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year hardcode sdgs

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Debby
2026-04-02 07:10:41 +07:00
parent 189e8895c9
commit ffd8cdf65e
1 changed files with 121 additions and 96 deletions
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217
scripts/bigquery_analytical_layer.py
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@@ -12,10 +12,11 @@ Filtering Order:
→ Semua baris tetap ada; label framework ditentukan di Step 6 → Semua baris tetap ada; label framework ditentukan di Step 6
6. Assign framework (MDGs/SDGs) per indicator PER ROW 6. Assign framework (MDGs/SDGs) per indicator PER ROW
→ Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' selalu → Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' selalu
→ Indikator DI SDG_ONLY_KEYWORDS + year >= sdg_transition_year → 'SDGs' → Indikator DI SDG_ONLY_KEYWORDS + year >= SDG_TRANSITION_YEAR → 'SDGs'
→ Indikator DI SDG_ONLY_KEYWORDS + year < sdg_transition_year → 'MDGs' → Indikator DI SDG_ONLY_KEYWORDS + year < SDG_TRANSITION_YEAR → 'MDGs'
→ sdg_transition_year = min(actual_start_year) dari semua SDG-only indicators → SDG_TRANSITION_YEAR = 2016 (HARDCODE — tanggal resmi SDGs berlaku)
yang lolos filter (= tahun pertama data SDG-only konsisten di semua countries) BUKAN dari actual_start_year data, karena data anaemia/FIES bisa ada
sebelum 2016 namun tetap harus dilabeli MDGs pada tahun-tahun tersebut.
7. Verify no gaps (dari actual_start_year per indikator, bukan start_year global) 7. Verify no gaps (dari actual_start_year per indikator, bukan start_year global)
8. Calculate norm_value_1_100 per indicator (min-max, direction-aware, global) 8. Calculate norm_value_1_100 per indicator (min-max, direction-aware, global)
9. Calculate YoY per indicator per country 9. Calculate YoY per indicator per country
@@ -23,12 +24,19 @@ Filtering Order:
11. Save analytical table 11. Save analytical table
FRAMEWORK LOGIC: FRAMEWORK LOGIC:
- sdg_transition_year dihitung SATU KALI dari actual_start_year SDG-only indicators - SDG_TRANSITION_YEAR = 2016 (HARDCODE, bukan auto-detect dari data)
- Semua SDG-only indicators menggunakan sdg_transition_year yang SAMA - Semua SDG-only indicators menggunakan SDG_TRANSITION_YEAR yang SAMA
sehingga label berubah serentak di satu titik waktu sehingga label berubah serentak di satu titik waktu
- Baris sebelum sdg_transition_year → 'MDGs' (data tetap ada, tidak dihapus) - SDG-only + year < SDG_TRANSITION_YEAR → 'MDGs' (data tetap ada, tidak dihapus)
- Baris mulai sdg_transition_year → 'SDGs' - SDG-only + year >= SDG_TRANSITION_YEAR → 'SDGs'
- Indikator non-SDG-only → 'MDGs' selalu - Non-SDG-only indicators → 'MDGs' selalu (di semua tahun)
ALASAN HARDCODE:
- SDGs resmi diadopsi PBB pada 25 September 2015 dan mulai berlaku 1 Januari 2016
- Indikator FIES dan anaemia punya data sebelum 2016 (dari MDGs era)
- Jika sdg_transition_year di-auto-detect dari min(actual_start_year),
maka akan = 2013 (karena data ada sejak 2013), sehingga semua tahun
berlabel SDGs — yang secara historis tidak tepat.
""" """
import pandas as pd import pandas as pd
@@ -58,10 +66,13 @@ from google.cloud import bigquery
# SDG-ONLY INDICATOR KEYWORDS # SDG-ONLY INDICATOR KEYWORDS
# ============================================================================= # =============================================================================
# Hanya indikator yang MURNI BARU di era SDGs yang didaftarkan di sini. # Hanya indikator yang MURNI BARU di era SDGs yang didaftarkan di sini.
# Indikator di set ini → 'SDGs' mulai dari sdg_transition_year. # Indikator di set ini → 'SDGs' mulai dari SDG_TRANSITION_YEAR (2016).
# Semua indikator lain (shared maupun tidak dikenal) → 'MDGs' di semua tahun. # Semua indikator lain (shared maupun tidak dikenal) → 'MDGs' di semua tahun.
