Make progress on porting to keras 3

scikeras/_saving_utils.py

@@ -26,8 +26,8 @@ def pack_keras_model(
     tp = type(model)
     out = BytesIO()
     if tp not in keras.saving.object_registration.GLOBAL_CUSTOM_OBJECTS:
-        module = '.'.join(tp.__qualname__.split('.')[:-1])
-        name = tp.__qualname__.split('.')[-1]
+        module = ".".join(tp.__qualname__.split(".")[:-1])
+        name = tp.__qualname__.split(".")[-1]
         keras.saving.register_keras_serializable(module, name)(tp)
     save_model(model, out)
     model_bytes = np.asarray(memoryview(out.getvalue()))

scikeras/_utils.py

@@ -1,5 +1,4 @@
 import inspect
-from types import FunctionType
 from typing import Any, Callable, Dict, Iterable, Mapping, Sequence, Type, Union
 
 from keras import losses as losses_mod

scikeras/wrappers.py

@@ -6,9 +6,10 @@
 from collections import defaultdict
 from typing import Any, Callable, Dict, Iterable, List, Mapping, Set, Tuple, Type, Union
 
-import numpy as np
-import tensorflow as tf
 import keras
+import numpy as np
+from keras import losses as losses_module
+from keras.models import Model
 from scipy.sparse import isspmatrix, lil_matrix
 from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
 from sklearn.exceptions import NotFittedError
@@ -18,8 +19,6 @@
 from sklearn.utils.class_weight import compute_sample_weight
 from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import _check_sample_weight, check_array, check_X_y
-from keras import losses as losses_module
-from keras.models import Model
 
 from scikeras._utils import (
     accepts_kwargs,
@@ -381,7 +380,9 @@ def _get_compile_kwargs(self):
                 strict=False,
             ),
         )
-        if compile_kwargs["metrics"] is not None and not isinstance(compile_kwargs['metrics'], (dict, list)):
+        if compile_kwargs["metrics"] is not None and not isinstance(
+            compile_kwargs["metrics"], (dict, list)
+        ):
             # Keras expects a list or dict of metrics, not a single metric
             compile_kwargs["metrics"] = [compile_kwargs["metrics"]]
         return compile_kwargs
@@ -537,7 +538,7 @@ def _fit_keras_model(
             self.history_ = defaultdict(list)
 
         for key, val in hist.history.items():
-            if not (key == 'loss' or key[:4] == 'val_'):
+            if not (key == "loss" or key[:4] == "val_"):
                 try:
                     key = metric_name(key)
                 except ValueError:

tests/multi_output_models.py

@@ -1,8 +1,8 @@
 from typing import List
 
 import numpy as np
-from sklearn.utils.multiclass import type_of_target
 from keras.backend import floatx as tf_floatx
+from sklearn.utils.multiclass import type_of_target
 
 from scikeras.utils.transformers import ClassifierLabelEncoder
 from scikeras.wrappers import KerasClassifier

tests/test_api.py

@@ -3,8 +3,15 @@
 from functools import partial
 from typing import Any, Dict
 
+import keras
 import numpy as np
 import pytest
+from keras import backend as K
+from keras import losses as losses_module
+from keras import metrics as metrics_module
+from keras.layers import Conv2D, Dense, Flatten, Input
+from keras.models import Model, Sequential
+from keras.utils import to_categorical
 from sklearn.calibration import CalibratedClassifierCV
 from sklearn.datasets import load_diabetes, load_digits, load_iris
 from sklearn.ensemble import (
@@ -17,13 +24,6 @@
 from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler
-import keras
-from keras import backend as K
-from keras import losses as losses_module
-from keras import metrics as metrics_module
-from keras.layers import Conv2D, Dense, Flatten, Input
-from keras.models import Model, Sequential
-from keras.utils import to_categorical
 
 from scikeras.wrappers import KerasClassifier, KerasRegressor
 

tests/test_basewrapper.py

@@ -1,10 +1,10 @@
 """Test that BaseWrapper for uses other than KerasClassifier and KerasRegressor.
 """
+import keras
 import numpy as np
+from keras import layers
 from sklearn.base import TransformerMixin
 from sklearn.metrics import mean_squared_error
-import keras
-from keras import layers
 
 from scikeras.wrappers import BaseWrapper
 

tests/test_callbacks.py

@@ -1,8 +1,8 @@
 from collections import defaultdict
 from typing import Any, DefaultDict, Dict
 
-import pytest
 import keras
+import pytest
 from keras.callbacks import Callback
 
 from scikeras.wrappers import KerasClassifier