1073 lines
47 KiB
Python
1073 lines
47 KiB
Python
"""
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BIGQUERY ANALYTICAL LAYER - DATA FILTERING
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fact_asean_food_security_selected disimpan di fs_asean_gold (layer='gold')
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Filtering Order:
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1. Load data (single years only)
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2. Determine year boundaries (2013 - auto-detected end year, baseline=2023 per syarat dosen)
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3. Filter complete indicators PER COUNTRY (auto-detect start year, no gaps)
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4. Filter countries with ALL pillars (FIXED SET)
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5. Filter indicators with consistent presence across FIXED countries
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6. Determine SDG start year & assign framework (MDGs/SDGs) per indicator PER ROW
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7. Verify no gaps
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8. Calculate norm_value_1_100 per indicator per country (min-max, direction-aware)
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9. Calculate YoY per indicator per country
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10. Analyze indicator availability by year
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11. Save analytical table
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NORMALISASI (Step 8):
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- norm_value_1_100 = min-max normalisasi nilai raw per indikator, skala 1-100
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- Direction-aware: lower_better diinvert sehingga nilai tinggi selalu = lebih baik
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- Normalisasi dilakukan GLOBAL per indikator (semua negara, semua tahun sekaligus)
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sehingga nilai antar negara dan antar tahun tetap comparable
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- Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio
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FRAMEWORK LOGIC (FIX - Row-Level Assignment):
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- SDG start year dideteksi dari data: tahun pertama indikator FIES/anaemia lengkap
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di semua fixed countries (setelah Step 3-5 filter selesai)
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- Framework di-assign PER BARIS (per tahun), bukan per indikator:
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* Jika row['year'] < sdg_start_year -> selalu 'MDGs'
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* Jika row['year'] >= sdg_start_year DAN
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nama ada di SDG_INDICATOR_KEYWORDS -> 'SDGs'
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* Selain itu -> 'MDGs'
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- Dengan demikian, indikator seperti "Prevalence of anemia" yang datanya dimulai
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sebelum era SDGs akan berlabel 'MDGs' untuk tahun-tahun pra-SDGs dan 'SDGs'
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untuk tahun-tahun pasca (>= sdg_start_year).
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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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from typing import Dict, List
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import json
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import sys
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if hasattr(sys.stdout, 'reconfigure'):
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sys.stdout.reconfigure(encoding='utf-8')
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from scripts.bigquery_config import get_bigquery_client, CONFIG, get_table_id
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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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truncate_table,
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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 INDICATOR KEYWORDS
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# =============================================================================
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SDG_INDICATOR_KEYWORDS = frozenset([
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# TARGET 2.1.1 — Prevalence of undernourishment (shared, sudah ada sebelum SDGs)
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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 — FIES (SDGs only)
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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 (shared)
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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 & Overweight (shared)
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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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"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 (SDGs only — listed here so rows >= sdg_start_year become SDGs)
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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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# Proxy keywords untuk deteksi era SDGs dari data (indikator murni baru di SDGs)
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_SDG_ERA_PROXY_KEYWORDS = frozenset([
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"food insecurity",
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"anemia",
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"anaemia",
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])
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# =============================================================================
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# THRESHOLD KONDISI (fixed absolute, skala 1-100)
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# =============================================================================
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# Digunakan untuk assign kondisi di analysis_layer.
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# Didefinisikan di sini agar konsisten antara kedua file.
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# bad : norm_value_1_100 < THRESHOLD_BAD
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# good : norm_value_1_100 > THRESHOLD_GOOD
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# moderate : di antara keduanya
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THRESHOLD_BAD = 40.0
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THRESHOLD_GOOD = 60.0
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def assign_condition(norm_value_1_100: float) -> str:
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"""
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Assign kondisi berdasarkan norm_value_1_100 (skala 1-100, sudah direction-aware).
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Nilai tinggi selalu berarti lebih baik (lower_better sudah diinvert).
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Returns: 'good' / 'moderate' / 'bad'
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"""
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if pd.isna(norm_value_1_100):
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return None
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if norm_value_1_100 > THRESHOLD_GOOD:
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return 'good'
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if norm_value_1_100 < THRESHOLD_BAD:
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return 'bad'
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return 'moderate'
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def assign_framework_for_row(
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indicator_name: str,
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row_year: int,
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sdg_start_year: int,
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) -> str:
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"""
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Tentukan framework (MDGs/SDGs) PER BARIS (per tahun), bukan per indikator.
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Logic:
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- Jika row_year < sdg_start_year → selalu 'MDGs', apapun nama indikatornya.
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- Jika row_year >= sdg_start_year DAN nama ada di SDG_INDICATOR_KEYWORDS → 'SDGs'.
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- Selain itu → 'MDGs'.
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Dengan cara ini, indikator seperti "Prevalence of anemia" yang datanya
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ada sebelum era SDGs akan berlabel 'MDGs' untuk tahun-tahun pra-SDGs,
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dan 'SDGs' untuk tahun-tahun pasca sdg_start_year.
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"""
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# Tahun sebelum era SDGs → selalu MDGs
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if row_year < sdg_start_year:
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return 'MDGs'
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# Tahun >= sdg_start_year: cek apakah nama ada di SDG list
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name_lower = str(indicator_name).lower().strip()
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if name_lower in SDG_INDICATOR_KEYWORDS:
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return 'SDGs'
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# Tidak ada di SDG list → MDGs
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return 'MDGs'
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# =============================================================================
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# ANALYTICAL LAYER CLASS
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# =============================================================================
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class AnalyticalLayerLoader:
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"""
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Analytical Layer Loader for BigQuery
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Output kolom fact_asean_food_security_selected:
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country_id, country_name,
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indicator_id, indicator_name, direction, framework,
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pillar_id, pillar_name,
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time_id, year, value,
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norm_value_1_100, <- min-max norm per indikator, skala 1-100, direction-aware
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yoy_change, yoy_pct
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PERUBAHAN (framework fix):
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- framework di-assign per baris (per tahun), bukan per indikator.
