-
Notifications
You must be signed in to change notification settings - Fork 253
Open
Description
Description
The function generate_candidates(X, strategy='graph_degree', target_rel='y', max_candidates=3) generates different output then shown in the example in the file, which is not correct as it is generating 3x the same array
Actual Behavior
import numpy as np
import networkx as nx
X = np.array([['a', 'y', 'b'],
['b', 'y', 'a'],
['a', 'y', 'c'],
['c', 'y', 'a'],
['a', 'y', 'd'],
['c', 'y', 'd'],
['b', 'y', 'c'],
['f', 'y', 'e']])
X_candidates = generate_candidates(X, strategy='graph_degree', target_rel='y', max_candidates=3)
array([['b', 'y', 'c'],
['b', 'y', 'c'],
['b', 'y', 'c']], dtype=object)
Expected Behavior
import numpy as np
import networkx as nx
X = np.array([['a', 'y', 'b'],
['b', 'y', 'a'],
['a', 'y', 'c'],
['c', 'y', 'a'],
['a', 'y', 'd'],
['c', 'y', 'd'],
['b', 'y', 'c'],
['f', 'y', 'e']])
X_candidates = generate_candidates(X, strategy='graph_degree', target_rel='y', max_candidates=3)
# ([['a', 'y', 'e'],
# ['f', 'y', 'a'],
# ['c', 'y', 'e']])
Steps to Reproduce
Run above mentioned code, ampligraph version = 1.4.0, numpy version = 1.19.5, networkx version=2.3
Metadata
Metadata
Assignees
Labels
No labels