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()) # compare tha accuracy by hand count = 0.0 # have to be in floating point predict = clf.predict(features_test) for i in range(0, len(predict)): if predict[i] == labels_test[i]: count += 1 accuracy = count / (len(predict)) print 'self-calculate: ', accuracy # using built-in function of sklearn acc = NBAccuracy(features_train, labels_train, features_test, labels_test) print 'using clf.score: ', acc # or print 'accuracy_score function', accuracy_score(predict, labels_test)
def submitAccuracy(): accuracy = NBAccuracy(features_train, labels_train, features_test, labels_test) return accuracy
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) ### 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()) pred = clf.predict(features_test) from classify import NBAccuracy # below are the two ways accuracy can be found print "Accuracy is: {}".format(NBAccuracy(clf, pred, labels_test)) print "Accuracy is: {}".format(clf.score(features_test, labels_test))
from class_vis import prettyPicture from prep_terrain_data import makeTerrainData from classify import NBAccuracy import matplotlib.pyplot as plt import numpy as np import pylab as pl features_train, labels_train, features_test, labels_test = makeTerrainData() def submitAccuracy(): accuracy = NBAccuracy(features_train, labels_train, features_test, labels_test) return accuracy if __name__ == "__main__": print "NBGaussian accuracy:", NBAccuracy(features_train, labels_train, features_test, labels_test)
def submitAccuracy(): accuracy = NBAccuracy(features_train, labels_train, features_test, labels_test) print("accuracy: ", accuracy) return accuracy
def submitAccuracy(): accuracy = NBAccuracy(features_train, labels_train, features_test, labels_test, 2) #return (accuracy) print(accuracy)