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- Baris dengan year < sdg_start_year selalu 'MDGs'.
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- Baris dengan year >= sdg_start_year dan nama di SDG_INDICATOR_KEYWORDS → 'SDGs'.
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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_clean = None
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self.df_indicator = None
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self.df_country = None
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self.df_pillar = None
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self.selected_country_ids = None
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self.start_year = 2013
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self.end_year = None
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self.baseline_year = 2023 # hardcode per syarat dosen (tahun terlengkap)
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self.sdg_start_year = None
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self.pipeline_metadata = {
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'source_class' : self.__class__.__name__,
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'start_time' : None,
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'end_time' : None,
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'duration_seconds' : None,
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'rows_fetched' : 0,
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'rows_transformed' : 0,
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'rows_loaded' : 0,
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'validation_metrics': {}
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}
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self.pipeline_start = None
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self.pipeline_end = None
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# ------------------------------------------------------------------
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# STEP 1: LOAD SOURCE DATA
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# ------------------------------------------------------------------
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def load_source_data(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 1: LOADING SOURCE DATA from fs_asean_gold")
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self.logger.info("=" * 80)
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try:
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query = f"""
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SELECT
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f.country_id,
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c.country_name,
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f.indicator_id,
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i.indicator_name,
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i.direction,
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f.pillar_id,
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p.pillar_name,
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f.time_id,
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t.year,
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t.start_year,
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t.end_year,
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t.is_year_range,
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f.value,
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f.source_id
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FROM `{get_table_id('fact_food_security', layer='gold')}` f
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JOIN `{get_table_id('dim_country', layer='gold')}` c ON f.country_id = c.country_id
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JOIN `{get_table_id('dim_indicator', layer='gold')}` i ON f.indicator_id = i.indicator_id
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JOIN `{get_table_id('dim_pillar', layer='gold')}` p ON f.pillar_id = p.pillar_id
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JOIN `{get_table_id('dim_time', layer='gold')}` t ON f.time_id = t.time_id
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"""
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self.logger.info("Loading fact table with dimensions...")
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self.df_clean = self.client.query(query).result().to_dataframe(
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create_bqstorage_client=False
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)
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self.logger.info(f" Loaded: {len(self.df_clean):,} rows")
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if 'is_year_range' in self.df_clean.columns:
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yr = self.df_clean['is_year_range'].value_counts()
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self.logger.info(
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f" Single years: {yr.get(False, 0):,} | "
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f"Year ranges: {yr.get(True, 0):,}"
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)
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self.df_indicator = read_from_bigquery(self.client, 'dim_indicator', layer='gold')
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self.df_country = read_from_bigquery(self.client, 'dim_country', layer='gold')
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self.df_pillar = read_from_bigquery(self.client, 'dim_pillar', layer='gold')
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self.logger.info(f" Indicators: {len(self.df_indicator)}")
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self.logger.info(f" Countries: {len(self.df_country)}")
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self.logger.info(f" Pillars: {len(self.df_pillar)}")
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self.pipeline_metadata['rows_fetched'] = len(self.df_clean)
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return True
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except Exception as e:
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self.logger.error(f"Error loading source data: {e}")
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raise
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# ------------------------------------------------------------------
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# STEP 2: DETERMINE YEAR BOUNDARIES
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# ------------------------------------------------------------------
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def determine_year_boundaries(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 2: DETERMINE YEAR BOUNDARIES")
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self.logger.info("=" * 80)
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# baseline_year = 2023 hardcode (syarat dosen: minimal 2023)
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df_baseline = self.df_clean[self.df_clean['year'] == self.baseline_year]
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baseline_indicator_count = df_baseline['indicator_id'].nunique()
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self.logger.info(f"\n Baseline year (hardcode, syarat dosen): {self.baseline_year}")
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self.logger.info(f" Baseline indicator count: {baseline_indicator_count}")
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years_sorted = sorted(self.df_clean['year'].unique(), reverse=True)
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selected_end_year = None
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self.logger.info(f"\n Scanning end_year (>= {self.baseline_year}):")
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for year in years_sorted:
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if year >= self.baseline_year:
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df_year = self.df_clean[self.df_clean['year'] == year]
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year_indicator_count = df_year['indicator_id'].nunique()
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status = "OK" if year_indicator_count >= baseline_indicator_count else "X"
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self.logger.info(f" [{status}] Year {int(year)}: {year_indicator_count} indicators")
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if year_indicator_count >= baseline_indicator_count and selected_end_year is None:
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selected_end_year = int(year)
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if selected_end_year is None:
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selected_end_year = self.baseline_year
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self.logger.warning(f" [!] Fallback to baseline: {selected_end_year}")
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else:
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self.logger.info(f"\n [OK] Selected end year: {selected_end_year}")
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self.end_year = selected_end_year
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original_count = len(self.df_clean)
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self.df_clean = self.df_clean[
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(self.df_clean['year'] >= self.start_year) &
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(self.df_clean['year'] <= self.end_year)
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].copy()
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self.logger.info(f"\n Filtering {self.start_year}-{self.end_year}:")
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self.logger.info(f" Rows before: {original_count:,}")
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self.logger.info(f" Rows after : {len(self.df_clean):,}")
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return self.df_clean
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# ------------------------------------------------------------------
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# STEP 3: FILTER COMPLETE INDICATORS PER COUNTRY
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# ------------------------------------------------------------------
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def filter_complete_indicators_per_country(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 3: FILTER COMPLETE INDICATORS PER COUNTRY (NO GAPS)")
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self.logger.info("=" * 80)
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grouped = self.df_clean.groupby([
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'country_id', 'country_name', 'indicator_id', 'indicator_name',
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'pillar_id', 'pillar_name'
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])
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valid_combinations = []
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removed_combinations = []
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for (country_id, country_name, indicator_id, indicator_name,
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pillar_id, pillar_name), group in grouped:
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years_present = sorted(group['year'].unique())
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start_year = int(min(years_present))
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end_year_actual = int(max(years_present))
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expected_years = list(range(start_year, self.end_year + 1))
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missing_years = [y for y in expected_years if y not in years_present]
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has_gap = len(missing_years) > 0
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is_complete = (
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end_year_actual >= self.end_year and
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not has_gap and
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(self.end_year - start_year) >= 4
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)
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if is_complete:
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valid_combinations.append({'country_id': country_id, 'indicator_id': indicator_id})
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else:
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reasons = []
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if end_year_actual < self.end_year:
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reasons.append(f"ends {end_year_actual}")
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if has_gap:
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gap_str = str(missing_years[:3])[1:-1]
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if len(missing_years) > 3:
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gap_str += "..."
