def default_setting(self):
"""
A default setting for data loading
:return:
"""
unknown_as_zero = False # using original labels, e.g., w.r.t. semi-supervised dataset
binary_rele = False # using original labels
train_presort, validation_presort, test_presort = True, True, True
train_batch_size, validation_batch_size, test_batch_size = 1, 1, 1
scale_data, scaler_id, scaler_level = get_default_scaler_setting(
data_id=self.data_id)
# more data settings that are rarely changed
self.data_dict = dict(data_id=self.data_id,
dir_data=self.dir_data,
min_docs=10,
min_rele=1,
scale_data=scale_data,
scaler_id=scaler_id,
scaler_level=scaler_level,
train_presort=train_presort,
validation_presort=validation_presort,
test_presort=test_presort,
train_batch_size=train_batch_size,
validation_batch_size=validation_batch_size,
test_batch_size=test_batch_size,
unknown_as_zero=unknown_as_zero,
binary_rele=binary_rele)
data_meta = get_data_meta(data_id=self.data_id) # add meta-information
self.data_dict.update(data_meta)
return self.data_dict
def default_setting(self):
"""
A default setting for data loading when performing adversarial ltr
"""
unknown_as_zero = False
binary_rele = False # using the original values
train_presort, validation_presort, test_presort = True, True, True
train_batch_size, validation_batch_size, test_batch_size = 1, 1, 1
scale_data, scaler_id, scaler_level = get_default_scaler_setting(
data_id=self.data_id)
# more data settings that are rarely changed
self.data_dict = dict(data_id=self.data_id,
dir_data=self.dir_data,
min_docs=10,
min_rele=1,
unknown_as_zero=unknown_as_zero,
binary_rele=binary_rele,
train_presort=train_presort,
validation_presort=validation_presort,
test_presort=test_presort,
train_batch_size=train_batch_size,
validation_batch_size=validation_batch_size,
test_batch_size=test_batch_size,
scale_data=scale_data,
scaler_id=scaler_id,
scaler_level=scaler_level)
data_meta = get_data_meta(data_id=self.data_id) # add meta-information
self.data_dict.update(data_meta)
return self.data_dict
def default_setting(self):
"""
A default setting for data loading when running lambdaMART
:return:
"""
unknown_as_zero = True if self.data_id in MSLETOR_SEMI else False # since lambdaMART is a supervised method
binary_rele = False # using the original values
presort = True # this setting leads to no difference for lambdaMART, but it can be altered to reused buffered data
scale_data, scaler_id, scaler_level = get_default_scaler_setting(
data_id=self.data_id)
# more data settings that are rarely changed
self.data_dict = dict(data_id=self.data_id,
dir_data=self.dir_data,
min_docs=10,
min_rele=1,
sample_rankings_per_q=1,
unknown_as_zero=unknown_as_zero,
binary_rele=binary_rele,
presort=presort,
scale_data=scale_data,
scaler_id=scaler_id,
scaler_level=scaler_level)
data_meta = get_data_meta(data_id=self.data_id) # add meta-information
self.data_dict.update(data_meta)
return self.data_dict
def default_setting(self):
"""
A default setting for data loading when running lambdaMART
"""
unknown_as_zero = True if self.data_id in MSLETOR_SEMI else False # since lambdaMART is a supervised method
binary_rele = False # using the original values
train_presort, validation_presort, test_presort = False, False, False
train_batch_size, validation_batch_size, test_batch_size = 1, 1, 1
scale_data, scaler_id, scaler_level = get_default_scaler_setting(
data_id=self.data_id)
# more data settings that are rarely changed
self.data_dict = dict(data_id=self.data_id,
dir_data=self.dir_data,
min_docs=10,
min_rele=1,
unknown_as_zero=unknown_as_zero,
binary_rele=binary_rele,
train_presort=train_presort,
validation_presort=validation_presort,
test_presort=test_presort,
train_batch_size=train_batch_size,
validation_batch_size=validation_batch_size,
test_batch_size=test_batch_size,
scale_data=scale_data,
scaler_id=scaler_id,
scaler_level=scaler_level)
data_meta = get_data_meta(data_id=self.data_id) # add meta-information
