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@@ -60,24 +60,6 @@ from sklearn.model_selection import StratifiedKFold
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from cdplib.log import Log
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-
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-
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-
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-aa = make_sliding_window_cv(data_set_size=50,
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- test_proportion=0.1,
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- train_proportion=0.6,
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- step_proportion=0.1)
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-
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-aa = list(aa)
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-
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-aa = make_sliding_window_cv(test_proportion=0.1,
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- train_proportion=0.6,
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- step_proportion=0.05,
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- index=pd.date_range(start=pd.to_datetime("2020-01-01"), periods=50))
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-
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-aa = list(aa)
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-
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-
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# TODO: write with yield !!!!
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def make_nested_expanding_cv(
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@@ -126,31 +108,6 @@ def make_nested_expanding_cv(
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"Exit with error: {}".format(e)))
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-
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-
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-for train_inds, test_inds in aa:
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- print(len(test_inds)/(len(train_inds) + len(test_inds)))
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- print(len(test_inds)/50)
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-
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-aaa = list(aaa)
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-
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-for aaa_cv in aaa:
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- for train_inds, test_inds in aaa_cv:
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- print(len(test_inds)/(len(train_inds) + len(test_inds)))
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- print(len(test_inds)/50)
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-
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-aaa = make_nested_expanding_cv(#data_set_size=50,
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- test_proportion=0.1,
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- start_train_proportion=0.6,
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- step_proportion=0.1,
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- index=pd.date_range(start=pd.to_datetime("2020-01-01"), periods=50))
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-
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-aaa = list(aaa)
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-
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def cv_slice_dataset(X, y, train_inds, test_inds)\
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-> Tuple[Union[pd.DataFrame, np.ndarray],
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Union[pd.Series, np.ndarray]]:
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