index int64 0 731k | package stringlengths 2 98 ⌀ | name stringlengths 1 76 | docstring stringlengths 0 281k ⌀ | code stringlengths 4 8.19k | signature stringlengths 2 42.8k ⌀ | embed_func_code listlengths 768 768 |
|---|---|---|---|---|---|---|
4,378 | pydantic.main | __getattr__ | null | def __getattr__(self, item: str) -> Any:
private_attributes = object.__getattribute__(self, '__private_attributes__')
if item in private_attributes:
attribute = private_attributes[item]
if hasattr(attribute, '__get__'):
return attribute.__get__(self, type(self)) # type: ignore
... | (self, item: str) -> Any | [
0.040573906153440475,
-0.0070080505684018135,
0.005547651089727879,
0.037158530205488205,
0.029711894690990448,
-0.08570164442062378,
0.07860960811376572,
-0.014575998298823833,
0.048711083829402924,
0.01377347856760025,
0.02443019486963749,
0.07151757925748825,
0.05087602138519287,
0.0468... |
4,379 | pydantic.main | __getstate__ | null | def __getstate__(self) -> dict[Any, Any]:
private = self.__pydantic_private__
if private:
private = {k: v for k, v in private.items() if v is not PydanticUndefined}
return {
'__dict__': self.__dict__,
'__pydantic_extra__': self.__pydantic_extra__,
'__pydantic_fields_set__': s... | (self) -> dict[typing.Any, typing.Any] | [
0.007885710336267948,
-0.017211059108376503,
0.018041620030999184,
-0.00011506723240017891,
-0.006607569754123688,
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-0.010871113277971745,
-0.02209290862083435,
0.017940105870366096,
-0.03613860905170441,
-0.011461734771728516,
0.01653738133609295,
-0.005855451337993145... |
4,380 | pydantic._internal._model_construction | hash_func | null | def make_hash_func(cls: type[BaseModel]) -> Any:
getter = operator.itemgetter(*cls.model_fields.keys()) if cls.model_fields else lambda _: 0
def hash_func(self: Any) -> int:
try:
return hash(getter(self.__dict__))
except KeyError:
# In rare cases (such as when using the ... | (self: Any) -> int | [
0.00581063749268651,
-0.08880949020385742,
0.0012284835102036595,
0.06495718657970428,
-0.016176527366042137,
-0.03464861586689949,
0.019063912332057953,
-0.02867657132446766,
-0.01260764803737402,
-0.025573978200554848,
0.01158540602773428,
0.011199823580682278,
0.0617649219930172,
0.0103... |
4,381 | dagster._config.pythonic_config.io_manager | __init__ | null | def __init__(self, **data: Any):
ConfigurableResourceFactory.__init__(self, **data)
| (self, **data: Any) | [
0.007448974996805191,
-0.032043974846601486,
0.01273016445338726,
-0.03352484852075577,
-0.018377112224698067,
0.023354990407824516,
0.019394097849726677,
0.061554402112960815,
0.022070376202464104,
-0.0067754448391497135,
0.029760215431451797,
0.05191979929804802,
-0.02886812388896942,
0.... |
4,382 | pydantic.main | __iter__ | So `dict(model)` works. | def __iter__(self) -> TupleGenerator:
"""So `dict(model)` works."""
yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
extra = self.__pydantic_extra__
if extra:
yield from extra.items()
| (self) -> Generator[Tuple[str, Any], NoneType, NoneType] | [
0.04748603329062462,
-0.025985313579440117,
-0.055044740438461304,
-0.03446625545620918,
-0.0028910243418067694,
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0.001238687545992434,
0.017667120322585106,
0.08846218138933182,
-0.015605654567480087,
0.007938450202345848,
0.041229307651519775,
0.022621870040893555,
... |
4,383 | pydantic._internal._repr | __pretty__ | Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects. | def __pretty__(self, fmt: typing.Callable[[Any], Any], **kwargs: Any) -> typing.Generator[Any, None, None]:
"""Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects."""
yield self.__repr_name__() + '('
yield 1
for name, value in self.__repr_args__():
if name is not No... | (self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, NoneType, NoneType] | [
0.020168708637356758,
-0.042477332055568695,
0.03259805217385292,
-0.020989008247852325,
0.020400531589984894,
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0.004614193923771381,
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-0.017538394778966904,
-0.04590119794011116,
0.021381326019763947,
0.017458148300647736,
0... |
4,384 | pydantic.main | __repr__ | null | def __repr__(self) -> str:
return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
| (self) -> str | [
0.02219986915588379,
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0.06238093972206116,
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0.004285663831979036,
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0.03699978068470955,
-0.024188173934817314,
-0.008337917737662792,
-0.009785923175513744,
-0.005796344019472599,
0.... |
4,385 | pydantic.main | __repr_args__ | null | def __repr_args__(self) -> _repr.ReprArgs:
for k, v in self.__dict__.items():
field = self.model_fields.get(k)
if field and field.repr:
yield k, v
# `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
# This can happen if a `ValidationError` is rais... | (self) -> '_repr.ReprArgs' | [
0.05479183793067932,
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0.033038489520549774,
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0.023691199719905853,
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0.04061891511082649,
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0.0726504772901535,
0.0016315010143443942,
-0.004160684067755938,
0.04966222494840622,
0.04077090322971344,
0.041720... |
4,386 | pydantic._internal._repr | __repr_name__ | Name of the instance's class, used in __repr__. | def __repr_name__(self) -> str:
"""Name of the instance's class, used in __repr__."""
return self.__class__.__name__
| (self) -> str | [
0.046195320785045624,
-0.04207461327314377,
0.036688774824142456,
0.01341037917882204,
-0.017142513766884804,
-0.039182886481285095,
0.02958597056567669,
-0.010455396957695484,
0.06058165803551674,
0.007559152320027351,
-0.05526811257004738,
-0.0236398596316576,
0.009578842669725418,
0.015... |
4,387 | pydantic._internal._repr | __repr_str__ | null | def __repr_str__(self, join_str: str) -> str:
return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__())
| (self, join_str: str) -> str | [
-0.010343417525291443,
-0.011919890530407429,
0.03527797386050224,
0.044841911643743515,
-0.007698445115238428,
-0.07728222757577896,
0.024452853947877884,
-0.02122984267771244,
0.05181342735886574,
-0.016246434301137924,
-0.02464553527534008,
0.02524109184741974,
0.00929243490099907,
0.05... |
4,388 | pydantic._internal._repr | __rich_repr__ | Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects. | def __rich_repr__(self) -> RichReprResult:
"""Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects."""
for name, field_repr in self.__repr_args__():
if name is None:
yield field_repr
else:
yield name, field_repr
| (self) -> 'RichReprResult' | [
0.02516397275030613,
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0.012926576659083366,
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0.02056942693889141,
-0.043188728392124176,
0.07612808793783188,
0.025199314579367638,
-0.022265873849391937,
0.013589251786470413,
0.010726496577262878,
0.... |
4,389 | dagster._config.pythonic_config.typing_utils | __set__ | null | def __set__(self, obj: Optional[object], value: Union[Any, "PartialResource[Any]"]) -> None:
# no-op implementation (only used to affect type signature)
setattr(obj, self._assigned_name, value)
| (self, obj: Optional[object], value: Union[Any, ForwardRef('PartialResource[Any]')]) -> None | [
0.020271416753530502,
-0.018138496205210686,
-0.01648244820535183,
0.006966673769056797,
-0.03351980075240135,
0.008102497085928917,
0.058334510773420334,
0.020514188334345818,
-0.00540166487917304,
-0.053340356796979904,
0.0034659961238503456,
0.004634333774447441,
0.0059348950162529945,
... |
4,390 | dagster._config.pythonic_config.typing_utils | __set_name__ | null | def __set_name__(self, _owner, name):
self._assigned_name = name
| (self, _owner, name) | [
0.001458510640077293,
-0.002779025351628661,
-0.01910712569952011,
0.030877117067575455,
-0.07065390795469284,
-0.010462213307619095,
0.010810387320816517,
0.048031069338321686,
0.051359955221414566,
-0.026036644354462624,
-0.018156016245484352,
-0.004611186683177948,
0.049050118774175644,
... |
4,391 | dagster._config.pythonic_config.config | __setattr__ | null | def __setattr__(self, name: str, value: Any):
