1323 lines
61 KiB
Python
1323 lines
61 KiB
Python
"""
|
|
BIGQUERY ANALYSIS LAYER - FOOD SECURITY AGGREGATION
|
|
Semua agregasi pakai norm_value dari _get_norm_value_df()
|
|
UPDATED: Simpan 6 tabel ke fs_asean_gold (layer='gold'):
|
|
- agg_pillar_composite
|
|
- agg_pillar_by_country
|
|
- agg_framework_by_country
|
|
- agg_framework_asean
|
|
- agg_narrative_overview (NEW)
|
|
- agg_narrative_pillar (NEW)
|
|
"""
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
from datetime import datetime
|
|
import logging
|
|
import json
|
|
import sys as _sys
|
|
|
|
from scripts.bigquery_config import get_bigquery_client
|
|
from scripts.bigquery_helpers import (
|
|
log_update,
|
|
load_to_bigquery,
|
|
read_from_bigquery,
|
|
setup_logging,
|
|
save_etl_metadata,
|
|
)
|
|
from google.cloud import bigquery
|
|
|
|
|
|
# =============================================================================
|
|
# KONSTANTA GLOBAL
|
|
# =============================================================================
|
|
|
|
DIRECTION_INVERT_KEYWORDS = frozenset({
|
|
"negative", "lower_better", "lower_is_better", "inverse", "neg",
|
|
})
|
|
|
|
DIRECTION_POSITIVE_KEYWORDS = frozenset({
|
|
"positive", "higher_better", "higher_is_better",
|
|
})
|
|
|
|
NORMALIZE_FRAMEWORKS_JOINTLY = False
|
|
|
|
|
|
# =============================================================================
|
|
# Windows CP1252 safe logging
|
|
# =============================================================================
|
|
|
|
class _SafeStreamHandler(logging.StreamHandler):
|
|
def emit(self, record):
|
|
try:
|
|
super().emit(record)
|
|
except UnicodeEncodeError:
|
|
try:
|
|
msg = self.format(record)
|
|
self.stream.write(
|
|
msg.encode("utf-8", errors="replace").decode("ascii", errors="replace")
|
|
+ self.terminator
|
|
)
|
|
self.flush()
|
|
except Exception:
|
|
self.handleError(record)
|
|
|
|
|
|
# =============================================================================
|
|
# HELPERS
|
|
# =============================================================================
|
|
|
|
def _should_invert(direction: str, logger=None, context: str = "") -> bool:
|
|
d = str(direction).lower().strip()
|
|
if d in DIRECTION_INVERT_KEYWORDS:
|
|
return True
|
|
if d in DIRECTION_POSITIVE_KEYWORDS:
|
|
return False
|
|
if logger:
|
|
logger.warning(
|
|
f" [DIRECTION WARNING] Unknown direction '{direction}' "
|
|
f"{'(' + context + ')' if context else ''}. Defaulting to positive (no invert)."
|
|
)
|
|
return False
|
|
|
|
|
|
def global_minmax(series: pd.Series, lo: float = 1.0, hi: float = 100.0) -> pd.Series:
|
|
values = series.dropna().values
|
|
if len(values) == 0:
|
|
return pd.Series(np.nan, index=series.index)
|
|
v_min, v_max = values.min(), values.max()
|
|
if v_min == v_max:
|
|
return pd.Series((lo + hi) / 2.0, index=series.index)
|
|
result = np.full(len(series), np.nan)
|
|
not_nan = series.notna()
|
|
raw = series[not_nan].values
|
|
result[not_nan.values] = lo + (raw - v_min) / (v_max - v_min) * (hi - lo)
|
|
return pd.Series(result, index=series.index)
|
|
|
|
|
|
def add_yoy(df: pd.DataFrame, group_cols: list, score_col: str) -> pd.DataFrame:
|
|
df = df.sort_values(group_cols + ["year"]).reset_index(drop=True)
|
|
if group_cols:
|
|
df["year_over_year_change"] = df.groupby(group_cols)[score_col].diff()
|
|
else:
|
|
df["year_over_year_change"] = df[score_col].diff()
|
|
return df
|
|
|
|
|
|
def safe_int(series: pd.Series, fill: int = 0, col_name: str = "", logger=None) -> pd.Series:
|
|
n_nan = series.isna().sum()
|
|
if n_nan > 0 and logger:
|
|
logger.warning(
|
|
f" [NaN WARNING] Kolom '{col_name}' punya {n_nan} NaN -> di-fill dengan {fill}"
|
|
)
|
|
return series.fillna(fill).astype(int)
|
|
|
|
|
|
def check_and_dedup(df: pd.DataFrame, key_cols: list, context: str = "", logger=None) -> pd.DataFrame:
|
|
dupes = df.duplicated(subset=key_cols, keep=False)
|
|
if dupes.any():
|
|
n_dupes = dupes.sum()
|
|
if logger:
|
|
logger.warning(
|
|
f" [DEDUP WARNING] {context}: {n_dupes} duplikat rows pada {key_cols}. "
|
|
f"Di-aggregate dengan mean."
|
|
)
|
|
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
|
|
agg_dict = {
|
|
c: ("mean" if c in numeric_cols else "first")
|
|
for c in df.columns if c not in key_cols
|
|
}
|
|
df = df.groupby(key_cols, as_index=False).agg(agg_dict)
|
|
return df
|
|
|
|
|
|
# =============================================================================
|
|
# NARRATIVE BUILDER FUNCTIONS (pure functions, easy to unit-test)
|
|
# =============================================================================
|
|
|
|
def _fmt_score(score) -> str:
|
|
"""Format score to 2 decimal places."""
|
|
if score is None or (isinstance(score, float) and np.isnan(score)):
|
|
return "N/A"
|
|
return f"{score:.2f}"
|
|
|
|
|
|
def _fmt_delta(delta) -> str:
|
|
"""Format YoY delta with sign and 2 decimal places."""
|
|
if delta is None or (isinstance(delta, float) and np.isnan(delta)):
|
|
return "N/A"
|
|
sign = "+" if delta >= 0 else ""
|
|
return f"{sign}{delta:.2f}"
|
|
|
|
|
|
def _build_overview_narrative(
|
|
year: int,
|
|
n_mdg: int,
|
|
n_sdg: int,
|
|
n_total_ind: int,
|
|
score: float,
|
|
yoy_val,
|
|
yoy_pct,
|
|
prev_year: int,
|
|
prev_score,
|
|
ranking_list: list,
|
|
most_improved_country,
|
|
most_improved_delta,
|
|
most_declined_country,
|
|
most_declined_delta,
|
|
) -> str:
|
|
"""
|
|
Compose a full English prose narrative for the Overview tab.
|
|
|
|
Narrative structure
|
|
-------------------
|
|
1. Indicator composition (MDGs first, then SDGs)
|
|
2. ASEAN score + YoY
|
|
3. Country ranking
|
|
4. Most improved / declined country
|
|
"""
|
|
|
|
# -- Sentence 1: indicator composition ----------------------------------
|
|
parts_ind = []
|
|
if n_mdg > 0:
|
|
parts_ind.append(f"{n_mdg} MDG indicator{'s' if n_mdg > 1 else ''}")
|
|
if n_sdg > 0:
|
|
parts_ind.append(f"{n_sdg} SDG indicator{'s' if n_sdg > 1 else ''}")
|
|
|
|
if parts_ind:
|
|
ind_detail = " and ".join(parts_ind)
|
|
sent1 = (
|
|
f"In {year}, the ASEAN food security assessment incorporated a total of "
|
|
f"{n_total_ind} indicator{'s' if n_total_ind != 1 else ''}, "
|
|
f"consisting of {ind_detail}."
|
|
)
|
|
else:
|
|
sent1 = (
|
|
f"In {year}, the ASEAN food security assessment incorporated "
|
|
f"{n_total_ind} indicator{'s' if n_total_ind != 1 else ''}."
