Test calibratedModel and fixed parameter method

Author: perellonieto

pycalib/__init__.py

@@ -1 +1 @@
-__version__ = '0.0.9.dev8'
+__version__ = '0.0.9.dev9'

pycalib/models/__init__.py

@@ -612,7 +612,7 @@ class _CalibratedClassifier(object):
     def __init__(self, base_estimator, method='beta',
                  score_type=None):
         self.base_estimator = base_estimator
-        self.calibrator = method
+        self.method = method
         self.score_type = score_type
 
     def _preproc(self, X):

pycalib/tests/models/test_init.py

@@ -1,12 +1,15 @@
 import unittest
 import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.datasets import make_blobs
 from pycalib.models import (IsotonicCalibration, LogisticCalibration,
-                            BinningCalibration, SigmoidCalibration)
+                            BinningCalibration, SigmoidCalibration,
+                            CalibratedModel)
 from numpy.testing import assert_array_equal
 
 
 class TestIsotonicCalibration(unittest.TestCase):
-    def test_dummy(self):
+    def test_fit_predict(self):
         S = np.array([[0.1, 0.9], [0.6, 0.4]])
         Y = np.array([1, 0])
         cal = IsotonicCalibration()
@@ -16,7 +19,7 @@ def test_dummy(self):
 
 
 class TestLogisticCalibration(unittest.TestCase):
-    def test_dummy(self):
+    def test_fit_predict(self):
         S = np.array([[0.1, 0.9], [0.6, 0.4]])
         Y = np.array([1, 0])
         cal = LogisticCalibration()
@@ -26,7 +29,7 @@ def test_dummy(self):
 
 
 class TestBinningCalibration(unittest.TestCase):
-    def test_dummy(self):
+    def test_fit_predict(self):
         S = np.array([[0.1, 0.9], [0.6, 0.4]])
         Y = np.array([1, 0])
         cal = BinningCalibration()
@@ -36,7 +39,7 @@ def test_dummy(self):
 
 
 class TestSigmoidCalibration(unittest.TestCase):
-    def test_dummy(self):
+    def test_fit_predict(self):
         S = np.array([[0.1, 0.9], [0.6, 0.4]])
         Y = np.array([1, 0])
         cal = SigmoidCalibration()
@@ -45,6 +48,18 @@ def test_dummy(self):
         assert_array_equal(Y, pred)
 
 
+class TestCalibratedModel(unittest.TestCase):
+    def test_fit_predict(self):
+        X, Y = make_blobs(n_samples=10000, centers=5, n_features=2,
+                                    random_state=42)
+        Y = (Y > 2).astype(int)
+        cal = CalibratedModel(LogisticRegression(), IsotonicCalibration())
+        cal.fit(X, Y)
+
+        pred = cal.predict(X)
+        self.assertGreater(np.mean(Y == pred), 0.7)
+
+
 def main():
     unittest.main()