Subpackages

• Distributions
  • Dirichlet
    • log_norm_constant()
    • log_norm_constant_multidim()
    • log_pdf()
    • pdf()
    • pdf_multidim()
  • Discrete decomposable log linear
    • conditional_prob_dec()
    • est_parameters()
    • get_all_counts()
    • hyperconsistent_cliques()
    • ll_complete_set_ratio()
    • locals_to_joint_prob_table()
    • log_likelihood_partial()
    • prob_dec()
    • read_local_hyper_consistent_parameters_from_json_file()
    • sample()
    • sample_hyper_consistent_counts()
    • sample_hyper_consistent_parameters()
    • sample_joint_prob_table()
    • sample_prob_table()
  • Graph intra-class
    • cov_matrix()
    • sample()
  • Graph inverse-Wishart
    • sample()
  • Gaussian graphical model
    • gaussian_marginal_log_likelihood()
    • log_likelihood()
    • log_likelihood_partial()
  • Junction tree clustering
  • Matrix multivariate normal
    • sample()
  • Multivariate students-t
    • log_pdf()
  • Sequential junction tree distribution
    • CondUniformGivenSizeJTDistribution
      • CondUniformGivenSizeJTDistribution.ll()
      • CondUniformGivenSizeJTDistribution.log_ratio()
    • CondUniformJTDistribution
      • CondUniformJTDistribution.ll()
      • CondUniformJTDistribution.log_ratio()
    • GGMJTPosterior
      • GGMJTPosterior.get_json_model()
      • GGMJTPosterior.init_model()
      • GGMJTPosterior.init_model_from_json()
      • GGMJTPosterior.ll_diff()
      • GGMJTPosterior.log_likelihood()
      • GGMJTPosterior.log_likelihood_partial()
      • GGMJTPosterior.log_ratio()
    • LogLinearJTPosterior
      • LogLinearJTPosterior.get_json_model()
      • LogLinearJTPosterior.init_model()
      • LogLinearJTPosterior.init_model_from_json()
      • LogLinearJTPosterior.log_likelihood()
      • LogLinearJTPosterior.log_likelihood_diff()
      • LogLinearJTPosterior.log_ratio()
    • SequentialJTDistribution
      • SequentialJTDistribution.log_ratio()
    • UniformJTDistribution
      • UniformJTDistribution.log_likelihood()
      • UniformJTDistribution.log_likelihood_partial()
      • UniformJTDistribution.log_ratio()
  • Wishart distribution
    • log_norm_constant()
    • logpdf()
    • normalizing_constant()
• Graph
  • Decomposable graph
    • all_dec_graphs()
    • gen_AR_graph()
    • junction_tree()
    • n_junction_trees()
    • naive_decomposable_graph()
    • peo()
    • sample()
    • sample_dec_graph()
    • sample_random_AR_graph()
    • separators()
  • Empirical graph distribution
    • GraphDistribution
      • GraphDistribution.add_graph()
      • GraphDistribution.from_json()
      • GraphDistribution.heatmap()
      • GraphDistribution.mode()
      • GraphDistribution.pdf()
      • GraphDistribution.read_from_dict()
      • GraphDistribution.to_json()
  • Undirected graph
    • from_json_file()
    • graph_to_tuple()
    • hash_graph()
    • plot()
    • plot_adjmat()
    • replace_node()
    • true_distribution()
    • tuple_to_graph()
  • Connect/disconnect cliques
    • connect_a()
    • connect_b()
    • connect_c()
    • connect_d()
    • connect_logprob()
    • connect_move()
    • connect_select_subsets()
    • disconnect_a()
    • disconnect_b()
    • disconnect_c()
    • disconnect_d()
    • disconnect_get_CXCY()
    • disconnect_get_neighbors()
    • disconnect_logprob_a()
    • disconnect_logprob_bcd()
    • disconnect_move()
    • disconnect_select_subsets()
  • Junction tree
    • JunctionTree
      • JunctionTree.add_edge()
      • JunctionTree.add_edges_from()
      • JunctionTree.connected_component_vertices()
      • JunctionTree.connected_components()
      • JunctionTree.fresh_copy()
      • JunctionTree.get_separators()
      • JunctionTree.log_n_junction_trees()
      • JunctionTree.log_nu()
      • JunctionTree.remove_edge()
      • JunctionTree.remove_edges_from()
      • JunctionTree.remove_node()
      • JunctionTree.to_graph()
      • JunctionTree.tuple()
    • forest_induced_by_sep()
    • from_prufer()
    • graph()
    • induced_subtree_nodes()
    • is_junction_tree()
    • jt_to_prufer()
    • log_n_junction_trees()
    • log_n_junction_trees_update_ratio()
    • log_nu()
    • n_junction_trees()
    • n_junction_trees_update()
    • n_subtrees()
    • n_subtrees_aux()
    • peo()
    • random_tree_from_forest()
    • randomize()
    • randomize_at_sep()
    • sample()
    • separators()
    • subtree_induced_by_subset()
    • to_prufer()
  • Junction tree collapser
    • backward_jt_traj_sample()
    • log_count_origins()
    • log_pdf()
    • possible_origins()
    • possible_origins_and_sets()
    • sample()
    • sample_new()
    • support()
    • support_subtree_nodes()
  • Junction tree expander
    • get_subtree_nodes()
    • pdf()
    • sample()
    • sample_cond_on_subtree_nodes()
    • subtree_cond_pdf()
  • Subtree sampler
    • pdf()
    • random_subtree()
  • Graph trajectory
    • Trajectory
      • Trajectory.add_sample()
      • Trajectory.empirical_distribution()
      • Trajectory.from_json()
      • Trajectory.get_adjvec_trajectory()
      • Trajectory.graph_diff_trajectory_df()
      • Trajectory.log_likelihood()
      • Trajectory.maximum_likelihood_graph()
      • Trajectory.read_file()
      • Trajectory.set_sampling_method()
      • Trajectory.set_sequential_distribution()
      • Trajectory.set_time()
      • Trajectory.set_trajectory()
      • Trajectory.size()
      • Trajectory.to_json()
      • Trajectory.write_adjvec_trajectory()
      • Trajectory.write_file()

