""" 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 (+ kolom performance_status: 'Good'/'Bad', threshold=60) - agg_narrative_overview - agg_narrative_pillar SOURCE TABLE: fact_asean_food_security_selected (sudah include nama + ID) n_indicators logic (sesuai agg_indicator_norm): - Setiap tahun dihitung dari indikator yang benar-benar hadir di tahun tsb. - Framework MDGs/SDGs per tahun mengikuti SDG_ONLY_KEYWORDS: * Indikator tidak di SDG_ONLY -> selalu MDGs * Indikator di SDG_ONLY + year >= sdgs_start_year -> SDGs * Indikator di SDG_ONLY + year < sdgs_start_year -> MDGs - Sehingga n_indicators MDGs dan SDGs bisa berbeda antar tahun. """ 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 # Threshold performance_status di agg_framework_asean PERFORMANCE_THRESHOLD = 60.0 # score >= 60 -> "Good", < 60 -> "Bad" # SDG_ONLY_KEYWORDS (sama persis dengan bigquery_aggraget_fact_selected_layer.py) SDG_ONLY_KEYWORDS: frozenset = frozenset([ "prevalence of undernourishment (percent) (3-year average)", "number of people undernourished (million) (3-year average)", "prevalence of severe food insecurity in the total population (percent) (3-year average)", "prevalence of severe food insecurity in the male adult population (percent) (3-year average)", "prevalence of severe food insecurity in the 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)", "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)", "percentage of children under 5 years affected by wasting (percent)", "number of children under 5 years affected by wasting (million)", "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)", "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)", ]) _SDG_ONLY_LOWER: frozenset = frozenset(k.lower() for k in SDG_ONLY_KEYWORDS) _FIES_DETECTION_LOWER: 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)", ]) # ============================================================================= # 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 def _performance_status(score) -> str: """Classify score into 'Good' or 'Bad' based on PERFORMANCE_THRESHOLD.""" if score is None or (isinstance(score, float) and np.isnan(score)): return "N/A" return "Good" if score >= PERFORMANCE_THRESHOLD else "Bad" # ============================================================================= # NARRATIVE HELPERS # ============================================================================= def _fmt_score(score) -> str: if score is None or (isinstance(score, float) and np.isnan(score)): return "N/A" return f"{score:.2f}" def _fmt_delta(delta) -> str: 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, performance_status: str, yoy_val, yoy_pct, prev_year: int, prev_score, prev_performance_status: str, ranking_list: list, most_improved_country, most_improved_delta, most_declined_country, most_declined_delta, ) -> str: # Sentence 1: indicator breakdown 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: score + performance status + YoY status_phrase = ( f"classified as \"{performance_status}\" performance " f"(threshold: {PERFORMANCE_THRESHOLD:.0f})" ) 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", representing a {abs_pct:.2f}% {trend_word} year-over-year" # Note if performance status changed status_change = "" if prev_performance_status not in ("N/A", None) and prev_performance_status != performance_status: status_change = ( f" This marks a shift from \"{prev_performance_status}\" in {prev_year} " f"to \"{performance_status}\" in {year}." ) sent2 = ( f"The ASEAN overall score (Total framework) reached {_fmt_score(score)}, " f"{status_phrase}, {direction_word} by {abs(yoy_val):.2f} points compared to " f"{prev_year} ({_fmt_score(prev_score)}, \"{prev_performance_status}\"){pct_clause}.{status_change}" ) else: sent2 = ( f"The ASEAN overall score (Total framework) reached {_fmt_score(score)} in {year}, " f"{status_phrase}. 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: 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 country 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:] 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: 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." ) 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)})." ) 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)}." ) 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:] 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.sdgs_start_year = None # Lookup: (indicator_id, year) -> framework label # Dibangun di _assign_framework_labels(), dipakai di _count_framework_indicators() self._ind_year_framework: pd.DataFrame = None # ========================================================================= # STEP 1: Load data # ========================================================================= 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, "fact_asean_food_security_selected", layer='gold' ) self.logger.info(f" fact_asean_food_security_selected : {len(self.df):,} rows") required_cols = { "country_id", "country_name", "indicator_id", "indicator_name", "direction", "pillar_id", "pillar_name", "time_id", "year", "value", } missing_cols = required_cols - set(self.df.columns) if missing_cols: raise ValueError( f"Kolom berikut tidak ditemukan di fact_asean_food_security_selected: {missing_cols}" ) 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 Rows loaded : {len(self.df):,}") 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: Detect sdgs_start_year + assign framework per (indicator, year) # Konsisten dengan logika di bigquery_aggraget_fact_selected_layer.py # ========================================================================= def _detect_sdgs_start_year(self) -> int: """Deteksi sdgs_start_year dari kehadiran FIES di data (metode eksplisit).""" 