Search is not available for this dataset
identifier
stringlengths 1
155
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stringlengths 2
6.09k
| docstring
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sequence | language
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is_pointer | (type_) | returns True, if type represents C++ pointer type, False otherwise | returns True, if type represents C++ pointer type, False otherwise | def is_pointer(type_):
"""returns True, if type represents C++ pointer type, False otherwise"""
return does_match_definition(type_,
cpptypes.pointer_t,
(cpptypes.const_t, cpptypes.volatile_t)) \
or does_match_definition(type_,
cpptypes.pointer_t,
(cpptypes.volatile_t, cpptypes.const_t)) | [
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is_calldef_pointer | (type_) | returns True, if type represents pointer to free/member function,
False otherwise | returns True, if type represents pointer to free/member function,
False otherwise | def is_calldef_pointer(type_):
"""returns True, if type represents pointer to free/member function,
False otherwise"""
if not is_pointer(type_):
return False
nake_type = remove_alias(type_)
nake_type = remove_cv(nake_type)
return isinstance(nake_type, cpptypes.compound_t) \
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remove_pointer | (type_) | removes pointer from the type definition
If type is not pointer type, it will be returned as is.
| removes pointer from the type definition | def remove_pointer(type_):
"""removes pointer from the type definition
If type is not pointer type, it will be returned as is.
"""
nake_type = remove_alias(type_)
if not is_pointer(nake_type):
return type_
elif isinstance(nake_type, cpptypes.volatile_t) and \
isinstance(nake_type.base, cpptypes.pointer_t):
return cpptypes.volatile_t(nake_type.base.base)
elif isinstance(nake_type, cpptypes.const_t) and \
isinstance(nake_type.base, cpptypes.pointer_t):
return cpptypes.const_t(nake_type.base.base)
elif isinstance(nake_type, cpptypes.volatile_t) \
and isinstance(nake_type.base, cpptypes.const_t) \
and isinstance(nake_type.base.base, cpptypes.pointer_t):
return (
cpptypes.volatile_t(cpptypes.const_t(nake_type.base.base.base))
)
else:
return nake_type.base | [
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is_reference | (type_) | returns True, if type represents C++ reference type, False otherwise | returns True, if type represents C++ reference type, False otherwise | def is_reference(type_):
"""returns True, if type represents C++ reference type, False otherwise"""
nake_type = remove_alias(type_)
return isinstance(nake_type, cpptypes.reference_t) | [
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is_array | (type_) | returns True, if type represents C++ array type, False otherwise | returns True, if type represents C++ array type, False otherwise | def is_array(type_):
"""returns True, if type represents C++ array type, False otherwise"""
nake_type = remove_alias(type_)
nake_type = remove_reference(nake_type)
nake_type = remove_cv(nake_type)
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array_size | (type_) | returns array size | returns array size | def array_size(type_):
"""returns array size"""
nake_type = remove_alias(type_)
nake_type = remove_reference(nake_type)
nake_type = remove_cv(nake_type)
assert isinstance(nake_type, cpptypes.array_t)
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array_item_type | (type_) | returns array item type | returns array item type | def array_item_type(type_):
"""returns array item type"""
if is_array(type_):
type_ = remove_alias(type_)
type_ = remove_cv(type_)
return type_.base
elif is_pointer(type_):
return remove_pointer(type_)
else:
raise RuntimeError(
"array_item_type functions takes as argument array or pointer " +
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remove_reference | (type_) | removes reference from the type definition
If type is not reference type, it will be returned as is.
| removes reference from the type definition | def remove_reference(type_):
"""removes reference from the type definition
If type is not reference type, it will be returned as is.
"""
nake_type = remove_alias(type_)
if not is_reference(nake_type):
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else:
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is_const | (type_) | returns True, if type represents C++ const type, False otherwise | returns True, if type represents C++ const type, False otherwise | def is_const(type_):
"""returns True, if type represents C++ const type, False otherwise"""
nake_type = remove_alias(type_)
if isinstance(nake_type, cpptypes.const_t):
return True
elif isinstance(nake_type, cpptypes.volatile_t):
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elif isinstance(nake_type, cpptypes.array_t):
return is_const(nake_type.base)
return False | [
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remove_const | (type_) | removes const from the type definition
If type is not const type, it will be returned as is
| removes const from the type definition | def remove_const(type_):
"""removes const from the type definition
If type is not const type, it will be returned as is
"""
nake_type = remove_alias(type_)
if not is_const(nake_type):
return type_
else:
# Handling for const and volatile qualified types. There is a
# difference in behavior between GCCXML and CastXML for cv-qual arrays.
# GCCXML produces the following nesting of types:
# -> volatile_t(const_t(array_t))
# while CastXML produces the following nesting:
# -> array_t(volatile_t(const_t))
# For both cases, we must unwrap the types, remove const_t, and add
# back the outer layers
if isinstance(nake_type, cpptypes.array_t):
is_v = is_volatile(nake_type)
if is_v:
result_type = nake_type.base.base.base
else:
result_type = nake_type.base.base
if is_v:
result_type = cpptypes.volatile_t(result_type)
return cpptypes.array_t(result_type, nake_type.size)
elif isinstance(nake_type, cpptypes.volatile_t):
return cpptypes.volatile_t(nake_type.base.base)
return nake_type.base | [
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remove_declarated | (type_) | removes type-declaration class-binder :class:`declarated_t` from
the `type_`
If `type_` is not :class:`declarated_t`, it will be returned as is
| removes type-declaration class-binder :class:`declarated_t` from
the `type_` | def remove_declarated(type_):
"""removes type-declaration class-binder :class:`declarated_t` from
the `type_`
If `type_` is not :class:`declarated_t`, it will be returned as is
"""
type_ = remove_alias(type_)
if isinstance(type_, cpptypes.elaborated_t):
type_ = type_.base
if isinstance(type_, cpptypes.declarated_t):
type_ = type_.declaration
return type_ | [
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is_same | (type1, type2) | returns True, if type1 and type2 are same types | returns True, if type1 and type2 are same types | def is_same(type1, type2):
"""returns True, if type1 and type2 are same types"""
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nake_type2 = remove_declarated(type2)
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is_elaborated | (type_) | returns True, if type represents C++ elaborated type, False otherwise | returns True, if type represents C++ elaborated type, False otherwise | def is_elaborated(type_):
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nake_type = remove_alias(type_)
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remove_elaborated | (type_) | removes type-declaration class-binder :class:`elaborated_t` from
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If `type_` is not :class:`elaborated_t`, it will be returned as is
| removes type-declaration class-binder :class:`elaborated_t` from
the `type_` | def remove_elaborated(type_):
"""removes type-declaration class-binder :class:`elaborated_t` from
the `type_`
If `type_` is not :class:`elaborated_t`, it will be returned as is
"""
nake_type = remove_alias(type_)
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is_volatile | (type_) | returns True, if type represents C++ volatile type, False otherwise | returns True, if type represents C++ volatile type, False otherwise | def is_volatile(type_):
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nake_type = remove_alias(type_)
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] | python | en | ['en', 'sr', 'en'] | True |
remove_volatile | (type_) | removes volatile from the type definition
If type is not volatile type, it will be returned as is
| removes volatile from the type definition | def remove_volatile(type_):
"""removes volatile from the type definition
If type is not volatile type, it will be returned as is
"""
nake_type = remove_alias(type_)
if not is_volatile(nake_type):
return type_
else:
if isinstance(nake_type, cpptypes.array_t):
is_c = is_const(nake_type)
if is_c:
base_type_ = nake_type.base.base.base
else:
base_type_ = nake_type.base.base
result_type = base_type_
if is_c:
result_type = cpptypes.const_t(result_type)
return cpptypes.array_t(result_type, nake_type.size)
return nake_type.base | [
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remove_cv | (type_) | removes const and volatile from the type definition | removes const and volatile from the type definition | def remove_cv(type_):
"""removes const and volatile from the type definition"""
nake_type = remove_alias(type_)
if not is_const(nake_type) and not is_volatile(nake_type):
return type_
result = nake_type
if is_const(result):
result = remove_const(result)
if is_volatile(result):
result = remove_volatile(result)
if is_const(result):
result = remove_const(result)
return result | [
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468,
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is_fundamental | (type_) | returns True, if type represents C++ fundamental type | returns True, if type represents C++ fundamental type | def is_fundamental(type_):
"""returns True, if type represents C++ fundamental type"""
return does_match_definition(
type_,
cpptypes.fundamental_t,
(cpptypes.const_t, cpptypes.volatile_t)) \
or does_match_definition(
type_,
cpptypes.fundamental_t,
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is_std_string | (type_) |
Returns True, if type represents C++ `std::string`, False otherwise.
|
Returns True, if type represents C++ `std::string`, False otherwise. | def is_std_string(type_):
"""
Returns True, if type represents C++ `std::string`, False otherwise.
