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 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