Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
Background.run | (self) |
Método para iniciar a animação do background
|
Método para iniciar a animação do background
| def run(self):
"""
Método para iniciar a animação do background
"""
# Enquanto o atributo "stop" for False, a animação continuará em um loop infinito
if not self.__stop:
# Move as imagens de background na posição X
self.move(self.__background[0], -10, 0)... | [
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Background.stop | (self) |
Método para parar a animação do background
|
Método para parar a animação do background
| def stop(self):
"""
Método para parar a animação do background
"""
self.__stop = True | [
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26
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a_descriptive_name | (optional_arguments) | A documentation string. | A documentation string. | def a_descriptive_name(optional_arguments):
"""A documentation string."""
# Your function's code goes here.
# Your function's code goes here.
# Your function's code goes here.
return optional_value | [
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] | [
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] | [
6,
25
] | python | en | ['en', 'cy', 'en'] | True |
fbeta | (
preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
beta: float = 1.0,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False
) |
Computes f_beta metric.
Works with binary, multiclass, and multilabel data.
Accepts probabilities from a model output or integer class values in prediction.
Works with multi-dimensional preds and target.
If preds and target are the same shape and preds is a float tensor, we use the ``self.thresho... |
Computes f_beta metric. | def fbeta(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
beta: float = 1.0,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False
) -> torch.Tensor:
"""
Computes f_beta metric.
Works with binary, multiclass, and multilabel data.
Accepts prob... | [
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f1 | (
preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False
) |
Computes F1 metric. F1 metrics correspond to a equally weighted average of the
precision and recall scores.
Works with binary, multiclass, and multilabel data.
Accepts probabilities from a model output or integer class values in prediction.
Works with multi-dimensional preds and target.
If pr... |
Computes F1 metric. F1 metrics correspond to a equally weighted average of the
precision and recall scores. | def f1(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False
) -> torch.Tensor:
"""
Computes F1 metric. F1 metrics correspond to a equally weighted average of the
precision and recall scores.
Works ... | [
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CompositionalMetric.__init__ | (
self,
operator: Callable,
metric_a: Union[Metric, int, float, torch.Tensor],
metric_b: Union[Metric, int, float, torch.Tensor, None],
) |
Args:
operator: the operator taking in one (if metric_b is None)
or two arguments. Will be applied to outputs of metric_a.compute()
and (optionally if metric_b is not None) metric_b.compute()
metric_a: first metric whose compute() result is the first arg... | def __init__(
self,
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metric_a: Union[Metric, int, float, torch.Tensor],
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):
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Args:
operator: the operator taking in one (if metric_b is None)
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] | [
41,
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] | python | en | ['en', 'error', 'th'] | False | |
find_position | (string, index, last_index, last_pos) |
Given a string and index, return (line, column)
|
Given a string and index, return (line, column)
| def find_position(string, index, last_index, last_pos):
"""
Given a string and index, return (line, column)
"""
lines = string.count('\n', last_index, index)
if lines > 0:
column = index - string.rfind('\n', last_index, index)
else:
column = last_pos[1] + (index - last_index)
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test_global_zero_only_logging_ddp_cpu | (tmpdir) |
Makes sure logging only happens from root zero
|
Makes sure logging only happens from root zero
| def test_global_zero_only_logging_ddp_cpu(tmpdir):
"""
Makes sure logging only happens from root zero
"""
model = TestModel()
model.training_epoch_end = None
trainer = Trainer(
accelerator='ddp_cpu',
num_processes=2,
default_root_dir=tmpdir,
limit_train_batches=1,... | [
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test_global_zero_only_logging_ddp_spawn | (tmpdir) |
Makes sure logging only happens from root zero
|
Makes sure logging only happens from root zero
| def test_global_zero_only_logging_ddp_spawn(tmpdir):
"""
Makes sure logging only happens from root zero
"""
model = TestModel()
model.training_epoch_end = None
trainer = Trainer(
accelerator='ddp_spawn',
gpus=2,
default_root_dir=tmpdir,
limit_train_batches=1,
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get_date_string | () |
Gets the current date and converts into a more user friendly string format.
|
Gets the current date and converts into a more user friendly string format.
| def get_date_string():
"""
Gets the current date and converts into a more user friendly string format.
"""
return dt.now().date().strftime("%b %d, %Y") | [
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get_datetime_string | () |
Gets the current date & time then converts into a more user friendly string format.
|
Gets the current date & time then converts into a more user friendly string format.
| def get_datetime_string():
"""
Gets the current date & time then converts into a more user friendly string format.
"""
return dt.now().time().strftime("%I:%M%p") + dt.now().date().strftime("\n%B %d,\n%Y") | [
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BehaviorRecord.__repr__ | (self) |
Returns field date of behavior record.
|
Returns field date of behavior record.
| def __repr__(self):
""""
Returns field date of behavior record.
