def train_al_and_report(model_name, kernel, warp, ard):
dataset_dir = os.path.join(MODEL_DIR, DATASET)
try:
os.makedirs(dataset_dir)
except OSError:
print "skipping output folder"
for fold in xrange(1):
fold_dir = os.path.join(SPLIT_DIR, DATASET, str(fold))
train_data = np.loadtxt(os.path.join(fold_dir, 'train'))
test_data = np.loadtxt(os.path.join(fold_dir, 'test'))
params_file = None
output_dir = os.path.join(dataset_dir, str(fold))
try:
os.makedirs(output_dir)
except OSError:
print "skipping output folder"
if ard:
iso_dir = output_dir.replace('True', 'False')
params_file = os.path.join(iso_dir, 'params')
# Split train into train and pool
pool_data = train_data[50:]
train_data = train_data[:50]
metrics_list = []
while True:
# Train gp
gp = util.train_gp_model(train_data, kernel, warp, ard, params_file)
# Get metrics on test
metrics = util.get_metrics(gp, test_data)
metrics_list.append([metrics[0], metrics[1], metrics[2][0],
metrics[2][1], metrics[3]])
# Predict pool and select instance in pool with higher variance
new_instance, new_i = util.query(gp, pool_data)
# Update train and pool
train_data = np.append(train_data, [new_instance], axis=0)
pool_data = np.delete(pool_data, (new_i), axis=0)
#if pool_data.shape[0] == 0:
# break
if train_data.shape[0] == 500:
break
print pool_data.shape
gc.collect(2)
# Final metrics on full train set (sanity check)
gp = util.train_gp_model(train_data, kernel, warp, ard, params_file)
metrics = util.get_metrics(gp, test_data)
metrics_list.append([metrics[0], metrics[1], metrics[2][0],
metrics[2][1], metrics[3]])
util.save_metrics_list(metrics_list, os.path.join(output_dir, 'metrics'))
def train_and_report(model_name, kernel, warp, ard):
dataset_dir = os.path.join(MODEL_DIR, DATASET)
try:
os.makedirs(dataset_dir)
except OSError:
print "skipping output folder"
for fold in xrange(10):
fold_dir = os.path.join(SPLIT_DIR, DATASET, str(fold))
train_data = np.loadtxt(os.path.join(fold_dir, 'train'))
test_data = np.loadtxt(os.path.join(fold_dir, 'test'))
params_file = None
output_dir = os.path.join(dataset_dir, str(fold))
try:
os.makedirs(output_dir)
except OSError:
print "skipping output folder"
if ard:
iso_dir = output_dir.replace('True', 'False')
params_file = os.path.join(iso_dir, 'params')
gp = util.train_gp_model(train_data, kernel, warp, ard, params_file)
util.save_parameters(gp, os.path.join(output_dir, 'params'))
util.save_gradients(gp, os.path.join(output_dir, 'grads'))
metrics = util.get_metrics(gp, test_data)
util.save_metrics(metrics, os.path.join(output_dir, 'metrics'))
util.save_cautious_curves(gp, test_data, os.path.join(output_dir, 'curves'))
util.save_predictions(gp, test_data, os.path.join(output_dir, 'preds'))
asym_metrics = util.get_asym_metrics(gp, test_data)
util.save_asym_metrics(asym_metrics, os.path.join(output_dir, 'asym_metrics'))
gc.collect(2) # buggy GPy has allocation cycles...
def train_and_report(model_name, kernel, warp, ard, likelihood='gaussian'):
dataset_dir = os.path.join(MODEL_DIR, DATASET)
try:
os.makedirs(dataset_dir)
except OSError:
print "skipping output folder"
for fold in xrange(10):
fold_dir = os.path.join(SPLIT_DIR, DATASET, str(fold))
train_data = np.loadtxt(os.path.join(fold_dir, 'train'))
test_data = np.loadtxt(os.path.join(fold_dir, 'test'))
output_dir = os.path.join(dataset_dir, str(fold))
params_file = None
if ard:
iso_dir = output_dir.replace('True', 'False')
params_file = os.path.join(iso_dir, 'params')
gp = util.train_gp_model(train_data, kernel, warp, ard, params_file, likelihood=likelihood)
metrics = util.get_metrics(gp, test_data)
try:
os.makedirs(output_dir)
except OSError:
print "skipping output folder"
util.save_parameters(gp, os.path.join(output_dir, 'params'))
util.save_metrics(metrics, os.path.join(output_dir, 'metrics'))
#util.save_gradients(gp, os.path.join(output_dir, 'grads'))
util.save_cautious_curves(gp, test_data, os.path.join(output_dir, 'curves'))
util.save_predictions(gp, test_data, os.path.join(output_dir, 'preds'))
