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2026-04-01 15:46:20 +07:00
parent 0f93ff6ecd
commit 6a55a91112
1 changed files with 183 additions and 140 deletions
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scripts/bigquery_analytical_layer.py
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@@ -19,21 +19,31 @@ NORMALISASI (Step 8):
- norm_value_1_100 = min-max normalisasi nilai raw per indikator, skala 1-100 - norm_value_1_100 = min-max normalisasi nilai raw per indikator, skala 1-100
- Direction-aware: lower_better diinvert sehingga nilai tinggi selalu = lebih baik - Direction-aware: lower_better diinvert sehingga nilai tinggi selalu = lebih baik
- 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
- Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio
FRAMEWORK LOGIC (Per-Row, bukan per indikator): FRAMEWORK LOGIC (Per-Row, threshold = sdg_start_year global):
- sdg_start_year dideteksi dari data: tahun pertama indikator FIES lengkap
di semua fixed countries (setelah Step 3-5 filter selesai) sdg_start_year dideteksi HANYA dari FIES ("food insecurity" / "food insecure"),
- Proxy deteksi sdg_start_year: HANYA FIES ("food insecurity", "food insecure") karena FIES adalah satu-satunya indikator yang murni baru di era SDGs.
Anemia TIDAK dipakai sebagai proxy karena datanya sudah ada sebelum era SDGs Anemia, stunting, wasting, undernourishment TIDAK dipakai sebagai proxy
- Framework di-assign PER BARIS (per year), bukan per indikator: karena data mereka sudah ada sebelum SDGs sehingga actual_start < sdg_start.
* row['year'] >= sdg_start_year AND nama ada di SDG_INDICATOR_KEYWORDS -> 'SDGs'
* Selain itu -> 'MDGs' Framework di-assign PER BARIS menggunakan sdg_start_year global:
- Ini menangani indikator "shared" (anemia, stunting, wasting, undernourishment) - Indikator ada di SDG_INDICATOR_KEYWORDS AND year >= sdg_start_year -> 'SDGs'
yang datanya ada sebelum SDGs: - Selain itu -> 'MDGs'
* row lama (year < sdg_start_year) -> 'MDGs'
* row baru (year >= sdg_start_year) -> 'SDGs' Efek per kategori indikator (contoh sdg_start_year = 2016):
Indikator shared (anemia, stunting, wasting, undernourishment):
data mulai 2013 -> year 2013, 2014, 2015 = 'MDGs' (year < 2016)
-> year 2016, 2017, ... = 'SDGs' (year >= 2016)
=> SPLIT: sebagian MDGs, sebagian SDGs ✓
Indikator FIES (murni SDGs):
data mulai 2016 (== sdg_start_year) -> seluruh baris = 'SDGs'
=> Selalu SDGs (tidak ada baris sebelum 2016) ✓
Indikator di luar SDG_INDICATOR_KEYWORDS:
-> selalu 'MDGs', tidak peduli tahunnya ✓
""" """
import pandas as pd import pandas as pd
@@ -61,16 +71,14 @@ from google.cloud import bigquery
# ============================================================================= # =============================================================================
# SDG INDICATOR KEYWORDS # SDG INDICATOR KEYWORDS
# Daftar nama indikator (lowercase) yang masuk SDG framework. # Indikator yang termasuk SDG framework (target 2.1 & 2.2).
# Indikator ini akan di-assign 'SDGs' untuk baris dengan year >= sdg_start_year, # Framework per baris ditentukan oleh sdg_start_year global (dari FIES proxy).
