Added a Command Line Interface

README.md

@@ -41,18 +41,51 @@ $ yay -Syyu python-fireflyalgorithm
 ## Usage:
 
 ```python
-import numpy as np
 from fireflyalgorithm import FireflyAlgorithm
-
-def sphere(x):
-    return np.sum(x ** 2)
+from fireflyalgorithm.problems import sphere
 
 FA = FireflyAlgorithm()
 best = FA.run(function=sphere, dim=10, lb=-5, ub=5, max_evals=10000)
 
 print(best)
 ```
 
+### Command line interface
+
+The package also comes with a simple command line interface which allows you to evaluate the algorithm on several
+popular test functions
+
+```shell
+firefly-algorithm -h
+```
+
+```text
+usage: firefly-algorithm [-h] --problem PROBLEM -d DIMENSION -l LOWER -u UPPER -nfes MAX_EVALS [-r RUNS] [--pop-size POP_SIZE] [--alpha ALPHA] [--beta-min BETA_MIN] [--gamma GAMMA] [--seed SEED]
+
+Evaluate the Firefly Algorithm on one or more test functions
+
+options:
+  -h, --help            show this help message and exit
+  --problem PROBLEM     Test problem to evaluate
+  -d DIMENSION, --dimension DIMENSION
+                        Dimension of the problem
+  -l LOWER, --lower LOWER
+                        Lower bounds of the problem
+  -u UPPER, --upper UPPER
+                        Upper bounds of the problem
+  -nfes MAX_EVALS, --max-evals MAX_EVALS
+                        Max number of fitness function evaluations
+  -r RUNS, --runs RUNS  Number of runs of the algorithm
+  --pop-size POP_SIZE   Population size
+  --alpha ALPHA         Randomness strength
+  --beta-min BETA_MIN   Attractiveness constant
+  --gamma GAMMA         Absorption coefficient
+  --seed SEED           Seed for the random number generator
+```
+
+**Note:** The CLI script can also run as a python module (python -m niaarm ...)
+
+
 ## Reference Papers:
 
 I. Fister Jr.,  X.-S. Yang,  I. Fister, J. Brest. [Memetic firefly algorithm for combinatorial optimization](http://www.iztok-jr-fister.eu/static/publications/44.pdf) in Bioinspired Optimization Methods and their Applications (BIOMA 2012), B. Filipic and J.Silc, Eds.

fireflyalgorithm/__init__.py

@@ -1,5 +1,5 @@
 from fireflyalgorithm.fireflyalgorithm import FireflyAlgorithm
 
-__all__ = ['FireflyAlgorithm']
+__all__ = ["FireflyAlgorithm"]
 
-__version__ = '0.3.4'
+__version__ = "0.3.4"

fireflyalgorithm/__main__.py

@@ -0,0 +1,6 @@
+import sys
+from fireflyalgorithm import cli
+
+
+if __name__ == "__main__":
+    sys.exit(cli.main())

fireflyalgorithm/cli.py

@@ -0,0 +1,79 @@
+import argparse
+import numpy as np
+from fireflyalgorithm.problems import PROBLEMS, get_problem
+from fireflyalgorithm.fireflyalgorithm import FireflyAlgorithm
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        prog="firefly-algorithm",
+        description="Evaluate the Firefly Algorithm on one or more test functions",
+    )
+
+    problem_functions = list(PROBLEMS.keys())
+    parser.add_argument(
+        "--problem",
+        type=str,
+        required=True,
+        choices=problem_functions,
+        metavar="PROBLEM",
+        help="Test problem to evaluate",
+    )
+    parser.add_argument(
+        "-d", "--dimension", type=int, required=True, help="Dimension of the problem"
+    )
+    parser.add_argument(
+        "-l", "--lower", type=float, required=True, help="Lower bounds of the problem"
+    )
+    parser.add_argument(
+        "-u", "--upper", type=float, required=True, help="Upper bounds of the problem"
+    )
+    parser.add_argument(
+        "-nfes",
+        "--max-evals",
+        type=int,
+        required=True,
+        help="Max number of fitness function evaluations",
+    )
+    parser.add_argument(
+        "-r", "--runs", type=int, default=1, help="Number of runs of the algorithm"
+    )
+    parser.add_argument("--pop-size", type=int, default=20, help="Population size")
+    parser.add_argument("--alpha", type=float, default=1.0, help="Randomness strength")
+    parser.add_argument(
+        "--beta-min", type=float, default=1.0, help="Attractiveness constant"
+    )
+    parser.add_argument(
+        "--gamma", type=float, default=0.01, help="Absorption coefficient"
+    )
+    parser.add_argument("--seed", type=int, help="Seed for the random number generator")
+    return parser
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    algorithm = FireflyAlgorithm(
+        args.pop_size, args.alpha, args.beta_min, args.gamma, args.seed
+    )
+    problem = get_problem(args.problem)
+    dim = args.dimension
+    lb = args.lower
+    ub = args.upper
+    max_evals = args.max_evals
+    runs = args.runs
+
+    fitness = np.empty(runs)
+    for i in range(runs):
+        fitness[i] = algorithm.run(problem, dim, lb, ub, max_evals)
+
+    if runs == 1:
+        print(f"Best fitness: {fitness[0]}")
+    else:
+        print(f"Best: {fitness.min()}")
+        print(f"Worst: {fitness.max()}")
+        print(f"Mean: {fitness.mean()}")
+        print(f"Std: {fitness.std()}")
+
+    return 0