You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
469 lines
15 KiB
469 lines
15 KiB
"""Axis classifier: correlate per-party PCA positions against ideology reference data
|
|
to assign honest, dynamic labels to political compass axes.
|
|
|
|
Public API: classify_axes(positions_by_window, axes, db_path) -> dict
|
|
"""
|
|
|
|
import logging
|
|
from collections import Counter
|
|
from pathlib import Path
|
|
from typing import Dict, List, Optional, Tuple
|
|
|
|
import numpy as np
|
|
import re
|
|
import json
|
|
|
|
_logger = logging.getLogger(__name__)
|
|
|
|
# Module-level caches — loaded once per process lifetime.
|
|
_ideology_cache: Optional[Dict[str, Dict[str, float]]] = None
|
|
_coalition_cache: Optional[Dict[str, set]] = None
|
|
|
|
# Correlation threshold above which we consider an axis "explained" by a dimension.
|
|
_THRESHOLD = 0.65
|
|
|
|
_LABELS = {
|
|
"lr": "Links\u2013Rechts",
|
|
"co": "Coalitie\u2013Oppositie",
|
|
"pc": "Progressief\u2013Conservatief",
|
|
"fallback_x": "Stempatroon As 1",
|
|
"fallback_y": "Stempatroon As 2",
|
|
}
|
|
|
|
_INTERPRETATION_TEMPLATES = {
|
|
"lr": "De {orientation} as weerspiegelt de klassieke links-rechts tegenstelling.",
|
|
"co": (
|
|
"De {orientation} as weerspiegelt stemgedrag van coalitie- versus "
|
|
"oppositiepartijen (r={r:.2f}). Links-rechts is minder dominant dit jaar."
|
|
),
|
|
"pc": "De {orientation} as weerspiegelt de progressief-conservatieve tegenstelling.",
|
|
"fallback": (
|
|
"De {orientation} as weerspiegelt een empirisch stempatroon "
|
|
"zonder duidelijke ideologische richting."
