leaderboard/irt/pyirt/multidim_one_param_logistic.py [202:215]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def summary(self, traces, sites):
        marginal = (
            EmpiricalMarginal(traces, sites)._get_samples_and_weights()[0].detach().cpu().numpy()
        )
        console.log(marginal)
        site_stats = {}
        for i in range(marginal.shape[1]):
            site_name = sites[i]
            marginal_site = pd.DataFrame(marginal[:, i]).transpose()
            describe = partial(pd.Series.describe, percentiles=[0.05, 0.25, 0.5, 0.75, 0.95])
            site_stats[site_name] = marginal_site.apply(describe, axis=1)[
                ["mean", "std", "5%", "25%", "50%", "75%", "95%"]
            ]
        return site_stats
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



leaderboard/irt/pyirt/two_param_logistic.py [199:212]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def summary(self, traces, sites):
        marginal = (
            EmpiricalMarginal(traces, sites)._get_samples_and_weights()[0].detach().cpu().numpy()
        )
        console.log(marginal)
        site_stats = {}
        for i in range(marginal.shape[1]):
            site_name = sites[i]
            marginal_site = pd.DataFrame(marginal[:, i]).transpose()
            describe = partial(pd.Series.describe, percentiles=[0.05, 0.25, 0.5, 0.75, 0.95])
            site_stats[site_name] = marginal_site.apply(describe, axis=1)[
                ["mean", "std", "5%", "25%", "50%", "75%", "95%"]
            ]
        return site_stats
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -