1265 lines
58 KiB
Python
1265 lines
58 KiB
Python
"""
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BIGQUERY ANALYSIS LAYER - FOOD SECURITY AGGREGATION
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Semua agregasi pakai norm_value dari _get_norm_value_df()
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UPDATED:
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- _classify_indicators() membaca kolom 'framework' langsung dari
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fact_asean_food_security_selected (sudah di-assign di analytical_layer
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berdasarkan SDG_INDICATOR_KEYWORDS + actual_start_year).
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- Kolom 'condition' (good/moderate/bad) ditambahkan ke semua tabel agregasi:
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* agg_pillar_composite
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* agg_pillar_by_country
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* agg_framework_by_country
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* agg_framework_asean
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Threshold fixed absolute (skala 1-100, direction-aware):
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bad : score < 40
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moderate : 40 <= score <= 60
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good : score > 60
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Simpan 6 tabel ke fs_asean_gold (layer='gold'):
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- agg_pillar_composite
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- agg_pillar_by_country
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- agg_framework_by_country
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- agg_framework_asean
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- agg_narrative_overview
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- agg_narrative_pillar
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SOURCE TABLE: fact_asean_food_security_selected
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"""
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import pandas as pd
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import numpy as np
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from datetime import datetime
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import logging
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import json
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import sys as _sys
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from scripts.bigquery_config import get_bigquery_client
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from scripts.bigquery_helpers import (
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log_update,
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load_to_bigquery,
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read_from_bigquery,
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setup_logging,
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save_etl_metadata,
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)
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from google.cloud import bigquery
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# =============================================================================
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# KONSTANTA GLOBAL
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# =============================================================================
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DIRECTION_INVERT_KEYWORDS = frozenset({
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"negative", "lower_better", "lower_is_better", "inverse", "neg",
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})
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DIRECTION_POSITIVE_KEYWORDS = frozenset({
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"positive", "higher_better", "higher_is_better",
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})
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NORMALIZE_FRAMEWORKS_JOINTLY = False
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# Threshold kondisi — fixed absolute, skala 1-100
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# Konsisten dengan THRESHOLD_BAD / THRESHOLD_GOOD di analytical_layer
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THRESHOLD_BAD = 40.0
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THRESHOLD_GOOD = 60.0
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def assign_condition(score) -> str:
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"""
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Assign kondisi berdasarkan score skala 1-100 (direction-aware, nilai tinggi = lebih baik).
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Returns: 'good' / 'moderate' / 'bad' / None jika NaN
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"""
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if score is None or (isinstance(score, float) and np.isnan(score)):
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return None
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if score > THRESHOLD_GOOD:
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return 'good'
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if score < THRESHOLD_BAD:
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return 'bad'
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return 'moderate'
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# =============================================================================
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# Windows CP1252 safe logging
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# =============================================================================
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class _SafeStreamHandler(logging.StreamHandler):
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def emit(self, record):
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try:
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super().emit(record)
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except UnicodeEncodeError:
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try:
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msg = self.format(record)
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self.stream.write(
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msg.encode("utf-8", errors="replace").decode("ascii", errors="replace")
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+ self.terminator
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)
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self.flush()
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except Exception:
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self.handleError(record)
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# =============================================================================
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# HELPERS
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# =============================================================================
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def _should_invert(direction: str, logger=None, context: str = "") -> bool:
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d = str(direction).lower().strip()
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if d in DIRECTION_INVERT_KEYWORDS:
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return True
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if d in DIRECTION_POSITIVE_KEYWORDS:
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return False
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if logger:
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logger.warning(
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f" [DIRECTION WARNING] Unknown direction '{direction}' "
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f"{'(' + context + ')' if context else ''}. Defaulting to positive (no invert)."
