build_scorecard

from _setup import artifact, row
 
from compileml.runtime import decide, score_micro
from compileml.scorecard import build_scorecard, score_from_scorecard, scorecard_to_markdown
 
card = build_scorecard(artifact)
 
print("base points (micro) ", card["base_points_micro"])
print("main effect tables  ", len(card["main_effects"]))
print("interaction grids   ", len(card["interactions"]))
print("whitebox max depth  ", card["whitebox_max_depth"])
print("exact               ", card["exact"])
 
# At depth 2 a tree can split on two different features, so most of the model
# lands in pairwise grids rather than one table per feature.
 
# The audit re-sum. The table below is the model, not a summary of it. Both
# sides are raw micro units: the scorecard reproduces the score before it is
# divided down to the display scale that decide() reports.
from_card = score_from_scorecard(card, row)
from_runtime = score_micro(artifact["model"], row)
 
print()
print("from the scorecard table :", from_card)
print("from the runtime         :", from_runtime)
print("identical                :", from_card == from_runtime)
print()
print("displayed as latent_int  :", decide(artifact, row, explain=False)["latent_int"])
 
# The whole scorecard, as `compileml scorecard` prints it: base points plus
# every table and grid, each cell in micro units and display points.
print()
print(scorecard_to_markdown(card))