|
|
),
|
|
}
|
|
|
|
|
|
# Simple keyword-based classifier for motion titles (fallback signal)
|
|
_KEYWORD_THRESHOLD = 0.4
|
|
|
|
_KEYWORDS: Dict[str, List[str]] = {
|
|
"Links\u2013Rechts": [
|
|
# economic
|
|
"belasting",
|
|
"uitkering",
|
|
"bijstand",
|
|
"minimumloon",
|
|
"cao",
|
|
"vakbond",
|
|
"bezuiniging",
|
|
"privatisering",
|
|
"subsidie",
|
|
"pensioen",
|
|
"aow",
|
|
"zorg",
|
|
# immigration
|
|
"asiel",
|
|
"asielaanvraag",
|
|
"migratie",
|
|
"vreemdeling",
|
|
"vluchtelingen",
|
|
"terugkeer",
|
|
"grenzen",
|
|
"opvang",
|
|
"statushouder",
|
|
],
|
|
"Progressief\u2013Conservatief": [
|
|
# environment
|
|
"klimaat",
|
|
"stikstof",
|
|
"duurzaam",
|
|
"duurzaamheid",
|
|
"co2",
|
|
"energietransitie",
|
|
"biodiversiteit",
|
|
# social
|
|
"euthanasie",
|
|
"abortus",
|
|
"lgbtq",
|
|
"transgender",
|
|
"diversiteit",
|
|
"traditi",
|
|
"gezin",
|
|
"religie",
|
|
"geloof",
|
|
],
|
|
"Nationaal\u2013Internationaal": [
|
|
"navo",
|
|
"nato",
|
|
"europees",
|
|
"europese",
|
|
" eu ",
|
|
"verdrag",
|
|
" vn ",
|
|
"internationaal",
|
|
],
|
|
}
|
|
|
|
# Pre-compiled regexes for keyword matching. We escape keywords but do NOT add
|
|
# word-boundaries because some keywords intentionally match substrings
|
|
# (e.g. 'traditi' matching 'tradities'). re.IGNORECASE makes lowercasing
|
|
# unnecessary during matching.
|
|
_KEYWORD_REGEXES: Dict[str, "re.Pattern[str]"] = {
|
|
cat: re.compile(
|
|
"|".join(re.escape(kw.strip()) for kw in kws),
|
|
re.IGNORECASE,
|
|
)
|
|
for cat, kws in _KEYWORDS.items()
|
|
}
|
|
|
|
|
|
def _classify_from_titles(titles: List[str]) -> Tuple[Optional[str], float]:
|
|
"""Classify a list of motion titles into an axis category using keyword matching.
|
|
|
|
Returns (category_label, confidence) where confidence = fraction of titles
|
|
containing at least one keyword from the winning category.
|
|
Returns (None, confidence) if confidence is below _KEYWORD_THRESHOLD.
|
|
"""
|
|
if not titles:
|
|
return None, 0.0
|
|
|
|
counts: Dict[str, int] = {cat: 0 for cat in _KEYWORDS}
|
|
for title in titles:
|
|
for cat, rx in _KEYWORD_REGEXES.items():
|
|
if rx.search(title):
|
|
counts[cat] += 1
|
|
|
|
# Determine the best category, but be deterministic on ties: if more than
|
|
# one category has the top count, return None to indicate ambiguity.
|
|
best_count = max(counts.values())
|
|
best_cats = [cat for cat, cnt in counts.items() if cnt == best_count]
|
|
confidence = best_count / len(titles)
|
|
|
|
if len(best_cats) != 1 or confidence < _KEYWORD_THRESHOLD:
|
|
return None, confidence
|
|
|
|
return best_cats[0], confidence
|
|
|
|
|
|
def _load_motion_vectors(db_path: str, window_id: str) -> Dict[int, np.ndarray]:
|
|
"""Load SVD motion vectors for a given window from DuckDB.
|
|
|
|
Returns {motion_id: vector_array}. Returns {} on any error.
|
|
"""
|
|
try:
|
|
import duckdb
|
|
|
|
conn = duckdb.connect(db_path, read_only=True)
|
|
rows = conn.execute(
|
|
"SELECT entity_id, vector FROM svd_vectors "
|
|
"WHERE entity_type = 'motion' AND window_id = ?",
|
|
[window_id],
|
|
).fetchall()
|
|
conn.close()
|
|
result: Dict[int, np.ndarray] = {}
|
|
for entity_id, vector_raw in rows:
|
|
try:
|
|
mid = int(entity_id)
|
|
vec = np.array(json.loads(vector_raw), dtype=float)
|
|
result[mid] = vec
|
|
except Exception:
|
|
continue
|
|
return result
|
|
except Exception as exc:
|
|
_logger.debug("Failed to load motion vectors for window %s: %s", window_id, exc)
|
|
return {}
|
|
|
|
|
|
def _project_motions(
|
|
motion_vecs: Dict[int, np.ndarray],
|
|
x_axis: np.ndarray,
|
|
y_axis: np.ndarray,
|
|
global_mean: np.ndarray,
|
|
) -> Dict[int, Tuple[float, float]]:
|
|
"""Project motion vectors onto the PCA axes after centering by global_mean.
|
|
|
|
Returns {motion_id: (x_score, y_score)}.
|
|
"""
|
|
projections: Dict[int, Tuple[float, float]] = {}
|
|
for mid, vec in motion_vecs.items():
|
|
try:
|
|
centered = vec - global_mean
|
|
x_score = float(np.dot(centered, x_axis))
|
|
y_score = float(np.dot(centered, y_axis))
|
|
projections[mid] = (x_score, y_score)
|
|
except Exception:
|
|
continue
|
|
return projections
|
|
|
|
|
|
def _top_motion_ids(
|
|
projections: Dict[int, Tuple[float, float]],
|
|
axis: str,
|
|
n: int = 5,
|
|
) -> Dict[str, List[int]]:
|
|
"""Return the top-n motion IDs at each pole of the given axis.
