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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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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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_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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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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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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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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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)
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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)
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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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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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