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sdgs year v3

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Debby
2026-04-01 07:13:07 +07:00
parent 8ae5018a62
commit 64e3095e7a
1 changed files with 71 additions and 103 deletions
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scripts/bigquery_analytical_layer.py
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@@ -22,17 +22,16 @@ NORMALISASI (Step 8):
sehingga nilai antar negara dan antar tahun tetap comparable sehingga nilai antar negara dan antar tahun tetap comparable
- Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio - Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio
FRAMEWORK LOGIC (FIX - Row-Level Assignment): FRAMEWORK LOGIC (Row-Level Assignment):
- SDG start year dideteksi dari data: tahun pertama indikator FIES/anaemia lengkap - SDG start year dideteksi dari data: tahun pertama indikator FIES/anaemia lengkap
di semua fixed countries (setelah Step 3-5 filter selesai) di semua fixed countries (setelah Step 3-5 filter selesai)
- Framework di-assign PER BARIS (per tahun), bukan per indikator: - Framework di-assign PER BARIS (per tahun):
* Jika row['year'] < sdg_start_year -> selalu 'MDGs' * year < sdg_start_year → selalu 'MDGs' (semua indikator)
* Jika row['year'] >= sdg_start_year DAN * year >= sdg_start_year + nama di SDG_ONLY_KEYWORDS → 'SDGs'
nama ada di SDG_INDICATOR_KEYWORDS -> 'SDGs' * selain itu (implisit) → 'MDGs'
* Selain itu -> 'MDGs' - Hanya FIES dan anaemia yang masuk SDG_ONLY_KEYWORDS karena murni baru di era SDGs.
- Dengan demikian, indikator seperti "Prevalence of anemia" yang datanya dimulai - Shared indicators (stunting, wasting, overweight, undernourishment) tidak terdaftar
sebelum era SDGs akan berlabel 'MDGs' untuk tahun-tahun pra-SDGs dan 'SDGs' di SDG_ONLY_KEYWORDS sehingga secara implisit selalu berlabel 'MDGs' di semua tahun.
untuk tahun-tahun pasca (>= sdg_start_year).
""" """
import pandas as pd import pandas as pd
@@ -59,13 +58,13 @@ from google.cloud import bigquery
# ============================================================================= # =============================================================================
# SDG INDICATOR KEYWORDS # SDG-ONLY INDICATOR KEYWORDS
# ============================================================================= # =============================================================================
# Hanya indikator yang MURNI BARU di era SDGs yang didaftarkan di sini.
# Baris dengan year >= sdg_start_year + nama ada di set ini → 'SDGs'.
# Semua indikator lain (shared maupun tidak dikenal) → 'MDGs' secara implisit.
SDG_INDICATOR_KEYWORDS = frozenset([ SDG_ONLY_KEYWORDS = frozenset([
# TARGET 2.1.1 — Prevalence of undernourishment (shared, sudah ada sebelum SDGs)
"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)",
@@ -79,15 +78,7 @@ SDG_INDICATOR_KEYWORDS = frozenset([
"number of moderately or severely food insecure people (million) (3-year average)", "number of moderately or severely food insecure people (million) (3-year average)",
"number of moderately or severely food insecure male adults (million) (3-year average)", "number of moderately or severely food insecure male adults (million) (3-year average)",
"number of moderately or severely food insecure female adults (million) (3-year average)", "number of moderately or severely food insecure female adults (million) (3-year average)",
# TARGET 2.2.1 — Stunting (shared) # TARGET 2.2.3 — Anaemia (SDGs only)
"percentage of children under 5 years of age who are stunted (modelled estimates) (percent)",
"number of children under 5 years of age who are stunted (modeled estimates) (million)",
# TARGET 2.2.2 — Wasting & Overweight (shared)
"percentage of children under 5 years affected by wasting (percent)",
"number of children under 5 years affected by wasting (million)",
"percentage of children under 5 years of age who are overweight (modelled estimates) (percent)",
"number of children under 5 years of age who are overweight (modeled estimates) (million)",
# TARGET 2.2.3 — Anaemia (SDGs only — listed here so rows >= sdg_start_year become SDGs)
"prevalence of anemia among women of reproductive age (15-49 years) (percent)", "prevalence of anemia among women of reproductive age (15-49 years) (percent)",
"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)",
]) ])
@@ -102,8 +93,6 @@ _SDG_ERA_PROXY_KEYWORDS = frozenset([
# ============================================================================= # =============================================================================
# THRESHOLD KONDISI (fixed absolute, skala 1-100) # THRESHOLD KONDISI (fixed absolute, skala 1-100)
# ============================================================================= # =============================================================================
