From b68e703c10b1d8effd13ae6a62eef8bb5b2a9f58 Mon Sep 17 00:00:00 2001
From: Gabriele Cavallaro <g.cavallaro@fz-juelich.de>
Date: Sat, 22 May 2021 09:07:59 +0000
Subject: [PATCH] Upload New File
---
experiments/QA_SVM/utils.py | 151 ++++++++++++++++++++++++++++++++++++
1 file changed, 151 insertions(+)
create mode 100644 experiments/QA_SVM/utils.py
diff --git a/experiments/QA_SVM/utils.py b/experiments/QA_SVM/utils.py
new file mode 100644
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--- /dev/null
+++ b/experiments/QA_SVM/utils.py
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+# Created by Dennis Willsch (d.willsch@fz-juelich.de)
+# Modified by Gabriele Cavallaro (g.cavallaro@fz-juelich.de)
+# and Madita Willsch (m.willsch@fz-juelich.de)
+
+import sys
+import re
+import json
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as cols
+from sklearn.metrics import roc_auc_score,average_precision_score,precision_recall_curve,roc_curve,accuracy_score,auc
+
+np.set_printoptions(precision=4, suppress=True)
+
+def kernel(xn, xm, gamma=-1): # here (xn.shape: NxD, xm.shape: ...xD) -> Nx...
+ if gamma == -1:
+ return xn @ xm.T
+ xn = np.atleast_2d(xn)
+ xm = np.atleast_2d(xm)
+ return np.exp(-gamma * np.sum((xn[:,None] - xm[None,:])**2, axis=-1)) # (N,1,D) - (1,...,D) -> (N,...,D) -> (N,...); see Hsu guide.pdf for formula
+
+# B = base
+# K = number of qubits per alpha
+# E = shift of exponent
+# decode binary -> alpha
+def decode(binary, B=10, K=3, E=0):
+ N = len(binary) // K
+ Bvec = float(B) ** (np.arange(K)-E)
+ return np.fromiter(binary,float).reshape(N,K) @ Bvec
+
+# encode alpha -> binary with B and K (for each n, the binary coefficients an,k such that sum_k an,k B**k is closest to alphan)
+def encode(alphas, B=10, K=3, E=0): # E allows for encodings with floating point numbers (limited precision of course)
+ N = len(alphas)
+ Bvec = float(B) ** (np.arange(K)-E) # B^(0-E) B^(1-E) B^(2-E) ... B^(K-1-E)
+ allvals = np.array(list(map(lambda n : np.fromiter(bin(n)[2:].zfill(K),float,K), range(2**K)))) @ Bvec # [[0,0,0],[0,0,1],...] @ [1, 10, 100]
+ return ''.join(list(map(lambda n : bin(n)[2:].zfill(K),np.argmin(np.abs(allvals[:,None] - alphas), axis=0))))
+
+def encode_as_vec(alphas, B=10, K=3, E=0):
+ return np.fromiter(encode(alphas,B,K,E), float)
+
+def loaddataset(datakey):
+ dataset = np.loadtxt(datakey, dtype=float, skiprows=1)
+ return dataset[:,2:], dataset[:,1] # data, labels
+
+def save_json(filename, var):
+ with open(filename,'w') as f:
+ f.write(str(json.dumps(var, indent=4, sort_keys=True, separators=(',', ': '), ensure_ascii=False)))
+
+def eval_classifier(x, alphas, data, label, gamma, b=0): # evaluates the distance to the hyper plane according to 16.5.32 on p. 891 (Numerical Recipes); sign is the assigned class; x.shape = ...xD
+ return np.sum((alphas * label)[:,None] * kernel(data, x, gamma), axis=0) + b
+
+def eval_on_sv(x, alphas, data, label, gamma, C):
+ return np.sum((alphas * (C-alphas) * label)[:,None] * kernel(data, x, gamma), axis=0)
+
+def eval_offset_search(alphas, data, label, gamma, C, useavgforb=True): # search for the best offset
+ maxacc=0
+ b1=-9
+ for i in np.linspace(-9,9,500):
+ acc = accuracy_score(label,np.sign(eval_classifier(data, alphas, data, label, gamma, i)))
+ if acc > maxacc:
+ maxacc = acc
+ b1=i
+ maxacc=0
+ b2=9
+ reversed_space=np.linspace(-9,9,500)[::-1]
+ for i in reversed_space:
+ acc = accuracy_score(label,np.sign(eval_classifier(data, alphas, data, label, gamma, i)))
+ if acc > maxacc:
+ maxacc = acc
+ b2=i
+ return (b1+b2)/2
+
+def eval_offset_MM(alphas, data, label, gamma, C, useavgforb=True): # evaluates offset b according to 16.5.37 (Mangasarian-Musicant variant) NOTE: does not seem to work with integer/very coarsely spaced alpha!
