1186 lines
50 KiB
Python
1186 lines
50 KiB
Python
"""
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BIGQUERY ANALYSIS LAYER - INDICATOR NORM AGGREGATION
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Tabel 1: agg_indicator_norm -> fs_asean_gold
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Tabel 2: agg_narrative_indicator -> fs_asean_gold
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=============================================================================
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agg_indicator_norm
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=============================================================================
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Tujuan:
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Menghitung norm_value per indikator per negara per tahun, sehingga dapat
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melihat performa setiap indikator secara individual (lower_better & higher_better
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sudah dibalik).
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Framework Classification Logic:
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- Semua indikator berlabel "MDGs" secara default.
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- Indikator yang ada dalam SDG_ONLY_KEYWORDS akan berlabel "SDGs" mulai dari
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sdgs_start_year (tahun pertama FIES hadir, dihitung otomatis).
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- Indikator yang SUDAH ADA sebelum sdgs_start_year DAN juga termasuk
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SDG_ONLY_KEYWORDS akan memiliki DUA label framework:
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* "MDGs" untuk year < sdgs_start_year
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* "SDGs" untuk year >= sdgs_start_year
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- Indikator yang TIDAK ada dalam SDG_ONLY_KEYWORDS selalu "MDGs".
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YoY Logic:
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- yoy_value : selisih absolut value vs tahun sebelumnya (per indikator, negara)
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- yoy_norm_value : selisih absolut norm_value vs tahun sebelumnya
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Performance Label Logic:
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- performance : "Good" jika norm_score_1_100 >= 60, "Bad" jika < 60, null jika null
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Output Schema (agg_indicator_norm):
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year, country_id, country_name,
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indicator_id, indicator_name, unit, direction,
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pillar_id, pillar_name,
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framework, -- "MDGs" | "SDGs"
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value, -- raw value asli
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norm_value, -- 0-1, direction sudah diperhitungkan
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norm_score_1_100, -- scaled 1-100 (global per indikator)
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yoy_value, -- perubahan absolut value YoY
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yoy_norm_value, -- perubahan absolut norm_value YoY
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performance -- "Good" | "Bad" | null
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=============================================================================
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agg_narrative_indicator
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=============================================================================
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Tujuan:
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Menghasilkan narasi otomatis 1 paragraf per indikator per tahun di level ASEAN
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(rata-rata seluruh negara ASEAN), dijalankan otomatis setelah agg_indicator_norm
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selesai dalam pipeline yang sama.
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Granularity:
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year x indicator_id (level ASEAN, bukan per negara)
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Output Schema (agg_narrative_indicator):
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year, indicator_id, indicator_name, unit, direction,
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pillar_name, framework,
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avg_value, -- rata-rata value ASEAN
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avg_norm_score_1_100, -- rata-rata norm_score_1_100 ASEAN
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performance, -- Good | Bad | null
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yoy_avg_value, -- perubahan avg_value vs tahun sebelumnya
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n_countries, -- jumlah negara yang punya data tahun ini
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narrative -- 1 paragraf narasi otomatis
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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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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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# SDG-ONLY KEYWORD SET
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# =============================================================================
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SDG_ONLY_KEYWORDS: frozenset = frozenset([
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# TARGET 2.1.1 - Undernourishment
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"prevalence of undernourishment (percent) (3-year average)",
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"number of people undernourished (million) (3-year average)",
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# TARGET 2.1.2 - Food Insecurity (FIES)
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"prevalence of severe food insecurity in the total population (percent) (3-year average)",
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"prevalence of severe food insecurity in the male adult population (percent) (3-year average)",
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"prevalence of severe food insecurity in the female adult population (percent) (3-year average)",
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"prevalence of moderate or severe food insecurity in the total population (percent) (3-year average)",
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"prevalence of moderate or severe food insecurity in the male adult population (percent) (3-year average)",
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"prevalence of moderate or severe food insecurity in the female adult population (percent) (3-year average)",
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"number of severely food insecure people (million) (3-year average)",
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"number of severely food insecure male adults (million) (3-year average)",
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"number of severely food insecure female adults (million) (3-year average)",
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"number of moderately or severely food insecure people (million) (3-year average)",
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"number of moderately or severely food insecure male adults (million) (3-year average)",
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"number of moderately or severely food insecure female adults (million) (3-year average)",
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# TARGET 2.2.1 - Stunting
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"percentage of children under 5 years of age who are stunted (modelled estimates) (percent)",
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"number of children under 5 years of age who are stunted (modeled estimates) (million)",
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# TARGET 2.2.2 - Wasting
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"percentage of children under 5 years affected by wasting (percent)",
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"number of children under 5 years affected by wasting (million)",
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# TARGET 2.2.2 - Overweight (children)
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"percentage of children under 5 years of age who are overweight (modelled estimates) (percent)",
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"number of children under 5 years of age who are overweight (modeled estimates) (million)",
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# TARGET 2.2.3 - Anaemia
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"prevalence of anemia among women of reproductive age (15-49 years) (percent)",
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"number of women of reproductive age (15-49 years) affected by anemia (million)",
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])
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# Lowercase set untuk matching case-insensitive
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_SDG_ONLY_LOWER: frozenset = frozenset(k.lower() for k in SDG_ONLY_KEYWORDS)
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# FIES-specific keywords untuk deteksi sdgs_start_year
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_FIES_DETECTION_KEYWORDS: frozenset = frozenset([
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"prevalence of severe food insecurity in the total population (percent) (3-year average)",
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"prevalence of moderate or severe food insecurity in the total population (percent) (3-year average)",
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"number of severely food insecure people (million) (3-year average)",
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"number of moderately or severely food insecure people (million) (3-year average)",
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])
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_FIES_DETECTION_LOWER: frozenset = frozenset(k.lower() for k in _FIES_DETECTION_KEYWORDS)
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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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# Threshold performance label
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_PERFORMANCE_THRESHOLD: float = 60.0
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# =============================================================================
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# PURE HELPERS — agg_indicator_norm
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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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result[not_nan.values] = lo + (series[not_nan].values - v_min) / (v_max - v_min) * (hi - lo)
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return pd.Series(result, index=series.index)
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def _compute_yoy(df: pd.DataFrame) -> pd.DataFrame:
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"""
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Hitung YoY untuk satu grup (indicator_id, country_id) yang sudah di-sort by year.
