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experiments/utils.py

+# 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() 
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