+ return np.sum(alphas*label)
+
+def eval_offset_avg(alphas, data, label, gamma, C, useavgforb=True): # evaluates offset b according to 16.5.33
+ cross = eval_classifier(data, alphas, data, label, gamma) # cross[i] = sum_j aj yj K(xj, xi) (error in Numerical Recipes)
+ if useavgforb:
+ return np.sum(alphas * (C-alphas) * (label - cross)) / np.sum(alphas * (C-alphas))
+ #return np.sum(label - cross) / num_sv
+ else: # this is actually not used, but we did a similar-in-spirit implementation in eval_finaltraining_avgscore.py
+ if np.isclose(np.sum(alphas * (C-alphas)),0):
+ print('no support vectors found, discarding this classifer')
+ return np.nan
+ bcandidates = [np.sum(alphas * (C-alphas) * (label - cross)) / np.sum(alphas * (C-alphas))] # average according to NR should be the first candidate
+ crosssorted = np.sort(cross)
+ crosscandidates = -(crosssorted[1:] + crosssorted[:-1])/2 # each value between f(xi) and the next higher f(xj) is a candidate
+ bcandidates += sorted(crosscandidates, key=lambda x:abs(x - bcandidates[0])) # try candidates closest to the average first
+ bnumcorrect = [(label == np.sign(cross + b)).sum() for b in bcandidates]
+ return bcandidates[np.argmax(bnumcorrect)]
+
+def eval_acc_auroc_auprc(label, score): # score is the distance to the hyper plane (output from eval_classifier)
+ precision,recall,_ = precision_recall_curve(label, score)
+ return accuracy_score(label,np.sign(score)), roc_auc_score(label,score), auc(recall,precision)
+
+
+################ This I/O functions are provided by http://hyperlabelme.uv.es/index.html ################
+
+def dataread(filename):
+ lasttag = 'description:'
+ # Open file and locate lasttag
+ f = open(filename, 'r')
+ nl = 1
+ for line in f:
+ if line.startswith(lasttag): break
+ nl += 1
+ f.close()
+
+ # Read data
+ data = np.loadtxt(filename, delimiter=',', skiprows=nl)
+ Y = data[:, 0]
+ X = data[:, 1:]
+ # Separate train/test
+ Xtest = X[Y < 0, :]
+ X = X[Y >= 0, :]
+ Y = Y[Y >= 0, None]
+
+ return X, Y, Xtest
+
+
+def datawrite(path,method, dataset, Yp):
+ filename = '{0}{1}_predictions.txt'.format(path, dataset)
+ res = True
+ try:
+ with open(filename, mode='w') as f:
+ f.write('{0} {1}'.format(method, dataset))
+ for v in Yp:
+ f.write(' {0}'.format(str(v)))
+ f.write('\n')
+ except Exception as e:
+ print('Error', e)
+ res = False
+ return res
+
+################
+
+
+def write_samples(X, Y,path):
+ f = open(path,"w")
+ f.write("id label data \n")
+ for i in range(0,X.shape[0]):
+ f.write(str(i)+" ")
+ if(Y[i]==1):
+ f.write("-1 ")
+ else:
+ f.write("1 ")
+ for j in range(0,X.shape[1]):
+ f.write(str(X[i,j])+" ")
+ f.write("\n")
+ f.close()
\ No newline at end of file
--
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