<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def single_feature_logliks(self, feature):
 """Calculate log-likelihoods of each modality's parameterization Used for plotting the estimates of a single feature Parameters featre : pandas.Series A single feature's values. All values must range from 0 to 1. Returns ------- logliks : pandas.DataFrame The log-likelihood the data, for each model, for each parameterization Raises ------ AssertionError If any value in ``x`` does not fall only between 0 and 1. """

        self.assert_less_than_or_equal_1(feature.values)
        self.assert_non_negative(feature.values)

        logliks = self._single_feature_logliks_one_step(
            feature, self.one_param_models)

        logsumexps = self.logliks_to_logsumexp(logliks)

        # If none of the one-parameter models passed, try the two-param models
        if (logsumexps <= self.logbf_thresh).all():
            logliks_two_params = self._single_feature_logliks_one_step(
                feature, self.two_param_models)
            logliks = pd.concat([logliks, logliks_two_params])
        return logliks