algorithm explaining updated

docs/source/algorithms/explainer.rst

@@ -111,39 +111,41 @@ Code sample
 
     lcppn.fit(x_train, y_train)
     explainer = Explainer(lcppn, data=x_train, mode="tree")
+
+    # One of the possible ways to get explanations
     explanations = explainer.explain(x_test)
 
 
 ++++++++++++++++++++++++++
 Filtering and Manipulation
 ++++++++++++++++++++++++++
 
-The Explanation object returned by the Explainer is built using the :literal:`xarray.Dataset` data structure, that enables the application of any xarray dataset operation. For example, filtering specific values can be quickly done. To illustrate the filtering operation, suppose we have SHAP values stored in the Explanation object named :literal:`explanation`.
+When you work with the `Explanation` object generated by the `Explainer`, you're leveraging the power of the `xarray.Dataset`. This structure is not just robust but also flexible, allowing for comprehensive dataset operations—especially filtering.
+
+**Practical Example: Filtering SHAP Values**
 
-A common use case is to extract SHAP values for only the predicted nodes. In Local classifier per parent node approach, each node except the leaf nodes represents a classifier. Hence, to find the SHAP values, we can pass the prediction until the penultimate element to obtain the SHAP values.
-To achieve this, we can use xarray's :literal:`.sel()` method:
+Consider a scenario where you need to focus only on SHAP values corresponding to predicted nodes. In the context of our `LocalClassifierPerParentNode` model, each node—except for the leaf nodes—acts as a classifier. This setup is particularly useful when you're looking to isolate SHAP values up to the penultimate node in your predictions. Here’s how you can do this efficiently using the `sel()` method from xarray:
 
 .. code-block:: python
 
+    # Creating a mask for selecting SHAP values for predicted classes
     mask = {'class': lcppn.predict(x_test).flatten()[:-1]}
-    x = explanations.sel(mask).shap_values
+    selected_shap_values = explanations.sel(mask).shap_values
 
-Also, we developed some helper functions built in the Explainer class as its methods to simplify standard explanation manipulation and filtering such as filtering explanations by level, or filtering explanations by class and returning its Shapley values. A basic example below is a continuation of the example from the beginning of this section:
+**Advanced Visualization: Multi-Plot SHAP Values**
 
-.. code-block:: python
+For an even deeper analysis, you might want to visualize the SHAP values. The `shap_multi_plot()` method not only filters the data but also provides a visual representation of the SHAP values for specified classes. Below is an example that illustrates how to plot SHAP values for the classes "Covid" and "Respiratory":
 
-    predictions = lcppn.predict(x_test)
-    # Get the correcponding samples
-    covid_idx = explainer.get_sample_indices(predictions, 'Covid')
-
-    # Filter the shap values
-    shap_values_covid = explainer.filter_by_class(explanations, 'Covid', covid_idx)
-    print(shap_values_covid)
+.. code-block:: python
 
-    # Filter explanations by level
-    level = 1
-    explanations_level_1 = explainer.filter_by_level(explanations, level)
-    print(explanations_level_1)
+    # Generating and plotting explanations for specific classes
+    explanations = explainer.shap_multi_plot(
+        class_names=["Covid", "Respiratory"],
+        features=x_test,
+        pred_class="Covid",
+        # Feature names specifiaction possible if x_train is a dataframe with specified columns_names
+        feature_names=x_train.columns.values
+    )