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)
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return False
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def global_minmax(series: pd.Series, lo: float = 1.0, hi: float = 100.0) -> pd.Series:
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values = series.dropna().values
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if len(values) == 0:
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return pd.Series(np.nan, index=series.index)
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v_min, v_max = values.min(), values.max()
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if v_min == v_max:
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return pd.Series((lo + hi) / 2.0, index=series.index)
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result = np.full(len(series), np.nan)
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not_nan = series.notna()
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raw = series[not_nan].values
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result[not_nan.values] = lo + (raw - v_min) / (v_max - v_min) * (hi - lo)
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return pd.Series(result, index=series.index)
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def add_yoy(df: pd.DataFrame, group_cols: list, score_col: str) -> pd.DataFrame:
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df = df.sort_values(group_cols + ["year"]).reset_index(drop=True)
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if group_cols:
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df["year_over_year_change"] = df.groupby(group_cols)[score_col].diff()
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else:
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df["year_over_year_change"] = df[score_col].diff()
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return df
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def safe_int(
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series: pd.Series, fill: int = 0, col_name: str = "", logger=None
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) -> pd.Series:
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n_nan = series.isna().sum()
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if n_nan > 0 and logger:
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logger.warning(
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f" [NaN WARNING] Kolom '{col_name}' punya {n_nan} NaN -> di-fill dengan {fill}"
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)
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return series.fillna(fill).astype(int)
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def check_and_dedup(
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df: pd.DataFrame, key_cols: list, context: str = "", logger=None
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) -> pd.DataFrame:
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dupes = df.duplicated(subset=key_cols, keep=False)
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if dupes.any():
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n_dupes = dupes.sum()
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if logger:
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logger.warning(
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f" [DEDUP WARNING] {context}: {n_dupes} duplikat rows pada {key_cols}. "
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f"Di-aggregate dengan mean."
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)
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numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
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agg_dict = {
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c: ("mean" if c in numeric_cols else "first")
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for c in df.columns if c not in key_cols
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}
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df = df.groupby(key_cols, as_index=False).agg(agg_dict)
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return df
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def add_condition_column(df: pd.DataFrame, score_col: str) -> pd.DataFrame:
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"""
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Tambahkan kolom 'condition' berdasarkan score_col.
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Threshold: bad < 40, moderate 40-60, good > 60 (skala 1-100).
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"""
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df['condition'] = df[score_col].apply(assign_condition)
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return df
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def log_condition_summary(df: pd.DataFrame, context: str, logger) -> None:
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"""Log distribusi kondisi untuk verifikasi."""
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dist = df['condition'].value_counts()
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logger.info(
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f" Condition distribution ({context}): " +
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" | ".join(f"{c}: {n:,}" for c, n in dist.items())
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)
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# =============================================================================
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# NARRATIVE BUILDER FUNCTIONS
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# =============================================================================
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def _fmt_score(score) -> str:
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if score is None or (isinstance(score, float) and np.isnan(score)):
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return "N/A"
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return f"{score:.2f}"
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def _fmt_delta(delta) -> str:
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if delta is None or (isinstance(delta, float) and np.isnan(delta)):
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return "N/A"
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sign = "+" if delta >= 0 else ""
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return f"{sign}{delta:.2f}"
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def _build_overview_narrative(
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year, n_mdg, n_sdg, n_total_ind, score, yoy_val, yoy_pct,
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prev_year, prev_score, ranking_list,
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most_improved_country, most_improved_delta,
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most_declined_country, most_declined_delta,
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) -> str:
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parts_ind = []
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if n_mdg > 0:
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parts_ind.append(f"{n_mdg} MDG indicator{'s' if n_mdg > 1 else ''}")
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if n_sdg > 0:
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parts_ind.append(f"{n_sdg} SDG indicator{'s' if n_sdg > 1 else ''}")
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if parts_ind:
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ind_detail = " and ".join(parts_ind)
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sent1 = (
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f"In {year}, the ASEAN food security assessment incorporated a total of "
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f"{n_total_ind} indicator{'s' if n_total_ind != 1 else ''}, "
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f"consisting of {ind_detail}."
