'''
Program to evaluate iris flower into its types - setosa, versicolor, virginica
using GaussianNB and rating the accuracy of the classifer
'''

from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

data = load_iris() # assining data

# Organizing Data
feature_name = data['feature_names']
feature = data['data']
label_name = data['target_names']
label = data['target']
desc = data['DESCR']

# Splitting our data
train_data, test_data, train_labels, test_labels = train_test_split(feature, label, test_size=0.3, random_state=0)

# assiging gaussianNB as 'classifier'
classifier = GaussianNB()

# feeding our data to classifier and assigning 'trained_classifier' to the fed classifier
trained_classifier = classifier.fit(train_data,train_labels)

# feeding the testing data to the classifier to predict the new data
prediction = trained_classifier.predict(test_data)

# rating our classifier from predicted labels and testing labels
print(accuracy_score(test_labels, prediction))