""" BIGQUERY ANALYSIS LAYER - FOOD SECURITY AGGREGATION Semua agregasi pakai norm_value dari _get_norm_value_df() FIXED: Hanya simpan 4 tabel ke fs_asean_gold (layer='gold'): - agg_pillar_composite - agg_pillar_by_country - agg_framework_by_country - agg_framework_asean """ import pandas as pd import numpy as np from datetime import datetime import logging import json import sys as _sys 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 # ============================================================================= # KONSTANTA GLOBAL # ============================================================================= DIRECTION_INVERT_KEYWORDS = frozenset({ "negative", "lower_better", "lower_is_better", "inverse", "neg", }) DIRECTION_POSITIVE_KEYWORDS = frozenset({ "positive", "higher_better", "higher_is_better", }) NORMALIZE_FRAMEWORKS_JOINTLY = False # ============================================================================= # Windows CP1252 safe logging # ============================================================================= class _SafeStreamHandler(logging.StreamHandler): def emit(self, record): try: super().emit(record) except UnicodeEncodeError: try: msg = self.format(record) self.stream.write( msg.encode("utf-8", errors="replace").decode("ascii", errors="replace") + self.terminator ) self.flush() except Exception: self.handleError(record) # ============================================================================= # 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() raw = series[not_nan].values result[not_nan.values] = lo + (raw - v_min) / (v_max - v_min) * (hi - lo) return pd.Series(result, index=series.index) def add_yoy(df: pd.DataFrame, group_cols: list, score_col: str) -> pd.DataFrame: df = df.sort_values(group_cols + ["year"]).reset_index(drop=True) if group_cols: df["year_over_year_change"] = df.groupby(group_cols)[score_col].diff() else: df["year_over_year_change"] = df[score_col].diff() return df def safe_int(series: pd.Series, fill: int = 0, col_name: str = "", logger=None) -> pd.Series: n_nan = series.isna().sum() if n_nan > 0 and logger: logger.warning( f" [NaN WARNING] Kolom '{col_name}' punya {n_nan} NaN -> di-fill dengan {fill}" ) return series.fillna(fill).astype(int) def check_and_dedup(df: pd.DataFrame, key_cols: list, context: str = "", logger=None) -> pd.DataFrame: dupes = df.duplicated(subset=key_cols, keep=False) if dupes.any(): n_dupes = dupes.sum() if logger: logger.warning( f" [DEDUP WARNING] {context}: {n_dupes} duplikat rows pada {key_cols}. " f"Di-aggregate dengan mean." ) numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() agg_dict = { c: ("mean" if c in numeric_cols else "first") for c in df.columns if c not in key_cols } df = df.groupby(key_cols, as_index=False).agg(agg_dict) return df # ============================================================================= # MAIN CLASS # ============================================================================= class FoodSecurityAggregator: def __init__(self, client: bigquery.Client): self.client = client self.logger = logging.getLogger(self.__class__.__name__) self.logger.propagate = False self.load_metadata = { "agg_pillar_composite": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None}, "agg_pillar_by_country": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None}, "agg_framework_by_country": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None}, "agg_framework_asean": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None}, } self.df = None self.dims = {} self.sdgs_start_year = None self.mdgs_indicator_ids = set() self.sdgs_indicator_ids = set() # ========================================================================= # STEP 1: Load data dari Gold layer # ========================================================================= def load_data(self): self.logger.info("=" * 70) self.logger.info("STEP 1: LOAD DATA from fs_asean_gold") self.logger.info("=" * 70) self.df = read_from_bigquery(self.client, "analytical_food_security", layer='gold') self.logger.info(f" analytical_food_security : {len(self.df):,} rows") self.dims["country"] = read_from_bigquery(self.client, "dim_country", layer='gold') self.dims["indicator"] = read_from_bigquery(self.client, "dim_indicator", layer='gold') self.dims["pillar"] = read_from_bigquery(self.client, "dim_pillar", layer='gold') self.dims["time"] = read_from_bigquery(self.client, "dim_time", layer='gold') ind_cols = ["indicator_id"] if "direction" in self.dims["indicator"].columns: ind_cols.append("direction") self.df = ( self.df .merge(self.dims["time"][["time_id", "year"]], on="time_id", how="left") .merge(self.dims["country"][["country_id", "country_name"]], on="country_id", how="left") .merge(self.dims["pillar"][["pillar_id", "pillar_name"]], on="pillar_id", how="left") .merge(self.dims["indicator"][ind_cols], on="indicator_id", how="left") ) if "direction" not in self.df.columns: self.df["direction"] = "positive" else: n_null_dir = self.df["direction"].isna().sum() if n_null_dir > 0: self.logger.warning(f" [DIRECTION] {n_null_dir} rows dengan direction NULL -> diisi 'positive'") self.df["direction"] = self.df["direction"].fillna("positive") dir_dist = self.df.drop_duplicates("indicator_id")["direction"].value_counts() self.logger.info(f"\n Distribusi direction per indikator:") for d, cnt in dir_dist.items(): tag = "INVERT" if _should_invert(d, self.logger, "load_data check") else "normal" self.logger.info(f" {d:<25} : {cnt:>3} indikator [{tag}]") self.logger.info(f"\n Setelah join: {len(self.df):,} rows") self.logger.info(f" Negara : {self.df['country_id'].nunique()}") self.logger.info(f" Indikator : {self.df['indicator_id'].nunique()}") self.logger.info(f" Tahun : {int(self.df['year'].min())} - {int(self.df['year'].max())}") # ========================================================================= # STEP 1b: Klasifikasi indikator ke MDGs / SDGs # ========================================================================= def _classify_indicators(self): self.logger.info("\n" + "=" * 70) self.logger.info("STEP 1b: KLASIFIKASI INDIKATOR -> MDGs / SDGs") self.logger.info("=" * 70) 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"\n Unique min_year per indikator: {unique_years}") if len(unique_years) == 1: gap_threshold = unique_years[0] + 1 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) largest_gap_size, y_before, y_after = gaps[0] gap_threshold = y_after self.logger.info(f" Gap terbesar: {y_before} -> {y_after} (selisih {largest_gap_size})") ind_min_year["framework"] = ind_min_year["min_year"].apply( lambda y: "MDGs" if int(y) < gap_threshold else "SDGs" ) sdgs_rows = ind_min_year[ind_min_year["framework"] == "SDGs"] self.sdgs_start_year = int(sdgs_rows["min_year"].min()) if not sdgs_rows.empty else int(self.df["year"].max()) + 1 self.logger.info(f" sdgs_start_year: {self.sdgs_start_year}") self.mdgs_indicator_ids = set(ind_min_year[ind_min_year["framework"] == "MDGs"]["indicator_id"].tolist()) self.sdgs_indicator_ids = set(ind_min_year[ind_min_year["framework"] == "SDGs"]["indicator_id"].tolist()) self.logger.info(f" MDGs: {len(self.mdgs_indicator_ids)} indicators") self.logger.info(f" SDGs: {len(self.sdgs_indicator_ids)} indicators") self.df = self.df.merge(ind_min_year[["indicator_id", "framework"]], on="indicator_id", how="left") # ========================================================================= # CORE HELPER: normalisasi raw value per indikator # ========================================================================= def _get_norm_value_df(self) -> pd.DataFrame: if "framework" not in self.df.columns: raise ValueError("Kolom 'framework' tidak ada. Pastikan _classify_indicators() dipanggil lebih dulu.") norm_parts = [] for ind_id, grp in self.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) continue raw = grp.loc[valid_mask, "value"].values v_min, v_max = raw.min(), 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) return pd.concat(norm_parts, ignore_index=True) # ========================================================================= # STEP 2: agg_pillar_composite -> Gold # ========================================================================= def calc_pillar_composite(self) -> pd.DataFrame: table_name = "agg_pillar_composite" self.load_metadata[table_name]["start_time"] = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info(f"STEP 2: {table_name} -> [Gold] fs_asean_gold") self.logger.info("=" * 70) df_normed = self._get_norm_value_df() df = ( df_normed .groupby(["pillar_id", "pillar_name", "year"]) .agg( pillar_norm =("norm_value", "mean"), n_indicators=("indicator_id", "nunique"), n_countries =("country_id", "nunique"), ) .reset_index() ) df["pillar_score_1_100"] = global_minmax(df["pillar_norm"]) df["rank_in_year"] = ( df.groupby("year")["pillar_score_1_100"] .rank(method="min", ascending=False) .astype(int) ) df = add_yoy(df, ["pillar_id"], "pillar_score_1_100") df["pillar_id"] = df["pillar_id"].astype(int) df["year"] = df["year"].astype(int) df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger) df["n_countries"] = safe_int(df["n_countries"], col_name="n_countries", logger=self.logger) df["rank_in_year"] = df["rank_in_year"].astype(int) df["pillar_norm"] = df["pillar_norm"].astype(float) df["pillar_score_1_100"] = df["pillar_score_1_100"].astype(float) schema = [ bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("pillar_norm", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("n_indicators", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("n_countries", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("pillar_score_1_100", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("rank_in_year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"), ] rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema) self._finalize(table_name, rows) return df # ========================================================================= # STEP 3: agg_pillar_by_country -> Gold # ========================================================================= def calc_pillar_by_country(self) -> pd.DataFrame: table_name = "agg_pillar_by_country" self.load_metadata[table_name]["start_time"] = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info(f"STEP 3: {table_name} -> [Gold] fs_asean_gold") self.logger.info("=" * 70) df_normed = self._get_norm_value_df() df = ( df_normed .groupby(["country_id", "country_name", "pillar_id", "pillar_name", "year"]) .agg(pillar_country_norm=("norm_value", "mean")) .reset_index() ) df["pillar_country_score_1_100"] = global_minmax(df["pillar_country_norm"]) df["rank_in_pillar_year"] = ( df.groupby(["pillar_id", "year"])["pillar_country_score_1_100"] .rank(method="min", ascending=False) .astype(int) ) df = add_yoy(df, ["country_id", "pillar_id"], "pillar_country_score_1_100") df["country_id"] = df["country_id"].astype(int) df["pillar_id"] = df["pillar_id"].astype(int) df["year"] = df["year"].astype(int) df["rank_in_pillar_year"] = df["rank_in_pillar_year"].astype(int) df["pillar_country_norm"] = df["pillar_country_norm"].astype(float) df["pillar_country_score_1_100"] = df["pillar_country_score_1_100"].astype(float) schema = [ bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("pillar_country_norm", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("pillar_country_score_1_100", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("rank_in_pillar_year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"), ] rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema) self._finalize(table_name, rows) return df # ========================================================================= # STEP 4: agg_framework_by_country -> Gold # ========================================================================= def _calc_country_composite_inmemory(self) -> pd.DataFrame: """Hitung country composite in-memory (tidak disimpan ke BQ).""" df_normed = self._get_norm_value_df() df = ( df_normed .groupby(["country_id", "country_name", "year"]) .agg( composite_score=("norm_value", "mean"), n_indicators =("indicator_id", "nunique"), ) .reset_index() ) df["score_1_100"] = global_minmax(df["composite_score"]) df["rank_in_asean"] = ( df.groupby("year")["score_1_100"] .rank(method="min", ascending=False) .astype(int) ) df = add_yoy(df, ["country_id"], "score_1_100") df["country_id"] = df["country_id"].astype(int) df["year"] = df["year"].astype(int) df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger) df["composite_score"] = df["composite_score"].astype(float) df["score_1_100"] = df["score_1_100"].astype(float) df["rank_in_asean"] = df["rank_in_asean"].astype(int) return df def calc_framework_by_country(self) -> pd.DataFrame: table_name = "agg_framework_by_country" self.load_metadata[table_name]["start_time"] = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info(f"STEP 4: {table_name} -> [Gold] fs_asean_gold") self.logger.info("=" * 70) country_composite = self._calc_country_composite_inmemory() df_normed = self._get_norm_value_df() parts = [] # Layer TOTAL agg_total = ( country_composite[[ "country_id", "country_name", "year", "score_1_100", "n_indicators", "composite_score" ]] .copy() .rename(columns={"score_1_100": "framework_score_1_100", "composite_score": "framework_norm"}) ) agg_total["framework"] = "Total" parts.append(agg_total) # Layer MDGs — Era pre-SDGs = Total pre_sdgs_rows = country_composite[country_composite["year"] < self.sdgs_start_year].copy() if not pre_sdgs_rows.empty: mdgs_pre = ( pre_sdgs_rows[["country_id", "country_name", "year", "score_1_100", "n_indicators", "composite_score"]] .copy() .rename(columns={"score_1_100": "framework_score_1_100", "composite_score": "framework_norm"}) ) mdgs_pre["framework"] = "MDGs" parts.append(mdgs_pre) # Layer MDGs — Era mixed if self.mdgs_indicator_ids: df_mdgs_mixed = df_normed[ (df_normed["indicator_id"].isin(self.mdgs_indicator_ids)) & (df_normed["year"] >= self.sdgs_start_year) ].copy() if not df_mdgs_mixed.empty: agg_mdgs_mixed = ( df_mdgs_mixed .groupby(["country_id", "country_name", "year"]) .agg(framework_norm=("norm_value", "mean"), n_indicators=("indicator_id", "nunique")) .reset_index() ) if not NORMALIZE_FRAMEWORKS_JOINTLY: agg_mdgs_mixed["framework_score_1_100"] = global_minmax(agg_mdgs_mixed["framework_norm"]) agg_mdgs_mixed["framework"] = "MDGs" parts.append(agg_mdgs_mixed) # Layer SDGs if self.sdgs_indicator_ids: df_sdgs = df_normed[ (df_normed["indicator_id"].isin(self.sdgs_indicator_ids)) & (df_normed["year"] >= self.sdgs_start_year) ].copy() if not df_sdgs.empty: agg_sdgs = ( df_sdgs .groupby(["country_id", "country_name", "year"]) .agg(framework_norm=("norm_value", "mean"), n_indicators=("indicator_id", "nunique")) .reset_index() ) if not NORMALIZE_FRAMEWORKS_JOINTLY: agg_sdgs["framework_score_1_100"] = global_minmax(agg_sdgs["framework_norm"]) agg_sdgs["framework"] = "SDGs" parts.append(agg_sdgs) df = pd.concat(parts, ignore_index=True) if NORMALIZE_FRAMEWORKS_JOINTLY: mixed_mask = (df["framework"].isin(["MDGs", "SDGs"])) & (df["year"] >= self.sdgs_start_year) if mixed_mask.any(): df.loc[mixed_mask, "framework_score_1_100"] = global_minmax(df.loc[mixed_mask, "framework_norm"]) df = check_and_dedup(df, ["country_id", "framework", "year"], context=table_name, logger=self.logger) df["rank_in_framework_year"] = ( df.groupby(["framework", "year"])["framework_score_1_100"] .rank(method="min", ascending=False) .astype(int) ) df = add_yoy(df, ["country_id", "framework"], "framework_score_1_100") df["country_id"] = df["country_id"].astype(int) df["year"] = df["year"].astype(int) df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger) df["rank_in_framework_year"] = safe_int(df["rank_in_framework_year"], col_name="rank_in_framework_year", logger=self.logger) df["framework_norm"] = df["framework_norm"].astype(float) df["framework_score_1_100"] = df["framework_score_1_100"].astype(float) self._validate_mdgs_equals_total(df, level="country") schema = [ bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("framework", "STRING", mode="REQUIRED"), bigquery.SchemaField("n_indicators", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("framework_norm", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("framework_score_1_100", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("rank_in_framework_year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"), ] rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema) self._finalize(table_name, rows) return df # ========================================================================= # STEP 5: agg_framework_asean -> Gold # ========================================================================= def calc_framework_asean(self) -> pd.DataFrame: table_name = "agg_framework_asean" self.load_metadata[table_name]["start_time"] = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info(f"STEP 5: {table_name} -> [Gold] fs_asean_gold") self.logger.info("=" * 70) df_normed = self._get_norm_value_df() country_composite = self._calc_country_composite_inmemory() country_norm = ( df_normed.groupby(["country_id", "country_name", "year"])["norm_value"] .mean().reset_index().rename(columns={"norm_value": "country_norm"}) ) asean_overall = ( country_norm.groupby("year") .agg(asean_norm=("country_norm", "mean"), std_norm=("country_norm", "std"), n_countries=("country_norm", "count")) .reset_index() ) asean_overall["asean_score_1_100"] = global_minmax(asean_overall["asean_norm"]) asean_comp = ( country_composite.groupby("year")["composite_score"] .mean().reset_index().rename(columns={"composite_score": "asean_composite"}) ) asean_overall = asean_overall.merge(asean_comp, on="year", how="left") parts = [] # Layer TOTAL total_cols = asean_overall[["year", "asean_score_1_100", "asean_norm", "std_norm", "n_countries"]].copy() total_cols = total_cols.rename(columns={ "asean_score_1_100": "framework_score_1_100", "asean_norm": "framework_norm", "n_countries": "n_countries_with_data", }) n_ind_total = df_normed.groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"}) total_cols = total_cols.merge(n_ind_total, on="year", how="left") total_cols["framework"] = "Total" parts.append(total_cols) # Layer MDGs — pre-SDGs = Total pre_sdgs = asean_overall[asean_overall["year"] < self.sdgs_start_year].copy() if not pre_sdgs.empty: mdgs_pre = pre_sdgs[["year", "asean_score_1_100", "asean_norm", "std_norm", "n_countries"]].copy() mdgs_pre = mdgs_pre.rename(columns={ "asean_score_1_100": "framework_score_1_100", "asean_norm": "framework_norm", "n_countries": "n_countries_with_data", }) n_ind_pre = df_normed[df_normed["year"] < self.sdgs_start_year].groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"}) mdgs_pre = mdgs_pre.merge(n_ind_pre, on="year", how="left") mdgs_pre["framework"] = "MDGs" parts.append(mdgs_pre) # Layer MDGs — mixed if self.mdgs_indicator_ids: df_mdgs_mixed = df_normed[ (df_normed["indicator_id"].isin(self.mdgs_indicator_ids)) & (df_normed["year"] >= self.sdgs_start_year) ].copy() if not df_mdgs_mixed.empty: cn = df_mdgs_mixed.groupby(["country_id", "year"])["norm_value"].mean().reset_index().rename(columns={"norm_value": "country_norm"}) asean_mdgs = cn.groupby("year").agg( framework_norm=("country_norm", "mean"), std_norm=("country_norm", "std"), n_countries_with_data=("country_id", "count"), ).reset_index() n_ind_mdgs = df_mdgs_mixed.groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"}) asean_mdgs = asean_mdgs.merge(n_ind_mdgs, on="year", how="left") if not NORMALIZE_FRAMEWORKS_JOINTLY: asean_mdgs["framework_score_1_100"] = global_minmax(asean_mdgs["framework_norm"]) asean_mdgs["framework"] = "MDGs" parts.append(asean_mdgs) # Layer SDGs if self.sdgs_indicator_ids: df_sdgs = df_normed[ (df_normed["indicator_id"].isin(self.sdgs_indicator_ids)) & (df_normed["year"] >= self.sdgs_start_year) ].copy() if not df_sdgs.empty: cn = df_sdgs.groupby(["country_id", "year"])["norm_value"].mean().reset_index().rename(columns={"norm_value": "country_norm"}) asean_sdgs = cn.groupby("year").agg( framework_norm=("country_norm", "mean"), std_norm=("country_norm", "std"), n_countries_with_data=("country_id", "count"), ).reset_index() n_ind_sdgs = df_sdgs.groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"}) asean_sdgs = asean_sdgs.merge(n_ind_sdgs, on="year", how="left") if not NORMALIZE_FRAMEWORKS_JOINTLY: asean_sdgs["framework_score_1_100"] = global_minmax(asean_sdgs["framework_norm"]) asean_sdgs["framework"] = "SDGs" parts.append(asean_sdgs) df = pd.concat(parts, ignore_index=True) if NORMALIZE_FRAMEWORKS_JOINTLY: mixed_mask = (df["framework"].isin(["MDGs", "SDGs"])) & (df["year"] >= self.sdgs_start_year) if mixed_mask.any(): df.loc[mixed_mask, "framework_score_1_100"] = global_minmax(df.loc[mixed_mask, "framework_norm"]) df = check_and_dedup(df, ["framework", "year"], context=table_name, logger=self.logger) df = add_yoy(df, ["framework"], "framework_score_1_100") df["year"] = df["year"].astype(int) df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger) df["n_countries_with_data"] = safe_int(df["n_countries_with_data"], col_name="n_countries_with_data", logger=self.logger) for col in ["framework_norm", "std_norm", "framework_score_1_100"]: df[col] = df[col].astype(float) self._validate_mdgs_equals_total(df, level="asean") schema = [ bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("framework", "STRING", mode="REQUIRED"), bigquery.SchemaField("n_indicators", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("n_countries_with_data", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("framework_norm", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("std_norm", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("framework_score_1_100", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"), ] rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema) self._finalize(table_name, rows) return