def __init__(
self,
LearnerParams,
PrivacyParams,
EvaluatorParams,
DatasetParams,
querypool,
available_actions,
):
self.pp = PrivacyParams
self.ev = EvaluatorParams
self.dd = DatasetParams
self.lp = LearnerParams
self.state = 0
self.query = None
self.querypool = querypool
self.available_actions = available_actions
self.action_space = spaces.Discrete(int(len(self.available_actions)))
self.observation_space = spaces.Discrete(int(
self.lp.observation_space)) # number of query
self.df, self.metadata = create_simulated_dataset(
self.dd.dataset_size, "dataset")
self.state_query_pair_base = {}
self.pool = list(range(self.lp.observation_space - 2, 1, -1))
self.info = {}
self.output = []
self.episode = 0
self.reward = 0
self.d1_dataset, self.d2_dataset, self.d1_metadata, self.d2_metadata = generate_neighbors(
self.df, self.metadata)
def learn(self, querypool, export_as_csv=False):
output = []
for i in range(len(querypool)):
df, metadata = create_simulated_dataset(self.dd.dataset_size, "dataset")
d1_dataset, d2_dataset, d1_metadata, d2_metadata = generate_neighbors(df, metadata)
d1 = PandasReader(d1_metadata, d1_dataset)
d2 = PandasReader(d2_metadata, d2_dataset)
eval = DPEvaluator()
pa = DPSingletonQuery()
key_metrics = eval.evaluate([d1_metadata, d1], [d2_metadata, d2], pa, querypool[i], self.pp, self.ev)
if key_metrics['__key__'].dp_res is None:
dp_res = key_metrics['__key__'].dp_res
error = key_metrics['__key__'].error
output.append({"query":querypool[i], "dpresult": dp_res, "jensen_shannon_divergence":None, "error": error})
else:
res_list = []
for key, metrics in key_metrics.items():
dp_res = metrics.dp_res
js_res = metrics.jensen_shannon_divergence
res_list.append([dp_res, js_res])
dp_res = np.all(np.array([res[0] for res in res_list]))
js_res = (np.array([res[1] for res in res_list])).max()
output.append({"query":querypool[i], "dpresult": dp_res,"jensen_shannon_divergence": js_res, "error":None})
if export_as_csv:
write_to_csv('Bandit.csv', output, flag='bandit')
else:
return output
def _compute_reward(self, query):
ast_transform = self.observe(query)
d1_query = query
d2_query = query.replace("d1.d1", "d2.d2")
d1_dataset, d2_dataset, d1_metadata, d2_metadata = generate_neighbors(
self.df, self.metadata)
d1 = PandasReader(d1_metadata, d1_dataset)
d2 = PandasReader(d2_metadata, d2_dataset)
eval = DPEvaluator()
pa = DPSingletonQuery()
key_metrics = eval.evaluate([d1_metadata, d1], [d2_metadata, d2], pa,
query, self.pp, self.ev)
message = None
if key_metrics["__key__"].dp_res is None:
dpresult = "DP_BUG"
self.reward = 1
message = key_metrics["__key__"].error
elif key_metrics["__key__"].dp_res == False:
self._game_ended = True
dpresult = "DP_FAIL"
self.reward = 20
message = "dp_res_False"
elif (key_metrics["__key__"].dp_res == True and
key_metrics["__key__"].jensen_shannon_divergence == math.inf):
self._game_ended = True
dpresult = "DP_BUG"
self.reward = 20
message = "jsdistance_is_inf"
else:
res_list = []
for key, metrics in key_metrics.items():
dp_res = metrics.dp_res
js_res = metrics.jensen_shannon_divergence
# ws_res = metrics.wasserstein_distance
res_list.append([dp_res, js_res])
dp_res = np.all(np.array([res[0] for res in res_list]))
js_res = (np.array([res[1] for res in res_list])).max()
# ws_res = (np.array([res[2] for res in res_list])).max()
if dp_res == True:
dpresult = "DP_PASS"
self.reward = js_res
return dpresult, self.reward, message, d1, d2