def processAnswer(self, exp_uid, args_json, db, ell): """ reporting back the reward of pulling the arm suggested by getQuery Expected input (in json structure with string keys): (str) query_uid : unique identifier of query (int) index_winner : index of arm must be {index_left,index_right} Expected output (comma separated): if error: return (JSON) '{}', (bool) False, (str) error else: return (JSON) '{}', (bool) True,'' Usage: processAnswer_args_json,didSucceed,message = app.processAnswer(db_API,exp_uid,processAnswer_args_json) Example input: processAnswer_args_json = {"query_uid": "4d02a9924f92138287edd17ca5feb6e1", "index_winner": 8} Example output: processAnswer_response_json = {} """ try: app_id = self.app_id log_entry = { 'exp_uid': exp_uid, 'task': 'processAnswer', 'json': args_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-CALL', log_entry) # convert args_json to args_dict try: args_dict = json.loads(args_json) except: error = "%s.processAnswer input args_json is in improper format" % self.app_id return '{}', False, error # check for the fields that must be contained in args or error occurs necessary_fields = ['index_winner', 'query_uid'] for field in necessary_fields: try: args_dict[field] except KeyError: error = "%s.processAnswer input arguments missing field: %s" % ( self.app_id, str(field)) return '{}', False, error # get list of algorithms associated with project alg_list, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'alg_list') # get alg_id query_uid = args_dict['query_uid'] alg_uid, didSucceed, message = db.get(app_id + ':queries', query_uid, 'alg_uid') for algorithm in alg_list: if alg_uid == algorithm['alg_uid']: alg_id = algorithm['alg_id'] alg_label = algorithm['alg_label'] num_reported_answers, didSucceed, message = db.increment( app_id + ':experiments', exp_uid, 'num_reported_answers_for_' + alg_uid) # get sandboxed database for the specific app_id,alg_id,exp_uid - closing off the rest of the database to the algorithm rc = ResourceClient(app_id, exp_uid, alg_uid, db) # get specific algorithm to make calls to alg = utils.get_app_alg(self.app_id, alg_id) targets, didSucceed, message = db.get(app_id + ':queries', query_uid, 'target_indices') for target in targets: if target['label'] == 'left': index_left = target['index'] if target['label'] == 'right': index_right = target['index'] if target['flag'] == 1: index_painted = target['index'] index_winner = args_dict['index_winner'] # update query doc timestamp_query_generated, didSucceed, message = db.get( app_id + ':queries', query_uid, 'timestamp_query_generated') datetime_query_generated = utils.str2datetime( timestamp_query_generated) timestamp_answer_received = args_dict.get('meta', {}).get( 'timestamp_answer_received', None) if timestamp_answer_received == None: datetime_answer_received = datetime_query_generated else: datetime_answer_received = utils.str2datetime( timestamp_answer_received) delta_datetime = datetime_answer_received - datetime_query_generated round_trip_time = delta_datetime.seconds + delta_datetime.microseconds / 1000000. response_time = float(args_dict.get('response_time', 0.)) db.set(app_id + ':queries', query_uid, 'response_time', response_time) db.set(app_id + ':queries', query_uid, 'network_delay', round_trip_time - response_time) db.set(app_id + ':queries', query_uid, 'index_winner', index_winner) # call processAnswer didSucceed, dt = utils.timeit(alg.processAnswer)( resource=rc, index_left=index_left, index_right=index_right, index_painted=index_painted, index_winner=index_winner) log_entry_durations = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'task': 'processAnswer', 'duration': dt } log_entry_durations.update(rc.getDurations()) meta = {'log_entry_durations': log_entry_durations} ############### predict_id = 'arm_ranking' params = {'alg_label': alg_label} predict_args_dict = {'predict_id': predict_id, 'params': params} predict_args_json = json.dumps(predict_args_dict) db.submit_job(app_id, exp_uid, 'predict', predict_args_json, ignore_result=True) ############### response_args_dict = {} args_out = {'args': response_args_dict, 'meta': meta} response_json = json.dumps(args_out) log_entry = { 'exp_uid': exp_uid, 'task': 'processAnswer', 'json': response_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-RESPONSE', log_entry) return response_json, True, "" except Exception, err: error = traceback.format_exc() log_entry = { 'exp_uid': exp_uid, 'task': 'processAnswer', 'error': error, 'timestamp': utils.datetimeNow(), 'args_json': args_json } ell.log(app_id + ':APP-EXCEPTION', log_entry) return '{}', False, error
