147 lines
4.6 KiB
Python
147 lines
4.6 KiB
Python
# -*- 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.
|
|
"""
|