on Steroids

tomaster implements algorithms for topological mode analysis.

The code is simple to read because it is written in pure Python.

The performance is good thanks to jit compilation with numba.

Usage

>>> from tomaster import tomato
>>> from sklearn import datasets
>>> X, y = datasets.make_moons(n_samples=1000, noise=0.05, random_state=1337)
>>> clusters = tomato(points=X, k=5, n_clusters=2)

>>> import matplotlib.pyplot as plt
>>> plt.scatter(*X.T, c=clusters)
>>> plt.show()

Installation

pip install tomaster

Testing

pytest

API

def tomato(
    *,
    points=None,
    k=None,
    neighbors=None,
    distances=None,
    density=None,
    metric="l2",
    bandwidth=None,
    raw: bool = False,
    tau=None,
    n_clusters=None,
    keep_cluster_labels: bool = False,
):
    """ToMATo clustering

    You can call this function with a lot of different signatures as it tries to build the missing parameters from the others.

    Parameters
    ----------

    points : np.ndarray
        Array of shape (n, dim)
    k : int
        Number of nearest neighbors to build the graph with
    neighbors : np.ndarray
        Array of shape (n, k)
    distances : np.ndarray
        Array of shape (n, k)
    density : np.ndarray
        Array of shape (n,)
    metric: str
        "l2" or "cosine"

    raw : bool
        if True, returns the merge edges

    tau : float
        Prominence threshold. If not specified, automatically selects the largest persistence gap.
    n_clusters : int
        Target number of clusters.

    keep_cluster_labels : bool
        If False, converts the labels to make them contiguous and start from 0.

    Returns
    -------
    clusters : np.ndarray
        if raw is False (default), array of shape (n,) containing the cluster indices
    edges : list
        if raw is True, spanning tree as list of (persistence, point1, point2)
    """


def tomato_img(
    img: np.ndarray, *, spatial_weight: float = 0, lab_space: bool = True, **kwargs
):
    """ToMATo for images

    Parameters
    ----------

    img : np.ndarray
        Image of shape (h, w) or (h, w, 3)
    spatial_weight : float
        Importance of the pixel positions in the distance function
    lab_space : bool
        If True, converts color images to the CIE L*a*b color space (<https://en.wikipedia.org/wiki/CIELAB_color_space>)

    see tomato() for other arguments.

    Returns
    -------

    clusters : np.ndarray
        Array of shape (h, w) containing the cluster indexes.
    """

References

• Chazal, Frédéric, Leonidas J. Guibas, Steve Y. Oudot, and Primoz Skraba. "Persistence-based clustering in riemannian manifolds." Journal of the ACM (JACM) 60, no. 6 (2013): 41. [pdf]

• Reference implementations: https://geometrica.saclay.inria.fr/data/ToMATo/, https://geometrica.saclay.inria.fr/data/Steve.Oudot/clustering/