Distributions

Auxiliary functions

trilearn.auxiliary_functions.gen_prec_mat(graph, a)

trilearn.auxiliary_functions.get_marg_counts(full_data, subset)

Returns a contingency table in dictionary form.

Parameters:

• data (np.array) – The data in n x p form.

• subset (list) – The subset of interest

trilearn.auxiliary_functions.group_trajectories_by_setting(trajlist)

trilearn.auxiliary_functions.is_pos_def(x)

trilearn.auxiliary_functions.l1_loss(m1, m2)

L1 loss.

Parameters:

• m1 (Numpy array) – A matrix

• m1 – A matrix

Returns:

float

trilearn.auxiliary_functions.l2_loss(m1, m2)

L2 loss between m1 and m2.

Parameters:

• m1 (Numpy array) – A matrix

• m1 – A matrix

Returns:

float

trilearn.auxiliary_functions.plot_graph_traj_statistics(graph_traj, write_to_file=False)

trilearn.auxiliary_functions.plot_heatmap(heatmap, cbar=False, annot=False, xticklabels=1, yticklabels=1)

trilearn.auxiliary_functions.plot_matrix(m, filename, extension, title='Adjmat')

Plots a 2-dim numpy array as heatmap. :param m: matrix to plot. :type m: numpy array

trilearn.auxiliary_functions.plot_multiple_traj_statistics(trajs, burnin_end, write_to_file=False, annot=False, output_directory='./', file_extension='eps')

trilearn.auxiliary_functions.random_element_from_coll(A)

trilearn.auxiliary_functions.random_subset(A)

Draws a random subset of elements in a list, inclding the empty set.

Parameters:

A (list) –

Returns:

Subset of A.

Return type:

set

trilearn.auxiliary_functions.read_all_trajectories_in_dir(directory)

trilearn.auxiliary_functions.sample_classification_datasets(mus, covmats, n_samples_in_each_class)

trilearn.auxiliary_functions.spc1(true_graph, est_graph)

Takes 2 adjacency matrices.

trilearn.auxiliary_functions.tpr(true_graph, est_graph)

Calculates the True positive rate of an estimated adjacency matrix.

Graph predictive classification

P. Green & A. Thomas MH-sampler

trilearn.mh_greenthomas.sample_trajectories_ggm_parallel(dataframe, n_samples, randomize=[1000], D=None, delta=1.0, reps=1, output_directory='.', **args)

trilearn.mh_greenthomas.sample_trajectories_ggm_to_file(dataframe, n_samples, randomize=[1000], D=None, delta=1.0, reps=1, output_directory='.', **args)

trilearn.mh_greenthomas.sample_trajectories_loglin_parallel(dataframe, n_samples, randomize=[1000], pseudo_obs=[1.0], reps=1, output_directory='.', **args)

trilearn.mh_greenthomas.sample_trajectories_loglin_to_file(dataframe, n_samples, randomize=[1000], pseudo_obs=[1.0], reps=1, output_directory='.', **args)

trilearn.mh_greenthomas.sample_trajectory(n_samples, randomize, sd)

trilearn.mh_greenthomas.sample_trajectory_ggm(dataframe, n_samples, randomize=1000, D=None, delta=1.0, cache={}, **args)

trilearn.mh_greenthomas.sample_trajectory_loglin(dataframe, n_samples, pseudo_obs=1.0, randomize=1000, cache={}, **args)

trilearn.mh_greenthomas.sample_trajectory_uniform(n_samples, randomize=100, graph_size=5, cache={}, **args)

trilearn.mh_greenthomas.trajectory_to_file(n_samples, randomize, seqdist, dir='.', reseed=False)

Writes the trajectory of graphs generated by particle Gibbs to file.

