Add feature to pass args to objective function (#144)

Resolves #143 This commit adds a `**kwargs` parameter to the `optimize()` method to pass arguments directly on the objective function. Additional tests and documentation were also provided. Committed with @bradahoward Signed-off-by: Lester James V. Miranda <[email protected]>
ljvmiranda921 · Jun 28, 2018 · 89f12cb · 89f12cb
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diff --git a/examples/basic_optimization_with_arguments.ipynb b/examples/basic_optimization_with_arguments.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Optimization with Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we will run a basic optimization using an objective function that needs parameterization.  We will use the ``single.GBestPSO`` and a version of the rosenbrock function to demonstrate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Python 3.5.2 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:53:06) \n",
+      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "# change directory to access pyswarms\n",
+    "sys.path.append('../')\n",
+    "\n",
+    "print(\"Running Python {}\".format(sys.version))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# import modules\n",
+    "import numpy as np\n",
+    "\n",
+    "# create a parameterized version of the classic Rosenbrock unconstrained optimzation function\n",
+    "def rosenbrock_with_args(x, a, b, c=0):\n",
+    "\n",
+    "    f = (a - x[:, 0]) ** 2 + b * (x[:, 1] - x[:, 0] ** 2) ** 2 + c\n",
+    "    return f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Arguments can either be passed in using a tuple or a dictionary, using the ``kwargs={}`` paradigm. First lets optimize the Rosenbrock function using keyword arguments.  Note in the definition of the Rosenbrock function above, there were two arguments that need to be passed other than the design variables, and one optional keyword argument, ``a``, ``b``, and ``c``, respectively"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pyswarms.single.global_best:Arguments Passed to Objective Function: {'c': 0, 'b': 100, 'a': 1}\n",
+      "INFO:pyswarms.single.global_best:Iteration 1/1000, cost: 1022.9667801907804\n",
+      "INFO:pyswarms.single.global_best:Iteration 101/1000, cost: 0.0011172801146408992\n",
+      "INFO:pyswarms.single.global_best:Iteration 201/1000, cost: 7.845605970774126e-07\n",
+      "INFO:pyswarms.single.global_best:Iteration 301/1000, cost: 1.313503109901238e-09\n",
+      "INFO:pyswarms.single.global_best:Iteration 401/1000, cost: 5.187079604907219e-10\n",
+      "INFO:pyswarms.single.global_best:Iteration 501/1000, cost: 1.0115283486088853e-10\n",
+      "INFO:pyswarms.single.global_best:Iteration 601/1000, cost: 2.329870757208421e-13\n",
+      "INFO:pyswarms.single.global_best:Iteration 701/1000, cost: 4.826176894160183e-15\n",
+      "INFO:pyswarms.single.global_best:Iteration 801/1000, cost: 3.125715456651088e-17\n",
+      "INFO:pyswarms.single.global_best:Iteration 901/1000, cost: 1.4236768129666014e-19\n",
+      "INFO:pyswarms.single.global_best:================================\n",
+      "Optimization finished!\n",
+      "Final cost: 0.0000\n",
+      "Best value: [0.99999999996210465, 0.9999999999218413]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyswarms.single.global_best import GlobalBestPSO\n",
+    "\n",
+    "# instatiate the optimizer\n",
+    "x_max = 10 * np.ones(2)\n",
+    "x_min = -1 * x_max\n",
+    "bounds = (x_min, x_max)\n",
+    "options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
+    "optimizer = GlobalBestPSO(n_particles=10, dimensions=2, options=options, bounds=bounds)\n",
+    "\n",
+    "# now run the optimization, pass a=1 and b=100 as a tuple assigned to args\n",
+    "\n",
+    "cost, pos = optimizer.optimize(rosenbrock_with_args, 1000, print_step=100, verbose=3, a=1, b=100, c=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to pass a dictionary of key word arguments by using ``**`` decorator when passing the dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pyswarms.single.global_best:Arguments Passed to Objective Function: {'c': 0, 'b': 100.0, 'a': 1.0}\n",
+      "INFO:pyswarms.single.global_best:Iteration 1/1000, cost: 1.996797703363527e-21\n",
+      "INFO:pyswarms.single.global_best:Iteration 101/1000, cost: 1.0061676299213387e-24\n",
+      "INFO:pyswarms.single.global_best:Iteration 201/1000, cost: 4.8140236741112245e-28\n",
+      "INFO:pyswarms.single.global_best:Iteration 301/1000, cost: 2.879342304056693e-29\n",
+      "INFO:pyswarms.single.global_best:Iteration 401/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 501/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 601/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 701/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 801/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 901/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:================================\n",
+      "Optimization finished!\n",
+      "Final cost: 0.0000\n",
+      "Best value: [1.0, 1.0]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "kwargs={\"a\": 1.0, \"b\": 100.0, 'c':0}\n",
+    "cost, pos = optimizer.optimize(rosenbrock_with_args, 1000, print_step=100, verbose=3, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Any key word arguments in the objective function can be left out as they will be passed the default as defined in the prototype. Note here, ``c`` is not passed into the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pyswarms.single.global_best:Arguments Passed to Objective Function: {'b': 100, 'a': 1}\n",
+      "INFO:pyswarms.single.global_best:Iteration 1/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 101/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 201/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 301/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 401/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 501/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 601/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 701/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 801/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:Iteration 901/1000, cost: 0.0\n",
+      "INFO:pyswarms.single.global_best:================================\n",
+      "Optimization finished!\n",
+      "Final cost: 0.0000\n",
+      "Best value: [1.0, 1.0]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cost, pos = optimizer.optimize(rosenbrock_with_args, 1000, print_step=100, verbose=3, a=1, b=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:anaconda3]",
+   "language": "python",
+   "name": "conda-env-anaconda3-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/pyswarms/base/base_discrete.py b/pyswarms/base/base_discrete.py
@@ -193,7 +193,7 @@ def _populate_history(self, hist):
         self.pos_history.append(hist.position)
         self.velocity_history.append(hist.velocity)
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1):
+    def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
         """Optimizes the swarm for a number of iterations.
 