def predict(self, exp_uid, args_json, db, ell): """ uses current model empirical estimates to forecast which index has the highest mean Expected input (in json structure with string keys): (string) predict_id : identifier for the desired prediction (list) params : dictionary of stat_id specific fields. ########## Description: uses current model empirical estimates to forecast the ranking of the arms in order of likelhood of being the best from most to least likely Expected input: (string) predict_id : 'arm_ranking' (dict) params : dictionary with fields (string) alg_label : describes target algorithm to use Expected output (in json structure): (list of dicts with fields): (int) index : index of target (int) rank : rank that the algorithm assigns to index (rank 0 is most likely the best arm) """ try: app_id = self.app_id log_entry = { 'exp_uid': exp_uid, 'task': 'predict', 'json': args_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-CALL', log_entry) # convert args_json to args_dict try: args_dict = json.loads(args_json) except: error = "%s.predict failed to convert input args_json due to improper format" % ( self.app_id) return '{}', False, error # check for the fields that must be contained in args or error occurs necessary_fields = ['predict_id', 'params'] for field in necessary_fields: try: args_dict[field] except KeyError: error = "%s.predict input arguments missing field: %s" % ( self.app_id, str(field)) return '{}', False, error predict_id = args_dict['predict_id'] params = args_dict['params'] if predict_id == "arm_ranking": alg_label = params['alg_label'] # get list of algorithms associated with project alg_list, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'alg_list') # get alg_id for algorithm in alg_list: if alg_label == algorithm['alg_label']: alg_id = algorithm['alg_id'] alg_uid = algorithm['alg_uid'] num_reported_answers, didSucceed, message = db.get( app_id + ':experiments', exp_uid, 'num_reported_answers_for_' + alg_uid) if type(num_reported_answers) != int: num_reported_answers = 0 # get sandboxed database for the specific app_id,alg_id,exp_uid - closing off the rest of the database to the algorithm rc = ResourceClient(app_id, exp_uid, alg_uid, db) # get specific algorithm to make calls to alg = utils.get_app_alg(self.app_id, alg_id) # call getQuery scores, precisions, dt = utils.timeit(alg.predict)(resource=rc) log_entry_durations = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'task': 'predict', 'duration': dt } log_entry_durations.update(rc.getDurations()) meta = {'log_entry_durations': log_entry_durations} import numpy ranks = (-numpy.array(scores)).argsort().tolist() n = len(scores) indexes = numpy.array(range(n))[ranks] scores = numpy.array(scores)[ranks] precisions = numpy.array(precisions)[ranks] ranks = range(n) targets = [] for index in range(n): targets.append({ 'index': indexes[index], 'rank': ranks[index], 'score': scores[index], 'precision': precisions[index] }) log_entry = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'timestamp': utils.datetimeNow() } log_entry.update({ 'targets': targets, 'num_reported_answers': num_reported_answers }) ell.log(app_id + ':ALG-EVALUATION', log_entry) response_args_dict = {} args_out = {'args': response_args_dict, 'meta': meta} predict_json = json.dumps(args_out) log_entry = { 'exp_uid': exp_uid, 'task': 'predict', 'json': predict_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-RESPONSE', log_entry) return predict_json, True, '' except Exception, err: error = traceback.format_exc() log_entry = { 'exp_uid': exp_uid, 'task': 'predict', 'error': str(error), 'timestamp': utils.datetimeNow(), 'args_json': args_json } didSucceed, message = ell.log(app_id + ':APP-EXCEPTION', log_entry) return '{}', False, error
def getQuery(self, exp_uid, args_json, db, ell): """ A request to ask which two arms to duel next Expected input (in jsonstructure with string keys): [optional] (string) participant_uid : unique identifier of session for a participant answering questions (that is, an email address is not good enough as the participant could participate in multiple exp_uids so it would not be unique against all experiments), if key non-existant particpant_uid is assigned as exp_uid. Expected output (in json structure with string keys): (list) target_indices : list that stores dictionary of targets with fields: { (int) index : the index of the target of relevance (str) label : in {'left','right'} for display (int) flag : integer for algorithm's use } (str) query_uid : unique identifier of query (used to look up for processAnswer) Usage: getQuery_response_json,didSucceed,message = app.getQuery(db_API,exp_uid,getQuery_args_json) Example input: getQuery_args_json = {"participant_uid": "0077110d03cf06b8f77d11acc399e8a7"} Example output: getQuery_response_json = {"query_uid": "4d02a9924f92138287edd17ca5feb6e1", "target_indices": [{"index": 9, "flag": 0, "label": "left"}, {"index": 