diff --git a/scripts/bigquery_analytical_layer.py b/scripts/bigquery_analytical_layer.py index bf1381e..969fdcc 100644 --- a/scripts/bigquery_analytical_layer.py +++ b/scripts/bigquery_analytical_layer.py @@ -8,7 +8,7 @@ Filtering Order: 3. Filter complete indicators PER COUNTRY (auto-detect start year, no gaps) 4. Filter countries with ALL pillars (FIXED SET) 5. Filter indicators with consistent presence across FIXED countries -6. Determine SDG start year & assign framework (MDGs/SDGs) per ROW per year +6. Determine SDG start year & assign framework (MDGs/SDGs) per indicator PER ROW 7. Verify no gaps 8. Calculate norm_value_1_100 per indicator per country (min-max, direction-aware) 9. Calculate YoY per indicator per country @@ -22,18 +22,17 @@ NORMALISASI (Step 8): sehingga nilai antar negara dan antar tahun tetap comparable - Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio -FRAMEWORK LOGIC (Per-Row, bukan per indikator): -- sdg_start_year dideteksi dari data: tahun pertama indikator FIES lengkap +FRAMEWORK LOGIC (FIX - Row-Level Assignment): +- SDG start year dideteksi dari data: tahun pertama indikator FIES/anaemia lengkap di semua fixed countries (setelah Step 3-5 filter selesai) -- Proxy deteksi sdg_start_year: HANYA FIES ("food insecurity", "food insecure") - Anemia TIDAK dipakai sebagai proxy karena datanya sudah ada sebelum era SDGs -- Framework di-assign PER BARIS (per year), bukan per indikator: - * row['year'] >= sdg_start_year AND nama ada di SDG_INDICATOR_KEYWORDS -> 'SDGs' - * Selain itu -> 'MDGs' -- Ini menangani indikator "shared" (anemia, stunting, wasting, undernourishment) - yang datanya ada sebelum SDGs: - * row lama (year < sdg_start_year) -> 'MDGs' - * row baru (year >= sdg_start_year) -> 'SDGs' +- Framework di-assign PER BARIS (per tahun), bukan per indikator: + * Jika row['year'] < sdg_start_year -> selalu 'MDGs' + * Jika row['year'] >= sdg_start_year DAN + nama ada di SDG_INDICATOR_KEYWORDS -> 'SDGs' + * Selain itu -> 'MDGs' +- Dengan demikian, indikator seperti "Prevalence of anemia" yang datanya dimulai + sebelum era SDGs akan berlabel 'MDGs' untuk tahun-tahun pra-SDGs dan 'SDGs' + untuk tahun-tahun pasca (>= sdg_start_year). """ import pandas as pd @@ -61,16 +60,13 @@ from google.cloud import bigquery # ============================================================================= # SDG INDICATOR KEYWORDS -# Daftar nama indikator (lowercase) yang masuk SDG framework. -# Indikator ini akan di-assign 'SDGs' untuk baris dengan year >= sdg_start_year, -# dan 'MDGs' untuk baris dengan year < sdg_start_year. # ============================================================================= SDG_INDICATOR_KEYWORDS = frozenset([ - # TARGET 2.1.1 — Prevalence of undernourishment (shared: ada sebelum SDGs) + # 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 — murni baru di era SDGs) + # 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 male adult population (percent) (3-year average)", "prevalence of severe food insecurity in the female adult population (percent) (3-year average)", @@ -83,35 +79,24 @@ SDG_INDICATOR_KEYWORDS = frozenset([ "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 female adults (million) (3-year average)", - # TARGET 2.2.1 — Stunting (shared: ada sebelum SDGs) + # TARGET 2.2.1 — Stunting (shared) "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: ada sebelum SDGs) + # 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 (shared: data ada sebelum SDGs, listed here agar - # baris >= sdg_start_year di-assign 'SDGs') + # 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)", "number