def run_al_simulation(self):
if RESULT_PKL_PATH not in os.listdir("."):
os.mkdir(RESULT_PKL_PATH)
results = {}
for split_interval in range(1, 3):
for batch_size in range(1, 6):
self._params['days_split'] = int(split_interval)
self._params['batch_size'] = int(batch_size)
self._params['start_interval'] = int(12 / split_interval)
TOTAL_DAYS = 48
TOTAL_COMMUNITIES = 586
GOAL = 0.65
total_time_intervals = TOTAL_DAYS / split_interval - self._params[
'start_interval'] + 1
self._params['queries_per_time'] = round(
(GOAL * TOTAL_COMMUNITIES) /
(total_time_intervals * batch_size) + 0.5)
self._database = RefaelDataLoader(
os.path.join("data", self._params['data_file_name']),
self._params)
results[(split_interval, batch_size)] = self.run_al()
pickle.dump(
results,
open(os.path.join(RESULT_PKL_PATH, "simulation_results.pkl"),
"wb"))
def __init__(self):
self._params = {
'logger_name': "logger",
# Data parameters
'days_split': 1,
'start_interval': 10,
'database': 'Refael',
'data_file_name': 'Refael_07_18.csv', # should be in ../data/
'date_format': "%Y-%m-%d", # Refael
'directed': True,
'white_label': 1,
# features + beta vectors parameters
'max_connected': False,
'ftr_pairs': 200,
'identical_bar': 0.99,
'context_beta': 1,
# ML- parameters
'learn_method': LearningMethod.XGBOOST,
# AL - parameters
'batch_size': 2,
'queries_per_time': 30,
'eps': 0.01,
'target_recall': 0.7,
'reveal_target': 0.6,
'dist_type': DistType.Euclidian
}
self._database = RefaelDataLoader(
os.path.join("data", self._params['data_file_name']), self._params)
self._ml_learner = MLCommunities(method=self._params['learn_method'])
def __init__(self):
self._params = {
'database': 'Refael',
'date_format': "%Y-%m-%d", # Refael
'directed': True,
'max_connected': False,
'logger_name': "logger",
'ftr_pairs': 300,
'identical_bar': 0.95,
'context_beta': 1,
}
self._database = RefaelDataLoader(
self._params['database'],
os.path.join("..", "data", "refael_001.csv"), self._params)
self._ml_learner = MLCommunities(method="RF")
class RefaelLearner:
def __init__(self):
self._params = {
'database': 'Refael',
'date_format': "%Y-%m-%d", # Refael
'directed': True,
'max_connected': False,
'logger_name': "logger",
'ftr_pairs': 300,
'identical_bar': 0.95,
'context_beta': 1,
}
self._database = RefaelDataLoader(
self._params['database'],
os.path.join("..", "data", "refael_001.csv"), self._params)
self._ml_learner = MLCommunities(method="RF")
def run_ml(self):
while self._database._forward_time():
beta_matrix, best_pairs, nodes_list, edges_list, labels = self._database._calc_curr_time(
)
self._ml_learner.forward_time_data(beta_matrix, best_pairs,
nodes_list, edges_list, labels)
self._ml_learner.run()
class RefaelLearner:
def __init__(self):
self._params = {
'logger_name': "logger",
# Data parameters
'days_split': 1,
'start_interval': 10,
'database': 'Refael',
'data_file_name': 'Refael_07_18.csv', # should be in ../data/
'date_format': "%Y-%m-%d", # Refael
'directed': True,
'white_label': 1,
# features + beta vectors parameters
'max_connected': False,
'ftr_pairs': 200,
'identical_bar': 0.99,
'context_beta': 1,
# ML- parameters
'learn_method': LearningMethod.XGBOOST,
# AL - parameters
'batch_size': 2,
'queries_per_time': 30,
'eps': 0.01,
'target_recall': 0.7,
'reveal_target': 0.6,
'dist_type': DistType.Euclidian
}
self._database = RefaelDataLoader(
os.path.join("data", self._params['data_file_name']), self._params)
self._ml_learner = MLCommunities(method=self._params['learn_method'])
# self._al_learner = ActiveLearning(self._params)
def run_ml(self):
# df = pd.DataFrame()
time = 0
while self._database.forward_time():
# print("----------------------------------- TIME " + str(time) + " ----------------------------------")
time += 1
beta_matrix, nodes_list, edges_list, labels = self._database.calc_curr_time(
)
self._ml_learner.forward_time_data(beta_matrix, nodes_list,
edges_list, labels)
print('starting to learn')
self._ml_learner.run()
# df = pd.concat([df, self._ml_learner.run()])
# if not os.path.exists('results'):
# os.mkdir('results')
# writer = pd.ExcelWriter(os.path.join(os.getcwd(), 'results', 'time_run.xlsx'))
# df.to_excel(writer, sheet_name='Sheet1', index=False)
# writer.save()
def run_al(self, pkl_result=False):
if RESULT_PKL_PATH not in os.listdir("."):
os.mkdir(RESULT_PKL_PATH)
timed_al = TimedActiveLearning(self._params, self._database.num_blacks)
# plot results y_axis
recall = []
# plot results x_axis
revealed = []
time = 0
while self._database.forward_time():
print("----------------------------------- TIME " + str(time) +
" ----------------------------------")
beta_matrix, nodes_list, edges_list, labels = self._database.calc_curr_time(
)
rv, rec = timed_al.step(beta_matrix, labels)
recall.append(rec)
revealed.append(rv)
time += 1
if pkl_result:
# save partial results to pkl
pickle.dump(
[revealed, recall],
open(
os.path.join(
RESULT_PKL_PATH, "al_results_split_" +
str(int(self._params['days_split'])) + "_start_" +
str(int(self._params['start_interval'])) + "_batch_" +
str(int(self._params['batch_size'])) + ".pkl"), "wb"))
return [revealed, recall]
def run_al_simulation(self):
if RESULT_PKL_PATH not in os.listdir("."):
os.mkdir(RESULT_PKL_PATH)
results = {}
for split_interval in range(1, 3):
for batch_size in range(1, 6):
self._params['days_split'] = int(split_interval)
self._params['batch_size'] = int(batch_size)
self._params['start_interval'] = int(12 / split_interval)
TOTAL_DAYS = 48
TOTAL_COMMUNITIES = 586
GOAL = 0.65
total_time_intervals = TOTAL_DAYS / split_interval - self._params[
'start_interval'] + 1
self._params['queries_per_time'] = round(
(GOAL * TOTAL_COMMUNITIES) /
(total_time_intervals * batch_size) + 0.5)
self._database = RefaelDataLoader(
os.path.join("data", self._params['data_file_name']),
self._params)
results[(split_interval, batch_size)] = self.run_al()
pickle.dump(
results,
open(os.path.join(RESULT_PKL_PATH, "simulation_results.pkl"),
"wb"))