Parameters:

• seq_dist (SequentialJTDistributions) – the distribution to be sampled from

• filename_prefix (string) – prefix to the filename

Returns:

Markov chain of underlying graphs of the junction trees sampled by pgibbs.

Return type:

Trajectory

trilearn.mh_greenthomas.trajectory_to_queue(n_samples, randomize, seqdist, queue, reseed=False)

Writes the trajectory of graphs generated by particle Gibbs to file.

Parameters:

• seq_dist (SequentialJTDistributions) – the distribution to be sampled from

• filename_prefix (string) – prefix to the filename

Returns:

Markov chain of underlying graphs of the junction trees sampled by pgibbs.

Return type:

Trajectory

Particle Gibbs

trilearn.pgibbs.sample_trajectories_ggm(dataframe, n_particles, n_samples, D=None, delta=1.0, alphas=[0.5], betas=[0.5], radii=[None], reset_cache=True, reps=1, **args)

trilearn.pgibbs.sample_trajectories_ggm_parallel(dataframe, n_particles, n_samples, D=None, delta=1.0, alphas=[0.5], betas=[0.5], radii=[None], reset_cache=True, reps=1, **args)

trilearn.pgibbs.sample_trajectories_ggm_to_file(dataframe, n_particles, n_samples, D=None, delta=1.0, alphas=[0.5], betas=[0.5], radii=[None], reset_cache=True, reps=1, output_directory='.', output_filename='trajectory.json', **args)

trilearn.pgibbs.sample_trajectories_loglin(dataframe, n_particles, n_samples, pseudo_observations=[1.0], alphas=[0.5], betas=[0.5], radii=[None], reset_cache=True, reps=1, **args)

trilearn.pgibbs.sample_trajectories_loglin_parallel(dataframe, n_particles, n_samples, pseudo_observations=[1.0], alphas=[0.5], betas=[0.5], radii=[None], reset_cache=True, reps=1, output_directory='.', **args)

trilearn.pgibbs.sample_trajectories_loglin_to_file(dataframe, n_particles, n_samples, pseudo_observations=[1.0], alphas=[0.5], betas=[0.5], radii=[None], reset_cache=True, reps=1, output_directory='.', output_filename='trajectory.json', **args)

trilearn.pgibbs.sample_trajectory(smc_N, alpha, beta, radius, n_samples, seq_dist, jt_traj=None, debug=False, reset_cache=True)

A particle Gibbs implementation for approximating distributions over junction trees.

Parameters:

• smc_N (int) – Number of particles in SMC in each Gibbs iteration

• n_samples (int) – Number of Gibbs iterations (samples)

• alpha (float) – sparsity parameter for the Christmas tree algorithm

• beta (float) – sparsity parameter for the Christmas tree algorithm

• radius (float) – defines the radius within which ned nodes are selected

• seq_dist (SequentialJTDistributions) – the distribution to be sampled from

Returns:

Markov chain of the underlying graphs of the junction trees sampled by pgibbs.

Return type:

Trajectory

trilearn.pgibbs.sample_trajectory_ggm(dataframe, n_particles, n_samples, D=None, delta=1.0, alpha=0.5, beta=0.5, radius=None, reset_cache=True, **args)

Particle Gibbs for approximating distributions over Gaussian graphical models.

Parameters:

• n_particles (int) – Number of particles in SMC in each Gibbs iteration

• n_samples (int) – Number of Gibbs iterations (samples)

• alpha (float) – sparsity parameter for the Christmas tree algorithm

• beta (float) – sparsity parameter for the Christmas tree algorithm

• radius (float) – defines the radius within which ned nodes are selected

• dataframe (np.matrix) – row matrix of data

• D (np.matrix) – matrix parameter for the hyper inverse wishart prior

• delta (float) – degrees of freedom for the hyper inverse wishart prior

• cache (dict) – cache for clique likelihoods

Returns:

Markov chain of the underlying graphs of the junction trees sampled by pgibbs.

Return type:

Trajectory

trilearn.pgibbs.sample_trajectory_loglin(dataframe, n_particles, n_samples, pseudo_obs=1.0, alpha=0.5, beta=0.5, radius=None, reset_cache=True, **args)

trilearn.pgibbs.trajectory_to_file(n_particles, n_samples, alpha, beta, radius, seqdist, node_labels, reset_cache=True, dir='.', output_filename='trajectory.csv', reseed=False)

Writes the trajectory of graphs generated by particle Gibbs to file.

