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
|
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52,676 |
bmitzkus_pulumi_onepassword._inputs
|
__init__
|
:param pulumi.Input[bool] digits: Use digits [0-9] when generating the password.
:param pulumi.Input[int] length: The length of the password to be generated.
:param pulumi.Input[bool] letters: Use letters [a-zA-Z] when generating the password.
:param pulumi.Input[bool] symbols: Use symbols [[email protected]_*] when generating the password.
|
def __init__(__self__, *,
digits: Optional[pulumi.Input[bool]] = None,
length: Optional[pulumi.Input[int]] = None,
letters: Optional[pulumi.Input[bool]] = None,
symbols: Optional[pulumi.Input[bool]] = None):
"""
:param pulumi.Input[bool] digits: Use digits [0-9] when generating the password.
:param pulumi.Input[int] length: The length of the password to be generated.
:param pulumi.Input[bool] letters: Use letters [a-zA-Z] when generating the password.
:param pulumi.Input[bool] symbols: Use symbols [[email protected]_*] when generating the password.
"""
if digits is not None:
pulumi.set(__self__, "digits", digits)
if length is not None:
pulumi.set(__self__, "length", length)
if letters is not None:
pulumi.set(__self__, "letters", letters)
if symbols is not None:
pulumi.set(__self__, "symbols", symbols)
|
(__self__, *, digits: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None, length: Union[int, Awaitable[int], ForwardRef('Output[T]'), NoneType] = None, letters: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None, symbols: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,677 |
bmitzkus_pulumi_onepassword._inputs
|
ItemSectionArgs
| null |
class ItemSectionArgs:
def __init__(__self__, *,
label: pulumi.Input[str],
fields: Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionFieldArgs']]]] = None,
id: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] label: The label for the section.
:param pulumi.Input[Sequence[pulumi.Input['ItemSectionFieldArgs']]] fields: A list of custom fields in the section.
:param pulumi.Input[str] id: A unique identifier for the section.
"""
pulumi.set(__self__, "label", label)
if fields is not None:
pulumi.set(__self__, "fields", fields)
if id is not None:
pulumi.set(__self__, "id", id)
@property
@pulumi.getter
def label(self) -> pulumi.Input[str]:
"""
The label for the section.
"""
return pulumi.get(self, "label")
@label.setter
def label(self, value: pulumi.Input[str]):
pulumi.set(self, "label", value)
@property
@pulumi.getter
def fields(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionFieldArgs']]]]:
"""
A list of custom fields in the section.
"""
return pulumi.get(self, "fields")
@fields.setter
def fields(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionFieldArgs']]]]):
pulumi.set(self, "fields", value)
@property
@pulumi.getter
def id(self) -> Optional[pulumi.Input[str]]:
"""
A unique identifier for the section.
"""
return pulumi.get(self, "id")
@id.setter
def id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "id", value)
|
(*, label: Union[str, Awaitable[str], ForwardRef('Output[T]')], fields: Union[Sequence[Union[ForwardRef('ItemSectionFieldArgs'), Awaitable[ForwardRef('ItemSectionFieldArgs')], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[ForwardRef('ItemSectionFieldArgs'), Awaitable[ForwardRef('ItemSectionFieldArgs')], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, id: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,679 |
bmitzkus_pulumi_onepassword._inputs
|
__init__
|
:param pulumi.Input[str] label: The label for the section.
:param pulumi.Input[Sequence[pulumi.Input['ItemSectionFieldArgs']]] fields: A list of custom fields in the section.
:param pulumi.Input[str] id: A unique identifier for the section.
|
def __init__(__self__, *,
label: pulumi.Input[str],
fields: Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionFieldArgs']]]] = None,
id: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] label: The label for the section.
:param pulumi.Input[Sequence[pulumi.Input['ItemSectionFieldArgs']]] fields: A list of custom fields in the section.
:param pulumi.Input[str] id: A unique identifier for the section.
"""
pulumi.set(__self__, "label", label)
if fields is not None:
pulumi.set(__self__, "fields", fields)
if id is not None:
pulumi.set(__self__, "id", id)
|
(__self__, *, label: Union[str, Awaitable[str], ForwardRef('Output[T]')], fields: Union[Sequence[Union[ForwardRef('ItemSectionFieldArgs'), Awaitable[ForwardRef('ItemSectionFieldArgs')], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[ForwardRef('ItemSectionFieldArgs'), Awaitable[ForwardRef('ItemSectionFieldArgs')], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, id: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,680 |
bmitzkus_pulumi_onepassword._inputs
|
ItemSectionFieldArgs
| null |
class ItemSectionFieldArgs:
def __init__(__self__, *,
label: pulumi.Input[str],
id: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input['ItemSectionFieldPasswordRecipeArgs']] = None,
purpose: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
value: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] label: The label for the field.
:param pulumi.Input[str] id: A unique identifier for the field.
:param pulumi.Input['ItemSectionFieldPasswordRecipeArgs'] password_recipe: Password for this item.
:param pulumi.Input[str] purpose: Purpose indicates this is a special field: a username, password, or notes field. One of ["USERNAME" "PASSWORD" "NOTES"]
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] value: The value of the field.
"""
pulumi.set(__self__, "label", label)
if id is not None:
pulumi.set(__self__, "id", id)
if password_recipe is not None:
pulumi.set(__self__, "password_recipe", password_recipe)
if purpose is not None:
pulumi.set(__self__, "purpose", purpose)
if type is not None:
pulumi.set(__self__, "type", type)
if value is not None:
pulumi.set(__self__, "value", value)
@property
@pulumi.getter
def label(self) -> pulumi.Input[str]:
"""
The label for the field.
"""
return pulumi.get(self, "label")
@label.setter
def label(self, value: pulumi.Input[str]):
pulumi.set(self, "label", value)
@property
@pulumi.getter
def id(self) -> Optional[pulumi.Input[str]]:
"""
A unique identifier for the field.
"""
return pulumi.get(self, "id")
@id.setter
def id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "id", value)
@property
@pulumi.getter(name="passwordRecipe")
def password_recipe(self) -> Optional[pulumi.Input['ItemSectionFieldPasswordRecipeArgs']]:
"""
Password for this item.
"""
return pulumi.get(self, "password_recipe")
@password_recipe.setter
def password_recipe(self, value: Optional[pulumi.Input['ItemSectionFieldPasswordRecipeArgs']]):
pulumi.set(self, "password_recipe", value)
@property
@pulumi.getter
def purpose(self) -> Optional[pulumi.Input[str]]:
"""
Purpose indicates this is a special field: a username, password, or notes field. One of ["USERNAME" "PASSWORD" "NOTES"]
"""
return pulumi.get(self, "purpose")
@purpose.setter
def purpose(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "purpose", value)
@property
@pulumi.getter
def type(self) -> Optional[pulumi.Input[str]]:
"""
The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "type", value)
@property
@pulumi.getter
def value(self) -> Optional[pulumi.Input[str]]:
"""
The value of the field.
"""
return pulumi.get(self, "value")
@value.setter
def value(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "value", value)
|
(*, label: Union[str, Awaitable[str], ForwardRef('Output[T]')], id: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password_recipe: Union[ForwardRef('ItemSectionFieldPasswordRecipeArgs'), Awaitable[ForwardRef('ItemSectionFieldPasswordRecipeArgs')], ForwardRef('Output[T]'), NoneType] = None, purpose: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, type: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, value: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,682 |
bmitzkus_pulumi_onepassword._inputs
|
__init__
|
:param pulumi.Input[str] label: The label for the field.
:param pulumi.Input[str] id: A unique identifier for the field.
:param pulumi.Input['ItemSectionFieldPasswordRecipeArgs'] password_recipe: Password for this item.
:param pulumi.Input[str] purpose: Purpose indicates this is a special field: a username, password, or notes field. One of ["USERNAME" "PASSWORD" "NOTES"]
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] value: The value of the field.
|
def __init__(__self__, *,
label: pulumi.Input[str],
id: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input['ItemSectionFieldPasswordRecipeArgs']] = None,
purpose: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
value: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] label: The label for the field.
:param pulumi.Input[str] id: A unique identifier for the field.
:param pulumi.Input['ItemSectionFieldPasswordRecipeArgs'] password_recipe: Password for this item.
:param pulumi.Input[str] purpose: Purpose indicates this is a special field: a username, password, or notes field. One of ["USERNAME" "PASSWORD" "NOTES"]
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] value: The value of the field.
"""
pulumi.set(__self__, "label", label)
if id is not None:
pulumi.set(__self__, "id", id)
if password_recipe is not None:
pulumi.set(__self__, "password_recipe", password_recipe)
if purpose is not None:
pulumi.set(__self__, "purpose", purpose)
if type is not None:
pulumi.set(__self__, "type", type)
if value is not None:
pulumi.set(__self__, "value", value)
|
(__self__, *, label: Union[str, Awaitable[str], ForwardRef('Output[T]')], id: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password_recipe: Union[ForwardRef('ItemSectionFieldPasswordRecipeArgs'), Awaitable[ForwardRef('ItemSectionFieldPasswordRecipeArgs')], ForwardRef('Output[T]'), NoneType] = None, purpose: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, type: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, value: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,683 |
bmitzkus_pulumi_onepassword._inputs
|
ItemSectionFieldPasswordRecipeArgs
| null |
class ItemSectionFieldPasswordRecipeArgs:
def __init__(__self__, *,
digits: Optional[pulumi.Input[bool]] = None,
length: Optional[pulumi.Input[int]] = None,
letters: Optional[pulumi.Input[bool]] = None,
symbols: Optional[pulumi.Input[bool]] = None):
"""
:param pulumi.Input[bool] digits: Use digits [0-9] when generating the password.
:param pulumi.Input[int] length: The length of the password to be generated.
:param pulumi.Input[bool] letters: Use letters [a-zA-Z] when generating the password.
:param pulumi.Input[bool] symbols: Use symbols [[email protected]_*] when generating the password.
"""
if digits is not None:
pulumi.set(__self__, "digits", digits)
if length is not None:
pulumi.set(__self__, "length", length)
if letters is not None:
pulumi.set(__self__, "letters", letters)
if symbols is not None:
pulumi.set(__self__, "symbols", symbols)
@property
@pulumi.getter
def digits(self) -> Optional[pulumi.Input[bool]]:
"""
Use digits [0-9] when generating the password.
"""
return pulumi.get(self, "digits")
@digits.setter
def digits(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "digits", value)
@property
@pulumi.getter
def length(self) -> Optional[pulumi.Input[int]]:
"""
The length of the password to be generated.
"""
return pulumi.get(self, "length")
@length.setter
def length(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "length", value)
@property
@pulumi.getter
def letters(self) -> Optional[pulumi.Input[bool]]:
"""
Use letters [a-zA-Z] when generating the password.
"""
return pulumi.get(self, "letters")
@letters.setter
def letters(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "letters", value)
@property
@pulumi.getter
def symbols(self) -> Optional[pulumi.Input[bool]]:
"""
Use symbols [[email protected]_*] when generating the password.
"""
return pulumi.get(self, "symbols")
@symbols.setter
def symbols(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "symbols", value)
|
(*, digits: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None, length: Union[int, Awaitable[int], ForwardRef('Output[T]'), NoneType] = None, letters: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None, symbols: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,686 |
bmitzkus_pulumi_onepassword.provider
|
Provider
| null |
class Provider(pulumi.ProviderResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
account: Optional[pulumi.Input[str]] = None,
op_cli_path: Optional[pulumi.Input[str]] = None,
service_account_token: Optional[pulumi.Input[str]] = None,
token: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
The provider type for the onepassword package. By default, resources use package-wide configuration
settings, however an explicit `Provider` instance may be created and passed during resource
construction to achieve fine-grained programmatic control over provider settings. See the
[documentation](https://www.pulumi.com/docs/reference/programming-model/#providers) for more information.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] account: A valid account's sign-in address or ID to use biometrics unlock. Can also be sourced from `OP_ACCOUNT` environment
variable. Provider will use the 1Password CLI if set.
:param pulumi.Input[str] op_cli_path: The path to the 1Password CLI binary. Can also be sourced from `OP_CLI_PATH` environment variable. Defaults to `op`.
:param pulumi.Input[str] service_account_token: A valid 1Password service account token. Can also be sourced from `OP_SERVICE_ACCOUNT_TOKEN` environment variable.
Provider will use the 1Password CLI if set.
:param pulumi.Input[str] token: A valid token for your 1Password Connect server. Can also be sourced from `OP_CONNECT_TOKEN` environment variable.
Provider will use 1Password Connect server if set.
:param pulumi.Input[str] url: The HTTP(S) URL where your 1Password Connect server can be found. Can also be sourced `OP_CONNECT_HOST` environment
variable. Provider will use 1Password Connect server if set.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[ProviderArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
The provider type for the onepassword package. By default, resources use package-wide configuration
settings, however an explicit `Provider` instance may be created and passed during resource
construction to achieve fine-grained programmatic control over provider settings. See the
[documentation](https://www.pulumi.com/docs/reference/programming-model/#providers) for more information.
:param str resource_name: The name of the resource.
:param ProviderArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ProviderArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
account: Optional[pulumi.Input[str]] = None,
op_cli_path: Optional[pulumi.Input[str]] = None,
service_account_token: Optional[pulumi.Input[str]] = None,
token: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
__props__=None):
opts = pulumi.ResourceOptions.merge(_utilities.get_resource_opts_defaults(), opts)
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ProviderArgs.__new__(ProviderArgs)
__props__.__dict__["account"] = account
__props__.__dict__["op_cli_path"] = op_cli_path
__props__.__dict__["service_account_token"] = service_account_token
__props__.__dict__["token"] = token
__props__.__dict__["url"] = url
super(Provider, __self__).__init__(
'onepassword',
resource_name,
__props__,
opts)
@property
@pulumi.getter
def account(self) -> pulumi.Output[Optional[str]]:
"""
A valid account's sign-in address or ID to use biometrics unlock. Can also be sourced from `OP_ACCOUNT` environment
variable. Provider will use the 1Password CLI if set.
"""
return pulumi.get(self, "account")
@property
@pulumi.getter(name="opCliPath")
def op_cli_path(self) -> pulumi.Output[Optional[str]]:
"""
The path to the 1Password CLI binary. Can also be sourced from `OP_CLI_PATH` environment variable. Defaults to `op`.
"""
return pulumi.get(self, "op_cli_path")
@property
@pulumi.getter(name="serviceAccountToken")
def service_account_token(self) -> pulumi.Output[Optional[str]]:
"""
A valid 1Password service account token. Can also be sourced from `OP_SERVICE_ACCOUNT_TOKEN` environment variable.
Provider will use the 1Password CLI if set.
"""
return pulumi.get(self, "service_account_token")
@property
@pulumi.getter
def token(self) -> pulumi.Output[Optional[str]]:
"""
A valid token for your 1Password Connect server. Can also be sourced from `OP_CONNECT_TOKEN` environment variable.
Provider will use 1Password Connect server if set.
"""
return pulumi.get(self, "token")
@property
@pulumi.getter
def url(self) -> pulumi.Output[Optional[str]]:
"""
The HTTP(S) URL where your 1Password Connect server can be found. Can also be sourced `OP_CONNECT_HOST` environment
variable. Provider will use 1Password Connect server if set.
"""
return pulumi.get(self, "url")
|
(resource_name: str, *args, **kwargs)
|
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] |
52,689 |
bmitzkus_pulumi_onepassword.provider
|
_internal_init
| null |
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
account: Optional[pulumi.Input[str]] = None,
op_cli_path: Optional[pulumi.Input[str]] = None,
service_account_token: Optional[pulumi.Input[str]] = None,
token: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
__props__=None):
opts = pulumi.ResourceOptions.merge(_utilities.get_resource_opts_defaults(), opts)
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ProviderArgs.__new__(ProviderArgs)
__props__.__dict__["account"] = account
__props__.__dict__["op_cli_path"] = op_cli_path
__props__.__dict__["service_account_token"] = service_account_token
__props__.__dict__["token"] = token
__props__.__dict__["url"] = url
super(Provider, __self__).__init__(
'onepassword',
resource_name,
__props__,
opts)
|
(__self__, resource_name: str, opts: Optional[pulumi.resource.ResourceOptions] = None, account: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, op_cli_path: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, service_account_token: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, token: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, url: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, __props__=None)
|
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] |
52,693 |
bmitzkus_pulumi_onepassword.provider
|
ProviderArgs
| null |
class ProviderArgs:
def __init__(__self__, *,
account: Optional[pulumi.Input[str]] = None,
op_cli_path: Optional[pulumi.Input[str]] = None,
service_account_token: Optional[pulumi.Input[str]] = None,
token: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a Provider resource.
:param pulumi.Input[str] account: A valid account's sign-in address or ID to use biometrics unlock. Can also be sourced from `OP_ACCOUNT` environment
variable. Provider will use the 1Password CLI if set.
:param pulumi.Input[str] op_cli_path: The path to the 1Password CLI binary. Can also be sourced from `OP_CLI_PATH` environment variable. Defaults to `op`.
:param pulumi.Input[str] service_account_token: A valid 1Password service account token. Can also be sourced from `OP_SERVICE_ACCOUNT_TOKEN` environment variable.
Provider will use the 1Password CLI if set.
:param pulumi.Input[str] token: A valid token for your 1Password Connect server. Can also be sourced from `OP_CONNECT_TOKEN` environment variable.
Provider will use 1Password Connect server if set.
:param pulumi.Input[str] url: The HTTP(S) URL where your 1Password Connect server can be found. Can also be sourced `OP_CONNECT_HOST` environment
variable. Provider will use 1Password Connect server if set.
"""
if account is not None:
pulumi.set(__self__, "account", account)
if op_cli_path is not None:
pulumi.set(__self__, "op_cli_path", op_cli_path)
if service_account_token is not None:
pulumi.set(__self__, "service_account_token", service_account_token)
if token is not None:
pulumi.set(__self__, "token", token)
if url is not None:
pulumi.set(__self__, "url", url)
@property
@pulumi.getter
def account(self) -> Optional[pulumi.Input[str]]:
"""
A valid account's sign-in address or ID to use biometrics unlock. Can also be sourced from `OP_ACCOUNT` environment
variable. Provider will use the 1Password CLI if set.
"""
return pulumi.get(self, "account")
@account.setter
def account(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "account", value)
@property
@pulumi.getter(name="opCliPath")
def op_cli_path(self) -> Optional[pulumi.Input[str]]:
"""
The path to the 1Password CLI binary. Can also be sourced from `OP_CLI_PATH` environment variable. Defaults to `op`.
"""
return pulumi.get(self, "op_cli_path")
@op_cli_path.setter
def op_cli_path(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "op_cli_path", value)
@property
@pulumi.getter(name="serviceAccountToken")
def service_account_token(self) -> Optional[pulumi.Input[str]]:
"""
A valid 1Password service account token. Can also be sourced from `OP_SERVICE_ACCOUNT_TOKEN` environment variable.
Provider will use the 1Password CLI if set.
"""
return pulumi.get(self, "service_account_token")
@service_account_token.setter
def service_account_token(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "service_account_token", value)
@property
@pulumi.getter
def token(self) -> Optional[pulumi.Input[str]]:
"""
A valid token for your 1Password Connect server. Can also be sourced from `OP_CONNECT_TOKEN` environment variable.
Provider will use 1Password Connect server if set.
"""
return pulumi.get(self, "token")
@token.setter
def token(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "token", value)
@property
@pulumi.getter
def url(self) -> Optional[pulumi.Input[str]]:
"""
The HTTP(S) URL where your 1Password Connect server can be found. Can also be sourced `OP_CONNECT_HOST` environment
variable. Provider will use 1Password Connect server if set.
"""
return pulumi.get(self, "url")
@url.setter
def url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "url", value)
|
(*, account: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, op_cli_path: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, service_account_token: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, token: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, url: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,695 |
bmitzkus_pulumi_onepassword.provider
|
__init__
|
The set of arguments for constructing a Provider resource.
:param pulumi.Input[str] account: A valid account's sign-in address or ID to use biometrics unlock. Can also be sourced from `OP_ACCOUNT` environment
variable. Provider will use the 1Password CLI if set.
:param pulumi.Input[str] op_cli_path: The path to the 1Password CLI binary. Can also be sourced from `OP_CLI_PATH` environment variable. Defaults to `op`.
:param pulumi.Input[str] service_account_token: A valid 1Password service account token. Can also be sourced from `OP_SERVICE_ACCOUNT_TOKEN` environment variable.
Provider will use the 1Password CLI if set.
:param pulumi.Input[str] token: A valid token for your 1Password Connect server. Can also be sourced from `OP_CONNECT_TOKEN` environment variable.
Provider will use 1Password Connect server if set.
:param pulumi.Input[str] url: The HTTP(S) URL where your 1Password Connect server can be found. Can also be sourced `OP_CONNECT_HOST` environment
variable. Provider will use 1Password Connect server if set.
|
def __init__(__self__, *,
account: Optional[pulumi.Input[str]] = None,
op_cli_path: Optional[pulumi.Input[str]] = None,
service_account_token: Optional[pulumi.Input[str]] = None,
token: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a Provider resource.
:param pulumi.Input[str] account: A valid account's sign-in address or ID to use biometrics unlock. Can also be sourced from `OP_ACCOUNT` environment
variable. Provider will use the 1Password CLI if set.
:param pulumi.Input[str] op_cli_path: The path to the 1Password CLI binary. Can also be sourced from `OP_CLI_PATH` environment variable. Defaults to `op`.
:param pulumi.Input[str] service_account_token: A valid 1Password service account token. Can also be sourced from `OP_SERVICE_ACCOUNT_TOKEN` environment variable.
Provider will use the 1Password CLI if set.
:param pulumi.Input[str] token: A valid token for your 1Password Connect server. Can also be sourced from `OP_CONNECT_TOKEN` environment variable.
Provider will use 1Password Connect server if set.
:param pulumi.Input[str] url: The HTTP(S) URL where your 1Password Connect server can be found. Can also be sourced `OP_CONNECT_HOST` environment
variable. Provider will use 1Password Connect server if set.
"""
if account is not None:
pulumi.set(__self__, "account", account)
if op_cli_path is not None:
pulumi.set(__self__, "op_cli_path", op_cli_path)
if service_account_token is not None:
pulumi.set(__self__, "service_account_token", service_account_token)
if token is not None:
pulumi.set(__self__, "token", token)
if url is not None:
pulumi.set(__self__, "url", url)
|
(__self__, *, account: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, op_cli_path: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, service_account_token: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, token: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, url: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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52,699 |
bmitzkus_pulumi_onepassword.get_item
|
get_item
|
Use this data source to get details of an item by its vault uuid and either the title or the uuid of the item.
## Example Usage
```python
import pulumi
import pulumi_onepassword as onepassword
example = onepassword.get_item(vault=var["demo_vault"],
uuid=onepassword_item["demo_sections"]["uuid"])
```
:param str note_value: Secure Note value.
:param str title: The title of the item to retrieve. This field will be populated with the title of the item if the item it looked up by its UUID.
:param str uuid: The UUID of the item to retrieve. This field will be populated with the UUID of the item if the item it looked up by its title.
:param str vault: The UUID of the vault the item is in.
|
def get_item(note_value: Optional[str] = None,
title: Optional[str] = None,
uuid: Optional[str] = None,
vault: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetItemResult:
"""
Use this data source to get details of an item by its vault uuid and either the title or the uuid of the item.
## Example Usage
```python
import pulumi
import pulumi_onepassword as onepassword
example = onepassword.get_item(vault=var["demo_vault"],
uuid=onepassword_item["demo_sections"]["uuid"])
```
:param str note_value: Secure Note value.
:param str title: The title of the item to retrieve. This field will be populated with the title of the item if the item it looked up by its UUID.
:param str uuid: The UUID of the item to retrieve. This field will be populated with the UUID of the item if the item it looked up by its title.
:param str vault: The UUID of the vault the item is in.
"""
__args__ = dict()
__args__['noteValue'] = note_value
__args__['title'] = title
__args__['uuid'] = uuid
__args__['vault'] = vault
opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)
__ret__ = pulumi.runtime.invoke('onepassword:index/getItem:getItem', __args__, opts=opts, typ=GetItemResult).value
return AwaitableGetItemResult(
category=pulumi.get(__ret__, 'category'),
database=pulumi.get(__ret__, 'database'),
hostname=pulumi.get(__ret__, 'hostname'),
id=pulumi.get(__ret__, 'id'),
note_value=pulumi.get(__ret__, 'note_value'),
password=pulumi.get(__ret__, 'password'),
port=pulumi.get(__ret__, 'port'),
sections=pulumi.get(__ret__, 'sections'),
tags=pulumi.get(__ret__, 'tags'),
title=pulumi.get(__ret__, 'title'),
type=pulumi.get(__ret__, 'type'),
url=pulumi.get(__ret__, 'url'),
username=pulumi.get(__ret__, 'username'),
uuid=pulumi.get(__ret__, 'uuid'),
vault=pulumi.get(__ret__, 'vault'))
|
(note_value: Optional[str] = None, title: Optional[str] = None, uuid: Optional[str] = None, vault: Optional[str] = None, opts: Optional[pulumi.invoke.InvokeOptions] = None) -> bmitzkus_pulumi_onepassword.get_item.AwaitableGetItemResult
|
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] |
52,701 |
bmitzkus_pulumi_onepassword.get_vault
|
get_vault
|
Use this data source to get details of a vault by either its name or uuid.
:param str name: The name of the vault to retrieve. This field will be populated with the name of the vault if the vault it looked up by its UUID.
:param str uuid: The UUID of the vault to retrieve. This field will be populated with the UUID of the vault if the vault it looked up by its name.
|
def get_vault(name: Optional[str] = None,
uuid: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetVaultResult:
"""
Use this data source to get details of a vault by either its name or uuid.
:param str name: The name of the vault to retrieve. This field will be populated with the name of the vault if the vault it looked up by its UUID.
:param str uuid: The UUID of the vault to retrieve. This field will be populated with the UUID of the vault if the vault it looked up by its name.
