727 lines
25 KiB
Python
727 lines
25 KiB
Python
# Author: Johannes Schönberger
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#
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# License: BSD 3 clause
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import warnings
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from numbers import Integral, Real
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import numpy as np
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from ..base import (
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BaseEstimator,
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MetaEstimatorMixin,
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MultiOutputMixin,
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RegressorMixin,
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_fit_context,
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clone,
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)
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from ..exceptions import ConvergenceWarning
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from ..utils import check_consistent_length, check_random_state
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from ..utils._bunch import Bunch
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from ..utils._param_validation import (
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HasMethods,
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Interval,
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Options,
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RealNotInt,
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StrOptions,
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)
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from ..utils.metadata_routing import (
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MetadataRouter,
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MethodMapping,
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_raise_for_params,
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_routing_enabled,
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process_routing,
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)
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from ..utils.random import sample_without_replacement
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from ..utils.validation import (
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_check_method_params,
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_check_sample_weight,
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_deprecate_positional_args,
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check_is_fitted,
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has_fit_parameter,
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)
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from ._base import LinearRegression
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_EPSILON = np.spacing(1)
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def _dynamic_max_trials(n_inliers, n_samples, min_samples, probability):
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"""Determine number trials such that at least one outlier-free subset is
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sampled for the given inlier/outlier ratio.
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Parameters
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----------
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n_inliers : int
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Number of inliers in the data.
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n_samples : int
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Total number of samples in the data.
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min_samples : int
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Minimum number of samples chosen randomly from original data.
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probability : float
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Probability (confidence) that one outlier-free sample is generated.
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Returns
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-------
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trials : int
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Number of trials.
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"""
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inlier_ratio = n_inliers / float(n_samples)
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nom = max(_EPSILON, 1 - probability)
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denom = max(_EPSILON, 1 - inlier_ratio**min_samples)
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if nom == 1:
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return 0
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if denom == 1:
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return float("inf")
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return abs(float(np.ceil(np.log(nom) / np.log(denom))))
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class RANSACRegressor(
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MetaEstimatorMixin,
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RegressorMixin,
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MultiOutputMixin,
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BaseEstimator,
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):
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"""RANSAC (RANdom SAmple Consensus) algorithm.
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RANSAC is an iterative algorithm for the robust estimation of parameters
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from a subset of inliers from the complete data set.
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Read more in the :ref:`User Guide <ransac_regression>`.
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Parameters
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----------
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estimator : object, default=None
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Base estimator object which implements the following methods:
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* `fit(X, y)`: Fit model to given training data and target values.
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* `score(X, y)`: Returns the mean accuracy on the given test data,
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which is used for the stop criterion defined by `stop_score`.
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Additionally, the score is used to decide which of two equally
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large consensus sets is chosen as the better one.
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* `predict(X)`: Returns predicted values using the linear model,
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which is used to compute residual error using loss function.
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If `estimator` is None, then
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:class:`~sklearn.linear_model.LinearRegression` is used for
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target values of dtype float.
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Note that the current implementation only supports regression
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estimators.
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min_samples : int (>= 1) or float ([0, 1]), default=None
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Minimum number of samples chosen randomly from original data. Treated
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as an absolute number of samples for `min_samples >= 1`, treated as a
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relative number `ceil(min_samples * X.shape[0])` for
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`min_samples < 1`. This is typically chosen as the minimal number of
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samples necessary to estimate the given `estimator`. By default a
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:class:`~sklearn.linear_model.LinearRegression` estimator is assumed and
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`min_samples` is chosen as ``X.shape[1] + 1``. This parameter is highly
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dependent upon the model, so if a `estimator` other than
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:class:`~sklearn.linear_model.LinearRegression` is used, the user must
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provide a value.
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residual_threshold : float, default=None
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Maximum residual for a data sample to be classified as an inlier.
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By default the threshold is chosen as the MAD (median absolute
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deviation) of the target values `y`. Points whose residuals are
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strictly equal to the threshold are considered as inliers.
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is_data_valid : callable, default=None
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This function is called with the randomly selected data before the
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model is fitted to it: `is_data_valid(X, y)`. If its return value is
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False the current randomly chosen sub-sample is skipped.
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is_model_valid : callable, default=None
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This function is called with the estimated model and the randomly
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selected data: `is_model_valid(model, X, y)`. If its return value is
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False the current randomly chosen sub-sample is skipped.
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Rejecting samples with this function is computationally costlier than
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with `is_data_valid`. `is_model_valid` should therefore only be used if
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the estimated model is needed for making the rejection decision.
