reformatted code

Author: zStupan

.flake8

@@ -1,5 +1,5 @@
 [flake8]
-ignore = E203,E266,E501,W503,D302,D401,D413,D414,D202,RST210,RST299,RST304,RST305,RST399,FI
+ignore = E126, E501, D302,D401,D413,D414,D202,RST210,RST299,RST304,RST305,RST399,FI
 exclude = 
     .git,
     __pycache__,
@@ -8,10 +8,9 @@ exclude =
     build,
     dist,
     scent.py
-per-file-ignores = 
+per-file-ignores =
     niapy/tests/*:B007,D100,D101,D102,D107,D208,D211,D413,E117,E302,E701,E704,F841,S110,W191,W293,D204,RST210,E101,D204,D205,D206,E741
-    niapy/algorithms/*:B305,B007,D100,D101,D102,D107,D204,D206,D413,E101,E117,E126,E302,E305,E701,E704,E711,E741,F812,RST201,RST203,RST204,RST210,RST301,W191
-    niapy/task/*:E701
-    niapy/util/*:W191,E117,E305,E302,E704,E701,RST203,D204,E306,D100
-    niapy/benchmarks/*:W191,E117,E305,E302,E704,E701,RST203,D204,E306,D206,D208
+    niapy/algorithms/*:B007,D100,D101,D102,D107,D204,D206,D413,RST201,RST203,RST204,RST210,RST301
+    niapy/util/*:RST203,D100
+    niapy/benchmarks/*:W191,RST203,D204,D206,D208
 

.pycodestyle.ini

@@ -3,21 +3,5 @@
 # E401 multiple imports on one line (checked by PyLint)
 # E402 module level import not at top of file (checked by PyLint)
 # E501: line too long (checked by PyLint)
-# E711: comparison to None (used to improve test style)
-# E712: comparison to True (used to improve test style)
-# W191: intentation contains tabs
-# E701: multiple statements on one line (dolon)
-# E704: multiple statements on one line (def)
-# E101: intentation contains mixed spaces nad tabs
-# E242: tab after ':'
-# E302: expected 2 blank lines
-# E305: expected 2 blank lines after class or function definition
-# E306: expected 1 blank line befor a nested definition
-# E274: tab vefore keyword
-# D204: 1 blank line required after class docstring
-# D413: Missing blank line after last section
-# E126: cntinuation line over-indented for hanging indent
-# E702: Multiple statemets on one line (semicolon)
-# E111 indentation is not a multiple of four
-# E741 ambiguous variable name 'I'
-ignore = E401,E402,E501,E711,E712,W504,W191,E701,E704,E101,E242,E302,E305,E306,E274,D204,D413,E126,E702,E111,E741,E117
+# E126: continuation line over-indented for hanging indent
+ignore = E401,E402,E501,E711,E712,E126

.pydocstyle.ini

@@ -1,18 +1,12 @@
 [pydocstyle]
 
 # D211: No blank lines allowed before class docstring
-add_select = D211
+# add_select = D211
 
 # D100: Missing docstring in public module
 # D101: Missing docstring in public class
 # D102: Missing docstring in public method
 # D103: Missing docstring in public function
 # D104: Missing docstring in public package
 # D105: Missing docstring in magic method
-# D107: Missing docstring in __init__
-# D202: No blank lines allowed after function docstring
-# D204: 1 blank line required after class docstring
-# D206: Docstring should be indented with spaces, not tabs
-# D413: Missing blank line after last section
-# D414: Section has no content ('Returns')
-add_ignore = D100,D101,D102,D103,D104,D105,D107,D202,D204,D206,D301,D302,D402,D413,D414
+add_ignore = D100,D101,D102,D103,D104,D105

