Version 0.1.10

CompStats/__init__.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = '0.1.9'
+__version__ = '0.1.10'
 from CompStats.bootstrap import StatisticSamples
 from CompStats.measurements import CI, SE, difference_p_value
 from CompStats.performance import performance, difference, all_differences, plot_performance, plot_difference

CompStats/interface.py

@@ -159,7 +159,7 @@ def __repr__(self):
         desc_se = [f'{k:0.4f}' for k in self.se]
         desc_se = ', '.join(desc_se)
         return f"<{self.__class__.__name__}({arg}={func_name}, statistic=[{desc}], se=[{desc_se}])>"
-        
+
     def __str__(self):
         """Prediction statistics with standard error in parenthesis"""
         if not isinstance(self.statistic, dict):
@@ -358,10 +358,11 @@ def plot(self, value_name:str=None,
         >>> perf.plot()
         """
         import seaborn as sns
-        if self.score_func is not None:
-            value_name = 'Score'
-        else:
-            value_name = 'Error'
+        if value_name is None:
+            if self.score_func is not None:
+                value_name = 'Score'
+            else:
+                value_name = 'Error'
         df = self.dataframe(value_name=value_name, var_name=var_name,
                             alg_legend=alg_legend, perf_names=perf_names)
         if var_name not in df.columns:

CompStats/utils.py

@@ -63,15 +63,18 @@ def dataframe(instance, value_name:str='Score',
               perf_names:list=None):
     """Dataframe"""
     import pandas as pd
+    statistic = instance.statistic
+    if not isinstance(statistic, dict):
+        iter = instance.statistic_samples.keys()
+    else:
+        iter = statistic    
     if isinstance(instance.best, str):
-        df = pd.DataFrame(dict(instance.statistic_samples.calls.items()))
+        calls = instance.statistic_samples.calls
+        df = pd.DataFrame({k: calls[k]
+                           for k in iter if k in calls})
         return df.melt(var_name=alg_legend,
                        value_name=value_name)
     df = pd.DataFrame()
-    if not isinstance(instance.statistic, dict):
-        iter = instance.statistic_samples.keys()
-    else:
-        iter = instance.statistic
     for key in iter:
         data = instance.statistic_samples[key]
         _df = pd.DataFrame(data,