def __init__(self,
message: str,
decimal_places: int = 3,
terminal_break: bool = False,
skip_output: bool = False):
"""Creates a new instance of ``Timer``.
Args:
message (str): The message to print at the end of the ``with`` block. Notice that the measured time, as
string ``" in X.XXXs"``, is appended to the provided message automatically.
this message
decimal_places (int, optional): The number of decimal places to print for the time, which is measured and
printed in seconds.
terminal_break (bool, optional): Indicates whether to add an additional line break to the printed message.
skip_output (bool, optional): If ``True``, then no output is printed to the screen.
"""
# sanitize args
insanity.sanitize_type("decimal_places", decimal_places, int)
insanity.sanitize_range("decimal_places", decimal_places, minimum=0)
# create the message to print at the end of the with-block
self._message = str(message).strip() + " in {:.%df}s" % decimal_places
if terminal_break:
self._message += "\n"
self._start = None # the time when the clock is started
self._total = 0 # the total time measured
self._skip_output = bool(
skip_output
) # indicates whether to print the time at the end of a ``with`` block
def max_branching_factor(self, max_branching_factor: int) -> None:
insanity.sanitize_type("max_branching_factor", max_branching_factor,
int)
insanity.sanitize_range("max_branching_factor",
max_branching_factor,
minimum=1)
self._max_branching_factor = max_branching_factor
def stop_prob(self, stop_prob: numbers.Real) -> None:
insanity.sanitize_type("stop_prob", stop_prob, numbers.Real)
insanity.sanitize_range("stop_prob",
stop_prob,
minimum=0,
maximum=1,
max_inclusive=False)
self._stop_prob = float(stop_prob)
def position(index: int) -> typing.Callable[[property], property]:
"""A decorator that allows for specifying the position of a property of a configuration class among all parsed
positional args.
Notice that ``argmagic`` ignore this decorator if positional args are not used or if the annotated property
defines an optional configuration value.
Args:
index (int): The index of the annotated configuration in the sequence of positional args.
"""
insanity.sanitize_type("index", index, int)
insanity.sanitize_range("index", index, minimum=0)
def _position(func: property) -> property:
if not isinstance(func, property):
raise TypeError(
"The decorator @position may be applied to properties only!")
func.fget.__dict__[argmagic.POSITION] = index
return func
return _position
def num_training_samples(self, num_training_samples: int) -> None:
insanity.sanitize_type("num_training_samples", num_training_samples, int)
insanity.sanitize_range("num_training_samples", num_training_samples, minimum=1)
self._num_training_samples = num_training_samples
def num_datasets(self, num_datasets: int) -> None:
insanity.sanitize_type("num_datasets", num_datasets, int)
insanity.sanitize_range("num_samples", num_datasets, minimum=1)
self._num_datasets = num_datasets
def generate_datasets(
self,
num_datasets: int,
num_training_samples: int,
output_dir: str
) -> None:
"""Generates datasets from the data that was provided to this instance of ``DatasetGenerator`, and writes them
to disk.
Args:
num_datasets (int): The total number of datasets to create.
num_training_samples (int): The number of training samples to create for each dataset.
output_dir (str): The path of the output directory.
