diff --git a/experiments/utils.py b/experiments/utils.py
deleted file mode 100644
index 785cf6d685fc6e42c121e6e12fea2144498d26bc..0000000000000000000000000000000000000000
--- a/experiments/utils.py
+++ /dev/null
@@ -1,151 +0,0 @@
-# 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