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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
test_no_feature_mapping | (feature_descriptions, nm_feature) | Testing if None is returned when the feature does not have corresponding mapping defined. | Testing if None is returned when the feature does not have corresponding mapping defined. | def test_no_feature_mapping(feature_descriptions, nm_feature):
"""Testing if None is returned when the feature does not have corresponding mapping defined."""
fd = FeatureDescriptor(feature_descriptions)
assert fd.mapping(nm_feature) is None | [
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test_no_feature_category | (feature_descriptions, nc_feature) | Testing if None is returned when the feature does not have corresponding category defined. | Testing if None is returned when the feature does not have corresponding category defined. | def test_no_feature_category(feature_descriptions, nc_feature):
"""Testing if None is returned when the feature does not have corresponding category defined."""
fd = FeatureDescriptor(feature_descriptions)
assert fd.category(nc_feature) is None | [
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TorchDecisionTreeClassifier.fit | (self, vectors, labels, criterion=None) |
Function which must be used after the initialisation to fit the binary tree and build the successive
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to solve a specific classification problem.
Args:
vectors (:class:`torch.FloatTensor`): Vectors tensor used to fit the decision t... |
Function which must be used after the initialisation to fit the binary tree and build the successive
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to solve a specific classification problem. | def fit(self, vectors, labels, criterion=None):
"""
Function which must be used after the initialisation to fit the binary tree and build the successive
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to solve a specific classification problem.
Args:
vectors (:class:`tor... | [
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TorchDecisionTreeClassifier._build_tree | (self, vectors, labels, func, depth) |
Private recursive function used to build the tree.
|
Private recursive function used to build the tree.
| def _build_tree(self, vectors, labels, func, depth):
"""
Private recursive function used to build the tree.
"""
if len(vectors) == 0:
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if depth == 0:
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TorchDecisionTreeClassifier.predict | (self, vector) |
Function which must be used after the the fitting of the binary tree. It calls recursively the different
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to classify the vector.
Args:
vector(:class:`torch.FloatTensor`): Vectors tensor which must be classified. It represents the ... |
Function which must be used after the the fitting of the binary tree. It calls recursively the different
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to classify the vector. | def predict(self, vector):
"""
Function which must be used after the the fitting of the binary tree. It calls recursively the different
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vector(:class:`torch.FloatTensor`): Vectors tensor which ... | [
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96,
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TorchDecisionTreeClassifier._classify | (self, vector, node) |
Private recursive function used to classify with the tree.
|
Private recursive function used to classify with the tree.
| def _classify(self, vector, node):
"""
Private recursive function used to classify with the tree.
"""
if node.results is not None:
return list(node.results.keys())[0]
else:
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TorchDecisionTreeRegressor.fit | (self, vectors, values, criterion=None) |
Function which must be used after the initialisation to fit the binary tree and build the successive
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to solve a specific regression problem.
Args:
vectors(:class:`torch.FloatTensor`): Vectors tensor used to fit the decision tree. ... |
Function which must be used after the initialisation to fit the binary tree and build the successive
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to solve a specific regression problem. | def fit(self, vectors, values, criterion=None):
"""
Function which must be used after the initialisation to fit the binary tree and build the successive
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TorchDecisionTreeRegressor._build_tree | (self, vectors, values, func, depth) |
Private recursive function used to build the tree.
|
Private recursive function used to build the tree.
| def _build_tree(self, vectors, values, func, depth):
"""
Private recursive function used to build the tree.
"""
if len(vectors) == 0:
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TorchDecisionTreeRegressor.predict | (self, vector) |
Function which must be used after the the fitting of the binary tree. It calls recursively the different
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to regress the vector.
Args:
vector(:class:`torch.FloatTensor`): Vectors tensor which must be regressed. It represents the da... |
Function which must be used after the the fitting of the binary tree. It calls recursively the different
:class:`Sklearn_PyTorch.decision_node.DecisionNode` to regress the vector. | def predict(self, vector):
"""
Function which must be used after the the fitting of the binary tree. It calls recursively the different
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TorchDecisionTreeRegressor._regress | (self, vector, node) |
Private recursive function used to regress on the tree.
|
Private recursive function used to regress on the tree.
| def _regress(self, vector, node):
"""
Private recursive function used to regress on the tree.
"""
if node.results is not None:
return node.results
else:
if split_function(vector, node.col, node.value):
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217,
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WorkflowView.get_initial | (self) | Returns initial data for the workflow.
