causalnex.evaluation.roc_auc¶

causalnex.evaluation.roc_auc(bn, data, node)[source]¶

Build a report of the micro-average Receiver-Operating Characteristics (ROC), and the Area Under the ROC curve Micro-average computes roc_auc over all predictions for all states of node.

Parameters

bn (BayesianNetwork) – model to compute roc_auc.
data (pd.DataFrame) – test data that will be used to calculate ROC.
node (str) – name of the variable to generate the report for.

Return type

Tuple[List[Tuple[float, float]], float]

Returns

roc - auc tuple

roc (List[Tuple[float, float]]): list of [(fpr, tpr)] observations.
auc float: auc for the node predictions.

Example:

 from causalnex.structure import StructureModel
 from causalnex.network import BayesianNetwork

 sm = StructureModel()
 sm.add_edges_from([
                    ('rush_hour', 'traffic'),
                    ('weather', 'traffic')
                    ])
 bn = BayesianNetwork(sm)
 import pandas as pd
 data = pd.DataFrame({
                      'rush_hour': [True, False, False, False, True, False, True],
                      'weather': ['Terrible', 'Good', 'Bad', 'Good', 'Bad', 'Bad', 'Good'],
                      'traffic': ['heavy', 'light', 'heavy', 'light', 'heavy', 'heavy', 'heavy']
                      }
 bn = bn.fit_node_states_and_cpds(data)
 test_data = pd.DataFrame({
                         'rush_hour': [False, False, True, True],
                         'weather': ['Good', 'Bad', 'Good', 'Bad'],
                         'traffic': ['light', 'heavy', 'heavy', 'light']
                         })
 from causalnex.evaluation import roc_auc
 roc, auc = roc_auc(bn, test_data, "traffic")
 print(auc)
0.75