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)
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else:
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sent1 = (
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f"In {year}, the ASEAN food security assessment incorporated "
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f"{n_total_ind} indicator{'s' if n_total_ind != 1 else ''}."
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)
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if yoy_val is not None and prev_score is not None:
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direction_word = "increasing" if yoy_val >= 0 else "decreasing"
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pct_clause = ""
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if yoy_pct is not None:
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abs_pct = abs(yoy_pct)
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trend_word = "improvement" if yoy_val >= 0 else "decline"
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pct_clause = f", which represents a {abs_pct:.2f}% {trend_word} year-over-year"
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sent2 = (
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f"The ASEAN overall score (Total framework) reached {_fmt_score(score)}, "
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f"{direction_word} by {abs(yoy_val):.2f} points compared to the previous year "
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f"({_fmt_score(prev_score)} in {prev_year}){pct_clause}."
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)
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else:
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sent2 = (
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f"The ASEAN overall score (Total framework) reached {_fmt_score(score)} in {year}; "
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f"no prior-year data is available for year-over-year comparison."
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)
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sent3 = ""
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if ranking_list:
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first = ranking_list[0]
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last = ranking_list[-1]
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middle = ranking_list[1:-1]
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if len(ranking_list) == 1:
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sent3 = (
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f"In terms of country performance, {first['country_name']} was the only "
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f"country assessed, scoring {_fmt_score(first['score'])} in {year}."
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)
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elif len(ranking_list) == 2:
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sent3 = (
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f"In terms of country performance, {first['country_name']} led the region "
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f"with a score of {_fmt_score(first['score'])}, while "
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f"{last['country_name']} recorded the lowest score of "
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f"{_fmt_score(last['score'])} in {year}."
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)
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else:
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middle_parts = [f"{c['country_name']} ({_fmt_score(c['score'])})" for c in middle]
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middle_str = (
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middle_parts[0] if len(middle_parts) == 1
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else ", ".join(middle_parts[:-1]) + f", and {middle_parts[-1]}"
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)
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sent3 = (
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f"In terms of country performance, {first['country_name']} led the region "
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f"with a score of {_fmt_score(first['score'])}, followed by {middle_str}. "
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f"At the other end, {last['country_name']} recorded the lowest score "
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f"of {_fmt_score(last['score'])} in {year}."
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)
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sent4_parts = []
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if most_improved_country and most_improved_delta is not None:
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sent4_parts.append(
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f"the most notable improvement was seen in {most_improved_country}, "
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f"which gained {_fmt_delta(most_improved_delta)} points from the previous year"
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)
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if most_declined_country and most_declined_delta is not None:
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if most_declined_delta < 0:
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sent4_parts.append(
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f"while {most_declined_country} experienced the largest decline "
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f"of {_fmt_delta(most_declined_delta)} points"
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)
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else:
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sent4_parts.append(
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f"while {most_declined_country} recorded the smallest gain "
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f"of {_fmt_delta(most_declined_delta)} points"
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)
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sent4 = ""
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if sent4_parts:
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sent4 = ", ".join(sent4_parts) + "."
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sent4 = sent4[0].upper() + sent4[1:]
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return " ".join(s for s in [sent1, sent2, sent3, sent4] if s)
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def _build_pillar_narrative(
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year, pillar_name, pillar_score, rank_in_year, n_pillars, yoy_val,
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top_country, top_country_score, bot_country, bot_country_score,
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strongest_pillar, strongest_score, weakest_pillar, weakest_score,
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most_improved_pillar, most_improved_delta,
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most_declined_pillar, most_declined_delta,
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) -> str:
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rank_suffix = {1: "st", 2: "nd", 3: "rd"}.get(rank_in_year, "th")
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sent1 = (
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f"In {year}, the {pillar_name} pillar scored {_fmt_score(pillar_score)}, "
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f"ranking {rank_in_year}{rank_suffix} out of {n_pillars} pillars assessed across ASEAN."
