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933c3706065cc2b431b90930a2f6ae6627740184
airflow-coolify/scripts/bigquery_aggraget_fact_selected_layer.py
Debby 933c370606 try again
2026-04-07 10:19:35 +07:00

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"""
BIGQUERY ANALYSIS LAYER - INDICATOR NORM AGGREGATION
Tabel: agg_indicator_norm -> fs_asean_gold
Tujuan:
Menghitung norm_value per indikator per negara per tahun, sehingga dapat
melihat performa setiap indikator secara individual (lower_better & higher_better
sudah dibalik).
Framework Classification Logic:
- Semua indikator berlabel "MDGs" secara default.
- Indikator yang ada dalam SDG_ONLY_KEYWORDS akan berlabel "SDGs" mulai dari
sdgs_start_year (tahun pertama FIES hadir, dihitung otomatis).
- Indikator yang SUDAH ADA sebelum sdgs_start_year DAN juga termasuk
SDG_ONLY_KEYWORDS akan memiliki DUA label framework:
* "MDGs" untuk year < sdgs_start_year
* "SDGs" untuk year >= sdgs_start_year
- Indikator yang TIDAK ada dalam SDG_ONLY_KEYWORDS selalu "MDGs".
YoY Logic:
- yoy_value : selisih absolut value vs tahun sebelumnya (per indikator, negara)
- yoy_norm_value : selisih absolut norm_value vs tahun sebelumnya
Performance Label Logic:
- performance : "Good" jika norm_score_1_100 >= 60, "Bad" jika < 60, null jika null
Output Schema (agg_indicator_norm):
year, country_id, country_name,
indicator_id, indicator_name, unit, direction,
pillar_id, pillar_name,
framework, -- "MDGs" | "SDGs"
value, -- raw value asli
norm_value, -- 0-1, direction sudah diperhitungkan
norm_score_1_100, -- scaled 1-100 (global per indikator)
yoy_value, -- perubahan absolut value YoY
yoy_norm_value, -- perubahan absolut norm_value YoY
performance -- "Good" | "Bad" | null
"""
import pandas as pd
import numpy as np
from datetime import datetime
import logging
import json
from scripts.bigquery_config import get_bigquery_client
from scripts.bigquery_helpers import (
log_update,
load_to_bigquery,
read_from_bigquery,
setup_logging,
save_etl_metadata,
)
from google.cloud import bigquery
# =============================================================================
# SDG-ONLY KEYWORD SET
# =============================================================================
SDG_ONLY_KEYWORDS: frozenset = frozenset([
# TARGET 2.1.1 - Undernourishment
"prevalence of undernourishment (percent) (3-year average)",
"number of people undernourished (million) (3-year average)",
# TARGET 2.1.2 - Food Insecurity (FIES)
"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)",
"prevalence of moderate or severe food insecurity in the total population (percent) (3-year average)",
"prevalence of moderate or severe food insecurity in the male adult population (percent) (3-year average)",
"prevalence of moderate or severe food insecurity in the female adult population (percent) (3-year average)",
"number of severely food insecure people (million) (3-year average)",
"number of severely food insecure male adults (million) (3-year average)",
"number of severely food insecure female adults (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 female adults (million) (3-year average)",
# TARGET 2.2.1 - Stunting
"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
"percentage of children under 5 years affected by wasting (percent)",
"number of children under 5 years affected by wasting (million)",
# TARGET 2.2.2 - Overweight (children)
"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
"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)",
])
# Lowercase set untuk matching case-insensitive
_SDG_ONLY_LOWER: frozenset = frozenset(k.lower() for k in SDG_ONLY_KEYWORDS)
# FIES-specific keywords untuk deteksi sdgs_start_year
_FIES_DETECTION_KEYWORDS: frozenset = frozenset([
"prevalence of severe food insecurity in the total population (percent) (3-year average)",
"prevalence of moderate or severe food insecurity in the total population (percent) (3-year average)",
"number of severely food insecure people (million) (3-year average)",
"number of moderately or severely food insecure people (million) (3-year average)",
])
_FIES_DETECTION_LOWER: frozenset = frozenset(k.lower() for k in _FIES_DETECTION_KEYWORDS)
DIRECTION_INVERT_KEYWORDS = frozenset({
"negative", "lower_better", "lower_is_better", "inverse", "neg",
})
DIRECTION_POSITIVE_KEYWORDS = frozenset({
"positive", "higher_better", "higher_is_better",
})
# Threshold performance label
_PERFORMANCE_THRESHOLD: float = 60.0
# =============================================================================
# PURE HELPERS
# =============================================================================
def _should_invert(direction: str, logger=None, context: str = "") -> bool:
d = str(direction).lower().strip()
if d in DIRECTION_INVERT_KEYWORDS:
return True
if d in DIRECTION_POSITIVE_KEYWORDS:
return False
if logger:
logger.warning(
f" [DIRECTION WARNING] Unknown direction '{direction}' "
f"{'(' + context + ')' if context else ''}. Defaulting to positive (no invert)."