"""
if utils.is_str(type_):
return type_ in string_equivalences
else:
type_ = remove_alias(type_)
type_ = remove_reference(type_)
type_ = remove_cv(type_)
return type_.decl_string in string_equivalences | [
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is_std_wstring | (type_) |
Returns True, if type represents C++ `std::wstring`, False otherwise.
|
Returns True, if type represents C++ `std::wstring`, False otherwise. | def is_std_wstring(type_):
"""
Returns True, if type represents C++ `std::wstring`, False otherwise.
"""
if utils.is_str(type_):
return type_ in wstring_equivalences
else:
type_ = remove_alias(type_)
type_ = remove_reference(type_)
type_ = remove_cv(type_)
return type_.decl_string in wstring_equivalences | [
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is_std_ostream | (type_) |
Returns True, if type represents C++ std::ostream, False otherwise.
|
Returns True, if type represents C++ std::ostream, False otherwise. | def is_std_ostream(type_):
"""
Returns True, if type represents C++ std::ostream, False otherwise.
"""
if utils.is_str(type_):
return type_ in ostream_equivalences
else:
type_ = remove_alias(type_)
type_ = remove_reference(type_)
type_ = remove_cv(type_)
return type_.decl_string in ostream_equivalences | [
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is_std_wostream | (type_) |
Returns True, if type represents C++ std::wostream, False otherwise.
|
Returns True, if type represents C++ std::wostream, False otherwise. | def is_std_wostream(type_):
"""
Returns True, if type represents C++ std::wostream, False otherwise.
"""
if utils.is_str(type_):
return type_ in wostream_equivalences
else:
type_ = remove_alias(type_)
type_ = remove_reference(type_)
type_ = remove_cv(type_)
return type_.decl_string in wostream_equivalences | [
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FortranFixedLexer._lex_fortran | (self, match, ctx=None) | Lex a line just as free form fortran without line break. | Lex a line just as free form fortran without line break. | def _lex_fortran(self, match, ctx=None):
"""Lex a line just as free form fortran without line break."""
lexer = FortranLexer()
text = match.group(0) + "\n"
for index, token, value in lexer.get_tokens_unprocessed(text):
value = value.replace('\n', '')
if value != '':
yield index, token, value | [
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176,
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] | [
183,
41
] | python | en | ['en', 'en', 'en'] | True |
registo | (request) |
Apresenta o formulário de registo ao utilizador.
Se o método for POST, cria o objeto Utilizador com os dados do formulário. Caso
seja outro, devolve o formulário sem dados introduzidos.
|
Apresenta o formulário de registo ao utilizador.
Se o método for POST, cria o objeto Utilizador com os dados do formulário. Caso
seja outro, devolve o formulário sem dados introduzidos.
| def registo(request):
"""
Apresenta o formulário de registo ao utilizador.
Se o método for POST, cria o objeto Utilizador com os dados do formulário. Caso
seja outro, devolve o formulário sem dados introduzidos.
"""
if request.method == 'POST':
form = UtilizadorRegistoForm(request.POST)
if form.is_valid():
form.save()
username = form.cleaned_data.get('username')
messages.success(
request, f'A sua conta foi criada com sucesso. Pode começar a utilizar o mySchool!')
return redirect('app-login')
else:
form = UtilizadorRegistoForm()
return render(request, 'utilizadores/registo.html', {'form': form}) | [
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] | python | en | ['en', 'ja', 'th'] | False |
perfil | (request) |
Apresenta o perfil ao utilizador, dentro da aplicação.
Se o método for POST, atualiza o objeto Perfil com os dados do formulário. Caso
seja outro, devolve o formulário sem dados introduzidos.
|
Apresenta o perfil ao utilizador, dentro da aplicação.
Se o método for POST, atualiza o objeto Perfil com os dados do formulário. Caso
seja outro, devolve o formulário sem dados introduzidos.
| def perfil(request):
"""
Apresenta o perfil ao utilizador, dentro da aplicação.
Se o método for POST, atualiza o objeto Perfil com os dados do formulário. Caso
seja outro, devolve o formulário sem dados introduzidos.
"""
if request.method == 'POST':
u_form = UtilizadorUpdateForm(request.POST, instance=request.user)
if u_form.is_valid():
u_form.save()
messages.success(
request, f'A sua conta foi atualizada!')
return redirect('app-perfil')
else:
u_form = UtilizadorUpdateForm(instance=request.user)
context = {
'u_form': u_form,
}
return render(request, 'utilizadores/perfil.html', context) | [
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RexxLexer.analyse_text | (text) |
Check for inital comment and patterns that distinguish Rexx from other
C-like languages.
|
Check for inital comment and patterns that distinguish Rexx from other
C-like languages.
| def analyse_text(text):
"""
Check for inital comment and patterns that distinguish Rexx from other
C-like languages.
"""
if re.search(r'/\*\**\s*rexx', text, re.IGNORECASE):
# Header matches MVS Rexx requirements, this is certainly a Rexx
# script.
return 1.0
elif text.startswith('/*'):
# Header matches general Rexx requirements; the source code might
# still be any language using C comments such as C++, C# or Java.
lowerText = text.lower()
result = sum(weight
for (pattern, weight) in RexxLexer.PATTERNS_AND_WEIGHTS
if pattern.search(lowerText)) + 0.01
return min(result, 1.0) | [
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EasytrieveLexer.analyse_text | (text) |
Perform a structural analysis for basic Easytrieve constructs.
|
Perform a structural analysis for basic Easytrieve constructs.
| def analyse_text(text):
"""
Perform a structural analysis for basic Easytrieve constructs.
"""
result = 0.0
lines = text.split('\n')
hasEndProc = False
hasHeaderComment = False
hasFile = False
hasJob = False
hasProc = False
hasParm = False
hasReport = False
def isCommentLine(line):
return EasytrieveLexer._COMMENT_LINE_REGEX.match(lines[0]) is not None
def isEmptyLine(line):
return not bool(line.strip())
# Remove possible empty lines and header comments.
while lines and (isEmptyLine(lines[0]) or isCommentLine(lines[0])):
if not isEmptyLine(lines[0]):
hasHeaderComment = True
del lines[0]
if EasytrieveLexer._MACRO_HEADER_REGEX.match(lines[0]):
# Looks like an Easytrieve macro.
result = 0.4
if hasHeaderComment:
result += 0.4
else:
# Scan the source for lines starting with indicators.
for line in lines:
words = line.split()
if (len(words) >= 2):
firstWord = words[0]
if not hasReport:
if not hasJob:
if not hasFile:
if not hasParm:
if firstWord == 'PARM':
hasParm = True
if firstWord == 'FILE':
hasFile = True
if firstWord == 'JOB':
hasJob = True
elif firstWord == 'PROC':
hasProc = True
elif firstWord == 'END-PROC':
hasEndProc = True
elif firstWord == 'REPORT':
hasReport = True
# Weight the findings.
if hasJob and (hasProc == hasEndProc):
if hasHeaderComment:
result += 0.1
if hasParm:
if hasProc:
# Found PARM, JOB and PROC/END-PROC:
# pretty sure this is Easytrieve.
result += 0.8
else:
# Found PARAM and JOB: probably this is Easytrieve
result += 0.5
else:
# Found JOB and possibly other keywords: might be Easytrieve
result += 0.11
if hasParm:
# Note: PARAM is not a proper English word, so this is
# regarded a much better indicator for Easytrieve than
# the other words.
result += 0.2
if hasFile:
result += 0.01
if hasReport:
result += 0.01
assert 0.0 <= result <= 1.0
return result | [
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1119,
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JclLexer.analyse_text | (text) |
Recognize JCL job by header.
|
Recognize JCL job by header.
| def analyse_text(text):
"""
Recognize JCL job by header.