|
|
)
|
|
|
|
# -- Sentence 2: ASEAN score + YoY -------------------------------------
|
|
if yoy_val is not None and prev_score is not None:
|
|
direction_word = "increasing" if yoy_val >= 0 else "decreasing"
|
|
pct_clause = ""
|
|
if yoy_pct is not None:
|
|
abs_pct = abs(yoy_pct)
|
|
trend_word = "improvement" if yoy_val >= 0 else "decline"
|
|
pct_clause = f", which represents a {abs_pct:.2f}% {trend_word} year-over-year"
|
|
sent2 = (
|
|
f"The ASEAN overall score (Total framework) reached {_fmt_score(score)}, "
|
|
f"{direction_word} by {abs(yoy_val):.2f} points compared to the previous year "
|
|
f"({_fmt_score(prev_score)} in {prev_year}){pct_clause}."
|
|
)
|
|
else:
|
|
sent2 = (
|
|
f"The ASEAN overall score (Total framework) reached {_fmt_score(score)} in {year}; "
|
|
f"no prior-year data is available for year-over-year comparison."
|
|
)
|
|
|
|
# -- Sentence 3: country ranking ----------------------------------------
|
|
sent3 = ""
|
|
if ranking_list:
|
|
top3 = ranking_list[:3]
|
|
last = ranking_list[-1]
|
|
if len(top3) >= 3:
|
|
after_first = (
|
|
f"{top3[1]['country_name']} ({_fmt_score(top3[1]['score'])}) and "
|
|
f"{top3[2]['country_name']} ({_fmt_score(top3[2]['score'])})."
|
|
)
|
|
elif len(top3) == 2:
|
|
after_first = f"{top3[1]['country_name']} ({_fmt_score(top3[1]['score'])})."
|
|
else:
|
|
after_first = "."
|
|
|
|
sent3 = (
|
|
f"In terms of country performance, {top3[0]['country_name']} led the region "
|
|
f"with a score of {_fmt_score(top3[0]['score'])}, followed by {after_first} "
|
|
f"At the other end, {last['country_name']} recorded the lowest score "
|
|
f"of {_fmt_score(last['score'])} in {year}."
|
|
)
|
|
|
|
# -- Sentence 4: most improved / declined ------------------------------
|
|
sent4_parts = []
|
|
if most_improved_country and most_improved_delta is not None:
|
|
sent4_parts.append(
|
|
f"the most notable improvement was seen in {most_improved_country}, "
|
|
f"which gained {_fmt_delta(most_improved_delta)} points from the previous year"
|
|
)
|
|
if most_declined_country and most_declined_delta is not None:
|
|
if most_declined_delta < 0:
|
|
sent4_parts.append(
|
|
f"while {most_declined_country} experienced the largest decline "
|
|
f"of {_fmt_delta(most_declined_delta)} points"
|
|
)
|
|
else:
|
|
sent4_parts.append(
|
|
f"while {most_declined_country} recorded the smallest gain "
|
|
f"of {_fmt_delta(most_declined_delta)} points"
|
|
)
|
|
|
|
sent4 = ""
|
|
if sent4_parts:
|
|
sent4 = ", ".join(sent4_parts) + "."
|
|
sent4 = sent4[0].upper() + sent4[1:]
|
|
|
|
# -- Assemble ----------------------------------------------------------
|
|
return " ".join(s for s in [sent1, sent2, sent3, sent4] if s)
|
|
|
|
|
|
def _build_pillar_narrative(
|
|
year: int,
|
|
pillar_name: str,
|
|
pillar_score: float,
|
|
rank_in_year: int,
|
|
n_pillars: int,
|
|
yoy_val,
|
|
top_country,
|
|
top_country_score,
|
|
bot_country,
|
|
bot_country_score,
|
|
strongest_pillar,
|
|
strongest_score,
|
|
weakest_pillar,
|
|
weakest_score,
|
|
most_improved_pillar,
|
|
most_improved_delta,
|
|
most_declined_pillar,
|
|
most_declined_delta,
|
|
) -> str:
|
|
"""
|
|
Compose a full English prose narrative for a single pillar in a given year.
|
|
|
|
Narrative structure
|
|
-------------------
|
|
1. Pillar score and rank
|
|
2. Strongest / weakest pillar context
|
|
3. Top / bottom country within this pillar
|
|
4. YoY movement for this pillar + biggest mover across all pillars
|
|
"""
|
|
|
|
# -- Sentence 1: pillar overview ----------------------------------------
|
|
rank_suffix = {1: "st", 2: "nd", 3: "rd"}.get(rank_in_year, "th")
|
|
sent1 = (
|
|
f"In {year}, the {pillar_name} pillar scored {_fmt_score(pillar_score)}, "
|
|
f"ranking {rank_in_year}{rank_suffix} out of {n_pillars} pillars assessed across ASEAN."
|
|
)
|
|
|
|
# -- Sentence 2: strongest / weakest context ----------------------------
|
|
sent2 = ""
|
|
if strongest_pillar and weakest_pillar:
|
|
if strongest_pillar == pillar_name:
|
|
sent2 = (
|
|
f"This made {pillar_name} the strongest performing pillar in {year}, "
|
|
f"compared to the weakest pillar, {weakest_pillar}, "
|
|
f"which scored {_fmt_score(weakest_score)}."
|
|
)
|
|
elif weakest_pillar == pillar_name:
|
|
sent2 = (
|
|
f"This made {pillar_name} the weakest performing pillar in {year}, "
|
|
f"compared to the strongest pillar, {strongest_pillar}, "
|
|
f"which scored {_fmt_score(strongest_score)}."
|
|
)
|
|
else:
|
|
sent2 = (
|
|
f"Across all pillars in {year}, {strongest_pillar} was the strongest "
|
|
f"(score: {_fmt_score(strongest_score)}), while {weakest_pillar} "
|
|
f"was the weakest (score: {_fmt_score(weakest_score)})."
|
|
)
|
|
|
|
# -- Sentence 3: country top / bottom within this pillar ---------------
|
|
sent3 = ""
|
|
if top_country and bot_country:
|
|
if top_country != bot_country:
|
|
sent3 = (
|
|
f"Within the {pillar_name} pillar, {top_country} led with a score of "
|
|
f"{_fmt_score(top_country_score)}, while {bot_country} recorded the lowest "
|
|
f"score of {_fmt_score(bot_country_score)}."
|
|
)
|
|
else:
|
|
sent3 = (
|
|
f"Within the {pillar_name} pillar, {top_country} was the only country "
|
|
f"with available data, scoring {_fmt_score(top_country_score)}."
|
|
)
|
|
|
|
# -- Sentence 4: YoY movement -------------------------------------------
|
|
if yoy_val is not None:
|
|
direction_word = "improved" if yoy_val >= 0 else "declined"
|
|
sent4 = (
|
|
f"Compared to the previous year, the {pillar_name} pillar "
|
|
f"{direction_word} by {abs(yoy_val):.2f} points"
|
|
)
|
|
else:
|
|
sent4 = (
|
|
f"No prior-year data is available to calculate year-over-year change "
|
|
f"for the {pillar_name} pillar in {year}"
|
|
)
|
|
|
|
if most_improved_pillar and most_improved_delta is not None \
|
|
and most_declined_pillar and most_declined_delta is not None \
|
|
and most_improved_pillar != most_declined_pillar:
|
|
sent4 += (
|
|
f". Across all pillars, {most_improved_pillar} showed the greatest improvement "
|
|
f"({_fmt_delta(most_improved_delta)} pts), while {most_declined_pillar} "
|
|
f"recorded the largest decline ({_fmt_delta(most_declined_delta)} pts)"
|
|
)
|
|
|
|
sent4 += "."