# Digunakan untuk assign kondisi di analysis_layer.
# Didefinisikan di sini agar konsisten antara kedua file.
# bad : norm_value_1_100 < THRESHOLD_BAD # bad : norm_value_1_100 < THRESHOLD_BAD
# good : norm_value_1_100 > THRESHOLD_GOOD # good : norm_value_1_100 > THRESHOLD_GOOD
# moderate : di antara keduanya # moderate : di antara keduanya
@@ -134,27 +123,29 @@ def assign_framework_for_row(
sdg_start_year: int, sdg_start_year: int,
) -> str: ) -> str:
""" """
Tentukan framework (MDGs/SDGs) PER BARIS (per tahun), bukan per indikator. Tentukan framework (MDGs/SDGs) PER BARIS (per tahun).
Logic: Logic:
- Jika row_year < sdg_start_year → selalu 'MDGs', apapun nama indikatornya. ─────────────────────────────────────────────────────────────────────────
- Jika row_year >= sdg_start_year DAN nama ada di SDG_INDICATOR_KEYWORDS → 'SDGs'. RULE 1: row_year < sdg_start_year
- Selain itu → 'MDGs'. → selalu 'MDGs', tanpa kecuali.
Dengan cara ini, indikator seperti "Prevalence of anemia" yang datanya RULE 2: row_year >= sdg_start_year AND nama ada di SDG_ONLY_KEYWORDS
ada sebelum era SDGs akan berlabel 'MDGs' untuk tahun-tahun pra-SDGs, → 'SDGs'
dan 'SDGs' untuk tahun-tahun pasca sdg_start_year.
RULE 3 (implisit): semua kondisi lain
→ 'MDGs'
Ini mencakup shared indicators (stunting, wasting, overweight,
undernourishment) yang tidak terdaftar di SDG_ONLY_KEYWORDS,
sehingga tidak perlu di-list secara eksplisit.
─────────────────────────────────────────────────────────────────────────
""" """
# Tahun sebelum era SDGs → selalu MDGs
if row_year < sdg_start_year: if row_year < sdg_start_year:
return 'MDGs' return 'MDGs'
# Tahun >= sdg_start_year: cek apakah nama ada di SDG list if str(indicator_name).lower().strip() in SDG_ONLY_KEYWORDS:
name_lower = str(indicator_name).lower().strip()
if name_lower in SDG_INDICATOR_KEYWORDS:
return 'SDGs' return 'SDGs'
# Tidak ada di SDG list → MDGs
return 'MDGs' return 'MDGs'
@@ -174,10 +165,10 @@ class AnalyticalLayerLoader:
norm_value_1_100, <- min-max norm per indikator, skala 1-100, direction-aware norm_value_1_100, <- min-max norm per indikator, skala 1-100, direction-aware
yoy_change, yoy_pct yoy_change, yoy_pct
PERUBAHAN (framework fix): FRAMEWORK LOGIC:
- framework di-assign per baris (per tahun), bukan per indikator. - year < sdg_start_year → 'MDGs' (semua indikator)
- Baris dengan year < sdg_start_year selalu 'MDGs'. - year >= sdg_start_year + nama di SDG_ONLY_KEYWORDS → 'SDGs' (FIES + anaemia)
- Baris dengan year >= sdg_start_year dan nama di SDG_INDICATOR_KEYWORDS → 'SDGs'. - selain itu (implisit) → 'MDGs'
""" """
def __init__(self, client: bigquery.Client): def __init__(self, client: bigquery.Client):
@@ -282,7 +273,6 @@ class AnalyticalLayerLoader:
self.logger.info("STEP 2: DETERMINE YEAR BOUNDARIES") self.logger.info("STEP 2: DETERMINE YEAR BOUNDARIES")
self.logger.info("=" * 80) self.logger.info("=" * 80)
# baseline_year = 2023 hardcode (syarat dosen: minimal 2023)
df_baseline = self.df_clean[self.df_clean['year'] == self.baseline_year] df_baseline = self.df_clean[self.df_clean['year'] == self.baseline_year]
baseline_indicator_count = df_baseline['indicator_id'].nunique() baseline_indicator_count = df_baseline['indicator_id'].nunique()
@@ -522,7 +512,7 @@ class AnalyticalLayerLoader:
return self.df_clean return self.df_clean
# ------------------------------------------------------------------ # ------------------------------------------------------------------
# STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL FIX) # STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL)
# ------------------------------------------------------------------ # ------------------------------------------------------------------
def determine_sdg_start_year(self): def determine_sdg_start_year(self):
@@ -530,8 +520,6 @@ class AnalyticalLayerLoader:
self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL)") self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL)")
self.logger.info("=" * 80) self.logger.info("=" * 80)
# actual_start_year per indikator = max(min_year per country)
# = konsisten dengan max_start_year di Step 5
indicator_actual_start = ( indicator_actual_start = (
self.df_clean self.df_clean
.groupby(['indicator_id', 'indicator_name', 'country_id'])['year'] .groupby(['indicator_id', 'indicator_name', 'country_id'])['year']
@@ -559,18 +547,10 @@ class AnalyticalLayerLoader:
for _, row in df_proxy.iterrows(): for _, row in df_proxy.iterrows():
self.logger.info(f" [{int(row['actual_start_year'])}] {row['indicator_name']}") self.logger.info(f" [{int(row['actual_start_year'])}] {row['indicator_name']}")
# ------------------------------------------------------------------ self.logger.info(f"\n Assigning framework PER ROW...")
# FIX: Assign framework PER BARIS (per tahun), bukan per indikator self.logger.info(f" year < {self.sdg_start_year} → MDGs (semua indikator)")
# ------------------------------------------------------------------ self.logger.info(f" year >= {self.sdg_start_year} + nama in SDG_ONLY_KEYWORDS → SDGs")
# Logic: self.logger.info(f" selain itu (implisit) → MDGs")
# row['year'] < sdg_start_year → 'MDGs' (apapun nama indikatornya)
# row['year'] >= sdg_start_year + nama di SDG_INDICATOR_KEYWORDS → 'SDGs'
# selain itu → 'MDGs'
# ------------------------------------------------------------------
self.logger.info(f"\n Assigning framework PER ROW (year-level)...")
self.logger.info(f" Rule: year < {self.sdg_start_year} → MDGs (always)")
self.logger.info(f" Rule: year >= {self.sdg_start_year} + name in SDG list → SDGs")
self.logger.info(f" Rule: year >= {self.sdg_start_year} + name NOT in SDG list → MDGs")
self.df_clean['framework'] = self.df_clean.apply( self.df_clean['framework'] = self.df_clean.apply(
lambda row: assign_framework_for_row( lambda row: assign_framework_for_row(
@@ -582,22 +562,24 @@ class AnalyticalLayerLoader:
) )
# Log ringkasan per indikator untuk verifikasi # Log ringkasan per indikator untuk verifikasi
self.logger.info(f"\n {'Framework Assignment per Indicator (sample)':}") self.logger.info(f"\n {'Framework Assignment per Indicator':}")
self.logger.info(f" {'-'*95}") self.logger.info(f" {'-'*100}")
self.logger.info( self.logger.info(
f" {'ID':<5} {'Indicator Name':<50} " f" {'ID':<5} {'Indicator Name':<52} "
f"{'Pre-SDG rows':<15} {'MDGs rows':<12} {'SDGs rows'}" f"{'Pre-SDG':<10} {'MDGs':<10} {'SDGs':<10} {'SDG-Only?'}"
) )
self.logger.info(f" {'-'*95}") self.logger.info(f" {'-'*100}")
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]
pre_sdg = (grp['year'] < self.sdg_start_year).sum() pre_sdg = (grp['year'] < self.sdg_start_year).sum()
mdgs_rows = (grp['framework'] == 'MDGs').sum() mdgs_rows = (grp['framework'] == 'MDGs').sum()
sdgs_rows = (grp['framework'] == 'SDGs').sum() sdgs_rows = (grp['framework'] == 'SDGs').sum()
is_sdg_only = ind_name.lower().strip() in SDG_ONLY_KEYWORDS
self.logger.info( self.logger.info(
f" {int(ind_id):<5} {ind_name[:48]:<50} " f" {int(ind_id):<5} {ind_name[:50]:<52} "
f"{pre_sdg:<15} {mdgs_rows:<12} {sdgs_rows}" f"{pre_sdg:<10} {mdgs_rows:<10} {sdgs_rows:<10} "
f"{'YES' if is_sdg_only else 'no'}"
) )
fw_summary = self.df_clean['framework'].value_counts() fw_summary = self.df_clean['framework'].value_counts()
@@ -605,7 +587,6 @@ class AnalyticalLayerLoader:
f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items() f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items()
)) ))
# Ringkasan unique indicators per framework di tahun terbaru (end_year)
end_year_df = self.df_clean[self.df_clean['year'] == self.end_year] end_year_df = self.df_clean[self.df_clean['year'] == self.end_year]
fw_ind_summary = end_year_df.groupby('framework')['indicator_id'].nunique() fw_ind_summary = end_year_df.groupby('framework')['indicator_id'].nunique()
self.logger.info(f" Indicators di year={self.end_year}: " + " | ".join( self.logger.info(f" Indicators di year={self.end_year}: " + " | ".join(
@@ -613,7 +594,7 @@ class AnalyticalLayerLoader:
)) ))
self.logger.info( self.logger.info(
f"\n [OK] 'framework' ditambahkan (row-level) — " f"\n [OK] 'framework' ditambahkan — "
f"MDGs: {(self.df_clean['framework'] == 'MDGs').sum():,} rows | " f"MDGs: {(self.df_clean['framework'] == 'MDGs').sum():,} rows | "
f"SDGs: {(self.df_clean['framework'] == 'SDGs').sum():,} rows" f"SDGs: {(self.df_clean['framework'] == 'SDGs').sum():,} rows"
) )
@@ -651,7 +632,7 @@ class AnalyticalLayerLoader:
