allThree.py

#!/usr/bin/env python
# encoding: utf-8
# author: Ernest
# created: 21/09/2016

"""
Description

"""

#!/usr/bin/python
from time import time
import matplotlib.pyplot as plt
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score


features_train, labels_train, features_test, labels_test = makeTerrainData()


### the training data (features_train, labels_train) have both "fast" and "slow"
### points mixed together--separate them so we can give them different colors
### in the scatterplot and identify them visually
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]


#### initial visualization
plt.xlim(0.0, 1.0)
plt.ylim(0.0, 1.0)
plt.scatter(bumpy_fast, grade_fast, color="b", label="fast")
plt.scatter(grade_slow, bumpy_slow, color="r", label="slow")
plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.show()
################################################################################


### your code here!  name your classifier object clf if you want the
### visualization code (prettyPicture) to show you the decision boundary

#t0 = time()
#knnClf = KNeighborsClassifier()
#knnClf.fit(features_train, labels_train)
#print "default knn training time:", round(time()-t0, 3), "s"

# t0 = time()
# adaBoostClf = AdaBoostClassifier(n_estimators=30,learning_rate=0.4)
# adaBoostClf.fit(features_train, labels_train)
# print "default adaBoost training time:", round(time()-t0, 3), "s"

t0 = time()
clf = RandomForestClassifier()
clf.fit(features_train, labels_train)
print "default randomForest training time:", round(time()-t0, 3), "s"

#knnPred = knnClf.predict(features_test)
#knnacc = accuracy_score(knnPred, labels_test)

# adaBoostPred = adaBoostClf.predict(features_test)
# adaBoostacc = accuracy_score(adaBoostPred, labels_test)

rfPred = clf.predict(features_test)
rfacc = accuracy_score(rfPred, labels_test)

# print "default knn accuracy:", knnacc
# print "default adaBoost accuracy:", adaBoostacc
print("default rf accuracy:", rfacc)



try:
    prettyPicture(clf, features_test, labels_test)
except NameError:
    print "unable to produce boundary"
    pass