Functions — github.com/chritoth/active-bayesian-causal-inference

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Functions218 in github.com/chritoth/active-bayesian-causal-inference

⨍ Functions 218◇ Types & classes 28

↓ 1 callersMethodinit_as_static

(self)

src/models/mechanisms.py:331

↓ 1 callersMethodintervention_info_gain

(self, interventions: dict, experiments: List[Experiment],

src/experimental_design/exp_designer_abci_dibs_gp.py:161

↓ 1 callersMethodintervention_info_gain

(self, interventions: dict, experiments: List[Ex

src/experimental_design/exp_designer_abci_categorical_gp.py:89

↓ 1 callersMethodinterventional_mll

(self, targets, node: str, interventions: dict, num_mc_samples=200, reduce=True)

src/environments/environment.py:170

↓ 1 callersMethodinterventional_mll

(self, targets, node: str, interventions: dict, graph: nx.DiGraph, num_mc_samples=50,

src/models/gp_model.py:259

↓ 1 callersMethodload_param_dict

(self, param_dict)

src/abci_categorical_gp.py:273

↓ 1 callersMethodload_param_dict

(self, param_dict)

src/abci_dibs_gp.py:479

↓ 1 callersMethodmechanism_log_hp_priors

(self, keys: List[str] = None)

src/models/gp_model.py:206

↓ 1 callersMethodmechanism_mlls

(self, experiments: List[Experiment], keys: List[str] = None, prior_mode=False)

src/models/gp_model.py:177

↓ 1 callersMethodnode_mll

(self, experiments: List[Experiment], node: str, graph: nx.DiGraph, prior_mode=False, use_cac

src/models/gp_model.py:120

↓ 1 callersMethodnormalize

Normalizes the distribution over graphs such that the sum over all graphs probabilities equals 1.

src/models/graph_models.py:225

↓ 1 callersMethodoptimize_gp_hyperparams

(self, experiments: List[Experiment], keys: List[str] = None, num_steps: int = 70,

src/models/gp_model.py:352

↓ 1 callersMethodparam_dict

(self)

src/abci_categorical_gp.py:265

↓ 1 callersMethodparam_dict

(self)

src/abci_dibs_gp.py:468

↓ 1 callersMethodparam_dict

(self)

src/environments/environment.py:200

↓ 1 callersFunctionparse_file_name

(filename: str)

src/utils/plotting.py:40

↓ 1 callersMethodparticle_similarities

(self, bandwidth=1.)

src/models/graph_models.py:539

↓ 1 callersFunctionrandom_search

(utility: callable, bounds: torch.Tensor, num_candidates=10)

src/experimental_design/optimization.py:49

↓ 1 callersMethodresample_particles

(self, threshold: float = 1e-2, use_cache: bool = False)

src/abci_dibs_gp.py:257

↓ 1 callersMethodrun_distributed

(self, experimenter_rref, args: dict)

src/experimental_design/exp_designer_base.py:27

↓ 1 callersFunctionrun_single_env

(env_file: str, output_dir: str, policy: str, num_experiments: int, batch_size: int, num_in

src/scripts/run_single_env.py:74

↓ 1 callersMethodsample_graphs

(self, num_samples: int, alpha: float = 1., fixed_edges: List[Tuple[int, int]] = None)

src/models/graph_models.py:454

↓ 1 callersMethodsample_soft_graphs

(self, num_samples: int, alpha: float = 1.)