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reasons.append(f"gap:{gap_str}")
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if (self.end_year - start_year) < 4:
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reasons.append(f"span={self.end_year - start_year}")
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removed_combinations.append({
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'country_name' : country_name,
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'indicator_name': indicator_name,
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'reasons' : ", ".join(reasons)
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})
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self.logger.info(f"\n [+] Valid: {len(valid_combinations):,}")
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self.logger.info(f" [-] Removed: {len(removed_combinations):,}")
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df_valid = pd.DataFrame(valid_combinations)
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df_valid['key'] = (
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df_valid['country_id'].astype(str) + '_' +
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df_valid['indicator_id'].astype(str)
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)
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self.df_clean['key'] = (
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self.df_clean['country_id'].astype(str) + '_' +
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self.df_clean['indicator_id'].astype(str)
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)
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original_count = len(self.df_clean)
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self.df_clean = self.df_clean[self.df_clean['key'].isin(df_valid['key'])].copy()
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self.df_clean = self.df_clean.drop('key', axis=1)
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self.logger.info(f"\n Rows before: {original_count:,}")
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self.logger.info(f" Rows after: {len(self.df_clean):,}")
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self.logger.info(f" Countries: {self.df_clean['country_id'].nunique()}")
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self.logger.info(f" Indicators: {self.df_clean['indicator_id'].nunique()}")
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return self.df_clean
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# ------------------------------------------------------------------
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# STEP 4: SELECT COUNTRIES WITH ALL PILLARS
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# ------------------------------------------------------------------
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def select_countries_with_all_pillars(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 4: SELECT COUNTRIES WITH ALL PILLARS (FIXED SET)")
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self.logger.info("=" * 80)
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total_pillars = self.df_clean['pillar_id'].nunique()
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country_pillar_count = self.df_clean.groupby(['country_id', 'country_name']).agg({
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'pillar_id' : 'nunique',
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'indicator_id': 'nunique',
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'year' : lambda x: f"{int(x.min())}-{int(x.max())}"
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}).reset_index()
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country_pillar_count.columns = [
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'country_id', 'country_name', 'pillar_count', 'indicator_count', 'year_range'
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]
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for _, row in country_pillar_count.sort_values('pillar_count', ascending=False).iterrows():
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status = "[+] KEEP" if row['pillar_count'] == total_pillars else "[-] REMOVE"
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self.logger.info(
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f" {status:<12} {row['country_name']:25s} "
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f"{row['pillar_count']}/{total_pillars} pillars"
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)
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selected_countries = country_pillar_count[
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country_pillar_count['pillar_count'] == total_pillars
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]
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self.selected_country_ids = selected_countries['country_id'].tolist()
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self.logger.info(f"\n FIXED SET: {len(self.selected_country_ids)} countries")
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original_count = len(self.df_clean)
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self.df_clean = self.df_clean[
|
|
self.df_clean['country_id'].isin(self.selected_country_ids)
|
|
].copy()
|
|
|
|
self.logger.info(f" Rows before: {original_count:,}")
|
|
self.logger.info(f" Rows after: {len(self.df_clean):,}")
|
|
return self.df_clean
|
|
|
|
# ------------------------------------------------------------------
|
|
# STEP 5: FILTER INDICATORS CONSISTENT ACROSS FIXED COUNTRIES
|
|
# ------------------------------------------------------------------
|
|
|
|
def filter_indicators_consistent_across_fixed_countries(self):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 5: FILTER INDICATORS WITH CONSISTENT PRESENCE")
|
|
self.logger.info("=" * 80)
|
|
|
|
indicator_country_start = self.df_clean.groupby([
|
|
'indicator_id', 'indicator_name', 'country_id'
|
|
])['year'].min().reset_index()
|
|
indicator_country_start.columns = [
|
|
'indicator_id', 'indicator_name', 'country_id', 'start_year'
|
|
]
|
|
|
|
indicator_max_start = indicator_country_start.groupby([
|
|
'indicator_id', 'indicator_name'
|
|
])['start_year'].max().reset_index()
|
|
indicator_max_start.columns = ['indicator_id', 'indicator_name', 'max_start_year']
|
|
|
|
valid_indicators = []
|
|
removed_indicators = []
|
|
|
|
for _, ind_row in indicator_max_start.iterrows():
|
|
indicator_id = ind_row['indicator_id']
|
|
indicator_name = ind_row['indicator_name']
|
|
max_start = int(ind_row['max_start_year'])
|
|
span = self.end_year - max_start
|
|
|
|
if span < 4:
|
|
removed_indicators.append({
|
|
'indicator_name': indicator_name,
|
|
'reason' : f"span={span} < 4"
|
|
})
|
|
continue
|
|
|
|
expected_years = list(range(max_start, self.end_year + 1))
|
|
ind_data = self.df_clean[self.df_clean['indicator_id'] == indicator_id]
|
|
all_years_complete = True
|
|
problematic_years = []
|
|
|
|
for year in expected_years:
|
|
country_count = ind_data[ind_data['year'] == year]['country_id'].nunique()
|
|
if country_count < len(self.selected_country_ids):
|
|
all_years_complete = False
|
|
problematic_years.append(f"{int(year)}({country_count})")
|
|
|
|
if all_years_complete:
|
|
valid_indicators.append(indicator_id)
|
|
else:
|
|
removed_indicators.append({
|
|
'indicator_name': indicator_name,
|
|
'reason' : f"missing countries in years: {', '.join(problematic_years[:5])}"
|
|
})
|
|
|
|
self.logger.info(f"\n [+] Valid: {len(valid_indicators)}")
|
|
self.logger.info(f" [-] Removed: {len(removed_indicators)}")
|
|
|
|
if not valid_indicators:
|
|
raise ValueError("No valid indicators found after filtering!")