"""
__args__ = dict()
__args__['name'] = name
__args__['uuid'] = uuid
opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)
__ret__ = pulumi.runtime.invoke('onepassword:index/getVault:getVault', __args__, opts=opts, typ=GetVaultResult).value
return AwaitableGetVaultResult(
description=pulumi.get(__ret__, 'description'),
id=pulumi.get(__ret__, 'id'),
name=pulumi.get(__ret__, 'name'),
uuid=pulumi.get(__ret__, 'uuid'))
|
(name: Optional[str] = None, uuid: Optional[str] = None, opts: Optional[pulumi.invoke.InvokeOptions] = None) -> bmitzkus_pulumi_onepassword.get_vault.AwaitableGetVaultResult
|
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] |
52,707 |
flaml.automl.automl
|
AutoML
|
The AutoML class.
Example:
```python
automl = AutoML()
automl_settings = {
"time_budget": 60,
"metric": 'accuracy',
"task": 'classification',
"log_file_name": 'mylog.log',
}
automl.fit(X_train = X_train, y_train = y_train, **automl_settings)
```
|
class AutoML(BaseEstimator):
"""The AutoML class.
Example:
```python
automl = AutoML()
automl_settings = {
"time_budget": 60,
"metric": 'accuracy',
"task": 'classification',
"log_file_name": 'mylog.log',
}
automl.fit(X_train = X_train, y_train = y_train, **automl_settings)
```
"""
__version__ = flaml_version
def __init__(self, **settings):
"""Constructor.
Many settings in fit() can be passed to the constructor too.
If an argument in fit() is provided, it will override the setting passed to the constructor.
If an argument in fit() is not provided but provided in the constructor, the value passed to the constructor will be used.
Args:
metric: A string of the metric name or a function,
e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted',
'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following input arguments:
```python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None,
config=None, groups_test=None, groups_train=None,
):
return metric_to_minimize, metrics_to_log
```
which returns a float number as the minimization objective,
and a dictionary as the metrics to log. E.g.,
```python
def custom_metric(
X_val, y_val, estimator, labels,
X_train, y_train, weight_val=None, weight_train=None,
*args,
):
from sklearn.metrics import log_loss
import time
start = time.time()
y_pred = estimator.predict_proba(X_val)
pred_time = (time.time() - start) / len(X_val)
val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
alpha = 0.5
return val_loss * (1 + alpha) - alpha * train_loss, {
"val_loss": val_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
```
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank',
'seq-classification', 'seq-regression', 'summarization',
or an instance of the Task class.
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging,
set it to be an empty string "".
estimator_list: A list of strings for estimator names, or 'auto'.
e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']```.
time_budget: A float number of the time budget in seconds.
Use -1 if no time limit.
max_iter: An integer of the maximal number of iterations.
sample: A boolean of whether to sample the training data during
search.
ensemble: boolean or dict | default=False. Whether to perform
ensemble after search. Can be a dict with keys 'passthrough'
and 'final_estimator' to specify the passthrough and
final_estimator in the stacker. The dict can also contain
'n_jobs' as the key to specify the number of jobs for the stacker.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the valiation data percentage for holdout.
n_splits: An integer of the number of folds for cross - validation.
log_type: A string of the log type, one of
['better', 'all'].
'better' only logs configs with better loss than previos iters
'all' logs all the tried configs.
model_history: A boolean of whether to keep the best
model per estimator. Make sure memory is large enough if setting to True.
log_training_metric: A boolean of whether to log the training
metric for each model.
mem_thres: A float of the memory size constraint in bytes.
pred_time_limit: A float of the prediction latency constraint in seconds.
It refers to the average prediction time per row in validation data.
train_time_limit: A float of the training time constraint in seconds.
verbose: int, default=3 | Controls the verbosity, higher means more
messages.
retrain_full: bool or str, default=True | whether to retrain the
selected model on the full training data when using holdout.
True - retrain only after search finishes; False - no retraining;
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
hpo_method: str, default="auto" | The hyperparameter
optimization method. By default, CFO is used for sequential
search and BlendSearch is used for parallel search.
No need to set when using flaml's default search space or using
a simple customized search space. When set to 'bs', BlendSearch
is used. BlendSearch can be tried when the search space is
complex, for example, containing multiple disjoint, discontinuous
subspaces. When set to 'random', random search is used.
starting_points: A dictionary or a str to specify the starting hyperparameter
config for the estimators | default="static".
If str:
- if "data", use data-dependent defaults;
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
```python
from flaml import AutoML
automl = AutoML()
X_train, y_train = load_iris(return_X_y=True)
automl.fit(X_train, y_train)
starting_points = automl.best_config_per_estimator
new_automl = AutoML()
new_automl.fit(X_train, y_train, starting_points=starting_points)
|
(**settings)
|
[
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] |
52,708 |
flaml.automl.automl
|
__del__
| null |
def __del__(self):
if (
hasattr(self, "_trained_estimator")
and self._trained_estimator
and hasattr(self._trained_estimator, "cleanup")
):
if self.preserve_checkpoint is False:
self._trained_estimator.cleanup()
del self._trained_estimator
|
(self)
|
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52,709 |
flaml.automl.automl
|
__init__
|
Constructor.
Many settings in fit() can be passed to the constructor too.
If an argument in fit() is provided, it will override the setting passed to the constructor.
If an argument in fit() is not provided but provided in the constructor, the value passed to the constructor will be used.
Args:
metric: A string of the metric name or a function,
e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted',
'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following input arguments:
```python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None,
config=None, groups_test=None, groups_train=None,
):
return metric_to_minimize, metrics_to_log
```
which returns a float number as the minimization objective,
and a dictionary as the metrics to log. E.g.,
```python
def custom_metric(
X_val, y_val, estimator, labels,
X_train, y_train, weight_val=None, weight_train=None,
*args,
):
from sklearn.metrics import log_loss
import time
start = time.time()
y_pred = estimator.predict_proba(X_val)
pred_time = (time.time() - start) / len(X_val)
val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
alpha = 0.5
return val_loss * (1 + alpha) - alpha * train_loss, {
"val_loss": val_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
```
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank',
'seq-classification', 'seq-regression', 'summarization',
or an instance of the Task class.
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging,
set it to be an empty string "".
estimator_list: A list of strings for estimator names, or 'auto'.
e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']```.
time_budget: A float number of the time budget in seconds.
Use -1 if no time limit.
max_iter: An integer of the maximal number of iterations.
sample: A boolean of whether to sample the training data during
search.
ensemble: boolean or dict | default=False. Whether to perform
ensemble after search. Can be a dict with keys 'passthrough'
and 'final_estimator' to specify the passthrough and
final_estimator in the stacker. The dict can also contain
'n_jobs' as the key to specify the number of jobs for the stacker.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the valiation data percentage for holdout.
n_splits: An integer of the number of folds for cross - validation.
log_type: A string of the log type, one of
['better', 'all'].
'better' only logs configs with better loss than previos iters
'all' logs all the tried configs.
model_history: A boolean of whether to keep the best
model per estimator. Make sure memory is large enough if setting to True.
log_training_metric: A boolean of whether to log the training
metric for each model.
mem_thres: A float of the memory size constraint in bytes.
pred_time_limit: A float of the prediction latency constraint in seconds.
It refers to the average prediction time per row in validation data.
train_time_limit: A float of the training time constraint in seconds.
verbose: int, default=3 | Controls the verbosity, higher means more
messages.
retrain_full: bool or str, default=True | whether to retrain the
selected model on the full training data when using holdout.
True - retrain only after search finishes; False - no retraining;
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
hpo_method: str, default="auto" | The hyperparameter
optimization method. By default, CFO is used for sequential
search and BlendSearch is used for parallel search.
No need to set when using flaml's default search space or using
a simple customized search space. When set to 'bs', BlendSearch
is used. BlendSearch can be tried when the search space is
complex, for example, containing multiple disjoint, discontinuous
subspaces. When set to 'random', random search is used.
starting_points: A dictionary or a str to specify the starting hyperparameter
config for the estimators | default="static".
If str:
- if "data", use data-dependent defaults;
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
```python
from flaml import AutoML
automl = AutoML()
X_train, y_train = load_iris(return_X_y=True)
automl.fit(X_train, y_train)
starting_points = automl.best_config_per_estimator
new_automl = AutoML()
new_automl.fit(X_train, y_train, starting_points=starting_points)
```
seed: int or None, default=None | The random seed for hpo.
n_concurrent_trials: [In preview] int, default=1 | The number of
concurrent trials. When n_concurrent_trials > 1, flaml performes
[parallel tuning](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)
and installation of ray or spark is required: `pip install flaml[ray]`
or `pip install flaml[spark]`. Please check
[here](https://spark.apache.org/docs/latest/api/python/getting_started/install.html)
for more details about installing Spark.
keep_search_state: boolean, default=False | Whether to keep data needed
for model search after fit(). By default the state is deleted for
space saving.
preserve_checkpoint: boolean, default=True | Whether to preserve the saved checkpoint
on disk when deleting automl. By default the checkpoint is preserved.
early_stop: boolean, default=False | Whether to stop early if the
search is considered to converge.
force_cancel: boolean, default=False | Whether to forcely cancel Spark jobs if the
search time exceeded the time budget.
append_log: boolean, default=False | Whether to directly append the log
records to the input log file if it exists.
auto_augment: boolean, default=True | Whether to automatically
augment rare classes.
min_sample_size: int, default=MIN_SAMPLE_TRAIN | the minimal sample
size when sample=True.
use_ray: boolean or dict.
If boolean: default=False | Whether to use ray to run the training
in separate processes. This can be used to prevent OOM for large
datasets, but will incur more overhead in time.
If dict: the dict contains the keywords arguments to be passed to
[ray.tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html).
use_spark: boolean, default=False | Whether to use spark to run the training
in parallel spark jobs. This can be used to accelerate training on large models
and large datasets, but will incur more overhead in time and thus slow down
training in some cases. GPU training is not supported yet when use_spark is True.
For Spark clusters, by default, we will launch one trial per executor. However,
sometimes we want to launch more trials than the number of executors (e.g., local mode).
In this case, we can set the environment variable `FLAML_MAX_CONCURRENT` to override
the detected `num_executors`. The final number of concurrent trials will be the minimum
of `n_concurrent_trials` and `num_executors`.
free_mem_ratio: float between 0 and 1, default=0. The free memory ratio to keep during training.
metric_constraints: list, default=[] | The list of metric constraints.
Each element in this list is a 3-tuple, which shall be expressed
in the following format: the first element of the 3-tuple is the name of the
metric, the second element is the inequality sign chosen from ">=" and "<=",
and the third element is the constraint value. E.g., `('val_loss', '<=', 0.1)`.
Note that all the metric names in metric_constraints need to be reported via
the metrics_to_log dictionary returned by a customized metric function.
The customized metric function shall be provided via the `metric` key word
argument of the fit() function or the automl constructor.
Find an example in the 4th constraint type in this [doc](/docs/Use-Cases/Task-Oriented-AutoML#constraint).
If `pred_time_limit` is provided as one of keyword arguments to fit() function or
the automl constructor, flaml will automatically (and under the hood)
add it as an additional element in the metric_constraints. Essentially 'pred_time_limit'
specifies a constraint about the prediction latency constraint in seconds.
custom_hp: dict, default=None | The custom search space specified by user.
It is a nested dict with keys being the estimator names, and values being dicts
per estimator search space. In the per estimator search space dict,
the keys are the hyperparameter names, and values are dicts of info ("domain",
"init_value", and "low_cost_init_value") about the search space associated with
the hyperparameter (i.e., per hyperparameter search space dict). When custom_hp
is provided, the built-in search space which is also a nested dict of per estimator
search space dict, will be updated with custom_hp. Note that during this nested dict update,
the per hyperparameter search space dicts will be replaced (instead of updated) by the ones
provided in custom_hp. Note that the value for "domain" can either be a constant
or a sample.Domain object.
e.g.,
```python
custom_hp = {
"transformer_ms": {
"model_path": {
"domain": "albert-base-v2",
},
"learning_rate": {
"domain": tune.choice([1e-4, 1e-5]),
}
}
}
```
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
e.g.,
```python
fit_kwargs_by_estimator = {
"transformer": {
"output_dir": "test/data/output/",
"fp16": False,
}
}
```
mlflow_logging: boolean, default=True | Whether to log the training results to mlflow.
This requires mlflow to be installed and to have an active mlflow run.
FLAML will create nested runs.
|
def __init__(self, **settings):
"""Constructor.
Many settings in fit() can be passed to the constructor too.
If an argument in fit() is provided, it will override the setting passed to the constructor.
If an argument in fit() is not provided but provided in the constructor, the value passed to the constructor will be used.
Args:
metric: A string of the metric name or a function,
e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted',
'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following input arguments:
```python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None,
config=None, groups_test=None, groups_train=None,
):
return metric_to_minimize, metrics_to_log
```
which returns a float number as the minimization objective,
and a dictionary as the metrics to log. E.g.,
```python
def custom_metric(
X_val, y_val, estimator, labels,
X_train, y_train, weight_val=None, weight_train=None,
*args,
):
from sklearn.metrics import log_loss
import time
start = time.time()
y_pred = estimator.predict_proba(X_val)
pred_time = (time.time() - start) / len(X_val)
val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
alpha = 0.5
return val_loss * (1 + alpha) - alpha * train_loss, {
"val_loss": val_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
```
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank',
'seq-classification', 'seq-regression', 'summarization',
or an instance of the Task class.
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging,
set it to be an empty string "".
estimator_list: A list of strings for estimator names, or 'auto'.
e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']```.
time_budget: A float number of the time budget in seconds.
Use -1 if no time limit.
max_iter: An integer of the maximal number of iterations.
sample: A boolean of whether to sample the training data during
search.
ensemble: boolean or dict | default=False. Whether to perform
ensemble after search. Can be a dict with keys 'passthrough'
and 'final_estimator' to specify the passthrough and
final_estimator in the stacker. The dict can also contain
'n_jobs' as the key to specify the number of jobs for the stacker.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the valiation data percentage for holdout.
n_splits: An integer of the number of folds for cross - validation.
log_type: A string of the log type, one of
['better', 'all'].
'better' only logs configs with better loss than previos iters
'all' logs all the tried configs.
model_history: A boolean of whether to keep the best
model per estimator. Make sure memory is large enough if setting to True.
log_training_metric: A boolean of whether to log the training
metric for each model.
mem_thres: A float of the memory size constraint in bytes.
pred_time_limit: A float of the prediction latency constraint in seconds.
It refers to the average prediction time per row in validation data.
train_time_limit: A float of the training time constraint in seconds.
verbose: int, default=3 | Controls the verbosity, higher means more
messages.
retrain_full: bool or str, default=True | whether to retrain the
selected model on the full training data when using holdout.
True - retrain only after search finishes; False - no retraining;
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
hpo_method: str, default="auto" | The hyperparameter
optimization method. By default, CFO is used for sequential
search and BlendSearch is used for parallel search.
No need to set when using flaml's default search space or using
a simple customized search space. When set to 'bs', BlendSearch
is used. BlendSearch can be tried when the search space is
complex, for example, containing multiple disjoint, discontinuous
subspaces. When set to 'random', random search is used.
starting_points: A dictionary or a str to specify the starting hyperparameter
config for the estimators | default="static".
If str:
- if "data", use data-dependent defaults;
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
```python
from flaml import AutoML
automl = AutoML()
X_train, y_train = load_iris(return_X_y=True)
automl.fit(X_train, y_train)
starting_points = automl.best_config_per_estimator
new_automl = AutoML()
new_automl.fit(X_train, y_train, starting_points=starting_points)
```
seed: int or None, default=None | The random seed for hpo.
n_concurrent_trials: [In preview] int, default=1 | The number of
concurrent trials. When n_concurrent_trials > 1, flaml performes
[parallel tuning](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)
and installation of ray or spark is required: `pip install flaml[ray]`
or `pip install flaml[spark]`. Please check
[here](https://spark.apache.org/docs/latest/api/python/getting_started/install.html)
for more details about installing Spark.
keep_search_state: boolean, default=False | Whether to keep data needed
for model search after fit(). By default the state is deleted for
space saving.
preserve_checkpoint: boolean, default=True | Whether to preserve the saved checkpoint
on disk when deletin
|
(self, **settings)
|
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] |
52,710 |
flaml.automl.automl
|
_decide_eval_method
| null |
def _decide_eval_method(self, eval_method, time_budget):
if not isinstance(self._split_type, str):
assert eval_method in [
"auto",
"cv",
], "eval_method must be 'auto' or 'cv' for custom data splitter."
assert self._state.X_val is None, "custom splitter and custom validation data can't be used together."
return "cv"
if self._state.X_val is not None and (
not isinstance(self._state.X_val, TimeSeriesDataset) or len(self._state.X_val.test_data) > 0
):
assert eval_method in [
"auto",
"holdout",
], "eval_method must be 'auto' or 'holdout' for custom validation data."
return "holdout"
if eval_method != "auto":
assert eval_method in [
"holdout",
"cv",
], "eval_method must be 'holdout', 'cv' or 'auto'."
return eval_method
nrow, dim = self._nrow, self._ndim
if (
time_budget < 0
or nrow * dim / 0.9 < SMALL_LARGE_THRES * (time_budget / 3600)
and nrow < CV_HOLDOUT_THRESHOLD
):
# time allows or sampling can be used and cv is necessary
return "cv"
else:
return "holdout"
|
(self, eval_method, time_budget)
|
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] |
52,711 |
flaml.automl.automl
|
_log_trial
| null |
def _log_trial(self, search_state, estimator):
if self._training_log:
self._training_log.append(
self._iter_per_learner[estimator],
search_state.metric_for_logging,
search_state.trial_time,
self._state.time_from_start,
search_state.val_loss,
search_state.config,
estimator,
search_state.sample_size,
)
if self._mlflow_logging and mlflow is not None and mlflow.active_run():
with mlflow.start_run(nested=True):
mlflow.log_metric("iter_counter", self._track_iter)
if (search_state.metric_for_logging is not None) and (
"intermediate_results" in search_state.metric_for_logging
):
for each_entry in search_state.metric_for_logging["intermediate_results"]:
with mlflow.start_run(nested=True):
mlflow.log_metrics(each_entry)
mlflow.log_metric("iter_counter", self._iter_per_learner[estimator])
del search_state.metric_for_logging["intermediate_results"]
if search_state.metric_for_logging:
mlflow.log_metrics(search_state.metric_for_logging)
mlflow.log_metric("trial_time", search_state.trial_time)
mlflow.log_metric("wall_clock_time", self._state.time_from_start)
mlflow.log_metric("validation_loss", search_state.val_loss)
mlflow.log_params(search_state.config)
mlflow.log_param("learner", estimator)
mlflow.log_param("sample_size", search_state.sample_size)
mlflow.log_metric("best_validation_loss", search_state.best_loss)
mlflow.log_param("best_config", search_state.best_config)
mlflow.log_param("best_learner", self._best_estimator)
mlflow.log_metric(
self._state.metric if isinstance(self._state.metric, str) else self._state.error_metric,
1 - search_state.val_loss
if self._state.error_metric.startswith("1-")
else -search_state.val_loss
if self._state.error_metric.startswith("-")
else search_state.val_loss,
)
|
(self, search_state, estimator)
|
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0.008917746134102345,
0.033412862569093704
] |
52,712 |
flaml.automl.automl
|
_prepare_data
| null |
def _prepare_data(self, eval_method, split_ratio, n_splits):
self._state.task.prepare_data(
self._state,
self._X_train_all,
self._y_train_all,
self._auto_augment,
eval_method,
self._split_type,
split_ratio,
n_splits,
self._df,
self._sample_weight_full,
)
self.data_size_full = self._state.data_size_full
|
(self, eval_method, split_ratio, n_splits)
|
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] |
52,713 |
flaml.automl.automl
|
_search
| null |
def _search(self):
# initialize the search_states
self._eci = []
self._state.best_loss = float("+inf")
self._state.time_from_start = 0
self._estimator_index = None
self._best_iteration = 0
self._time_taken_best_iter = 0
self._config_history = {}
self._max_iter_per_learner = 10000
self._iter_per_learner = dict([(e, 0) for e in self.estimator_list])
self._iter_per_learner_fullsize = dict([(e, 0) for e in self.estimator_list])
self._fullsize_reached = False
self._trained_estimator = None
self._best_estimator = None
self._retrained_config = {}
self._warn_threshold = 10
self._selected = None
self.modelcount = 0
if self._max_iter < 2 and self.estimator_list and self._state.retrain_final:
# when max_iter is 1, no need to search
self.modelcount = self._max_iter
self._max_iter = 0
self._best_estimator = estimator = self.estimator_list[0]
self._selected = state = self._search_states[estimator]
state.best_config_sample_size = self._state.data_size[0]
state.best_config = state.init_config[0] if state.init_config else {}
elif self._use_ray is False and self._use_spark is False:
self._search_sequential()
else:
self._search_parallel()
# Add a checkpoint for the current best config to the log.
if self._training_log:
self._training_log.checkpoint()
self._state.time_from_start = time.time() - self._start_time_flag
if self._best_estimator:
self._selected = self._search_states[self._best_estimator]
self.modelcount = sum(search_state.total_iter for search_state in self._search_states.values())
if self._trained_estimator:
logger.info(f"selected model: {self._trained_estimator.model}")
estimators = []
if self._ensemble and self._state.task in (
"binary",
"multiclass",
"regression",
):
search_states = list(x for x in self._search_states.items() if x[1].best_config)
search_states.sort(key=lambda x: x[1].best_loss)
estimators = [
(
x[0],
x[1].learner_class(
task=self._state.task,
n_jobs=self._state.n_jobs,
**AutoMLState.sanitize(x[1].best_config),
),
)
for x in search_states[:2]
]
estimators += [
(
x[0],
x[1].learner_class(
task=self._state.task,
n_jobs=self._state.n_jobs,
**AutoMLState.sanitize(x[1].best_config),
),
)
for x in search_states[2:]
if x[1].best_loss < 4 * self._selected.best_loss
]
logger.info([(estimator[0], estimator[1].params) for estimator in estimators])
if len(estimators) > 1:
if self._state.task.is_classification():
from sklearn.ensemble import StackingClassifier as Stacker
else:
from sklearn.ensemble import StackingRegressor as Stacker
if self._use_ray is not False:
import ray
n_cpus = ray.is_initialized() and ray.available_resources()["CPU"] or os.cpu_count()
elif self._use_spark:
from flaml.tune.spark.utils import get_n_cpus
n_cpus = get_n_cpus()
else:
n_cpus = os.cpu_count()
ensemble_n_jobs = (
-self._state.n_jobs # maximize total parallelization degree
if abs(self._state.n_jobs) == 1 # 1 and -1 correspond to min/max parallelization
else max(1, int(n_cpus / 2 / self._state.n_jobs))
# the total degree of parallelization = parallelization degree per estimator * parallelization degree of ensemble
)
if isinstance(self._ensemble, dict):
final_estimator = self._ensemble.get("final_estimator", self._trained_estimator)
passthrough = self._ensemble.get("passthrough", True)
ensemble_n_jobs = self._ensemble.get("n_jobs", ensemble_n_jobs)
else:
final_estimator = self._trained_estimator
passthrough = True
stacker = Stacker(
estimators,
final_estimator,
n_jobs=ensemble_n_jobs,
passthrough=passthrough,
)
sample_weight_dict = (
(self._sample_weight_full is not None) and {"sample_weight": self._sample_weight_full} or {}
)
for e in estimators:
e[1].__class__.init()
import joblib
try:
logger.info("Building ensemble with tuned estimators")
stacker.fit(
self._X_train_all,
self._y_train_all,
**sample_weight_dict, # NOTE: _search is after kwargs is updated to fit_kwargs_by_estimator
)
logger.info(f"ensemble: {stacker}")
self._trained_estimator = stacker
self._trained_estimator.model = stacker
except ValueError as e:
if passthrough:
logger.warning(
"Using passthrough=False for ensemble because the data contain categorical features."
)
stacker = Stacker(
estimators,
final_estimator,
n_jobs=self._state.n_jobs,
passthrough=False,
)
stacker.fit(
self._X_train_all,
self._y_train_all,
**sample_weight_dict, # NOTE: _search is after kwargs is updated to fit_kwargs_by_estimator
)
logger.info(f"ensemble: {stacker}")
self._trained_estimator = stacker
self._trained_estimator.model = stacker
else:
raise e
except joblib.externals.loky.process_executor.TerminatedWorkerError:
logger.error(
"No enough memory to build the ensemble."
" Please try increasing available RAM, decreasing n_jobs for ensemble, or disabling ensemble."
)
elif self._state.retrain_final:
# reset time budget for retraining
if self._max_iter > 1:
self._state.time_budget = -1
if (
self._state.task.is_ts_forecast()
or self._trained_estimator is None
or self._trained_estimator.model is None
or (
self._state.time_budget < 0
or self._state.time_budget - self._state.time_from_start
> self._selected.est_retrain_time(self.data_size_full)
)
and self._selected.best_config_sample_size == self._state.data_size[0]
):
state = self._search_states[self._best_estimator]
(
self._trained_estimator,
retrain_time,
) = self._state._train_with_config(
self._best_estimator,
state.best_config,
self.data_size_full,
)
logger.info("retrain {} for {:.1f}s".format(self._best_estimator, retrain_time))
state.best_config_train_time = retrain_time
if self._trained_estimator:
logger.info(f"retrained model: {self._trained_estimator.model}")
else:
logger.info("not retraining because the time budget is too small.")
|
(self)
|
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] |
52,714 |
flaml.automl.automl
|
_search_parallel
| null |
def _search_parallel(self):
if self._use_ray is not False:
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest import ConcurrencyLimiter
else:
from ray.tune.search import ConcurrencyLimiter
import ray
except (ImportError, AssertionError):
raise ImportError("use_ray=True requires installation of ray. " "Please run pip install flaml[ray]")
else:
from flaml.tune.searcher.suggestion import ConcurrencyLimiter
if self._hpo_method in ("cfo", "grid"):
from flaml import CFO as SearchAlgo
elif "bs" == self._hpo_method:
from flaml import BlendSearch as SearchAlgo
elif "random" == self._hpo_method:
from flaml import RandomSearch as SearchAlgo
elif "optuna" == self._hpo_method:
if self._use_ray is not False:
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo
else:
from ray.tune.search.optuna import OptunaSearch as SearchAlgo
except (ImportError, AssertionError):
from flaml.tune.searcher.suggestion import (
OptunaSearch as SearchAlgo,
)
else:
from flaml.tune.searcher.suggestion import OptunaSearch as SearchAlgo
else:
raise NotImplementedError(
f"hpo_method={self._hpo_method} is not recognized. " "'auto', 'cfo' and 'bs' are supported."