Defaults to using the GET parameters
to allow pre-seeding of the workflow context values.
| Returns initial data for the workflow. | def get_initial(self):
"""Returns initial data for the workflow.
Defaults to using the GET parameters
to allow pre-seeding of the workflow context values.
"""
return copy.copy(self.request.GET) | [
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WorkflowView.get_workflow | (self) | Returns the instantiated workflow class. | Returns the instantiated workflow class. | def get_workflow(self):
"""Returns the instantiated workflow class."""
extra_context = self.get_initial()
entry_point = self.request.GET.get("step", None)
workflow = self.workflow_class(self.request,
context_seed=extra_context,
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WorkflowView.get_context_data | (self, **kwargs) | Returns the template context, including the workflow class.
This method should be overridden in subclasses to provide additional
context data to the template.
| Returns the template context, including the workflow class. | def get_context_data(self, **kwargs):
"""Returns the template context, including the workflow class.
This method should be overridden in subclasses to provide additional
context data to the template.
"""
context = super(WorkflowView, self).get_context_data(**kwargs)
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WorkflowView.get_layout | (self) | Returns classes for the workflow element in template.
The returned classes are determied based on
the workflow characteristics.
| Returns classes for the workflow element in template. | def get_layout(self):
"""Returns classes for the workflow element in template.
The returned classes are determied based on
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"""
if self.request.is_ajax():
layout = ['modal', ]
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layout = ['static_page', ]
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WorkflowView.get_template_names | (self) | Returns the template name to use for this request. | Returns the template name to use for this request. | def get_template_names(self):
"""Returns the template name to use for this request."""
if self.request.is_ajax():
template = self.ajax_template_name
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WorkflowView.get | (self, request, *args, **kwargs) | Handler for HTTP GET requests. | Handler for HTTP GET requests. | def get(self, request, *args, **kwargs):
"""Handler for HTTP GET requests."""
try:
context = self.get_context_data(**kwargs)
except exceptions.NotAvailable:
exceptions.handle(request)
self.set_workflow_step_errors(context)
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WorkflowView.validate_steps | (self, request, workflow, start, end) | Validates the workflow steps from ``start`` to ``end``, inclusive.
Returns a dict describing the validation state of the workflow.
| Validates the workflow steps from ``start`` to ``end``, inclusive. | def validate_steps(self, request, workflow, start, end):
"""Validates the workflow steps from ``start`` to ``end``, inclusive.
Returns a dict describing the validation state of the workflow.
"""
errors = {}
for step in workflow.steps[start:end + 1]:
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WorkflowView.post | (self, request, *args, **kwargs) | Handler for HTTP POST requests. | Handler for HTTP POST requests. | def post(self, request, *args, **kwargs):
"""Handler for HTTP POST requests."""
context = self.get_context_data(**kwargs)
workflow = context[self.context_object_name]
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MetadatadefinitionsPage.json_load_template | (self, namespace_template_name) | Read template for namespace creation
:param namespace_template_name: Path to template
:return = json data container
| Read template for namespace creation | def json_load_template(self, namespace_template_name):
"""Read template for namespace creation
:param namespace_template_name: Path to template
:return = json data container
"""
try:
with open(namespace_template_name, 'r') as template:
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UtilsTestCase.test_time_plus_minutes | (self) |
Assert time_plus_minutes helper functions returns correct
result of time + minutes calculation.
|
Assert time_plus_minutes helper functions returns correct
result of time + minutes calculation.
| def test_time_plus_minutes(self):
"""
Assert time_plus_minutes helper functions returns correct
result of time + minutes calculation.
"""
time = dt.time()
self.assertEqual(time, time_plus_minutes(time, 0))
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StudentTestCase.test_student_id_validation | (self) |
Assert that ValidationError is raised when student has incorrect student id number set.
|
Assert that ValidationError is raised when student has incorrect student id number set.
| def test_student_id_validation(self):
"""
Assert that ValidationError is raised when student has incorrect student id number set.
"""
with self.assertRaises(ValidationError):
student = Student.objects.create(
account=self.student,
student_id="1... | [
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StudentTestCase.test_student_user_type_validation | (self) |
Assert that ValidationError is raised when Student account
has user_type different than student.
|
Assert that ValidationError is raised when Student account
has user_type different than student.
| def test_student_user_type_validation(self):
"""
Assert that ValidationError is raised when Student account
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"""
Student.objects.create(
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LecturerTestCase.test_lecturer_account_validator | (self) |
Assert that ValidationError is raised when Lecturer account
has user_type different than teacher.
|
Assert that ValidationError is raised when Lecturer account
has user_type different than teacher.
| def test_lecturer_account_validator(self):
"""
Assert that ValidationError is raised when Lecturer account
has user_type different than teacher.
