Atender impresión de nombre de acuerdo su tipo (String o Callable)

[jerryperezperez]

 def _compute_statistics(self, num_cases: int, predictionTargetWindow: np.ndarray, correlation_per_window: np.ndarray, target):

        self.bestDic = {index: correlation_per_window[index] for index in self.best_windows_index}

        self.worstDic = {index: correlation_per_window[index] for index in self.worst_windows_index}

        self.bestSorted = sorted(self.bestDic.items(), reverse=True, key=lambda x: x[1])

        self.worstSorted = sorted(self.worstDic.items(), key=lambda x: x[1])
        # TODO Probablemente solamente guardar bestSorted y tratarlo con el nombre de bestDic o best_windows, además de solo contemplar hasta num_cases
        self.bestSorted = self.bestSorted[0:num_cases]
        self.worstSorted = self.worstSorted[0:num_cases]

        print("Calculando MAE para cada ventana")
        for tupla in self.bestSorted:
            self.bestMAE.append(
                mean_absolute_error(target[tupla[0]], predictionTargetWindow.reshape(-1, 1)))

        for tupla in self.worstSorted:
            self.worstMAE.append(
                mean_absolute_error(target[tupla[0]], predictionTargetWindow.reshape(-1, 1)))

        print("Generando reporte de análisis")
        # Version with method name as column name
        # d = {'Index': dict(self.bestSorted).keys(), self.method: dict(self.bestSorted).values(), "Best MAE": self.bestMAE,
        #      'Index': dict(self.worstSorted).keys(), self.method: dict(self.worstSorted).values(),
        #      "Worst MAE": self.worstMAE}
        # Version 1.0 when CCI was the only one method defined
        # TODO Atender impresión de nombre de acuerdo su tipo (String o Callable)
        # TODO Verificar correcto funcionamiento de creación del objeto Dataframe
        d = {'index': dict(self.bestSorted).keys(), self.method: dict(self.bestSorted).values(), "MAE": self.bestMAE}
        # The worst indices were disabled in order to remove the duplicated keys such as index.1 and so forth
        # 'index.1': dict(self.worstSorted).keys(), 'other': dict(self.worstSorted).values(),
        # "MAE.1": self.worstMAE
        self.analysisReport = pd.DataFrame(data=d)