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
Allow analysis modules to be imported in lightweight test environments
without duckdb installed. Modules that need duckdb for actual queries
still require it at runtime, but import-time failures are handled gracefully.
Two related bugs fixed:
1. Label alignment: Removed static left_pole/right_pole from SVD_THEMES
entries. These labels assumed a fixed flip direction but could mismatch
with runtime flip computation, causing right-wing parties to appear on
the wrong side. Labels are now always derived from positive_pole,
negative_pole, and the runtime flip direction.
2. Score mismatch: Changed tijdtraject view for components 3-10 from
load_party_scores_all_windows_aligned() to load_party_scores_all_windows().
Procrustes alignment rotates the full 50-dim vector space to align
components 1-2, but this also transforms components 3-10, making their
scores incomparable with the single-window view. Per-window flip
computation already handles orientation alignment for these components.
Also updated svd_labels.py to prefer analysis.config as the canonical
source for SVD_THEMES, falling back to explorer only when config is
unavailable.
Key findings:
- Coalition started losing votes structurally from 2019
- Not that 'right' won, but that government lost
- Added government_win_rate.png visualization
- Updated analysis with party vote counts
- Add polarization_analysis.png: spread over time for all axes
- Add axis1_deep_dive.png: focus on Axis 1 (coalition vs opposition)
- Add Dutch blog post on parliamentary polarization findings
- Add script to find motions closest to semantic gravity per axis/window
- Document Axis 1 semantic shift: from administrative law (2016)
to migration/asylum policy (2026)
- Shows that 'coalition' votes on different topics over time
- Implement SVD axis stability using Lasso regression on fused embeddings
- Add overtone shift analysis to detect semantic content changes
- Implement semantic drift tracking for motion content over time
- Add party voting analysis with cross-ideological voting patterns
- Generate markdown report with visualizations
- Add comprehensive test suite with 12 passing tests
See reports/drift/report.md for analysis results.
- Add compute_overtone_shift(): tracks semantic gravity movement across windows
even when party ordering stays the same
- Update _generate_report() with overtone shift section including dimension-level
analysis and inflection point detection
- Update methodology section to reflect new metrics
- All 12 tests pass
Key finding: no axes exceed 0.7 stability threshold — semantic features
defining each SVD axis shift significantly across windows (0.06-0.51 range)
- 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
- refactoring-streamlit-data-loading.md: update test count
164/164 → 173/173 (7 new axis validation tests added)
- svd-component-labels-mismatch.md: SVD_THEMES moved from
explorer.py:434-611 → analysis/config.py:67+ per the
refactoring that extracted constants to analysis/config.py
- 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
- Add AGENTS.md with documented solutions reference
- Include SVD label convention (right-wing parties on right side)
- Document SVD insight: labels reflect voting patterns, not semantics
- Fix SQL verification example to use Python approach
The component captures voting unity of the right-wing coalition vs left
opposition, NOT semantic content like 'defense' or 'EU integration'.
Motions about elderly care (Dobbe) appear because the left votes for them
while the right coalition votes against - this is coalition-opposition
polarization, not policy domain.
Bug: report_per_component used scored[:args.report_top_n] which took
top N by score (all positive for components with only positive scores).
JSON correctly separated positive and negative poles.
Fix: Use same positive/negative separation logic for report as JSON.
- Each motion now assigned to exactly one component (highest absolute score)
- Added --exclusive flag (default: True) for backward compatibility
- Added markdown report generation with motion details for label review
- Added --report-top-n for report size (default: 20 per component)
- Updated JSON output with 'exclusive' flag for transparency
- Add Dutch paragraph explaining Rice index and party discipline patterns
- Analysis covers high discipline parties (PVV, SGP) vs lower discipline parties
- Explains what discipline reveals about party dynamics
- Add _load_mp_vectors_by_party_for_window() to load SVD vectors for specific windows
- Add load_party_axis_scores_for_window() cached function
- Add year selector UI for components 3-10 similar to components 1-2
- Uses get_uniform_dim_windows() to get available windows
- 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
Previously the st.plotly_chart call was wrapped in 'except Exception: pass'
which silently swallowed all rendering errors. The user would see no chart
and no error message.
Now:
- Exception message is shown via st.error()
- Diagnostics JSON is shown when debug is enabled (EXPLORER_DEBUG_TRAJECTORIES=1
or UI checkbox), even when trace_count > 0
This reveals the actual root cause when the chart fails to render.
- 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
- Extract shared helper that both load_party_axis_scores and
load_party_mp_vectors delegate to, eliminating ~40 lines of
duplicated DB query + vector parsing code
- Remove dead code in load_party_axis_scores that queried mp_metadata
twice (first without ORDER BY, then again with ORDER BY, overwriting)
- Fix _cached_bootstrap_cis parameter: remove _ prefix so Streamlit
actually hashes the input dict instead of caching with no key
- 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
Use one DuckDB write connection for the entire update loop instead of
opening/closing per row, wrapped in try/finally for proper cleanup.
Move 'import duckdb' to module level with other imports.
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.
Move rng initialization before the party loop so each party gets a
unique segment of the random stream instead of identical sequences.
Replace Python bootstrap loop with vectorized numpy indexing.
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.
sgeboers