Class RANSACRegressorScikitsLearnNode

RANSAC (RANdom SAmple Consensus) algorithm.

This node has been automatically generated by wrapping the ``sklearn.linear_model.ransac.RANSACRegressor`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

RANSAC is an iterative algorithm for the robust estimation of parameters
from a subset of inliers from the complete data set. More information can
be found in the general documentation of linear models.

A detailed description of the algorithm can be found in the documentation
of the ``linear_model`` sub-package.

Read more in the :ref:`User Guide <ransac_regression>`.

**Parameters**

base_estimator : object, optional
    Base estimator object which implements the following methods:


     * `fit(X, y)`: Fit model to given training data and target values.
     * `score(X, y)`: Returns the mean accuracy on the given test data,
       which is used for the stop criterion defined by `stop_score`.
       Additionally, the score is used to decide which of two equally
       large consensus sets is chosen as the better one.

    If `base_estimator` is None, then
    ``base_estimator=sklearn.linear_model.LinearRegression()`` is used for
    target values of dtype float.

    Note that the current implementation only supports regression
    estimators.

min_samples : int (>= 1) or float ([0, 1]), optional
    Minimum number of samples chosen randomly from original data. Treated
    as an absolute number of samples for `min_samples >= 1`, treated as a
    relative number `ceil(min_samples * X.shape[0]`) for
    `min_samples < 1`. This is typically chosen as the minimal number of
    samples necessary to estimate the given `base_estimator`. By default a
    ``sklearn.linear_model.LinearRegression()`` estimator is assumed and
    `min_samples` is chosen as ``X.shape[1] + 1``.

residual_threshold : float, optional
    Maximum residual for a data sample to be classified as an inlier.
    By default the threshold is chosen as the MAD (median absolute
    deviation) of the target values `y`.

is_data_valid : callable, optional
    This function is called with the randomly selected data before the
    model is fitted to it: `is_data_valid(X, y)`. If its return value is
    False the current randomly chosen sub-sample is skipped.

is_model_valid : callable, optional
    This function is called with the estimated model and the randomly
    selected data: `is_model_valid(model, X, y)`. If its return value is
    False the current randomly chosen sub-sample is skipped.
    Rejecting samples with this function is computationally costlier than
    with `is_data_valid`. `is_model_valid` should therefore only be used if
    the estimated model is needed for making the rejection decision.

max_trials : int, optional
    Maximum number of iterations for random sample selection.

stop_n_inliers : int, optional
    Stop iteration if at least this number of inliers are found.

stop_score : float, optional
    Stop iteration if score is greater equal than this threshold.

stop_probability : float in range [0, 1], optional
    RANSAC iteration stops if at least one outlier-free set of the training
    data is sampled in RANSAC. This requires to generate at least N
    samples (iterations)::


        N >= log(1 - probability) / log(1 - e**m)

    where the probability (confidence) is typically set to high value such
    as 0.99 (the default) and e is the current fraction of inliers w.r.t.
    the total number of samples.

residual_metric : callable, optional
    Metric to reduce the dimensionality of the residuals to 1 for
    multi-dimensional target values ``y.shape[1] > 1``. By default the sum
    of absolute differences is used::


        lambda dy: np.sum(np.abs(dy), axis=1)

random_state : integer or numpy.RandomState, optional
    The generator used to initialize the centers. If an integer is
    given, it fixes the seed. Defaults to the global numpy random
    number generator.

**Attributes**

``estimator_`` : object
    Best fitted model (copy of the `base_estimator` object).

``n_trials_`` : int
    Number of random selection trials until one of the stop criteria is
    met. It is always ``<= max_trials``.

``inlier_mask_`` : bool array of shape [n_samples]
    Boolean mask of inliers classified as ``True``.

**References**

.. [1] http://en.wikipedia.org/wiki/RANSAC
.. [2] http://www.cs.columbia.edu/~belhumeur/courses/compPhoto/ransac.pdf
.. [3] http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf

Overrides: object.__init__

Predict using the estimated model.

This node has been automatically generated by wrapping the sklearn.linear_model.ransac.RANSACRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

This is a wrapper for estimator_.predict(X).

Parameters

X : numpy array of shape [n_samples, n_features]

Returns

y : array, shape = [n_samples] or [n_samples, n_targets]

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit estimator using RANSAC algorithm.

This node has been automatically generated by wrapping the sklearn.linear_model.ransac.RANSACRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like or sparse matrix, shape [n_samples, n_features]

Training data.

y : array-like, shape = [n_samples] or [n_samples, n_targets]

Target values.

Raises

ValueError

If no valid consensus set could be found. This occurs if is_data_valid and is_model_valid return False for all max_trials randomly chosen sub-samples.

Overrides: Node.stop_training