Commit 4c42cf99 authored by Gabriele Cavallaro's avatar Gabriele Cavallaro
Browse files

Delete utils.py

parent 5d810480
# 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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