Add four test files covering:
- test_config.py: SVD_THEMES structure validation
- test_explorer_labels.py: label derivation from positive/negative poles and flip
- test_svd_axis_alignment.py: right-wing centroid on RIGHT side for all axes
- test_validate_svd_themes.py: theme validation script tests
- Replace Procrustes-based stability with Ridge regression on fused embeddings
- For each SVD axis, fit Ridge: SVD_score ~ fused_embedding per window
- Compare weight vectors via max(cosine similarity, Jaccard top-100)
- Add --regression-alpha CLI argument (default 1.0)
- Keep party-based fallback for windows with < 50 motions
- Update tests for new regression-based approach
Key finding: regression weights show moderate stability (0.06-0.51)
but no axes exceed 0.7 threshold — semantic features defining each
axis shift significantly across windows
- Add scripts/motion_drift.py: analyzes SVD axis stability, semantic drift,
and cross-ideological voting patterns across annual windows
- Add analysis/motion_drift.py: core analysis functions with Procrustes
alignment fallback using party-based sign consistency
- Add matplotlib dependency for static chart generation
- Add tests/test_motion_drift.py: 12 tests covering all analysis functions
- Report output: markdown with embedded PNG charts
Key findings from real data:
- No axes are fully stable (>0.7) across 2019-2026
- All axes show moderate consistency (0.40-0.47) — stable within periods
but flip between cabinet periods (2019/2022/2026 vs 2023/2024/2025)
- Party voting analysis detects cross-ideological voting patterns
- Add CANONICAL_RIGHT (PVV, FVD, JA21, SGP) and CANONICAL_LEFT frozensets
to analysis/config.py as the canonical source of truth
- Update analysis/svd_labels.py to import from config; re-export as
RIGHT_PARTIES/LEFT_PARTIES for backward compatibility
- Add build_window_party_scores helper to analysis/explorer_data.py
- Add 7 integration tests in tests/test_axis_political_orientation.py
validating that canonical right parties appear on the right side of SVD
axes (x=component 1, y=component 2) using real DuckDB data
- Changed _render_party_axis_chart_1d from horizontal bar chart to scatter plot
- Same format as components 1-2: markers on horizontal line with axis arrows- Axis labels now show correct direction with arrows (← left | right →)
- Ensures consistent visualization across all SVD components
- Lock x_label/y_label to Links-Rechts / Progressief-Conservatief after
classify_axes; Procrustes sign-fixing in compute_2d_axes already ensures
the correct orientation so the heuristic _should_swap_axes call is removed
- Remove visual error bars from party axis chart; 95% CI is now shown in
hover text (party: score, N=n, 95%-BI: [low, high]) to keep the 1D
scatter clean
- Remove show_ci checkbox and parameter — CI is always accessible on hover
- Update tests to match new hover format and absence of error_x
- Add load_party_mp_vectors() to return raw per-MP SVD vectors by party
- Extract _build_party_axis_figure() as pure function for testability
- Modify _render_party_axis_chart to accept bootstrap_data and delegate
to the new builder
- When bootstrap_data present: show error_x bars, diamond markers for
N=1 parties, and N=count in hover text
- Wire up bootstrap computation in build_svd_components_tab via cached
_cached_bootstrap_cis wrapper
- Add 6 tests covering figure construction, bootstrap rendering, flip
behavior, and importability
Enable backfilling body_text for existing motions that lack it (2016-2018 data).
New extract_besluit_id() and update_existing_motions() helpers support the
--update-existing mode, while --no-skip-details enables detail fetching during
normal downloads. Includes 7 tests covering URL parsing, DB update flow, and
argparse wiring.
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.
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.
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
- fetch_mp_metadata: use real OData URL with pagination (1200 records, 5 pages)
uses Fractie.Afkorting not NaamNL for abbreviation matching
skips Verwijderd=true records
- upsert_mp_metadata: keep most recent membership (prefer active over ended,
then higher Van date) so current party affiliations are not overwritten by historical
- compute_anchor_axis: anchor directly on party-level SVD entities (GroenLinks-PvdA etc)
before falling back to mp_metadata individual MP lookup
- test_fetch_mp_metadata: fix mock for timeout kwarg + pagination + Afkorting field
- Generated anchor axis HTML for 2025-Q2 through 2026-Q1 in outputs/