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Kolom yang ditambahkan:
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yoy_value : value - value_prev
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yoy_norm_value : norm_value - norm_value_prev
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Baris pertama tiap grup selalu null (tidak ada tahun sebelumnya).
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"""
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df = df.sort_values("year").copy()
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df["value_prev"] = df["value"].shift(1)
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df["norm_value_prev"] = df["norm_value"].shift(1)
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df["yoy_value"] = np.where(
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df["value"].notna() & df["value_prev"].notna(),
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df["value"] - df["value_prev"],
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np.nan,
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)
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df["yoy_norm_value"] = np.where(
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df["norm_value"].notna() & df["norm_value_prev"].notna(),
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df["norm_value"] - df["norm_value_prev"],
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np.nan,
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)
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df = df.drop(columns=["value_prev", "norm_value_prev"])
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return df
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# =============================================================================
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# PURE HELPERS — agg_narrative_indicator
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# =============================================================================
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def _is_lower_better(direction: str) -> bool:
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return str(direction).lower().strip() in DIRECTION_INVERT_KEYWORDS
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def _format_value(value: float, unit: str) -> str:
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"""Format nilai dengan unit yang sesuai."""
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if pd.isna(value):
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return "N/A"
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unit = str(unit).strip() if unit else ""
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if abs(value) >= 1000:
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formatted = f"{value:,.1f}"
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elif abs(value) >= 10:
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formatted = f"{value:.2f}"
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else:
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formatted = f"{value:.3f}"
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return f"{formatted} {unit}".strip()
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def _format_yoy(yoy: float, unit: str, lower_better: bool) -> tuple:
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"""
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Kembalikan (direction_word, change_desc, is_positive_trend).
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is_positive_trend: True jika perubahan menguntungkan sesuai direction.
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"""
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unit = str(unit).strip() if unit else ""
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abs_yoy = abs(yoy)
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if abs_yoy >= 1000:
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yoy_str = f"{abs_yoy:,.1f}"
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elif abs_yoy >= 10:
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yoy_str = f"{abs_yoy:.2f}"
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else:
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yoy_str = f"{abs_yoy:.3f}"
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change_desc = f"{yoy_str} {unit}".strip()
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is_positive = (yoy < 0) if lower_better else (yoy > 0)
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direction_word = "decreased by" if yoy < 0 else "increased by"
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return direction_word, change_desc, is_positive
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def _build_narrative(row: pd.Series) -> str:
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"""
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Bangun 1 paragraf narasi ASEAN-level untuk satu baris (year x indicator_id).
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"""
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year = int(row["year"])
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ind_name = str(row["indicator_name"]).strip()
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unit = str(row["unit"]).strip() if row["unit"] else ""
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direction = str(row["direction"]).strip()
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pillar = str(row["pillar_name"]).strip()
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framework = str(row["framework"]).strip()
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avg_val = row["avg_value"]
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avg_score = row["avg_norm_score_1_100"]
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performance = row["performance"]
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yoy = row["yoy_avg_value"]
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n_countries = int(row["n_countries"]) if not pd.isna(row["n_countries"]) else 0
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lower_better = _is_lower_better(direction)
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direction_label = (
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"lower values indicate better outcomes" if lower_better
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else "higher values indicate better outcomes"
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)
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# --- Bagian 1: Nilai rata-rata ASEAN ---
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val_str = _format_value(avg_val, unit)
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sentence1 = (
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f"In {year}, the ASEAN regional average for {ind_name} stood at {val_str}"
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)
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if n_countries > 0:
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sentence1 += (
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f", based on data from {n_countries} "
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f"ASEAN member state{'s' if n_countries > 1 else ''}"
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)
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sentence1 += "."
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# --- Bagian 2: Score dan performance ---
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if not pd.isna(avg_score):
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score_str = f"{avg_score:.1f} out of 100"
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if performance == "Good":
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perf_phrase = (
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f"The region achieved a normalized score of {score_str}, "
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f"classified as Good performance — meeting the 60-point threshold — "
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f"under the {framework} framework ({pillar} pillar)."
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)
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elif performance == "Bad":
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perf_phrase = (
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f"The region recorded a normalized score of {score_str}, "
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f"classified as Bad performance — falling below the 60-point threshold — "
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f"under the {framework} framework ({pillar} pillar)."
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)
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else:
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perf_phrase = (
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f"The region recorded a normalized score of {score_str} "
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f"under the {framework} framework ({pillar} pillar)."
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)
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else:
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perf_phrase = (
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f"Performance could not be assessed due to insufficient data "
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f"under the {framework} framework ({pillar} pillar)."
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)
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# --- Bagian 3 & 4: Arah + YoY ---
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direction_phrase = f"Since {direction_label} for this indicator"
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if not pd.isna(yoy) and yoy != 0:
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direction_word, change_desc, is_positive = _format_yoy(yoy, unit, lower_better)
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if is_positive:
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trend_word = "a positive trend"
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tone = "reflecting improvements in regional food security performance"
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else:
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trend_word = "a deteriorating trend"
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tone = "signaling the need for greater regional attention and policy response"
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yoy_phrase = (
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f"{direction_phrase}, the regional average {direction_word} {change_desc} "
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f"compared to {year - 1}, reflecting {trend_word} — {tone}."
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)
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elif pd.isna(yoy):
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yoy_phrase = (
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f"No prior year data is available for comparison, "
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f"as this is the earliest recorded year for this indicator in the dataset."
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)
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else:
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yoy_phrase = (
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f"{direction_phrase}, the regional average remained stable "
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f"compared to {year - 1}, with no measurable change year-on-year."
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)
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return f"{sentence1} {perf_phrase} {yoy_phrase}"
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# =============================================================================
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# MAIN CLASS
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# =============================================================================
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class IndicatorNormAggregator:
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"""
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Hitung norm_value per indikator untuk seluruh data di
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fact_asean_food_security_selected, lalu simpan ke agg_indicator_norm.
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Setelah selesai, otomatis menjalankan pipeline agg_narrative_indicator.
