bnlearn.bnlearn

Bayesian techniques for structure learning, parameter learning, inference and sampling.

bnlearn.bnlearn.adjmat2dict(adjmat)

Convert adjacency matrix to dict.

Parameters:

adjmat (pd.DataFrame) – Adjacency matrix.

Returns:

graph – Graph.

Return type:

dict

bnlearn.bnlearn.adjmat2vec(adjmat, min_weight=1)

Convert adjacency matrix into vector with source and target.

Parameters:

• adjmat (pd.DataFrame()) – Adjacency matrix.

• min_weight (float) – edges are returned with a minimum weight.

• absolute (bool) – True: Make all values absolute False: Keep as is

Returns:

nodes that are connected based on source and target

Return type:

pd.DataFrame()

Examples

>>> import bnlearn as bn
>>> source=['Cloudy','Cloudy','Sprinkler','Rain']
>>> target=['Sprinkler','Rain','Wet_Grass','Wet_Grass']
>>> adjmat = vec2adjmat(source, target)
>>> vector = bn.adjmat2vec(adjmat)

bnlearn.bnlearn.check_model(DAG, verbose=3)

Check if the CPDs associated with the nodes are consistent.

Parameters:

• DAG (Object.) – Object containing CPDs.

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Return type:

None.

bnlearn.bnlearn.compare_networks(model_1, model_2, pos=None, showfig=True, figsize=(15, 8), verbose=3)

Compare networks of two models.

Parameters:

• model_1 (dict) – Results of model 1.

• model_2 (dict) – Results of model 2.

• pos (graph, optional) – Coordinates of the network. If there are provided, the same structure will be used to plot the network.. The default is None.

• showfig (bool, optional) – plot figure. The default is True.

• figsize (tuple, optional) – Figure size.. The default is (15,8).

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

scores : Score of differences between the two input models. adjmat_diff : Adjacency matrix depicting the differences between the two input models.

Return type:

tuple containing (scores, adjmat_diff)

bnlearn.bnlearn.compute_logp(p_value)

bnlearn.bnlearn.convert_edges_with_time_slice(edges, time_slice=0)

bnlearn.bnlearn.dag2adjmat(model, verbose=3)

Convert model into adjacency matrix.

Parameters:

• model (object) – Model object.

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

adjacency matrix.

Return type:

pd.DataFrame

Examples

>>> import bnlearn as bn
>>> # Load DAG
>>> DAG = bn.import_DAG('Sprinkler')
>>> # Extract edges from model and store in adjacency matrix
>>> adjmat=bn.dag2adjmat(DAG['model'])

bnlearn.bnlearn.df2onehot(df, y_min=10, perc_min_num=0.8, dtypes='pandas', excl_background=None, verbose=3)

Convert dataframe to one-hot matrix.

Parameters:

• df (pd.DataFrame()) – Input dataframe for which the rows are the features, and colums are the samples.

• dtypes (list of str or 'pandas', optional) – Representation of the columns in the form of [‘cat’,’num’]. By default the dtype is determiend based on the pandas dataframe.

• y_min (int [0..len(y)], optional) – Minimal number of sampels that must be present in a group. All groups with less then y_min samples are labeled as _other_ and are not used in the enriching model. The default is None.

• perc_min_num (float [None, 0..1], optional) – Force column (int or float) to be numerical if unique non-zero values are above percentage. The default is None. Alternative can be 0.8

• verbose (int, optional) – Print message to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

One-hot dataframe.

Return type:

pd.DataFrame()

bnlearn.bnlearn.get_edge_properties(model, color='#000000', weight=1, minscale=1, maxscale=5, verbose=3)

Collect edge properties.

Parameters:

• model (dict) – dict containing (initialized) model.

• color (str, (Default: '#000000')) – The default color of the edges.

• weight (float, (Default: 1)) – The default weight of the edges.

• minscale (float, (Default: 1)) – The minimum weight of the edge in case of test statisics are used.

• maxscale (float, (Default: 10)) – The maximum weight of the edge in case of test statisics are used.

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

Edge properties.

Return type:

dict.

