def alumni():
len_data = get_data_length()
alumni = get_all_alumni()
# POST METHOD
if request.method == "POST":
file = check_upload_file(request)
if not file:
flash('Ouch! Terjadi Error, Silahkan Periksa Ulang File Anda!',
'danger')
return redirect(request.url)
try:
timestr = time.strftime("%Y%m%d")
filename = timestr + "_" + secure_filename(file.filename)
path = os.path.join(Config.ROOT_DIRECTORY, "NBC", "static",
"upload", "alumni", filename)
file.save(path)
csv = pd.read_csv(path)
for i, row in csv.iterrows():
# check if the data already exist
exst = get_an_alumni(row['NIM'])
if exst:
flash(
'ID Terduplikasi, Tidak ada perubahan yang dilakukan.',
'danger')
return redirect(request.url)
for index, row in csv.iterrows():
data_alumni = Alumni(id=row['NIM'],
school_type=row['Tipe Sekolah'],
gender=row['Gender'],
school_city=row['Kota Sekolah'],
ket_lulus=row['Keterangan Lulus'])
# make ipk
ipk = (row['IPS_1'] + row['IPS_2'] + row['IPS_3'] +
row['IPS_4']) / 4
# save the IP value
data_nilai = Nilai(id_alumni=row['NIM'],
semester_1=row['IPS_1'],
semester_2=row['IPS_2'],
semester_3=row['IPS_3'],
semester_4=row['IPS_4'],
ipk=float("%.2f" % ipk))
save_to_db(data_alumni)
save_to_db(data_nilai)
flash('Successfully menambahkan data alumni', 'success')
return redirect(request.url)
except Exception as e:
flash('Error: {}'.format(e), 'danger')
return redirect(request.url)
return render_template('admin_data_alumni.html',
data=alumni,
len_data=len_data,
modal_for='alumni')
def training():
training_data = get_all_training()
len_data = get_data_length()
if request.method == "POST":
file = check_upload_file(request)
if not file:
flash('Ouch! Terjadi Error, Silahkan Periksa Ulang File Anda!',
'danger')
return redirect(request.url)
try:
# should delete this or no ?
delete_all_training()
timestr = time.strftime("%Y%m%d")
filename = timestr + "_" + secure_filename(file.filename)
path = os.path.join(Config.ROOT_DIRECTORY, "NBC", "static",
"upload", "training", filename)
file.save(path)
csv = pd.read_csv(path)
for i, row in csv.iterrows():
# check if the data already exist
exst = get_a_training(row['NIM'])
if exst:
flash(
'ID Terduplikasi, Tidak ada perubahan yang dilakukan.',
'danger')
return redirect(request.url)
for index, row in csv.iterrows():
# make ipk
ipk = (row['IPS_1'] + row['IPS_2'] + row['IPS_3'] +
row['IPS_4']) / 4
data_training = Training(id=row['NIM'],
school_type=row['Tipe Sekolah'],
gender=row['Gender'],
school_city=row['Kota Sekolah'],
ket_lulus=row['Keterangan Lulus'],
semester_1=row['IPS_1'],
semester_2=row['IPS_2'],
semester_3=row['IPS_3'],
semester_4=row['IPS_4'],
ipk=float("%.2f" % ipk))
save_to_db(data_training)
flash('Success menambahkan data alumni', 'success')
return redirect(
url_for('dashboard.cross_validation', dt='training'))
except Exception as e:
flash('Error: {}'.format(e), 'danger')
return redirect(request.url)
return render_template('admin_data_training.html',
data=training_data,
len_data=len_data,
modal_for='data training')
def register():
if request.method == "POST":
email = request.form.get('email')
password = request.form.get('password')
first_name = request.form.get('firstname')
last_name = request.form.get('lastname')
phone = request.form.get('phone')
if not email or not password or not first_name or not last_name:
flash('Please fill all form!', 'danger')
return redirect(url_for('dashboard.create_user'))
if phone:
phone = '+62' + phone
data = User(email=email,
password=password,
first_name=first_name,
last_name=last_name,
registered_on=datetime.datetime.utcnow(),
phone_number=phone,
role='Admin')
save_to_db(data)
flash('User berhasil dibuat', 'success')
return redirect(url_for('auth.login'))
return render_template('register.html')
def create_alumni():
if request.method == "POST":
try:
# get data from form
id = request.form.get('angkatan') + '.11.' + request.form.get(
'id').zfill(4)
semester_1 = float(request.form.get('semester_1'))
semester_2 = float(request.form.get('semester_2'))
semester_3 = float(request.form.get('semester_3'))
semester_4 = float(request.form.get('semester_4'))
ipk = (semester_1 + semester_2 + semester_3 + semester_4) / 4
# check if the nim is conflict or not
exst = get_an_alumni(id)
if exst:
flash('Error: NIM Conflict!', 'danger')
return redirect(request.url)
# make an Alumni model object
que_alumni = Alumni(id=id,
school_type=request.form.get('school_type'),
gender=request.form.get('gender'),
school_city=request.form.get('school_city'),
ket_lulus=request.form.get('ket_lulus'))
que_nilai = Nilai(id_alumni=id,
semester_1=semester_1,
semester_2=semester_2,
semester_3=semester_3,
semester_4=semester_4,
ipk=float("%.2f" % ipk))