of women of reproductive age (15-49 years) affected by anemia (million)", ]) -# ============================================================================= -# SDG ERA PROXY KEYWORDS -# HANYA indikator yang MURNI baru di era SDGs (FIES saja). -# Dipakai untuk mendeteksi sdg_start_year dari data. -# -# PENTING — Anemia/anaemia TIDAK dipakai sebagai proxy: -# Data anemia sudah ada sebelum era SDGs sehingga actual_start_year-nya -# lebih awal dari sdg_start_year. Jika dipakai sebagai proxy, sdg_start_year -# akan terdeteksi terlalu awal dan seluruh baris anemia akan menjadi 'SDGs'. -# FIES adalah satu-satunya indikator yang benar-benar murni baru di era SDGs -# dan dapat dipakai sebagai penanda tahun mulainya era SDGs. -# ============================================================================= +# Proxy keywords untuk deteksi era SDGs dari data (indikator murni baru di SDGs) _SDG_ERA_PROXY_KEYWORDS = frozenset([ "food insecurity", - "food insecure", + "anemia", + "anaemia", ]) # ============================================================================= @@ -119,8 +104,8 @@ _SDG_ERA_PROXY_KEYWORDS = frozenset([ # ============================================================================= # Digunakan untuk assign kondisi di analysis_layer. # Didefinisikan di sini agar konsisten antara kedua file. -# bad : norm_value_1_100 < THRESHOLD_BAD -# good : norm_value_1_100 > THRESHOLD_GOOD +# bad : norm_value_1_100 < THRESHOLD_BAD +# good : norm_value_1_100 > THRESHOLD_GOOD # moderate : di antara keduanya THRESHOLD_BAD = 40.0 @@ -143,41 +128,33 @@ def assign_condition(norm_value_1_100: float) -> str: return 'moderate' -def assign_framework_per_row( +def assign_framework_for_row( indicator_name: str, - year: int, + row_year: int, sdg_start_year: int, ) -> str: """ - Tentukan framework (MDGs/SDGs) per BARIS (per row year), bukan per indikator. + Tentukan framework (MDGs/SDGs) PER BARIS (per tahun), bukan per indikator. Logic: - - 'SDGs' jika KEDUA kondisi terpenuhi: - 1. Nama indikator ada di SDG_INDICATOR_KEYWORDS - 2. year (tahun baris ini) >= sdg_start_year - - 'MDGs' untuk semua kasus lain. + - 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'. + - Selain itu → 'MDGs'. - Mengapa per row, bukan per indikator? - Indikator "shared" seperti anemia, stunting, wasting, undernourishment - memiliki data yang ada SEBELUM era SDGs dimulai. Jika assign dilakukan - per indikator menggunakan actual_start_year, indikator-indikator ini - akan selalu di-assign 'MDGs' karena actual_start_year < sdg_start_year. - Dengan assign per row menggunakan year baris: - - baris lama (year < sdg_start_year) -> 'MDGs' (benar: belum era SDGs) - - baris baru (year >= sdg_start_year) -> 'SDGs' (benar: sudah era SDGs) - - Contoh anemia (sdg_start_year = 2016): - - row year=2013 -> 'MDGs' - - row year=2014 -> 'MDGs' - - row year=2015 -> 'MDGs' - - row year=2016 -> 'SDGs' - - row year=2017 -> 'SDGs' - - ... + Dengan cara ini, indikator seperti "Prevalence of anemia" yang datanya + ada sebelum era SDGs akan berlabel 'MDGs' untuk tahun-tahun pra-SDGs, + dan 'SDGs' untuk tahun-tahun pasca sdg_start_year. """ - name_lower = str(indicator_name).lower().strip() - in_sdg_list = name_lower in SDG_INDICATOR_KEYWORDS - if in_sdg_list and int(year) >= sdg_start_year: + # Tahun sebelum era SDGs → selalu MDGs + if row_year < sdg_start_year: + return 'MDGs' + + # Tahun >= sdg_start_year: cek apakah nama ada di SDG list + name_lower = str(indicator_name).lower().strip() + if name_lower in SDG_INDICATOR_KEYWORDS: return 'SDGs' + + # Tidak ada di SDG list → MDGs return 