🎨 visualizations and display fixes

src/njab/plotting/metrics.py

@@ -4,6 +4,7 @@
 
 from njab.sklearn.types import ResultsSplit, Results
 
+LIMITS = (-0.05, 1.05)
 
 def plot_split_auc(result: ResultsSplit, name: str,
                    ax: matplotlib.axes.Axes) -> matplotlib.axes.Axes:
@@ -15,6 +16,8 @@ def plot_split_auc(result: ResultsSplit, name: str,
                     xlabel='false positive rate',
                     ylabel='true positive rate',
                     style='.-',
+                    ylim=LIMITS,
+                    xlim=LIMITS,
                     ax=ax)
     return ax
 
@@ -42,6 +45,8 @@ def plot_split_prc(result: ResultsSplit, name: str,
                     xlabel='true positive rate',
                     ylabel='precision',
                     style='.-',
+                    ylim=LIMITS,
+                    xlim=LIMITS,
                     ax=ax)
     return ax
 

src/njab/sklearn/pca.py

@@ -39,6 +39,7 @@ def run_pca(
 def plot_explained_variance(pca: sklearn.decomposition.PCA, ax=None):
     exp_var = pd.Series(
         pca.explained_variance_ratio_).to_frame('explained variance')
+    exp_var.index += 1  # start at 1
     exp_var["explained variance (cummulated)"] = exp_var[
         'explained variance'].cumsum()
     exp_var.index.name = 'PC'

src/njab/sklearn/scoring.py

@@ -8,17 +8,24 @@ class ConfusionMatrix():
     def __init__(self, y_true, y_pred):
         self.cm_ = sklm.confusion_matrix(y_true, y_pred)
 
-    @property
-    def as_dataframe(self):
+    def as_dataframe(self, names=('true', 'pred')) -> pd.DataFrame:
         """Create pandas.DataFrame and return.
         Names rows and columns."""
         if not hasattr(self, 'df'):
+            true_name, pred_name = names
             self.df = pd.DataFrame(self.cm_)
-            self.df.index.name = 'true'
-            self.df.columns = pd.MultiIndex.from_product([['pred'],
+            self.df.index.name = true_name
+            self.df.columns = pd.MultiIndex.from_product([[pred_name],
                                                           self.df.columns])
         return self.df
 
+    def classification_label(self) -> dict:
+        tn, fp, fn, tp = self.cm_.ravel()
+        return {'TN': tn, 'FP': fp, 'FN': fn, 'TP': tp}
+
+    def as_classification_series(self) -> pd.Series:
+        return pd.Series(self.classification_label())
+
     @property
     def as_array(self):
         """Return sklearn.metrics.confusion_matrix array"""