def list_searchable_parameters():
print('here', file=sys.stdout)
inputs = REC_DATA.list_input_keys_values()
print('inputs', inputs, file=sys.stdout)
targets = look_up_table.LOOK_UP_TABLE['campaign_objective']
print('targets', targets, file=sys.stdout)
return json_response({"inputs": inputs, "targets": targets})
def list_searchable_parameters():
""" returns a plain text of charset='utf-8'
with inputs and target as a key:value pair
"""
# output 'here' with the help of file=sys.stdout
print('here', file=sys.stdout)
# saved all searchable parameters into inputs in a key : value format (json)
inputs = REC_DATA.list_input_keys_values()
# output 'inputs' with the aid of file=sys.stdout
print('inputs', inputs, file=sys.stdout)
# save a portion of the searchable parameter by specifying the key name 'campaign_objective'
targets = look_up_table.LOOK_UP_TABLE['campaign_objective']
print('targets', targets, file=sys.stdout)
# return a text file with inputs as key to searchable parameters and targets key to targets
return json_response({"inputs": inputs, "targets": targets})
def get_feature_output():
"""Returns a dictionary with all data used in the rec ending and their metadata."""
res = {"game_features": REC_DATA.game_features}
return json_response(res)
def make_recommendation():
"""Based on the user's objective, this function selects matches and returns scores and meta data
"""
event_rates = ['click-through-event', 'first_dropped', 'impression']
# Load the input
json_dict = load_input(request)
#json_dict = ast.literal_eval(json_str)
if VERBOSE:
print('json_dict', json_dict, file=sys.stdout)
# beware campaign_objective also sent in
slice_parameters = json_dict #[{i: json_dict[i]} for i in json_dict if i != 'campaign_objective']
# set default objects if none given
objectives = json_dict.get(
'campaign_objective',
look_up_table.LOOK_UP_TABLE['campaign_objective'])
if isinstance(objectives, list) is False:
objectives = [objectives]
print('objectives', objectives, file=sys.stdout)
# assure the objectives are reasonable
for obj in objectives:
assert obj in look_up_table.LOOK_UP_TABLE['campaign_objective']
# identify rows matching the input query params
matching_rows = REC_DATA.extract_data_slice(slice_parameters)
# summ all events for each line_item_id matching above results
gm_kys_view = REC_DATA.sum_events(matching_rows, ['first_key'],
event_rates)
# get a list of unique game ids
uniq_games = list(gm_kys_view.keys())
for game_id in uniq_games:
# calculate rates, and scores
gm_kys_view[game_id]['click_through_rate'] = REC_DATA.calculates_rates(
gm_kys_view[game_id]['click-through-event'],
gm_kys_view[game_id]['impression'])
gm_kys_view[game_id]['engagement_rate'] = REC_DATA.calculates_rates(
gm_kys_view[game_id]['first_dropped'],
gm_kys_view[game_id]['impression'])
# calculate the specific score for this game
gm_kys_view[game_id]['rec_scores'] = REC_DATA.calculate_score(
[gm_kys_view[game_id][obj] for obj in objectives])
# sort the games based on 'decreasing' score
ind_sort = np.argsort(
[gm_kys_view[game_id]['rec_scores'] for game_id in uniq_games])[::-1]
# generate a results list of score and games
rec_score = []
for i in ind_sort:
game_id = uniq_games[i]
# get all the additional feautures for this game
game_features = REC_DATA.extract_game_features(game_id=game_id)
rec_score.append({
'game_id': game_id,
'score': gm_kys_view[game_id]['rec_scores'],
'game_features': game_features
})
if VERBOSE:
print('rec_score', rec_score, file=sys.stdout)
pass
return json_response(rec_score)
