def repeat_predictions(filename):
"""
A convenience function to re run the predictions from an experiment
"""
expstring = open(filename, 'r').read()
exp = eval(expstring)
print experiment_fields_to_str(exp)
if not exp.make_predictions:
print 'This experiment does not make predictions'
return None
data_sets = list(gen_all_datasets(exp.data_dir))
for r, file in data_sets:
# Check if this experiment has already been done.
output_file = os.path.join(exp.results_dir, file + "_result.txt")
if os.path.isfile(output_file):
print 'Predictions for %s' % file
data_file = os.path.join(r, file + ".mat")
X, y, D, Xtest, ytest = gpml.load_mat(data_file)
prediction_file = os.path.join(exp.results_dir, os.path.splitext(os.path.split(data_file)[-1])[0] + "_predictions.mat")
best_model = parse_results(output_file)
predictions = jc.make_predictions(X, y, Xtest, ytest, best_model, local_computation=True,
max_jobs=exp.max_jobs, verbose=exp.verbose, random_seed=exp.random_seed)
scipy.io.savemat(prediction_file, predictions, appendmat=False)
print "Finished file %s" % file
else:
print 'Results not found for %s' % file
def perform_experiment(data_file, output_file, exp):
if exp.make_predictions:
X, y, D, Xtest, ytest = gpml.load_mat(data_file)
prediction_file = os.path.join(
exp.results_dir,
os.path.splitext(os.path.split(data_file)[-1])[0] +
"_predictions.mat")
else:
X, y, D = gpml.load_mat(data_file)
perform_kernel_search(X, y, D, data_file, output_file, exp)
best_model = parse_results(output_file)
if exp.make_predictions:
print '\nMaking predictions\n'
predictions = jc.make_predictions(X,
y,
Xtest,
ytest,
best_model,
local_computation=True,
max_jobs=exp.max_jobs,
verbose=exp.verbose,
random_seed=exp.random_seed)
scipy.io.savemat(prediction_file, predictions, appendmat=False)
os.system('reset') # Stop terminal from going invisible.
def perform_experiment(data_file, output_file, exp):
if exp.make_predictions:
X, y, D, Xtest, ytest = gpml.load_mat(data_file, y_dim=1)
prediction_file = os.path.join(
exp.results_dir,
os.path.splitext(os.path.split(data_file)[-1])[0] +
"_predictions.mat")
else:
X, y, D = gpml.load_mat(data_file, y_dim=1)
perform_kernel_search(X, y, D, data_file, output_file, exp)
best_scored_kernel = parse_results(output_file)
if exp.make_predictions:
predictions = jc.make_predictions(
X,
y,
Xtest,
ytest,
best_scored_kernel,
local_computation=exp.local_computation,
max_jobs=exp.max_jobs,
verbose=exp.verbose,
zero_mean=exp.zero_mean,
random_seed=exp.random_seed)
scipy.io.savemat(prediction_file, predictions, appendmat=False)
def calculate_model_fits(data_file, output_file, exp):
prediction_file = os.path.join(exp.results_dir, os.path.splitext(os.path.split(data_file)[-1])[0] + "_predictions.mat")
X, y, D, = gpml.load_mat(data_file, y_dim=1)
Xtest = X
ytest = y
best_scored_kernel = parse_results(output_file)
predictions = jc.make_predictions(X, y, Xtest, ytest, best_scored_kernel, local_computation=exp.local_computation,
max_jobs=exp.max_jobs, verbose=exp.verbose, zero_mean=exp.zero_mean, random_seed=exp.random_seed)
scipy.io.savemat(prediction_file, predictions, appendmat=False)
os.system('reset') # Stop terminal from going invisible.
def perform_experiment(data_file, output_file, exp):
if exp.make_predictions:
X, y, D, Xtest, ytest = gpml.load_mat(data_file)
prediction_file = os.path.join(exp.results_dir, os.path.splitext(os.path.split(data_file)[-1])[0] + "_predictions.mat")
else:
X, y, D = gpml.load_mat(data_file)
perform_kernel_search(X, y, D, data_file, output_file, exp)
best_model = parse_results(output_file)
if exp.make_predictions:
print '\nMaking predictions\n'
predictions = jc.make_predictions(X, y, Xtest, ytest, best_model, local_computation=True,
max_jobs=exp.max_jobs, verbose=exp.verbose, random_seed=exp.random_seed)
scipy.io.savemat(prediction_file, predictions, appendmat=False)
os.system('reset') # Stop terminal from going invisible.
def repeat_predictions(filename):
"""
A convenience function to re run the predictions from an experiment
"""
expstring = open(filename, 'r').read()
exp = eval(expstring)
print experiment_fields_to_str(exp)
if not exp.make_predictions:
print 'This experiment does not make predictions'
return None
data_sets = list(gen_all_datasets(exp.data_dir))
for r, file in data_sets:
# Check if this experiment has already been done.
output_file = os.path.join(exp.results_dir, file + "_result.txt")
if os.path.isfile(output_file):
print 'Predictions for %s' % file
data_file = os.path.join(r, file + ".mat")
X, y, D, Xtest, ytest = gpml.load_mat(data_file)
prediction_file = os.path.join(
exp.results_dir,
os.path.splitext(os.path.split(data_file)[-1])[0] +
"_predictions.mat")
best_model = parse_results(output_file)
predictions = jc.make_predictions(X,
y,
Xtest,
ytest,
best_model,
local_computation=True,
max_jobs=exp.max_jobs,
verbose=exp.verbose,
random_seed=exp.random_seed)
scipy.io.savemat(prediction_file, predictions, appendmat=False)
print "Finished file %s" % file
else:
print 'Results not found for %s' % file
