@ -400,173 +400,6 @@ def _compute_stability_fallback(
" windows " : window_list ,
}
# Compute sign consistency across windows
window_list = sorted ( party_axes . keys ( ) )
stability_matrix = np . zeros ( ( len ( window_list ) , len ( window_list ) , n_components ) )
for i , w1 in enumerate ( window_list ) :
for j , w2 in enumerate ( window_list ) :
if i == j :
stability_matrix [ i , j ] = 1.0
continue
for comp in range ( 1 , n_components + 1 ) :
s1 = np . sign ( party_axes [ w1 ] . get ( comp , 0 ) )
s2 = np . sign ( party_axes [ w2 ] . get ( comp , 0 ) )
stability_matrix [ i , j , comp - 1 ] = 1.0 if s1 == s2 and s1 != 0 else 0.0
n_windows = len ( window_list )
avg_stability = np . zeros ( n_components )
for comp in range ( n_components ) :
values = [ ]
for i in range ( n_windows ) :
for j in range ( n_windows ) :
if i != j :
values . append ( stability_matrix [ i , j , comp ] )
avg_stability [ comp ] = np . mean ( values ) if values else 0.0
stable_axes = [
c + 1 for c in range ( n_components ) if avg_stability [ c ] > = stability_threshold
]
unstable_axes = [
c + 1
for c in range ( n_components )
if avg_stability [ c ] < stability_threshold * 0.5
]
reordered_axes = [
c + 1
for c in range ( n_components )
if stability_threshold * 0.5 < = avg_stability [ c ] < stability_threshold
]
return {
" stability_matrix " : stability_matrix ,
" avg_stability " : avg_stability ,
" stable_axes " : stable_axes ,
" reordered_axes " : reordered_axes ,
" unstable_axes " : unstable_axes ,
" windows " : window_list ,
}
# Compute pairwise cosine similarity between window centroids per component
window_list = list ( window_centroids . keys ( ) )
stability_matrix = np . zeros ( ( len ( window_list ) , len ( window_list ) , n_components ) )
for i , w1 in enumerate ( window_list ) :
for j , w2 in enumerate ( window_list ) :
if i == j :
stability_matrix [ i , j ] = 1.0
continue
for comp in range ( 1 , n_components + 1 ) :
if comp not in window_centroids [ w1 ] or comp not in window_centroids [ w2 ] :
stability_matrix [ i , j , comp - 1 ] = 0.0
continue
a = window_centroids [ w1 ] [ comp ]
b = window_centroids [ w2 ] [ comp ]
norm_a = np . linalg . norm ( a )
norm_b = np . linalg . norm ( b )
if norm_a == 0 or norm_b == 0 :
stability_matrix [ i , j , comp - 1 ] = 0.0
else :
stability_matrix [ i , j , comp - 1 ] = np . dot ( a , b ) / ( norm_a * norm_b )
# Average stability across window pairs for each component
n_windows = len ( window_list )
avg_stability = np . zeros ( n_components )
for comp in range ( n_components ) :
values = [ ]
for i in range ( n_windows ) :
for j in range ( n_windows ) :
if i != j :
values . append ( stability_matrix [ i , j , comp ] )
avg_stability [ comp ] = np . mean ( values ) if values else 0.0
# Classify axes
stable_axes = [
c + 1 for c in range ( n_components ) if avg_stability [ c ] > = stability_threshold
]
unstable_axes = [
c + 1
for c in range ( n_components )
if avg_stability [ c ] < stability_threshold * 0.5
]
reordered_axes = [
c + 1
for c in range ( n_components )
if stability_threshold * 0.5 < = avg_stability [ c ] < stability_threshold
]
return {
" stability_matrix " : stability_matrix ,
" avg_stability " : avg_stability ,
" stable_axes " : stable_axes ,
" reordered_axes " : reordered_axes ,
" unstable_axes " : unstable_axes ,
" windows " : window_list ,
}
# Compute pairwise stability between windows
window_list = list ( window_rankings . keys ( ) )
stability_matrix = np . zeros ( ( len ( window_list ) , len ( window_list ) , n_components ) )