"""
result = 0.0
lines = text.split('\n')
if len(lines) > 0:
if JclLexer._JOB_HEADER_PATTERN.match(lines[0]):
result = 1.0
assert 0.0 <= result <= 1.0
return result | [
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ColumnDistinctValues._get_evaluation_dependencies | (
cls,
metric: MetricConfiguration,
configuration: Optional[ExpectationConfiguration] = None,
execution_engine: Optional[ExecutionEngine] = None,
runtime_configuration: Optional[Dict] = None,
) | Returns a dictionary of given metric names and their corresponding configuration,
specifying the metric types and their respective domains | Returns a dictionary of given metric names and their corresponding configuration,
specifying the metric types and their respective domains | def _get_evaluation_dependencies(
cls,
metric: MetricConfiguration,
configuration: Optional[ExpectationConfiguration] = None,
execution_engine: Optional[ExecutionEngine] = None,
runtime_configuration: Optional[Dict] = None,
):
"""Returns a dictionary of given metric names and their corresponding configuration,
specifying the metric types and their respective domains"""
dependencies: dict = super()._get_evaluation_dependencies(
metric=metric,
configuration=configuration,
execution_engine=execution_engine,
runtime_configuration=runtime_configuration,
)
if isinstance(
execution_engine, (SqlAlchemyExecutionEngine, SparkDFExecutionEngine)
):
dependencies["column.value_counts"] = MetricConfiguration(
metric_name="column.value_counts",
metric_domain_kwargs=metric.metric_domain_kwargs,
metric_value_kwargs={
"sort": "value",
"collate": None,
},
)
return dependencies | [
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ColumnDistinctValuesCount._get_evaluation_dependencies | (
cls,
metric: MetricConfiguration,
configuration: Optional[ExpectationConfiguration] = None,
execution_engine: Optional[ExecutionEngine] = None,
runtime_configuration: Optional[Dict] = None,
) | Returns a dictionary of given metric names and their corresponding configuration,
specifying the metric types and their respective domains | Returns a dictionary of given metric names and their corresponding configuration,
specifying the metric types and their respective domains | def _get_evaluation_dependencies(
cls,
metric: MetricConfiguration,
configuration: Optional[ExpectationConfiguration] = None,
execution_engine: Optional[ExecutionEngine] = None,
runtime_configuration: Optional[Dict] = None,
):
"""Returns a dictionary of given metric names and their corresponding configuration,
specifying the metric types and their respective domains"""
dependencies: dict = super()._get_evaluation_dependencies(
metric=metric,
configuration=configuration,
execution_engine=execution_engine,
runtime_configuration=runtime_configuration,
)
if isinstance(
execution_engine, (SqlAlchemyExecutionEngine, SparkDFExecutionEngine)
):
dependencies["column.value_counts"] = MetricConfiguration(
metric_name="column.value_counts",
metric_domain_kwargs=metric.metric_domain_kwargs,
metric_value_kwargs={
"sort": "value",
"collate": None,
},
)
return dependencies | [
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ExpectColumnMedianToBeBetween.validate_configuration | (self, configuration: Optional[ExpectationConfiguration]) |
Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
necessary configuration arguments have been provided for the validation of the expectation.
Args:
configuration (OPTIONAL[ExpectationConfiguration]): \
An optional Expectation Configuration entry that will be used to configure the expectation
Returns:
True if the configuration has been validated successfully. Otherwise, raises an exception
|
Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
necessary configuration arguments have been provided for the validation of the expectation. | def validate_configuration(self, configuration: Optional[ExpectationConfiguration]):
"""
Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
necessary configuration arguments have been provided for the validation of the expectation.
Args:
configuration (OPTIONAL[ExpectationConfiguration]): \
An optional Expectation Configuration entry that will be used to configure the expectation
Returns:
True if the configuration has been validated successfully. Otherwise, raises an exception
"""
super().validate_configuration(configuration)
self.validate_metric_value_between_configuration(configuration=configuration) | [
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_f | (x, with_defaults) |
A small helper function.
|
A small helper function. | def _f(x, with_defaults):
"""
A small helper function.
"""
return x.build_decl_string(with_defaults) | [
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type_t.clone | (self) | returns new instance of the type | returns new instance of the type | def clone(self):
"""returns new instance of the type"""
answer = self._clone_impl()
return answer | [
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compound_t.base | (self) | reference to internal/base class | reference to internal/base class | def base(self):
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array_t.size | (self) | returns array size | returns array size | def size(self):
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return self._size | [
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array_t.size | (self, size) | sometimes there is a need to update the size of the array | sometimes there is a need to update the size of the array | def size(self, size):
"""sometimes there is a need to update the size of the array"""
self.cache.reset()
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calldef_type_t.return_type | (self) | reference to :class:`return type <type_t>` | reference to :class:`return type <type_t>` | def return_type(self):
"""reference to :class:`return type <type_t>`"""
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calldef_type_t.arguments_types | (self) | list of argument :class:`types <type_t>` | list of argument :class:`types <type_t>` | def arguments_types(self):
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free_function_type_t.create_decl_string | (
return_type, arguments_types, with_defaults=True) |
Returns free function type
:param return_type: function return type
:type return_type: :class:`type_t`
:param arguments_types: list of argument :class:`type <type_t>`
:rtype: :class:`free_function_type_t`
|
Returns free function type | def create_decl_string(
return_type, arguments_types, with_defaults=True):
"""
Returns free function type
:param return_type: function return type
:type return_type: :class:`type_t`
:param arguments_types: list of argument :class:`type <type_t>`
:rtype: :class:`free_function_type_t`
"""
return free_function_type_t.NAME_TEMPLATE % {
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'arguments': ','.join(
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free_function_type_t.create_typedef | (self, typedef_name, unused=None, with_defaults=True) | returns string, that contains valid C++ code, that defines typedef
to function type
:param name: the desired name of typedef
| returns string, that contains valid C++ code, that defines typedef
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:param name: the desired name of typedef
"""
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member_function_type_t.has_const | (self) | describes, whether function has const modifier | describes, whether function has const modifier | def has_const(self):
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member_function_type_t.class_inst | (self) | reference to parent :class:`class <declaration_t>` | reference to parent :class:`class <declaration_t>` | def class_inst(self):
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member_function_type_t.create_typedef | (
self,
typedef_name,
class_alias=None,
with_defaults=True) | creates typedef to the function type
:param typedef_name: desired type name
:rtype: string
| creates typedef to the function type | def create_typedef(
self,
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:rtype: string
"""
has_const_str = ''
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member_variable_type_t.variable_type | (self) | describes member variable :class:`type <type_t>` | describes member variable :class:`type <type_t>` | def variable_type(self):
"""describes member variable :class:`type <type_t>`"""
return self._mv_type | [
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declarated_t.declaration | (self) | reference to :class:`declaration_t` | reference to :class:`declaration_t` | def declaration(self):
"""reference to :class:`declaration_t`"""
return self._declaration | [
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factorial | (n) | Return the factorial of n, an exact integer >= 0.
If the result is small enough to fit in an int, return an int.
Else return a long.
>>> [factorial(n) for n in range(6)]
[1, 1, 2, 6, 24, 120]
>>> factorial(-1)
Traceback (most recent call last):
...
ValueError: n must be >= 0
Factorials of floats are OK, but the float must be an exact integer:
>>> factorial(30.1)
Traceback (most recent call last):
...
ValueError: n must be exact integer
It must also not be ridiculously large:
>>> factorial(1e100)
Traceback (most recent call last):
...
OverflowError: n too large
| Return the factorial of n, an exact integer >= 0. | def factorial(n):
"""Return the factorial of n, an exact integer >= 0.
If the result is small enough to fit in an int, return an int.
Else return a long.
>>> [factorial(n) for n in range(6)]
[1, 1, 2, 6, 24, 120]
>>> factorial(-1)
Traceback (most recent call last):
...
ValueError: n must be >= 0
Factorials of floats are OK, but the float must be an exact integer:
>>> factorial(30.1)
Traceback (most recent call last):
...
ValueError: n must be exact integer
It must also not be ridiculously large:
>>> factorial(1e100)
Traceback (most recent call last):
...
OverflowError: n too large
"""
import math
if not n >= 0:
raise ValueError("n must be >= 0")
if math.floor(n) != n:
raise ValueError("n must be exact integer")
if n+1 == n: # catch a value like 1e300
raise OverflowError("n too large")
result = 1
factor = 2
while factor <= n:
result *= factor
factor += 1
return result | [
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43,
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Renderer._get_column_list_from_evrs | (cls, evrs) |
Get list of column names.
If expect_table_columns_to_match_ordered_list EVR is present, use it as the list, including the order.
Otherwise, get the list of all columns mentioned in the expectations and order it alphabetically.
:param evrs:
:return: list of columns with best effort sorting
|
Get list of column names. | def _get_column_list_from_evrs(cls, evrs):
"""
Get list of column names.
If expect_table_columns_to_match_ordered_list EVR is present, use it as the list, including the order.
Otherwise, get the list of all columns mentioned in the expectations and order it alphabetically.