"""
return f'<BehaviorRecord: {self.date}>' | [
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BehaviorRecord.payload | (self) |
Returns the dictionary representation of the Baavior Record instance.
|
Returns the dictionary representation of the Baavior Record instance.
| def payload(self):
"""
Returns the dictionary representation of the Baavior Record instance.
"""
_payload = {
"date" : self.date,
"happy_point_count": self.happy_point_count,
"sad_point_count": self.sad_point_count,
"bonus_point_count": self.bonus_point_count
}
return _payload | [
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Alarm.__repr__ | (self) |
Returns the name of the Alarm instance
|
Returns the name of the Alarm instance
| def __repr__(self):
"""
Returns the name of the Alarm instance
"""
return f"<Alarm: {self.name}>" | [
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Alarm.payload | (self) |
Returns the dictionary representation of the Alarm model instance.
@return alarm object
@rtype dict
|
Returns the dictionary representation of the Alarm model instance.
| def payload(self):
"""
Returns the dictionary representation of the Alarm model instance.
@return alarm object
@rtype dict
"""
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TestLineSplitter.test_no_delimiter | (self) | Test LineSplitter w/o delimiter | Test LineSplitter w/o delimiter | def test_no_delimiter(self):
"Test LineSplitter w/o delimiter"
strg = " 1 2 3 4 5 # test"
test = LineSplitter()(strg)
assert_equal(test, ['1', '2', '3', '4', '5'])
test = LineSplitter('')(strg)
assert_equal(test, ['1', '2', '3', '4', '5']) | [
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TestLineSplitter.test_space_delimiter | (self) | Test space delimiter | Test space delimiter | def test_space_delimiter(self):
"Test space delimiter"
strg = " 1 2 3 4 5 # test"
test = LineSplitter(' ')(strg)
assert_equal(test, ['1', '2', '3', '4', '', '5'])
test = LineSplitter(' ')(strg)
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TestLineSplitter.test_tab_delimiter | (self) | Test tab delimiter | Test tab delimiter | def test_tab_delimiter(self):
"Test tab delimiter"
strg = " 1\t 2\t 3\t 4\t 5 6"
test = LineSplitter('\t')(strg)
assert_equal(test, ['1', '2', '3', '4', '5 6'])
strg = " 1 2\t 3 4\t 5 6"
test = LineSplitter('\t')(strg)
assert_equal(test, ['1 2', '3 4', '5 ... | [
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TestLineSplitter.test_other_delimiter | (self) | Test LineSplitter on delimiter | Test LineSplitter on delimiter | def test_other_delimiter(self):
"Test LineSplitter on delimiter"
strg = "1,2,3,4,,5"
test = LineSplitter(',')(strg)
assert_equal(test, ['1', '2', '3', '4', '', '5'])
#
strg = " 1,2,3,4,,5 # test"
test = LineSplitter(',')(strg)
assert_equal(test, ['1', '2',... | [
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TestLineSplitter.test_constant_fixed_width | (self) | Test LineSplitter w/ fixed-width fields | Test LineSplitter w/ fixed-width fields | def test_constant_fixed_width(self):
"Test LineSplitter w/ fixed-width fields"
strg = " 1 2 3 4 5 # test"
test = LineSplitter(3)(strg)
assert_equal(test, ['1', '2', '3', '4', '', '5', ''])
#
strg = " 1 3 4 5 6# test"
test = LineSplitter(20)(strg)... | [
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TestNameValidator.test_case_sensitivity | (self) | Test case sensitivity | Test case sensitivity | def test_case_sensitivity(self):
"Test case sensitivity"
names = ['A', 'a', 'b', 'c']
test = NameValidator().validate(names)
assert_equal(test, ['A', 'a', 'b', 'c'])
test = NameValidator(case_sensitive=False).validate(names)
assert_equal(test, ['A', 'A_1', 'B', 'C'])
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TestNameValidator.test_excludelist | (self) | Test excludelist | Test excludelist | def test_excludelist(self):
"Test excludelist"
names = ['dates', 'data', 'Other Data', 'mask']
validator = NameValidator(excludelist=['dates', 'data', 'mask'])
test = validator.validate(names)
assert_equal(test, ['dates_', 'data_', 'Other_Data', 'mask_']) | [
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TestNameValidator.test_missing_names | (self) | Test validate missing names | Test validate missing names | def test_missing_names(self):
"Test validate missing names"
namelist = ('a', 'b', 'c')
validator = NameValidator()
assert_equal(validator(namelist), ['a', 'b', 'c'])
namelist = ('', 'b', 'c')
assert_equal(validator(namelist), ['f0', 'b', 'c'])
namelist = ('a', 'b'... | [
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TestNameValidator.test_validate_nb_names | (self) | Test validate nb names | Test validate nb names | def test_validate_nb_names(self):
"Test validate nb names"
namelist = ('a', 'b', 'c')
validator = NameValidator()
assert_equal(validator(namelist, nbfields=1), ('a',))
assert_equal(validator(namelist, nbfields=5, defaultfmt="g%i"),
['a', 'b', 'c', 'g0', 'g1']... | [
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TestNameValidator.test_validate_wo_names | (self) | Test validate no names | Test validate no names | def test_validate_wo_names(self):
"Test validate no names"
namelist = None
validator = NameValidator()
assert_(validator(namelist) is None)
assert_equal(validator(namelist, nbfields=3), ['f0', 'f1', 'f2']) | [
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"nbfie... | [
129,
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] | [
134,
73
] | python | en | ['lv', 'en', 'it'] | False |
TestStringConverter.test_creation | (self) | Test creation of a StringConverter | Test creation of a StringConverter | def test_creation(self):
"Test creation of a StringConverter"
converter = StringConverter(int, -99999)
assert_equal(converter._status, 1)
assert_equal(converter.default, -99999) | [
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TestStringConverter.test_upgrade | (self) | Tests the upgrade method. | Tests the upgrade method. | def test_upgrade(self):
"Tests the upgrade method."
