PHATET.py

from scipy import sparse
import graphtools
from sklearn.exceptions import NotFittedError
import phate
import numpy as np

class PHATET(phate.PHATE):
    """
    PHATET is an adaptation of PHATE which incorporates random jumps into the diffusion operator.
    This improvement is based on Google's PageRank algorithm and makes the PHATE algorithm more
    robust to parameter selection.
    """

    def __init__(self, beta = 0.9, **kwargs):
        super(PHATET, self).__init__(**kwargs)

        self.beta = beta

    @property
    def diff_op(self):
        """diff_op :  array-like, shape=[n_samples, n_samples] or [n_landmark, n_landmark]
        The diffusion operator built from the graph
        """
        if self.graph is not None:
            if isinstance(self.graph, graphtools.graphs.LandmarkGraph):
                diff_op = self.graph.landmark_op
            else:
                diff_op = self.graph.diff_op
            if sparse.issparse(diff_op):
                diff_op = diff_op.toarray()

            dim = diff_op.shape[0]

            diff_op_tele = self.beta * diff_op + (1 - self.beta) * 1 / dim * np.ones((dim, dim))


            return diff_op_tele

        else:
            raise NotFittedError(
                "This PHATE instance is not fitted yet. Call "
                "'fit' with appropriate arguments before "
                "using this method."
            )