|
|
|
|
axis: 'x' or 'y'
|
|
Returns {'+': [motion_ids], '-': [motion_ids]} (highest positive first,
|
|
most negative first in the '-' list).
|
|
"""
|
|
idx = 0 if axis == "x" else 1
|
|
sorted_ids = sorted(projections, key=lambda mid: projections[mid][idx])
|
|
neg_ids = sorted_ids[:n] # most negative
|
|
pos_ids = sorted_ids[-n:][::-1] # most positive
|
|
return {"+": pos_ids, "-": neg_ids}
|
|
|
|
|
|
def _fetch_motion_titles(
|
|
db_path: str,
|
|
motion_ids: List[int],
|
|
) -> Dict[int, Tuple[str, str]]:
|
|
"""Fetch (title, date) for a list of motion IDs from DuckDB.
|
|
|
|
Returns {motion_id: (title, date_str)}. Missing IDs are omitted.
|
|
Returns {} on any DB error.
|
|
"""
|
|
if not motion_ids:
|
|
return {}
|
|
try:
|
|
import duckdb
|
|
|
|
placeholders = ", ".join("?" * len(motion_ids))
|
|
conn = duckdb.connect(db_path, read_only=True)
|
|
rows = conn.execute(
|
|
f"SELECT id, title, date FROM motions WHERE id IN ({placeholders})",
|
|
motion_ids,
|
|
).fetchall()
|
|
conn.close()
|
|
return {int(row[0]): (str(row[1]), str(row[2])) for row in rows}
|
|
except Exception as exc:
|
|
_logger.debug("Failed to fetch motion titles: %s", exc)
|
|
return {}
|
|
|
|
|
|
def _load_ideology(csv_path: Path) -> Dict[str, Dict[str, float]]:
|
|
"""Load party ideology scores from CSV.
|
|
|
|
Returns {party_name: {"left_right": float, "progressive": float}}.
|
|
Returns {} on any error (caller should treat empty as 'skip classification').
|
|
"""
|
|
global _ideology_cache
|
|
if _ideology_cache is not None:
|
|
return _ideology_cache
|
|
result: Dict[str, Dict[str, float]] = {}
|
|
try:
|
|
with open(csv_path, encoding="utf-8") as fh:
|
|
lines = fh.read().splitlines()
|
|
header = [h.strip() for h in lines[0].split(",")]
|
|
lr_idx = header.index("left_right")
|
|
pc_idx = header.index("progressive")
|
|
for line in lines[1:]:
|
|
if not line.strip():
|
|
continue
|
|
parts = [p.strip() for p in line.split(",")]
|
|
if len(parts) <= max(lr_idx, pc_idx):
|
|
continue
|
|
result[parts[0]] = {
|
|
"left_right": float(parts[lr_idx]),
|
|
"progressive": float(parts[pc_idx]),
|
|
}
|
|
except FileNotFoundError:
|
|
_logger.warning(
|
|
"party_ideologies.csv not found at %s — axis labels will be generic",
|
|
csv_path,
|
|
)
|
|
return {}
|
|
except Exception as exc:
|
|
_logger.warning("Failed to load party_ideologies.csv: %s", exc)
|
|
return {}
|
|
_ideology_cache = result
|
|
return result
|
|
|
|
|
|
def _load_coalition(csv_path: Path) -> Dict[str, set]:
|
|
"""Load coalition membership from CSV.
|
|
|
|
Returns {window_id: set_of_party_names}.
|
|
Returns {} on any error (coalition dimension will be skipped).
