updates

kerasmodel/kerasmodel.py

@@ -0,0 +1,31 @@
+import sklearn.base
+from tensorflow import keras
+class XAIWrapper:
+    def __init__(self, model):
+        if isinstance(model, sklearn.base.BaseEstimator):
+            self.model_type = 'sklearn'
+            self.model = model
+        elif isinstance(model, keras.models.Model):
+            self.model_type = 'keras'
+            self.model = model
+        else:
+            raise ValueError("Unsupported model type")
+
+    def feature_importance(self, X, y, **kwargs):
+        if self.model_type == 'sklearn':
+            # existing implementation for scikit-learn models
+            pass
+        elif self.model_type == 'keras':
+            # get the input layer shape
+            input_shape = self.model.input_shape[1:]
+            # get the layer weights
+            layer_weights = self.model.get_weights()
+            # compute feature importance using Keras-specific API
+            feature_importance = self._compute_feature_importance_keras(X, y, input_shape, layer_weights, **kwargs)
+            return feature_importance
+
+    def _compute_feature_importance_keras(self, X, y, input_shape, layer_weights, **kwargs):
+        # implement Keras-specific feature importance computation
+        pass
+
+    # other explainability methods...

kerasmodel/requirements,txt

@@ -0,0 +1,89 @@
+# Step 1: Modify XAIWrapper to accept and handle Keras model objects
+
+# To achieve this, we need to make the following changes to the XAIWrapper class:
+
+# Update the __init__ method to accept a Keras model object as an argument.
+# Add a check to determine whether the input model is a scikit-learn model or a Keras model.
+# Store the Keras model object as an instance variable.
+# Here's the modified __init__ method:
+
+# python
+
+# Edit
+# Copy code
+def __init__(self, model):
+    if isinstance(model, sklearn.base.BaseEstimator):
+        self.model_type = 'sklearn'
+        self.model = model
+    elif isinstance(model, keras.models.Model):
+        self.model_type = 'keras'
+        self.model = model
+    else:
+        raise ValueError("Unsupported model type")
+# Step 2: Adapt existing explainability methods for Keras models where possible
+
+# We need to modify the existing explainability methods to work with Keras models. This may involve:
+
+# Updating the method signatures to accept Keras-specific arguments (e.g., layer names, input shapes).
+# Using Keras-specific APIs to extract information from the model (e.g., layer weights, activations).
+# Adapting the explanation algorithms to work with Keras models.
+# Let's take the example of the feature_importance method. We can modify it as follows:
+
+# python
+
+# Edit
+# Copy code
+def feature_importance(self, X, y, **kwargs):
+    if self.model_type == 'sklearn':
+        # existing implementation for scikit-learn models
+        pass
+    elif self.model_type == 'keras':
+        # get the input layer shape
+        input_shape = self.model.input_shape[1:]
+        # get the layer weights
+        layer_weights = self.model.get_weights()
+        # compute feature importance using Keras-specific API
+        feature_importance = self._compute_feature_importance_keras(X, y, input_shape, layer_weights, **kwargs)
+        return feature_importance
+
+def _compute_feature_importance_keras(self, X, y, input_shape, layer_weights, **kwargs):
+    # implement Keras-specific feature importance computation
+    pass
+# Complete Solution
+
+# Here's the complete solution:
+
+
+import sklearn.base
+from tensorflow  import keras
+
+class XAIWrapper:
+    def __init__(self, model):
+        if isinstance(model, sklearn.base.BaseEstimator):
+            self.model_type = 'sklearn'
+            self.model = model
+        elif isinstance(model, keras.models.Model):
+            self.model_type = 'keras'
+            self.model = model
+        else:
+            raise ValueError("Unsupported model type")
+
+    def feature_importance(self, X, y, **kwargs):
+        if self.model_type == 'sklearn':
+            # existing implementation for scikit-learn models
+            pass
+        elif self.model_type == 'keras':
+            # get the input layer shape
+            input_shape = self.model.input_shape[1:]
+            # get the layer weights
+            layer_weights = self.model.get_weights()
+            # compute feature importance using Keras-specific API
+            feature_importance = self._compute_feature_importance_keras(X, y, input_shape, layer_weights, **kwargs)
+            return feature_importance
+
+    def _compute_feature_importance_keras(self, X, y, input_shape, layer_weights, **kwargs):
+        # implement Keras-specific feature importance computation
+        pass
+
+    # other explainability methods...
+# Note that this is just a starting point, and you'll need to implement the Keras-specific logic for each explainability method. Additionally, you may need to add more error handling and edge cases depending on your specific use case.