def addFeatures(data_dict, features_list):
"""
"""
# we don't want to inadvertently change the data_dict
# just yet so take a copy of data_dict so we can play
# with some of the features
# (yes it is wasteful to copy the whole thing)
data_dict_copy = deepcopy(data_dict)
# now we must will rescale the features so that we
# have a level playing field
#(technically unecessary with these choices as they
# are of similar scale)
data_dict_rescale = rescale(data_dict_copy, ['from_poi_to_this_person',
'from_this_person_to_poi',
'shared_receipt_with_poi'])
#data_dict_rescale = data_dict_copy
# now create the new feature
cnt = 0
for person, features in data_dict_rescale.iteritems():
frompoi = features['from_poi_to_this_person']
frompoi = 0 if frompoi == 'NaN' else frompoi
topoi = features['from_this_person_to_poi']
topoi = 0 if topoi == 'NaN' else topoi
sharedpoi = features['shared_receipt_with_poi']
sharedpoi = 0 if sharedpoi == 'NaN' else sharedpoi
#print str(frompoi) + " : " + str(topoi) + " : " + str(sharedpoi)
shared_messages = frompoi + topoi + sharedpoi
# now add to data_dict
data_dict[person]['shared_messages'] = shared_messages
cnt += 1
print "data_dict size: " + str(len(data_dict))
print "cnt: " + str(cnt)
features_list.append('shared_messages')
return (data_dict, features_list)
### Please name your classifier clf for easy export below. ### Note that if you want to do PCA or other multi-stage operations, ### you'll need to use Pipelines. For more info: ### http://scikit-learn.org/stable/modules/pipeline.html from sklearn import neighbors k = 3 clf = neighbors.KNeighborsClassifier(n_neighbors=k) # perform feature re-scaling # NOTE: only want to rescale the input features in_features = copy.deepcopy(features_list) in_features.pop(0) my_dataset = rescale(my_dataset, in_features) ### Task 5: Tune your classifier to achieve better than .3 precision and recall ### using our testing script. ### Because of the small size of the dataset, the script uses stratified ### shuffle split cross validation. For more info: ### http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html t0 = time() test_classifier(clf, my_dataset, features_list) print "time:", round(time() -t0, 3), "s" ### Dump your classifier, dataset, and features_list so ### anyone can run/check your results.
