Commit fd0a6b8a authored by sjmonagi's avatar sjmonagi

images only

parent 0a76107d
......@@ -38,6 +38,13 @@ def plotting_training_log(num_episode, plotted_data, successes, failures, loss,
plt.ylabel("Successful/Failed Trajectories and Ratio")
plt.savefig("failed_success_ratio" + str(num_episode) + ".png")
# plots of successful failed trajectories and F1 Score between them
plotted_data.plot(x="Episodes", y=["Successful trajectories", "Failed trajectories", "F1"],
title="Agent Learning Ratio")
plt.xlabel("Episodes")
plt.ylabel("Successful/Failed Trajectories and F1 Score")
plt.savefig("failed_success_F1" + str(num_episode) + ".png")
# plot of successful trajectories
plotted_data.plot(x="Episodes", y=["Successful trajectories"],
title="Successful Trajectories")
......@@ -58,6 +65,12 @@ def plotting_training_log(num_episode, plotted_data, successes, failures, loss,
plt.ylabel("Ratio")
plt.savefig("Ratio" + str(num_episode) + ".png")
plotted_data.plot(x="Episodes", y=["F1"],
title="F1 Score between Successful and Failed Trajectory")
plt.xlabel("Episodes")
plt.ylabel("F1 Score")
plt.savefig("F1" + str(num_episode) + ".png")
plotted_data.plot(x="Episodes", y=["loss"],
title="HER-DRQN model loss")
plt.xlabel("Episodes")
......@@ -68,7 +81,7 @@ def plotting_training_log(num_episode, plotted_data, successes, failures, loss,
def validate(n, nodes_num, top_view, env, envT, ae, ae_sess, distance_threshold, model):
print("### Validation ###")
plotted_data_val = pd.DataFrame(
columns=["Episodes", "Successful trajectories", "Failed trajectories", "Ratio", "loss", "epsilon", "num_steps"])
columns=["Episodes", "Successful trajectories", "Failed trajectories", "Ratio", "F1"])
val_success = 0
val_failures = 0
for i in range(100):
......@@ -134,13 +147,31 @@ def validate(n, nodes_num, top_view, env, envT, ae, ae_sess, distance_threshold,
print("validation_success:", val_success, "validation_failures:", val_failures, "steps_num",num_steps)
plotted_data_val = plotted_data_val.append({"Episodes": str(i),
"Successes": val_success / (i+1),
"Failures": val_failures / (i+1),
"Ratio": (val_success / (val_failures + 0.1)),
"num_steps": num_steps}, ignore_index=True)
plotted_data_val.plot(x="Episodes", y=["Successes", "Failures", "Ratio"],
"Successful trajectories": val_success / (i+1),
"Failed trajectories": val_failures / (i+1),
"Ratio": (val_success / (val_failures + 1)),
"F1": ((1 - (val_failures / (n + 1))) * (val_success / (n + 1))) /
((1 - (val_failures / (n + 1))) + ((val_success / (n + 1))) + 1)
}, ignore_index=True)
plotted_data_val.plot(x="Episodes", y=["Successful trajectories", "Failed trajectories", "Ratio"],
title="Validation Agent Learning Ratio")
plt.xlabel("Episodes")
plt.ylabel("Successful/Failed Trajectories and Ratio")
plt.savefig("Vaildation_failed_success_ratio " +str(n) + str(i) + ".png")
plotted_data_val.plot(x="Episodes", y=["Successful trajectories", "Failed trajectories", "F1"],
title="Validation Agent Learning F1")
plt.xlabel("Episodes")
plt.ylabel("Successful/Failed Trajectories and F1 Score")
plt.savefig("Vaildation_failed_success_F1 " +str(n) + str(i) + ".png")
plotted_data_val.plot(x="Episodes", y=["F1"],
title="F1 Score")
plt.xlabel("Episodes")
plt.ylabel("F1 Score between Successful and Failed Trajectory")
plt.savefig("F1 Score" +str(n) + str(i) + ".png")
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