Examples

>>> # Example 1:
>>> import bnlearn as bn
>>> edges = [('A', 'B'), ('A', 'C'), ('A', 'D')]
>>>
>>> # Create DAG and store in model
>>> model = bn.make_DAG(edges)
>>>
>>> # Adjust the properties
>>> edge_properties = bn.get_edge_properties(model)
>>> edge_properties[('A', 'B')]['weight']=10
>>> edge_properties[('A', 'B')]['color']='#8A0707'
>>>
>>> # Make plot
>>> fig = bn.plot(model, interactive=False, edge_properties=edge_properties)

>>> # Example 2:
>>> # Load asia DAG
>>> df = bn.import_example(data='asia')
>>>
>>> # Structure learning of sampled dataset
>>> model = bn.structure_learning.fit(df)
>>>
>>> # Compute edge weights based on chi_square test statistic
>>> model = bn.independence_test(model, df, test='chi_square')
>>>
>>> # Get the edge properties
>>> edge_properties = bn.get_edge_properties(model)
>>> # Make adjustments
>>> edge_properties[('tub', 'either')]['color']='#8A0707'
>>>
>>> # Make plot
>>> fig = bn.plot(model, interactive=True, edge_properties=edge_properties)

bnlearn.bnlearn.get_node_properties(model, node_color='#ADD8E6', node_size=None, verbose=3)

Collect node properties.

Parameters:

• model (dict) – dict containing (initialized) model.

• node_color (str, (Default: '#000000')) – The default color of the edges.

• node_size (float, (Default: 1)) – The default weight of the edges.

• 3. (Print progress to screen. The default is) – 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

Node properties.

Return type:

dict.

Examples

>>> import bnlearn as bn
>>> edges = [('A', 'B'), ('A', 'C'), ('A', 'D')]
>>> # Create DAG and store in model
>>> model = bn.make_DAG(edges)
>>> node_properties = bn.get_node_properties(model)
>>> # Adjust the properties
>>> node_properties['A']['node_size']=100
>>> node_properties['A']['node_color']='#000000'
>>> # Make plot
>>> fig = bn.plot(model, interactive=False, node_properties=node_properties)
>>>
>>> # Example: Specify all nodes
>>> node_properties = bn.get_node_properties(model, node_size=10, node_color='#000000')
>>> bn.plot(model, interactive=True, node_properties=node_properties)

bnlearn.bnlearn.has_valid_time_slice(edges)

Example usage edges = [(‘A’, ‘B’), (‘A’, ‘C’), (‘A’, ‘D’)] edgest = [((‘A’, 0), (‘B’, 0)), ((‘A’, 0), (‘C’, 0)), ((‘A’, 0), (‘D’, 0))]

# Check edges has_valid_time_slice(edges)

# Check edges has_valid_time_slice(edgest)

bnlearn.bnlearn.import_DAG(filepath='sprinkler', CPD=True, checkmodel=True, verbose=3)

Import Directed Acyclic Graph.

Parameters:

• filepath (str, (default: sprinkler)) – Pre-defined examples are depicted below, or provide the absolute file path to the .bif model file.. The default is ‘sprinkler’. ‘sprinkler’, ‘alarm’, ‘andes’, ‘asia’, ‘sachs’, ‘filepath/to/model.bif’,

• CPD (bool, optional) – Directed Acyclic Graph (DAG). The default is True.

• checkmodel (bool) – Check the validity of the model. The default is True

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

model : BayesianNetwork adjmat : Adjacency matrix

Return type:

dict containing model and adjmat.

Examples

>>> import bnlearn as bn
>>> model = bn.import_DAG('sprinkler')
>>> fig = bn.plot(model)

bnlearn.bnlearn.import_example(data='sprinkler', url=None, sep=',', n=10000, verbose=3)

Load example dataset.

Parameters:

• data (str, (default: sprinkler)) –

  Pre-defined examples.

  • ’sprinkler’

  • ’alarm’

  • ’andes’

  • ’asia’

  • ’sachs’

  • ’water’

  Continous data sets:

  • ’auto_mpg’

• n (int, optional) – Number of samples to generate. The default is 10000.

• verbose (int, (default: 3)) – Print progress to screen. 0: None, 1: Error, 2: Warning, 3: Info, 4: Debug, 5: Trace

Returns:

df

Return type:

pd.DataFrame()

bnlearn.bnlearn.independence_test(model, df, test='chi_square', alpha=0.05, prune=False, verbose=3)

Compute edge strength using test statistic.

