def read_file(self, file_path):
try:
f = open(file_path, 'r')
except Exception as e:
logging.debug('Error opening %s: %s' % (file_path, e))
return
load_save.load(f)
f.close()
def read_file(self, file_path):
try:
f = open(file_path, 'r')
except Exception as e:
logging.error('Could not open %s: %s' % (file_path, e))
return
load_save.load(f)
f.close()
def read_file(self, file_path):
try:
f = open(file_path, 'r')
except Exception as e:
logging.error("Couldn't open %s: %s" % (file_path, e))
return
load_save.load(f)
f.close()
def read_file(self, file_path):
try:
f = open(file_path, 'r')
except Exception as e:
logging.debug('Error opening %s: %s' % (file_path, e))
return
load_save.load(f)
f.close()
def load():
dir = ''
dir = os.environ.get('SUGAR_ACTIVITY_ROOT')
if dir == None: dir = ''
fname = os.path.join(dir, 'data', 'sudoku.dat')
try:
f = open(fname, 'r')
except:
return None #****
load_save.load(f)
f.close
def load():
dir = ''
dir = os.environ.get('SUGAR_ACTIVITY_ROOT')
if dir is None:
dir = ''
fname = os.path.join(dir, 'data', 'Countries.dat')
try:
f = open(fname, 'r')
except BaseException:
return None # ****
try:
load_save.load(f)
except BaseException:
pass
f.close
def load():
directory = ''
directory = os.environ.get('SUGAR_ACTIVITY_ROOT')
if directory is None:
directory = ''
fname = os.path.join(directory, 'data', 'follow.dat')
try:
f = open(fname, 'r')
except Exception as e:
logging.error('Could not open %s: %s' % (fname, e))
return None
try:
load_save.load(f)
except Exception as e:
logging.error('load_save failed: %s' % (e))
f.close
def __init__(self, search_space, fixed_space, evaluator, input_file=None, output_file=None):
"""Instantiate the GaussianProcessSearch and create the GaussianProcessRegressor
Args:
search_space (list): List of skopt.space.Dimension objects (Integer, Real,
or Categorical) whose name must match the correspondent variable name in the
evaluator function
fixed_space (dict): Dictionary of parameters that will be passed by default to the
evaluator function. The keys must match the correspondent names in the function.
evaluator (function): Function of which we want to estimate the maximum. It must take
the union of search_space and fixed_space as parameters and return a scalar value.
input_file (str): Path to the file containing points in the search space and
corresponding values that are already known.
output_file (str): Path to the file where updated results will be stored.
"""
self.search_space = search_space
self.fixed_space = fixed_space
self.evaluator = evaluator
self.input_file = input_file
self.output_file = output_file
self.x_values = []
self.y_values = []
self.solutions = []
if input_file is not None:
try:
data_dict = load_save.load(data_file=input_file)
self.x_values, self.y_values = self._extract_values(data_dict)
except OSError as e:
raise OSError('Cannot read input file. \n' + str(e))
def load():
dir = ''
dir = os.environ.get('SUGAR_ACTIVITY_ROOT')
if dir is None:
dir = ''
fname = os.path.join(dir, 'data', 'spiro.dat')
try:
f = open(fname, 'r')
except Exception as e:
logging.error('Could not open %s: %s' % (fname, e))
return None
try:
load_save.load(f)
except Exception as e:
logging.error('load_save failed: %s' % (e))
f.close
def load():
directory = ''
directory = os.environ.get('SUGAR_ACTIVITY_ROOT')
if directory is None:
directory = ''
fname = os.path.join(directory, 'data', 'Letters.dat')
if os.path.exists(fname):
try:
f = open(fname, 'r')
except Exception as e:
logging.error('Could not open %s: %s' % (fname, e))
return None
try:
load_save.load(f)
except Exception as e:
logging.error('load_save failed: %s' % (e))
f.close
def load():
dir = ''
dir = os.environ.get('SUGAR_ACTIVITY_ROOT')
if dir is None:
dir = ''
fname = os.path.join(dir, 'data', 'triples.dat')
if os.path.exists(fname):
try:
f = open(fname, 'r')
except Exception as e:
logging.error('Could not open %s: %s' % (fname, e))
return None
try:
load_save.load(f)
except Exception as e:
logging.error('load_save failed: %s' % (e))
f.close