SDG_ONLY_KEYWORDS = frozenset([ SDG_ONLY_KEYWORDS = frozenset([
# TARGET 2.1.1
"prevalence of undernourishment (percent) (3-year average)",
"number of people undernourished (million) (3-year average)",
# TARGET 2.1.2 — FIES (SDGs only) # TARGET 2.1.2 — FIES (SDGs only)
"prevalence of severe food insecurity in the total population (percent) (3-year average)", "prevalence of severe food insecurity in the total population (percent) (3-year average)",
"prevalence of severe food insecurity in the male adult population (percent) (3-year average)", "prevalence of severe food insecurity in the male adult population (percent) (3-year average)",
@@ -80,6 +91,15 @@ SDG_ONLY_KEYWORDS = frozenset([
"number of women of reproductive age (15-49 years) affected by anemia (million)", "number of women of reproductive age (15-49 years) affected by anemia (million)",
]) ])
# =============================================================================
# SDG TRANSITION YEAR — HARDCODE
# =============================================================================
# SDGs resmi berlaku mulai 1 Januari 2016 (diadopsi PBB 25 September 2015).
# Nilai ini TIDAK boleh dihitung dari data karena indikator FIES/anaemia
# punya data historis sebelum 2016 yang harus tetap dilabeli 'MDGs'.
SDG_TRANSITION_YEAR = 2016
# ============================================================================= # =============================================================================
# THRESHOLD KONDISI (fixed absolute, skala 1-100) # THRESHOLD KONDISI (fixed absolute, skala 1-100)
# ============================================================================= # =============================================================================
@@ -119,13 +139,11 @@ class AnalyticalLayerLoader:
yoy_change, yoy_pct yoy_change, yoy_pct
FRAMEWORK LOGIC: FRAMEWORK LOGIC:
- SDG_TRANSITION_YEAR = 2016 (HARDCODE — tanggal resmi SDGs berlaku)
- Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' di SEMUA tahun - Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' di SEMUA tahun
- Indikator DI SDG_ONLY_KEYWORDS: - Indikator DI SDG_ONLY_KEYWORDS:
year < sdg_transition_year → 'MDGs' (data tetap ada, tidak dihapus) year < SDG_TRANSITION_YEAR (2016) → 'MDGs' (data tetap ada, tidak dihapus)
year >= sdg_transition_year → 'SDGs' year >= SDG_TRANSITION_YEAR (2016) → 'SDGs'
- sdg_transition_year = min(actual_start_year) dari semua SDG-only indicators
yang lolos filter Step 3-5. Semua SDG-only indicators menggunakan
sdg_transition_year yang SAMA agar label berubah serentak.
""" """
def __init__(self, client: bigquery.Client): def __init__(self, client: bigquery.Client):
@@ -140,12 +158,14 @@ class AnalyticalLayerLoader:
self.selected_country_ids = None self.selected_country_ids = None
self.indicator_max_start_map = {} # indicator_id → max_start_year (dari Step 5) self.indicator_max_start_map = {} # indicator_id → max_start_year (dari Step 5)
self.sdg_transition_year = None # tahun SDGs mulai berlaku (dari Step 6)
self.start_year = 2013 self.start_year = 2013
self.end_year = None self.end_year = None
self.baseline_year = 2023 self.baseline_year = 2023
# SDG_TRANSITION_YEAR diambil dari konstanta modul (HARDCODE = 2016)
self.sdg_transition_year = SDG_TRANSITION_YEAR
self.pipeline_metadata = { self.pipeline_metadata = {
'source_class' : self.__class__.__name__, 'source_class' : self.__class__.__name__,
'start_time' : None, 'start_time' : None,
@@ -455,14 +475,14 @@ class AnalyticalLayerLoader:
# Filter hanya indikator yang valid. # Filter hanya indikator yang valid.
# PENTING: TIDAK menghapus baris year < max_start_year. # PENTING: TIDAK menghapus baris year < max_start_year.
# Semua baris tetap ada — label framework ditentukan di Step 6. # Semua baris tetap ada — label framework ditentukan di Step 6.