"""
# sanitize args
insanity.sanitize_type("num_datasets", num_datasets, int)
insanity.sanitize_range("num_datasets", num_datasets, minimum=1)
insanity.sanitize_type("num_training_samples", num_training_samples, int)
insanity.sanitize_range("num_training_samples", num_training_samples, minimum=1)
# create patterns for the names of the directories that are created for the single datasets and for
# the base names of training samples
output_dir_pattern = "{:0" + str(len(str(num_datasets - 1))) + "d}"
sample_filename_pattern = "{:0" + str(len(str(num_training_samples - 1))) + "d}"
for dataset_idx in range(num_datasets):
print("generating dataset #{}...".format(dataset_idx))
# assemble needed paths
ds_output_dir = os.path.join(output_dir, output_dir_pattern.format(dataset_idx))
train_dir = os.path.join(ds_output_dir, "train")
dev_dir = os.path.join(ds_output_dir, "dev")
test_dir = os.path.join(ds_output_dir, "test")
# create folder structure for storing the current dataset
if not os.path.isdir(ds_output_dir):
os.mkdir(ds_output_dir)
if not os.path.isdir(train_dir):
os.mkdir(train_dir)
if not os.path.isdir(dev_dir):
os.mkdir(dev_dir)
if not os.path.isdir(test_dir):
os.mkdir(test_dir)
# split countries into train/dev/test
train, dev, test = self._split_countries()
# write selected dev+test countries to disk
with open(os.path.join(ds_output_dir, "countries.dev.txt"), "w") as f:
for c in dev:
f.write("{}\n".format(c))
with open(os.path.join(ds_output_dir, "countries.test.txt"), "w") as f:
for c in test:
f.write("{}\n".format(c))
# create training samples + write them to disk
for sample_idx in range(num_training_samples):
print("generating training sample #{}...".format(sample_idx))
sample = self._generate_sample(train)
kg_writer.KgWriter.write(sample, train_dir, sample_filename_pattern.format(sample_idx))
# create evaluation sample + write it to disk
print("generating dev sample... ")
dev_sample = self._generate_sample(train, inf_countries=dev, minimal=True)
kg_writer.KgWriter.write(dev_sample, dev_dir, "dev")
# create test sample + write it to disk
print("generating test sample...")
test_sample = self._generate_sample(train, inf_countries=test, minimal=True)
kg_writer.KgWriter.write(test_sample, test_dir, "test")
# print statistics about test sample
num_spec = len([t for t in test_sample.triples if not t.inferred])
num_inf = len([t for t in test_sample.triples if t.inferred])
print("number triples in test sample: {} ({} spec / {} inf)".format(num_spec + num_inf, num_spec, num_inf))
print("OK\n")
def max_tree_size(self, max_tree_size: int) -> None:
insanity.sanitize_type("max_tree_size", max_tree_size, int)
insanity.sanitize_range("max_tree_size", max_tree_size, minimum=1)
self._max_tree_size = max_tree_size
def max_tree_depth(self, max_tree_depth: int) -> None:
insanity.sanitize_type("max_tree_depth", max_tree_depth, int)
insanity.sanitize_range("max_tree_depth", max_tree_depth, minimum=1)
self._max_tree_depth = max_tree_depth
def read(cls, input_dir: str, basename: str, index: int = None) -> knowledge_graph.KnowledgeGraph:
"""Loads a knowledge graph from the specified location.
Args:
input_dir (str): The directory that contains all of the files.
basename (str): The base name, i.e., the prefix, included in all files' names.
index (int, optional): If this is provided, then ``input_dir`` and ``basename`` are assumed to specify a
sequence of knowledge graphs, and ``index`` specifies the element of this sequence to retrieve.
Returns:
:class:`knowledge_graph.KnowledgeGraph`: A knowledge graph that has been populated according to the read
information.
Raises:
ValueError: If ``input_dir`` does not refer to an existing directory or if any of the needed files is
missing.