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Alur agg_indicator_norm:
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1. Load fact_asean_food_security_selected
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2. Load dim_indicator -> ambil kolom unit
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3. Merge unit ke df
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4. Deteksi sdgs_start_year (tahun pertama FIES hadir di data)
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5. Assign framework per baris mengikuti aturan MDGs/SDGs dual-label
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6. Hitung norm_value per indikator (direction-aware, 0-1)
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7. Hitung YoY per (indicator_id, country_id)
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8. Scale ke 1-100 per indikator (global)
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9. Assign performance label (Good/Bad)
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10. Simpan ke BigQuery -> agg_indicator_norm
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11. Summary log agg_indicator_norm
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Alur agg_narrative_indicator (lanjutan, pakai df_final yang sudah ada):
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12. Agregasi ke level ASEAN (year x indicator_id)
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13. Hitung YoY avg_value per indikator
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14. Assign performance berdasarkan avg_norm_score
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15. Build narrative 1 paragraf per baris
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16. Simpan ke BigQuery -> agg_narrative_indicator
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17. Summary log agg_narrative_indicator
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"""
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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.df = None
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self.df_unit = None
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self.sdgs_start_year = None
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self.pipeline_start = None
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self.pipeline_metadata = {
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"rows_fetched" : 0,
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"rows_loaded" : 0,
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"rows_loaded_narrative" : 0,
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"start_time" : None,
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"end_time" : None,
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}
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# =========================================================================
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# STEP 1: Load fact table
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# =========================================================================
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def load_data(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 1: LOAD DATA — fact_asean_food_security_selected")
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self.logger.info("=" * 80)
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self.df = read_from_bigquery(
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self.client, "fact_asean_food_security_selected", layer="gold"
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)
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required = {
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"country_id", "country_name",
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"indicator_id", "indicator_name", "direction",
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"pillar_id", "pillar_name",
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"year", "value",
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}
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missing = required - set(self.df.columns)
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if missing:
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raise ValueError(f"Kolom tidak ditemukan: {missing}")
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n_null = self.df["direction"].isna().sum()
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if n_null > 0:
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self.logger.warning(f" {n_null} rows direction NULL -> diisi 'positive'")
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self.df["direction"] = self.df["direction"].fillna("positive")
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self.pipeline_metadata["rows_fetched"] = len(self.df)
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self.logger.info(f" Rows : {len(self.df):,}")
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self.logger.info(f" Countries : {self.df['country_id'].nunique()}")
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self.logger.info(f" Indicators: {self.df['indicator_id'].nunique()}")
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self.logger.info(
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f" Years : {int(self.df['year'].min())} - {int(self.df['year'].max())}"
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)
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# =========================================================================
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# STEP 2: Load unit dari dim_indicator
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# =========================================================================
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def load_units(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 2: LOAD UNIT — dim_indicator")
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self.logger.info("=" * 80)
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dim = read_from_bigquery(self.client, "dim_indicator", layer="gold")
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if "indicator_id" not in dim.columns or "unit" not in dim.columns:
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raise ValueError(
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f"dim_indicator harus punya kolom 'indicator_id' dan 'unit'. "
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f"Kolom tersedia: {list(dim.columns)}"
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)
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self.df_unit = (
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dim[["indicator_id", "unit"]]
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.drop_duplicates(subset=["indicator_id"])
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.copy()
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)
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self.df_unit["indicator_id"] = self.df_unit["indicator_id"].astype(int)
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self.df_unit["unit"] = self.df_unit["unit"].fillna("").astype(str)
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n_missing_unit = self.df_unit["unit"].eq("").sum()
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self.logger.info(f" dim_indicator rows (unique indicator_id): {len(self.df_unit):,}")
