MultiTaskLasso
¶
-
class
ibex.sklearn.linear_model.
MultiTaskLasso
(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic')¶ Bases:
sklearn.linear_model.coordinate_descent.MultiTaskLasso
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer
The optimization objective for Lasso is:
(1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
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.
- alpha : float, optional
- Constant that multiplies the L1/L2 term. Defaults to 1.0
- 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).
- normalize : boolean, optional, default False
- This parameter is ignored when
fit_intercept
is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=False
. - copy_X : boolean, optional, 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. - random_state : int, RandomState instance or None, optional, 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 the
RandomState instance used by np.random. Used when
selection
== ‘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_ : array, shape (n_tasks, n_features)
- Parameter vector (W in the cost function formula).
Note that
coef_
stores the transpose ofW
,W.T
. - intercept_ : array, shape (n_tasks,)
- independent term in decision function.
- n_iter_ : int
- number of iterations run by the coordinate descent solver to reach the specified tolerance.
>>> from sklearn import linear_model >>> clf = linear_model.MultiTaskLasso(alpha=0.1) >>> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 0], [1, 1], [2, 2]]) MultiTaskLasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False) >>> print(clf.coef_) [[ 0.89393398 0. ] [ 0.89393398 0. ]] >>> print(clf.intercept_) [ 0.10606602 0.10606602]
Lasso, MultiTaskElasticNet
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortran-contiguous numpy array.
-
fit
(X, y)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Fit MultiTaskElasticNet model with coordinate descent
- X : ndarray, shape (n_samples, n_features)
- Data
- y : ndarray, shape (n_samples, n_tasks)
- Target. Will be cast to X’s dtype if necessary
Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortran-contiguous numpy array if necessary.
To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.
- A parameter
-
predict
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Predict using the linear model
- X : {array-like, sparse matrix}, shape = (n_samples, n_features)
- Samples.
- C : array, shape = (n_samples,)
- Returns predicted values.
- A parameter
-
score
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - 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.
- 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.
- score : float
- R^2 of self.predict(X) wrt. y.
- A parameter
- A parameter