# insert into database
save_to_db(que_alumni)
save_to_db(que_nilai)
flash('Success membuat data alumni.', 'success')
return redirect(url_for('dashboard.create_alumni'))
except Exception as e:
flash('Error: {}'.format(e))
return redirect(request.url)
return render_template('admin_create_alumni.html')
def cross_validation(dt):
try:
# query all the data
if dt == 'alumni':
dtobj = get_all_alumni()
else:
dtobj = get_all_training()
if dtobj.empty:
flash('Tidak ada data.', 'danger')
return redirect(url_for('dashboard.alumni'))
# convert class label into number, so that it will not be encoded
dtobj['ket_lulus'] = dtobj['ket_lulus'].replace(
['Tidak Tepat Waktu', 'Tepat Waktu'], [0, 1])
# based on you should never change what you itterating over, make a list and make a new data frame
mydf = []
for i, row in dtobj.iterrows():
mydf.append({
'id': row['id'],
'school_type': row['school_type'],
'gender': row['gender'],
'school_city': row['school_city'],
'semester_1': convert_nilai(row['semester_1']),
'semester_2': convert_nilai(row['semester_2']),
'semester_3': convert_nilai(row['semester_3']),
'semester_4': convert_nilai(row['semester_4']),
'ipk': convert_nilai(row['ipk']),
'ket_lulus': row['ket_lulus']
})
df = pd.DataFrame(mydf)
# One Hot Encoder
enc = pd.get_dummies(df.drop(['id'], axis=1))
# Split X, and Y
x = np.array(enc.drop('ket_lulus', axis=1))
y = np.array(enc['ket_lulus'])
# determine the label for confusion matrix
labels = np.unique(y)
# create the model
model = MultinomialNB()
# initialize kfold
kf = KFold(n_splits=10)
# initialize confusion matrix
cf = np.array([[0, 0], [0, 0]])
f1 = []
recall = []
precision = []
scores = []
for train_index, test_index in kf.split(x):
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
# fit the model, then predict the test fold
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
# compute confusion matrix, ROC curve and AUC
cf += confusion_matrix(y_test, y_pred, labels=labels)
# calculate the accuracy, f1-score, recall, precision
f1.append(f1_score(y_test, y_pred))
recall.append(recall_score(y_test, y_pred))
precision.append(precision_score(y_test, y_pred))
scores.append(model.score(x_test, y_test))
dfcv = pd.concat([
pd.DataFrame(scores, columns=['accuracy']),
pd.DataFrame(precision, columns=['precision']),
pd.DataFrame(recall, columns=['recall']),
pd.DataFrame(f1, columns=['f1'])
],
axis=1)
path_model_kfold = os.path.join(Config.STATIC_DIRECTORY,
'kfold_model.png')
path_result_kfold = os.path.join(Config.STATIC_DIRECTORY,
'kfold_result.png')
create_bar_chart(path_model_kfold, f1, recall, precision, scores)
if dt == 'training':
dfcf = pd.DataFrame(cf, columns=['P_Negative', 'P_Positive'])
create_bar_chart(path_result_kfold, f1, recall, precision, scores)
dfcv.to_csv('current_model_cv.csv', index=False, encoding='utf-8')
dfcf.to_csv('current_model_cf.csv', index=False, encoding='utf-8')
# if create model button is clicked
if request.method == "POST":
delete_all_training()
create_bar_chart(path_result_kfold, f1, recall, precision, scores)
dtobj['ket_lulus'] = dtobj['ket_lulus'].replace(
[0, 1], ['Tidak Tepat Waktu', 'Tepat Waktu'])
for i, row in dtobj.iterrows():
data = Training(id=row['id'],
school_type=row['school_type'],
gender=row['gender'],
school_city=row['school_city'],
semester_1=row['semester_1'],
semester_2=row['semester_2'],
semester_3=row['semester_3'],
semester_4=row['semester_4'],
ipk=row['ipk'],
ket_lulus=row['ket_lulus'])
save_to_db(data)
dfcf = pd.DataFrame(cf, columns=['P_Negative', 'P_Positive'])
dfcv.to_csv('current_model_cv.csv', index=False, encoding='utf-8')
dfcf.to_csv('current_model_cf.csv', index=False, encoding='utf-8')
flash('Successfully create a model', 'success')
return redirect(url_for('dashboard.index'))
# < END POST REQUEST >
return render_template('cross_validation.html',
scores=dfcv,
cf=cf,
dt=dt)
except Exception as e:
flash('Error: {}'.format(e), 'danger')
return redirect(url_for('dashboard.alumni'))
def predict_csv():
train_data = get_all_training()
if request.method == "POST":
try:
file = check_upload_file(request)
if not file:
flash('Ouch! Terjadi Error, Silahkan Periksa Ulang File Anda!',
'danger')
return redirect(request.url)
timestr = time.strftime("%Y%m%d")
filename = timestr + "_" + secure_filename(file.filename)
path = os.path.join(Config.ROOT_DIRECTORY, "NBC", "static",
"upload", "prediksi", filename)
file.save(path)
csv = pd.read_csv(path)
for i, row in csv.iterrows():
exst = get_a_prediction(row['NIM'])
if exst:
flash(
'ID Terduplikasi, Tidak ada perubahan yang dilakukan.',
'danger')
return redirect(request.url)
# END FOR
# make list of dict
data_target = []
for index, row in csv.iterrows():
