def parse_graec(s):
# do GRAEC SCORE
enumeration_encoder = Human_Functions.load_dict_from_csv(Name_functions.S_GRAEC_enumeration_dictionary(s))
best_acc_train = -1
best_acc_test = -1
best_acc_enum = -1
best_f1_train = -1
best_f1_test = -1
best_f1_enum = -1
with open(Name_functions.full_GRAEC_table(), 'a+') as wf:
for e in enumeration_encoder:
df_train = DataFrameOperations.import_df(fn=Name_functions.S_GRAEC_train_predictions(s, e))
acc_train = Metrics.accuracy(df_train['True_label'], df_train['Predicted_label'])
f1_train = Metrics.f1(df_train['True_label'], df_train['Predicted_label'])
df_test = DataFrameOperations.import_df(fn=Name_functions.S_GRAEC_test_predictions(s, e))
acc_test = Metrics.accuracy(df_test['True_label'], df_test['Predicted_label'])
f1_test = Metrics.f1(df_test['True_label'], df_test['Predicted_label'])
wf.write(enumeration_encoder[e] + ';{};ACC;{}\n'.format(s, acc_test))
wf.write(enumeration_encoder[e] + ';{};F1;{}\n'.format(s, f1_test))
if acc_train > best_acc_train:
best_acc_train = acc_train
best_acc_test = acc_test
best_acc_enum = e
if f1_train > best_f1_train:
best_f1_train = f1_train
best_f1_test = f1_test
best_f1_enum = e
with open(Name_functions.S_GRAEC_score(s, 'accuracy'), 'w+') as wf:
wf.write(enumeration_encoder[best_acc_enum] + '\n')
wf.write('{}\n'.format(best_acc_test))
with open(Name_functions.S_GRAEC_score(s, 'f1'), 'w+') as wf:
wf.write(enumeration_encoder[best_f1_enum] + '\n')
wf.write('{}\n'.format(best_f1_test))
def run():
for metric in ['accuracy', 'f1']:
best_graec_score = -1
best_graec_parameters = None
fig, ax = plt.subplots()
y = []
x_labels = []
colours = []
with open(Name_functions.metric_table(metric), 'w+') as wf:
wf.write('\\begin{table}[]\n')
wf.write('\\centering\n')
wf.write('\\begin{tabular}{|c|c|c|c|}\n')
wf.write('\\hline\n')
wf.write('$S$ & $\\beta$ & $\\tau$ & {}\\\\\n'.format(
Parameters.Tex_dict[metric]))
wf.write('\\hline\n')
for S in Parameters.S_values:
names = ['N{}', 'R{}', 'GR{}']
fn_scores = [
Name_functions.S_naive_score(S, metric),
Name_functions.S_recent_score(S, metric),
Name_functions.S_GRAEC_score(S, metric)
]
colour_values = ['r', 'orange', 'b']
for i in range(3):
with open(fn_scores[i], 'r') as rf:
if i == 2:
# Our solution
(B, T, P) = rf.readline()[:-1].split(';')[0:4]
score = float(rf.readline()[:-1])
wf.write('{}&{}&{}&{:.3f}\\\\\n'.format(
S, B, T, score))
if score > best_graec_score:
best_graec_score = score
best_graec_parameters = '{};{};{};{}\n'.format(
S, B, T, P)
else:
score = float(rf.readline()[:-1])
x_labels.append(names[i].format(S))
y.append(score)
colours.append(colour_values[i])
wf.write('\\hline\n')
wf.write('\\end{tabular}\n')
wf.write(
'\\caption{{Optimal values for $S$, $\\beta$, and $\\tau$, and their {} scores}}\n'
.format(Parameters.Tex_dict[metric]))
wf.write('\\label{}\n')
wf.write('\\end{table}\n')
ax.bar(range(len(x_labels)), y, color=colours)
ax.set_xticks(range(len(x_labels)))
ax.set_xticklabels(x_labels)
ax.set_xlabel('Method')
ax.set_ylim(0, max(y) + 0.05)
ax.set_ylabel('{} score'.format(metric))
ax.set_title(
'{} scores for different concept drift solutions'.format(metric))
for (xp, yp) in zip(range(len(x_labels)), y):
ax.text(xp - 0.36, yp, '{:.2f}'.format(yp))
# Save graph to disc
fn = Name_functions.metric_figure(metric)
fig.set_size_inches(20 / 2.56, 10 / 2.56)
plt.savefig(fn, bbox_inches='tight')
plt.close()
# Save best graec parameters to disc
fn = Name_functions.best_graec()
with open(fn, 'w+') as wf:
wf.write(best_graec_parameters)