8, "flag": 1, "label": "right"}]} """ try: app_id = self.app_id log_entry = { 'exp_uid': exp_uid, 'task': 'getQuery', 'json': args_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-CALL', log_entry) # convert args_json to args_dict try: args_dict = json.loads(args_json) except: error = "%s.initExp input args_json is in improper format" % self.app_id return '{}', False, error # get list of algorithms associated with project alg_list, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'alg_list') alg_label_to_alg_id = {} alg_label_to_alg_uid = {} for algorithm in alg_list: alg_label_to_alg_id[ algorithm['alg_label']] = algorithm['alg_id'] alg_label_to_alg_uid[ algorithm['alg_label']] = algorithm['alg_uid'] algorithm_management_settings, didSucceed, message = db.get( app_id + ':experiments', exp_uid, 'algorithm_management_settings') # ASSIGN ALGORITHM TO PARTICIPANT if 'participant_uid' in args_dict: participant_uid = args_dict['participant_uid'] else: participant_uid = exp_uid participant_doc_exists, didSucceed, message = db.exists( app_id + ':participants', participant_uid, 'participant_uid') first_participant_query = not participant_doc_exists if first_participant_query: db.set(app_id + ':participants', participant_uid, 'participant_uid', participant_uid) db.set(app_id + ':participants', participant_uid, 'exp_uid', exp_uid) participant_to_algorithm_management, didSucceed, message = db.get( app_id + ':experiments', exp_uid, 'participant_to_algorithm_management') if (participant_uid == exp_uid) or ( participant_to_algorithm_management == 'one_to_many') or (first_participant_query): if algorithm_management_settings[ 'mode'] == 'fixed_proportions': proportions_list = algorithm_management_settings['params'][ 'proportions'] prop = [ prop_item['proportion'] for prop_item in proportions_list ] prop_item = numpy.random.choice(alg_list, p=prop) else: raise Exception('algorithm_management_mode : ' + algorithm_management_settings['mode'] + ' not implemented') alg_id = alg_label_to_alg_id[prop_item['alg_label']] alg_uid = alg_label_to_alg_uid[prop_item['alg_label']] alg_label = prop_item['alg_label'] if (first_participant_query) and ( participant_to_algorithm_management == 'one_to_one'): db.set(app_id + ':participants', participant_uid, 'alg_id', alg_id) db.set(app_id + ':participants', participant_uid, 'alg_uid', alg_uid) elif (participant_to_algorithm_management == 'one_to_one'): # If here, then alg_uid should already be assigned in participant doc alg_id, didSucceed, message = db.get(app_id + ':participants', participant_uid, 'alg_id') alg_uid, didSucceed, message = db.get(app_id + ':participants', participant_uid, 'alg_uid') else: raise Exception('participant_to_algorithm_management : ' + participant_to_algorithm_management + ' not implemented') # get sandboxed database for the specific app_id,alg_id,exp_uid - closing off the rest of the database to the algorithm rc = ResourceClient(app_id, exp_uid, alg_uid, db) # get specific algorithm to make calls to alg = utils.get_app_alg(self.app_id, alg_id) # call getQuery index_left, index_right, index_painted, dt = utils.timeit( alg.getQuery)(resource=rc) # check for context context_type, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'context_type') context, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'context') # log log_entry_durations = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'task': 'getQuery', 'duration': dt } log_entry_durations.update(rc.getDurations()) meta = {'log_entry_durations': log_entry_durations} # create JSON query payload if index_left == index_painted: targets = [{ 'index': index_left, 'label': 'left', 'flag': 1 }, { 'index': index_right, 'label': 'right', 'flag': 0 }] else: targets = [{ 'index': index_left, 'label': 'left', 'flag': 0 }, { 'index': index_right, 'label': 'right', 'flag': 1 }] timestamp = str(utils.datetimeNow()) query_uid = utils.getNewUID() query = {} query['query_uid'] = query_uid query['target_indices'] = targets # save query data to database query_doc = {} query_doc.update(query) query_doc['participant_uid'] = participant_uid query_doc['alg_uid'] = alg_uid query_doc['exp_uid'] = exp_uid query_doc['alg_label'] = alg_label query_doc['timestamp_query_generated'] = timestamp db.set_doc(app_id + ':queries', query_uid, query_doc) # add context after updating query doc to avoid redundant information query['context_type'] = context_type query['context'] = context args_out = {'args': query, 'meta': meta} response_json = json.dumps(args_out) log_entry = { 'exp_uid': exp_uid, 'task': 'getQuery', 'json': response_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-RESPONSE', log_entry) return response_json, True, '' except Exception, err: error = traceback.format_exc() log_entry = { 'exp_uid': exp_uid, 'task': 'getQuery', 'error': error, 'timestamp': utils.datetimeNow(), 'args_json': args_json } ell.log(app_id + ':APP-EXCEPTION', log_entry) return '{}', False, error
