from pyxvis.io.data import load_features
from pyxvis.io.plots import show_clf_results
from pyxvis.learning.classifiers import clf_model, define_classifier
from pyxvis.learning.classifiers import train_classifier, test_classifier
(X, d, Xt,
dt) = load_features('../data/F2/F2') # load training and testing data
ss_cl = ['nn(10,)', 'nn(12,6)']
for cl_name in ss_cl:
(name, params) = clf_model(cl_name) # function name and parameters
clf = define_classifier([name, params]) # classifier definition
clf = train_classifier(clf, X, d) # classifier training
d0 = test_classifier(clf, X) # classification of training
ds = test_classifier(clf, Xt) # classification of testing
show_clf_results(clf, X, d, Xt, dt, d0, ds,
cl_name) # display results and decision lines
from pyxvis.learning.evaluation import cross_validation
# List of classifiers
ss_cl = [
'dmin', 'lda', 'qda', 'maha', 'knn3', 'knn5', 'knn7', 'knn11', 'knn15',
'bayes-naive', 'bayes-kde', 'adaboost', 'lr', 'rf', 'tree', 'svm-lin',
'svm-rbf(0.1,1)', 'svm-rbf(0.1,0.5)', 'svm-rbf(0.5,1)',
'svm-pol(0.05,0.1,2)', 'svm-pol(0.05,0.5,2)', 'svm-pol(0.05,0.5,3)',
'svm-sig(0.1,1)', 'svm-sig(0.1,0.5)', 'svm-sig(0.5,1)', 'nn(10,)',
'nn(20,)', 'nn(12,6)', 'nn(20,10,4)'
]
(X, d) = load_features('../data/G3/G3',
full=1) # load training and testing data
n = len(ss_cl)
folds = 10
acc = np.zeros((n, ))
for k in range(n):
(name, params) = clf_model(ss_cl[k]) # function name and parameters
acc[k] = cross_validation([name, params], X, d, folds=folds)
print(f'{k:3d}' + ') ' + f'{ss_cl[k]:20s}' + ': ' +
f'CV-Accuracy = {acc[k]:.4f}')
ks = np.argmax(acc)
print('-----------------------------------------------')
print('Best Classifier:')
print(f'{ks:3d}' + ') ' + f'{ss_cl[ks]:20s}' + ': ' +
f'CV-Accuracy = {acc[ks]:.4f}')
print('-----------------------------------------------')
def best_features_classifier(ss_fs,ff,ss_cl,X,d,Xt,dt,stit,folds=5):
ncl = len(ss_cl) # number of classifiers
nfs = len(ss_fs) # number of feature selectors
nfk = len(ff) # number of feature selectors
accbest = 0 # best achieved accuracy
acctbest = 0 # best achieved accuracy
yacc = np.zeros((1000,))
yacct = np.zeros((1000,))
nt = np.zeros((1000,))
clfs = [None] * 1000
fses = [None] * 1000
t = 1
for k in range(nfk):
nfik = ff[k]
for i in range(nfs):
fs = ss_fs[i]
print('analyzing feature felector: sfs+'+fs+' with '+str(nfik)+' features...')
(fsname,fspar) = fse_model(fs)
ss = fsel([fsname,fspar],X,d,nfik,cv = folds, show = 1)
Xf = X[:,ss]
Xft = Xt[:,ss]
for j in range(ncl):
cl = ss_cl[j]
# print('analyzing classifier '+cl+'...')
(clname,clpar) = clf_model(cl)
acc = cross_validation([clname,clpar],Xf,d,folds)
_,acct,_ = hold_out([clname,clpar],Xf,d,Xft,dt)
stn = f'{nfik:2d}'
stt = 'feat='+stn+', fsel='+fs+', clf='+cl
stb = ''
sti = '(iter = '+str(t)+')'
if acc>accbest:
accbest = acc
stb = sti + ' *** new train max found ***'
yacc[t] = acc
yacct[t] = acct
nt[t] = nfik
clfs[t] = cl
fses[t] = fs
if acct > acctbest:
stb = sti + ' *** new train/test max found ***'
tbest = t
acctbest = acct
accbest0 = acc
fsnbest = fsname
fspbest = fspar
fsbest = fs
clbest = cl
clnbest = clname
clpbest = clpar
nbest = nfik
ssbest = ss
t = t + 1
print(f'{stt:38s} > acc = {acc:.4f}/{acct:.4f} <best acc = {accbest:.4f}/{acctbest:.4f}> (Train/Test) '+stb)
print('---------------------------------------------------------------------------')
print(' Best iteration: '+str(tbest)+' (maximum of testing accuracy)')
print(' Feature Selector: ' +fsbest+' with '+str(nbest)+' features')
print(' : ('+fsnbest+', '+fspbest+')')
print(' Classifier: ' +clbest)
print(' : ('+clnbest+', '+clpbest+') CrossVal with '+str(folds)+' folds')
print(f' Training-Acc: {accbest0:.4f} ')
print(f' Testing-Acc: {acctbest:.4f} ')
y1 = yacc[0:t]
y2 = yacct[0:t]
x = range(t)
# fig = plt.figure()
fig = plt.figure(figsize=(t+2,5))
ax = fig.add_subplot(111)
plt.plot(x[1:t], y1[1:t], marker='o', markerfacecolor='blue', markersize=6, label = 'Training')
plt.plot(x[1:t], y2[1:t], marker='o', markerfacecolor='red', markersize=6, label = 'Testing')
plt.xlabel('iterations')
plt.ylabel('accuracy')
plt.title(stit)
yf = 0.405
dy = 0.05
dd = 0.02
fts = 10
ax.text(dd, yf, 'features:', fontsize=fts)
ax.text(dd, yf-dy, 'f-selector:', fontsize=fts)
ax.text(dd, yf-dy*2, 'classifier:', fontsize=fts)
ax.text(dd, yf-dy*3, 'acc-train:', fontsize=fts)
ax.text(dd, yf-dy*4, 'acc-test:', fontsize=fts)
for i in range(1,t):
col = 'black'
if i==tbest:
col = 'green'
di = i+dd
ax.text(di, yf, str(int(nt[i])), fontsize=fts,color=col)
ax.text(di, yf-dy, fses[i], fontsize=fts,color=col)
ax.text(di, yf-dy*2, clfs[i], fontsize=fts,color=col)
accs = f'{y1[i]:.4f}'
accts = f'{y2[i]:.4f}'
ax.text(di, yf-dy*3, accs, fontsize=fts,color=col)
ax.text(di, yf-dy*4, accts, fontsize=fts,color=col)
ax.text(tbest-0.5, yf+0.2, 'best test accuracy', fontsize=fts,color='green')
ax.annotate(' ',xy=(tbest, acctbest-0.05), xytext=(tbest, yf+0.2),
arrowprops=dict(facecolor='green', shrink=0.05))
ax.plot(tbest,acctbest, 'o')
plt.xticks(range(t+1))
plt.grid(True)
plt.legend()
plt.xlim(0, t)
plt.ylim(0, 1.1)
plt.show()
return clbest, ssbest