# -*- coding: utf-8 -*-
"""
Cross-validation for ARIMA and pipeline estimators.
See: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py
""" # noqa: E501
import numpy as np
import numbers
import warnings
import time
from traceback import format_exception_only
from sklearn import base
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.utils import indexable
from ._split import check_cv
from .. import metrics
from ..utils import check_endog
from ..arima.warnings import ModelFitWarning
from ..compat.sklearn import safe_indexing
__all__ = [
'cross_validate',
'cross_val_predict',
'cross_val_score',
]
_valid_scoring = {
'mean_absolute_error': mean_absolute_error,
'mean_squared_error': mean_squared_error,
'smape': metrics.smape,
}
_valid_averaging = {
'mean': np.nanmean,
'median': np.nanmedian,
}
def _check_callables(x, dct, varname):
if callable(x):
return x
if isinstance(x, str):
try:
return dct[x]
except KeyError:
valid_keys = list(dct.keys())
raise ValueError('%s can be a callable or a string in %s'
% (varname, str(valid_keys)))
raise TypeError('expected a callable or a string, but got %r (type=%s)'
% (x, type(x)))
def _check_averaging(method):
return _check_callables(method, _valid_averaging, "averaging")
def _check_scoring(metric):
return _check_callables(metric, _valid_scoring, "metric")
def _safe_split(y, exog, train, test):
"""Performs the CV indexing given the indices"""
y_train, y_test = y.take(train), y.take(test)
if exog is None:
exog_train = exog_test = None
else:
exog_train, exog_test = \
safe_indexing(exog, train), safe_indexing(exog, test)
return y_train, y_test, exog_train, exog_test
def _fit_and_score(fold, estimator, y, exog, scorer, train, test, verbose,
error_score):
"""Fit estimator and compute scores for a given dataset split."""
msg = 'fold=%i' % fold
if verbose > 1:
print("[CV] %s %s" % (msg, (64 - len(msg)) * '.'))
start_time = time.time()
y_train, y_test, exog_train, exog_test = _safe_split(y, exog, train, test)
try:
estimator.fit(y_train, exogenous=exog_train)
except Exception as e:
fit_time = time.time() - start_time
score_time = 0.0
if error_score == 'raise':
raise
else:
test_scores = error_score
warnings.warn("Estimator fit failed. The score on this train-test "
"partition will be set to %f. Details: \n%s"
% (error_score,
format_exception_only(type(e), e)[0]),
ModelFitWarning)
else:
fit_time = time.time() - start_time
# forecast h periods into the future and compute the score
preds = estimator.predict(n_periods=len(test), exogenous=exog_test)
test_scores = scorer(y_test, preds)
score_time = time.time() - start_time - fit_time
if verbose > 2:
total_time = score_time + fit_time
msg += ", score=%.3f [time=%.3f sec]" % (test_scores, total_time)
print(msg)
# TODO: if we ever want train scores, we'll need to change this signature
return test_scores, fit_time, score_time
def _fit_and_predict(fold, estimator, y, exog, train, test, verbose):
"""Fit estimator and compute scores for a given dataset split."""
msg = 'fold=%i' % fold
if verbose > 1:
print("[CV] %s %s" % (msg, (64 - len(msg)) * '.'))
start_time = time.time()
y_train, _, exog_train, exog_test = _safe_split(y, exog, train, test)
# scikit doesn't handle failures on cv predict, so we won't either.
estimator.fit(y_train, exogenous=exog_train)
fit_time = time.time() - start_time
# forecast h periods into the future
start_time = time.time()
preds = estimator.predict(n_periods=len(test), exogenous=exog_test)
pred_time = time.time() - start_time
if verbose > 2:
total_time = pred_time + fit_time
msg += " [time=%.3f sec]" % (total_time)
print(msg)
return preds, test
[docs]def cross_validate(estimator, y, exogenous=None, scoring=None, cv=None,
verbose=0, error_score=np.nan):
"""Evaluate metric(s) by cross-validation and also record fit/score times.
Parameters
----------
estimator : estimator
An estimator object that implements the ``fit`` method
y : array-like or iterable, shape=(n_samples,)
The time-series array.
exogenous : array-like, shape=[n_obs, n_vars], optional (default=None)
An optional 2-d array of exogenous variables.
scoring : str or callable, optional (default=None)
The scoring metric to use. If a callable, must adhere to the signature
``metric(true, predicted)``. Valid string scoring metrics include:
- 'smape'
- 'mean_absolute_error'
- 'mean_squared_error'
cv : BaseTSCrossValidator or None, optional (default=None)
An instance of cross-validation. If None, will use a RollingForecastCV
verbose : integer, optional
The verbosity level.
error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised.
If a numeric value is given, ModelFitWarning is raised. This parameter
does not affect the refit step, which will always raise the error.