Description

Compute the edge strength using a statistical test of independence based using the model structure (DAG) and the data. For the pairs in the DAG (either by structure learning or user-defined), an statistical test is performed. Any two variables are associated if the test’s p-value < significance_level.

param model:

The (learned) model which needs to be tested.

type model:

Instance of bnlearn.structure_learning.

param df:

The dataset against which to test the model structure.

type df:

pandas.DataFrame instance

param test:

The statistical test to compute associations.

• chi_square

• g_sq

• log_likelihood

• freeman_tuckey

• modified_log_likelihood

• neyman

• cressie_read

type test:

str or function

param alpha:

A value between 0 and 1. If p_value < significance_level, the variables are considered uncorrelated.

type alpha:

float

param prune:

True: Keep only edges that are significant (<=alpha) based on the independence test.

type prune:

bool (default: False)

returns:

df – The dataset against which to test the model structure.

rtype:

pandas.DataFrame instance

Examples

>>> import bnlearn as bn
>>> df = bn.import_example(data='asia')
>>> # Structure learning of sampled dataset
>>> model = bn.structure_learning.fit(df)
>>> # Compute arc strength
>>> model = bn.independence_test(model, df, test='chi_square')
>>> print(model['independence_test'])
>>>
>>>       source     target  stat_test       p_value  chi_square  dof
>>> 0     Cloudy  Sprinkler       True  8.383708e-53  233.906474    1
>>> 1  Sprinkler  Wet_Grass       True  1.196919e-23  100.478455    1
>>> 2       Rain     Cloudy       True  1.080606e-87  394.061629    1
>>> 3       Rain  Wet_Grass       True  3.886511e-64  285.901702    1

bnlearn.bnlearn.load(filepath='bnlearn_model.pkl', verbose=3)

Load learned model.

Parameters:

• filepath (str) – Pathname to stored pickle files.

• verbose (int, optional) – Show message. A higher number gives more information. The default is 3.

Return type:

Object.

bnlearn.bnlearn.make_DAG(DAG, CPD=None, methodtype='bayes', checkmodel=True, verbose=3)

Create Directed Acyclic Graph based on list.

Parameters:

• DAG (list) – list containing source and target in the form of [(‘A’,’B’), (‘B’,’C’)].

• CPD (list, array-like) – Containing TabularCPD for each node.

• methodtype (str (default: 'bayes')) –

  • ‘bayes’: Bayesian model

  • ’nb’ or ‘naivebayes’: Special case of Bayesian Model where the only edges in the model are from the feature variables to the dependent variable. Or in other words, each tuple should start with the same variable name such as: edges = [(‘A’, ‘B’), (‘A’, ‘C’), (‘A’, ‘D’)]

  • ’markov’: Markov model

  • ’DBN’: DynamicBayesianNetwork

• checkmodel (bool) – Check the validity of the model. The default is True

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

• ‘adjmat’: Adjacency matrix

• ’model’: pgmpy.models

• ’methodtype’: methodtype

• ’model_edges’: Edges

Return type:

dict keys

Examples

>>> import bnlearn as bn
>>> edges = [('A', 'B'), ('A', 'C'), ('A', 'D')]
>>> DAG = bn.make_DAG(edges, methodtype='naivebayes')
>>> fig = bn.plot(DAG)

Examples

>>> import bnlearn as bn
>>> edges = [('A', 'B'), ('A', 'C'), ('A', 'D')]
>>> DAG = bn.make_DAG(edges, methodtype='markov')
>>> fig = bn.plot(DAG)

Examples

>>> import bnlearn as bn
>>> edges = [('A', 'B'), ('A', 'C'), ('A', 'D')]
>>> DAG = bn.make_DAG(edges, methodtype='DBN')
>>> fig = bn.plot(DAG)
>>>
>>> # Set custom timeslice
>>> edges = [(('A', 0), ('B', 1)), (('A', 0), ('C', 0)), (('A', 0), ('D', 0))]
>>> DAG = bn.make_DAG(edges, methodtype='DBN')
>>> fig = bn.plot(DAG)

bnlearn.bnlearn.plot(model, pos=None, scale=1, interactive=False, title='bnlearn Directed Acyclic Graph (DAG)', node_color=None, node_size=None, node_properties=None, edge_properties=None, edge_labels='weight', params_interactive={'bgcolor': '#ffffff', 'figsize': (1500, 800), 'filepath': None, 'font_color': '#000000', 'minmax_distance': [100, 250], 'notebook': False, 'show_slider': True}, params_static={'alpha': 0.8, 'arrowsize': 20, 'arrowstyle': '-|>', 'dpi': 200, 'edge_alpha': 0.8, 'facecolor': 'white', 'figsize': (10, 10), 'font_color': '#000000', 'font_family': 'sans-serif', 'font_size': 10, 'height': None, 'layout': 'graphviz_layout', 'maxscale': 5, 'minscale': 1, 'node_shape': 'o', 'showplot': True, 'visible': True, 'width': None}, verbose=3)

Plot the learned stucture.