)
return False
def global_minmax(series: pd.Series, lo: float = 1.0, hi: float = 100.0) -> pd.Series:
values = series.dropna().values
if len(values) == 0:
return pd.Series(np.nan, index=series.index)
v_min, v_max = values.min(), values.max()
if v_min == v_max:
return pd.Series((lo + hi) / 2.0, index=series.index)
result = np.full(len(series), np.nan)
not_nan = series.notna()
result[not_nan.values] = lo + (series[not_nan].values - v_min) / (v_max - v_min) * (hi - lo)
return pd.Series(result, index=series.index)
def _compute_yoy(df: pd.DataFrame) -> pd.DataFrame:
"""
Hitung YoY untuk satu grup (indicator_id, country_id) yang sudah di-sort by year.
Kolom yang ditambahkan:
yoy_value : value - value_prev
yoy_norm_value : norm_value - norm_value_prev
Baris pertama tiap grup selalu null (tidak ada tahun sebelumnya).
"""
df = df.sort_values("year").copy()
df["value_prev"] = df["value"].shift(1)
df["norm_value_prev"] = df["norm_value"].shift(1)
df["yoy_value"] = np.where(
df["value"].notna() & df["value_prev"].notna(),
df["value"] - df["value_prev"],
np.nan,
)
df["yoy_norm_value"] = np.where(
df["norm_value"].notna() & df["norm_value_prev"].notna(),
df["norm_value"] - df["norm_value_prev"],
np.nan,
)
df = df.drop(columns=["value_prev", "norm_value_prev"])
return df
# =============================================================================
# MAIN CLASS
# =============================================================================
class IndicatorNormAggregator:
"""
Hitung norm_value per indikator untuk seluruh data di
fact_asean_food_security_selected, lalu simpan ke agg_indicator_norm.