return True return True
# ------------------------------------------------------------------ # ------------------------------------------------------------------
# STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR PER COUNTRY # STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR
# ------------------------------------------------------------------ # ------------------------------------------------------------------
def calculate_norm_value(self): def calculate_norm_value(self):
@@ -663,16 +644,7 @@ class AnalyticalLayerLoader:
- Normalisasi dilakukan GLOBAL per indikator (semua negara + semua tahun sekaligus) - Normalisasi dilakukan GLOBAL per indikator (semua negara + semua tahun sekaligus)
sehingga nilai antar negara dan antar tahun tetap comparable. sehingga nilai antar negara dan antar tahun tetap comparable.
- lower_better diinvert: nilai tinggi selalu = kondisi lebih baik. - lower_better diinvert: nilai tinggi selalu = kondisi lebih baik.
Contoh: undernourishment 5% (rendah = baik) → norm tinggi setelah invert.
- Skala 1-100 (bukan 0-100) untuk menghindari nilai absolut nol di Looker Studio. - Skala 1-100 (bukan 0-100) untuk menghindari nilai absolut nol di Looker Studio.
- Kolom ini memungkinkan perbandingan lintas indikator yang berbeda satuan
(persen, juta orang, dll) karena sudah dinormalisasi ke skala yang sama.
Catatan:
- Berbeda dengan norm_value di _get_norm_value_df() di analysis_layer
yang skala 0-1 dan dipakai untuk agregasi composite score.
- norm_value_1_100 ini adalah per baris (per country per year per indicator),
untuk ditampilkan langsung di Looker Studio.
""" """
self.logger.info("\n" + "=" * 80) self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR") self.logger.info("STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR")
@@ -706,15 +678,11 @@ class AnalyticalLayerLoader:
normed = np.full(len(grp), np.nan) normed = np.full(len(grp), np.nan)
if v_min == v_max: if v_min == v_max:
# Semua nilai sama → beri nilai tengah (50.5 pada skala 1-100)
normed[valid_mask.values] = 50.5 normed[valid_mask.values] = 50.5
else: else:
# Min-max ke 0-1 dulu
scaled = (raw - v_min) / (v_max - v_min) scaled = (raw - v_min) / (v_max - v_min)
# Invert jika lower_better
if do_invert: if do_invert:
scaled = 1.0 - scaled scaled = 1.0 - scaled
# Scale ke 1-100
normed[valid_mask.values] = 1.0 + scaled * 99.0 normed[valid_mask.values] = 1.0 + scaled * 99.0
grp['norm_value_1_100'] = normed grp['norm_value_1_100'] = normed
@@ -727,7 +695,6 @@ class AnalyticalLayerLoader:
self.df_clean = pd.concat(norm_parts, ignore_index=True) self.df_clean = pd.concat(norm_parts, ignore_index=True)
# Statistik ringkasan
valid_norm = self.df_clean['norm_value_1_100'].notna().sum() valid_norm = self.df_clean['norm_value_1_100'].notna().sum()
null_norm = self.df_clean['norm_value_1_100'].isna().sum() null_norm = self.df_clean['norm_value_1_100'].isna().sum()
self.logger.info(f"\n norm_value_1_100 — valid: {valid_norm:,} | null: {null_norm:,}") self.logger.info(f"\n norm_value_1_100 — valid: {valid_norm:,} | null: {null_norm:,}")
@@ -737,7 +704,6 @@ class AnalyticalLayerLoader:
f"{self.df_clean['norm_value_1_100'].max():.2f}" f"{self.df_clean['norm_value_1_100'].max():.2f}"
) )
# Log distribusi kondisi berdasarkan threshold
self.df_clean['_condition_preview'] = self.df_clean['norm_value_1_100'].apply(assign_condition) self.df_clean['_condition_preview'] = self.df_clean['norm_value_1_100'].apply(assign_condition)
cond_dist = self.df_clean['_condition_preview'].value_counts() cond_dist = self.df_clean['_condition_preview'].value_counts()
