def __init__(self, client=None, **kwargs):
super(Lasso, self).__init__(client=client, **kwargs)
kwargs['shuffle'] = False
if 'selection' in kwargs:
if kwargs['selection'] == 'random':
kwargs['shuffle'] = True
del kwargs['selection']
self.solver = CD(client=client, **kwargs)
class Lasso(BaseEstimator):
"""
Lasso extends LinearRegression by providing L1 regularization on the
coefficients when predicting response y with a linear combination of the
predictors in X. It can zero some of the coefficients for feature
selection and improves the conditioning of the problem.
cuML's Lasso an array-like object or cuDF DataFrame and
uses coordinate descent to fit a linear model.
Parameters
-----------
alpha : float (default = 1.0)
Constant that multiplies the L1 term.
alpha = 0 is equivalent to an ordinary least square, solved by the
LinearRegression class.
For numerical reasons, using alpha = 0 with the Lasso class is not
advised.
Given this, you should use the LinearRegression class.
fit_intercept : boolean (default = True)
If True, Lasso tries to correct for the global mean of y.
If False, the model expects that you have centered the data.
normalize : boolean (default = False)
If True, the predictors in X will be normalized by dividing by it's L2
norm.
If False, no scaling will be done.
max_iter : int (default = 1000)
The maximum number of iterations
tol : float (default = 1e-3)
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 than tol.
selection : {'cyclic', 'random'} (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.
Attributes
-----------
coef_ : array, shape (n_features)
The estimated coefficients for the linear regression model.
intercept_ : array
The independent term. If `fit_intercept` is False, will be 0.
For additional docs, see `scikitlearn's Lasso
<https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html>`_.
"""
def __init__(self, client=None, **kwargs):
super(Lasso, self).__init__(client=client, **kwargs)
kwargs['shuffle'] = False
if 'selection' in kwargs:
if kwargs['selection'] == 'random':
kwargs['shuffle'] = True
del kwargs['selection']
self.solver = CD(client=client, **kwargs)
def fit(self, X, y):
"""
Fit the model with X and y.
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
y : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
"""
self.solver.fit(X, y)
return self
def predict(self, X, delayed=True):
"""
Predicts the y for X.
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
delayed : bool (default = True)
Whether to do a lazy prediction (and return Delayed objects) or an
eagerly executed one.
Returns
-------
y : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
"""
return self.solver.predict(X, delayed=delayed)
class ElasticNet(BaseEstimator):
"""
ElasticNet extends LinearRegression with combined L1 and L2 regularizations
on the coefficients when predicting response y with a linear combination of
the predictors in X. It can reduce the variance of the predictors, force
some coefficients to be small, and improves the conditioning of the
problem.
cuML's ElasticNet an array-like object or cuDF DataFrame, uses coordinate
descent to fit a linear model.
Parameters
-----------
alpha : float (default = 1.0)
Constant that multiplies the L1 term.
alpha = 0 is equivalent to an ordinary least square, solved by the
LinearRegression object.
For numerical reasons, using alpha = 0 with the Lasso object is not
advised.
Given this, you should use the LinearRegression object.
l1_ratio: float (default = 0.5)
The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is
an L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
fit_intercept : boolean (default = True)
If True, Lasso tries to correct for the global mean of y.
If False, the model expects that you have centered the data.
normalize : boolean (default = False)
If True, the predictors in X will be normalized by dividing by it's L2
norm.
If False, no scaling will be done.
max_iter : int (default = 1000)
The maximum number of iterations
tol : float (default = 1e-3)
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 than tol.
selection : {'cyclic', 'random'} (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.
handle : cuml.Handle
If it is None, a new one is created just for this class.
output_type : (optional) {'input', 'cudf', 'cupy', 'numpy'} default = None
Use it to control output type of the results and attributes.
If None it'll inherit the output type set at the
module level, cuml.output_type. If that has not been changed, by
default the estimator will mirror the type of the data used for each
fit or predict call.
If set, the estimator will override the global option for its behavior.
Attributes
-----------
coef_ : array, shape (n_features)
The estimated coefficients for the linear regression model.
intercept_ : array
The independent term. If `fit_intercept` is False, will be 0.
For additional docs, see `scikitlearn's ElasticNet
<https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`_.
"""
def __init__(self, client=None, **kwargs):
super(ElasticNet, self).__init__(client=client, **kwargs)
kwargs['shuffle'] = False
if 'selection' in kwargs:
if kwargs['selection'] == 'random':
kwargs['shuffle'] = True
del kwargs['selection']
self.solver = CD(client=client, **kwargs)
def fit(self, X, y):
"""
Fit the model with X and y.
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
y : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
"""
self.solver.fit(X, y)
return self
def predict(self, X, delayed=True):
"""
Predicts the y for X.
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
delayed : bool (default = True)
Whether to do a lazy prediction (and return Delayed objects) or an
eagerly executed one.
Returns
-------
y : Dask cuDF DataFrame or CuPy backed Dask Array
Dense matrix (floats or doubles) of shape (n_samples, n_features).
"""
return self.solver.predict(X)