:param evrs:
:return: list of columns with best effort sorting
"""
evrs_ = evrs if isinstance(evrs, list) else evrs.results
expect_table_columns_to_match_ordered_list_evr = cls._find_evr_by_type(
evrs_, "expect_table_columns_to_match_ordered_list"
)
# Group EVRs by column
sorted_columns = sorted(
list(
{
evr.expectation_config.kwargs["column"]
for evr in evrs_
if "column" in evr.expectation_config.kwargs
}
)
)
if expect_table_columns_to_match_ordered_list_evr:
ordered_columns = expect_table_columns_to_match_ordered_list_evr.result[
"observed_value"
]
else:
ordered_columns = []
# only return ordered columns from expect_table_columns_to_match_ordered_list evr if they match set of column
# names from entire evr
if set(sorted_columns) == set(ordered_columns):
return ordered_columns
else:
return sorted_columns | [
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Renderer._group_and_order_expectations_by_column | (cls, expectations) | Group expectations by column. | Group expectations by column. | def _group_and_order_expectations_by_column(cls, expectations):
"""Group expectations by column."""
expectations_by_column = {}
ordered_columns = []
for expectation in expectations.expectations:
if "column" in expectation.kwargs:
column = expectation.kwargs["column"]
else:
column = "_nocolumn"
if column not in expectations_by_column:
expectations_by_column[column] = []
expectations_by_column[column].append(expectation)
# if possible, get the order of columns from expect_table_columns_to_match_ordered_list
if (
expectation.expectation_type
== "expect_table_columns_to_match_ordered_list"
):
exp_column_list = expectation.kwargs["column_list"]
if exp_column_list and len(exp_column_list) > 0:
ordered_columns = exp_column_list
# Group items by column
sorted_columns = sorted(list(expectations_by_column.keys()))
# only return ordered columns from expect_table_columns_to_match_ordered_list evr if they match set of column
# names from entire evr, else use alphabetic sort
if set(sorted_columns) == set(ordered_columns):
return expectations_by_column, ordered_columns
else:
return expectations_by_column, sorted_columns | [
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CounterfactualValueEstimator.predict_best | (self) |
Predict the best treatment group based on the highest counterfactual
value for a treatment.
|
Predict the best treatment group based on the highest counterfactual
value for a treatment.
| def predict_best(self):
'''
Predict the best treatment group based on the highest counterfactual
value for a treatment.
'''
self._get_counterfactuals()
self._get_counterfactual_values()
return self.best_treatment | [
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CounterfactualValueEstimator.predict_counterfactuals | (self) |
Predict the counterfactual values for each treatment group.
|
Predict the counterfactual values for each treatment group.
| def predict_counterfactuals(self):
'''
Predict the counterfactual values for each treatment group.
'''
self._get_counterfactuals()
self._get_counterfactual_values()
return self.expected_values | [
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CounterfactualValueEstimator._get_counterfactuals | (self) |
Get an array of counterfactual outcomes based on control outcome and
the array of conditional average treatment effects.
|
Get an array of counterfactual outcomes based on control outcome and
the array of conditional average treatment effects.
| def _get_counterfactuals(self):
'''
Get an array of counterfactual outcomes based on control outcome and
the array of conditional average treatment effects.
'''
conditions = self.treatment_names.copy()
conditions.insert(0, self.control_name)
cates_with_control = np.c_[np.zeros(self.cate.shape[0]), self.cate]
cates_flat = cates_with_control.flatten()
cates_filt = [actual_group == poss_group
for actual_group in self.treatment
for poss_group in conditions]
control_outcome = self.y_proba - cates_flat[cates_filt]
self.counterfactuals = cates_with_control + control_outcome[:, None] | [
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] | python | en | ['en', 'error', 'th'] | False |
CounterfactualValueEstimator._get_counterfactual_values | (self) |
Calculate the expected value of assigning a unit to each of the
treatment conditions given the value of conversion and the conversion
and impression costs associated with the treatment.
|
Calculate the expected value of assigning a unit to each of the
treatment conditions given the value of conversion and the conversion
and impression costs associated with the treatment.
| def _get_counterfactual_values(self):
'''
Calculate the expected value of assigning a unit to each of the
treatment conditions given the value of conversion and the conversion
and impression costs associated with the treatment.
'''
self.expected_values = ((self.value[:, None] - self.conversion_cost) *
self.counterfactuals - self.impression_cost)
self.best_treatment = np.argmax(self.expected_values, axis=1) | [
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decl_factory_t.__init__ | (self) | creates declarations factory | creates declarations factory | def __init__(self):
"""creates declarations factory"""
object.__init__(self) | [
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decl_factory_t.create_member_function | (self, *arguments, **keywords) | creates instance of class that describes member function
declaration | creates instance of class that describes member function
declaration | def create_member_function(self, *arguments, **keywords):
"""creates instance of class that describes member function
declaration"""
return member_function_t(*arguments, **keywords) | [
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decl_factory_t.create_constructor | (self, *arguments, **keywords) | creates instance of class that describes constructor declaration | creates instance of class that describes constructor declaration | def create_constructor(self, *arguments, **keywords):
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decl_factory_t.create_destructor | (self, *arguments, **keywords) | creates instance of class that describes destructor declaration | creates instance of class that describes destructor declaration | def create_destructor(self, *arguments, **keywords):
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decl_factory_t.create_member_operator | (self, *arguments, **keywords) | creates instance of class that describes member operator
declaration | creates instance of class that describes member operator
declaration | def create_member_operator(self, *arguments, **keywords):
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decl_factory_t.create_casting_operator | (self, *arguments, **keywords) | creates instance of class that describes casting operator
declaration | creates instance of class that describes casting operator
declaration | def create_casting_operator(self, *arguments, **keywords):
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decl_factory_t.create_free_function | (self, *arguments, **keywords) | creates instance of class that describes free function
declaration | creates instance of class that describes free function
declaration | def create_free_function(self, *arguments, **keywords):
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decl_factory_t.create_free_operator | (self, *arguments, **keywords) | creates instance of class that describes free operator
declaration | creates instance of class that describes free operator
declaration | def create_free_operator(self, *arguments, **keywords):
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decl_factory_t.create_class_declaration | (self, *arguments, **keywords) | creates instance of class that describes class declaration | creates instance of class that describes class declaration | def create_class_declaration(self, *arguments, **keywords):
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decl_factory_t.create_class | (self, *arguments, **keywords) | creates instance of class that describes class definition
declaration | creates instance of class that describes class definition
declaration | def create_class(self, *arguments, **keywords):
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decl_factory_t.create_enumeration | (self, *arguments, **keywords) | creates instance of class that describes enumeration declaration | creates instance of class that describes enumeration declaration | def create_enumeration(self, *arguments, **keywords):
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decl_factory_t.create_namespace | (self, *arguments, **keywords) | creates instance of class that describes namespace declaration | creates instance of class that describes namespace declaration | def create_namespace(self, *arguments, **keywords):
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decl_factory_t.create_typedef | (self, *arguments, **keywords) | creates instance of class that describes typedef declaration | creates instance of class that describes typedef declaration | def create_typedef(self, *arguments, **keywords):
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decl_factory_t.create_variable | (self, *arguments, **keywords) | creates instance of class that describes variable declaration | creates instance of class that describes variable declaration | def create_variable(self, *arguments, **keywords):
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bobby_columnar_table_multi_batch | () |
# TODO: <Alex>ALEX -- Add DocString</Alex>
|
# TODO: <Alex>ALEX -- Add DocString</Alex>
| def bobby_columnar_table_multi_batch():
"""
# TODO: <Alex>ALEX -- Add DocString</Alex>
"""
verbose_profiler_config_file_path: str = file_relative_path(
__file__, "bobby_user_workflow_verbose_profiler_config.yml"
)
verbose_profiler_config: str
with open(verbose_profiler_config_file_path) as f:
verbose_profiler_config = f.read()
my_row_count_range_rule_expectation_configurations_oneshot_sampling_method: List[
ExpectationConfiguration
] = [
ExpectationConfiguration(
**{
"kwargs": {"min_value": 7505, "max_value": 8495},
"expectation_type": "expect_table_row_count_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "table.row_count",
"domain_kwargs": {},
},
"num_batches": 2,
},
},
},
),
]
my_column_ranges_rule_expectation_configurations_oneshot_sampling_method: List[
ExpectationConfiguration
] = [
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "VendorID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "VendorID",
"min_value": 1,
"max_value": 1,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "VendorID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "VendorID",
"min_value": 4,
"max_value": 4,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "passenger_count"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "passenger_count",
"min_value": 0,
"max_value": 1,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "passenger_count"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "passenger_count",
"min_value": 6,
"max_value": 6,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "trip_distance"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "trip_distance",
"min_value": 0.0,
"max_value": 0.0,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "trip_distance"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "trip_distance",
"min_value": 37.62,
"max_value": 57.85,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "RatecodeID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "RatecodeID",
