updated plot_lcppn_explainer to use platypus dataset

docs/examples/plot_lcppn_explainer.py

@@ -6,40 +6,56 @@
 
 A minimalist example showing how to use HiClass Explainer to obtain SHAP values of LCPPN model.
 A detailed summary of the Explainer class has been given at Algorithms Overview Section for :ref:`Hierarchical Explainability`.
+SHAP values are calculated based on a synthetic platypus diseases dataset that can be downloaded `here <https://gist.githubusercontent.com/ashishpatel16/9306f8ed3ed101e7ddcb519776bcbd80/raw/3f225c3f80dd8cbb1b6252f6c372a054ec968705/platypus_diseases.csv>`_.
 """
-import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from hiclass import LocalClassifierPerParentNode, Explainer
+import requests
+import pandas as pd
+import shap
+
+# Download training data
+url = "https://gist.githubusercontent.com/ashishpatel16/9306f8ed3ed101e7ddcb519776bcbd80/raw/3f225c3f80dd8cbb1b6252f6c372a054ec968705/platypus_diseases.csv"
+path = "platypus_diseases.csv"
+response = requests.get(url)
+with open(path, "wb") as file:
+    file.write(response.content)
+
+# Load training data into pandas dataframe
+training_data = pd.read_csv(path).fillna(" ")
 
 # Define data
-X_train = np.array(
-    [
-        [40.7, 1.0, 1.0, 2.0, 5.0, 2.0, 1.0, 5.0, 34.3],
-        [39.2, 0.0, 2.0, 4.0, 1.0, 3.0, 1.0, 2.0, 34.1],
-        [40.6, 0.0, 3.0, 1.0, 4.0, 5.0, 0.0, 6.0, 27.7],
-        [36.5, 0.0, 3.0, 1.0, 2.0, 2.0, 0.0, 2.0, 39.9],
-    ]
-)
-X_test = np.array([[35.5, 0.0, 1.0, 1.0, 3.0, 3.0, 0.0, 2.0, 37.5]])
-Y_train = np.array(
-    [
-        ["Gastrointestinal", "Norovirus", ""],
-        ["Respiratory", "Covid", ""],
-        ["Allergy", "External", "Bee Allergy"],
-        ["Respiratory", "Cold", ""],
-    ]
-)
+X_train = training_data.drop(["label"], axis=1)
+X_test = X_train[:100]  # Use first 100 samples as test set
+Y_train = training_data["label"]
+Y_train = [eval(my) for my in Y_train]
 
 # Use random forest classifiers for every node
 rfc = RandomForestClassifier()
 classifier = LocalClassifierPerParentNode(
     local_classifier=rfc, replace_classifiers=False
 )
 
-# Train local classifier per node
+# Train local classifier per parent node
 classifier.fit(X_train, Y_train)
 
 # Define Explainer
 explainer = Explainer(classifier, data=X_train, mode="tree")
-explanations = explainer.explain(X_test)
+explanations = explainer.explain(X_test.values)
 print(explanations)
+
+# Filter samples which only predicted "Respiratory" at first level
+respiratory_idx = classifier.predict(X_test)[:, 0] == "Respiratory"
+
+# Specify additional filters to obtain only level 0
+shap_filter = {"level": 0, "class": "Respiratory", "sample": respiratory_idx}
+
+# Use .sel() method to apply the filter and obtain filtered results
+shap_val_respiratory = explanations.sel(shap_filter)
+
+# Plot feature importance on test set
+shap.plots.violin(
+    shap_val_respiratory.shap_values,
+    feature_names=X_train.columns.values,
+    plot_size=(13, 8),
+)