README.md

@@ -114,7 +114,7 @@ from niapy.task import StoppingTask
 # we will run 10 repetitions of Grey Wolf Optimizer against Pinter benchmark function
 for i in range(10):
     task = StoppingTask(D=10, nFES=1000, benchmark='pinter')
-    algorithm = GreyWolfOptimizer(NP=20)
+    algorithm = GreyWolfOptimizer(population_size=20)
     best = algorithm.run(task)
     print(best[-1])
 ```
@@ -165,7 +165,7 @@ for i in range(10):
     task = StoppingTask(D=20, nGEN=100, optType=OptimizationType.MINIMIZATION, benchmark=MyBenchmark())
 
     # parameter is population size
-    algo = GreyWolfOptimizer(NP=20)
+    algo = GreyWolfOptimizer(population_size=20)
 
     # running algorithm returns best found minimum
     best = algo.run(task)

docs/source/changelog.rst

@@ -619,7 +619,7 @@ Changelog <https://github.com/NiaOrg/NiaPy/compare/1.0.0...1.0.1>`__
    (`GregaVrbancic <https://github.com/GregaVrbancic>`__)
 -  Fix paper `#115 <https://github.com/NiaOrg/NiaPy/pull/115>`__
    (`GregaVrbancic <https://github.com/GregaVrbancic>`__)
--  arguments: Ts->integer; TournamentSelection: use shuffled indices in
+-  arguments: Ts->integer; tournament_selection: use shuffled indices in
    … `#114 <https://github.com/NiaOrg/NiaPy/pull/114>`__
    (`mlaky88 <https://github.com/mlaky88>`__)
 

docs/source/getting_started.rst

@@ -213,7 +213,7 @@ The complete example would look something like this.
         task = StoppingTask(D=20, nGEN=100, optType=OptimizationType.MINIMIZATION, benchmark=MyBenchmark())
 
         # parameter is population size
-        algo = GreyWolfOptimizer(NP=20, InitPopFunc=MyInit)
+        algo = GreyWolfOptimizer(NP=20, initialization_function=MyInit)
 
         # running algorithm returns best found minimum
         best = algo.run(task)

examples/about_algorithm.py

@@ -2,10 +2,11 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 # End of fix
 
 from niapy.algorithms.basic import FireflyAlgorithm
 
 algo = FireflyAlgorithm()
-print(algo.algorithmInfo())
+print(algo.info())

examples/advanced_example.py

@@ -2,31 +2,35 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 
 from niapy.task import StoppingTask, OptimizationType
 from niapy.benchmarks import Benchmark
 from niapy.algorithms.basic import GreyWolfOptimizer
 
+
 # our custom benchmark class
 class MyBenchmark(Benchmark):
-	def __init__(self):
-		Benchmark.__init__(self, -10, 10)
+    def __init__(self):
+        Benchmark.__init__(self, -10, 10)
+
+    def function(self):
+        def evaluate(D, sol):
+            val = 0.0
+            for i in range(D): val += sol[i] ** 2
+            return val
 
-	def function(self):
-		def evaluate(D, sol):
-			val = 0.0
-			for i in range(D): val += sol[i] ** 2
-			return val
-		return evaluate
+        return evaluate
 
 
 # we will run 10 repetitions of Grey Wolf Optimizer against our custom MyBenchmark benchmark function
 for i in range(10):
-    task = StoppingTask(D=20, nGEN=100, optType=OptimizationType.MINIMIZATION, benchmark=MyBenchmark())
+    task = StoppingTask(max_iters=100, dimension=20, optimization_type=OptimizationType.MINIMIZATION,
+                        benchmark=MyBenchmark())
 
     # parameter is population size
-    algo = GreyWolfOptimizer(NP=20)
+    algo = GreyWolfOptimizer(population_size=20)
 
     # running algorithm returns best found minimum
     best = algo.run(task)

examples/advanced_example_custom_pop.py

@@ -2,38 +2,44 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 
 from niapy.task import StoppingTask, OptimizationType
 from niapy.benchmarks import Benchmark
 from niapy.algorithms.basic import GreyWolfOptimizer
 from numpy import random as rand, apply_along_axis
 
+
 # our custom benchmark class
 class MyBenchmark(Benchmark):
-	def __init__(self):
-		Benchmark.__init__(self, -10, 10)
+    def __init__(self):
+
+        Benchmark.__init__(self, -10, 10)
 
-	def function(self):
