# -*- coding: utf-8 -*-
#
# Base ARIMA pre-processing classes. Don't import this in __init__, or we'll
# potentially get circular imports in sub-classes
from sklearn.base import BaseEstimator, TransformerMixin
import abc
from ..compat.numpy import DTYPE
from ..utils import check_exog, check_endog
__all__ = [
"BaseTransformer"
]
class BaseTransformer(BaseEstimator, TransformerMixin, metaclass=abc.ABCMeta):
"""A base pre-processing transformer
A subclass of the scikit-learn ``TransformerMixin``, the purpose of the
``BaseTransformer`` is to learn characteristics from the training set and
apply them in a transformation to the test set. For instance, a transformer
aimed at normalizing features in an exogenous array would learn the means
and standard deviations of the training features in the ``fit`` method, and
then center and scale the features in the ``transform`` method.
The ``fit`` method should only ever be applied to the *training* set to
avoid any data leakage, while ``transform`` may be applied to any dataset
of the same schema.
"""
@staticmethod
def _check_y_X(y, X):
"""Validate input"""
# Do not force finite, since a transformer's goal may be imputation.
if y is not None:
y = check_endog(
y,
dtype=DTYPE,
copy=True,
force_all_finite=False,
preserve_series=False,
)
if X is not None:
X = check_exog(
X,
dtype=None,
copy=True,
force_all_finite=False,
)
return y, X
def fit_transform(self, y, X=None, **kwargs):
"""Fit and transform the arrays
Parameters
----------
y : array-like or None, shape=(n_samples,)
The endogenous (time-series) array.
X : array-like or None, shape=(n_samples, n_features), optional
The exogenous array of additional covariates.
**kwargs : keyword args
Keyword arguments required by the transform function.
"""
self.fit(y, X)
return self.transform(y, X, **kwargs)
@abc.abstractmethod
def fit(self, y, X):
"""Fit the transformer
The purpose of the ``fit`` method is to learn a set of statistics or
characteristics from the training set, and store them as "fit
attributes" within the instance. A transformer *must* be fit before
the transformation can be applied to a dataset in the ``transform``
method.
Parameters
----------
y : array-like or None, shape=(n_samples,)
The endogenous (time-series) array.
X : array-like or None, shape=(n_samples, n_features)
The exogenous array of additional covariates.
Returns
-------
self : BaseTransformer
The scikit-learn convention is for the ``fit`` method to return
the instance of the transformer, ``self``. This allows us to
string ``fit(...).transform(...)`` calls together.
"""
@abc.abstractmethod
def transform(self, y, X, **kwargs):
"""Transform the new array
Apply the transformation to the array after learning the training set's
characteristics in the ``fit`` method.
Parameters
----------
y : array-like or None, shape=(n_samples,)
The endogenous (time-series) array.
X : array-like or None, shape=(n_samples, n_features)
The exogenous array of additional covariates.
**kwargs : keyword args
Keyword arguments required by the transform function.
Returns
-------
y : array-like or None
The transformed y array
X : array-like or None
The transformed X array
"""
class UpdatableMixin:
"""Transformers that may update their params, like ARIMAs"""
def _check_endog(self, y):
if y is None:
raise ValueError("endog array cannot be None when updating")
# TODO: remove default None value for X when we remove kwargs
def update_and_transform(self, y, X=None, **kwargs):
"""Update the params and return the transformed arrays
Parameters
----------
y : array-like or None, shape=(n_samples,)
The endogenous (time-series) array.
X : array-like or None, shape=(n_samples, n_features)
The exogenous array of additional covariates.
**kwargs : keyword args
Keyword arguments required by the transform function.
"""