def draw_conf_matrix(confusion_matrix: ConfusionMatrix, algo_name: str,
fold_iter: int):
classification_classes = confusion_matrix.classes
confusion_matrix_df = DataFrame(confusion_matrix.to_array())
row_sums = confusion_matrix_df.sum(axis=1).to_list()
x_labels = [
"{} ({})".format(label, row_sums[index])
for index, label in enumerate(classification_classes)
]
temp_df = confusion_matrix_df.T
shape = temp_df.shape
annotations = [[
f'''{classification_classes[row]}\n{classification_classes[col]}\n{temp_df[row][col]}'''
for col in range(0, shape[0])
] for row in range(0, shape[1])]
confusion_matrix_heatmap = sns.heatmap(
confusion_matrix_df.T,
annot=DataFrame(annotations).T,
xticklabels=x_labels,
yticklabels=classification_classes,
cmap="OrRd",
linewidths=0.1,
linecolor="black",
fmt='',
cbar=False,
)
fig = confusion_matrix_heatmap.get_figure()
fig.savefig("./test_result_data/{}-fold-{}.svg".format(
algo_name, fold_iter),
bbox_inches='tight')
fig.savefig("./test_result_data/{}-fold-{}.png".format(
algo_name, fold_iter),
bbox_inches='tight')
plt.clf()
pass
def generate_and_save_report(
y_pred: list,
y_true: list,
corpus_name: str,
algorithm_name: str,
fold: int,
) -> None:
report = DataFrame(
classification_report(
y_pred,
y_true,
zero_division=0,
output_dict=True,
))
# Save laporan ke Excel
report.to_excel("./reports/{}-{}-fold-{}.xlsx".format(
corpus_name, algorithm_name, fold))
# Buat plot Confusion Matrix untuk masing-masing kelas pengelompokan
confusion_matrix = ConfusionMatrix(
actual_vector=y_true,
predict_vector=y_pred,
)
confusion_matrix_positions = DataFrame(confusion_matrix.position())
confusion_matrix_positions = confusion_matrix_positions.applymap(
lambda positions: len(positions))
pos_classes = confusion_matrix.classes
pos_classes.sort()
report_text = ""
for pos_class in pos_classes:
tp = confusion_matrix_positions[pos_class]["TP"]
tn = confusion_matrix_positions[pos_class]["TN"]
fp = confusion_matrix_positions[pos_class]["FP"]
fn = confusion_matrix_positions[pos_class]["FN"]
accuracy = tp + tn / (tp + tn + fp + fn)
report_text += get_pos_class_report_text(algorithm_name, report,
pos_class, tp, tn, fp, fn,
accuracy)
# Simpan teks laporan
with open(
"./reports/{}-{}-fold-{}.txt".format(corpus_name, algorithm_name,
fold_counter),
"w") as report_filehandle:
report_filehandle.write(report_text)
confusion_matrix_df = DataFrame(confusion_matrix.to_array())
row_sums = confusion_matrix_df.sum().to_list()
y_labels = pos_classes
x_labels = [
"{} ({})".format(label, row_sums[index])
for index, label in enumerate(pos_classes)
]
temp_df = confusion_matrix_df.T
shape = temp_df.shape
annotations = [[
f'''{pos_classes[col]}\n{pos_classes[row]}\n{temp_df[row][col]}'''
for col in range(0, shape[0])
] for row in range(0, shape[1])]
confusion_matrix_heatmap = sns.heatmap(
confusion_matrix_df,
annot=annotations,
xticklabels=x_labels,
yticklabels=y_labels,
cmap="Greens",
linewidths=0.1,
linecolor="black",
fmt='',
cbar=False,
)
fig = confusion_matrix_heatmap.get_figure()
fig.savefig("./reports/{}-{}-fold-{}.svg".format(
corpus_name,
algorithm_name,
fold,
),
bbox_inches='tight')
plt.clf()
pass