|
|
sent4 = sent4[0].upper() + sent4[1:]
|
|
|
|
# -- Assemble ----------------------------------------------------------
|
|
return " ".join(s for s in [sent1, sent2, sent3, sent4] if s)
|
|
|
|
|
|
# =============================================================================
|
|
# MAIN CLASS
|
|
# =============================================================================
|
|
|
|
class FoodSecurityAggregator:
|
|
|
|
def __init__(self, client: bigquery.Client):
|
|
self.client = client
|
|
self.logger = logging.getLogger(self.__class__.__name__)
|
|
self.logger.propagate = False
|
|
|
|
self.load_metadata = {
|
|
"agg_pillar_composite": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
|
|
"agg_pillar_by_country": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
|
|
"agg_framework_by_country": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
|
|
"agg_framework_asean": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
|
|
"agg_narrative_overview": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
|
|
"agg_narrative_pillar": {"rows_loaded": 0, "status": "pending", "start_time": None, "end_time": None},
|
|
}
|
|
|
|
self.df = None
|
|
self.dims = {}
|
|
|
|
self.sdgs_start_year = None
|
|
self.mdgs_indicator_ids = set()
|
|
self.sdgs_indicator_ids = set()
|
|
|
|
# =========================================================================
|
|
# STEP 1: Load data dari Gold layer
|
|
# =========================================================================
|
|
|
|
def load_data(self):
|
|
self.logger.info("=" * 70)
|
|
self.logger.info("STEP 1: LOAD DATA from fs_asean_gold")
|
|
self.logger.info("=" * 70)
|
|
|
|
self.df = read_from_bigquery(self.client, "analytical_food_security", layer='gold')
|
|
self.logger.info(f" analytical_food_security : {len(self.df):,} rows")
|
|
|
|
self.dims["country"] = read_from_bigquery(self.client, "dim_country", layer='gold')
|
|
self.dims["indicator"] = read_from_bigquery(self.client, "dim_indicator", layer='gold')
|
|
self.dims["pillar"] = read_from_bigquery(self.client, "dim_pillar", layer='gold')
|
|
self.dims["time"] = read_from_bigquery(self.client, "dim_time", layer='gold')
|
|
|
|
ind_cols = ["indicator_id"]
|
|
if "direction" in self.dims["indicator"].columns:
|
|
ind_cols.append("direction")
|
|
|
|
self.df = (
|
|
self.df
|
|
.merge(self.dims["time"][["time_id", "year"]], on="time_id", how="left")
|
|
.merge(self.dims["country"][["country_id", "country_name"]], on="country_id", how="left")
|
|
.merge(self.dims["pillar"][["pillar_id", "pillar_name"]], on="pillar_id", how="left")
|
|
.merge(self.dims["indicator"][ind_cols], on="indicator_id", how="left")
|
|
)
|
|
|
|
if "direction" not in self.df.columns:
|
|
self.df["direction"] = "positive"
|
|
else:
|
|
n_null_dir = self.df["direction"].isna().sum()
|
|
if n_null_dir > 0:
|
|
self.logger.warning(f" [DIRECTION] {n_null_dir} rows dengan direction NULL -> diisi 'positive'")
|
|
self.df["direction"] = self.df["direction"].fillna("positive")
|
|
|
|
dir_dist = self.df.drop_duplicates("indicator_id")["direction"].value_counts()
|
|
self.logger.info(f"\n Distribusi direction per indikator:")
|
|
for d, cnt in dir_dist.items():
|
|
tag = "INVERT" if _should_invert(d, self.logger, "load_data check") else "normal"
|
|
self.logger.info(f" {d:<25} : {cnt:>3} indikator [{tag}]")
|
|
|
|
self.logger.info(f"\n Setelah join: {len(self.df):,} rows")
|
|
self.logger.info(f" Negara : {self.df['country_id'].nunique()}")
|
|
self.logger.info(f" Indikator : {self.df['indicator_id'].nunique()}")
|
|
self.logger.info(f" Tahun : {int(self.df['year'].min())} - {int(self.df['year'].max())}")
|
|
|
|
# =========================================================================
|
|
# STEP 1b: Klasifikasi indikator ke MDGs / SDGs
|
|
# =========================================================================
|
|
|
|
def _classify_indicators(self):
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info("STEP 1b: KLASIFIKASI INDIKATOR -> MDGs / SDGs")
|
|
self.logger.info("=" * 70)
|
|
|
|
ind_min_year = (
|
|
self.df.groupby("indicator_id")["year"]
|
|
.min().reset_index()
|
|
.rename(columns={"year": "min_year"})
|
|
)
|
|
|
|
unique_years = sorted(ind_min_year["min_year"].unique())
|
|
self.logger.info(f"\n Unique min_year per indikator: {unique_years}")
|
|
|
|
if len(unique_years) == 1:
|
|
gap_threshold = unique_years[0] + 1
|
|
self.logger.info(" Hanya 1 cluster -> semua = MDGs")
|
|
else:
|
|
gaps = [
|
|
(unique_years[i+1] - unique_years[i], unique_years[i], unique_years[i+1])
|
|
for i in range(len(unique_years) - 1)
|
|
]
|
|
gaps.sort(reverse=True)
|
|
largest_gap_size, y_before, y_after = gaps[0]
|
|
gap_threshold = y_after
|
|
self.logger.info(f" Gap terbesar: {y_before} -> {y_after} (selisih {largest_gap_size})")
|
|
|
|
ind_min_year["framework"] = ind_min_year["min_year"].apply(
|
|
lambda y: "MDGs" if int(y) < gap_threshold else "SDGs"
|
|
)
|
|
|
|
sdgs_rows = ind_min_year[ind_min_year["framework"] == "SDGs"]
|
|
self.sdgs_start_year = int(sdgs_rows["min_year"].min()) if not sdgs_rows.empty else int(self.df["year"].max()) + 1
|
|
|
|
self.logger.info(f" sdgs_start_year: {self.sdgs_start_year}")
|
|
|
|
self.mdgs_indicator_ids = set(ind_min_year[ind_min_year["framework"] == "MDGs"]["indicator_id"].tolist())
|
|
self.sdgs_indicator_ids = set(ind_min_year[ind_min_year["framework"] == "SDGs"]["indicator_id"].tolist())
|
|
|
|
self.logger.info(f" MDGs: {len(self.mdgs_indicator_ids)} indicators")
|
|
self.logger.info(f" SDGs: {len(self.sdgs_indicator_ids)} indicators")
|
|
|
|
self.df = self.df.merge(ind_min_year[["indicator_id", "framework"]], on="indicator_id", how="left")
|
|
|
|
# =========================================================================
|
|
# CORE HELPER: normalisasi raw value per indikator
|
|
# =========================================================================
|
|
|
|
def _get_norm_value_df(self) -> pd.DataFrame:
|
|
if "framework" not in self.df.columns:
|
|
raise ValueError("Kolom 'framework' tidak ada. Pastikan _classify_indicators() dipanggil lebih dulu.")