def daemonProcess(self, exp_uid, args_json, db, ell): try: app_id = self.app_id log_entry = { 'exp_uid': exp_uid, 'task': 'daemonProcess', 'json': args_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-CALL', log_entry) # convert args_json to args_dict try: args_dict = json.loads(args_json) except: error = "%s.daemonProcess input args_json is in improper format" % self.app_id return '{}', False, error # check for the fields that must be contained in args or error occurs necessary_fields = ['alg_uid', 'daemon_args'] for field in necessary_fields: try: args_dict[field] except KeyError: error = "%s.daemonProcess input arguments missing field: %s" % ( self.app_id, str(field)) return '{}', False, error alg_daemon_args = args_dict['daemon_args'] alg_uid = args_dict['alg_uid'] alg_id, didSucceed, message = db.get(app_id + ':algorithms', alg_uid, 'alg_id') # get sandboxed database for the specific app_id,alg_id,exp_uid - closing off the rest of the database to the algorithm # rc = ResourceClient(app_id,exp_uid,alg_uid,db) rc = ResourceClient(app_id, exp_uid, alg_uid, db) # get specific algorithm to make calls to alg = utils.get_app_alg(self.app_id, alg_id) didSucceed, dt = utils.timeit(alg.daemonProcess)( resource=rc, daemon_args_dict=alg_daemon_args) log_entry_durations = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'task': 'daemonProcess', 'duration': dt } log_entry_durations.update(rc.getDurations()) meta = {'log_entry_durations': log_entry_durations} daemon_message = {} args_out = {'args': daemon_message, 'meta': meta} response_json = json.dumps(args_out) log_entry = { 'exp_uid': exp_uid, 'task': 'daemonProcess', 'json': response_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-RESPONSE', log_entry) return response_json, True, '' except Exception, err: error = traceback.format_exc() log_entry = { 'exp_uid': exp_uid, 'task': 'daemonProcess', 'error': error, 'timestamp': utils.datetimeNow(), 'args_json': args_json } ell.log(app_id + ':APP-EXCEPTION', log_entry) return '{}', False, error
def initExp(self, exp_uid, args_json, db, ell): """ initialize the project and necessary experiments Expected input (in json structure with string keys): (int) n: number of arms (float) failure_probability : confidence [optional] (list of dicts) alg_list : with fields (Defaults given by Info.get_app_default_alg_list) (string) alg_id : valid alg_id for this app_id (string) alg_label : unique identifier for algorithm (e.g. may have experiment with repeated alg_id's, but alg_labels must be unqiue, will also be used for plot legends [optional] (string) test_alg_label : must be one of the alg_label's in alg_list (Default is self) [optional] (dict) algorithm_management_settings : dictionary with fields (string) 'mode' and (dict) 'params'. mode in {'pure_exploration','explore_exploit','fixed_proportions'}. Default is 'fixed_proportions' and allocates uniform probability to each algorithm. If mode=fixed_proportions then params is a dictionary that contains the field 'proportions' which is a list of dictionaries with fields 'alg_label' and 'proportion' for all algorithms in alg_list. All proportions must be positive and sum to 1 over all algs in alg_list [optional] (string) participant_to_algorithm_management : in {'one_to_one','one_to_many'}. Default is 'one_to_many'. [optional] (string) instructions [optional] (string) debrief [optional] (int) num_tries Expected output: if error: return (JSON) '{}', (bool) False, (str) error_str else: return (JSON) '{}', (bool) True,'' Usage: initExp_response_json,didSucceed,message = app.initExp(db_API,exp_uid,initExp_args_json) Example input: initExp_args_json = {"participant_to_algorithm_management": "one_to_many", "alg_list": [{"alg_label": "BR_LilUCB", "alg_id": "BR_LilUCB", "params": {}}], "algorithm_management_settings": {"params": {"proportions": [{"alg_label": "BR_LilUCB", "proportion": 1.0}]}, "mode": "fixed_proportions"}, "failure_probability": 0.01, "n": 10} Example output: initExp_response_json = {} """ try: app_id = self.app_id # remove any reminants of an experiment if it exists didSucceed, message = db.delete_docs_with_filter( 'experiments_admin', {'exp_uid': exp_uid}) didSucceed, message = db.delete_docs_with_filter( app_id + ':experiments', {'exp_uid': exp_uid}) didSucceed, message = db.delete_docs_with_filter( app_id + ':queries', {'exp_uid': exp_uid}) didSucceed, message = db.delete_docs_with_filter( app_id + ':participants', {'exp_uid': exp_uid}) didSucceed, message = db.delete_docs_with_filter( app_id + ':algorithms', {'exp_uid': exp_uid}) didSucceed, message = ell.delete_logs_with_filter( app_id + ':APP-CALL', {'exp_uid': exp_uid}) didSucceed, message = ell.delete_logs_with_filter( app_id + ':APP-RESPONSE', {'exp_uid': exp_uid}) didSucceed, message = ell.delete_logs_with_filter( app_id + ':APP-EXCEPTION', {'exp_uid': exp_uid}) didSucceed, message = ell.delete_logs_with_filter( app_id + ':ALG-DURATION', {'exp_uid': exp_uid}) didSucceed, message = ell.delete_logs_with_filter( app_id + ':ALG-EVALUATION', {'exp_uid': exp_uid}) # add indexes (only adds them if they do not already exist) didSucceed, message = db.ensure_index('experiments_admin', {'exp_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':experiments', {'exp_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':queries', {'query_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':queries', {'exp_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':queries', {'alg_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':queries', {'participant_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':participants', {'participant_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':participants', {'exp_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':algorithms', {'alg_uid': 1}) didSucceed, message = db.ensure_index(app_id + ':algorithms', {'exp_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':APP-CALL', {'exp_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':APP-CALL', {'timestamp': 1}) didSucceed, message = ell.ensure_index(app_id + ':APP-CALL', { 'exp_uid': 1, 'timestamp': 1 }) didSucceed, message = ell.ensure_index(app_id + ':APP-CALL', { 'exp_uid': 1, 'task': 1 }) didSucceed, message = ell.ensure_index(app_id + ':APP-RESPONSE', {'exp_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':APP-RESPONSE', {'timestamp': 1}) didSucceed, message = ell.ensure_index(app_id + ':APP-RESPONSE', { 'exp_uid': 1, 'timestamp': 1 }) didSucceed, message = ell.ensure_index(app_id + ':APP-RESPONSE', { 'exp_uid': 1, 'task': 1 }) didSucceed, message = ell.ensure_index(app_id + ':APP-EXCEPTION', {'exp_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':APP-EXCEPTION', {'timestamp': 1}) didSucceed, message = ell.ensure_index(app_id + ':APP-EXCEPTION', { 'exp_uid': 1, 'timestamp': 1 }) didSucceed, message = ell.ensure_index(app_id + ':APP-EXCEPTION', { 'exp_uid': 1, 'task': 1 }) didSucceed, message = ell.ensure_index(app_id + ':ALG-DURATION', {'exp_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':ALG-DURATION', {'alg_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':ALG-DURATION', {'timestamp': 1}) didSucceed, message = ell.ensure_index(app_id + ':ALG-DURATION', { 'exp_uid': 1, 'timestamp': 1 }) didSucceed, message = ell.ensure_index(app_id + ':ALG-DURATION', { 'alg_uid': 1, 'task': 1 }) didSucceed, message = ell.ensure_index(app_id + ':ALG-EVALUATION', {'exp_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':ALG-EVALUATION', {'alg_uid': 1}) didSucceed, message = ell.ensure_index(app_id + ':ALG-EVALUATION', {'timestamp': 1}) didSucceed, message = ell.ensure_index(app_id + ':ALG-EVALUATION', { 'exp_uid': 1, 'timestamp': 1 }) db.set('experiments_admin', exp_uid, 'exp_uid', exp_uid) db.set('experiments_admin', exp_uid, 'app_id', app_id) db.set('experiments_admin', exp_uid, 'start_date', utils.datetime2str(utils.datetimeNow())) log_entry = { 'exp_uid': exp_uid, 'task': 'initExp', 'json': args_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-CALL', log_entry) # convert args_json to args_dict try: args_dict = json.loads(args_json) except: error = "%s.initExp input args_json is in improper format" % self.app_id return '{}', False, error # check for the fields that must be contained in args or error occurs necessary_fields = ['n', 'failure_probability'] for field in necessary_fields: try: args_dict[field] except KeyError: error = "%s.initExp input arguments missing field: %s" % ( self.app_id, str(field)) return '{}', False, error n = args_dict['n'] delta = args_dict['failure_probability'] if 'alg_list' in args_dict: alg_list = args_dict['alg_list'] supportedAlgs = utils.get_app_supported_algs(self.app_id) for algorithm in alg_list: if algorithm['alg_id'] not in supportedAlgs: error = "%s.initExp unsupported algorithm '%s' in alg_list" % ( self.app_id, alg_id) return '{}', False, error else: alg_list = utils.get_app_default_alg_list(self.app_id) if 'instructions' not in args_dict: instructions = utils.get_app_default_instructions(app_id) else: instructions = args_dict['instructions'] if 'debrief' not in args_dict: debrief = utils.get_app_default_instructions(app_id) else: debrief = args_dict['debrief'] if 'num_tries' not in args_dict: num_tries = utils.get_app_default_num_tries(app_id) else: num_tries = args_dict['num_tries'] if 'context_type' not in args_dict: context_type = 'none' else: context_type = args_dict['context_type'] if 'context' not in args_dict: context = '' else: context = args_dict['context'] # ALGORITHM_MANAGEMENT_MODE FORMATTING CHECK if 'algorithm_management_settings' not in args_dict: params = {} params['proportions'] = [] for algorithm in alg_list: params['proportions'].append({ 'alg_label': algorithm['alg_label'], 'proportion': 1. / len(alg_list) }) algorithm_management_settings = {} algorithm_management_settings['mode'] = 'fixed_proportions' algorithm_management_settings['params'] = params else: algorithm_management_settings = args_dict[ 'algorithm_management_settings'] try: mode = algorithm_management_settings['mode'] params = algorithm_management_settings['params'] except: error = "%s.initExp algorithm_management_settings must be a dictionary with fields 'mode' and 'params'" % ( self.app_id) return '{}', False, error if mode == 'fixed_proportions': try: algorithm_proportions_list = params['proportions'] except: error = "%s.initExp algorithm_management_settings['params'] must be a dictionary with field 'proportions'" % ( self.app_id) return '{}', False, error # check if alg_labels are properly labeled for proportion_item in algorithm_proportions_list: proportion = proportion_item['proportion'] target_alg_label = proportion_item['alg_label'] target_alg_label_in_alg_list = False for algorithm in alg_list: if algorithm['alg_label'] == target_alg_label: target_alg_label_in_alg_list = True if not target_alg_label_in_alg_list: error = "%s.initExp algorithm_management_settings['params']['proportions'] must be a list of dictionaries, each dictionary containing the fields 'alg_label' and 'proportion'. The 'alg_label' value must be one of the alg_labels in a provided alg_list and 'proportion' must be nonnegative and sum to 1 : '%s' not in provided alg_list" % ( self.app_id, target_alg_label) return '{}', False, error elif mode == 'pure_exploration': error = "%s.initExp Sorry, '%s' is not yet supported." % ( self.app_id, mode) return '{}', False, error elif mode == 'explore_exploit': error = "%s.initExp Sorry, '%s' is not yet supported." % ( self.app_id, mode) return '{}', False, error else: error = "%s.initExp unsupported algorithm_management_mode: '%s'. Must be in {'pure_exploration','explore_exploit','fixed_proportions'}" % ( self.app_id, algorithm_management_mode) return '{}', False, error # ALGORITHM_MANAGEMENT_MODE FORMATTING CHECK if 'participant_to_algorithm_management' not in args_dict: participant_to_algorithm_management = 'one_to_many' else: participant_to_algorithm_management = args_dict[ 'participant_to_algorithm_management'] if participant_to_algorithm_management not in [ 'one_to_many', 'one_to_one' ]: error = "%s.initExp unsupported participant_to_algorithm_management: '%s'. Must be in {'one_to_many','one_to_one'}" % ( self.app_id, participant_to_algorithm_management) return '{}', False, error # assign uid to each algorithm and save it for algorithm in alg_list: alg_uid = utils.getNewUID() algorithm['alg_uid'] = alg_uid db.set(app_id + ':algorithms', alg_uid, 'alg_uid', alg_uid) db.set(app_id + ':algorithms', alg_uid, 'exp_uid', exp_uid) db.set(app_id + ':experiments', exp_uid, 'exp_uid', exp_uid) db.set(app_id + ':experiments', exp_uid, 'app_id', app_id) db.set(app_id + ':experiments', exp_uid, 'n', n) db.set(app_id + ':experiments', exp_uid, 'failure_probability', delta) db.set(app_id + ':experiments', exp_uid, 'alg_list', alg_list) db.set(app_id + ':experiments', exp_uid, 'algorithm_management_settings', algorithm_management_settings) db.set(app_id + ':experiments', exp_uid, 'participant_to_algorithm_management', participant_to_algorithm_management) db.set(app_id + ':experiments', exp_uid, 'instructions', instructions) db.set(app_id + ':experiments', exp_uid, 'debrief', debrief) db.set(app_id + ':experiments', exp_uid, 'context_type', context_type) db.set(app_id + ':experiments', exp_uid, 'context', context) db.set(app_id + ':experiments', exp_uid, 'num_tries', num_tries) # now create intitialize each algorithm for algorithm in alg_list: alg_id = algorithm['alg_id'] alg_uid = algorithm['alg_uid'] db.set(app_id + ':algorithms', alg_uid, 'alg_id', alg_id) db.set(app_id + ':algorithms', alg_uid, 'alg_uid', alg_uid) db.set(app_id + ':algorithms', alg_uid, 'exp_uid', exp_uid) # get sandboxed database for the specific app_id,alg_id,exp_uid - closing off the rest of the database to the algorithm rc = ResourceClient(app_id, exp_uid, alg_uid, db) # get specific algorithm to make calls to alg = utils.get_app_alg(self.app_id, alg_id) # call initExp didSucceed, dt = utils.timeit(alg.initExp)( resource=rc, n=n, failure_probability=delta) log_entry = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'task': 'initExp', 'duration': dt, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':ALG-DURATION', log_entry) response_json = '{}' log_entry = { 'exp_uid': exp_uid, 'task': 'initExp', 'json': response_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-RESPONSE', log_entry) return response_json, True, '' except Exception, err: error = traceback.format_exc() log_entry = { 'exp_uid': exp_uid, 'task': 'initExp', 'error': error, 'timestamp': utils.datetimeNow(), 'args_json': args_json } ell.log(app_id + ':APP-EXCEPTION', log_entry) return '{}', False, error