"""
y, exog = indexable(y, exogenous)
y = check_endog(y, copy=False)
cv = check_cv(cv)
scoring = _check_scoring(scoring)
# validate the error score
if not (error_score == "raise" or isinstance(error_score, numbers.Number)):
raise ValueError('error_score should be the string "raise" or a '
'numeric value')
# TODO: in the future we might consider joblib for parallelizing, but it
# . could cause cross threads in parallelism..
results = [
_fit_and_score(fold, base.clone(estimator), y, exog,
scorer=scoring,
train=train,
test=test,
verbose=verbose,
error_score=error_score)
for fold, (train, test) in enumerate(cv.split(y, exog))]
scores, fit_times, score_times = list(zip(*results))
ret = {
'test_score': np.array(scores),
'fit_time': np.array(fit_times),
'score_time': np.array(score_times),
}
return ret
[docs]def cross_val_predict(estimator, y, exogenous=None, cv=None, verbose=0,
averaging="mean"):
"""Generate cross-validated estimates for each input data point
Parameters
----------
estimator : estimator
An estimator object that implements the ``fit`` method
y : array-like or iterable, shape=(n_samples,)
The time-series array.
exogenous : array-like, shape=[n_obs, n_vars], optional (default=None)
An optional 2-d array of exogenous variables.
cv : BaseTSCrossValidator or None, optional (default=None)
An instance of cross-validation. If None, will use a RollingForecastCV.
Note that for cross-validation predictions, the CV step cannot exceed
the CV horizon, or there will be a gap between fold predictions.
verbose : integer, optional
The verbosity level.
averaging : str or callable, one of ["median", "mean"] (default="mean")
Unlike normal CV, time series CV might have different folds (windows)
forecasting the same time step. After all forecast windows are made,
we build a matrix of y x n_folds, populating each fold's forecasts like
so::
nan nan nan # training samples
nan nan nan
nan nan nan
nan nan nan
1 nan nan # test samples
4 3 nan
3 2.5 3.5
nan 6 5
nan nan 4
We then average each time step's forecasts to end up with our final
prediction results.
Examples
--------
>>> import pmdarima as pm
>>> from pmdarima.model_selection import cross_val_predict,\
... RollingForecastCV
>>> y = pm.datasets.load_wineind()
>>> cv = RollingForecastCV(h=14, step=12)
>>> preds = cross_val_predict(
... pm.ARIMA((1, 1, 2), seasonal_order=(0, 1, 1, 12)), y, cv=cv)
>>> preds[:5]
array([30710.45743168, 34902.94929722, 17994.16587163, 22127.71167249,
25473.60876435])
"""
y, exog = indexable(y, exogenous)
y = check_endog(y, copy=False)
cv = check_cv(cv)
avgfunc = _check_averaging(averaging)
# need to be careful here:
# >>> cv = RollingForecastCV(step=6, 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,
# 60, 61, 62, 63]),
# array([64, 65, 66, 67])) <~~ 64 vs. 61
if cv.step > cv.horizon:
raise ValueError("CV step cannot be > CV horizon, or there will be a "
"gap in predictions between folds")
# clone estimator to make sure all folds are independent
prediction_blocks = [
_fit_and_predict(fold, base.clone(estimator), y, exog,
train=train,
test=test,
verbose=verbose,) # TODO: fit params?
for fold, (train, test) in enumerate(cv.split(y, exog))]
# Unlike normal CV, time series CV might have different folds (windows)
# forecasting the same time step. In this stage, we build a matrix of
# y x n_folds, populating each fold's forecasts like so:
pred_matrix = np.ones((y.shape[0], len(prediction_blocks))) * np.nan
for i, (pred_block, test_indices) in enumerate(prediction_blocks):
pred_matrix[test_indices, i] = pred_block
# from there, we need to apply nanmean (or some other metric) along rows
# to agree on a forecast for a sample.
test_mask = ~(np.isnan(pred_matrix).all(axis=1))
predictions = pred_matrix[test_mask]
return avgfunc(predictions, axis=1)
[docs]def cross_val_score(estimator, y, exogenous=None, scoring=None, cv=None,
verbose=0, error_score=np.nan):
"""Evaluate a score by cross-validation
Parameters
----------
estimator : estimator
An estimator object that implements the ``fit`` method
y : array-like or iterable, shape=(n_samples,)
The time-series array.
exogenous : array-like, shape=[n_obs, n_vars], optional (default=None)
An optional 2-d array of exogenous variables.
scoring : str or callable, optional (default=None)
The scoring metric to use. If a callable, must adhere to the signature
``metric(true, predicted)``. Valid string scoring metrics include:
- 'smape'
- 'mean_absolute_error'
- 'mean_squared_error'
cv : BaseTSCrossValidator or None, optional (default=None)
An instance of cross-validation. If None, will use a RollingForecastCV
verbose : integer, optional
The verbosity level.
error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised.
If a numeric value is given, ModelFitWarning is raised. This parameter
does not affect the refit step, which will always raise the error.
"""
cv_results = cross_validate(estimator=estimator, y=y, exogenous=exogenous,
scoring=scoring, cv=cv,
verbose=verbose,
error_score=error_score)
return cv_results['test_score']