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Implement predict and score methods & tests

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This commit is contained in:
Ricardo Montañana Gómez
2020-05-13 12:42:09 +02:00
parent c4de782a3f
commit 8f71eeb316
6 changed files with 70 additions and 221 deletions
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6
tests/Snode_test.py
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@@ -11,9 +11,9 @@ class Snode_test(unittest.TestCase):
def __init__(self, *args, **kwargs):
self._random_state = 1
self._model = Stree(random_state=self._random_state,
self._clf = Stree(random_state=self._random_state,
use_predictions=True)
self._model.fit(*self._get_Xy())
self._clf.fit(*self._get_Xy())
super(Snode_test, self).__init__(*args, **kwargs)
def _get_Xy(self):
@@ -42,4 +42,4 @@ class Snode_test(unittest.TestCase):
return
check_leave(node.get_down())
check_leave(node.get_up())
check_leave(self._model._tree)
check_leave(self._clf._tree)

34
tests/Stree_test.py
View File

@@ -11,9 +11,9 @@ class Stree_test(unittest.TestCase):
def __init__(self, *args, **kwargs):
self._random_state = 1
self._model = Stree(random_state=self._random_state,
use_predictions=True)
self._model.fit(*self._get_Xy())
self._clf = Stree(random_state=self._random_state,
use_predictions=False)
self._clf.fit(*self._get_Xy())
super(Stree_test, self).__init__(*args, **kwargs)
def _get_Xy(self):
@@ -25,7 +25,7 @@ class Stree_test(unittest.TestCase):
def _check_tree(self, node: Snode):
if node.is_leaf():
return
y_prediction = node._model.predict(node._X)
y_prediction = node._clf.predict(node._X)
y_down = node.get_down()._y
y_up = node.get_up()._y
# Is a correct partition in terms of cadinality?
@@ -55,7 +55,7 @@ class Stree_test(unittest.TestCase):
def test_build_tree(self):
"""Check if the tree is built the same way as predictions of models
"""
self._check_tree(self._model._tree)
self._check_tree(self._clf._tree)
def _get_file_data(self, file_name: str) -> tuple:
"""Return X, y from data, y is the last column in array
@@ -94,14 +94,32 @@ class Stree_test(unittest.TestCase):
def test_subdatasets(self):
"""Check if the subdatasets files have the same predictions as the tree itself
"""
model = self._model._tree._model
model = self._clf._tree._clf
X, y = self._get_Xy()
model.fit(X, y)
self._model.save_sub_datasets()
with open(self._model.get_catalog_name()) as cat_file:
self._clf.save_sub_datasets()
with open(self._clf.get_catalog_name()) as cat_file:
catalog = csv.reader(cat_file, delimiter=',')
for row in catalog:
X, y = self._get_Xy()
x_file, y_file = self._get_file_data(row[0])
y_original = np.array(self._find_out(x_file, X, y), dtype=int)
self.assertTrue(np.array_equal(y_file, y_original))
def test_single_prediction(self):
X, y = self._get_Xy()
yp = self._clf.predict((X[0, :].reshape(-1, X.shape[1])))
self.assertEqual(yp[0], y[0])
def test_multiple_prediction(self):
X, y = self._get_Xy()
yp = self._clf.predict(X[:23, :])
self.assertListEqual(y[:23].tolist(), yp.tolist())
def test_score(self):
X, y = self._get_Xy()
accuracy_score = self._clf.score(X, y, print_out=False)
yp = self._clf.predict(X)
right = (yp == y).astype(int)
accuracy_computed = sum(right) / len(y)
self.assertEqual(accuracy_score, accuracy_computed)