|
|
self.logger.info(f" Indicator dengan unit kosong : {n_missing_unit}")
|
|
|
|
fact_ids = set(self.df["indicator_id"].astype(int).unique())
|
|
dim_ids = set(self.df_unit["indicator_id"].unique())
|
|
orphan = fact_ids - dim_ids
|
|
if orphan:
|
|
self.logger.warning(
|
|
f" [WARNING] {len(orphan)} indicator_id di fact tidak ditemukan di "
|
|
f"dim_indicator (unit akan diisi ''): {sorted(orphan)}"
|
|
)
|
|
|
|
# =========================================================================
|
|
# STEP 3: Merge unit ke df
|
|
# =========================================================================
|
|
|
|
def _merge_unit(self):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 3: MERGE UNIT -> fact df")
|
|
self.logger.info("=" * 80)
|
|
|
|
before = len(self.df)
|
|
self.df = self.df.merge(self.df_unit, on="indicator_id", how="left")
|
|
self.df["unit"] = self.df["unit"].fillna("").astype(str)
|
|
after = len(self.df)
|
|
|
|
assert before == after, (
|
|
f"Row count berubah setelah merge unit: {before} -> {after}"
|
|
)
|
|
|
|
n_empty = self.df["unit"].eq("").sum()
|
|
self.logger.info(
|
|
f" Merge OK. Rows: {after:,} | Rows dengan unit kosong: {n_empty}"
|
|
)
|
|
unique_units = self.df["unit"].value_counts().to_dict()
|
|
self.logger.info(" Distribusi unit (top 10):")
|
|
for u, cnt in list(unique_units.items())[:10]:
|
|
label = repr(u) if u == "" else u
|
|
self.logger.info(f" {label:<30}: {cnt:,} rows")
|
|
|
|
# =========================================================================
|
|
# STEP 4: Deteksi sdgs_start_year
|
|
# =========================================================================
|
|
|
|
def _detect_sdgs_start_year(self) -> int:
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 4: DETECT sdgs_start_year (first FIES year)")
|
|
self.logger.info("=" * 80)
|
|
|
|
fies_rows = self.df[
|
|
self.df["indicator_name"].str.lower().str.strip().isin(_FIES_DETECTION_LOWER)
|
|
]
|
|
if not fies_rows.empty:
|
|
sdgs_start = int(fies_rows["year"].min())
|
|
n_fies_ind = fies_rows["indicator_name"].nunique()
|
|
self.logger.info(f" [Metode 1 - FIES explicit] sdgs_start_year = {sdgs_start}")
|
|
self.logger.info(f" FIES indicators found: {n_fies_ind}, first year = {sdgs_start}")
|
|
for nm in fies_rows["indicator_name"].unique():
|
|
min_y = int(fies_rows[fies_rows["indicator_name"] == nm]["year"].min())
|
|
self.logger.info(f" - {nm[:60]} (first year: {min_y})")
|
|
return sdgs_start
|
|
|
|
self.logger.info(" [Metode 1] Tidak ada FIES rows -> fallback gap-terbesar")
|
|
ind_min_year = (
|
|
self.df.groupby("indicator_id")["year"]
|
|
.min().reset_index()
|
|
.rename(columns={"year": "min_year"})
|
|
)
|
|
unique_years = sorted(ind_min_year["min_year"].unique())
|
|
self.logger.info(f" Unique min_year per indikator: {unique_years}")
|
|
|
|
if len(unique_years) == 1:
|
|
sdgs_start = int(unique_years[0]) + 9999
|
|
self.logger.info(" Hanya 1 cluster -> semua MDGs")
|
|
else:
|
|
gaps = [
|
|
(unique_years[i+1] - unique_years[i], unique_years[i], unique_years[i+1])
|
|
for i in range(len(unique_years) - 1)
|
|
]
|
|
gaps.sort(reverse=True)
|
|
_, y_before, y_after = gaps[0]
|
|
sdgs_start = int(y_after)
|
|
self.logger.info(
|
|
f" Gap terbesar: {y_before} -> {y_after} -> sdgs_start_year = {sdgs_start}"
|
|
)
|
|
|
|
return sdgs_start
|
|
|
|
# =========================================================================
|
|
# STEP 5: Assign framework
|
|
# =========================================================================
|
|
|
|
def _assign_framework(self):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 5: ASSIGN FRAMEWORK PER BARIS")
|
|
self.logger.info(f" sdgs_start_year = {self.sdgs_start_year}")
|
|
self.logger.info("=" * 80)
|
|
|
|
df = self.df.copy()
|
|
|
|
df["_is_sdg_kw"] = df["indicator_name"].str.lower().str.strip().isin(_SDG_ONLY_LOWER)
|
|
df["framework"] = "MDGs"
|
|
|
|
mask_sdgs = df["_is_sdg_kw"] & (df["year"] >= self.sdgs_start_year)
|
|
df.loc[mask_sdgs, "framework"] = "SDGs"
|
|
|
|
df = df.drop(columns=["_is_sdg_kw"])
|
|
|
|
fw_dist = df["framework"].value_counts()
|
|
self.logger.info("\n Framework distribution (rows):")
|
|
for fw, cnt in fw_dist.items():
|
|
self.logger.info(f" {fw:<6}: {cnt:,} rows")
|
|
|
|
dual = (
|
|
df.groupby("indicator_id")["framework"]
|
|
.nunique()
|
|
.reset_index()
|
|
.rename(columns={"framework": "n_frameworks"})
|
|
)
|
|
dual_ids = dual[dual["n_frameworks"] > 1]["indicator_id"].tolist()
|
|
self.logger.info(
|
|
f"\n Indikator dengan DUAL framework (MDGs + SDGs): {len(dual_ids)}"
|
|
)
|
|
if dual_ids:
|
|
for iid in dual_ids:
|
|
ind_name = df[df["indicator_id"] == iid]["indicator_name"].iloc[0]
|
|
yr_range = df[df["indicator_id"] == iid][["year", "framework"]].drop_duplicates()
|
|
mdgs_yrs = sorted(yr_range[yr_range["framework"] == "MDGs"]["year"].tolist())
|
|
sdgs_yrs = sorted(yr_range[yr_range["framework"] == "SDGs"]["year"].tolist())
|
|
self.logger.info(
|
|
f" [{iid}] {ind_name[:55]}\n"
|
|
f" MDGs years: {mdgs_yrs}\n"
|
|
f" SDGs years: {sdgs_yrs}"
|
|
)
|
|
|
|
self.df = df
|
|
|
|
# =========================================================================
|
|
# STEP 6: Hitung norm_value per indikator (direction-aware)
|
|
# =========================================================================
|
|
|
|
def _compute_norm_values(self) -> pd.DataFrame:
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 6: COMPUTE NORM_VALUE PER INDICATOR (direction-aware)")
|
|
self.logger.info("=" * 80)
|
|
|
|
df = self.df.copy()
|
|
norm_parts = []
|
|
|
|
for ind_id, grp in df.groupby("indicator_id"):
|
|
grp = grp.copy()
|
|
direction = str(grp["direction"].iloc[0])
|
|
do_invert = _should_invert(
|
|
direction, self.logger, context=f"indicator_id={ind_id}"
|
|
)
|
|
|
|
valid_mask = grp["value"].notna()
|
|
n_valid = valid_mask.sum()
|
|
|
|
if n_valid < 2:
|
|
grp["norm_value"] = np.nan
|
|
norm_parts.append(grp)
|
|
self.logger.warning(
|
|
f" [SKIP] indicator_id={ind_id}: only {n_valid} valid values"
|
|
)
|
|
continue
|
|
|
|
raw = grp.loc[valid_mask, "value"].values
|
|
v_min = raw.min()
|
|
v_max = raw.max()
|
|
|
|
normed = np.full(len(grp), np.nan)
|
|
if v_min == v_max:
|
|
normed[valid_mask.values] = 0.5
|
|
else:
|
|
normed[valid_mask.values] = (raw - v_min) / (v_max - v_min)
|
|
|
|
if do_invert:
|
|
normed = np.where(np.isnan(normed), np.nan, 1.0 - normed)
|
|
|
|
grp["norm_value"] = normed
|
|
norm_parts.append(grp)
|
|
|
|
df_normed = pd.concat(norm_parts, ignore_index=True)
|
|
|
|
self.logger.info(f" norm_value computed: {df_normed['indicator_id'].nunique()} indicators")
|
|
self.logger.info(
|
|
f" norm_value range : "
|
|
f"{df_normed['norm_value'].min():.4f} - {df_normed['norm_value'].max():.4f}"
|
|
)
|
|
self.logger.info(f" norm_value nulls : {df_normed['norm_value'].isna().sum()}")
|
|
return df_normed
|
|
|
|
# =========================================================================
|
|
# STEP 7: Hitung YoY per (indicator_id, country_id)
|
|
# =========================================================================
|
|
|
|
def _compute_yoy_columns(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 7: COMPUTE YoY COLUMNS (per indicator, per country)")
|
|
self.logger.info("=" * 80)
|
|
|
|
parts = []
|
|
groups = df.groupby(["indicator_id", "country_id"], sort=False)
|
|
self.logger.info(f" Processing {groups.ngroups:,} (indicator x country) groups...")
