ReportDeco.generate_report fails when predict_proba returns 1.0 on train data

[resivalex]

     68                          "target_descr": "___ОПИСАНИЕ ЦЕЛЕВОГО СОБЫТИЯ___",
     69                          "non_target_descr": "___ОПИСАНИЕ НЕЦЕЛЕВОГО СОБЫТИЯ___"}
---> 70         autowoe_model.generate_report(report_params)
     71 
     72         accuracy_on_valid = accuracy_score(test_data[target_name], (test_preds > 0.5).astype(int))

~/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.8/site-packages/autowoe/lib/report/report.py in generate_report(self, report_params, groupby)
    377 
    378             # Split score into 10 bins for train and test
--> 379             train_binned, test_binned = self.__get_binned_data(10)
    380             names = ['binned_train_total.png', 'binned_train_posneg.png']
    381             plot_binned(

~/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.8/site-packages/autowoe/lib/report/report.py in __get_binned_data(self, bin_count)
    527                 df['ScoreBin'] = pd.cut(df['Score'], bins, retbins=False)
    528             else:
--> 529                 df['ScoreBin'], bins = pd.cut(df['Score'], bin_count, retbins=True)
    530 
    531         return train_binned, test_binned

~/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.8/site-packages/pandas/core/reshape/tile.py in cut(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)
    241         if np.isinf(mn) or np.isinf(mx):
    242             # GH 24314
--> 243             raise ValueError(
    244                 "cannot specify integer `bins` when input data contains infinity"
    245             )

ValueError: cannot specify integer `bins` when input data contains infinity