Alur:
1. Load fact_asean_food_security_selected
2. Load dim_indicator -> ambil kolom unit
3. Merge unit ke df
4. Deteksi sdgs_start_year (tahun pertama FIES hadir di data)
5. Assign framework per baris mengikuti aturan MDGs/SDGs dual-label
6. Hitung norm_value per indikator (direction-aware, 0-1)
7. Hitung YoY per (indicator_id, country_id)
8. Scale ke 1-100 per indikator (global)
9. Assign performance label (Good/Bad)
10. Simpan ke BigQuery
"""
def __init__(self, client: bigquery.Client):
self.client = client
self.logger = logging.getLogger(self.__class__.__name__)
self.logger.propagate = False
self.df = None
self.df_unit = None
self.sdgs_start_year = None
self.pipeline_start = None
self.pipeline_metadata = {
"rows_fetched": 0,
"rows_loaded" : 0,
"start_time" : None,
"end_time" : None,
}
# =========================================================================
# STEP 1: Load fact table
# =========================================================================
def load_data(self):
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 1: LOAD DATA — fact_asean_food_security_selected")
self.logger.info("=" * 80)
self.df = read_from_bigquery(
self.client, "fact_asean_food_security_selected", layer="gold"
)
required = {
"country_id", "country_name",
"indicator_id", "indicator_name", "direction",
"pillar_id", "pillar_name",
"year", "value",
}
missing = required - set(self.df.columns)
if missing:
raise ValueError(f"Kolom tidak ditemukan: {missing}")
n_null = self.df["direction"].isna().sum()
if n_null > 0:
self.logger.warning(f" {n_null} rows direction NULL -> diisi 'positive'")
self.df["direction"] = self.df["direction"].fillna("positive")
self.pipeline_metadata["rows_fetched"] = len(self.df)
self.logger.info(f" Rows : {len(self.df):,}")
self.logger.info(f" Countries : {self.df['country_id'].nunique()}")
self.logger.info(f" Indicators: {self.df['indicator_id'].nunique()}")
self.logger.info(
f" Years : {int(self.df['year'].min())} - {int(self.df['year'].max())}"
)
# =========================================================================
# STEP 2: Load unit dari dim_indicator
# =========================================================================
def load_units(self):
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 2: LOAD UNIT — dim_indicator")
self.logger.info("=" * 80)
dim = read_from_bigquery(self.client, "dim_indicator", layer="gold")
if "indicator_id" not in dim.columns or "unit" not in dim.columns:
raise ValueError(
f"dim_indicator harus punya kolom 'indicator_id' dan 'unit'. "
f"Kolom tersedia: {list(dim.columns)}"
)
self.df_unit = (
dim[["indicator_id", "unit"]]
.drop_duplicates(subset=["indicator_id"])
.copy()
)
self.df_unit["indicator_id"] = self.df_unit["indicator_id"].astype(int)
self.df_unit["unit"] = self.df_unit["unit"].fillna("").astype(str)
n_missing_unit = self.df_unit["unit"].eq("").sum()
self.logger.info(f" dim_indicator rows (unique indicator_id): {len(self.df_unit):,}")
self.logger.info(f" Indicator dengan unit kosong : {n_missing_unit}")
fact_ids = set(self.df["indicator_id"].astype(int).unique())
dim_ids = set(self.df_unit["indicator_id"].unique())
orphan = fact_ids - dim_ids
if orphan:
self.logger.warning(
f" [WARNING] {len(orphan)} indicator_id di fact tidak ditemukan di "
f"dim_indicator (unit akan diisi ''): {sorted(orphan)}"
)
# =========================================================================
# STEP 3: Merge unit ke df
# =========================================================================
def _merge_unit(self):
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 3: MERGE UNIT -> fact df")
self.logger.info("=" * 80)
before = len(self.df)