src/models/graph_models.py:440

↓ 1 callersMethodsave

(self, path)

src/abci_categorical_gp.py:280

↓ 1 callersMethodsave

(self, path)

src/abci_dibs_gp.py:489

↓ 1 callersMethodscm_info_gain

(self, interventions: dict, inner_mc_graphs: List[List[nx.DiGraph]],

src/experimental_design/exp_designer_abci_dibs_gp.py:119

↓ 1 callersMethodscm_info_gain

(self, interventions: dict, batch_size: int = 1, num_exp_per_graph: int = 1, mode='n-best',

src/experimental_design/exp_designer_abci_categorical_gp.py:72

↓ 1 callersMethodselect_hyperparameters

(self, prior_mode: bool = False)

src/models/mechanisms.py:292

↓ 1 callersMethodset_log_prob

Sets the log probability of a given graph. Parameters ---------- log_prob: torch.tensor The new log prab

src/models/graph_models.py:209

↓ 1 callersMethodsort_by_prob

Returns a list of all graphs sorted by their probabilities. Parameters ---------- descending: bool If tr

src/models/graph_models.py:264

↓ 1 callersMethodsort_edges

(self, adj_mat: torch.Tensor, edge_weights: torch.Tensor, descending=True)

src/models/graph_models.py:508

↓ 1 callersMethodupdate_latent_particles

(self, num_steps: int = 100, log_interval: int = 25)

src/abci_dibs_gp.py:278

Method__init__

(self, env: Environment, policy, num_workers: int = 1, linear: bool = False)

src/abci_categorical_gp.py:17

Method__init__

(self, env: Environment, policy: str, num_particles: int = 5, num_mc_graphs: int = 40, embedd

src/abci_dibs_gp.py:18

Method__init__

(self, env: Environment, policy='observational', num_workers: int = 1)

src/abci_base.py:11

Method__init__

(self, num_nodes: int, p: float = None, mechanism_model='gp-model',

src/environments/generic_environments.py:10

Method__init__

(self, num_nodes: int, num_parents_per_node: int = 2, mechanism_model='gp-mo

src/environments/generic_environments.py:39

Method__init__

(self, num_nodes: int = None, mechanism_model='gp-model', frac_non_intervena

src/environments/generic_environments.py:67

Method__init__

(self, num_nodes: int, mechanism_model='gp-model', frac_non_intervenable_nod

src/environments/generic_environments.py:90

Method__init__

(self, num_nodes: int, mechanism_model='gp-model', frac_non_intervenable_nod

src/environments/generic_environments.py:111

Method__init__

(self, num_nodes: int, mechanism_model='gp-model', frac_non_intervenable_nod

src/environments/generic_environments.py:130

Method__init__

(self, num_nodes: int, mechanism_model='gp-model', frac_non_intervenable_nod

src/environments/generic_environments.py:171

Method__init__

(self, num_nodes: int, mechanism_model='gp-model', frac_non_intervenable_nod

src/environments/generic_environments.py:189

Method__init__

(self, query_nodes: List[str], intervention_targets: Dict[str, dist.Distribution], sample_que

src/environments/environment.py:13

Method__init__

(self, num_nodes: int, mechanism_model: Optional[str] = 'gp-model', frac_non

src/environments/environment.py:41

Method__init__

(self, interventions: dict, data: Dict[str, torch.tensor])

src/environments/experiment.py:10

Method__init__

(self, intervention_bounds: Dict[str, Tuple[float, float]], opt_strategy: str = 'gp-ucb', dis

src/experimental_design/exp_designer_abci_dibs_gp.py:15

Method__init__

(self, intervention_bounds: Dict[str, Tuple[float, float]], opt_strategy: str = 'gp-ucb', dis

src/experimental_design/exp_designer_base.py:14

Method__init__

(self, intervention_bounds: Dict[str, Tuple[float, float]], opt_strategy: str = 'gp-ucb', dis

src/experimental_design/exp_designer_abci_categorical_gp.py:16

Method__init__

(self, env_name: str, num_nodes: int, timestamp: str, abci_model: str, policy: str, num_exper

src/utils/plotting.py:54

Method__init__

(self, mu_0: float = 0., kappa_0: float = 0.1, alpha_0: float = 50., beta_0: float = 25., sta

src/models/mechanisms.py:78

Method__init__

(self, train_x, train_y, likelihood, in_size: int)

src/models/mechanisms.py:220

Method__init__

(self, train_x, train_y, likelihood)

src/models/mechanisms.py:271

Method__init__

(self, in_size: int, static=False, linear=False)

src/models/mechanisms.py:311

Method__init__

(self, node_labels: List[str], linear: bool = False)

src/models/gp_model.py:28

Method__init__

Parameters ---------- node_labels : List[str] List of node labels.

