def eval_byexp(model_name, size, partition, userstrata=-1, window=50, sliding_step=50, write=False):
exp_filter = ExpFilter(size, partition, userstrata=userstrata)
device_and_exps = exp_filter.get_expId_bydevice()
convnet = cPickle.load(open('../models/%s.pkl' % model_name))
results = {}
results['total'] = [0, 0]
for device, exp in device_and_exps:
dataset = load_data([exp], window=window, sliding_step=sliding_step)
if dataset.start_idx < 1:
continue
sharedset = shared_dataset(dataset)
convnet_inst_num = dataset.start_idx
convnet_scores = convnet.score(sharedset)
convnet_errors = 1 - convnet_scores
res = (dataset.start_idx, convnet_errors)
results[exp] = res
results['total'][0] = results['total'][0] + res[0]
results['total'][1] = results['total'][1] + res[0] * res[1]
if write is False:
return
with open("../../results/%s_results.txt" % model_name, 'w') as f:
line = "The total error is %r" % (results['total'][1] / results['total'][0])
f.write(line + "\n")
print line
for device, exp in device_and_exps:
res = results.get(exp, None)
if res is None:
"%s, %s skipped" % (device, exp)
continue
line = "%s, %s the error is %r for %i insts" % (device, exp, res[1], res[0])
f.write(line + "\n")
print line
def eval_exps(self, exp_ids): preparedset = load_prepared_data(exp_ids) prepared_x = preparedset[0] prepared_y = numpy.asarray(preparedset[1][:,0], 'int32') mv_logreg_error = 1-self.mv_logreg.score(prepared_x, prepared_y) # dataset = load_data(exp_ids, window=self.window, sliding_step=self.sliding_step) # if dataset is None: # return 0, 0, 0, 0 # sharedset = shared_dataset(dataset, self.convnet.static_sensor) # convnet_inst_num = dataset[1].shape[0] dataset = cc_preprocessor.load_data(exp_ids, window=self.window, sliding_step=self.sliding_step) if dataset.start_idx < 1: return 0, 0, 0, 0 sharedset = cc_multimodal_conv.shared_dataset(dataset) convnet_inst_num = dataset.start_idx """ shared_x = theano.shared(prepared_x, borrow=True) shared_y = theano.shared(prepared_y, borrow=True) sharedset = [shared_x, shared_y] """ if self.mv_rf is None: convnet_scores = self.convnet.score(sharedset) print convnet_scores else: repr_x, repr_y = self.convnet.input_to_feature(sharedset, layer=self.layer) print repr_x.shape convnet_scores = self.mv_rf.score(repr_x, repr_y[:,0]) convnet_errors = 1-convnet_scores logreg_inst_num = len(preparedset[1]) return logreg_inst_num, mv_logreg_error, convnet_inst_num, convnet_errors
y_preds = [predict_model(i) for i in xrange(n_test_batches)]
y_preds.append(predict_remainder())
y_preds = numpy.reshape(y_preds, (n_test_batches * batch_size + batch_size, len(self.label_struct)))
y_preds = y_preds[:test_size,]
return y_preds
def shared_dataset(data, borrow=True):
shared_a = theano.shared(numpy.asarray(data.accData.get(), dtype=theano.config.floatX), borrow=borrow)
shared_l = theano.shared(numpy.asarray(data.linData.get(), dtype=theano.config.floatX), borrow=borrow)
shared_g = theano.shared(numpy.asarray(data.gyroData.get(), dtype=theano.config.floatX), borrow=borrow)
shared_m = theano.shared(numpy.asarray(data.magData.get(), dtype=theano.config.floatX), borrow=borrow)
shared_y = theano.shared(numpy.asarray(data.targetData.get(), dtype='int32'), borrow=borrow)
return shared_a, shared_l, shared_g, shared_m, shared_y
if __name__ == '__main__':
data_size = 3 # 0: mini, 1: small, 2: medium, 3: large
sliding_step = 50
window = 50
train_set = load_data(ExpFilter(data_size, 'train').get_expId(), window=window, sliding_step=sliding_step)
train_shared = shared_dataset(train_set)
valid_shared = None
label_struct = [2]
ccnet = ConvNet(label_struct, train_set.n_features(), filter_h=1, presample_size=1, nkerns=[10,10,10,30])
ccnet.learn(train_shared, valid_shared, learning_rate=0.05, momentum_params={'start':0.05, 'end':0.95, 'interval':100}, n_epochs=200)