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max_trials : int, default=100
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Maximum number of iterations for random sample selection.
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max_skips : int, default=np.inf
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Maximum number of iterations that can be skipped due to finding zero
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inliers or invalid data defined by ``is_data_valid`` or invalid models
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defined by ``is_model_valid``.
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.. versionadded:: 0.19
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stop_n_inliers : int, default=np.inf
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Stop iteration if at least this number of inliers are found.
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stop_score : float, default=np.inf
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Stop iteration if score is greater equal than this threshold.
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stop_probability : float in range [0, 1], default=0.99
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RANSAC iteration stops if at least one outlier-free set of the training
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data is sampled in RANSAC. This requires to generate at least N
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samples (iterations)::
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N >= log(1 - probability) / log(1 - e**m)
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where the probability (confidence) is typically set to high value such
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as 0.99 (the default) and e is the current fraction of inliers w.r.t.
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the total number of samples.
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loss : str, callable, default='absolute_error'
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String inputs, 'absolute_error' and 'squared_error' are supported which
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find the absolute error and squared error per sample respectively.
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If ``loss`` is a callable, then it should be a function that takes
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two arrays as inputs, the true and predicted value and returns a 1-D
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array with the i-th value of the array corresponding to the loss
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on ``X[i]``.
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If the loss on a sample is greater than the ``residual_threshold``,
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then this sample is classified as an outlier.
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.. versionadded:: 0.18
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random_state : int, RandomState instance, default=None
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The generator used to initialize the centers.
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Pass an int for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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Attributes
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----------
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estimator_ : object
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Best fitted model (copy of the `estimator` object).
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n_trials_ : int
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Number of random selection trials until one of the stop criteria is
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met. It is always ``<= max_trials``.
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inlier_mask_ : bool array of shape [n_samples]
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Boolean mask of inliers classified as ``True``.
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n_skips_no_inliers_ : int
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Number of iterations skipped due to finding zero inliers.
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.. versionadded:: 0.19
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n_skips_invalid_data_ : int
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Number of iterations skipped due to invalid data defined by
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``is_data_valid``.
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.. versionadded:: 0.19
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n_skips_invalid_model_ : int
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Number of iterations skipped due to an invalid model defined by
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``is_model_valid``.
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.. versionadded:: 0.19
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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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TheilSenRegressor : Theil-Sen Estimator robust multivariate regression model.
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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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.. [1] https://en.wikipedia.org/wiki/RANSAC
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.. [2] https://www.sri.com/wp-content/uploads/2021/12/ransac-publication.pdf
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.. [3] http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf
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Examples
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--------
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>>> from sklearn.linear_model import RANSACRegressor
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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 = RANSACRegressor(random_state=0).fit(X, y)
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>>> reg.score(X, y)
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0.9885...
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>>> reg.predict(X[:1,])
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array([-31.9417...])
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""" # noqa: E501
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_parameter_constraints: dict = {
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"estimator": [HasMethods(["fit", "score", "predict"]), None],
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"min_samples": [
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Interval(Integral, 1, None, closed="left"),
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Interval(RealNotInt, 0, 1, closed="both"),
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None,
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],
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"residual_threshold": [Interval(Real, 0, None, closed="left"), None],
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"is_data_valid": [callable, None],
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"is_model_valid": [callable, None],
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"max_trials": [
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Interval(Integral, 0, None, closed="left"),
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Options(Real, {np.inf}),
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],
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"max_skips": [
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Interval(Integral, 0, None, closed="left"),
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Options(Real, {np.inf}),
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],
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"stop_n_inliers": [
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Interval(Integral, 0, None, closed="left"),
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Options(Real, {np.inf}),
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],
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"stop_score": [Interval(Real, None, None, closed="both")],
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"stop_probability": [Interval(Real, 0, 1, closed="both")],
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"loss": [StrOptions({"absolute_error", "squared_error"}), callable],
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"random_state": ["random_state"],
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}
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def __init__(
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self,
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estimator=None,
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*,
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min_samples=None,
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residual_threshold=None,
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is_data_valid=None,
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is_model_valid=None,
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max_trials=100,
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max_skips=np.inf,
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stop_n_inliers=np.inf,
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stop_score=np.inf,
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stop_probability=0.99,
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loss="absolute_error",
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random_state=None,
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):
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self.estimator = estimator
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self.min_samples = min_samples
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self.residual_threshold = residual_threshold
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self.is_data_valid = is_data_valid
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self.is_model_valid = is_model_valid
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self.max_trials = max_trials
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self.max_skips = max_skips
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self.stop_n_inliers = stop_n_inliers
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self.stop_score = stop_score
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self.stop_probability = stop_probability
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self.random_state = random_state
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self.loss = loss
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@_fit_context(
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# RansacRegressor.estimator is not validated yet
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prefer_skip_nested_validation=False
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)
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# TODO(1.7): remove `sample_weight` from the signature after deprecation
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# cycle; for backwards compatibility: pop it from `fit_params` before the
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# `_raise_for_params` check and reinsert it after the check
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@_deprecate_positional_args(version="1.7")
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def fit(self, X, y, *, sample_weight=None, **fit_params):
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"""Fit estimator using RANSAC algorithm.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Training data.