|
|
|
|
norm_parts = []
|
|
for ind_id, grp in self.df.groupby("indicator_id"):
|
|
grp = grp.copy()
|
|
direction = str(grp["direction"].iloc[0])
|
|
do_invert = _should_invert(direction, self.logger, context=f"indicator_id={ind_id}")
|
|
valid_mask = grp["value"].notna()
|
|
n_valid = valid_mask.sum()
|
|
|
|
if n_valid < 2:
|
|
grp["norm_value"] = np.nan
|
|
norm_parts.append(grp)
|
|
continue
|
|
|
|
raw = grp.loc[valid_mask, "value"].values
|
|
v_min, v_max = raw.min(), raw.max()
|
|
normed = np.full(len(grp), np.nan)
|
|
if v_min == v_max:
|
|
normed[valid_mask.values] = 0.5
|
|
else:
|
|
normed[valid_mask.values] = (raw - v_min) / (v_max - v_min)
|
|
|
|
if do_invert:
|
|
normed = np.where(np.isnan(normed), np.nan, 1.0 - normed)
|
|
|
|
grp["norm_value"] = normed
|
|
norm_parts.append(grp)
|
|
|
|
return pd.concat(norm_parts, ignore_index=True)
|
|
|
|
# =========================================================================
|
|
# STEP 2: agg_pillar_composite -> Gold
|
|
# =========================================================================
|
|
|
|
def calc_pillar_composite(self) -> pd.DataFrame:
|
|
table_name = "agg_pillar_composite"
|
|
self.load_metadata[table_name]["start_time"] = datetime.now()
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info(f"STEP 2: {table_name} -> [Gold] fs_asean_gold")
|
|
self.logger.info("=" * 70)
|
|
|
|
df_normed = self._get_norm_value_df()
|
|
|
|
df = (
|
|
df_normed
|
|
.groupby(["pillar_id", "pillar_name", "year"])
|
|
.agg(
|
|
pillar_norm =("norm_value", "mean"),
|
|
n_indicators=("indicator_id", "nunique"),
|
|
n_countries =("country_id", "nunique"),
|
|
)
|
|
.reset_index()
|
|
)
|
|
|
|
df["pillar_score_1_100"] = global_minmax(df["pillar_norm"])
|
|
df["rank_in_year"] = (
|
|
df.groupby("year")["pillar_score_1_100"]
|
|
.rank(method="min", ascending=False)
|
|
.astype(int)
|
|
)
|
|
df = add_yoy(df, ["pillar_id"], "pillar_score_1_100")
|
|
|
|
df["pillar_id"] = df["pillar_id"].astype(int)
|
|
df["year"] = df["year"].astype(int)
|
|
df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger)
|
|
df["n_countries"] = safe_int(df["n_countries"], col_name="n_countries", logger=self.logger)
|
|
df["rank_in_year"] = df["rank_in_year"].astype(int)
|
|
df["pillar_norm"] = df["pillar_norm"].astype(float)
|
|
df["pillar_score_1_100"] = df["pillar_score_1_100"].astype(float)
|
|
|
|
schema = [
|
|
bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_norm", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_indicators", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_countries", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_score_1_100", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("rank_in_year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"),
|
|
]
|
|
rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema)
|
|
self._finalize(table_name, rows)
|
|
return df
|
|
|
|
# =========================================================================
|
|
# STEP 3: agg_pillar_by_country -> Gold
|
|
# =========================================================================
|
|
|
|
def calc_pillar_by_country(self) -> pd.DataFrame:
|
|
table_name = "agg_pillar_by_country"
|
|
self.load_metadata[table_name]["start_time"] = datetime.now()
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info(f"STEP 3: {table_name} -> [Gold] fs_asean_gold")
|
|
self.logger.info("=" * 70)
|
|
|
|
df_normed = self._get_norm_value_df()
|
|
|
|
df = (
|
|
df_normed
|
|
.groupby(["country_id", "country_name", "pillar_id", "pillar_name", "year"])
|
|
.agg(pillar_country_norm=("norm_value", "mean"))
|
|
.reset_index()
|
|
)
|
|
|
|
df["pillar_country_score_1_100"] = global_minmax(df["pillar_country_norm"])
|
|
df["rank_in_pillar_year"] = (
|
|
df.groupby(["pillar_id", "year"])["pillar_country_score_1_100"]
|
|
.rank(method="min", ascending=False)
|
|
.astype(int)
|
|
)
|
|
df = add_yoy(df, ["country_id", "pillar_id"], "pillar_country_score_1_100")
|
|
|
|
df["country_id"] = df["country_id"].astype(int)
|
|
df["pillar_id"] = df["pillar_id"].astype(int)
|
|
df["year"] = df["year"].astype(int)
|
|
df["rank_in_pillar_year"] = df["rank_in_pillar_year"].astype(int)
|
|
df["pillar_country_norm"] = df["pillar_country_norm"].astype(float)
|
|
df["pillar_country_score_1_100"] = df["pillar_country_score_1_100"].astype(float)
|
|
|
|
schema = [
|
|
bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_country_norm", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_country_score_1_100", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("rank_in_pillar_year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"),
|
|
]
|
|
rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema)
|
|
self._finalize(table_name, rows)
|
|
return df
|
|
|
|
# =========================================================================
|
|
# STEP 4: agg_framework_by_country -> Gold
|
|
# =========================================================================
|
|
|
|
def _calc_country_composite_inmemory(self) -> pd.DataFrame:
|
|
"""Hitung country composite in-memory (tidak disimpan ke BQ)."""
|
|
df_normed = self._get_norm_value_df()
|
|
df = (
|
|
df_normed
|
|
.groupby(["country_id", "country_name", "year"])
|
|
.agg(
|
|
composite_score=("norm_value", "mean"),
|
|
n_indicators =("indicator_id", "nunique"),
|
|
)
|
|
.reset_index()
|
|
)
|
|
df["score_1_100"] = global_minmax(df["composite_score"])
|
|
df["rank_in_asean"] = (
|
|
df.groupby("year")["score_1_100"]
|
|
.rank(method="min", ascending=False)
|
|
.astype(int)
|
|
)
|
|
df = add_yoy(df, ["country_id"], "score_1_100")
|
|
df["country_id"] = df["country_id"].astype(int)
|
|
df["year"] = df["year"].astype(int)
|
|
df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger)
|
|
df["composite_score"] = df["composite_score"].astype(float)
|
|
df["score_1_100"] = df["score_1_100"].astype(float)
|
|
df["rank_in_asean"] = df["rank_in_asean"].astype(int)
|
|
return df
|
|
|
|
def calc_framework_by_country(self) -> pd.DataFrame:
|
|
table_name = "agg_framework_by_country"
|
|
self.load_metadata[table_name]["start_time"] = datetime.now()
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info(f"STEP 4: {table_name} -> [Gold] fs_asean_gold")
|
|
self.logger.info("=" * 70)
|
|
|
|
country_composite = self._calc_country_composite_inmemory()
|
|
df_normed = self._get_norm_value_df()
|
|
parts = []
|
|
|
|
# Layer TOTAL
|
|
agg_total = (
|
|
country_composite[[
|
|
"country_id", "country_name", "year",
|
|
"score_1_100", "n_indicators", "composite_score"
|
|
]]
|
|
.copy()
|
|
.rename(columns={"score_1_100": "framework_score_1_100", "composite_score": "framework_norm"})
|
|
)
|
|
agg_total["framework"] = "Total"
|
|
parts.append(agg_total)
|
|
|
|
# Layer MDGs — Era pre-SDGs = Total
|
|
pre_sdgs_rows = country_composite[country_composite["year"] < self.sdgs_start_year].copy()
