From fee7f513fe699b509475b5fa0c0f802d26d117f7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edyta=20Fr=C4=85szczak?= <efraszczak@motoinsight.com>
Date: Fri, 5 Jul 2024 10:59:53 +0200
Subject: [PATCH] jupyter and some documentation updates

---
 README.md | 36 ++++++++++++++++++++++++++++++++++++
 src/aa.py | 29 -----------------------------
 2 files changed, 36 insertions(+), 29 deletions(-)
 delete mode 100644 src/aa.py

diff --git a/README.md b/README.md
index 67498f8..8bad272 100644
--- a/README.md
+++ b/README.md
@@ -219,6 +219,42 @@ result, evaluation = source_detector.detect_sources_and_evaluate(G=G,
 print(evaluation)
 
 
+```
+
+For performing ensemble source detection, use 'EnsembleSourceDetector' class and configure it with 'EnsembleSourceDetectionConfig' object. This approach allows for seamless source detection and result evaluation.
+
+```python
+
+import networkx as nx
+
+from nsdlib.common.models import SourceDetectionConfig, \
+    EnsembleSourceDetectionConfig
+from nsdlib.source_detection import SourceDetector, EnsembleSourceDetector
+from nsdlib.taxonomies import NodeEvaluationAlgorithm, EnsembleVotingType
+
+G = nx.karate_club_graph()
+
+config_netsleuth = SourceDetectionConfig(
+    node_evaluation_algorithm=NodeEvaluationAlgorithm.NETSLEUTH,
+)
+
+config_degree = SourceDetectionConfig(
+    node_evaluation_algorithm=NodeEvaluationAlgorithm.CENTRALITY_DEGREE,
+)
+
+ensemble_config = EnsembleSourceDetectionConfig(
+    detection_configs=[config_netsleuth, config_degree],
+    voting_type=EnsembleVotingType.HARD,
+    classifier_weights=[0.5, 0.5],
+)
+
+source_detector = EnsembleSourceDetector(ensemble_config)
+
+result, evaluation = source_detector.detect_sources_and_evaluate(G=G,
+                                        IG=G, real_sources=[0,33])
+print(evaluation)
+
+
 ```
 
 - by importing and using specific method, each method has appropriate prefix to understand what is the purpose of it:
diff --git a/src/aa.py b/src/aa.py
deleted file mode 100644
index a7e462b..0000000
--- a/src/aa.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import networkx as nx
-
-import nsdlib as nsd
-from nsdlib import NodeEvaluationAlgorithm, PropagationReconstructionAlgorithm
-
-G = nx.karate_club_graph()
-IG = G.copy()
-IG.remove_nodes_from([10, 15, 20, 33])
-real_sources = [0, 8]
-
-EIG = nsd.reconstruct_propagation(G, IG, PropagationReconstructionAlgorithm.SBRP)
-
-outbreaks = nsd.identify_outbreaks(EIG, nsd.OutbreaksDetectionAlgorithm.LEIDEN)
-outbreaks_G = nsd.create_subgraphs_based_on_outbreaks(EIG, outbreaks)
-detected_sources = []
-for outbreak in outbreaks_G:
-    nodes_evaluation = nsd.evaluate_nodes(
-        outbreak, NodeEvaluationAlgorithm.CENTRALITY_AVERAGE_DISTANCE
-    )
-    outbreak_detected_source = max(nodes_evaluation, key=nodes_evaluation.get)
-    print(f"Outbreak: {outbreak}, Detected Source: {outbreak_detected_source}")
-    detected_sources.append(outbreak_detected_source)
-
-evaluation = nsd.compute_source_detection_evaluation(
-    G=EIG,
-    real_sources=real_sources,
-    detected_sources=detected_sources,
-)
-print(evaluation)