from .resource import ConfigurableResourceFactory
# This is a hack to allow us to set attributes on the class that are not part of the
# config schema. Pydantic will normally raise an error if you try to set an attribute
# that is not part of the schema.
... | (self, name: str, value: Any) | [
0.05019550397992134,
-0.014814645983278751,
0.007058742921799421,
0.03102361038327217,
0.014272410422563553,
-0.008327186107635498,
0.0193074531853199,
0.026492072269320488,
0.008399806916713715,
-0.05782553181052208,
0.013400960713624954,
0.030829954892396927,
0.036620255559682846,
0.0462... |
4,392 | pydantic.main | __setstate__ | null | def __setstate__(self, state: dict[Any, Any]) -> None:
_object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__'])
_object_setattr(self, '__pydantic_extra__', state['__pydantic_extra__'])
_object_setattr(self, '__pydantic_private__', state['__pydantic_private__'])
_object_setattr(... | (self, state: dict[typing.Any, typing.Any]) -> NoneType | [
-0.02323097176849842,
-0.0025588127318769693,
0.03192466124892235,
0.015173624269664288,
-0.03782203048467636,
-0.011812661774456501,
-0.046390242874622345,
0.008362074382603168,
-0.0020524277351796627,
-0.044956233352422714,
-0.03432662785053253,
0.0596548393368721,
0.01410707924515009,
0... |
4,393 | pydantic.main | __str__ | null | def __str__(self) -> str:
return self.__repr_str__(' ')
| (self) -> str | [
0.006124191917479038,
-0.02720538154244423,
0.041021015495061874,
0.016055773943662643,
0.00567275658249855,
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0.004224755335599184,
-0.014556667767465115,
0.08088362962007523,
-0.041906848549842834,
-0.03638741001486778,
-0.020851213485002518,
-0.0008900946704670787,
0... |
4,394 | pydantic.main | _calculate_keys | null | @typing_extensions.deprecated(
'The private method `_calculate_keys` will be removed and should no longer be used.',
category=None,
)
def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any:
warnings.warn(
'The private method `_calculate_keys` will be removed and should no longer be used.',
... | (self, *args: Any, **kwargs: Any) -> Any | [
-0.05444582551717758,
-0.058921780437231064,
-0.03327244892716408,
0.04147254303097725,
-0.03577268868684769,
-0.026558512821793556,
0.00717726768925786,
-0.003166827140375972,
0.05395626649260521,
-0.014599313959479332,
0.014809124171733856,
-0.03720639646053314,
0.060740139335393906,
-0.... |
4,395 | pydantic.main | _check_frozen | null | def _check_frozen(self, name: str, value: Any) -> None:
if self.model_config.get('frozen', None):
typ = 'frozen_instance'
elif getattr(self.model_fields.get(name), 'frozen', False):
typ = 'frozen_field'
else:
return
error: pydantic_core.InitErrorDetails = {
'type': typ,
... | (self, name: str, value: Any) -> NoneType | [
0.11290347576141357,
-0.014709255658090115,
-0.03647065535187721,
0.024647941812872887,
-0.03612496331334114,
-0.09838435053825378,
0.026099853217601776,
0.010033751837909222,
-0.02660110965371132,
-0.0000816295578260906,
0.04559696465730667,
0.035468146204948425,
0.02941851131618023,
-0.0... |
4,396 | dagster._config.pythonic_config.config | _convert_to_config_dictionary | Converts this Config object to a Dagster config dictionary, in the same format as the dictionary
accepted as run config or as YAML in the launchpad.
Inner fields are recursively converted to dictionaries, meaning nested config objects
or EnvVars will be converted to the appropriate dictionary r... | def _convert_to_config_dictionary(self) -> Mapping[str, Any]:
"""Converts this Config object to a Dagster config dictionary, in the same format as the dictionary
accepted as run config or as YAML in the launchpad.
Inner fields are recursively converted to dictionaries, meaning nested config objects
or E... | (self) -> Mapping[str, Any] | [
0.0239920224994421,
-0.027313360944390297,
0.0013383340556174517,
-0.047695234417915344,
-0.026096224784851074,
0.0017792880535125732,
-0.00553900096565485,
-0.023785728961229324,
-0.01929882913827896,
-0.014791298657655716,
-0.03195498138666153,
-0.027560913935303688,
0.01609095185995102,
... |
4,397 | pydantic.main | _copy_and_set_values | null | @typing_extensions.deprecated(
'The private method `_copy_and_set_values` will be removed and should no longer be used.',
category=None,
)
def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any:
warnings.warn(
'The private method `_copy_and_set_values` will be removed and should no longer ... | (self, *args: Any, **kwargs: Any) -> Any | [
-0.019725654274225235,
-0.03799014911055565,
0.0044378372840583324,
-0.00035414635203778744,
-0.07458871603012085,
-0.009532325901091099,
-0.012628592550754547,
0.00813204888254404,
0.06616964936256409,
-0.03193677216768265,
-0.03851199150085449,
-0.003237599041312933,
0.01388971321284771,
... |
4,398 | dagster._config.pythonic_config.resource | _get_initialize_and_run_fn | null | def _get_initialize_and_run_fn(self) -> Callable:
return self._initialize_and_run_cm if self._is_cm_resource else self._initialize_and_run
| (self) -> Callable | [
0.01994732767343521,
-0.04892932251095772,
0.011860116384923458,
0.025513630360364914,
0.03231500834226608,
0.04459809884428978,
0.02311115339398384,
0.02928653545677662,
-0.00037961677298881114,
-0.01593755930662155,
0.02436314895749092,
0.044361233711242676,
0.02167305164039135,
0.005887... |
4,399 | dagster._config.pythonic_config.config | _get_non_default_public_field_values | null | def _get_non_default_public_field_values(self) -> Mapping[str, Any]:
return self.__class__._get_non_default_public_field_values_cls(dict(self)) # noqa: SLF001
| (self) -> Mapping[str, Any] | [
0.043788325041532516,
-0.01906406879425049,
-0.0017275726422667503,
0.012709379196166992,
-0.031217845156788826,
-0.012527071870863438,
0.046948306262493134,
-0.057921431958675385,
0.0736866146326065,
0.016685400158166885,
-0.001234910567291081,
0.032450586557388306,
0.004713929258286953,
... |
4,400 | dagster._config.pythonic_config.resource | _initialize_and_run | null | def _initialize_and_run(self, context: InitResourceContext) -> TResValue:
with self._resolve_and_update_nested_resources(context) as has_nested_resource:
updated_resource = has_nested_resource.with_replaced_resource_context( # noqa: SLF001
context
)._with_updated_values(context.resource... | (self, context: dagster._core.execution.context.init.InitResourceContext) -> ~TResValue | [
0.06108705699443817,
-0.04897681623697281,
-0.036259278655052185,
-0.019415682181715965,
0.02111254446208477,
-0.008779032155871391,
0.02632816508412361,
0.015057424083352089,
0.037973999977111816,
-0.014226854778826237,
0.041939303278923035,
0.06440933793783188,
-0.0057470062747597694,
0.... |
4,401 | dagster._config.pythonic_config.resource | _initialize_and_run_cm | null | def _is_dagster_maintained(self) -> bool:
return self._dagster_maintained
| (self, context: dagster._core.execution.context.init.InitResourceContext) -> Generator[~TResValue, NoneType, NoneType] | [
0.08559146523475647,
0.052028145641088486,
-0.009615707211196423,
0.020864585414528847,
0.04746193438768387,
-0.005961905233561993,
-0.002876796294003725,
0.037729568779468536,
-0.05082826316356659,
-0.018614809960126877,
-0.012740395963191986,
-0.029880352318286896,
0.038862790912389755,
... |
4,402 | pydantic.main | _iter | null | @typing_extensions.deprecated(
'The private method `_iter` will be removed and should no longer be used.', category=None
)
def _iter(self, *args: Any, **kwargs: Any) -> Any:
warnings.warn(
'The private method `_iter` will be removed and should no longer be used.',
category=PydanticDeprecatedSinc... | (self, *args: Any, **kwargs: Any) -> Any | [
-0.01400976162403822,
-0.03485226258635521,
-0.05536813288927078,
-0.00882782693952322,
-0.07676678895950317,
-0.010496286675333977,
0.009498742409050465,
0.019121073186397552,
0.08439403027296066,
0.0049612391740083694,
0.006404588930308819,
-0.035187721252441406,
0.005720431916415691,
0.... |
4,403 | dagster._config.pythonic_config.resource | _resolve_and_update_nested_resources | Updates any nested resources with the resource values from the context.
In this case, populating partially configured resources or
resources that return plain Python types.