'MDGs' @@ -197,11 +174,10 @@ class AnalyticalLayerLoader: norm_value_1_100, <- min-max norm per indikator, skala 1-100, direction-aware yoy_change, yoy_pct - Catatan framework: - Framework di-assign PER BARIS (per year), sehingga indikator shared - seperti anemia dapat memiliki framework berbeda di baris yang berbeda: - - baris sebelum sdg_start_year -> 'MDGs' - - baris sejak sdg_start_year -> 'SDGs' + PERUBAHAN (framework fix): + - framework di-assign per baris (per tahun), bukan per indikator. + - Baris dengan year < sdg_start_year selalu 'MDGs'. + - Baris dengan year >= sdg_start_year dan nama di SDG_INDICATOR_KEYWORDS → 'SDGs'. """ def __init__(self, client: bigquery.Client): @@ -306,14 +282,6 @@ class AnalyticalLayerLoader: self.logger.info("STEP 2: DETERMINE YEAR BOUNDARIES") self.logger.info("=" * 80) - # Filter single years only (is_year_range == False) - if 'is_year_range' in self.df_clean.columns: - before = len(self.df_clean) - self.df_clean = self.df_clean[self.df_clean['is_year_range'] == False].copy() - self.logger.info( - f" Filter single years only: {before:,} -> {len(self.df_clean):,} rows" - ) - # baseline_year = 2023 hardcode (syarat dosen: minimal 2023) df_baseline = self.df_clean[self.df_clean['year'] == self.baseline_year] baseline_indicator_count = df_baseline['indicator_id'].nunique() @@ -529,11 +497,6 @@ class AnalyticalLayerLoader: self.logger.info(f"\n [+] Valid: {len(valid_indicators)}") self.logger.info(f" [-] Removed: {len(removed_indicators)}") - if removed_indicators: - self.logger.info(f"\n Removed indicators:") - for item in removed_indicators: - self.logger.info(f" [-] {item['indicator_name'][:60]} | {item['reason']}") - if not valid_indicators: raise ValueError("No valid indicators found after filtering!") @@ -559,18 +522,13 @@ class AnalyticalLayerLoader: return self.df_clean # ------------------------------------------------------------------ - # STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK PER ROW + # STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL FIX) # ------------------------------------------------------------------ def determine_sdg_start_year(self): self.logger.info("\n" + "=" * 80) - self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK PER ROW") + self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL)") self.logger.info("=" * 80) - self.logger.info( - " Proxy: FIES only (food insecurity/food insecure).\n" - " Anemia TIDAK dipakai sebagai proxy — datanya ada sebelum era SDGs.\n" - " Framework di-assign PER BARIS (year), bukan per indikator." - ) # actual_start_year per indikator = max(min_year per country) # = konsisten dengan max_start_year di Step 5 @@ -583,7 +541,7 @@ class AnalyticalLayerLoader: ) indicator_actual_start.columns = ['indicator_id', 'indicator_name', 'actual_start_year'] - # Deteksi sdg_start_year dari proxy SDGs-only (FIES saja, BUKAN anemia) + # Deteksi sdg_start_year dari proxy SDGs-only (FIES & anaemia) proxy_mask = indicator_actual_start['indicator_name'].str.lower().apply( lambda n: any(kw in n for kw in _SDG_ERA_PROXY_KEYWORDS) ) @@ -591,78 +549,71 @@ class AnalyticalLayerLoader: if df_proxy.empty: raise ValueError( - "Tidak ada indikator proxy SDGs (FIES) yang lolos filter. " - "Pastikan indikator FIES (food insecurity/food insecure) ada di data." + "Tidak ada indikator proxy SDGs (FIES/anaemia) yang lolos filter. " + "Pastikan indikator FIES dan anaemia ada di data." ) self.sdg_start_year = int(df_proxy['actual_start_year'].min()) self.logger.info(f"\n sdg_start_year = {self.sdg_start_year}") - self.logger.info(f" Proxy indicators (FIES only):") + self.logger.info(f" Proxy indicators (penentu sdg_start_year):") for _, row in df_proxy.iterrows(): self.logger.info(f" [{int(row['actual_start_year'])}] {row['indicator_name']}") - # ---------------------------------------------------------------- - # Assign framework PER BARIS menggunakan year baris, bukan actual_start_year - # Sehingga indikator "shared" (anemia, stunting, dll) mendapat: - # - 'MDGs' untuk baris sebelum sdg_start_year - # - 'SDGs' untuk baris sejak sdg_start_year - # ---------------------------------------------------------------- + # ------------------------------------------------------------------ + # FIX: Assign framework PER BARIS (per tahun), bukan per indikator + # ------------------------------------------------------------------ + # Logic: + # 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( - lambda row: assign_framework_per_row( + lambda row: assign_framework_for_row( indicator_name = row['indicator_name'], - year = int(row['year']), + row_year = int(row['year']), sdg_start_year = self.sdg_start_year, ), axis=1 ) - # ---------------------------------------------------------------- - # Logging: ringkasan per indikator (frameworks apa yang muncul) - # ---------------------------------------------------------------- - ind_fw_summary = ( - self.df_clean - .groupby(['indicator_id', 'indicator_name'])['framework'] - .unique() - .reset_index() - ) - ind_fw_summary['frameworks'] = ind_fw_summary['framework'].apply( - lambda x: '/'.join(sorted(x)) - ) - ind_fw_summary = ind_fw_summary.merge( - indicator_actual_start[['indicator_id', 'actual_start_year']], - on='indicator_id', how='left' + # Log ringkasan per indikator untuk verifikasi + self.logger.info(f"\n {'Framework Assignment per Indicator (sample)':}") + self.logger.info(f" {'-'*95}") + self.logger.info( + f" {'ID':<5} {'Indicator Name':<50} " + f"{'Pre-SDG rows':<15} {'MDGs rows':<12} {'SDGs rows'}" ) + self.logger.info(f" {'-'*95}") - self.logger.info(f"\n Framework assignment per indikator:") - self.logger.info(f" {'-'*85}") - self.logger.info(f" {'ID':<5} {'Frameworks':<18} {'ActualStart':<13} {'Indicator Name'}") - self.logger.info(f" {'-'*85}") - for _, row in ind_fw_summary.sort_values( - ['frameworks', 'actual_start_year', 'indicator_name'] - ).iterrows(): + for ind_id, grp in self.df_clean.groupby('indicator_id'): + ind_name = grp['indicator_name'].iloc[0] + pre_sdg = (grp['year'] < self.sdg_start_year).sum() + mdgs_rows = (grp['framework'] == 'MDGs').sum() + sdgs_rows = (grp['framework'] == 'SDGs').sum() self.logger.info( - f" {int(row['indicator_id']):<5} {row['frameworks']:<18} " - f"{int(row['actual_start_year']):<13} {row['indicator_name'][:48]}" + f" {int(ind_id):<5} {ind_name[:48]:<50} " + f"{pre_sdg:<15} {mdgs_rows:<12} {sdgs_rows}" ) - # Indikator dengan framework split (MDGs/SDGs) — highlight untuk validasi - split_inds = ind_fw_summary[ind_fw_summary['frameworks'] == 'MDGs/SDGs'] - if not split_inds.empty: - self.logger.info( - f"\n [INFO] {len(split_inds)} indikator memiliki framework split " - f"(MDGs sebelum {self.sdg_start_year}, SDGs sejak {self.sdg_start_year}):" - ) - for _, row in split_inds.iterrows(): - self.logger.info(f" - {row['indicator_name'][:60]}") - fw_summary = self.df_clean['framework'].value_counts() - self.logger.info( - f"\n Ringkasan rows: " + - " | ".join(f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items()) - ) + self.logger.info(f"\n Ringkasan rows: " + " | ".join( + 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] + fw_ind_summary = end_year_df.groupby('framework')['indicator_id'].nunique() + self.logger.info(f" Indicators di year={self.end_year}: " + " | ".join( + f"{fw}: {cnt}" for fw, cnt in fw_ind_summary.items() + )) self.logger.info( - f"\n [OK] 'framework' ditambahkan per row — " + f"\n [OK] 'framework' ditambahkan (row-level) — " f"MDGs: {(self.df_clean['framework'] == 'MDGs').sum():,} rows | " f"SDGs: {(self.df_clean['framework'] == 'SDGs').sum():,} rows" ) @@ -700,7 +651,7 @@ class AnalyticalLayerLoader: return True # ------------------------------------------------------------------ - # STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR + # STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR PER COUNTRY # ------------------------------------------------------------------ def calculate_norm_value(self): @@ -731,7 +682,7 @@ class AnalyticalLayerLoader: "negative", "lower_better", "lower_is_better", "inverse", "neg", }) - df = self.df_clean.copy() + df = self.df_clean.copy() norm_parts = [] indicators = df.groupby(['indicator_id', 'indicator_name', 'direction']) @@ -862,45 +813,40 @@ class AnalyticalLayerLoader: 'start_year', 'end_year', 'country_count' ] - # Framework summary per indikator (bisa MDGs, SDGs, atau MDGs/SDGs split) - ind_fw = ( - self.df_clean + # Framework per indikator di end_year (untuk display — representasi terbaru) + fw_at_end = ( + self.df_clean[self.df_clean['year'] == self.end_year] .groupby('indicator_id')['framework'] - .unique() + .first() .reset_index() ) - ind_fw['framework_label'] = ind_fw['framework'].apply( - lambda x: '/'.join(sorted(x)) - ) - indicator_details = indicator_details.merge( - ind_fw[['indicator_id', 'framework_label']], - on='indicator_id', how='left' - ) + indicator_details = indicator_details.merge(fw_at_end, on='indicator_id', how='left') + indicator_details['framework'] = indicator_details['framework'].fillna('MDGs') indicator_details['year_range'] = ( indicator_details['start_year'].astype(int).astype(str) + '-' + indicator_details['end_year'].astype(int).astype(str) ) indicator_details = indicator_details.sort_values( - ['framework_label', 'pillar_name', 'start_year', 'indicator_name'] + ['framework', 'pillar_name', 'start_year', 'indicator_name'] ) self.logger.info(f"\nTotal Indicators: {len(indicator_details)}") - self.logger.info(f"Framework breakdown (per indicator label):") - for fw, count in indicator_details.groupby('framework_label').size().items(): + self.logger.info(f"Framework breakdown (at end_year={self.end_year}):") + for fw, count in indicator_details.groupby('framework').size().items(): self.logger.info(f" {fw}: {count} indicators") - self.logger.info(f"\n{'-'*115}") + self.logger.info(f"\n{'-'*110}") self.logger.info( f"{'ID':<5} {'Indicator Name':<55} {'Pillar':<15} " - f"{'Framework':<15} {'Years':<12} {'Dir':<8} {'Countries'}" + f"{'Framework':<10} {'Years':<12} {'Dir':<8} {'Countries'}" ) - self.logger.info(f"{'-'*115}") + self.logger.info(f"{'-'*110}") for _, row in indicator_details.iterrows(): direction = 'higher+' if row['direction'] == 'higher_better' else 'lower-' self.logger.info( f"{int(row['indicator_id']):<5} {row['indicator_name'][:52]:<55} " - f"{row['pillar_name'][:13]:<15} {row['framework_label']:<15} " + f"{row['pillar_name'][:13]:<15} {row['framework']:<10} " f"{row['year_range']:<12} {direction:<8} {int(row['country_count'])}" ) @@ -969,16 +915,14 @@ class AnalyticalLayerLoader: for fw, cnt in fw_dist_rows.items(): self.logger.info(f" {fw}: {cnt:,} rows") - # Framework distribution per indikator (label) - ind_fw_label = ( - analytical_df - .groupby('indicator_id')['framework'] - .unique() - .apply(lambda