self.df = self.df.merge(self.df_unit, on="indicator_id", how="left")
self.df["unit"] = self.df["unit"].fillna("").astype(str)
after = len(self.df)
assert before == after, (
f"Row count berubah setelah merge unit: {before} -> {after}"
)
n_empty = self.df["unit"].eq("").sum()
self.logger.info(
f" Merge OK. Rows: {after:,} | Rows dengan unit kosong: {n_empty}"
)
unique_units = self.df["unit"].value_counts().to_dict()
self.logger.info(" Distribusi unit (top 10):")
for u, cnt in list(unique_units.items())[:10]:
label = repr(u) if u == "" else u
self.logger.info(f" {label:<30}: {cnt:,} rows")
# =========================================================================
# STEP 4: Deteksi sdgs_start_year
# =========================================================================
def _detect_sdgs_start_year(self) -> int:
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 4: DETECT sdgs_start_year (first FIES year)")
self.logger.info("=" * 80)
fies_rows = self.df[
self.df["indicator_name"].str.lower().str.strip().isin(_FIES_DETECTION_LOWER)
]
if not fies_rows.empty:
sdgs_start = int(fies_rows["year"].min())
n_fies_ind = fies_rows["indicator_name"].nunique()
self.logger.info(f" [Metode 1 - FIES explicit] sdgs_start_year = {sdgs_start}")
self.logger.info(f" FIES indicators found: {n_fies_ind}, first year = {sdgs_start}")
for nm in fies_rows["indicator_name"].unique():
min_y = int(fies_rows[fies_rows["indicator_name"] == nm]["year"].min())
self.logger.info(f" - {nm[:60]} (first year: {min_y})")
return sdgs_start
self.logger.info(" [Metode 1] Tidak ada FIES rows -> fallback gap-terbesar")
ind_min_year = (
self.df.groupby("indicator_id")["year"]
.min().reset_index()
.rename(columns={"year": "min_year"})
)
unique_years = sorted(ind_min_year["min_year"].unique())
self.logger.info(f" Unique min_year per indikator: {unique_years}")
if len(unique_years) == 1:
sdgs_start = int(unique_years[0]) + 9999
self.logger.info(" Hanya 1 cluster -> semua MDGs")
else:
gaps = [
(unique_years[i+1] - unique_years[i], unique_years[i], unique_years[i+1])
for i in range(len(unique_years) - 1)
]
gaps.sort(reverse=True)
_, y_before, y_after = gaps[0]
sdgs_start = int(y_after)
self.logger.info(
f" Gap terbesar: {y_before} -> {y_after} -> sdgs_start_year = {sdgs_start}"
)
return sdgs_start
# =========================================================================
# STEP 5: Assign framework
# =========================================================================
def _assign_framework(self):
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 5: ASSIGN FRAMEWORK PER BARIS")
self.logger.info(f" sdgs_start_year = {self.sdgs_start_year}")
self.logger.info("=" * 80)
df = self.df.copy()
df["_is_sdg_kw"] = df["indicator_name"].str.lower().str.strip().isin(_SDG_ONLY_LOWER)
df["framework"] = "MDGs"
mask_sdgs = df["_is_sdg_kw"] & (df["year"] >= self.sdgs_start_year)
df.loc[mask_sdgs, "framework"] = "SDGs"
df = df.drop(columns=["_is_sdg_kw"])
fw_dist = df["framework"].value_counts()
self.logger.info("\n Framework distribution (rows):")
for fw, cnt in fw_dist.items():
self.logger.info(f" {fw:<6}: {cnt:,} rows")
dual = (
df.groupby("indicator_id")["framework"]
.nunique()
.reset_index()
.rename(columns={"framework": "n_frameworks"})
)
dual_ids = dual[dual["n_frameworks"] > 1]["indicator_id"].tolist()
self.logger.info(
f"\n Indikator dengan DUAL framework (MDGs + SDGs): {len(dual_ids)}"
)
if dual_ids:
for iid in dual_ids:
ind_name = df[df["indicator_id"] == iid]["indicator_name"].iloc[0]
yr_range = df[df["indicator_id"] == iid][["year", "framework"]].drop_duplicates()
mdgs_yrs = sorted(yr_range[yr_range["framework"] == "MDGs"]["year"].tolist())