"""
# //////// Sanitize Args ---------------------------------------------------------------------------------------
# ensure that the inputs are strings
input_dir = str(input_dir)
basename = str(basename)
if index is not None:
insanity.sanitize_type("index", index, int)
insanity.sanitize_range("index", index, minimum=0)
# assemble all needed paths
# the used postfixes have the following meanings:
# * _vocab -> definition of a class/relation/literal
# * _spec -> user-defined data
# * _inf -> inferred data
individual_spec = os.path.join(input_dir, basename + io.INDIVIDUALS_SPEC_EXT)
classes_vocab = os.path.join(input_dir, basename + io.CLASSES_VOCAB_EXT)
classes_spec = os.path.join(input_dir, basename + io.CLASSES_SPEC_EXT)
classes_inf = os.path.join(input_dir, basename + io.CLASSES_INF_EXT)
classes_pred = os.path.join(input_dir, basename + io.CLASSES_PRED_EXT)
relations_vocab = os.path.join(input_dir, basename + io.RELATIONS_VOCAB_EXT)
relations_spec = os.path.join(input_dir, basename + io.RELATIONS_SPEC_EXT)
relations_inf = os.path.join(input_dir, basename + io.RELATIONS_INF_EXT)
relations_pred = os.path.join(input_dir, basename + io.RELATIONS_PRED_EXT)
literals_vocab = os.path.join(input_dir, basename + io.LITERALS_VOCAB_EXT)
literals_spec = os.path.join(input_dir, basename + io.LITERALS_SPEC_EXT)
literals_inf = os.path.join(input_dir, basename + io.LITERALS_INF_EXT)
literals_pred = os.path.join(input_dir, basename + io.LITERALS_PRED_EXT)
if index is not None:
classes_spec += "." + str(index)
classes_inf += "." + str(index)
classes_pred += "." + str(index)
relations_spec += "." + str(index)
relations_inf += "." + str(index)
relations_pred += "." + str(index)
literals_spec += "." + str(index)
literals_inf += "." + str(index)
literals_pred += "." + str(index)
# check whether the input directory exists
if not os.path.isdir(input_dir):
raise ValueError("The provided <input_dir> does not exist: '{}'!".format(input_dir))
# check whether all of the needed files exist:
if not os.path.isfile(individual_spec):
raise ValueError("Missing file: '{}'!".format(individual_spec))
if not os.path.isfile(classes_vocab):
raise ValueError("Missing file: '{}'!".format(classes_vocab))
if not os.path.isfile(classes_spec):
raise ValueError("Missing file: '{}'!".format(classes_spec))
if not os.path.isfile(classes_inf):
raise ValueError("Missing file: '{}'!".format(classes_inf))
if not os.path.isfile(classes_pred):
raise ValueError("Missing file: '{}'!".format(classes_pred))
if not os.path.isfile(relations_vocab):
raise ValueError("Missing file: '{}'!".format(relations_vocab))
if not os.path.isfile(relations_spec):
raise ValueError("Missing file: '{}'!".format(relations_spec))
if not os.path.isfile(relations_inf):
raise ValueError("Missing file: '{}'!".format(relations_inf))
if not os.path.isfile(relations_pred):
raise ValueError("Missing file: '{}'!".format(relations_pred))
if not os.path.isfile(literals_vocab):
raise ValueError("Missing file: '{}'!".format(literals_vocab))
if not os.path.isfile(literals_spec):
raise ValueError("Missing file: '{}'!".format(literals_spec))
if not os.path.isfile(literals_inf):
raise ValueError("Missing file: '{}'!".format(literals_inf))
if not os.path.isfile(literals_pred):
raise ValueError("Missing file: '{}'!".format(literals_pred))
# //////// Read Vocabulary ---------------------------------------------------------------------------------
# create new empty knowledge graph
kg = knowledge_graph.KnowledgeGraph()
# read classes
with open(classes_vocab, "r") as f:
for index, line in enumerate(f):
if line == "":
continue
m = re.match(cls.VOCAB_REGEX, line)
assert int(m.group("index")) == index
kg.classes.add(ctf.ClassTypeFactory.create_class(m.group("name")))
# read relations
with open(relations_vocab, "r") as f:
for index, line in enumerate(f):
if line == "":
continue
m = re.match(cls.VOCAB_REGEX, line)
assert int(m.group("index")) == index
kg.relations.add(rtf.RelationTypeFactory.create_relation(m.group("name")))
# read literals
with open(literals_vocab, "r") as f:
for index, line in enumerate(f):
if line == "":
continue
m = re.match(cls.VOCAB_REGEX, line)
assert int(m.group("index")) == index
kg.literals.add(ltf.LiteralTypeFactory.create_literal(m.group("name")))
# //////// Read Individuals --------------------------------------------------------------------------------
with open(individual_spec, "r") as f:
for index, line in enumerate(f):
if line == "":
continue
m = re.match(cls.VOCAB_REGEX, line)
assert int(m.group("index")) == index
kg.individuals.add(individual_factory.IndividualFactory.create_individual(m.group("name")))
# //////// Read Class Memberships --------------------------------------------------------------------------
# read specified memberships
with open(classes_spec, "r") as f:
