Module fri.model.base_initmodel
Base class for our initial baseline models which are used in Gridsearch. They store the constant parameters needed in the model and the dynamic instance attributes when fitted.
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"""
Base class for our initial baseline models which are used in Gridsearch.
They store the constant parameters needed in the model
and the dynamic instance attributes when fitted.
"""
from abc import ABC, abstractmethod
from sklearn.base import BaseEstimator
class InitModel(ABC, BaseEstimator):
HYPERPARAMETER = {}
SOLVER_PARAMS = {"solver": "ECOS"}
def __init__(self, **parameters):
self.model_state = {}
self.constraints = {}
@abstractmethod
def fit(self, X, y, **kwargs):
pass
@abstractmethod
def predict(self, X):
pass
@abstractmethod
def score(self, X, y, **kwargs):
pass
def make_scorer(self):
return None, None
@property
def L1_factor(self):
try:
return self.constraints["w_l1"]
except:
raise NotImplementedError(
"Baseline model does not provide (L1) normalization constant. Expected l1 norm of model weights (e.g. w)."
)
class LUPI_InitModel(InitModel):
@property
def L1_factor_priv(self):
try:
return self.constraints["w_priv_l1"]
except:
raise NotImplementedError(
"Baseline model does not provide LUPI (L1) normalization constant. Expected l1 norm of LUPI model weights (e.g. w_priv)."
)
Classes
InitModel
class InitModel(
**parameters
)
Helper class that provides a standard way to create an ABC using inheritance.
View Source
class InitModel(ABC, BaseEstimator):
HYPERPARAMETER = {}
SOLVER_PARAMS = {"solver": "ECOS"}
def __init__(self, **parameters):
self.model_state = {}
self.constraints = {}
@abstractmethod
def fit(self, X, y, **kwargs):
pass
@abstractmethod
def predict(self, X):
pass
@abstractmethod
def score(self, X, y, **kwargs):
pass
def make_scorer(self):
return None, None
@property
def L1_factor(self):
try:
return self.constraints["w_l1"]
except:
raise NotImplementedError(
"Baseline model does not provide (L1) normalization constant. Expected l1 norm of model weights (e.g. w)."
)
Ancestors (in MRO)
- abc.ABC
- sklearn.base.BaseEstimator
Descendants
- fri.model.base_initmodel.LUPI_InitModel
- fri.model.classification.Classification_SVM
- fri.model.lupi_classification.LUPI_Classification_SVM
- fri.model.ordinal_regression.OrdinalRegression_SVM
- fri.model.regression.Regression_SVR
Class variables
HYPERPARAMETER
SOLVER_PARAMS
Instance variables
L1_factor
Methods
fit
def fit(
self,
X,
y,
**kwargs
)
View Source
@abstractmethod
def fit(self, X, y, **kwargs):
pass
get_params
def get_params(
self,
deep=True
)
Get parameters for this estimator.
Parameters
deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
params : dict Parameter names mapped to their values.
View Source
def get_params(self, deep=True):
"""
Get parameters for this estimator.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : dict
Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
value = getattr(self, key)
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
make_scorer
def make_scorer(
self
)
View Source
def make_scorer(self):
return None, None
predict
def predict(
self,
X
)
View Source
@abstractmethod
def predict(self, X):
pass
score
def score(
self,
X,
y,
**kwargs
)
View Source
@abstractmethod
def score(self, X, y, **kwargs):
pass
set_params
def set_params(
self,
**params
)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
Parameters
**params : dict Estimator parameters.
Returns
self : estimator instance Estimator instance.
View Source
def set_params(self, **params):
"""
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : estimator instance
Estimator instance.
"""
if not params:
# Simple optimization to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
nested_params = defaultdict(dict) # grouped by prefix
for key, value in params.items():
key, delim, sub_key = key.partition('__')
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self))
if delim:
nested_params[key][sub_key] = value
else:
setattr(self, key, value)
valid_params[key] = value
for key, sub_params in nested_params.items():
valid_params[key].set_params(**sub_params)
return self
LUPI_InitModel
class LUPI_InitModel(
**parameters
)
Helper class that provides a standard way to create an ABC using inheritance.
View Source
class LUPI_InitModel(InitModel):
@property
def L1_factor_priv(self):
try:
return self.constraints["w_priv_l1"]
except:
raise NotImplementedError(
"Baseline model does not provide LUPI (L1) normalization constant. Expected l1 norm of LUPI model weights (e.g. w_priv)."
)
Ancestors (in MRO)
- fri.model.base_initmodel.InitModel
- abc.ABC
- sklearn.base.BaseEstimator
Descendants
- fri.model.lupi_ordinal_regression.LUPI_OrdinalRegression_SVM
- fri.model.lupi_regression.LUPI_Regression_SVM
Class variables
HYPERPARAMETER
SOLVER_PARAMS
Instance variables
L1_factor
L1_factor_priv
Methods
fit
def fit(
self,
X,
y,
**kwargs
)
View Source
@abstractmethod
def fit(self, X, y, **kwargs):
pass
get_params
def get_params(
self,
deep=True
)
Get parameters for this estimator.
Parameters
deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
params : dict Parameter names mapped to their values.
View Source
def get_params(self, deep=True):
"""
Get parameters for this estimator.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : dict
Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
value = getattr(self, key)
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
make_scorer
def make_scorer(
self
)
View Source
def make_scorer(self):
return None, None
predict
def predict(
self,
X
)
View Source
@abstractmethod
def predict(self, X):
pass
score
def score(
self,
X,
y,
**kwargs
)
View Source
@abstractmethod
def score(self, X, y, **kwargs):
pass
set_params
def set_params(
self,
**params
)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
Parameters
**params : dict Estimator parameters.
Returns
self : estimator instance Estimator instance.
View Source
def set_params(self, **params):
"""
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : estimator instance
Estimator instance.
"""
if not params:
# Simple optimization to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
nested_params = defaultdict(dict) # grouped by prefix
for key, value in params.items():
key, delim, sub_key = key.partition('__')
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self))
if delim:
nested_params[key][sub_key] = value
else:
setattr(self, key, value)
valid_params[key] = value
for key, sub_params in nested_params.items():
valid_params[key].set_params(**sub_params)
return self