run precommit

src/tabpfn/classifier.py

@@ -33,13 +33,13 @@
     determine_precision,
     initialize_tabpfn_model,
 )
+from tabpfn.config import ModelInterfaceConfig
 from tabpfn.constants import (
     PROBABILITY_EPSILON_ROUND_ZERO,
     SKLEARN_16_DECIMAL_PRECISION,
     XType,
     YType,
 )
-from tabpfn.config import ModelInterfaceConfig
 from tabpfn.preprocessing import (
     ClassifierEnsembleConfig,
     EnsembleConfig,

src/tabpfn/preprocessing.py

@@ -153,7 +153,7 @@ def to_dict(self) -> dict:
         }
 
     @classmethod
-    def from_dict(cls, config_dict: dict) -> "PreprocessorConfig":
+    def from_dict(cls, config_dict: dict) -> PreprocessorConfig:
         """Create a config from a dictionary.
 
         Args:

src/tabpfn/regressor.py

@@ -37,10 +37,6 @@
     determine_precision,
     initialize_tabpfn_model,
 )
-from tabpfn.constants import (
-    XType,
-    YType,
-)
 from tabpfn.config import ModelInterfaceConfig
 from tabpfn.model.bar_distribution import FullSupportBarDistribution
 from tabpfn.model.preprocessing import (
@@ -70,6 +66,10 @@
     from sklearn.pipeline import Pipeline
     from torch.types import _dtype
 
+    from tabpfn.constants import (
+        XType,
+        YType,
+    )
     from tabpfn.inference import (
         InferenceEngine,
     )

tests/test_classifier_interface.py

@@ -171,49 +171,50 @@ def test_classifier_in_pipeline(X_y: tuple[np.ndarray, np.ndarray]) -> None:
         rtol=0.1,
     ), "Class probabilities are not properly balanced in pipeline"
 
+
 def test_dict_vs_object_preprocessor_config(X_y: tuple[np.ndarray, np.ndarray]) -> None:
     """Test that dict configs behave identically to PreprocessorConfig objects."""
     X, y = X_y
-    
+
     # Define same config as both dict and object
     dict_config = {
         "name": "quantile_uni_coarse",
-        "append_original": False, # changed from default
+        "append_original": False,  # changed from default
         "categorical_name": "ordinal_very_common_categories_shuffled",
         "global_transformer_name": "svd",
         "subsample_features": -1,
     }
-    
+
     object_config = PreprocessorConfig(
         name="quantile_uni_coarse",
-        append_original=False, # changed from default
+        append_original=False,  # changed from default
         categorical_name="ordinal_very_common_categories_shuffled",
         global_transformer_name="svd",
         subsample_features=-1,
     )
-    
+
     # Create two models with same random state
     model_dict = TabPFNClassifier(
         inference_config={"PREPROCESS_TRANSFORMS": [dict_config]},
         n_estimators=2,
-        random_state=42
+        random_state=42,
     )
-    
+
     model_obj = TabPFNClassifier(
         inference_config={"PREPROCESS_TRANSFORMS": [object_config]},
         n_estimators=2,
-        random_state=42
+        random_state=42,
     )
-    
+
     # Fit both models
     model_dict.fit(X, y)
     model_obj.fit(X, y)
-    
+
     # Compare predictions
     pred_dict = model_dict.predict(X)
     pred_obj = model_obj.predict(X)
     np.testing.assert_array_equal(pred_dict, pred_obj)
-    
+
     # Compare probabilities
     prob_dict = model_dict.predict_proba(X)
     prob_obj = model_obj.predict_proba(X)

tests/test_regressor_interface.py

@@ -157,64 +157,63 @@ def test_regressor_in_pipeline(X_y: tuple[np.ndarray, np.ndarray]) -> None:
         X.shape[0],
     ), "Quantile predictions shape is incorrect"
 
+
 def test_dict_vs_object_preprocessor_config(X_y: tuple[np.ndarray, np.ndarray]) -> None:
     """Test that dict configs behave identically to PreprocessorConfig objects."""
     X, y = X_y
-    
+
     # Define same config as both dict and object
     dict_config = {
         "name": "quantile_uni",
-        "append_original": False, # changed from default
+        "append_original": False,  # changed from default
         "categorical_name": "ordinal_very_common_categories_shuffled",
         "global_transformer_name": "svd",
         "subsample_features": -1,
     }
-    
+
     object_config = PreprocessorConfig(
         name="quantile_uni",
-        append_original=False, # changed from default
+        append_original=False,  # changed from default
         categorical_name="ordinal_very_common_categories_shuffled",
         global_transformer_name="svd",
         subsample_features=-1,
     )
-    
+
     # Create two models with same random state
     model_dict = TabPFNRegressor(
         inference_config={"PREPROCESS_TRANSFORMS": [dict_config]},
         n_estimators=2,
-        random_state=42
+        random_state=42,
     )
-    
+
     model_obj = TabPFNRegressor(
         inference_config={"PREPROCESS_TRANSFORMS": [object_config]},
         n_estimators=2,
-        random_state=42
+        random_state=42,
     )
-    
+
     # Fit both models
     model_dict.fit(X, y)
     model_obj.fit(X, y)
-    
+
     # Compare predictions for different output types
     for output_type in ["mean", "median", "mode"]:
         pred_dict = model_dict.predict(X, output_type=output_type)
         pred_obj = model_obj.predict(X, output_type=output_type)
         np.testing.assert_array_almost_equal(
-            pred_dict, 
+            pred_dict,
             pred_obj,
-            err_msg=f"Predictions differ for output_type={output_type}"
+            err_msg=f"Predictions differ for output_type={output_type}",
         )
-    
+
     # Compare quantile predictions
     quantiles = [0.1, 0.5, 0.9]
     quant_dict = model_dict.predict(X, output_type="quantiles", quantiles=quantiles)
     quant_obj = model_obj.predict(X, output_type="quantiles", quantiles=quantiles)
-    
+
     for q_dict, q_obj in zip(quant_dict, quant_obj):
         np.testing.assert_array_almost_equal(
             q_dict,
             q_obj,
-            err_msg=f"Quantile predictions differ"
+            err_msg="Quantile predictions differ",
         )
-
-