self.data_dict.update(data_meta)
return self.data_dict
def default_setting(self):
"""
A default setting for data loading
:return:
"""
unknown_as_zero = True if self.data_id in MSLETOR_SEMI else False
binary_rele = False # using the original values
presort = True # a default setting
scale_data, scaler_id, scaler_level = get_default_scaler_setting(
data_id=self.data_id)
# more data settings that are rarely changed
self.data_dict = dict(data_id=self.data_id,
dir_data=self.dir_data,
min_docs=10,
min_rele=1,
sample_rankings_per_q=1,
unknown_as_zero=unknown_as_zero,
binary_rele=binary_rele,
presort=presort,
scale_data=scale_data,
scaler_id=scaler_id,
scaler_level=scaler_level)
data_meta = get_data_meta(data_id=self.data_id) # add meta-information
self.data_dict.update(data_meta)
return self.data_dict
def grid_search(self): """ Iterator of settings for data loading when performing adversarial ltr :param debug: :param data_id: :param dir_data: :return: """ ''' common settings without grid-search ''' binary_rele, unknown_as_zero = False, False common_data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, min_docs=10, min_rele=1, unknown_as_zero=unknown_as_zero, binary_rele=binary_rele) data_meta = get_data_meta(data_id=self.data_id) # add meta-information common_data_dict.update(data_meta) ''' some settings for grid-search ''' choice_presort = [True] if self.debug else [True] choice_sample_rankings_per_q = [1] if self.debug else [1] # number of sample rankings per query choice_scale_data, choice_scaler_id, choice_scaler_level = get_default_scaler_setting(data_id=self.data_id, grid_search=True) for scale_data, scaler_id, scaler_level, presort, sample_rankings_per_q in product(choice_scale_data, choice_scaler_id, choice_scaler_level, choice_presort, choice_sample_rankings_per_q): self.data_dict = dict(presort=presort, sample_rankings_per_q=sample_rankings_per_q, scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level) self.data_dict.update(common_data_dict) yield self.data_dict
def grid_search(self):
if self.data_json is not None: # using json file
choice_presort = self.json_dict['presort']
choice_min_docs = self.json_dict['min_docs']
choice_min_rele = self.json_dict['min_rele']
choice_binary_rele = self.json_dict['binary_rele']
choice_unknown_as_zero = self.json_dict['unknown_as_zero']
choice_sample_rankings_per_q = self.json_dict[
'sample_rankings_per_q']
base_data_dict = dict(data_id=self.data_id,
dir_data=self.json_dict["dir_data"])
else:
choice_min_docs = [10]
choice_min_rele = [1]
choice_presort = [True]
choice_binary_rele = [False]
choice_unknown_as_zero = [True]
choice_sample_rankings_per_q = [
1
] # number of sample rankings per query
base_data_dict = dict(data_id=self.data_id, dir_data=self.dir_data)
data_meta = get_data_meta(data_id=self.data_id) # add meta-information
base_data_dict.update(data_meta)
choice_scale_data, choice_scaler_id, choice_scaler_level = get_default_scaler_setting(
data_id=self.data_id, grid_search=True)
for min_docs, min_rele, sample_rankings_per_q in product(
choice_min_docs, choice_min_rele,
choice_sample_rankings_per_q):
threshold_dict = dict(min_docs=min_docs,
min_rele=min_rele,
sample_rankings_per_q=sample_rankings_per_q)
for binary_rele, unknown_as_zero, presort in product(
choice_binary_rele, choice_unknown_as_zero,
choice_presort):
custom_dict = dict(binary_rele=binary_rele,
unknown_as_zero=unknown_as_zero,
presort=presort)
for scale_data, scaler_id, scaler_level in product(
choice_scale_data, choice_scaler_id,
choice_scaler_level):
scale_dict = dict(scale_data=scale_data,
scaler_id=scaler_id,
scaler_level=scaler_level)
self.data_dict = dict()
self.data_dict.update(base_data_dict)
self.data_dict.update(threshold_dict)
self.data_dict.update(custom_dict)
self.data_dict.update(scale_dict)
yield self.data_dict