"min_value": 1,
"max_value": 1,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "RatecodeID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "RatecodeID",
"min_value": 5,
"max_value": 6,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "PULocationID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "PULocationID",
"min_value": 1,
"max_value": 1,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "PULocationID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "PULocationID",
"min_value": 265,
"max_value": 265,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "DOLocationID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "DOLocationID",
"min_value": 1,
"max_value": 1,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "DOLocationID"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "DOLocationID",
"min_value": 265,
"max_value": 265,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "payment_type"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "payment_type",
"min_value": 1,
"max_value": 1,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "payment_type"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "payment_type",
"min_value": 4,
"max_value": 4,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "fare_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "fare_amount",
"min_value": -51.84,
"max_value": -21.16,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "fare_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "fare_amount",
"min_value": 228.94,
"max_value": 2990.05,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "extra"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "extra",
"min_value": -36.53,
"max_value": -1.18,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "extra"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "extra",
"min_value": 4.51,
"max_value": 6.99,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "mta_tax"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "mta_tax",
"min_value": -0.5,
"max_value": -0.5,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "mta_tax"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "mta_tax",
"min_value": 0.69,
"max_value": 37.32,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "tip_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "tip_amount",
"min_value": 0.0,
"max_value": 0.0,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "tip_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "tip_amount",
"min_value": 46.84,
"max_value": 74.86,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "tolls_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "tolls_amount",
"min_value": 0.0,
"max_value": 0.0,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "tolls_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "tolls_amount",
"min_value": 26.4,
"max_value": 497.67,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "improvement_surcharge"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "improvement_surcharge",
"min_value": -0.3,
"max_value": -0.3,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "improvement_surcharge"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "improvement_surcharge",
"min_value": 0.3,
"max_value": 0.3,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "total_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "total_amount",
"min_value": -52.66,
"max_value": -24.44,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "total_amount"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "total_amount",
"min_value": 550.18,
"max_value": 2992.47,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_min_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.min",
"domain_kwargs": {"column": "congestion_surcharge"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "congestion_surcharge",
"min_value": -2.49,
"max_value": -0.01,
"mostly": 1.0,
},
},
),
ExpectationConfiguration(
**{
"expectation_type": "expect_column_max_to_be_between",
"meta": {
"profiler_details": {
"metric_configuration": {
"metric_name": "column.max",
"domain_kwargs": {"column": "congestion_surcharge"},
},
"num_batches": 2,
}
},
"kwargs": {
"column": "congestion_surcharge",
"min_value": 0.01,
"max_value": 2.49,
"mostly": 1.0,
},
},
),
]
expectation_configurations: List[ExpectationConfiguration] = []
expectation_configurations.extend(
my_row_count_range_rule_expectation_configurations_oneshot_sampling_method
)
expectation_configurations.extend(
my_column_ranges_rule_expectation_configurations_oneshot_sampling_method
)
expectation_suite_name_oneshot_sampling_method: str = (
"bobby_columnar_table_multi_batch_oneshot_sampling_method"
)
expected_expectation_suite_oneshot_sampling_method: ExpectationSuite = (
ExpectationSuite(
expectation_suite_name=expectation_suite_name_oneshot_sampling_method
)
)
expectation_configuration: ExpectationConfiguration
for expectation_configuration in expectation_configurations:
expected_expectation_suite_oneshot_sampling_method.add_expectation(
expectation_configuration
)
yaml = YAML()
profiler_config: dict = yaml.load(verbose_profiler_config)
expected_expectation_suite_oneshot_sampling_method.add_citation(
comment="Suite created by Rule-Based Profiler with the configuration included.",
profiler_config=profiler_config,
)
return {
"profiler_config": verbose_profiler_config,
"test_configuration_oneshot_sampling_method": {
"expectation_suite_name": expectation_suite_name_oneshot_sampling_method,
"expected_expectation_suite": expected_expectation_suite_oneshot_sampling_method,
},
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14,
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] | [
687,
5
] | python | en | ['en', 'error', 'th'] | False |
DragonNet.__init__ | (self, neurons_per_layer=200, targeted_reg=True, ratio=1., val_split=0.2,
batch_size=64, epochs=30, learning_rate=1e-3, reg_l2=0.01, loss_func=dragonnet_loss_binarycross,
verbose=True) |
Initializes a Dragonnet.
|
Initializes a Dragonnet.
| def __init__(self, neurons_per_layer=200, targeted_reg=True, ratio=1., val_split=0.2,
batch_size=64, epochs=30, learning_rate=1e-3, reg_l2=0.01, loss_func=dragonnet_loss_binarycross,
verbose=True):
"""
Initializes a Dragonnet.
"""
self.neurons_per_layer = neurons_per_layer
self.targeted_reg = targeted_reg
self.ratio = ratio
self.val_split = val_split
self.batch_size = batch_size
self.epochs = epochs
self.learning_rate = learning_rate
self.loss_func = loss_func
self.reg_l2 = reg_l2
self.verbose = verbose | [
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DragonNet.make_dragonnet | (self, input_dim) |
Neural net predictive model. The dragon has three heads.
Args:
input_dim (int): number of rows in input
Returns:
model (keras.models.Model): DragonNet model
|
Neural net predictive model. The dragon has three heads. | def make_dragonnet(self, input_dim):
"""
Neural net predictive model. The dragon has three heads.
Args:
input_dim (int): number of rows in input
Returns:
model (keras.models.Model): DragonNet model
"""
inputs = Input(shape=(input_dim,), name='input')
# representation
x = Dense(units=self.neurons_per_layer, activation='elu', kernel_initializer='RandomNormal')(inputs)
x = Dense(units=self.neurons_per_layer, activation='elu', kernel_initializer='RandomNormal')(x)
x = Dense(units=self.neurons_per_layer, activation='elu', kernel_initializer='RandomNormal')(x)
t_predictions = Dense(units=1, activation='sigmoid')(x)
# HYPOTHESIS
y0_hidden = Dense(units=int(self.neurons_per_layer / 2),
activation='elu',
kernel_regularizer=l2(self.reg_l2))(x)
y1_hidden = Dense(units=int(self.neurons_per_layer/2),
activation='elu',
kernel_regularizer=l2(self.reg_l2))(x)
# second layer
y0_hidden = Dense(units=int(self.neurons_per_layer/2),
activation='elu',
kernel_regularizer=l2(self.reg_l2))(y0_hidden)
y1_hidden = Dense(units=int(self.neurons_per_layer / 2),
activation='elu',
kernel_regularizer=l2(self.reg_l2))(y1_hidden)
# third
y0_predictions = Dense(units=1,
activation=None,
kernel_regularizer=l2(self.reg_l2),
name='y0_predictions')(y0_hidden)
y1_predictions = Dense(units=1,
activation=None,
kernel_regularizer=l2(self.reg_l2),
name='y1_predictions')(y1_hidden)
dl = EpsilonLayer()
epsilons = dl(t_predictions, name='epsilon')
concat_pred = Concatenate(1)([y0_predictions, y1_predictions, t_predictions, epsilons])
model = Model(inputs=inputs, outputs=concat_pred)
return model | [
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97,
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DragonNet.fit | (self, X, treatment, y, p=None) |
Fits the DragonNet model.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
|
Fits the DragonNet model. | def fit(self, X, treatment, y, p=None):
"""
Fits the DragonNet model.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
"""
X, treatment, y = convert_pd_to_np(X, treatment, y)
y = np.hstack((y.reshape(-1, 1), treatment.reshape(-1, 1)))
self.dragonnet = self.make_dragonnet(X.shape[1])
metrics = [regression_loss, binary_classification_loss, treatment_accuracy, track_epsilon]
if self.targeted_reg:
loss = make_tarreg_loss(ratio=self.ratio, dragonnet_loss=self.loss_func)
else:
loss = self.loss_func
self.dragonnet.compile(
optimizer=Adam(lr=self.learning_rate),
loss=loss, metrics=metrics)
adam_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=2, min_delta=0.),
ReduceLROnPlateau(monitor='loss', factor=0.5, patience=5, verbose=self.verbose, mode='auto',
min_delta=1e-8, cooldown=0, min_lr=0)
]
self.dragonnet.fit(X, y,
callbacks=adam_callbacks,
validation_split=self.val_split,
epochs=self.epochs,
batch_size=self.batch_size,
verbose=self.verbose)
sgd_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=40, min_delta=0.),
ReduceLROnPlateau(monitor='loss', factor=0.5, patience=5, verbose=self.verbose, mode='auto',
min_delta=0., cooldown=0, min_lr=0)
]
sgd_lr = 1e-5
momentum = 0.9
self.dragonnet.compile(optimizer=SGD(lr=sgd_lr, momentum=momentum, nesterov=True), loss=loss, metrics=metrics)
self.dragonnet.fit(X, y,
callbacks=sgd_callbacks,
validation_split=self.val_split,
epochs=300,
batch_size=self.batch_size,
verbose=self.verbose) | [
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155,
48
] | python | en | ['en', 'error', 'th'] | False |
DragonNet.predict | (self, X, treatment=None, y=None, p=None) |
Calls predict on fitted DragonNet.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
Returns:
(np.array): a 2D array with shape (X.shape[0], 4),
where each row takes the form of (outcome do(t=0), outcome do(t=1), propensity, epsilon)
|
Calls predict on fitted DragonNet. | def predict(self, X, treatment=None, y=None, p=None):
"""
Calls predict on fitted DragonNet.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
Returns:
(np.array): a 2D array with shape (X.shape[0], 4),
where each row takes the form of (outcome do(t=0), outcome do(t=1), propensity, epsilon)
"""
return self.dragonnet.predict(X) | [
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DragonNet.predict_propensity | (self, X) |
Predicts the individual propensity scores.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
Returns:
(np.array): propensity score vector
|
Predicts the individual propensity scores. | def predict_propensity(self, X):
"""
Predicts the individual propensity scores.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
Returns:
(np.array): propensity score vector
"""
preds = self.predict(X)
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DragonNet.predict_tau | (self, X) |
Predicts the individual treatment effect (tau / "ITE").
