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_pipeline.py
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_pipeline.py
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#!/usr/bin/env python3 -u
# -*- coding: utf-8 -*-
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__author__ = ["Markus Löning", "Martin Walter"]
__all__ = ["TransformedTargetForecaster", "ForecastingPipeline"]
from sklearn.base import clone
from sktime.base import _HeterogenousMetaEstimator
from sktime.forecasting.base._base import BaseForecaster
from sktime.forecasting.base._base import DEFAULT_ALPHA
from sktime.transformations.base import _SeriesToSeriesTransformer
from sktime.utils.validation.series import check_series
class _Pipeline(
BaseForecaster,
_HeterogenousMetaEstimator,
):
def _check_steps(self):
"""Check Steps
Parameters
----------
self : an instance of self
Returns
-------
step : Returns step.
"""
names, estimators = zip(*self.steps)
# validate names
self._check_names(names)
# validate estimators
transformers = estimators[:-1]
forecaster = estimators[-1]
valid_transformer_type = _SeriesToSeriesTransformer
for transformer in transformers:
if not isinstance(transformer, valid_transformer_type):
raise TypeError(
f"All intermediate steps should be "
f"instances of {valid_transformer_type}, "
f"but transformer: {transformer} is not."
)
valid_forecaster_type = BaseForecaster
if not isinstance(forecaster, valid_forecaster_type):
raise TypeError(
f"Last step of {self.__class__.__name__} must be of type: "
f"{valid_forecaster_type}, "
f"but forecaster: {forecaster} is not."
)
# Shallow copy
return list(self.steps)
def _iter_transformers(self, reverse=False):
# exclude final forecaster
steps = self.steps_[:-1]
if reverse:
steps = reversed(steps)
for idx, (name, transformer) in enumerate(steps):
yield idx, name, transformer
def __len__(self):
"""
Returns the length of the Pipeline
"""
return len(self.steps)
@property
def named_steps(self):
"""Map the steps to a dictionary"""
return dict(self.steps)
def get_params(self, deep=True):
"""Get parameters for this estimator.
Parameters
----------
deep : boolean, optional
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.
"""
return self._get_params("steps", deep=deep)
def set_params(self, **kwargs):
"""Set the parameters of this estimator.
Valid parameter keys can be listed with ``get_params()``.
Returns
-------
self
"""
self._set_params("steps", **kwargs)
return self
class ForecastingPipeline(_Pipeline):
"""
Pipeline for forecasting with exogenous data to apply transformers
to the exogenous serieses. The forecaster can also be a
TransformedTargetForecaster containing transformers to
transform y. ForecastingPipeline is only applying the given transformers
to X.
Parameters
----------
steps : list
List of tuples like ("name", forecaster/transformer)
Example
-------
>>> from sktime.datasets import load_longley
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.compose import ForecastingPipeline
>>> from sktime.transformations.series.impute import Imputer
>>> from sktime.transformations.series.adapt import TabularToSeriesAdaptor
>>> from sktime.forecasting.base import ForecastingHorizon
>>> from sktime.forecasting.model_selection import temporal_train_test_split
>>> from sklearn.preprocessing import MinMaxScaler
>>> y, X = load_longley()
>>> y_train, _, X_train, X_test = temporal_train_test_split(y, X)
>>> fh = ForecastingHorizon(X_test.index, is_relative=False)
>>> pipe = ForecastingPipeline(steps=[
... ("imputer", Imputer(method="mean")),
... ("minmaxscaler", TabularToSeriesAdaptor(MinMaxScaler())),
... ("forecaster", NaiveForecaster(strategy="drift"))])
>>> pipe.fit(y_train, X_train)
ForecastingPipeline(...)
>>> y_pred = pipe.predict(fh=fh, X=X_test)
"""
_required_parameters = ["steps"]
_tags = {
"univariate-only": False,
"requires-fh-in-fit": False,
"handles-missing-data": False,
}
def __init__(self, steps):
self.steps = steps
self.steps_ = self._check_steps()
super(ForecastingPipeline, self).__init__()
def _fit(self, y, X=None, fh=None):
"""Fit to training data.
Parameters
----------
y : pd.Series
Target time series to which to fit the forecaster.
fh : int, list or np.array, optional (default=None)
The forecasters horizon with the steps ahead to to predict.
X : pd.DataFrame, required
Exogenous variables are ignored
Returns
-------
self : returns an instance of self.
"""
# Some transformers can not deal with X=None, therefore X is mandatory
self._set_y_X(y, X)
# If X is not given, just passthrough the data without transformation
if self._X is not None:
# transform X
for step_idx, name, transformer in self._iter_transformers():
t = clone(transformer)
X = t.fit_transform(X)
self.steps_[step_idx] = (name, t)
# fit forecaster
name, forecaster = self.steps[-1]
f = clone(forecaster)
f.fit(y, X, fh)
self.steps_[-1] = (name, f)
return self
def _predict(self, fh=None, X=None, return_pred_int=False, alpha=DEFAULT_ALPHA):
"""Forecast time series at future horizon.
