improve plots

teng_ml/tracker/epoch_tracker.py

@@ -111,26 +111,28 @@ class EpochTracker:
         """
         @param model_dir: Optional. If given, save to model_dir as svg
         """
-        fig, ax = plt.subplots(nrows=3, ncols=1, sharex=True, layout="tight")
+        fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, layout="tight", figsize=(6, 6))
 
         ax[0].plot(self.epochs, self.accuracies, color="red")
         ax[0].set_ylabel("Accuracy")
+        ax[0].grid("minor")
 
         ax[1].plot(self.epochs, self.learning_rate, color="green")
         ax[1].set_ylabel("Learning Rate")
+        ax[1].grid("minor")
 
-        ax[2].plot(self.epochs, self.loss, color="blue")
-        ax[2].set_ylabel("Loss")
+        # ax[2].plot(self.epochs, self.loss, color="blue")
+        # ax[2].set_ylabel("Loss")
 
         fig.suptitle(title)
-        ax[2].set_xlabel("Epoch")
+        ax[-1].set_xlabel("Epoch")
         plt.tight_layout()
         if model_dir is not None:
             fig.savefig(f"{model_dir}/{name}.svg")
 
         return fig, ax
 
-    def plot_predictions(self, title="Predictions per Label", ep=-1, model_dir=None, name="img_training_predictions"):
+    def plot_predictions(self, title="Predictions per Label", ep=-1, model_dir=None, name="img_training_predictions", empty_zero=True):
         """
         @param model_dir: Optional. If given, save to model_dir as svg
         @param ep: Epoch, defaults to last
@@ -141,8 +143,23 @@ class EpochTracker:
 
         N = len(self.labels)
         label_names = self.labels.get_labels()
+        # print(label_names)
+        replace = {
+            "cloth": "fabric",
+            "foam": "foam_PDMS_pure",
+            "foil": "bubble_wrap",
+            "rigid_foam": "foam_PE",
+            "fabric_PP": "fabric",
+            "foam_PDMS_white": "foam_PDMS_pure",
+            "foam_PDMS_black": "foam_PEDOT",
+            "bubble_wrap_PE": "bubble_wrap",
+        }
+        label_names = [ replace[label] if label in replace else label for label in label_names ]
 
-        fig, ax = plt.subplots(layout="tight")
+        if len(label_names) > 6:
+            fig, ax = plt.subplots(layout="tight", figsize=(7, 6))
+        else:
+            fig, ax = plt.subplots(layout="tight", figsize=(6, 5))
         im = ax.imshow(normalized_predictions, cmap='Blues')  # cmap='BuPu', , norm=colors.PowerNorm(1./2.)
         ax.set_xticks(np.arange(N))
         ax.set_yticks(np.arange(N))
@@ -155,14 +172,21 @@ class EpochTracker:
         for i in range(1, N):
             ax.axhline(i-0.5, color='black', linewidth=1)
 
+        # for i in range(1, N):
+            # ax.axvline(i-0.5, color='#bbb', linewidth=1)
+
         # rotate the x-axis labels for better readability
         plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
 
         # create annotations
         for i in range(N):
             for j in range(N):
-                text = ax.text(j, i, round(normalized_predictions[i, j], 2),
-                               ha="center", va="center", color="black")
+                val = round(normalized_predictions[i, j], 2)
+                if empty_zero and val == 0: continue
+                color = "black"
+                if normalized_predictions[i, j] >= 0.6: color = "white"
+                text = ax.text(j, i, val,
+                               ha="center", va="center", color=color)
 
         # add colorbar
         cbar = ax.figure.colorbar(im, ax=ax)