         Performs the optimization to evaluate the objective
@@ -210,6 +210,8 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1):
             amount of steps for printing into console.
         verbose : int (the default is 1)
             verbosity setting.
+        kwargs : dict
+            arguments for objective function
 
         Raises
         ------

diff --git a/pyswarms/base/base_single.py b/pyswarms/base/base_single.py
@@ -224,7 +224,7 @@ def _populate_history(self, hist):
         self.pos_history.append(hist.position)
         self.velocity_history.append(hist.velocity)
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1):
+    def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
         """Optimizes the swarm for a number of iterations.
 
         Performs the optimization to evaluate the objective
@@ -241,6 +241,8 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1):
             amount of steps for printing into console.
         verbose : int (the default is 1)
             verbosity setting.
+        kwargs : dict
+            arguments for objective function
 
         Raises
         ------

diff --git a/pyswarms/discrete/binary.py b/pyswarms/discrete/binary.py
@@ -149,7 +149,7 @@ def __init__(
         # Initialize the topology
         self.top = Ring()
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1):
+    def optimize(self, objective_func, iters, print_step=1, verbose=1,**kwargs):
         """Optimizes the swarm for a number of iterations.
 
         Performs the optimization to evaluate the objective
@@ -165,17 +165,22 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1):
             amount of steps for printing into console.
         verbose : int  (the default is 1)
             verbosity setting.
+        kwargs : dict
+            arguments for objective function
 
         Returns
         -------
         tuple
             the local best cost and the local best position among the
             swarm.
         """
+        cli_print("Arguments Passed to Objective Function: {}".format(kwargs),
+                  verbose, 2, logger=self.logger)
+
         for i in range(iters):
             # Compute cost for current position and personal best
-            self.swarm.current_cost = objective_func(self.swarm.position)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos)
+            self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
+            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )
@@ -187,8 +192,7 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1):
             # Print to console
             if i % print_step == 0:
                 cli_print(
-                    "Iteration %s/%s, cost: %s"
-                    % (i + 1, iters, np.min(self.swarm.best_cost)),
+                    "Iteration {}/{}, cost: {}".format(i + 1, iters, np.min(self.swarm.best_cost)),
                     verbose,
                     2,
                     logger=self.logger,

diff --git a/pyswarms/single/global_best.py b/pyswarms/single/global_best.py
@@ -131,7 +131,7 @@ def __init__(
         # Initialize the topology
         self.top = Star()
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1):
+    def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
         """Optimizes the swarm for a number of iterations.
 
         Performs the optimization to evaluate the objective
@@ -147,16 +147,22 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1):
             amount of steps for printing into console.
         verbose : int  (default is 1)
             verbosity setting.
+        kwargs : dict
+            arguments for the objective function
 
         Returns
         -------
         tuple
             the global best cost and the global best position.
         """
+
+        cli_print("Arguments Passed to Objective Function: {}".format(kwargs),
+                  verbose, 2, logger=self.logger)
+
         for i in range(iters):
             # Compute cost for current position and personal best
-            self.swarm.current_cost = objective_func(self.swarm.position)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos)
+            self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
+            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )
@@ -169,8 +175,7 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1):
             # Print to console
             if i % print_step == 0:
                 cli_print(
-                    "Iteration %s/%s, cost: %s"
-                    % (i + 1, iters, self.swarm.best_cost),
+                    "Iteration {}/{}, cost: {}".format(i + 1, iters, self.swarm.best_cost),
                     verbose,
                     2,
                     logger=self.logger,