Add reporter module

docs/api/_pyswarms.utils.rst

@@ -10,3 +10,4 @@ functionalities.
    pyswarms.utils.functions
    pyswarms.utils.search
    pyswarms.utils.plotters
+   pyswarms.utils.reporter

docs/api/pyswarms.utils.reporter.rst

@@ -0,0 +1,10 @@
+pyswarms.utils.reporter package
+================================
+
+.. automodule:: pyswarms.utils.reporter.reporter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:
+   :special-members: __init__
+

pyswarms/backend/generators.py

@@ -9,12 +9,15 @@
 
 """
 
-# Import modules
+import logging
+
 import numpy as np
 
-# Import from package
+from ..utils.reporter import Reporter
 from .swarms import Swarm
 
+rep = Reporter(logger=logging.getLogger(__name__))
+
 
 def generate_swarm(
     n_particles, dimensions, bounds=None, center=1.00, init_pos=None
@@ -67,7 +70,12 @@ def generate_swarm(
                 low=min_bounds, high=max_bounds, size=(n_particles, dimensions)
             )
     except ValueError:
+        rep.logger.exception(
+            "Please check the size and value of bounds and dimensions"
+        )
         raise
+    except TypeError:
+        rep.logger.exception("Invalid input type!")
     else:
         return pos
 
@@ -103,7 +111,11 @@ def generate_discrete_swarm(
                 size=(n_particles, dimensions)
             ).argsort(axis=1)
     except ValueError:
-        raise
+        rep.logger.exception(
+            "Please check the size and value of bounds and dimensions"
+        )
+    except TypeError:
+        rep.logger.exception("Invalid input type!")
     else:
         return pos
 
@@ -132,8 +144,12 @@ def generate_velocity(n_particles, dimensions, clamp=None):
         velocity = (max_velocity - min_velocity) * np.random.random_sample(
             size=(n_particles, dimensions)
         ) + min_velocity
-    except (ValueError, TypeError):
-        raise
+    except ValueError:
+        rep.logger.exception(
+            "Please check the size and value of clamp and dimensions"
+        )
+    except TypeError:
+        rep.logger.exception("Invalid input type!")
     else:
         return velocity
 

pyswarms/backend/operators.py

@@ -8,14 +8,13 @@
 to specify how the swarm will behave.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 
-# Create a logger
-logger = logging.getLogger(__name__)
+from ..utils.reporter import Reporter
+
+rep = Reporter(logger=logging.getLogger(__name__))
 
 
 def compute_pbest(swarm):
@@ -67,9 +66,9 @@ def compute_pbest(swarm):
             ~mask_cost, swarm.pbest_cost, swarm.current_cost
         )
     except AttributeError:
-        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
-        logger.error(msg)
-        raise
+        rep.logger.exception(
+            "Please pass a Swarm class. You passed {}".format(type(swarm))
+        )
     else:
         return (new_pbest_pos, new_pbest_cost)
 
@@ -137,13 +136,11 @@ def compute_velocity(swarm, clamp):
             )
             updated_velocity = np.where(~mask, swarm.velocity, temp_velocity)
     except AttributeError:
-        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
-        logger.error(msg)
-        raise
+        rep.logger.exception(
+            "Please pass a Swarm class. You passed {}".format(type(swarm))
+        )
     except KeyError:
-        msg = "Missing keyword in swarm.options"
-        logger.error(msg)
-        raise
+        rep.logger.exception("Missing keyword in swarm.options")
     else:
         return updated_velocity
 
@@ -187,8 +184,8 @@ def compute_position(swarm, bounds):
             temp_position = np.where(~mask, swarm.position, temp_position)
         position = temp_position
     except AttributeError:
-        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
-        logger.error(msg)
-        raise
+        rep.logger.exception(
+            "Please pass a Swarm class. You passed {}".format(type(swarm))
+        )
     else:
         return position

pyswarms/backend/swarms.py

@@ -8,9 +8,8 @@
 as input to most backend cases.
 """
 
