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⚡ Bolt: _factor_fit 행렬 곱셈 기반 차원 루프 벡터화 최적화 #156

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⚡ Bolt: _factor_fit 행렬 곱셈 기반 차원 루프 벡터화 최적화 #156

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⚡ Bolt: _factor_fit의 행렬 곱셈을 이용한 차원 반복문 벡터화 최적화
seonghobae Jul 13, 2026
79c2aa2
⚡ Bolt: _factor_fit의 행렬 곱셈을 이용한 차원 반복문 벡터화 최적화
seonghobae Jul 13, 2026
03fc64b
⚡ Bolt: _factor_fit의 행렬 곱셈을 이용한 차원 반복문 벡터화 최적화
seonghobae Jul 13, 2026
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53 changes: 27 additions & 26 deletions python/fast_mlsirm/diagnostics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -461,36 +461,37 @@ def _factor_fit(
if factors.shape != (y.shape[1],):
raise ValueError("factor_id length must match number of items")

rows = []
for factor in np.unique(factors):
cols = factors == factor
rows.append(
(
float(factor),
float(observed[:, cols].sum()),
float((y[:, cols] * observed[:, cols]).sum()),
float((prob[:, cols] * observed[:, cols]).sum()),
float(residual[:, cols].sum()),
float((variance[:, cols] * observed[:, cols]).sum()),
float((residual[:, cols] * residual[:, cols]).sum()),
float(pearson_sq[:, cols].sum()),
)
)
unique_factors = np.unique(factors)
# Optimized factorization: vectorize looping over dimensions using matrix multiplication
mask = (factors[:, None] == unique_factors[None, :])

obs_sum = observed.sum(axis=0)
y_obs_sum = (y * observed).sum(axis=0)
prob_obs_sum = (prob * observed).sum(axis=0)
res_sum = residual.sum(axis=0)
var_obs_sum = (variance * observed).sum(axis=0)
res_sq_sum = (residual * residual).sum(axis=0)
pearson_sum = pearson_sq.sum(axis=0)

count = obs_sum @ mask
score = y_obs_sum @ mask
expected = prob_obs_sum @ mask
raw = res_sum @ mask
variance_sum = var_obs_sum @ mask
res_sq_sum_fact = res_sq_sum @ mask
pearson_sum_fact = pearson_sum @ mask

table = np.asarray(rows, dtype=np.float64)
variance_sum = table[:, 5]
count = table[:, 1]
safe_count = np.maximum(count, 1.0)
safe_variance = np.maximum(variance_sum, 1e-12)
return {
"factor_id": table[:, 0],
"observed_count": count,
"score": table[:, 2],
"expected_score": table[:, 3],
"raw_residual": table[:, 4],
"standardized_residual": table[:, 4] / np.sqrt(safe_variance),
"infit_mnsq": table[:, 6] / safe_variance,
"outfit_mnsq": table[:, 7] / safe_count,
"factor_id": unique_factors.astype(np.float64),
"observed_count": count.astype(np.float64),
"score": score.astype(np.float64),
"expected_score": expected.astype(np.float64),
"raw_residual": raw.astype(np.float64),
"standardized_residual": (raw / np.sqrt(safe_variance)).astype(np.float64),
"infit_mnsq": (res_sq_sum_fact / safe_variance).astype(np.float64),
"outfit_mnsq": (pearson_sum_fact / safe_count).astype(np.float64),
}


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