-
Notifications
You must be signed in to change notification settings - Fork 2
/
app.py
592 lines (479 loc) · 32.5 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
from asyncio import Transport
import pandas as pd
import streamlit as st
from streamlit_plotly_events import plotly_events
import util
import mapping
from sklearn import ensemble
from sklearn.multioutput import MultiOutputRegressor
from collections import Counter
def changeprov():
indexgantiprov = df['provinsi'].loc[lambda x: x==st.session_state.state_name_provinsi].index[0]
st.session_state.selectboxchanged = 1
st.session_state.index_provinsi = indexgantiprov
if 'selected_points' in locals():
selected_points.append({'pointIndex' : indexgantiprov})
idx = int(selected_points[0]['pointIndex'])
else:
selected_points = [{'pointIndex': indexgantiprov}, {'pointIndex': indexgantiprov}]
idx = int(selected_points[0]['pointIndex'])
def changetarget():
if ('ever_clicked' in st.session_state):
del st.session_state['ever_clicked']
if 'selectboxchanged' not in st.session_state:
st.session_state['selectboxchanged'] = 0
st.set_page_config(page_title="Capaian Indikator Utama Pembangunan di Indonesia", layout="wide")
st.title('Capaian Indikator Utama Pembangunan di Indonesia')
tab1, tab2, tab3, tab4 = st.tabs(["1. Visualisasi Data", "2. Prediksi Data", "3. Simulasi Satu Indikator", "4. Simulasi Seluruh Indikator"])
with tab1:
inputcol1, inputcol2 = st.columns(2)
with inputcol1:
tahun = st.selectbox('Tahun', ([str(x) for x in range(2021, 2009, -1)]))
with inputcol2:
target = st.selectbox('Indikator', ('Indeks Pembangunan Manusia', 'Tingkat Kemiskinan', 'Rasio Gini', 'Laju Pertumbuhan Ekonomi', 'Tingkat Pengangguran Terbuka'), key=0, on_change=changetarget)
filetarget = 'Indeks Pembangunan Manusia.xlsx'
sasaran = 'IPM.xlsx'
column = ['IPM']
flag='IPM'
if (target == 'Tingkat Kemiskinan'):
filetarget = 'persentasemiskin.xlsx'
sasaran = 'TK.xlsx'
column = ['Kemiskinan']
flag='Kemiskinan'
elif (target == 'Rasio Gini'):
filetarget = 'giniratio.xlsx'
sasaran = 'GINI.xlsx'
column = ['Gini']
flag='Gini'
elif (target == 'Laju Pertumbuhan Ekonomi'):
filetarget = 'Laju PDRB.xlsx'
sasaran = 'LPE.xlsx'
column = ['LPE']
flag='LPE'
elif (target == 'Tingkat Pengangguran Terbuka'):
filetarget = 'pengangguran.xlsx'
sasaran = 'TPT.xlsx'
column = ['TPT']
flag='TPT'
column.append("Inflasi")
column.append("KFD")
Inflasi = pd.read_excel('Inflasi.xlsx')
KFD = pd.read_excel('KFD.xlsx')
exec('{} = pd.read_excel("{}")'.format(column[0], filetarget))
filesasaran = pd.read_excel(sasaran).sort_values(by='tahun').reset_index().drop('index', axis=1)
filesasaran.fillna("", inplace = True)
provinsi=['ACEH','SUMATERA_UTARA','SUMATERA_BARAT','RIAU', 'JAMBI', 'SUMATERA_SELATAN', 'BENGKULU', 'LAMPUNG', 'BANGKA_BELITUNG', 'KEPRI', 'DKI_JAKARTA', 'JAWA_BARAT', 'JAWA_TENGAH', 'DI_YOGYAKARTA', 'JAWA_TIMUR', 'BANTEN', 'BALI', 'NTB', 'NTT', 'KALIMANTAN_BARAT', 'KALIMANTAN_TENGAH', 'KALIMANTAN_SELATAN', 'KALIMANTAN_TIMUR', 'KALIMANTAN_UTARA', 'SULAWESI_UTARA', 'SULAWESI_TENGAH', 'SULAWESI_SELATAN', 'SULAWESI_TENGGARA', 'GORONTALO', 'SULAWESI_BARAT', 'MALUKU', 'MALUKU_UTARA', 'PAPUA_BARAT', 'PAPUA']
for x in provinsi:
exec('{} = pd.DataFrame(columns=column)'.format(x))
countX=0
countY=0
for i in provinsi:
for j in column:
exec('{}["{}"]={}.loc[{}:{},2010:2021].T'.format(i,j,j,countX,countY))
countX+=1
countY+=1