-# Import modules
 import numpy as np
-from attr import attrs, attrib
+from attr import attrib, attrs
 from attr.validators import instance_of
 
 

pyswarms/backend/topology/base.py

@@ -12,32 +12,28 @@
 :mod:`pyswarms.backend.swarms.Swarm` module.
 """
 
-# Import from stdlib
 import abc
 import logging
 
-# Import from package
-from ...utils.console_utils import cli_print
+from ...utils.reporter import Reporter
 
 
 class Topology(abc.ABC):
     def __init__(self, static, **kwargs):
         """Initializes the class"""
 
         # Initialize logger
-        self.logger = logging.getLogger(__name__)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
         # Initialize attributes
         self.static = static
         self.neighbor_idx = None
 
         if self.static:
-            cli_print(
-                "Running on `dynamic` topology, neighbors are updated regularly."
-                "Set `static=True` for fixed neighbors.",
-                1,
-                0,
-                self.logger,
+            self.rep.log(
+                "Running on `dynamic` topology,"
+                "set `static=True` for fixed neighbors.",
+                lvl=10,
             )
 
     @abc.abstractmethod

pyswarms/backend/topology/pyramid.py

@@ -6,20 +6,15 @@
 This class implements a pyramid topology. In this topology, the particles are connected by N-dimensional simplices.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 from scipy.spatial import Delaunay
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Pyramid(Topology):
     def __init__(self, static=False):
@@ -32,6 +27,7 @@ def __init__(self, static=False):
             is static or dynamic
         """
         super(Pyramid, self).__init__(static)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm):
         """Update the global best using a pyramid neighborhood approach
@@ -102,10 +98,9 @@ def compute_gbest(self, swarm):
                     best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
                 ]
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)

pyswarms/backend/topology/random.py

@@ -6,21 +6,16 @@
 This class implements a random topology. All particles are connected in a random fashion.
 """
 
-# Import from stdlib
-import logging
 import itertools
+import logging
 
-# Import modules
 import numpy as np
 from scipy.sparse.csgraph import connected_components, dijkstra
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Random(Topology):
     def __init__(self, static=False):
@@ -32,6 +27,7 @@ def __init__(self, static=False):
             a boolean that decides whether the topology
             is static or dynamic"""
         super(Random, self).__init__(static)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm, k):
         """Update the global best using a random neighborhood approach
@@ -91,10 +87,9 @@ def compute_gbest(self, swarm, k):
             ]
 
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)

pyswarms/backend/topology/ring.py

@@ -9,20 +9,15 @@
 optimizers.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 from scipy.spatial import cKDTree
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Ring(Topology):
     def __init__(self, static=False):
@@ -34,6 +29,7 @@ def __init__(self, static=False):
             a boolean that decides whether the topology
             is static or dynamic"""
         super(Ring, self).__init__(static)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm, p, k):
         """Update the global best using a ring-like neighborhood approach
@@ -86,10 +82,9 @@ def compute_gbest(self, swarm, p, k):
                 best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
             ]
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)

pyswarms/backend/topology/star.py

@@ -9,23 +9,19 @@
 optimizers.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Star(Topology):
     def __init__(self):
         super(Star, self).__init__(static=True)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm):
         """Update the global best using a star topology
@@ -68,10 +64,9 @@ def compute_gbest(self, swarm):
             best_pos = swarm.pbest_pos[np.argmin(swarm.pbest_cost)]
             best_cost = np.min(swarm.pbest_cost)
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)

pyswarms/backend/topology/von_neumann.py

@@ -6,18 +6,16 @@
 This class implements a Von Neumann topology.
 """
 
-# Import from stdlib
 import logging
 
 from .ring import Ring
-
-# Create a logger
-logger = logging.getLogger(__name__)
+from ...utils.reporter import Reporter
 
 
 class VonNeumann(Ring):
     def __init__(self):
         super(VonNeumann, self).__init__(static=True)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm, p, r):
         """Updates the global best using a neighborhood approach