Parameters:

• model (dict) – Learned model from the .fit() function.

• pos (graph, optional) – Coordinates of the network. If there are provided, the same structure will be used to plot the network.. The default is None.

• scale (int, optional) – Scaling parameter for the network. A larger number will linearily increase the network.. The default is 1.

• interactive (Bool, (default: True)) – True: Interactive web-based graph. False: Static plot

• title (str, optional) – Title for the plots.

• node_color (str, optional) – Color each node in the network using a hex-color, such as ‘#8A0707’

• node_size (int, optional) – Set the node size for each node in the network. The default size when using static plolts is 800, and for interactive plots it is 10.

• node_properties (dict (default: None)) –

  Dictionary containing custom node_color and node_size parameters for the network. The node properties can easily be retrieved using the function: node_properties = bn.get_node_properties(model) node_properties = {‘node1’:{‘node_color’:’#8A0707’,’node_size’:10},

  ’node2’:{‘node_color’:’#000000’,’node_size’:30}}

• edge_properties (dict (default: None)) – Dictionary containing custom node_color and node_size parameters for the network. The edge properties can be retrieved with: edge_properties = bn.get_edge_properties(model)

• edge_labels (Bool (default: True)) – None: Do not show edge labels ‘weight’: Show the input values in the array ‘pvalue’: Show the edge pvalues (this requires doing the independence_test: model = bn.independence_test(model, df)

• params_interactive (dict.) – Dictionary containing various settings in case of creating interactive plots.

• params_static (dict.) – Dictionary containing various settings in case of creating static plots. layout: ‘graphviz_layout’, ‘spring_layout’, ‘planar_layout’, ‘shell_layout’, ‘spectral_layout’, ‘pydot_layout’, ‘circular_layout’, ‘spring_layout’, ‘random_layout’, ‘bipartite_layout’, ‘multipartite_layout’,

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: Error, 2: Warning, 3: Info (default), 4: Debug, 5: Trace

Returns:

poslist.

Positions of the nodes.

GGraph.

Graph model

node_properties: dict.

Node properties.

Return type:

dict containing pos and G

Examples

>>> import bnlearn as bn
>>>
>>> # Load asia DAG
>>> df = bn.import_example(data='asia')
>>>
>>> # Structure learning of sampled dataset
>>> model = bn.structure_learning.fit(df)
>>>
>>> # plot static
>>> fig = bn.plot(model)
>>>
>>> # plot interactive
>>> fig = bn.plot(model, interactive=True)
>>>
>>> # plot interactive with various settings
>>> fig = bn.plot(model, interactive=True, node_color='#8A0707', node_size=35, params_interactive = {'figsize':(800, 600), 'font_color': 'node_color', 'bgcolor':'#0f0f0f0f'})
>>>
>>> # plot with node properties
>>> node_properties = bn.get_node_properties(model)
>>> # Make some changes
>>> node_properties['xray']['node_color']='#8A0707'
>>> node_properties['xray']['node_size']=50
>>> # Plot
>>> fig = bn.plot(model, interactive=True, node_properties=node_properties)
>>>

bnlearn.bnlearn.plot_graphviz(model, edge_labels='weight', params={'detect_cycle': False, 'ignore_shape': False, 'path': None, 'path_color': None, 'prediction_coefs': None, 'prediction_feature_importance': None, 'prediction_feature_indices': None, 'prediction_line_color': 'red', 'prediction_target_label': 'Y(pred)'}, verbose=3)

Plot a causal or Bayesian network using Graphviz based on an adjacency matrix.

This function visualizes the causal or Bayesian model structure in model[‘adjmat’] using the Graphviz library. The user can customize various aspects of the plot such as colors, path highlights, and edge labels.

Parameters:

• model (dict) – A dictionary containing the network model. Must include an adjacency matrix under the key “adjmat” and when the independence_test is performed, the -log10(pvalues) will be shown on the edges to highlight the strength of the significant relationships between variables.

