fixing kmf plot

lifelines/plotting.py

@@ -224,11 +224,11 @@ def set_kwargs_color(kwargs):
     if int(major_version) == 1 and int(minor_version) < 5:
         raise ValueError('Matplotlib versions less than 1.5 no longer supported.')
     elif int(major_version) == 2:
-        kwargs['color'] = coalesce(kwargs.get('c'), kwargs.get('color'),
-                                   kwargs["ax"]._get_lines.get_next_color())
+        kwargs['c'] = coalesce(kwargs.get('c'), kwargs.get('color'),
+                               kwargs["ax"]._get_lines.get_next_color())
     else:
-        kwargs['color'] = coalesce(kwargs.get('c'), kwargs.get('color'),
-                                   next(kwargs["ax"]._get_lines.prop_cycler)['color'])
+        kwargs['c'] = coalesce(kwargs.get('c'), kwargs.get('color'),
+                               next(kwargs["ax"]._get_lines.prop_cycler)['color'])
 
 
 def set_kwargs_drawstyle(kwargs):
@@ -267,7 +267,7 @@ def _plot_loglogs(loc=None, iloc=None, show_censors=False, censor_styles=None, *
 
         # plot censors
         ax = kwargs['ax']
-        colour = kwargs['color']
+        colour = kwargs['c']
 
         if show_censors and cls.event_table['censored'].sum() > 0:
             cs = {
@@ -353,7 +353,7 @@ def plot(loc=None, iloc=None, show_censors=False,
 
         # plot censors
         ax = kwargs['ax']
-        colour = kwargs['color']
+        colour = kwargs['c']
 
         if show_censors and cls.event_table['censored'].sum() > 0:
             cs = {

tests/utils/test_utils.py

@@ -329,6 +329,14 @@ def test_both_concordance_index_function_deal_with_ties_the_same_way():
     assert fast_cindex(actual_times, predicted_times, obs) == slow_cindex(actual_times, predicted_times, obs) == 1.0
 
 
+def test_concordance_index_function_exits():
+    N = 10 * 1000
+    actual_times = np.random.exponential(1, size=N)
+    predicted_times = np.random.exponential(1, size=N)
+    obs = np.ones(N)
+    assert fast_cindex(actual_times, predicted_times, obs)
+
+
 def test_survival_table_from_events_with_non_negative_T_and_no_lagged_births():
     n = 10
     T = np.arange(n)