""" BIGQUERY ANALYSIS LAYER - FOOD SECURITY AGGREGATION Semua agregasi pakai norm_value dari _get_norm_value_df() UPDATED: Simpan 6 tabel ke fs_asean_gold (layer='gold'): - agg_pillar_composite - agg_pillar_by_country - agg_framework_by_country - agg_framework_asean - agg_narrative_overview - agg_narrative_pillar """ 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 # ============================================================================= # NARRATIVE BUILDER FUNCTIONS (pure functions, easy to unit-test) # ============================================================================= def _fmt_score(score) -> str: """Format score to 2 decimal places.""" if score is None or (isinstance(score, float) and np.isnan(score)): return "N/A" return f"{score:.2f}" def _fmt_delta(delta) -> str: """Format YoY delta with sign and 2 decimal places.""" if delta is None or (isinstance(delta, float) and np.isnan(delta)): return "N/A" sign = "+" if delta >= 0 else "" return f"{sign}{delta:.2f}" def _build_overview_narrative( year: int, n_mdg: int, n_sdg: int, n_total_ind: int, score: float, yoy_val, yoy_pct, prev_year: int, prev_score, ranking_list: list, most_improved_country, most_improved_delta, most_declined_country, most_declined_delta, ) -> str: """ Compose a full English prose narrative for the Overview tab. Narrative structure ------------------- 1. Indicator composition (MDGs first, then SDGs) 2. ASEAN score + YoY 3. Country ranking 4. Most improved / declined country """ # -- Sentence 1: indicator composition ---------------------------------- parts_ind = [] if n_mdg > 0: parts_ind.append(f"{n_mdg} MDG indicator{'s' if n_mdg > 1 else ''}") if n_sdg > 0: parts_ind.append(f"{n_sdg} SDG indicator{'s' if n_sdg > 1 else ''}") if parts_ind: ind_detail = " and ".join(parts_ind) sent1 = ( f"In {year}, the ASEAN food security assessment incorporated a total of " f"{n_total_ind} indicator{'s' if n_total_ind != 1 else ''}, " f"consisting of {ind_detail}." ) else: sent1 = ( f"In {year}, the ASEAN food security assessment incorporated " f"{n_total_ind} indicator{'s' if n_total_ind != 1 else ''}." ) # -- Sentence 2: ASEAN score + YoY ------------------------------------- if yoy_val is not None and prev_score is not None: direction_word = "increasing" if yoy_val >= 0 else "decreasing" pct_clause = "" if yoy_pct is not None: abs_pct = abs(yoy_pct) trend_word = "improvement" if yoy_val >= 0 else "decline" pct_clause = f", which represents a {abs_pct:.2f}% {trend_word} year-over-year" sent2 = ( f"The ASEAN overall score (Total framework) reached {_fmt_score(score)}, " f"{direction_word} by {abs(yoy_val):.2f} points compared to the previous year " f"({_fmt_score(prev_score)} in {prev_year}){pct_clause}." ) else: sent2 = ( f"The ASEAN overall score (Total framework) reached {_fmt_score(score)} in {year}; " f"no prior-year data is available for year-over-year comparison." ) # -- Sentence 3: country ranking ---------------------------- sent3 = "" if ranking_list: first = ranking_list[0] last = ranking_list[-1] middle = ranking_list[1:-1] if len(ranking_list) == 1: sent3 = ( f"In terms of country performance, {first['country_name']} was the only " f"country assessed, scoring {_fmt_score(first['score'])} in {year}." ) elif len(ranking_list) == 2: sent3 = ( f"In terms of country performance, {first['country_name']} led the region " f"with a score of {_fmt_score(first['score'])}, while " f"{last['country_name']} recorded the lowest score of " f"{_fmt_score(last['score'])} in {year}." ) else: # Susun semua negara di tengah: "B (xx.xx), C (xx.xx), ..., and Y (xx.xx)" middle_parts = [ f"{c['country_name']} ({_fmt_score(c['score'])})" for c in middle ] if len(middle_parts) == 1: middle_str = middle_parts[0] else: middle_str = ", ".join(middle_parts[:-1]) + f", and {middle_parts[-1]}" sent3 = ( f"In terms of country performance, {first['country_name']} led the region " f"with a score of {_fmt_score(first['score'])}, followed by {middle_str}. " f"At the other end, {last['country_name']} recorded the lowest score " f"of {_fmt_score(last['score'])} in {year}." ) # -- Sentence 4: most improved / declined ------------------------------ sent4_parts = [] if most_improved_country and most_improved_delta is not None: sent4_parts.append( f"the