Class SGDRegressorScikitsLearnNode

Linear model fitted by minimizing a regularized empirical loss with SGD

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

SGD stands for Stochastic Gradient Descent: the gradient of the loss is
estimated each sample at a time and the model is updated along the way with
a decreasing strength schedule (aka learning rate).

The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.

This implementation works with data represented as dense numpy arrays of
floating point values for the features.

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

**Parameters**

loss : str, 'squared_loss', 'huber', 'epsilon_insensitive',                 or 'squared_epsilon_insensitive'
    The loss function to be used. Defaults to 'squared_loss' which refers
    to the ordinary least squares fit. 'huber' modifies 'squared_loss' to
    focus less on getting outliers correct by switching from squared to
    linear loss past a distance of epsilon. 'epsilon_insensitive' ignores
    errors less than epsilon and is linear past that; this is the loss
    function used in SVR. 'squared_epsilon_insensitive' is the same but
    becomes squared loss past a tolerance of epsilon.

penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
    The penalty (aka regularization term) to be used. Defaults to 'l2'
    which is the standard regularizer for linear SVM models. 'l1' and
    'elasticnet' might bring sparsity to the model (feature selection)
    not achievable with 'l2'.

alpha : float
    Constant that multiplies the regularization term. Defaults to 0.0001
    Also used to compute learning_rate when set to 'optimal'.

l1_ratio : float
    The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
    l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
    Defaults to 0.15.

fit_intercept : bool
    Whether the intercept should be estimated or not. If False, the
    data is assumed to be already centered. Defaults to True.

n_iter : int, optional
    The number of passes over the training data (aka epochs). The number
    of iterations is set to 1 if using partial_fit.
    Defaults to 5.

shuffle : bool, optional
    Whether or not the training data should be shuffled after each epoch.
    Defaults to True.

random_state : int seed, RandomState instance, or None (default)
    The seed of the pseudo random number generator to use when
    shuffling the data.

verbose : integer, optional
    The verbosity level.

epsilon : float
    Epsilon in the epsilon-insensitive loss functions; only if `loss` is
    'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
    For 'huber', determines the threshold at which it becomes less
    important to get the prediction exactly right.
    For epsilon-insensitive, any differences between the current prediction
    and the correct label are ignored if they are less than this threshold.

learning_rate : string, optional
    The learning rate:

    - constant: eta = eta0
    - optimal: eta = 1.0/(alpha * t)
    - invscaling: eta = eta0 / pow(t, power_t) [default]


eta0 : double, optional
    The initial learning rate [default 0.01].

power_t : double, optional
    The exponent for inverse scaling learning rate [default 0.25].

warm_start : bool, optional
    When set to True, reuse the solution of the previous call to fit as
    initialization, otherwise, just erase the previous solution.

average : bool or int, optional
    When set to True, computes the averaged SGD weights and stores the
    result in the ``coef_`` attribute. If set to an int greater than 1,
    averaging will begin once the total number of samples seen reaches
    average. So ``average=10 will`` begin averaging after seeing 10
    samples.

**Attributes**

``coef_`` : array, shape (n_features,)
    Weights assigned to the features.

``intercept_`` : array, shape (1,)
    The intercept term.

``average_coef_`` : array, shape (n_features,)
    Averaged weights assigned to the features.

``average_intercept_`` : array, shape (1,)
    The averaged intercept term.

**Examples**

>>> import numpy as np
>>> from sklearn import linear_model
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = linear_model.SGDRegressor()
>>> clf.fit(X, y)
... #doctest: +NORMALIZE_WHITESPACE
SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,
             fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
             loss='squared_loss', n_iter=5, penalty='l2', power_t=0.25,
             random_state=None, shuffle=True, verbose=0, warm_start=False)

See also

Ridge, ElasticNet, Lasso, SVR

Overrides: object.__init__

DEPRECATED: Support to use estimators as feature selectors will be removed in version 0.19. Use SelectFromModel instead.

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

Reduce X to its most important features.

        Uses ``coef_`` or ``feature_importances_`` to determine the most
        important features.  For models with a ``coef_`` for each class, the
        absolute sum over the classes is used.

        Parameters
        ----------
        X : array or scipy sparse matrix of shape [n_samples, n_features]
            The input samples.

        threshold : string, float or None, optional (default=None)
            The threshold value to use for feature selection. Features whose
            importance is greater or equal are kept while the others are
            discarded. If "median" (resp. "mean"), then the threshold value is
            the median (resp. the mean) of the feature importances. A scaling
            factor (e.g., "1.25*mean") may also be used. If None and if
            available, the object attribute ``threshold`` is used. Otherwise,
            "mean" is used by default.

        Returns
        -------
        X_r : array of shape [n_samples, n_selected_features]
            The input samples with only the selected features.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit linear model with Stochastic Gradient Descent.

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

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Training data

y : numpy array, shape (n_samples,)

Target values

coef_init : array, shape (n_features,)

The initial coefficients to warm-start the optimization.

intercept_init : array, shape (1,)

The initial intercept to warm-start the optimization.

sample_weight : array-like, shape (n_samples,), optional

Weights applied to individual samples (1. for unweighted).

Returns

self : returns an instance of self.

Overrides: Node.stop_training