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allow arbitrary cross validation fold indices (#3353)

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* allow arbitrary cross validation fold indices

 - use training indices passed to `folds` parameter in `training.cv`
 - update doc string

* add tests for arbitrary fold indices
This commit is contained in:
Oliver Laslett
2018-06-30 20:23:49 +01:00
committed by Philip Hyunsu Cho
parent 594bcea83e
commit 18813a26ab
2 changed files with 92 additions and 9 deletions
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40
python-package/xgboost/training.py
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@@ -231,22 +231,39 @@ def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
np.random.seed(seed)
if stratified is False and folds is None:
# Do standard k-fold cross validation
if shuffle is True:
idx = np.random.permutation(dall.num_row())
else:
idx = np.arange(dall.num_row())
idset = np.array_split(idx, nfold)
elif folds is not None and isinstance(folds, list):
idset = [x[1] for x in folds]
nfold = len(idset)
out_idset = np.array_split(idx, nfold)
in_idset = [
np.concatenate([out_idset[i] for i in range(nfold) if k != i])
for k in range(nfold)
]
elif folds is not None:
# Use user specified custom split using indices
try:
in_idset = [x[0] for x in folds]
out_idset = [x[1] for x in folds]
except TypeError:
# Custom stratification using Sklearn KFoldSplit object
splits = list(folds.split(X=dall.get_label(), y=dall.get_label()))
in_idset = [x[0] for x in splits]
out_idset = [x[1] for x in splits]
nfold = len(out_idset)
else:
# Do standard stratefied shuffle k-fold split
sfk = XGBStratifiedKFold(n_splits=nfold, shuffle=True, random_state=seed)
idset = [x[1] for x in sfk.split(X=dall.get_label(), y=dall.get_label())]
splits = list(sfk.split(X=dall.get_label(), y=dall.get_label()))
in_idset = [x[0] for x in splits]
out_idset = [x[1] for x in splits]
nfold = len(out_idset)
ret = []
for k in range(nfold):
dtrain = dall.slice(np.concatenate([idset[i] for i in range(nfold) if k != i]))
dtest = dall.slice(idset[k])
dtrain = dall.slice(in_idset[k])
dtest = dall.slice(out_idset[k])
# run preprocessing on the data set if needed
if fpreproc is not None:
dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy())
@@ -308,8 +325,13 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
Number of folds in CV.
stratified : bool
Perform stratified sampling.
folds : a KFold or StratifiedKFold instance
Sklearn KFolds or StratifiedKFolds.
folds : a KFold or StratifiedKFold instance or list of fold indices
Sklearn KFolds or StratifiedKFolds object.
Alternatively may explicitly pass sample indices for each fold.
For `n` folds, `folds` should be a length `n` list of tuples.
Each tuple is `(in,out)` where `in` is a list of indices to be used
as the training samples for the `n`th fold and `out` is a list of
indices to be used as the testing samples for the `n`th fold.
metrics : string or list of strings
Evaluation metrics to be watched in CV.
obj : function