econml.sklearn_extensions.linear_model.WeightedLasso
- class econml.sklearn_extensions.linear_model.WeightedLasso(alpha=1.0, fit_intercept=True, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')[source]
Bases:
econml.sklearn_extensions.linear_model.WeightedModelMixin
,sklearn.linear_model._coordinate_descent.Lasso
Version of sklearn Lasso that accepts weights.
- Parameters
alpha (float, optional) – Constant that multiplies the L1 term. Defaults to 1.0.
alpha = 0
is equivalent to ordinary least squares, solved by theLinearRegression
object. For numerical reasons, usingalpha = 0
with Lasso is not advised. Given this, you should use theLinearRegression
object.fit_intercept (bool, default True) – 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).
precompute (True | False | array_like, default False) – 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. For sparse input this option is alwaysTrue
to preserve sparsity.copy_X (bool, default True) – If
True
, X will be copied; else, it may be overwritten.max_iter (int, optional) – The maximum number of iterations
tol (float, optional) – The tolerance for the optimization: if the updates are smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.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. See the Glossary.
positive (bool, optional) – When set to
True
, forces the coefficients to be positive.random_state (int, RandomState instance, or None, default None) – The seed of the pseudo random number generator that selects a random feature to update. If int, random_state is the seed used by the random number generator; If
RandomState
instance, random_state is the random number generator; If None, the random number generator is theRandomState
instance used bynp.random
. Used whenselection='random'
.selection (str, default ‘cyclic’) – If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e-4.
- coef_
parameter vector (w in the cost function formula)
- Type
array, shape (n_features,) | (n_targets, n_features)
- n_iter_
number of iterations run by the coordinate descent solver to reach the specified tolerance.
- Type
int | array_like, shape (n_targets,)
- __init__(alpha=1.0, fit_intercept=True, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')[source]
Methods
__init__
([alpha, fit_intercept, precompute, ...])fit
(X, y[, sample_weight, check_input])Fit model with coordinate descent.
get_params
([deep])Get parameters for this estimator.
path
(X, y, *[, l1_ratio, eps, n_alphas, ...])Compute elastic net path with coordinate descent.
predict
(X)Predict using the linear model.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
Attributes
Sparse representation of the fitted coef_.
- fit(X, y, sample_weight=None, check_input=True)[source]
Fit model with coordinate descent.
- Parameters
X (ndarray or scipy.sparse matrix, (n_samples, n_features)) – Data
y (ndarray, shape (n_samples,) or (n_samples, n_targets)) – Target. Will be cast to X’s dtype if necessary
sample_weight (numpy array of shape [n_samples]) – Individual weights for each sample. The weights will be normalized internally.
check_input (bool, default True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
- static path(X, y, *, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None, verbose=False, return_n_iter=False, positive=False, check_input=True, **params)
Compute elastic net path with coordinate descent.
The elastic net optimization function varies for mono and multi-outputs.
For mono-output tasks it is:
1 / (2 * n_samples) * ||y - Xw||^2_2 + alpha * l1_ratio * ||w||_1 + 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
For multi-output tasks it is:
(1 / (2 * n_samples)) * ||Y - XW||_Fro^2 + alpha * l1_ratio * ||W||_21 + 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
Where:
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the User Guide.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If
y
is mono-output thenX
can be sparse.y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_targets)) – Target values.
l1_ratio (float, default=0.5) – Number between 0 and 1 passed to elastic net (scaling between l1 and l2 penalties).
l1_ratio=1
corresponds to the Lasso.eps (float, default=1e-3) – Length of the path.
eps=1e-3
means thatalpha_min / alpha_max = 1e-3
.n_alphas (int, default=100) – Number of alphas along the regularization path.
alphas (ndarray, default=None) – List of alphas where to compute the models. If None alphas are set automatically.
precompute (‘auto’, bool or array-like of shape (n_features, n_features), default=’auto’) – 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.Xy (array-like of shape (n_features,) or (n_features, n_targets), default=None) – Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.
copy_X (bool, default=True) – If
True
, X will be copied; else, it may be overwritten.coef_init (ndarray of shape (n_features, ), default=None) – The initial values of the coefficients.
verbose (bool or int, default=False) – Amount of verbosity.
return_n_iter (bool, default=False) – Whether to return the number of iterations or not.
positive (bool, default=False) – If set to True, forces coefficients to be positive. (Only allowed when
y.ndim == 1
).check_input (bool, default=True) – If set to False, the input validation checks are skipped (including the Gram matrix when provided). It is assumed that they are handled by the caller.
**params (kwargs) – Keyword arguments passed to the coordinate descent solver.
- Returns
alphas (ndarray of shape (n_alphas,)) – The alphas along the path where models are computed.
coefs (ndarray of shape (n_features, n_alphas) or (n_targets, n_features, n_alphas)) – Coefficients along the path.
dual_gaps (ndarray of shape (n_alphas,)) – The dual gaps at the end of the optimization for each alpha.
n_iters (list of int) – The number of iterations taken by the coordinate descent optimizer to reach the specified tolerance for each alpha. (Is returned when
return_n_iter
is set to True).
See also
MultiTaskElasticNet
Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.
MultiTaskElasticNetCV
Multi-task L1/L2 ElasticNet with built-in cross-validation.
ElasticNet
Linear regression with combined L1 and L2 priors as regularizer.
ElasticNetCV
Elastic Net model with iterative fitting along a regularization path.
Notes
For an example, see examples/linear_model/plot_lasso_coordinate_descent_path.py.
- predict(X)
Predict using the linear model.
- Parameters
X (array-like or sparse matrix, shape (n_samples, n_features)) – Samples.
- Returns
C – Returns predicted values.
- Return type
array, shape (n_samples,)
- score(X, y, sample_weight=None)
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares
((y_true - y_pred)** 2).sum()
and \(v\) is the total sum of squares((y_true - y_true.mean()) ** 2).sum()
. The 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
X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
- Returns
score – \(R^2\) of
self.predict(X)
w.r.t. y.- Return type
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- property sparse_coef_
Sparse representation of the fitted coef_.