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)
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sent2 = ""
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if strongest_pillar and weakest_pillar:
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if strongest_pillar == pillar_name:
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sent2 = (
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f"This made {pillar_name} the strongest performing pillar in {year}, "
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f"compared to the weakest pillar, {weakest_pillar}, "
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f"which scored {_fmt_score(weakest_score)}."
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)
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elif weakest_pillar == pillar_name:
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sent2 = (
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f"This made {pillar_name} the weakest performing pillar in {year}, "
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f"compared to the strongest pillar, {strongest_pillar}, "
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f"which scored {_fmt_score(strongest_score)}."
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)
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else:
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sent2 = (
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f"Across all pillars in {year}, {strongest_pillar} was the strongest "
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f"(score: {_fmt_score(strongest_score)}), while {weakest_pillar} "
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f"was the weakest (score: {_fmt_score(weakest_score)})."
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)
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sent3 = ""
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if top_country and bot_country:
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if top_country != bot_country:
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sent3 = (
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f"Within the {pillar_name} pillar, {top_country} led with a score of "
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f"{_fmt_score(top_country_score)}, while {bot_country} recorded the lowest "
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f"score of {_fmt_score(bot_country_score)}."
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)
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else:
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sent3 = (
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f"Within the {pillar_name} pillar, {top_country} was the only country "
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f"with available data, scoring {_fmt_score(top_country_score)}."
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)
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if yoy_val is not None:
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direction_word = "improved" if yoy_val >= 0 else "declined"
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sent4 = (
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f"Compared to the previous year, the {pillar_name} pillar "
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f"{direction_word} by {abs(yoy_val):.2f} points"
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)
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else:
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sent4 = (
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f"No prior-year data is available to calculate year-over-year change "
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f"for the {pillar_name} pillar in {year}"
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)
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if (most_improved_pillar and most_improved_delta is not None
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and most_declined_pillar and most_declined_delta is not None
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and most_improved_pillar != most_declined_pillar):
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sent4 += (
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f". Across all pillars, {most_improved_pillar} showed the greatest improvement "
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f"({_fmt_delta(most_improved_delta)} pts), while {most_declined_pillar} "
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f"recorded the largest decline ({_fmt_delta(most_declined_delta)} pts)"
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)
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sent4 += "."
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sent4 = sent4[0].upper() + sent4[1:]
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return " ".join(s for s in [sent1, sent2, sent3, sent4] if s)
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# =============================================================================
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# MAIN CLASS
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# =============================================================================
|
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|
