def get_all_business_types():
businesses = loader.get_challengeset()
idtoloc = loader.get_idtoloc()
business_types_dict = loader.get_business_types()
print "Done {} of {}".format(len(business_types_dict), len(businesses))
for business in businesses:
unique_id = business['unique_id']
if unique_id not in business_types_dict.keys():
print business['name']
closest_place, best_sim = None, 0
lat, lon = idtoloc[unique_id]
for place in get_places(lat, lon):
sim = cosine_sim(place['name'], business['name'])
if sim > best_sim:
closest_place = place
best_sim = sim
if closest_place:
types = filter(lambda x: not x in ['point_of_interest', 'establishment', 'sublocality', 'route',
'real', 'political', 'of', 'or', 'local', 'locality', 'intersection',
'1'], closest_place['types'])
types = " ".join(types).replace("_", " ")
else:
types = None
print types
business_types_dict[unique_id] = types
loader.dump_business_dict(business_types_dict)
def clean_business_types():
business_types_dict = loader.get_business_types()
for k in business_types_dict.keys():
if business_types_dict[k] is None:
business_types_dict[k] = ''
strs = business_types_dict[k].split(' ')
s = []
for st in strs:
if st not in [
None, 'agency', 'place', 'food', 'point_of_interest',
'establishment', 'sublocality', 'route', 'real',
'political', 'of', 'or', 'local', 'locality',
'intersection', '1'
]:
s += [st]
if len(s) == 0:
business_types_dict[k] = ''
else:
business_types_dict[k] = ' '.join(s)
loader.dump_business_dict(business_types_dict)
print "Score: ", total
print "Total: ", max_potential
def getPredictionScoreOfTrainingSet():
"""
Returns the score of the classifications on the validation set.
"""
hand_classified_set = loader.get_hand_classifiedset()
algo_classified_set = loader.get_algo_classifiedset()
total = 0
scores = []
for uid, actual in hand_classified_set.iteritems():
guess = algo_classified_set[uid]
scores.append(util.score_prediction(guess, actual))
total = sum(scores)
max_potential = len(hand_classified_set.keys()) * 6
return total / float(max_potential)
def writeClassification(business_uid, naics_code):
loader.write_row_algo_classified_set(business_uid, naics_code)
if __name__ == "__main__":
naics_items = loader.get_naics_data_for_level(6)
business_types = loader.get_business_types()
naics_dict = loader.get_naics_dict()
featurizer = None
manager.run()
return render_template('classifypage.html', business=business, naics_dict=naics_dict, agent=agent)
@app.route('/c/<agent>/<test>/<business_uid>/<naics_code>', methods=['POST'])
def classifyBusiness(agent, test, business_uid, naics_code):
if request.method == 'POST':
loader.write_row_classified_set(business_uid, naics_code)
if test != 'test':
loader.write_row_hand_classified_set(business_uid, naics_code)
return redirect('/classifier/' + agent)
else:
return abort(405) # 405 Method Not Allowed
@app.route('/database')
@app.route('/database/<int:page>', methods=['GET'])
def databaseView(page=1):
businesses = dbh.getBusinessPage(page)
return render_template('database.html', businesses=businesses, hand_classified_set=hand_classified_set,
algo_classified_set=algo_classified_set, naics_dict=naics_dict)
if __name__ == "__main__":
naics_items = loader.get_naics_data_for_level(6)
hand_classified_set = loader.get_hand_classifiedset()
algo_classified_set = loader.get_algo_classifiedset()
unclassified_business_ids = loader.get_unclassified_business_ids()
business_types = loader.get_business_types()
naics_dict = loader.get_naics_dict()
app.run(debug=True)
def __init__(self):
self.google_types = loader.get_business_types()
self.model = loader.get_word2vecmodel()
print "Loaded Models"
def __init__(self):
self.google_types = loader.get_business_types()
self.model = loader.get_word2vecmodel()
print "Loaded Models"
