Remove travis ci and set codecov percentage

.travis.yml

@@ -1,20 +0,0 @@
-language: python
-os: linux
-dist: xenial
-install:
-  - pip install -r requirements.txt
-  - pip install --upgrade codecov coverage black flake8
-notifications:
-  email:
-    recipients:
-      - ricardo.montanana@alu.uclm.es
-    on_success: never # default: change
-    on_failure: always # default: always
-# command to run tests
-script:
-  - black --check --diff stree
-  - flake8 --count stree
-  - coverage run -m unittest -v stree.tests
-after_success:
-  - codecov
-  - bash <(curl -Ls https://coverage.codacy.com/get.sh)

README.md

@@ -1,6 +1,7 @@
-[![Build Status](https://travis-ci.com/Doctorado-ML/STree.svg?branch=master)](https://travis-ci.com/Doctorado-ML/STree)
+[![Codeship Status for Doctorado-ML/STree](https://app.codeship.com/projects/8b2bd350-8a1b-0138-5f2c-3ad36f3eb318/status?branch=master)](https://app.codeship.com/projects/399170)
 [![codecov](https://codecov.io/gh/doctorado-ml/stree/branch/master/graph/badge.svg)](https://codecov.io/gh/doctorado-ml/stree)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/35fa3dfd53a24a339344b33d9f9f2f3d)](https://www.codacy.com/gh/Doctorado-ML/STree?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/STree&utm_campaign=Badge_Grade)
+
 # Stree
 
 Oblique Tree classifier based on SVM nodes. The nodes are built and splitted with sklearn LinearSVC models.Stree is a sklearn estimator and can be integrated in pipelines, grid searches, etc.

codecov.yml

@@ -2,10 +2,10 @@ overage:
   status:
     project:
       default:
-        target: auto
+        target: 90%
     patch:
       default:
-        target: auto
+        target: 90%
 comment:
   layout: "reach, diff, flags, files"
   behavior: default

stree/tests/Strees_test.py

@@ -41,22 +41,6 @@ class Stree_test(unittest.TestCase):
         except KeyError:
             pass
 
-    def _get_Xy(self):
-        X, y = make_classification(
-            n_samples=1500,
-            n_features=3,
-            n_informative=3,
-            n_redundant=0,
-            n_repeated=0,
-            n_classes=2,
-            n_clusters_per_class=2,
-            class_sep=1.5,
-            flip_y=0,
-            weights=[0.5, 0.5],
-            random_state=self._random_state,
-        )
-        return X, y
-
     def _check_tree(self, node: Snode):
         """Check recursively that the nodes that are not leaves have the
         correct number of labels and its sons have the right number of elements