"""
decision_tree_classification_with_scikit_learn.py
creates, trains, and prediction tests a decision tree classifier
"""

# Import libraries.
from sklearn.datasets import load_iris
from sklearn import tree
import graphviz

# Load iris measurements test data.
X, y = load_iris(return_X_y=True)

# Create a decision tree classifier
classifier = tree.DecisionTreeClassifier()

# Train the classifier with the test data.
classifier = classifier.fit(X, y)

# Export the classifier structure.
dot_data = tree.export_graphviz(classifier, out_file=None)

# Graph the structure.
graph = graphviz.Source(dot_data)

# Output the graph into a pdf file.
graph.render("iris")

# Create an array of values for a prediction input.
X_new = X - .1

# Predict results based on the X training data and modified X_new data.
classifier.predict(X)
classifier.predict(X_new)