converter = StringConverter()
assert_equal(converter._status, 0)
# test int
assert_equal(converter.upgrade('0'), 0)
assert_equal(converter._status, 1)
# On systems where long defaults to 32-bit, the statuses w... | [
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TestStringConverter.test_missing | (self) | Tests the use of missing values. | Tests the use of missing values. | def test_missing(self):
"Tests the use of missing values."
converter = StringConverter(missing_values=('missing',
'missed'))
converter.upgrade('0')
assert_equal(converter('0'), 0)
assert_equal(converter(''), converter.default)
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TestStringConverter.test_upgrademapper | (self) | Tests updatemapper | Tests updatemapper | def test_upgrademapper(self):
"Tests updatemapper"
dateparser = _bytes_to_date
StringConverter.upgrade_mapper(dateparser, date(2000, 1, 1))
convert = StringConverter(dateparser, date(2000, 1, 1))
test = convert('2001-01-01')
assert_equal(test, date(2001, 1, 1))
te... | [
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TestStringConverter.test_string_to_object | (self) | Make sure that string-to-object functions are properly recognized | Make sure that string-to-object functions are properly recognized | def test_string_to_object(self):
"Make sure that string-to-object functions are properly recognized"
old_mapper = StringConverter._mapper[:] # copy of list
conv = StringConverter(_bytes_to_date)
assert_equal(conv._mapper, old_mapper)
assert_(hasattr(conv, 'default')) | [
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TestStringConverter.test_keep_default | (self) | Make sure we don't lose an explicit default | Make sure we don't lose an explicit default | def test_keep_default(self):
"Make sure we don't lose an explicit default"
converter = StringConverter(None, missing_values='',
default=-999)
converter.upgrade('3.14159265')
assert_equal(converter.default, -999)
assert_equal(converter.type, np.... | [
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TestStringConverter.test_keep_default_zero | (self) | Check that we don't lose a default of 0 | Check that we don't lose a default of 0 | def test_keep_default_zero(self):
"Check that we don't lose a default of 0"
converter = StringConverter(int, default=0,
missing_values="N/A")
assert_equal(converter.default, 0) | [
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TestStringConverter.test_keep_missing_values | (self) | Check that we're not losing missing values | Check that we're not losing missing values | def test_keep_missing_values(self):
"Check that we're not losing missing values"
converter = StringConverter(int, default=0,
missing_values="N/A")
assert_equal(
converter.missing_values, {'', 'N/A'}) | [
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TestStringConverter.test_int64_dtype | (self) | Check that int64 integer types can be specified | Check that int64 integer types can be specified | def test_int64_dtype(self):
"Check that int64 integer types can be specified"
converter = StringConverter(np.int64, default=0)
val = "-9223372036854775807"
assert_(converter(val) == -9223372036854775807)
val = "9223372036854775807"
assert_(converter(val) == 92233720368547... | [
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TestStringConverter.test_uint64_dtype | (self) | Check that uint64 integer types can be specified | Check that uint64 integer types can be specified | def test_uint64_dtype(self):
"Check that uint64 integer types can be specified"
converter = StringConverter(np.uint64, default=0)
val = "9223372043271415339"
assert_(converter(val) == 9223372043271415339) | [
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TestMiscFunctions.test_has_nested_dtype | (self) | Test has_nested_dtype | Test has_nested_dtype | def test_has_nested_dtype(self):
"Test has_nested_dtype"
ndtype = np.dtype(float)
assert_equal(has_nested_fields(ndtype), False)
ndtype = np.dtype([('A', '|S3'), ('B', float)])
assert_equal(has_nested_fields(ndtype), False)
ndtype = np.dtype([('A', int), ('B', [('BA', flo... | [
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TestMiscFunctions.test_easy_dtype | (self) | Test ndtype on dtypes | Test ndtype on dtypes | def test_easy_dtype(self):
"Test ndtype on dtypes"
# Simple case
ndtype = float
assert_equal(easy_dtype(ndtype), np.dtype(float))
# As string w/o names
ndtype = "i4, f8"
assert_equal(easy_dtype(ndtype),
np.dtype([('f0', "i4"), ('f1', "f8")]))
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TestMiscFunctions.test_flatten_dtype | (self) | Testing flatten_dtype | Testing flatten_dtype | def test_flatten_dtype(self):
"Testing flatten_dtype"
# Standard dtype
dt = np.dtype([("a", "f8"), ("b", "f8")])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, float])
# Recursive dtype
dt = np.dtype([("a", [("aa", '|S1'), ("ab", '|S2')]), ("b", int)])
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DeterministicTuringMachine.run | (self) | Run step until we reach accept or reject | Run step until we reach accept or reject | def run(self) -> Tuple[bool, str]:
"""Run step until we reach accept or reject"""
while True:
result = self.step()
if isinstance(result, bool):
return result, "".join(self.tape.content).rstrip(BLANK) | [
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ModelHooks.setup | (self, stage: str) |
Called at the beginning of fit and test.