sdgs_yrs = sorted(yr_range[yr_range["framework"] == "SDGs"]["year"].tolist())
self.logger.info(
f" [{iid}] {ind_name[:55]}\n"
f" MDGs years: {mdgs_yrs}\n"
f" SDGs years: {sdgs_yrs}"
)
self.df = df
# =========================================================================
# STEP 6: Hitung norm_value per indikator (direction-aware)
# =========================================================================
def _compute_norm_values(self) -> pd.DataFrame:
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 6: COMPUTE NORM_VALUE PER INDICATOR (direction-aware)")
self.logger.info("=" * 80)
df = self.df.copy()
norm_parts = []
for ind_id, grp in df.groupby("indicator_id"):
grp = grp.copy()
direction = str(grp["direction"].iloc[0])
do_invert = _should_invert(
direction, self.logger, context=f"indicator_id={ind_id}"
)
valid_mask = grp["value"].notna()
n_valid = valid_mask.sum()
if n_valid < 2:
grp["norm_value"] = np.nan
norm_parts.append(grp)
self.logger.warning(
f" [SKIP] indicator_id={ind_id}: only {n_valid} valid values"
)
continue
raw = grp.loc[valid_mask, "value"].values
v_min = raw.min()
v_max = raw.max()
normed = np.full(len(grp), np.nan)
if v_min == v_max:
normed[valid_mask.values] = 0.5
else:
normed[valid_mask.values] = (raw - v_min) / (v_max - v_min)
if do_invert:
normed = np.where(np.isnan(normed), np.nan, 1.0 - normed)
grp["norm_value"] = normed
norm_parts.append(grp)
df_normed = pd.concat(norm_parts, ignore_index=True)
self.logger.info(f" norm_value computed: {df_normed['indicator_id'].nunique()} indicators")
self.logger.info(
f" norm_value range : "
f"{df_normed['norm_value'].min():.4f} - {df_normed['norm_value'].max():.4f}"
)
self.logger.info(f" norm_value nulls : {df_normed['norm_value'].isna().sum()}")
return df_normed
# =========================================================================
# STEP 7: Hitung YoY per (indicator_id, country_id)
# =========================================================================
def _compute_yoy_columns(self, df: pd.DataFrame) -> pd.DataFrame:
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 7: COMPUTE YoY COLUMNS (per indicator, per country)")
self.logger.info("=" * 80)
parts = []
groups = df.groupby(["indicator_id", "country_id"], sort=False)
self.logger.info(f" Processing {groups.ngroups:,} (indicator x country) groups...")
for (ind_id, country_id), grp in groups:
parts.append(_compute_yoy(grp))
df_out = pd.concat(parts, ignore_index=True)
self.logger.info(
f" yoy_value nulls : {df_out['yoy_value'].isna().sum():,}"
)
self.logger.info(
f" yoy_value range : "
f"{df_out['yoy_value'].min():.4f} - {df_out['yoy_value'].max():.4f}"
)
self.logger.info(
f" yoy_norm_value nulls: {df_out['yoy_norm_value'].isna().sum():,}"
)
self.logger.info(
f" yoy_norm_value range: "
f"{df_out['yoy_norm_value'].min():.4f} - {df_out['yoy_norm_value'].max():.4f}"
)
return df_out
# =========================================================================
# STEP 8: Scale ke 1-100
# =========================================================================
def _compute_scores(self, df: pd.DataFrame) -> pd.DataFrame:
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 8: SCALE TO 1-100")
self.logger.info("=" * 80)
score_parts = []
for ind_id, grp in df.groupby("indicator_id"):
grp = grp.copy()
grp["norm_score_1_100"] = global_minmax(grp["norm_value"])
score_parts.append(grp)
df = pd.concat(score_parts, ignore_index=True)
self.logger.info(
f" norm_score_1_100 range: "
f"{df['norm_score_1_100'].min():.2f} - {df['norm_score_1_100'].max():.2f}"
)
return df