Returns a new instance of the resource.
| def _is_dagster_maintained(self) -> bool:
return self._dagster_maintained
| (self, context: dagster._core.execution.context.init.InitResourceContext) -> Generator[dagster._config.pythonic_config.resource.ConfigurableResourceFactory, NoneType, NoneType] | [
0.08559146523475647,
0.052028145641088486,
-0.009615707211196423,
0.020864585414528847,
0.04746193438768387,
-0.005961905233561993,
-0.002876796294003725,
0.037729568779468536,
-0.05082826316356659,
-0.018614809960126877,
-0.012740395963191986,
-0.029880352318286896,
0.038862790912389755,
... |
4,404 | dagster._config.pythonic_config.resource | _resolve_required_resource_keys | null | def _resolve_required_resource_keys(
self, resource_mapping: Mapping[int, str]
) -> AbstractSet[str]:
from dagster._core.execution.build_resources import wrap_resource_for_execution
# All dependent resources which are not fully configured
# must be specified to the Definitions object so that the
# r... | (self, resource_mapping: Mapping[int, str]) -> AbstractSet[str] | [
0.024239111691713333,
-0.03481383994221687,
-0.06293341517448425,
0.031834524124860764,
-0.01725059747695923,
0.033691998571157455,
0.0689656063914299,
-0.014188524335622787,
0.04792650043964386,
-0.015512663871049881,
0.03840005025267601,
0.028248311951756477,
0.025857504457235336,
-0.034... |
4,405 | dagster._config.pythonic_config.resource | _with_updated_values | Returns a new instance of the resource with the given values.
Used when initializing a resource at runtime.
| def _with_updated_values(
self, values: Optional[Mapping[str, Any]]
) -> "ConfigurableResourceFactory[TResValue]":
"""Returns a new instance of the resource with the given values.
Used when initializing a resource at runtime.
"""
values = check.opt_mapping_param(values, "values", key_type=str)
#... | (self, values: Optional[Mapping[str, Any]]) -> dagster._config.pythonic_config.resource.ConfigurableResourceFactory | [
0.05769340693950653,
-0.07257261872291565,
-0.029576076194643974,
-0.058860406279563904,
-0.011068236082792282,
0.017477601766586304,
0.00023590659839101136,
0.02468927763402462,
0.04737278074026108,
-0.015371536836028099,
-0.004127795342355967,
0.11341750621795654,
0.005009881220757961,
0... |
4,406 | pydantic.main | copy | Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```py
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}... | @typing_extensions.deprecated(
'The `copy` method is deprecated; use `model_copy` instead. '
'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
category=None,
)
def copy(
self: Model,
*,
include: AbstractSetIntStr | MappingIntStrAny | None = None,
... | (self: 'Model', *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'typing.Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Model' | [
-0.007945128716528416,
-0.03266330435872078,
-0.008926008827984333,
0.014938803389668465,
-0.0677984282374382,
-0.034095391631126404,
0.003347253194078803,
-0.033075276762247086,
-0.009475301019847393,
-0.030073782429099083,
-0.027346935123205185,
-0.018263986334204674,
0.01553713995963335,
... |
4,407 | dagster_snowflake.snowflake_io_manager | create_io_manager | null | def create_io_manager(self, context) -> DbIOManager:
return DbIOManager(
db_client=SnowflakeDbClient(),
io_manager_name="SnowflakeIOManager",
database=self.database,
schema=self.schema_,
type_handlers=self.type_handlers(),
default_load_type=self.default_load_type(),
... | (self, context) -> dagster._core.storage.db_io_manager.DbIOManager | [
0.015683015808463097,
-0.06511104851961136,
-0.025747962296009064,
-0.015930064022541046,
0.02719365432858467,
-0.018848899751901627,
0.008134306408464909,
0.09947826713323593,
-0.02455846779048443,
-0.010238795541226864,
-0.008340179920196533,
-0.00782778300344944,
-0.045822955667972565,
... |
4,408 | dagster._config.pythonic_config.io_manager | create_resource | null | def create_resource(self, context: InitResourceContext) -> TResValue:
return self.create_io_manager(context)
| (self, context: dagster._core.execution.context.init.InitResourceContext) -> ~TResValue | [
0.04039858281612396,
-0.036672670394182205,
-0.033084750175476074,
-0.05650971829891205,
0.054508764296770096,
0.019785301759839058,
0.02170000784099102,
0.0678599625825882,
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-0.05502625182271004,
0.09038795530796051,
0.0936998799443245,
-0.009478660300374031,
-0.0084... |
4,409 | dagster_snowflake.snowflake_io_manager | default_load_type | If an asset or op is not annotated with an return type, default_load_type will be used to
determine which TypeHandler to use to store and load the output.
If left unimplemented, default_load_type will return None. In that case, if there is only
one TypeHandler, the I/O manager will default to l... | @staticmethod
def default_load_type() -> Optional[Type]:
"""If an asset or op is not annotated with an return type, default_load_type will be used to
determine which TypeHandler to use to store and load the output.
If left unimplemented, default_load_type will return None. In that case, if there is only
... | () -> Optional[Type] | [
0.03348575159907341,
-0.09964878112077713,
-0.03551632910966873,
-0.0031234254129230976,
0.03149277716875076,
0.014975519850850105,
0.006087036337703466,
0.02329525351524353,
-0.028352899476885796,
-0.02914256788790226,
0.017626553773880005,
-0.005114050582051277,
-0.037377696484327316,
-0... |
4,410 | pydantic.main | dict | null | @typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None)
def dict( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: boo... | (self, *, include: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, exclude: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) -> Dict[str, Any] | [
-0.003605614183470607,
-0.03452107682824135,
-0.013271159492433071,
-0.03203998878598213,
-0.021365942433476448,
-0.020080771297216415,
-0.03730561211705208,
-0.01077221892774105,
-0.04669448733329773,
-0.07653898000717163,
-0.02697070874273777,
-0.006582031026482582,
-0.016537630930542946,
... |
4,411 | dagster._config.pythonic_config.resource | get_resource_context | Returns the context that this resource was initialized with. | def get_resource_context(self) -> InitResourceContext:
"""Returns the context that this resource was initialized with."""
return check.not_none(
self._state__internal__.resource_context,
additional_message="Attempted to get context before resource was initialized.",
)
| (self) -> dagster._core.execution.context.init.InitResourceContext | [
0.012936768122017384,
-0.028973162174224854,
0.01050532329827547,
0.004688882268965244,
0.0557190477848053,
0.005062415264546871,
0.10817142575979233,
0.03968265280127525,
0.061250120401382446,
-0.03617469221353531,
0.0341515839099884,
0.017224276438355446,
-0.0031553092412650585,
-0.01702... |
4,412 | dagster._config.pythonic_config.io_manager | get_resource_definition | null | def __init__(
self,
configurable_resource_cls: Type,
resource_fn: ResourceFunction,
config_schema: Any,
description: Optional[str],
resolve_resource_keys: Callable[[Mapping[int, str]], AbstractSet[str]],
nested_resources: Mapping[str, Any],
input_config_schema: Optional[Union[CoercableTo... | (self) -> dagster._config.pythonic_config.io_manager.ConfigurableIOManagerFactoryResourceDefinition | [
0.012116905301809311,
-0.05369234085083008,
-0.013006218709051609,
-0.026605313643813133,
-0.003459987696260214,
0.031385377049446106,
0.017628800123929977,
0.01441429927945137,
-0.033738356083631516,
-0.04494741931557655,
0.011542555876076221,
0.05647144466638565,
0.016989605501294136,
0.... |
4,413 | pydantic.main | json | null | @typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None)
def json( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none... | (self, *, include: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, exclude: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[... | [
-0.051316939294338226,
-0.07367749512195587,
0.04689556360244751,
-0.00926404818892479,
-0.004045376554131508,
-0.00019918270118068904,
-0.03997357562184334,
-0.02217935398221016,
-0.04410502314567566,
-0.08146926015615463,
-0.0020113629288971424,
-0.01332754548639059,
-0.014469129964709282,... |
4,414 | pydantic.main | model_copy | Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to... | def model_copy(self: Model, *, update: dict[str, Any] | None = None, deep: bool = False) -> Model:
"""Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Args:
update: Values to change/add in the new model. Note: the data is not validated
... | (self: ~Model, *, update: Optional[dict[str, Any]] = None, deep: bool = False) -> ~Model | [
0.011153331957757473,
-0.02194472774863243,
0.0223638117313385,
0.010096099227666855,
-0.05646195635199547,
-0.050861477851867676,
-0.010505657643079758,
-0.01805868186056614,
-0.010572330094873905,
-0.02301148511469364,
-0.022535255178809166,
-0.009381752461194992,
-0.029526326805353165,
... |
4,415 | pydantic.main | model_dump | Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output... | def model_dump(
self,
*,
mode: Literal['json', 'python'] | str = 'python',
include: IncEx = None,
exclude: IncEx = None,
context: dict[str, Any] | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
rou... | (self, *, mode: Union[Literal['json', 'python'], str] = 'python', include: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, exclude: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, context: Optional[dict[str, Any]] = None, by_alias: bool = False, exclude_unset: boo... | [
0.0032076281495392323,
-0.010475371964275837,
0.004639807622879744,
-0.038373325020074844,
0.032280877232551575,
-0.03502702713012695,
-0.015749430283904076,
-0.04313816502690315,
-0.05699620768427849,
-0.10606314241886139,
-0.026443039998412132,
-0.0019243494607508183,
-0.036918412894010544... |
4,416 | pydantic.main | model_dump_json | Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Fi... | def model_dump_json(
self,
*,
indent: int | None = None,
include: IncEx = None,
exclude: IncEx = None,
context: dict[str, Any] | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = Fa... | (self, *, indent: Optional[int] = None, include: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, exclude: Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], NoneType] = None, context: Optional[dict[str, Any]] = None, by_alias: bool = False, exclude_unset: bool = False, exclude_default... | [
-0.022473175078630447,
-0.023243172094225883,
0.020575042814016342,
-0.034202203154563904,
0.024747353047132492,
-0.05325515940785408,
-0.04204542934894562,
-0.0613848976790905,
-0.05715886503458023,
-0.09791500121355057,
-0.019106676802039146,
-0.025481536984443665,
-0.04405100643634796,
... |
4,417 | pydantic.main | model_post_init | Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
| def model_post_init(self, __context: Any) -> None:
"""Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
"""
pass
| (self, _BaseModel__context: Any) -> NoneType | [
-0.01661718264222145,
0.029840903356671333,
0.015198751352727413,
0.008196373470127583,
0.0314568392932415,
-0.021815100684762,
0.02479560114443302,
0.03147479146718979,
0.06406278163194656,
-0.029499763622879982,
0.009462187997996807,
0.02508287876844406,
0.0046368311159312725,
0.00814699... |
4,418 | dagster._config.pythonic_config.resource | process_config_and_initialize | Initializes this resource, fully processing its config and returning the prepared
resource value.