)
space = self.search_space
self._state.time_from_start = time.time() - self._start_time_flag
time_budget_s = self._state.time_budget - self._state.time_from_start if self._state.time_budget >= 0 else None
if self._hpo_method != "optuna":
min_resource = self.min_resource
if isinstance(min_resource, dict):
_min_resource_set = set(min_resource.values())
min_resource_all_estimator = min(_min_resource_set)
if len(_min_resource_set) > 1:
logger.warning(
"Using the min FLAML_sample_size of all the provided starting points as the starting sample size in the case of parallel search."
)
else:
min_resource_all_estimator = min_resource
search_alg = SearchAlgo(
metric="val_loss",
space=space,
low_cost_partial_config=self.low_cost_partial_config,
points_to_evaluate=self.points_to_evaluate,
cat_hp_cost=self.cat_hp_cost,
resource_attr=self.resource_attr,
min_resource=min_resource_all_estimator,
max_resource=self.max_resource,
config_constraints=[(partial(size, self._state.learner_classes), "<=", self._mem_thres)],
metric_constraints=self.metric_constraints,
seed=self._seed,
time_budget_s=time_budget_s,
num_samples=self._max_iter,
allow_empty_config=True,
)
else:
# if self._hpo_method is optuna, sometimes the search space and the initial config dimension do not match
# need to remove the extra keys from the search space to be consistent with the initial config
converted_space = SearchAlgo.convert_search_space(space)
removed_keys = set(space.keys()).difference(converted_space.keys())
new_points_to_evaluate = []
for idx in range(len(self.points_to_evaluate)):
r = self.points_to_evaluate[idx].copy()
for each_key in removed_keys:
r.pop(each_key)
new_points_to_evaluate.append(r)
search_alg = SearchAlgo(
metric="val_loss",
mode="min",
points_to_evaluate=[p for p in new_points_to_evaluate if len(p) == len(converted_space)],
)
search_alg = ConcurrencyLimiter(search_alg, self._n_concurrent_trials)
resources_per_trial = self._state.resources_per_trial
if self._use_spark:
# use spark as parallel backend
analysis = tune.run(
self.trainable,
search_alg=search_alg,
config=space,
metric="val_loss",
mode="min",
time_budget_s=time_budget_s,
num_samples=self._max_iter,
verbose=max(self.verbose - 2, 0),
use_ray=False,
use_spark=True,
force_cancel=self._force_cancel,
# raise_on_failed_trial=False,
# keep_checkpoints_num=1,
# checkpoint_score_attr="min-val_loss",
)
else:
# use ray as parallel backend
analysis = ray.tune.run(
self.trainable,
search_alg=search_alg,
config=space,
metric="val_loss",
mode="min",
resources_per_trial=resources_per_trial,
time_budget_s=time_budget_s,
num_samples=self._max_iter,
verbose=max(self.verbose - 2, 0),
raise_on_failed_trial=False,
keep_checkpoints_num=1,
checkpoint_score_attr="min-val_loss",
**self._use_ray if isinstance(self._use_ray, dict) else {},
)
# logger.info([trial.last_result for trial in analysis.trials])
trials = sorted(
(
trial
for trial in analysis.trials
if trial.last_result and trial.last_result.get("wall_clock_time") is not None
),
key=lambda x: x.last_result["wall_clock_time"],
)
for self._track_iter, trial in enumerate(trials):
result = trial.last_result
better = False
if result:
config = result["config"]
estimator = config.get("ml", config)["learner"]
search_state = self._search_states[estimator]
search_state.update(result, 0)
wall_time = result.get("wall_clock_time")
if wall_time is not None:
self._state.time_from_start = wall_time
self._iter_per_learner[estimator] += 1
if search_state.sample_size == self._state.data_size[0]:
if not self._fullsize_reached:
self._fullsize_reached = True
if search_state.best_loss < self._state.best_loss:
self._state.best_loss = search_state.best_loss
self._best_estimator = estimator
self._config_history[self._track_iter] = (
self._best_estimator,
config,
self._time_taken_best_iter,
)
self._trained_estimator = search_state.trained_estimator
self._best_iteration = self._track_iter
self._time_taken_best_iter = self._state.time_from_start
better = True
self._search_states[estimator].best_config = config
if better or self._log_type == "all":
self._log_trial(search_state, estimator)
|
(self)
|
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] |
52,715 |
flaml.automl.automl
|
_search_sequential
| null |
def _search_sequential(self):
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest import ConcurrencyLimiter
else:
from ray.tune.search import ConcurrencyLimiter
except (ImportError, AssertionError):
from flaml.tune.searcher.suggestion import ConcurrencyLimiter
if self._hpo_method in ("cfo", "grid"):
from flaml import CFO as SearchAlgo
elif "optuna" == self._hpo_method:
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo
else:
from ray.tune.search.optuna import OptunaSearch as SearchAlgo
except (ImportError, AssertionError):
from flaml.tune.searcher.suggestion import OptunaSearch as SearchAlgo
elif "bs" == self._hpo_method:
from flaml import BlendSearch as SearchAlgo
elif "random" == self._hpo_method:
from flaml.tune.searcher import RandomSearch as SearchAlgo
elif "cfocat" == self._hpo_method:
from flaml.tune.searcher.cfo_cat import CFOCat as SearchAlgo
else:
raise NotImplementedError(
f"hpo_method={self._hpo_method} is not recognized. " "'cfo' and 'bs' are supported."
)
est_retrain_time = next_trial_time = 0
best_config_sig = None
better = True # whether we find a better model in one trial
for self._track_iter in range(self._max_iter):
if self._estimator_index is None:
estimator = self._active_estimators[0]
else:
estimator = self._select_estimator(self._active_estimators)
if not estimator:
break
logger.info(f"iteration {self._track_iter}, current learner {estimator}")
search_state = self._search_states[estimator]
self._state.time_from_start = time.time() - self._start_time_flag
time_left = self._state.time_budget - self._state.time_from_start
budget_left = (
time_left
if not self._retrain_in_budget
or better
or (not self.best_estimator)
or self._search_states[self.best_estimator].sample_size < self._state.data_size[0]
else time_left - est_retrain_time
)
if not search_state.search_alg:
search_state.training_function = partial(
AutoMLState._compute_with_config_base,
state=self._state,
estimator=estimator,
)
search_space = search_state.search_space
if self._sample:
resource_attr = "FLAML_sample_size"
min_resource = (
self._min_sample_size[estimator]
if isinstance(self._min_sample_size, dict) and estimator in self._min_sample_size
else self._min_sample_size_input
)
max_resource = self._state.data_size[0]
else:
resource_attr = min_resource = max_resource = None
learner_class = self._state.learner_classes.get(estimator)
if "grid" == self._hpo_method: # for synthetic exp only
points_to_evaluate = []
space = search_space
keys = list(space.keys())
domain0, domain1 = space[keys[0]], space[keys[1]]
for x1 in range(domain0.lower, domain0.upper + 1):
for x2 in range(domain1.lower, domain1.upper + 1):
points_to_evaluate.append(
{
keys[0]: x1,
keys[1]: x2,
}
)
self._max_iter_per_learner = len(points_to_evaluate)
low_cost_partial_config = None
else:
points_to_evaluate = search_state.init_config.copy()
low_cost_partial_config = search_state.low_cost_partial_config
time_budget_s = (
min(budget_left, self._state.train_time_limit or np.inf) if self._state.time_budget >= 0 else None
)
if self._hpo_method in ("bs", "cfo", "grid", "cfocat", "random"):
algo = SearchAlgo(
metric="val_loss",
mode="min",
space=search_space,
points_to_evaluate=points_to_evaluate,
low_cost_partial_config=low_cost_partial_config,
cat_hp_cost=search_state.cat_hp_cost,
resource_attr=resource_attr,
min_resource=min_resource,
max_resource=max_resource,
config_constraints=[(learner_class.size, "<=", self._mem_thres)],
metric_constraints=self.metric_constraints,
seed=self._seed,
allow_empty_config=True,
time_budget_s=time_budget_s,
num_samples=self._max_iter,
)
else:
# if self._hpo_method is optuna, sometimes the search space and the initial config dimension do not match
# need to remove the extra keys from the search space to be consistent with the initial config
converted_space = SearchAlgo.convert_search_space(search_space)
removed_keys = set(search_space.keys()).difference(converted_space.keys())
new_points_to_evaluate = []
for idx in range(len(points_to_evaluate)):
r = points_to_evaluate[idx].copy()
for each_key in removed_keys:
r.pop(each_key)
new_points_to_evaluate.append(r)
points_to_evaluate = new_points_to_evaluate
algo = SearchAlgo(
metric="val_loss",
mode="min",
space=search_space,
points_to_evaluate=[p for p in points_to_evaluate if len(p) == len(search_space)],
)
search_state.search_alg = ConcurrencyLimiter(algo, max_concurrent=1)
# search_state.search_alg = algo
else:
search_space = None
if self._hpo_method in ("bs", "cfo", "cfocat"):
search_state.search_alg.searcher.set_search_properties(
metric=None,
mode=None,
metric_target=self._state.best_loss,
)
start_run_time = time.time()
analysis = tune.run(
search_state.training_function,
search_alg=search_state.search_alg,
time_budget_s=time_budget_s,
verbose=max(self.verbose - 3, 0),
use_ray=False,
use_spark=False,
)
time_used = time.time() - start_run_time
better = False
if analysis.trials:
result = analysis.trials[-1].last_result
search_state.update(result, time_used=time_used)
if self._estimator_index is None:
# update init eci estimate
eci_base = search_state.init_eci
self._eci.append(search_state.estimated_cost4improvement)
for e in self.estimator_list[1:]:
self._eci.append(self._search_states[e].init_eci / eci_base * self._eci[0])
self._estimator_index = 0
min_budget = max(10 * self._eci[0], sum(self._eci))
max_budget = 10000 * self._eci[0]
if search_state.sample_size:
ratio = search_state.data_size[0] / search_state.sample_size
min_budget *= ratio
max_budget *= ratio
logger.info(
f"Estimated sufficient time budget={max_budget:.0f}s."
f" Estimated necessary time budget={min_budget:.0f}s."
|
(self)
|
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] |
52,716 |
flaml.automl.automl
|
_select_estimator
| null |
def _select_estimator(self, estimator_list):
if self._learner_selector == "roundrobin":
self._estimator_index += 1
if self._estimator_index == len(estimator_list):
self._estimator_index = 0
return estimator_list[self._estimator_index]
min_estimated_cost, selected = np.inf, None
inv = []
untried_exists = False
for i, estimator in enumerate(estimator_list):
if estimator in self._search_states and (
self._search_states[estimator].sample_size
): # sample_size=None meaning no result
search_state = self._search_states[estimator]
if (
self._state.time_budget >= 0
and self._search_states[estimator].time2eval_best
> self._state.time_budget - self._state.time_from_start
or self._iter_per_learner_fullsize[estimator] >= self._max_iter_per_learner
):
inv.append(0)
continue
estimated_cost = search_state.estimated_cost4improvement
if search_state.sample_size < self._state.data_size[0] and self._state.time_budget >= 0:
estimated_cost = min(
estimated_cost,
search_state.time2eval_best
* min(
SAMPLE_MULTIPLY_FACTOR,
self._state.data_size[0] / search_state.sample_size,
),
)
gap = search_state.best_loss - self._state.best_loss
if gap > 0 and not self._ensemble:
delta_loss = (search_state.best_loss_old - search_state.best_loss) or search_state.best_loss
delta_time = (search_state.total_time_used - search_state.time_best_found_old) or 1e-10
speed = delta_loss / delta_time
if speed:
estimated_cost = max(2 * gap / speed, estimated_cost)
estimated_cost = estimated_cost or 1e-9
inv.append(1 / estimated_cost)
else:
estimated_cost = self._eci[i]
inv.append(0)
untried_exists = True
if estimated_cost < min_estimated_cost:
min_estimated_cost = estimated_cost
selected = estimator
if untried_exists or not selected:
state = self._search_states.get(selected)
if not (state and state.sample_size):
return selected
s = sum(inv)
p = self._random.rand()
q = 0
for i in range(len(inv)):
if inv[i]:
q += inv[i] / s
if p < q:
return estimator_list[i]
|
(self, estimator_list)
|
[
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] |
52,717 |
flaml.automl.automl
|
add_learner
|
Add a customized learner.
Args:
learner_name: A string of the learner's name.
learner_class: A subclass of flaml.automl.model.BaseEstimator.
|
def add_learner(self, learner_name, learner_class):
"""Add a customized learner.
Args:
learner_name: A string of the learner's name.
learner_class: A subclass of flaml.automl.model.BaseEstimator.
"""
self._state.learner_classes[learner_name] = learner_class
|
(self, learner_name, learner_class)
|
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] |
52,718 |
flaml.automl.automl
|
best_model_for_estimator
|
Return the best model found for a particular estimator.
Args:
estimator_name: a str of the estimator's name.
Returns:
An object storing the best model for estimator_name.
If `model_history` was set to False during fit(), then the returned model
is untrained unless estimator_name is the best estimator.
If `model_history` was set to True, then the returned model is trained.
|
def best_model_for_estimator(self, estimator_name: str):
"""Return the best model found for a particular estimator.
Args:
estimator_name: a str of the estimator's name.
Returns:
An object storing the best model for estimator_name.
If `model_history` was set to False during fit(), then the returned model
is untrained unless estimator_name is the best estimator.
If `model_history` was set to True, then the returned model is trained.
"""
state = self._search_states.get(estimator_name)
return state and getattr(state, "trained_estimator", None)
|
(self, estimator_name: str)
|
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52,719 |
flaml.automl.automl
|
fit
|
Find a model for a given task.
Args:
X_train: A numpy array or a pandas dataframe of training data in
shape (n, m). For time series forecsat tasks, the first column of X_train
must be the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric).
When using ray, X_train can be a ray.ObjectRef.
y_train: A numpy array or a pandas series of labels in shape (n, ).
dataframe: A dataframe of training data including label column.
For time series forecast tasks, dataframe must be specified and must have
at least two columns, timestamp and label, where the first
column is the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric).
When using ray, dataframe can be a ray.ObjectRef.
label: A str of the label column name for, e.g., 'label';
Note: If X_train and y_train are provided,
dataframe and label are ignored;
If not, dataframe and label must be provided.
metric: A string of the metric name or a function,
e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted',
'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following input arguments:
```python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None,
config=None, groups_test=None, groups_train=None,
):
return metric_to_minimize, metrics_to_log
```
which returns a float number as the minimization objective,
and a dictionary as the metrics to log. E.g.,
```python
def custom_metric(
X_val, y_val, estimator, labels,
X_train, y_train, weight_val=None, weight_train=None,
*args,
):
from sklearn.metrics import log_loss
import time
start = time.time()
y_pred = estimator.predict_proba(X_val)
pred_time = (time.time() - start) / len(X_val)
val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
alpha = 0.5
return val_loss * (1 + alpha) - alpha * train_loss, {
"val_loss": val_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
```
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast_regression',
'ts_forecast_classification', 'rank', 'seq-classification',
'seq-regression', 'summarization', or an instance of Task class
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging,
set it to be an empty string "".
estimator_list: A list of strings for estimator names, or 'auto'.
e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']```.
time_budget: A float number of the time budget in seconds.
Use -1 if no time limit.
max_iter: An integer of the maximal number of iterations.
NOTE: when both time_budget and max_iter are unspecified,
only one model will be trained per estimator.
sample: A boolean of whether to sample the training data during
search.
ensemble: boolean or dict | default=False. Whether to perform
ensemble after search. Can be a dict with keys 'passthrough'
and 'final_estimator' to specify the passthrough and
final_estimator in the stacker. The dict can also contain
'n_jobs' as the key to specify the number of jobs for the stacker.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the valiation data percentage for holdout.
n_splits: An integer of the number of folds for cross - validation.
log_type: A string of the log type, one of
['better', 'all'].
'better' only logs configs with better loss than previos iters
'all' logs all the tried configs.
model_history: A boolean of whether to keep the trained best
model per estimator. Make sure memory is large enough if setting to True.
Default value is False: best_model_for_estimator would return a
untrained model for non-best learner.
log_training_metric: A boolean of whether to log the training
metric for each model.
mem_thres: A float of the memory size constraint in bytes.
pred_time_limit: A float of the prediction latency constraint in seconds.
It refers to the average prediction time per row in validation data.
train_time_limit: None or a float of the training time constraint in seconds.
X_val: None or a numpy array or a pandas dataframe of validation data.
y_val: None or a numpy array or a pandas series of validation labels.
sample_weight_val: None or a numpy array of the sample weight of
validation data of the same shape as y_val.
groups_val: None or array-like | group labels (with matching length
to y_val) or group counts (with sum equal to length of y_val)
for validation data. Need to be consistent with groups.
groups: None or array-like | Group labels (with matching length to
y_train) or groups counts (with sum equal to length of y_train)
for training data.
verbose: int, default=3 | Controls the verbosity, higher means more
messages.
retrain_full: bool or str, default=True | whether to retrain the
selected model on the full training data when using holdout.
True - retrain only after search finishes; False - no retraining;
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
hpo_method: str, default="auto" | The hyperparameter
optimization method. By default, CFO is used for sequential
search and BlendSearch is used for parallel search.
No need to set when using flaml's default search space or using
a simple customized search space. When set to 'bs', BlendSearch
is used. BlendSearch can be tried when the search space is
complex, for example, containing multiple disjoint, discontinuous
subspaces. When set to 'random', random search is used.
starting_points: A dictionary or a str to specify the starting hyperparameter
config for the estimators | default="data".
If str:
- if "data", use data-dependent defaults;
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
```python
from flaml import AutoML
automl = AutoML()
X_train, y_train = load_iris(return_X_y=True)
automl.fit(X_train, y_train)
starting_points = automl.best_config_per_estimator
new_automl = AutoML()
new_automl.fit(X_train, y_train, starting_points=starting_points)
```
seed: int or None, default=None | The random seed for hpo.
n_concurrent_trials: [In preview] int, default=1 | The number of
concurrent trials. When n_concurrent_trials > 1, flaml performes
[parallel tuning](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)
and installation of ray or spark is required: `pip install flaml[ray]`
or `pip install flaml[spark]`. Please check
[here](https://spark.apache.org/docs/latest/api/python/getting_started/install.html)
for more details about installing Spark.
keep_search_state: boolean, default=False | Whether to keep data needed
for model search after fit(). By default the state is deleted for
space saving.
preserve_checkpoint: boolean, default=True | Whether to preserve the saved checkpoint
on disk when deleting automl. By default the checkpoint is preserved.
early_stop: boolean, default=False | Whether to stop early if the
search is considered to converge.
force_cancel: boolean, default=False | Whether to forcely cancel the PySpark job if overtime.
append_log: boolean, default=False | Whether to directly append the log
records to the input log file if it exists.
auto_augment: boolean, default=True | Whether to automatically
augment rare classes.
min_sample_size: int, default=MIN_SAMPLE_TRAIN | the minimal sample
size when sample=True.
use_ray: boolean or dict.
If boolean: default=False | Whether to use ray to run the training
in separate processes. This can be used to prevent OOM for large
datasets, but will incur more overhead in time.
If dict: the dict contains the keywords arguments to be passed to
[ray.tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html).
use_spark: boolean, default=False | Whether to use spark to run the training
in parallel spark jobs. This can be used to accelerate training on large models
and large datasets, but will incur more overhead in time and thus slow down
training in some cases.
free_mem_ratio: float between 0 and 1, default=0. The free memory ratio to keep during training.
metric_constraints: list, default=[] | The list of metric constraints.
Each element in this list is a 3-tuple, which shall be expressed
in the following format: the first element of the 3-tuple is the name of the
metric, the second element is the inequality sign chosen from ">=" and "<=",
and the third element is the constraint value. E.g., `('precision', '>=', 0.9)`.
Note that all the metric names in metric_constraints need to be reported via
the metrics_to_log dictionary returned by a customized metric function.
The customized metric function shall be provided via the `metric` key word argument
of the fit() function or the automl constructor.
Find examples in this [test](https://github.com/microsoft/FLAML/tree/main/test/automl/test_constraints.py).
If `pred_time_limit` is provided as one of keyword arguments to fit() function or
the automl constructor, flaml will automatically (and under the hood)
add it as an additional element in the metric_constraints. Essentially 'pred_time_limit'
specifies a constraint about the prediction latency constraint in seconds.
custom_hp: dict, default=None | The custom search space specified by user
Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the
domain of the custom search space can either be a value of a sample.Domain object.
```python
custom_hp = {
"transformer_ms": {
"model_path": {
"domain": "albert-base-v2",
},
"learning_rate": {
"domain": tune.choice([1e-4, 1e-5]),
}
}
}
```
time_col: for a time series task, name of the column containing the timestamps. If not
provided, defaults to the first column of X_train/X_val
cv_score_agg_func: customized cross-validation scores aggregate function. Default to average metrics across folds. If specificed, this function needs to
have the following input arguments:
* val_loss_folds: list of floats, the loss scores of each fold;
* log_metrics_folds: list of dicts/floats, the metrics of each fold to log.
This function should return the final aggregate result of all folds. A float number of the minimization objective, and a dictionary as the metrics to log or None.
E.g.,
```python
def cv_score_agg_func(val_loss_folds, log_metrics_folds):
metric_to_minimize = sum(val_loss_folds)/len(val_loss_folds)
metrics_to_log = None
for single_fold in log_metrics_folds:
if metrics_to_log is None:
metrics_to_log = single_fold
elif isinstance(metrics_to_log, dict):
metrics_to_log = {k: metrics_to_log[k] + v for k, v in single_fold.items()}
else:
metrics_to_log += single_fold
if metrics_to_log:
n = len(val_loss_folds)
metrics_to_log = (
{k: v / n for k, v in metrics_to_log.items()}
if isinstance(metrics_to_log, dict)
else metrics_to_log / n
)
return metric_to_minimize, metrics_to_log
```
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
mlflow_logging: boolean, default=None | Whether to log the training results to mlflow.
Default value is None, which means the logging decision is made based on
AutoML.__init__'s mlflow_logging argument.
This requires mlflow to be installed and to have an active mlflow run.
FLAML will create nested runs.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
For TransformersEstimator, available fit_kwargs can be found from
[TrainingArgumentsForAuto](nlp/huggingface/training_args).
e.g.,
```python
fit_kwargs_by_estimator = {
"transformer": {
"output_dir": "test/data/output/",
"fp16": False,
},
"tft": {
"max_encoder_length": 1,
"min_encoder_length": 1,
"static_categoricals": [],
"static_reals": [],
"time_varying_known_categoricals": [],
"time_varying_known_reals": [],
"time_varying_unknown_categoricals": [],
"time_varying_unknown_reals": [],
"variable_groups": {},
"lags": {},
}
}
```
**fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such as sample_weight. Below are a few examples of
estimator-specific parameters:
period: int | forecast horizon for all time series forecast tasks.
gpu_per_trial: float, default = 0 | A float of the number of gpus per trial,
only used by TransformersEstimator, XGBoostSklearnEstimator, and
TemporalFusionTransformerEstimator.
group_ids: list of strings of column names identifying a time series, only
used by TemporalFusionTransformerEstimator, required for
'ts_forecast_panel' task. `group_ids` is a parameter for TimeSeriesDataSet object
from PyTorchForecasting.
For other parameters to describe your dataset, refer to
[TimeSeriesDataSet PyTorchForecasting](https://pytorch-forecasting.readthedocs.io/en/stable/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html).
To specify your variables, use `static_categoricals`, `static_reals`,
`time_varying_known_categoricals`, `time_varying_known_reals`,
`time_varying_unknown_categoricals`, `time_varying_unknown_reals`,
`variable_groups`. To provide more information on your data, use
`max_encoder_length`, `min_encoder_length`, `lags`.
log_dir: str, default = "lightning_logs" | Folder into which to log results
for tensorboard, only used by TemporalFusionTransformerEstimator.
max_epochs: int, default = 20 | Maximum number of epochs to run training,
only used by TemporalFusionTransformerEstimator.
batch_size: int, default = 64 | Batch size for training model, only
used by TemporalFusionTransformerEstimator.
|
def fit(
self,
X_train=None,
y_train=None,
dataframe=None,
label=None,
metric=None,
task: Optional[Union[str, Task]] = None,
n_jobs=None,
# gpu_per_trial=0,
log_file_name=None,
estimator_list=None,
time_budget=None,
max_iter=None,
sample=None,
ensemble=None,
eval_method=None,
log_type=None,
model_history=None,
split_ratio=None,
n_splits=None,
log_training_metric=None,
mem_thres=None,
pred_time_limit=None,
train_time_limit=None,
X_val=None,
y_val=None,
sample_weight_val=None,
groups_val=None,
groups=None,
verbose=None,
retrain_full=None,
split_type=None,
learner_selector=None,
hpo_method=None,
starting_points=None,
seed=None,
n_concurrent_trials=None,
keep_search_state=None,
preserve_checkpoint=True,
early_stop=None,
force_cancel=None,
append_log=None,
auto_augment=None,
min_sample_size=None,
use_ray=None,
use_spark=None,
free_mem_ratio=0,
metric_constraints=None,
custom_hp=None,
time_col=None,
cv_score_agg_func=None,
skip_transform=None,
mlflow_logging=None,
fit_kwargs_by_estimator=None,
**fit_kwargs,
):
"""Find a model for a given task.
Args:
X_train: A numpy array or a pandas dataframe of training data in
shape (n, m). For time series forecsat tasks, the first column of X_train
must be the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric).
When using ray, X_train can be a ray.ObjectRef.
y_train: A numpy array or a pandas series of labels in shape (n, ).
dataframe: A dataframe of training data including label column.
