def test_repr(self): m = ff.MeanZero() k = ff.SqExpKernel() l = ff.LikGauss() regression_model = ff.GPModel(mean=m, kernel=k, likelihood=l) print regression_model print ff.repr_to_model(regression_model.__repr__()) assert regression_model == ff.repr_to_model(regression_model.__repr__())
def test_repr(self): m = ff.MeanZero() k = ff.SqExpKernel() l = ff.LikGauss() regression_model = ff.GPModel(mean=m, kernel=k, likelihood=l) print regression_model print ff.repr_to_model(regression_model.__repr__()) assert regression_model == ff.repr_to_model( regression_model.__repr__())
def parse_results(results_filenames, max_level=None): ''' Returns the best kernel in an experiment output file as a ScoredKernel ''' if not isinstance(results_filenames, list): # Backward compatibility wth specifying a single file results_filenames = [results_filenames] # Read relevant lines of file(s) result_tuples = [] for results_filename in results_filenames: lines = [] with open(results_filename) as results_file: score = None for line in results_file: if line.startswith('score = '): score = line[8:-2] elif line.startswith("GPModel"): lines.append(line) elif (not max_level is None) and (len(re.findall('Level [0-9]+', line)) > 0): level = int(line.split(' ')[2]) if level > max_level: break result_tuples += [ff.repr_to_model(line.strip()) for line in lines] if not score is None: best_tuple = sorted(result_tuples, key=lambda a_model : GPModel.score(a_model, score))[0] else: best_tuple = sorted(result_tuples, key=GPModel.score)[0] return best_tuple
def parse_results(results_filenames, max_level=None): ''' Returns the best kernel in an experiment output file as a ScoredKernel ''' if not isinstance(results_filenames, list): # Backward compatibility wth specifying a single file results_filenames = [results_filenames] # Read relevant lines of file(s) result_tuples = [] for results_filename in results_filenames: lines = [] with open(results_filename) as results_file: score = None for line in results_file: if line.startswith('score = '): score = line[8:-2] elif line.startswith("GPModel"): lines.append(line) elif (not max_level is None) and (len( re.findall('Level [0-9]+', line)) > 0): level = int(line.split(' ')[2]) if level > max_level: break result_tuples += [ff.repr_to_model(line.strip()) for line in lines] if not score is None: best_tuple = sorted( result_tuples, key=lambda a_model: GPModel.score(a_model, score))[0] else: best_tuple = sorted(result_tuples, key=GPModel.score)[0] return best_tuple