def test_add_node(self):
ch = hmcdatasets.load_shades_class_hierachy()
old_number = len(ch.nodes_())
ch.add_node("additional node", ch.root)
new_number = len(ch.nodes_())
# Adding a node should increase node count by 1
self.assertEqual(old_number + 1, new_number)
def test_predict(self):
ch = hmcdatasets.load_shades_class_hierachy()
X, y = hmcdatasets.load_shades_data()
dt = hmc.DecisionTreeHierarchicalClassifier(ch)
dt = dt.fit(X, y)
predictions = dt.predict(X)
# One prediction for each observation
self.assertEqual(len(predictions), len(X))
def test_add_redundant_node(self):
ch = hmcdatasets.load_shades_class_hierachy()
ch.add_node("redundant_node", ch.root)
old_number = len(ch.nodes_())
ch.add_node("redundant_node", ch.root)
new_number = len(ch.nodes_())
# Adding a redundant node should not increase node count
self.assertEqual(old_number, new_number)
def test_fit(self):
ch = hmcdatasets.load_shades_class_hierachy()
ch.print_()
X, y = hmcdatasets.load_shades_data()
dt = hmc.DecisionTreeHierarchicalClassifier(ch)
dt = dt.fit(X, y)
trees_fit = True
for stage in dt.stages:
if "tree" not in stage.keys():
trees_fit = False
# After the fit each stage should have a tree
self.assertEqual(trees_fit, True)
def test_score(self):
ch = hmcdatasets.load_shades_class_hierachy()
X, y = hmcdatasets.load_shades_data()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.50,
random_state=0)
dt = hmc.DecisionTreeHierarchicalClassifier(ch)
dt = dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
metrics.classification_report(ch, y_test, pd.DataFrame(y_pred))
def test_add_dag_node(self):
ch = hmcdatasets.load_shades_class_hierachy()
# Adding a child with a new parent should throw an exception
self.assertRaises(ValueError, ch.add_node, "slate", "light")
def test_add_root_node(self):
ch = hmcdatasets.load_shades_class_hierachy()
# Adding the root as a child should throw an exception
self.assertRaises(ValueError, ch.add_node, "colors", "light")
def test_get_descendants(self):
ch = hmcdatasets.load_shades_class_hierachy()
self.assertEqual(ch._get_descendants("dark"),
["black", "gray", "ash", "slate"])
self.assertEqual(len(ch._get_descendants("slate")), 0)
def test_get_ancestors(self):
ch = hmcdatasets.load_shades_class_hierachy()
self.assertEqual(ch._get_ancestors("ash"), ["gray", "dark"])
self.assertEqual(len(ch._get_ancestors("colors")), 0)
def test_get_children(self):
ch = hmcdatasets.load_shades_class_hierachy()
self.assertEqual(ch._get_children("dark"), ["black", "gray"])
def test_get_parent(self):
ch = hmcdatasets.load_shades_class_hierachy()
self.assertEqual(ch._get_parent("black"), "dark")
def test_count_nodes(self):
ch = hmcdatasets.load_shades_class_hierachy()
self.assertEqual(len(ch.nodes_()), 7)