For time series forecast tasks, dataframe must be specified and must have
at least two columns, timestamp and label, where the first
column is the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric).
When using ray, dataframe can be a ray.ObjectRef.
label: A str of the label column name for, e.g., 'label';
Note: If X_train and y_train are provided,
dataframe and label are ignored;
If not, dataframe and label must be provided.
metric: A string of the metric name or a function,
e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted',
'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following input arguments:
```python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None,
config=None, groups_test=None, groups_train=None,
):
return metric_to_minimize, metrics_to_log
```
which returns a float number as the minimization objective,
and a dictionary as the metrics to log. E.g.,
```python
def custom_metric(
X_val, y_val, estimator, labels,
X_train, y_train, weight_val=None, weight_train=None,
*args,
):
from sklearn.metrics import log_loss
import time
start = time.time()
y_pred = estimator.predict_proba(X_val)
pred_time = (time.time() - start) / len(X_val)
val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
alpha = 0.5
return val_loss * (1 + alpha) - alpha * train_loss, {
"val_loss": val_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
```
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast_regression',
'ts_forecast_classification', 'rank', 'seq-classification',
'seq-regression', 'summarization', or an instance of Task class
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging,
set it to be an empty string "".
estimator_list: A list of strings for estimator names, or 'auto'.
e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']```.
time_budget: A float number of the time budget in seconds.
Use -1 if no time limit.
max_iter: An integer of the maximal number of iterations.
NOTE: when both time_budget and max_iter are unspecified,
only one model will be trained per estimator.
sample: A boolean of whether to sample the training data during
search.
ensemble: boolean or dict | default=False. Whether to perform
ensemble after search. Can be a dict with keys 'passthrough'
and 'final_estimator' to specify the passthrough and
final_estimator in the stacker. The dict can also contain
'n_jobs' as the key to specify the number of jobs for the stacker.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the valiation data percentage for holdout.
n_splits: An integer of the number of folds for cross - validation.
log_type: A string of the log type, one of
['better', 'all'].
'better' only logs configs with better loss than previos iters
'all' logs all the tried configs.
model_history: A boolean of whether to keep the trained best
model per estimator. Make sure memory is large enough if setting to True.
Default value is False: best_model_for_estimator would return a
untrained model for non-best learner.
log_training_metric: A boolean of whether to log the training
metric for each model.
mem_thres: A float of the memory size constraint in bytes.
pred_time_limit: A float of the prediction latency constraint in seconds.
It refers to the average prediction time per row in validation data.
train_time_limit: None or a float of the training time constraint in seconds.
X_val: None or a numpy array or a pandas dataframe of validation data.
y_val: None or a numpy array or a pandas series of validation labels.
sample_weight_val: None or a numpy array of the sample weight of
validation data of the same shape as y_val.
groups_val: None or array-like | group labels (with matching length
to y_val) or group counts (with sum equal to length of y_val)
for validation data. Need to be consistent with groups.
groups: None or array-like | Group labels (with matching length to
y_train) or groups counts (with sum equal to length of y_train)
for training data.
verbose: int, default=3 | Controls the verbosity, higher means more
messages.
retrain_full: bool or str, default=True | whether to retrain the
selected model on the full training data when using holdout.
True - retrain only after search finishes; False - no retraining;
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to
|
(self, X_train=None, y_train=None, dataframe=None, label=None, metric=None, task: Union[str, flaml.automl.task.task.Task, NoneType] = None, n_jobs=None, log_file_name=None, estimator_list=None, time_budget=None, max_iter=None, sample=None, ensemble=None, eval_method=None, log_type=None, model_history=None, split_ratio=None, n_splits=None, log_training_metric=None, mem_thres=None, pred_time_limit=None, train_time_limit=None, X_val=None, y_val=None, sample_weight_val=None, groups_val=None, groups=None, verbose=None, retrain_full=None, split_type=None, learner_selector=None, hpo_method=None, starting_points=None, seed=None, n_concurrent_trials=None, keep_search_state=None, preserve_checkpoint=True, early_stop=None, force_cancel=None, append_log=None, auto_augment=None, min_sample_size=None, use_ray=None, use_spark=None, free_mem_ratio=0, metric_constraints=None, custom_hp=None, time_col=None, cv_score_agg_func=None, skip_transform=None, mlflow_logging=None, fit_kwargs_by_estimator=None, **fit_kwargs)
|
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] |
52,720 |
flaml.automl.automl
|
get_estimator_from_log
|
Get the estimator from log file.
Args:
log_file_name: A string of the log file name.
record_id: An integer of the record ID in the file,
0 corresponds to the first trial.
task: A string of the task type,
'binary', 'multiclass', 'regression', 'ts_forecast', 'rank',
or an instance of the Task class.
Returns:
An estimator object for the given configuration.
|
def get_estimator_from_log(self, log_file_name: str, record_id: int, task: Union[str, Task]):
"""Get the estimator from log file.
Args:
log_file_name: A string of the log file name.
record_id: An integer of the record ID in the file,
0 corresponds to the first trial.
task: A string of the task type,
'binary', 'multiclass', 'regression', 'ts_forecast', 'rank',
or an instance of the Task class.
Returns:
An estimator object for the given configuration.
"""
with training_log_reader(log_file_name) as reader:
record = reader.get_record(record_id)
estimator = record.learner
config = AutoMLState.sanitize(record.config)
if isinstance(task, str):
task = task_factory(task)
estimator, _ = train_estimator(
X_train=None,
y_train=None,
config_dic=config,
task=task,
estimator_name=estimator,
estimator_class=self._state.learner_classes.get(estimator),
eval_metric="train_time",
)
return estimator
|
(self, log_file_name: str, record_id: int, task: Union[str, flaml.automl.task.task.Task])
|
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] |
52,721 |
flaml.automl.automl
|
get_params
| null |
def get_params(self, deep: bool = False) -> dict:
return self._settings.copy()
|
(self, deep: bool = False) -> dict
|
[
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] |
52,722 |
flaml.automl.automl
|
pickle
| null |
def pickle(self, output_file_name):
import pickle
estimator_to_training_function = {}
for estimator in self.estimator_list:
search_state = self._search_states[estimator]
if hasattr(search_state, "training_function"):
estimator_to_training_function[estimator] = search_state.training_function
del search_state.training_function
with open(output_file_name, "wb") as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
|
(self, output_file_name)
|
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] |
52,723 |
flaml.automl.automl
|
predict
|
Predict label from features.
Args:
X: A numpy array or pandas dataframe or pyspark.pandas dataframe
of featurized instances, shape n * m,
or for time series forcast tasks:
a pandas dataframe with the first column containing
timestamp values (datetime type) or an integer n for
the predict steps (only valid when the estimator is
arima or sarimax). Other columns in the dataframe
are assumed to be exogenous variables (categorical
or numeric).
**pred_kwargs: Other key word arguments to pass to predict() function of
the searched learners, such as per_device_eval_batch_size.
```python
multivariate_X_test = DataFrame({
'timeStamp': pd.date_range(start='1/1/2022', end='1/07/2022'),
'categorical_col': ['yes', 'yes', 'no', 'no', 'yes', 'no', 'yes'],
'continuous_col': [105, 107, 120, 118, 110, 112, 115]
})
model.predict(multivariate_X_test)
```
Returns:
A array-like of shape n * 1: each element is a predicted
label for an instance.
|
def predict(
self,
X: Union[np.array, DataFrame, List[str], List[List[str]], psDataFrame],
**pred_kwargs,
):
"""Predict label from features.
Args:
X: A numpy array or pandas dataframe or pyspark.pandas dataframe
of featurized instances, shape n * m,
or for time series forcast tasks:
a pandas dataframe with the first column containing
timestamp values (datetime type) or an integer n for
the predict steps (only valid when the estimator is
arima or sarimax). Other columns in the dataframe
are assumed to be exogenous variables (categorical
or numeric).
**pred_kwargs: Other key word arguments to pass to predict() function of
the searched learners, such as per_device_eval_batch_size.
```python
multivariate_X_test = DataFrame({
'timeStamp': pd.date_range(start='1/1/2022', end='1/07/2022'),
'categorical_col': ['yes', 'yes', 'no', 'no', 'yes', 'no', 'yes'],
'continuous_col': [105, 107, 120, 118, 110, 112, 115]
})
model.predict(multivariate_X_test)
```
Returns:
A array-like of shape n * 1: each element is a predicted
label for an instance.
"""
estimator = getattr(self, "_trained_estimator", None)
if estimator is None:
logger.warning("No estimator is trained. Please run fit with enough budget.")
return None
X = self._state.task.preprocess(X, self._transformer)
y_pred = estimator.predict(X, **pred_kwargs)
if isinstance(y_pred, np.ndarray) and y_pred.ndim > 1 and isinstance(y_pred, np.ndarray):
y_pred = y_pred.flatten()
if self._label_transformer:
return self._label_transformer.inverse_transform(Series(y_pred.astype(int)))
else:
return y_pred
|
(self, X: Union[<built-in function array>, pandas.core.frame.DataFrame, List[str], List[List[str]], flaml.automl.spark.psDataFrame], **pred_kwargs)
|
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] |
52,724 |
flaml.automl.automl
|
predict_proba
|
Predict the probability of each class from features, only works for
classification problems.
Args:
X: A numpy array of featurized instances, shape n * m.
**pred_kwargs: Other key word arguments to pass to predict_proba() function of
the searched learners, such as per_device_eval_batch_size.
Returns:
A numpy array of shape n * c. c is the # classes. Each element at
(i, j) is the probability for instance i to be in class j.
|
def predict_proba(self, X, **pred_kwargs):
"""Predict the probability of each class from features, only works for
classification problems.
Args:
X: A numpy array of featurized instances, shape n * m.
**pred_kwargs: Other key word arguments to pass to predict_proba() function of
the searched learners, such as per_device_eval_batch_size.
Returns:
A numpy array of shape n * c. c is the # classes. Each element at
(i, j) is the probability for instance i to be in class j.
"""
estimator = getattr(self, "_trained_estimator", None)
if estimator is None:
logger.warning("No estimator is trained. Please run fit with enough budget.")
return None
X = self._state.task.preprocess(X, self._transformer)
proba = self._trained_estimator.predict_proba(X, **pred_kwargs)
return proba
|
(self, X, **pred_kwargs)
|
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] |
52,725 |
flaml.automl.automl
|
retrain_from_log
|
Retrain from log file.
This function is intended to retrain the logged configurations.
NOTE: In some rare case, the last config is early stopped to meet time_budget and it's the best config.
But the logged config's ITER_HP (e.g., n_estimators) is not reduced.
Args:
log_file_name: A string of the log file name.
X_train: A numpy array or dataframe of training data in shape n*m.
For time series forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric).
y_train: A numpy array or series of labels in shape n*1.
dataframe: A dataframe of training data including label column.
For time series forecast tasks, dataframe must be specified and should
have at least two columns: timestamp and label, where the first
column is the timestamp column (datetime type). Other columns
in the dataframe are assumed to be exogenous variables
(categorical or numeric).
label: A str of the label column name, e.g., 'label';
Note: If X_train and y_train are provided,
dataframe and label are ignored;
If not, dataframe and label must be provided.
time_budget: A float number of the time budget in seconds.
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank',
'seq-classification', 'seq-regression', 'summarization',
or an instance of Task class.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the validation data percentage for holdout.
n_splits: An integer of the number of folds for cross-validation.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
groups: None or array-like | Group labels (with matching length to
y_train) or groups counts (with sum equal to length of y_train)
for training data.
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
train_best: A boolean of whether to train the best config in the
time budget; if false, train the last config in the budget.
train_full: A boolean of whether to train on the full data. If true,
eval_method and sample_size in the log file will be ignored.
record_id: the ID of the training log record from which the model will
be retrained. By default `record_id = -1` which means this will be
ignored. `record_id = 0` corresponds to the first trial, and
when `record_id >= 0`, `time_budget` will be ignored.
auto_augment: boolean, default=True | Whether to automatically
augment rare classes.
custom_hp: dict, default=None | The custom search space specified by user
Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the
domain of the custom search space can either be a value or a sample.Domain object.
```python
custom_hp = {
"transformer_ms": {
"model_path": {
"domain": "albert-base-v2",
},
"learning_rate": {
"domain": tune.choice([1e-4, 1e-5]),
}
}
}
```
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
e.g.,
```python
fit_kwargs_by_estimator = {
"transformer": {
"output_dir": "test/data/output/",
"fp16": False,
}
}
```
**fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such as sample_weight. Below are a few examples of
estimator-specific parameters:
period: int | forecast horizon for all time series forecast tasks.
gpu_per_trial: float, default = 0 | A float of the number of gpus per trial,
only used by TransformersEstimator, XGBoostSklearnEstimator, and
TemporalFusionTransformerEstimator.
group_ids: list of strings of column names identifying a time series, only
used by TemporalFusionTransformerEstimator, required for
'ts_forecast_panel' task. `group_ids` is a parameter for TimeSeriesDataSet object
from PyTorchForecasting.
For other parameters to describe your dataset, refer to
[TimeSeriesDataSet PyTorchForecasting](https://pytorch-forecasting.readthedocs.io/en/stable/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html).
To specify your variables, use `static_categoricals`, `static_reals`,
`time_varying_known_categoricals`, `time_varying_known_reals`,
`time_varying_unknown_categoricals`, `time_varying_unknown_reals`,
`variable_groups`. To provide more information on your data, use
`max_encoder_length`, `min_encoder_length`, `lags`.
log_dir: str, default = "lightning_logs" | Folder into which to log results
for tensorboard, only used by TemporalFusionTransformerEstimator.
max_epochs: int, default = 20 | Maximum number of epochs to run training,
only used by TemporalFusionTransformerEstimator.
batch_size: int, default = 64 | Batch size for training model, only
used by TemporalFusionTransformerEstimator.
|
def retrain_from_log(
self,
log_file_name,
X_train=None,
y_train=None,
dataframe=None,
label=None,
time_budget=np.inf,
task: Optional[Union[str, Task]] = None,
eval_method=None,
split_ratio=None,
n_splits=None,
split_type=None,
groups=None,
n_jobs=-1,
# gpu_per_trial=0,
train_best=True,
train_full=False,
record_id=-1,
auto_augment=None,
custom_hp=None,
skip_transform=None,
preserve_checkpoint=True,
fit_kwargs_by_estimator=None,
**fit_kwargs,
):
"""Retrain from log file.
This function is intended to retrain the logged configurations.
NOTE: In some rare case, the last config is early stopped to meet time_budget and it's the best config.
But the logged config's ITER_HP (e.g., n_estimators) is not reduced.
Args:
log_file_name: A string of the log file name.
X_train: A numpy array or dataframe of training data in shape n*m.
For time series forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric).
y_train: A numpy array or series of labels in shape n*1.
dataframe: A dataframe of training data including label column.
For time series forecast tasks, dataframe must be specified and should
have at least two columns: timestamp and label, where the first
column is the timestamp column (datetime type). Other columns
in the dataframe are assumed to be exogenous variables
(categorical or numeric).
label: A str of the label column name, e.g., 'label';
Note: If X_train and y_train are provided,
dataframe and label are ignored;
If not, dataframe and label must be provided.
time_budget: A float number of the time budget in seconds.
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank',
'seq-classification', 'seq-regression', 'summarization',
or an instance of Task class.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the validation data percentage for holdout.
n_splits: An integer of the number of folds for cross-validation.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
groups: None or array-like | Group labels (with matching length to
y_train) or groups counts (with sum equal to length of y_train)
for training data.
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
train_best: A boolean of whether to train the best config in the
time budget; if false, train the last config in the budget.
train_full: A boolean of whether to train on the full data. If true,
eval_method and sample_size in the log file will be ignored.
record_id: the ID of the training log record from which the model will
be retrained. By default `record_id = -1` which means this will be
ignored. `record_id = 0` corresponds to the first trial, and
when `record_id >= 0`, `time_budget` will be ignored.
auto_augment: boolean, default=True | Whether to automatically
augment rare classes.
custom_hp: dict, default=None | The custom search space specified by user
Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the
domain of the custom search space can either be a value or a sample.Domain object.
```python
custom_hp = {
"transformer_ms": {
"model_path": {
"domain": "albert-base-v2",
},
"learning_rate": {
"domain": tune.choice([1e-4, 1e-5]),
}
}
}
```
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
e.g.,
```python
fit_kwargs_by_estimator = {
"transformer": {
"output_dir": "test/data/output/",
"fp16": False,
}
}
```
**fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such as sample_weight. Below are a few examples of
estimator-specific parameters:
period: int | forecast horizon for all time series forecast tasks.
gpu_per_trial: float, default = 0 | A float of the number of gpus per trial,
only used by TransformersEstimator, XGBoostSklearnEstimator, and
TemporalFusionTransformerEstimator.
group_ids: list of strings of column names identifying a time series, only
used by TemporalFusionTransformerEstimator, required for
'ts_forecast_panel' task. `group_ids` is a parameter for TimeSeriesDataSet object
from PyTorchForecasting.
For other parameters to describe your dataset, refer to
[TimeSeriesDataSet PyTorchForecasting](https://pytorch-forecasting.readthedocs.io/en/stable/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html).
To specify your variables, use `static_categoricals`, `static_reals`,
`time_varying_known_categoricals`, `time_varying_known_reals`,
`time_varying_unknown_categoricals`, `time_varying_unknown_reals`,
`variable_groups`. To provide more information on your data, use
`max_encoder_length`, `min_encoder_length`, `lags`.
log_dir: str, default = "lightning_logs" | Folder into which to log results
for tensorboard, only used by TemporalFusionTransformerEstimator.
max_epochs: int, default = 20 | Maximum number of epochs to run training,
only used by TemporalFusionTransformerEstimator.
batch_size: int, default = 64 | Batch size for training model, only
used by TemporalFusionTransformerEstimator.
"""
task = task or self._settings.get("task")
if isinstance(task, str):
task = task_factory(task)
eval_method = eval_method or self._settings.get("eval_method")
split_ratio = split_ratio or self._settings.get("split_ratio")
n_splits = n_splits or self._settings.get("n_splits")
split_type = split_type or self._settings.get("split_type")
auto_augment = self._settings.get("auto_augment") if auto_augment is None else auto_augment
self._state.task = task
self._estimator_type = "classifier" if task.is_classification() else "regressor"
self._state.fit_kwargs = fit_kwargs
self._state.custom_hp = custom_hp or self._settings.get("custom_hp")
self._skip_transform = self._settings.get("skip_transform") if skip_transform is None else skip_transform
self._state.fit_kwargs_by_estimator = fit_kwargs_by_estimator or self._settings.get("fit_kwargs_by_estimator")
self.preserve_checkpoint = (
self._settings.get("preserve
|
(self, log_file_name, X_train=None, y_train=None, dataframe=None, label=None, time_budget=inf, task: Union[str, flaml.automl.task.task.Task, NoneType] = None, eval_method=None, split_ratio=None, n_splits=None, split_type=None, groups=None, n_jobs=-1, train_best=True, train_full=False, record_id=-1, auto_augment=None, custom_hp=None, skip_transform=None, preserve_checkpoint=True, fit_kwargs_by_estimator=None, **fit_kwargs)
|
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] |
52,726 |
flaml.automl.automl
|
save_best_config
| null |
def save_best_config(self, filename):
best = {
"class": self.best_estimator,
"hyperparameters": self.best_config,
}
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "w") as f:
json.dump(best, f)
|
(self, filename)
|
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] |
52,727 |
flaml.automl.automl
|
score
| null |
def score(
self,
X: Union[DataFrame, psDataFrame],
y: Union[Series, psSeries],
**kwargs,
):
estimator = getattr(self, "_trained_estimator", None)
if estimator is None:
logger.warning("No estimator is trained. Please run fit with enough budget.")
return None
X = self._state.task.preprocess(X, self._transformer)
if self._label_transformer:
y = self._label_transformer.transform(y)
return estimator.score(X, y, **kwargs)
|
(self, X: Union[pandas.core.frame.DataFrame, flaml.automl.spark.psDataFrame], y: Union[pandas.core.series.Series, flaml.automl.spark.psDataFrame], **kwargs)
|
[
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0.009615619666874409,
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0.02263232134282589,
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] |
52,728 |
flaml.onlineml.autovw
|
AutoVW
|
Class for the AutoVW algorithm.
|
class AutoVW:
"""Class for the AutoVW algorithm."""
WARMSTART_NUM = 100
AUTOMATIC = "_auto"
VW_INTERACTION_ARG_NAME = "interactions"
def __init__(
self,
max_live_model_num: int,
search_space: dict,
init_config: Optional[dict] = {},
min_resource_lease: Optional[Union[str, float]] = "auto",
automl_runner_args: Optional[dict] = {},
scheduler_args: Optional[dict] = {},
model_select_policy: Optional[str] = "threshold_loss_ucb",
metric: Optional[str] = "mae_clipped",
random_seed: Optional[int] = None,
model_selection_mode: Optional[str] = "min",
cb_coef: Optional[float] = None,
):
"""Constructor.
Args:
max_live_model_num: An int to specify the maximum number of
'live' models, which, in other words, is the maximum number
of models allowed to update in each learning iteraction.
search_space: A dictionary of the search space. This search space
includes both hyperparameters we want to tune and fixed
hyperparameters. In the latter case, the value is a fixed value.
init_config: A dictionary of a partial or full initial config,
e.g. {'interactions': set(), 'learning_rate': 0.5}
min_resource_lease: string or float | The minimum resource lease
assigned to a particular model/trial. If set as 'auto', it will
be calculated automatically.
automl_runner_args: A dictionary of configuration for the OnlineTrialRunner.
If set {}, default values will be used, which is equivalent to using
the following configs.
Example:
```python
automl_runner_args = {
"champion_test_policy": 'loss_ucb', # the statistic test for a better champion
"remove_worse": False, # whether to do worse than test
}
```
scheduler_args: A dictionary of configuration for the scheduler.
If set {}, default values will be used, which is equivalent to using the
following config.
Example:
```python
scheduler_args = {
"keep_challenger_metric": 'ucb', # what metric to use when deciding the top performing challengers
"keep_challenger_ratio": 0.5, # denotes the ratio of top performing challengers to keep live
"keep_champion": True, # specifcies whether to keep the champion always running
}
```
model_select_policy: A string in ['threshold_loss_ucb',
'threshold_loss_lcb', 'threshold_loss_avg', 'loss_ucb', 'loss_lcb',
'loss_avg'] to specify how to select one model to do prediction from
the live model pool. Default value is 'threshold_loss_ucb'.
metric: A string in ['mae_clipped', 'mae', 'mse', 'absolute_clipped',
'absolute', 'squared'] to specify the name of the loss function used
for calculating the progressive validation loss in ChaCha.
random_seed: An integer of the random seed used in the searcher
(more specifically this the random seed for ConfigOracle).
model_selection_mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
cb_coef: A float coefficient (optional) used in the sample complexity bound.
"""
self._max_live_model_num = max_live_model_num
self._search_space = search_space
self._init_config = init_config
self._online_trial_args = {
"metric": metric,
"min_resource_lease": min_resource_lease,
"cb_coef": cb_coef,
}
self._automl_runner_args = automl_runner_args
self._scheduler_args = scheduler_args
self._model_select_policy = model_select_policy
self._model_selection_mode = model_selection_mode
self._random_seed = random_seed
self._trial_runner = None
self._best_trial = None
# code for debugging purpose
self._prediction_trial_id = None
self._iter = 0
def _setup_trial_runner(self, vw_example):
"""Set up the _trial_runner based on one vw_example."""
# setup the default search space for the namespace interaction hyperparameter
search_space = self._search_space.copy()
for k, v in self._search_space.items():
if k == self.VW_INTERACTION_ARG_NAME and v == self.AUTOMATIC:
raw_namespaces = self.get_ns_feature_dim_from_vw_example(vw_example).keys()
search_space[k] = polynomial_expansion_set(init_monomials=set(raw_namespaces))
# setup the init config based on the input _init_config and search space
init_config = self._init_config.copy()
for k, v in search_space.items():
if k not in init_config.keys():
if isinstance(v, PolynomialExpansionSet):
init_config[k] = set()
elif not isinstance(v, Categorical) and not isinstance(v, Float):
init_config[k] = v
searcher_args = {
"init_config": init_config,
"space": search_space,
"random_seed": self._random_seed,
"online_trial_args": self._online_trial_args,
}
logger.info("original search_space %s", self._search_space)
logger.info("original init_config %s", self._init_config)
logger.info("searcher_args %s", searcher_args)
logger.info("scheduler_args %s", self._scheduler_args)
logger.info("automl_runner_args %s", self._automl_runner_args)
searcher = ChampionFrontierSearcher(**searcher_args)
scheduler = ChaChaScheduler(**self._scheduler_args)
self._trial_runner = OnlineTrialRunner(
max_live_model_num=self._max_live_model_num,
searcher=searcher,
scheduler=scheduler,
**self._automl_runner_args,
)
def predict(self, data_sample):
"""Predict on the input data sample.
Args:
data_sample: one data example in vw format.
"""
if self._trial_runner is None:
self._setup_trial_runner(data_sample)
self._best_trial = self._select_best_trial()
self._y_predict = self._best_trial.predict(data_sample)
# code for debugging purpose
if self._prediction_trial_id is None or self._prediction_trial_id != self._best_trial.trial_id:
self._prediction_trial_id = self._best_trial.trial_id
logger.info(
"prediction trial id changed to %s at iter %s, resource used: %s",
self._prediction_trial_id,
self._iter,
self._best_trial.result.resource_used,
)
return self._y_predict
def learn(self, data_sample):
"""Perform one online learning step with the given data sample.
Args:
data_sample: one data example in vw format. It will be used to
update the vw model.
