Refactor test abstractions

This commit adds test abstractions to PySwarms Resolves #240, #241 Signed-off-by: Lester James V. Miranda <[email protected]>
ljvmiranda921 · Oct 1, 2018 · bafdcf1 · bafdcf1
1 parent 0d2b11c
commit bafdcf1
Show file tree

Hide file tree

Showing 55 changed files with 1,010 additions and 1,486 deletions.
diff --git a/.coveragerc b/.coveragerc
@@ -0,0 +1,5 @@
+[report]
+exclude_lines =
+    pragma: no cover
+    @abc.abstractmethod
+    pos = init_pos
diff --git a/docs/conf.py b/docs/conf.py
@@ -13,8 +13,12 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys
+# Import standard library
 import os
+import sys
+
+# Import from pyswarms
+import pyswarms
 
 # If extensions (or modules to document with autodoc) are in another
 # directory, add these directories to sys.path here. If the directory is
@@ -32,7 +36,6 @@
 # sys.path.insert(0, project_root)
 sys.path.insert(0, os.path.abspath("../"))
 
-import pyswarms
 
 # -- General configuration ---------------------------------------------
 

diff --git a/pyswarms/backend/generators.py b/pyswarms/backend/generators.py
@@ -9,8 +9,10 @@
 
 """
 
+# Import standard library
 import logging
 
+# Import modules
 import numpy as np
 
 from ..utils.reporter import Reporter
@@ -44,6 +46,14 @@ def generate_swarm(
     -------
     numpy.ndarray
         swarm matrix of shape (n_particles, n_dimensions)
+
+    Raises
+    ------
+    ValueError
+        When the shapes and values of bounds, dimensions, and init_pos
+        are inconsistent.
+    TypeError
+        When the argument passed to bounds is not an iterable.
     """
     try:
         if (init_pos is not None) and (bounds is None):
@@ -70,12 +80,13 @@ 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"
-        )
+        msg = "Bounds and/or init_pos should be of size ({},)"
+        rep.logger.exception(msg.format(dimensions))
         raise
     except TypeError:
-        rep.logger.exception("Invalid input type!")
+        msg = "generate_swarm() takes an int for n_particles and dimensions and an array for bounds"
+        rep.logger.exception(msg)
+        raise
     else:
         return pos
 
@@ -96,6 +107,18 @@ def generate_discrete_swarm(
     init_pos : :code:`numpy.ndarray` (default is :code:`None`)
         option to explicitly set the particles' initial positions. Set to
         :code:`None` if you wish to generate the particles randomly.
+
+    Returns
+    -------
+    numpy.ndarray
+        swarm matrix of shape (n_particles, n_dimensions)
+
+    Raises
+    ------
+    ValueError
+        When init_pos during binary=True does not contain two unique values.
+    TypeError
+        When the argument passed to n_particles or dimensions is incorrect.
     """
     try:
         if (init_pos is not None) and binary:
@@ -111,11 +134,12 @@ def generate_discrete_swarm(
                 size=(n_particles, dimensions)
             ).argsort(axis=1)
     except ValueError:
-        rep.logger.exception(
-            "Please check the size and value of bounds and dimensions"
-        )
+        rep.logger.exception("Please check the size and value of dimensions")
+        raise
     except TypeError:
-        rep.logger.exception("Invalid input type!")
+        msg = "generate_discrete_swarm() takes an int for n_particles and dimensions"
+        rep.logger.exception(msg)
+        raise
     else:
         return pos
 
@@ -145,11 +169,13 @@ def generate_velocity(n_particles, dimensions, clamp=None):
             size=(n_particles, dimensions)
         ) + min_velocity
     except ValueError:
-        rep.logger.exception(
-            "Please check the size and value of clamp and dimensions"
-        )
+        msg = "Please check clamp shape: {} != {}"
+        rep.logger.exception(msg.format(len(clamp), dimensions))
+        raise
     except TypeError:
-        rep.logger.exception("Invalid input type!")
+        msg = "generate_velocity() takes an int for n_particles and dimensions and an array for clamp"
+        rep.logger.exception(msg)
+        raise
     else:
         return velocity
 

diff --git a/pyswarms/backend/operators.py b/pyswarms/backend/operators.py
@@ -8,8 +8,10 @@
 to specify how the swarm will behave.
 """
 
