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.
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
- Replace gtfs/bokeh deploy with motief/streamlit (port 8501)
- Update inventory to motief.sgeboers.nl
- Remove stale .drone.yml
- Add CI guard to forbid .env in repo
- Add env removal report and secrets rotation checklist
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