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class FoodSecurityAggregator:
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def __init__(self, client: bigquery.Client):
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self.client = client
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self.logger = logging.getLogger(self.__class__.__name__)
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self.logger.propagate = False
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self.load_metadata = {
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"agg_pillar_composite": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
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"agg_pillar_by_country": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
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"agg_framework_by_country": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
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"agg_framework_asean": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
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"agg_narrative_overview": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
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"agg_narrative_pillar": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
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}
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|
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self.df = None
|
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self.dims = {}
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|
|
self.sdgs_start_year = None
|
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self.mdgs_indicator_ids = set()
|
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self.sdgs_indicator_ids = set()
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|
|
# =========================================================================
|
|
# STEP 1: Load data
|
|
# =========================================================================
|
|
|
|
def load_data(self):
|
|
self.logger.info("=" * 70)
|
|
self.logger.info("STEP 1: LOAD DATA from fs_asean_gold")
|
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self.logger.info("=" * 70)
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|
|
|
self.df = read_from_bigquery(
|
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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", "framework",
|
|
"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: {missing_cols}\n"
|
|
f"Pastikan pipeline dijalankan berurutan:\n"
|
|
f" 1. bigquery_cleaned_layer.py\n"
|
|
f" 2. bigquery_dimensional_model.py\n"
|
|
f" 3. bigquery_analytical_layer.py\n"
|
|
f" 4. bigquery_analysis_layer.py (file ini)"
|
|
)
|
|
|
|
self.df["direction"] = self.df["direction"].fillna("positive")
|
|
self.df["framework"] = self.df["framework"].fillna("MDGs")
|
|
|
|
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") else "normal"
|
|
self.logger.info(f" {d:<25} : {cnt:>3} [{tag}]")
|
|
|
|
fw_dist = self.df.drop_duplicates("indicator_id")["framework"].value_counts()
|
|
self.logger.info(f"\n Distribusi framework per indikator:")
|
|
for fw, cnt in fw_dist.items():
|
|
self.logger.info(f" {fw:<10} : {cnt:>3}")
|
|
|
|
self.logger.info(
|
|
f"\n Rows: {len(self.df):,} | Negara: {self.df['country_id'].nunique()} | "
|
|
f"Indikator: {self.df['indicator_id'].nunique()} | "
|
|
f"Tahun: {int(self.df['year'].min())}-{int(self.df['year'].max())}"
|
|
)
|
|
|
|
# =========================================================================
|
|
# STEP 1b: Klasifikasi indikator
|
|
# =========================================================================
|
|
|
|
def _classify_indicators(self):
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info("STEP 1b: KLASIFIKASI INDIKATOR -> MDGs / SDGs")
|
|
self.logger.info("=" * 70)
|
|
|
|
self.mdgs_indicator_ids = set(
|
|
self.df[self.df["framework"] == "MDGs"]["indicator_id"].unique().tolist()
|
|
)
|
|
self.sdgs_indicator_ids = set(
|
|
self.df[self.df["framework"] == "SDGs"]["indicator_id"].unique().tolist()
|
|
)
|
|
|
|
# sdgs_start_year: ambil dari proxy SDGs-only (FIES/anaemia)
|
|
# Konsisten dengan cara analytical_layer mendeteksinya
|
|
_PROXY_KW = frozenset(['food insecurity', 'anemia', 'anaemia'])
|
|
proxy_mask = (
|
|
(self.df["framework"] == "SDGs") &
|
|
self.df["indicator_name"].str.lower().apply(
|
|
lambda n: any(kw in n for kw in _PROXY_KW)
|
|
)
|
|
)
|
|
df_proxy = self.df[proxy_mask]
|
|
|
|
if not df_proxy.empty:
|
|
self.sdgs_start_year = int(df_proxy["year"].min())
|
|
self.logger.info(
|
|
f"\n sdgs_start_year = {self.sdgs_start_year} "
|
|
f"(dari proxy FIES/anaemia di tabel)"
|
|
)
|
|
else:
|
|
# Fallback: min year dari semua SDGs rows
|
|
sdgs_rows = self.df[self.df["framework"] == "SDGs"]
|
|
if not sdgs_rows.empty:
|
|
self.sdgs_start_year = int(sdgs_rows["year"].min())
|
|
self.logger.warning(
|
|
f" [WARN] Proxy tidak ditemukan, fallback ke min(year) SDGs: "
|
|
f"{self.sdgs_start_year}"
|
|
)
|
|
else:
|
|
self.sdgs_start_year = int(self.df["year"].max()) + 1
|
|
self.logger.warning(
|
|
f" [WARN] Tidak ada SDGs. sdgs_start_year = {self.sdgs_start_year}"
|
|
)
|
|
|
|
self.logger.info(f" MDGs : {len(self.mdgs_indicator_ids)} indikator")
|
|
self.logger.info(f" SDGs : {len(self.sdgs_indicator_ids)} indikator")
