# from timeit import default_timer
from unittest import TestCase
import networkx as nx
# import numpy as np
import numpy as np
import scipy.sparse as sp
from sklearn.metrics.scorer import _PredictScorer
# from utils.Extractor import load_dataset
from utils.metrics import hierarchical_f_measure, hierarchical_f_measure_scorer
class TestHierarchicalFMeasure(TestCase):
def test_simple_f_score(self):
graph = nx.DiGraph()
graph.add_nodes_from([0, 1, 2, 3])
graph.add_edges_from([(0, 1), (1, 2), (1, 3)])
y_true = sp.csr_matrix([[0, 0, 1, 0]])
y_pred = sp.csr_matrix([[0, 0, 0, 1]])
self.assertEquals(0.5, hierarchical_f_measure(self.imitate_tr(graph, 0), y_true, y_pred))
def test_simple_f_score_dense(self):
graph = nx.DiGraph()
graph.add_nodes_from([0,1, 2, 3])
graph.add_edges_from([(0, 1), (1, 2), (1, 3)])
y_true = np.matrix([[0, 0,1, 0]])
y_pred = np.matrix([[0, 0,0, 1]])
self.assertEquals(0.5, hierarchical_f_measure(self.imitate_tr(graph, 0), y_true, y_pred))
def test_ill_defined(self):
graph = nx.DiGraph()
graph.add_nodes_from([0, 1, 2])
graph.add_edges_from([(0, 1), (0, 2)])
y_true = sp.csr_matrix([[0, 1, 0]])
y_pred = sp.csr_matrix([[0, 0, 0]])
self.assertEquals(0, hierarchical_f_measure(self.imitate_tr(graph, 0), y_true, y_pred))
def test_more_complex(self):
graph = nx.DiGraph()
graph.add_nodes_from([0, 1, 2, 3, 4, 5])
graph.add_edges_from([(0, 1), (1, 2), (2, 3), (2, 4), (3, 5), (1, 3)])
y_true = sp.csr_matrix([[0,0, 0, 0, 1, 1], [0,1, 0, 0, 0, 0]])
y_pred = sp.csr_matrix([[0,0, 0, 1, 0, 1], [0,0, 0, 0, 0, 1]])
# int = 4, ta = 5, pa = 4, p = 4/4, r = 4/5, f = 2*(4/4)*(4/5)/(4/4+4/5)
self.assertEquals((2 * (4 / 5) / (1 + 4 / 5) + 2 * (1 / 1) * (1 / 4) / (1 + 1 / 4)) / 2,
hierarchical_f_measure(self.imitate_tr(graph, 0), y_true, y_pred))
def test_make_scorer(self):
graph = nx.DiGraph()
scorer = hierarchical_f_measure_scorer(graph)
self.assertTrue(callable(scorer))
self.assertIsInstance(scorer, _PredictScorer)
def imitate_tr(self, graph, root):
def tr():
pass
tr.nx_graph = graph
tr.nx_root = root
return tr
#
# def test_speed(self):
# _, _, tr = load_dataset('econ62k')
# graph = tr.nx_graph
#
# def random_labels():
# def set_random_ones(n_nodes):
# ids = np.random.choice(n_nodes, 5)
# zeros = sp.dok_matrix((1, n_nodes), dtype=np.bool_)
# for index in ids:
# zeros[0, index] = True
# return zeros
#
# number_of_nodes = graph.number_of_nodes()
# matrix = set_random_ones(number_of_nodes)
# for i in range(0, 62000):
# zeros = set_random_ones(number_of_nodes)
# matrix = sp.vstack((matrix, zeros))
# return sp.csr_matrix(matrix)
#
# y_true = random_labels()
# y_pred = random_labels()
# print('random constructed')
#
# start = default_timer()
# hierarchical_f_measure(graph, y_true, y_pred)
# print(default_timer() - start)