"""
self._iter += 1
self._trial_runner.step(data_sample, (self._y_predict, self._best_trial))
def _select_best_trial(self):
"""Select a best trial from the running trials according to the _model_select_policy."""
best_score = float("+inf") if self._model_selection_mode == "min" else float("-inf")
new_best_trial = None
for trial in self._trial_runner.running_trials:
if trial.result is not None and (
"threshold" not in self._model_select_policy or trial.result.resource_used >= self.WARMSTART_NUM
):
score = trial.result.get_score(self._model_select_policy)
if ("min" == self._model_selection_mode and score < best_score) or (
"max" == self._model_selection_mode and score > best_score
):
best_score = score
new_best_tri
|
(max_live_model_num: int, search_space: dict, init_config: Optional[dict] = {}, min_resource_lease: Union[str, float, NoneType] = 'auto', automl_runner_args: Optional[dict] = {}, scheduler_args: Optional[dict] = {}, model_select_policy: Optional[str] = 'threshold_loss_ucb', metric: Optional[str] = 'mae_clipped', random_seed: Optional[int] = None, model_selection_mode: Optional[str] = 'min', cb_coef: Optional[float] = None)
|
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] |
52,729 |
flaml.onlineml.autovw
|
__init__
|
Constructor.
Args:
max_live_model_num: An int to specify the maximum number of
'live' models, which, in other words, is the maximum number
of models allowed to update in each learning iteraction.
search_space: A dictionary of the search space. This search space
includes both hyperparameters we want to tune and fixed
hyperparameters. In the latter case, the value is a fixed value.
init_config: A dictionary of a partial or full initial config,
e.g. {'interactions': set(), 'learning_rate': 0.5}
min_resource_lease: string or float | The minimum resource lease
assigned to a particular model/trial. If set as 'auto', it will
be calculated automatically.
automl_runner_args: A dictionary of configuration for the OnlineTrialRunner.
If set {}, default values will be used, which is equivalent to using
the following configs.
Example:
```python
automl_runner_args = {
"champion_test_policy": 'loss_ucb', # the statistic test for a better champion
"remove_worse": False, # whether to do worse than test
}
```
scheduler_args: A dictionary of configuration for the scheduler.
If set {}, default values will be used, which is equivalent to using the
following config.
Example:
```python
scheduler_args = {
"keep_challenger_metric": 'ucb', # what metric to use when deciding the top performing challengers
"keep_challenger_ratio": 0.5, # denotes the ratio of top performing challengers to keep live
"keep_champion": True, # specifcies whether to keep the champion always running
}
```
model_select_policy: A string in ['threshold_loss_ucb',
'threshold_loss_lcb', 'threshold_loss_avg', 'loss_ucb', 'loss_lcb',
'loss_avg'] to specify how to select one model to do prediction from
the live model pool. Default value is 'threshold_loss_ucb'.
metric: A string in ['mae_clipped', 'mae', 'mse', 'absolute_clipped',
'absolute', 'squared'] to specify the name of the loss function used
for calculating the progressive validation loss in ChaCha.
random_seed: An integer of the random seed used in the searcher
(more specifically this the random seed for ConfigOracle).
model_selection_mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
cb_coef: A float coefficient (optional) used in the sample complexity bound.
|
def __init__(
self,
max_live_model_num: int,
search_space: dict,
init_config: Optional[dict] = {},
min_resource_lease: Optional[Union[str, float]] = "auto",
automl_runner_args: Optional[dict] = {},
scheduler_args: Optional[dict] = {},
model_select_policy: Optional[str] = "threshold_loss_ucb",
metric: Optional[str] = "mae_clipped",
random_seed: Optional[int] = None,
model_selection_mode: Optional[str] = "min",
cb_coef: Optional[float] = None,
):
"""Constructor.
Args:
max_live_model_num: An int to specify the maximum number of
'live' models, which, in other words, is the maximum number
of models allowed to update in each learning iteraction.
search_space: A dictionary of the search space. This search space
includes both hyperparameters we want to tune and fixed
hyperparameters. In the latter case, the value is a fixed value.
init_config: A dictionary of a partial or full initial config,
e.g. {'interactions': set(), 'learning_rate': 0.5}
min_resource_lease: string or float | The minimum resource lease
assigned to a particular model/trial. If set as 'auto', it will
be calculated automatically.
automl_runner_args: A dictionary of configuration for the OnlineTrialRunner.
If set {}, default values will be used, which is equivalent to using
the following configs.
Example:
```python
automl_runner_args = {
"champion_test_policy": 'loss_ucb', # the statistic test for a better champion
"remove_worse": False, # whether to do worse than test
}
```
scheduler_args: A dictionary of configuration for the scheduler.
If set {}, default values will be used, which is equivalent to using the
following config.
Example:
```python
scheduler_args = {
"keep_challenger_metric": 'ucb', # what metric to use when deciding the top performing challengers
"keep_challenger_ratio": 0.5, # denotes the ratio of top performing challengers to keep live
"keep_champion": True, # specifcies whether to keep the champion always running
}
```
model_select_policy: A string in ['threshold_loss_ucb',
'threshold_loss_lcb', 'threshold_loss_avg', 'loss_ucb', 'loss_lcb',
'loss_avg'] to specify how to select one model to do prediction from
the live model pool. Default value is 'threshold_loss_ucb'.
metric: A string in ['mae_clipped', 'mae', 'mse', 'absolute_clipped',
'absolute', 'squared'] to specify the name of the loss function used
for calculating the progressive validation loss in ChaCha.
random_seed: An integer of the random seed used in the searcher
(more specifically this the random seed for ConfigOracle).
model_selection_mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
cb_coef: A float coefficient (optional) used in the sample complexity bound.
"""
self._max_live_model_num = max_live_model_num
self._search_space = search_space
self._init_config = init_config
self._online_trial_args = {
"metric": metric,
"min_resource_lease": min_resource_lease,
"cb_coef": cb_coef,
}
self._automl_runner_args = automl_runner_args
self._scheduler_args = scheduler_args
self._model_select_policy = model_select_policy
self._model_selection_mode = model_selection_mode
self._random_seed = random_seed
self._trial_runner = None
self._best_trial = None
# code for debugging purpose
self._prediction_trial_id = None
self._iter = 0
|
(self, max_live_model_num: int, search_space: dict, init_config: Optional[dict] = {}, min_resource_lease: Union[str, float, NoneType] = 'auto', automl_runner_args: Optional[dict] = {}, scheduler_args: Optional[dict] = {}, model_select_policy: Optional[str] = 'threshold_loss_ucb', metric: Optional[str] = 'mae_clipped', random_seed: Optional[int] = None, model_selection_mode: Optional[str] = 'min', cb_coef: Optional[float] = None)
|
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] |
52,730 |
flaml.onlineml.autovw
|
_select_best_trial
|
Select a best trial from the running trials according to the _model_select_policy.
|
def _select_best_trial(self):
"""Select a best trial from the running trials according to the _model_select_policy."""
best_score = float("+inf") if self._model_selection_mode == "min" else float("-inf")
new_best_trial = None
for trial in self._trial_runner.running_trials:
if trial.result is not None and (
"threshold" not in self._model_select_policy or trial.result.resource_used >= self.WARMSTART_NUM
):
score = trial.result.get_score(self._model_select_policy)
if ("min" == self._model_selection_mode and score < best_score) or (
"max" == self._model_selection_mode and score > best_score
):
best_score = score
new_best_trial = trial
if new_best_trial is not None:
logger.debug("best_trial resource used: %s", new_best_trial.result.resource_used)
return new_best_trial
else:
# This branch will be triggered when the resource consumption all trials are smaller
# than the WARMSTART_NUM threshold. In this case, we will select the _best_trial
# selected in the previous iteration.
if self._best_trial is not None and self._best_trial.status == Trial.RUNNING:
logger.debug("old best trial %s", self._best_trial.trial_id)
return self._best_trial
else:
# this will be triggered in the first iteration or in the iteration where we want
# to select the trial from the previous iteration but that trial has been paused
# (i.e., self._best_trial.status != Trial.RUNNING) by the scheduler.
logger.debug(
"using champion trial: %s",
self._trial_runner.champion_trial.trial_id,
)
return self._trial_runner.champion_trial
|
(self)
|
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] |
52,731 |
flaml.onlineml.autovw
|
_setup_trial_runner
|
Set up the _trial_runner based on one vw_example.
|
def _setup_trial_runner(self, vw_example):
"""Set up the _trial_runner based on one vw_example."""
# setup the default search space for the namespace interaction hyperparameter
search_space = self._search_space.copy()
for k, v in self._search_space.items():
if k == self.VW_INTERACTION_ARG_NAME and v == self.AUTOMATIC:
raw_namespaces = self.get_ns_feature_dim_from_vw_example(vw_example).keys()
search_space[k] = polynomial_expansion_set(init_monomials=set(raw_namespaces))
# setup the init config based on the input _init_config and search space
init_config = self._init_config.copy()
for k, v in search_space.items():
if k not in init_config.keys():
if isinstance(v, PolynomialExpansionSet):
init_config[k] = set()
elif not isinstance(v, Categorical) and not isinstance(v, Float):
init_config[k] = v
searcher_args = {
"init_config": init_config,
"space": search_space,
"random_seed": self._random_seed,
"online_trial_args": self._online_trial_args,
}
logger.info("original search_space %s", self._search_space)
logger.info("original init_config %s", self._init_config)
logger.info("searcher_args %s", searcher_args)
logger.info("scheduler_args %s", self._scheduler_args)
logger.info("automl_runner_args %s", self._automl_runner_args)
searcher = ChampionFrontierSearcher(**searcher_args)
scheduler = ChaChaScheduler(**self._scheduler_args)
self._trial_runner = OnlineTrialRunner(
max_live_model_num=self._max_live_model_num,
searcher=searcher,
scheduler=scheduler,
**self._automl_runner_args,
)
|
(self, vw_example)
|
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] |
52,732 |
flaml.onlineml.autovw
|
get_ns_feature_dim_from_vw_example
|
Get a dictionary of feature dimensionality for each namespace singleton.
|
@staticmethod
def get_ns_feature_dim_from_vw_example(vw_example) -> dict:
"""Get a dictionary of feature dimensionality for each namespace singleton."""
return get_ns_feature_dim_from_vw_example(vw_example)
|
(vw_example) -> dict
|
[
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] |
52,733 |
flaml.onlineml.autovw
|
learn
|
Perform one online learning step with the given data sample.
Args:
data_sample: one data example in vw format. It will be used to
update the vw model.
|
def learn(self, data_sample):
"""Perform one online learning step with the given data sample.
Args:
data_sample: one data example in vw format. It will be used to
update the vw model.
"""
self._iter += 1
self._trial_runner.step(data_sample, (self._y_predict, self._best_trial))
|
(self, data_sample)
|
[
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] |
52,734 |
flaml.onlineml.autovw
|
predict
|
Predict on the input data sample.
Args:
data_sample: one data example in vw format.
|
def predict(self, data_sample):
"""Predict on the input data sample.
Args:
data_sample: one data example in vw format.
"""
if self._trial_runner is None:
self._setup_trial_runner(data_sample)
self._best_trial = self._select_best_trial()
self._y_predict = self._best_trial.predict(data_sample)
# code for debugging purpose
if self._prediction_trial_id is None or self._prediction_trial_id != self._best_trial.trial_id:
self._prediction_trial_id = self._best_trial.trial_id
logger.info(
"prediction trial id changed to %s at iter %s, resource used: %s",
self._prediction_trial_id,
self._iter,
self._best_trial.result.resource_used,
)
return self._y_predict
|
(self, data_sample)
|
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] |
52,735 |
flaml.tune.searcher.blendsearch
|
BlendSearch
|
class for BlendSearch algorithm.
|
class BlendSearch(Searcher):
"""class for BlendSearch algorithm."""
lagrange = "_lagrange" # suffix for lagrange-modified metric
LocalSearch = FLOW2
def __init__(
self,
metric: Optional[str] = None,
mode: Optional[str] = None,
space: Optional[dict] = None,
low_cost_partial_config: Optional[dict] = None,
cat_hp_cost: Optional[dict] = None,
points_to_evaluate: Optional[List[dict]] = None,
evaluated_rewards: Optional[List] = None,
time_budget_s: Union[int, float] = None,
num_samples: Optional[int] = None,
resource_attr: Optional[str] = None,
min_resource: Optional[float] = None,
max_resource: Optional[float] = None,
reduction_factor: Optional[float] = None,
global_search_alg: Optional[Searcher] = None,
config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None,
metric_constraints: Optional[List[Tuple[str, str, float]]] = None,
seed: Optional[int] = 20,
cost_attr: Optional[str] = "auto",
cost_budget: Optional[float] = None,
experimental: Optional[bool] = False,
lexico_objectives: Optional[dict] = None,
use_incumbent_result_in_evaluation=False,
allow_empty_config=False,
):
"""Constructor.
Args:
metric: A string of the metric name to optimize for.
mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
space: A dictionary to specify the search space.
low_cost_partial_config: A dictionary from a subset of
controlled dimensions to the initial low-cost values.
E.g., ```{'n_estimators': 4, 'max_leaves': 4}```.
cat_hp_cost: A dictionary from a subset of categorical dimensions
to the relative cost of each choice.
E.g., ```{'tree_method': [1, 1, 2]}```.
I.e., the relative cost of the three choices of 'tree_method'
is 1, 1 and 2 respectively.
points_to_evaluate: Initial parameter suggestions to be run first.
evaluated_rewards (list): If you have previously evaluated the
parameters passed in as points_to_evaluate you can avoid
re-running those trials by passing in the reward attributes
as a list so the optimiser can be told the results without
needing to re-compute the trial. Must be the same or shorter length than
points_to_evaluate. When provided, `mode` must be specified.
time_budget_s: int or float | Time budget in seconds.
num_samples: int | The number of configs to try. -1 means no limit on the
number of configs to try.
resource_attr: A string to specify the resource dimension and the best
performance is assumed to be at the max_resource.
min_resource: A float of the minimal resource to use for the resource_attr.
max_resource: A float of the maximal resource to use for the resource_attr.
reduction_factor: A float of the reduction factor used for
incremental pruning.
global_search_alg: A Searcher instance as the global search
instance. If omitted, Optuna is used. The following algos have
known issues when used as global_search_alg:
- HyperOptSearch raises exception sometimes
- TuneBOHB has its own scheduler
config_constraints: A list of config constraints to be satisfied.
E.g., ```config_constraints = [(mem_size, '<=', 1024**3)]```.
`mem_size` is a function which produces a float number for the bytes
needed for a config.
It is used to skip configs which do not fit in memory.
metric_constraints: A list of metric constraints to be satisfied.
E.g., `['precision', '>=', 0.9]`. The sign can be ">=" or "<=".
seed: An integer of the random seed.
cost_attr: None or str to specify the attribute to evaluate the cost of different trials.
Default is "auto", which means that we will automatically choose the cost attribute to use (depending
on the nature of the resource budget). When cost_attr is set to None, cost differences between different trials will be omitted
in our search algorithm. When cost_attr is set to a str different from "auto" and "time_total_s",
this cost_attr must be available in the result dict of the trial.
cost_budget: A float of the cost budget. Only valid when cost_attr is a str different from "auto" and "time_total_s".
lexico_objectives: dict, default=None | It specifics information needed to perform multi-objective
optimization with lexicographic preferences. This is only supported in CFO currently.
When lexico_objectives is not None, the arguments metric, mode will be invalid.
This dictionary shall contain the following fields of key-value pairs:
- "metrics": a list of optimization objectives with the orders reflecting the priorities/preferences of the
objectives.
- "modes" (optional): a list of optimization modes (each mode either "min" or "max") corresponding to the
objectives in the metric list. If not provided, we use "min" as the default mode for all the objectives.
- "targets" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the
metric names (provided in "metric"), and the values are the numerical target values.
- "tolerances" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in "metrics"), and the values are the absolute/percentage tolerance in the form of numeric/string.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": 0.01, "pred_time": 0.0},
"targets": {"error_rate": 0.0},
}
```
We also support percentage tolerance.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": "5%", "pred_time": "0%"},
"targets": {"error_rate": 0.0},
}
```
experimental: A bool of whether to use experimental features.
"""
self._eps = SEARCH_THREAD_EPS
self._input_cost_attr = cost_attr
if cost_attr == "auto":
if time_budget_s is not None:
self.cost_attr = TIME_TOTAL_S
else:
self.cost_attr = None
self._cost_budget = None
else:
self.cost_attr = cost_attr
self._cost_budget = cost_budget
self.penalty = PENALTY # penalty term for constraints
self._metric, self._mode = metric, mode
self._use_incumbent_result_in_evaluation = use_incumbent_result_in_evaluation
self.lexico_objectives = lexico_objectives
init_config = low_cost_partial_config or {}
if not init_config:
logger.info(
"No low-cost partial config given to the search algorithm. "
"For cost-frugal search, "
"consider providing low-cost values for cost-related hps via "
"'low_cost_partial_config'. More info can be found at "
"https://microsoft.github.io/FLAML/docs/FAQ#about-low_cost_partial_config-in-tune"
)
if evaluated_rewards:
assert mode, "mode must be specified when
|
(metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, time_budget_s: Union[int, float] = None, num_samples: Optional[int] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, global_search_alg: Optional[flaml.tune.searcher.suggestion.Searcher] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, seed: Optional[int] = 20, cost_attr: Optional[str] = 'auto', cost_budget: Optional[float] = None, experimental: Optional[bool] = False, lexico_objectives: Optional[dict] = None, use_incumbent_result_in_evaluation=False, allow_empty_config=False)
|
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52,736 |
flaml.tune.searcher.blendsearch
|
__init__
|
Constructor.
Args:
metric: A string of the metric name to optimize for.
mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
space: A dictionary to specify the search space.
low_cost_partial_config: A dictionary from a subset of
controlled dimensions to the initial low-cost values.
E.g., ```{'n_estimators': 4, 'max_leaves': 4}```.
cat_hp_cost: A dictionary from a subset of categorical dimensions
to the relative cost of each choice.
E.g., ```{'tree_method': [1, 1, 2]}```.
I.e., the relative cost of the three choices of 'tree_method'
is 1, 1 and 2 respectively.
points_to_evaluate: Initial parameter suggestions to be run first.
evaluated_rewards (list): If you have previously evaluated the
parameters passed in as points_to_evaluate you can avoid
re-running those trials by passing in the reward attributes
as a list so the optimiser can be told the results without
needing to re-compute the trial. Must be the same or shorter length than
points_to_evaluate. When provided, `mode` must be specified.
time_budget_s: int or float | Time budget in seconds.
num_samples: int | The number of configs to try. -1 means no limit on the
number of configs to try.
resource_attr: A string to specify the resource dimension and the best
performance is assumed to be at the max_resource.
min_resource: A float of the minimal resource to use for the resource_attr.
max_resource: A float of the maximal resource to use for the resource_attr.
reduction_factor: A float of the reduction factor used for
incremental pruning.
global_search_alg: A Searcher instance as the global search
instance. If omitted, Optuna is used. The following algos have
known issues when used as global_search_alg:
- HyperOptSearch raises exception sometimes
- TuneBOHB has its own scheduler
config_constraints: A list of config constraints to be satisfied.
E.g., ```config_constraints = [(mem_size, '<=', 1024**3)]```.
`mem_size` is a function which produces a float number for the bytes
needed for a config.
It is used to skip configs which do not fit in memory.
metric_constraints: A list of metric constraints to be satisfied.
E.g., `['precision', '>=', 0.9]`. The sign can be ">=" or "<=".
seed: An integer of the random seed.
cost_attr: None or str to specify the attribute to evaluate the cost of different trials.
Default is "auto", which means that we will automatically choose the cost attribute to use (depending
on the nature of the resource budget). When cost_attr is set to None, cost differences between different trials will be omitted
in our search algorithm. When cost_attr is set to a str different from "auto" and "time_total_s",
this cost_attr must be available in the result dict of the trial.
cost_budget: A float of the cost budget. Only valid when cost_attr is a str different from "auto" and "time_total_s".
lexico_objectives: dict, default=None | It specifics information needed to perform multi-objective
optimization with lexicographic preferences. This is only supported in CFO currently.
When lexico_objectives is not None, the arguments metric, mode will be invalid.
This dictionary shall contain the following fields of key-value pairs:
- "metrics": a list of optimization objectives with the orders reflecting the priorities/preferences of the
objectives.
- "modes" (optional): a list of optimization modes (each mode either "min" or "max") corresponding to the
objectives in the metric list. If not provided, we use "min" as the default mode for all the objectives.
- "targets" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the
metric names (provided in "metric"), and the values are the numerical target values.
- "tolerances" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in "metrics"), and the values are the absolute/percentage tolerance in the form of numeric/string.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": 0.01, "pred_time": 0.0},
"targets": {"error_rate": 0.0},
}
```
We also support percentage tolerance.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": "5%", "pred_time": "0%"},
"targets": {"error_rate": 0.0},
}
```
experimental: A bool of whether to use experimental features.
|
def __init__(
self,
metric: Optional[str] = None,
mode: Optional[str] = None,
space: Optional[dict] = None,
low_cost_partial_config: Optional[dict] = None,
cat_hp_cost: Optional[dict] = None,
points_to_evaluate: Optional[List[dict]] = None,
evaluated_rewards: Optional[List] = None,
time_budget_s: Union[int, float] = None,
num_samples: Optional[int] = None,
resource_attr: Optional[str] = None,
min_resource: Optional[float] = None,
max_resource: Optional[float] = None,
reduction_factor: Optional[float] = None,
global_search_alg: Optional[Searcher] = None,
config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None,
metric_constraints: Optional[List[Tuple[str, str, float]]] = None,
seed: Optional[int] = 20,
cost_attr: Optional[str] = "auto",
cost_budget: Optional[float] = None,
experimental: Optional[bool] = False,
lexico_objectives: Optional[dict] = None,
use_incumbent_result_in_evaluation=False,
allow_empty_config=False,
):
"""Constructor.
Args:
metric: A string of the metric name to optimize for.
mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
space: A dictionary to specify the search space.
low_cost_partial_config: A dictionary from a subset of
controlled dimensions to the initial low-cost values.
E.g., ```{'n_estimators': 4, 'max_leaves': 4}```.
cat_hp_cost: A dictionary from a subset of categorical dimensions
to the relative cost of each choice.
E.g., ```{'tree_method': [1, 1, 2]}```.
I.e., the relative cost of the three choices of 'tree_method'
is 1, 1 and 2 respectively.
points_to_evaluate: Initial parameter suggestions to be run first.
evaluated_rewards (list): If you have previously evaluated the
parameters passed in as points_to_evaluate you can avoid
re-running those trials by passing in the reward attributes
as a list so the optimiser can be told the results without
needing to re-compute the trial. Must be the same or shorter length than
points_to_evaluate. When provided, `mode` must be specified.
time_budget_s: int or float | Time budget in seconds.
num_samples: int | The number of configs to try. -1 means no limit on the
number of configs to try.
resource_attr: A string to specify the resource dimension and the best
performance is assumed to be at the max_resource.
min_resource: A float of the minimal resource to use for the resource_attr.
max_resource: A float of the maximal resource to use for the resource_attr.
reduction_factor: A float of the reduction factor used for
incremental pruning.
global_search_alg: A Searcher instance as the global search
instance. If omitted, Optuna is used. The following algos have
known issues when used as global_search_alg:
- HyperOptSearch raises exception sometimes
- TuneBOHB has its own scheduler
config_constraints: A list of config constraints to be satisfied.
E.g., ```config_constraints = [(mem_size, '<=', 1024**3)]```.
`mem_size` is a function which produces a float number for the bytes
needed for a config.
It is used to skip configs which do not fit in memory.
metric_constraints: A list of metric constraints to be satisfied.
E.g., `['precision', '>=', 0.9]`. The sign can be ">=" or "<=".
seed: An integer of the random seed.
cost_attr: None or str to specify the attribute to evaluate the cost of different trials.
Default is "auto", which means that we will automatically choose the cost attribute to use (depending
on the nature of the resource budget). When cost_attr is set to None, cost differences between different trials will be omitted
in our search algorithm. When cost_attr is set to a str different from "auto" and "time_total_s",
this cost_attr must be available in the result dict of the trial.
cost_budget: A float of the cost budget. Only valid when cost_attr is a str different from "auto" and "time_total_s".
lexico_objectives: dict, default=None | It specifics information needed to perform multi-objective
optimization with lexicographic preferences. This is only supported in CFO currently.
When lexico_objectives is not None, the arguments metric, mode will be invalid.
This dictionary shall contain the following fields of key-value pairs:
- "metrics": a list of optimization objectives with the orders reflecting the priorities/preferences of the
objectives.
- "modes" (optional): a list of optimization modes (each mode either "min" or "max") corresponding to the
objectives in the metric list. If not provided, we use "min" as the default mode for all the objectives.
- "targets" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the
metric names (provided in "metric"), and the values are the numerical target values.
- "tolerances" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in "metrics"), and the values are the absolute/percentage tolerance in the form of numeric/string.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": 0.01, "pred_time": 0.0},
"targets": {"error_rate": 0.0},
}
```
We also support percentage tolerance.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": "5%", "pred_time": "0%"},
"targets": {"error_rate": 0.0},
}
```
experimental: A bool of whether to use experimental features.
