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chore: upgrade backend dependencies (#2641)
chore: upgrade backend dependencies (#2641)
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@@ -56,7 +56,7 @@ def diffexp_ttest_from_mean_var(meanA, varA, nA, meanB, varB, nB, top_n, diffexp
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# degrees of freedom for Welch's t-test
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with np.errstate(divide="ignore", invalid="ignore"):
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dof = sum_vn ** 2 / (vnA ** 2 / (nA - 1) + vnB ** 2 / (nB - 1))
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dof = sum_vn**2 / (vnA**2 / (nA - 1) + vnB**2 / (nB - 1))
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dof[np.isnan(dof)] = 1
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# Welch's t-test score calculation
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@@ -97,7 +97,7 @@ def estimate_approximate_distribution(X) -> XApproximateDistribution:
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if Xdata.size > CHUNKSIZE:
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min_val = max_val = Xdata[0]
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with concurrent.futures.ThreadPoolExecutor() as tp:
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for (_min, _max) in tp.map(min_max, [Xdata[i : i + CHUNKSIZE] for i in range(0, Xdata.size, CHUNKSIZE)]):
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for _min, _max in tp.map(min_max, [Xdata[i : i + CHUNKSIZE] for i in range(0, Xdata.size, CHUNKSIZE)]):
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min_val = min(_min, min_val)
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max_val = max(_max, max_val)
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