59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
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import numpy as ν
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import csv
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import get_emb
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MAX_RES = 10
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φ = open('beta', 'r'); # coefficients
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Δ = [float(el) for el in (open('Delta', 'r').read().split('\n')[:-1])]
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#α = -0.8569279; # some magic constant
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α = 0;
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M = open('titles2.text', 'r').read().strip().split('\n') # TITLE\nAUTHORS\nACCEPTED?
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T = ν.array(M[0::4])
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A = ν.array(M[1::4])
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O = ν.array(M[2::4])
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X = ν.loadtxt(open('embeddings2.nsv', 'rb'), delimiter=',', skiprows=0)
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NN = open('NN', 'r').read().split('\n')
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β = φ.read().split('\n');
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β = β[:-1]
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β = [float(el) for el in β]
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def get(θ):
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return(get_emb.get_embedding(θ))
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def percent(χ):
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γ = α + ν.dot(χ, β)
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π = ν.exp(γ) / (1 + ν.exp(γ))
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return(str(π)[2:4] + '%')
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def closest(χ, n):
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n = abs(n)
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n = n % MAX_RES
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if n == 0: n = MAX_RES
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ψ = ν.array(ν.dot(X, χ))
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topn = T[ν.argsort(ψ)[-n:]]
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aopn = A[ν.argsort(ψ)[-n:]]
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oopn = O[ν.argsort(ψ)[-n:]]
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print(ν.argsort(ψ)[-n:])
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out = ""#"tail prob = " + str(percentile_far(ν.max(ψ))) + "\n"
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for i in reversed(range(len(topn))):
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if oopn[i] == "TRUE":
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p = "presented"
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else:
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p = "online-only"
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out += topn[i] + " <i>(" + aopn[i] + ", " + p + ")</i>\n"
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tailprob = int(percentile_far(ν.max(ψ))*100)
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return [out, tailprob]
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def percentile_far(q_dist):
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return sum(1*(ν.array(Δ)<=q_dist)) / len(Δ) # fraction of abstracts further from their nearest neighbor than χ
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