sys.path.append('/home/ml/adoomr/Archive/mlproject4/python')
from get_data import get_train_val_test
from normalize import normalize
SAVE_DIRECTORY = '/home/ml/adoomr/Archive/mlproject4/sanctuary/'
RADIUS = 2
NPOINTS = 8
def lbp_transform(X, radius, npts, img_height=41, img_width=41): # X is a matrix of images
def single_lbp_transform(x): # x is a single image
return mahotas.features.lbp(x.reshape(img_height, img_width), radius, npts)
return np.asarray(map(single_lbp_transform, X))
trainX, trainY, valX, valY, testX, testY = get_train_val_test()
changed_train_X = lbp_transform(trainX, RADIUS, NPOINTS)
changed_val_X = lbp_transform(valX, RADIUS, NPOINTS)
changed_test_X = lbp_transform(testX, RADIUS, NPOINTS)
save_dir = os.path.join(SAVE_DIRECTORY, 'lbp_dataset_%d_%d' % (RADIUS, NPOINTS))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
np.save(os.path.join(save_dir, 'lbp_trainX.npy'), changed_train_X)
np.save(os.path.join(save_dir, 'lbp_valX.npy'), changed_val_X)
np.save(os.path.join(save_dir, 'lbp_testX.npy'), changed_test_X)
np.save(os.path.join(save_dir, 'lbp_trainY.npy'), trainY)
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score, confusion_matrix, classification_report
from sklearn.ensemble import RandomForestClassifier
def test_classifier(model, X, y, testX, testy):
print 'Fitting model %s...'%(type(model).__name__)
model.fit(X,y)
yhat = model.predict(testX)
return f1_score(testy, yhat), confusion_matrix(testy, yhat), classification_report(testy, yhat)
if __name__ == '__main__':
data_directory = '../sanctuary/lbp_dataset_2_8/'
prefix = 'lbp_minsplit2_minsamples2_nfeatures'
if not prefix.startswith('lbp'):
trainX,trainy, valX,valy, testX,testy = get_train_val_test()
else:
trainX = np.load(data_directory + 'lbp_trainX.npy')
trainy = np.load(data_directory + 'lbp_trainY.npy')
testX = np.load(data_directory + 'lbp_testX.npy')
testy = np.load(data_directory + 'lbp_testY.npy')
trainX = normalize(trainX)
#valX = normalize(valX)
testX = normalize(testX)
names = []
scores = []
matrices = []
reports = []