for i , w1 in enumerate ( window_list ) :
for j , w2 in enumerate ( window_list ) :
if i == j :
stability_matrix [ i , j ] = 1.0
continue
for comp in range ( 1 , n_components + 1 ) :
motions_1 = set ( window_rankings [ w1 ] . get ( comp , [ ] ) )
motions_2 = set ( window_rankings [ w2 ] . get ( comp , [ ] ) )
if not motions_1 or not motions_2 :
stability_matrix [ i , j , comp - 1 ] = 0.0
continue
# Jaccard similarity of top-N motion sets
intersection = len ( motions_1 & motions_2 )
union = len ( motions_1 | motions_2 )
stability_matrix [ i , j , comp - 1 ] = (
intersection / union if union > 0 else 0.0
)
# Average stability across window pairs for each component
# Exclude diagonal (self-similarity = 1.0)
n_windows = len ( window_list )
avg_stability = np . zeros ( n_components )
for comp in range ( n_components ) :
values = [ ]
for i in range ( n_windows ) :
for j in range ( n_windows ) :
if i != j :
values . append ( stability_matrix [ i , j , comp ] )
avg_stability [ comp ] = np . mean ( values ) if values else 0.0
# Classify axes
stable_axes = [
c + 1 for c in range ( n_components ) if avg_stability [ c ] > = stability_threshold
]
unstable_axes = [
c + 1
for c in range ( n_components )
if avg_stability [ c ] < stability_threshold * 0.5
]
reordered_axes = [
c + 1
for c in range ( n_components )
if stability_threshold * 0.5 < = avg_stability [ c ] < stability_threshold
]
return {
" stability_matrix " : stability_matrix ,
" avg_stability " : avg_stability ,
" stable_axes " : stable_axes ,
" reordered_axes " : reordered_axes ,
" unstable_axes " : unstable_axes ,
" windows " : window_list ,
}
def compute_overtone_shift (
con : duckdb . DuckDBPyConnection ,
@ -798,6 +631,7 @@ def compute_semantic_drift(
" window_after " : w_after ,
" drift " : float ( drift ) ,
" median_drift " : float ( median_drift ) ,
" transition_index " : i + 1 ,
}
)
@ -848,7 +682,7 @@ def compute_party_voting(
# Get party votes for this window
# Parse window year for date filtering
year = int ( w . split ( " - " ) [ 0 ] ) if " - " not in w else int ( w . split ( " -Q " ) [ 0 ] )
year = int ( w . split ( " - " ) [ 0 ] )
year_start = f " { year } -01-01 "
year_end = f " { year } -12-31 "
@ -932,10 +766,10 @@ def compute_party_voting(
continue
for motion_id in party_motions [ party ] :
if motion_id not in motion_scores :
if str ( motion_id ) not in motion_scores :
continue
scores = motion_scores [ motion_id ]
scores = motion_scores [ str ( motion_id ) ]
# Check if motion is ideologically opposite
for axis in stable_axes :
comp_idx = axis - 1
@ -1044,7 +878,7 @@ def _generate_report(
ax . set_yticks ( range ( len ( windows ) ) )
ax . set_yticklabels ( windows )
ax . set_title ( f " Axis { axis } Stability " )
fig . colorbar ( im , ax = ax , label = " Jaccard Similarity " )
fig . colorbar ( im , ax = ax , label = " Stability (cosine + Jaccard) " )
plt . tight_layout ( )
fig_path = os . path . join ( output_dir , " axis_stability.png " )
@ -1079,9 +913,8 @@ def _generate_report(
# Mark inflection points
inflections = drift_result . get ( " inflection_points " , { } ) . get ( axis , [ ] )
for inf in inflections :
ax . axvline (
x = list ( drift_series . keys ( ) ) . index ( axis ) + 1 , color = " red " , alpha = 0.3
)
x_pos = inf . get ( " transition_index " , 1 )
ax . axvline ( x = x_pos , color = " red " , alpha = 0.3 , linestyle = " -- " )
ax . set_xlabel ( " Window Transition " )
ax . set_ylabel ( " Cosine Distance " )