| def process_config_and_initialize(self) -> TResValue:
"""Initializes this resource, fully processing its config and returning the prepared
resource value.
"""
from dagster._config.post_process import post_process_config
return self.from_resource_context(
build_init_resource_context(
... | (self) -> ~TResValue | [
0.03538506478071213,
-0.04040434956550598,
-0.006086552981287241,
-0.0355815514922142,
0.01638859137892723,
0.04344092682003975,
0.037332046777009964,
0.007435150910168886,
0.007430685218423605,
-0.016335003077983856,
0.031669724732637405,
0.09081154316663742,
0.01774611882865429,
0.020827... |
4,419 | dagster._config.pythonic_config.resource | setup_for_execution | Optionally override this method to perform any pre-execution steps
needed before the resource is used in execution.
| def setup_for_execution(self, context: InitResourceContext) -> None:
"""Optionally override this method to perform any pre-execution steps
needed before the resource is used in execution.
"""
pass
| (self, context: dagster._core.execution.context.init.InitResourceContext) -> NoneType | [
0.007221629377454519,
0.01181404571980238,
-0.03618214651942253,
-0.03000088594853878,
0.053385209292173386,
0.010734501294791698,
0.032926104962825775,
0.031324200332164764,
-0.013973134569823742,
0.00013195036444813013,
0.035224489867687225,
0.02362809143960476,
0.030662542209029198,
-0.... |
4,420 | dagster._config.pythonic_config.resource | teardown_after_execution | Optionally override this method to perform any post-execution steps
needed after the resource is used in execution.
teardown_after_execution will be called even if any part of the run fails.
It will not be called if setup_for_execution fails.
| def teardown_after_execution(self, context: InitResourceContext) -> None:
"""Optionally override this method to perform any post-execution steps
needed after the resource is used in execution.
teardown_after_execution will be called even if any part of the run fails.
It will not be called if setup_for_e... | (self, context: dagster._core.execution.context.init.InitResourceContext) -> NoneType | [
0.030429214239120483,
0.03039483167231083,
-0.025185761973261833,
-0.041019268333911896,
0.03221714496612549,
-0.004736298695206642,
-0.009111572988331318,
-0.0016149402363225818,
0.0014215342234820127,
0.02767855115234852,
0.023621322587132454,
0.018670128658413887,
0.017174454405903816,
... |
4,421 | dagster_snowflake.snowflake_io_manager | type_handlers | type_handlers should return a list of the TypeHandlers that the I/O manager can use.
.. code-block:: python
from dagster_snowflake import SnowflakeIOManager
from dagster_snowflake_pandas import SnowflakePandasTypeHandler
from dagster_snowflake_pyspark import SnowflakePySpar... | @staticmethod
@abstractmethod
def type_handlers() -> Sequence[DbTypeHandler]:
"""type_handlers should return a list of the TypeHandlers that the I/O manager can use.
.. code-block:: python
from dagster_snowflake import SnowflakeIOManager
from dagster_snowflake_pandas import SnowflakePandasTypeHa... | () -> Sequence[dagster._core.storage.db_io_manager.DbTypeHandler] | [
0.01178663782775402,
-0.10502105951309204,
-0.0594070740044117,
0.01797739416360855,
-0.024400053545832634,
0.006377286743372679,
-0.012512589804828167,
0.020709797739982605,
-0.002855915343388915,
-0.0015968423103913665,
0.01721111312508583,
0.0042725298553705215,
-0.00041181393316946924,
... |
4,422 | dagster._config.pythonic_config.resource | with_replaced_resource_context | Returns a new instance of the resource with the given resource init context bound. | def with_replaced_resource_context(
self, resource_context: InitResourceContext
) -> "ConfigurableResourceFactory[TResValue]":
"""Returns a new instance of the resource with the given resource init context bound."""
# This utility is used to create a copy of this resource, without adjusting
# any values... | (self, resource_context: dagster._core.execution.context.init.InitResourceContext) -> dagster._config.pythonic_config.resource.ConfigurableResourceFactory | [
0.05554303899407387,
-0.062190767377614975,
-0.017400337383151054,
-0.03843331336975098,
-0.003414680017158389,
0.019961347803473473,
0.03825168311595917,
0.0389418825507164,
0.06887482106685638,
-0.037743113934993744,
0.018353905528783798,
0.039268821477890015,
-0.04722429811954498,
0.014... |
4,423 | dagster._config.pythonic_config.resource | with_resource_context | null | @property
def required_resource_keys(self) -> AbstractSet[str]:
return _resolve_required_resource_keys_for_resource(
self._resource, self._resource_id_to_key_mapping
)
| (self, resource_context: dagster._core.execution.context.init.InitResourceContext) -> dagster._config.pythonic_config.resource.ConfigurableResourceFactory | [
-0.0076745981350541115,
-0.04944615438580513,
-0.04941191151738167,
0.012284493073821068,
0.0114198699593544,
0.0292944498360157,
0.05872586742043495,
-0.03455067053437233,
0.10361777245998383,
-0.014142148196697235,
0.034156881272792816,
0.009459488093852997,
0.05941071733832359,
-0.00579... |
4,424 | dagster._config.pythonic_config.resource | yield_for_execution | Optionally override this method to perform any lifecycle steps
before or after the resource is used in execution. By default, calls
setup_for_execution before yielding, and teardown_after_execution after yielding.
Note that if you override this method and want setup_for_execution or
tea... | def _is_dagster_maintained(self) -> bool:
return self._dagster_maintained
| (self, context: dagster._core.execution.context.init.InitResourceContext) -> Generator[~TResValue, NoneType, NoneType] | [
0.08565548807382584,
0.05202654376626015,
-0.009682069532573223,
0.0208972729742527,
0.04742714390158653,
-0.005957555491477251,
-0.0028787909541279078,
0.037795066833496094,
-0.050860028713941574,
-0.018547579646110535,
-0.012706674635410309,
-0.029896097257733345,
0.03879493474960327,
-0... |
4,425 | dagster_snowflake.resources | SnowflakeResource | A resource for connecting to the Snowflake data warehouse.
If connector configuration is not set, SnowflakeResource.get_connection() will return a
`snowflake.connector.Connection <https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-api#object-connection>`__
object. If connector=... | class SnowflakeResource(ConfigurableResource, IAttachDifferentObjectToOpContext):
"""A resource for connecting to the Snowflake data warehouse.