"""
Lecturer.objects.create(account=self.teacher).clean()
with self.assertRaises(ValidationError):
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EnrollmentTestCase.test_student_typing | (self) |
Assert that ValidationError is raised when Enrollment is linked to user other than student.
|
Assert that ValidationError is raised when Enrollment is linked to user other than student.
| def test_student_typing(self):
"""
Assert that ValidationError is raised when Enrollment is linked to user other than student.
"""
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ClassTimeCase.test_end_property | (self) |
Assert that ClassTime.end property returns correct end time
(ClassTime.start + ClassTime.duration_minutes).
|
Assert that ClassTime.end property returns correct end time
(ClassTime.start + ClassTime.duration_minutes).
| def test_end_property(self):
"""
Assert that ClassTime.end property returns correct end time
(ClassTime.start + ClassTime.duration_minutes).
"""
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AnsiToWin32.should_wrap | (self) |
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AnsiToWin32.write_and_convert | (self, text) |
Write the given text to our wrapped stream, stripping any ANSI
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main | (args=None, plugins=None) | return exit code, after performing an in-process test run.
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get_plugin_manager | () |
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This function can be used by integration with other tools, like hooking
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_get_plugin_specs_as_list | (specs) |
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which case it is returned as a list. Specs can also be `None` in which case an
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getcfg | (args, warnfunc=None) |
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note: warnfunc is an optional function used to warn
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Search the list of arguments for a valid ini-file for pytest,
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create_terminal_writer | (config, *args, **kwargs) | Create a TerminalWriter instance configured according to the options
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PytestPluginManager.addhooks | (self, module_or_class) |
.. deprecated:: 2.8
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|
.. deprecated:: 2.8 | def addhooks(self, module_or_class):
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PytestPluginManager.hasplugin | (self, name) | Return True if the plugin with the given name is registered. | Return True if the plugin with the given name is registered. | def hasplugin(self, name):
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PytestPluginManager._set_initial_conftests | (self, namespace) | load initial conftest files given a preparsed "namespace".
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Parser.getgroup | (self, name, description="", after=None) | get (or create) a named option Group.
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MyOptionParser.parse_args | (self, args=None, namespace=None) | allow splitting of positional arguments | allow splitting of positional arguments | def parse_args(self, args=None, namespace=None):
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Config.add_cleanup | (self, func) | Add a function to be called when the config object gets out of
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Config.warn | (self, code, message, fslocation=None, nodeid=None) | generate a warning for this test session. | generate a warning for this test session. | def warn(self, code, message, fslocation=None, nodeid=None):
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Config.fromdictargs | (cls, option_dict, args) | constructor useable for subprocesses. | constructor useable for subprocesses. | def fromdictargs(cls, option_dict, args):
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Config._consider_importhook | (self, args) | Install the PEP 302 import hook if using assertion rewriting.
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Config._mark_plugins_for_rewrite | (self, hook) |
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Config.addinivalue_line | (self, name, line) | add a line to an ini-file option. The option must have been
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Config.getini | (self, name) | return configuration value from an :ref:`ini file <inifiles>`. If the
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Config.getoption | (self, name, default=notset, skip=False) | return command line option value.
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Config.getvalue | (self, name, path=None) | (deprecated, use getoption()) | (deprecated, use getoption()) | def getvalue(self, name, path=None):
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Config.getvalueorskip | (self, name, path=None) | (deprecated, use getoption(skip=True)) | (deprecated, use getoption(skip=True)) | def getvalueorskip(self, name, path=None):
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glibc_version_string | () | Returns glibc version string, or None if not using glibc. | Returns glibc version string, or None if not using glibc. | def glibc_version_string():
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libc_ver | () | Try to determine the glibc version
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AttrList.__init__ | (self, attributes) |
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SolanoHookTests.test_solano_message_001 | (self) |
Build notifications are generated by Solano Labs after build completes.