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()) self.logger.info(f" [FIES explicit] sdgs_start_year = {sdgs_start}") return sdgs_start # Fallback: gap terbesar pada distribusi min_year 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()) if len(unique_years) == 1: self.logger.info(" [Fallback] Hanya 1 cluster -> semua MDGs") return int(unique_years[0]) + 9999 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] self.logger.info(f" [Fallback gap] sdgs_start_year = {y_after} (gap {y_before}->{y_after})") return int(y_after) def _assign_framework_labels(self): """ Buat lookup table _ind_year_framework: DataFrame(indicator_id, year, framework). Aturan (identik dengan IndicatorNormAggregator._assign_framework): - Indikator TIDAK di SDG_ONLY_KEYWORDS -> selalu "MDGs" - Indikator DI SDG_ONLY_KEYWORDS: year < sdgs_start_year -> "MDGs" year >= sdgs_start_year -> "SDGs" Juga attach kolom 'framework' ke self.df untuk dipakai _get_norm_value_df(). """ self.logger.info("\n" + "=" * 70) self.logger.info("STEP 1b: ASSIGN FRAMEWORK LABELS (per indicator per year)") self.logger.info(f" sdgs_start_year = {self.sdgs_start_year}") self.logger.info("=" * 70) 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"]) self.df = df # Build compact lookup (unique indicator_id x year x framework) self._ind_year_framework = ( self.df[["indicator_id", "year", "framework"]] .drop_duplicates() .reset_index(drop=True) ) # Log distribusi fw_dist = self.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") # n_indicators per framework per year ind_fw_yr = ( self._ind_year_framework .groupby(["year", "framework"])["indicator_id"] .nunique() .reset_index() .rename(columns={"indicator_id": "n_indicators"}) .sort_values(["year", "framework"]) ) self.logger.info( f"\n {'Year':<6} {'Framework':<8} {'n_indicators'}" ) self.logger.info(" " + "-" * 30) for _, r in ind_fw_yr.iterrows(): self.logger.info( f" {int(r['year']):<6} {r['framework']:<8} {int(r['n_indicators'])}" ) def _count_framework_indicators(self, year: int, framework: str) -> int: """ Hitung jumlah indikator unik untuk framework tertentu di tahun tertentu. Menggunakan _ind_year_framework yang dibangun di _assign_framework_labels(). """ mask = ( (self._ind_year_framework["year"] == year) & (self._ind_year_framework["framework"] == framework) ) return int(self._ind_year_framework.loc[mask, "indicator_id"].nunique()) # ========================================================================= # 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 _assign_framework_labels() 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 # ========================================================================= 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 # ========================================================================= 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 # ========================================================================= def _calc_country_composite_inmemory(self) -> pd.DataFrame: 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 = [] # 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) # MDGs pre-SDGs 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) # MDGs mixed (year >= sdgs_start_year, hanya indikator MDGs) mdgs_indicator_ids = set( self._ind_year_framework[self._ind_year_framework["framework"] == "MDGs"]["indicator_id"] ) if mdgs_indicator_ids: df_mdgs_mixed = df_normed[ (df_normed["indicator_id"].isin(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) # SDGs sdgs_indicator_ids = set( self._ind_year_framework[self._ind_year_framework["framework"] == "SDGs"]["indicator_id"] ) if sdgs_indicator_ids: df_sdgs = df_normed[ (df_normed["indicator_id"].isin(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 (+ performance_status) # ========================================================================= 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(f" performance_status threshold: {PERFORMANCE_THRESHOLD}") 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 = [] # ------------------------------------------------------------------ # Helper: hitung n_indicators per framework per year dari lookup # ------------------------------------------------------------------ def _n_ind(year_val, framework_val): return self._count_framework_indicators(year_val, framework_val) # TOTAL total_cols = asean_overall[[ "year", "asean_score_1_100", "asean_norm", "std_norm", "n_countries" ]].copy().rename(columns={ "asean_score_1_100": "framework_score_1_100", "asean_norm" : "framework_norm", "n_countries" : "n_countries_with_data", }) # n_indicators Total = semua indikator yang hadir di tahun tsb total_cols["n_indicators"] = total_cols["year"].apply( lambda y: int(self._ind_year_framework[ self._ind_year_framework["year"] == y ]["indicator_id"].nunique()) ) total_cols["framework"] = "Total" parts.append(total_cols) # MDGs pre-SDGs 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().rename(columns={ "asean_score_1_100": "framework_score_1_100", "asean_norm" : "framework_norm", "n_countries" : "n_countries_with_data", }) # Pre-SDGs era: semua indikator