+# Import standard library
 import logging
 
+# Import modules
 import numpy as np
 
 from ..utils.reporter import Reporter
@@ -69,6 +71,7 @@ def compute_pbest(swarm):
         rep.logger.exception(
             "Please pass a Swarm class. You passed {}".format(type(swarm))
         )
+        raise
     else:
         return (new_pbest_pos, new_pbest_cost)
 
@@ -139,8 +142,10 @@ def compute_velocity(swarm, clamp):
         rep.logger.exception(
             "Please pass a Swarm class. You passed {}".format(type(swarm))
         )
+        raise
     except KeyError:
         rep.logger.exception("Missing keyword in swarm.options")
+        raise
     else:
         return updated_velocity
 
@@ -187,5 +192,6 @@ def compute_position(swarm, bounds):
         rep.logger.exception(
             "Please pass a Swarm class. You passed {}".format(type(swarm))
         )
+        raise
     else:
         return position
diff --git a/pyswarms/backend/swarms.py b/pyswarms/backend/swarms.py
@@ -8,6 +8,7 @@
 as input to most backend cases.
 """
 
+# Import modules
 import numpy as np
 from attr import attrib, attrs
 from attr.validators import instance_of

diff --git a/pyswarms/backend/topology/base.py b/pyswarms/backend/topology/base.py
@@ -12,6 +12,7 @@
 :mod:`pyswarms.backend.swarms.Swarm` module.
 """
 
+# Import standard library
 import abc
 import logging
 
@@ -33,7 +34,7 @@ def __init__(self, static, **kwargs):
             self.rep.log(
                 "Running on `dynamic` topology,"
                 "set `static=True` for fixed neighbors.",
-                lvl=10,
+                lvl=logging.DEBUG,
             )
 
     @abc.abstractmethod

diff --git a/pyswarms/backend/topology/pyramid.py b/pyswarms/backend/topology/pyramid.py
@@ -6,8 +6,10 @@
 This class implements a pyramid topology. In this topology, the particles are connected by N-dimensional simplices.
 """
 
+# Import standard library
 import logging
 
+# Import modules
 import numpy as np
 from scipy.spatial import Delaunay
 
@@ -29,7 +31,7 @@ def __init__(self, static=False):
         super(Pyramid, self).__init__(static)
         self.rep = Reporter(logger=logging.getLogger(__name__))
 
-    def compute_gbest(self, swarm):
+    def compute_gbest(self, swarm, **kwargs):
         """Update the global best using a pyramid neighborhood approach
 
         This topology uses the :code:`Delaunay` class from :code:`scipy`. To prevent precision errors in the Delaunay

diff --git a/pyswarms/backend/topology/random.py b/pyswarms/backend/topology/random.py
@@ -6,9 +6,11 @@
 This class implements a random topology. All particles are connected in a random fashion.
 """
 
+# Import standard library
 import itertools
 import logging
 
+# Import modules
 import numpy as np
 from scipy.sparse.csgraph import connected_components, dijkstra
 
@@ -18,23 +20,19 @@
 
 
 class Random(Topology):
-    def __init__(self, k, static=False):
+    def __init__(self, static=False):
         """Initializes the class
 
         Parameters
         ----------
-        k : int
-            number of neighbors to be considered. Must be a
-            positive integer less than :code:`n_particles-1`
         static : bool (Default is :code:`False`)
             a boolean that decides whether the topology
             is static or dynamic
         """
         super(Random, self).__init__(static)
-        self.k = k
         self.rep = Reporter(logger=logging.getLogger(__name__))
 
-    def compute_gbest(self, swarm):
+    def compute_gbest(self, swarm, k, **kwargs):
         """Update the global best using a random neighborhood approach
 
