abstracted PlotModelHistory to be not only able to plot loss but also other...

abstracted PlotModelHistory to be not only able to plot loss but also other metrics from the history callback object for a given plot metric

src/plotting/training_monitoring.py

@@ -19,12 +19,13 @@ lr_object = Union[Dict, LearningRateDecay]
 
 class PlotModelHistory:
     """
-    Plots history of all losses for a training event. For default loss and val_loss are plotted. If further losses are
-    provided (name must somehow include the word `loss`), this additional information is added to the plot with an
-    separate y-axis scale on the right side (shared for all additional losses). The plot is saved locally. For a proper
-    saving behaviour, the parameter filename must include the absolute path for the plot.
+    Plots history of all plot_metrics (default: loss) for a training event. For default plot_metric and val_plot_metric
+    are plotted. If further metrics are provided (name must somehow include the word `<plot_metric>`), this additional
+    information is added to the plot with an separate y-axis scale on the right side (shared for all additional
+    metrics). The plot is saved locally. For a proper saving behaviour, the parameter filename must include the absolute
+    path for the plot.
     """
-    def __init__(self, filename: str, history: history_object):
+    def __init__(self, filename: str, history: history_object, plot_metric: str = "loss"):
         """
         Sets attributes and create plot
         :param filename: saving name of the plot to create (preferably absolute path if possible), the filename needs a
@@ -34,31 +35,34 @@ class PlotModelHistory:
         if isinstance(history, keras.callbacks.History):
             history = history.history
         self._data = pd.DataFrame.from_dict(history)
+        self._plot_metric = plot_metric
         self._additional_columns = self._filter_columns(history)
         self._plot(filename)
 
-    @staticmethod
-    def _filter_columns(history: Dict) -> List[str]:
+    def _filter_columns(self, history: Dict) -> List[str]:
         """
-        Select only columns named like %loss%. The default losses 'loss' and 'val_loss' are also removed.
-        :param history: a dict with at least 'loss' and 'val_loss' as keys (can be derived from keras History.history)
-        :return: filtered columns including all loss variations except loss and val_loss.
+        Select only columns named like %<plot_metric>%. The default metrics '<plot_metric>' and 'val_<plot_metric>' are
+        also removed.
+        :param history: a dict with at least '<plot_metric>' and 'val_<plot_metric>' as keys (can be derived from keras
+            History.history)
+        :return: filtered columns including all plot_metric variations except <plot_metric> and val_<plot_metric>.
         """
-        cols = list(filter(lambda x: "loss" in x, history.keys()))
-        cols.remove("val_loss")
-        cols.remove("loss")
+        cols = list(filter(lambda x: self._plot_metric in x, history.keys()))
+        cols.remove(f"val_{self._plot_metric}")
+        cols.remove(self._plot_metric)
         return cols
 
     def _plot(self, filename: str) -> None:
         """
-        Actual plot routine. Plots loss and val_loss as default. If more losses are provided, they will be added with
-        an additional yaxis on the right side. The plot is saved in filename.
+        Actual plot routine. Plots <plot_metric> and val_<plot_metric> as default. If more plot_metrics are provided,
+        they will be added with an additional yaxis on the right side. The plot is saved in filename.
         :param filename: name (including total path) of the plot to save.
         """
-        ax = self._data[["loss", "val_loss"]].plot(linewidth=0.7)
+        ax = self._data[[self._plot_metric, f"val_{self._plot_metric}"]].plot(linewidth=0.7)
         if len(self._additional_columns) > 0:
             self._data[self._additional_columns].plot(linewidth=0.7, secondary_y=True, ax=ax)
-        ax.set(xlabel="epoch", ylabel="loss", title=f"Model loss: best = {self._data[['val_loss']].min().values}")
+        title = f"Model {self._plot_metric}: best = {self._data[[f'val_{self._plot_metric}']].min().values}"
+        ax.set(xlabel="epoch", ylabel=self._plot_metric, title=title)
         ax.axhline(y=0, color="gray", linewidth=0.5)
         plt.tight_layout()
         plt.savefig(filename)