linear_model.Lars()

class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True, positive=False) [source]

Least Angle Regression model a.k.a. LAR

Parameters:	n_nonzero_coefs : int, optional Target number of non-zero coefficients. Use `np.inf` for no limit. fit_intercept : boolean Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). positive : boolean (default=False) Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. This parameter is ignored when `fit_intercept` is set to False. When the regressors are normalized, note that this makes the hyperparameters learnt more robust and almost independent of the number of samples. The same property is not valid for standardized data. However, if you wish to standardize, please use `preprocessing.StandardScaler` before calling `fit` on an estimator with `normalize=False`. precompute : True \| False \| ?auto? \| array-like Whether to use a precomputed Gram matrix to speed up calculations. If set to `'auto'` let us decide. The Gram matrix can also be passed as argument. copy_X : boolean, optional, default True If `True`, X will be copied; else, it may be overwritten. eps : float, optional The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the `tol` parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization. fit_path : boolean If True the full path is stored in the `coef_path_` attribute. If you compute the solution for a large problem or many targets, setting `fit_path` to `False` will lead to a speedup, especially with a small alpha.
Attributes:	alphas_ : array, shape (n_alphas + 1,) \| list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration. `n_alphas` is either `n_nonzero_coefs` or `n_features`, whichever is smaller. active_ : list, length = n_alphas \| list of n_targets such lists Indices of active variables at the end of the path. coef_path_ : array, shape (n_features, n_alphas + 1) \| list of n_targets such arrays The varying values of the coefficients along the path. It is not present if the `fit_path` parameter is `False`. coef_ : array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula). intercept_ : float \| array, shape (n_targets,) Independent term in decision function. n_iter_ : array-like or int The number of iterations taken by lars_path to find the grid of alphas for each target.

Examples

>>> from sklearn import linear_model
>>> clf = linear_model.Lars(n_nonzero_coefs=1)
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
... 
Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True,
   n_nonzero_coefs=1, normalize=True, positive=False, precompute='auto',
   verbose=False)
>>> print(clf.coef_) 
[ 0. -1.11...]

Methods

`decision_function`(\args, \\*kwargs)	DEPRECATED: and will be removed in 0.19.
`fit`(X, y[, Xy])	Fit the model using X, y as training data.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict using the linear model
`score`(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
`set_params`(\\params)	Set the parameters of this estimator.

__init__(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True, positive=False) [source]

decision_function(*args, **kwargs) [source]

DEPRECATED: and will be removed in 0.19.

Decision function of the linear model.

Parameters:

Parameters:	X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples.
Returns:	C : array, shape = (n_samples,) Returns predicted values.

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

Samples.

Returns:

C : array, shape = (n_samples,)

Returns predicted values.

fit(X, y, Xy=None) [source]

Fit the model using X, y as training data.

Parameters:

Parameters:	X : array-like, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Target values. Xy : array-like, shape (n_samples,) or (n_samples, n_targets), optional Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.
Returns:	self : object returns an instance of self.

X : array-like, shape (n_samples, n_features)

Training data.

y : array-like, shape (n_samples,) or (n_samples, n_targets)

Target values.

Xy : array-like, shape (n_samples,) or (n_samples, n_targets), optional

Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.

Returns:

self : object

returns an instance of self.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X) [source]

Predict using the linear model

Parameters:

Parameters:	X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples.
Returns:	C : array, shape = (n_samples,) Returns predicted values.

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

Samples.

Returns:

C : array, shape = (n_samples,)

Returns predicted values.

score(X, y, sample_weight=None) [source]

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters:

Parameters:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float R^2 of self.predict(X) wrt. y.

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True values for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

R^2 of self.predict(X) wrt. y.

set_params(**params) [source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it?s possible to update each component of a nested object.

Returns:	self :

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html

doc_scikit_learn

2017-01-15 04:23:19

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