|
|
|
|
original_count = len(self.df_clean)
|
|
self.df_clean = self.df_clean[
|
|
self.df_clean['indicator_id'].isin(valid_indicators)
|
|
].copy()
|
|
|
|
self.df_clean = self.df_clean.merge(
|
|
indicator_max_start[['indicator_id', 'max_start_year']],
|
|
on='indicator_id', how='left'
|
|
)
|
|
self.df_clean = self.df_clean[
|
|
self.df_clean['year'] >= self.df_clean['max_start_year']
|
|
].copy()
|
|
self.df_clean = self.df_clean.drop('max_start_year', axis=1)
|
|
|
|
self.logger.info(f"\n Rows before: {original_count:,}")
|
|
self.logger.info(f" Rows after: {len(self.df_clean):,}")
|
|
self.logger.info(f" Countries: {self.df_clean['country_id'].nunique()}")
|
|
self.logger.info(f" Indicators: {self.df_clean['indicator_id'].nunique()}")
|
|
self.logger.info(f" Pillars: {self.df_clean['pillar_id'].nunique()}")
|
|
return self.df_clean
|
|
|
|
# ------------------------------------------------------------------
|
|
# STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL FIX)
|
|
# ------------------------------------------------------------------
|
|
|
|
def determine_sdg_start_year(self):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL)")
|
|
self.logger.info("=" * 80)
|
|
|
|
# actual_start_year per indikator = max(min_year per country)
|
|
# = konsisten dengan max_start_year di Step 5
|
|
indicator_actual_start = (
|
|
self.df_clean
|
|
.groupby(['indicator_id', 'indicator_name', 'country_id'])['year']
|
|
.min().reset_index()
|
|
.groupby(['indicator_id', 'indicator_name'])['year']
|
|
.max().reset_index()
|
|
)
|
|
indicator_actual_start.columns = ['indicator_id', 'indicator_name', 'actual_start_year']
|
|
|
|
# Deteksi sdg_start_year dari proxy SDGs-only (FIES & anaemia)
|
|
proxy_mask = indicator_actual_start['indicator_name'].str.lower().apply(
|
|
lambda n: any(kw in n for kw in _SDG_ERA_PROXY_KEYWORDS)
|
|
)
|
|
df_proxy = indicator_actual_start[proxy_mask]
|
|
|
|
if df_proxy.empty:
|
|
raise ValueError(
|
|
"Tidak ada indikator proxy SDGs (FIES/anaemia) yang lolos filter. "
|
|
"Pastikan indikator FIES dan anaemia ada di data."
|
|
)
|
|
|
|
self.sdg_start_year = int(df_proxy['actual_start_year'].min())
|
|
self.logger.info(f"\n sdg_start_year = {self.sdg_start_year}")
|
|
self.logger.info(f" Proxy indicators (penentu sdg_start_year):")
|
|
for _, row in df_proxy.iterrows():
|
|
self.logger.info(f" [{int(row['actual_start_year'])}] {row['indicator_name']}")
|
|
|
|
# ------------------------------------------------------------------
|
|
# FIX: Assign framework PER BARIS (per tahun), bukan per indikator
|
|
# ------------------------------------------------------------------
|
|
# Logic:
|
|
# row['year'] < sdg_start_year → 'MDGs' (apapun nama indikatornya)
|
|
# row['year'] >= sdg_start_year + nama di SDG_INDICATOR_KEYWORDS → 'SDGs'
|
|
# selain itu → 'MDGs'
|
|
# ------------------------------------------------------------------
|
|
self.logger.info(f"\n Assigning framework PER ROW (year-level)...")
