def submit_accuracy():
features_train, labels_train, features_test, labels_test = make_terrain_data(
)
accuracy = nb_accuracy(features_train, labels_train, features_test,
labels_test)
print accuracy
return accuracy
def make_prediction():
features_train, labels_train, features_test, labels_test = make_terrain_data(
)
# 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()
clf = RandomForestClassifier(min_samples_split=50)
# clf = AdaBoostClassifier(n_estimators=100, random_state=0)
clf.fit(features_train, labels_train)
prediction = clf.predict(features_test)
print("accuracy: {}".format(accuracy_score(labels_test, prediction)))
try:
pretty_picture(clf, features_test, labels_test)
except NameError:
pass
Here we use Random Forest Classifier to identify whether a
car should go fast or slow according to the type of terrain.
'''
import matplotlib.pyplot as plt
import sys
from time import time
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
sys.path.append("../../pre_process/")
from prep_terrain_data import make_terrain_data
from vis_terrain_data import pretty_picture
print("Creating terrain dataset and split into test/train.")
features_train, labels_train, features_test, labels_test = make_terrain_data()
# 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
