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lasso.py
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lasso.py
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#
# Copyright (c) 2019-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from cuml.linear_model.elastic_net import ElasticNet
from cuml.internals.api_decorators import device_interop_preparation
class Lasso(ElasticNet):
"""
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 can take array-like objects, either in host as
NumPy arrays or in device (as Numba or `__cuda_array_interface__`
compliant), in addition to cuDF objects. It uses coordinate descent to fit
a linear model.
This estimator supports cuML's experimental device selection capabilities.
It can be configured to run on either the CPU or the GPU.
To learn more, please see :ref:`device-selection`.
Examples
--------
.. code-block:: python
>>> import numpy as np
>>> import cudf
>>> from cuml.linear_model import Lasso
>>> ls = Lasso(alpha = 0.1, solver='qn')
>>> X = cudf.DataFrame()
>>> X['col1'] = np.array([0, 1, 2], dtype = np.float32)
>>> X['col2'] = np.array([0, 1, 2], dtype = np.float32)
>>> y = cudf.Series( np.array([0.0, 1.0, 2.0], dtype = np.float32) )
>>> result_lasso = ls.fit(X, y)
>>> print(result_lasso.coef_)
0 0.425
1 0.425
dtype: float32
>>> print(result_lasso.intercept_)
0.150000...
>>> X_new = cudf.DataFrame()
>>> X_new['col1'] = np.array([3,2], dtype = np.float32)
>>> X_new['col2'] = np.array([5,5], dtype = np.float32)
>>> preds = result_lasso.predict(X_new)
>>> print(preds)
0 3.549997
1 3.124997
dtype: float32
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.
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 the
column-wise standard deviation.
If False, no scaling will be done.
Note: this is in contrast to sklearn's deprecated `normalize` flag,
which divides by the column-wise L2 norm; but this is the same as if
using sklearn's StandardScaler.
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.
solver : {'cd', 'qn'} (default='cd')
Choose an algorithm:
* 'cd' - coordinate descent
* 'qn' - quasi-newton
You may find the alternative 'qn' algorithm is faster when the number
of features is sufficiently large, but the sample size is small.
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
Specifies the cuml.handle that holds internal CUDA state for
computations in this model. Most importantly, this specifies the CUDA
stream that will be used for the model's computations, so users can
run different models concurrently in different streams by creating
handles in several streams.
If it is None, a new one is created.
output_type : {'input', 'array', 'dataframe', 'series', 'df_obj', \
'numba', 'cupy', 'numpy', 'cudf', 'pandas'}, default=None
Return results and set estimator attributes to the indicated output
type. If None, the output type set at the module level
(`cuml.global_settings.output_type`) will be used. See
:ref:`output-data-type-configuration` for more info.
verbose : int or boolean, default=False
Sets logging level. It must be one of `cuml.common.logger.level_*`.
See :ref:`verbosity-levels` for more info.
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.
Notes
-----
For additional docs, see `scikitlearn's Lasso
<https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html>`_.
"""
_cpu_estimator_import_path = "sklearn.linear_model.Lasso"
@device_interop_preparation
def __init__(
self,
*,
alpha=1.0,
fit_intercept=True,
normalize=False,
max_iter=1000,
tol=1e-3,
solver="cd",
selection="cyclic",
handle=None,
output_type=None,
verbose=False,
):
# Lasso is just a special case of ElasticNet
super().__init__(
l1_ratio=1.0,
alpha=alpha,
fit_intercept=fit_intercept,
normalize=normalize,
max_iter=max_iter,
tol=tol,
solver=solver,
selection=selection,
handle=handle,
output_type=output_type,
verbose=verbose,
)
def get_param_names(self):
return list(set(super().get_param_names()) - {"l1_ratio"})