def getQuery(self, exp_uid, args_json, db, ell): """ A request to ask which k arms to duel next Expected input (in jsonstructure with string keys): (int) k : number of objects to display [optional] (string) participant_uid : unique identifier of session for a participant answering questions (that is, an email address is not good enough as the participant could participate in multiple exp_uids so it would not be unique against all experiments), if key non-existant particpant_uid is assigned as exp_uid. Expected output (in json structure with string keys): (list) target_indices : list of k target indexes e.g. [ (int) target_index_1, ... , (int) target_index_k ] (str) query_uid : unique identifier of query (used to look up for processAnswer) """ try: app_id = self.app_id log_entry = { 'exp_uid': exp_uid, 'task': 'getQuery', 'json': args_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-CALL', log_entry) # convert args_json to args_dict try: args_dict = json.loads(args_json) except: error = "%s.initExp input args_json is in improper format" % self.app_id return '{}', False, error # get list of algorithms associated with project alg_list, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'alg_list') alg_label_to_alg_id = {} alg_label_to_alg_uid = {} for algorithm in alg_list: alg_label_to_alg_id[ algorithm['alg_label']] = algorithm['alg_id'] alg_label_to_alg_uid[ algorithm['alg_label']] = algorithm['alg_uid'] algorithm_management_settings, didSucceed, message = db.get( app_id + ':experiments', exp_uid, 'algorithm_management_settings') # ASSIGN ALGORITHM TO PARTICIPANT if 'participant_uid' in args_dict: participant_uid = args_dict['participant_uid'] else: participant_uid = exp_uid participant_doc_exists, didSucceed, message = db.exists( app_id + ':participants', participant_uid, 'participant_uid') first_participant_query = not participant_doc_exists if first_participant_query: db.set(app_id + ':participants', participant_uid, 'participant_uid', participant_uid) db.set(app_id + ':participants', participant_uid, 'exp_uid', exp_uid) participant_to_algorithm_management, didSucceed, message = db.get( app_id + ':experiments', exp_uid, 'participant_to_algorithm_management') if (participant_uid == exp_uid) or ( participant_to_algorithm_management == 'one_to_many') or (first_participant_query): if algorithm_management_settings[ 'mode'] == 'fixed_proportions': proportions_list = algorithm_management_settings['params'][ 'proportions'] prop = [ prop_item['proportion'] for prop_item in proportions_list ] prop_item = numpy.random.choice(alg_list, p=prop) else: raise Exception('algorithm_management_mode : ' + algorithm_management_settings['mode'] + ' not implemented') alg_id = alg_label_to_alg_id[prop_item['alg_label']] alg_uid = alg_label_to_alg_uid[prop_item['alg_label']] alg_label = prop_item['alg_label'] if (first_participant_query) and ( participant_to_algorithm_management == 'one_to_one'): db.set(app_id + ':participants', participant_uid, 'alg_id', alg_id) db.set(app_id + ':participants', participant_uid, 'alg_uid', alg_uid) elif (participant_to_algorithm_management == 'one_to_one'): # If here, then alg_uid should already be assigned in participant doc alg_id, didSucceed, message = db.get(app_id + ':participants', participant_uid, 'alg_id') alg_uid, didSucceed, message = db.get(app_id + ':participants', participant_uid, 'alg_uid') else: raise Exception('participant_to_algorithm_management : ' + participant_to_algorithm_management + ' not implemented') # get sandboxed database for the specific app_id,alg_id,exp_uid - closing off the rest of the database to the algorithm rc = ResourceClient(app_id, exp_uid, alg_uid, db) # get specific algorithm to make calls to alg = utils.get_app_alg(self.app_id, alg_id) # call getQuery targets, dt = utils.timeit(alg.getQuery)(resource=rc) # update targets target_list = [] for target in targets: target_list.append({'index': target}) # check for context context_type, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'context_type') context, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'context') # log log_entry_durations = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'task': 'getQuery', 'duration': dt } log_entry_durations.update(rc.getDurations()) meta = {'log_entry_durations': log_entry_durations} # create JSON query payload timestamp = str(utils.datetimeNow()) query_uid = utils.getNewUID() query = {} query['query_uid'] = query_uid query['target_indices'] = target_list # save query data to database query_doc = {} query_doc.update(query) query_doc['participant_uid'] = participant_uid query_doc['alg_uid'] = alg_uid query_doc['exp_uid'] = exp_uid query_doc['alg_label'] = alg_label query_doc['timestamp_query_generated'] = timestamp for field in query_doc: db.set(app_id + ':queries', query_uid, field, query_doc[field]) # add context after updating query doc to avoid redundant information query['context_type'] = context_type query['context'] = context args_out = {'args': query, 'meta': meta} response_json = json.dumps(args_out) log_entry = { 'exp_uid': exp_uid, 'task': 'getQuery', 'json': response_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-RESPONSE', log_entry) return response_json, True, '' except Exception, err: error = traceback.format_exc() log_entry = { 'exp_uid': exp_uid, 'task': 'getQuery', 'error': error, 'timestamp': utils.datetimeNow(), 'args_json': args_json } ell.log(app_id + ':APP-EXCEPTION', log_entry) return '{}', False, error
def predict(self, exp_uid, args_json, db, ell): """ Description: uses current model empirical estimates to forecast the ranking of the arms in order of likelhood of being the best from most to least likely Expected input: (string) predict_id : 'predict_labels' (dict) params : dictionary with fields (string) alg_label : describes target algorithm to use Expected output (in json structure): (list of dicts with fields): (int) index : index of target (int) rank : rank that the algorithm assigns to index (rank 0 is most likely the best arm) """ try: app_id = self.app_id log_entry = { 'exp_uid': exp_uid, 'task': 'predict', 'json': args_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-CALL', log_entry) # convert args_json to args_dict try: args_dict = json.loads(args_json) except: error = "%s.predict failed to convert input args_json due to improper format" % ( self.app_id) return '{}', False, error # check for the fields that must be contained in args or error occurs necessary_fields = ['predict_id', 'params'] for field in necessary_fields: try: args_dict[field] except KeyError: error = "%s.predict input arguments missing field: %s" % ( self.app_id, str(field)) return '{}', False, error predict_id = args_dict['predict_id'] params = args_dict['params'] if predict_id == "predict_labels": alg_label = params['alg_label'] # get list of algorithms associated with project alg_list, didSucceed, message = db.get(app_id + ':experiments', exp_uid, 'alg_list') # get alg_id for algorithm in alg_list: if alg_label == algorithm['alg_label']: alg_id = algorithm['alg_id'] alg_uid = algorithm['alg_uid'] num_reported_answers, didSucceed, message = db.get( app_id + ':experiments', exp_uid, 'num_reported_answers_for_' + alg_uid) if type(num_reported_answers) != int: num_reported_answers = 0 # get sandboxed database for the specific app_id,alg_id,exp_uid - closing off the rest of the database to the algorithm rc = ResourceClient(app_id, exp_uid, alg_uid, db) # get specific algorithm to make calls to alg = utils.get_app_alg(self.app_id, alg_id) # call getQuery predicted_labels, dt = utils.timeit(alg.predict)(resource=rc) log_entry_durations = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'task': 'predict', 'duration': dt } log_entry_durations.update(rc.getDurations()) meta = {'log_entry_durations': log_entry_durations} test_labels, didSucceed, message = db.get( app_id + ':experiments', exp_uid, 'test_labels') num_errors = 0 for answer in test_labels: target_index, target_label = answer if target_label * predicted_labels[target_index] < 0: num_errors += 1. error = num_errors / float(max(1., len(test_labels))) log_entry = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'timestamp': utils.datetimeNow() } log_entry.update({ 'error': error, 'num_reported_answers': num_reported_answers }) ell.log(app_id + ':ALG-EVALUATION', log_entry) response_args_dict = { 'exp_uid': exp_uid, 'alg_uid': alg_uid, 'error': error, 'num_reported_answers': num_reported_answers } args_out = {'args': response_args_dict, 'meta': meta} predict_json = json.dumps(args_out) log_entry = { 'exp_uid': exp_uid, 'task': 'predict', 'json': predict_json, 'timestamp': utils.datetimeNow() } ell.log(app_id + ':APP-RESPONSE', log_entry) return predict_json, True, '' except Exception, err: error = traceback.format_exc() log_entry = { 'exp_uid': exp_uid, 'task': 'predict', 'error': str(error), 'timestamp': utils.datetimeNow(), 'args_json': args_json } didSucceed, message = ell.log(app_id + ':APP-EXCEPTION', log_entry) return '{}', False, error