WIP

docs/features.rst

@@ -38,3 +38,12 @@ These functions can be used as benchmark tests for assessing the performance of
 algorithm.
 
 * :mod:`pyswarms.utils.functions.single_obj` - single-objective test functions
+
+Search
+~~~~~~
+
+These search methods can be used to compare the relative performance of hyperparameter value combinations in reducing a specified objective function. 
+
+* :mod:`pyswarms.utils.search.grid_search` - exhaustive search of optimal performance on selected objective function over cartesian products of provided hyperparameter values
+
+* :mod:`pyswarms.utils.search.random_search` - search for optimal performance on selected objective function over combinations of randomly selected hyperparameter values within specified bounds for specified number of selection iterations

pyswarms/utils/search/base_search.py

@@ -16,7 +16,7 @@ def __init__(self, optimizer, n_particles, dimensions, options,
 
         Attributes
         ----------
-        optimizer: PySwarms class
+        optimizer: pyswarms.single
             either LocalBestPSO or GlobalBestPSO
         n_particles : int
             number of particles in the swarm.
@@ -63,7 +63,14 @@ def __init__(self, optimizer, n_particles, dimensions, options,
         self.iters = iters
 
     def generate_score(self, options):
-        """Generates score for optimizer's performance on objective function."""
+        """Generates score for optimizer's performance on objective function.
+
+        Parameters
+        ----------
+
+        options: dict
+            a dict of 5 hyperparameter values ('c1', 'c2', 'w', 'k', 'p' 
+        """
 
         #Intialize optimizer
         f = self.optimizer(self.n_particles, self.dims, options,
@@ -74,7 +81,16 @@ def generate_score(self, options):
 
     def search(self, maximum=False):
         """Compares optimizer's objective function performance scores
-        for all combinations of provided parameters."""
+        for all combinations of provided parameters.
+
+        Parameters
+        ----------
+
+        maximum: bool
+            a bool defaulting to False, returning the minimum value for the 
+            objective function. If set to True, will return the maximum value
+            for the objective function. 
+        """
 
         #Assign parameter keys
         params = self.options.keys()
@@ -85,11 +101,12 @@ def search(self, maximum=False):
         #Calculate scores for all hyperparameter combinations
         scores = [self.generate_score(i) for i in grid]
 
-        #Select optimization function
+        #Default behavior
+        idx, self.best_score = min(enumerate(scores), key=op.itemgetter(1))
+
+        #Catches the maximum bool flag
         if maximum:
             idx, self.best_score = max(enumerate(scores), key=op.itemgetter(1))
-        else:
-            idx, self.best_score = min(enumerate(scores), key=op.itemgetter(1))
 
         #Return optimum hyperparameter value property from grid using index
         self.best_options = op.itemgetter(idx)(grid)

pyswarms/utils/search/grid_search.py

@@ -4,14 +4,14 @@
 Hyperparameter grid search.
 
 Compares the relative performance of hyperparameter value combinations in
-reducing a specified objective function.
+optimizing a specified objective function.
 
 For each hyperparameter, user can provide either a single value or a list
-of possible values, and their cartesian product is taken to produce a grid
-of all possible combinations. These combinations are then tested to produce
-a list of objective function scores. The default of the optimize method
-returns the hyperparameters that yield the minimum score, yet maximum score
-can also be evaluated.
+of possible values. The cartesian products of these hyperparameters are taken
+to produce a grid of all possible combinations. These combinations are then
+tested to produce a list of objective function scores. The search method
+default returns the minimum objective function score and hyperparameters that
+yield the minimum score, yet maximum score can also be evaluated.
 
 Parameters
 ----------
@@ -54,6 +54,15 @@ class GridSearch(SearchBase):
     """Exhaustive search of optimal performance on selected objective function
     over all combinations of specified hyperparameter values."""
 
+    def __init__(self, optimizer, n_particles, dimensions, options,
+                 objective_func, iters,bounds=None, velocity_clamp=None):
+        """Initializes the paramsearch."""
+
+        # Assign attributes
+        super().__init__(optimizer, n_particles, dimensions, options,
+                objective_func, iters, bounds=bounds,
+                velocity_clamp=velocity_clamp)
+
     def generate_grid(self):
         """Generates the grid of all hyperparameter value combinations."""
 
@@ -67,3 +76,4 @@ def generate_grid(self):
 
         #Return list of dicts for all hyperparameter value combinations
         return [dict(zip(*[params, list(x)])) for x in list_of_products]
+

pyswarms/utils/search/random_search.py

@@ -3,15 +3,15 @@
 """
 Hyperparameter random search.
 
-Compares the relative performance of randomly genreated hyperparameter value
-combinations in reducing a specified objective function.
+Compares the relative performance of combinations of randomly generated
+hyperparameter values in optimizing a specified objective function.
 
 User provides lists of bounds for the uniform random value generation of
 'c1', 'c2', and 'w', and the random integer value generation of 'k'.
 Combinations of values are generated for the number of iterations specified,
 and the generated grid of combinations is used in the search method to find
-the optimal parameters for the objective function. The default of the search
-method returns the objective function score and hyperparameters that yield
+the optimal parameters for the objective function. The search method default
+returns the minimum objective function score and hyperparameters that yield
 the minimum score, yet maximum score can also be evaluated.
 
 Parameters
@@ -52,10 +52,12 @@
 import random
 from pyswarms.utils.search.base_search import SearchBase
 
+from past.builtins import xrange
+
 class RandomSearch(SearchBase):
     """Search of optimal performance on selected objective function
-    over combinations of hyperparameter values within specified bounds
-    for specified number of selection iterations."""
+    over combinations of randomly selected hyperparameter values
+    within specified bounds for specified number of selection iterations."""
 
     def __init__(self, optimizer, n_particles, dimensions, options,
                  objective_func, iters, n_selection_iters,
@@ -82,7 +84,7 @@ def generate_grid(self):
 
         #Remove 'p' to hold as a constant in the paramater combinations
         p = options.pop('p')
-        params['p'] = [p for _ in range(self.n_selection_iters)]
+        params['p'] = [p for _ in xrange(self.n_selection_iters)]
 
         #Assign generators based on parameter type
         param_generators = {
@@ -103,5 +105,5 @@ def generate_grid(self):
                  'w': params['w'][i],
                  'k': params['k'][i],
                  'p': params['p'][i]}
-                for i in range(self.n_selection_iters)]
+                for i in xrange(self.n_selection_iters)]