Fix bug with tensorflow 2.15, remove dependencies constraints

Author: antoinedemathelin

.github/workflows/run-test-coverage.yml

@@ -16,10 +16,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.11
       uses: actions/setup-python@v2
       with:
-        python-version: '3.8'
+        python-version: '3.11'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/run-test.yml

@@ -13,7 +13,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.11] #[3.6, 3.7, 3.8, 3.9]
         os: [ubuntu-latest, windows-latest, macos-latest]
         exclude:
           - os: windows-latest

.gitignore

@@ -21,4 +21,5 @@ docs_build/
 docs/html/
 docs/doctrees/
 adapt/datasets.py
-datasets/
+datasets/
+debug.ipynb

adapt/base.py

@@ -13,7 +13,10 @@
 from sklearn.metrics.pairwise import KERNEL_PARAMS
 from sklearn.exceptions import NotFittedError
 from tensorflow.keras import Model
-from tensorflow.keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
+try:
+    from tensorflow.keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
+except:
+    from scikeras.wrappers import KerasClassifier, KerasRegressor
 try:
     from tensorflow.keras.optimizers.legacy import RMSprop
 except:
@@ -1623,7 +1626,7 @@ def _get_length_dataset(self, dataset, domain="src"):
 
 
     def _check_for_batch(self, dataset):
-        if dataset.__class__.__name__ == "BatchDataset":
+        if "BatchDataset" in dataset.__class__.__name__:
             return True
         if hasattr(dataset, "_input_dataset"):
             return self._check_for_batch(dataset._input_dataset)

adapt/instance_based/_iwc.py

@@ -170,7 +170,7 @@ def fit_weights(self, Xs, Xt, warm_start=False, **kwargs):
         else:
             y_pred = self.classifier_.predict(Xs).ravel()
 
-        self.weights_ = 1. / (y_pred + EPS) - 1.
+        self.weights_ = 1. / np.clip(y_pred, EPS, 1.) - 1.
 
         return self.weights_
 

adapt/instance_based/_tradaboost.py

@@ -218,7 +218,7 @@ def fit(self, X, y, Xt=None, yt=None,
         self : returns an instance of self
         """
         set_random_seed(self.random_state)
-        tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+        # tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
         Xs, ys = check_arrays(X, y, accept_sparse=True)
         Xt, yt = self._get_target_data(Xt, yt)
@@ -757,9 +757,9 @@ class TwoStageTrAdaBoostR2(TrAdaBoostR2):
     --------
     >>> from sklearn.linear_model import Ridge
     >>> from adapt.utils import make_regression_da
-    >>> from adapt.instance_based import TrAdaBoostR2
+    >>> from adapt.instance_based import TwoStageTrAdaBoostR2
     >>> Xs, ys, Xt, yt = make_regression_da()
-    >>> model = TrAdaBoostR2(Ridge(), n_estimators=10, Xt=Xt[:10], yt=yt[:10], random_state=0)
+    >>> model = TwoStageTrAdaBoostR2(Ridge(), n_estimators=10, Xt=Xt[:10], yt=yt[:10], random_state=0)
     >>> model.fit(Xs, ys)
     Iteration 0 - Cross-validation score: 0.2956 (0.0905)
     Iteration 1 - Cross-validation score: 0.2956 (0.0905)
@@ -814,7 +814,7 @@ def fit(self, X, y, Xt=None, yt=None,
             sample_weight_tgt=None,
             **fit_params):
         set_random_seed(self.random_state)
-        tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+        # tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
         Xs, ys = check_arrays(X, y, accept_sparse=True)
         Xt, yt = self._get_target_data(Xt, yt)

adapt/utils.py

@@ -11,7 +11,10 @@
 from sklearn.utils import check_array
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
-from tensorflow.keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
+try:
+    from tensorflow.keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
+except:
+    from scikeras.wrappers import KerasClassifier, KerasRegressor
 import tensorflow as tf
 import tensorflow.keras.backend as K
 from tensorflow.keras import Sequential, Model

requirements.txt

@@ -1,5 +1,6 @@
 numpy
 scipy
-tensorflow < 2.12
+tensorflow
 scikit-learn
-cvxopt <= 1.3.0
+cvxopt
+scikeras

setup.py

@@ -13,7 +13,7 @@
     author_email='[email protected]',
     license='BSD-2',
     packages=find_packages(exclude=["tests"]),
-    install_requires=["numpy>=1.16", "scipy>=1.0", "tensorflow<2.12", "scikit-learn>=0.2", "cvxopt<=1.3.0"],
+    install_requires=["numpy", "scipy", "tensorflow", "scikit-learn", "cvxopt", "scikeras"],
     zip_safe=False,
     long_description=long_description,
     long_description_content_type='text/markdown'