most notable improvement was seen in {most_improved_country}, " f"which gained {_fmt_delta(most_improved_delta)} points from the previous year" ) if most_declined_country and most_declined_delta is not None: if most_declined_delta < 0: sent4_parts.append( f"while {most_declined_country} experienced the largest decline " f"of {_fmt_delta(most_declined_delta)} points" ) else: sent4_parts.append( f"while {most_declined_country} recorded the smallest gain " f"of {_fmt_delta(most_declined_delta)} points" ) sent4 = "" if sent4_parts: sent4 = ", ".join(sent4_parts) + "." sent4 = sent4[0].upper() + sent4[1:] # -- Assemble ---------------------------------------------------------- return " ".join(s for s in [sent1, sent2, sent3, sent4] if s) def _build_pillar_narrative( year: int, pillar_name: str, pillar_score: float, rank_in_year: int, n_pillars: int, yoy_val, top_country, top_country_score, bot_country, bot_country_score, strongest_pillar, strongest_score, weakest_pillar, weakest_score, most_improved_pillar, most_improved_delta, most_declined_pillar, most_declined_delta, ) -> str: """ Compose a full English prose narrative for a single pillar in a given year. Narrative structure ------------------- 1. Pillar score and rank 2. Strongest / weakest pillar context 3. Top / bottom country within this pillar 4. YoY movement for this pillar + biggest mover across all pillars """ # -- Sentence 1: pillar overview ---------------------------------------- rank_suffix = {1: "st", 2: "nd", 3: "rd"}.get(rank_in_year, "th") sent1 = ( f"In {year}, the {pillar_name} pillar scored {_fmt_score(pillar_score)}, " f"ranking {rank_in_year}{rank_suffix} out of {n_pillars} pillars assessed across ASEAN." ) # -- Sentence 2: strongest / weakest context ---------------------------- sent2 = "" if strongest_pillar and weakest_pillar: if strongest_pillar == pillar_name: sent2 = ( f"This made {pillar_name} the strongest performing pillar in {year}, " f"compared to the weakest pillar, {weakest_pillar}, " f"which scored {_fmt_score(weakest_score)}." ) elif weakest_pillar == pillar_name: sent2 = ( f"This made {pillar_name} the weakest performing pillar in {year}, " f"compared to the strongest pillar, {strongest_pillar}, " f"which scored {_fmt_score(strongest_score)}." ) else: sent2 = ( f"Across all pillars in {year}, {strongest_pillar} was the strongest " f"(score: {_fmt_score(strongest_score)}), while {weakest_pillar} " f"was the weakest (score: {_fmt_score(weakest_score)})." ) # -- Sentence 3: country top / bottom within this pillar --------------- sent3 = "" if top_country and bot_country: if top_country != bot_country: sent3 = ( f"Within the {pillar_name} pillar, {top_country} led with a score of " f"{_fmt_score(top_country_score)}, while {bot_country} recorded the lowest " f"score of {_fmt_score(bot_country_score)}." ) else: sent3 = ( f"Within the {pillar_name} pillar, {top_country} was the only country " f"with available data, scoring {_fmt_score(top_country_score)}." ) # -- Sentence 4: YoY movement ------------------------------------------- if yoy_val is not None: direction_word = "improved" if yoy_val >= 0 else "declined" sent4 = ( f"Compared to the previous year, the {pillar_name} pillar " f"{direction_word} by {abs(yoy_val):.2f} points" ) else: sent4 = ( f"No prior-year data is available to calculate year-over-year change " f"for the {pillar_name} pillar in {year}" ) if most_improved_pillar and most_improved_delta is not None \ and most_declined_pillar and most_declined_delta is not None \ and most_improved_pillar != most_declined_pillar: sent4 += ( f". Across all pillars, {most_improved_pillar} showed the greatest improvement " f"({_fmt_delta(most_improved_delta)} pts), while {most_declined_pillar} " f"recorded the largest decline ({_fmt_delta(most_declined_delta)} pts)" ) sent4 += "." sent4 = sent4[0].upper() + sent4[1:] # -- Assemble ---------------------------------------------------------- return " ".join(s for s in [sent1, sent2, sent3, sent4] if s) # ============================================================================= # 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}, "agg_narrative_overview": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None}, "agg_narrative_pillar": {"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 # ========================================================================= # STEP 6: agg_narrative_overview -> Gold (NEW) # # Sumber data : df_framework_asean (framework='Total') + df_framework_by_country # Granularity : 1 row per year # Columns : year, n_mdg_indicators, n_sdg_indicators, n_total_indicators, # asean_total_score, yoy_change, yoy_change_pct, # country_ranking_json, most_improved_country, most_improved_delta, # most_declined_country, most_declined_delta, narrative_overview # ========================================================================= def calc_narrative_overview( self, df_framework_asean: pd.DataFrame, df_framework_by_country: pd.DataFrame, ) -> pd.DataFrame: table_name = "agg_narrative_overview" self.load_metadata[table_name]["start_time"] = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info(f"STEP 6: {table_name} -> [Gold] fs_asean_gold") self.logger.info("=" * 70) # ASEAN-level Total framework rows only, sorted by year # PENTING: filter framework='Total' dulu sebelum apapun asean_total = ( df_framework_asean[df_framework_asean["framework"] == "Total"] .sort_values("year") .reset_index(drop=True) ) # Buat lookup score per tahun untuk ambil prev_score yang akurat # Tidak mengandalkan score - yoy_val karena floating point bisa drift score_by_year = dict(zip( asean_total["year"].astype(int), asean_total["framework_score_1_100"].astype(float), )) # Country-level Total framework rows (ranking + YoY per country) country_total = ( df_framework_by_country[df_framework_by_country["framework"] == "Total"] .copy() ) # Indicator counts per year per framework (self.df already has 'framework' column) ind_year = self.df.drop_duplicates(subset=["indicator_id", "year", "framework"]) records = [] for _, row in asean_total.iterrows(): yr = int(row["year"]) score = float(row["framework_score_1_100"]) yoy = row["year_over_year_change"] yoy_val = float(yoy) if pd.notna(yoy) else None # -- Indicator counts per framework for this year --------------- yr_ind = ind_year[ind_year["year"] == yr] n_mdg = int(yr_ind[yr_ind["framework"] == "MDGs"]["indicator_id"].nunique()) n_sdg = int(yr_ind[yr_ind["framework"] == "SDGs"]["indicator_id"].nunique()) n_total_ind = int(yr_ind["indicator_id"].nunique()) # -- prev_score diambil langsung dari lookup, bukan score - yoy_val # Ini memastikan nilai konsisten 100% dengan tabel agg_framework_asean prev_score = score_by_year.get(yr - 1, None) # -- YoY % ----------------------------------------------------- yoy_pct = ( (yoy_val / prev_score * 100) if (yoy_val is not None and prev_score is not None and prev_score != 0) else None ) # -- Country ranking for this year ----------------------------- yr_country = ( country_total[country_total["year"] == yr] .sort_values("rank_in_framework_year") .reset_index(drop=True) ) ranking_list = [] for _, cr in yr_country.iterrows(): cr_yoy = cr.get("year_over_year_change", None) ranking_list.append({ "rank": int(cr["rank_in_framework_year"]), "country_name": str(cr["country_name"]), "score": round(float(cr["framework_score_1_100"]), 2), "yoy_change": round(float(cr_yoy), 2) if pd.notna(cr_yoy) else None, }) country_ranking_json = json.dumps(ranking_list, ensure_ascii=False) # -- Most improved / declined country -------------------------- yr_country_yoy = yr_country.dropna(subset=["year_over_year_change"]) if not yr_country_yoy.empty: best_idx = yr_country_yoy["year_over_year_change"].idxmax() worst_idx = yr_country_yoy["year_over_year_change"].idxmin() most_improved_country = str(yr_country_yoy.loc[best_idx, "country_name"]) most_improved_delta = round(float(yr_country_yoy.loc[best_idx, "year_over_year_change"]), 2) most_declined_country = str(yr_country_yoy.loc[worst_idx, "country_name"]) most_declined_delta = round(float(yr_country_yoy.loc[worst_idx, "year_over_year_change"]), 2) else: most_improved_country = most_declined_country = None most_improved_delta = most_declined_delta = None # -- Build narrative ------------------------------------------- narrative = _build_overview_narrative( year = yr, n_mdg = n_mdg, n_sdg = n_sdg, n_total_ind = n_total_ind, score = score, yoy_val = yoy_val, yoy_pct = yoy_pct, prev_year = yr - 1, prev_score = prev_score, ranking_list = ranking_list, most_improved_country = most_improved_country, most_improved_delta = most_improved_delta, most_declined_country = most_declined_country, most_declined_delta = most_declined_delta, ) records.append({ "year": yr, "n_mdg_indicators": n_mdg, "n_sdg_indicators": n_sdg, "n_total_indicators": n_total_ind, "asean_total_score": round(score, 2), "yoy_change": yoy_val, "yoy_change_pct": round(yoy_pct, 2) if yoy_pct is not None else None, "country_ranking_json": country_ranking_json, "most_improved_country": most_improved_country, "most_improved_delta": most_improved_delta, "most_declined_country": most_declined_country, "most_declined_delta": most_declined_delta, "narrative_overview": narrative, }) df = pd.DataFrame(records) df["year"] = df["year"].astype(int) df["n_mdg_indicators"] = df["n_mdg_indicators"].astype(int) df["n_sdg_indicators"] = df["n_sdg_indicators"].astype(int) df["n_total_indicators"] = df["n_total_indicators"].astype(int) df["asean_total_score"] = df["asean_total_score"].astype(float) for col in ["yoy_change", "yoy_change_pct", "most_improved_delta", "most_declined_delta"]: df[col] = pd.to_numeric(df[col], errors="coerce").astype(float) schema = [ bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("n_mdg_indicators", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("n_sdg_indicators", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("n_total_indicators", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("asean_total_score", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("yoy_change", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("yoy_change_pct", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("country_ranking_json", "STRING", mode="REQUIRED"), bigquery.SchemaField("most_improved_country", "STRING", mode="NULLABLE"), bigquery.SchemaField("most_improved_delta", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("most_declined_country", "STRING", mode="NULLABLE"), bigquery.SchemaField("most_declined_delta", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("narrative_overview", "STRING", mode="REQUIRED"), ] 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 7: agg_narrative_pillar -> Gold (NEW) # # Sumber data : df_pillar_composite + df_pillar_by_country # Granularity : 1 row per (year, pillar_id) # Columns : year, pillar_id, pillar_name, pillar_score, rank_in_year, # yoy_change, top_country, top_country_score, # bottom_country, bottom_country_score, narrative_pillar # ========================================================================= def calc_narrative_pillar( self, df_pillar_composite: pd.DataFrame, df_pillar_by_country: pd.DataFrame, ) -> pd.DataFrame: table_name = "agg_narrative_pillar" self.load_metadata[table_name]["start_time"] = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info(f"STEP 7: {table_name} -> [Gold] fs_asean_gold") self.logger.info("=" * 70) records = [] years = sorted(df_pillar_composite["year"].unique()) for yr in years: yr_pillars = ( df_pillar_composite[df_pillar_composite["year"] == yr] .sort_values("rank_in_year") .reset_index(drop=True) ) yr_country_pillar = df_pillar_by_country[df_pillar_by_country["year"] == yr] # Strongest / weakest pillar this year (for context sentence) strongest_pillar = yr_pillars.iloc[0] if len(yr_pillars) > 0 else None weakest_pillar = yr_pillars.iloc[-1] if len(yr_pillars) > 0 else None # Biggest improvement / decline across all pillars this year yr_pillars_yoy = yr_pillars.dropna(subset=["year_over_year_change"]) if not yr_pillars_yoy.empty: best_p_idx = yr_pillars_yoy["year_over_year_change"].idxmax() worst_p_idx = yr_pillars_yoy["year_over_year_change"].idxmin() most_improved_pillar = str(yr_pillars_yoy.loc[best_p_idx, "pillar_name"]) most_improved_delta = round(float(yr_pillars_yoy.loc[best_p_idx, "year_over_year_change"]), 2) most_declined_pillar = str(yr_pillars_yoy.loc[worst_p_idx, "pillar_name"]) most_declined_delta = round(float(yr_pillars_yoy.loc[worst_p_idx, "year_over_year_change"]), 2) else: most_improved_pillar = most_declined_pillar = None most_improved_delta = most_declined_delta = None for _, prow in yr_pillars.iterrows(): p_id = int(prow["pillar_id"]) p_name = str(prow["pillar_name"]) p_score = float(prow["pillar_score_1_100"]) p_rank = int(prow["rank_in_year"]) p_yoy = prow["year_over_year_change"] p_yoy_val = float(p_yoy) if pd.notna(p_yoy) else None # Top / bottom country within this pillar & year p_country = ( yr_country_pillar[yr_country_pillar["pillar_id"] == p_id] .sort_values("rank_in_pillar_year") .reset_index(drop=True) ) if not p_country.empty: top_country = str(p_country.iloc[0]["country_name"]) top_country_score = round(float(p_country.iloc[0]["pillar_country_score_1_100"]), 2) bot_country = str(p_country.iloc[-1]["country_name"]) bot_country_score = round(float(p_country.iloc[-1]["pillar_country_score_1_100"]), 2) else: top_country = bot_country = None top_country_score = bot_country_score = None # -- Build narrative --------------------------------------- narrative = _build_pillar_narrative( year = yr, pillar_name = p_name, pillar_score = p_score, rank_in_year = p_rank, n_pillars = len(yr_pillars), yoy_val = p_yoy_val, top_country = top_country, top_country_score = top_country_score, bot_country = bot_country, bot_country_score = bot_country_score, strongest_pillar = str(strongest_pillar["pillar_name"]) if strongest_pillar is not None else None, strongest_score = round(float(strongest_pillar["pillar_score_1_100"]), 2) if strongest_pillar is not None else None, weakest_pillar = str(weakest_pillar["pillar_name"]) if weakest_pillar is not None else None, weakest_score = round(float(weakest_pillar["pillar_score_1_100"]), 2) if weakest_pillar is not None else None, most_improved_pillar = most_improved_pillar, most_improved_delta = most_improved_delta, most_declined_pillar = most_declined_pillar, most_declined_delta = most_declined_delta, ) records.append({ "year": yr, "pillar_id": p_id, "pillar_name": p_name, "pillar_score": round(p_score, 2), "rank_in_year": p_rank, "yoy_change": p_yoy_val, "top_country": top_country, "top_country_score": top_country_score, "bottom_country": bot_country, "bottom_country_score": bot_country_score, "narrative_pillar": narrative, }) df = pd.DataFrame(records) df["year"] = df["year"].astype(int) df["pillar_id"] = df["pillar_id"].astype(int) df["rank_in_year"] = df["rank_in_year"].astype(int) for col in ["pillar_score", "yoy_change", "top_country_score", "bottom_country_score"]: df[col] = pd.to_numeric(df[col], errors="coerce").astype(float) schema = [ bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("pillar_score", "FLOAT", mode="REQUIRED"), bigquery.SchemaField("rank_in_year", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("yoy_change", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("top_country", "STRING", mode="NULLABLE"), bigquery.SchemaField("top_country_score", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("bottom_country", "STRING", mode="NULLABLE"), bigquery.SchemaField("bottom_country_score", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("narrative_pillar", "STRING", mode="REQUIRED"), ] 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 — 6 tabel (4 lama + 2 narrative baru) # ========================================================================= def run(self): start = datetime.now() self.logger.info("\n" + "=" * 70) self.logger.info("FOOD SECURITY AGGREGATION v9.0 — 6 TABLES -> fs_asean_gold") self.logger.info(" agg_pillar_composite | agg_pillar_by_country") self.logger.info(" agg_framework_by_country| agg_framework_asean") self.logger.info(" agg_narrative_overview | agg_narrative_pillar") self.logger.info("=" * 70) self.load_data() self._classify_indicators() # -- 4 tabel lama (tidak ada perubahan) ---------------------------- df_pillar_composite = self.calc_pillar_composite() df_pillar_by_country = self.calc_pillar_by_country() df_framework_by_country = self.calc_framework_by_country() df_framework_asean = self.calc_framework_asean() # -- 2 tabel narrative baru ---------------------------------------- self.calc_narrative_overview( df_framework_asean = df_framework_asean, df_framework_by_country = df_framework_by_country, ) self.calc_narrative_pillar( df_pillar_composite = df_pillar_composite, df_pillar_by_country = df_pillar_by_country, ) 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. Menjalankan 6 tabel sekaligus: 4 agregasi + 2 narrative. """ 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-> 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)