Merge pull request #119 from antoinedemathelin/master

fix: Fix compatibility issue with latest Tensorflow version

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

@@ -13,13 +13,8 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: ['3.8', '3.9', '3.10', '3.11']
         os: [ubuntu-latest, windows-latest, macos-latest]
-        exclude:
-          - os: windows-latest
-            python-version: 3.9
-          - os: ubuntu-latest
-            python-version: 3.6
     runs-on: ${{ matrix.os }}
     steps:
     - uses: actions/checkout@v2

.gitignore

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

README.md

@@ -1,7 +1,7 @@
 # ADAPT
 
 [![PyPI version](https://badge.fury.io/py/adapt.svg)](https://pypi.org/project/adapt)
-[![Build Status](https://github.com/adapt-python/adapt/workflows/build/badge.svg)](https://github.com/adapt-python/adapt/actions)
+[![Build Status](https://github.com/adapt-python/adapt/actions/workflows/run-test.yml/badge.svg)](https://github.com/adapt-python/adapt/actions)
 [![Python Version](https://img.shields.io/badge/python-3.6%20|%203.7%20|%203.8|%203.9-blue)](https://img.shields.io/badge/python-3.6%20|%203.7%20|%203.8|%203.9-blue)
 [![Codecov Status](https://codecov.io/gh/adapt-python/adapt/branch/master/graph/badge.svg?token=IWQXMYGY2Q)](https://codecov.io/gh/adapt-python/adapt)
 
@@ -72,11 +72,12 @@ The following dependencies are required and will be installed with the library:
 - `tensorflow` (>= 2.0)
 - `scikit-learn`
 - `cvxopt`
+- `scikeras`
 
 If for some reason, these packages failed to install, you can do it manually with:
 
 ```
-pip install numpy scipy tensorflow scikit-learn cvxopt
+pip install numpy scipy tensorflow scikit-learn cvxopt scikeras
 ```
 
 Finally import the module in your python scripts with:
@@ -87,6 +88,15 @@ import adapt
 
 A simple example of usage is given in the [Quick-Start](#Quick-Start) below.
 
+### Stable environments [Updated Dec 2023]
+
+ADAPT sometimes encounters incompatibility issue after a new Tensorflow release. In this case, you can use the following environment, which has passed all tests. ADAPT should work well on it:
+- OS: `ubuntu-22.04, windows-2022, macos-12`
+- Python versions: `3.8 to 3.11`
+
+```
+pip install numpy==1.26.2 scipy==1.11.4 tensorflow==2.15.0 scikit-learn==1.3.2 cvxopt==1.3.2 scikeras==0.12.0
+```
 
 ## ADAPT Guideline
 

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'