APBN = pd.read_csv('Peta APBN Data.csv', header=None)
APBN[0][0] = 'Tahun'
APBN.columns = APBN.iloc[0]
APBN = APBN.iloc[1:]
APBN = APBN.astype({"Tahun": int})
APBN = APBN.astype({"Tahun": str})
APBN = APBN.set_index('Tahun', drop=True)
mappingprovinsiAPBN = mapping.mappingprovinsiAPBN
for x in mappingprovinsiAPBN.keys():
firstcol = (x-1)*11
lastcol = x*11
exec('{} = APBN.iloc[:, {}:{}]'.format(mappingprovinsiAPBN[x], firstcol, lastcol))
exec('for col in {}.columns: {}[col] = {}[col].astype(int)'.format(mappingprovinsiAPBN[x],mappingprovinsiAPBN[x],mappingprovinsiAPBN[x]))
for x in provinsi:
exec('{}.index = {}.index.map(str)'.format(x, x))
exec('{} = pd.concat([{}, {}APBN], axis=1, join="inner")'.format(x, x, x))
st.success('Data ' + target + ' per Provinsi')
exec('df_mapping = {}'.format(column[0]))
df, indomap = util.read_map(df_mapping, provinsi)
col1, col2 = st.columns((4, 1))
with col1:
do_refresh = st.button('Refresh')
peta = util.plot_map(df, indomap, tahun)
selected_points = plotly_events(peta)
if (st.session_state.selectboxchanged == 1):
selected_points.append({'pointIndex' : -1})
if ('ever_clicked' in st.session_state):
selected_points.append({'pointIndex' : -1})
if (len(selected_points) > 0):
st.session_state.ever_clicked = 1
idx = int(selected_points[0]['pointIndex'])
if (st.session_state.selectboxchanged == 1):
idx = st.session_state.index_provinsi
st.session_state.selectboxchanged = 0
if (selected_points[0]['pointIndex'] == -1):
idx = st.session_state.index_provinsi
name_provinsi = df.iloc[idx]['provinsi']
st.session_state['state_name_provinsi'] = name_provinsi
selected_provinsi = df['variabel'].iloc[idx]
exec('results = util.prediction({})'.format(selected_provinsi))
with col2:
st.selectbox('Provinsi', (df['provinsi']), key='state_name_provinsi' , on_change=changeprov)
if 'selected_provinsi' in locals():
exec('temp_df={}[[column[0]]].reset_index().drop("index", axis=1)'.format(selected_provinsi))
temp_df=pd.concat([temp_df, filesasaran], axis=1)
util.is_target(temp_df, flag)
resultsallcompiled = []
if (len(selected_points) > 0):
targets = ['Indeks Pembangunan Manusia', 'Tingkat Kemiskinan', 'Rasio Gini', 'Laju Pertumbuhan Ekonomi', 'Tingkat Pengangguran Terbuka']
filetargets = ['Indeks Pembangunan Manusia.xlsx', 'persentasemiskin.xlsx', 'giniratio.xlsx', 'Laju PDRB.xlsx', 'pengangguran.xlsx']
sasarans = ['IPM.xlsx', 'TK.xlsx', 'GINI.xlsx', 'LPE.xlsx', 'TPT.xlsx']
columns = [['IPM'], ['Kemiskinan'], ['Gini'], ['LPE'], ['TPT']]
for k in range(len(columns)):
columns[k].append("Inflasi")
columns[k].append("KFD")
for i in range(len(targets)):
target2 = targets[i]
filetarget2 = filetargets[i]
sasaran2 = sasarans[i]
column2 = columns[i]
exec('{} = pd.read_excel("{}")'.format(column2[0], filetarget2))
filesasaran = pd.read_excel(sasaran2).sort_values(by='tahun').reset_index().drop('index', axis=1)
filesasaran.fillna("", inplace = True)
provinsi=['ACEH','SUMATERA_UTARA','SUMATERA_BARAT','RIAU', 'JAMBI', 'SUMATERA_SELATAN', 'BENGKULU', 'LAMPUNG', 'BANGKA_BELITUNG', 'KEPRI', 'DKI_JAKARTA', 'JAWA_BARAT', 'JAWA_TENGAH', 'DI_YOGYAKARTA', 'JAWA_TIMUR', 'BANTEN', 'BALI', 'NTB', 'NTT', 'KALIMANTAN_BARAT', 'KALIMANTAN_TENGAH', 'KALIMANTAN_SELATAN', 'KALIMANTAN_TIMUR', 'KALIMANTAN_UTARA', 'SULAWESI_UTARA', 'SULAWESI_TENGAH', 'SULAWESI_SELATAN', 'SULAWESI_TENGGARA', 'GORONTALO', 'SULAWESI_BARAT', 'MALUKU', 'MALUKU_UTARA', 'PAPUA_BARAT', 'PAPUA']
for x in provinsi:
exec('{} = pd.DataFrame(columns=column2)'.format(x))
countX=0
countY=0
for i in provinsi:
for j in column2:
exec('{}["{}"]={}.loc[{}:{},2010:2021].T'.format(i,j,j,countX,countY))
countX+=1
countY+=1
APBN = pd.read_csv('Peta APBN Data.csv', header=None)
APBN[0][0] = 'Tahun'
APBN.columns = APBN.iloc[0]
APBN = APBN.iloc[1:]
APBN = APBN.astype({"Tahun": int})
APBN = APBN.astype({"Tahun": str})
APBN = APBN.set_index('Tahun', drop=True)
mappingprovinsiAPBN = mapping.mappingprovinsiAPBN
for x in mappingprovinsiAPBN.keys():
firstcol = (x-1)*11
lastcol = x*11
exec('{} = APBN.iloc[:, {}:{}]'.format(mappingprovinsiAPBN[x], firstcol, lastcol))
exec('for col in {}.columns: {}[col] = {}[col].astype(int)'.format(mappingprovinsiAPBN[x],mappingprovinsiAPBN[x],mappingprovinsiAPBN[x]))
for x in provinsi:
exec('{}.index = {}.index.map(str)'.format(x, x))
exec('{} = pd.concat([{}, {}APBN], axis=1, join="inner")'.format(x, x, x))
exec('df_mapping = {}'.format(column2[0]))
df, indomap = util.read_map(df_mapping, provinsi)
selected_provinsi = df['variabel'].iloc[idx]
exec('resultsall = util.prediction({})'.format(selected_provinsi))
resultsallcompiled.append(resultsall)
col1, col2 = st.columns((4,1))
with col1:
with st.expander("Keterangan Rentang"):
st.write('Semakin rendah Tingkat Pengangguran Terbuka, Tingkat Kemiskinan, dan Rasio Gini berarti semakin baik')
with col2:
with st.expander("Keterangan Indikator"):
st.write('🟥 Masih jauh dari target dalam RKP (>5% deviasi dari nilai target)')
st.write('🟨 Mendekati target dalam RKP (5% deviasi dari nilai target)')
st.write('🟩 Sudah memenuhi target dalam RKP (>= atau <=)')
st.write('⬜ Target belum tersedia dalam RKP pada tahun tersebut')
with tab2:
if 'results' not in locals():
st.warning('Silakan Pilih Provinsi pada Peta')
if 'name_provinsi' in locals():
st.subheader('Prediksi ' + target + ' pada Provinsi ' + name_provinsi)
if 'results' in locals():
st.success('Hasil Prediksi ' + target)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.write('')
with col2:
st.write('Data Asli')
st.write(results['y_test'])
with col3:
st.write('Hasil Prediksi')
st.write(results['y_pred'])
col4.metric("Root Mean Squared Error (RMSE)", str('{:.3f}'.format(results['RMSE'])))
with col5:
st.write('')
with st.expander("Keterangan Hasil Prediksi"):
st.write('Dilakukan pelatihan terhadap model pada data historis tahun 2010-2019 dan dilakukan pengujian pada data tahun 2020-2021')
st.write('Semakin rendah hasil Root Mean Squared Error (RMSE) berarti semakin baik')
st.success('Analisis Fungsi Anggaran Utama dalam Memprediksi ' + target)
col1, col2, col3, col4 = st.columns(4)
with col1:
st.write('')
with col2:
st.write('Tingkat Keutamaan Fungsi Anggaran')
results['importance_df'].drop(results['importance_df']
[(results['importance_df']['Fungsi Anggaran'] == 'Inflasi') | (results['importance_df']['Fungsi Anggaran'] == 'KFD')].index, inplace=True)
st.write(results['importance_df'].style.applymap(util.is_feature_importance, subset=['Keutamaan']))
with col3:
st.write('Fungsi Anggaran Utama')
st.write(results['dfprovakhir'].iloc[:, 1:])
with col4:
st.write('')
col1, col2 = st.columns(2)
with col1:
with st.expander("Keterangan Tingkat Keutamaan"):
st.write('🟩 Tingkat keutamaan >= 0.5')
st.write('🟨 0.2 =< Tingkat keutamaan < 0.5')
st.write('🟧 0.1 =< Tingkat keutamaan < 0.2')
st.write('🟥 Tingkat keutamaan < 0.1')
with col2:
