example.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn import tree
import graphviz

from trustee import ClassificationTrustee
from trustee.report.trust import TrustReport

OUTPUT_PATH = "out/"
REPORT_PATH = f"{OUTPUT_PATH}/report/trust_report.obj"

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

pipeline = Pipeline([
    ('scaler', StandardScaler()),  # Step 1: Standardize the data
    ('classifier', LogisticRegression())  # Step 2: Apply logistic regression
])

pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')

trust_report = TrustReport(
    pipeline,
    X_train=X_train,
    y_train=y_train,
    X_test=X_test,
    y_test=y_test,
    max_iter=5,
    num_pruning_iter=5,
    train_size=0.7,
    trustee_num_iter=5,
    trustee_num_stability_iter=5,
    trustee_sample_size=0.3,
    analyze_branches=True,
    analyze_stability=True,
    top_k=1,
    verbose=True,
    class_names=iris.target_names,
    feature_names=iris.feature_names,
    is_classify=True,
)

trust_report.save(OUTPUT_PATH)

trustee = ClassificationTrustee(expert=pipeline)
trustee.fit(X_train, y_train, num_iter=5, num_stability_iter=5, samples_size=0.3, verbose=True)

dt, pruned_dt, agreement, reward = trustee.explain()
print(f"Model explanation training (agreement, fidelity): ({agreement}, {reward})")
print(f"Model Explanation size: {dt.tree_.node_count}")
print(f"Top-k Prunned Model explanation size: {pruned_dt.tree_.node_count}")

# Output decision tree to pdf
dot_data = tree.export_graphviz(
    dt,
    class_names=iris.target_names,
    feature_names=iris.feature_names,
    filled=True,
    rounded=True,
    special_characters=True,
)
graph = graphviz.Source(dot_data)
graph.render("dt_explanation")

# Output pruned decision tree to pdf
dot_data = tree.export_graphviz(
    pruned_dt,
    class_names=iris.target_names,
    feature_names=iris.feature_names,
    filled=True,
    rounded=True,
    special_characters=True,
)
graph = graphviz.Source(dot_data)
graph.render("pruned_dt_explation")