Parameters:

• n_particles (int) – Number of particles in SMC in each Gibbs iteration

• n_samples (int) – Number of Gibbs iterations (samples)

• alpha (float) – sparsity parameter for the Christmas tree algorithm

• beta (float) – sparsity parameter for the Christmas tree algorithm

• radius (float) – defines the radius within which ned nodes are selected

• seq_dist (SequentialJTDistributions) – the distribution to be sampled from

• filename_prefix (string) – prefix to the filename

Returns:

Markov chain of underlying graphs of the junction trees sampled by pgibbs.

Return type:

Trajectory

trilearn.pgibbs.trajectory_to_queue(n_particles, n_samples, alpha, beta, radius, seqdist, queue, reset_cache=True, reseed=False)

Writes the trajectory of graphs generated by particle Gibbs to file.

Parameters:

• n_particles (int) – Number of particles in SMC in each Gibbs iteration

• n_samples (int) – Number of Gibbs iterations (samples)

• alpha (float) – sparsity parameter for the Christmas tree algorithm

• beta (float) – sparsity parameter for the Christmas tree algorithm

• radius (float) – defines the radius within which ned nodes are selected

• seq_dist (SequentialJTDistributions) – the distribution to be sampled from

• filename_prefix (string) – prefix to the filename

Returns:

Markov chain of underlying graphs of the junction trees sampled by pgibbs.

Return type:

Trajectory

Stochastic set process

trilearn.set_process.backward_order_neigh_log_prob(from_order, to_order, radius, maxradius)

Probability of generating order from_order from the larger order to_order under the restriction that no hole greater than radius is created.

trilearn.set_process.backward_order_neigh_set(from_order, radius, maxradius)

Returns the list of nodes that can be removed from from_order (the greater order).

trilearn.set_process.backward_perm_traj_sample(p, radius)

Samples a permutation tajectory with maximum p indices.

trilearn.set_process.gen_backward_order_neigh(from_order, radius, maxradius)

Returns: A permutation wit one less element than fromm_order

trilearn.set_process.gen_order_neigh(from_order, radius, total_set)

Returns a list with one more element than from_order

such that the new element is within the radius and belongs to total_set.

Parameters:

• from_order (list) – list of elements

• radius (int) – specifies the radius within which the new element can be taken

• total_set (list) – the full set of elements

Returns:

numpy array

trilearn.set_process.order_neigh_log_prob(from_order, to_order, radius, total_set)

Since the perm trajectory is Markovian, the ratio becomes only the probability.

trilearn.set_process.order_neigh_set(current_order, radius, total_set)

The set of neighbors of current_order with maximal distance radius (of total_set current_order).

SMC

Sequential Monte Carlo sampler for junction tree distributions.

trilearn.smc.approximate(N, alpha, beta, radius, seq_dist, debug=False, neig_set_cache={})

Sequential Monte Carlo for junction trees using the christmas tree algorithm as proposal kernel.

Parameters:

• N (int) – number

• alpha (float) – sparsity parameter for the Christmas tree algorithm

• beta (float) – sparsity parameter for the Christmas tree algorithm

• radius (float) – defines the radius within which ned nodes are selected

• seqdist (SequentialJTDistributions) – the distribution to be sampled from

Returns:

(new_trees, log_w)

References:

trilearn.smc.approximate_cond(N, alpha, beta, radius, seq_dist, T_cond, perm_cond, debug=False, neig_set_cache={})

SMC an junction trees conditioned on the trajectories T_cond and perm_cond.

trilearn.smc.est_dec_max_clique_size(order, n_particles, alpha=0.5, beta=0.5, n_smc_estimates=1, debug=False)

trilearn.smc.est_log_norm_consts(order, n_particles, sequential_distribution, alpha=0.5, beta=0.5, n_smc_estimates=1, debug=False)

trilearn.smc.est_n_dec_graphs(order, n_particles, alpha=0.5, beta=0.5, n_smc_estimates=1, debug=False)

trilearn.smc.get_smc_trajs(Is)

This method is made for visualizing the collapsing in SMC.

trilearn.smc.get_traj(n, i, Is)

trilearn.smc.smc_approximate_ggm(N, alpha, beta, radius, X, D, delta)

trilearn.smc.smc_ggm_graphs(N, alpha, beta, radius, X, D, delta)

trilearn.smc.uniform_dec_maxl_clique_size_samples(order, n_particles, alpha=0.5, beta=0.5, debug=False)

trilearn.smc.uniform_dec_samples(order, n_particles, alpha=0.5, beta=0.5, debug=False)