with st.expander("Keterangan Fungsi Anggaran"):
st.write('Fungsi anggaran utama didapat dari feature importance')
st.write('Fungsi anggaran dalam miliar rupiah')
targets = ['Indeks Pembangunan Manusia', 'Tingkat Kemiskinan', 'Rasio Gini', 'Laju Pertumbuhan Ekonomi', 'Tingkat Pengangguran Terbuka']
features1 = []
features2 = []
features3 = []
listsemua = []
for i in range (len(resultsallcompiled)):
resultsallcompiled[i]['importance_df'].drop(resultsallcompiled[i]['importance_df']
[(resultsallcompiled[i]['importance_df']['Fungsi Anggaran'] == 'Inflasi') | (resultsallcompiled[i]['importance_df']['Fungsi Anggaran'] == 'KFD')].index, inplace=True)
features1.append(resultsallcompiled[i]['importance_df']['Fungsi Anggaran'].iloc[0])
features2.append(resultsallcompiled[i]['importance_df']['Fungsi Anggaran'].iloc[1])
features3.append(resultsallcompiled[i]['importance_df']['Fungsi Anggaran'].iloc[2])
listsemua.append(resultsallcompiled[i]['importance_df']['Fungsi Anggaran'].iloc[0])
listsemua.append(resultsallcompiled[i]['importance_df']['Fungsi Anggaran'].iloc[1])
listsemua.append(resultsallcompiled[i]['importance_df']['Fungsi Anggaran'].iloc[2])
dfalltarget = pd.DataFrame({'Target': targets, 'Anggaran Utama 1': features1, 'Anggaran Utama 2': features2, 'Anggaran Utama 3': features3})
listcountsemua = []
counterresults = Counter(listsemua)
for key in counterresults.keys():
listcountsemua.append((key,counterresults[key]))
sorted_by_second = sorted(listcountsemua, key=lambda tup: tup[1], reverse=True)
dfalltargettop3 = resultsallcompiled[0]['dfprovawal'][[sorted_by_second[0][0], sorted_by_second[1][0], sorted_by_second[2][0]]]
for i in range(len(resultsallcompiled)):
dfalltargettop3[targets[i]] = resultsallcompiled[i]['dfprov'][[resultsallcompiled[i]['dfprov'].columns[0]]]
st.success('Analisis Fungsi Anggaran Utama dalam Memprediksi Seluruh 5 Indikator Utama Pembangunan')
lsa = []
lsb = []
for i in range(len(sorted_by_second)):
lsa.append(sorted_by_second[i][0])
lsb.append(sorted_by_second[i][1]/15)
dftop3counts = pd.DataFrame({'Fungsi Anggaran':lsa, 'Keutamaan': lsb})
col1, col2, col3, col4 = st.columns(4)
with col1:
st.write('')
with col2:
st.write('Tingkat Keutamaan Fungsi Anggaran')
st.write(dftop3counts.style.applymap(util.is_feature_importance, subset=['Keutamaan']))
with col3:
st.write('Fungsi Anggaran Utama')
st.write(dfalltargettop3.iloc[:, :3])
with col4:
st.write('')
col1, col2 = st.columns(2)
with col1:
with st.expander("Keterangan Tingkat Keutamaan"):
st.write('🟩 Tingkat keutamaan >= 0.5')
st.write('🟨 0.2 =< Tingkat keutamaan < 0.5')
st.write('🟧 0.1 =< Tingkat keutamaan < 0.2')
st.write('🟥 Tingkat keutamaan < 0.1')
with col2:
with st.expander("Keterangan Fungsi Anggaran"):
st.write('Fungsi anggaran utama didapat dari kesamaan fungsi anggaran utama pada seluruh 5 indikator utama pembangunan')
with tab3:
if 'results' not in locals():
st.warning('Silakan Pilih Provinsi pada Peta')
if 'results' in locals():
st.subheader('Simulasi Belanja Pemerintah Per Fungsi terhadap Capaian ' + target + ' pada Provinsi ' + name_provinsi)
st.success('Simulasi ' + target + ' yang Akan Dicapai Berdasarkan Fungsi Anggaran Utama yang Dikeluarkan' )
dflima = results['dfprovakhir'].copy()
dflima['Inflasi'] = results['dfprovawal']['Inflasi']
dflima['KFD'] = results['dfprovawal']['KFD']
f1val = results['dfprovakhir'].iloc[-1:, 1:][results['dfprovakhir'].iloc[-1:, 1:].columns[0]].iloc[0]
