Removed irrelvant plotting code for clarity while working

decodertest.py

@@ -155,72 +155,7 @@ def main():
     plt.savefig("sample_spectrogram.png")
     plt.close()
     exit()
-    print(f"Test loss: {test_loss}, test accuracy: {test_accuracy}")
-    for predict, groundtruth, key in zip(test_predict, test_set[1], keys):
-        conf_matrix = confusion_matrix(
-                np.argmax(predict, axis=1),
-                np.argmax(groundtruth, axis=1),
-                labels=range(len(categories[key].values())),
-                normalize="pred"
-        )
-        plt.pcolormesh(conf_matrix, edgecolors="black", linewidth=0.5)#origin="upper")
-        plt.gca().set_aspect("equal")
-        plt.colorbar()
-        plt.xticks([int(num) for num in categories[key].keys()],
-                   categories[key].values(), rotation=270)
-        plt.yticks([int(num) for num in categories[key].keys()],
-                   categories[key].values())
-        plt.xlabel("True label")
-        plt.ylabel("Predicted label")
-        plt.gcf().set_size_inches(len(categories[key])/10+4,
-                                  len(categories[key])/10+3)
-        plt.savefig(f"/app/workdir/figures/confusion_matrix_{key}.png",
-                    bbox_inches="tight")
-        plt.close()
-        with open(f"confusion_matrix_{key}.json", 'w') as f:
-            confusion_dict = {"prediction": predict.tolist(),
-                              "true": groundtruth.tolist(),
-                              "matrix": conf_matrix.tolist()}
-            json.dump(confusion_dict, f)
-
-        label_binarizer = LabelBinarizer().fit(groundtruth)
-        y_onehot_test = label_binarizer.transform(groundtruth)
-        fpr, tpr, _ = roc_curve(
-                groundtruth.ravel(),
-                predict.ravel()
-        )
-        roc_auc = auc(fpr, tpr)
-        plt.plot(fpr, tpr, label=f"AUC = {roc_auc:.2f}")
-        plt.savefig(f"/app/workdir/figures/roc_curve_{key}.png",
-                    bbox_inches="tight")
-        with open(f"roc_fpr_tpr_{key}.json", 'w') as f:
-            roc_dict = {"fpr": fpr.tolist(),
-                        "tpr": tpr.tolist(),
-                        "auc": roc_auc}
-            json.dump(roc_dict, f)
-        print("Done")
-
-    # Save the hyperparameters and metric to csv
-    metric = {
-        "head_size": HEAD_SIZE,
-        "num_heads": NUM_HEADS,
-        "ff_dim": FF_DIM,
-        "num_transformer_blocks": NUM_TRANSFORMER_BLOCKS,
-        "mlp_units": MLP_UNITS[0],
-        "dropout": DROPOUT,
-        "mlp_dropout": MLP_DROPOUT,
-        "batch_size": BATCH_SIZE,
-        "epochs": EPOCHS,
-        "test_loss": test_loss,
-        "test_accuracy": test_accuracy
-    }
-    if not os.path.exists("/app/workdir/metrics.csv"):
-        with open("/app/workdir/metrics.csv", "w") as f:
-            f.write(",".join(metric.keys()) + "\n")
-    with open("/app/workdir/metrics.csv", "a") as f:
-        f.write(",".join([str(value) for value in metric.values()]) + "\n")
-
-    return
+
 
 if __name__ == "__main__":
     main()