src/models/graph_models.py:161

Method__init__

(self, node_labels: List[str], embedding_size: int, num_particles: int, std: float = 1.)

src/models/graph_models.py:371

Methodadj_mat_to_graph_key

(self, adj_mat: torch.Tensor)

src/models/graph_models.py:515

Methodcompare

(graph1, graph2)

src/models/graph_models.py:279

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:23

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:52

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:77

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:101

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:122

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:141

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:160

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:182

Methodconstruct_graph

(self, num_nodes: int)

src/environments/generic_environments.py:200

Methodedge_log_probs

(self, alpha: float = 1.)

src/models/graph_models.py:404

Methodedge_probs

Returns the matrix of edge probabilities. Returns ---------- torch.Tensor Matrix of edge probabilities.

src/models/graph_models.py:284

Methodexpected_noise_entropy

(self, prior_mode: bool = False)

src/models/mechanisms.py:175

Methodexpected_noise_entropy

(self, interventions, graph: nx.DiGraph, use_cache=False)

src/models/gp_model.py:154

Methodforward

Computes the mechanism output for a given input tensor. Must be implemented by all child classes. Parameters ----------

src/models/mechanisms.py:49

Methodforward

(self, inputs: torch.Tensor, prior_mode=False)

src/models/mechanisms.py:122

Methodforward

(self, x)

src/models/mechanisms.py:229

Methodforward

(self, x)

src/models/mechanisms.py:279

Methodforward

(self, inputs: torch.Tensor, prior_mode=False)

src/models/mechanisms.py:349

Methodforward

(ctx, adj_mat: torch.Tensor, round_edge_weights=False)

src/models/graph_models.py:566

Methodget_limit_graphs

(self)

src/models/graph_models.py:532

Methodhyperparam_log_prior

(self, prior_mode: bool = False)

src/models/mechanisms.py:284

Methodinit_design_process

(self, args: dict)

src/experimental_design/exp_designer_abci_dibs_gp.py:20

Methodinit_design_process

(self, args: dict)

src/experimental_design/exp_designer_abci_categorical_gp.py:22

Functioninit_plot_style

Initialize the plot style for pyplot.

src/utils/plotting.py:10

Methodload

(cls, path, num_workers: int = 1)

src/abci_categorical_gp.py:284

Methodload

(cls, path, num_workers: int = 1)

src/abci_dibs_gp.py:493

Methodload_param_dict

(self, param_dict)

src/abci_base.py:128

Methodload_param_dict

(cls, param_dict)

src/environments/environment.py:35

Methodload_param_dict

(self, param_dict)

src/models/mechanisms.py:201

Methodload_param_dict

(self, param_dict)

src/models/mechanisms.py:261

Methodload_param_dict

(self, param_dict)

src/models/mechanisms.py:307

Methodload_param_dict

(self, param_dict)

src/models/mechanisms.py:406

Methodload_param_dict

(self, param_dict)

src/models/gp_model.py:401

Methodload_param_dict

Sets the parameters of this class instance with the parameter values given in `param_dict`. Parameters ---------- pa

src/models/graph_models.py:354

Methodload_param_dict

(self, param_dict)

src/models/graph_models.py:553

Methodload_results

(self, stats_names: List[str], base_dir: str = '../results/')

src/utils/plotting.py:85

Methodmll

(self, inputs: torch.Tensor, targets: torch.Tensor, prior_mode=False, reduce=True)

src/models/mechanisms.py:153

src/environments/environment.py:28

Methodparam_dict

(self)

src/models/mechanisms.py:186

Methodparam_dict

(self)

src/models/mechanisms.py:255

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