f2val = results['dfprovakhir'].iloc[-1:, 1:][results['dfprovakhir'].iloc[-1:, 1:].columns[1]].iloc[0]
f3val = results['dfprovakhir'].iloc[-1:, 1:][results['dfprovakhir'].iloc[-1:, 1:].columns[2]].iloc[0]
f4val = results['dfprovawal'].iloc[-1:, 1:][results['dfprovawal'].iloc[-1:, 1:].columns[0]].iloc[0]
f5val = results['dfprovawal'].iloc[-1:, 1:][results['dfprovawal'].iloc[-1:, 1:].columns[1]].iloc[0]
with st.form("form_1"):
st.info('Faktor Anggaran')
col1, col2, col3 = st.columns(3)
with col1:
f1 = st.number_input('Anggaran ' + results['dfprovakhir'].columns[1] + ' (Dalam Milyar Rupiah)', value=f1val)
with col2:
f2 = st.number_input('Anggaran ' + results['dfprovakhir'].columns[2] + ' (Dalam Milyar Rupiah)', value=f2val)
with col3:
f3 = st.number_input('Anggaran ' + results['dfprovakhir'].columns[3] + ' (Dalam Milyar Rupiah)', value=f3val)
st.info('Faktor Lainnya')
col1, col2, col3, col4 = st.columns(4)
with col1:
st.write('')
with col2:
f4 = st.number_input('Faktor Inflasi', value=f4val)
with col3:
f5 = st.number_input('Faktor Kapasitas Fiskal Daerah', value=f5val)
with col4:
st.write('')
submitted = st.form_submit_button("Hitung", on_click=changeprov)
if submitted:
X = dflima.iloc[:, 1:6]
y = results['dfprov'][[results['dfprov'].columns[0]]]
X_train = X[:12]
y_train = y[:12]
regressor = ensemble.GradientBoostingRegressor(random_state=0)
regressor.fit(X_train, y_train)
y_pred = regressor.predict([[f1, f2, f3, f4, f5]])
st.metric('Prediksi Capaian ' +target+':', str('{:.3f}'.format(y_pred[0])))
if (target == 'Indeks Pembangunan Manusia'):
if y_pred[0] >= 73.41:
st.warning('Sudah Mencapai Target RKP')
else:
st.error('Belum Mencapai Target RKP')
elif (target == 'Tingkat Kemiskinan'):
if y_pred[0] <= 8.5:
st.warning('Sudah Mencapai Target RKP')
else:
st.error('Belum Mencapai Target RKP')
elif (target == 'Rasio Gini'):
if y_pred[0] <= 0.376:
st.warning('Sudah Mencapai Target RKP')
else:
st.error('Belum Mencapai Target RKP')
elif (target == 'Laju Pertumbuhan Ekonomi'):
if y_pred[0] >= 5.2 and y_pred[0] <= 5.5:
st.warning('Sudah Mencapai Target RKP')
else:
st.error('Belum Mencapai Target RKP')
else:
if y_pred[0] <= 6.3:
st.warning('Sudah Mencapai Target RKP')
else:
st.error('Belum Mencapai Target RKP')
st.success('Simulasi Fungsi Anggaran Utama yang Perlu Dikeluarkan untuk Mencapai ' + target + ' yang Diinginkan')
with st.form("form_2"):
st.info('Faktor Anggaran')
col1, col2, col3 = st.columns(3)
with col1:
st.write('')
with col2:
f1 = st.number_input('Capaian ' + target + ' yang Diinginkan', value=results['y_test'].iloc[-1:].iloc[0])
with col3:
st.write('')
col1, col2, col3 = st.columns(3)
with col1:
st.write('')
with col2:
if (target == 'Indeks Pembangunan Manusia'):
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">73.41 - 73.46</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target Indeks Pembangunan Manusia RKP Tahun 2022</span></p>', unsafe_allow_html=True)
elif (target == 'Tingkat Kemiskinan'):
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">8.50 - 9.00</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target Tingkat Kemiskinan RKP Tahun 2022</span></p>', unsafe_allow_html=True)
elif (target == 'Rasio Gini'):
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">0.376 - 0.378</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target Rasio Gini RKP Tahun 2022</span></p>', unsafe_allow_html=True)