• edge_labels (Bool (default: True)) – None: Do not show edge labels ‘weight’: Show the input values in the array ‘pvalue’: Show the edge pvalues (this requires doing the independence_test: model = bn.independence_test(model, df)

• params (dict, optional) –

  A dictionary of parameters to control the visualization. Default values are provided, but users can modify specific parameters. The available options are:

  • prediction_feature_indices (list or None): The feature indices to be used in the prediction path, or None for no specific feature highlights.

  • prediction_target_label (str): Label for the prediction target node.

  • prediction_line_color (str): Color for the prediction path in the graph.

  • prediction_coefs (list or None): Coefficients for the prediction line (optional).

  • prediction_feature_importance (list or None): Feature importance values for features in the prediction path.

  • path (list or None): Specific path between nodes to highlight, or None to ignore.

  • path_color (str or None): Color of the path to highlight, or None.

  • detect_cycle (bool): If True, attempt to detect cycles in the graph.

  • ignore_shape (bool): If True, ignore the node shapes when plotting.

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

dot_graph – A Graphviz Source object containing the DOT source code for the graph. This can be rendered or exported. Returns None if no edges are found in the adjacency matrix.

Return type:

graphviz.Source or None

Notes

• This function depends on the graphviz and lingam.utils libraries.

• If the independence_test key is present in model, the graph’s edges are updated based on the p-values from the test, and edges are weighted accordingly.

• If the Graphviz executable is not set in the system environment, it will attempt to set it using the setgraphviz function.

Examples

>>> # Import library
>>> import bnlearn as bn
>>>
>>> # Load example mixed dataset
>>> df = bn.import_example(data='sprinkler')
>>>
>>> # Structure learning
>>> model = bn.structure_learning.fit(df)
>>>
>>> # Independence test
>>> model = bn.independence_test(model, df, test='chi_square', prune=False)
>>>
>>> # Dot graph
>>> dotgraph = bn.plot_graphviz(model)
>>> dotgraph
>>> # Create pdf
>>> dotgraph.view(filename=r'c:/temp/dotgraph')

bnlearn.bnlearn.predict(model, df, variables, to_df=True, method='max', verbose=3)

Predict on data from a Bayesian network.

Description

The inference on the dataset is performed sample-wise by using all the available nodes as evidence (obviously, with the exception of the node whose values we are predicting). The states with highest probability are returned.

param model:

An object of class from bn.fit.

type model:

Object

param df:

Each row in the DataFrame will be predicted

type df:

pd.DataFrame

param variables:

The label(s) of node(s) to be predicted.

type variables:

str or list of str

param to_df:

The output is converted to dataframe output. Note that this heavily impacts the speed.

type to_df:

Bool, (default is True)

param method:

The method that is used to select the for the inferences. ‘max’ : Return the variable values based on the maximum probability. None : Returns all Probabilities

type method:

str

param verbose:

Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

type verbose:

int, optional

returns:

P – Predict() returns a dict with the evidence and states that resulted in the highest probability for the input variable.

rtype:

dict or DataFrame

Examples

>>> import bnlearn as bn
>>> model = bn.import_DAG('sprinkler')
>>>
>>> # Make single inference
>>> query = bn.inference.fit(model, variables=['Rain', 'Cloudy'], evidence={'Wet_Grass':1})
>>> print(query)
>>> print(bn.query2df(query))
>>>
>>> # Lets create an example dataset with 100 samples and make inferences on the entire dataset.
>>> df = bn.sampling(model, n=1000)
>>>
>>> # Each sample will be assesed and the states with highest probability are returned.
>>> Pout = bn.predict(model, df, variables=['Rain', 'Cloudy'])
>>>
>>> print(Pout)
>>> #     Cloudy  Rain         p
>>> # 0        0     0  0.647249
>>> # 1        0     0  0.604230
>>> # ..     ...   ...       ...
>>> # 998      0     0  0.604230
>>> # 999      1     1  0.878049

bnlearn.bnlearn.print_CPD(DAG, checkmodel=False, verbose=3)

Print DAG-model to screen.

Parameters:

• DAG (pgmpy.models.BayesianNetwork) – model of the DAG.

• checkmodel (bool) – Check the validity of the model. The default is True

Returns:

Dictionary containing the CPDs.

