Source code for KDEpy.TreeKDE

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Module for the TreeKDE.
from scipy.spatial import cKDTree
import numbers
import numpy as np
from KDEpy.BaseKDE import BaseKDE

[docs]class TreeKDE(BaseKDE): """ This class implements a tree-based computation of a kernel density estimate. It works by segmenting the space recursively into smaller parts. This makes computing a kernel density estimate at a location easier, since we are able to query the tree structure for nearby points instead of having to evaluate the kernel function on all data points. For kernels without finite support, their support is approximated. The ``scipy`` k-d tree is used as the underlying algorithm. Parameters ---------- kernel : str The kernel function. See cls._available_kernels.keys() for choices. bw : float, str or array-like Bandwidth or bandwidth selection method. If a float is passed, it is the standard deviation of the kernel. If a string it passed, it is the bandwidth selection method, see cls._bw_methods.keys() for choices. If an array-like it passed, it is the bandwidth of each point. norm : float The p-norm used to compute the distances in higher dimensions. Examples -------- >>> data = np.random.randn(2**10) >>> # (1) Automatic bw selection using Improved Sheather Jones >>> x, y = TreeKDE(bw='ISJ').fit(data).evaluate() >>> # (2) Explicit choice of kernel and bw (standard deviation of kernel) >>> x, y = TreeKDE(kernel='triweight', bw=0.5).fit(data).evaluate() >>> weights = data + 10 >>> # (3) Using a grid and weights for the data >>> y = TreeKDE(kernel='epa', bw=0.5).fit(data, weights).evaluate(x) References ---------- - Friedman, Jerome H., Jon Louis Bentley, and Raphael Ari Finkel. An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Trans. Math. Softw. 3, no. 3 (September 1977): 209–226. - Maneewongvatana, Songrit, and David M. Mount. It’s Okay to Be Skinny, If Your Friends Are Fat. In Center for Geometric Computing 4th Annual Workshop on Computational Geometry, 2:1–8, 1999. - Silverman, B. W. Density Estimation for Statistics and Data Analysis. Boca Raton: Chapman and Hall, 1986. Page 99 for reference to kd-tree. - Scipy implementation, at ``scipy.spatial.KDTree``. """ def __init__(self, kernel="gaussian", bw=1, norm=2.0): super().__init__(kernel, bw) self.norm = norm
[docs] def fit(self, data, weights=None): """ Fit the KDE to the data. This validates the data and stores it. Computations are performed upon evaluation on a grid. Parameters ---------- data: array-like The data points. weights: array-like One weight per data point. Numbers of observations must match the data points. Returns ------- self Returns the instance. Examples -------- >>> data = [1, 3, 4, 7] >>> weights = [3, 4, 2, 1] >>> kde = TreeKDE().fit(data, weights=None) >>> kde = TreeKDE().fit(data, weights=weights) >>> x, y = kde() """ # Sets super().fit(data, weights) return self
[docs] def evaluate(self, grid_points=None, eps=10e-4): """ Evaluate on grid points. Parameters ---------- grid_points: array-like, int, tuple or None A grid (mesh) to evaluate on. High dimensional grids must have shape (obs, dims). If an integer is passed, it's the number of grid points on an equidistant grid. If a tuple is passed, it's the number of grid points in each dimension. If None, a grid will be automatically created. eps: float The maximal total error in absolute terms when estimating the effective support of a kernel which has infinite support. Setting this too high will produced a jagged estimate. Returns ------- y: array-like If a grid is supplied, `y` is returned. If no grid is supplied, a tuple (`x`, `y`) is returned. Examples -------- >>> kde = TreeKDE().fit([1, 3, 4, 7]) >>> # Two ways to evaluate, either with a grid or without >>> x, y = kde.evaluate() >>> x, y = kde.evaluate(256) >>> y = kde.evaluate(x) """ # This method sets self.grid points and verifies it super().evaluate(grid_points) evaluated = np.zeros(self.grid_points.shape[0]) # For every data point, compute the kernel and add to the grid obs, dims = bw = if isinstance(bw, numbers.Number): bw = np.asfarray(np.ones(obs) * bw) else: bw = np.asarray_chkfinite(bw, dtype=float) # Initialize the tree structure for fast lookups of neighbors tree = cKDTree( # Compute the kernel radius maximal_bw = np.max(bw) if not eps > 0: raise ValueError("eps must be > 0.") kernel_radius = self.kernel.practical_support(maximal_bw, eps) # Since we iterate through grid points, we need the maximum bw to # ensure that we get data points that are close enough for i, grid_point in enumerate(self.grid_points): # Query for data points that are close to this grid point # TODO: Is this epsilon value sensible? # Scipy 1.3.0 introduced error: ValueError: ndarray is not C-contiguous grid_point = np.ascontiguousarray(grid_point) indices = tree.query_ball_point(x=grid_point, r=kernel_radius, p=self.norm, eps=eps * obs ** 0.5) # Use broadcasting to find x-values (distances) x = grid_point -[indices] kernel_estimates = self.kernel(x, bw=bw[indices], norm=self.norm) if self.weights is not None: weights_subset = self.weights[indices] assert kernel_estimates.shape == weights_subset.shape assert kernel_estimates.shape == bw[indices].shape # Unpack the (n, 1) arrays to (n,) and compute the doc product if self.weights is not None: evaluated[i] +=, weights_subset) else: evaluated[i] += np.sum(kernel_estimates) / obs return self._evalate_return_logic(evaluated, self.grid_points)
if __name__ == "__main__": import pytest # --durations=10 <- May be used to show potentially slow tests pytest.main(args=[".", "--doctest-modules", "-v"])