This is a good hook when you need to build models dynamically or adjust something about them.
This hook is called on every process when using DDP.
Args:
stage: either 'fit' or 'test'
Example::
class LitModel(...... |
Called at the beginning of fit and test.
This is a good hook when you need to build models dynamically or adjust something about them.
This hook is called on every process when using DDP. | def setup(self, stage: str) -> None:
"""
Called at the beginning of fit and test.
This is a good hook when you need to build models dynamically or adjust something about them.
This hook is called on every process when using DDP.
Args:
stage: either 'fit' or 'test'
... | [
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ModelHooks.teardown | (self, stage: str) |
Called at the end of fit and test.
Args:
stage: either 'fit' or 'test'
|
Called at the end of fit and test. | def teardown(self, stage: str) -> None:
"""
Called at the end of fit and test.
Args:
stage: either 'fit' or 'test'
""" | [
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ModelHooks.on_fit_start | (self) |
Called at the very beginning of fit.
If on DDP it is called on every process
|
Called at the very beginning of fit.
If on DDP it is called on every process
| def on_fit_start(self) -> None:
"""
Called at the very beginning of fit.
If on DDP it is called on every process
""" | [
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ModelHooks.on_fit_end | (self) |
Called at the very end of fit.
If on DDP it is called on every process
|
Called at the very end of fit.
If on DDP it is called on every process
| def on_fit_end(self) -> None:
"""
Called at the very end of fit.
If on DDP it is called on every process
""" | [
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ModelHooks.on_train_start | (self) |
Called at the beginning of training after sanity check.
|
Called at the beginning of training after sanity check.
| def on_train_start(self) -> None:
"""
Called at the beginning of training after sanity check.
""" | [
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ModelHooks.on_train_end | (self) |
Called at the end of training before logger experiment is closed.
|
Called at the end of training before logger experiment is closed.
| def on_train_end(self) -> None:
"""
Called at the end of training before logger experiment is closed.
""" | [
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ModelHooks.on_validation_start | (self) |
Called at the beginning of validation.
|
Called at the beginning of validation.
| def on_validation_start(self) -> None:
"""
Called at the beginning of validation.
""" | [
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ModelHooks.on_validation_end | (self) |
Called at the end of validation.
|
Called at the end of validation.
| def on_validation_end(self) -> None:
"""
Called at the end of validation.
""" | [
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ModelHooks.on_pretrain_routine_start | (self) |
Called at the beginning of the pretrain routine (between fit and train start).
- fit
- pretrain_routine start
- pretrain_routine end
- training_start
|
Called at the beginning of the pretrain routine (between fit and train start). | def on_pretrain_routine_start(self) -> None:
"""
Called at the beginning of the pretrain routine (between fit and train start).
- fit
- pretrain_routine start
- pretrain_routine end
- training_start
""" | [
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ModelHooks.on_pretrain_routine_end | (self) |
Called at the end of the pretrain routine (between fit and train start).
- fit
- pretrain_routine start
- pretrain_routine end
- training_start
|
Called at the end of the pretrain routine (between fit and train start). | def on_pretrain_routine_end(self) -> None:
"""
Called at the end of the pretrain routine (between fit and train start).
- fit
- pretrain_routine start
- pretrain_routine end
- training_start
""" | [
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ModelHooks.on_train_batch_start | (self, batch: Any, batch_idx: int, dataloader_idx: int) |
Called in the training loop before anything happens for that batch.
If you return -1 here, you will skip training for the rest of the current epoch.
Args:
batch: The batched data as it is returned by the training DataLoader.
batch_idx: the index of the batch
... |
Called in the training loop before anything happens for that batch. | def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""
Called in the training loop before anything happens for that batch.
If you return -1 here, you will skip training for the rest of the current epoch.
Args:
batch: The batched data as... | [
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ModelHooks.on_train_batch_end | (self, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int) |
Called in the training loop after the batch.
Args:
outputs: The outputs of training_step_end(training_step(x))
batch: The batched data as it is returned by the training DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the datalo... |
Called in the training loop after the batch. | def on_train_batch_end(self, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""
Called in the training loop after the batch.
