def __init__(self,
batch_size: int = 256,
epochs: int = 100,
activation_fn: str = 'softmax',
num_negative: int = 500,
optimizer: optimizers = optimizers.Adam(),
loss: losses = 'categorical_crossentropy',
validation_step: int = 1,
normalize_score: bool = False,
bow_feature=None,
vocabulary=None):
self._model_params = InputParamTuple(num_negative=num_negative,
batch_size=batch_size,
epochs=epochs,
validation_step=validation_step,
optimizer=optimizer,
loss=loss,
activation_fn=activation_fn,
normalize=normalize_score) # TODO
self._data = None
self._model = None
self._best_model = None
self._normalize_score = normalize_score
self._doubler = Doubler(embedding_node_dim=300,
embedding_doc_dim=64,
bow_feature=bow_feature,
vocabulary=vocabulary)
def gen():
d = {
"RMED1_regrets": [],
"RMED2_regrets": [],
"IF_regrets": [],
"BTM_regrets": [],
"SAVAGE_regrets": [],
"DOUBLER_regrets": []
}
horizon = 100000
samples = 10
for i in range(samples):
print("On sample number", i)
x = RMED('RMED1', len(pref_mat), horizon, generator_fnc, f_rmed,
regret_fn)
reg_RMED = np.array(x.algo())
#reg_BTM[0] contains the regret values, [1] contains the best arm
print("RMED done, best arm : ", reg_RMED[1])
d["RMED1_regrets"].append(list(np.around(reg_RMED[0], 3)))
# json.dump(RMED1_regrets, open("RMED1_regrets.json", "w"))
x = RMED('RMED2', len(pref_mat), horizon, generator_fnc, f_rmed,
regret_fn)
reg_RMED = np.array(x.algo())
print("RMED done, best arm : ", reg_RMED[1])
d["RMED2_regrets"].append(list(np.around(reg_RMED[0], 3)))
# json.dump(RMED2_regrets, open("RMED2_regrets.json", "w"))
x = InterleavedFilter(len(pref_mat), horizon, generator_fnc, regret_fn)
reg_IF = np.array(x.algo())
print("IF done, best arm : ", reg_IF[1])
d["IF_regrets"].append(list(np.around(reg_IF[0], 3)))
# json.dump(IF_regrets, open("IF_regrets.json", "w"))
x = BeatTheMean(len(pref_mat), horizon, generator_fnc, regret_fn)
reg_BTM = np.array(x.algo())
print("BTM done, best arm : ", reg_BTM[1])
d["BTM_regrets"].append(list(np.around(reg_BTM[0], 3)))
# json.dump(BTM_regrets, open("BTM_regrets.json", "w"))
x = Doubler(horizon, pref_mat, regret_fn)
reg_DOUBLER = x.run()
print("Doubler done, best arm : ", reg_DOUBLER[1])
d["DOUBLER_regrets"].append(reg_DOUBLER[0])
# json.dump(DOUBLER_regrets, open("DOUBLER_regrets.json", "w"))
x = SAVAGE(horizon, pref_mat, regret_fn)
reg_SAVAGE = x.run()
print("SAVAGE done, best arm : ", reg_SAVAGE[1])
d["SAVAGE_regrets"].append(reg_SAVAGE[0])
# json.dump(SAVAGE_regrets, open("SAVAGE_regrets.json", "w"))
if i % 3 == 0:
print("Dumped all")
dump_all(d)
dump_all(d)
d['RUCB_regrets'] = run_rucb(samples, horizon, pref_mat)
print("RUCB done")
d['RCS_regrets'] = run_rcs(samples, horizon, pref_mat)
print("RCS done")
dump_all(d)
