import pylab as pl
from matplotlib.ticker import NullFormatter
from sklearn.lda import LDA
import samples
import features
signals, backgrounds = samples.get_samples('2jet', purpose='train')
X_train, X_test,\
w_train, w_test,\
y_train, y_test = samples.make_classification(
*(samples.make_train_test(signals, backgrounds,
branches=features.hh_2jet_vars,
train_fraction=.5,
same_size_train=True,
same_size_test=True)),
standardize=True)
print X_train
print X_test
print w_train
print w_test
# standardize
X_train = standardize(X_train)
X_test = standardize(X_test)
lda = LDA(n_components=2)
def perform_pca(channel):
signals, backgrounds = samples.get_samples(channel, purpose='train')
if channel == '01jet':
branches = features.hh_01jet_vars
else:
branches = features.hh_2jet_vars
X_train, X_test,\
w_train, w_test,\
y_train, y_test = samples.make_classification(
*(samples.make_train_test(signals, backgrounds,
branches=branches,
train_fraction=.5,
max_sig_train=2000,
max_bkg_train=2000,
max_sig_test=2000,
max_bkg_test=2000,
same_size_train=True,
same_size_test=True,
norm_sig_to_bkg_train=True,
norm_sig_to_bkg_test=True)),
standardize=True)
print X_train
print X_test
print w_train
print w_test
print w_train.min(), w_train.max()
pca = PCA(n_components=2)
# fit only on background
pca.fit(X_train[y_train == 0])
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
xmin = X_test_pca[:, 0].min()
xmax = X_test_pca[:, 0].max()
ymin = X_test_pca[:, 1].min()
ymax = X_test_pca[:, 1].max()
width = xmax - xmin
height = ymax - ymin
xmin -= width*.1
xmax += width*.1
ymin -= height*.1
ymax += height*.1
# fit support vector machine on output of PCA
clf = svm.SVC(C=100, gamma=.01, probability=True, scale_C=True)
clf.fit(X_train_pca, y_train, sample_weight=w_train)
# plot the decision function
xx, yy = np.meshgrid(np.linspace(xmin, xmax, 500), np.linspace(ymin, ymax, 500))
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
channel_name = samples.CHANNEL_NAMES[channel]
target_names = ['%s Signal' % channel_name,
'%s Background' % channel_name]
target_values = [1, 0]
# Percentage of variance explained for each components
print 'explained variance ratio (first two components):', \
pca.explained_variance_ratio_
# plot PCA and SVM output
pl.figure()
# plot support vector machine decision function
pl.set_cmap(pl.cm.jet)
pl.contourf(xx, yy, Z, alpha=0.75)
for c, i, target_name in zip("rb", target_values, target_names):
pl.scatter(X_test_pca[y_test == i, 0], X_test_pca[y_test == i, 1],
c=c, label=target_name,
s=w_test[y_test == i]*10,
alpha=0.9)
pl.xlim((xmin, xmax))
pl.ylim((ymin, ymax))
pl.legend()
pl.xlabel('Principal Component [arb. units]')
pl.ylabel('Secondary Component [arb. units]')
pl.title('Principal Component Analysis\n'
'and Support Vector Machine Decision Function')
pl.savefig('pca_%s.png' % channel)
# testing:
signals, backgrounds = samples.get_samples(channel, purpose='test',
mass=125)
signal_train, signal_weight_train, \
signal_test, signal_weight_test, \
background_train, background_weight_train, \
background_test, background_weight_test = samples.make_train_test(
signals, backgrounds,
branches=branches,
train_fraction=.5,
norm_sig_to_bkg_train=False,
norm_sig_to_bkg_test=False)
sample_test = np.concatenate((background_test, signal_test))
sample_test = samples.std(sample_test)
background_test, signal_test = sample_test[:len(background_test)], \
sample_test[len(background_test):]
signal_test = pca.transform(signal_test)
background_test = pca.transform(background_test)
pl.figure()
pl.hist(clf.predict_proba(background_test)[:,-1],
weights=background_weight_test, bins=30, range=(0, 1),
label='Background', color='b')
pl.hist(clf.predict_proba(signal_test)[:,-1],
weights=signal_weight_test*10, bins=30, range=(0, 1),
label='Signal x 10', color='r')
pl.legend()
pl.ylabel('Events')
pl.xlabel('Support Vector Machine Signal Probability')
pl.savefig('pca_svm_score_%s.png' % channel)
import pylab as pl
from matplotlib.ticker import NullFormatter
from sklearn.lda import LDA
import samples
import features
signals, backgrounds = samples.get_samples('2jet', purpose='train')
X_train, X_test,\
w_train, w_test,\
y_train, y_test = samples.make_classification(
*(samples.make_train_test(signals, backgrounds,
branches=features.hh_2jet_vars,
train_fraction=.5,
same_size_train=True,
same_size_test=True)),
standardize=True)
print X_train
print X_test
print w_train
print w_test
# standardize
X_train = standardize(X_train)
X_test = standardize(X_test)
lda = LDA(n_components=2)
