Merge branch 'master' into python-3.13

.github/workflows/lock.yml

@@ -2,8 +2,10 @@ name: 'Lock Inactive Threads'
 
 on:
   schedule:
-    # midnight UTC, every Wednesday
+    # midnight UTC, every Wednesday, for Issues
     - cron: '0 0 * * 3'
+    # midnight UTC, every Thursday, for PRs
+    - cron: '0 0 * * 4'
   # allow manual triggering from GitHub UI
   workflow_dispatch:
 
@@ -42,4 +44,4 @@ jobs:
           # what should the locking status be?
           issue-lock-reason: 'resolved'
           pr-lock-reason: 'resolved'
-          process-only: 'issues, prs'
+          process-only: ${{ github.event.schedule == '0 0 * * 3' && 'issues' || 'prs' }}

.pre-commit-config.yaml

@@ -17,6 +17,11 @@ repos:
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
+  - repo: https://github.com/adrienverge/yamllint
+    rev: v1.35.1
+    hooks:
+      - id: yamllint
+        args: ["--strict"]
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
     rev: v0.8.3

.yamllint.yml

@@ -0,0 +1,14 @@
+# default config: https://yamllint.readthedocs.io/en/stable/configuration.html#default-configuration
+extends: default
+
+rules:
+  document-start: disable
+  line-length:
+    max: 999  # temporarily increase allowed line length
+  truthy:
+    # prevent treating GitHub Workflow "on" key as boolean value
+    check-keys: false
+
+  # temporarily disabled rules
+  indentation: disable
+  comments-indentation: disable

tests/python_package_test/test_engine.py

@@ -3909,12 +3909,14 @@ def test_predict_regression_output_shape():
     # 1-round model
     bst = lgb.train(params, dtrain, num_boost_round=1)
     assert bst.predict(X).shape == (n_samples,)
+    assert bst.predict(X, raw_score=True).shape == (n_samples,)
     assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1)
     assert bst.predict(X, pred_leaf=True).shape == (n_samples, 1)
 
     # 2-round model
     bst = lgb.train(params, dtrain, num_boost_round=2)
     assert bst.predict(X).shape == (n_samples,)
+    assert bst.predict(X, raw_score=True).shape == (n_samples,)
     assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1)
     assert bst.predict(X, pred_leaf=True).shape == (n_samples, 2)
 
@@ -3929,12 +3931,14 @@ def test_predict_binary_classification_output_shape():
     # 1-round model
     bst = lgb.train(params, dtrain, num_boost_round=1)
     assert bst.predict(X).shape == (n_samples,)
+    assert bst.predict(X, raw_score=True).shape == (n_samples,)
     assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1)
     assert bst.predict(X, pred_leaf=True).shape == (n_samples, 1)
 
     # 2-round model
     bst = lgb.train(params, dtrain, num_boost_round=2)
     assert bst.predict(X).shape == (n_samples,)
+    assert bst.predict(X, raw_score=True).shape == (n_samples,)
     assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1)
     assert bst.predict(X, pred_leaf=True).shape == (n_samples, 2)
 
@@ -3950,12 +3954,14 @@ def test_predict_multiclass_classification_output_shape():
     # 1-round model
     bst = lgb.train(params, dtrain, num_boost_round=1)
     assert bst.predict(X).shape == (n_samples, n_classes)
+    assert bst.predict(X, raw_score=True).shape == (n_samples, n_classes)
     assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_classes * (n_features + 1))
     assert bst.predict(X, pred_leaf=True).shape == (n_samples, n_classes)
 
     # 2-round model
     bst = lgb.train(params, dtrain, num_boost_round=2)
     assert bst.predict(X).shape == (n_samples, n_classes)
+    assert bst.predict(X, raw_score=True).shape == (n_samples, n_classes)
     assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_classes * (n_features + 1))
     assert bst.predict(X, pred_leaf=True).shape == (n_samples, n_classes * 2)