Replace warning with logging

optimizer/__init__.py

@@ -1,7 +1,7 @@
 """optimizer
 A Python package that implements different type of Optimization Algorithm
 """
-from .util import FileManager, Randomizer
+from .util import FileManager, Randomizer, logger
 from .optimizer import Optimizer
 from .mopso import MOPSO
 from .objective import Objective, ElementWiseObjective, BatchObjective

optimizer/mopso.py

@@ -22,8 +22,7 @@
 import math
 import numpy as np
 import math
-import warnings
-from optimizer import Optimizer, FileManager, Randomizer
+from optimizer import Optimizer, FileManager, Randomizer, logger
 from numba import njit, jit
 import scipy.stats as stats
 
@@ -202,7 +201,7 @@ def __init__(self,
  self.num_particles = num_particles
 
  if len(lower_bounds) != len(upper_bounds):
- warnings.warn(f"Warning: lower_bounds and upper_bounds have different lengths."
+ logger.warning(f"Warning: lower_bounds and upper_bounds have different lengths."
  f"The lowest length ({min(len(lower_bounds), len(upper_bounds))}) is taken.")
  self.num_params = min(len(lower_bounds), len(upper_bounds))
  self.lower_bounds = lower_bounds
@@ -219,7 +218,7 @@ def __init__(self,
  'spread', 'lower_bounds', 'upper_bounds', 'random', 'gaussian'}
 
  if initial_particles_position == 'spread':
- warnings.warn(f"Initial distribution set to 'random'.")
+ logger.warning(f"Initial distribution set to 'random'.")
  initial_particles_position = 'random'
  # self.spread_particles()
 
@@ -297,9 +296,9 @@ def check_types(self):
  f"Upper bound {i} is not acceptable")
 
  if lb_types != ub_types:
- warnings.warn(
+ logger.warning(
  "Warning: lower_bounds and upper_bounds are of different types")
- warnings.warn("Keeping the least restrictive type")
+ logger.warning("Keeping the least restrictive type")
  for i in range(self.num_params):
  if lb_types[i] == float or ub_types[i] == float:
  self.lower_bounds[i] = float(self.lower_bounds[i])
@@ -329,16 +328,21 @@ def insert_nodes(self, param_list, is_bool=False):
  return param_list
 
  def get_nodes(self):
- all_nodes = [[self.lower_bounds[idx], self.upper_bounds[idx]]
- for idx in range(self.num_params)]
+
+ def ndcube(*args):
+ return list(itertools.product(*map(lambda x: [x[0], x[1]], args)))
+
+ bounds = list(zip(self.lower_bounds, self.upper_bounds))
+ all_nodes = ndcube(*bounds)
+ print(len(all_nodes))
+ exit()
  indices_with_bool = [idx for idx, node in enumerate(
  all_nodes) if any(isinstance(val, bool) for val in node)]
  all_nodes = [[2 if isinstance(val, bool) and val else 0 if isinstance(
  val, bool) and not val else val for val in node] for node in all_nodes]
 
  if self.num_particles < self.num_params:
- warnings.warn(f"Warning: not enough particles, now you are running with {
- len(all_nodes[0])} particles")
+ logger.warning(f"Warning: not enough particles, now you are running with {len(all_nodes[0])} particles")
 
  particle_count = len(all_nodes[0])
  while particle_count < self.num_particles:

optimizer/util.py

@@ -1,7 +1,34 @@
 import os
 import json
 import numpy as np
+import logging
 
+class CustomFormatter(logging.Formatter):
+
+ grey = "\x1b[38;20m"
+ yellow = "\x1b[33;20m"
+ red = "\x1b[31;20m"
+ bold_red = "\x1b[31;1m"
+ reset = "\x1b[0m"
+ format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s (%(filename)s:%(lineno)d)"
+
+ FORMATS = {
+ logging.DEBUG: grey + format + reset,
+ logging.INFO: grey + format + reset,
+ logging.WARNING: yellow + format + reset,
+ logging.ERROR: red + format + reset,
+ logging.CRITICAL: bold_red + format + reset
+ }
+
+ def format(self, record):
+ log_fmt = self.FORMATS.get(record.levelno)
+ formatter = logging.Formatter(log_fmt)
+ return formatter.format(record)
+
+logger = logging.getLogger("Optimizer")
+handler = logging.StreamHandler()
+handler.setFormatter(CustomFormatter())
+logger.addHandler(handler)
 
 class Randomizer:
  rng = np.random.default_rng()

tests/zdt1.py

@@ -4,7 +4,7 @@
 import matplotlib.pyplot as plt
 import matplotlib.animation as animation
 import os
-
+import logging
 
 num_agents = 100
 num_iterations = 200
@@ -13,6 +13,7 @@
 lb = [0.] * num_params
 ub = [1.] * num_params
 
+optimizer.logger.setLevel(logging.DEBUG)
 
 def zdt1_objective1(x):
  return x[0]