class Experiment():
def __init__(self, _type, bandits, agent, runs, exp_nums, logdir, k_vec,
k_probs):
self._type = _type
self.esr_vector = []
self.esr_probs = []
self.f1_score = []
self.f1 = []
#self.plotter = Plotter()
self.metrics = Metrics()
self.k_vec = k_vec
self.k_probs = k_probs
self.f1_df = pd.DataFrame()
self.esrBandit = Agent(0, 10)
self.logdir = logdir
for i in range(len(k_vec)):
self.esrBandit.manual_distribution(k_vec[i], k_probs[i])
if self._type == "bandit":
self.bandits = bandits
self.agent = agent
self.runs = runs
self.exp_nums = exp_nums
def run(self):
if self._type == "bandit":
avg_log = self.logdir + 'average' + '/'
if not os.path.exists(avg_log):
os.makedirs(avg_log, exist_ok=True)
for run in range(self.runs):
self.run_df = pd.DataFrame()
start = time.perf_counter()
run_log = self.logdir + 'run_' + str(run + 1) + '/'
if not os.path.exists(run_log):
os.makedirs(run_log, exist_ok=True)
for i in range(self.exp_nums):
self.esr_vector = []
self.esr_probs = []
if i == 0:
for j in range(len(self.bandits)):
_return_ = self.bandits[j].pull_arm()
self.agent.update(j, _return_)
action = self.agent.select_action()
_return_ = self.bandits[action].pull_arm()
self.agent.update(action, _return_)
esr_index = self.agent.esr_dominance()
self.esr_agent = deepcopy(self.agent)
self.esr_agent.distribution = np.array(
self.esr_agent.distribution)[esr_index]
for val in esr_index:
self.esr_vector.append(
self.agent.distribution[val].get_distribution()[0])
self.esr_probs.append(
self.agent.distribution[val].get_distribution()[1])
#self.f1.append(self.metrics.precision_recall(self.esr_vector, self.esr_probs, self.k_vec, self.k_probs))
self.f1.append(
self.metrics.pr_kl(self.esr_agent, self.esrBandit))
self.run_df['run' + str(run)] = self.f1
self.run_df['mean'] = self.run_df.mean(axis=1)
self.f1_df['run' + str(run)] = self.f1
end = time.perf_counter()
#self.run_df['average'] = self.f1_df.mean(axis=1)
#print(self.f1_df)
#self.f1_score = self.f1_df['Average']
#self.run_df['average'] = np.mean(np.array(self.f1_score).reshape(-1, 10), axis=1)
self.run_df.to_csv(run_log + "/f1_score.csv", index=False)
ser = SER(self.k_vec, self.k_probs)
ser_expectations = ser.expectations()
ser_pareto_front = ser.pareto_front()
print("")
print(
'**** Run ' + str(run + 1) + ' - Execution Time: ' +
str(round((end - start), 2)) + ' seconds ****', )
print(str(len(esr_index)) + " distributions in the ESR set")
print("ESR Vector and Probabilities")
for a in range(len(self.esr_vector)):
print(self.esr_vector[a])
print(self.esr_probs[a])
print(" ")
print("")
print("SER - Pareto Front")
print("Number of policies on the pareto front : " +
str(len(ser_pareto_front)))
print(ser_pareto_front)
print("")
self.plotter = Plotter(self.esr_vector, self.esr_probs,
run_log, self.exp_nums, True, True)
self.plotter.plot_run()
self.f1_df['mean'] = self.f1_df.mean(axis=1)
#self.f1_df['average'] = np.mean(np.array(self.f1_df['mean']).reshape(-1, 10), axis=1)
self.f1_df.to_csv(avg_log + "/f1_score.csv", index=False)
self.plotter = Plotter(self.esr_vector, self.esr_probs, avg_log,
self.exp_nums, True, True)
self.plotter.plot_run()
return
