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

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>`_.
"""
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/1152c0b9613c2bda144a38fc4f74b5fe12255f4d/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 = 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 parent node
classifier.fit(X_train, Y_train)

# Define Explainer
explainer = Explainer(classifier, data=X_train, 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", "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),
)