"""
self._eps = SEARCH_THREAD_EPS
self._input_cost_attr = cost_attr
if cost_attr == "auto":
if time_budget_s is not None:
self.cost_attr = TIME_TOTAL_S
else:
self.cost_attr = None
self._cost_budget = None
else:
self.cost_attr = cost_attr
self._cost_budget = cost_budget
self.penalty = PENALTY # penalty term for constraints
self._metric, self._mode = metric, mode
self._use_incumbent_result_in_evaluation = use_incumbent_result_in_evaluation
self.lexico_objectives = lexico_objectives
init_config = low_cost_partial_config or {}
if not init_config:
logger.info(
"No low-cost partial config given to the search algorithm. "
"For cost-frugal search, "
"consider providing low-cost values for cost-related hps via "
"'low_cost_partial_config'. More info can be found at "
"https://microsoft.github.io/FLAML/docs/FAQ#about-low_cost_partial_config-in-tune"
)
if evaluated_rewards:
assert mode, "mode must be specified when evaluted_rewards is provided."
self._points_to_evaluate = []
self._evaluated_rewards = []
n = len(evaluated_rewards)
self._evaluated_points = points_to_evaluate[:n]
new_points_to_evaluate = points_to_evaluate[n:]
self._all_rewards = evaluated_rewards
best = max(evaluated_rewards) if mode == "max" else min(evaluated_rewards)
# only keep the best points as start points
for i, r in enumerate(evaluated_rewards):
if r == best:
p = points_to_evaluate[i]
self._points_to_evaluate.append(p)
self._evaluated_rewards.append(r)
self._points_to_evaluate.extend(new_
|
(self, metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, time_budget_s: Union[int, float, NoneType] = None, num_samples: Optional[int] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, global_search_alg: Optional[flaml.tune.searcher.suggestion.Searcher] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, seed: Optional[int] = 20, cost_attr: Optional[str] = 'auto', cost_budget: Optional[float] = None, experimental: Optional[bool] = False, lexico_objectives: Optional[dict] = None, use_incumbent_result_in_evaluation=False, allow_empty_config=False)
|
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] |
52,737 |
flaml.tune.searcher.blendsearch
|
_clean
|
delete thread and increase admissible region if converged,
merge local threads if they are close
|
def _clean(self, thread_id: int):
"""delete thread and increase admissible region if converged,
merge local threads if they are close
"""
assert thread_id
todelete = set()
for id in self._search_thread_pool:
if id and id != thread_id:
if self._inferior(id, thread_id):
todelete.add(id)
for id in self._search_thread_pool:
if id and id != thread_id:
if self._inferior(thread_id, id):
todelete.add(thread_id)
break
create_new = False
if self._search_thread_pool[thread_id].converged:
self._is_ls_ever_converged = True
todelete.add(thread_id)
self._expand_admissible_region(
self._ls_bound_min,
self._ls_bound_max,
self._search_thread_pool[thread_id].space,
)
if self._candidate_start_points:
if not self._started_from_given:
# remove start points whose perf is worse than the converged
obj = self._search_thread_pool[thread_id].obj_best1
worse = [
trial_id
for trial_id, r in self._candidate_start_points.items()
if r and r[self._ls.metric] * self._ls.metric_op >= obj
]
# logger.info(f"remove candidate start points {worse} than {obj}")
for trial_id in worse:
del self._candidate_start_points[trial_id]
if self._candidate_start_points and self._started_from_low_cost:
create_new = True
for id in todelete:
del self._search_thread_pool[id]
if create_new:
self._create_thread_from_best_candidate()
|
(self, thread_id: int)
|
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] |
52,738 |
flaml.tune.searcher.blendsearch
|
_create_condition
|
create thread condition
|
def _create_condition(self, result: Dict) -> bool:
"""create thread condition"""
if len(self._search_thread_pool) < 2:
return True
obj_median = np.median([thread.obj_best1 for id, thread in self._search_thread_pool.items() if id])
return result[self._ls.metric] * self._ls.metric_op < obj_median
|
(self, result: Dict) -> bool
|
[
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0.029873523861169815,
0.027965575456619263
] |
52,739 |
flaml.tune.searcher.blendsearch
|
_create_thread
| null |
def _create_thread(self, config, result, space):
if self.lexico_objectives is None:
obj = result[self._ls.metric]
else:
obj = {k: result[k] for k in self.lexico_objectives["metrics"]}
self._search_thread_pool[self._thread_count] = SearchThread(
self._ls.mode,
self._ls.create(
config,
obj,
cost=result.get(self.cost_attr, 1),
space=space,
),
self.cost_attr,
self._eps,
)
self._thread_count += 1
self._update_admissible_region(
unflatten_dict(config),
self._ls_bound_min,
self._ls_bound_max,
space,
self._ls.space,
)
|
(self, config, result, space)
|
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] |
52,740 |
flaml.tune.searcher.blendsearch
|
_create_thread_from_best_candidate
| null |
def _create_thread_from_best_candidate(self):
# find the best start point
best_trial_id = None
obj_best = None
for trial_id, r in self._candidate_start_points.items():
if r and (best_trial_id is None or r[self._ls.metric] * self._ls.metric_op < obj_best):
best_trial_id = trial_id
obj_best = r[self._ls.metric] * self._ls.metric_op
if best_trial_id:
# create a new thread
config = {}
result = self._candidate_start_points[best_trial_id]
for key, value in result.items():
if key.startswith("config/"):
config[key[7:]] = value
self._started_from_given = True
del self._candidate_start_points[best_trial_id]
self._create_thread(config, result, self._subspace.get(best_trial_id, self._ls.space))
|
(self)
|
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] |
52,741 |
flaml.tune.searcher.blendsearch
|
_expand_admissible_region
|
expand the admissible region for the subspace `space`
|
def _expand_admissible_region(self, lower, upper, space):
"""expand the admissible region for the subspace `space`"""
for key in upper:
ub = upper[key]
if isinstance(ub, list):
choice = space[key].get("_choice_")
if choice:
self._expand_admissible_region(lower[key][choice], upper[key][choice], space[key])
elif isinstance(ub, dict):
self._expand_admissible_region(lower[key], ub, space[key])
else:
upper[key] += self._ls.STEPSIZE
lower[key] -= self._ls.STEPSIZE
|
(self, lower, upper, space)
|
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] |
52,742 |
flaml.tune.searcher.blendsearch
|
_inferior
|
whether thread id1 is inferior to id2
|
def _inferior(self, id1: int, id2: int) -> bool:
"""whether thread id1 is inferior to id2"""
t1 = self._search_thread_pool[id1]
t2 = self._search_thread_pool[id2]
if t1.obj_best1 < t2.obj_best2:
return False
elif t1.resource and t1.resource < t2.resource:
return False
elif t2.reach(t1):
return True
return False
|
(self, id1: int, id2: int) -> bool
|
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0.07306184619665146
] |
52,743 |
flaml.tune.searcher.blendsearch
|
_init_search
|
initialize the search
|
def _init_search(self):
"""initialize the search"""
self._start_time = time.time()
self._time_used = 0
self._set_deadline()
self._is_ls_ever_converged = False
self._subspace = {} # the subspace for each trial id
self._metric_target = np.inf * self._ls.metric_op
self._search_thread_pool = {
# id: int -> thread: SearchThread
0: SearchThread(self._ls.mode, self._gs, self.cost_attr, self._eps)
}
self._thread_count = 1 # total # threads created
self._init_used = self._ls.init_config is None
self._trial_proposed_by = {} # trial_id: str -> thread_id: int
self._ls_bound_min = normalize(
self._ls.init_config.copy(),
self._ls.space,
self._ls.init_config,
{},
recursive=True,
)
self._ls_bound_max = normalize(
self._ls.init_config.copy(),
self._ls.space,
self._ls.init_config,
{},
recursive=True,
)
self._gs_admissible_min = self._ls_bound_min.copy()
self._gs_admissible_max = self._ls_bound_max.copy()
if self._metric_constraints:
self._metric_constraint_satisfied = False
self._metric_constraint_penalty = [self.penalty for _ in self._metric_constraints]
else:
self._metric_constraint_satisfied = True
self._metric_constraint_penalty = None
self.best_resource = self._ls.min_resource
i = 0
# config_signature: tuple -> result: Dict
self._result = {}
self._cost_used = 0
while self._evaluated_rewards:
# go over the evaluated rewards
trial_id = f"trial_for_evaluated_{i}"
self.suggest(trial_id)
i += 1
|
(self)
|
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] |
52,744 |
flaml.tune.searcher.blendsearch
|
_select_thread
|
thread selector; use can_suggest to check LS availability
|
def _select_thread(self) -> Tuple:
"""thread selector; use can_suggest to check LS availability"""
# calculate min_eci according to the budget left
min_eci = np.inf
if self.cost_attr == TIME_TOTAL_S:
now = time.time()
min_eci = self._deadline - now
if min_eci <= 0:
# return -1, -1
# keep proposing new configs assuming no budget left
min_eci = 0
elif self._num_samples and self._num_samples > 0:
# estimate time left according to num_samples limitation
num_finished = len(self._result)
num_proposed = num_finished + len(self._trial_proposed_by)
num_left = max(self._num_samples - num_proposed, 0)
if num_proposed > 0:
time_used = now - self._start_time + self._time_used
min_eci = min(min_eci, time_used / num_finished * num_left)
# print(f"{min_eci}, {time_used / num_finished * num_left}, {num_finished}, {num_left}")
elif self.cost_attr is not None and self._cost_budget:
min_eci = max(self._cost_budget - self._cost_used, 0)
elif self._num_samples and self._num_samples > 0:
num_finished = len(self._result)
num_proposed = num_finished + len(self._trial_proposed_by)
min_eci = max(self._num_samples - num_proposed, 0)
# update priority
max_speed = 0
for thread in self._search_thread_pool.values():
if thread.speed > max_speed:
max_speed = thread.speed
for thread in self._search_thread_pool.values():
thread.update_eci(self._metric_target, max_speed)
if thread.eci < min_eci:
min_eci = thread.eci
for thread in self._search_thread_pool.values():
thread.update_priority(min_eci)
top_thread_id = backup_thread_id = 0
priority1 = priority2 = self._search_thread_pool[0].priority
for thread_id, thread in self._search_thread_pool.items():
if thread_id and thread.can_suggest:
priority = thread.priority
if priority > priority1:
priority1 = priority
top_thread_id = thread_id
if priority > priority2 or backup_thread_id == 0:
priority2 = priority
backup_thread_id = thread_id
return top_thread_id, backup_thread_id
|
(self) -> Tuple
|
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] |
52,745 |
flaml.tune.searcher.blendsearch
|
_set_deadline
| null |
def _set_deadline(self):
if self._time_budget_s is not None:
self._deadline = self._time_budget_s + self._start_time
self._set_eps()
else:
self._deadline = np.inf
|
(self)
|
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] |
52,746 |
flaml.tune.searcher.blendsearch
|
_set_eps
|
set eps for search threads according to time budget
|
def _set_eps(self):
"""set eps for search threads according to time budget"""
self._eps = max(min(self._time_budget_s / 1000.0, 1.0), 1e-9)
|
(self)
|
[
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] |
52,747 |
flaml.tune.searcher.blendsearch
|
_should_skip
|
if config is None or config's result is known or constraints are violated
return True; o.w. return False
|
def _should_skip(self, choice, trial_id, config, space) -> bool:
"""if config is None or config's result is known or constraints are violated
return True; o.w. return False
"""
if config is None:
return True
config_signature = self._ls.config_signature(config, space)
exists = config_signature in self._result
if not exists:
# check constraints
exists = self._violate_config_constriants(config, config_signature)
if exists: # suggested before (including violate constraints)
if choice >= 0: # not fallback to rs
result = self._result.get(config_signature)
if result: # finished
self._search_thread_pool[choice].on_trial_complete(trial_id, result, error=False)
if choice:
# local search thread
self._clean(choice)
# else: # running
# # tell the thread there is an error
# self._search_thread_pool[choice].on_trial_complete(
# trial_id, {}, error=True)
return True
return False
|
(self, choice, trial_id, config, space) -> bool
|
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] |
52,748 |
flaml.tune.searcher.blendsearch
|
_update_admissible_region
| null |
def _update_admissible_region(
self,
config,
admissible_min,
admissible_max,
subspace: Dict = {},
space: Dict = {},
):
# update admissible region
normalized_config = normalize(config, subspace, config, {})
for key in admissible_min:
value = normalized_config[key]
if isinstance(admissible_max[key], list):
domain = space[key]
choice = indexof(domain, value)
self._update_admissible_region(
value,
admissible_min[key][choice],
admissible_max[key][choice],
subspace[key],
domain[choice],
)
if len(admissible_max[key]) > len(domain.categories):
# points + index
normal = (choice + 0.5) / len(domain.categories)
admissible_max[key][-1] = max(normal, admissible_max[key][-1])
admissible_min[key][-1] = min(normal, admissible_min[key][-1])
elif isinstance(value, dict):
self._update_admissible_region(
value,
admissible_min[key],
admissible_max[key],
subspace[key],
space[key],
)
else:
if value > admissible_max[key]:
admissible_max[key] = value
elif value < admissible_min[key]:
admissible_min[key] = value
|
(self, config, admissible_min, admissible_max, subspace: Dict = {}, space: Dict = {})
|
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52,749 |
flaml.tune.searcher.blendsearch
|
_valid
|
config validator
|
def _valid(self, config: Dict, space: Dict, subspace: Dict, lower: Dict, upper: Dict) -> bool:
"""config validator"""
normalized_config = normalize(config, subspace, config, {})
for key, lb in lower.items():
if key in config:
value = normalized_config[key]
if isinstance(lb, list):
domain = space[key]
index = indexof(domain, value)
nestedspace = subspace[key]
lb = lb[index]
ub = upper[key][index]
elif isinstance(lb, dict):
nestedspace = subspace[key]
domain = space[key]
ub = upper[key]
else:
nestedspace = None
if nestedspace:
valid = self._valid(value, domain, nestedspace, lb, ub)
if not valid:
return False
elif value + self._ls.STEPSIZE < lower[key] or value > upper[key] + self._ls.STEPSIZE:
return False
return True
|
(self, config: Dict, space: Dict, subspace: Dict, lower: Dict, upper: Dict) -> bool
|
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] |
52,750 |
flaml.tune.searcher.blendsearch
|
_violate_config_constriants
|
check if config violates config constraints.
If so, set the result to worst and return True.
|
def _violate_config_constriants(self, config, config_signature):
"""check if config violates config constraints.
If so, set the result to worst and return True.
"""
if not self._config_constraints:
return False
for constraint in self._config_constraints:
func, sign, threshold = constraint
value = func(config)
if (
sign == "<="
and value > threshold
or sign == ">="
and value < threshold
or sign == ">"
and value <= threshold
or sign == "<"
and value > threshold
):
self._result[config_signature] = {
self._metric: np.inf * self._ls.metric_op,
"time_total_s": 1,
}
return True
return False
|
(self, config, config_signature)
|
[
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] |
52,751 |
flaml.tune.searcher.blendsearch
|
on_trial_complete
|
search thread updater and cleaner.
|
def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False):
"""search thread updater and cleaner."""
metric_constraint_satisfied = True
if result and not error and self._metric_constraints:
# account for metric constraints if any
objective = result[self._metric]
for i, constraint in enumerate(self._metric_constraints):
metric_constraint, sign, threshold = constraint
value = result.get(metric_constraint)
if value:
sign_op = 1 if sign == "<=" else -1
violation = (value - threshold) * sign_op
if violation > 0:
# add penalty term to the metric
objective += self._metric_constraint_penalty[i] * violation * self._ls.metric_op
metric_constraint_satisfied = False
if self._metric_constraint_penalty[i] < self.penalty:
self._metric_constraint_penalty[i] += violation
result[self._metric + self.lagrange] = objective
if metric_constraint_satisfied and not self._metric_constraint_satisfied:
# found a feasible point
self._metric_constraint_penalty = [1 for _ in self._metric_constraints]
self._metric_constraint_satisfied |= metric_constraint_satisfied
thread_id = self._trial_proposed_by.get(trial_id)
if thread_id in self._search_thread_pool:
self._search_thread_pool[thread_id].on_trial_complete(trial_id, result, error)
del self._trial_proposed_by[trial_id]
if result:
config = result.get("config", {})
if not config:
for key, value in result.items():
if key.startswith("config/"):
config[key[7:]] = value
if self._allow_empty_config and not config:
return
signature = self._ls.config_signature(config, self._subspace.get(trial_id, {}))
if error: # remove from result cache
del self._result[signature]
else: # add to result cache
self._cost_used += result.get(self.cost_attr, 0)
self._result[signature] = result
# update target metric if improved
objective = result[self._ls.metric]
if (objective - self._metric_target) * self._ls.metric_op < 0:
self._metric_target = objective
if self._ls.resource:
self._best_resource = config[self._ls.resource_attr]
if thread_id:
if not self._metric_constraint_satisfied:
# no point has been found to satisfy metric constraint
self._expand_admissible_region(
self._ls_bound_min,
self._ls_bound_max,
self._subspace.get(trial_id, self._ls.space),
)
if self._gs is not None and self._experimental and (not self._ls.hierarchical):
self._gs.add_evaluated_point(flatten_dict(config), objective)
# TODO: recover when supported
# converted = convert_key(config, self._gs.space)
# logger.info(converted)
# self._gs.add_evaluated_point(converted, objective)
elif metric_constraint_satisfied and self._create_condition(result):
# thread creator
thread_id = self._thread_count
self._started_from_given = self._candidate_start_points and trial_id in self._candidate_start_points
if self._started_from_given:
del self._candidate_start_points[trial_id]
else:
self._started_from_low_cost = True
self._create_thread(config, result, self._subspace.get(trial_id, self._ls.space))
# reset admissible region to ls bounding box
self._gs_admissible_min.update(self._ls_bound_min)
self._gs_admissible_max.update(self._ls_bound_max)
# cleaner
if thread_id and thread_id in self._search_thread_pool:
# local search thread
self._clean(thread_id)
if trial_id in self._subspace and not (
self._candidate_start_points and trial_id in self._candidate_start_points
):
del self._subspace[trial_id]
|
(self, trial_id: str, result: Optional[Dict] = None, error: bool = False)
|
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] |
52,752 |
flaml.tune.searcher.blendsearch
|
on_trial_result
|
receive intermediate result.
|
def on_trial_result(self, trial_id: str, result: Dict):
"""receive intermediate result."""
if trial_id not in self._trial_proposed_by:
return
thread_id = self._trial_proposed_by[trial_id]
if thread_id not in self._search_thread_pool:
return
if result and self._metric_constraints:
result[self._metric + self.lagrange] = result[self._metric]
self._search_thread_pool[thread_id].on_trial_result(trial_id, result)
|
(self, trial_id: str, result: Dict)
|
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] |
52,753 |
flaml.tune.searcher.blendsearch
|
restore
|
restore states from checkpoint.
|
def restore(self, checkpoint_path: str):
"""restore states from checkpoint."""
with open(checkpoint_path, "rb") as inputFile:
state = pickle.load(inputFile)
self.__dict__ = state.__dict__
self._start_time = time.time()
self._set_deadline()
|
(self, checkpoint_path: str)
|
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] |
52,754 |
flaml.tune.searcher.blendsearch
|
save
|
save states to a checkpoint path.
|
def save(self, checkpoint_path: str):
"""save states to a checkpoint path."""
self._time_used += time.time() - self._start_time
self._start_time = time.time()
save_object = self
with open(checkpoint_path, "wb") as outputFile:
pickle.dump(save_object, outputFile)
|
(self, checkpoint_path: str)
|
[
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0.08311901241540909,
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0.0027461459394544363,
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] |
52,755 |
flaml.tune.searcher.blendsearch
|
set_search_properties
| null |
def set_search_properties(
self,
metric: Optional[str] = None,
mode: Optional[str] = None,
config: Optional[Dict] = None,
**spec,
) -> bool:
metric_changed = mode_changed = False
if metric and self._metric != metric:
metric_changed = True
self._metric = metric
if self._metric_constraints:
# metric modified by lagrange
metric += self.lagrange
# TODO: don't change metric for global search methods that
# can handle constraints already
if mode and self._mode != mode:
mode_changed = True
self._mode = mode
if not self._ls.space:
# the search space can be set only once
if self._gs is not None:
# define-by-run is not supported via set_search_properties
self._gs.set_search_properties(metric, mode, config)
self._gs.space = config
if config:
add_cost_to_space(config, self._ls.init_config, self._cat_hp_cost)
self._ls.set_search_properties(metric, mode, config)
self._init_search()
else:
if metric_changed or mode_changed:
# reset search when metric or mode changed
self._ls.set_search_properties(metric, mode)
if self._gs is not None:
self._gs = GlobalSearch(
space=self._gs._space,
metric=metric,
mode=mode,
seed=self._gs_seed,
)
self._gs.space = self._ls.space
self._init_search()
if spec:
# CFO doesn't need these settings
if "time_budget_s" in spec:
self._time_budget_s = spec["time_budget_s"] # budget from now
now = time.time()
self._time_used += now - self._start_time
self._start_time = now
self._set_deadline()
if self._input_cost_attr == "auto" and self._time_budget_s:
self.cost_attr = self._ls.cost_attr = TIME_TOTAL_S
if "metric_target" in spec:
self._metric_target = spec.get("metric_target")
num_samples = spec.get("num_samples")
if num_samples is not None:
self._num_samples = (
(num_samples + len(self._result) + len(self._trial_proposed_by))
if num_samples > 0 # 0 is currently treated the same as -1
else num_samples
)
return True
|
(self, metric: Optional[str] = None, mode: Optional[str] = None, config: Optional[Dict] = None, **spec) -> bool
|
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] |
52,756 |
flaml.tune.searcher.blendsearch
|
suggest
|
choose thread, suggest a valid config.
|
def suggest(self, trial_id: str) -> Optional[Dict]:
"""choose thread, suggest a valid config."""
if self._init_used and not self._points_to_evaluate:
if self._cost_budget and self._cost_used >= self._cost_budget:
return None
choice, backup = self._select_thread()
config = self._search_thread_pool[choice].suggest(trial_id)
if not choice and config is not None and self._ls.resource:
config[self._ls.resource_attr] = self.best_resource
elif choice and config is None:
# local search thread finishes
if self._search_thread_pool[choice].converged:
self._expand_admissible_region(
self._ls_bound_min,
self._ls_bound_max,
self._search_thread_pool[choice].space,
)
del self._search_thread_pool[choice]
return
# preliminary check; not checking config validation
space = self._search_thread_pool[choice].space
skip = self._should_skip(choice, trial_id, config, space)
use_rs = 0
if skip:
if choice:
return
# use rs when BO fails to suggest a config
config, space = self._ls.complete_config({})
skip = self._should_skip(-1, trial_id, config, space)
if skip:
return
use_rs = 1
if choice or self._valid(
config,
self._ls.space,
space,
self._gs_admissible_min,
self._gs_admissible_max,
):
# LS or valid or no backup choice
self._trial_proposed_by[trial_id] = choice
self._search_thread_pool[choice].running += use_rs
else: # invalid config proposed by GS
if choice == backup:
# use CFO's init point
init_config = self._ls.init_config
config, space = self._ls.complete_config(init_config, self._ls_bound_min, self._ls_bound_max)
self._trial_proposed_by[trial_id] = choice
self._search_thread_pool[choice].running += 1
else:
thread = self._search_thread_pool[backup]
config = thread.suggest(trial_id)
space = thread.space
skip = self._should_skip(backup, trial_id, config, space)
if skip:
return
self._trial_proposed_by[trial_id] = backup
choice = backup
if not choice: # global search
# temporarily relax admissible region for parallel proposals
self._update_admissible_region(
config,
self._gs_admissible_min,
self._gs_admissible_max,
space,
self._ls.space,
)
else:
self._update_admissible_region(
config,
self._ls_bound_min,
self._ls_bound_max,
space,
self._ls.space,
)
self._gs_admissible_min.update(self._ls_bound_min)
self._gs_admissible_max.update(self._ls_bound_max)
signature = self._ls.config_signature(config, space)
self._result[signature] = {}
self._subspace[trial_id] = space
else: # use init config
if self._candidate_start_points is not None and self._points_to_evaluate:
self._candidate_start_points[trial_id] = None
reward = None
if self._points_to_evaluate:
init_config = self._points_to_evaluate.pop(0)
if self._evaluated_rewards:
reward = self._evaluated_rewards.pop(0)
else:
init_config = self._ls.init_config
if self._allow_empty_config and not init_config:
assert reward is None, "Empty config can't have reward."
return init_config
config, space = self._ls.complete_config(init_config, self._ls_bound_min, self._ls_bound_max)
config_signature = self._ls.config_signature(config, space)
if reward is None:
result = self._result.get(config_signature)
if result: # tried before
return
elif result is None: # not tried before
if self._violate_config_constriants(config, config_signature):
# violate config constraints
return
self._result[config_signature] = {}
else: # running but no result yet
return
self._init_used = True
self._trial_proposed_by[trial_id] = 0
self._search_thread_pool[0].running += 1
self._subspace[trial_id] = space
if reward is not None:
result = {self._metric: reward, self.cost_attr: 1, "config": config}
# result = self._result[config_signature]
self.on_trial_complete(trial_id, result)
return
if self._use_incumbent_result_in_evaluation:
if self._trial_proposed_by[trial_id] > 0:
choice_thread = self._search_thread_pool[self._trial_proposed_by[trial_id]]
config[INCUMBENT_RESULT] = choice_thread.best_result
return config
|
(self, trial_id: str) -> Optional[Dict]
|
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] |
52,757 |
flaml.tune.searcher.blendsearch
|
BlendSearchTuner
|
Tuner class for NNI.
|
class BlendSearchTuner(BlendSearch, NNITuner):
"""Tuner class for NNI."""
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
"""Receive trial's final result.
Args:
parameter_id: int.
parameters: object created by `generate_parameters()`.
value: final metrics of the trial, including default metric.
"""
result = {
"config": parameters,
self._metric: extract_scalar_reward(value),
self.cost_attr: 1 if isinstance(value, float) else value.get(self.cost_attr, value.get("sequence", 1)),
# if nni does not report training cost,
# using sequence as an approximation.
# if no sequence, using a constant 1
}
self.on_trial_complete(str(parameter_id), result)
...
def generate_parameters(self, parameter_id, **kwargs) -> Dict:
"""Returns a set of trial (hyper-)parameters, as a serializable object.
Args:
parameter_id: int.
"""
return self.suggest(str(parameter_id))
...
def update_search_space(self, search_space):
"""Required by NNI.
Tuners are advised to support updating search space at run-time.
If a tuner can only set search space once before generating first hyper-parameters,
it should explicitly document this behaviour.
Args:
search_space: JSON object created by experiment owner.
