def show():
results = datasets.np_load_data(RESULTS_FOLDER_PATH, "results.npy")
for result in results:
plot.plt_validation_curve(result[2], result[3], result[1], result[0],
plot_tile="Validation curve for TOR/NonTor dataset with Decision Tree")
result = datasets.pk_load(RESULTS_FOLDER_PATH, "learning_curves")
plot.plt_learning_curve(result[0], result[1], result[2], "Learning curve for TOR/NonTor dataset with Decision Tree")
def show():
results = datasets.np_load_data(RESULTS_FOLDER_PATH, "results.npy")
for result in results:
plot.plt_validation_curve(
result[2],
result[3],
result[1],
result[0],
plot_tile=
"Validation curve for CICAndMal2017 dataset with Random Forest")
def show(scor='roc_auc'):
results = datasets.np_load_data(RESULTS_FOLDER_PATH, "results.npy")
for result in results:
plot.plt_validation_curve(
result[2],
result[3],
result[1],
result[0],
plot_tile=
"Validation curve for CICIDS2017 dataset with Linear Regression")
def show():
for parameter_dir in os.listdir(RESULTS_FOLDER_PATH):
result_dir = os.path.join(RESULTS_FOLDER_PATH, parameter_dir)
if not os.path.isdir(result_dir):
continue
parameter_name = parameter_dir.replace("_", " ").capitalize()
for file in os.listdir(result_dir):
if file.endswith(".npy"):
if "roc_fpr_tpr_thres" in file:
fpr_tpr_thres = datasets.np_load_data(result_dir, file)
plot.initialize_roc_plt(parameter_name)
for i in range(0, len(fpr_tpr_thres)):
plot.plt_add_roc_curve(fpr_tpr_thres[i][1],
fpr_tpr_thres[i][2],
fpr_tpr_thres[i][0])
elif "roc_auc_scores" in file:
auc_score = datasets.np_load_data(result_dir, file)
plot.plot_auc_score(auc_score[:, 0], auc_score[:, 1],
parameter_name)
plot.show()