Working predict and score (basic)

README.md

@@ -1,8 +1,6 @@
 [![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
+# Odte
 Oblique Decision Tree Ensemble

odte/Odte.py

@@ -12,6 +12,7 @@ from sklearn.utils import check_consistent_length
 from sklearn.metrics._classification import _weighted_sum, _check_targets
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.base import BaseEstimator, ClassifierMixin
+from scipy.stats import mode
 from sklearn.utils.validation import (
     check_X_y,
     check_array,
@@ -76,9 +77,9 @@ class Odte(BaseEstimator, ClassifierMixin):
         self, X: np.array, y: np.array, sample_weight: np.array = None
     ) -> "Odte":
         # Check parameters are Ok.
-        if self.n_estimators < 10:
+        if self.n_estimators < 3:
             raise ValueError(
-                f"n_estimators must be greater than 9... got (n_estimators=\
+                f"n_estimators must be greater than 3... got (n_estimators=\
                     {self.n_estimators:f})"
             )
         # the rest of parameters are checked in estimator
@@ -115,13 +116,13 @@ class Odte(BaseEstimator, ClassifierMixin):
     def _get_bootstrap_n_samples(self, n_samples) -> int:
         if self.max_samples is None:
             return n_samples
-        if type(self.max_samples) == int:
+        if isinstance(self.max_samples, int):
             if not (1 <= self.max_samples <= n_samples):
                 message = f"max_samples should be in the range 1 to \
                     {n_samples} but got {self.max_samples}"
                 raise ValueError(message)
             return self.max_samples
-        if type(self.max_samples) == float:
+        if isinstance(self.max_samples, float):
             if not (0 < self.max_samples < 1):
                 message = f"max_samples should be in the range (0, 1)\
                     but got {self.max_samples}"
@@ -137,7 +138,10 @@ class Odte(BaseEstimator, ClassifierMixin):
         check_is_fitted(self, ["estimators_"])
         # Input validation
         X = check_array(X)
-        return np.ones((X.shape[0]),)
+        result = np.empty((X.shape[0], self.n_estimators))
+        for index, tree in enumerate(self.estimators_):
+            result[:, index] = tree.predict(X)
+        return mode(result, axis=1).mode.ravel()
 
     def score(
         self, X: np.array, y: np.array, sample_weight: np.array = None
@@ -148,7 +152,7 @@ class Odte(BaseEstimator, ClassifierMixin):
         X, y = check_X_y(X, y)
         y_pred = self.predict(X).reshape(y.shape)
         # Compute accuracy for each possible representation
-        y_type, y_true, y_pred = _check_targets(y, y_pred)
+        _, y_true, y_pred = _check_targets(y, y_pred)
         check_consistent_length(y_true, y_pred, sample_weight)
         score = y_true == y_pred
         return _weighted_sum(score, sample_weight, normalize=True)

odte/tests/Odte_tests.py

@@ -52,24 +52,37 @@ class Odte_test(unittest.TestCase):
             self.assertLessEqual(value, 1000)
 
     def test_bogus_n_estimator(self):
-        values = [0, -1]
+        values = [0, -1, 2]
         for n_estimators in values:
             with self.assertRaises(ValueError):
                 tclf = Odte(n_estimators=n_estimators)
                 tclf.fit(*load_dataset(self._random_state))
 
+    def test_simple_predict(self):
+        warnings.filterwarnings("ignore", category=ConvergenceWarning)
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
+        X, y = [[1, 2], [5, 6], [9, 10], [16, 17]], [0, 1, 1, 2]
+        expected = [0, 1, 1, 0]
+        tclf = Odte(
+            random_state=self._random_state, n_estimators=10, kernel="rbf"
+        )
+        computed = tclf.fit(X, y).predict(X)
+        self.assertListEqual(expected, computed.tolist())
+
     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)
+        expected = y
+        tclf = Odte(
+            random_state=self._random_state, n_estimators=10, kernel="linear"
+        )
         computed = tclf.fit(X, y).predict(X)
-        self.assertListEqual(expected.tolist(), computed.tolist())
+        self.assertListEqual(expected[:27].tolist(), computed[:27].tolist())
 
     def test_score(self):
         X, y = load_dataset(self._random_state)
-        expected = 0.5
-        tclf = Odte(random_state=self._random_state)
+        expected = 0.9526666666666667
+        tclf = Odte(random_state=self._random_state, n_estimators=10)
         computed = tclf.fit(X, y).score(X, y)
         self.assertAlmostEqual(expected, computed)

requirements.txt

@@ -2,4 +2,4 @@ numpy
 scikit-learn
 pandas
 ipympl
-stree
+git+https://github.com/doctorado-ml/stree