If connector configuration is not set, SnowflakeResource.get_connection() will return a
`snowflake.connector.Connection <https://docs.snowflake.com/en/developer-gu... | (*, account: Optional[str] = None, user: str, password: Optional[str] = None, database: Optional[str] = None, schema: Optional[str] = None, role: Optional[str] = None, warehouse: Optional[str] = None, private_key: Optional[str] = None, private_key_password: Optional[str] = None, private_key_path: Optional[str] = None, ... | [
0.04083690419793129,
-0.1095312237739563,
-0.062194257974624634,
0.031798724085092545,
0.010353672318160534,
-0.046594105660915375,
0.001341282855719328,
0.03743211179971695,
-0.004846674390137196,
-0.032582856714725494,
-0.04692426696419716,
-0.023173240944743156,
0.00866159237921238,
0.0... |
4,434 | dagster._config.pythonic_config.resource | __init__ | null | def __init__(self, **data: Any):
resource_pointers, data_without_resources = separate_resource_params(self.__class__, data)
schema = infer_schema_from_config_class(
self.__class__, fields_to_omit=set(resource_pointers.keys())
)
# Populate config values
super().__init__(**data_without_resourc... | (self, **data: Any) | [
0.019202113151550293,
-0.055583056062459946,
0.006179606541991234,
-0.02183620072901249,
-0.01829545386135578,
0.033555980771780014,
0.024241236969828606,
-0.013838501647114754,
-0.028230542317032814,
-0.03578922897577286,
0.025863680988550186,
0.07501421868801117,
-0.01137620210647583,
0.... |
4,458 | dagster_snowflake.resources | _snowflake_private_key | null | def _snowflake_private_key(self, config) -> bytes:
# If the user has defined a path to a private key, we will use that.
if config.get("private_key_path", None) is not None:
# read the file from the path.
with open(config.get("private_key_path"), "rb") as key:
private_key = key.read()... | (self, config) -> bytes | [
0.05476564168930054,
-0.040620628744363785,
-0.056320853531360626,
0.07313194125890732,
-0.034362755715847015,
-0.03660300001502037,
-0.012673118151724339,
0.053395576775074005,
0.08035256713628769,
-0.009326636791229248,
0.018245957791805267,
-0.008812862448394299,
-0.000022691008780384436,... |
4,461 | dagster._config.pythonic_config.resource | create_resource | Returns the object that this resource hands to user code, accessible by ops or assets
through the context or resource parameters. This works like the function decorated
with @resource when using function-based resources.
For ConfigurableResource, this function will return itself, passing
... | def create_resource(self, context: InitResourceContext) -> TResValue:
"""Returns the object that this resource hands to user code, accessible by ops or assets
through the context or resource parameters. This works like the function decorated
with @resource when using function-based resources.
For Config... | (self, context: dagster._core.execution.context.init.InitResourceContext) -> ~TResValue | [
0.03887272998690605,
-0.07319612801074982,
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0.040056295692920685,
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0.039094649255275726,
0.023393912240862846,
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-0.06631665676832199,
0.07449065148830414,
0.07223448157310486,
0.014877787791192532,
-0.004... |
4,463 | dagster_snowflake.resources | get_connection | Gets a connection to Snowflake as a context manager.
If connector configuration is not set, SnowflakeResource.get_connection() will return a
`snowflake.connector.Connection <https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-api#object-connection>`__
If connector="s... | @compat_model_validator(mode="before")
def validate_authentication(cls, values):
auths_set = 0
auths_set += 1 if values.get("password") is not None else 0
auths_set += 1 if values.get("private_key") is not None else 0
auths_set += 1 if values.get("private_key_path") is not None else 0
# if authentic... | (self, raw_conn: bool = True) -> Iterator[Union[Any, snowflake.connector.connection.SnowflakeConnection]] | [
0.051467154175043106,
-0.08256960660219193,
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0.0390261746942997,
0.008270845748484135,
0.0014290004037320614,
-0.0021637496538460255,
0.045431796461343765,
... |
4,464 | dagster_snowflake.resources | get_object_to_set_on_execution_context | null | def get_object_to_set_on_execution_context(self) -> Any:
# Directly create a SnowflakeConnection here for backcompat since the SnowflakeConnection
# has methods this resource does not have
return SnowflakeConnection(
config=self._resolved_config_dict,
log=get_dagster_logger(),
snowfl... | (self) -> Any | [
0.0337679460644722,
-0.11543548107147217,
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-0.02322787046432495,
-0.04407339543104172,
-0.023642977699637413,
-0.0013795496197417378,
-... |
4,466 | dagster._config.pythonic_config.resource | get_resource_definition | null | import contextlib
import inspect
from typing import (
AbstractSet,
Any,
Callable,
Dict,
Generator,
Generic,
List,
Mapping,
NamedTuple,
Optional,
Set,
Type,
TypeVar,
Union,
cast,
)
from typing_extensions import TypeAlias, TypeGuard, get_args, get_origin
from ... | (self) -> dagster._config.pythonic_config.resource.ConfigurableResourceFactoryResourceDefinition | [
0.08229289948940277,
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0.007669590879231691,
0.009054510854184628,
-0.0012261316878721118,
-... |
4,478 | dagster_snowflake.snowflake_io_manager | build_snowflake_io_manager | Builds an IO manager definition that reads inputs from and writes outputs to Snowflake.
Args:
type_handlers (Sequence[DbTypeHandler]): Each handler defines how to translate between
slices of Snowflake tables and an in-memory type - e.g. a Pandas DataFrame. If only
one DbTypeHandler ... | def build_snowflake_io_manager(
type_handlers: Sequence[DbTypeHandler], default_load_type: Optional[Type] = None
) -> IOManagerDefinition:
"""Builds an IO manager definition that reads inputs from and writes outputs to Snowflake.
Args:
type_handlers (Sequence[DbTypeHandler]): Each handler defines h... | (type_handlers: Sequence[dagster._core.storage.db_io_manager.DbTypeHandler], default_load_type: Optional[Type] = None) -> dagster._core.storage.io_manager.IOManagerDefinition | [
0.023008622229099274,
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0.03129172697663307,
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0.04055653139948845,
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0.0009152318234555423,
-0.030350929126143456,
0.006196988746523857,
-0.03219161927700043,
0.... |
4,479 | dagster_snowflake.resources | fetch_last_updated_timestamps | Fetch the last updated times of a list of tables in Snowflake.
If the underlying query to fetch the last updated time returns no results, a ValueError will be raised.
Args:
snowflake_connection (Union[SqlDbConnection, SnowflakeConnection]): A connection to Snowflake.
Accepts either a Snowf... | def fetch_last_updated_timestamps(
*,
snowflake_connection: Union[SqlDbConnection, snowflake.connector.SnowflakeConnection],
schema: str,
tables: Sequence[str],
database: Optional[str] = None,
) -> Mapping[str, datetime]:
"""Fetch the last updated times of a list of tables in Snowflake.
If ... | (*, snowflake_connection: Union[Any, snowflake.connector.connection.SnowflakeConnection], schema: str, tables: Sequence[str], database: Optional[str] = None) -> Mapping[str, datetime.datetime] | [
0.04945294186472893,
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0.05477558821439743,
0.03275015950202942,
-0.058072447776794434,
-0.05533168464899063,
-0.029910091310739517,
0.02... |
4,483 | dagster_snowflake.ops | snowflake_op_for_query | This function is an op factory that constructs an op to execute a snowflake query.
Note that you can only use `snowflake_op_for_query` if you know the query you'd like to
execute at graph construction time. If you'd like to execute queries dynamically during
job execution, you should manually execute those... | def snowflake_op_for_query(sql, parameters=None):
"""This function is an op factory that constructs an op to execute a snowflake query.
