""" 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)