def test_feature(feature_impl, X_train, y_train, X_test, y_test):
start = time.time()
print 'calculating features...',
worker = MultiProcessWorker(feature_impl)
X_train = worker.run(X_train)
del worker
worker = MultiProcessWorker(feature_impl)
X_test = worker.run(X_test)
del worker
print 'time elapsed: ', time.time() - start
start = time.time()
gmmset = GMMSet()
print 'training ...',
gmmset.fit(X_train, y_train)
nr_correct = 0
print 'time elapsed: ', time.time() - start
print 'predicting...',
start = time.time()
pool = multiprocessing.Pool(concurrency)
predictions = []
for x_test, label_true in zip(*(X_test, y_test)):
predictions.append(
pool.apply_async(predict_task, args=(gmmset, x_test)))
pool.close()
for ind, (x_test, label_true) in enumerate(zip(*(X_test, y_test))):
label_pred = predictions[ind].get()
if label_pred == label_true:
nr_correct += 1
print 'time elapsed: ', time.time() - start
print("{}/{} {:.6f}".format(nr_correct, len(y_test),
float(nr_correct) / len(y_test)))
def _compute(self, data):
worker = MultiProcessWorker(BOB.extract)
args = []
names = []
for (name, fs, signal) in data:
args.append((fs, signal))
names.append(name)
rst = worker.run(args)
del worker
return zip(names, rst)
def _compute(self, data):
worker = MultiProcessWorker(BOB.extract)
args = []
names = []
for (name, fs, signal) in data:
args.append((fs, signal))
names.append(name)
rst = worker.run(args)
del worker
return zip(names, rst)
def test_feature(feature_impl, X_train, y_train, X_test, y_test):
start = time.time()
print 'calculating features...',
worker = MultiProcessWorker(feature_impl)
X_train = worker.run(X_train)
del worker
worker = MultiProcessWorker(feature_impl)
X_test = worker.run(X_test)
del worker
print 'time elapsed: ', time.time() - start
start = time.time()
gmmset = GMMSet()
print 'training ...' ,
gmmset.fit(X_train, y_train)
nr_correct = 0
print 'time elapsed: ', time.time() - start
print 'predicting...',
start = time.time()
pool = multiprocessing.Pool(concurrency)
predictions = []
for x_test, label_true in zip(*(X_test, y_test)):
predictions.append(pool.apply_async(predict_task, args = (gmmset, x_test)))
pool.close()
for ind, (x_test, label_true) in enumerate(zip(*(X_test, y_test))):
label_pred = predictions[ind].get()
if label_pred == label_true:
nr_correct += 1
print 'time elapsed: ', time.time() - start
print("{}/{} {:.6f}".format(nr_correct, len(y_test),
float(nr_correct) / len(y_test)))