|
|
self.logger.info(f" Rule: year < {self.sdg_start_year} → MDGs (always)")
|
|
self.logger.info(f" Rule: year >= {self.sdg_start_year} + name in SDG list → SDGs")
|
|
self.logger.info(f" Rule: year >= {self.sdg_start_year} + name NOT in SDG list → MDGs")
|
|
|
|
self.df_clean['framework'] = self.df_clean.apply(
|
|
lambda row: assign_framework_for_row(
|
|
indicator_name = row['indicator_name'],
|
|
row_year = int(row['year']),
|
|
sdg_start_year = self.sdg_start_year,
|
|
),
|
|
axis=1
|
|
)
|
|
|
|
# Log ringkasan per indikator untuk verifikasi
|
|
self.logger.info(f"\n {'Framework Assignment per Indicator (sample)':}")
|
|
self.logger.info(f" {'-'*95}")
|
|
self.logger.info(
|
|
f" {'ID':<5} {'Indicator Name':<50} "
|
|
f"{'Pre-SDG rows':<15} {'MDGs rows':<12} {'SDGs rows'}"
|
|
)
|
|
self.logger.info(f" {'-'*95}")
|
|
|
|
for ind_id, grp in self.df_clean.groupby('indicator_id'):
|
|
ind_name = grp['indicator_name'].iloc[0]
|
|
pre_sdg = (grp['year'] < self.sdg_start_year).sum()
|
|
mdgs_rows = (grp['framework'] == 'MDGs').sum()
|
|
sdgs_rows = (grp['framework'] == 'SDGs').sum()
|
|
self.logger.info(
|
|
f" {int(ind_id):<5} {ind_name[:48]:<50} "
|
|
f"{pre_sdg:<15} {mdgs_rows:<12} {sdgs_rows}"
|
|
)
|
|
|
|
fw_summary = self.df_clean['framework'].value_counts()
|
|
self.logger.info(f"\n Ringkasan rows: " + " | ".join(
|
|
f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items()
|
|
))
|
|
|
|
# Ringkasan unique indicators per framework di tahun terbaru (end_year)
|
|
end_year_df = self.df_clean[self.df_clean['year'] == self.end_year]
|
|
fw_ind_summary = end_year_df.groupby('framework')['indicator_id'].nunique()
|
|
self.logger.info(f" Indicators di year={self.end_year}: " + " | ".join(
|
|
f"{fw}: {cnt}" for fw, cnt in fw_ind_summary.items()
|
|
))
|
|
|
|
self.logger.info(
|
|
f"\n [OK] 'framework' ditambahkan (row-level) — "
|
|
f"MDGs: {(self.df_clean['framework'] == 'MDGs').sum():,} rows | "
|
|
f"SDGs: {(self.df_clean['framework'] == 'SDGs').sum():,} rows"
|
|
)
|
|
return self.df_clean
|
|
|
|
# ------------------------------------------------------------------
|
|
# STEP 7: VERIFY NO GAPS
|
|
# ------------------------------------------------------------------
|
|
|
|
def verify_no_gaps(self):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 7: VERIFY NO GAPS")
|
|
self.logger.info("=" * 80)
|
|
|
|
expected_countries = len(self.selected_country_ids)
|
|
verification = self.df_clean.groupby(
|
|
['indicator_id', 'year']
|
|
)['country_id'].nunique().reset_index()
|
|
verification.columns = ['indicator_id', 'year', 'country_count']
|
|
all_good = (verification['country_count'] == expected_countries).all()
|
|
|
|
if all_good:
|
|
self.logger.info(
|
|
f" VERIFICATION PASSED — all combinations have {expected_countries} countries"
|
|
)
|
|
else:
|
|
bad = verification[verification['country_count'] != expected_countries]
|
|
for _, row in bad.head(10).iterrows():
|
|
self.logger.error(
|
|
f" Indicator {int(row['indicator_id'])}, Year {int(row['year'])}: "
|
|
f"{int(row['country_count'])} countries (expected {expected_countries})"
|
|
)
|
|
raise ValueError("Gap verification failed!")
|
|
|
|
return True
|
|
|
|
# ------------------------------------------------------------------
|
|
# STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR PER COUNTRY
|
|
# ------------------------------------------------------------------
|
|
|
|
def calculate_norm_value(self):
|
|
"""
|
|
Hitung norm_value_1_100 per indikator — min-max normalisasi skala 1-100,
|
|
direction-aware.
|
|
|
|
CARA KERJA:
|
|
- Normalisasi dilakukan GLOBAL per indikator (semua negara + semua tahun sekaligus)
|
|
sehingga nilai antar negara dan antar tahun tetap comparable.
|
|
- lower_better diinvert: nilai tinggi selalu = kondisi lebih baik.
|
|
Contoh: undernourishment 5% (rendah = baik) → norm tinggi setelah invert.
|
|
- Skala 1-100 (bukan 0-100) untuk menghindari nilai absolut nol di Looker Studio.
|
|
- Kolom ini memungkinkan perbandingan lintas indikator yang berbeda satuan
|
|
(persen, juta orang, dll) karena sudah dinormalisasi ke skala yang sama.
|
|
|
|
Catatan:
|
|
- Berbeda dengan norm_value di _get_norm_value_df() di analysis_layer
|
|
yang skala 0-1 dan dipakai untuk agregasi composite score.
|
|
- norm_value_1_100 ini adalah per baris (per country per year per indicator),
|
|
untuk ditampilkan langsung di Looker Studio.