|
|
"""
|
|
global _coalition_cache
|
|
if _coalition_cache is not None:
|
|
return _coalition_cache
|
|
result: Dict[str, set] = {}
|
|
try:
|
|
with open(csv_path, encoding="utf-8") as fh:
|
|
lines = fh.read().splitlines()
|
|
for line in lines[1:]:
|
|
if not line.strip():
|
|
continue
|
|
parts = [p.strip() for p in line.split(",")]
|
|
if len(parts) < 2:
|
|
continue
|
|
wid, party = parts[0], parts[1]
|
|
result.setdefault(wid, set()).add(party)
|
|
except FileNotFoundError:
|
|
_logger.warning(
|
|
"coalition_membership.csv not found at %s — coalition axis detection disabled",
|
|
csv_path,
|
|
)
|
|
return {}
|
|
except Exception as exc:
|
|
_logger.warning("Failed to load coalition_membership.csv: %s", exc)
|
|
return {}
|
|
_coalition_cache = result
|
|
return result
|
|
|
|
|
|
def _window_year(window_id: str) -> Optional[str]:
|
|
"""Extract year string from window_id.
|
|
|
|
Returns None for 'current_parliament'.
|
|
'2016' → '2016', '2016-Q3' → '2016'.
|
|
"""
|
|
if window_id == "current_parliament":
|
|
return None
|
|
return window_id.split("-")[0]
|
|
|
|
|
|
def _pearsonr(x: List[float], y: List[float]) -> float:
|
|
"""Pearson r; returns 0.0 for degenerate input (< 3 points or zero variance)."""
|
|
if len(x) < 3:
|
|
return 0.0
|
|
xa = np.array(x, dtype=float)
|
|
ya = np.array(y, dtype=float)
|
|
if xa.std() < 1e-12 or ya.std() < 1e-12:
|
|
return 0.0
|
|
return float(np.corrcoef(xa, ya)[0, 1])
|
|
|
|
|
|
def _assign_label(
|
|
r_lr: float,
|
|
r_co: float,
|
|
r_pc: float,
|
|
axis: str,
|
|
) -> Tuple[str, str, float]:
|
|
"""Assign label, interpretation and quality score for one axis.
|
|
|
|
Priority: left-right > coalition > progressive > fallback.
|
|
Returns (label, interpretation_string, quality_score).
|
|
"""
|
|
orientation = "horizontale" if axis == "x" else "verticale"
|
|
fallback_label = _LABELS["fallback_x"] if axis == "x" else _LABELS["fallback_y"]
|
|
quality = max(abs(r_lr), abs(r_co), abs(r_pc))
|
|
|
|
if abs(r_lr) >= _THRESHOLD:
|
|
return (
|
|
_LABELS["lr"],
|
|
_INTERPRETATION_TEMPLATES["lr"].format(orientation=orientation),
|
|
quality,
|
|
)
|
|
if abs(r_co) >= _THRESHOLD:
|
|
return (
|
|
_LABELS["co"],
|
|
_INTERPRETATION_TEMPLATES["co"].format(orientation=orientation, r=r_co),
|
|
quality,
|
|
)
|
|
if abs(r_pc) >= _THRESHOLD:
|
|
return (
|
|
_LABELS["pc"],
|
|
_INTERPRETATION_TEMPLATES["pc"].format(orientation=orientation),
|
|
quality,
|
|
)
|
|
return (
|
|
fallback_label,
|
|
_INTERPRETATION_TEMPLATES["fallback"].format(orientation=orientation),
|
|
quality,
|
|
)
|
|
|
|
|
|
def classify_axes(
|
|
positions_by_window: Dict[str, Dict[str, Tuple[float, float]]],
|
|
axes: dict,
|
|
db_path: str,
|
|
) -> dict:
|
|
"""Classify compass axes by correlating per-party positions against ideology reference data.
|
|
|
|
Enriches ``axes`` with:
|
|
x_label, y_label — global label (modal across annual windows)
|
|
x_quality, y_quality — {window_id: float} max |r| for each window
|
|
x_interpretation — {window_id: str} Dutch explanation per window
|
|
y_interpretation — {window_id: str} Dutch explanation per window
|
|
|
|
Returns the original ``axes`` dict unchanged if reference data is unavailable.
