# Source code for pmdarima.model_selection._split

```
# -*- coding: utf-8 -*-
import abc
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.utils.validation import indexable
from sklearn.model_selection import train_test_split as tts
__all__ = [
'check_cv',
'train_test_split',
'RollingForecastCV',
'SlidingWindowForecastCV'
]
[docs]def train_test_split(*arrays, test_size=None, train_size=None):
"""Split arrays or matrices into sequential train and test subsets
Creates train/test splits over endogenous arrays an optional exogenous
arrays. This is a wrapper of scikit-learn's ``train_test_split`` that
does not shuffle.
Parameters
----------
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.
test_size : float, int or None, optional (default=None)
If float, should be between 0.0 and 1.0 and represent the proportion
of the dataset to include in the test split. If int, represents the
absolute number of test samples. If None, the value is set to the
complement of the train size. If ``train_size`` is also None, it will
be set to 0.25.
train_size : float, int, or None, (default=None)
If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.
Returns
-------
splitting : list, length=2 * len(arrays)
List containing train-test split of inputs.
Examples
--------
>>> import pmdarima as pm
>>> from pmdarima.model_selection import train_test_split
>>> y = pm.datasets.load_sunspots()
>>> y_train, y_test = train_test_split(y, test_size=50)
>>> y_test.shape
(50,)
The split is sequential:
>>> import numpy as np
>>> from numpy.testing import assert_array_equal
>>> assert_array_equal(y, np.concatenate([y_train, y_test]))
"""
return tts(
*arrays,
shuffle=False,
stratify=None,
test_size=test_size,
train_size=train_size)
class BaseTSCrossValidator(BaseEstimator, metaclass=abc.ABCMeta):
"""Base class for time series cross validators
Based on the scikit-learn base cross-validator with alterations to fit the
time series interface.
"""
def __init__(self, h, step):
if h < 1:
raise ValueError("h must be a positive value")
if step < 1:
raise ValueError("step must be a positive value")
self.h = h
self.step = step
@property
def horizon(self):
"""The forecast horizon for the cross-validator"""
return self.h
def split(self, y, X=None):
"""Generate indices to split data into training and test sets
Parameters
----------
y : array-like or iterable, shape=(n_samples,)
The time-series array.
X : array-like, shape=[n_obs, n_vars], optional (default=None)
An optional 2-d array of exogenous variables.
Yields
------
train : np.ndarray
The training set indices for the split
test : np.ndarray
The test set indices for the split
"""
y, X = indexable(y, X)
indices = np.arange(y.shape[0])
for train_index, test_index in self._iter_train_test_masks(y, X):
train_index = indices[train_index]
test_index = indices[test_index]
yield train_index, test_index
def _iter_train_test_masks(self, y, X):
"""Generate boolean masks corresponding to test sets"""
for train_index, test_index in self._iter_train_test_indices(y, X):
train_mask = np.zeros(y.shape[0], dtype=bool)
test_mask = np.zeros(y.shape[0], dtype=bool)
train_mask[train_index] = True
test_mask[test_index] = True
yield train_mask, test_mask
@abc.abstractmethod
def _iter_train_test_indices(self, y, X):
"""Yields the train/test indices"""
[docs]class RollingForecastCV(BaseTSCrossValidator):
"""Use a rolling forecast to perform cross validation
Sometimes called “evaluation on a rolling forecasting origin” [1], this
approach to CV incrementally grows the training size while using a single
future sample as a test sample, e.g.:
With h == 1::
array([15136., 16733., 20016., 17708., 18019., 19227., 22893., 23739.])
1st: ~~~~ tr ~~~~ tr ~~~~ te
2nd: ~~~~ tr ~~~~ tr ~~~~ tr ~~~~ te
3rd: ~~~~ tr ~~~~ tr ~~~~ tr ~~~~ tr ~~~~ te
With h == 2::
array([15136., 16733., 20016., 17708., 18019., 19227., 22893., 23739.])
1st: ~~~~ tr ~~~~ tr ~~~~ te ~~~~ te
2nd: ~~~~ tr ~~~~ tr ~~~~ tr ~~~~ te ~~~~ te
3rd: ~~~~ tr ~~~~ tr ~~~~ tr ~~~~ tr ~~~~ te ~~~~ te
Parameters
----------
h : int, optional (default=1)
The forecasting horizon, or the number of steps into the future after
the last training sample for the test set.
step : int, optional (default=1)
The size of step taken to increase the training sample size.
initial : int, optional (default=None)
The initial training size. If None, will use 1 // 3 the length of the
time series.