elif (target == 'Laju Pertumbuhan Ekonomi'):
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">5.20 - 5.50</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target Laju Pertumbuhan Ekonomi RKP Tahun 2022</span></p>', unsafe_allow_html=True)
else:
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">5.50 - 6.30</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target Tingkat Pengangguran Terbuka RKP Tahun 2022</span></p>', unsafe_allow_html=True)
with col3:
st.write('')
st.info('Faktor Lainnya')
col1, col2, col3, col4 = st.columns(4)
with col1:
st.write('')
with col2:
f2 = st.number_input('Faktor Inflasi', value=f4val)
with col3:
f3 = st.number_input('Faktor Kapasitas Fiskal Daerah', value=f5val)
with col4:
st.write('')
submitted = st.form_submit_button("Hitung", on_click=changeprov)
if submitted:
X = dflima.iloc[:, 1:4]
y = results['dfprovawal'][[results['dfprovawal'].columns[0], 'Inflasi', 'KFD']]
y_train = X[:12]
X_train = y[:12]
regressor = MultiOutputRegressor(ensemble.GradientBoostingRegressor(random_state=0))
regressor.fit(X_train, y_train)
y_pred = regressor.predict([[f1,f2,f3]])
percentage1 = ((float(y_pred[:,0:1])-f1val)/f1val)*100
percentage2 = ((float(y_pred[:,1:2])-f2val)/f2val)*100
percentage3 = ((float(y_pred[:,2:3])-f3val)/f3val)*100
st.warning('Anggaran Saat Ini')
col1, col2, col3 = st.columns(3)
with col1:
st.metric('Anggaran ' + X.columns[0] + ' Saat Ini (Dalam Miliar Rupiah): ', str(f1val))
with col2:
st.metric('Anggaran ' + X.columns[1] + ' Saat Ini (Dalam Miliar Rupiah): ', str(f2val))
with col3:
st.metric('Anggaran ' + X.columns[2] + ' Saat Ini (Dalam Miliar Rupiah): ', str(f3val))
st.warning('Prediksi Anggaran yang Perlu Dikeluarkan')
col1, col2, col3 = st.columns(3)
with col1:
st.metric('Prediksi Anggaran ' + X.columns[0] +' (Dalam Miliar Rupiah): ', str('{:.3f}'.format(float(y_pred[:,0:1]))), str('{:.3f}'.format(percentage1)) + '%', delta_color="inverse")
with col2:
st.metric('Prediksi Anggaran ' + X.columns[1] +' (Dalam Miliar Rupiah): ', str('{:.3f}'.format(float(y_pred[:,1:2]))), str('{:.3f}'.format(percentage2)) + '%', delta_color="inverse")
with col3:
st.metric('Prediksi Anggaran ' + X.columns[2] +' (Dalam Miliar Rupiah): ', str('{:.3f}'.format(float(y_pred[:,2:3]))), str('{:.3f}'.format(percentage3)) + '%', delta_color="inverse")
with tab4:
if 'results' not in locals():
st.warning('Silakan Pilih Provinsi pada Peta')
if 'results' in locals():
st.subheader('Simulasi Belanja Pemerintah Per Fungsi terhadap Seluruh Indikator Utama Pembangunan pada Provinsi ' + name_provinsi)
with st.form("form_3"):
st.info('Faktor Anggaran')
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.write('Capaian ' + targets[0] + ' yang Diinginkan: ')
f1 = st.number_input('', value=73.46, label_visibility='collapsed')
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">73.46</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target ' + targets[0] + ' RKP Tahun 2022</span></p>', unsafe_allow_html=True)
with col2:
st.write('Capaian ' + targets[1] + ' yang Diinginkan: ')
f2 = st.number_input('', value=9.0, label_visibility='collapsed')
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">9.0</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target ' + targets[1] + ' RKP Tahun 2022</span></p>', unsafe_allow_html=True)
with col3:
st.write('Capaian ' + targets[2] + ' yang Diinginkan: ')
