FIX check parameter in LogisticRegression

Author: TomDLT

sklearn/linear_model/logistic.py

@@ -1003,9 +1003,15 @@ def fit(self, X, y):
         self : object
             Returns self.
         """
-        if self.C < 0:
+        if not isinstance(self.C, numbers.Number) or self.C < 0:
             raise ValueError("Penalty term must be positive; got (C=%r)"
                              % self.C)
+        if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0:
+            raise ValueError("Maximum number of iteration must be positive;"
+                             " got (max_iter=%r)" % self.max_iter)
+        if not isinstance(self.tol, numbers.Number) or self.tol < 0:
+            raise ValueError("Tolerance for stopping criteria must be "
+                             "positive; got (tol=%r)" % self.tol)
 
         X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64, order="C")
         self.classes_ = np.unique(y)

sklearn/linear_model/tests/test_logistic.py

@@ -5,6 +5,7 @@
 from sklearn.utils.testing import assert_almost_equal
 from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_array_almost_equal
+from sklearn.utils.testing import assert_raises_regexp
 from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_raises
@@ -67,7 +68,23 @@ def test_predict_2_classes():
 
 def test_error():
     # Test for appropriate exception on errors
-    assert_raises(ValueError, LogisticRegression(C=-1).fit, X, Y1)
+    msg = "Penalty term must be positive"
+    assert_raises_regexp(ValueError, msg,
+                         LogisticRegression(C=-1).fit, X, Y1)
+    assert_raises_regexp(ValueError, msg,
+                         LogisticRegression(C="test").fit, X, Y1)
+
+    msg = "Tolerance for stopping criteria must be positive"
+    assert_raises_regexp(ValueError, msg,
+                         LogisticRegression(tol=-1).fit, X, Y1)
+    assert_raises_regexp(ValueError, msg,
+                         LogisticRegression(tol="test").fit, X, Y1)
+
+    msg = "Maximum number of iteration must be positive"
+    assert_raises_regexp(ValueError, msg,
+                         LogisticRegression(max_iter=-1).fit, X, Y1)
+    assert_raises_regexp(ValueError, msg,
+                         LogisticRegression(max_iter="test").fit, X, Y1)
 
 
 def test_predict_3_classes():