# ===============================================================================
# Copyright 2024 Intel 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.
# ===============================================================================
import numbers
import warnings
import numpy as np
from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin
from sklearn.linear_model import Ridge as _sklearn_Ridge
from sklearn.metrics import r2_score
from sklearn.utils import gen_batches
from sklearn.utils.validation import check_is_fitted, check_X_y
from daal4py.sklearn._n_jobs_support import control_n_jobs
from daal4py.sklearn.utils.validation import sklearn_check_version
from ..utils.validation import validate_data
if sklearn_check_version("1.2"):
from sklearn.utils._param_validation import Interval
from onedal.linear_model import IncrementalRidge as onedal_IncrementalRidge
from .._device_offload import dispatch, wrap_output_data
from .._utils import PatchingConditionsChain, _add_inc_serialization_note
from ..base import oneDALEstimator
[docs]
@control_n_jobs(
decorated_methods=["fit", "partial_fit", "predict", "score", "_onedal_finalize_fit"]
)
class IncrementalRidge(MultiOutputMixin, RegressorMixin, oneDALEstimator, BaseEstimator):
"""
Incremental estimator for Ridge Regression.
Allows to train Ridge Regression if data is split into batches.
Parameters
----------
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set
to False, no intercept will be used in calculations
(i.e. data is expected to be centered).
alpha : float, default=1.0
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
copy_X : bool, default=True
If True, X will be copied; else, it may be overwritten.
n_jobs : int, default=None
The number of jobs to use for the computation.
batch_size : int, default=None
The number of samples to use for each batch. Only used when calling
``fit``. If ``batch_size`` is ``None``, then ``batch_size``
is inferred from the data and set to ``5 * n_features``, to provide a
balance between approximation accuracy and memory consumption.
Attributes
----------
coef_ : array of shape (n_features, ) or (n_targets, n_features)
Estimated coefficients for the ridge regression problem.
If multiple targets are passed during the fit (y 2D), this
is a 2D array of shape (n_targets, n_features), while if only
one target is passed, this is a 1D array of length n_features.
intercept_ : float or array of shape (n_targets,)
Independent term in the linear model. Set to 0.0 if
`fit_intercept = False`.
n_features_in_ : int
Number of features seen during :term:`fit`.
n_samples_seen_ : int
The number of samples processed by the estimator. Will be reset on
new calls to fit, but increments across ``partial_fit`` calls.
It should be not less than `n_features_in_` if `fit_intercept`
is False and not less than `n_features_in_` + 1 if `fit_intercept`
is True to obtain regression coefficients.
batch_size_ : int
Inferred batch size from ``batch_size``.
Notes
-----
Sparse data formats are not supported. Input dtype must be ``float32`` or ``float64``.
%incremental_serialization_note%
"""
__doc__ = _add_inc_serialization_note(__doc__)
_onedal_incremental_ridge = staticmethod(onedal_IncrementalRidge)
if sklearn_check_version("1.2"):
_parameter_constraints: dict = {
"fit_intercept": ["boolean"],
"alpha": [Interval(numbers.Real, 0, None, closed="left")],
"copy_X": ["boolean"],
"n_jobs": [Interval(numbers.Integral, -1, None, closed="left"), None],
"batch_size": [Interval(numbers.Integral, 1, None, closed="left"), None],
}
def __init__(
self, fit_intercept=True, alpha=1.0, copy_X=True, n_jobs=None, batch_size=None
):
self.fit_intercept = fit_intercept
self.alpha = alpha
self.copy_X = copy_X
self.n_jobs = n_jobs
self.batch_size = batch_size
def _onedal_supported(self, method_name, *data):
patching_status = PatchingConditionsChain(
f"sklearn.linear_model.{self.__class__.__name__}.{method_name}"
)
return patching_status
_onedal_cpu_supported = _onedal_supported
_onedal_gpu_supported = _onedal_supported
def _onedal_predict(self, X, queue=None):
if sklearn_check_version("1.2"):
self._validate_params()
X = validate_data(self, X, accept_sparse=False, reset=False)
assert hasattr(self, "_onedal_estimator")
if self._need_to_finalize:
self._onedal_finalize_fit()
return self._onedal_estimator.predict(X, queue=queue)
def _onedal_score(self, X, y, sample_weight=None, queue=None):
return r2_score(
y, self._onedal_predict(X, queue=queue), sample_weight=sample_weight
)
def _onedal_partial_fit(self, X, y, check_input=True, queue=None):
first_pass = not hasattr(self, "n_samples_seen_") or self.n_samples_seen_ == 0
if sklearn_check_version("1.2"):
self._validate_params()
if check_input:
X, y = validate_data(
self,
X,
y,
dtype=[np.float64, np.float32],
reset=first_pass,
copy=self.copy_X,
multi_output=True,
ensure_all_finite=False,