self.logger.info(f"\n Distribusi kondisi (threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}):") self.logger.info(f"\n Distribusi kondisi (threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}):")
@@ -813,7 +779,6 @@ class AnalyticalLayerLoader:
'start_year', 'end_year', 'country_count' 'start_year', 'end_year', 'country_count'
] ]
# Framework per indikator di end_year (untuk display — representasi terbaru)
fw_at_end = ( fw_at_end = (
self.df_clean[self.df_clean['year'] == self.end_year] self.df_clean[self.df_clean['year'] == self.end_year]
.groupby('indicator_id')['framework'] .groupby('indicator_id')['framework']
@@ -909,13 +874,11 @@ class AnalyticalLayerLoader:
self.logger.info(f" Total rows: {len(analytical_df):,}") self.logger.info(f" Total rows: {len(analytical_df):,}")
# Framework distribution per row
fw_dist_rows = analytical_df['framework'].value_counts() fw_dist_rows = analytical_df['framework'].value_counts()
self.logger.info(f" Framework distribution (rows):") self.logger.info(f" Framework distribution (rows):")
for fw, cnt in fw_dist_rows.items(): for fw, cnt in fw_dist_rows.items():
self.logger.info(f" {fw}: {cnt:,} rows") self.logger.info(f" {fw}: {cnt:,} rows")
# Framework distribution per unique indicator (at end_year)
fw_dist_ind = ( fw_dist_ind = (
analytical_df[analytical_df['year'] == self.end_year] analytical_df[analytical_df['year'] == self.end_year]
.drop_duplicates('indicator_id')['framework'] .drop_duplicates('indicator_id')['framework']
@@ -972,7 +935,12 @@ class AnalyticalLayerLoader:
'sdg_start_year' : self.sdg_start_year, 'sdg_start_year' : self.sdg_start_year,
'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' : 'row-level: year < sdg_start_year → MDGs always', 'framework_logic' : (
'row-level: year < sdg_start_year → MDGs always; '
'year >= sdg_start_year + SDG_ONLY_KEYWORDS → SDGs; '
'else (implicit) → MDGs'
),
'sdg_only_keywords_count' : len(SDG_ONLY_KEYWORDS),
'condition_thresholds' : { 'condition_thresholds' : {
'bad' : f'< {THRESHOLD_BAD}', 'bad' : f'< {THRESHOLD_BAD}',
'moderate': f'{THRESHOLD_BAD}-{THRESHOLD_GOOD}', 'moderate': f'{THRESHOLD_BAD}-{THRESHOLD_GOOD}',
@@ -1007,7 +975,7 @@ class AnalyticalLayerLoader:
self.logger.info("Output: fact_asean_food_security_selected -> fs_asean_gold") self.logger.info("Output: fact_asean_food_security_selected -> fs_asean_gold")
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("Framework: row-level (year < sdg_start_year → MDGs always)") self.logger.info("Framework: year < sdg_start_year → MDGs | SDG_ONLY → SDGs | else → MDGs (implicit)")
self.logger.info("=" * 80) self.logger.info("=" * 80)
self.load_source_data() self.load_source_data()
@@ -1017,8 +985,8 @@ class AnalyticalLayerLoader:
self.filter_indicators_consistent_across_fixed_countries() self.filter_indicators_consistent_across_fixed_countries()
self.determine_sdg_start_year() self.determine_sdg_start_year()
self.verify_no_gaps() self.verify_no_gaps()
self.calculate_norm_value() # Step 8: norm_value_1_100 self.calculate_norm_value()
self.calculate_yoy() # Step 9: yoy_change, yoy_pct self.calculate_yoy()
self.analyze_indicator_availability_by_year() self.analyze_indicator_availability_by_year()
self.save_analytical_table() self.save_analytical_table()
@@ -1058,7 +1026,7 @@ if __name__ == "__main__":
print("Output: fact_asean_food_security_selected -> fs_asean_gold") print("Output: fact_asean_food_security_selected -> fs_asean_gold")
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(f"Framework: row-level (year < sdg_start_year → MDGs always)") print(f"Framework: year < sdg_start_year → MDGs | SDG_ONLY → SDGs | else → MDGs (implicit)")
print("=" * 80) print("=" * 80)
logger = setup_logging() logger = setup_logging()