From 4394bf9ca036f35edd319b8b1fbba85eabeadc87 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 18 Apr 2023 05:46:47 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 elk/training/reporter.py   | 12 ++++++++++--
 elk/training/supervised.py |  6 +++++-
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/elk/training/reporter.py b/elk/training/reporter.py
index 60bbcd12..16ce5f35 100644
--- a/elk/training/reporter.py
+++ b/elk/training/reporter.py
@@ -132,7 +132,11 @@ def score_contrast_set(self, labels: Tensor, contrast_set: Tensor) -> EvalResult
             cal_err = 0.0
 
         Y_one_hot = to_one_hot(Y, c).long().flatten()
-        auroc_result = RocAucResult(-1., -1., -1.) if len(labels.unique()) == 1 else roc_auc_ci(Y_one_hot.cpu(), logits.cpu().flatten())
+        auroc_result = (
+            RocAucResult(-1.0, -1.0, -1.0)
+            if len(labels.unique()) == 1
+            else roc_auc_ci(Y_one_hot.cpu(), logits.cpu().flatten())
+        )
 
         raw_preds = logits.argmax(dim=-1).long()
         raw_acc = accuracy(Y, raw_preds.flatten())
@@ -172,7 +176,11 @@ def score(self, labels: Tensor, hiddens: Tensor) -> EvalResult:
         preds = probs.gt(0.5).to(torch.int)
         acc = preds.flatten().eq(labels).float().mean().item()
 
-        auroc_result = RocAucResult(-1., -1., -1.) if len(labels.unique()) == 1 else roc_auc_ci(labels.cpu(), logits.cpu().flatten())
+        auroc_result = (
+            RocAucResult(-1.0, -1.0, -1.0)
+            if len(labels.unique()) == 1
+            else roc_auc_ci(labels.cpu(), logits.cpu().flatten())
+        )
 
         return EvalResult(
             auroc=auroc_result.estimate,
diff --git a/elk/training/supervised.py b/elk/training/supervised.py
index ded52fb6..8832613e 100644
--- a/elk/training/supervised.py
+++ b/elk/training/supervised.py
@@ -34,7 +34,11 @@ def evaluate_supervised(
         raise ValueError(f"Invalid val_h shape: {val_h.shape}")
 
     lr_acc = accuracy(labels, raw_preds.flatten())
-    lr_auroc = RocAucResult(-1., -1., -1.) if len(labels.unique()) == 1 else roc_auc_ci(labels, logits.flatten())
+    lr_auroc = (
+        RocAucResult(-1.0, -1.0, -1.0)
+        if len(labels.unique()) == 1
+        else roc_auc_ci(labels, logits.flatten())
+    )
 
     return lr_auroc, assert_type(float, lr_acc)