Args:
outputs: The outputs of training_step_end(training_step(x))
batch: The batched data as it is returned by the traini... | [
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ModelHooks.on_validation_model_eval | (self) |
Sets the model to eval during the val loop
|
Sets the model to eval during the val loop
| def on_validation_model_eval(self) -> None:
"""
Sets the model to eval during the val loop
"""
self.eval() | [
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ModelHooks.on_validation_model_train | (self) |
Sets the model to train during the val loop
|
Sets the model to train during the val loop
| def on_validation_model_train(self) -> None:
"""
Sets the model to train during the val loop
"""
self.train() | [
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ModelHooks.on_validation_batch_start | (self, batch: Any, batch_idx: int, dataloader_idx: int) |
Called in the validation loop before anything happens for that batch.
Args:
batch: The batched data as it is returned by the validation DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
|
Called in the validation loop before anything happens for that batch. | def on_validation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""
Called in the validation loop before anything happens for that batch.
Args:
batch: The batched data as it is returned by the validation DataLoader.
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ModelHooks.on_validation_batch_end | (self, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int) |
Called in the validation loop after the batch.
Args:
outputs: The outputs of validation_step_end(validation_step(x))
batch: The batched data as it is returned by the validation DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of th... |
Called in the validation loop after the batch. | def on_validation_batch_end(self, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""
Called in the validation loop after the batch.
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ModelHooks.on_test_batch_start | (self, batch: Any, batch_idx: int, dataloader_idx: int) |
Called in the test loop before anything happens for that batch.
Args:
batch: The batched data as it is returned by the test DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
|
Called in the test loop before anything happens for that batch. | def on_test_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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Called in the test loop before anything happens for that batch.
Args:
batch: The batched data as it is returned by the test DataLoader.
batch_idx: the index of the batch
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ModelHooks.on_test_batch_end | (self, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int) |
Called in the test loop after the batch.
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batch: The batched data as it is returned by the test DataLoader.
batch_idx: the index of the batch
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Called in the test loop after the batch. | def on_test_batch_end(self, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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Called in the test loop after the batch.
Args:
outputs: The outputs of test_step_end(test_step(x))
batch: The batched data as it is returned by the test DataLoader.
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ModelHooks.on_test_model_train | (self) |
Sets the model to train during the test loop
|
Sets the model to train during the test loop
| def on_test_model_train(self) -> None:
"""
Sets the model to train during the test loop
"""
self.train() | [
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ModelHooks.on_test_model_eval | (self) |
Sets the model to eval during the test loop
|
Sets the model to eval during the test loop
| def on_test_model_eval(self) -> None:
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Sets the model to eval during the test loop
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ModelHooks.on_predict_model_eval | (self) |
Sets the model to eval during the predict loop
|
Sets the model to eval during the predict loop
| def on_predict_model_eval(self) -> None:
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Sets the model to eval during the predict loop
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ModelHooks.on_epoch_start | (self) |
Called in the training loop at the very beginning of the epoch.
|
Called in the training loop at the very beginning of the epoch.
| def on_epoch_start(self) -> None:
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Called in the training loop at the very beginning of the epoch.
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ModelHooks.on_epoch_end | (self) |
Called in the training loop at the very end of the epoch.
|
Called in the training loop at the very end of the epoch.
| def on_epoch_end(self) -> None:
"""
Called in the training loop at the very end of the epoch.
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ModelHooks.on_train_epoch_start | (self) |
Called in the training loop at the very beginning of the epoch.
|
Called in the training loop at the very beginning of the epoch.
| def on_train_epoch_start(self) -> None:
"""
Called in the training loop at the very beginning of the epoch.
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ModelHooks.on_train_epoch_end | (self, outputs) |
Called in the training loop at the very end of the epoch.
|
Called in the training loop at the very end of the epoch.
| def on_train_epoch_end(self, outputs) -> None:
"""
Called in the training loop at the very end of the epoch.
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ModelHooks.on_validation_epoch_start | (self) |
Called in the validation loop at the very beginning of the epoch.
|
Called in the validation loop at the very beginning of the epoch.
| def on_validation_epoch_start(self) -> None:
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Called in the validation loop at the very beginning of the epoch.
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ModelHooks.on_validation_epoch_end | (self) |
Called in the validation loop at the very end of the epoch.
|
Called in the validation loop at the very end of the epoch.
| def on_validation_epoch_end(self) -> None:
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Called in the validation loop at the very end of the epoch.
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ModelHooks.on_test_epoch_start | (self) |
Called in the test loop at the very beginning of the epoch.
|
Called in the test loop at the very beginning of the epoch.
| def on_test_epoch_start(self) -> None:
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Called in the test loop at the very beginning of the epoch.
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ModelHooks.on_test_epoch_end | (self) |
Called in the test loop at the very end of the epoch.
|
Called in the test loop at the very end of the epoch.
| def on_test_epoch_end(self) -> None:
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Called in the test loop at the very end of the epoch.