|
|
if not pre_sdgs_rows.empty:
|
|
mdgs_pre = (
|
|
pre_sdgs_rows[["country_id", "country_name", "year", "score_1_100", "n_indicators", "composite_score"]]
|
|
.copy()
|
|
.rename(columns={"score_1_100": "framework_score_1_100", "composite_score": "framework_norm"})
|
|
)
|
|
mdgs_pre["framework"] = "MDGs"
|
|
parts.append(mdgs_pre)
|
|
|
|
# Layer MDGs — Era mixed
|
|
if self.mdgs_indicator_ids:
|
|
df_mdgs_mixed = df_normed[
|
|
(df_normed["indicator_id"].isin(self.mdgs_indicator_ids)) &
|
|
(df_normed["year"] >= self.sdgs_start_year)
|
|
].copy()
|
|
if not df_mdgs_mixed.empty:
|
|
agg_mdgs_mixed = (
|
|
df_mdgs_mixed
|
|
.groupby(["country_id", "country_name", "year"])
|
|
.agg(framework_norm=("norm_value", "mean"), n_indicators=("indicator_id", "nunique"))
|
|
.reset_index()
|
|
)
|
|
if not NORMALIZE_FRAMEWORKS_JOINTLY:
|
|
agg_mdgs_mixed["framework_score_1_100"] = global_minmax(agg_mdgs_mixed["framework_norm"])
|
|
agg_mdgs_mixed["framework"] = "MDGs"
|
|
parts.append(agg_mdgs_mixed)
|
|
|
|
# Layer SDGs
|
|
if self.sdgs_indicator_ids:
|
|
df_sdgs = df_normed[
|
|
(df_normed["indicator_id"].isin(self.sdgs_indicator_ids)) &
|
|
(df_normed["year"] >= self.sdgs_start_year)
|
|
].copy()
|
|
if not df_sdgs.empty:
|
|
agg_sdgs = (
|
|
df_sdgs
|
|
.groupby(["country_id", "country_name", "year"])
|
|
.agg(framework_norm=("norm_value", "mean"), n_indicators=("indicator_id", "nunique"))
|
|
.reset_index()
|
|
)
|
|
if not NORMALIZE_FRAMEWORKS_JOINTLY:
|
|
agg_sdgs["framework_score_1_100"] = global_minmax(agg_sdgs["framework_norm"])
|
|
agg_sdgs["framework"] = "SDGs"
|
|
parts.append(agg_sdgs)
|
|
|
|
df = pd.concat(parts, ignore_index=True)
|
|
|
|
if NORMALIZE_FRAMEWORKS_JOINTLY:
|
|
mixed_mask = (df["framework"].isin(["MDGs", "SDGs"])) & (df["year"] >= self.sdgs_start_year)
|
|
if mixed_mask.any():
|
|
df.loc[mixed_mask, "framework_score_1_100"] = global_minmax(df.loc[mixed_mask, "framework_norm"])
|
|
|
|
df = check_and_dedup(df, ["country_id", "framework", "year"], context=table_name, logger=self.logger)
|
|
df["rank_in_framework_year"] = (
|
|
df.groupby(["framework", "year"])["framework_score_1_100"]
|
|
.rank(method="min", ascending=False)
|
|
.astype(int)
|
|
)
|
|
df = add_yoy(df, ["country_id", "framework"], "framework_score_1_100")
|
|
|
|
df["country_id"] = df["country_id"].astype(int)
|
|
df["year"] = df["year"].astype(int)
|
|
df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger)
|
|
df["rank_in_framework_year"] = safe_int(df["rank_in_framework_year"], col_name="rank_in_framework_year", logger=self.logger)
|
|
df["framework_norm"] = df["framework_norm"].astype(float)
|
|
df["framework_score_1_100"] = df["framework_score_1_100"].astype(float)
|
|
|
|
self._validate_mdgs_equals_total(df, level="country")
|
|
|
|
schema = [
|
|
bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_indicators", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework_norm", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework_score_1_100", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("rank_in_framework_year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"),
|
|
]
|
|
rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema)
|
|
self._finalize(table_name, rows)
|
|
return df
|
|
|
|
# =========================================================================
|
|
# STEP 5: agg_framework_asean -> Gold
|
|
# =========================================================================
|
|
|
|
def calc_framework_asean(self) -> pd.DataFrame:
|
|
table_name = "agg_framework_asean"
|
|
self.load_metadata[table_name]["start_time"] = datetime.now()
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info(f"STEP 5: {table_name} -> [Gold] fs_asean_gold")
|
|
self.logger.info("=" * 70)
|
|
|
|
df_normed = self._get_norm_value_df()
|
|
country_composite = self._calc_country_composite_inmemory()
|
|
|
|
country_norm = (
|
|
df_normed.groupby(["country_id", "country_name", "year"])["norm_value"]
|
|
.mean().reset_index().rename(columns={"norm_value": "country_norm"})
|
|
)
|
|
asean_overall = (
|
|
country_norm.groupby("year")
|
|
.agg(asean_norm=("country_norm", "mean"), std_norm=("country_norm", "std"),
|
|
n_countries=("country_norm", "count"))
|
|
.reset_index()
|
|
)
|
|
asean_overall["asean_score_1_100"] = global_minmax(asean_overall["asean_norm"])
|
|
asean_comp = (
|
|
country_composite.groupby("year")["composite_score"]
|
|
.mean().reset_index().rename(columns={"composite_score": "asean_composite"})
|
|
)
|
|
asean_overall = asean_overall.merge(asean_comp, on="year", how="left")
|
|
|
|
parts = []
|
|
|
|
# Layer TOTAL
|
|
total_cols = asean_overall[["year", "asean_score_1_100", "asean_norm", "std_norm", "n_countries"]].copy()
|
|
total_cols = total_cols.rename(columns={
|
|
"asean_score_1_100": "framework_score_1_100",
|
|
"asean_norm": "framework_norm",
|
|
"n_countries": "n_countries_with_data",
|
|
})
|
|
n_ind_total = df_normed.groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"})
|
|
total_cols = total_cols.merge(n_ind_total, on="year", how="left")
|
|
total_cols["framework"] = "Total"
|
|
parts.append(total_cols)
|
|
|
|
# Layer MDGs — pre-SDGs = Total
|
|
pre_sdgs = asean_overall[asean_overall["year"] < self.sdgs_start_year].copy()
|
|
if not pre_sdgs.empty:
|
|
mdgs_pre = pre_sdgs[["year", "asean_score_1_100", "asean_norm", "std_norm", "n_countries"]].copy()
|
|
mdgs_pre = mdgs_pre.rename(columns={
|
|
"asean_score_1_100": "framework_score_1_100",
|
|
"asean_norm": "framework_norm",
|
|
"n_countries": "n_countries_with_data",
|
|
})
|
|
n_ind_pre = df_normed[df_normed["year"] < self.sdgs_start_year].groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"})
|
|
mdgs_pre = mdgs_pre.merge(n_ind_pre, on="year", how="left")
|
|
mdgs_pre["framework"] = "MDGs"
|
|
parts.append(mdgs_pre)
|
|
|
|
# Layer MDGs — mixed
|
|
if self.mdgs_indicator_ids:
|
|
df_mdgs_mixed = df_normed[
|
|
(df_normed["indicator_id"].isin(self.mdgs_indicator_ids)) &
|
|
(df_normed["year"] >= self.sdgs_start_year)
|
|
].copy()
|
|
if not df_mdgs_mixed.empty:
|
|
cn = df_mdgs_mixed.groupby(["country_id", "year"])["norm_value"].mean().reset_index().rename(columns={"norm_value": "country_norm"})
|
|
asean_mdgs = cn.groupby("year").agg(
|
|
framework_norm=("country_norm", "mean"),
|
|
std_norm=("country_norm", "std"),
|
|
n_countries_with_data=("country_id", "count"),
|
|
).reset_index()
|
|
n_ind_mdgs = df_mdgs_mixed.groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"})
|
|
asean_mdgs = asean_mdgs.merge(n_ind_mdgs, on="year", how="left")
|
|
if not NORMALIZE_FRAMEWORKS_JOINTLY:
|
|
asean_mdgs["framework_score_1_100"] = global_minmax(asean_mdgs["framework_norm"])
|
|
asean_mdgs["framework"] = "MDGs"
|
|
parts.append(asean_mdgs)
|
|
|
|
# Layer SDGs
|
|
if self.sdgs_indicator_ids:
|
|
df_sdgs = df_normed[