# max_start_year disimpan sebagai lookup untuk Step 6 & 7. # max_start_year disimpan sebagai lookup untuk Step 7.
# ---------------------------------------------------------------- # ----------------------------------------------------------------
original_count = len(self.df_clean) original_count = len(self.df_clean)
self.df_clean = self.df_clean[ self.df_clean = self.df_clean[
self.df_clean['indicator_id'].isin(valid_indicators) self.df_clean['indicator_id'].isin(valid_indicators)
].copy() ].copy()
# Simpan max_start_year per indicator_id untuk Step 6 dan Step 7 # Simpan max_start_year per indicator_id untuk Step 7
self.indicator_max_start_map = ( self.indicator_max_start_map = (
indicator_max_start[indicator_max_start['indicator_id'].isin(valid_indicators)] indicator_max_start[indicator_max_start['indicator_id'].isin(valid_indicators)]
.set_index('indicator_id')['max_start_year'] .set_index('indicator_id')['max_start_year']
@@ -484,86 +504,79 @@ class AnalyticalLayerLoader:
# STEP 6: ASSIGN FRAMEWORK PER ROW # STEP 6: ASSIGN FRAMEWORK PER ROW
# ------------------------------------------------------------------ # ------------------------------------------------------------------
def determine_sdg_start_year(self): def assign_framework(self):
self.logger.info("\n" + "=" * 80) self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 6: ASSIGN FRAMEWORK PER ROW") self.logger.info("STEP 6: ASSIGN FRAMEWORK PER ROW")
self.logger.info("=" * 80) self.logger.info("=" * 80)
# ---------------------------------------------------------------- # ----------------------------------------------------------------
# Bangun tabel actual_start_year per indikator dari # SDG_TRANSITION_YEAR = 2016 (HARDCODE)
# indicator_max_start_map yang sudah ditetapkan di Step 5. # SDGs diadopsi PBB 25 September 2015, berlaku 1 Januari 2016.
#
# PENTING — TIDAK dihitung dari data:
# Jika auto-detect dari min(actual_start_year SDG-only indicators),
# hasilnya = 2013 (karena data FIES/anaemia ada sejak 2013).
# Akibatnya year >= 2013 → SDGs → SEMUA tahun berlabel SDGs.
# Ini secara historis salah karena SDGs belum berlaku di 2013-2015.
# ---------------------------------------------------------------- # ----------------------------------------------------------------
indicator_actual_start = pd.DataFrame([ self.logger.info(f"\n SDG_TRANSITION_YEAR : {self.sdg_transition_year} (HARDCODE)")
{'indicator_id': ind_id, 'actual_start_year': int(start_yr)} self.logger.info(f" Alasan : SDGs resmi berlaku 1 Januari 2016")
for ind_id, start_yr in self.indicator_max_start_map.items() self.logger.info(f" Bukan auto-detect : data FIES/anaemia ada sejak 2013,")
]) self.logger.info(f" tapi tahun 2013-2015 harus tetap MDGs")
# Merge indicator_name untuk logging # ----------------------------------------------------------------
indicator_actual_start = indicator_actual_start.merge( # Identifikasi indikator SDG-only berdasarkan SDG_ONLY_KEYWORDS
self.df_clean[['indicator_id', 'indicator_name']].drop_duplicates(), # ----------------------------------------------------------------
on='indicator_id', how='left' indicator_info = (
self.df_clean[['indicator_id', 'indicator_name']]
.drop_duplicates()
.copy()
) )
indicator_info['is_sdg_only'] = (
# Tandai mana yang SDG-only indicator_info['indicator_name']
indicator_actual_start['is_sdg_only'] = ( .str.lower()
indicator_actual_start['indicator_name'] .str.strip()
.str.lower().str.strip()
.isin(SDG_ONLY_KEYWORDS) .isin(SDG_ONLY_KEYWORDS)
) )
sdg_only_ids = set(
indicator_info.loc[indicator_info['is_sdg_only'], 'indicator_id']
)
non_sdg_ids = set(
indicator_info.loc[~indicator_info['is_sdg_only'], 'indicator_id']
)
self.logger.info(f"\n SDG-only indicators ({len(sdg_only_ids)}):")
for _, row in indicator_info[indicator_info['is_sdg_only']].iterrows():
actual_start = self.indicator_max_start_map.get(row['indicator_id'], '?')