|
|
|
|
for (ind_id, country_id), grp in groups:
|
|
parts.append(_compute_yoy(grp))
|
|
|
|
df_out = pd.concat(parts, ignore_index=True)
|
|
|
|
self.logger.info(
|
|
f" yoy_value nulls : {df_out['yoy_value'].isna().sum():,}"
|
|
)
|
|
self.logger.info(
|
|
f" yoy_value range : "
|
|
f"{df_out['yoy_value'].min():.4f} - {df_out['yoy_value'].max():.4f}"
|
|
)
|
|
self.logger.info(
|
|
f" yoy_norm_value nulls: {df_out['yoy_norm_value'].isna().sum():,}"
|
|
)
|
|
self.logger.info(
|
|
f" yoy_norm_value range: "
|
|
f"{df_out['yoy_norm_value'].min():.4f} - {df_out['yoy_norm_value'].max():.4f}"
|
|
)
|
|
return df_out
|
|
|
|
# =========================================================================
|
|
# STEP 8: Scale ke 1-100
|
|
# =========================================================================
|
|
|
|
def _compute_scores(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 8: SCALE TO 1-100")
|
|
self.logger.info("=" * 80)
|
|
|
|
score_parts = []
|
|
for ind_id, grp in df.groupby("indicator_id"):
|
|
grp = grp.copy()
|
|
grp["norm_score_1_100"] = global_minmax(grp["norm_value"])
|
|
score_parts.append(grp)
|
|
df = pd.concat(score_parts, ignore_index=True)
|
|
|
|
self.logger.info(
|
|
f" norm_score_1_100 range: "
|
|
f"{df['norm_score_1_100'].min():.2f} - {df['norm_score_1_100'].max():.2f}"
|
|
)
|
|
return df
|
|
|
|
# =========================================================================
|
|
# STEP 9: Assign performance label
|
|
# =========================================================================
|
|
|
|
def _assign_performance(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
"""
|
|
performance = "Good" jika norm_score_1_100 >= 60
|
|
= "Bad" jika norm_score_1_100 < 60
|
|
= null jika norm_score_1_100 null
|
|
"""
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info(
|
|
f"STEP 9: ASSIGN PERFORMANCE LABEL "
|
|
f"(threshold >= {_PERFORMANCE_THRESHOLD} -> Good)"
|
|
)
|
|
self.logger.info("=" * 80)
|
|
|
|
df = df.copy()
|
|
df["performance"] = pd.NA
|
|
|
|
has_score = df["norm_score_1_100"].notna()
|
|
df.loc[has_score & (df["norm_score_1_100"] >= _PERFORMANCE_THRESHOLD), "performance"] = "Good"
|
|
df.loc[has_score & (df["norm_score_1_100"] < _PERFORMANCE_THRESHOLD), "performance"] = "Bad"
|
|
|
|
n_good = (df["performance"] == "Good").sum()
|
|
n_bad = (df["performance"] == "Bad").sum()
|
|
n_null = df["performance"].isna().sum()
|
|
total = len(df)
|
|
|
|
self.logger.info(f" Good : {n_good:,} ({n_good/total*100:.1f}%)")
|
|
self.logger.info(f" Bad : {n_bad:,} ({n_bad/total*100:.1f}%)")
|
|
self.logger.info(f" Null : {n_null:,} ({n_null/total*100:.1f}%)")
|
|
return df
|
|
|
|
# =========================================================================
|
|
# STEP 10: Save agg_indicator_norm to BigQuery
|
|
# =========================================================================
|
|
|
|
def _save(self, df: pd.DataFrame) -> int:
|
|
table_name = "agg_indicator_norm"
|
|
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info(f"STEP 10: SAVE -> [Gold] {table_name}")
|
|
self.logger.info("=" * 80)
|
|
|
|
out = df[[
|
|
"year",
|
|
"country_id",
|
|
"country_name",
|
|
"indicator_id",
|
|
"indicator_name",
|
|
"unit",
|
|
"direction",
|
|
"pillar_id",
|
|
"pillar_name",
|
|
"framework",
|
|
"value",
|
|
"norm_value",
|
|
"norm_score_1_100",
|
|
"yoy_value",
|
|
"yoy_norm_value",
|
|
"performance",
|
|
]].copy()
|
|
|
|
out = out.sort_values(
|
|
["year", "country_name", "pillar_name", "indicator_name"]
|
|
).reset_index(drop=True)
|
|
|
|
# Cast
|
|
out["year"] = out["year"].astype(int)
|
|
out["country_id"] = out["country_id"].astype(int)
|
|
out["country_name"] = out["country_name"].astype(str)
|
|
out["indicator_id"] = out["indicator_id"].astype(int)
|
|
out["indicator_name"] = out["indicator_name"].astype(str)
|
|
out["unit"] = out["unit"].astype(str)
|
|
out["direction"] = out["direction"].astype(str)
|
|
out["pillar_id"] = out["pillar_id"].astype(int)
|
|
out["pillar_name"] = out["pillar_name"].astype(str)
|
|
out["framework"] = out["framework"].astype(str)
|
|
out["value"] = out["value"].astype(float)
|
|
out["norm_value"] = out["norm_value"].astype(float)
|
|
out["norm_score_1_100"] = out["norm_score_1_100"].astype(float)
|
|
out["yoy_value"] = pd.to_numeric(out["yoy_value"], errors="coerce").astype(float)
|
|
out["yoy_norm_value"] = pd.to_numeric(out["yoy_norm_value"], errors="coerce").astype(float)
|
|
out["performance"] = out["performance"].astype(str).replace("nan", pd.NA).astype("string")
|
|
|
|
self.logger.info(f" Columns : {list(out.columns)}")
|
|
self.logger.info(f" Total rows : {len(out):,}")
|
|
self.logger.info(f" Countries : {out['country_id'].nunique()}")
|
|
self.logger.info(f" Indicators : {out['indicator_id'].nunique()}")
|
|
self.logger.info(f" Years : {int(out['year'].min())} - {int(out['year'].max())}")
|
|
self.logger.info(f" Frameworks : {dict(out['framework'].value_counts())}")
|
|
self.logger.info(f" Performance: {dict(out['performance'].value_counts())}")
|
|
|
|
schema = [
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("indicator_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("indicator_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("unit", "STRING", mode="NULLABLE"),
|
|
