From 1d392d534f4a90aa2eb5a6c69f14ea3d8cfa489e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= <rmontanana@gmail.com>
Date: Thu, 11 Jun 2020 13:45:24 +0200
Subject: [PATCH] #6 - Update tests and codecov conf

---
 codecov.yml                        |  3 ---
 stree/Strees.py                    |  7 +------
 stree/tests/Strees_grapher_test.py | 17 ++++++++++++++++-
 3 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/codecov.yml b/codecov.yml
index 08f7b1b..222249f 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -3,9 +3,6 @@ overage:
     project:
       default:
         target: 90%
-    patch:
-      default:
-        target: 90%
 comment:
   layout: "reach, diff, flags, files"
   behavior: default
diff --git a/stree/Strees.py b/stree/Strees.py
index 85a073e..55ec85e 100644
--- a/stree/Strees.py
+++ b/stree/Strees.py
@@ -19,7 +19,6 @@ from sklearn.utils.validation import (
     check_is_fitted,
     _check_sample_weight,
 )
-from sklearn.utils.sparsefuncs import count_nonzero
 from sklearn.metrics._classification import _weighted_sum, _check_targets
 
 
@@ -422,11 +421,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         # Compute accuracy for each possible representation
         y_type, y_true, y_pred = _check_targets(y, y_pred)
         check_consistent_length(y_true, y_pred, sample_weight)
-        if y_type.startswith("multilabel"):
-            differing_labels = count_nonzero(y_true - y_pred, axis=1)
-            score = differing_labels == 0
-        else:
-            score = y_true == y_pred
+        score = y_true == y_pred
         return _weighted_sum(score, sample_weight, normalize=True)
 
     def __iter__(self) -> Siterator:
diff --git a/stree/tests/Strees_grapher_test.py b/stree/tests/Strees_grapher_test.py
index c7f6f15..8f39bb7 100644
--- a/stree/tests/Strees_grapher_test.py
+++ b/stree/tests/Strees_grapher_test.py
@@ -68,6 +68,11 @@ class Stree_grapher_test(unittest.TestCase):
         self.assertEqual(accuracy_score, accuracy_computed)
         self.assertGreater(accuracy_score, 0.86)
 
+    def test_score_4dims(self):
+        X, y = get_dataset(self._random_state, n_features=4)
+        accuracy_score = self._clf.score(X, y)
+        self.assertEqual(accuracy_score, 0.95)
+
     def test_save_all(self):
         folder_name = os.path.join(os.sep, "tmp", "stree")
         if os.path.isdir(folder_name):
@@ -171,11 +176,13 @@ class Snode_graph_test(unittest.TestCase):
 
     def test_plot_hyperplane_with_distribution(self):
         plt.close()
+        # select a pure node
+        node = self._clf._tree_gr.get_down().get_up().get_up()
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             matplotlib.use("Agg")
             num_figures_before = plt.gcf().number
-            self._clf._tree_gr.plot_hyperplane(plot_distribution=True)
+            node.plot_hyperplane(plot_distribution=True)
             num_figures_after = plt.gcf().number
         self.assertEqual(1, num_figures_after - num_figures_before)
 
@@ -209,3 +216,11 @@ class Snode_graph_test(unittest.TestCase):
         self.assertEqual(x, xx)
         self.assertEqual(y, yy)
         self.assertEqual(z, zz)
+
+    def test_cmap_change(self):
+        node = Snode_graph(Snode(None, None, None, "test"))
+        self.assertEqual("jet", node._get_cmap())
+        # make node pure
+        node._belief = 1.0
+        node._class = 1
+        self.assertEqual("jet_r", node._get_cmap())