"""
==============================================
DL85Classifier example to export tree as image
==============================================
"""
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from dl85 import DL85Classifier
import graphviz


print("######################################################################\n"
      "#                      DL8.5 default classifier                      #\n"
      "######################################################################")

# read the dataset and split into features and targets
dataset = np.genfromtxt("../datasets/anneal.txt", delimiter=' ')
X, y = dataset[:, 1:], dataset[:, 0]
# split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

clf = DL85Classifier(max_depth=2)
clf.fit(X, y)
y_pred = clf.predict(X_test)

# show results
print("Model built in", round(clf.runtime_, 4), "seconds")
print("Found tree:", clf.tree_)
print("Confusion Matrix below\n", confusion_matrix(y_test, y_pred))
print("Accuracy on training set =", round(clf.accuracy_, 4))
print("Accuracy on test set =", round(accuracy_score(y_test, y_pred), 4))

# print the tree
dot = clf.export_graphviz()
graph = graphviz.Source(dot, format="png")
graph.render("plots/anneal_odt")