ipk = (row['IPS_1'] + row['IPS_2'] + row['IPS_3'] +
row['IPS_4']) / 4
# save the data into database
# the reason is, i want to store the value with number data type, not discret.
obj_testing = Testing(id=row['NIM'],
school_type=row['Tipe Sekolah'],
gender=row['Gender'],
school_city=row['Kota Sekolah'],
semester_1=row['IPS_1'],
semester_2=row['IPS_2'],
semester_3=row['IPS_3'],
semester_4=row['IPS_4'],
ipk=float("%.2f" % ipk))
save_to_db(obj_testing)
# convert all IP and insert it to list
data_target.append({
'id': row['NIM'],
'school_type': row['Tipe Sekolah'],
'gender': row['Gender'],
'school_city': row['Kota Sekolah'],
'semester_1': convert_nilai(row['IPS_1']),
'semester_2': convert_nilai(row['IPS_2']),
'semester_3': convert_nilai(row['IPS_3']),
'semester_4': convert_nilai(row['IPS_4']),
'ipk': convert_nilai(ipk)
})
# END FOR
y_pred = predict_data(train_data, data_target)
# make data frame from y pred
y_pred = pd.DataFrame(y_pred, columns=['result'])
y_pred['result'] = y_pred['result'].replace(
[0, 1], ['Tidak Tepat Waktu', 'Tepat Waktu'])
# insert into database
# combine the result and the data
dfr = pd.concat([pd.DataFrame(data_target), y_pred],
axis=1,
sort=True)
for i, row in dfr.iterrows():
obj_hasil = Hasil(id_testing=row['id'], result=row['result'])
save_to_db(obj_hasil)
flash('Prediksi Berhasil', 'success')
return redirect(url_for('dashboard.predict'))
except Exception as e:
flash('Error: {}'.format(e), 'danger')
return redirect(request.url)
return render_template('admin_prediksi_csv.html')
def create_predict():
if request.method == "POST":
try:
# get training data for prediction
train_data = get_all_training()
if train_data.empty:
flash(
'Tidak ada data training, silahkan buat model terlebih dahulu.',
'danger')
return redirect(request.url)
if not request.form.get('id'):
flash('Fill all empty form!', 'danger')
return redirect(request.url)
# get the data from frontend form
id = request.form.get('angkatan') + '.11.' + request.form.get(
'id').zfill(4)
# check if the nim already in databases
exst = get_a_prediction(id)
# if already in databases, return with alert error
if exst:
flash('Error, NIM Duplicated!', 'danger')
return redirect(request.url)
semester_1 = float(request.form.get('semester_1'))
semester_2 = float(request.form.get('semester_2'))
semester_3 = float(request.form.get('semester_3'))
semester_4 = float(request.form.get('semester_4'))
ipk = (semester_1 + semester_2 + semester_3 + semester_4) / 4
# change the value into categorical
test_data = {
'id': id,
'school_type': request.form.get('school_type'),
'gender': request.form.get('gender'),
'school_city': request.form.get('school_city'),
'semester_1': convert_nilai(semester_1),
'semester_2': convert_nilai(semester_2),
'semester_3': convert_nilai(semester_3),
'semester_4': convert_nilai(semester_4),
'ipk': convert_nilai(ipk)
}
y_pred = predict_data(train_data, test_data, index=True)
# convert the result into string
if y_pred == 0:
y_pred = 'Tidak Tepat Waktu'
else:
y_pred = 'Tepat Waktu'
# insert into database
que_test = Testing(id=test_data['id'],
school_type=test_data['school_type'],
gender=test_data['gender'],
school_city=test_data['school_city'],
semester_1=semester_1,
semester_2=semester_2,
semester_3=semester_3,
semester_4=semester_4,
ipk=float("%.2f" % ipk))
que_hasil = Hasil(id_testing=test_data['id'], result=y_pred)
save_to_db(que_test)
save_to_db(que_hasil)
flash('Success memprediksi data.', 'success')
return redirect(url_for('dashboard.predict'))
except Exception as e:
flash('Error: {}'.format(e), 'danger')
return redirect(request.url)
return render_template('admin_create_prediksi.html')
