457 lines
15 KiB
Python
457 lines
15 KiB
Python
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"""
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A Theil-Sen Estimator for Multiple Linear Regression Model
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"""
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# Author: Florian Wilhelm <florian.wilhelm@gmail.com>
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#
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# License: BSD 3 clause
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import warnings
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from itertools import combinations
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from numbers import Integral, Real
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import numpy as np
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from joblib import effective_n_jobs
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from scipy import linalg
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from scipy.linalg.lapack import get_lapack_funcs
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from scipy.special import binom
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from ..base import RegressorMixin, _fit_context
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from ..exceptions import ConvergenceWarning
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from ..utils import check_random_state
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from ..utils._param_validation import Interval
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from ..utils.parallel import Parallel, delayed
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from ._base import LinearModel
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_EPSILON = np.finfo(np.double).eps
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def _modified_weiszfeld_step(X, x_old):
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"""Modified Weiszfeld step.
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This function defines one iteration step in order to approximate the
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spatial median (L1 median). It is a form of an iteratively re-weighted
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least squares method.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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Training vector, where `n_samples` is the number of samples and
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`n_features` is the number of features.
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x_old : ndarray of shape = (n_features,)
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Current start vector.
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Returns
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-------
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x_new : ndarray of shape (n_features,)
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New iteration step.
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References
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----------
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- On Computation of Spatial Median for Robust Data Mining, 2005
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T. Kärkkäinen and S. Äyrämö
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http://users.jyu.fi/~samiayr/pdf/ayramo_eurogen05.pdf
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"""
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diff = X - x_old
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diff_norm = np.sqrt(np.sum(diff**2, axis=1))
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mask = diff_norm >= _EPSILON
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# x_old equals one of our samples
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is_x_old_in_X = int(mask.sum() < X.shape[0])
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diff = diff[mask]
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diff_norm = diff_norm[mask][:, np.newaxis]
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quotient_norm = linalg.norm(np.sum(diff / diff_norm, axis=0))
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if quotient_norm > _EPSILON: # to avoid division by zero
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new_direction = np.sum(X[mask, :] / diff_norm, axis=0) / np.sum(
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1 / diff_norm, axis=0
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)
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else:
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new_direction = 1.0
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quotient_norm = 1.0
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return (
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max(0.0, 1.0 - is_x_old_in_X / quotient_norm) * new_direction
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+ min(1.0, is_x_old_in_X / quotient_norm) * x_old
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)
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def _spatial_median(X, max_iter=300, tol=1.0e-3):
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"""Spatial median (L1 median).
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The spatial median is member of a class of so-called M-estimators which
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are defined by an optimization problem. Given a number of p points in an
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n-dimensional space, the point x minimizing the sum of all distances to the
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p other points is called spatial median.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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Training vector, where `n_samples` is the number of samples and
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`n_features` is the number of features.
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max_iter : int, default=300
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Maximum number of iterations.
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tol : float, default=1.e-3
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Stop the algorithm if spatial_median has converged.
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Returns
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-------
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spatial_median : ndarray of shape = (n_features,)
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Spatial median.
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n_iter : int
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Number of iterations needed.
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References
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----------
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- On Computation of Spatial Median for Robust Data Mining, 2005
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T. Kärkkäinen and S. Äyrämö
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http://users.jyu.fi/~samiayr/pdf/ayramo_eurogen05.pdf
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"""
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if X.shape[1] == 1:
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return 1, np.median(X.ravel(), keepdims=True)
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tol **= 2 # We are computing the tol on the squared norm
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spatial_median_old = np.mean(X, axis=0)
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for n_iter in range(max_iter):
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spatial_median = _modified_weiszfeld_step(X, spatial_median_old)
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if np.sum((spatial_median_old - spatial_median) ** 2) < tol:
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break
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else:
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spatial_median_old = spatial_median
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else:
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warnings.warn(
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"Maximum number of iterations {max_iter} reached in "
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"spatial median for TheilSen regressor."
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"".format(max_iter=max_iter),
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ConvergenceWarning,
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)
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return n_iter, spatial_median
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def _breakdown_point(n_samples, n_subsamples):
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"""Approximation of the breakdown point.
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Parameters
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----------
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n_samples : int
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Number of samples.
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n_subsamples : int
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Number of subsamples to consider.
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Returns
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-------
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breakdown_point : float
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Approximation of breakdown point.
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"""
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return (
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1
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- (
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0.5 ** (1 / n_subsamples) * (n_samples - n_subsamples + 1)
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+ n_subsamples
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- 1
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)
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/ n_samples
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)
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def _lstsq(X, y, indices, fit_intercept):
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"""Least Squares Estimator for TheilSenRegressor class.
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This function calculates the least squares method on a subset of rows of X
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and y defined by the indices array. Optionally, an intercept column is
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added if intercept is set to true.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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Design matrix, where `n_samples` is the number of samples and
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`n_features` is the number of features.
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y : ndarray of shape (n_samples,)
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Target vector, where `n_samples` is the number of samples.
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indices : ndarray of shape (n_subpopulation, n_subsamples)
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Indices of all subsamples with respect to the chosen subpopulation.