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K2KAuthPlugin.get_plugin | (self, service_provider=None, auth_url=None, plugins=None,
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ScaledDotProductAttention.__init__ | (self, d_model, d_k, d_v, h) |
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
:param d_v: Dimensionality of values
:param h: Number of heads
|
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
:param d_v: Dimensionality of values
:param h: Number of heads
| def __init__(self, d_model, d_k, d_v, h):
'''
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
:param d_v: Dimensionality of values
:param h: Number of heads
'''
super(ScaledDotProductAttention, self).__init__()
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ScaledDotProductAttention.forward | (self, queries, keys, values, attention_mask=None, attention_weights=None) |
Computes
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:param attention_mask: Mask over attention values (b_s, h, nq, nk). True indicates masking.
:param attention_weights: Multiplicative weights ... |
Computes
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:param keys: Keys (b_s, nk, d_model)
:param values: Values (b_s, nk, d_model)
:param attention_mask: Mask over attention values (b_s, h, nq, nk). True indicates masking.
:param attention_weights: Multiplicative weights ... | def forward(self, queries, keys, values, attention_mask=None, attention_weights=None):
'''
Computes
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:param keys: Keys (b_s, nk, d_model)
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ScaledDotProductAttentionMemory.__init__ | (self, d_model, d_k, d_v, h, m) |
:param d_model: Output dimensionality of the model
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:param d_v: Dimensionality of values
:param h: Number of heads
:param m: Number of memory slots
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:param d_model: Output dimensionality of the model
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ScaledDotProductAttentionMemory.forward | (self, queries, keys, values, attention_mask=None, attention_weights=None) |
Computes
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:param attention_mask: Mask over attention values (b_s, h, nq, nk). True indicates masking.
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:param values: Values (b_s, nk, d_model)
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:param attention_weights: Multiplicative weights ... | def forward(self, queries, keys, values, attention_mask=None, attention_weights=None):
'''
Computes
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test_zipimport_hook | (testdir, tmpdir) | Test package loader is being used correctly (see #1837). | Test package loader is being used correctly (see #1837). | def test_zipimport_hook(testdir, tmpdir):
"""Test package loader is being used correctly (see #1837)."""
zipapp = pytest.importorskip('zipapp')
testdir.tmpdir.join('app').ensure(dir=1)
testdir.makepyfile(**{
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import pytest
def main():
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test_deferred_hook_checking | (testdir) |
Check hooks as late as possible (#1821).
|
Check hooks as late as possible (#1821).
| def test_deferred_hook_checking(testdir):
"""
Check hooks as late as possible (#1821).
"""
testdir.syspathinsert()
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class Hooks(object):
def pytest_my_hook(self, config):
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test_fixture_values_leak | (testdir) | Ensure that fixture objects are properly destroyed by the garbage collector at the end of their expected
life-times (#2981).
| Ensure that fixture objects are properly destroyed by the garbage collector at the end of their expected
life-times (#2981).
| def test_fixture_values_leak(testdir):
"""Ensure that fixture objects are properly destroyed by the garbage collector at the end of their expected
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testdir.makepyfile("""
import attr
import gc
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TestGeneralUsage.test_namespace_import_doesnt_confuse_import_hook | (self, testdir) |
Ref #383. Python 3.3's namespace package messed with our import hooks
Importing a module that didn't exist, even if the ImportError was
gracefully handled, would make our test crash.
Use recwarn here to silence this warning in Python 2.7:
ImportWarning: Not importing direct... |
Ref #383. Python 3.3's namespace package messed with our import hooks
Importing a module that didn't exist, even if the ImportError was
gracefully handled, would make our test crash. | def test_namespace_import_doesnt_confuse_import_hook(self, testdir):
"""
Ref #383. Python 3.3's namespace package messed with our import hooks
Importing a module that didn't exist, even if the ImportError was
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TestGeneralUsage.test_plugins_given_as_strings | (self, tmpdir, monkeypatch) | test that str values passed to main() as `plugins` arg
are interpreted as module names to be imported and registered.
#855.
| test that str values passed to main() as `plugins` arg
are interpreted as module names to be imported and registered.
#855.
| def test_plugins_given_as_strings(self, tmpdir, monkeypatch):
"""test that str values passed to main() as `plugins` arg
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#855.