|
|
"""
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR")
|
|
self.logger.info("=" * 80)
|
|
|
|
DIRECTION_INVERT = frozenset({
|
|
"negative", "lower_better", "lower_is_better", "inverse", "neg",
|
|
})
|
|
|
|
df = self.df_clean.copy()
|
|
norm_parts = []
|
|
|
|
indicators = df.groupby(['indicator_id', 'indicator_name', 'direction'])
|
|
self.logger.info(f"\n {'ID':<5} {'Direction':<15} {'Invert':<8} {'Min':>10} {'Max':>10} {'Indicator Name'}")
|
|
self.logger.info(f" {'-'*90}")
|
|
|
|
for (ind_id, ind_name, direction), grp in indicators:
|
|
grp = grp.copy()
|
|
do_invert = str(direction).lower().strip() in DIRECTION_INVERT
|
|
valid_mask = grp['value'].notna()
|
|
n_valid = valid_mask.sum()
|
|
|
|
if n_valid < 2:
|
|
grp['norm_value_1_100'] = np.nan
|
|
norm_parts.append(grp)
|
|
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:
|
|
# Semua nilai sama → beri nilai tengah (50.5 pada skala 1-100)
|
|
normed[valid_mask.values] = 50.5
|
|
else:
|
|
# Min-max ke 0-1 dulu
|
|
scaled = (raw - v_min) / (v_max - v_min)
|
|
# Invert jika lower_better
|
|
if do_invert:
|
|
scaled = 1.0 - scaled
|
|
# Scale ke 1-100
|
|
normed[valid_mask.values] = 1.0 + scaled * 99.0
|
|
|
|
grp['norm_value_1_100'] = normed
|
|
|
|
self.logger.info(
|
|
f" {int(ind_id):<5} {direction:<15} {'YES' if do_invert else 'no':<8} "
|
|
f"{v_min:>10.3f} {v_max:>10.3f} {ind_name[:45]}"
|
|
)
|
|
norm_parts.append(grp)
|
|
|
|
self.df_clean = pd.concat(norm_parts, ignore_index=True)
|
|
|
|
# Statistik ringkasan
|
|
valid_norm = self.df_clean['norm_value_1_100'].notna().sum()
|
|
null_norm = self.df_clean['norm_value_1_100'].isna().sum()
|
|
self.logger.info(f"\n norm_value_1_100 — valid: {valid_norm:,} | null: {null_norm:,}")
|
|
self.logger.info(
|
|
f" Range aktual: "
|
|
f"{self.df_clean['norm_value_1_100'].min():.2f} - "
|
|
f"{self.df_clean['norm_value_1_100'].max():.2f}"
|
|
)
|
|
|
|
# Log distribusi kondisi berdasarkan threshold
|
|
self.df_clean['_condition_preview'] = self.df_clean['norm_value_1_100'].apply(assign_condition)
|
|
cond_dist = self.df_clean['_condition_preview'].value_counts()
|
|
self.logger.info(f"\n Distribusi kondisi (threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}):")
|
|
for cond, cnt in cond_dist.items():
|
|
self.logger.info(f" {cond}: {cnt:,} rows")
|
|
self.df_clean = self.df_clean.drop(columns=['_condition_preview'])
|
|
|
|
self.logger.info(f"\n [OK] Kolom 'norm_value_1_100' ditambahkan ke df_clean")
|
|
return self.df_clean
|
|
|
|
# ------------------------------------------------------------------
|
|
# STEP 9: CALCULATE YOY
|
|
# ------------------------------------------------------------------
|
|
|
|
def calculate_yoy(self):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 9: CALCULATE YEAR-OVER-YEAR (YoY) PER INDICATOR PER COUNTRY")
|
|
self.logger.info("=" * 80)
|
|
|
|
df = self.df_clean.sort_values(['country_id', 'indicator_id', 'year']).copy()
|
|
|
|
df['value_prev'] = df.groupby(['country_id', 'indicator_id'])['value'].shift(1)
|
|
df['yoy_change'] = df['value'] - df['value_prev']
|
|
df['yoy_pct'] = np.where(
|
|
df['value_prev'].notna() & (df['value_prev'] != 0),
|
|
(df['yoy_change'] / df['value_prev'].abs()) * 100,
|
|
np.nan
|
|
)
|
|
df = df.drop(columns=['value_prev'])
|
|
|
|
total_rows = len(df)
|
|
valid_yoy = df['yoy_pct'].notna().sum()
|
|
null_yoy = df['yoy_pct'].isna().sum()
|
|
|
|
self.logger.info(f" Total rows : {total_rows:,}")
|
|
self.logger.info(f" YoY calculated : {valid_yoy:,}")
|
|
self.logger.info(f" YoY NULL (base yr): {null_yoy:,}")
|
|
|
|
self.df_clean = df
|
|
self.logger.info(f" [OK] Kolom 'yoy_change', 'yoy_pct' ditambahkan")
|
|
return self.df_clean
|
|
|
|
# ------------------------------------------------------------------
|
|
# STEP 10: ANALYZE INDICATOR AVAILABILITY BY YEAR
|
|
# ------------------------------------------------------------------
|
|
|
|
def analyze_indicator_availability_by_year(self):
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("STEP 10: ANALYZE INDICATOR AVAILABILITY BY YEAR")
|
|
self.logger.info("=" * 80)
|
|
|
|
year_stats = self.df_clean.groupby('year').agg({
|
|
'indicator_id': 'nunique',
|
|
'country_id' : 'nunique'
|
|
}).reset_index()
|
|
year_stats.columns = ['year', 'indicator_count', 'country_count']
|
|
|
|
self.logger.info(f"\n{'Year':<8} {'Indicators':<15} {'Countries':<12} {'Rows'}")
|
|
self.logger.info("-" * 50)
|
|
for _, row in year_stats.iterrows():
|
|
year = int(row['year'])
|
|
row_count = len(self.df_clean[self.df_clean['year'] == year])
|
|
self.logger.info(
|
|
f"{year:<8} {int(row['indicator_count']):<15} "
|
|
f"{int(row['country_count']):<12} {row_count:,}"
|
|
)
|
|
|
|
indicator_details = self.df_clean.groupby([
|
|
'indicator_id', 'indicator_name', 'pillar_name', 'direction'
|
|
]).agg({'year': ['min', 'max'], 'country_id': 'nunique'}).reset_index()