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fit_intercept : bool
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Fit intercept or not.
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Returns
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-------
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weights : ndarray of shape (n_subpopulation, n_features + intercept)
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Solution matrix of n_subpopulation solved least square problems.
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"""
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fit_intercept = int(fit_intercept)
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n_features = X.shape[1] + fit_intercept
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n_subsamples = indices.shape[1]
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weights = np.empty((indices.shape[0], n_features))
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X_subpopulation = np.ones((n_subsamples, n_features))
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# gelss need to pad y_subpopulation to be of the max dim of X_subpopulation
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y_subpopulation = np.zeros((max(n_subsamples, n_features)))
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(lstsq,) = get_lapack_funcs(("gelss",), (X_subpopulation, y_subpopulation))
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for index, subset in enumerate(indices):
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X_subpopulation[:, fit_intercept:] = X[subset, :]
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y_subpopulation[:n_subsamples] = y[subset]
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weights[index] = lstsq(X_subpopulation, y_subpopulation)[1][:n_features]
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return weights
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class TheilSenRegressor(RegressorMixin, LinearModel):
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"""Theil-Sen Estimator: robust multivariate regression model.
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The algorithm calculates least square solutions on subsets with size
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n_subsamples of the samples in X. Any value of n_subsamples between the
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number of features and samples leads to an estimator with a compromise
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between robustness and efficiency. Since the number of least square
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solutions is "n_samples choose n_subsamples", it can be extremely large
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and can therefore be limited with max_subpopulation. If this limit is
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reached, the subsets are chosen randomly. In a final step, the spatial
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median (or L1 median) is calculated of all least square solutions.
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Read more in the :ref:`User Guide <theil_sen_regression>`.
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Parameters
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----------
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fit_intercept : bool, default=True
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Whether to calculate the intercept for this model. If set
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to false, no intercept will be used in calculations.
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copy_X : bool, default=True
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If True, X will be copied; else, it may be overwritten.
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max_subpopulation : int, default=1e4
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Instead of computing with a set of cardinality 'n choose k', where n is
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the number of samples and k is the number of subsamples (at least
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number of features), consider only a stochastic subpopulation of a
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given maximal size if 'n choose k' is larger than max_subpopulation.
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For other than small problem sizes this parameter will determine
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memory usage and runtime if n_subsamples is not changed. Note that the
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data type should be int but floats such as 1e4 can be accepted too.
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n_subsamples : int, default=None
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Number of samples to calculate the parameters. This is at least the
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number of features (plus 1 if fit_intercept=True) and the number of
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samples as a maximum. A lower number leads to a higher breakdown
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point and a low efficiency while a high number leads to a low
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breakdown point and a high efficiency. If None, take the
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minimum number of subsamples leading to maximal robustness.
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If n_subsamples is set to n_samples, Theil-Sen is identical to least
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squares.
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max_iter : int, default=300
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Maximum number of iterations for the calculation of spatial median.
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tol : float, default=1e-3
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Tolerance when calculating spatial median.
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random_state : int, RandomState instance or None, default=None
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A random number generator instance to define the state of the random
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permutations generator. Pass an int for reproducible output across
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multiple function calls.
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See :term:`Glossary <random_state>`.
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n_jobs : int, default=None
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Number of CPUs to use during the cross validation.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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verbose : bool, default=False
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Verbose mode when fitting the model.
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Attributes
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----------
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coef_ : ndarray of shape (n_features,)
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Coefficients of the regression model (median of distribution).
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intercept_ : float
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Estimated intercept of regression model.
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breakdown_ : float
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Approximated breakdown point.
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n_iter_ : int
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Number of iterations needed for the spatial median.
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n_subpopulation_ : int
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Number of combinations taken into account from 'n choose k', where n is
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the number of samples and k is the number of subsamples.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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See Also
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--------
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HuberRegressor : Linear regression model that is robust to outliers.
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RANSACRegressor : RANSAC (RANdom SAmple Consensus) algorithm.
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SGDRegressor : Fitted by minimizing a regularized empirical loss with SGD.
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References
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----------
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- Theil-Sen Estimators in a Multiple Linear Regression Model, 2009
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Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang
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http://home.olemiss.edu/~xdang/papers/MTSE.pdf
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Examples
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--------
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>>> from sklearn.linear_model import TheilSenRegressor
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>>> from sklearn.datasets import make_regression
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>>> X, y = make_regression(
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... n_samples=200, n_features=2, noise=4.0, random_state=0)
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>>> reg = TheilSenRegressor(random_state=0).fit(X, y)
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>>> reg.score(X, y)
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0.9884...
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>>> reg.predict(X[:1,])
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array([-31.5871...])