# dan 'MDGs' untuk baris dengan year < sdg_start_year.
# ============================================================================= # =============================================================================
SDG_INDICATOR_KEYWORDS = frozenset([ SDG_INDICATOR_KEYWORDS = frozenset([
# TARGET 2.1.1 — Prevalence of undernourishment (shared: ada sebelum SDGs) # TARGET 2.1.1 — Prevalence of undernourishment (shared: ada sebelum SDGs)
"prevalence of undernourishment (percent) (3-year average)", "prevalence of undernourishment (percent) (3-year average)",
"number of people undernourished (million) (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 (murni baru di era SDGs)
"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)",
"prevalence of severe food insecurity in the female adult population (percent) (3-year average)", "prevalence of severe food insecurity in the female adult population (percent) (3-year average)",
@@ -91,23 +99,19 @@ SDG_INDICATOR_KEYWORDS = frozenset([
"number of children under 5 years affected by wasting (million)", "number of children under 5 years affected by wasting (million)",
"percentage of children under 5 years of age who are overweight (modelled estimates) (percent)", "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)", "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 # TARGET 2.2.3 — Anaemia (shared: ada sebelum SDGs)
# baris >= sdg_start_year di-assign '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)",
]) ])
# ============================================================================= # =============================================================================
# SDG ERA PROXY KEYWORDS # SDG ERA PROXY KEYWORDS
# HANYA indikator yang MURNI baru di era SDGs (FIES saja). # HANYA FIES — dipakai HANYA untuk mendeteksi sdg_start_year dari data.
# Dipakai untuk mendeteksi sdg_start_year dari data.
# #
# PENTING — Anemia/anaemia TIDAK dipakai sebagai proxy: # KRITIS — anemia/stunting/wasting/undernourishment TIDAK boleh ada di sini:
# Data anemia sudah ada sebelum era SDGs sehingga actual_start_year-nya # Data mereka sudah ada sebelum era SDGs sehingga actual_start_year < sdg_start_year.
# lebih awal dari sdg_start_year. Jika dipakai sebagai proxy, sdg_start_year # Jika dipakai sebagai proxy, sdg_start_year terdeteksi terlalu awal (misal 2013)
# akan terdeteksi terlalu awal dan seluruh baris anemia akan menjadi 'SDGs'. # sehingga seluruh baris indikator shared menjadi 'SDGs' — SALAH.
# FIES adalah satu-satunya indikator yang benar-benar murni baru di era SDGs
# dan dapat dipakai sebagai penanda tahun mulainya era SDGs.
# ============================================================================= # =============================================================================
_SDG_ERA_PROXY_KEYWORDS = frozenset([ _SDG_ERA_PROXY_KEYWORDS = frozenset([
"food insecurity", "food insecurity",
@@ -117,21 +121,13 @@ _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
# good : norm_value_1_100 > THRESHOLD_GOOD
# moderate : di antara keduanya
THRESHOLD_BAD = 40.0 THRESHOLD_BAD = 40.0
THRESHOLD_GOOD = 60.0 THRESHOLD_GOOD = 60.0
def assign_condition(norm_value_1_100: float) -> str: def assign_condition(norm_value_1_100: float) -> str:
""" """
Assign kondisi berdasarkan norm_value_1_100 (skala 1-100, sudah direction-aware). Assign kondisi berdasarkan norm_value_1_100 (skala 1-100, direction-aware).
Nilai tinggi selalu berarti lebih baik (lower_better sudah diinvert).
Returns: 'good' / 'moderate' / 'bad' Returns: 'good' / 'moderate' / 'bad'
""" """
if pd.isna(norm_value_1_100): if pd.isna(norm_value_1_100):
@@ -149,30 +145,27 @@ def assign_framework_per_row(
sdg_start_year: int, sdg_start_year: int,
) -> str: ) -> str:
""" """
Tentukan framework (MDGs/SDGs) per BARIS (per row year), bukan per indikator. Tentukan framework (MDGs/SDGs) per BARIS menggunakan sdg_start_year GLOBAL.
