class PowerTransformedTargetRegressor(sklearn.compose.TransformedTargetRegressor):
def __init__(
self,
regressor=None,
*,
power_transformer_method="box-cox",
power_transformer_standardize=True,
**kwargs
):
self.regressor = regressor
self.power_transformer_method = power_transformer_method
self.power_transformer_standardize = power_transformer_standardize
self.transformer = PowerTransformer(
method=self.power_transformer_method,
standardize=self.power_transformer_standardize,
)
self.func = None
self.inverse_func = None
self.check_inverse = False
self._fit_vars = set()
self.set_params(**kwargs)
def _carry_over_regressor_fit_vars(self):
self._clear_regressor_fit_vars()
for k, v in get_all_object_vars_and_properties(self.regressor_).items():
if is_fit_var(k):
try:
setattr(self, k, v)
self._fit_vars.add(k)
except:
pass
def _clear_regressor_fit_vars(self, all: bool = False):
vars_to_remove = []
try:
for var in self._fit_vars:
if all or var not in get_all_object_vars_and_properties(self.regressor_):
vars_to_remove.append(var)
for var in vars_to_remove:
try:
delattr(self, var)
self._fit_vars.remove(var)
except:
pass
except:
pass
def fit(self, X, y, **fit_params):
"""Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like of shape (n_samples,)
Target values.
**fit_params : dict
Parameters passed to the ``fit`` method of the underlying
regressor.
Returns
-------
self : object
"""
r = super().fit(X, y, **fit_params)
self._carry_over_regressor_fit_vars()
return r
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 pipelines). 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 : object
Estimator instance.
"""
if "power_transformer_method" in params:
self.power_transformer_method = params["power_transformer_method"]
params.pop("power_transformer_method")
self.transformer.set_params(**{"method": self.power_transformer_method})
if "power_transformer_standardize" in params:
self.power_transformer_standardize = params["power_transformer_standardize"]
params.pop("power_transformer_standardize")
self.transformer.set_params(
**{"standardize": self.power_transformer_standardize}
)
return self.regressor.set_params(**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 : mapping of string to any
Parameter names mapped to their values.
"""
r = self.regressor.get_params(deep=deep)
r["power_transformer_method"] = self.power_transformer_method
r["power_transformer_standardize"] = self.power_transformer_standardize
r["regressor"] = self.regressor
return r
class PowerTransformedTargetRegressor(
sklearn.compose.TransformedTargetRegressor):
def __init__(
self,
regressor=None,
*,
power_transformer_method="box-cox",
power_transformer_standardize=True,
**kwargs,
):
self.regressor = regressor
self.power_transformer_method = power_transformer_method
self.power_transformer_standardize = power_transformer_standardize
self.transformer = PowerTransformer(
method=self.power_transformer_method,
standardize=self.power_transformer_standardize,
)
self.func = None
self.inverse_func = None
self.check_inverse = False
self._fit_vars = set()
self.set_params(**kwargs)
def __getattr__(self, name: str):
# override getattr to allow grabbing of regressor attrs
if name not in ("regressor", "regressor_"):
if hasattr(self, "regressor_"):
return getattr(self.regressor_, name)
return getattr(self.regressor, name)
def fit(self, X, y, **fit_params):
"""Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like of shape (n_samples,)
Target values.
**fit_params : dict
Parameters passed to the ``fit`` method of the underlying
regressor.
Returns
-------
self : object
"""
# workaround - for some reason, if data is in float32, super().fit() will return an array of 0s
# this also happens in pure scikit-learn
y = y.astype("float64")
super().fit(X, y, **fit_params)
return self
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 pipelines). 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 : object
Estimator instance.
"""
if "regressor" in params:
self.regressor = params.pop("regressor")
if "power_transformer_method" in params:
self.power_transformer_method = params.pop(
"power_transformer_method")
self.transformer.set_params(
**{"method": self.power_transformer_method})
if "power_transformer_standardize" in params:
self.power_transformer_standardize = params.pop(
"power_transformer_standardize")
self.transformer.set_params(
**{"standardize": self.power_transformer_standardize})
return self.regressor.set_params(**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 : mapping of string to any
Parameter names mapped to their values.
"""
r = self.regressor.get_params(deep=deep)
r["power_transformer_method"] = self.power_transformer_method
r["power_transformer_standardize"] = self.power_transformer_standardize
r["regressor"] = self.regressor
return r