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
Returns:
(np.array): treatment effect vector
|
Predicts the individual treatment effect (tau / "ITE"). | def predict_tau(self, X):
"""
Predicts the individual treatment effect (tau / "ITE").
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
Returns:
(np.array): treatment effect vector
"""
preds = self.predict(X)
return (preds[:, 1] - preds[:, 0]).reshape(-1, 1) | [
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DragonNet.fit_predict | (self, X, treatment, y, p=None, return_components=False) |
Fits the DragonNet model and then predicts.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
return_components (bool, optional): whether to return
Returns:
(np.array): predictions based on return_components flag
if return_components=False (default), each row is treatment effect
if return_components=True, each row is (outcome do(t=0), outcome do(t=1), propensity, epsilon)
|
Fits the DragonNet model and then predicts. | def fit_predict(self, X, treatment, y, p=None, return_components=False):
"""
Fits the DragonNet model and then predicts.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
return_components (bool, optional): whether to return
Returns:
(np.array): predictions based on return_components flag
if return_components=False (default), each row is treatment effect
if return_components=True, each row is (outcome do(t=0), outcome do(t=1), propensity, epsilon)
"""
self.fit(X, treatment, y)
return self.predict_tau(X) | [
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ExceptionAppend | (e, msg) | Append a message to the given exception's message. | Append a message to the given exception's message. | def ExceptionAppend(e, msg):
"""Append a message to the given exception's message."""
if not e.args:
e.args = (msg,)
elif len(e.args) == 1:
e.args = (str(e.args[0]) + ' ' + msg,)
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FindQualifiedTargets | (target, qualified_list) |
Given a list of qualified targets, return the qualified targets for the
specified |target|.
|
Given a list of qualified targets, return the qualified targets for the
specified |target|.
| def FindQualifiedTargets(target, qualified_list):
"""
Given a list of qualified targets, return the qualified targets for the
specified |target|.
"""
return [t for t in qualified_list if ParseQualifiedTarget(t)[1] == target] | [
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GetEnvironFallback | (var_list, default) | Look up a key in the environment, with fallback to secondary keys
and finally falling back to a default value. | Look up a key in the environment, with fallback to secondary keys
and finally falling back to a default value. | def GetEnvironFallback(var_list, default):
"""Look up a key in the environment, with fallback to secondary keys
and finally falling back to a default value."""
for var in var_list:
if var in os.environ:
return os.environ[var]
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InvertRelativePath | (path, toplevel_dir=None) | Given a path like foo/bar that is relative to toplevel_dir, return
the inverse relative path back to the toplevel_dir.
E.g. os.path.normpath(os.path.join(path, InvertRelativePath(path)))
should always produce the empty string, unless the path contains symlinks.
| Given a path like foo/bar that is relative to toplevel_dir, return
the inverse relative path back to the toplevel_dir. | def InvertRelativePath(path, toplevel_dir=None):
"""Given a path like foo/bar that is relative to toplevel_dir, return
the inverse relative path back to the toplevel_dir.
E.g. os.path.normpath(os.path.join(path, InvertRelativePath(path)))
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"""
if not path:
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toplevel_dir = '.' if toplevel_dir is None else toplevel_dir
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EncodePOSIXShellArgument | (argument) | Encodes |argument| suitably for consumption by POSIX shells.
argument may be quoted and escaped as necessary to ensure that POSIX shells
treat the returned value as a literal representing the argument passed to
this function. Parameter (variable) expansions beginning with $ are allowed
to remain intact without escaping the $, to allow the argument to contain
references to variables to be expanded by the shell.
| Encodes |argument| suitably for consumption by POSIX shells. | def EncodePOSIXShellArgument(argument):
"""Encodes |argument| suitably for consumption by POSIX shells.
argument may be quoted and escaped as necessary to ensure that POSIX shells
treat the returned value as a literal representing the argument passed to
this function. Parameter (variable) expansions beginning with $ are allowed
to remain intact without escaping the $, to allow the argument to contain
references to variables to be expanded by the shell.
"""
if not isinstance(argument, str):
argument = str(argument)
if _quote.search(argument):
quote = '"'
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quote = ''
encoded = quote + re.sub(_escape, r'\\\1', argument) + quote
return encoded | [
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EncodePOSIXShellList | (list) | Encodes |list| suitably for consumption by POSIX shells.
Returns EncodePOSIXShellArgument for each item in list, and joins them
together using the space character as an argument separator.
| Encodes |list| suitably for consumption by POSIX shells. | def EncodePOSIXShellList(list):
"""Encodes |list| suitably for consumption by POSIX shells.
Returns EncodePOSIXShellArgument for each item in list, and joins them
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encoded_arguments = []
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DeepDependencyTargets | (target_dicts, roots) | Returns the recursive list of target dependencies. | Returns the recursive list of target dependencies. | def DeepDependencyTargets(target_dicts, roots):
"""Returns the recursive list of target dependencies."""
dependencies = set()
pending = set(roots)
while pending:
# Pluck out one.
r = pending.pop()
# Skip if visited already.
if r in dependencies:
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# Add it.
dependencies.add(r)
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pending.update(set(spec.get('dependencies_original', [])))
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BuildFileTargets | (target_list, build_file) | From a target_list, returns the subset from the specified build_file.
| From a target_list, returns the subset from the specified build_file.
| def BuildFileTargets(target_list, build_file):
"""From a target_list, returns the subset from the specified build_file.
"""
return [p for p in target_list if BuildFile(p) == build_file] | [
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AllTargets | (target_list, target_dicts, build_file) | Returns all targets (direct and dependencies) for the specified build_file.
| Returns all targets (direct and dependencies) for the specified build_file.
| def AllTargets(target_list, target_dicts, build_file):
"""Returns all targets (direct and dependencies) for the specified build_file.
"""
bftargets = BuildFileTargets(target_list, build_file)
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WriteOnDiff | (filename) | Write to a file only if the new contents differ.
Arguments:
filename: name of the file to potentially write to.
Returns:
A file like object which will write to temporary file and only overwrite
the target if it differs (on close).
| Write to a file only if the new contents differ. | def WriteOnDiff(filename):
"""Write to a file only if the new contents differ.
Arguments:
filename: name of the file to potentially write to.
Returns:
A file like object which will write to temporary file and only overwrite
the target if it differs (on close).
"""
class Writer(object):
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def __init__(self):
# Pick temporary file.
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dir=os.path.split(filename)[0])
try:
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# Don't leave turds behind.
os.unlink(self.tmp_path)
raise
def __getattr__(self, attrname):
# Delegate everything else to self.tmp_file
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def close(self):
try:
# Close tmp file.
self.tmp_file.close()
# Determine if different.
same = False
try:
same = filecmp.cmp(self.tmp_path, filename, False)
except OSError, e:
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raise
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os.unlink(self.tmp_path)
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os.umask(umask)
os.chmod(self.tmp_path, 0666 & ~umask)
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# NOTE: on windows (but not cygwin) rename will not replace an
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# is no way to make the switch atomic.