f3 = st.number_input('', value=0.378, label_visibility='collapsed')
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">0.378</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target ' + targets[2] + ' RKP Tahun 2022</span></p>', unsafe_allow_html=True)
with col4:
st.write('Capaian ' + targets[3] + ' yang Diinginkan: ')
f4 = st.number_input('', value=5.5, label_visibility='collapsed')
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">5.5</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target ' + targets[3] + ' RKP Tahun 2022</span></p>', unsafe_allow_html=True)
with col5:
st.write('Capaian ' + targets[4] + ' yang Diinginkan: ')
f5 = st.number_input('', value=6.3, label_visibility='collapsed')
st.markdown('<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.1/css/all.css" crossorigin="anonymous"><p style="background-color: rgb(220, 137, 40); color: rgb(250, 250, 250); font-size: 34px; border-radius: 7px; padding-left: 12px; padding-top: 6px; padding-bottom: 6px; line-height: 27px;"><span style="color: rgb(0, 0, 0)">6.3</span><br><span style="font-size: 18px; margin-top: 0px; color: white; line-height: 5px;">Target ' + targets[4] + ' RKP Tahun 2022</span></p>', unsafe_allow_html=True)
st.info('Faktor Lainnya')
col1, col2, col3, col4 = st.columns(4)
with col1:
st.write('')
with col2:
f6 = st.number_input('Faktor Inflasi', value=f4val)
with col3:
f7 = st.number_input('Faktor Kapasitas Fiskal Daerah', value=f5val)
with col4:
st.write('')
f1val = dfalltargettop3.iloc[:, 0:1].iloc[len(dfalltargettop3)-1].iloc[0]
f2val = dfalltargettop3.iloc[:, 1:2].iloc[len(dfalltargettop3)-1].iloc[0]
f3val = dfalltargettop3.iloc[:, 2:3].iloc[len(dfalltargettop3)-1].iloc[0]
submitted = st.form_submit_button("Hitung", on_click=changeprov)
if submitted:
dfalltargettop3['Inflasi'] = dflima['Inflasi']
dfalltargettop3['KFD'] = dflima['KFD']
X = dfalltargettop3.iloc[:, 3:10]
y = dfalltargettop3.iloc[:, 0:3]
X_train = X[:12]
y_train = y[:12]
regressor = MultiOutputRegressor(ensemble.GradientBoostingRegressor(random_state=0))
regressor.fit(X_train, y_train)
y_pred = regressor.predict([[f1, f2, f3, f4, f5, f6, f7]])
percentage1 = ((float(y_pred[:,0:1])-f1val)/f1val)*100
percentage2 = ((float(y_pred[:,1:2])-f2val)/f2val)*100
percentage3 = ((float(y_pred[:,2:3])-f3val)/f3val)*100
st.warning('Anggaran Saat Ini')
col1, col2, col3 = st.columns(3)
with col1:
st.metric('Anggaran ' + dfalltargettop3.columns[0] + ' Saat Ini (Dalam Miliar Rupiah): ', str(f1val))
with col2:
st.metric('Anggaran ' + dfalltargettop3.columns[1] + ' Saat Ini (Dalam Miliar Rupiah): ', str(f2val))
with col3:
st.metric('Anggaran ' + dfalltargettop3.columns[2] + ' Saat Ini (Dalam Miliar Rupiah): ', str(f3val))
st.warning('Prediksi Anggaran yang Perlu Dikeluarkan')
col1, col2, col3 = st.columns(3)
with col1:
st.metric('Prediksi Anggaran ' + dfalltargettop3.columns[0] + ' (Dalam Miliar Rupiah): ', str('{:.3f}'.format(float(y_pred[:,0:1]))), str('{:.3f}'.format(percentage1)) + '%', delta_color="inverse")
with col2:
st.metric('Prediksi Anggaran ' + dfalltargettop3.columns[1] + ' (Dalam Miliar Rupiah): ', str('{:.3f}'.format(float(y_pred[:,1:2]))), str('{:.3f}'.format(percentage2)) + '%', delta_color="inverse")
with col3:
st.metric('Prediksi Anggaran ' + dfalltargettop3.columns[2] + ' (Dalam Miliar Rupiah): ', str('{:.3f}'.format(float(y_pred[:,2:3]))), str('{:.3f}'.format(percentage3)) + '%', delta_color="inverse")