def main(): db_helper = ApeicDBHelper() users = db_helper.get_users() for user in users: logs = db_helper.get_logs(user) preprocessor = Preprocessor(logs) preprocessor.extract_stay_points()
def main(): # TODO: ignore some user's data? db = ApeicDBHelper() logs = [] for user in db.get_users(): logs.extend(db.get_logs(user)) analyzer = RealDataAnalyzer() analyzer.get_env_context_distrs(logs)
def main(): db_helper = ApeicDBHelper() hits = 0.0 misses = 0.0 users = db_helper.get_users() for user in users: logs = db_helper.get_logs(user) training_data, testing_data = split(logs) predictor = TAPPredictor() predictor.train(training_data) for i in xrange(2, len(testing_data)): candidates = predictor.predict(testing_data[i-2]['application'], testing_data[i-1]['application'], k) if testing_data[i]['application'] in candidates: hits += 1.0 else: misses += 1.0 print k, hits/(hits + misses)
def main(): db_helper = ApeicDBHelper() users = db_helper.get_users() accuracies = [] for user in users: if user == '11d1ef9f845ec10e': continue print colored(user, attrs=['blink']) logs = db_helper.get_logs(user) predictor = APPRushPredictor() training_logs, testing_logs = predictor.split(logs, 0.8) predictor.train(training_logs) hits = 0.0 misses = 0.0 # testing_logs = training_logs last_log = testing_logs[0] for log in testing_logs[1:]: if log['application'] != last_log['application'] or log['id'] == testing_logs[-1]['id']: candidates = predictor.predict(last_log, log, 5) if log['application'] in candidates: hits += 1 else: misses += 1 last_log = log acc = hits/(hits + misses) accuracies.append(acc) print acc print sum(accuracies)/len(accuracies)
def main(): db_helper = ApeicDBHelper() users = db_helper.get_users() accuracies = [] for user in users: if user == '11d1ef9f845ec10e': continue print colored(user, attrs=['blink']) logs = db_helper.get_logs(user) predictor = APPRushPredictor() training_logs, testing_logs = predictor.split(logs, 0.8) predictor.train(training_logs) hits = 0.0 misses = 0.0 # testing_logs = training_logs last_log = testing_logs[0] for log in testing_logs[1:]: if log['application'] != last_log['application'] or log[ 'id'] == testing_logs[-1]['id']: candidates = predictor.predict(last_log, log, 5) if log['application'] in candidates: hits += 1 else: misses += 1 last_log = log acc = hits / (hits + misses) accuracies.append(acc) print acc print sum(accuracies) / len(accuracies)