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EnsureDirExists | (path) | Make sure the directory for |path| exists. | Make sure the directory for |path| exists. | def EnsureDirExists(path):
"""Make sure the directory for |path| exists."""
try:
os.makedirs(os.path.dirname(path))
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GetFlavor | (params) | Returns |params.flavor| if it's set, the system's default flavor else. | Returns |params.flavor| if it's set, the system's default flavor else. | def GetFlavor(params):
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flavors = {
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409,
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] | [
436,
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CopyTool | (flavor, out_path) | Finds (flock|mac|win)_tool.gyp in the gyp directory and copies it
to |out_path|. | Finds (flock|mac|win)_tool.gyp in the gyp directory and copies it
to |out_path|. | def CopyTool(flavor, out_path):
"""Finds (flock|mac|win)_tool.gyp in the gyp directory and copies it
to |out_path|."""
# aix and solaris just need flock emulation. mac and win use more complicated
# support scripts.
prefix = {
'aix': 'flock',
'solaris': 'flock',
'mac': 'mac',
'win': 'win'
}.get(flavor, None)
if not prefix:
return
# Slurp input file.
source_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), '%s_tool.py' % prefix)
with open(source_path) as source_file:
source = source_file.readlines()
# Add header and write it out.
tool_path = os.path.join(out_path, 'gyp-%s-tool' % prefix)
with open(tool_path, 'w') as tool_file:
tool_file.write(
''.join([source[0], '# Generated by gyp. Do not edit.\n'] + source[1:]))
# Make file executable.
os.chmod(tool_path, 0755) | [
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TopologicallySorted | (graph, get_edges) | r"""Topologically sort based on a user provided edge definition.
Args:
graph: A list of node names.
get_edges: A function mapping from node name to a hashable collection
of node names which this node has outgoing edges to.
Returns:
A list containing all of the node in graph in topological order.
It is assumed that calling get_edges once for each node and caching is
cheaper than repeatedly calling get_edges.
Raises:
CycleError in the event of a cycle.
Example:
graph = {'a': '$(b) $(c)', 'b': 'hi', 'c': '$(b)'}
def GetEdges(node):
return re.findall(r'\$\(([^))]\)', graph[node])
print TopologicallySorted(graph.keys(), GetEdges)
==>
['a', 'c', b']
| r"""Topologically sort based on a user provided edge definition. | def TopologicallySorted(graph, get_edges):
r"""Topologically sort based on a user provided edge definition.
Args:
graph: A list of node names.
get_edges: A function mapping from node name to a hashable collection
of node names which this node has outgoing edges to.
Returns:
A list containing all of the node in graph in topological order.
It is assumed that calling get_edges once for each node and caching is
cheaper than repeatedly calling get_edges.
Raises:
CycleError in the event of a cycle.
Example:
graph = {'a': '$(b) $(c)', 'b': 'hi', 'c': '$(b)'}
def GetEdges(node):
return re.findall(r'\$\(([^))]\)', graph[node])
print TopologicallySorted(graph.keys(), GetEdges)
==>
['a', 'c', b']
"""
get_edges = memoize(get_edges)
visited = set()
visiting = set()
ordered_nodes = []
def Visit(node):
if node in visiting:
raise CycleError(visiting)
if node in visited:
return
visited.add(node)
visiting.add(node)
for neighbor in get_edges(node):
Visit(neighbor)
visiting.remove(node)
ordered_nodes.insert(0, node)
for node in sorted(graph):
Visit(node)
return ordered_nodes | [
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BaseNotebookRenderer.add_code_cell | (self, code: str, lint: bool = False) |
Add the given code as a new code cell.
Args:
code: Code to render into the notebook cell
lint: Whether to lint the code before adding it
Returns:
Nothing, adds a cell to the class instance notebook
|
Add the given code as a new code cell.
Args:
code: Code to render into the notebook cell
lint: Whether to lint the code before adding it | def add_code_cell(self, code: str, lint: bool = False) -> None:
"""
Add the given code as a new code cell.
Args:
code: Code to render into the notebook cell
lint: Whether to lint the code before adding it
Returns:
Nothing, adds a cell to the class instance notebook
"""
if lint:
code: str = lint_code(code).rstrip("\n")
cell = nbformat.v4.new_code_cell(code)
self._notebook["cells"].append(cell) | [
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BaseNotebookRenderer.add_markdown_cell | (self, markdown: str) |
Add the given markdown as a new markdown cell.
Args:
markdown: Code to render into the notebook cell
Returns:
Nothing, adds a cell to the class instance notebook
|
Add the given markdown as a new markdown cell.
Args:
markdown: Code to render into the notebook cell | def add_markdown_cell(self, markdown: str) -> None:
"""
Add the given markdown as a new markdown cell.
Args:
markdown: Code to render into the notebook cell
Returns:
Nothing, adds a cell to the class instance notebook
"""
cell = nbformat.v4.new_markdown_cell(markdown)
self._notebook["cells"].append(cell) | [
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BaseNotebookRenderer.write_notebook_to_disk | (
cls, notebook: nbformat.NotebookNode, notebook_file_path: str
) |
Write a given Jupyter notebook to disk.
Args:
notebook: Jupyter notebook
notebook_file_path: Location to write notebook
|
Write a given Jupyter notebook to disk.
Args:
notebook: Jupyter notebook
notebook_file_path: Location to write notebook
| def write_notebook_to_disk(
cls, notebook: nbformat.NotebookNode, notebook_file_path: str
) -> None:
"""
Write a given Jupyter notebook to disk.
Args:
notebook: Jupyter notebook
notebook_file_path: Location to write notebook
"""
with open(notebook_file_path, "w") as f:
nbformat.write(notebook, f) | [
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BaseNotebookRenderer.render | (self) |
Render a notebook from parameters.
|
Render a notebook from parameters.
| def render(self):
"""
Render a notebook from parameters.
"""
raise NotImplementedError | [
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BaseNotebookRenderer.render_to_disk | (
self,
notebook_file_path: str,
) |
Render a notebook to disk from arguments
|
Render a notebook to disk from arguments
| def render_to_disk(
self,
notebook_file_path: str,
) -> None:
"""
Render a notebook to disk from arguments
"""
raise NotImplementedError | [
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nested_update | (
d: Union[Iterable, dict], u: Union[Iterable, dict], dedup: bool = False
) | update d with items from u, recursively and joining elements | update d with items from u, recursively and joining elements | def nested_update(
d: Union[Iterable, dict], u: Union[Iterable, dict], dedup: bool = False
):
"""update d with items from u, recursively and joining elements"""
for k, v in u.items():
if isinstance(v, Mapping):
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l2 = v or []
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d[k] = l1 + l2
else:
d[k] = v
return d | [
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in_databricks | () |
Tests whether we are in a Databricks environment.
Returns:
bool
|
Tests whether we are in a Databricks environment. | def in_databricks() -> bool:
"""
Tests whether we are in a Databricks environment.
Returns:
bool
"""
return "DATABRICKS_RUNTIME_VERSION" in os.environ | [
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convert_to_json_serializable | (data) |
Helper function to convert an object to one that is json serializable
Args:
data: an object to attempt to convert a corresponding json-serializable object
Returns:
(dict) A converted test_object
Warning:
test_obj may also be converted in place.
|
Helper function to convert an object to one that is json serializable
Args:
data: an object to attempt to convert a corresponding json-serializable object
Returns:
(dict) A converted test_object
Warning:
test_obj may also be converted in place.
| def convert_to_json_serializable(data):
"""
Helper function to convert an object to one that is json serializable
Args:
data: an object to attempt to convert a corresponding json-serializable object
Returns:
(dict) A converted test_object
Warning:
test_obj may also be converted in place.