# =========================================================================
# STEP 9: Assign performance label
# =========================================================================
def _assign_performance(self, df: pd.DataFrame) -> pd.DataFrame:
"""
performance = "Good" jika norm_score_1_100 >= 60
= "Bad" jika norm_score_1_100 < 60
= null jika norm_score_1_100 null
"""
self.logger.info("\n" + "=" * 80)
self.logger.info(
f"STEP 9: ASSIGN PERFORMANCE LABEL "
f"(threshold >= {_PERFORMANCE_THRESHOLD} -> Good)"
)
self.logger.info("=" * 80)
df = df.copy()
df["performance"] = pd.NA
has_score = df["norm_score_1_100"].notna()
df.loc[has_score & (df["norm_score_1_100"] >= _PERFORMANCE_THRESHOLD), "performance"] = "Good"
df.loc[has_score & (df["norm_score_1_100"] < _PERFORMANCE_THRESHOLD), "performance"] = "Bad"
n_good = (df["performance"] == "Good").sum()
n_bad = (df["performance"] == "Bad").sum()
n_null = df["performance"].isna().sum()
total = len(df)
self.logger.info(f" Good : {n_good:,} ({n_good/total*100:.1f}%)")
self.logger.info(f" Bad : {n_bad:,} ({n_bad/total*100:.1f}%)")
self.logger.info(f" Null : {n_null:,} ({n_null/total*100:.1f}%)")
return df
# =========================================================================
# STEP 10: Save to BigQuery
# =========================================================================
def _save(self, df: pd.DataFrame) -> int:
table_name = "agg_indicator_norm"
self.logger.info("\n" + "=" * 80)
self.logger.info(f"STEP 10: SAVE -> [Gold] {table_name}")
self.logger.info("=" * 80)
out = df[[
"year",
"country_id",
"country_name",
"indicator_id",
"indicator_name",
"unit",
"direction",
"pillar_id",
"pillar_name",
"framework",
"value",
"norm_value",
"norm_score_1_100",
"yoy_value",
"yoy_norm_value",
"performance",
]].copy()
out = out.sort_values(
["year", "country_name", "pillar_name", "indicator_name"]
).reset_index(drop=True)
# Cast
out["year"] = out["year"].astype(int)
out["country_id"] = out["country_id"].astype(int)
out["country_name"] = out["country_name"].astype(str)
out["indicator_id"] = out["indicator_id"].astype(int)
out["indicator_name"] = out["indicator_name"].astype(str)
out["unit"] = out["unit"].astype(str)
out["direction"] = out["direction"].astype(str)
out["pillar_id"] = out["pillar_id"].astype(int)
out["pillar_name"] = out["pillar_name"].astype(str)
out["framework"] = out["framework"].astype(str)
out["value"] = out["value"].astype(float)
out["norm_value"] = out["norm_value"].astype(float)
out["norm_score_1_100"] = out["norm_score_1_100"].astype(float)
out["yoy_value"] = pd.to_numeric(out["yoy_value"], errors="coerce").astype(float)
out["yoy_norm_value"] = pd.to_numeric(out["yoy_norm_value"], errors="coerce").astype(float)
out["performance"] = out["performance"].astype(str).replace("nan", pd.NA).astype("string")
self.logger.info(f" Columns : {list(out.columns)}")
self.logger.info(f" Total rows : {len(out):,}")
self.logger.info(f" Countries : {out['country_id'].nunique()}")
self.logger.info(f" Indicators : {out['indicator_id'].nunique()}")
self.logger.info(f" Years : {int(out['year'].min())} - {int(out['year'].max())}")
self.logger.info(f" Frameworks : {dict(out['framework'].value_counts())}")
self.logger.info(f" Performance: {dict(out['performance'].value_counts())}")
schema = [
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"),
bigquery.SchemaField("indicator_id", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("indicator_name", "STRING", mode="REQUIRED"),
bigquery.SchemaField("unit", "STRING", mode="NULLABLE"),
bigquery.SchemaField("direction", "STRING", mode="REQUIRED"),
bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
bigquery.SchemaField("framework", "STRING", mode="REQUIRED"),
bigquery.SchemaField("value", "FLOAT", mode="REQUIRED"),
