def _inference_epoch_end(self, output_results: List[Dict[str, Tensor]],
stage: str) -> None:
all_y = torch.cat([_r["y"] for _r in output_results], 0)
all_s = torch.cat([_r["s"] for _r in output_results], 0)
all_preds = torch.cat([_r["preds"] for _r in output_results], 0)
dt = em.DataTuple(
x=pd.DataFrame(torch.rand_like(all_s,
dtype=float).detach().cpu().numpy(),
columns=["x0"]),
s=pd.DataFrame(all_s.detach().cpu().numpy(), columns=["s"]),
y=pd.DataFrame(all_y.detach().cpu().numpy(), columns=["y"]),
)
results = em.run_metrics(
predictions=em.Prediction(
hard=pd.Series(all_preds.detach().cpu().numpy())),
actual=dt,
metrics=[em.Accuracy(),
em.RenyiCorrelation(),
em.Yanovich()],
per_sens_metrics=[em.Accuracy(),
em.ProbPos(),
em.TPR()],
)
tm_acc = self.val_acc if stage == "val" else self.test_acc
acc = tm_acc.compute().item()
results_dict = {f"{stage}/acc": acc}
results_dict.update(
{f"{stage}/{self.target}_{k}": v
for k, v in results.items()})
self.log_dict(results_dict)
def inference_epoch_end(self, outputs: EPOCH_OUTPUT,
stage: Stage) -> Dict[str, float]:
targets_all = aggregate_over_epoch(outputs=outputs, metric="targets")
subgroup_inf_all = aggregate_over_epoch(outputs=outputs,
metric="subgroup_inf")
logits_y_all = aggregate_over_epoch(outputs=outputs, metric="logits_y")
preds_y_all = hard_prediction(logits_y_all)
dt = em.DataTuple(
x=pd.DataFrame(
torch.rand_like(subgroup_inf_all).detach().cpu().numpy(),
columns=["x0"],
),
s=pd.DataFrame(subgroup_inf_all.detach().cpu().numpy(),
columns=["s"]),
y=pd.DataFrame(targets_all.detach().cpu().numpy(), columns=["y"]),
)
return em.run_metrics(
predictions=em.Prediction(
hard=pd.Series(preds_y_all.detach().cpu().numpy())),
actual=dt,
metrics=[em.Accuracy(),
em.RenyiCorrelation(),
em.Yanovich()],
per_sens_metrics=[em.Accuracy(),
em.ProbPos(),
em.TPR()],
)
def inference_epoch_end(self, outputs: EPOCH_OUTPUT, stage: Stage) -> Dict[str, float]:
targets_all = aggregate_over_epoch(outputs=outputs, metric="targets")
subgroup_inf_all = aggregate_over_epoch(outputs=outputs, metric="subgroup_inf")
preds_all = aggregate_over_epoch(outputs=outputs, metric="preds")
mean_preds = preds_all.mean(-1)
mean_preds_s0 = preds_all[subgroup_inf_all == 0].mean(-1)
mean_preds_s1 = preds_all[subgroup_inf_all == 1].mean(-1)
dt = em.DataTuple(
x=pd.DataFrame(
torch.rand_like(subgroup_inf_all, dtype=torch.float).detach().cpu().numpy(),
columns=["x0"],
),
s=pd.DataFrame(subgroup_inf_all.detach().cpu().numpy(), columns=["s"]),
y=pd.DataFrame(targets_all.detach().cpu().numpy(), columns=["y"]),
)
results_dict = em.run_metrics(
predictions=em.Prediction(hard=pd.Series((preds_all > 0).detach().cpu().numpy())),
actual=dt,
metrics=[em.Accuracy(), em.RenyiCorrelation(), em.Yanovich()],
per_sens_metrics=[em.Accuracy(), em.ProbPos(), em.TPR()],
)
results_dict.update(
{
"DP_Gap": float((mean_preds_s0 - mean_preds_s1).abs().item()),
"mean_pred": float(mean_preds.item()),
}
)
return results_dict