"""
# If it's one of our types, we use our own conversion; this can move to full schema
# once nesting goes all the way down
if isinstance(data, (SerializableDictDot, SerializableDotDict)):
return data.to_json_dict()
# Handling "float(nan)" separately is required by Python-3.6 and Pandas-0.23 versions.
if isinstance(data, float) and np.isnan(data):
return None
if isinstance(data, (str, int, float, bool)):
# No problem to encode json
return data
if isinstance(data, dict):
new_dict = {}
for key in data:
# A pandas index can be numeric, and a dict key can be numeric, but a json key must be a string
new_dict[str(key)] = convert_to_json_serializable(data[key])
return new_dict
if isinstance(data, (list, tuple, set)):
new_list = []
for val in data:
new_list.append(convert_to_json_serializable(val))
return new_list
if isinstance(data, (np.ndarray, pd.Index)):
# test_obj[key] = test_obj[key].tolist()
# If we have an array or index, convert it first to a list--causing coercion to float--and then round
# to the number of digits for which the string representation will equal the float representation
return [convert_to_json_serializable(x) for x in data.tolist()]
if isinstance(data, (datetime.datetime, datetime.date)):
return data.isoformat()
if isinstance(data, uuid.UUID):
return str(data)
# Use built in base type from numpy, https://docs.scipy.org/doc/numpy-1.13.0/user/basics.types.html
# https://github.com/numpy/numpy/pull/9505
if np.issubdtype(type(data), np.bool_):
return bool(data)
if np.issubdtype(type(data), np.integer) or np.issubdtype(type(data), np.uint):
return int(data)
if np.issubdtype(type(data), np.floating):
# Note: Use np.floating to avoid FutureWarning from numpy
return float(round(data, sys.float_info.dig))
# Note: This clause has to come after checking for np.ndarray or we get:
# `ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()`
if data is None:
# No problem to encode json
return data
try:
if not isinstance(data, list) and pd.isna(data):
# pd.isna is functionally vectorized, but we only want to apply this to single objects
# Hence, why we test for `not isinstance(list))`
return None
except TypeError:
pass
except ValueError:
pass
if isinstance(data, pd.Series):
# Converting a series is tricky since the index may not be a string, but all json
# keys must be strings. So, we use a very ugly serialization strategy
index_name = data.index.name or "index"
value_name = data.name or "value"
return [
{
index_name: convert_to_json_serializable(idx),
value_name: convert_to_json_serializable(val),
}
for idx, val in data.iteritems()
]
if isinstance(data, pd.DataFrame):
return convert_to_json_serializable(data.to_dict(orient="records"))
if pyspark and isinstance(data, pyspark.sql.DataFrame):
# using StackOverflow suggestion for converting pyspark df into dictionary
# https://stackoverflow.com/questions/43679880/pyspark-dataframe-to-dictionary-columns-as-keys-and-list-of-column-values-ad-di
return convert_to_json_serializable(
dict(zip(data.schema.names, zip(*data.collect())))
)
if isinstance(data, decimal.Decimal):
if requires_lossy_conversion(data):
logger.warning(
f"Using lossy conversion for decimal {data} to float object to support serialization."
)
return float(data)
if isinstance(data, RunIdentifier):
return data.to_json_dict()
else:
raise TypeError(
"%s is of type %s which cannot be serialized."
% (str(data), type(data).__name__)
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] | [
115,
0
] | [
231,
9
] | python | en | ['en', 'error', 'th'] | False |
ensure_json_serializable | (data) |
Helper function to convert an object to one that is json serializable
Args:
data: an object to attempt to convert a corresponding json-serializable object
Returns:
(dict) A converted test_object
Warning:
test_obj may also be converted in place.
|
Helper function to convert an object to one that is json serializable
Args:
data: an object to attempt to convert a corresponding json-serializable object
Returns:
(dict) A converted test_object
Warning:
test_obj may also be converted in place.
| def ensure_json_serializable(data):
"""
Helper function to convert an object to one that is json serializable
Args:
data: an object to attempt to convert a corresponding json-serializable object
Returns:
(dict) A converted test_object
Warning:
test_obj may also be converted in place.
"""
if isinstance(data, (SerializableDictDot, SerializableDotDict)):
return
if isinstance(data, ((str,), (int,), float, bool)):
# No problem to encode json
return
if isinstance(data, dict):
for key in data:
str(key) # key must be cast-able to string
ensure_json_serializable(data[key])
return
if isinstance(data, (list, tuple, set)):
for val in data:
ensure_json_serializable(val)
return
if isinstance(data, (np.ndarray, pd.Index)):
# test_obj[key] = test_obj[key].tolist()
# If we have an array or index, convert it first to a list--causing coercion to float--and then round
# to the number of digits for which the string representation will equal the float representation
_ = [ensure_json_serializable(x) for x in data.tolist()]
return
if isinstance(data, (datetime.datetime, datetime.date)):
return
# Use built in base type from numpy, https://docs.scipy.org/doc/numpy-1.13.0/user/basics.types.html
# https://github.com/numpy/numpy/pull/9505
if np.issubdtype(type(data), np.bool_):
return
if np.issubdtype(type(data), np.integer) or np.issubdtype(type(data), np.uint):
return
if np.issubdtype(type(data), np.floating):
# Note: Use np.floating to avoid FutureWarning from numpy
return
# Note: This clause has to come after checking for np.ndarray or we get:
# `ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()`
if data is None:
# No problem to encode json
return
try:
if not isinstance(data, list) and pd.isna(data):
# pd.isna is functionally vectorized, but we only want to apply this to single objects
# Hence, why we test for `not isinstance(list))`
return
except TypeError:
pass
except ValueError:
pass
if isinstance(data, pd.Series):
# Converting a series is tricky since the index may not be a string, but all json
# keys must be strings. So, we use a very ugly serialization strategy
index_name = data.index.name or "index"
value_name = data.name or "value"
_ = [
{
index_name: ensure_json_serializable(idx),
value_name: ensure_json_serializable(val),
}
for idx, val in data.iteritems()
]
return
if pyspark and isinstance(data, pyspark.sql.DataFrame):
# using StackOverflow suggestion for converting pyspark df into dictionary
# https://stackoverflow.com/questions/43679880/pyspark-dataframe-to-dictionary-columns-as-keys-and-list-of-column-values-ad-di
return ensure_json_serializable(
dict(zip(data.schema.names, zip(*data.collect())))
)
if isinstance(data, pd.DataFrame):
return ensure_json_serializable(data.to_dict(orient="records"))
if isinstance(data, decimal.Decimal):
return
if isinstance(data, RunIdentifier):
return
else:
raise InvalidExpectationConfigurationError(
"%s is of type %s which cannot be serialized to json"
% (str(data), type(data).__name__)
) | [
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] | [
234,
0
] | [
336,
9
] | python | en | ['en', 'error', 'th'] | False |
substitute_all_strftime_format_strings | (
data: Union[dict, list, str, Any], datetime_obj: Optional[datetime.datetime] = None
) |
This utility function will iterate over input data and for all strings, replace any strftime format
elements using either the provided datetime_obj or the current datetime
|
This utility function will iterate over input data and for all strings, replace any strftime format
elements using either the provided datetime_obj or the current datetime
| def substitute_all_strftime_format_strings(
data: Union[dict, list, str, Any], datetime_obj: Optional[datetime.datetime] = None
) -> Union[str, Any]:
"""
This utility function will iterate over input data and for all strings, replace any strftime format
elements using either the provided datetime_obj or the current datetime
"""
datetime_obj: datetime.datetime = datetime_obj or datetime.datetime.now()
if isinstance(data, dict) or isinstance(data, OrderedDict):
return {
k: substitute_all_strftime_format_strings(v, datetime_obj=datetime_obj)
for k, v in data.items()
}
elif isinstance(data, list):
return [
substitute_all_strftime_format_strings(el, datetime_obj=datetime_obj)
for el in data
]
elif isinstance(data, str):
return get_datetime_string_from_strftime_format(data, datetime_obj=datetime_obj)
else:
return data | [
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343,
0
] | [
365,
19
] | python | en | ['en', 'error', 'th'] | False |
get_datetime_string_from_strftime_format | (
format_str: str, datetime_obj: Optional[datetime.datetime] = None
) |
This utility function takes a string with strftime format elements and substitutes those elements using
either the provided datetime_obj or current datetime
|
This utility function takes a string with strftime format elements and substitutes those elements using
either the provided datetime_obj or current datetime
| def get_datetime_string_from_strftime_format(
format_str: str, datetime_obj: Optional[datetime.datetime] = None
) -> str:
"""
This utility function takes a string with strftime format elements and substitutes those elements using
either the provided datetime_obj or current datetime
"""
datetime_obj: datetime.datetime = datetime_obj or datetime.datetime.now()
return datetime_obj.strftime(format_str) | [
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sniff_s3_compression | (s3_url: S3Url) | Attempts to get read_csv compression from s3_url | Attempts to get read_csv compression from s3_url | def sniff_s3_compression(s3_url: S3Url) -> str:
"""Attempts to get read_csv compression from s3_url"""
return _SUFFIX_TO_PD_KWARG.get(s3_url.suffix) | [
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] | [
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] | [
464,
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] | python | en | ['en', 'en', 'en'] | True |
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