def __init__(self, fit_intercept: bool = True, threshold: float = 1,
n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
ModelLipschitz.__init__(self)
self.n_threads = n_threads
self.threshold = threshold
def fit(self, features, labels):
"""Set the data into the model object
Parameters
----------
features : {`numpy.ndarray`, `scipy.sparse.csr_matrix`}, shape=(n_samples, n_features)
The features matrix, either dense or sparse
labels : `numpy.ndarray`, shape=(n_samples,)
The labels vector
Returns
-------
output : `ModelHuber`
The current instance with given data
"""
ModelFirstOrder.fit(self, features, labels)
ModelGeneralizedLinear.fit(self, features, labels)
ModelLipschitz.fit(self, features, labels)
self._set("_model", _ModelHuber(self.features,
self.labels,
self.fit_intercept,
self.threshold,
self.n_threads))
return self
def __init__(self, fit_intercept: bool = True, smoothness: float = 1.,
n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
ModelLipschitz.__init__(self)
self.n_threads = n_threads
self.smoothness = smoothness
def __init__(self,
fit_intercept: bool = True,
link: str = "exponential",
n_threads: int = 1):
ModelSecondOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
ModelSelfConcordant.__init__(self)
self._set("_link", None)
self.link = link
self.n_threads = n_threads
def fit(self, features, labels):
"""Set the data into the model object
Parameters
----------
features : {`numpy.ndarray`, `scipy.sparse.csr_matrix`}, shape=(n_samples, n_features)
The features matrix, either dense or sparse
labels : `numpy.ndarray`, shape=(n_samples,)
The labels vector
Returns
-------
output : `ModelPoisReg`
The current instance with given data
"""
ModelFirstOrder.fit(self, features, labels)
ModelGeneralizedLinear.fit(self, features, labels)
self._set("_model", self._build_cpp_model(features.dtype))
return self
def set_model(self, model: ModelGeneralizedLinear):
"""Set model in the solver
Parameters
----------
model : `ModelGeneralizedLinear`
Sets the model in the solver. The model gives the first
order information about the model (loss, gradient, among
other things). SAGA only accepts childs of `ModelGeneralizedLinear`
Returns
-------
output : `Solver`
The `Solver` with given model
"""
if not isinstance(model, ModelGeneralizedLinear):
raise ValueError("SAGA accepts only childs of "
"`ModelGeneralizedLinear`")
if hasattr(model, "n_threads"):
model.n_threads = self.n_threads
return SolverFirstOrderSto.set_model(self, model)
def __init__(self, fit_intercept: bool = True, n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
self.n_threads = n_threads
def __init__(self, fit_intercept: bool = True):
ModelGeneralizedLinear.__init__(self, fit_intercept)