bigquery.SchemaField("direction", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("value", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("norm_value", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("norm_score_1_100", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("yoy_value", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("yoy_norm_value", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("performance", "STRING", mode="NULLABLE"),
|
|
]
|
|
|
|
rows_loaded = load_to_bigquery(
|
|
self.client, out, table_name,
|
|
layer="gold", write_disposition="WRITE_TRUNCATE", schema=schema,
|
|
)
|
|
|
|
log_update(self.client, "DW", table_name, "full_load", rows_loaded)
|
|
self.logger.info(f" [OK] {table_name}: {rows_loaded:,} rows -> [Gold] fs_asean_gold")
|
|
|
|
metadata = {
|
|
"source_class" : self.__class__.__name__,
|
|
"table_name" : table_name,
|
|
"execution_timestamp": self.pipeline_start,
|
|
"duration_seconds" : (datetime.now() - self.pipeline_start).total_seconds(),
|
|
"rows_fetched" : self.pipeline_metadata["rows_fetched"],
|
|
"rows_transformed" : rows_loaded,
|
|
"rows_loaded" : rows_loaded,
|
|
"completeness_pct" : 100.0,
|
|
"config_snapshot" : json.dumps({
|
|
"sdgs_start_year" : self.sdgs_start_year,
|
|
"sdg_only_keywords_n" : len(SDG_ONLY_KEYWORDS),
|
|
"layer" : "gold",
|
|
"normalization" : "per_indicator_global_minmax",
|
|
"direction_handling" : "lower_better_inverted",
|
|
"yoy_columns" : ["yoy_value", "yoy_norm_value"],
|
|
"performance_threshold": _PERFORMANCE_THRESHOLD,
|
|
"unit_source" : "dim_indicator",
|
|
"framework_logic" : (
|
|
"SDG_ONLY_KEYWORDS: MDGs if year < sdgs_start_year, "
|
|
"SDGs if year >= sdgs_start_year. "
|
|
"Non-SDG_ONLY: always MDGs."
|
|
),
|
|
}),
|
|
"validation_metrics" : json.dumps({
|
|
"total_rows" : rows_loaded,
|
|
"n_indicators" : int(out["indicator_id"].nunique()),
|
|
"n_countries" : int(out["country_id"].nunique()),
|
|
"sdgs_start_year": self.sdgs_start_year,
|
|
}),
|
|
}
|
|
save_etl_metadata(self.client, metadata)
|
|
self.logger.info(" Metadata -> [AUDIT] etl_metadata")
|
|
return rows_loaded
|
|
|
|
# =========================================================================
|
|
# STEP 11: Summary log agg_indicator_norm
|
|
# =========================================================================
|
|
|
|
def _log_summary(self, df: pd.DataFrame):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 11: SUMMARY — agg_indicator_norm")
|
|
self.logger.info("=" * 80)
|
|
|
|
summary = (
|
|
df.groupby(["framework", "year"])
|
|
.agg(
|
|
n_indicators=("indicator_id", "nunique"),
|
|
n_countries =("country_id", "nunique"),
|
|
avg_score =("norm_score_1_100", "mean"),
|
|
)
|
|
.reset_index()
|
|
)
|
|
self.logger.info(
|
|
f"\n{'Framework':<8} {'Year':<6} {'Indicators':<12} {'Countries':<12} {'Avg Score'}"
|
|
)
|
|
self.logger.info("-" * 55)
|
|
for _, r in summary.iterrows():
|
|
self.logger.info(
|
|
f"{r['framework']:<8} {int(r['year']):<6} "
|
|
f"{int(r['n_indicators']):<12} {int(r['n_countries']):<12} "
|
|
f"{r['avg_score']:.2f}"
|
|
)
|
|
|
|
# Performance summary per framework
|
|
self.logger.info("\n Performance summary per Framework:")
|
|
perf_fw = (
|
|
df[df["performance"].notna()]
|
|
.groupby(["framework", "performance"])
|
|
.size()
|
|
.reset_index(name="count")
|
|
)
|
|
for fw in perf_fw["framework"].unique():
|
|
sub = perf_fw[perf_fw["framework"] == fw]
|
|
total = sub["count"].sum()
|
|
self.logger.info(f" [{fw}]")
|
|
for _, r in sub.iterrows():
|
|
self.logger.info(
|
|
f" {r['performance']:<6}: {int(r['count']):,} "
|
|
f"({r['count']/total*100:.1f}%)"
|
|
)
|
|
|
|
# Top 5 & Bottom 5 indikator
|
|
ind_avg = (
|
|
df.groupby(["indicator_id", "indicator_name", "unit", "pillar_name", "direction"])
|
|
["norm_score_1_100"].mean()
|
|
.reset_index()
|
|
.sort_values("norm_score_1_100", ascending=False)
|
|
)
|
|
|
|
self.logger.info(
|
|
"\n TOP 5 Indicators (avg norm_score_1_100 across all years & countries):"
|
|
)
|
|
for _, r in ind_avg.head(5).iterrows():
|
|
tag = "[lower+]" if r["direction"] in DIRECTION_INVERT_KEYWORDS else "[higher+]"
|
|
unit = f"[{r['unit']}]" if r["unit"] else ""
|
|
self.logger.info(
|
|
f" [{int(r['indicator_id'])}] {r['indicator_name'][:50]:<52} "
|
|
f"{r['norm_score_1_100']:.2f} {tag} {unit}"
|
|
)
|
|
|
|
self.logger.info("\n BOTTOM 5 Indicators:")
|
|
for _, r in ind_avg.tail(5).iterrows():
|
|
tag = "[lower+]" if r["direction"] in DIRECTION_INVERT_KEYWORDS else "[higher+]"
|
|
unit = f"[{r['unit']}]" if r["unit"] else ""
|
|
self.logger.info(
|
|
f" [{int(r['indicator_id'])}] {r['indicator_name'][:50]:<52} "
|
|
f"{r['norm_score_1_100']:.2f} {tag} {unit}"
|
|
)
|
|
|
|
# Indikator per pillar
|
|
pillar_summary = (
|
|
df.drop_duplicates(subset=["indicator_id", "pillar_name"])
|
|
.groupby("pillar_name")["indicator_id"]
|
|
.count()
|
|
.reset_index()
|
|
.rename(columns={"indicator_id": "n_indicators"})
|
|
)
|
|
self.logger.info("\n Indicators per pillar:")
|
|
for _, r in pillar_summary.iterrows():
|
|
self.logger.info(f" {r['pillar_name']:<30}: {r['n_indicators']}")
|
|
|
|
# =========================================================================
|
|
# STEP 12-16: agg_narrative_indicator (lanjutan dari df_final)
|
|
# =========================================================================
|
|
|
|
def _build_narrative_table(self, df_final: pd.DataFrame):
|
|
"""
|
|
Pipeline agg_narrative_indicator yang dijalankan otomatis
|
|
setelah agg_indicator_norm selesai. Memakai df_final yang sudah ada
|
|
di memori, tanpa re-load dari BigQuery.