Logic: Rules:
- 'SDGs' jika KEDUA kondisi terpenuhi: 1. Indikator TIDAK ada di SDG_INDICATOR_KEYWORDS -> selalu 'MDGs'
1. Nama indikator ada di SDG_INDICATOR_KEYWORDS 2. Indikator ada di SDG_INDICATOR_KEYWORDS:
2. year (tahun baris ini) >= sdg_start_year - year >= sdg_start_year -> 'SDGs'
- 'MDGs' untuk semua kasus lain. - year < sdg_start_year -> 'MDGs'
Mengapa per row, bukan per indikator? sdg_start_year dideteksi dari FIES (proxy murni SDGs), bukan dari
Indikator "shared" seperti anemia, stunting, wasting, undernourishment actual_start_year masing-masing indikator. Ini memastikan indikator
memiliki data yang ada SEBELUM era SDGs dimulai. Jika assign dilakukan shared (anemia, stunting, wasting, undernourishment) yang datanya
per indikator menggunakan actual_start_year, indikator-indikator ini ada sebelum SDGs tetap mendapat label 'MDGs' untuk baris sebelum
akan selalu di-assign 'MDGs' karena actual_start_year < sdg_start_year. sdg_start_year dan 'SDGs' untuk baris sejak 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): Contoh (sdg_start_year = 2016):
- row year=2013 -> 'MDGs' anemia year=2013 -> 'MDGs' (ada di SDG list, tapi year < 2016)
- row year=2014 -> 'MDGs' anemia year=2015 -> 'MDGs'
- row year=2015 -> 'MDGs' anemia year=2016 -> 'SDGs' (year >= 2016)
- row year=2016 -> 'SDGs' anemia year=2023 -> 'SDGs'
- row year=2017 -> 'SDGs' FIES year=2016 -> 'SDGs' (tidak ada baris FIES sebelum 2016)
- ... non-SDG year=any -> 'MDGs' (tidak ada di SDG_INDICATOR_KEYWORDS)
""" """
name_lower = str(indicator_name).lower().strip() name_lower = str(indicator_name).lower().strip()
in_sdg_list = name_lower in SDG_INDICATOR_KEYWORDS in_sdg_list = name_lower in SDG_INDICATOR_KEYWORDS
@@ -187,21 +180,28 @@ def assign_framework_per_row(
class AnalyticalLayerLoader: class AnalyticalLayerLoader:
""" """
Analytical Layer Loader for BigQuery Analytical Layer Loader for BigQuery.
Output kolom fact_asean_food_security_selected: Output kolom fact_asean_food_security_selected:
country_id, country_name, country_id, country_name,
indicator_id, indicator_name, direction, framework, indicator_id, indicator_name, direction, framework,
pillar_id, pillar_name, pillar_id, pillar_name,
time_id, year, value, time_id, year, value,
norm_value_1_100, <- min-max norm per indikator, skala 1-100, direction-aware norm_value_1_100,
yoy_change, yoy_pct yoy_change, yoy_pct
Catatan framework: Framework logic (sdg_start_year global dari FIES proxy):
Framework di-assign PER BARIS (per year), sehingga indikator shared Indikator shared (anemia, stunting, wasting, undernourishment):
seperti anemia dapat memiliki framework berbeda di baris yang berbeda: year < sdg_start_year -> 'MDGs' (misal 2013-2015)
- baris sebelum sdg_start_year -> 'MDGs' year >= sdg_start_year -> 'SDGs' (misal 2016-2023)
- baris sejak sdg_start_year -> 'SDGs' => SPLIT: sebagian MDGs, sebagian SDGs ✓
Indikator FIES (murni SDGs):
seluruh baris -> 'SDGs'
(tidak ada data FIES sebelum sdg_start_year) ✓
Indikator di luar SDG_INDICATOR_KEYWORDS:
seluruh baris -> 'MDGs' ✓
""" """
def __init__(self, client: bigquery.Client): def __init__(self, client: bigquery.Client):
@@ -218,9 +218,9 @@ class AnalyticalLayerLoader:
self.start_year = 2013 self.start_year = 2013
self.end_year = None self.end_year = None
self.baseline_year = 2023 # hardcode per syarat dosen (tahun terlengkap) self.baseline_year = 2023 # hardcode per syarat dosen