Note that you can only use `snowflake_op_for_query` if you know the query you'd like to
execute at graph construction time. If you'd like to execute queries dynamically du... | (sql, parameters=None) | [
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0.023732254281640053,
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0.0062535288743674755,
-0.010602068156003952,
-0.013050069101154804,
0.018497982993721962,
-... |
4,487 | ifconf.common | ConfigBuilder | null | class ConfigBuilder(metaclass=abc.ABCMeta):
@abc.abstractmethod
def add_attr(self, name, default=None, required=False, hidden=False, help=''):
pass
@abc.abstractmethod
def add_attr_boolean(self, name, default=False, required=False, hidden=False, help=''):
pass
@abc.abstractmethod
... | () | [
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0.0007170853787101805,
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-0.03533344343304634,
0.01094275526702404,
0.04537155106663704,
0.014570043422281742,
0.... |
4,488 | ifconf.common | add_attr | null | @abc.abstractmethod
def add_attr(self, name, default=None, required=False, hidden=False, help=''):
pass
| (self, name, default=None, required=False, hidden=False, help='') | [
0.0008908712188713253,
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0.05298366770148277,
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-0.00628972752019763,
0.02219315804541111,
0.021176805719733238,
0.07077818363904953,
0.06... |
4,489 | ifconf.common | add_attr_boolean | null | @abc.abstractmethod
def add_attr_boolean(self, name, default=False, required=False, hidden=False, help=''):
pass
| (self, name, default=False, required=False, hidden=False, help='') | [
0.00525978347286582,
-0.003003503195941448,
0.057440172880887985,
0.009651284664869308,
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0.00955944787710905,
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-0.014159671030938625,
0.013049271889030933,
0.025948263704776764,
0.038037415593862534,
0... |
4,490 | ifconf.common | add_attr_dict | null | @abc.abstractmethod
def add_attr_dict(self, name, default={}, required=False, hidden=False, help=''):
pass
| (self, name, default={}, required=False, hidden=False, help='') | [
0.00895700603723526,
0.011889309622347355,
0.02866579033434391,
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0.0022076533641666174,
0.00802591536194086,
0.006749352905899286,
-0.014341320842504501,
0.02639072760939598,
0.0369739830493927,
0.04765835031867027,
0.0626... |
4,491 | ifconf.common | add_attr_float | null | @abc.abstractmethod
def add_attr_float(self, name, default=0.0, required=False, hidden=False, help=''):
pass
| (self, name, default=0.0, required=False, hidden=False, help='') | [
0.015280394814908504,
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0.06132543087005615,
0.06231071427464485,
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0.011653530411422253,
0.02957550436258316,
-0.007147555239498615,
0.050521280616521835,
... |
4,492 | ifconf.common | add_attr_int | null | @abc.abstractmethod
def add_attr_int(self, name, default=0, required=False, hidden=False, help=''):
pass
| (self, name, default=0, required=False, hidden=False, help='') | [
0.0032179115805774927,
-0.00046723405830562115,
0.054393354803323746,
0.02315392717719078,
0.01837613247334957,
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0.015887001529335976,
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-0.0333443284034729,
0.008770427666604519,
0.02661198191344738,
0.06214474141597748,
0.0... |
4,493 | ifconf.common | add_attr_list | null | @abc.abstractmethod
def add_attr_list(self, name, default=[], required=False, hidden=False, help=''):
pass
| (self, name, default=[], required=False, hidden=False, help='') | [
-0.000990512315183878,
0.005523500498384237,
0.021618131548166275,
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0.009721361100673676,
0.026427824050188065,
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0.013001469895243645,
0.020207513123750687,
0.006313785910606384,
0.0030804136767983437,
0.04632942005991936,
... |
4,494 | ifconf.common | add_attr_path | null | @abc.abstractmethod
def add_attr_path(self, name, default=None, required=False, hidden=False, help=''):
pass
| (self, name, default=None, required=False, hidden=False, help='') | [
0.01108130719512701,
0.011139022186398506,
0.051481906324625015,
0.02605426497757435,
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0.03131458908319473,
0.02887406200170517,
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0.012111934833228588,
0.05807792395353317,
0.001455246121622622,
0.09188250452280045,
0.0631898... |
4,497 | ifconf.common | config_callback | null | def config_callback(section = None):
def _decorator(func):
if hasattr(func, '__SECTION__'):
return func
func.__MODULE_NAME__ = get_module_name_for(func)
func.__SECTION__ = section if type(section) == str and section else '{}_{}'.format(func.__MODULE_NAME__, func.__name__)
... | (section=None) | [
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0.020473027601838112,
0.030155280604958534,
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0.0011453290935605764,
0.035230234265327454,
-0.0002473607601132244,
0.0945015549659729,
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-0.054490815848112106,
-0.01183867547661066,
-0.04541478678584099,
0.04863642901182175,
... |
4,498 | ifconf.main | configure_main | null | def configure_main(argparser = None
, with_default_args = True
, config_arg = 'config.ini'
, config_path = []
, with_config_logging = True
, callback_methods = []):
global __MAIN_CONFIG__
__MAIN_CONFIG__ = Config(argp... | (argparser=None, with_default_args=True, config_arg='config.ini', config_path=[], with_config_logging=True, callback_methods=[]) | [
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0.01891603134572506,
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0.03181159868836403,
0.011803527362644672,
-0.05587446317076683,
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-0.07566412538290024,
-0.008603375405073166,
... |
4,499 | ifconf.module | configure_module | null | def configure_module(*callback_methods, override = {}, immutable = True):
config = get_main_config()
loaders = [ConfigLoader.load(config_callback()(callback), config) for callback in callback_methods]
loaders[0].prepend_name_value_dict(override)
return reduce(lambda a,b: a.append_name_values(b), loaders... | (*callback_methods, override={}, immutable=True) | [
-0.016965433955192566,
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0.05782756954431534,
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0.0067564561031758785,
-0.018232816830277443,
0.0036885179579257965,
... |
4,505 | face_recognition_models | cnn_face_detector_model_location | null | def cnn_face_detector_model_location():
return resource_filename(__name__, "models/mmod_human_face_detector.dat")
| () | [
0.03511763736605644,
-0.03570660948753357,
0.004502438008785248,
0.015488202683627605,
0.01079480815678835,
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0.004148132633417845,
-0.04255344718694687,
0.032... |
4,506 | face_recognition_models | face_recognition_model_location | null | def face_recognition_model_location():
return resource_filename(__name__, "models/dlib_face_recognition_resnet_model_v1.dat")
| () | [
0.047633469104766846,
-0.04816867783665657,
-0.02481069043278694,
0.016973724588751793,
0.0128927668556571,
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0.05998146906495094,
0.0330299511551857,
-0.03761744126677513,
-0.00373928458429873,
0.0018791996408253908,
-0.05562335252761841,
0.04201... |
4,507 | face_recognition_models | pose_predictor_five_point_model_location | null | def pose_predictor_five_point_model_location():
return resource_filename(__name__, "models/shape_predictor_5_face_landmarks.dat")
| () | [
0.0292782261967659,
-0.04094656556844711,
-0.01966795139014721,
0.039622243493795395,
0.022137630730867386,
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0.0063844784162938595,
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0.010326121933758259,
0.008133834227919579,
0.006800565868616104,
-0.02172601781785488,
0.... |
4,508 | face_recognition_models | pose_predictor_model_location | null | def pose_predictor_model_location():
return resource_filename(__name__, "models/shape_predictor_68_face_landmarks.dat")
| () | [
0.04584551230072975,
-0.027609026059508324,
-0.02657368592917919,
0.027318403124809265,
0.031205464154481888,
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0.051004040986299515,
0.008141937665641308,
0.010798398405313492,
0.004159517586231232,
-0.008859409019351006,
-0.018200160935521126,
... |
4,509 | optbinning.binning.binning_process | BinningProcess | Binning process to compute optimal binning of variables in a dataset,
given a binary, continuous or multiclass target dtype.
Parameters
----------
variable_names : array-like
List of variable names.
max_n_prebins : int (default=20)
The maximum number of bins after pre-binning (preb... | class BinningProcess(Base, BaseEstimator, BaseBinningProcess):
"""Binning process to compute optimal binning of variables in a dataset,
given a binary, continuous or multiclass target dtype.
Parameters
----------
variable_names : array-like
List of variable names.
max_n_prebins : int (... | (variable_names, max_n_prebins=20, min_prebin_size=0.05, min_n_bins=None, max_n_bins=None, min_bin_size=None, max_bin_size=None, max_pvalue=None, max_pvalue_policy='consecutive', selection_criteria=None, fixed_variables=None, categorical_variables=None, special_codes=None, split_digits=None, binning_fit_params=None, bi... | [
0.05543598160147667,
-0.023499617353081703,
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0.014947297982871532,
-0.012857372872531414,
-0.022132016718387604,
-0.07951346039772034,
-0.0077481321059167385,
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-0.07392748445272446,
-0.04172145202755928,
0.02829585038125515,
0.010122171603143215,
... |
4,510 | sklearn.base | __getstate__ | null | def __getstate__(self):
if getattr(self, "__slots__", None):
raise TypeError(
"You cannot use `__slots__` in objects inheriting from "
"`sklearn.base.BaseEstimator`."
)
try:
state = super().__getstate__()
if state is None:
# For Python 3.11+, e... | (self) | [
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0.005366118159145117,
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0.003921036142855883,
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0.06076325848698616,
-0.015879614278674126,
... |
4,511 | optbinning.binning.binning_process | __init__ | null | def __init__(self, variable_names, max_n_prebins=20, min_prebin_size=0.05,
min_n_bins=None, max_n_bins=None, min_bin_size=None,
max_bin_size=None, max_pvalue=None,
max_pvalue_policy="consecutive", selection_criteria=None,
fixed_variables=None, categorical_variables=No... | (self, variable_names, max_n_prebins=20, min_prebin_size=0.05, min_n_bins=None, max_n_bins=None, min_bin_size=None, max_bin_size=None, max_pvalue=None, max_pvalue_policy='consecutive', selection_criteria=None, fixed_variables=None, categorical_variables=None, special_codes=None, split_digits=None, binning_fit_params=No... | [
0.021731017157435417,
-0.017425984144210815,
-0.05319982022047043,
-0.004663039464503527,
-0.016557816416025162,
-0.006318821106106043,
-0.07525303959846497,
-0.029428163543343544,
-0.04475085809826851,
-0.05627867951989174,
-0.04671989381313324,
0.0827711820602417,
-0.014696181751787663,
... |
4,512 | sklearn.base | __repr__ | null | def __repr__(self, N_CHAR_MAX=700):
