def score()

in covid19_spread/bar.py [0:0]

• 38 lines of code
• 7 McCabe index (conditional complexity)

    def score(self, t, ys):
        assert t.size(-1) == ys.size(-1), (t.size(), ys.size())
        length = ys.size(-1) - self.window + 1

        # beta evolution
        beta = self.beta(t, ys)

        Z = th.zeros(0).sum()
        if self.self_correlation:
            ws = F.softplus(self.z)
            ws = ws.expand(self.M, self.z.size(1))
            # self-correlation
            Z = F.conv1d(
                F.pad(ys.unsqueeze(0) if ys.ndim == 2 else ys, (self.z.size(1) - 1, 0)),
                ws.unsqueeze(1),
                groups=self.M,
            )
            Z = Z.squeeze(0)
            Z = Z.div(float(self.z.size(1)))

        # cross-correlation
        Ys = th.zeros(0).sum(0)
        W = th.zeros(1, 1)
        if self.cross_correlation:
            W = self.metapopulation_weights()
            Ys = th.stack(
                [
                    F.pad(ys.narrow(-1, i, length), (self.window - 1, 0))
                    for i in range(self.window)
                ]
            )
            orig_shape = Ys.shape
            Ys = Ys.view(-1, Ys.size(-2), Ys.size(-1)) if Ys.ndim == 4 else Ys
            Ys = (
                th.bmm(W.unsqueeze(0).expand(Ys.size(0), self.M, self.M), Ys)
                .view(orig_shape)
                .mean(dim=0)
            )
        with th.no_grad():
            self.train_stats = (Z.mean().item(), Ys.mean().item())

        if self.features is not None:
            Ys = Ys + F.softplus(self.w_feat(self.features))

        Ys = beta * (Z + Ys) / self.neighbors
        return Ys, beta, W