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y : array-like of shape (n_samples,) or (n_samples, n_targets)
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Target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Individual weights for each sample
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raises error if sample_weight is passed and estimator
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fit method does not support it.
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.. versionadded:: 0.18
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**fit_params : dict
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Parameters routed to the `fit` method of the sub-estimator via the
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metadata routing API.
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.. versionadded:: 1.5
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Only available if
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`sklearn.set_config(enable_metadata_routing=True)` is set. See
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:ref:`Metadata Routing User Guide <metadata_routing>` for more
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details.
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Returns
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-------
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self : object
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Fitted `RANSACRegressor` estimator.
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Raises
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------
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ValueError
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If no valid consensus set could be found. This occurs if
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`is_data_valid` and `is_model_valid` return False for all
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`max_trials` randomly chosen sub-samples.
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"""
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# Need to validate separately here. We can't pass multi_output=True
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# because that would allow y to be csr. Delay expensive finiteness
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# check to the estimator's own input validation.
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_raise_for_params(fit_params, self, "fit")
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check_X_params = dict(accept_sparse="csr", force_all_finite=False)
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check_y_params = dict(ensure_2d=False)
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X, y = self._validate_data(
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X, y, validate_separately=(check_X_params, check_y_params)
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)
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check_consistent_length(X, y)
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if self.estimator is not None:
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estimator = clone(self.estimator)
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else:
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estimator = LinearRegression()
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if self.min_samples is None:
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if not isinstance(estimator, LinearRegression):
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raise ValueError(
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"`min_samples` needs to be explicitly set when estimator "
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"is not a LinearRegression."
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)
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min_samples = X.shape[1] + 1
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elif 0 < self.min_samples < 1:
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min_samples = np.ceil(self.min_samples * X.shape[0])
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elif self.min_samples >= 1:
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min_samples = self.min_samples
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if min_samples > X.shape[0]:
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raise ValueError(
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"`min_samples` may not be larger than number "
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"of samples: n_samples = %d." % (X.shape[0])
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)
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if self.residual_threshold is None:
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# MAD (median absolute deviation)
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residual_threshold = np.median(np.abs(y - np.median(y)))
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else:
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residual_threshold = self.residual_threshold
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if self.loss == "absolute_error":
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if y.ndim == 1:
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loss_function = lambda y_true, y_pred: np.abs(y_true - y_pred)
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else:
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loss_function = lambda y_true, y_pred: np.sum(
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np.abs(y_true - y_pred), axis=1
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)
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elif self.loss == "squared_error":
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if y.ndim == 1:
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loss_function = lambda y_true, y_pred: (y_true - y_pred) ** 2
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else:
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loss_function = lambda y_true, y_pred: np.sum(
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(y_true - y_pred) ** 2, axis=1
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)
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elif callable(self.loss):
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loss_function = self.loss
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random_state = check_random_state(self.random_state)
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try: # Not all estimator accept a random_state
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estimator.set_params(random_state=random_state)
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except ValueError:
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pass
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estimator_fit_has_sample_weight = has_fit_parameter(estimator, "sample_weight")
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estimator_name = type(estimator).__name__
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if sample_weight is not None and not estimator_fit_has_sample_weight:
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raise ValueError(
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"%s does not support sample_weight. Sample"