self.logger.info(
f" [SDG-only] id={int(row['indicator_id'])} "
f"actual_start={actual_start} | {row['indicator_name']}"
)
self.logger.info(f"\n Non-SDG-only indicators ({len(non_sdg_ids)}): → MDGs selalu")
# ---------------------------------------------------------------- # ----------------------------------------------------------------
# sdg_transition_year = min(actual_start_year) dari semua SDG-only # Validasi: pastikan ada SDG-only indicators yang lolos filter
# indicators yang lolos filter.
# Ini adalah satu titik waktu di mana semua SDG-only indicators
# berubah dari 'MDGs' ke 'SDGs' secara SERENTAK.
# ---------------------------------------------------------------- # ----------------------------------------------------------------
sdg_only_df = indicator_actual_start[indicator_actual_start['is_sdg_only']] if not sdg_only_ids:
if sdg_only_df.empty:
raise ValueError( raise ValueError(
"Tidak ada indikator SDG-only (FIES/anaemia) yang lolos filter. " "Tidak ada indikator SDG-only (FIES/anaemia) yang lolos filter. "
"Pastikan indikator FIES dan anaemia ada di data." "Pastikan nama indikator di SDG_ONLY_KEYWORDS cocok dengan data BigQuery."
) )
self.sdg_transition_year = int(sdg_only_df['actual_start_year'].min())
self.logger.info(f"\n SDG-only indicators dan actual_start_year masing-masing:")
self.logger.info(f" {'-'*80}")
for _, row in sdg_only_df.iterrows():
self.logger.info(
f" [SDG-only] actual_start={int(row['actual_start_year'])} | "
f"{row['indicator_name']}"
)
self.logger.info(
f"\n sdg_transition_year = {self.sdg_transition_year} "
f"(min actual_start_year dari semua SDG-only indicators)"
)
self.logger.info(f"\n Logika assign framework (PER BARIS):")
self.logger.info(f" ──────────────────────────────────────────────────────────")
self.logger.info(f" Indikator TIDAK di SDG_ONLY_KEYWORDS:")
self.logger.info(f" → 'MDGs' di semua tahun")
self.logger.info(f" Indikator DI SDG_ONLY_KEYWORDS:")
self.logger.info(f" year < {self.sdg_transition_year} → 'MDGs' (data tetap ada)")
self.logger.info(f" year >= {self.sdg_transition_year} → 'SDGs'")
self.logger.info(f" ──────────────────────────────────────────────────────────")
# ---------------------------------------------------------------- # ----------------------------------------------------------------
# Assign framework dengan vectorized operation menggunakan # Assign framework dengan vectorized np.where:
# sdg_transition_year (SATU nilai untuk semua SDG-only indicators) #
# Kondisi SDG-only AND year >= SDG_TRANSITION_YEAR → 'SDGs'
# Semua kondisi lain (non-SDG-only ATAU year < SDG_TRANSITION_YEAR) → 'MDGs'
#
# Hasilnya dalam 1 indikator SDG-only (misal anaemia, data mulai 2013):
# 2013, 2014, 2015 → 'MDGs' (data tetap ada)
# 2016, 2017, ... → 'SDGs'
# ---------------------------------------------------------------- # ----------------------------------------------------------------
# Tandai apakah setiap baris adalah SDG-only indicator
sdg_only_ids = set(
indicator_actual_start.loc[
indicator_actual_start['is_sdg_only'], 'indicator_id'
]
)
self.df_clean['_is_sdg_only'] = self.df_clean['indicator_id'].isin(sdg_only_ids) self.df_clean['_is_sdg_only'] = self.df_clean['indicator_id'].isin(sdg_only_ids)
# Assign framework:
# - Bukan SDG-only → 'MDGs'
# - SDG-only AND year >= sdg_transition_year → 'SDGs'
# - SDG-only AND year < sdg_transition_year → 'MDGs'
self.df_clean['framework'] = np.where( self.df_clean['framework'] = np.where(
self.df_clean['_is_sdg_only'] & self.df_clean['_is_sdg_only'] &
(self.df_clean['year'] >= self.sdg_transition_year), (self.df_clean['year'] >= self.sdg_transition_year),
@@ -571,19 +584,26 @@ class AnalyticalLayerLoader:
'MDGs' 'MDGs'
) )
# Drop kolom bantu
self.df_clean = self.df_clean.drop(columns=['_is_sdg_only']) self.df_clean = self.df_clean.drop(columns=['_is_sdg_only'])
# ---------------------------------------------------------------- # ----------------------------------------------------------------
# Log verifikasi per indikator # Log verifikasi per indikator — tampilkan split MDGs/SDGs per tahun
# ---------------------------------------------------------------- # ----------------------------------------------------------------
self.logger.info(f"\n Logika assign framework (PER BARIS):")