|
|
"""
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 12-16: agg_narrative_indicator")
|
|
self.logger.info(" Level : ASEAN (year x indicator_id)")
|
|
self.logger.info("=" * 80)
|
|
|
|
# -- STEP 12: Agregasi ke level ASEAN --
|
|
self.logger.info("\n--- STEP 12: AGGREGATE TO ASEAN LEVEL ---")
|
|
dim_cols = ["indicator_name", "unit", "direction", "pillar_name", "framework"]
|
|
agg_dict = {col: "first" for col in dim_cols}
|
|
agg_dict["value"] = "mean"
|
|
agg_dict["norm_score_1_100"] = "mean"
|
|
agg_dict["country_id"] = "count"
|
|
|
|
df_agg = (
|
|
df_final.groupby(["year", "indicator_id"])
|
|
.agg(agg_dict)
|
|
.reset_index()
|
|
.rename(columns={
|
|
"value" : "avg_value",
|
|
"norm_score_1_100": "avg_norm_score_1_100",
|
|
"country_id" : "n_countries",
|
|
})
|
|
)
|
|
self.logger.info(f" Rows : {len(df_agg):,}")
|
|
self.logger.info(f" Inds : {df_agg['indicator_id'].nunique()}")
|
|
self.logger.info(
|
|
f" Years : {int(df_agg['year'].min())} - {int(df_agg['year'].max())}"
|
|
)
|
|
|
|
# -- STEP 13: YoY avg_value per indikator --
|
|
self.logger.info("\n--- STEP 13: COMPUTE YoY avg_value ---")
|
|
parts = []
|
|
for ind_id, grp in df_agg.groupby("indicator_id"):
|
|
grp = grp.sort_values("year").copy()
|
|
grp["prev_avg_value"] = grp["avg_value"].shift(1)
|
|
grp["yoy_avg_value"] = np.where(
|
|
grp["avg_value"].notna() & grp["prev_avg_value"].notna(),
|
|
grp["avg_value"] - grp["prev_avg_value"],
|
|
np.nan,
|
|
)
|
|
grp = grp.drop(columns=["prev_avg_value"])
|
|
parts.append(grp)
|
|
df_agg = pd.concat(parts, ignore_index=True)
|
|
self.logger.info(f" yoy_avg_value nulls: {df_agg['yoy_avg_value'].isna().sum():,}")
|
|
|
|
# -- STEP 14: Assign performance --
|
|
self.logger.info("\n--- STEP 14: ASSIGN PERFORMANCE ---")
|
|
df_agg["performance"] = pd.NA
|
|
has_score = df_agg["avg_norm_score_1_100"].notna()
|
|
df_agg.loc[has_score & (df_agg["avg_norm_score_1_100"] >= _PERFORMANCE_THRESHOLD), "performance"] = "Good"
|
|
df_agg.loc[has_score & (df_agg["avg_norm_score_1_100"] < _PERFORMANCE_THRESHOLD), "performance"] = "Bad"
|
|
n_good = (df_agg["performance"] == "Good").sum()
|
|
n_bad = (df_agg["performance"] == "Bad").sum()
|
|
self.logger.info(f" Good: {n_good:,} | Bad: {n_bad:,}")
|
|
|
|
# -- STEP 15: Build narrative --
|
|
self.logger.info("\n--- STEP 15: BUILD NARRATIVE ---")
|
|
df_agg["narrative"] = df_agg.apply(_build_narrative, axis=1)
|
|
self.logger.info(f" Narratives generated: {len(df_agg):,}")
|
|
self.logger.info("\n Sample (first 2):")
|
|
for _, row in df_agg.head(2).iterrows():
|
|
self.logger.info(
|
|
f"\n [{int(row['year'])}] {row['indicator_name'][:50]}"
|
|
f"\n -> {row['narrative'][:250]}..."
