Pure numpy function that computes bootstrap confidence intervals for
party centroid vectors. Handles N>=2 (bootstrap), N=1 (degenerate CI),
and N=0 (excluded) cases. Uses np.random.default_rng for reproducibility.
- PC2: rename 'maatschappelijke verantwoordelijkheid' to 'institutioneel
progressivisme' (less normatively loaded), rewrite explanation with actual
party scores (CU=-59, SGP=-25, VVD=-15 — all strongly negative, not
'near the middle'), update pole descriptions
- PC3: remove speculative motivation claim about PVV, state factual
observation that PVV/SP/PvdD/GL-PvdA vote alike despite opposing PC1
- PC7-PC10: add '(indicatief)' to labels — these axes explain <4% EVR
and may be below noise level
- PC7: add explicit fragility warning in explanation
- PC8: clarify DENK/SP negative scores mean active opposition voting,
not lack of focus; note Volt N=1 unreliability
- Scree plot: soften claim that later axes are 'meaningful'
Major corrections:
- Fix PC2 factual error: CU/CDA/SGP/D66 are strongly negative (-13 to -58), not near zero
- Correct methodology: party scores use single-window SVD, not Procrustes pipeline
- Correct centering: global (after stacking), not per-window
- Fix Groep Markuszower misclassification on PC4 (positive, not negative pool)
- Fix D66/PC4-PC5 cross-reference error
- Fix PC8/DENK interpretation (negative = voting against, not absence of focus)
Additions:
- Party sizes (N=) for all 17 parties across all axes
- Party size reliability table (D66=26 to Volt=1)
- All 5 flip values documented (PC3,4,7,9,10), not just PC3
- Vector-space mismatch table (single-window scores vs Procrustes EVR)
- Cautionary '(indicatief label)' on PC7-PC10
- New follow-up steps: bootstrap CIs, dimensionality testing, varimax, external validation
- Softened causal claims (kabinetscrisis correlation, PVV motivations)
- Less normatively loaded PC2 label
- Re-ran generate_svd_json.py for current_parliament window (100 rows, 10 components)
- Computed party centroid scores per axis from 150 matched MPs
- Updated all 10 SVD_THEMES entries with accurate labels, Dutch explanations
and correct positive/negative pole party attributions
- Key findings: PC1=rechts-links, PC2=populistisch nationalisme vs mainstream,
PC3=verzorgingsstaat vs bezuinigingen, PC6=klimaat & energie,
PC8=Europese defensie-integratie
- Added axis_analysis_data.json and party_svd_scores.json as analysis artifacts
Both _load_window_ids and _load_mp_vectors_for_window only read from the DB.
Opening without read_only=True caused an IOException when Streamlit already held
a read-only lock, silently returning an empty scree plot.
Previously load_scree_data computed L2-norms per dimension on current_parliament
vectors only, giving ~11% for PC1. This was inconsistent with the compass which
uses all windows + Procrustes alignment and gets PC1=24.1%.
Added compute_svd_spectrum() helper to political_axis.py that reuses the same
alignment pipeline. load_scree_data now delegates to it. _render_scree_plot
no longer re-normalizes (inputs are already EVR percentages). Hover label
updated to 'verklaarde variantie'.
Quarterly windows (29 of 41 total) diluted PC1 explained variance ratio
from ~20% down to ~14.6%. The fix splits the vector collection loop into:
- pca_vecs: annual windows only (re.match r'^\d{4}$') -> M_pca used for SVD
- all_vecs: every window -> M used for projections onto derived axes
Centering for SVD and global_mean for projection both now use M_pca.mean(axis=0)
so axes are consistent. Falls back to all windows if no annual windows exist.
Replaces static ideology CSV as primary axis classification signal with
per-year motion projection + Dutch keyword classifier. Adds axis-swap
logic so left-right is conventionally on X when present. Adds Option C
UI expander showing top motions per axis pole.
The global PCA X-axis flip uses centroids averaged across all windows,
which can leave individual windows with left/right inverted (e.g. PvdA
appearing right of VVD in 2020). Mirror the existing per-window Y-axis
correction to also check and flip X values per window.
Replace hardcoded 'Links-Rechts' / 'Progressief-Conservatief' axis labels
with values from classify_axes(). Add per-year interpretation caption when
axis quality score is below the 0.65 correlation threshold.
classify_axes() correlates per-party PCA positions against party_ideologies.csv
to assign honest dynamic labels (Links-Rechts, Coalitie-Oppositie, etc.)
instead of always assuming the first PCA axis is left-right.
Add design for honest PCA axis labeling — validates each compass axis
against a party ideology reference CSV and labels dynamically (Links–Rechts,
Coalitie–Oppositie, or fallback) instead of hardcoding Left–Right always.
The global orientation check using party centroids averaged across all
windows was insufficient — individual windows (notably 2023) could still
have conservative parties above progressive ones on the Y-axis.
Added a per-window flip in compute_2d_axes (PCA branch) that checks
prog_avg_y vs cons_avg_y for each window independently and negates all
Y values in that window when cons > prog. Flipped window IDs are stored
in axis_def['y_flipped_windows'] for diagnostics.
Moved the canonical party set definitions outside the orientation try-
block so they are always in scope for the per-window correction.
Added test_per_window_y_orientation to cover the case where one window
is globally fine but locally inverted.
- Rename app to 'Motief: de stematlas' in Home.py
- Remove PCA variance caption from compass tab
- Hardcode db_path and window_size; remove sidebar inputs
- Change trajectories default to [CDA, D66, VVD]
- Move quiz to pages/1_Stemwijzer.py; wrap in st.form
- Remove quiz tab from main explorer
- Add pytest dev dep + fix test fixtures (_load_mp_vectors_for_window)
- Add test_pca_axis_orientation with proper PCA variance dominance
sgeboers