)
if first_pass:
self.n_samples_seen_ = X.shape[0]
self.n_features_in_ = X.shape[1]
else:
self.n_samples_seen_ += X.shape[0]
onedal_params = {
"fit_intercept": self.fit_intercept,
"alpha": self.alpha,
"copy_X": self.copy_X,
}
if not hasattr(self, "_onedal_estimator"):
self._onedal_estimator = self._onedal_incremental_ridge(**onedal_params)
self._onedal_estimator.partial_fit(X, y, queue=queue)
self._need_to_finalize = True
def _onedal_finalize_fit(self):
assert hasattr(self, "_onedal_estimator")
is_underdetermined = self.n_samples_seen_ < self.n_features_in_ + int(
self.fit_intercept
)
if is_underdetermined:
raise ValueError("Not enough samples to finalize")
self._onedal_estimator.finalize_fit()
self._save_attributes()
self._need_to_finalize = False
def _onedal_fit(self, X, y, queue=None):
if sklearn_check_version("1.2"):
self._validate_params()
X, y = validate_data(
self,
X,
y,
dtype=[np.float64, np.float32],
copy=self.copy_X,
multi_output=True,
ensure_2d=True,
)
n_samples, n_features = X.shape
is_underdetermined = n_samples < n_features + int(self.fit_intercept)
if is_underdetermined:
raise ValueError("Not enough samples to run oneDAL backend")
if self.batch_size is None:
self.batch_size_ = 5 * n_features
else:
self.batch_size_ = self.batch_size
self.n_samples_seen_ = 0
if hasattr(self, "_onedal_estimator"):
self._onedal_estimator._reset()
for batch in gen_batches(n_samples, self.batch_size_):
X_batch, y_batch = X[batch], y[batch]
self._onedal_partial_fit(X_batch, y_batch, check_input=False, queue=queue)
if sklearn_check_version("1.2"):
self._validate_params()
# finite check occurs on onedal side
self.n_features_in_ = n_features
if n_samples == 1:
warnings.warn(
"Only one sample available. You may want to reshape your data array"
)
self._onedal_finalize_fit()
return self
[docs]
def partial_fit(self, X, y, check_input=True):
"""
Incrementally fits with X and y. X and y are processed as a single batch.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values, where `n_samples` is the number of samples and
`n_targets` is the number of targets.
check_input : bool, default=True
Run validate_data on X and y.
Returns
-------
self : IncrementalRidge
Returns the instance itself.
"""
dispatch(
self,
"partial_fit",
{
"onedal": self.__class__._onedal_partial_fit,
"sklearn": None,
},
X,
y,
check_input=check_input,
)
return self
[docs]
def fit(self, X, y):
"""
Fit the model with X and y, using minibatches of size ``batch_size``.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples and
``n_features`` is the number of features. It is necessary for
``n_samples`` to be not less than ``n_features`` if ``fit_intercept``
is False and not less than ``n_features`` + 1 if ``fit_intercept``
is True.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values, where `n_samples` is the number of samples and
`n_targets` is the number of targets.
Returns
-------
self : IncrementalRidge
Returns the instance itself.
"""
dispatch(
self,
"fit",
{
"onedal": self.__class__._onedal_fit,
"sklearn": None,
},
X,
y,
)
return self
[docs]
@wrap_output_data
def predict(self, X, y=None):
check_is_fitted(
self,
msg=f"This {self.__class__.__name__} instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
)
return dispatch(
self,
"predict",
{
"onedal": self.__class__._onedal_predict,
"sklearn": None,
},
X,
)
[docs]
@wrap_output_data
def score(self, X, y, sample_weight=None):
check_is_fitted(
self,
msg=f"This {self.__class__.__name__} instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
)
return dispatch(
self,
"score",
{
"onedal": self.__class__._onedal_score,
"sklearn": None,
},
X,
y,
sample_weight=sample_weight,
)
score.__doc__ = _sklearn_Ridge.score.__doc__
predict.__doc__ = _sklearn_Ridge.predict.__doc__
@property
def coef_(self):
if hasattr(self, "_onedal_estimator") and self._need_to_finalize:
self._onedal_finalize_fit()
return self._coef
@coef_.setter
def coef_(self, value):
if hasattr(self, "_onedal_estimator"):
self._onedal_estimator.coef_ = value
# checking if the model is already fitted and if so, deleting the model
if hasattr(self._onedal_estimator, "_onedal_model"):
del self._onedal_estimator._onedal_model
self._coef = value
@property
def intercept_(self):
if hasattr(self, "_onedal_estimator") and self._need_to_finalize:
self._onedal_finalize_fit()
return self._intercept
@intercept_.setter
def intercept_(self, value):
if hasattr(self, "_onedal_estimator"):
self._onedal_estimator.intercept_ = value
# checking if the model is already fitted and if so, deleting the model
if hasattr(self._onedal_estimator, "_onedal_model"):
del self._onedal_estimator._onedal_model
self._intercept = value
def _save_attributes(self):
self.n_features_in_ = self._onedal_estimator.n_features_in_
self._coef = self._onedal_estimator.coef_
self._intercept = self._onedal_estimator.intercept_