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ModelHooks.on_test_start | (self) |
Called at the beginning of testing.
|
Called at the beginning of testing.
| def on_test_start(self) -> None:
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Called at the beginning of testing.
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ModelHooks.on_test_end | (self) |
Called at the end of testing.
|
Called at the end of testing.
| def on_test_end(self) -> None:
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Called at the end of testing.
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ModelHooks.on_before_zero_grad | (self, optimizer: Optimizer) |
Called after optimizer.step() and before optimizer.zero_grad().
Called in the training loop after taking an optimizer step and before zeroing grads.
Good place to inspect weight information with weights updated.
This is where it is called::
for optimizer in optimizers:
... |
Called after optimizer.step() and before optimizer.zero_grad(). | def on_before_zero_grad(self, optimizer: Optimizer) -> None:
"""
Called after optimizer.step() and before optimizer.zero_grad().
Called in the training loop after taking an optimizer step and before zeroing grads.
Good place to inspect weight information with weights updated.
T... | [
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ModelHooks.on_after_backward | (self) |
Called in the training loop after loss.backward() and before optimizers do anything.
This is the ideal place to inspect or log gradient information.
Example::
def on_after_backward(self):
# example to inspect gradient information in tensorboard
if s... |
Called in the training loop after loss.backward() and before optimizers do anything.
This is the ideal place to inspect or log gradient information. | def on_after_backward(self) -> None:
"""
Called in the training loop after loss.backward() and before optimizers do anything.
This is the ideal place to inspect or log gradient information.
Example::
def on_after_backward(self):
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ModelHooks.on_post_move_to_device | (self) |
Called in the ``parameter_validation`` decorator after :meth:`~pytorch_lightning.core.LightningModule.to`
is called. This is a good place to tie weights between modules after moving them to a device. Can be
used when training models with weight sharing properties on TPU.
Addresses the ... |
Called in the ``parameter_validation`` decorator after :meth:`~pytorch_lightning.core.LightningModule.to`
is called. This is a good place to tie weights between modules after moving them to a device. Can be
used when training models with weight sharing properties on TPU. | def on_post_move_to_device(self) -> None:
"""
Called in the ``parameter_validation`` decorator after :meth:`~pytorch_lightning.core.LightningModule.to`
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DataHooks.prepare_data | (self) |
Use this to download and prepare data.
.. warning:: DO NOT set state to the model (use `setup` instead)
since this is NOT called on every GPU in DDP/TPU
Example::
def prepare_data(self):
# good
download_data()
tokenize()... |
Use this to download and prepare data. | def prepare_data(self) -> None:
"""
Use this to download and prepare data.
.. warning:: DO NOT set state to the model (use `setup` instead)
since this is NOT called on every GPU in DDP/TPU
Example::
def prepare_data(self):
# good
... | [
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DataHooks.train_dataloader | (self) |
Implement a PyTorch DataLoader for training.
Return:
Single PyTorch :class:`~torch.utils.data.DataLoader`.
The dataloader you return will not be called every epoch unless you set
:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
... |
Implement a PyTorch DataLoader for training. | def train_dataloader(self) -> DataLoader:
"""
Implement a PyTorch DataLoader for training.
Return:
Single PyTorch :class:`~torch.utils.data.DataLoader`.
The dataloader you return will not be called every epoch unless you set
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DataHooks.test_dataloader | (self) | r"""
Implement one or multiple PyTorch DataLoaders for testing.
The dataloader you return will not be called every epoch unless you set
:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
For data processing use the following pattern:
... | r"""
Implement one or multiple PyTorch DataLoaders for testing. | def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
r"""
Implement one or multiple PyTorch DataLoaders for testing.
The dataloader you return will not be called every epoch unless you set
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DataHooks.val_dataloader | (self) | r"""
Implement one or multiple PyTorch DataLoaders for validation.
The dataloader you return will not be called every epoch unless you set
:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
It's recommended that all data downloads and preparation... | r"""
Implement one or multiple PyTorch DataLoaders for validation. | def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
r"""
Implement one or multiple PyTorch DataLoaders for validation.
The dataloader you return will not be called every epoch unless you set
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DataHooks.predict_dataloader | (self) | r"""
Implement one or multiple PyTorch DataLoaders for prediction.
It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
- :meth:`~pytorch_lightning.trainer.Trainer.fit`
- ...
- :meth:`prepare_data`
- :meth:`train_dataloader`
-... | r"""
Implement one or multiple PyTorch DataLoaders for prediction. | def predict_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
r"""
Implement one or multiple PyTorch DataLoaders for prediction.
It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
- :meth:`~pytorch_lightning.trainer.Trainer.fit`
- ..... | [
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DataHooks.transfer_batch_to_device | (self, batch: Any, device: Optional[torch.device] = None) |
Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors
wrapped in a custom data structure.