self.sdg_start_year = None self.sdg_start_year = None # dideteksi HANYA dari FIES proxy di Step 6
self.pipeline_metadata = { self.pipeline_metadata = {
'source_class' : self.__class__.__name__, 'source_class' : self.__class__.__name__,
@@ -306,7 +306,6 @@ 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)
# Filter single years only (is_year_range == False)
if 'is_year_range' in self.df_clean.columns: if 'is_year_range' in self.df_clean.columns:
before = len(self.df_clean) before = len(self.df_clean)
self.df_clean = self.df_clean[self.df_clean['is_year_range'] == False].copy() self.df_clean = self.df_clean[self.df_clean['is_year_range'] == False].copy()
@@ -314,7 +313,6 @@ class AnalyticalLayerLoader:
f" Filter single years only: {before:,} -> {len(self.df_clean):,} rows" 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] 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()
@@ -542,6 +540,7 @@ class AnalyticalLayerLoader:
self.df_clean['indicator_id'].isin(valid_indicators) self.df_clean['indicator_id'].isin(valid_indicators)
].copy() ].copy()
# Trim baris di bawah max_start_year per indikator
self.df_clean = self.df_clean.merge( self.df_clean = self.df_clean.merge(
indicator_max_start[['indicator_id', 'max_start_year']], indicator_max_start[['indicator_id', 'max_start_year']],
on='indicator_id', how='left' on='indicator_id', how='left'
@@ -567,13 +566,16 @@ class AnalyticalLayerLoader:
self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK PER ROW") self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK PER ROW")
self.logger.info("=" * 80) self.logger.info("=" * 80)
self.logger.info( self.logger.info(
" Proxy: FIES only (food insecurity/food insecure).\n" " sdg_start_year dideteksi HANYA dari FIES proxy\n"
" Anemia TIDAK dipakai sebagai proxy — datanya ada sebelum era SDGs.\n" " (food insecurity / food insecure — murni baru di era SDGs).\n"
" Framework di-assign PER BARIS (year), bukan per indikator." " Anemia/stunting/wasting/undernourishment TIDAK dipakai sebagai proxy.\n\n"
" Framework per baris (threshold = sdg_start_year global):\n"
" SDG_INDICATOR_KEYWORDS + year >= sdg_start_year -> 'SDGs'\n"
" SDG_INDICATOR_KEYWORDS + year < sdg_start_year -> 'MDGs' [SPLIT]\n"
" Indikator di luar SDG_INDICATOR_KEYWORDS -> selalu 'MDGs'"
) )
# actual_start_year per indikator = max(min_year per country) # Hitung actual_start_year per indikator (untuk logging & validasi)
# = 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']
@@ -583,7 +585,9 @@ class AnalyticalLayerLoader:
) )
indicator_actual_start.columns = ['indicator_id', 'indicator_name', 'actual_start_year'] 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 HANYA dari FIES proxy
# ------------------------------------------------------------------
proxy_mask = indicator_actual_start['indicator_name'].str.lower().apply( proxy_mask = indicator_actual_start['indicator_name'].str.lower().apply(
lambda n: any(kw in n for kw in _SDG_ERA_PROXY_KEYWORDS) lambda n: any(kw in n for kw in _SDG_ERA_PROXY_KEYWORDS)
) )
@@ -591,22 +595,46 @@ class AnalyticalLayerLoader:
if df_proxy.empty: if df_proxy.empty:
raise ValueError( raise ValueError(
"Tidak ada indikator proxy SDGs (FIES) yang lolos filter. " "Tidak ada indikator FIES (food insecurity/food insecure) yang lolos filter. "
"Pastikan indikator FIES (food insecurity/food insecure) ada di data." "Pastikan indikator FIES ada di data dan lolos Step 3-5."