for individual_index, line in enumerate(f): # run through all individuals
if line == "":
continue
current_ind = kg.individuals[individual_index]
for class_index, mem in enumerate(map(int, re.findall(cls.MEMBERSHIPS_REGEX, line))):
if mem != 0: # only consider specified memberships
current_ind.classes.add(
class_membership.ClassMembership(
kg.classes[class_index],
mem == 1
)
)
# read inferred memberships
with open(classes_inf, "r") as f:
for individual_index, line in enumerate(f): # run through all individuals
if line == "":
continue
current_ind = kg.individuals[individual_index]
for class_index, mem in enumerate(map(int, re.findall(cls.MEMBERSHIPS_REGEX, line))):
if mem != 0: # only consider specified memberships
current_ind.classes.add(
class_membership.ClassMembership(
kg.classes[class_index],
mem == 1,
inferred=True
)
)
# read memberships that are prediction targets
with open(classes_pred, "r") as f:
for individual_index, line in enumerate(f): # run through all individuals
if line == "":
continue
current_ind = kg.individuals[individual_index]
for class_index, mem in enumerate(map(int, re.findall(cls.MEMBERSHIPS_REGEX, line))):
if mem != 0: # only consider specified memberships
current_ind.classes.add(
class_membership.ClassMembership(
kg.classes[class_index],
mem == 1,
prediction=True
)
)
# //////// Read Literals -----------------------------------------------------------------------------------
# read specified literals
with open(literals_spec, "r") as f:
for line in f:
if line == "":
continue
# parse read line
m = re.match(cls.TRIPLE_REGEX, line)
# fetch respective individual, literal, and value
current_ind = kg.individuals[int(m.group("subject"))]
current_lit = kg.literals[int(m.group("predicate"))]
current_value = m.group("object")
# add literal to individual
current_ind.literals.add(
literal_value.LiteralValue(
current_lit,
current_value
)
)
# read inferred literals
with open(literals_inf, "r") as f:
for line in f:
if line == "":
continue
# parse read line
m = re.match(cls.TRIPLE_REGEX, line)
# fetch respective individual, literal, and value
current_ind = kg.individuals[int(m.group("subject"))]
current_lit = kg.literals[int(m.group("predicate"))]
current_value = m.group("object")
# add literal to individual
current_ind.literals.add(
literal_value.LiteralValue(
current_lit,
current_value,
inferred=True
)
)
# read literals that are prediction targets
with open(literals_pred, "r") as f:
for line in f:
if line == "":
continue
# parse read line
m = re.match(cls.TRIPLE_REGEX, line)
# fetch respective individual, literal, and value
current_ind = kg.individuals[int(m.group("subject"))]
current_lit = kg.literals[int(m.group("predicate"))]
current_value = m.group("object")
# add literal to individual
current_ind.literals.add(
literal_value.LiteralValue(
current_lit,
current_value,
prediction=True
)
)
# //////// Read Triples ------------------------------------------------------------------------------------
# read specified triples
with open(relations_spec, "r") as f:
for line in f:
if line == "":
continue
# parse read line
m = re.match(cls.TYPED_TRIPLE_REGEX, line)
# fetch respective individual, literal, and value
positive = m.group("type") == "+"
sub = kg.individuals[int(m.group("subject"))]
pred = kg.relations[int(m.group("predicate"))]
obj = kg.individuals[int(m.group("object"))]
# add triple
kg.triples.add(triple.Triple(sub, pred, obj, positive))
# read inferred triples
with open(relations_inf, "r") as f:
for line in f:
if line == "":
continue
# parse read line
m = re.match(cls.TYPED_TRIPLE_REGEX, line)
# fetch respective individual, literal, and value
positive = m.group("type") == "+"
sub = kg.individuals[int(m.group("subject"))]
pred = kg.relations[int(m.group("predicate"))]
obj = kg.individuals[int(m.group("object"))]
# add triple
kg.triples.add(triple.Triple(sub, pred, obj, positive, inferred=True))
# read triples that are prediction targets
with open(relations_pred, "r") as f:
for line in f:
if line == "":
continue
# parse read line
m = re.match(cls.TYPED_TRIPLE_REGEX, line)
# fetch respective individual, literal, and value
positive = m.group("type") == "+"
sub = kg.individuals[int(m.group("subject"))]
pred = kg.relations[int(m.group("predicate"))]
obj = kg.individuals[int(m.group("object"))]
# add triple
kg.triples.add(triple.Triple(sub, pred, obj, positive, prediction=True))
return kg
def __init__(
self,
model: encoder.Encoder,
word_emb: nn.Embedding,
pos_emb: nn.Embedding,
mask_index: int,
prediction_rate: numbers.Real = 0.15,
mask_rate: numbers.Real = 0.8,
random_rate: numbers.Real = 0.1
):
"""Creates a new instance of ``BERTLoss`.
