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mr_svm.py
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import os, sys, time, glob, librosa, itertools, argparse
os.environ["KERAS_BACKEND"] = 'theano'
import numpy as np
import cPickle as pickle
from sklearn.utils import shuffle
from sklearn import preprocessing, decomposition
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
from sklearn.svm import SVC, NuSVC, LinearSVC
'''
Material Recognition GAN
'''
def firstDeriv(x, t):
# First derivative of measurements with respect to time
dx = np.zeros(np.shape(x), np.float)
dx[0:-1] = np.diff(x, axis=0)/np.diff(t, axis=0)
dx[-1] = (x[-1] - x[-2])/(t[-1] - t[-2])
return dx
def dataset(modalities=0, forcetempTime=4, contactmicTime=0.2, leaveObjectOut=False, verbose=False, deriv=False):
materials = ['plastic', 'glass', 'fabric', 'metal', 'wood', 'ceramic']
X = []
y = []
objects = dict()
for m, material in enumerate(materials):
if verbose:
print 'Processing', material
sys.stdout.flush()
with open('data_processed/processed_0.1sbefore_%s_times_%.2f_%.2f.pkl' % (material, forcetempTime, contactmicTime), 'rb') as f:
allData = pickle.load(f)
for j, (objName, objData) in enumerate(allData.iteritems()):
if leaveObjectOut:
objects[objName] = {'x': [], 'y': []}
X = objects[objName]['x']
y = objects[objName]['y']
for i in xrange(len(objData['temperature'])):
y.append(m)
if deriv:
objData['force0'][i] = firstDeriv(objData['force0'][i], objData['forceTime'][i])
objData['force1'][i] = firstDeriv(objData['force1'][i], objData['forceTime'][i])
objData['temperature'][i] = firstDeriv(objData['temperature'][i], objData['temperatureTime'][i])
if modalities > 2:
# Mel-scaled power (energy-squared) spectrogram
sr = 48000
S = librosa.feature.melspectrogram(np.array(objData['contact'][i]), sr=sr, n_mels=128)
# Convert to log scale (dB)
log_S = librosa.logamplitude(S, ref_power=np.max)
if modalities == 0:
X.append(objData['force0'][i] + objData['force1'][i])
elif modalities == 1:
X.append(objData['temperature'][i])
elif modalities == 2:
X.append(objData['temperature'][i] + objData['force0'][i] + objData['force1'][i])
elif modalities == 3:
X.append(log_S.flatten())
elif modalities == 4:
X.append(objData['temperature'][i] + log_S.flatten().tolist())
elif modalities == 5:
X.append(objData['temperature'][i] + objData['force0'][i] + objData['force1'][i] + log_S.flatten().tolist())
elif modalities == 6:
X.append(objData['force0'][i] + objData['force1'][i] + log_S.flatten().tolist())
if leaveObjectOut:
return objects
else:
X = np.array(X)
y = np.array(y)
if verbose:
print 'X:', np.shape(X), 'y:', np.shape(y)
return X, y
def mr_svm(X, y, percentlabeled=50, trainTestSets=None, verbose=False):
# Non Deterministic output
np.random.seed(np.random.randint(1e9))
materials = ['plastic', 'glass', 'fabric', 'metal', 'wood', 'ceramic']
test_ratio = 200*len(materials)
num_labeled_examples = int(10*percentlabeled)
# Split into train and test sets
if trainTestSets is None:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_ratio, stratify=y)
else:
X_train, X_test, y_train, y_test = trainTestSets
if verbose:
print 'Num of class examples in test set:', [np.sum(y_test == i) for i in xrange(len(materials))]
print 'X_train:', np.shape(X_train), 'y_train:', np.shape(y_train), 'X_test:', np.shape(X_test), 'y_test:', np.shape(y_test)
# Scale data to zero mean and unit variance
scaler = preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Select labeled data
X_train, y_train = shuffle(X_train, y_train)
x_labeled = np.concatenate([X_train[y_train==j][:num_labeled_examples] for j in xrange(len(materials))], axis=0)
y_labeled = np.concatenate([[j]*num_labeled_examples for j in xrange(len(materials))], axis=0)
if verbose:
print 'x_labeled:', np.shape(x_labeled), 'y_labeled:', np.shape(y_labeled)
svm = SVC(kernel='rbf', C=1.0)
# Train on x_labeled, y_labeled. Test on X_test, y_test
svm.fit(x_labeled, y_labeled)
testerror = 1.0 - svm.score(X_test, y_test)
test_err = 1.0 - np.mean(svm.predict(X_test) == y_test)
if verbose:
print 'Test error:', testerror, test_err
sys.stdout.flush()
return testerror
if __name__ == '__main__':
modalities = ['Force', 'Temperature', 'Force and Temperature', 'Contact mic', 'Temperature and Contact Mic', 'Force, Temperature, and Contact Mic', 'Force and Contact Mic']
parser = argparse.ArgumentParser(description='Collecting data from a spinning platter of objects.')
parser.add_argument('-t', '--tables', nargs='+', help='[Required] Tables to recompute', required=True)
parser.add_argument('-v', '--verbose', help='Verbose', action='store_true')
args = parser.parse_args()
if '2' in args.tables:
# Test various amounts of labeled training data
print '\n', '-'*25, 'Testing various amounts of labeled training data', '-'*25
print '-'*100
for modality in [2, 5]:
print '-'*25, modalities[modality], 'modality', '-'*25
X, y = dataset(modalities=modality)
for percent in [1, 2, 4, 8, 16, 50, 100]:
print '-'*15, 'Percentage of training data labeled: %d%%' % percent, '-'*15
errors = []
# Average over Stratified 6-fold. Training set: 6000, Test set: 1200
skf = StratifiedKFold(n_splits=6, shuffle=True)
for trainIdx, testIdx in skf.split(X, y):
errors.append(mr_svm(None, None, percentlabeled=percent, trainTestSets=[X[trainIdx], X[testIdx], y[trainIdx], y[testIdx]], verbose=args.verbose))
# print 'Test error:', errors[-1], 'Test accuracy:', 1.0-errors[-1]
sys.stdout.flush()
print 'Average error:', np.mean(errors), 'Average accuracy:', np.mean(1.0-np.array(errors))
sys.stdout.flush()
if '4' in args.tables:
# Test generalization with leave-one-object-out validation
print '\n', '-'*25, 'Testing generalization with leave-one-object-out validation', '-'*25
print '-'*100
for modality in [2, 5]:
print '-'*25, modalities[modality], 'modality', '-'*25
objects = dataset(modalities=modality, leaveObjectOut=True)
for percent in [1, 4, 16, 50, 100]:
print '-'*15, 'Percentage of training data labeled: %d%%' % percent, '-'*15
errors = []
# Average over all 72 training and test set splits
for objName, objData in objects.iteritems():
Xtest = np.array(objData['x'])
ytest = np.array(objData['y'])
Xtrain = np.array(list(itertools.chain.from_iterable([data['x'] for name, data in objects.iteritems() if name != objName])))
ytrain = np.array(list(itertools.chain.from_iterable([data['y'] for name, data in objects.iteritems() if name != objName])))
errors.append(mr_svm(None, None, percentlabeled=percent, trainTestSets=[Xtrain, Xtest, ytrain, ytest], verbose=args.verbose))
print objName, 'Test error:', errors[-1], 'Test accuracy:', 1.0-errors[-1]
sys.stdout.flush()
print 'Average leave-one-object-out error:', np.mean(errors), 'Average accuracy:', np.mean(1.0-np.array(errors))
sys.stdout.flush()