berlabel MDGs mdgs_pre["n_indicators"] = mdgs_pre["year"].apply( lambda y: _n_ind(y, "MDGs") ) mdgs_pre["framework"] = "MDGs" parts.append(mdgs_pre) # MDGs mixed mdgs_indicator_ids = set( self._ind_year_framework[self._ind_year_framework["framework"] == "MDGs"]["indicator_id"] ) if mdgs_indicator_ids: df_mdgs_mixed = df_normed[ (df_normed["indicator_id"].isin(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() asean_mdgs["n_indicators"] = asean_mdgs["year"].apply( lambda y: _n_ind(y, "MDGs") ) 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) # SDGs sdgs_indicator_ids = set( self._ind_year_framework[self._ind_year_framework["framework"] == "SDGs"]["indicator_id"] ) if sdgs_indicator_ids: df_sdgs = df_normed[ (df_normed["indicator_id"].isin(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() asean_sdgs["n_indicators"] = asean_sdgs["year"].apply( lambda y: _n_ind(y, "SDGs") ) 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") # performance_status df["performance_status"] = df["framework_score_1_100"].apply(_performance_status) 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) df["performance_status"] = df["performance_status"].astype(str) self._validate_mdgs_equals_total(df, level="asean") # Log performance summary self.logger.info(f"\n performance_status summary (threshold={PERFORMANCE_THRESHOLD}):") for fw in df["framework"].unique(): sub = df[df["framework"] == fw].sort_values("year") for _, r in sub.iterrows(): self.logger.info( f" {fw:<8} {int(r['year'])}: " f"score={r['framework_score_1_100']:.2f} " f"n_ind={int(r['n_indicators'])} " f"-> {r['performance_status']}" ) 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"), bigquery.SchemaField("performance_status", "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 6: agg_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_total = ( df_framework_asean[df_framework_asean["framework"] == "Total"] .sort_values("year") .reset_index(drop=True) ) score_by_year = dict(zip(asean_total["year"].astype(int), asean_total["framework_score_1_100"].astype(float))) status_by_year = dict(zip(asean_total["year"].astype(int), asean_total["performance_status"].astype(str))) country_total = df_framework_by_country[df_framework_by_country["framework"] == "Total"].copy() records = [] for _, row in asean_total.iterrows(): yr = int(row["year"]) score = float(row["framework_score_1_100"]) perf_status = str(row["performance_status"]) yoy = row["year_over_year_change"] yoy_val = float(yoy) if pd.notna(yoy) else None # n_indicators per framework per year (dari lookup) n_mdg = self._count_framework_indicators(yr, "MDGs") n_sdg = self._count_framework_indicators(yr, "SDGs") n_total_ind = int( self._ind_year_framework[ self._ind_year_framework["year"] == yr ]["indicator_id"].nunique() ) prev_score = score_by_year.get(yr - 1, None) prev_status = status_by_year.get(yr - 1, "N/A") 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 ) 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) 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 narrative = _build_overview_narrative( year = yr, n_mdg = n_mdg, n_sdg = n_sdg, n_total_ind = n_total_ind, score = score, performance_status = perf_status, yoy_val = yoy_val, yoy_pct = yoy_pct, prev_year = yr - 1, prev_score = prev_score, prev_performance_status = prev_status, 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), "performance_status": perf_status, "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) df["performance_status"] = df["performance_status"].astype(str) 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("performance_status", "STRING", 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 # ========================================================================= 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_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 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 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 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" [OK] {table_name}: {rows_loaded:,} rows -> [Gold] fs_asean_gold") 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 — 6 TABLES -> fs_asean_gold") self.logger.info(" Source : fact_asean_food_security_selected") self.logger.info(" Outputs : 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(f" Performance threshold: {PERFORMANCE_THRESHOLD} (Good/Bad)") self.logger.info("=" * 70) self.load_data() self.sdgs_start_year = self._detect_sdgs_start_year() self._assign_framework_labels() 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() 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 = "[OK]" if meta["status"] == "success" else "[FAIL]" self.logger.info(f" {icon} {tbl:<35} {meta['rows_loaded']:>10,}") # ============================================================================= # AIRFLOW TASK # ============================================================================= def run_aggregation(): """ Airflow task: Hitung semua agregasi dari fact_asean_food_security_selected. 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 # ============================================================================= 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" Source : fact_asean_food_security_selected") print(f" NORMALIZE_FRAMEWORKS_JOINTLY : {NORMALIZE_FRAMEWORKS_JOINTLY}") print(f" PERFORMANCE_THRESHOLD : {PERFORMANCE_THRESHOLD}") 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)