def test_default_small():
features, labels = load()
selected = np.concatenate( [np.where(labels < 2)[0], np.where(labels == 2)[0][:6]] )
features = features[selected]
labels = labels[selected]
learner = defaultclassifier('fast')
# For version 0.3.8, the line below led to an error
milk.nfoldcrossvalidation(features, labels, classifier=learner)
def test_default_small():
features, labels = load()
selected = np.concatenate(
[np.where(labels < 2)[0],
np.where(labels == 2)[0][:6]])
features = features[selected]
labels = labels[selected]
learner = defaultclassifier('fast')
# For version 0.3.8, the line below led to an error
milk.nfoldcrossvalidation(features, labels, classifier=learner)
def trainMe(directory):
classes = []
labels = []
features = []
print('[+] Reading files')
gestures = check_output(['ls', directory]).split()
print('[+] Extracting features')
for gesture in gestures:
classes.append(gesture)
gesture_dir = directory + '/' + gesture
files = check_output(['ls', gesture_dir]).split()
for filename in files:
file_dir = gesture_dir + '/' + filename
labels.append(len(classes))
image = cv.imread(file_dir)
image = resizeImage(image, width=500)
features.append(getFVector(image))
features = np.array(features)
labels = np.array(labels)
print('[+] Training')
classifier = milk.defaultclassifier()
model = classifier.train(features, labels)
print('[+] Cross validation')
confusion_matrix, names = milk.nfoldcrossvalidation(features, labels, learner=classifier)
print('[+] Accuracy %.2f' % (float(confusion_matrix.trace())/float(confusion_matrix.sum())))
return model, classes
#Apply KPCA for feature reduction
# will select best 440 features out of one image
kpca = KernelPCA(n_components=440, kernel='rbf')
Features = kpca.fit_transform(Features)
labels = [1] * len(positives) + [0] * len(negatives) #Labels
#creating SVM Classifier
learner = milk.defaultclassifier()
#model = learner.train(Features, labels)
start = timer()
cm, names, preds = milk.nfoldcrossvalidation(Features,
labels,
nfolds=10,
classifier=learner,
return_predictions=True)
end = timer()
print(end - start) #Time taken to perform validation
TP = cm[0][0] # class Spliced, predicted as Spliced
TN = cm[1][1] #class Authentic, predicted as Authentic
FN = cm[0][1] #class Spliced, predicted as Authentic
FP = cm[1][0] #class Authentic, predicted as Spliced
print("Accuracy=", ((TN + TP) / float(TN + TP + FP + FN)) * 100.00)
print("Precision=", (TP / float(TP + FP)) * 100.00)
print("Recall=", (TP / float(TP + FN)) * 100.00)
"""
#Xgboost with KFold-cross Validation
import pickle
import sys
import milk
import milk.supervised
import milk.supervised.adaboost
import milk.supervised.multi
assert(sys.argv[1])
features = pickle.load(open(sys.argv[1], 'r'))
#learner = milk.supervised.tree_learner()
#learner = milk.supervised.adaboost.boost_learner(weak)
#learner = milk.supervised.multi.one_against_one(learner)
learner = milk.defaultlearner(mode='really-slow')
model = learner.train(*features)
pickle.dump(model, open('trainer.pik', 'w'))
cmat,names, preds = milk.nfoldcrossvalidation(*features,
classifier=learner,
return_predictions=1)
print cmat
print names
print preds
no_y_total = 0.0 hh = 0.0 aa = 0.0 learner_mode = 0 for feature_ii in range(0,slope_mode_cases[slope_mode][1]): len_train = len(features[feature_ii]) if len_train < 5: continue if learner_mode == 0: weak = milk.supervised.tree.stump_learner() learner = milk.supervised.adaboost.boost_learner(weak) learner = milk.supervised.multi.one_against_one(learner) else: learner = milk.defaultclassifier() learner = milk.supervised.defaultclassifier() cmat,names,predictions = milk.nfoldcrossvalidation(features[feature_ii][0:len_train/2],labels[feature_ii][0:len_train/2],classifier=learner,return_predictions=True) colors = "rgby" codes = "xo" T0 = [] T1 = [] L0 = [] for kk in range(0,len(features[feature_ii])): kk0,kk1 = features[feature_ii][kk] L0.append(labels[feature_ii][kk]) T0.append(kk0) T1.append(kk1) for y,x,r,p in zip(T0,T1,labels[feature_ii],predictions): if ( r + p < 2 or ( r == 1 and p == 1)) or \ ( r + p > 4 or ( r == 2 and p == 2)): code = codes[1] else:
def __call__(self, i):
return milk.nfoldcrossvalidation(value(self.features), value(self.labels), folds=[i], **value(self.kwargs))
def __call__(self, i):
return milk.nfoldcrossvalidation(
self.features,
self.labels,
folds=[i],
**self.kwargs)
# https://pypi.python.org/pypi/milk/ import numpy as np import milk # prepare data # 2d array of features: 100 examples of 10 features each features = np.random.rand(100, 10) labels = np.zeros(100) features[50:] += .5 labels[50:] = 1 confusion_matrix, names = milk.nfoldcrossvalidation(features, labels) print 'Accuracy:', confusion_matrix.trace() / float(confusion_matrix.sum()) # train learner = milk.defaultclassifier() model = learner.train(features, labels) # Now you can use the model on new examples: example = np.random.rand(10) print model.apply(example) example2 = np.random.rand(10) example2 += .5 print model.apply(example2)
