updated example

examples/sampling_example.py

@@ -3,7 +3,7 @@
 from ontosample.classic_samplers import *
 from ontosample.lpc_samplers import *
 from ontosample.lpf_samplers import *
-from ontolearn.knowledge_base import KnowledgeBase
+from ontolearn_light.knowledge_base import KnowledgeBase
 
 # 0. Load json that stores the learning problem
 with open("learning_problems/uncle_lp.json") as json_file:
@@ -14,16 +14,18 @@
 print(f'Initial individuals: {kb.individuals_count()}')
 
 # 2. Initialize learning problem (required only for LPF and LPC samplers)
+# This does not necessarily need to be the set of learning problem nodes. It can be any set of nodes that you want to
+# certainly include in the sample (LPF) or have the sample graph centralized at them (LPC).
 pos = set(map(OWLNamedIndividual, map(IRI.create, set(examples['positive_examples']))))
 neg = set(map(OWLNamedIndividual, map(IRI.create, set(examples['negative_examples']))))
 lp = pos.union(neg)
 
 # 3. Initialize the sampler and generate the sample
 sampler = RandomWalkerJumpsSamplerLPCentralized(kb, lp)
-sampled_kb = sampler.sample(30)
+sampled_kb = sampler.sample(30)  # will generate a sample with 30 nodes
 
-print(f'Removed individuals: {len(sampler.get_removed_nodes())}')
+print(f'Removed individuals: {kb.individuals_count() - sampled_kb.individuals_count()}')
 print(f'Remaining individuals: {sampled_kb.individuals_count()}')
 
-# 4. Save the sampled ontology
-sampler.save_sample()
+# 4. Save the sampled ontology into a file named 'sampled_kb' (this file will be created on runtime)
+sampler.save_sample(sampled_kb, 'sampled_kb')