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Fix corpsepose clipping error in interacted labor model (remove cache-order dependency) #6

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Fix corpsepose clipping error in interacted labor model (remove cache-order dependency) #6

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457ae24
Pass clip-model curve directly to corpsepose transform to avoid cache…
marinecdf Mar 2, 2026
911280d
Restore prepare_interp_raw after corpsepose transform fix
marinecdf Mar 3, 2026
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52 changes: 45 additions & 7 deletions interpret/specification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -327,6 +327,7 @@ def create_curvegen(csvv, covariator, regions, farmer='full', specconf=None, get

# Clause to set curve baseline extents if configured.
clipping_cfg = specconf.get('clipping', False)
clipmodel = None
if clipping_cfg:
# Validate clipping configuration option.
user_assert(clipping_cfg in ['boatpose', 'downdog', True, 'corpsepose', 'plankpose'],
Expand Down Expand Up @@ -375,7 +376,19 @@ def create_curvegen(csvv, covariator, regions, farmer='full', specconf=None, get
curve_global_extrema = curve_extrema(curve)
baselineexts = {r: curve_global_extrema for r in regions}

def transform(region, curve):
# instead of accepting exactly one curve, transform can now accept one or more curves
def transform(region, *curves):
# for the special corpsepose/plankpose clipping configurations, we need both current model curve and clip-model curve to perform transform
if clipping_cfg in ['corpsepose', 'plankpose']:
# force there to be exactly two curves in that case: clip_reference_curve and current model curve: curves
user_assert(len(curves) == 2, "corpsepose/plankpose requires both clip-model and target curves")
clip_reference_curve, curve = curves
# for other clipping configurations, we only need the current model curve
else:
user_assert(len(curves) == 1, "expected a single curve for transform")
curve = curves[0]
clip_reference_curve = None

if isinstance(curve, smart_curve.SmartCurve):
final_curve = curve
else:
Expand Down Expand Up @@ -420,9 +433,20 @@ def transform(region, curve):
direction=ucurve_direction,
)
else: # 'corpsepose' or 'plankpose'
# making sure the clip model's (low-risk) curve was passed in to clip against
user_assert(
clip_reference_curve is not None,
"clip-model curve was not provided to clipping transform"
)

# if not in right curve format already, convert
if not isinstance(clip_reference_curve, smart_curve.SmartCurve):
clip_reference_curve = smart_curve.CoefficientsCurve(
clip_reference_curve.ccs, weathernames
)

# Also shift clipmodel down
clip_curve = othermodels[specconf.get('clip-model')].current_curves[region]
clip_curve = smart_curve.ShiftedCurve(clip_curve, -clip_curve.univariate(baselineexts[region]))
clip_curve = smart_curve.ShiftedCurve(clip_reference_curve, -clip_reference_curve.univariate(baselineexts[region]))

if clipping_cfg == 'corpsepose':
final_curve = smart_curve.MinimumCurve(final_curve, clip_curve)
Expand Down Expand Up @@ -450,21 +474,35 @@ def transform(region, curve):

return final_curve

# By default, transform only receives the current model's curve
transform_curvegens = [curr_curvegen]

# For corpsepose/plankpose, transform now needs two curves:
# 1) the clip-model reference curve (minlost_lo)
# 2) the current model curve (minlost_hi)
# so clipping does not depend on the cache lookup of the clip-model's curve
if clipping_cfg in ['corpsepose', 'plankpose']:
transform_curvegens = [othermodels[clipmodel].curvegen, curr_curvegen]

# Pass the prepared curve generator(s) into transform
# In the corpsepose/plankpose case this supplies two curves
# Otherwise it preserves the original single-curve behavior
if clipping_cfg and specconf.get('goodmoney', False):
final_curvegen = curvegen.TransformCurveGenerator(transform, "Clipping and Good Money transformation", curr_curvegen)
final_curvegen = curvegen.TransformCurveGenerator(transform, "Clipping and Good Money transformation", *transform_curvegens)
elif clipping_cfg:
final_curvegen = curvegen.TransformCurveGenerator(transform, "Clipping transformation", curr_curvegen)
final_curvegen = curvegen.TransformCurveGenerator(transform, "Clipping transformation", *transform_curvegens)
elif specconf.get('goodmoney', False):
final_curvegen = curvegen.TransformCurveGenerator(transform, "Good Money transformation", curr_curvegen)
final_curvegen = curvegen.TransformCurveGenerator(transform, "Good Money transformation", *transform_curvegens)
else:
final_curvegen = curvegen.TransformCurveGenerator(transform, "Smart curve transformation", curr_curvegen)
final_curvegen = curvegen.TransformCurveGenerator(transform, "Smart curve transformation", *transform_curvegens)
final_curvegen.deltamethod_passthrough = True

if covariator:
final_curvegen = curvegen.FarmerCurveGenerator(final_curvegen, covariator, farmer)

return final_curvegen


def prepare_interp_raw(csvv, weatherbundle, economicmodel, qvals, farmer='full', specconf=None, config=None):
"""

Expand Down