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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def single_feature_fit(self, feature):
 """Get the log2 bayes factor of the fit for each modality"""

        if np.isfinite(feature).sum() == 0:
            series = pd.Series(index=MODALITY_ORDER)
        else:
            logbf_one_param = pd.Series(
                {k: v.logsumexp_logliks(feature) for
                 k, v in self.one_param_models.items()})

            # Check if none of the previous features fit
            if (logbf_one_param <= self.logbf_thresh).all():
                logbf_two_param = pd.Series(
                    {k: v.logsumexp_logliks(feature)
                     for k, v in self.two_param_models.items()})
                series = pd.concat([logbf_one_param, logbf_two_param])
                series[NULL_MODEL] = self.logbf_thresh
            else:
                series = logbf_one_param
        series.index.name = 'Modality'
        series.name = self.score_name
        return series