|
|
)
|
|
|
|
# -- STEP 16: Save --
|
|
self.logger.info("\n--- STEP 16: SAVE -> [Gold] agg_narrative_indicator ---")
|
|
out = df_agg[[
|
|
"year", "indicator_id", "indicator_name", "unit", "direction",
|
|
"pillar_name", "framework",
|
|
"avg_value", "avg_norm_score_1_100", "performance",
|
|
"yoy_avg_value", "n_countries", "narrative",
|
|
]].copy()
|
|
|
|
out = out.sort_values(["year", "pillar_name", "indicator_name"]).reset_index(drop=True)
|
|
|
|
out["year"] = out["year"].astype(int)
|
|
out["indicator_id"] = out["indicator_id"].astype(int)
|
|
out["indicator_name"] = out["indicator_name"].astype(str)
|
|
out["unit"] = out["unit"].fillna("").astype(str)
|
|
out["direction"] = out["direction"].astype(str)
|
|
out["pillar_name"] = out["pillar_name"].astype(str)
|
|
out["framework"] = out["framework"].astype(str)
|
|
out["avg_value"] = out["avg_value"].astype(float)
|
|
out["avg_norm_score_1_100"] = out["avg_norm_score_1_100"].astype(float)
|
|
out["performance"] = out["performance"].astype(str).replace("nan", pd.NA).astype("string")
|
|
out["yoy_avg_value"] = pd.to_numeric(out["yoy_avg_value"], errors="coerce").astype(float)
|
|
out["n_countries"] = out["n_countries"].astype(int)
|
|
out["narrative"] = out["narrative"].astype(str)
|
|
|
|
schema = [
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("indicator_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("indicator_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("unit", "STRING", mode="NULLABLE"),
|
|
bigquery.SchemaField("direction", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("avg_value", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("avg_norm_score_1_100", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("performance", "STRING", mode="NULLABLE"),
|
|
bigquery.SchemaField("yoy_avg_value", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("n_countries", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("narrative", "STRING", mode="NULLABLE"),
|
|
]
|
|
|
|
rows_loaded = load_to_bigquery(
|
|
self.client, out, "agg_narrative_indicator",
|
|
layer="gold", write_disposition="WRITE_TRUNCATE", schema=schema,
|
|
)
|
|
|
|
log_update(self.client, "DW", "agg_narrative_indicator", "full_load", rows_loaded)
|
|
self.logger.info(
|
|
f" [OK] agg_narrative_indicator: {rows_loaded:,} rows -> [Gold] fs_asean_gold"
|
|
)
|
|
|
|
metadata = {
|
|
"source_class" : self.__class__.__name__,
|
|
"table_name" : "agg_narrative_indicator",
|
|
"execution_timestamp": self.pipeline_start,
|
|
"duration_seconds" : (datetime.now() - self.pipeline_start).total_seconds(),
|
|
"rows_fetched" : self.pipeline_metadata["rows_fetched"],
|
|
"rows_transformed" : rows_loaded,
|
|
"rows_loaded" : rows_loaded,
|
|
"completeness_pct" : 100.0,
|
|
"config_snapshot" : json.dumps({
|
|
"source_table" : "agg_indicator_norm (in-memory df_final)",
|
|
"granularity" : "year x indicator_id (ASEAN level)",
|
|
"aggregation" : "mean across ASEAN countries",
|
|
"performance_threshold": _PERFORMANCE_THRESHOLD,
|
|
"yoy_column" : "yoy_avg_value",
|
|
"layer" : "gold",
|
|
}),
|
|
"validation_metrics" : json.dumps({
|
|
"total_rows" : rows_loaded,
|
|
"n_indicators": int(out["indicator_id"].nunique()),
|
|
"year_min" : int(out["year"].min()),
|
|
"year_max" : int(out["year"].max()),
|
|
}),
|
|
}
|
|
save_etl_metadata(self.client, metadata)
|
|
self.logger.info(" Metadata -> [AUDIT] etl_metadata")
|
|
|
|
self.pipeline_metadata["rows_loaded_narrative"] = rows_loaded
|
|
|
|
# =========================================================================
|
|
# RUN
|
|
# =========================================================================
|
|
|
|
def run(self):
|
|
self.pipeline_start = datetime.now()
|
|
self.pipeline_metadata["start_time"] = self.pipeline_start
|
|
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("INDICATOR NORM AGGREGATION")
|
|
self.logger.info(" Source : fact_asean_food_security_selected")
|
|
self.logger.info(" Dim : dim_indicator (unit)")
|
|
self.logger.info(" Output : agg_indicator_norm -> fs_asean_gold")
|
|
self.logger.info(" agg_narrative_indicator -> fs_asean_gold")
|
|
self.logger.info("=" * 80)
|
|
|
|
self.load_data()
|
|
self.load_units()
|
|
self._merge_unit()
|
|
self.sdgs_start_year = self._detect_sdgs_start_year()
|
|
self._assign_framework()
|
|
df_normed = self._compute_norm_values()
|
|
df_yoy = self._compute_yoy_columns(df_normed)
|
|
df_scored = self._compute_scores(df_yoy)
|
|
df_final = self._assign_performance(df_scored)
|
|
rows_loaded = self._save(df_final)
|
|
self.pipeline_metadata["rows_loaded"] = rows_loaded
|
|
self._log_summary(df_final)
|
|
|
|
# Lanjut build agg_narrative_indicator dari df_final (tanpa re-load BQ)
|
|
self._build_narrative_table(df_final)
|
|
|
|
self.pipeline_metadata["end_time"] = datetime.now()
|
|
duration = (
|
|
self.pipeline_metadata["end_time"] - self.pipeline_start
|
|
).total_seconds()
|
|
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("COMPLETED")
|
|
self.logger.info("=" * 80)
|
|
self.logger.info(f" Duration : {duration:.2f}s")
|
|
self.logger.info(f" Rows Fetched : {self.pipeline_metadata['rows_fetched']:,}")
|
|
self.logger.info(f" Rows Loaded (norm) : {rows_loaded:,}")
|
|
self.logger.info(f" Rows Loaded (narrative) : {self.pipeline_metadata['rows_loaded_narrative']:,}")
|
|
self.logger.info(f" sdgs_start_year : {self.sdgs_start_year}")
|
|
|
|
|
|
# =============================================================================
|
|
# AIRFLOW TASK <-- tidak berubah
|
|
# =============================================================================
|
|
|
|
def run_indicator_norm_aggregation():
|
|
"""
|
|
Airflow task: Build agg_indicator_norm + agg_narrative_indicator.
|
|
Dipanggil setelah analytical_layer_to_gold selesai.
|
|
"""
|
|
client = get_bigquery_client()
|
|
agg = IndicatorNormAggregator(client)
|
|
agg.run()
|
|
print(f"agg_indicator_norm loaded : {agg.pipeline_metadata['rows_loaded']:,} rows")
|
|
print(f"agg_narrative_indicator loaded: {agg.pipeline_metadata['rows_loaded_narrative']:,} rows")
|
|
|
|
|
|
# =============================================================================
|
|
# MAIN
|
|
# =============================================================================
|
|
|
|
if __name__ == "__main__":
|
|
import sys, io
|
|
if sys.stdout.encoding and sys.stdout.encoding.lower() not in ("utf-8", "utf8"):
|
|
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
|
|
if sys.stderr.encoding and sys.stderr.encoding.lower() not in ("utf-8", "utf8"):
|
|
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace")
|
|
|
|
print("=" * 80)
|
|
print("INDICATOR NORM AGGREGATION -> fs_asean_gold")
|
|
print(" Source : fact_asean_food_security_selected")
|
|
print(" Dim : dim_indicator (unit)")
|
|
print(" Output : agg_indicator_norm")
|
|
print(" agg_narrative_indicator")
|
|
print("=" * 80)
|
|
|
|
logger = setup_logging()
|
|
client = get_bigquery_client()
|
|
agg = IndicatorNormAggregator(client)
|
|
agg.run()
|
|
|
|
print("\n" + "=" * 80)
|
|
print("[OK] COMPLETED")
|
|
print("=" * 80) |