|
|
(df_normed["indicator_id"].isin(self.sdgs_indicator_ids)) &
|
|
(df_normed["year"] >= self.sdgs_start_year)
|
|
].copy()
|
|
if not df_sdgs.empty:
|
|
cn = df_sdgs.groupby(["country_id", "year"])["norm_value"].mean().reset_index().rename(columns={"norm_value": "country_norm"})
|
|
asean_sdgs = cn.groupby("year").agg(
|
|
framework_norm=("country_norm", "mean"),
|
|
std_norm=("country_norm", "std"),
|
|
n_countries_with_data=("country_id", "count"),
|
|
).reset_index()
|
|
n_ind_sdgs = df_sdgs.groupby("year")["indicator_id"].nunique().reset_index().rename(columns={"indicator_id": "n_indicators"})
|
|
asean_sdgs = asean_sdgs.merge(n_ind_sdgs, on="year", how="left")
|
|
if not NORMALIZE_FRAMEWORKS_JOINTLY:
|
|
asean_sdgs["framework_score_1_100"] = global_minmax(asean_sdgs["framework_norm"])
|
|
asean_sdgs["framework"] = "SDGs"
|
|
parts.append(asean_sdgs)
|
|
|
|
df = pd.concat(parts, ignore_index=True)
|
|
|
|
if NORMALIZE_FRAMEWORKS_JOINTLY:
|
|
mixed_mask = (df["framework"].isin(["MDGs", "SDGs"])) & (df["year"] >= self.sdgs_start_year)
|
|
if mixed_mask.any():
|
|
df.loc[mixed_mask, "framework_score_1_100"] = global_minmax(df.loc[mixed_mask, "framework_norm"])
|
|
|
|
df = check_and_dedup(df, ["framework", "year"], context=table_name, logger=self.logger)
|
|
df = add_yoy(df, ["framework"], "framework_score_1_100")
|
|
|
|
df["year"] = df["year"].astype(int)
|
|
df["n_indicators"] = safe_int(df["n_indicators"], col_name="n_indicators", logger=self.logger)
|
|
df["n_countries_with_data"] = safe_int(df["n_countries_with_data"], col_name="n_countries_with_data", logger=self.logger)
|
|
for col in ["framework_norm", "std_norm", "framework_score_1_100"]:
|
|
df[col] = df[col].astype(float)
|
|
|
|
self._validate_mdgs_equals_total(df, level="asean")
|
|
|
|
schema = [
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_indicators", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_countries_with_data", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("framework_norm", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("std_norm", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("framework_score_1_100", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("year_over_year_change", "FLOAT", mode="NULLABLE"),
|
|
]
|
|
rows = load_to_bigquery(self.client, df, table_name, layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema)
|
|
self._finalize(table_name, rows)
|
|
return df
|
|
|
|
# =========================================================================
|
|
# STEP 6: agg_narrative_overview -> Gold (NEW)
|
|
#
|
|
# Sumber data : df_framework_asean (framework='Total') + df_framework_by_country
|
|
# Granularity : 1 row per year
|
|
# Columns : year, n_mdg_indicators, n_sdg_indicators, n_total_indicators,
|
|
# asean_total_score, yoy_change, yoy_change_pct,
|
|
# country_ranking_json, most_improved_country, most_improved_delta,
|
|
# most_declined_country, most_declined_delta, narrative_overview
|
|
# =========================================================================
|
|
|
|
def calc_narrative_overview(
|
|
self,
|
|
df_framework_asean: pd.DataFrame,
|
|
df_framework_by_country: pd.DataFrame,
|
|
) -> pd.DataFrame:
|
|
table_name = "agg_narrative_overview"
|
|
self.load_metadata[table_name]["start_time"] = datetime.now()
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info(f"STEP 6: {table_name} -> [Gold] fs_asean_gold")
|
|
self.logger.info("=" * 70)
|
|
|
|
# ASEAN-level Total framework rows, sorted by year
|
|
asean_total = (
|
|
df_framework_asean[df_framework_asean["framework"] == "Total"]
|
|
.sort_values("year")
|
|
.reset_index(drop=True)
|
|
)
|
|
|
|
# Country-level Total framework rows (ranking + YoY per country)
|
|
country_total = (
|
|
df_framework_by_country[df_framework_by_country["framework"] == "Total"]
|
|
.copy()
|
|
)
|
|
|
|
# Indicator counts per year per framework (self.df already has 'framework' column)
|
|
ind_year = self.df.drop_duplicates(subset=["indicator_id", "year", "framework"])
|
|
|
|
records = []
|
|
|
|
for _, row in asean_total.iterrows():
|
|
yr = int(row["year"])
|
|
score = float(row["framework_score_1_100"])
|
|
yoy = row["year_over_year_change"]
|
|
yoy_val = float(yoy) if pd.notna(yoy) else None
|
|
|
|
# -- Indicator counts per framework for this year ---------------
|
|
yr_ind = ind_year[ind_year["year"] == yr]
|
|
n_mdg = int(yr_ind[yr_ind["framework"] == "MDGs"]["indicator_id"].nunique())
|
|
n_sdg = int(yr_ind[yr_ind["framework"] == "SDGs"]["indicator_id"].nunique())
|
|
n_total_ind = int(yr_ind["indicator_id"].nunique())
|
|
|
|
# -- YoY % -----------------------------------------------------
|
|
prev_score = score - yoy_val if yoy_val is not None else None
|
|
yoy_pct = (
|
|
(yoy_val / prev_score * 100)
|
|
if (yoy_val is not None and prev_score and prev_score != 0)
|
|
else None
|
|
)
|
|
|
|
# -- Country ranking for this year -----------------------------
|
|
yr_country = (
|
|
country_total[country_total["year"] == yr]
|
|
.sort_values("rank_in_framework_year")
|
|
.reset_index(drop=True)
|
|
)
|
|
|
|
ranking_list = []
|
|
for _, cr in yr_country.iterrows():
|
|
cr_yoy = cr.get("year_over_year_change", None)
|
|
ranking_list.append({
|
|
"rank": int(cr["rank_in_framework_year"]),
|
|
"country_name": str(cr["country_name"]),
|
|
"score": round(float(cr["framework_score_1_100"]), 2),
|
|
"yoy_change": round(float(cr_yoy), 2) if pd.notna(cr_yoy) else None,
|
|
})
|
|
country_ranking_json = json.dumps(ranking_list, ensure_ascii=False)
|
|
|
|
# -- Most improved / declined country --------------------------
|
|
yr_country_yoy = yr_country.dropna(subset=["year_over_year_change"])
|
|
if not yr_country_yoy.empty:
|
|
best_idx = yr_country_yoy["year_over_year_change"].idxmax()
|
|
worst_idx = yr_country_yoy["year_over_year_change"].idxmin()
|
|
most_improved_country = str(yr_country_yoy.loc[best_idx, "country_name"])
|
|
most_improved_delta = round(float(yr_country_yoy.loc[best_idx, "year_over_year_change"]), 2)
|
|
most_declined_country = str(yr_country_yoy.loc[worst_idx, "country_name"])
|
|
most_declined_delta = round(float(yr_country_yoy.loc[worst_idx, "year_over_year_change"]), 2)
|
|
else:
|
|
most_improved_country = most_declined_country = None
|
|
most_improved_delta = most_declined_delta = None
|
|
|
|
# -- Build narrative -------------------------------------------
|
|
narrative = _build_overview_narrative(
|
|
year = yr,
|
|
n_mdg = n_mdg,
|
|
n_sdg = n_sdg,
|
|
n_total_ind = n_total_ind,
|
|
score = score,
|
|
yoy_val = yoy_val,
|
|
yoy_pct = yoy_pct,
|
|
prev_year = yr - 1,
|
|
prev_score = prev_score,
|
|
ranking_list = ranking_list,
|
|
most_improved_country = most_improved_country,
|
|
most_improved_delta = most_improved_delta,
|
|
most_declined_country = most_declined_country,
|
|
most_declined_delta = most_declined_delta,
