updating docs for cv
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@ -283,10 +283,14 @@ def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None):
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ret.append(CVPack(dtrain, dtest, plst))
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return ret
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def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True, trial=None):
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def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True, trial=0):
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# pylint: disable=invalid-name
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"""
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Aggregate cross-validation results.
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If show_progress is true, progress is displayed in every call. If
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show_progress is an integer, progress will only be displayed every
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`show_progress` trees, tracked via trial.
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"""
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cvmap = {}
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idx = rlist[0].split()[0]
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@ -320,8 +324,6 @@ def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True, trial=None)
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index.extend([k + '-mean', k + '-std'])
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results.extend([mean, std])
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if as_pandas:
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try:
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import pandas as pd
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@ -376,9 +378,11 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
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as_pandas : bool, default True
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Return pd.DataFrame when pandas is installed.
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If False or pandas is not installed, return np.ndarray
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show_progress : bool or None, default None
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show_progress : bool, int, or None, default None
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Whether to display the progress. If None, progress will be displayed
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when np.ndarray is returned.
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when np.ndarray is returned. If True, progress will be displayed at
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boosting stage. If an integer is given, progress will be displayed
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is printed at every given `show_progress` boosting stage.
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show_stdv : bool, default True
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Whether to display the standard deviation in progress.
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Results are not affected, and always contains std.
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