"""
config = {}
for key, value in search_space.items():
v = value.get("_value")
_type = value["_type"]
if _type == "choice":
config[key] = choice(v)
elif _type == "randint":
config[key] = randint(*v)
elif _type == "uniform":
config[key] = uniform(*v)
elif _type == "quniform":
config[key] = quniform(*v)
elif _type == "loguniform":
config[key] = loguniform(*v)
elif _type == "qloguniform":
config[key] = qloguniform(*v)
elif _type == "normal":
config[key] = randn(*v)
elif _type == "qnormal":
config[key] = qrandn(*v)
else:
raise ValueError(f"unsupported type in search_space {_type}")
# low_cost_partial_config is passed to constructor,
# which is before update_search_space() is called
init_config = self._ls.init_config
add_cost_to_space(config, init_config, self._cat_hp_cost)
self._ls = self.LocalSearch(
init_config,
self._ls.metric,
self._mode,
config,
self._ls.resource_attr,
self._ls.min_resource,
self._ls.max_resource,
self._ls.resource_multiple_factor,
cost_attr=self.cost_attr,
seed=self._ls.seed,
lexico_objectives=self.lexico_objectives,
)
if self._gs is not None:
self._gs = GlobalSearch(
space=config,
metric=self._metric,
mode=self._mode,
sampler=self._gs._sampler,
)
self._gs.space = config
self._init_search()
|
(metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, time_budget_s: Union[int, float] = None, num_samples: Optional[int] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, global_search_alg: Optional[flaml.tune.searcher.suggestion.Searcher] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, seed: Optional[int] = 20, cost_attr: Optional[str] = 'auto', cost_budget: Optional[float] = None, experimental: Optional[bool] = False, lexico_objectives: Optional[dict] = None, use_incumbent_result_in_evaluation=False, allow_empty_config=False)
|
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] |
52,773 |
flaml.tune.searcher.blendsearch
|
generate_parameters
|
Returns a set of trial (hyper-)parameters, as a serializable object.
Args:
parameter_id: int.
|
def generate_parameters(self, parameter_id, **kwargs) -> Dict:
"""Returns a set of trial (hyper-)parameters, as a serializable object.
Args:
parameter_id: int.
"""
return self.suggest(str(parameter_id))
|
(self, parameter_id, **kwargs) -> Dict
|
[
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] |
52,776 |
flaml.tune.searcher.blendsearch
|
receive_trial_result
|
Receive trial's final result.
Args:
parameter_id: int.
parameters: object created by `generate_parameters()`.
value: final metrics of the trial, including default metric.
|
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
"""Receive trial's final result.
Args:
parameter_id: int.
parameters: object created by `generate_parameters()`.
value: final metrics of the trial, including default metric.
"""
result = {
"config": parameters,
self._metric: extract_scalar_reward(value),
self.cost_attr: 1 if isinstance(value, float) else value.get(self.cost_attr, value.get("sequence", 1)),
# if nni does not report training cost,
# using sequence as an approximation.
# if no sequence, using a constant 1
}
self.on_trial_complete(str(parameter_id), result)
|
(self, parameter_id, parameters, value, **kwargs)
|
[
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] |
52,781 |
flaml.tune.searcher.blendsearch
|
update_search_space
|
Required by NNI.
Tuners are advised to support updating search space at run-time.
If a tuner can only set search space once before generating first hyper-parameters,
it should explicitly document this behaviour.
Args:
search_space: JSON object created by experiment owner.
|
def update_search_space(self, search_space):
"""Required by NNI.
Tuners are advised to support updating search space at run-time.
If a tuner can only set search space once before generating first hyper-parameters,
it should explicitly document this behaviour.
Args:
search_space: JSON object created by experiment owner.
"""
config = {}
for key, value in search_space.items():
v = value.get("_value")
_type = value["_type"]
if _type == "choice":
config[key] = choice(v)
elif _type == "randint":
config[key] = randint(*v)
elif _type == "uniform":
config[key] = uniform(*v)
elif _type == "quniform":
config[key] = quniform(*v)
elif _type == "loguniform":
config[key] = loguniform(*v)
elif _type == "qloguniform":
config[key] = qloguniform(*v)
elif _type == "normal":
config[key] = randn(*v)
elif _type == "qnormal":
config[key] = qrandn(*v)
else:
raise ValueError(f"unsupported type in search_space {_type}")
# low_cost_partial_config is passed to constructor,
# which is before update_search_space() is called
init_config = self._ls.init_config
add_cost_to_space(config, init_config, self._cat_hp_cost)
self._ls = self.LocalSearch(
init_config,
self._ls.metric,
self._mode,
config,
self._ls.resource_attr,
self._ls.min_resource,
self._ls.max_resource,
self._ls.resource_multiple_factor,
cost_attr=self.cost_attr,
seed=self._ls.seed,
lexico_objectives=self.lexico_objectives,
)
if self._gs is not None:
self._gs = GlobalSearch(
space=config,
metric=self._metric,
mode=self._mode,
sampler=self._gs._sampler,
)
self._gs.space = config
self._init_search()
|
(self, search_space)
|
[
0.022442296147346497,
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0.013811340555548668,
0.040935907512903214,
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0.03562873229384422,
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] |
52,782 |
flaml.tune.searcher.blendsearch
|
CFO
|
class for CFO algorithm.
|
class CFO(BlendSearchTuner):
"""class for CFO algorithm."""
__name__ = "CFO"
def suggest(self, trial_id: str) -> Optional[Dict]:
# Number of threads is 1 or 2. Thread 0 is a vacuous thread
assert len(self._search_thread_pool) < 3, len(self._search_thread_pool)
if len(self._search_thread_pool) < 2:
# When a local thread converges, the number of threads is 1
# Need to restart
self._init_used = False
return super().suggest(trial_id)
def _select_thread(self) -> Tuple:
for key in self._search_thread_pool:
if key:
return key, key
def _create_condition(self, result: Dict) -> bool:
"""create thread condition"""
if self._points_to_evaluate:
# still evaluating user-specified init points
# we evaluate all candidate start points before we
# create the first local search thread
return False
if len(self._search_thread_pool) == 2:
return False
if self._candidate_start_points and self._thread_count == 1:
# result needs to match or exceed the best candidate start point
obj_best = min(
(self._ls.metric_op * r[self._ls.metric] for r in self._candidate_start_points.values() if r),
default=-np.inf,
)
return result[self._ls.metric] * self._ls.metric_op <= obj_best
else:
return True
def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False):
super().on_trial_complete(trial_id, result, error)
if self._candidate_start_points and trial_id in self._candidate_start_points:
# the trial is a candidate start point
self._candidate_start_points[trial_id] = result
if len(self._search_thread_pool) < 2 and not self._points_to_evaluate:
self._create_thread_from_best_candidate()
|
(metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, time_budget_s: Union[int, float] = None, num_samples: Optional[int] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, global_search_alg: Optional[flaml.tune.searcher.suggestion.Searcher] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, seed: Optional[int] = 20, cost_attr: Optional[str] = 'auto', cost_budget: Optional[float] = None, experimental: Optional[bool] = False, lexico_objectives: Optional[dict] = None, use_incumbent_result_in_evaluation=False, allow_empty_config=False)
|
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] |
52,785 |
flaml.tune.searcher.blendsearch
|
_create_condition
|
create thread condition
|
def _create_condition(self, result: Dict) -> bool:
"""create thread condition"""
if self._points_to_evaluate:
# still evaluating user-specified init points
# we evaluate all candidate start points before we
# create the first local search thread
return False
if len(self._search_thread_pool) == 2:
return False
if self._candidate_start_points and self._thread_count == 1:
# result needs to match or exceed the best candidate start point
obj_best = min(
(self._ls.metric_op * r[self._ls.metric] for r in self._candidate_start_points.values() if r),
default=-np.inf,
)
return result[self._ls.metric] * self._ls.metric_op <= obj_best
else:
return True
|
(self, result: Dict) -> bool
|
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] |
52,791 |
flaml.tune.searcher.blendsearch
|
_select_thread
| null |
def _select_thread(self) -> Tuple:
for key in self._search_thread_pool:
if key:
return key, key
|
(self) -> Tuple
|
[
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] |
52,799 |
flaml.tune.searcher.blendsearch
|
on_trial_complete
| null |
def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False):
super().on_trial_complete(trial_id, result, error)
if self._candidate_start_points and trial_id in self._candidate_start_points:
# the trial is a candidate start point
self._candidate_start_points[trial_id] = result
if len(self._search_thread_pool) < 2 and not self._points_to_evaluate:
self._create_thread_from_best_candidate()
|
(self, trial_id: str, result: Optional[Dict] = None, error: bool = False)
|
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] |
52,805 |
flaml.tune.searcher.blendsearch
|
suggest
| null |
def suggest(self, trial_id: str) -> Optional[Dict]:
# Number of threads is 1 or 2. Thread 0 is a vacuous thread
assert len(self._search_thread_pool) < 3, len(self._search_thread_pool)
if len(self._search_thread_pool) < 2:
# When a local thread converges, the number of threads is 1
# Need to restart
self._init_used = False
return super().suggest(trial_id)
|
(self, trial_id: str) -> Optional[Dict]
|
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] |
52,807 |
flaml.tune.searcher.flow2
|
FLOW2
|
Local search algorithm FLOW2, with adaptive step size.
|
class FLOW2(Searcher):
"""Local search algorithm FLOW2, with adaptive step size."""
STEPSIZE = 0.1
STEP_LOWER_BOUND = 0.0001
def __init__(
self,
init_config: dict,
metric: Optional[str] = None,
mode: Optional[str] = None,
space: Optional[dict] = None,
resource_attr: Optional[str] = None,
min_resource: Optional[float] = None,
max_resource: Optional[float] = None,
resource_multiple_factor: Optional[float] = None,
cost_attr: Optional[str] = "time_total_s",
seed: Optional[int] = 20,
lexico_objectives=None,
):
"""Constructor.
Args:
init_config: a dictionary of a partial or full initial config,
e.g., from a subset of controlled dimensions
to the initial low-cost values.
E.g., {'epochs': 1}.
metric: A string of the metric name to optimize for.
mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
space: A dictionary to specify the search space.
resource_attr: A string to specify the resource dimension and the best
performance is assumed to be at the max_resource.
min_resource: A float of the minimal resource to use for the resource_attr.
max_resource: A float of the maximal resource to use for the resource_attr.
resource_multiple_factor: A float of the multiplicative factor
used for increasing resource.
cost_attr: A string of the attribute used for cost.
seed: An integer of the random seed.
lexico_objectives: dict, default=None | It specifics information needed to perform multi-objective
optimization with lexicographic preferences. When lexico_objectives is not None, the arguments metric,
mode will be invalid. This dictionary shall contain the following fields of key-value pairs:
- "metrics": a list of optimization objectives with the orders reflecting the priorities/preferences of the
objectives.
- "modes" (optional): a list of optimization modes (each mode either "min" or "max") corresponding to the
objectives in the metric list. If not provided, we use "min" as the default mode for all the objectives
- "targets" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the
metric names (provided in "metric"), and the values are the numerical target values.
- "tolerances" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in "metrics"), and the values are the absolute/percentage tolerance in the form of numeric/string.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": 0.01, "pred_time": 0.0},
"targets": {"error_rate": 0.0},
}
```
We also support percentage tolerance.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": "5%", "pred_time": "0%"},
"targets": {"error_rate": 0.0},
}
```
"""
if mode:
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."
else:
mode = "min"
super(FLOW2, self).__init__(metric=metric, mode=mode)
# internally minimizes, so "max" => -1
if mode == "max":
self.metric_op = -1.0
elif mode == "min":
self.metric_op = 1.0
self.space = space or {}
self._space = flatten_dict(self.space, prevent_delimiter=True)
self._random = np.random.RandomState(seed)
self.rs_random = sample._BackwardsCompatibleNumpyRng(seed + 19823)
self.seed = seed
self.init_config = init_config
self.best_config = flatten_dict(init_config)
self.resource_attr = resource_attr
self.min_resource = min_resource
self.lexico_objectives = lexico_objectives
if self.lexico_objectives is not None:
if "modes" not in self.lexico_objectives.keys():
self.lexico_objectives["modes"] = ["min"] * len(self.lexico_objectives["metrics"])
for t_metric, t_mode in zip(self.lexico_objectives["metrics"], self.lexico_objectives["modes"]):
if t_metric not in self.lexico_objectives["tolerances"].keys():
self.lexico_objectives["tolerances"][t_metric] = 0
if t_metric not in self.lexico_objectives["targets"].keys():
self.lexico_objectives["targets"][t_metric] = -float("inf") if t_mode == "min" else float("inf")
self.resource_multiple_factor = resource_multiple_factor or SAMPLE_MULTIPLY_FACTOR
self.cost_attr = cost_attr
self.max_resource = max_resource
self._resource = None
self._f_best = None # only use for lexico_comapre. It represent the best value achieved by lexico_flow.
self._step_lb = np.inf
self._histories = None # only use for lexico_comapre. It records the result of historical configurations.
if space is not None:
self._init_search()
def _init_search(self):
self._tunable_keys = []
self._bounded_keys = []
self._unordered_cat_hp = {}
hier = False
for key, domain in self._space.items():
assert not (
isinstance(domain, dict) and "grid_search" in domain
), f"{key}'s domain is grid search, not supported in FLOW^2."
if callable(getattr(domain, "get_sampler", None)):
self._tunable_keys.append(key)
sampler = domain.get_sampler()
# the step size lower bound for uniform variables doesn't depend
# on the current config
if isinstance(sampler, sample.Quantized):
q = sampler.q
sampler = sampler.get_sampler()
if str(sampler) == "Uniform":
self._step_lb = min(self._step_lb, q / (domain.upper - domain.lower + 1))
elif isinstance(domain, sample.Integer) and str(sampler) == "Uniform":
self._step_lb = min(self._step_lb, 1.0 / (domain.upper - domain.lower))
if isinstance(domain, sample.Categorical):
if not domain.ordered:
self._unordered_cat_hp[key] = len(domain.categories)
if not hier:
for cat in domain.categories:
if isinstance(cat, dict):
hier = True
break
if str(sampler) != "Normal":
self._bounded_keys.append(key)
if not hier:
self._space_keys = sorted(self._tunable_keys)
self.hierarchical = hier
if self.resource_attr and self.resource_attr not in self._space and self.max_resource:
self.min_resource = self.min_resource or self._min_resource()
self._resource = self._round(self.min_resource)
if not hier:
self._space_keys.append(self.resource_attr)
else:
self._resource = None
self.incumbent = {}
self.incumbent = self.normalize(self.best_config) # flattened
self.best_obj = self.cost_incumbent = None
self.dim = len(self._tunable_keys) # total # tunable dimensions
self._direction_tried = None
self._num_complete4incumbent = self._cost_complete4incumbent = 0
self._num_allowed4incumbent = 2 * self.dim
self._pro
|
(init_config: dict, metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, resource_multiple_factor: Optional[float] = None, cost_attr: Optional[str] = 'time_total_s', seed: Optional[int] = 20, lexico_objectives=None)
|
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] |
52,808 |
flaml.tune.searcher.flow2
|
__init__
|
Constructor.
Args:
init_config: a dictionary of a partial or full initial config,
e.g., from a subset of controlled dimensions
to the initial low-cost values.
E.g., {'epochs': 1}.
metric: A string of the metric name to optimize for.
mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
space: A dictionary to specify the search space.
resource_attr: A string to specify the resource dimension and the best
performance is assumed to be at the max_resource.
min_resource: A float of the minimal resource to use for the resource_attr.
max_resource: A float of the maximal resource to use for the resource_attr.
resource_multiple_factor: A float of the multiplicative factor
used for increasing resource.
cost_attr: A string of the attribute used for cost.
seed: An integer of the random seed.
lexico_objectives: dict, default=None | It specifics information needed to perform multi-objective
optimization with lexicographic preferences. When lexico_objectives is not None, the arguments metric,
mode will be invalid. This dictionary shall contain the following fields of key-value pairs:
- "metrics": a list of optimization objectives with the orders reflecting the priorities/preferences of the
objectives.
- "modes" (optional): a list of optimization modes (each mode either "min" or "max") corresponding to the
objectives in the metric list. If not provided, we use "min" as the default mode for all the objectives
- "targets" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the
metric names (provided in "metric"), and the values are the numerical target values.
- "tolerances" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in "metrics"), and the values are the absolute/percentage tolerance in the form of numeric/string.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": 0.01, "pred_time": 0.0},
"targets": {"error_rate": 0.0},
}
```
We also support percentage tolerance.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": "5%", "pred_time": "0%"},
"targets": {"error_rate": 0.0},
}
```
|
def __init__(
self,
init_config: dict,
metric: Optional[str] = None,
mode: Optional[str] = None,
space: Optional[dict] = None,
resource_attr: Optional[str] = None,
min_resource: Optional[float] = None,
max_resource: Optional[float] = None,
resource_multiple_factor: Optional[float] = None,
cost_attr: Optional[str] = "time_total_s",
seed: Optional[int] = 20,
lexico_objectives=None,
):
"""Constructor.
Args:
init_config: a dictionary of a partial or full initial config,
e.g., from a subset of controlled dimensions
to the initial low-cost values.
E.g., {'epochs': 1}.
metric: A string of the metric name to optimize for.
mode: A string in ['min', 'max'] to specify the objective as
minimization or maximization.
space: A dictionary to specify the search space.
resource_attr: A string to specify the resource dimension and the best
performance is assumed to be at the max_resource.
min_resource: A float of the minimal resource to use for the resource_attr.
max_resource: A float of the maximal resource to use for the resource_attr.
resource_multiple_factor: A float of the multiplicative factor
used for increasing resource.
cost_attr: A string of the attribute used for cost.
seed: An integer of the random seed.
lexico_objectives: dict, default=None | It specifics information needed to perform multi-objective
optimization with lexicographic preferences. When lexico_objectives is not None, the arguments metric,
mode will be invalid. This dictionary shall contain the following fields of key-value pairs:
- "metrics": a list of optimization objectives with the orders reflecting the priorities/preferences of the
objectives.
- "modes" (optional): a list of optimization modes (each mode either "min" or "max") corresponding to the
objectives in the metric list. If not provided, we use "min" as the default mode for all the objectives
- "targets" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the
metric names (provided in "metric"), and the values are the numerical target values.
- "tolerances" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in "metrics"), and the values are the absolute/percentage tolerance in the form of numeric/string.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": 0.01, "pred_time": 0.0},
"targets": {"error_rate": 0.0},
}
```
We also support percentage tolerance.
E.g.,
```python
lexico_objectives = {
"metrics": ["error_rate", "pred_time"],
"modes": ["min", "min"],
"tolerances": {"error_rate": "5%", "pred_time": "0%"},
"targets": {"error_rate": 0.0},
}
```
"""
if mode:
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."
else:
mode = "min"
super(FLOW2, self).__init__(metric=metric, mode=mode)
# internally minimizes, so "max" => -1
if mode == "max":
self.metric_op = -1.0
elif mode == "min":
self.metric_op = 1.0
self.space = space or {}
self._space = flatten_dict(self.space, prevent_delimiter=True)
self._random = np.random.RandomState(seed)
self.rs_random = sample._BackwardsCompatibleNumpyRng(seed + 19823)
self.seed = seed
self.init_config = init_config
self.best_config = flatten_dict(init_config)
self.resource_attr = resource_attr
self.min_resource = min_resource
self.lexico_objectives = lexico_objectives
if self.lexico_objectives is not None:
if "modes" not in self.lexico_objectives.keys():
self.lexico_objectives["modes"] = ["min"] * len(self.lexico_objectives["metrics"])
for t_metric, t_mode in zip(self.lexico_objectives["metrics"], self.lexico_objectives["modes"]):
if t_metric not in self.lexico_objectives["tolerances"].keys():
self.lexico_objectives["tolerances"][t_metric] = 0
if t_metric not in self.lexico_objectives["targets"].keys():
self.lexico_objectives["targets"][t_metric] = -float("inf") if t_mode == "min" else float("inf")
self.resource_multiple_factor = resource_multiple_factor or SAMPLE_MULTIPLY_FACTOR
self.cost_attr = cost_attr
self.max_resource = max_resource
self._resource = None
self._f_best = None # only use for lexico_comapre. It represent the best value achieved by lexico_flow.
self._step_lb = np.inf
self._histories = None # only use for lexico_comapre. It records the result of historical configurations.
if space is not None:
self._init_search()
|
(self, init_config: dict, metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, resource_multiple_factor: Optional[float] = None, cost_attr: Optional[str] = 'time_total_s', seed: Optional[int] = 20, lexico_objectives=None)
|
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] |
52,809 |
flaml.tune.searcher.flow2
|
_increase_resource
| null |
def _increase_resource(self, trial_id):
# consider increasing resource using sum eval cost of complete
# configs
old_resource = self._resource
self._resource = self._round(self._resource * self.resource_multiple_factor)
self.cost_incumbent *= self._resource / old_resource
config = self.best_config.copy()
config[self.resource_attr] = self._resource
self._direction_tried = None
self._configs[trial_id] = (config, self.step)
return unflatten_dict(config)
|
(self, trial_id)
|
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] |
52,810 |
flaml.tune.searcher.flow2
|
_init_search
| null |
def _init_search(self):
self._tunable_keys = []
self._bounded_keys = []
self._unordered_cat_hp = {}
hier = False
for key, domain in self._space.items():
assert not (
isinstance(domain, dict) and "grid_search" in domain
), f"{key}'s domain is grid search, not supported in FLOW^2."
if callable(getattr(domain, "get_sampler", None)):
self._tunable_keys.append(key)
sampler = domain.get_sampler()
# the step size lower bound for uniform variables doesn't depend
# on the current config
if isinstance(sampler, sample.Quantized):
q = sampler.q
sampler = sampler.get_sampler()
if str(sampler) == "Uniform":
self._step_lb = min(self._step_lb, q / (domain.upper - domain.lower + 1))
elif isinstance(domain, sample.Integer) and str(sampler) == "Uniform":
self._step_lb = min(self._step_lb, 1.0 / (domain.upper - domain.lower))
if isinstance(domain, sample.Categorical):
if not domain.ordered:
self._unordered_cat_hp[key] = len(domain.categories)
if not hier:
for cat in domain.categories:
if isinstance(cat, dict):
hier = True
break
if str(sampler) != "Normal":
self._bounded_keys.append(key)
if not hier:
self._space_keys = sorted(self._tunable_keys)
self.hierarchical = hier
if self.resource_attr and self.resource_attr not in self._space and self.max_resource:
self.min_resource = self.min_resource or self._min_resource()
self._resource = self._round(self.min_resource)
if not hier:
self._space_keys.append(self.resource_attr)
else:
self._resource = None
self.incumbent = {}
self.incumbent = self.normalize(self.best_config) # flattened
self.best_obj = self.cost_incumbent = None
self.dim = len(self._tunable_keys) # total # tunable dimensions
self._direction_tried = None
self._num_complete4incumbent = self._cost_complete4incumbent = 0
self._num_allowed4incumbent = 2 * self.dim
self._proposed_by = {} # trial_id: int -> incumbent: Dict
self.step_ub = np.sqrt(self.dim)
self.step = self.STEPSIZE * self.step_ub
lb = self.step_lower_bound
if lb > self.step:
self.step = lb * 2
# upper bound
self.step = min(self.step, self.step_ub)
# maximal # consecutive no improvements
self.dir = 2 ** (min(9, self.dim))
self._configs = {} # dict from trial_id to (config, stepsize)
self._K = 0
self._iter_best_config = 1
self.trial_count_proposed = self.trial_count_complete = 1
self._num_proposedby_incumbent = 0
self._reset_times = 0
# record intermediate trial cost
self._trial_cost = {}
self._same = False # whether the proposed config is the same as best_config
self._init_phase = True # initial phase to increase initial stepsize
self._trunc = 0
# no truncation by default. when > 0, it means how many
# non-zero dimensions to keep in the random unit vector
|
(self)
|
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] |
52,811 |
flaml.tune.searcher.flow2
|
_min_resource
|
automatically decide minimal resource
|
def _min_resource(self) -> float:
"""automatically decide minimal resource"""
return self.max_resource / np.pow(self.resource_multiple_factor, 5)
|
(self) -> float
|
[
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] |
52,812 |
flaml.tune.searcher.flow2
|
_project
|
project normalized config in the feasible region and set resource_attr
|
def _project(self, config):
"""project normalized config in the feasible region and set resource_attr"""
for key in self._bounded_keys:
value = config[key]
config[key] = max(0, min(1, value))
if self._resource:
config[self.resource_attr] = self._resource
|
(self, config)
|
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] |
52,813 |
flaml.tune.searcher.flow2
|
_round
|
round the resource to self.max_resource if close to it
|
def _round(self, resource) -> float:
"""round the resource to self.max_resource if close to it"""
if resource * self.resource_multiple_factor > self.max_resource:
return self.max_resource
return resource
|
(self, resource) -> float
|
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] |
52,814 |
flaml.tune.searcher.flow2
|
complete_config
|
Generate a complete config from the partial config input.
Add minimal resource to config if available.
|
def complete_config(
self,
partial_config: Dict,
lower: Optional[Dict] = None,
upper: Optional[Dict] = None,
) -> Tuple[Dict, Dict]:
"""Generate a complete config from the partial config input.
Add minimal resource to config if available.