Examples
--------
With a step size of one and a forecasting horizon of one, the training size
will grow by 1 for each step, and the test index will be 1 + the last
training index:
>>> import pmdarima as pm
>>> from pmdarima.model_selection import RollingForecastCV
>>> wineind = pm.datasets.load_wineind()
>>> cv = RollingForecastCV()
>>> cv_generator = cv.split(wineind)
>>> next(cv_generator)
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57]), array([58]))
>>> next(cv_generator)
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58]), array([59]))
With a step size of 2 and a forecasting horizon of 4, the training size
will grow by 2 for each step, and the test index will 4 + the last index
in the training fold:
>>> cv = RollingForecastCV(step=2, h=4)
>>> cv_generator = cv.split(wineind)
>>> next(cv_generator)
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57]), array([58, 59, 60, 61]))
>>> next(cv_generator)
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59]), array([60, 61, 62, 63]))
See Also
--------
SlidingWindowForecastCV
References
----------
.. [1] https://robjhyndman.com/hyndsight/tscv/
"""
[docs] def __init__(self, h=1, step=1, initial=None):
super().__init__(h, step)
self.initial = initial
def _iter_train_test_indices(self, y, X):
"""Yields the train/test indices"""
n_samples = y.shape[0]
initial = self.initial
step = self.step
h = self.h
if initial is not None:
if initial < 1:
raise ValueError("Initial training size must be a positive "
"integer")
elif initial + h > n_samples:
raise ValueError("The initial training size + forecasting "
"horizon would exceed the length of the "
"given timeseries!")
else:
# if it's 1, we have another problem..
initial = max(1, n_samples // 3)
# Determine the number of iterations that will take place. Must
# guarantee that the forecasting horizon will not over-index the series
all_indices = np.arange(n_samples)
window_start = 0
window_end = initial
while True:
if window_end + h > n_samples:
break
train_indices = all_indices[window_start: window_end]
test_indices = all_indices[window_end: window_end + h]
window_end += step
yield train_indices, test_indices
[docs]class SlidingWindowForecastCV(BaseTSCrossValidator):
"""Use a sliding window to perform cross validation
This approach to CV slides a window over the training samples while using
several future samples as a test set. While similar to the
:class:`RollingForecastCV`, it differs in that the train set does not grow,
but rather shifts.
Parameters
----------
h : int, optional (default=1)
The forecasting horizon, or the number of steps into the future after
the last training sample for the test set.
step : int, optional (default=1)
The size of step taken between training folds.
window_size : int or None, optional (default=None)
The size of the rolling window to use. If None, a rolling window of
size n_samples // 5 will be used.
Examples
--------
With a step size of one and a forecasting horizon of one, the training size
will grow by 1 for each step, and the test index will be 1 + the last
training index. Notice the sliding window also adjusts where the training
sample begins for each fold:
>>> import pmdarima as pm
>>> from pmdarima.model_selection import SlidingWindowForecastCV
>>> wineind = pm.datasets.load_wineind()
>>> cv = SlidingWindowForecastCV()
>>> cv_generator = cv.split(wineind)
>>> next(cv_generator)
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34]), array([35]))
>>> next(cv_generator)
(array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35]), array([36]))
With a step size of 4, a forecasting horizon of 6, and a window size of 12,
the training size will grow by 4 for each step, and the test index will 6 +
the last index in the training fold:
>>> cv = SlidingWindowForecastCV(step=4, h=6, window_size=12)
>>> cv_generator = cv.split(wineind)
>>> next(cv_generator)
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),
array([12, 13, 14, 15, 16, 17]))
>>> next(cv_generator)
(array([ 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]),
array([16, 17, 18, 19, 20, 21]))
See Also
--------
RollingForecastCV
References
----------
.. [1] https://robjhyndman.com/hyndsight/tscv/
"""
[docs] def __init__(self, h=1, step=1, window_size=None):
super().__init__(h, step)
self.window_size = window_size
def _iter_train_test_indices(self, y, X):
"""Yields the train/test indices"""
n_samples = y.shape[0]
window_size = self.window_size
step = self.step
h = self.h
if window_size is not None:
if window_size + h > n_samples:
raise ValueError("The window_size + forecasting "
"horizon would exceed the length of the "
"given timeseries!")
else:
# TODO: what's a good sane default for this?
window_size = max(3, n_samples // 5)
if window_size < 3:
raise ValueError("window_size must be > 2")
indices = np.arange(n_samples)
window_start = 0
while True:
window_end = window_start + window_size
if window_end + h > n_samples:
break
train_indices = indices[window_start: window_end]
test_indices = indices[window_end: window_end + h]
window_start += step
yield train_indices, test_indices
[docs]def check_cv(cv=None):
"""Input checker utility for building a cross-validator
Parameters
----------
cv : BaseTSCrossValidator or None, optional (default=None)
An instance of CV or None. Possible inputs:
- None, to use a default RollingForecastCV
- A BaseTSCrossValidator as a passthrough
"""
cv = RollingForecastCV() if cv is None else cv
if not isinstance(cv, BaseTSCrossValidator):
raise TypeError("cv should be an instance of BaseTSCrossValidator or "
"None, but got %r (type=%s)" % (cv, type(cv)))
return cv
```