# N_CHAR_MAX is the (approximate) maximum number of non-blank
# characters to render. We pass it as an optional parameter to ease
# the tests.
from .utils._pprint import _EstimatorPrettyPrinter
N_MAX_ELEMENTS_TO_SHOW = 30 # number of elements to show in sequences... | (self, N_CHAR_MAX=700) | [
0.022142434492707253,
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0.03323216736316681,
-0.021642563864588737,
0.015727423131465912,
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0.020883500576019287,
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-0.005054251290857792,
0.07627661526203156,
0.0265301913022995,
... |
4,513 | sklearn.base | __setstate__ | null | def __setstate__(self, state):
if type(self).__module__.startswith("sklearn."):
pickle_version = state.pop("_sklearn_version", "pre-0.18")
if pickle_version != __version__:
warnings.warn(
InconsistentVersionWarning(
estimator_name=self.__class__.__name... | (self, state) | [
0.006920168176293373,
0.012480197474360466,
0.017249874770641327,
0.02870996855199337,
-0.019979342818260193,
-0.036153972148895264,
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0.02268124185502529,
0.0008271117694675922,
0.004742107354104519,
-0.03926023840904236,
0.041465867310762405,
0.007283178623765707,
0.... |
4,514 | sklearn.base | __sklearn_clone__ | null | def __sklearn_clone__(self):
return _clone_parametrized(self)
| (self) | [
-0.015217680484056473,
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0.05367782339453697,
0.00959612987935543,
-0.08853311836719513,
0.015276500955224037,
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0.03993065655231476,
0.060769885778427124,
0.044669900089502335,
-0.0461152009665966,
0.0028296818491071463,
-0.008881881833076477,
0.0... |
4,515 | optbinning.binning.binning_process | _binning_selection_criteria | null | def _binning_selection_criteria(self):
for i, name in enumerate(self.variable_names):
optb = self._binned_variables[name]
optb.binning_table.build()
n_bins = len(optb.splits)
if isinstance(optb, OptimalPWBinning) or optb.dtype == "numerical":
n_bins += 1
if isinst... | (self) | [
0.0248646792024374,
-0.03609274700284004,
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0.03673030063509941,
-0.005901819095015526,
-... |
4,516 | sklearn.base | _check_feature_names | Set or check the `feature_names_in_` attribute.
.. versionadded:: 1.0
Parameters
----------
X : {ndarray, dataframe} of shape (n_samples, n_features)
The input samples.
reset : bool
Whether to reset the `feature_names_in_` attribute.
If Fals... | def _check_feature_names(self, X, *, reset):
"""Set or check the `feature_names_in_` attribute.
.. versionadded:: 1.0
Parameters
----------
X : {ndarray, dataframe} of shape (n_samples, n_features)
The input samples.
reset : bool
Whether to reset the `feature_names_in_` attribute... | (self, X, *, reset) | [
0.0045377155765891075,
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0.01729603484272957,
0.016621669754385948,
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0.020097972825169563,
0.021636664867401123,
0.09034589678049088,
-0.00638272101059556,
-0.021484695374965668,
-0.009545587003231049,
0.029197148978710175,
-... |
4,517 | optbinning.binning.base | _check_is_fitted | null | def _check_is_fitted(self):
if not self._is_fitted:
raise NotFittedError("This {} instance is not fitted yet. Call "
"'fit' with appropriate arguments."
.format(self.__class__.__name__))
| (self) | [
0.051112741231918335,
0.008767640218138695,
0.009679128415882587,
-0.0004899244522675872,
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0.009340575896203518,
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0.03251839801669121,
0.06413398683071136,
0.035035837441682816,
-0.04607785865664482,
0.044550031423568726,
-0.... |
4,518 | sklearn.base | _check_n_features | Set the `n_features_in_` attribute, or check against it.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input samples.
reset : bool
If True, the `n_features_in_` attribute is set to `X.shape[1]`.
If False and t... | def _check_n_features(self, X, reset):
"""Set the `n_features_in_` attribute, or check against it.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input samples.
reset : bool
If True, the `n_features_in_` attribute is set to `X.shape[1]`.
... | (self, X, reset) | [
0.005124994553625584,
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0.018185174092650414,
0.02194110117852688,
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0.003411408979445696,
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0.012006358243525028,
-0.032263148576021194,
-0.0199415422976017,
0.019040841609239578,
-0.00465... |
4,519 | optbinning.binning.binning_process | _fit | null | def _fit(self, X, y, sample_weight, check_input):
time_init = time.perf_counter()
if self.verbose:
logger.info("Binning process started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params())
# check X dtype
if not isinstance(X, (pd.DataFrame, np.ndarray))... | (self, X, y, sample_weight, check_input) | [
0.052776772528886795,
-0.01366965938359499,
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0.008657450787723064,
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-0.06339552253484726,
-0.04287122189998627,
0.007706518284976482,
0.01866205781698227,
-0... |
4,520 | optbinning.binning.binning_process | _fit_disk | null | def _fit_disk(self, input_path, target, **kwargs):
time_init = time.perf_counter()
if self.verbose:
logger.info("Binning process started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params())
# Input file extension
extension = input_path.split(".")[1]
... | (self, input_path, target, **kwargs) | [
0.05389077588915825,
-0.006731361150741577,
-0.04890458285808563,
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-0.03348727524280548,
-0.06681498885154724,
-0.014519794844090939,
0.04208347201347351,
0.014619519002735615,
-0.... |
4,521 | optbinning.binning.binning_process | _fit_from_dict | null | def _fit_from_dict(self, dict_optb):
time_init = time.perf_counter()
if self.verbose:
logger.info("Binning process started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params())
if not isinstance(dict_optb, dict):
raise TypeError("dict_optb must be a ... | (self, dict_optb) | [
0.03991638869047165,
-0.02542567066848278,
-0.07073306292295456,
-0.03525182977318764,
-0.06232156604528427,
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-0.05471298098564148,
0.011776099912822247,
-0.03553858771920204,
-0.05085134133696556,
-0.04828965663909912,
0.07264477014541626,
-0.01079157181084156,
-0.019... |
4,522 | sklearn.utils._estimator_html_repr | _get_doc_link | Generates a link to the API documentation for a given estimator.
This method generates the link to the estimator's documentation page
by using the template defined by the attribute `_doc_link_template`.
Returns
-------
url : str
The URL to the API documentation for ... | def _get_doc_link(self):
"""Generates a link to the API documentation for a given estimator.
This method generates the link to the estimator's documentation page
by using the template defined by the attribute `_doc_link_template`.
Returns
-------
url : str
The URL to the API documentatio... | (self) | [
0.010168884880840778,
-0.03472078591585159,
-0.004028618801385164,
-0.02348920702934265,
0.015317454934120178,
-0.005950171500444412,
0.02643910050392151,
0.03330996632575989,
-0.03737752139568329,
0.07856608182191849,
0.018835339695215225,
0.0022284514270722866,
0.019513264298439026,
0.01... |
4,523 | sklearn.utils._metadata_requests | _get_metadata_request | Get requested data properties.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
request : MetadataRequest
A :class:`~sklearn.utils.metadata_routing.MetadataRequest` instance.
| def _get_metadata_request(self):
"""Get requested data properties.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
request : MetadataRequest
A :class:`~sklearn.utils.metadata_routing.MetadataRequest` instance.
"""
if hasattr(s... | (self) | [
0.03515271097421646,
0.01016147155314684,
-0.023042337968945503,
-0.04554079845547676,
0.019634030759334564,
0.015183287672698498,
0.0189451165497303,
0.03498954698443413,
0.05982668697834015,
0.011212971061468124,
0.007188267074525356,
0.02810041233897209,
0.01463034376502037,
-0.02998585... |
4,524 | sklearn.base | _get_tags | null | def _get_tags(self):
collected_tags = {}
for base_class in reversed(inspect.getmro(self.__class__)):
if hasattr(base_class, "_more_tags"):
# need the if because mixins might not have _more_tags
# but might do redundant work in estimators
# (i.e. calling more tags on B... | (self) | [
0.0007553865434601903,
-0.05209498107433319,
-0.03291675075888634,
-0.009166749194264412,
-0.04065859317779541,
0.004281683824956417,
0.004518394824117422,
-0.04440883919596672,
0.052949000149965286,
0.003395178122445941,
0.02066347748041153,
-0.0007780133164487779,
0.03391929343342781,
-0... |
4,525 | sklearn.base | _more_tags | null | def _more_tags(self):
return _DEFAULT_TAGS
| (self) | [
-0.0006326489383354783,
-0.0549827478826046,
0.03883618116378784,
-0.02817733772099018,
-0.0045950873754918575,
0.03206447511911392,
0.037147652357816696,
-0.04326857253909111,
0.08337114751338959,
-0.04038400202989578,
0.0046874284744262695,
0.013323553837835789,
-0.01439647376537323,
0.0... |
4,526 | sklearn.base | _repr_html_inner | This function is returned by the @property `_repr_html_` to make
`hasattr(estimator, "_repr_html_") return `True` or `False` depending
on `get_config()["display"]`.
| def _repr_html_inner(self):
"""This function is returned by the @property `_repr_html_` to make
`hasattr(estimator, "_repr_html_") return `True` or `False` depending
on `get_config()["display"]`.