bigquery.SchemaField("norm_value", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("norm_score_1_100", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("yoy_value", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("yoy_norm_value", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("performance", "STRING", mode="NULLABLE"),
]
rows_loaded = load_to_bigquery(
self.client, out, table_name,
layer="gold", write_disposition="WRITE_TRUNCATE", schema=schema,
)
log_update(self.client, "DW", table_name, "full_load", rows_loaded)
self.logger.info(f" [OK] {table_name}: {rows_loaded:,} rows -> [Gold] fs_asean_gold")
metadata = {
"source_class" : self.__class__.__name__,
"table_name" : table_name,
"execution_timestamp": self.pipeline_start,
"duration_seconds" : (datetime.now() - self.pipeline_start).total_seconds(),
"rows_fetched" : self.pipeline_metadata["rows_fetched"],
"rows_transformed" : rows_loaded,
"rows_loaded" : rows_loaded,
"completeness_pct" : 100.0,
"config_snapshot" : json.dumps({
"sdgs_start_year" : self.sdgs_start_year,
"sdg_only_keywords_n" : len(SDG_ONLY_KEYWORDS),
"layer" : "gold",
"normalization" : "per_indicator_global_minmax",
"direction_handling" : "lower_better_inverted",
"yoy_columns" : ["yoy_value", "yoy_norm_value"],
"performance_threshold": _PERFORMANCE_THRESHOLD,
"unit_source" : "dim_indicator",
"framework_logic" : (
"SDG_ONLY_KEYWORDS: MDGs if year < sdgs_start_year, "
"SDGs if year >= sdgs_start_year. "
"Non-SDG_ONLY: always MDGs."
),
}),
"validation_metrics" : json.dumps({
"total_rows" : rows_loaded,
"n_indicators" : int(out["indicator_id"].nunique()),
"n_countries" : int(out["country_id"].nunique()),
"sdgs_start_year": self.sdgs_start_year,
}),
}
save_etl_metadata(self.client, metadata)
self.logger.info(" Metadata -> [AUDIT] etl_metadata")
return rows_loaded
# =========================================================================
# STEP 11: Summary log
# =========================================================================
def _log_summary(self, df: pd.DataFrame):
self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 11: SUMMARY")
self.logger.info("=" * 80)
summary = (
df.groupby(["framework", "year"])
.agg(
n_indicators=("indicator_id", "nunique"),
n_countries =("country_id", "nunique"),
avg_score =("norm_score_1_100", "mean"),
)
.reset_index()
)
self.logger.info(
f"\n{'Framework':<8} {'Year':<6} {'Indicators':<12} {'Countries':<12} {'Avg Score'}"
)
self.logger.info("-" * 55)
for _, r in summary.iterrows():
self.logger.info(
f"{r['framework']:<8} {int(r['year']):<6} "
f"{int(r['n_indicators']):<12} {int(r['n_countries']):<12} "
f"{r['avg_score']:.2f}"
)
# Performance summary per framework
self.logger.info("\n Performance summary per Framework:")
perf_fw = (
df[df["performance"].notna()]
.groupby(["framework", "performance"])
.size()
.reset_index(name="count")
)
for fw in perf_fw["framework"].unique():
sub = perf_fw[perf_fw["framework"] == fw]
total = sub["count"].sum()
self.logger.info(f" [{fw}]")
for _, r in sub.iterrows():
self.logger.info(
f" {r['performance']:<6}: {int(r['count']):,} "
f"({r['count']/total*100:.1f}%)"
)
# Top 5 & Bottom 5 indikator
ind_avg = (
df.groupby(["indicator_id", "indicator_name", "unit", "pillar_name", "direction"])
["norm_score_1_100"].mean()
.reset_index()
.sort_values("norm_score_1_100", ascending=False)
)
self.logger.info(
"\n TOP 5 Indicators (avg norm_score_1_100 across all years & countries):"
)
for _, r in ind_avg.head(5).iterrows():
tag = "[lower+]" if r["direction"] in DIRECTION_INVERT_KEYWORDS else "[higher+]"
unit = f"[{r['unit']}]" if r["unit"] else ""
self.logger.info(
