WIP: Update to Python 3.9 and TensorFlow 2

Pipfile

@@ -0,0 +1,38 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+protobuf = "*"
+lime = "*"
+mip = "*"
+numpy = "*"
+pandas = "*"
+recourse = "*"
+scikit-learn = "*"
+h5py = "*"
+dice-ml = "*"
+ipython = "*"
+xgboost = "*"
+causalgraphicalmodels = "*"
+tensorflow = "*"
+torch = "*"
+torchvision = "*"
+
+[dev-packages]
+pre-commit = "*"
+pytest = "*"
+sphinx = "*"
+sphinx-rtd-theme = "*"
+sphinx-autodoc-typehints = "*"
+numpydoc = "*"
+imageio = "*"
+ipython = "*"
+jinja2 = "*"
+networkx = "*"
+scipy = "*"
+markupsafe = "*"
+
+[requires]
+python_version = "3.9"

carla/data/pipelining/steps.py

@@ -79,7 +79,7 @@ def encode(
         Whole DataFrame with encoded values
     """
     output = df.copy()
-    encoded_features = fitted_encoder.get_feature_names(features)
+    encoded_features = fitted_encoder.get_feature_names_out(features)
     output[encoded_features] = fitted_encoder.transform(output[features])
     output = output.drop(features, axis=1)
 
@@ -107,7 +107,7 @@ def decode(
         Whole DataFrame with encoded values
     """
     output = df.copy()
-    encoded_features = fitted_encoder.get_feature_names(features)
+    encoded_features = fitted_encoder.get_feature_names_out(features)
 
     # Prevent errors for datasets without categorical data
     # inverse_transform cannot handle these cases

carla/models/api/mlmodel.py

@@ -171,7 +171,7 @@ def get_mutable_mask(self):
         # get categorical features
         categorical = self.data.categorical
         # get the binary encoded categorical features
-        encoded_categorical = self.data.encoder.get_feature_names(categorical)
+        encoded_categorical = self.data.encoder.get_feature_names_out(categorical)
         # get the immutables, where the categorical features are in encoded format
         immutable = [
             encoded_categorical[categorical.index(i)] if i in categorical else i

carla/models/catalog/ANN_TF/model_ann.py

@@ -7,7 +7,7 @@
 
 
 def weighted_binary_cross_entropy(target, output):
-    loss = 0.7 * (target * tf.log(output)) + 0.3 * ((1 - target) * tf.log(1 - output))
+    loss = 0.7 * (target * tf.math.log(output)) + 0.3 * ((1 - target) * tf.math.log(1 - output))
     return tf.negative(tf.reduce_mean(loss, axis=-1))
 
 

carla/models/catalog/catalog.py

@@ -98,7 +98,7 @@ def __init__(
                 encoded_features = data.categorical
             else:
                 encoded_features = list(
-                    data.encoder.get_feature_names(data.categorical)
+                    data.encoder.get_feature_names_out(data.categorical)
                 )
 
             self._catalog = None

carla/recourse_methods/__init__.py

@@ -2,18 +2,18 @@
 
 from .api import RecourseMethod
 from .catalog import (
-    CCHVAE,
-    CEM,
-    CRUD,
-    FOCUS,
-    ActionableRecourse,
-    CausalRecourse,
-    Clue,
-    Dice,
-    Face,
-    FeatureTweak,
+    # CCHVAE,
+    # CEM,
+    # CRUD,
+    # FOCUS,
+    # ActionableRecourse,
+    # CausalRecourse,
+    # Clue,
+    # Dice,
+    # Face,
+    # FeatureTweak,
     GrowingSpheres,
-    Revise,
-    Roar,
-    Wachter,
+    # Revise,
+    # Roar,
+    # Wachter,
 )

carla/recourse_methods/catalog/__init__.py

@@ -1,15 +1,15 @@
-# flake8: noqa
-from .actionable_recourse import ActionableRecourse
-from .causal_recourse import CausalRecourse
-from .cchvae import CCHVAE
-from .cem import CEM
-from .clue import Clue
-from .crud import CRUD
-from .dice import Dice
-from .face import Face
-from .feature_tweak import FeatureTweak
-from .focus import FOCUS
+# # flake8: noqa
+# from .actionable_recourse import ActionableRecourse
+# from .causal_recourse import CausalRecourse
+# from .cchvae import CCHVAE
+# from .cem import CEM
+# from .clue import Clue
+# from .crud import CRUD
+# from .dice import Dice
+# from .face import Face
+# from .feature_tweak import FeatureTweak
+# from .focus import FOCUS
 from .growing_spheres import GrowingSpheres
-from .revise import Revise
-from .roar import Roar
-from .wachter import Wachter
+# from .revise import Revise
+# from .roar import Roar
+# from .wachter import Wachter