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" weights are only used for the calibration"
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" itself." % estimator_name
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)
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if sample_weight is not None:
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fit_params["sample_weight"] = sample_weight
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if _routing_enabled():
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routed_params = process_routing(self, "fit", **fit_params)
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else:
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routed_params = Bunch()
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routed_params.estimator = Bunch(fit={}, predict={}, score={})
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if sample_weight is not None:
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sample_weight = _check_sample_weight(sample_weight, X)
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routed_params.estimator.fit = {"sample_weight": sample_weight}
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n_inliers_best = 1
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score_best = -np.inf
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inlier_mask_best = None
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X_inlier_best = None
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y_inlier_best = None
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inlier_best_idxs_subset = None
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self.n_skips_no_inliers_ = 0
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self.n_skips_invalid_data_ = 0
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self.n_skips_invalid_model_ = 0
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# number of data samples
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n_samples = X.shape[0]
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sample_idxs = np.arange(n_samples)
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self.n_trials_ = 0
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max_trials = self.max_trials
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while self.n_trials_ < max_trials:
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self.n_trials_ += 1
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if (
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self.n_skips_no_inliers_
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+ self.n_skips_invalid_data_
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+ self.n_skips_invalid_model_
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) > self.max_skips:
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break
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# choose random sample set
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subset_idxs = sample_without_replacement(
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n_samples, min_samples, random_state=random_state
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)
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X_subset = X[subset_idxs]
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y_subset = y[subset_idxs]
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# check if random sample set is valid
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if self.is_data_valid is not None and not self.is_data_valid(
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X_subset, y_subset
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):
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self.n_skips_invalid_data_ += 1
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continue
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# cut `fit_params` down to `subset_idxs`
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fit_params_subset = _check_method_params(
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X, params=routed_params.estimator.fit, indices=subset_idxs
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)
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# fit model for current random sample set
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estimator.fit(X_subset, y_subset, **fit_params_subset)
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# check if estimated model is valid
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if self.is_model_valid is not None and not self.is_model_valid(
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estimator, X_subset, y_subset
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):
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self.n_skips_invalid_model_ += 1
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continue
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# residuals of all data for current random sample model
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y_pred = estimator.predict(X)
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residuals_subset = loss_function(y, y_pred)
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# classify data into inliers and outliers
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inlier_mask_subset = residuals_subset <= residual_threshold
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n_inliers_subset = np.sum(inlier_mask_subset)
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# less inliers -> skip current random sample
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if n_inliers_subset < n_inliers_best:
|
|
self.n_skips_no_inliers_ += 1
|
|
continue
|
|
|
|
# extract inlier data set
|
|
inlier_idxs_subset = sample_idxs[inlier_mask_subset]
|
|
X_inlier_subset = X[inlier_idxs_subset]
|
|
y_inlier_subset = y[inlier_idxs_subset]
|
|
|
|
# cut `fit_params` down to `inlier_idxs_subset`
|
|
score_params_inlier_subset = _check_method_params(
|
|
X, params=routed_params.estimator.score, indices=inlier_idxs_subset
|
|
)
|
|
|
|
# score of inlier data set
|
|
score_subset = estimator.score(
|
|
X_inlier_subset,
|
|
y_inlier_subset,
|
|
**score_params_inlier_subset,
|
|
)
|
|
|
|
# same number of inliers but worse score -> skip current random
|
|
# sample
|
|
if n_inliers_subset == n_inliers_best and score_subset < score_best:
|
|
continue
|
|
|
|
# save current random sample as best sample
|
|
n_inliers_best = n_inliers_subset
|
|
score_best = score_subset
|
|
inlier_mask_best = inlier_mask_subset
|
|
X_inlier_best = X_inlier_subset
|
|
y_inlier_best = y_inlier_subset
|
|
inlier_best_idxs_subset = inlier_idxs_subset
|
|
|
|
max_trials = min(
|
|
max_trials,
|
|
_dynamic_max_trials(
|
|
n_inliers_best, n_samples, min_samples, self.stop_probability
|
|
),
|
|
)
|
|
|
|
# break if sufficient number of inliers or score is reached
|
|
if n_inliers_best >= self.stop_n_inliers or score_best >= self.stop_score:
|
|
break
|
|
|
|
# if none of the iterations met the required criteria
|
|
if inlier_mask_best is None:
|
|
if (
|
|
self.n_skips_no_inliers_
|
|
+ self.n_skips_invalid_data_
|
|
+ self.n_skips_invalid_model_
|
|
) > self.max_skips:
|
|
raise ValueError(
|
|
"RANSAC skipped more iterations than `max_skips` without"
|
|
" finding a valid consensus set. Iterations were skipped"
|
|
" because each randomly chosen sub-sample failed the"
|
|
" passing criteria. See estimator attributes for"
|
|
" diagnostics (n_skips*)."