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TestGeneralUsage.test_parametrized_with_null_bytes | (self, testdir) | Test parametrization with values that contain null bytes and unicode characters (#2644, #2957) | Test parametrization with values that contain null bytes and unicode characters (#2644, #2957) | def test_parametrized_with_null_bytes(self, testdir):
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p = testdir.makepyfile(u"""
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import pytest
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TestInvocationVariants.test_cmdline_python_namespace_package | (self, testdir, monkeypatch) |
test --pyargs option with namespace packages (#1567)
|
test --pyargs option with namespace packages (#1567)
| def test_cmdline_python_namespace_package(self, testdir, monkeypatch):
"""
test --pyargs option with namespace packages (#1567)
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TestInvocationVariants.test_cmdline_python_package_symlink | (self, testdir, monkeypatch) |
test --pyargs option with packages with path containing symlink can
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|
test --pyargs option with packages with path containing symlink can
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TestInvocationVariants.test_core_backward_compatibility | (self) | Test backward compatibility for get_plugin_manager function. See #787. | Test backward compatibility for get_plugin_manager function. See #787. | def test_core_backward_compatibility(self):
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TestInvocationVariants.test_has_plugin | (self, request) | Test hasplugin function of the plugin manager (#932). | Test hasplugin function of the plugin manager (#932). | def test_has_plugin(self, request):
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start_new_thread | (function, args, kwargs={}) | Dummy implementation of _thread.start_new_thread().
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exit | () | Dummy implementation of _thread.exit(). | Dummy implementation of _thread.exit(). | def exit():
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get_ident | () | Dummy implementation of _thread.get_ident().
Since this module should only be used when _threadmodule is not
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| Dummy implementation of _thread.get_ident(). | def get_ident():
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allocate_lock | () | Dummy implementation of _thread.allocate_lock(). | Dummy implementation of _thread.allocate_lock(). | def allocate_lock():
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stack_size | (size=None) | Dummy implementation of _thread.stack_size(). | Dummy implementation of _thread.stack_size(). | def stack_size(size=None):
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_set_sentinel | () | Dummy implementation of _thread._set_sentinel(). | Dummy implementation of _thread._set_sentinel(). | def _set_sentinel():
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interrupt_main | () | Set _interrupt flag to True to have start_new_thread raise
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LockType.acquire | (self, waitflag=None, timeout=-1) | Dummy implementation of acquire().
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LockType.release | (self) | Release the dummy lock. | Release the dummy lock. | def release(self):
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precook | (s, n=4) |
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cook_refs | (refs, n=4) | Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: re... | Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: re... | def cook_refs(refs, n=4): ## lhuang: oracle will call with "average"
'''Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : num... | [
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] | python | en | ['en', 'en', 'en'] | True |
cook_test | (test, n=4) | Takes a test sentence and returns an object that
encapsulates everything that BLEU needs to know about it.
:param test: list of string : hypothesis sentence for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (dict)
| Takes a test sentence and returns an object that
encapsulates everything that BLEU needs to know about it.
:param test: list of string : hypothesis sentence for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (dict)
| def cook_test(test, n=4):
'''Takes a test sentence and returns an object that
encapsulates everything that BLEU needs to know about it.
:param test: list of string : hypothesis sentence for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (di... | [
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43,
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] | python | en | ['en', 'en', 'en'] | True |
CiderScorer.__init__ | (self, refs, test=None, n=4, sigma=6.0, doc_frequency=None, ref_len=None) | singular instance | singular instance | def __init__(self, refs, test=None, n=4, sigma=6.0, doc_frequency=None, ref_len=None):
''' singular instance '''
self.n = n
self.sigma = sigma
self.crefs = []
self.ctest = []
self.doc_frequency = defaultdict(float)
self.ref_len = None
for k in refs.keys()... | [
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49,
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] | [
72,
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] | python | en | ['en', 'de', 'en'] | False |
CiderScorer.compute_doc_freq | (self) |
Compute term frequency for reference data.
This will be used to compute idf (inverse document frequency later)
The term frequency is stored in the object
:return: None
|
Compute term frequency for reference data.
This will be used to compute idf (inverse document frequency later)
The term frequency is stored in the object
:return: None
| def compute_doc_freq(self):
'''
Compute term frequency for reference data.
This will be used to compute idf (inverse document frequency later)
The term frequency is stored in the object
:return: None
'''
for refs in self.crefs:
# refs, k ref captions o... | [
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] | python | en | ['en', 'error', 'th'] | False |
_current_component | (view_func, dashboard=None, panel=None) | Sets the currently-active dashboard and/or panel on the request. | Sets the currently-active dashboard and/or panel on the request. | def _current_component(view_func, dashboard=None, panel=None):
"""Sets the currently-active dashboard and/or panel on the request."""
@functools.wraps(view_func, assigned=available_attrs(view_func))
def dec(request, *args, **kwargs):
if dashboard:
request.horizon['dashboard'] = dashboard... | [
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] | python | en | ['en', 'en', 'en'] | True |
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