Parameters
----------
fh : int, list, np.array or ForecastingHorizon
Forecasting horizon
X : pd.DataFrame, required
Exogenous time series
return_pred_int : bool, optional (default=False)
If True, returns prediction intervals for given alpha values.
alpha : float or list, optional (default=DEFAULT_ALPHA)
Returns
-------
y_pred : pd.Series
Point predictions
y_pred_int : pd.DataFrame - only if return_pred_int=True
Prediction intervals
"""
forecaster = self.steps_[-1][1]
# If X is not given, just passthrough the data without transformation
if self._X is not None:
# transform X before doing prediction
for _, _, transformer in self._iter_transformers():
X = transformer.transform(X)
y_pred = forecaster.predict(fh, X, return_pred_int=return_pred_int, alpha=alpha)
return y_pred
def _update(self, y, X=None, update_params=True):
"""Update fitted parameters
Parameters
----------
y : pd.Series
X : pd.DataFrame, required
update_params : bool, optional (default=True)
Returns
-------
self : an instance of self
"""
# If X is not given, just passthrough the data without transformation
if self._X is not None:
for step_idx, name, transformer in self._iter_transformers():
if hasattr(transformer, "update"):
transformer.update(X, update_params=update_params)
self.steps_[step_idx] = (name, transformer)
name, forecaster = self.steps_[-1]
forecaster.update(y=y, X=X, update_params=update_params)
self.steps_[-1] = (name, forecaster)
return self
# removed transform and inverse_transform as long as y can only be a pd.Series
# def transform(self, Z, X=None):
# self.check_is_fitted()
# Zt = check_series(Z, enforce_multivariate=True)
# for _, _, transformer in self._iter_transformers():
# Zt = transformer.transform(Zt)
# return Zt
# def inverse_transform(self, Z, X=None):
# self.check_is_fitted()
# Zt = check_series(Z, enforce_multivariate=True)
# for _, _, transformer in self._iter_transformers(reverse=True):
# if not _has_tag(transformer, "skip-inverse-transform"):
# Zt = transformer.inverse_transform(Zt)
# return Zt
class TransformedTargetForecaster(_Pipeline, _SeriesToSeriesTransformer):
"""
Meta-estimator for forecasting transformed time series.
Pipeline functionality to apply transformers to the target series.
Parameters
----------
steps : list
List of tuples like ("name", forecaster/transformer)
Example
-------
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.compose import TransformedTargetForecaster
>>> from sktime.transformations.series.impute import Imputer
>>> from sktime.transformations.series.detrend import Deseasonalizer
>>> y = load_airline()
>>> pipe = TransformedTargetForecaster(steps=[
... ("imputer", Imputer(method="mean")),
... ("detrender", Deseasonalizer()),
... ("forecaster", NaiveForecaster(strategy="drift"))])
>>> pipe.fit(y)
TransformedTargetForecaster(...)
>>> y_pred = pipe.predict(fh=[1,2,3])
"""
_required_parameters = ["steps"]
_tags = {
"univariate-only": True,
"requires-fh-in-fit": False,
"handles-missing-data": False,
}
def __init__(self, steps):
self.steps = steps
self.steps_ = self._check_steps()
super(TransformedTargetForecaster, self).__init__()
def _fit(self, y, X=None, fh=None):
"""Fit to training data.
Parameters
----------
y : pd.Series
Target time series to which to fit the forecaster.
fh : int, list or np.array, optional (default=None)
The forecasters horizon with the steps ahead to to predict.
X : pd.DataFrame, optional (default=None)
Exogenous variables are ignored
Returns
-------
self : returns an instance of self.
"""
self._set_y_X(y, X)
# transform
for step_idx, name, transformer in self._iter_transformers():
t = clone(transformer)
y = t.fit_transform(y)
self.steps_[step_idx] = (name, t)
# fit forecaster
name, forecaster = self.steps[-1]
f = clone(forecaster)
f.fit(y, X, fh)
self.steps_[-1] = (name, f)
return self
def _predict(self, fh=None, X=None, return_pred_int=False, alpha=DEFAULT_ALPHA):
"""Forecast time series at future horizon.
Parameters
----------
fh : int, list, np.array or ForecastingHorizon
Forecasting horizon
X : pd.DataFrame, optional (default=None)
Exogenous time series
return_pred_int : bool, optional (default=False)
If True, returns prediction intervals for given alpha values.
alpha : float or list, optional (default=DEFAULT_ALPHA)
Returns
-------
y_pred : pd.Series
Point predictions
y_pred_int : pd.DataFrame - only if return_pred_int=True
Prediction intervals
"""
forecaster = self.steps_[-1][1]
y_pred = forecaster.predict(fh, X, return_pred_int=return_pred_int, alpha=alpha)
for _, _, transformer in self._iter_transformers(reverse=True):
# skip sktime transformers where inverse transform
# is not wanted ur meaningful (e.g. Imputer, HampelFilter)
skip_trafo = transformer.get_tag("skip-inverse-transform", False)
if not skip_trafo:
y_pred = transformer.inverse_transform(y_pred)
return y_pred
def _update(self, y, X=None, update_params=True):
"""Update fitted parameters
Parameters
----------
y : pd.Series
X : pd.DataFrame, optional (default=None)
update_params : bool, optional (default=True)
Returns
-------
self : an instance of self
"""
for step_idx, name, transformer in self._iter_transformers():
if hasattr(transformer, "update"):
transformer.update(y, X, update_params=update_params)
self.steps_[step_idx] = (name, transformer)
name, forecaster = self.steps_[-1]
forecaster.update(y=y, X=X, update_params=update_params)
self.steps_[-1] = (name, forecaster)
return self
def transform(self, Z, X=None):
self.check_is_fitted()
zt = check_series(Z, enforce_univariate=True)
for _, _, transformer in self._iter_transformers():
zt = transformer.transform(zt, X)
return zt
def inverse_transform(self, Z, X=None):
self.check_is_fitted()
zt = check_series(Z, enforce_univariate=True)
for _, _, transformer in self._iter_transformers(reverse=True):
if not transformer.get_tag("skip-inverse-transform", False):
zt = transformer.inverse_transform(zt, X)
return zt