) )
self.sdg_start_year = int(df_proxy['actual_start_year'].min()) 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"\n sdg_start_year = {self.sdg_start_year} (dari FIES proxy)")
self.logger.info(f" FIES proxy indicators:")
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']}")
# ---------------------------------------------------------------- # Log indikator shared yang akan split (ada di SDG list, data mulai sebelum sdg_start_year)
# Assign framework PER BARIS menggunakan year baris, bukan actual_start_year shared_sdg = indicator_actual_start[
# Sehingga indikator "shared" (anemia, stunting, dll) mendapat: ~proxy_mask &
# - 'MDGs' untuk baris sebelum sdg_start_year indicator_actual_start['indicator_name'].str.lower().isin(SDG_INDICATOR_KEYWORDS) &
# - 'SDGs' untuk baris sejak sdg_start_year (indicator_actual_start['actual_start_year'] < self.sdg_start_year)
# ---------------------------------------------------------------- ]
if not shared_sdg.empty:
self.logger.info(
f"\n Indikator shared yang akan SPLIT MDGs/SDGs "
f"(data mulai < sdg_start_year={self.sdg_start_year}):"
)
for _, row in shared_sdg.iterrows():
n_mdgs = len(self.df_clean[
(self.df_clean['indicator_id'] == row['indicator_id']) &
(self.df_clean['year'] < self.sdg_start_year)
])
n_sdgs = len(self.df_clean[
(self.df_clean['indicator_id'] == row['indicator_id']) &
(self.df_clean['year'] >= self.sdg_start_year)
])
self.logger.info(
f" [actual_start={int(row['actual_start_year'])}] "
f"{row['indicator_name'][:50]} "
f"| MDGs rows: {n_mdgs:,} | SDGs rows: {n_sdgs:,}"
)
# ------------------------------------------------------------------
# Assign framework PER BARIS menggunakan sdg_start_year global
# ------------------------------------------------------------------
self.df_clean['framework'] = self.df_clean.apply( self.df_clean['framework'] = self.df_clean.apply(
lambda row: assign_framework_per_row( lambda row: assign_framework_per_row(
indicator_name = row['indicator_name'], indicator_name = row['indicator_name'],
@@ -616,9 +644,9 @@ class AnalyticalLayerLoader:
axis=1 axis=1
) )
# ---------------------------------------------------------------- # ------------------------------------------------------------------
# Logging: ringkasan per indikator (frameworks apa yang muncul) # Logging ringkasan per indikator
# ---------------------------------------------------------------- # ------------------------------------------------------------------
ind_fw_summary = ( ind_fw_summary = (
self.df_clean self.df_clean
.groupby(['indicator_id', 'indicator_name'])['framework'] .groupby(['indicator_id', 'indicator_name'])['framework']
@@ -634,9 +662,9 @@ class AnalyticalLayerLoader:
) )
self.logger.info(f"\n Framework assignment per indikator:") self.logger.info(f"\n Framework assignment per indikator:")
self.logger.info(f" {'-'*85}") self.logger.info(f" {'-'*90}")
self.logger.info(f" {'ID':<5} {'Frameworks':<18} {'ActualStart':<13} {'Indicator Name'}") self.logger.info(f" {'ID':<5} {'Frameworks':<18} {'ActualStart':<13} {'Indicator Name'}")
self.logger.info(f" {'-'*85}") self.logger.info(f" {'-'*90}")
for _, row in ind_fw_summary.sort_values( for _, row in ind_fw_summary.sort_values(
['frameworks', 'actual_start_year', 'indicator_name'] ['frameworks', 'actual_start_year', 'indicator_name']
).iterrows(): ).iterrows():
@@ -645,24 +673,48 @@ class AnalyticalLayerLoader:
f"{int(row['actual_start_year']):<13} {row['indicator_name'][:48]}" f"{int(row['actual_start_year']):<13} {row['indicator_name'][:48]}"
) )