self.logger.info(f" {'─'*72}")
self.logger.info(f" Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' di semua tahun")
self.logger.info(f" Indikator DI SDG_ONLY_KEYWORDS:")
self.logger.info(f" year < {self.sdg_transition_year} → 'MDGs' (data tetap ada, tidak dihapus)")
self.logger.info(f" year >= {self.sdg_transition_year} → 'SDGs'")
self.logger.info(f" {'─'*72}")
self.logger.info(f"\n Verifikasi framework per indikator:") self.logger.info(f"\n Verifikasi framework per indikator:")
self.logger.info(f" {'-'*110}") self.logger.info(f" {'─'*115}")
self.logger.info( self.logger.info(
f" {'ID':<5} {'Indicator Name':<52} {'Data From':<12} " f" {'ID':<5} {'Indicator Name':<52} {'Data From':<11} "
f"{'MDGs rows':<12} {'SDGs rows':<12} {'Note'}" f"{'MDGs rows':<11} {'SDGs rows':<11} {'Note'}"
) )
self.logger.info(f" {'-'*110}") self.logger.info(f" {'─'*115}")
for ind_id, grp in self.df_clean.groupby('indicator_id'): for ind_id, grp in self.df_clean.groupby('indicator_id'):
ind_name = grp['indicator_name'].iloc[0] ind_name = grp['indicator_name'].iloc[0]
@@ -593,13 +613,17 @@ class AnalyticalLayerLoader:
data_from = int(grp['year'].min()) data_from = int(grp['year'].min())
if is_sdg_only: if is_sdg_only:
note = f"SDGs from {self.sdg_transition_year}, MDGs before" mdgs_yrs = sorted(grp[grp['framework'] == 'MDGs']['year'].unique())
sdgs_yrs = sorted(grp[grp['framework'] == 'SDGs']['year'].unique())
yr_range_mdgs = f"{min(mdgs_yrs)}-{max(mdgs_yrs)}" if mdgs_yrs else "-"
yr_range_sdgs = f"{min(sdgs_yrs)}-{max(sdgs_yrs)}" if sdgs_yrs else "-"
note = f"MDGs:{yr_range_mdgs} | SDGs:{yr_range_sdgs}"
else: else:
note = "MDGs always" note = "MDGs always"
self.logger.info( self.logger.info(
f" {int(ind_id):<5} {ind_name[:50]:<52} {data_from:<12} " f" {int(ind_id):<5} {ind_name[:50]:<52} {data_from:<11} "
f"{mdgs_rows:<12} {sdgs_rows:<12} {note}" f"{mdgs_rows:<11} {sdgs_rows:<11} {note}"
) )
fw_summary = self.df_clean['framework'].value_counts() fw_summary = self.df_clean['framework'].value_counts()
@@ -978,12 +1002,13 @@ class AnalyticalLayerLoader:
'end_year' : self.end_year, 'end_year' : self.end_year,
'baseline_year' : self.baseline_year, 'baseline_year' : self.baseline_year,
'sdg_transition_year' : self.sdg_transition_year, 'sdg_transition_year' : self.sdg_transition_year,
'sdg_transition_source' : 'HARDCODE — SDGs resmi berlaku 1 Jan 2016',
'fixed_countries' : len(self.selected_country_ids), 'fixed_countries' : len(self.selected_country_ids),
'norm_scale' : '1-100 per indicator global minmax direction-aware', 'norm_scale' : '1-100 per indicator global minmax direction-aware',
'framework_logic' : ( 'framework_logic' : (
'sdg_transition_year = min(actual_start_year) dari SDG-only indicators; ' f'SDG_TRANSITION_YEAR={SDG_TRANSITION_YEAR} (HARDCODE); '
'SDG-only year >= sdg_transition_year → SDGs; ' 'SDG-only + year >= SDG_TRANSITION_YEAR → SDGs; '
'SDG-only year < sdg_transition_year → MDGs (data tetap ada); ' 'SDG-only + year < SDG_TRANSITION_YEAR → MDGs (data tetap ada); '
'non-SDG-only → MDGs selalu' 'non-SDG-only → MDGs selalu'
), ),
'sdg_only_keywords_count': len(SDG_ONLY_KEYWORDS), 'sdg_only_keywords_count': len(SDG_ONLY_KEYWORDS),
@@ -1022,8 +1047,8 @@ class AnalyticalLayerLoader:
self.logger.info("Kolom baru: norm_value_1_100 (min-max 1-100, direction-aware)") self.logger.info("Kolom baru: norm_value_1_100 (min-max 1-100, direction-aware)")
self.logger.info(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}") self.logger.info(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
self.logger.info( self.logger.info(
"Framework: SDG-only indicators → SDGs mulai sdg_transition_year, " f"Framework: SDG_TRANSITION_YEAR={SDG_TRANSITION_YEAR} (HARDCODE). "
"MDGs sebelumnya (data tetap ada). Non-SDG-only → MDGs selalu." "SDG-only + year >= 2016 → SDGs; sebelumnya MDGs. Non-SDG-only → MDGs selalu."