"""
return estimator_html_repr(self)
| (self) | [
0.05660282075405121,
-0.06867533177137375,
0.025455746799707413,
0.004117588046938181,
0.030301997438073158,
0.0006650658906437457,
0.01814325712621212,
-0.010830764658749104,
0.00958902109414339,
0.03383751958608627,
0.008127385750412941,
0.00467809708788991,
0.01971268281340599,
0.002968... |
4,527 | sklearn.base | _repr_mimebundle_ | Mime bundle used by jupyter kernels to display estimator | def _repr_mimebundle_(self, **kwargs):
"""Mime bundle used by jupyter kernels to display estimator"""
output = {"text/plain": repr(self)}
if get_config()["display"] == "diagram":
output["text/html"] = estimator_html_repr(self)
return output
| (self, **kwargs) | [
0.03578951582312584,
-0.08578288555145264,
0.04075736925005913,
-0.016810227185487747,
0.03260589390993118,
-0.020448653027415276,
-0.009288481436669827,
0.017719833180308342,
0.01906675100326538,
0.024471912533044815,
0.04502552002668381,
0.022162912413477898,
-0.022092942148447037,
0.029... |
4,528 | optbinning.binning.binning_process | _support_selection_criteria | null | def _support_selection_criteria(self):
self._support = np.full(self._n_variables, True, dtype=bool)
if self.selection_criteria is None:
return
default_metrics_info = _METRICS[self._target_dtype]
criteria_metrics = self.selection_criteria.keys()
binning_metrics = pd.DataFrame.from_dict(self._... | (self) | [
0.054883718490600586,
-0.02738884836435318,
-0.04933526739478111,
0.0005196150741539896,
-0.007023914251476526,
0.027936626225709915,
-0.011255932971835136,
-0.0545303151011467,
-0.06488506495952606,
-0.001981273991987109,
0.01975531131029129,
0.04792165011167526,
0.02427888847887516,
-0.0... |
4,529 | optbinning.binning.binning_process | _transform | null | def _transform(self, X, metric, metric_special, metric_missing,
show_digits, check_input):
# Check X dtype
if not isinstance(X, (pd.DataFrame, np.ndarray)):
raise TypeError("X must be a pandas.DataFrame or numpy.ndarray.")
n_samples, n_variables = X.shape
mask = self.get_support()... | (self, X, metric, metric_special, metric_missing, show_digits, check_input) | [
0.006037148647010326,
-0.05576341971755028,
0.01981273479759693,
0.009639021940529346,
-0.034667424857616425,
-0.02467356249690056,
-0.011179904453456402,
0.004700420890003443,
0.002683177124708891,
-0.033423054963350296,
-0.019598858430981636,
0.029223298653960228,
0.022690344601869583,
-... |
4,530 | optbinning.binning.binning_process | _transform_disk | null | def _transform_disk(self, input_path, output_path, chunksize, metric,
metric_special, metric_missing, show_digits, **kwargs):
# check input_path and output_path extensions
input_extension = input_path.split(".")[1]
output_extension = output_path.split(".")[1]
if input_extension != "c... | (self, input_path, output_path, chunksize, metric, metric_special, metric_missing, show_digits, **kwargs) | [
0.0182142723351717,
-0.017868882045149803,
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0.0014336247695609927,
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0.036001887172460556,
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-0.056115806102752686,
0.021251676604151726,
0.04579472169280052,
0.024096069857478142,
0.0... |
4,531 | sklearn.base | _validate_data | Validate input data and set or check the `n_features_in_` attribute.
Parameters
----------
X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features), default='no validation'
The input samples.
If `'no_validation'`, no validation is perfo... | def _validate_data(
self,
X="no_validation",
y="no_validation",
reset=True,
validate_separately=False,
cast_to_ndarray=True,
**check_params,
):
"""Validate input data and set or check the `n_features_in_` attribute.
Parameters
----------
X : {array-like, sparse matrix, datafr... | (self, X='no_validation', y='no_validation', reset=True, validate_separately=False, cast_to_ndarray=True, **check_params) | [
0.015838082879781723,
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0.03471894934773445,
0.006748739629983902,
-0.012912328355014324,
-0.005597942974418402,
-0.004905514419078827,
0.03585024178028107,
0.005929528269916773,
-0.0030062124133110046,
0.0076801045797765255,
-0.01831522211432457,
0.05359981581568718,
... |
4,532 | sklearn.base | _validate_params | Validate types and values of constructor parameters
The expected type and values must be defined in the `_parameter_constraints`
class attribute, which is a dictionary `param_name: list of constraints`. See
the docstring of `validate_parameter_constraints` for a description of the
accep... | def _validate_params(self):
"""Validate types and values of constructor parameters
The expected type and values must be defined in the `_parameter_constraints`
class attribute, which is a dictionary `param_name: list of constraints`. See
the docstring of `validate_parameter_constraints` for a descriptio... | (self) | [
0.026387397199869156,
0.014883355237543583,
0.04443436488509178,
-0.015152981504797935,
-0.03249892219901085,
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-0.005095931235700846,
-0.015476532280445099,
0.011872531846165657,
0.00929310917854309,
-0.02065335214138031,
0.019035596400499344,
0.023529361933469772,
-0... |
4,533 | optbinning.binning.binning_process | fit | Fit the binning process. Fit the optimal binning to all variables
according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples.
.. versionch... | def fit(self, X, y, sample_weight=None, check_input=False):
"""Fit the binning process. Fit the optimal binning to all variables
according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is th... | (self, X, y, sample_weight=None, check_input=False) | [
0.04616078734397888,
-0.013100175186991692,
-0.015964698046445847,
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-0.029010137543082237,
-0.03751248121261597,
0.03784089535474777,
0.02444879524409771,
0.001994447084143758,
-0.01840045489370823,
-0.04287661984562874,
0.020033415406942368,
0.0... |
4,534 | optbinning.binning.binning_process | fit_disk | Fit the binning process according to the given training data on
disk.
Parameters
----------
input_path : str
Any valid string path to a file with extension .csv or .parquet.
target : str
Target column.
**kwargs : keyword arguments
Ke... | def fit_disk(self, input_path, target, **kwargs):
"""Fit the binning process according to the given training data on
disk.
Parameters
----------
input_path : str
Any valid string path to a file with extension .csv or .parquet.
target : str
Target column.
**kwargs : keyword ar... | (self, input_path, target, **kwargs) | [
0.04764252156019211,
0.004022679757326841,
-0.049461472779512405,
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0.053029414266347885,
0.009636941365897655,
-0.03840784728527069,
0.02551778219640255,
-0.020970404148101807,
0.016571691259741783,
-0.... |
4,535 | optbinning.binning.binning_process | fit_from_dict | Fit the binning process from a dict of OptimalBinning objects
already fitted.
Parameters
----------
dict_optb : dict
Dictionary with OptimalBinning objects for binary, continuous
or multiclass target. All objects must share the same class.
Returns
... | def fit_from_dict(self, dict_optb):
"""Fit the binning process from a dict of OptimalBinning objects
already fitted.
Parameters
----------
dict_optb : dict
Dictionary with OptimalBinning objects for binary, continuous
or multiclass target. All objects must share the same class.
R... | (self, dict_optb) | [
0.05840994790196419,
-0.026106927543878555,
-0.06540320068597794,
-0.018986856564879417,
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0.011839609593153,
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-0.007908170111477375,
-0.02572646550834179,
0.05533001944422722,
-0.01534529309719801,
0.0103... |
4,536 | optbinning.binning.binning_process | fit_transform | Fit the binning process according to the given training data, then
transform it.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples.
y : array-like of shape (n_samples,)
... | def fit_transform(self, X, y, sample_weight=None, metric=None,
metric_special=0, metric_missing=0, show_digits=2,
check_input=False):
"""Fit the binning process according to the given training data, then
transform it.
Parameters
----------
X : {array-like, sparse ... | (self, X, y, sample_weight=None, metric=None, metric_special=0, metric_missing=0, show_digits=2, check_input=False) | [
0.02537260204553604,
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0.04076070338487625,
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0.04502374678850174,
0.0287194661796093,
0.006146825850009918,
-0.031841959804296494,
-0.02812114544212818,
0.05220361426472664,
-0.0... |
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