Return type:

dict

Examples

>>> # Import library
>>> import bnlearn as bn
>>>
>>> # Load example dataset
>>> df = bn.import_example('sprinkler')
>>>
>>> # Set edges
>>> edges = [('Cloudy', 'Sprinkler'),
>>>          ('Cloudy', 'Rain'),
>>>          ('Sprinkler', 'Wet_Grass'),
>>>          ('Rain', 'Wet_Grass')]
>>>
>>> # Make the actual Bayesian DAG
>>> DAG = bn.make_DAG(edges)
>>> model = bn.parameter_learning.fit(DAG, df)
>>>
>>> # Gather and store the CPDs in dictionary contaning dataframes for each node.
>>> CPD = bn.print_CPD(model, verbose=0)
>>>
>>> CPD['Cloudy']
>>> CPD['Rain']
>>> CPD['Wet_Grass']
>>> CPD['Sprinkler']
>>>
>>> # Print nicely
>>> from tabulate import tabulate
>>> print(tabulate(CPD['Cloudy'], tablefmt="grid", headers="keys"))

bnlearn.bnlearn.query2df(query, variables=None, groupby=None, verbose=3)

Convert query from inference model to a dataframe.

Parameters:

• query (Object from the inference model.) – Convert query object to a dataframe.

• variables (list) – Order or select variables.

• groupby (list of strings (default: None)) – The query is grouped on the variable name by taking the maximum P value for each catagory.

Returns:

df – Dataframe with inferences.

Return type:

pd.DataFrame()

bnlearn.bnlearn.sampling(DAG, n=1000, methodtype='bayes', verbose=0)

Generate sample(s) using the joint distribution of the network.

Parameters:

• DAG (dict) – Contains model and the adjmat of the DAG.

• methodtype (str (default: 'bayes')) –

  • ‘bayes’: Forward sampling using Bayesian.

  • ’gibbs’ : Gibbs sampling.

• n (int, optional) – Number of samples to generate. The default is 1000.

• verbose (int, optional) – Print progress to screen. The default is 3. 0: None, 1: ERROR, 2: WARN, 3: INFO (default), 4: DEBUG, 5: TRACE

Returns:

df – Dataframe containing sampled data from the input DAG model.

Return type:

pd.DataFrame().

Example

>>> # Example 1
>>>
>>> # Import library
>>> import bnlearn as bn
>>> # Load DAG with model
>>> DAG = bn.import_DAG('sprinkler')
>>> # Sampling
>>> df = bn.sampling(DAG, n=1000, methodtype='bayes')
>>>
>>> # Example 2:
>>>
>>> # Load example dataset
>>> df = bn.import_example('sprinkler')
>>> edges = [('Cloudy', 'Sprinkler'),
>>>         ('Cloudy', 'Rain'),
>>>         ('Sprinkler', 'Wet_Grass'),
>>>         ('Rain', 'Wet_Grass')]
>>>
>>> # Make the actual Bayesian DAG
>>> DAG = bn.make_DAG(edges, verbose=3, methodtype='bayes')
>>> # Fit model
>>> model = bn.parameter_learning.fit(DAG, df, verbose=3, methodtype='bayes')
>>> # Sampling using gibbs
>>> df = bn.sampling(model, n=100, methodtype='gibbs', verbose=0)

bnlearn.bnlearn.save(model, filepath='bnlearn_model.pkl', overwrite=False, verbose=3)

Save learned model in pickle file.

Parameters:

• filepath (str, (default: 'bnlearn_model.pkl')) – Pathname to store pickle files.

• overwrite (bool, (default=False)) – Overwite file if exists.

• verbose (int, optional) – Show message. A higher number gives more informatie. The default is 3.

Returns:

bool – Status whether the file is saved.

Return type:

[True, False]

bnlearn.bnlearn.structure_scores(model, df, scoring_method=['k2', 'bic', 'bdeu', 'bds'], verbose=3, **kwargs)

Compute structure scores.

Each model can be scored based on its structure. However, the score doesn’t have very straight forward interpretebility but can be used to compare different models. A higher score represents a better fit. This method only needs the model structure to compute the score. The structure score functionality can be found here: bnlearn.bnlearn.structure_scores().

Parameters:

• model (The bnlearn instance such as pgmpy.base.DAG or pgmpy.models.BayesianNetwork) – The model whose score needs to be computed.

• df (pd.DataFrame instance) – The dataset against which to score the model.

• scoring_method (str ( k2 | bdeu | bds | bic )) – The following four scoring methods are supported currently: 1) K2Score 2) BDeuScore 3) BDsScore 4) BicScore

• kwargs (kwargs) – Any additional parameters parameters that needs to be passed to the scoring method.