|
|
)
|
|
|
|
records.append({
|
|
"year": yr,
|
|
"n_mdg_indicators": n_mdg,
|
|
"n_sdg_indicators": n_sdg,
|
|
"n_total_indicators": n_total_ind,
|
|
"asean_total_score": round(score, 2),
|
|
"yoy_change": yoy_val,
|
|
"yoy_change_pct": round(yoy_pct, 2) if yoy_pct is not None else None,
|
|
"country_ranking_json": country_ranking_json,
|
|
"most_improved_country": most_improved_country,
|
|
"most_improved_delta": most_improved_delta,
|
|
"most_declined_country": most_declined_country,
|
|
"most_declined_delta": most_declined_delta,
|
|
"narrative_overview": narrative,
|
|
})
|
|
|
|
df = pd.DataFrame(records)
|
|
df["year"] = df["year"].astype(int)
|
|
df["n_mdg_indicators"] = df["n_mdg_indicators"].astype(int)
|
|
df["n_sdg_indicators"] = df["n_sdg_indicators"].astype(int)
|
|
df["n_total_indicators"] = df["n_total_indicators"].astype(int)
|
|
df["asean_total_score"] = df["asean_total_score"].astype(float)
|
|
for col in ["yoy_change", "yoy_change_pct", "most_improved_delta", "most_declined_delta"]:
|
|
df[col] = pd.to_numeric(df[col], errors="coerce").astype(float)
|
|
|
|
schema = [
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_mdg_indicators", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_sdg_indicators", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("n_total_indicators", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("asean_total_score", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("yoy_change", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("yoy_change_pct", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("country_ranking_json", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("most_improved_country", "STRING", mode="NULLABLE"),
|
|
bigquery.SchemaField("most_improved_delta", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("most_declined_country", "STRING", mode="NULLABLE"),
|
|
bigquery.SchemaField("most_declined_delta", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("narrative_overview", "STRING", mode="REQUIRED"),
|
|
]
|
|
rows = load_to_bigquery(
|
|
self.client, df, table_name, layer='gold',
|
|
write_disposition="WRITE_TRUNCATE", schema=schema,
|
|
)
|
|
self._finalize(table_name, rows)
|
|
return df
|
|
|
|
# =========================================================================
|
|
# STEP 7: agg_narrative_pillar -> Gold (NEW)
|
|
#
|
|
# Sumber data : df_pillar_composite + df_pillar_by_country
|
|
# Granularity : 1 row per (year, pillar_id)
|
|
# Columns : year, pillar_id, pillar_name, pillar_score, rank_in_year,
|
|
# yoy_change, top_country, top_country_score,
|
|
# bottom_country, bottom_country_score, narrative_pillar
|
|
# =========================================================================
|
|
|
|
def calc_narrative_pillar(
|
|
self,
|
|
df_pillar_composite: pd.DataFrame,
|
|
df_pillar_by_country: pd.DataFrame,
|
|
) -> pd.DataFrame:
|
|
table_name = "agg_narrative_pillar"
|
|
self.load_metadata[table_name]["start_time"] = datetime.now()
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info(f"STEP 7: {table_name} -> [Gold] fs_asean_gold")
|
|
self.logger.info("=" * 70)
|
|
|
|
records = []
|
|
years = sorted(df_pillar_composite["year"].unique())
|
|
|
|
for yr in years:
|
|
yr_pillars = (
|
|
df_pillar_composite[df_pillar_composite["year"] == yr]
|
|
.sort_values("rank_in_year")
|
|
.reset_index(drop=True)
|
|
)
|
|
yr_country_pillar = df_pillar_by_country[df_pillar_by_country["year"] == yr]
|
|
|
|
# Strongest / weakest pillar this year (for context sentence)
|
|
strongest_pillar = yr_pillars.iloc[0] if len(yr_pillars) > 0 else None
|
|
weakest_pillar = yr_pillars.iloc[-1] if len(yr_pillars) > 0 else None
|
|
|
|
# Biggest improvement / decline across all pillars this year
|
|
yr_pillars_yoy = yr_pillars.dropna(subset=["year_over_year_change"])
|
|
if not yr_pillars_yoy.empty:
|
|
best_p_idx = yr_pillars_yoy["year_over_year_change"].idxmax()
|
|
worst_p_idx = yr_pillars_yoy["year_over_year_change"].idxmin()
|
|
most_improved_pillar = str(yr_pillars_yoy.loc[best_p_idx, "pillar_name"])
|
|
most_improved_delta = round(float(yr_pillars_yoy.loc[best_p_idx, "year_over_year_change"]), 2)
|
|
most_declined_pillar = str(yr_pillars_yoy.loc[worst_p_idx, "pillar_name"])
|
|
most_declined_delta = round(float(yr_pillars_yoy.loc[worst_p_idx, "year_over_year_change"]), 2)
|
|
else:
|
|
most_improved_pillar = most_declined_pillar = None
|
|
most_improved_delta = most_declined_delta = None
|
|
|
|
for _, prow in yr_pillars.iterrows():
|
|
p_id = int(prow["pillar_id"])
|
|
p_name = str(prow["pillar_name"])
|
|
p_score = float(prow["pillar_score_1_100"])
|
|
p_rank = int(prow["rank_in_year"])
|
|
p_yoy = prow["year_over_year_change"]
|
|
p_yoy_val = float(p_yoy) if pd.notna(p_yoy) else None
|
|
|
|
# Top / bottom country within this pillar & year
|
|
p_country = (
|
|
yr_country_pillar[yr_country_pillar["pillar_id"] == p_id]
|
|
.sort_values("rank_in_pillar_year")
|
|
.reset_index(drop=True)
|
|
)
|
|
if not p_country.empty:
|
|
top_country = str(p_country.iloc[0]["country_name"])
|
|
top_country_score = round(float(p_country.iloc[0]["pillar_country_score_1_100"]), 2)
|
|
bot_country = str(p_country.iloc[-1]["country_name"])
|
|
bot_country_score = round(float(p_country.iloc[-1]["pillar_country_score_1_100"]), 2)
|
|
else:
|
|
top_country = bot_country = None
|
|
top_country_score = bot_country_score = None
|
|
|
|
# -- Build narrative ---------------------------------------
|
|
narrative = _build_pillar_narrative(
|
|
year = yr,
|
|
pillar_name = p_name,
|
|
pillar_score = p_score,
|
|
rank_in_year = p_rank,
|
|
n_pillars = len(yr_pillars),
|
|
yoy_val = p_yoy_val,
|
|
top_country = top_country,
|
|
top_country_score = top_country_score,
|
|
bot_country = bot_country,
|
|
bot_country_score = bot_country_score,
|
|
strongest_pillar = str(strongest_pillar["pillar_name"]) if strongest_pillar is not None else None,
|
|
strongest_score = round(float(strongest_pillar["pillar_score_1_100"]), 2) if strongest_pillar is not None else None,
|
|
weakest_pillar = str(weakest_pillar["pillar_name"]) if weakest_pillar is not None else None,
|
|
weakest_score = round(float(weakest_pillar["pillar_score_1_100"]), 2) if weakest_pillar is not None else None,
|
|
most_improved_pillar = most_improved_pillar,
|
|
most_improved_delta = most_improved_delta,
|
|
most_declined_pillar = most_declined_pillar,
|
|
most_declined_delta = most_declined_delta,
|
|
)
|
|
|
|
records.append({
|
|
"year": yr,
|
|
"pillar_id": p_id,
|
|
"pillar_name": p_name,
|
|
"pillar_score": round(p_score, 2),
|
|
"rank_in_year": p_rank,
|
|
"yoy_change": p_yoy_val,
|
|
"top_country": top_country,
|
|
"top_country_score": top_country_score,
|
|
"bottom_country": bot_country,
|
|
"bottom_country_score": bot_country_score,
|
|
"narrative_pillar": narrative,
|
|
})
|
|
|
|