) )
self.logger.info("=" * 80) self.logger.info("=" * 80)
@@ -1032,7 +1057,7 @@ class AnalyticalLayerLoader:
self.filter_complete_indicators_per_country() self.filter_complete_indicators_per_country()
self.select_countries_with_all_pillars() self.select_countries_with_all_pillars()
self.filter_indicators_consistent_across_fixed_countries() self.filter_indicators_consistent_across_fixed_countries()
self.determine_sdg_start_year() self.assign_framework()
self.verify_no_gaps() self.verify_no_gaps()
self.calculate_norm_value() self.calculate_norm_value()
self.calculate_yoy() self.calculate_yoy()
@@ -1047,7 +1072,7 @@ class AnalyticalLayerLoader:
self.logger.info("=" * 80) self.logger.info("=" * 80)
self.logger.info(f" Duration : {duration:.2f}s") self.logger.info(f" Duration : {duration:.2f}s")
self.logger.info(f" Year Range : {self.start_year}-{self.end_year}") self.logger.info(f" Year Range : {self.start_year}-{self.end_year}")
self.logger.info(f" SDG Transition Year: {self.sdg_transition_year}") self.logger.info(f" SDG Transition Year: {self.sdg_transition_year} (HARDCODE)")
self.logger.info(f" Countries : {len(self.selected_country_ids)}") self.logger.info(f" Countries : {len(self.selected_country_ids)}")
self.logger.info(f" Indicators : {self.df_clean['indicator_id'].nunique()}") self.logger.info(f" Indicators : {self.df_clean['indicator_id'].nunique()}")
self.logger.info(f" Rows Loaded : {self.pipeline_metadata['rows_loaded']:,}") self.logger.info(f" Rows Loaded : {self.pipeline_metadata['rows_loaded']:,}")
@@ -1076,8 +1101,8 @@ if __name__ == "__main__":
print(f"Norm: min-max 1-100 per indicator, direction-aware") print(f"Norm: min-max 1-100 per indicator, direction-aware")
print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}") print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
print( print(
"Framework: SDG-only → SDGs mulai sdg_transition_year, MDGs sebelumnya. " f"Framework: SDG_TRANSITION_YEAR={SDG_TRANSITION_YEAR} (HARDCODE). "
"Non-SDG-only → MDGs selalu." "SDG-only + year >= 2016 → SDGs; sebelumnya MDGs. Non-SDG-only → MDGs selalu."
) )
print("=" * 80) print("=" * 80)
@@ -1088,6 +1113,6 @@ if __name__ == "__main__":
print("\n" + "=" * 80) print("\n" + "=" * 80)
print("[OK] COMPLETED") print("[OK] COMPLETED")
print(f" SDG Transition Year : {loader.sdg_transition_year}") print(f" SDG Transition Year : {loader.sdg_transition_year} (HARDCODE)")
print(f" Rows Loaded : {loader.pipeline_metadata['rows_loaded']:,}") print(f" Rows Loaded : {loader.pipeline_metadata['rows_loaded']:,}")
print("=" * 80) print("=" * 80)