Predict and score structure

.gitignore

@@ -127,3 +127,7 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+.idea
+.vscode
+.pre-commit-config.yaml

README.md

@@ -1,2 +1,8 @@
+[![Codeship Status for Doctorado-ML/Odte](https://app.codeship.com/projects/c279cef0-8f1b-0138-f3d2-5e67174268f2/status?branch=master)](https://app.codeship.com/projects/399830)
+
+[![codecov](https://codecov.io/gh/Doctorado-ML/odte/branch/master/graph/badge.svg)](https://codecov.io/gh/Doctorado-ML/odte)
+
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/c85f935ac6a0482ab67d3ebed4611459)](https://www.codacy.com/gh/Doctorado-ML/Odte?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/Odte&utm_campaign=Badge_Grade)
+
 # odte
 Oblique Decision Tree Ensemble

odte/Odte.py

@@ -91,6 +91,7 @@ class Odte(BaseEstimator, ClassifierMixin):
         self.n_classes_ = self.classes_.shape[0]
         self.estimators_ = []
         self._train(X, y, sample_weight)
+        return self
 
     def _train(
         self, X: np.array, y: np.array, sample_weight: np.array
@@ -109,7 +110,7 @@ class Odte(BaseEstimator, ClassifierMixin):
             weights_update = np.bincount(indices, minlength=n_samples)
             current_weights = weights * weights_update
             # train the classifier
-            clf.fit(X[indices, :], y[indices, :], current_weights[indices, :])
+            clf.fit(X[indices, :], y[indices], current_weights[indices])
 
     def _get_bootstrap_n_samples(self, n_samples) -> int:
         if self.max_samples is None:
@@ -131,14 +132,15 @@ class Odte(BaseEstimator, ClassifierMixin):
             {type(self.max_samples)}"
         )
 
-    def predict(self, X: np.array):
+    def predict(self, X: np.array) -> np.array:
         # todo
         check_is_fitted(self, ["estimators_"])
         # Input validation
         X = check_array(X)
+        return np.ones((X.shape[0]),)
 
     def score(
-        self, X: np.array, y: np.array, sample_weight: np.array
+        self, X: np.array, y: np.array, sample_weight: np.array = None
     ) -> float:
         # todo
         check_is_fitted(self, ["estimators_"])

odte/tests/Odte_tests.py

@@ -1,6 +1,9 @@
 import unittest
 import numpy as np
 
+import warnings
+from sklearn.exceptions import ConvergenceWarning
+
 from odte import Odte
 from .utils import load_dataset
 
@@ -47,3 +50,26 @@ class Odte_test(unittest.TestCase):
         for value in computed.tolist():
             self.assertGreaterEqual(value, 101)
             self.assertLessEqual(value, 1000)
+
+    def test_bogus_n_estimator(self):
+        values = [0, -1]
+        for n_estimators in values:
+            with self.assertRaises(ValueError):
+                tclf = Odte(n_estimators=n_estimators)
+                tclf.fit(*load_dataset(self._random_state))
+
+    def test_predict(self):
+        warnings.filterwarnings("ignore", category=ConvergenceWarning)
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
+        X, y = load_dataset(self._random_state)
+        expected = np.ones(y.shape[0])
+        tclf = Odte(random_state=self._random_state)
+        computed = tclf.fit(X, y).predict(X)
+        self.assertListEqual(expected.tolist(), computed.tolist())
+
+    def test_score(self):
+        X, y = load_dataset(self._random_state)
+        expected = 0.5
+        tclf = Odte(random_state=self._random_state)
+        computed = tclf.fit(X, y).score(X, y)
+        self.assertAlmostEqual(expected, computed)