def load_setting(self): if self.use_json: choice_min_docs = self.json_dict['min_docs'] choice_min_rele = self.json_dict['min_rele'] choice_binary_rele = self.json_dict['binary_rele'] choice_unknown_as_zero = self.json_dict['unknown_as_zero'] choice_train_presort = self.json_dict['train_presort'] choice_train_batch_size = self.json_dict['train_batch_size'] # hard-coding for rarely changed settings base_data_dict = dict(data_id=self.data_id, dir_data=self.json_dict["dir_data"], test_presort=True, validation_presort=True, validation_batch_size=1, test_batch_size=1) else: choice_min_docs = [10] choice_min_rele = [1] choice_binary_rele = [False] choice_unknown_as_zero = [False] choice_train_presort = [True] choice_train_batch_size = [1] # number of sample rankings per query base_data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, test_presort=True, validation_presort=True, validation_batch_size=1, test_batch_size=1) data_meta = get_data_meta(data_id=self.data_id) # add meta-information base_data_dict.update(data_meta) choice_scale_data, choice_scaler_id, choice_scaler_level = get_default_scaler_setting(data_id=self.data_id, grid_search=True) for min_docs, min_rele, train_batch_size in product(choice_min_docs, choice_min_rele, choice_train_batch_size): threshold_dict = dict(min_docs=min_docs, min_rele=min_rele, train_batch_size=train_batch_size) for binary_rele, unknown_as_zero, train_presort in product(choice_binary_rele, choice_unknown_as_zero, choice_train_presort): custom_dict = dict(binary_rele=binary_rele, unknown_as_zero=unknown_as_zero, train_presort=train_presort) for scale_data, scaler_id, scaler_level in product(choice_scale_data, choice_scaler_id, choice_scaler_level): scale_dict = dict(scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level) self.data_dict = dict() self.data_dict.update(base_data_dict) self.data_dict.update(threshold_dict) self.data_dict.update(custom_dict) self.data_dict.update(scale_dict) return self.data_dict
def grid_search(self):
"""
Iterator of settings for data loading when performing adversarial ltr
"""
if self.ad_data_json is not None: # using json file
choice_min_docs = self.json_dict['min_docs']
choice_min_rele = self.json_dict['min_rele']
choice_binary_rele = self.json_dict['binary_rele']
choice_unknown_as_zero = self.json_dict['unknown_as_zero']
choice_train_presort = self.json_dict['train_presort']
choice_train_batch_size = self.json_dict['train_batch_size']
base_data_dict = dict(data_id=self.data_id,
dir_data=self.json_dict["dir_data"],
test_presort=True,
validation_presort=True,
validation_batch_size=1,
test_batch_size=1)
else:
choice_min_docs = [10]
choice_min_rele = [1]
choice_binary_rele = [False]
choice_unknown_as_zero = [False]
choice_train_presort = [True]
choice_train_batch_size = [1
] # number of sample rankings per query
base_data_dict = dict(data_id=self.data_id,
dir_data=self.dir_data,
test_presort=True,
validation_presort=True,
validation_batch_size=1,
test_batch_size=1)
data_meta = get_data_meta(data_id=self.data_id) # add meta-information
base_data_dict.update(data_meta)
choice_scale_data, choice_scaler_id, choice_scaler_level = get_default_scaler_setting(
data_id=self.data_id, grid_search=True)
for min_docs, min_rele, train_batch_size in product(
choice_min_docs, choice_min_rele, choice_train_batch_size):
threshold_dict = dict(min_docs=min_docs,
min_rele=min_rele,
train_batch_size=train_batch_size)
for binary_rele, unknown_as_zero, train_presort in product(
choice_binary_rele, choice_unknown_as_zero,
choice_train_presort):
custom_dict = dict(binary_rele=binary_rele,
unknown_as_zero=unknown_as_zero,
train_presort=train_presort)
for scale_data, scaler_id, scaler_level in product(
choice_scale_data, choice_scaler_id,
choice_scaler_level):
scale_dict = dict(scale_data=scale_data,
scaler_id=scaler_id,
scaler_level=scaler_level)
self.data_dict = dict()
self.data_dict.update(base_data_dict)
self.data_dict.update(threshold_dict)
self.data_dict.update(custom_dict)
self.data_dict.update(scale_dict)
yield self.data_dict