x: '/'.join(sorted(x))) + # Framework distribution per unique indicator (at end_year) + fw_dist_ind = ( + analytical_df[analytical_df['year'] == self.end_year] + .drop_duplicates('indicator_id')['framework'] .value_counts() ) - self.logger.info(f" Framework distribution (per indicator label):") - for fw, cnt in ind_fw_label.items(): + self.logger.info(f" Framework distribution (indicators at year={self.end_year}):") + for fw, cnt in fw_dist_ind.items(): self.logger.info(f" {fw}: {cnt} indicators") self.logger.info( @@ -1022,26 +966,24 @@ class AnalyticalLayerLoader: 'rows_loaded' : rows_loaded, 'completeness_pct' : 100.0, 'config_snapshot' : json.dumps({ - 'start_year' : self.start_year, - 'end_year' : self.end_year, - 'baseline_year' : self.baseline_year, - 'sdg_start_year' : self.sdg_start_year, - 'fixed_countries' : len(self.selected_country_ids), - 'norm_scale' : '1-100 per indicator global minmax direction-aware', - 'framework_assignment' : 'per-row by year (not per-indicator)', - 'sdg_proxy_keywords' : list(_SDG_ERA_PROXY_KEYWORDS), - 'condition_thresholds' : { + 'start_year' : self.start_year, + 'end_year' : self.end_year, + 'baseline_year' : self.baseline_year, + 'sdg_start_year' : self.sdg_start_year, + 'fixed_countries' : len(self.selected_country_ids), + 'norm_scale' : '1-100 per indicator global minmax direction-aware', + 'framework_logic' : 'row-level: year < sdg_start_year → MDGs always', + 'condition_thresholds': { 'bad' : f'< {THRESHOLD_BAD}', 'moderate': f'{THRESHOLD_BAD}-{THRESHOLD_GOOD}', 'good' : f'> {THRESHOLD_GOOD}', }, }), 'validation_metrics' : json.dumps({ - 'fixed_countries' : len(self.selected_country_ids), - 'total_indicators' : int(self.df_clean['indicator_id'].nunique()), - 'sdg_start_year' : self.sdg_start_year, - 'framework_dist_rows' : fw_dist_rows.to_dict(), - 'framework_dist_inds' : ind_fw_label.to_dict(), + 'fixed_countries' : len(self.selected_country_ids), + 'total_indicators': int(self.df_clean['indicator_id'].nunique()), + 'sdg_start_year' : self.sdg_start_year, + 'framework_dist_rows': fw_dist_rows.to_dict(), }) } save_etl_metadata(self.client, metadata) @@ -1064,9 +1006,8 @@ class AnalyticalLayerLoader: self.logger.info("\n" + "=" * 80) 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("Framework: per-row by year (shared indicators split MDGs/SDGs)") - self.logger.info(f"SDG Proxy: FIES only (food insecurity/food insecure)") 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("=" * 80) self.load_source_data() @@ -1074,10 +1015,10 @@ class AnalyticalLayerLoader: self.filter_complete_indicators_per_country() self.select_countries_with_all_pillars() self.filter_indicators_consistent_across_fixed_countries() - self.determine_sdg_start_year() # Step 6: per-row framework assignment + self.determine_sdg_start_year() self.verify_no_gaps() - self.calculate_norm_value() # Step 8: norm_value_1_100 - self.calculate_yoy() # Step 9: yoy_change, yoy_pct + self.calculate_norm_value() # Step 8: norm_value_1_100 + self.calculate_yoy() # Step 9: yoy_change, yoy_pct self.analyze_indicator_availability_by_year() self.save_analytical_table() @@ -1116,8 +1057,8 @@ if __name__ == "__main__": print("BIGQUERY ANALYTICAL LAYER - DATA FILTERING") print("Output: fact_asean_food_security_selected -> fs_asean_gold") print(f"Norm: min-max 1-100 per indicator, direction-aware") - print(f"Framework: per-row by year | SDG Proxy: FIES only") print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}") + print(f"Framework: row-level (year < sdg_start_year → MDGs always)") print("=" * 80) logger = setup_logging()