"""
disturb = self._reset_times and partial_config == self.init_config
# if not the first time to complete init_config, use random gaussian
config, space = complete_config(partial_config, self.space, self, disturb, lower, upper)
if partial_config == self.init_config:
self._reset_times += 1
if self._resource:
config[self.resource_attr] = self.min_resource
return config, space
|
(self, partial_config: Dict, lower: Optional[Dict] = None, upper: Optional[Dict] = None) -> Tuple[Dict, Dict]
|
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] |
52,815 |
flaml.tune.searcher.flow2
|
config_signature
|
Return the signature tuple of a config.
|
def config_signature(self, config, space: Dict = None) -> tuple:
"""Return the signature tuple of a config."""
config = flatten_dict(config)
space = flatten_dict(space) if space else self._space
value_list = []
# self._space_keys doesn't contain keys with const values,
# e.g., "eval_metric": ["logloss", "error"].
keys = sorted(config.keys()) if self.hierarchical else self._space_keys
for key in keys:
value = config[key]
if key == self.resource_attr:
value_list.append(value)
else:
# key must be in space
domain = space[key]
if self.hierarchical and not (
domain is None or type(domain) in (str, int, float) or isinstance(domain, sample.Domain)
):
# not domain or hashable
# get rid of list type for hierarchical search space.
continue
if isinstance(domain, sample.Integer):
value_list.append(int(round(value)))
else:
value_list.append(value)
return tuple(value_list)
|
(self, config, space: Optional[Dict] = None) -> tuple
|
[
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] |
52,816 |
flaml.tune.searcher.flow2
|
create
| null |
def create(self, init_config: Dict, obj: float, cost: float, space: Dict) -> Searcher:
# space is the subspace where the init_config is located
flow2 = self.__class__(
init_config,
self.metric,
self.mode,
space,
self.resource_attr,
self.min_resource,
self.max_resource,
self.resource_multiple_factor,
self.cost_attr,
self.seed + 1,
self.lexico_objectives,
)
if self.lexico_objectives is not None:
flow2.best_obj = {}
for k, v in obj.items():
flow2.best_obj[k] = (
-v if self.lexico_objectives["modes"][self.lexico_objectives["metrics"].index(k)] == "max" else v
)
else:
flow2.best_obj = obj * self.metric_op # minimize internally
flow2.cost_incumbent = cost
self.seed += 1
return flow2
|
(self, init_config: Dict, obj: float, cost: float, space: Dict) -> flaml.tune.searcher.suggestion.Searcher
|
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] |
52,817 |
flaml.tune.searcher.flow2
|
denormalize
|
denormalize each dimension in config from [0,1].
|
def denormalize(self, config):
"""denormalize each dimension in config from [0,1]."""
return denormalize(config, self._space, self.best_config, self.incumbent, self._random)
|
(self, config)
|
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] |
52,818 |
flaml.tune.searcher.flow2
|
lexico_compare
| null |
def lexico_compare(self, result) -> bool:
if self._histories is None:
self._histories, self._f_best = defaultdict(list), {}
for k in self.lexico_objectives["metrics"]:
self._histories[k].append(result[k])
self.update_fbest()
return True
else:
for k in self.lexico_objectives["metrics"]:
self._histories[k].append(result[k])
self.update_fbest()
for k_metric, k_mode in zip(self.lexico_objectives["metrics"], self.lexico_objectives["modes"]):
k_target = (
self.lexico_objectives["targets"][k_metric]
if k_mode == "min"
else -self.lexico_objectives["targets"][k_metric]
)
if not isinstance(self.lexico_objectives["tolerances"][k_metric], str):
tolerance_bound = self._f_best[k_metric] + self.lexico_objectives["tolerances"][k_metric]
else:
assert (
self.lexico_objectives["tolerances"][k_metric][-1] == "%"
), "String tolerance of {} should use %% as the suffix".format(k_metric)
tolerance_bound = self._f_best[k_metric] * (
1 + 0.01 * float(self.lexico_objectives["tolerances"][k_metric].replace("%", ""))
)
if (result[k_metric] < max(tolerance_bound, k_target)) and (
self.best_obj[k_metric]
< max(
tolerance_bound,
k_target,
)
):
continue
elif result[k_metric] < self.best_obj[k_metric]:
return True
else:
return False
for k_metr in self.lexico_objectives["metrics"]:
if result[k_metr] == self.best_obj[k_metr]:
continue
elif result[k_metr] < self.best_obj[k_metr]:
return True
else:
return False
|
(self, result) -> bool
|
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] |
52,819 |
flaml.tune.searcher.flow2
|
normalize
|
normalize each dimension in config to [0,1].
|
def normalize(self, config, recursive=False) -> Dict:
"""normalize each dimension in config to [0,1]."""
return normalize(config, self._space, self.best_config, self.incumbent, recursive)
|
(self, config, recursive=False) -> Dict
|
[
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] |
52,820 |
flaml.tune.searcher.flow2
|
on_trial_complete
|
Compare with incumbent.
If better, move, reset num_complete and num_proposed.
If not better and num_complete >= 2*dim, num_allowed += 2.
|
def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False):
"""
Compare with incumbent.
If better, move, reset num_complete and num_proposed.
If not better and num_complete >= 2*dim, num_allowed += 2.
"""
self.trial_count_complete += 1
if not error and result:
obj = (
result.get(self._metric)
if self.lexico_objectives is None
else {k: result[k] for k in self.lexico_objectives["metrics"]}
)
if obj:
obj = (
{
k: -obj[k] if m == "max" else obj[k]
for k, m in zip(
self.lexico_objectives["metrics"],
self.lexico_objectives["modes"],
)
}
if isinstance(obj, dict)
else obj * self.metric_op
)
if (
self.best_obj is None
or (self.lexico_objectives is None and obj < self.best_obj)
or (self.lexico_objectives is not None and self.lexico_compare(obj))
):
self.best_obj = obj
self.best_config, self.step = self._configs[trial_id]
self.incumbent = self.normalize(self.best_config)
self.cost_incumbent = result.get(self.cost_attr, 1)
if self._resource:
self._resource = self.best_config[self.resource_attr]
self._num_complete4incumbent = 0
self._cost_complete4incumbent = 0
self._num_proposedby_incumbent = 0
self._num_allowed4incumbent = 2 * self.dim
self._proposed_by.clear()
if self._K > 0:
self.step *= np.sqrt(self._K / self._oldK)
self.step = min(self.step, self.step_ub)
self._iter_best_config = self.trial_count_complete
if self._trunc:
self._trunc = min(self._trunc + 1, self.dim)
return
elif self._trunc:
self._trunc = max(self._trunc >> 1, 1)
proposed_by = self._proposed_by.get(trial_id)
if proposed_by == self.incumbent:
self._num_complete4incumbent += 1
cost = result.get(self.cost_attr, 1) if result else self._trial_cost.get(trial_id)
if cost:
self._cost_complete4incumbent += cost
if self._num_complete4incumbent >= 2 * self.dim and self._num_allowed4incumbent == 0:
self._num_allowed4incumbent = 2
if self._num_complete4incumbent == self.dir and (not self._resource or self._resource == self.max_resource):
self._num_complete4incumbent -= 2
self._num_allowed4incumbent = max(self._num_allowed4incumbent, 2)
|
(self, trial_id: str, result: Optional[Dict] = None, error: bool = False)
|
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] |
52,821 |
flaml.tune.searcher.flow2
|
on_trial_result
|
Early update of incumbent.
|
def on_trial_result(self, trial_id: str, result: Dict):
"""Early update of incumbent."""
if result:
obj = (
result.get(self._metric)
if self.lexico_objectives is None
else {k: result[k] for k in self.lexico_objectives["metrics"]}
)
if obj:
obj = (
{
k: -obj[k] if m == "max" else obj[k]
for k, m in zip(
self.lexico_objectives["metrics"],
self.lexico_objectives["modes"],
)
}
if isinstance(obj, dict)
else obj * self.metric_op
)
if (
self.best_obj is None
or (self.lexico_objectives is None and obj < self.best_obj)
or (self.lexico_objectives is not None and self.lexico_compare(obj))
):
self.best_obj = obj
config = self._configs[trial_id][0]
if self.best_config != config:
self.best_config = config
if self._resource:
self._resource = config[self.resource_attr]
self.incumbent = self.normalize(self.best_config)
self.cost_incumbent = result.get(self.cost_attr, 1)
self._cost_complete4incumbent = 0
self._num_complete4incumbent = 0
self._num_proposedby_incumbent = 0
self._num_allowed4incumbent = 2 * self.dim
self._proposed_by.clear()
self._iter_best_config = self.trial_count_complete
cost = result.get(self.cost_attr, 1)
# record the cost in case it is pruned and cost info is lost
self._trial_cost[trial_id] = cost
|
(self, trial_id: str, result: Dict)
|
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] |
52,822 |
flaml.tune.searcher.flow2
|
rand_vector_gaussian
| null |
def rand_vector_gaussian(self, dim, std=1.0):
return self._random.normal(0, std, dim)
|
(self, dim, std=1.0)
|
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] |
52,823 |
flaml.tune.searcher.flow2
|
rand_vector_unit_sphere
| null |
def rand_vector_unit_sphere(self, dim, trunc=0) -> np.ndarray:
vec = self._random.normal(0, 1, dim)
if 0 < trunc < dim:
vec[np.abs(vec).argsort()[: dim - trunc]] = 0
mag = np.linalg.norm(vec)
return vec / mag
|
(self, dim, trunc=0) -> numpy.ndarray
|
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] |
52,824 |
flaml.tune.searcher.flow2
|
reach
|
whether the incumbent can reach the incumbent of other.
|
def reach(self, other: Searcher) -> bool:
"""whether the incumbent can reach the incumbent of other."""
config1, config2 = self.best_config, other.best_config
incumbent1, incumbent2 = self.incumbent, other.incumbent
if self._resource and config1[self.resource_attr] > config2[self.resource_attr]:
# resource will not decrease
return False
for key in self._unordered_cat_hp:
# unordered cat choice is hard to reach by chance
if config1[key] != config2.get(key):
return False
delta = np.array([incumbent1[key] - incumbent2.get(key, np.inf) for key in self._tunable_keys])
return np.linalg.norm(delta) <= self.step
|
(self, other: flaml.tune.searcher.suggestion.Searcher) -> bool
|
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] |
52,825 |
flaml.tune.searcher.flow2
|
set_search_properties
| null |
def set_search_properties(
self,
metric: Optional[str] = None,
mode: Optional[str] = None,
config: Optional[Dict] = None,
) -> bool:
if metric:
self._metric = metric
if mode:
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."
self._mode = mode
if mode == "max":
self.metric_op = -1.0
elif mode == "min":
self.metric_op = 1.0
if config:
self.space = config
self._space = flatten_dict(self.space)
self._init_search()
return True
|
(self, metric: Optional[str] = None, mode: Optional[str] = None, config: Optional[Dict] = None) -> bool
|
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] |
52,826 |
flaml.tune.searcher.flow2
|
suggest
|
Suggest a new config, one of the following cases:
1. same incumbent, increase resource.
2. same resource, move from the incumbent to a random direction.
3. same resource, move from the incumbent to the opposite direction.
|
def suggest(self, trial_id: str) -> Optional[Dict]:
"""Suggest a new config, one of the following cases:
1. same incumbent, increase resource.
2. same resource, move from the incumbent to a random direction.
3. same resource, move from the incumbent to the opposite direction.
"""
# TODO: better decouple FLOW2 config suggestion and stepsize update
self.trial_count_proposed += 1
if (
self._num_complete4incumbent > 0
and self.cost_incumbent
and self._resource
and self._resource < self.max_resource
and (self._cost_complete4incumbent >= self.cost_incumbent * self.resource_multiple_factor)
):
return self._increase_resource(trial_id)
self._num_allowed4incumbent -= 1
move = self.incumbent.copy()
if self._direction_tried is not None:
# return negative direction
for i, key in enumerate(self._tunable_keys):
move[key] -= self._direction_tried[i]
self._direction_tried = None
else:
# propose a new direction
self._direction_tried = self.rand_vector_unit_sphere(self.dim, self._trunc) * self.step
for i, key in enumerate(self._tunable_keys):
move[key] += self._direction_tried[i]
self._project(move)
config = self.denormalize(move)
self._proposed_by[trial_id] = self.incumbent
self._configs[trial_id] = (config, self.step)
self._num_proposedby_incumbent += 1
best_config = self.best_config
if self._init_phase:
if self._direction_tried is None:
if self._same:
same = not any(key not in best_config or value != best_config[key] for key, value in config.items())
if same:
# increase step size
self.step += self.STEPSIZE
self.step = min(self.step, self.step_ub)
else:
same = not any(key not in best_config or value != best_config[key] for key, value in config.items())
self._same = same
if self._num_proposedby_incumbent == self.dir and (not self._resource or self._resource == self.max_resource):
# check stuck condition if using max resource
self._num_proposedby_incumbent -= 2
self._init_phase = False
if self.step < self.step_lower_bound:
return None
# decrease step size
self._oldK = self._K or self._iter_best_config
self._K = self.trial_count_proposed + 1
self.step *= np.sqrt(self._oldK / self._K)
if self._init_phase:
return unflatten_dict(config)
if self._trunc == 1 and self._direction_tried is not None:
# random
for i, key in enumerate(self._tunable_keys):
if self._direction_tried[i] != 0:
for _, generated in generate_variants_compatible(
{"config": {key: self._space[key]}}, random_state=self.rs_random
):
if generated["config"][key] != best_config[key]:
config[key] = generated["config"][key]
return unflatten_dict(config)
break
elif len(config) == len(best_config):
for key, value in best_config.items():
if value != config[key]:
return unflatten_dict(config)
# print('move to', move)
self.incumbent = move
return unflatten_dict(config)
|
(self, trial_id: str) -> Optional[Dict]
|
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] |
52,827 |
flaml.tune.searcher.flow2
|
update_fbest
| null |
def update_fbest(
self,
):
obj_initial = self.lexico_objectives["metrics"][0]
feasible_index = np.array([*range(len(self._histories[obj_initial]))])
for k_metric in self.lexico_objectives["metrics"]:
k_values = np.array(self._histories[k_metric])
feasible_value = k_values.take(feasible_index)
self._f_best[k_metric] = np.min(feasible_value)
if not isinstance(self.lexico_objectives["tolerances"][k_metric], str):
tolerance_bound = self._f_best[k_metric] + self.lexico_objectives["tolerances"][k_metric]
else:
assert (
self.lexico_objectives["tolerances"][k_metric][-1] == "%"
), "String tolerance of {} should use %% as the suffix".format(k_metric)
tolerance_bound = self._f_best[k_metric] * (
1 + 0.01 * float(self.lexico_objectives["tolerances"][k_metric].replace("%", ""))
)
feasible_index_filter = np.where(
feasible_value
<= max(
tolerance_bound,
self.lexico_objectives["targets"][k_metric],
)
)[0]
feasible_index = feasible_index.take(feasible_index_filter)
|
(self)
|
[
0.07621940970420837,
-0.0232179444283247,
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0.024281641468405724,
-0.026537412777543068,
-0.031085634604096413,
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] |
52,828 |
flaml.tune.searcher.blendsearch
|
RandomSearch
|
Class for random search.
|
class RandomSearch(CFO):
"""Class for random search."""
def suggest(self, trial_id: str) -> Optional[Dict]:
if self._points_to_evaluate:
return super().suggest(trial_id)
config, _ = self._ls.complete_config({})
return config
def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False):
return
def on_trial_result(self, trial_id: str, result: Dict):
return
|
(metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, time_budget_s: Union[int, float] = None, num_samples: Optional[int] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, global_search_alg: Optional[flaml.tune.searcher.suggestion.Searcher] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, seed: Optional[int] = 20, cost_attr: Optional[str] = 'auto', cost_budget: Optional[float] = None, experimental: Optional[bool] = False, lexico_objectives: Optional[dict] = None, use_incumbent_result_in_evaluation=False, allow_empty_config=False)
|
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] |
52,845 |
flaml.tune.searcher.blendsearch
|
on_trial_complete
| null |
def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False):
return
|
(self, trial_id: str, result: Optional[Dict] = None, error: bool = False)
|
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] |
52,846 |
flaml.tune.searcher.blendsearch
|
on_trial_result
| null |
def on_trial_result(self, trial_id: str, result: Dict):
return
|
(self, trial_id: str, result: Dict)
|
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] |
52,851 |
flaml.tune.searcher.blendsearch
|
suggest
| null |
def suggest(self, trial_id: str) -> Optional[Dict]:
if self._points_to_evaluate:
return super().suggest(trial_id)
config, _ = self._ls.complete_config({})
return config
|
(self, trial_id: str) -> Optional[Dict]
|
[
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0.03407135233283043,
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0.008422375656664371,
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] |
52,860 |
duckduckgo_search.duckduckgo_search_async
|
AsyncDDGS
|
DuckDuckgo_search async class to get search results from duckduckgo.com.
|
class AsyncDDGS:
"""DuckDuckgo_search async class to get search results from duckduckgo.com."""
_executor: Optional[ThreadPoolExecutor] = None
def __init__(
self,
headers: Optional[Dict[str, str]] = None,
proxy: Optional[str] = None,
proxies: Union[Dict[str, str], str, None] = None, # deprecated
timeout: Optional[int] = 10,
) -> None:
"""Initialize the AsyncDDGS object.
Args:
headers (dict, optional): Dictionary of headers for the HTTP client. Defaults to None.
proxy (str, optional): proxy for the HTTP client, supports http/https/socks5 protocols.
example: "http://user:[email protected]:3128". Defaults to None.
timeout (int, optional): Timeout value for the HTTP client. Defaults to 10.
"""
self.proxy: Optional[str] = proxy
assert self.proxy is None or isinstance(self.proxy, str), "proxy must be a str"
if not proxy and proxies:
warnings.warn("'proxies' is deprecated, use 'proxy' instead.", stacklevel=1)
self.proxy = proxies.get("http") or proxies.get("https") if isinstance(proxies, dict) else proxies
self._asession = requests.AsyncSession(
headers=headers,
proxy=self.proxy,
timeout=timeout,
impersonate="chrome",
allow_redirects=False,
)
self._asession.headers["Referer"] = "https://duckduckgo.com/"
self._exception_event = asyncio.Event()
async def __aenter__(self) -> "AsyncDDGS":
return self
async def __aexit__(
self,
exc_type: Optional[Type[BaseException]] = None,
exc_val: Optional[BaseException] = None,
exc_tb: Optional[TracebackType] = None,
) -> None:
await self._asession.__aexit__(exc_type, exc_val, exc_tb)
def __del__(self) -> None:
if hasattr(self, "_asession") and self._asession._closed is False:
with suppress(RuntimeError, RuntimeWarning):
asyncio.create_task(self._asession.close())
@cached_property
def parser(self) -> Optional["LHTMLParser"]:
"""Get HTML parser."""
return LHTMLParser(remove_blank_text=True, remove_comments=True, remove_pis=True, collect_ids=False)
@classmethod
def _get_executor(cls, max_workers: int = 1) -> ThreadPoolExecutor:
"""Get ThreadPoolExecutor. Default max_workers=1, because >=2 leads to a big overhead"""
if cls._executor is None:
cls._executor = ThreadPoolExecutor(max_workers=max_workers)
return cls._executor
@property
def executor(cls) -> Optional[ThreadPoolExecutor]:
return cls._get_executor()
async def _aget_url(
self,
method: str,
url: str,
data: Optional[Union[Dict[str, str], bytes]] = None,
params: Optional[Dict[str, str]] = None,
) -> bytes:
if self._exception_event.is_set():
raise DuckDuckGoSearchException("Exception occurred in previous call.")
try:
resp = await self._asession.request(method, url, data=data, params=params)
except Exception as ex:
self._exception_event.set()
if "time" in str(ex).lower():
raise TimeoutException(f"{url} {type(ex).__name__}: {ex}") from ex
raise DuckDuckGoSearchException(f"{url} {type(ex).__name__}: {ex}") from ex
logger.debug(f"_aget_url() {resp.url} {resp.status_code} {resp.elapsed:.2f} {len(resp.content)}")
if resp.status_code == 200:
return cast(bytes, resp.content)
self._exception_event.set()
if resp.status_code in (202, 301, 403):
raise RatelimitException(f"{resp.url} {resp.status_code} Ratelimit")
raise DuckDuckGoSearchException(f"{resp.url} return None. {params=} {data=}")
async def _aget_vqd(self, keywords: str) -> str:
"""Get vqd value for a search query."""
resp_content = await self._aget_url("POST", "https://duckduckgo.com", data={"q": keywords})
return _extract_vqd(resp_content, keywords)
async def text(
self,
keywords: str,
region: str = "wt-wt",
safesearch: str = "moderate",
timelimit: Optional[str] = None,
backend: str = "api",
max_results: Optional[int] = None,
) -> List[Dict[str, str]]:
"""DuckDuckGo text search generator. Query params: https://duckduckgo.com/params.
Args:
keywords: keywords for query.
region: wt-wt, us-en, uk-en, ru-ru, etc. Defaults to "wt-wt".
safesearch: on, moderate, off. Defaults to "moderate".
timelimit: d, w, m, y. Defaults to None.
backend: api, html, lite. Defaults to api.
api - collect data from https://duckduckgo.com,
html - collect data from https://html.duckduckgo.com,
lite - collect data from https://lite.duckduckgo.com.
max_results: max number of results. If None, returns results only from the first response. Defaults to None.
Returns:
List of dictionaries with search results, or None if there was an error.
Raises:
DuckDuckGoSearchException: Base exception for duckduckgo_search errors.
RatelimitException: Inherits from DuckDuckGoSearchException, raised for exceeding API request rate limits.
TimeoutException: Inherits from DuckDuckGoSearchException, raised for API request timeouts.
"""
if LXML_AVAILABLE is False and backend != "api":
backend = "api"
warnings.warn("lxml is not installed. Using backend='api'.", stacklevel=2)
if backend == "api":
results = await self._text_api(keywords, region, safesearch, timelimit, max_results)
elif backend == "html":
results = await self._text_html(keywords, region, safesearch, timelimit, max_results)
elif backend == "lite":
results = await self._text_lite(keywords, region, timelimit, max_results)
return results
async def _text_api(
self,
keywords: str,
region: str = "wt-wt",
safesearch: str = "moderate",
timelimit: Optional[str] = None,
max_results: Optional[int] = None,
) -> List[Dict[str, str]]:
"""DuckDuckGo text search generator. Query params: https://duckduckgo.com/params.
Args:
keywords: keywords for query.
region: wt-wt, us-en, uk-en, ru-ru, etc. Defaults to "wt-wt".
safesearch: on, moderate, off. Defaults to "moderate".
timelimit: d, w, m, y. Defaults to None.
max_results: max number of results. If None, returns results only from the first response. Defaults to None.
Returns:
List of dictionaries with search results.
Raises:
DuckDuckGoSearchException: Base exception for duckduckgo_search errors.
RatelimitException: Inherits from DuckDuckGoSearchException, raised for exceeding API request rate limits.
TimeoutException: Inherits from DuckDuckGoSearchException, raised for API request timeouts.
"""
assert keywords, "keywords is mandatory"
vqd = await self._aget_vqd(keywords)
payload = {
"q": keywords,
"kl": region,
"l": region,
"p": "",
"s": "0",
"df": "",
"vqd": vqd,
"ex": "",
}
safesearch = safesearch.lower()
if safesearch == "moderate":
payload["ex"] = "-1"
elif safesearch == "off":
payload["ex"] = "-2"
elif safesearch == "on": # strict
payload["p"] = "1"
if timelimit:
payload["df"] = timelimit
cache = set()
results: List[Optional[Dict[str, str]]] = [None] * 1100
async def _text_api_page(s: int, page: int) -> None:
priority = page * 100
payload["s"] = f"{s}"
resp_content = await self._aget_url("GET", "https://links.duckduckgo.com/d
|
(headers: Optional[Dict[str, str]] = None, proxy: Optional[str] = None, proxies: Union[Dict[str, str], str, NoneType] = None, timeout: Optional[int] = 10) -> None
|
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] |
52,861 |
duckduckgo_search.duckduckgo_search_async
|
__aenter__
| null |
def __init__(
self,
headers: Optional[Dict[str, str]] = None,
proxy: Optional[str] = None,
proxies: Union[Dict[str, str], str, None] = None, # deprecated
timeout: Optional[int] = 10,
) -> None:
"""Initialize the AsyncDDGS object.
Args:
headers (dict, optional): Dictionary of headers for the HTTP client. Defaults to None.
proxy (str, optional): proxy for the HTTP client, supports http/https/socks5 protocols.
example: "http://user:[email protected]:3128". Defaults to None.
timeout (int, optional): Timeout value for the HTTP client. Defaults to 10.
"""
self.proxy: Optional[str] = proxy
assert self.proxy is None or isinstance(self.proxy, str), "proxy must be a str"
if not proxy and proxies:
warnings.warn("'proxies' is deprecated, use 'proxy' instead.", stacklevel=1)
self.proxy = proxies.get("http") or proxies.get("https") if isinstance(proxies, dict) else proxies
self._asession = requests.AsyncSession(
headers=headers,
proxy=self.proxy,
timeout=timeout,
impersonate="chrome",
allow_redirects=False,
)
self._asession.headers["Referer"] = "https://duckduckgo.com/"
self._exception_event = asyncio.Event()
|
(self) -> duckduckgo_search.duckduckgo_search_async.AsyncDDGS
|
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] |
52,863 |
duckduckgo_search.duckduckgo_search_async
|
__del__
| null |
def __del__(self) -> None:
if hasattr(self, "_asession") and self._asession._closed is False:
with suppress(RuntimeError, RuntimeWarning):
asyncio.create_task(self._asession.close())
|
(self) -> NoneType
|
[
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] |
52,864 |
duckduckgo_search.duckduckgo_search_async
|
__init__
|
Initialize the AsyncDDGS object.
Args:
headers (dict, optional): Dictionary of headers for the HTTP client. Defaults to None.
proxy (str, optional): proxy for the HTTP client, supports http/https/socks5 protocols.
example: "http://user:[email protected]:3128". Defaults to None.
timeout (int, optional): Timeout value for the HTTP client. Defaults to 10.
|
def __init__(
self,
headers: Optional[Dict[str, str]] = None,
proxy: Optional[str] = None,
proxies: Union[Dict[str, str], str, None] = None, # deprecated
timeout: Optional[int] = 10,
) -> None:
"""Initialize the AsyncDDGS object.
Args:
headers (dict, optional): Dictionary of headers for the HTTP client. Defaults to None.
proxy (str, optional): proxy for the HTTP client, supports http/https/socks5 protocols.
example: "http://user:[email protected]:3128". Defaults to None.
timeout (int, optional): Timeout value for the HTTP client. Defaults to 10.
"""
self.proxy: Optional[str] = proxy
assert self.proxy is None or isinstance(self.proxy, str), "proxy must be a str"
if not proxy and proxies:
warnings.warn("'proxies' is deprecated, use 'proxy' instead.", stacklevel=1)
self.proxy = proxies.get("http") or proxies.get("https") if isinstance(proxies, dict) else proxies
self._asession = requests.AsyncSession(
headers=headers,
proxy=self.proxy,
timeout=timeout,
impersonate="chrome",
allow_redirects=False,
)
self._asession.headers["Referer"] = "https://duckduckgo.com/"
self._exception_event = asyncio.Event()
|
(self, headers: Optional[Dict[str, str]] = None, proxy: Optional[str] = None, proxies: Union[Dict[str, str], str, NoneType] = None, timeout: Optional[int] = 10) -> NoneType
|
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