df # ========================================================================= # HELPERS # ========================================================================= def _validate_mdgs_equals_total(self, df: pd.DataFrame, level: str = ""): self.logger.info(f"\n Validasi MDGs < {self.sdgs_start_year} == Total [{level}]:") group_by = ["year"] if level.startswith("asean") else ["country_id", "year"] mdgs_pre = df[(df["framework"] == "MDGs") & (df["year"] < self.sdgs_start_year)][group_by + ["framework_score_1_100"]].rename(columns={"framework_score_1_100": "mdgs_score"}) total_pre = df[(df["framework"] == "Total") & (df["year"] < self.sdgs_start_year)][group_by + ["framework_score_1_100"]].rename(columns={"framework_score_1_100": "total_score"}) if mdgs_pre.empty and total_pre.empty: self.logger.info(f" -> Tidak ada data pre-{self.sdgs_start_year} (skip)") return if mdgs_pre.empty or total_pre.empty: self.logger.warning(f" -> [WARNING] Salah satu kosong: MDGs={len(mdgs_pre)}, Total={len(total_pre)}") return check = mdgs_pre.merge(total_pre, on=group_by) max_diff = (check["mdgs_score"] - check["total_score"]).abs().max() status = "OK (identik)" if max_diff < 0.01 else f"MISMATCH! max_diff={max_diff:.6f}" self.logger.info(f" -> {status} (n_checked={len(check)})") def _finalize(self, table_name: str, rows_loaded: int): self.load_metadata[table_name].update({ "rows_loaded": rows_loaded, "status": "success", "end_time": datetime.now(), }) log_update(self.client, "DW", table_name, "full_load", rows_loaded) self.logger.info(f" ✓ {table_name}: {rows_loaded:,} rows → [Gold] fs_asean_gold") self.logger.info(f" Metadata → [AUDIT] etl_logs") def _fail(self, table_name: str, error: Exception): self.load_metadata[table_name].update({"status": "failed", "end_time": datetime.now()}) self.logger.error(f" [FAIL] {table_name}: {error}") log_update(self.client, "DW", table_name, "full_load", 0, "failed", str(error)) # ========================================================================= # RUN # ========================================================================= def run(self): start = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info("FOOD SECURITY AGGREGATION v8.0 — 4 TABLES -> fs_asean_gold") self.logger.info("=" * 70) self.load_data() self._classify_indicators() self.calc_pillar_composite() self.calc_pillar_by_country() self.calc_framework_by_country() self.calc_framework_asean() duration = (datetime.now() - start).total_seconds() total_rows = sum(m["rows_loaded"] for m in self.load_metadata.values()) self.logger.info("\n" + "=" * 70) self.logger.info("SELESAI") self.logger.info("=" * 70) self.logger.info(f" Durasi : {duration:.2f}s") self.logger.info(f" Total rows : {total_rows:,}") for tbl, meta in self.load_metadata.items(): icon = "✓" if meta["status"] == "success" else "✗" self.logger.info(f" {icon} {tbl:<35} {meta['rows_loaded']:>10,}") # ============================================================================= # AIRFLOW TASK FUNCTIONS # ============================================================================= def run_aggregation(): """ Airflow task: Hitung semua agregasi dari analytical_food_security. Dipanggil setelah analytical_layer_to_gold selesai. """ from scripts.bigquery_config import get_bigquery_client client = get_bigquery_client() agg = FoodSecurityAggregator(client) agg.run() total = sum(m["rows_loaded"] for m in agg.load_metadata.values()) print(f"Aggregation completed: {total:,} total rows loaded") # ============================================================================= # MAIN EXECUTION # ============================================================================= if __name__ == "__main__": import 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("=" * 70) print("FOOD SECURITY AGGREGATION 4 TABLES -> fs_asean_gold") print(f" NORMALIZE_FRAMEWORKS_JOINTLY = {NORMALIZE_FRAMEWORKS_JOINTLY}") print("=" * 70) logger = setup_logging() for handler in logger.handlers: handler.__class__ = _SafeStreamHandler client = get_bigquery_client() agg = FoodSecurityAggregator(client) agg.run() print("\n" + "=" * 70) print("[OK] SELESAI") print("=" * 70)