# Indikator dengan framework split (MDGs/SDGs) — highlight untuk validasi # Ringkasan per kategori
mdgs_only = ind_fw_summary[ind_fw_summary['frameworks'] == 'MDGs']
sdgs_only = ind_fw_summary[ind_fw_summary['frameworks'] == 'SDGs']
split_inds = ind_fw_summary[ind_fw_summary['frameworks'] == 'MDGs/SDGs'] split_inds = ind_fw_summary[ind_fw_summary['frameworks'] == 'MDGs/SDGs']
if not mdgs_only.empty:
self.logger.info(
f"\n [MDGs only — {len(mdgs_only)} indikator] "
f"Tidak ada di SDG_INDICATOR_KEYWORDS:"
)
for _, row in mdgs_only.iterrows():
self.logger.info(f" - {row['indicator_name'][:65]}")
if not sdgs_only.empty:
self.logger.info(
f"\n [SDGs only — {len(sdgs_only)} indikator] "
f"Data mulai = sdg_start_year, tidak ada baris sebelumnya:"
)
for _, row in sdgs_only.iterrows():
self.logger.info(
f" - [{int(row['actual_start_year'])}] {row['indicator_name'][:65]}"
)
if not split_inds.empty: if not split_inds.empty:
self.logger.info( self.logger.info(
f"\n [INFO] {len(split_inds)} indikator memiliki framework split " f"\n [SPLIT MDGs/SDGs — {len(split_inds)} indikator] "
f"(MDGs sebelum {self.sdg_start_year}, SDGs sejak {self.sdg_start_year}):" f"Baris < {self.sdg_start_year} = MDGs | "
f"Baris >= {self.sdg_start_year} = SDGs:"
) )
for _, row in split_inds.iterrows(): for _, row in split_inds.iterrows():
self.logger.info(f" - {row['indicator_name'][:60]}") self.logger.info(
f" - [actual_start={int(row['actual_start_year'])}] "
f"{row['indicator_name'][:65]}"
)
fw_summary = self.df_clean['framework'].value_counts() fw_summary = self.df_clean['framework'].value_counts()
self.logger.info( self.logger.info(
f"\n Ringkasan rows: " + f"\n Ringkasan rows: " +
" | ".join(f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items()) " | ".join(f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items())
) )
self.logger.info( self.logger.info(
f"\n [OK] 'framework' ditambahkan per row — " 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"
) )
@@ -704,25 +756,6 @@ class AnalyticalLayerLoader:
# ------------------------------------------------------------------ # ------------------------------------------------------------------
def calculate_norm_value(self): def calculate_norm_value(self):
"""
Hitung norm_value_1_100 per indikator — min-max normalisasi skala 1-100,
direction-aware.
CARA KERJA:
- Normalisasi dilakukan GLOBAL per indikator (semua negara + semua tahun sekaligus)
sehingga nilai antar negara dan antar tahun tetap comparable.
- 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.
- 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")
self.logger.info("=" * 80) self.logger.info("=" * 80)
@@ -735,7 +768,10 @@ class AnalyticalLayerLoader:
norm_parts = [] norm_parts = []
indicators = df.groupby(['indicator_id', 'indicator_name', 'direction']) indicators = df.groupby(['indicator_id', 'indicator_name', 'direction'])
self.logger.info(f"\n {'ID':<5} {'Direction':<15} {'Invert':<8} {'Min':>10} {'Max':>10} {'Indicator Name'}") self.logger.info(
f"\n {'ID':<5} {'Direction':<15} {'Invert':<8} "
f"{'Min':>10} {'Max':>10} {'Indicator Name'}"
)
self.logger.info(f" {'-'*90}") self.logger.info(f" {'-'*90}")
for (ind_id, ind_name, direction), grp in indicators: for (ind_id, ind_name, direction), grp in indicators:
@@ -755,15 +791,11 @@ 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
@@ -776,7 +808,6 @@ 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:,}")
@@ -786,15 +817,17 @@ 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 "
f"(threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}):"
)
