feat: Add support for CFVAE

experiments/experimental_setup.yaml

@@ -138,3 +138,7 @@ recourse_methods:
     hyperparams:
       loss_type: "BCE"
       binary_cat_features: True
+  cfvae:
+    hyperparams:
+       encoded_size: 10
+       train: True

experiments/results.csv

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+cfvae,boston_housing,linear,13.0,5.336862650203253,3.2526255520168155,1.0,0.0,12.0,,,
+cfvae,boston_housing,linear,13.0,4.347691256195981,2.2922527020522363,1.0,0.0,11.0,,,
+cfvae,boston_housing,linear,13.0,5.048199482370351,3.1680938340144644,1.0,0.0,12.0,,,
+cfvae,boston_housing,linear,13.0,4.753947094619414,2.7730740140209846,1.0,0.0,12.0,,,
+cfvae,boston_housing,linear,13.0,4.987738864930418,2.9458328755782937,1.0,0.0,12.0,,,
+cfvae,boston_housing,linear,13.0,4.395711857725537,2.3681790491332655,1.0,0.0,11.0,,,
+cfvae,boston_housing,linear,13.0,4.872102300158955,3.0965358976933275,1.0,0.0,12.0,,,
+cfvae,boston_housing,linear,13.0,5.2932850010953345,3.337514840368252,1.0,0.0,12.0,,,
+cfvae,boston_housing,linear,12.0,4.3720457902112075,2.4644088510009925,1.0,0.0,11.0,,,
+cfvae,boston_housing,linear,13.0,4.724121581970545,2.864823450317629,1.0,0.0,12.0,,,

experiments/run_experiment.py

@@ -164,6 +164,8 @@ def initialize_recourse_method(
         return Revise(mlmodel, data, hyperparams)
     elif "wachter" in method:
         return Wachter(mlmodel, hyperparams)
+    elif "cfvae" in method:
+        return CFVAE(mlmodel, hyperparams)
     else:
         raise ValueError("Recourse method not known")
 
@@ -193,7 +195,7 @@ def create_parser():
     -r, --recourse_method: Specifies recourse methods for the experiment.
         Default: ["dice", "cchvae", "cem", "cem_vae", "clue", "cruds", "face_knn", "face_epsilon", "gs", "mace", "revise", "wachter"].
         Choices: ["dice", "ar", "causal_recourse", "cchvae", "cem", "cem_vae", "claproar", "clue", "cruds", "face_knn", "face_epsilon", "feature_tweak",
-            "focus", "gravitational", "greedy", "gs", "mace", "revise", "wachter"].
+            "focus", "gravitational", "greedy", "gs", "mace", "revise", "wachter", "cfvae"].
     -n, --number_of_samples: Specifies the number of instances per dataset.
         Default: 20.
     -s, --train_split: Specifies the split of the available data used for training.
@@ -260,6 +262,7 @@ def create_parser():
             "gs",
             "revise",
             "wachter",
+            "cfvae",
         ],
         choices=[
             "dice",
@@ -281,6 +284,7 @@ def create_parser():
             "mace",
             "revise",
             "wachter",
+            "cfvae",
         ],
         help="Recourse methods for experiment",
     )
@@ -360,6 +364,7 @@ def create_parser():
         "gravitational",
         "wachter",
         "revise",
+        "cfvae",
     ]
     sklearn_methods = ["feature_tweak", "focus", "mace"]
 

methods/__init__.py

@@ -20,4 +20,5 @@
     Revise,
     Roar,
     Wachter,
+    CFVAE,
 )

methods/api/recourse_method.py

@@ -42,4 +42,4 @@ def get_counterfactuals(self, factuals: pd.DataFrame):
         pd.DataFrame
             Encoded and normalised counterfactual examples.
         """
-        pass
+        return pd.DataFrame()

methods/catalog/__init__.py

@@ -17,3 +17,4 @@
 from .revise import Revise
 from .roar import Roar
 from .wachter import Wachter
+from .cfvae import CFVAE

methods/catalog/cfvae/__init__.py

@@ -0,0 +1,3 @@
+# flake8: noqa
+
+from .model import CFVAE