Args:
model (encoder.Encoder): The encoder model being pretrained.
word_emb (nn.Embedding): The used word embeddings.
pos_emb (nn.Embedding): The used positional embeddings.
mask_index (int): The index of the mask token.
prediction_rate (numbers.Real, optional): The percentage of tokens in each training sequence that
predictions are computed for, which is set to ``0.8``, by default.
mask_rate (numbers.Real, optional): Among all tokens that predictions are computed for, the percentage of
tokens that are replaced with the mask token, as specified by ``mask_index``. This is set to ``0.8``, by
default.
random_rate (numbers.Real, optional): Among all tokens that predictions are computed for, the percentage of
tokens that are randomly replaced with other tokens. This is set to ``0.1``, by default.
"""
super().__init__()
# sanitize args
insanity.sanitize_type("model", model, encoder.Encoder)
insanity.sanitize_type("word_emb", word_emb, nn.Embedding)
insanity.sanitize_type("pos_emb", word_emb, nn.Embedding)
if pos_emb.embedding_dim != word_emb.embedding_dim:
raise ValueError("<pos_emb> is not compatible with <word_emb>!")
insanity.sanitize_type("mask_index", mask_index, int)
if mask_index < 0 or mask_index >= word_emb.num_embeddings:
raise ValueError("The <mask_index> does not exist in <word_emb>!")
insanity.sanitize_type("prediction_rate", prediction_rate, numbers.Real)
prediction_rate = float(prediction_rate)
insanity.sanitize_range("prediction_rate", prediction_rate, minimum=0, maximum=1)
insanity.sanitize_type("mask_rate", mask_rate, numbers.Real)
mask_rate = float(mask_rate)
insanity.sanitize_range("mask_rate", mask_rate, minimum=0, maximum=1)
insanity.sanitize_type("random_rate", random_rate, numbers.Real)
random_rate = float(random_rate)
insanity.sanitize_range("random_rate", random_rate, minimum=0, maximum=1)
if mask_rate + random_rate > 1:
raise ValueError("<mask_rate> + <random_rate> has to be at most 1!")
# store args
self._mask_index = mask_index
self._mask_rate = mask_rate
self._model = model
self._pad_index = model.pad_index
self._pos_emb = pos_emb
self._prediction_rate = prediction_rate
self._random_rate = random_rate
self._word_emb = word_emb
# create an output layer, which is trained together with the model, for predicting masked tokens
self._output_layer = nn.Sequential(
nn.Linear(self._word_emb.embedding_dim, self._word_emb.num_embeddings),
nn.Softmax(dim=1)
)
# create the used loss function
self._loss = nn.CrossEntropyLoss()
def position(self, position: typing.Union[int, None]):
insanity.sanitize_type("position", position, int, none_allowed=True)
if position is not None:
insanity.sanitize_range("position", position, minimum=0)
self._position = position