The data types listed below (and any arbitrary nesting of them) are supported out of the box:
- :class:`torch.Tensor` or anything that implements `.to(...)`
- :... |
Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors
wrapped in a custom data structure. | def transfer_batch_to_device(self, batch: Any, device: Optional[torch.device] = None) -> Any:
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DataHooks.on_before_batch_transfer | (self, batch, dataloader_idx) |
Override to alter or apply batch augmentations to your batch before it is transferred to the device.
.. warning:: dataloader_idx always returns 0, and will be updated to support the true idx in the future.
Note:
This hook only runs on single GPU training and DDP (no data-parallel)... |
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DataHooks.on_after_batch_transfer | (self, batch, dataloader_idx) |
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CheckpointHooks.on_load_checkpoint | (self, checkpoint: Dict[str, Any]) | r"""
Called by Lightning to restore your model.
If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.
Args:
checkpoint: Loaded checkpoint
Example::
def on_load_checkpoint(self, checkpoint):
# 99% of the time... | r"""
Called by Lightning to restore your model.
If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this. | def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
r"""
Called by Lightning to restore your model.
If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.
Args:
checkpoint: Loaded checkpoint
Example::
d... | [
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CheckpointHooks.on_save_checkpoint | (self, checkpoint: Dict[str, Any]) | r"""
Called by Lightning when saving a checkpoint to give you a chance to store anything
else you might want to save.
Args:
checkpoint: Checkpoint to be saved
Example::
def on_save_checkpoint(self, checkpoint):
# 99% of use cases you don't need ... | r"""
Called by Lightning when saving a checkpoint to give you a chance to store anything
else you might want to save. | def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
r"""
Called by Lightning when saving a checkpoint to give you a chance to store anything
else you might want to save.
Args:
checkpoint: Checkpoint to be saved
Example::
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test_qr_mode_full_future_warning | () | Check mode='full' FutureWarning.
In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
deprecated. The release date will probably be sometime in the summer
of 2013.
| Check mode='full' FutureWarning. | def test_qr_mode_full_future_warning():
"""Check mode='full' FutureWarning.
In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
deprecated. The release date will probably be sometime in the summer
of 2013.
"""
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9,
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21,
63
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test_module_exists | () | Test if the some 3rd party libs are available | Test if the some 3rd party libs are available | def test_module_exists():
"""Test if the some 3rd party libs are available"""
assert _module_available("torch")
assert _module_available("torch.nn.parallel")
assert not _module_available("torch.nn.asdf")
assert not _module_available("asdf")
assert not _module_available("asdf.bla.asdf") | [
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read_dataset | (filepath: str) | Read a pandas DataFrame from csv and return data.
:param filepath: String with the filepath of the file
:type filepath: str
:return: A pandas Dataframe with the data inside the file
:rtype: pd.DataFrame
| Read a pandas DataFrame from csv and return data. | def read_dataset(filepath: str) -> pd.DataFrame:
"""Read a pandas DataFrame from csv and return data.
:param filepath: String with the filepath of the file
:type filepath: str
:return: A pandas Dataframe with the data inside the file
:rtype: pd.DataFrame
"""
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drop_values | (data: pd.DataFrame, drop_duplicates: list, to_drop: list) | Drop data with no sense, duplicates or NaN values.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param drop_duplicates: List with the column tags to drop the duplicates
:type drop_duplicates: list
:param to_drop: List with the column tags to be dropped
:type to_drop: lis... | Drop data with no sense, duplicates or NaN values. | def drop_values(data: pd.DataFrame, drop_duplicates: list, to_drop: list) -> pd.DataFrame:
"""Drop data with no sense, duplicates or NaN values.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param drop_duplicates: List with the column tags to drop the duplicates
:type drop_d... | [
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48,
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fill_empty_values | (data: pd.DataFrame, labels: list) | Fill empty values with zeros.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param labels: List with column tags where fill empty values
:type labels: list
:return: A pandas Dataframe with no empty values
:rtype: pd.DataFrame
| Fill empty values with zeros. | def fill_empty_values(data: pd.DataFrame, labels: list) -> pd.DataFrame:
"""Fill empty values with zeros.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param labels: List with column tags where fill empty values
:type labels: list
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64,
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to_datetime | (data: pd.DataFrame, labels: list) | Change format to datetime.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param labels: List with column tags where dates will be transform to datetime format
:type labels: list
:return: A pandas Dataframe with dates in datetime format
:rtype: pd.DataFrame
| Change format to datetime. | def to_datetime(data: pd.DataFrame, labels: list) -> pd.DataFrame:
"""Change format to datetime.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param labels: List with column tags where dates will be transform to datetime format
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to_last_date | (data: pd.DataFrame, ref_label: str) | Use the last date of the last year registered considered as the end of the study period.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param ref_label: List with column tags where dates will be filled with the last date registered
:type ref_label: str
:return: A pandas Dataf... | Use the last date of the last year registered considered as the end of the study period. | def to_last_date(data: pd.DataFrame, ref_label: str) -> pd.DataFrame:
"""Use the last date of the last year registered considered as the end of the study period.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param ref_label: List with column tags where dates will be filled with ... | [
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83,
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eliminate_spurious_data | (data: pd.DataFrame, sort_value: str) | Clean the dataset from spurious data, negative years and non-sense data.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param sort_value: String tag value that will be used to sort the Dataframe
:type sort_value: str
:return: A pandas Dataframe with spurious data cleaned
... | Clean the dataset from spurious data, negative years and non-sense data. | def eliminate_spurious_data(data: pd.DataFrame, sort_value: str) -> pd.DataFrame:
"""Clean the dataset from spurious data, negative years and non-sense data.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param sort_value: String tag value that will be used to sort the Dataframe
... | [
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123,
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non_numerical_recoding | (data: pd.DataFrame, non_numerical: str) | Map all the non numerical data to a categorical number.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param non_numerical: String tag with the column with non numerical values
:type non_numerical: str
:return: A pandas Dataframe with the non_numerical column transformed to n... | Map all the non numerical data to a categorical number. | def non_numerical_recoding(data: pd.DataFrame, non_numerical: str) -> pd.DataFrame:
"""Map all the non numerical data to a categorical number.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param non_numerical: String tag with the column with non numerical values
:type non_nu... | [
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")... | [
126,
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145,
15
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data_normalization | (data: pd.DataFrame) | Normalize quantitative data of the given dataframe if their skewness is greater than 2.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:return: A pandas dataframe with the data with skewness > 2 been normalized
:rtype: pd.DataFrame
| Normalize quantitative data of the given dataframe if their skewness is greater than 2. | def data_normalization(data: pd.DataFrame) -> pd.DataFrame:
"""Normalize quantitative data of the given dataframe if their skewness is greater than 2.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:return: A pandas dataframe with the data with skewness > 2 been normalized
:rt... | [
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"# Include only quantitat... | [
148,
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] | [
178,
55
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split_X_t | (data: pd.DataFrame, x_tags: list, label_tag: str) | Split X and t from a dataset using their colum tags.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param x_tags: List with the column tags to be used for the characteristics matrix
:type x_tags: list
:param label_tag: List with the column tag of the label column
:type la... | Split X and t from a dataset using their colum tags. | def split_X_t(data: pd.DataFrame, x_tags: list, label_tag: str) -> tuple:
"""Split X and t from a dataset using their colum tags.
:param data: Pandas Dataframe with the data
:type data: pd.DataFrame
:param x_tags: List with the column tags to be used for the characteristics matrix
:type x_tags: lis... | [
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"t... | [
181,
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195,
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test_accelerator_choice_ddp_cpu_custom_cluster | (device_count_mock) |
Test that we choose the custom cluster even when SLURM or TE flags are around
|
Test that we choose the custom cluster even when SLURM or TE flags are around
| def test_accelerator_choice_ddp_cpu_custom_cluster(device_count_mock):
"""
Test that we choose the custom cluster even when SLURM or TE flags are around
"""
class CustomCluster(ClusterEnvironment):
def master_address(self):
return 'asdf'
class CB(Callback):
def on_fit... | [
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291,
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320,
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test_plugin_accelerator_choice | (accelerator, plugin) |
Ensure that when a plugin and accelerator is passed in, that the plugin takes precedent.
|
Ensure that when a plugin and accelerator is passed in, that the plugin takes precedent.
| def test_plugin_accelerator_choice(accelerator, plugin):
"""
Ensure that when a plugin and accelerator is passed in, that the plugin takes precedent.
"""
trainer = Trainer(accelerator=accelerator, plugins=plugin, num_processes=2)
assert isinstance(trainer.accelerator.training_type_plugin, DDPSharded... | [
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415,
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initdb | () | Initialize the database. | Initialize the database. | def initdb():
"""Initialize the database."""
click.echo('Init the db')
db.create_all() | [
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28,
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] | [
31,
19
] | python | en | ['en', 'zu', 'en'] | True |
renewdb | () | Remove the database. | Remove the database. | def renewdb():
"""Remove the database."""
click.echo('Renew the db')
db.drop_all()
db.create_all() | [
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] | python | en | ['en', 'en', 'en'] | True |
list_routes | () | List all routes on flask service | List all routes on flask service | def list_routes():
"""List all routes on flask service"""
output = []
for rule in app.url_map.iter_rules():
methods = ','.join(rule.methods)
line = urllib.parse.unquote(
"{:50s} {:20s} {}".format(rule.endpoint, methods, rule))
output.append(line)
for line in sort... | [
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KafkaBrokerIntegrationTest.test_kafka_consumer_with_no_messages_received | (self) |
This test verifies that consumer sends fetches correctly, and receives nothing.
|
This test verifies that consumer sends fetches correctly, and receives nothing.
| def test_kafka_consumer_with_no_messages_received(self):
"""
This test verifies that consumer sends fetches correctly, and receives nothing.
"""
consumer = KafkaConsumer(
bootstrap_servers=KafkaBrokerIntegrationTest.kafka_address(), fetch_max_wait_ms=500)
consumer.assign([To... | [
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"a... | [
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