Returns:

Model score – A score value for the model.

Return type:

float

Examples

>>> import bnlearn as bn
>>> # Load example dataset
>>>
>>> df = bn.import_example('sprinkler')
>>> edges = [('Cloudy', 'Sprinkler'), ('Cloudy', 'Rain'), ('Sprinkler', 'Wet_Grass'), ('Rain', 'Wet_Grass')]
>>>
>>> # Make the Bayesian DAG
>>> DAG = bn.make_DAG(edges)
>>> model = bn.parameter_learning.fit(DAG, df)
>>>
>>> # Structure scores are stored in the model dictionary.
>>> model['structure_scores']
>>>
>>> # Compute the structure score for as specific scoring-method.
>>> bn.structure_scores(model, df, scoring_method="bic")

bnlearn.bnlearn.to_bayesiannetwork(model, verbose=3)

Convert adjacency matrix to BayesianNetwork.

Convert a adjacency to a Bayesian model. This is required as some of the functionalities, such as structure_learning output a DAGmodel. If the output of structure_learning is provided, the adjmat is extracted and processed.

Parameters:

model (pd.DataFrame()) – Adjacency matrix.

Raises:

Exception – The input should not be None and if a model (as dict) is provided, the key ‘adjmat’ should be included.

Returns:

BayesianNetwork – BayesianNetwork that can be used in parameter_learning.fit.

Return type:

Object

bnlearn.bnlearn.to_undirected(adjmat)

Transform directed adjacency matrix to undirected.

Parameters:

adjmat (np.array()) – Adjacency matrix.

Returns:

Directed adjacency matrix – Converted adjmat with undirected edges.

Return type:

pd.DataFrame()

bnlearn.bnlearn.topological_sort(adjmat, start=None)

Topological sort.

Description

Get nodes list in the topological sort order.

param adjmat:

Adjacency matrix.

type adjmat:

pd.DataFrame or bnlearn object.

param start:

Start position. The default is None and the whole network is examined.

type start:

str, optional

returns:

Topological sort order.

rtype:

list

Example

import bnlearn as bn DAG = bn.import_DAG(‘sprinkler’, verbose=0) bn.topological_sort(DAG, ‘Rain’) bn.topological_sort(DAG)

References

https://stackoverflow.com/questions/47192626/deceptively-simple-implementation-of-topological-sorting-in-python

bnlearn.bnlearn.vec2adjmat(source, target, weights=None, symmetric: bool = True, aggfunc='sum', verbose=3) → DataFrame

Convert source and target into adjacency matrix.

Parameters:

• source (list) – The source node.

• target (list) – The target node.

• weights (list of int) – The Weights between the source-target values

• symmetric (bool, optional) – Make the adjacency matrix symmetric with the same number of rows as columns. The default is True.

• aggfunc (str, optional) – Aggregate function in case multiple values exists for the same relationship. ‘sum’ (default)

Returns:

adjacency matrix.

Return type:

pd.DataFrame

Examples

>>> source = ['Cloudy', 'Cloudy', 'Sprinkler', 'Rain']
>>> target = ['Sprinkler', 'Rain', 'Wet_Grass', 'Wet_Grass']
>>> vec2adjmat(source, target)
>>>
>>> weights = [1, 2, 1, 3]
>>> vec2adjmat(source, target, weights=weight)

bnlearn.bnlearn.vec2df(source, target, weights=None)

Convert source-target edges into sparse dataframe.

Description

Convert edges between source and taget into a dataframe based on the weight. A weight of 2 will result that a row with the edge is created 2x.

param source:

The source node.

type source:

array-like

param target:

The target node.

type target:

array-like

param weights:

The Weights between the source-target values

type weights:

array-like of int

rtype:

pd.DataFrame

Examples

>>> # Example 1
>>> import bnlearn as bn
>>> source=['Cloudy','Cloudy','Sprinkler','Rain']
>>> target=['Sprinkler','Rain','Wet_Grass','Wet_Grass']
>>> weights=[1,2,1,3]
>>> df = bn.vec2df(source, target, weights=weights)

>>> # Example 2
>>> import bnlearn as bn
>>> vec = bn.import_example("stormofswords")
>>> df = bn.vec2df(vec['source'], vec['target'], weights=vec['weight'])

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