# -*- coding: utf-8 -*-
"""
=========================================
Explaining Local Classifier Per Node
=========================================

A minimalist example showing how to use HiClass Explainer to obtain SHAP values of LCPN 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 LocalClassifierPerNode, Explainer
from hiclass.datasets import load_platypus
import shap

# Load train and test splits
X_train, X_test, Y_train, Y_test = load_platypus()

# Use random forest classifiers for every node
rfc = RandomForestClassifier()
classifier = LocalClassifierPerNode(local_classifier=rfc, replace_classifiers=False)

# Train local classifier per node
classifier.fit(X_train, Y_train)

# Define Explainer
explainer = Explainer(classifier, data=X_train.values, mode="tree")
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_1", "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),
)