         This uses random class from :code:`numpy` to give every particle k
@@ -51,6 +49,9 @@ def compute_gbest(self, swarm):
         ----------
         swarm : pyswarms.backend.swarms.Swarm
             a Swarm instance
+        k : int
+            number of neighbors to be considered. Must be a
+            positive integer less than :code:`n_particles-1`
 
         Returns
         -------
@@ -62,7 +63,7 @@ def compute_gbest(self, swarm):
         try:
             # Check if the topology is static or dynamic and assign neighbors
             if (self.static and self.neighbor_idx is None) or not self.static:
-                adj_matrix = self.__compute_neighbors(swarm, self.k)
+                adj_matrix = self.__compute_neighbors(swarm, k)
                 self.neighbor_idx = np.array(
                     [
                         adj_matrix[i].nonzero()[0]
@@ -83,10 +84,14 @@ def compute_gbest(self, swarm):
             ).astype(int)
 
             # Obtain best cost and position
-            best_cost = np.min(swarm.pbest_cost[best_neighbor])
-            best_pos = swarm.pbest_pos[
-                best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
-            ]
+            if np.min(swarm.pbest_cost) < swarm.best_cost:
+                best_cost = np.min(swarm.pbest_cost[best_neighbor])
+                best_pos = swarm.pbest_pos[
+                    best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
+                ]
+            else:
+                # Just get the previous best_pos and best_cost
+                best_pos, best_cost = swarm.best_pos, swarm.best_cost
 
         except AttributeError:
             self.rep.logger.exception(

diff --git a/pyswarms/backend/topology/ring.py b/pyswarms/backend/topology/ring.py
@@ -9,8 +9,10 @@
 optimizers.
 """
 
+# Import standard library
 import logging
 
+# Import modules
 import numpy as np
 from scipy.spatial import cKDTree
 
@@ -20,27 +22,19 @@
 
 
 class Ring(Topology):
-    def __init__(self, p, k, static=False):
+    def __init__(self, static=False):
         """Initializes the class
 
         Parameters
         ----------
         static : bool (Default is :code:`False`)
             a boolean that decides whether the topology
             is static or dynamic
-        p: int {1,2}
-            the Minkowski p-norm to use. 1 is the
-            sum-of-absolute values (or L1 distance) while 2 is
-            the Euclidean (or L2) distance.
-        k : int
-            number of neighbors to be considered. Must be a
-            positive integer less than :code:`n_particles`
         """
         super(Ring, self).__init__(static)
-        self.p, self.k = p, k
         self.rep = Reporter(logger=logging.getLogger(__name__))
 
-    def compute_gbest(self, swarm):
+    def compute_gbest(self, swarm, p, k, **kwargs):
         """Update the global best using a ring-like neighborhood approach
 
         This uses the cKDTree method from :code:`scipy` to obtain the nearest
@@ -50,6 +44,13 @@ def compute_gbest(self, swarm):
         ----------
         swarm : pyswarms.backend.swarms.Swarm
             a Swarm instance
+        p: int {1,2}
+            the Minkowski p-norm to use. 1 is the
+            sum-of-absolute values (or L1 distance) while 2 is
+            the Euclidean (or L2) distance.
+        k : int
+            number of neighbors to be considered. Must be a
+            positive integer less than :code:`n_particles`
 
         Returns
         -------
@@ -63,14 +64,12 @@ def compute_gbest(self, swarm):
             if (self.static and self.neighbor_idx is None) or not self.static:
                 # Obtain the nearest-neighbors for each particle
                 tree = cKDTree(swarm.position)
-                _, self.neighbor_idx = tree.query(
-                    swarm.position, p=self.p, k=self.k
-                )
+                _, self.neighbor_idx = tree.query(swarm.position, p=p, k=k)
 
             # Map the computed costs to the neighbour indices and take the
             # argmin. If k-neighbors is equal to 1, then the swarm acts
             # independently of each other.
-            if self.k == 1:
+            if k == 1:
                 # The minimum index is itself, no mapping needed.
                 best_neighbor = swarm.pbest_cost[self.neighbor_idx][
                     :, np.newaxis