df = pd.DataFrame(records)
|
|
df["year"] = df["year"].astype(int)
|
|
df["pillar_id"] = df["pillar_id"].astype(int)
|
|
df["rank_in_year"] = df["rank_in_year"].astype(int)
|
|
for col in ["pillar_score", "yoy_change", "top_country_score", "bottom_country_score"]:
|
|
df[col] = pd.to_numeric(df[col], errors="coerce").astype(float)
|
|
|
|
schema = [
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_score", "FLOAT", mode="REQUIRED"),
|
|
bigquery.SchemaField("rank_in_year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("yoy_change", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("top_country", "STRING", mode="NULLABLE"),
|
|
bigquery.SchemaField("top_country_score", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("bottom_country", "STRING", mode="NULLABLE"),
|
|
bigquery.SchemaField("bottom_country_score", "FLOAT", mode="NULLABLE"),
|
|
bigquery.SchemaField("narrative_pillar", "STRING", mode="REQUIRED"),
|
|
]
|
|
rows = load_to_bigquery(
|
|
self.client, df, table_name, layer='gold',
|
|
write_disposition="WRITE_TRUNCATE", schema=schema,
|
|
)
|
|
self._finalize(table_name, rows)
|
|
return df
|
|
|
|
# =========================================================================
|
|
# HELPERS
|
|
# =========================================================================
|
|
|
|
def _validate_mdgs_equals_total(self, df: pd.DataFrame, level: str = ""):
|
|
self.logger.info(f"\n Validasi MDGs < {self.sdgs_start_year} == Total [{level}]:")
|
|
group_by = ["year"] if level.startswith("asean") else ["country_id", "year"]
|
|
mdgs_pre = df[(df["framework"] == "MDGs") & (df["year"] < self.sdgs_start_year)][group_by + ["framework_score_1_100"]].rename(columns={"framework_score_1_100": "mdgs_score"})
|
|
total_pre = df[(df["framework"] == "Total") & (df["year"] < self.sdgs_start_year)][group_by + ["framework_score_1_100"]].rename(columns={"framework_score_1_100": "total_score"})
|
|
if mdgs_pre.empty and total_pre.empty:
|
|
self.logger.info(f" -> Tidak ada data pre-{self.sdgs_start_year} (skip)")
|
|
return
|
|
if mdgs_pre.empty or total_pre.empty:
|
|
self.logger.warning(f" -> [WARNING] Salah satu kosong: MDGs={len(mdgs_pre)}, Total={len(total_pre)}")
|
|
return
|
|
check = mdgs_pre.merge(total_pre, on=group_by)
|
|
max_diff = (check["mdgs_score"] - check["total_score"]).abs().max()
|
|
status = "OK (identik)" if max_diff < 0.01 else f"MISMATCH! max_diff={max_diff:.6f}"
|
|
self.logger.info(f" -> {status} (n_checked={len(check)})")
|
|
|
|
def _finalize(self, table_name: str, rows_loaded: int):
|
|
self.load_metadata[table_name].update({
|
|
"rows_loaded": rows_loaded, "status": "success", "end_time": datetime.now(),
|
|
})
|
|
log_update(self.client, "DW", table_name, "full_load", rows_loaded)
|
|
self.logger.info(f" ✓ {table_name}: {rows_loaded:,} rows → [Gold] fs_asean_gold")
|
|
self.logger.info(f" Metadata → [AUDIT] etl_logs")
|
|
|
|
def _fail(self, table_name: str, error: Exception):
|
|
self.load_metadata[table_name].update({"status": "failed", "end_time": datetime.now()})
|
|
self.logger.error(f" [FAIL] {table_name}: {error}")
|
|
log_update(self.client, "DW", table_name, "full_load", 0, "failed", str(error))
|
|
|
|
# =========================================================================
|
|
# RUN — 6 tabel (4 lama + 2 narrative baru)
|
|
# =========================================================================
|
|
|
|
def run(self):
|
|
start = datetime.now()
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info("FOOD SECURITY AGGREGATION v9.0 — 6 TABLES -> fs_asean_gold")
|
|
self.logger.info(" agg_pillar_composite | agg_pillar_by_country")
|
|
self.logger.info(" agg_framework_by_country| agg_framework_asean")
|
|
self.logger.info(" agg_narrative_overview | agg_narrative_pillar")
|
|
self.logger.info("=" * 70)
|
|
|
|
self.load_data()
|
|
self._classify_indicators()
|
|
|
|
# -- 4 tabel lama (tidak ada perubahan) ----------------------------
|
|
df_pillar_composite = self.calc_pillar_composite()
|
|
df_pillar_by_country = self.calc_pillar_by_country()
|
|
df_framework_by_country = self.calc_framework_by_country()
|
|
df_framework_asean = self.calc_framework_asean()
|
|
|
|
# -- 2 tabel narrative baru ----------------------------------------
|
|
self.calc_narrative_overview(
|
|
df_framework_asean = df_framework_asean,
|
|
df_framework_by_country = df_framework_by_country,
|
|
)
|
|
self.calc_narrative_pillar(
|
|
df_pillar_composite = df_pillar_composite,
|
|
df_pillar_by_country = df_pillar_by_country,
|
|
)
|
|
|
|
duration = (datetime.now() - start).total_seconds()
|
|
total_rows = sum(m["rows_loaded"] for m in self.load_metadata.values())
|
|
|
|
self.logger.info("\n" + "=" * 70)
|
|
self.logger.info("SELESAI")
|
|
self.logger.info("=" * 70)
|
|
self.logger.info(f" Durasi : {duration:.2f}s")
|
|
self.logger.info(f" Total rows : {total_rows:,}")
|
|
for tbl, meta in self.load_metadata.items():
|
|
icon = "✓" if meta["status"] == "success" else "✗"
|
|
self.logger.info(f" {icon} {tbl:<35} {meta['rows_loaded']:>10,}")
|
|
|
|
|
|
# =============================================================================
|
|
# AIRFLOW TASK FUNCTIONS
|
|
# =============================================================================
|
|
|
|
def run_aggregation():
|
|
"""
|
|
Airflow task: Hitung semua agregasi dari analytical_food_security.
|
|
Dipanggil setelah analytical_layer_to_gold selesai.
|
|
Menjalankan 6 tabel sekaligus: 4 agregasi + 2 narrative.
|
|
"""
|
|
from scripts.bigquery_config import get_bigquery_client
|
|
client = get_bigquery_client()
|
|
agg = FoodSecurityAggregator(client)
|
|
agg.run()
|
|
total = sum(m["rows_loaded"] for m in agg.load_metadata.values())
|
|
print(f"Aggregation completed: {total:,} total rows loaded")
|
|
|
|
|
|
# =============================================================================
|
|
# MAIN EXECUTION
|
|
# =============================================================================
|
|
|
|
if __name__ == "__main__":
|
|
import io
|
|
|
|
if _sys.stdout.encoding and _sys.stdout.encoding.lower() not in ("utf-8", "utf8"):
|
|
_sys.stdout = io.TextIOWrapper(_sys.stdout.buffer, encoding="utf-8", errors="replace")
|
|
if _sys.stderr.encoding and _sys.stderr.encoding.lower() not in ("utf-8", "utf8"):
|
|
_sys.stderr = io.TextIOWrapper(_sys.stderr.buffer, encoding="utf-8", errors="replace")
|
|
|
|
print("=" * 70)
|
|
print("FOOD SECURITY AGGREGATION v9.0 — 6 TABLES -> fs_asean_gold")
|
|
print(f" NORMALIZE_FRAMEWORKS_JOINTLY = {NORMALIZE_FRAMEWORKS_JOINTLY}")
|
|
print("=" * 70)
|
|
|
|
logger = setup_logging()
|
|
for handler in logger.handlers:
|
|
handler.__class__ = _SafeStreamHandler
|
|
|
|
client = get_bigquery_client()
|
|
agg = FoodSecurityAggregator(client)
|
|
agg.run()
|
|
|
|
print("\n" + "=" * 70)
|
|
print("[OK] SELESAI")
|
|
print("=" * 70) |