|
|
indicator_details.columns = [
|
|
'indicator_id', 'indicator_name', 'pillar_name', 'direction',
|
|
'start_year', 'end_year', 'country_count'
|
|
]
|
|
|
|
# Framework per indikator di end_year (untuk display — representasi terbaru)
|
|
fw_at_end = (
|
|
self.df_clean[self.df_clean['year'] == self.end_year]
|
|
.groupby('indicator_id')['framework']
|
|
.first()
|
|
.reset_index()
|
|
)
|
|
indicator_details = indicator_details.merge(fw_at_end, on='indicator_id', how='left')
|
|
indicator_details['framework'] = indicator_details['framework'].fillna('MDGs')
|
|
|
|
indicator_details['year_range'] = (
|
|
indicator_details['start_year'].astype(int).astype(str) + '-' +
|
|
indicator_details['end_year'].astype(int).astype(str)
|
|
)
|
|
indicator_details = indicator_details.sort_values(
|
|
['framework', 'pillar_name', 'start_year', 'indicator_name']
|
|
)
|
|
|
|
self.logger.info(f"\nTotal Indicators: {len(indicator_details)}")
|
|
self.logger.info(f"Framework breakdown (at end_year={self.end_year}):")
|
|
for fw, count in indicator_details.groupby('framework').size().items():
|
|
self.logger.info(f" {fw}: {count} indicators")
|
|
|
|
self.logger.info(f"\n{'-'*110}")
|
|
self.logger.info(
|
|
f"{'ID':<5} {'Indicator Name':<55} {'Pillar':<15} "
|
|
f"{'Framework':<10} {'Years':<12} {'Dir':<8} {'Countries'}"
|
|
)
|
|
self.logger.info(f"{'-'*110}")
|
|
for _, row in indicator_details.iterrows():
|
|
direction = 'higher+' if row['direction'] == 'higher_better' else 'lower-'
|
|
self.logger.info(
|
|
f"{int(row['indicator_id']):<5} {row['indicator_name'][:52]:<55} "
|
|
f"{row['pillar_name'][:13]:<15} {row['framework']:<10} "
|
|
f"{row['year_range']:<12} {direction:<8} {int(row['country_count'])}"
|
|
)
|
|
|
|
return year_stats
|
|
|
|
# ------------------------------------------------------------------
|
|
# STEP 11: SAVE ANALYTICAL TABLE
|
|
# ------------------------------------------------------------------
|
|
|
|
def save_analytical_table(self):
|
|
table_name = 'fact_asean_food_security_selected'
|
|
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info(f"STEP 11: SAVE TO [DW/Gold] {table_name} -> fs_asean_gold")
|
|
self.logger.info("=" * 80)
|
|
|
|
try:
|
|
if 'framework' not in self.df_clean.columns:
|
|
raise ValueError("Kolom 'framework' tidak ada. Pastikan Step 6 sudah dijalankan.")
|
|
if 'norm_value_1_100' not in self.df_clean.columns:
|
|
raise ValueError("Kolom 'norm_value_1_100' tidak ada. Pastikan Step 8 sudah dijalankan.")
|
|
if 'yoy_change' not in self.df_clean.columns:
|
|
raise ValueError("Kolom 'yoy_change' tidak ada. Pastikan Step 9 sudah dijalankan.")
|
|
|
|
analytical_df = self.df_clean[[
|
|
'country_id',
|
|
'country_name',
|
|
'indicator_id',
|
|
'indicator_name',
|
|
'direction',
|
|
'framework',
|
|
'pillar_id',
|
|
'pillar_name',
|
|
'time_id',
|
|
'year',
|
|
'value',
|
|
'norm_value_1_100',
|
|
'yoy_change',
|
|
'yoy_pct',
|
|
]].copy()
|
|
|
|
analytical_df = analytical_df.sort_values(
|
|
['year', 'country_name', 'pillar_name', 'indicator_name']
|
|
).reset_index(drop=True)
|
|
|
|
analytical_df['country_id'] = analytical_df['country_id'].astype(int)
|
|
analytical_df['country_name'] = analytical_df['country_name'].astype(str)
|
|
analytical_df['indicator_id'] = analytical_df['indicator_id'].astype(int)
|
|
analytical_df['indicator_name'] = analytical_df['indicator_name'].astype(str)
|
|
analytical_df['direction'] = analytical_df['direction'].astype(str)
|
|
analytical_df['framework'] = analytical_df['framework'].astype(str)
|
|
analytical_df['pillar_id'] = analytical_df['pillar_id'].astype(int)
|
|
analytical_df['pillar_name'] = analytical_df['pillar_name'].astype(str)
|
|
analytical_df['time_id'] = analytical_df['time_id'].astype(int)
|
|
analytical_df['year'] = analytical_df['year'].astype(int)
|
|
analytical_df['value'] = analytical_df['value'].astype(float)
|
|
analytical_df['norm_value_1_100'] = analytical_df['norm_value_1_100'].astype(float)
|
|
analytical_df['yoy_change'] = analytical_df['yoy_change'].astype(float)
|
|
analytical_df['yoy_pct'] = analytical_df['yoy_pct'].astype(float)
|
|
|
|
self.logger.info(f" Total rows: {len(analytical_df):,}")
|
|
|
|
# Framework distribution per row
|
|
fw_dist_rows = analytical_df['framework'].value_counts()
|
|
self.logger.info(f" Framework distribution (rows):")
|
|
for fw, cnt in fw_dist_rows.items():
|
|
self.logger.info(f" {fw}: {cnt:,} rows")
|
|
|
|
# Framework distribution per unique indicator (at end_year)
|
|
fw_dist_ind = (
|
|
analytical_df[analytical_df['year'] == self.end_year]
|
|
.drop_duplicates('indicator_id')['framework']
|
|
.value_counts()
|
|
)
|
|
self.logger.info(f" Framework distribution (indicators at year={self.end_year}):")
|
|
for fw, cnt in fw_dist_ind.items():
|
|
self.logger.info(f" {fw}: {cnt} indicators")
|
|
