Nodes module

class bamt.nodes.base.BaseNode(name: str)

Bases: object

Base class for nodes.

static choose_serialization(model) → str | Exception

static get_path_joblib(node_name: str, specific: str = '') → str

Parameters:

• node_name – name of node

• specific – more specific unique name for node.

• example (For) –

• combination. –

Returns:

Path to save a joblib file.

static get_dist(node_info, pvals)

class bamt.nodes.composite_continuous_node.CompositeContinuousNode(name, regressor: object | None = None)

Bases: GaussianNode

class bamt.nodes.composite_discrete_node.CompositeDiscreteNode(name, classifier: object | None = None)

Bases: LogitNode

Class for composite discrete node.

class bamt.nodes.conditional_gaussian_node.ConditionalGaussianNode(name, regressor: object | None = None)

Bases: BaseNode

Main class for Conditional Gaussian Node

fit_parameters(data: DataFrame) → Dict[str, Dict[str, CondGaussParams]]

Train params for Conditional Gaussian Node. Return: {“hybcprob”: {<combination of outputs from discrete parents> : CondGaussParams}}

get_dist(node_info, pvals)

choose(node_info: Dict[str, Dict[str, CondGaussParams]], pvals: List[str | float]) → float

Return value from ConditionalLogit node params: node_info: nodes info from distributions pvals: parent values

predict(node_info: Dict[str, Dict[str, CondGaussParams]], pvals: List[str | float]) → float

Return value from ConditionalLogit node params: node_info: nodes info from distributions pvals: parent values

class bamt.nodes.conditional_logit_node.ConditionalLogitNode(name: str, classifier: object | None = None)

Bases: BaseNode

Main class for Conditional Logit Node

fit_parameters(data: DataFrame) → Dict[str, Dict[str, LogitParams]]

Train params on data Return: {“hybcprob”: {<combination of outputs from discrete parents> : LogitParams}}

static get_dist(node_info, pvals, **kwargs)

choose(node_info: Dict[str, Dict[str, LogitParams]], pvals: List[str | float]) → str

Return value from ConditionalLogit node params: node_info: nodes info from distributions pvals: parent values

static predict(node_info: Dict[str, Dict[str, LogitParams]], pvals: List[str | float]) → str

Return value from ConditionalLogit node params: node_info: nodes info from distributions pvals: parent values

class bamt.nodes.conditional_mixture_gaussian_node.ConditionalMixtureGaussianNode(name)

Bases: BaseNode

Main class for Conditional Mixture Gaussian Node

fit_parameters(data: DataFrame) → Dict[str, Dict[str, CondMixtureGaussParams]]

Train params for Conditional Mixture Gaussian Node. Return: {“hybcprob”: {<combination of outputs from discrete parents> : CondMixtureGaussParams}}

static get_dist(node_info, pvals)

choose(node_info: Dict[str, Dict[str, CondMixtureGaussParams]], pvals: List[str | float]) → float | None

Function to get value from ConditionalMixtureGaussian node params: node_info: nodes info from distributions pvals: parent values

static predict(node_info: Dict[str, Dict[str, CondMixtureGaussParams]], pvals: List[str | float]) → float | None

Function to get prediction from ConditionalMixtureGaussian node params: node_info: nodes info from distributions pvals: parent values

class bamt.nodes.discrete_node.DiscreteNode(name)

Bases: BaseNode

Main class of Discrete Node

fit_parameters(data: DataFrame, num_workers: int = 1)

Train params for Discrete Node data: DataFrame to train on num_workers: number of Parallel Workers Method returns probas dict with the following format {[<combinations>: value]} and vals, list of appeared values in combinations

static get_dist(node_info, pvals)

choose(node_info: Dict[str, float | str], pvals: List[str]) → str

Return value from discrete node params: node_info: nodes info from distributions pvals: parent values

static predict(node_info: Dict[str, float | str], pvals: List[str]) → str

function for prediction based on evidence values in discrete node

Parameters:

• node_info (Dict[str, Union[float, str]]) – parameters of node

• pvals (List[str]) – values in parents nodes

Returns:

prediction

Return type:

str

class bamt.nodes.logit_node.LogitNode(name, classifier: object | None = None)

Bases: BaseNode

Main class for logit node

fit_parameters(data: DataFrame, **kwargs) → LogitParams

get_dist(node_info, pvals)

choose(node_info: LogitParams, pvals: List[float]) → str

Return value from Logit node params: node_info: nodes info from distributions pvals: parent values

static predict(node_info: LogitParams, pvals: List[float]) → str

Return prediction from Logit node params: node_info: nodes info from distributions pvals: parent values

class bamt.nodes.mixture_gaussian_node.MixtureGaussianNode(name)

Bases: BaseNode

Main class for Mixture Gaussian Node

fit_parameters(data: DataFrame) → MixtureGaussianParams

Train params for Mixture Gaussian Node

static get_dist(node_info, pvals)

choose(node_info: MixtureGaussianParams, pvals: List[str | float]) → float | None

Func to get value from current node node_info: nodes info from distributions pvals: parent values Return value from MixtureGaussian node

static predict(node_info: MixtureGaussianParams, pvals: List[str | float]) → float | None

Func to get prediction from current node node_info: nodes info from distributions pvals: parent values Return value from MixtureGaussian node

class bamt.nodes.schema.DiscreteParams

Bases: TypedDict

cprob: List[list | Any] | Dict[str, list]

vals: List[str]

class bamt.nodes.schema.MixtureGaussianParams

Bases: TypedDict

mean: List[float]

coef: List[float]

covars: List[float]

class bamt.nodes.schema.GaussianParams

Bases: TypedDict

regressor: str

regressor_obj: str | bool | bytes | None

variance: ndarray | float

mean: ndarray | float

serialization: str

class bamt.nodes.schema.CondGaussParams

Bases: TypedDict

regressor: str

regressor_obj: str | bool | bytes | None

variance: ndarray | float

mean: ndarray | float

serialization: str

class bamt.nodes.schema.CondMixtureGaussParams

Bases: TypedDict

mean: List[float] | None

coef: List[float]

covars: List[float] | None

class bamt.nodes.schema.LogitParams

Bases: TypedDict

classes: List[int]

classifier: str

classifier_obj: str | bool | bytes | None

serialization: str

class bamt.nodes.schema.HybcprobParams

Bases: TypedDict

hybcprob: Dict[str, CondGaussParams]