f" [{int(r['indicator_id'])}] {r['indicator_name'][:50]:<52} "
f"{r['norm_score_1_100']:.2f} {tag} {unit}"
)
self.logger.info("\n BOTTOM 5 Indicators:")
for _, r in ind_avg.tail(5).iterrows():
tag = "[lower+]" if r["direction"] in DIRECTION_INVERT_KEYWORDS else "[higher+]"
unit = f"[{r['unit']}]" if r["unit"] else ""
self.logger.info(
f" [{int(r['indicator_id'])}] {r['indicator_name'][:50]:<52} "
f"{r['norm_score_1_100']:.2f} {tag} {unit}"
)
# Indikator per pillar
pillar_summary = (
df.drop_duplicates(subset=["indicator_id", "pillar_name"])
.groupby("pillar_name")["indicator_id"]
.count()
.reset_index()
.rename(columns={"indicator_id": "n_indicators"})
)
self.logger.info("\n Indicators per pillar:")
for _, r in pillar_summary.iterrows():
self.logger.info(f" {r['pillar_name']:<30}: {r['n_indicators']}")
# =========================================================================
# RUN
# =========================================================================
def run(self):
self.pipeline_start = datetime.now()
self.pipeline_metadata["start_time"] = self.pipeline_start
self.logger.info("\n" + "=" * 80)
self.logger.info("INDICATOR NORM AGGREGATION")
self.logger.info(" Source : fact_asean_food_security_selected")
self.logger.info(" Dim : dim_indicator (unit)")
self.logger.info(" Output : agg_indicator_norm -> fs_asean_gold")
self.logger.info("=" * 80)
self.load_data()
self.load_units()
self._merge_unit()
self.sdgs_start_year = self._detect_sdgs_start_year()
self._assign_framework()
df_normed = self._compute_norm_values()
df_yoy = self._compute_yoy_columns(df_normed)
df_scored = self._compute_scores(df_yoy)
df_final = self._assign_performance(df_scored)
rows_loaded = self._save(df_final)
self.pipeline_metadata["rows_loaded"] = rows_loaded
self._log_summary(df_final)
self.pipeline_metadata["end_time"] = datetime.now()
duration = (
self.pipeline_metadata["end_time"] - self.pipeline_start
).total_seconds()
self.logger.info("\n" + "=" * 80)
self.logger.info("COMPLETED")
self.logger.info("=" * 80)
self.logger.info(f" Duration : {duration:.2f}s")
self.logger.info(f" Rows Fetched : {self.pipeline_metadata['rows_fetched']:,}")
self.logger.info(f" Rows Loaded : {rows_loaded:,}")
self.logger.info(f" sdgs_start_year : {self.sdgs_start_year}")
# =============================================================================
# AIRFLOW TASK
# =============================================================================
def run_indicator_norm_aggregation():
"""
Airflow task: Build agg_indicator_norm.
Dipanggil setelah analytical_layer_to_gold selesai.
"""
client = get_bigquery_client()
agg = IndicatorNormAggregator(client)
agg.run()
print(f"agg_indicator_norm loaded: {agg.pipeline_metadata['rows_loaded']:,} rows")
# =============================================================================
# MAIN
# =============================================================================
if __name__ == "__main__":
import sys, io
if sys.stdout.encoding and sys.stdout.encoding.lower() not in ("utf-8", "utf8"):
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
if sys.stderr.encoding and sys.stderr.encoding.lower() not in ("utf-8", "utf8"):
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace")
print("=" * 80)
print("INDICATOR NORM AGGREGATION -> fs_asean_gold")
print(" Source : fact_asean_food_security_selected")
print(" Dim : dim_indicator (unit)")
print(" Output : agg_indicator_norm")
print("=" * 80)
logger = setup_logging()
client = get_bigquery_client()
agg = IndicatorNormAggregator(client)
agg.run()
print("\n" + "=" * 80)
print("[OK] COMPLETED")
print("=" * 80)
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