""" Complete the code in ClassifyNB.py with the sklearn
Naive Bayes classifier to classify the terrain data.
The objective of this exercise is to recreate the decision
boundary found in the lesson video, and make a plot that
visually shows the decision boundary """
from prep_terrain_data import makeTerrainData
from class_viz import prettyPicture, output_image
from ClassifyNB import classify
from AccuracyNB import accuracy
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the training data (features_train, labels_train) have both "fast" and "slow" points mixed
### in together--separate them so we can give them different colors in the scatterplot,
### and visually identify them
grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]
bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]
grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]
bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]
clf = classify(features_train, labels_train)
### draw the decision boundary with the text points overlaid
prettyPicture(clf, features_test, labels_test)
output_image("test.png", "png", open("test.png", "rb").read())
print(accuracy(clf, features_test, labels_test))
### in together--separate them so we can give them different colors in the scatterplot,
### and visually identify them
grade_fast = [
features_train[ii][0] for ii in range(0, len(features_train))
if labels_train[ii] == 0
]
bumpy_fast = [
features_train[ii][1] for ii in range(0, len(features_train))
if labels_train[ii] == 0
]
grade_slow = [
features_train[ii][0] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
bumpy_slow = [
features_train[ii][1] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
# You will need to complete this function imported from the ClassifyNB script.
# Be sure to change to that code tab to complete this quiz.
clf = classify(features_train, labels_train)
pred = clf.predict(features_test)
accuracy = sum(pred == labels_test) / len(pred)
# print clf.score(features_test, labels_test)
### draw the decision boundary with the text points overlaid
prettyPicture(clf, features_test, labels_test)
# output_image("test.png", "png", open("test.png", "rb").read())