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"""
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_parameter_constraints: dict = {
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"fit_intercept": ["boolean"],
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"copy_X": ["boolean"],
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# target_type should be Integral but can accept Real for backward compatibility
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"max_subpopulation": [Interval(Real, 1, None, closed="left")],
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"n_subsamples": [None, Integral],
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"max_iter": [Interval(Integral, 0, None, closed="left")],
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"tol": [Interval(Real, 0.0, None, closed="left")],
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"random_state": ["random_state"],
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"n_jobs": [None, Integral],
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"verbose": ["verbose"],
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}
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def __init__(
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self,
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*,
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fit_intercept=True,
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copy_X=True,
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max_subpopulation=1e4,
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n_subsamples=None,
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max_iter=300,
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tol=1.0e-3,
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random_state=None,
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n_jobs=None,
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verbose=False,
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):
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self.fit_intercept = fit_intercept
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self.copy_X = copy_X
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self.max_subpopulation = max_subpopulation
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self.n_subsamples = n_subsamples
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self.max_iter = max_iter
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self.tol = tol
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self.random_state = random_state
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self.n_jobs = n_jobs
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self.verbose = verbose
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def _check_subparams(self, n_samples, n_features):
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n_subsamples = self.n_subsamples
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if self.fit_intercept:
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n_dim = n_features + 1
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else:
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n_dim = n_features
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if n_subsamples is not None:
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if n_subsamples > n_samples:
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raise ValueError(
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"Invalid parameter since n_subsamples > "
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"n_samples ({0} > {1}).".format(n_subsamples, n_samples)
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)
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if n_samples >= n_features:
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if n_dim > n_subsamples:
|
||
|
|
plus_1 = "+1" if self.fit_intercept else ""
|
||
|
|
raise ValueError(
|
||
|
|
"Invalid parameter since n_features{0} "
|
||
|
|
"> n_subsamples ({1} > {2})."
|
||
|
|
"".format(plus_1, n_dim, n_subsamples)
|
||
|
|
)
|
||
|
|
else: # if n_samples < n_features
|
||
|
|
if n_subsamples != n_samples:
|
||
|
|
raise ValueError(
|
||
|
|
"Invalid parameter since n_subsamples != "
|
||
|
|
"n_samples ({0} != {1}) while n_samples "
|
||
|
|
"< n_features.".format(n_subsamples, n_samples)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
n_subsamples = min(n_dim, n_samples)
|
||
|
|
|
||
|
|
all_combinations = max(1, np.rint(binom(n_samples, n_subsamples)))
|
||
|
|
n_subpopulation = int(min(self.max_subpopulation, all_combinations))
|
||
|
|
|
||
|
|
return n_subsamples, n_subpopulation
|
||
|
|
|
||
|
|
@_fit_context(prefer_skip_nested_validation=True)
|
||
|
|
def fit(self, X, y):
|
||
|
|
"""Fit linear model.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : ndarray of shape (n_samples, n_features)
|
||
|
|
Training data.
|
||
|
|
y : ndarray of shape (n_samples,)
|
||
|
|
Target values.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : returns an instance of self.
|
||
|
|
Fitted `TheilSenRegressor` estimator.
|
||
|
|
"""
|
||
|
|
random_state = check_random_state(self.random_state)
|
||
|
|
X, y = self._validate_data(X, y, y_numeric=True)
|
||
|
|
n_samples, n_features = X.shape
|
||
|
|
n_subsamples, self.n_subpopulation_ = self._check_subparams(
|
||
|
|
n_samples, n_features
|
||
|
|
)
|
||
|
|
self.breakdown_ = _breakdown_point(n_samples, n_subsamples)
|
||
|
|
|
||
|
|
if self.verbose:
|
||
|
|
print("Breakdown point: {0}".format(self.breakdown_))
|
||
|
|
print("Number of samples: {0}".format(n_samples))
|
||
|
|
tol_outliers = int(self.breakdown_ * n_samples)
|
||
|
|
print("Tolerable outliers: {0}".format(tol_outliers))
|
||
|
|
print("Number of subpopulations: {0}".format(self.n_subpopulation_))
|
||
|
|
|
||
|
|
# Determine indices of subpopulation
|
||
|
|
if np.rint(binom(n_samples, n_subsamples)) <= self.max_subpopulation:
|
||
|
|
indices = list(combinations(range(n_samples), n_subsamples))
|
||
|
|
else:
|
||
|
|
indices = [
|
||
|
|
random_state.choice(n_samples, size=n_subsamples, replace=False)
|
||
|
|
for _ in range(self.n_subpopulation_)
|
||
|
|
]
|
||
|
|
|
||
|
|
n_jobs = effective_n_jobs(self.n_jobs)
|
||
|
|
index_list = np.array_split(indices, n_jobs)
|
||
|
|
weights = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
|
||
|
|
delayed(_lstsq)(X, y, index_list[job], self.fit_intercept)
|
||
|
|
for job in range(n_jobs)
|
||
|
|
)
|
||
|
|
weights = np.vstack(weights)
|
||
|
|
self.n_iter_, coefs = _spatial_median(
|
||
|
|
weights, max_iter=self.max_iter, tol=self.tol
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.fit_intercept:
|
||
|
|
self.intercept_ = coefs[0]
|
||
|
|
self.coef_ = coefs[1:]
|
||
|
|
else:
|
||
|
|
self.intercept_ = 0.0
|
||
|
|
self.coef_ = coefs
|
||
|
|
|
||
|
|
return self
|