|
|
self.logger.info(
|
|
f" norm_value_1_100 range: "
|
|
f"{analytical_df['norm_value_1_100'].min():.2f} - "
|
|
f"{analytical_df['norm_value_1_100'].max():.2f}"
|
|
)
|
|
|
|
schema = [
|
|
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("direction", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("time_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("value", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("norm_value_1_100", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("yoy_change", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("yoy_pct", "FLOAT", mode="NULLABLE"),
|
|
]
|
|
|
|
rows_loaded = load_to_bigquery(
|
|
self.client, analytical_df, table_name,
|
|
layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
|
|
)
|
|
|
|
self.pipeline_metadata['rows_loaded'] = rows_loaded
|
|
log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
|
|
|
|
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({
|
|
'start_year' : self.start_year,
|
|
'end_year' : self.end_year,
|
|
'baseline_year' : self.baseline_year,
|
|
'sdg_start_year' : self.sdg_start_year,
|
|
'fixed_countries' : len(self.selected_country_ids),
|
|
'norm_scale' : '1-100 per indicator global minmax direction-aware',
|
|
'framework_logic' : 'row-level: year < sdg_start_year → MDGs always',
|
|
'condition_thresholds': {
|
|
'bad' : f'< {THRESHOLD_BAD}',
|
|
'moderate': f'{THRESHOLD_BAD}-{THRESHOLD_GOOD}',
|
|
'good' : f'> {THRESHOLD_GOOD}',
|
|
},
|
|
}),
|
|
'validation_metrics' : json.dumps({
|
|
'fixed_countries' : len(self.selected_country_ids),
|
|
'total_indicators': int(self.df_clean['indicator_id'].nunique()),
|
|
'sdg_start_year' : self.sdg_start_year,
|
|
'framework_dist_rows': fw_dist_rows.to_dict(),
|
|
})
|
|
}
|
|
save_etl_metadata(self.client, metadata)
|
|
|
|
self.logger.info(f" [OK] {table_name}: {rows_loaded:,} rows -> fs_asean_gold")
|
|
return rows_loaded
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error saving: {e}")
|
|
raise
|
|
|
|
# ------------------------------------------------------------------
|
|
# 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("Output: fact_asean_food_security_selected -> fs_asean_gold")
|
|
self.logger.info("Kolom baru: norm_value_1_100 (min-max 1-100, direction-aware)")
|
|
self.logger.info(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
|
|
self.logger.info("Framework: row-level (year < sdg_start_year → MDGs always)")
|
|
self.logger.info("=" * 80)
|
|
|
|
self.load_source_data()
|
|
self.determine_year_boundaries()
|
|
self.filter_complete_indicators_per_country()
|
|
self.select_countries_with_all_pillars()
|
|
self.filter_indicators_consistent_across_fixed_countries()
|
|
self.determine_sdg_start_year()
|
|
self.verify_no_gaps()
|
|
self.calculate_norm_value() # Step 8: norm_value_1_100
|
|
self.calculate_yoy() # Step 9: yoy_change, yoy_pct
|
|
self.analyze_indicator_availability_by_year()
|
|
self.save_analytical_table()
|
|
|
|
self.pipeline_end = datetime.now()
|
|
duration = (self.pipeline_end - 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" Year Range : {self.start_year}-{self.end_year}")
|
|
self.logger.info(f" SDG Start Yr : {self.sdg_start_year}")
|
|
self.logger.info(f" Countries : {len(self.selected_country_ids)}")
|
|
self.logger.info(f" Indicators : {self.df_clean['indicator_id'].nunique()}")
|
|
self.logger.info(f" Rows Loaded : {self.pipeline_metadata['rows_loaded']:,}")
|
|
|
|
|
|
# =============================================================================
|
|
# AIRFLOW TASK FUNCTION
|
|
# =============================================================================
|
|
|
|
def run_analytical_layer():
|
|
from scripts.bigquery_config import get_bigquery_client
|
|
client = get_bigquery_client()
|
|
loader = AnalyticalLayerLoader(client)
|
|
loader.run()
|
|
print(f"Analytical layer loaded: {loader.pipeline_metadata['rows_loaded']:,} rows")
|
|
|
|
|
|
# =============================================================================
|
|
# MAIN EXECUTION
|
|
# =============================================================================
|
|
|
|
if __name__ == "__main__":
|
|
print("=" * 80)
|
|
print("BIGQUERY ANALYTICAL LAYER - DATA FILTERING")
|
|
print("Output: fact_asean_food_security_selected -> fs_asean_gold")
|
|
print(f"Norm: min-max 1-100 per indicator, direction-aware")
|
|
print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
|
|
print(f"Framework: row-level (year < sdg_start_year → MDGs always)")
|
|
print("=" * 80)
|
|
|
|
logger = setup_logging()
|
|
client = get_bigquery_client()
|
|
loader = AnalyticalLayerLoader(client)
|
|
loader.run()
|
|
|
|
print("\n" + "=" * 80)
|
|
print("[OK] COMPLETED")
|
|
print(f" SDG Start Year : {loader.sdg_start_year}")
|
|
print(f" Rows Loaded : {loader.pipeline_metadata['rows_loaded']:,}")
|
|
print("=" * 80) |