|
|
|
|
for fw in ["MDGs", "SDGs"]:
|
|
fw_inds = (
|
|
self.df[self.df["framework"] == fw]
|
|
.drop_duplicates("indicator_id")[["indicator_id", "indicator_name"]]
|
|
.sort_values("indicator_name")
|
|
)
|
|
self.logger.info(f"\n {fw} indicators ({len(fw_inds)}):")
|
|
for _, row in fw_inds.iterrows():
|
|
self.logger.info(f" [{int(row['indicator_id'])}] {row['indicator_name']}")
|
|
|
|
# =========================================================================
|
|
# CORE HELPER: normalisasi 0-1 per indikator (untuk composite score)
|
|
# =========================================================================
|
|
|
|
def _get_norm_value_df(self) -> pd.DataFrame:
|
|
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}")
|
|
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 = add_condition_column(df, "pillar_score_1_100")
|
|
log_condition_summary(df, table_name, self.logger)
|
|
|
|
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"),
|
|
bigquery.SchemaField("condition", "STRING", 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}")
|
|
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 = add_condition_column(df, "pillar_country_score_1_100")
|
|
log_condition_summary(df, table_name, self.logger)
|
|
|
|
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"),
|
|
bigquery.SchemaField("condition", "STRING", 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}")
|
|
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 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)
|
|
|
|
# Layer MDGs mixed (setelah SDGs masuk)
|
|
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 = add_condition_column(df, "framework_score_1_100")
|
|
log_condition_summary(df, table_name, self.logger)
|
|
|
|
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"),
|
|
bigquery.SchemaField("condition", "STRING", 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
|
|
# =========================================================================
|
|
|
|
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}")
|
|
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"])
|
|
|
|
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
|
|
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 = add_condition_column(df, "framework_score_1_100")
|
|
log_condition_summary(df, table_name, self.logger)
|
|
|
|
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"),
|
|
bigquery.SchemaField("condition", "STRING", 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 & 7: Narrative (tidak ada perubahan)
|
|
# =========================================================================
|
|
|
|
def calc_narrative_overview(self, df_framework_asean, df_framework_by_country):
|
|
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}")
|
|
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)))
|
|
country_total = df_framework_by_country[df_framework_by_country["framework"] == "Total"].copy()
|
|
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
|
|
|
|
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 = score_by_year.get(yr - 1, None)
|
|
yoy_pct = ((yoy_val / prev_score * 100) if (yoy_val is not None and prev_score 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,
|
|
})
|
|
|
|
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, 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" : json.dumps(ranking_list, ensure_ascii=False),
|
|
"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
|
|
|
|
def calc_narrative_pillar(self, df_pillar_composite, df_pillar_by_country):
|
|
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}")
|
|
self.logger.info("=" * 70)
|
|
|
|
records = []
|
|
for yr in sorted(df_pillar_composite["year"].unique()):
|
|
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_country = yr_country_pillar[yr_country_pillar["pillar_id"] == p_id].sort_values("rank_in_pillar_year").reset_index(drop=True)
|
|
top_country = bot_country = None
|
|
top_country_score = bot_country_score = None
|
|
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)
|
|
|
|
p_yoy = prow["year_over_year_change"]
|
|
narrative = _build_pillar_narrative(
|
|
year=yr, pillar_name=str(prow["pillar_name"]),
|
|
pillar_score=float(prow["pillar_score_1_100"]),
|
|
rank_in_year=int(prow["rank_in_year"]), n_pillars=len(yr_pillars),
|
|
yoy_val=float(p_yoy) if pd.notna(p_yoy) else None,
|
|
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" : str(prow["pillar_name"]),
|
|
"pillar_score" : round(float(prow["pillar_score_1_100"]), 2),
|
|
"rank_in_year" : int(prow["rank_in_year"]),
|
|
"yoy_change" : float(p_yoy) if pd.notna(p_yoy) else None,
|
|
"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")
|
|
|
|
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(f" Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
|
|
self.logger.info("=" * 70)
|
|
|
|
self.load_data()
|
|
self._classify_indicators()
|
|
|
|
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 & MAIN
|
|
# =============================================================================
|
|
|
|
def run_aggregation():
|
|
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")
|
|
|
|
|
|
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"Condition threshold: bad<{THRESHOLD_BAD}, moderate {THRESHOLD_BAD}-{THRESHOLD_GOOD}, good>{THRESHOLD_GOOD}")
|
|
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) |