|
|
"""
|
|
data_dir = Path(db_path).parent
|
|
ideology = _load_ideology(data_dir / "party_ideologies.csv")
|
|
if not ideology:
|
|
return axes # no reference data — preserve existing behaviour
|
|
|
|
coalition = _load_coalition(data_dir / "coalition_membership.csv")
|
|
|
|
x_quality: Dict[str, float] = {}
|
|
y_quality: Dict[str, float] = {}
|
|
x_interpretation: Dict[str, str] = {}
|
|
y_interpretation: Dict[str, str] = {}
|
|
annual_x_labels: List[str] = []
|
|
annual_y_labels: List[str] = []
|
|
|
|
for wid, pos_dict in positions_by_window.items():
|
|
year = _window_year(wid)
|
|
is_current = wid == "current_parliament"
|
|
is_annual = not is_current and "-" not in wid # e.g. "2016" not "2016-Q3"
|
|
|
|
# Only use parties present in both the positions and the ideology reference.
|
|
parties = [p for p in pos_dict if p in ideology]
|
|
if len(parties) < 5:
|
|
_logger.debug(
|
|
"Skipping axis classification for %s: only %d reference parties (need 5)",
|
|
wid,
|
|
len(parties),
|
|
)
|
|
continue
|
|
|
|
party_x = [pos_dict[p][0] for p in parties]
|
|
party_y = [pos_dict[p][1] for p in parties]
|
|
ref_lr = [ideology[p]["left_right"] for p in parties]
|
|
ref_pc = [ideology[p]["progressive"] for p in parties]
|
|
|
|
# Coalition dummy: +1 if in government that year, -1 otherwise.
|
|
# current_parliament and windows with no coalition data use a neutral vector.
|
|
if year and coalition and year in coalition:
|
|
gov_set = coalition[year]
|
|
ref_co = [1.0 if p in gov_set else -1.0 for p in parties]
|
|
else:
|
|
ref_co = [0.0] * len(parties) # neutral — will never exceed threshold
|
|
|
|
r_lr_x = _pearsonr(party_x, ref_lr)
|
|
r_co_x = _pearsonr(party_x, ref_co)
|
|
r_pc_x = _pearsonr(party_x, ref_pc)
|
|
x_lbl, x_int, x_q = _assign_label(r_lr_x, r_co_x, r_pc_x, "x")
|
|
|
|
r_lr_y = _pearsonr(party_y, ref_lr)
|
|
r_co_y = _pearsonr(party_y, ref_co)
|
|
r_pc_y = _pearsonr(party_y, ref_pc)
|
|
y_lbl, y_int, y_q = _assign_label(r_lr_y, r_co_y, r_pc_y, "y")
|
|
|
|
x_quality[wid] = x_q
|
|
y_quality[wid] = y_q
|
|
x_interpretation[wid] = x_int
|
|
y_interpretation[wid] = y_int
|
|
|
|
# Only annual windows vote on the global label (not quarterly, not current_parliament).
|
|
if is_annual:
|
|
annual_x_labels.append(x_lbl)
|
|
annual_y_labels.append(y_lbl)
|
|
|
|
def _modal(labels: List[str], fallback: str) -> str:
|
|
if not labels:
|
|
return fallback
|
|
return Counter(labels).most_common(1)[0][0]
|
|
|
|
enriched = dict(axes)
|
|
enriched["x_label"] = _modal(annual_x_labels, "Links\u2013Rechts")
|
|
enriched["y_label"] = _modal(annual_y_labels, "Progressief\u2013Conservatief")
|
|
enriched["x_quality"] = x_quality
|
|
enriched["y_quality"] = y_quality
|
|
enriched["x_interpretation"] = x_interpretation
|
|
enriched["y_interpretation"] = y_interpretation
|
|
return enriched
|
|
|