for cond, cnt in cond_dist.items(): for cond, cnt in cond_dist.items():
self.logger.info(f" {cond}: {cnt:,} rows") self.logger.info(f" {cond}: {cnt:,} rows")
self.df_clean = self.df_clean.drop(columns=['_condition_preview']) self.df_clean = self.df_clean.drop(columns=['_condition_preview'])
self.logger.info(f"\n [OK] Kolom 'norm_value_1_100' ditambahkan ke df_clean") self.logger.info(f"\n [OK] Kolom 'norm_value_1_100' ditambahkan")
return self.df_clean return self.df_clean
# ------------------------------------------------------------------ # ------------------------------------------------------------------
@@ -862,7 +895,6 @@ class AnalyticalLayerLoader:
'start_year', 'end_year', 'country_count' 'start_year', 'end_year', 'country_count'
] ]
# Framework summary per indikator (bisa MDGs, SDGs, atau MDGs/SDGs split)
ind_fw = ( ind_fw = (
self.df_clean self.df_clean
.groupby('indicator_id')['framework'] .groupby('indicator_id')['framework']
@@ -963,13 +995,11 @@ 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 indikator (label)
ind_fw_label = ( ind_fw_label = (
analytical_df analytical_df
.groupby('indicator_id')['framework'] .groupby('indicator_id')['framework']
@@ -1028,7 +1058,11 @@ 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_assignment' : 'per-row by year (not per-indicator)', 'framework_assignment' : (
f'per-row, sdg_start_year={self.sdg_start_year} global (FIES proxy only). '
'SDG_INDICATOR_KEYWORDS + year >= sdg_start_year -> SDGs, else MDGs. '
'Shared indicators (anemia/stunting/wasting/undernourishment) split MDGs/SDGs.'
),
'sdg_proxy_keywords' : list(_SDG_ERA_PROXY_KEYWORDS), 'sdg_proxy_keywords' : list(_SDG_ERA_PROXY_KEYWORDS),
'condition_thresholds' : { 'condition_thresholds' : {
'bad' : f'< {THRESHOLD_BAD}', 'bad' : f'< {THRESHOLD_BAD}',
@@ -1064,8 +1098,12 @@ class AnalyticalLayerLoader:
self.logger.info("\n" + "=" * 80) self.logger.info("\n" + "=" * 80)
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("Framework: per-row by year (shared indicators split MDGs/SDGs)") self.logger.info(
self.logger.info(f"SDG Proxy: FIES only (food insecurity/food insecure)") "Framework: per-row, threshold = sdg_start_year global (dari FIES proxy)\n"
" SDG_INDICATOR_KEYWORDS + year >= sdg_start_year -> 'SDGs'\n"
" SDG_INDICATOR_KEYWORDS + year < sdg_start_year -> 'MDGs' [SPLIT]\n"
" Indikator di luar SDG_INDICATOR_KEYWORDS -> selalu 'MDGs'"
)
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("=" * 80) self.logger.info("=" * 80)
@@ -1074,10 +1112,10 @@ 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() # Step 6: per-row framework assignment 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()
@@ -1089,7 +1127,7 @@ 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 Start Yr : {self.sdg_start_year}") self.logger.info(f" SDG Start Yr : {self.sdg_start_year} (dari FIES proxy)")
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']:,}")
@@ -1116,7 +1154,12 @@ if __name__ == "__main__":
print("BIGQUERY ANALYTICAL LAYER - DATA FILTERING") print("BIGQUERY ANALYTICAL LAYER - DATA FILTERING")
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"Framework: per-row by year | SDG Proxy: FIES only") print(
"Framework: per-row, threshold = sdg_start_year global (dari FIES proxy)\n"
" SDG_INDICATOR_KEYWORDS + year >= sdg_start_year -> SDGs\n"
" SDG_INDICATOR_KEYWORDS + year < sdg_start_year -> MDGs [SPLIT]\n"
" Indikator di luar SDG_INDICATOR_KEYWORDS -> selalu MDGs"
)
print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}") print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
print("=" * 80) print("=" * 80)