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"RANSAC could not find a valid consensus set. All"
|
|
" `max_trials` iterations were skipped because each"
|
|
" randomly chosen sub-sample failed the passing criteria."
|
|
" See estimator attributes for diagnostics (n_skips*)."
|
|
)
|
|
else:
|
|
if (
|
|
self.n_skips_no_inliers_
|
|
+ self.n_skips_invalid_data_
|
|
+ self.n_skips_invalid_model_
|
|
) > self.max_skips:
|
|
warnings.warn(
|
|
(
|
|
"RANSAC found a valid consensus set but exited"
|
|
" early due to skipping more iterations than"
|
|
" `max_skips`. See estimator attributes for"
|
|
" diagnostics (n_skips*)."
|
|
),
|
|
ConvergenceWarning,
|
|
)
|
|
|
|
# estimate final model using all inliers
|
|
fit_params_best_idxs_subset = _check_method_params(
|
|
X, params=routed_params.estimator.fit, indices=inlier_best_idxs_subset
|
|
)
|
|
|
|
estimator.fit(X_inlier_best, y_inlier_best, **fit_params_best_idxs_subset)
|
|
|
|
self.estimator_ = estimator
|
|
self.inlier_mask_ = inlier_mask_best
|
|
return self
|
|
|
|
def predict(self, X, **params):
|
|
"""Predict using the estimated model.
|
|
|
|
This is a wrapper for `estimator_.predict(X)`.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like or sparse matrix} of shape (n_samples, n_features)
|
|
Input data.
|
|
|
|
**params : dict
|
|
Parameters routed to the `predict` method of the sub-estimator via
|
|
the metadata routing API.
|
|
|
|
.. versionadded:: 1.5
|
|
|
|
Only available if
|
|
`sklearn.set_config(enable_metadata_routing=True)` is set. See
|
|
:ref:`Metadata Routing User Guide <metadata_routing>` for more
|
|
details.
|
|
|
|
Returns
|
|
-------
|
|
y : array, shape = [n_samples] or [n_samples, n_targets]
|
|
Returns predicted values.
|
|
"""
|
|
check_is_fitted(self)
|
|
X = self._validate_data(
|
|
X,
|
|
force_all_finite=False,
|
|
accept_sparse=True,
|
|
reset=False,
|
|
)
|
|
|
|
_raise_for_params(params, self, "predict")
|
|
|
|
if _routing_enabled():
|
|
predict_params = process_routing(self, "predict", **params).estimator[
|
|
"predict"
|
|
]
|
|
else:
|
|
predict_params = {}
|
|
|
|
return self.estimator_.predict(X, **predict_params)
|
|
|
|
def score(self, X, y, **params):
|
|
"""Return the score of the prediction.
|
|
|
|
This is a wrapper for `estimator_.score(X, y)`.
|
|
|
|
Parameters
|
|
----------
|
|
X : (array-like or sparse matrix} of shape (n_samples, n_features)
|
|
Training data.
|
|
|
|
y : array-like of shape (n_samples,) or (n_samples, n_targets)
|
|
Target values.
|
|
|
|
**params : dict
|
|
Parameters routed to the `score` method of the sub-estimator via
|
|
the metadata routing API.
|
|
|
|
.. versionadded:: 1.5
|
|
|
|
Only available if
|
|
`sklearn.set_config(enable_metadata_routing=True)` is set. See
|
|
:ref:`Metadata Routing User Guide <metadata_routing>` for more
|
|
details.
|
|
|
|
Returns
|
|
-------
|
|
z : float
|
|
Score of the prediction.
|
|
"""
|
|
check_is_fitted(self)
|
|
X = self._validate_data(
|
|
X,
|
|
force_all_finite=False,
|
|
accept_sparse=True,
|
|
reset=False,
|
|
)
|
|
|
|
_raise_for_params(params, self, "score")
|
|
if _routing_enabled():
|
|
score_params = process_routing(self, "score", **params).estimator["score"]
|
|
else:
|
|
score_params = {}
|
|
|
|
return self.estimator_.score(X, y, **score_params)
|
|
|
|
def get_metadata_routing(self):
|
|
"""Get metadata routing of this object.
|
|
|
|
Please check :ref:`User Guide <metadata_routing>` on how the routing
|
|
mechanism works.
|
|
|
|
.. versionadded:: 1.5
|
|
|
|
Returns
|
|
-------
|
|
routing : MetadataRouter
|
|
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
|
|
routing information.
|
|
"""
|
|
router = MetadataRouter(owner=self.__class__.__name__).add(
|
|
estimator=self.estimator,
|
|
method_mapping=MethodMapping()
|
|
.add(caller="fit", callee="fit")
|
|
.add(caller="fit", callee="score")
|
|
.add(caller="score", callee="score")
|
|
.add(caller="predict", callee="predict"),
|
|
)
|
|
return router
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
"_xfail_checks": {
|
|
"check_sample_weights_invariance": (
|
|
"zero sample_weight is not equivalent to removing samples"
|
|
),
|
|
}
|
|
}
|