-		def evaluate(D, sol):
-			val = 0.0
-			for i in range(D): val += sol[i] ** 2
-			return val
-		return evaluate
+    def function(self):
+        def evaluate(D, sol):
+            val = 0.0
+            for i in range(D): val += sol[i] ** 2
+            return val
+
+        return evaluate
 
 
 # custom initialization population function
 def MyInit(task, NP, rnd=rand, **kwargs):
-    pop = 0.2 + rnd.rand(NP, task.D) * task.bRange
+    pop = 0.2 + rnd.rand(NP, task.dimension) * task.range
     fpop = apply_along_axis(task.eval, 1, pop)
     return pop, fpop
 
+
 # we will run 10 repetitions of Grey Wolf Optimizer against our custom MyBenchmark benchmark function
 for i in range(10):
-    task = StoppingTask(D=20, nGEN=100, optType=OptimizationType.MINIMIZATION, benchmark=MyBenchmark())
+    task = StoppingTask(max_iters=100, dimension=20, optimization_type=OptimizationType.MINIMIZATION,
+                        benchmark=MyBenchmark())
 
     # parameter is population size
-    algo = GreyWolfOptimizer(NP=20, InitPopFunc=MyInit)
+    algo = GreyWolfOptimizer(NP=20, initialization_function=MyInit)
 
     # running algorithm returns best found minimum
     best = algo.run(task)

examples/basic_customized_example.py

@@ -1,6 +1,7 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 # End of fix
 
@@ -17,10 +18,11 @@
     # second parameter takes the number of function evaluations
     # third parameter is benchmark optimization type
     # forth parameter is benchmark function
-    task = StoppingTask(D=20, nGEN=100, optType=OptimizationType.MINIMIZATION, benchmark=pinterCustom)
+    task = StoppingTask(max_iters=100, dimension=20, optimization_type=OptimizationType.MINIMIZATION,
+                        benchmark=pinterCustom)
 
     # parameter is population size
-    algo = GreyWolfOptimizer(NP=20)
+    algo = GreyWolfOptimizer(population_size=20)
 
     # running algorithm returns best found minimum
     best = algo.run(task)

examples/basic_example.py

@@ -1,6 +1,7 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 # End of fix
 
@@ -9,7 +10,7 @@
 
 # we will run 10 repetitions of Grey Wolf Optimizer against Pinter benchmark function
 for i in range(10):
-    task = StoppingTask(D=10, nFES=1000, benchmark='pinter')
-    algorithm = GreyWolfOptimizer(NP=20)
+    task = StoppingTask(max_evals=1000, dimension=10, benchmark='pinter')
+    algorithm = GreyWolfOptimizer(population_size=20)
     best = algorithm.run(task)
     print(best[-1])

examples/basic_stats.py

@@ -2,6 +2,7 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 # End of fix
 
@@ -15,9 +16,10 @@
 stats = np.zeros(NUM_RUNS)
 
 for i in range(NUM_RUNS):
-    task = StoppingTask(D=10, nFES=10000, optType=OptimizationType.MINIMIZATION, benchmark=Sphere())
-    print ("Working on run: " + str(i+1))
-    algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
+    task = StoppingTask(max_evals=10000, dimension=10, optimization_type=OptimizationType.MINIMIZATION,
+                        benchmark=Sphere())
+    print("Working on run: " + str(i + 1))
+    algo = DifferentialEvolution(population_size=40, CR=0.9, F=0.5)
     best = algo.run(task)
     stats[i] = best[1]  # save best
 

examples/custom_benchmark.py

@@ -2,27 +2,32 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 
 from niapy.task import StoppingTask, OptimizationType
 from niapy.benchmarks import Benchmark
 from niapy.algorithms.basic import ParticleSwarmAlgorithm
 
+
 class MyBenchmark(Benchmark):
-	def __init__(self):
-		Benchmark.__init__(self, -10, 10)
+    def __init__(self):
+
+        Benchmark.__init__(self, -10, 10)
+
+    def function(self):
+        def evaluate(D, sol):
+            val = 0.0
+            for i in range(D): val += sol[i] ** 2
+            return val
 
-	def function(self):
-		def evaluate(D, sol):
-			val = 0.0
-			for i in range(D): val += sol[i] ** 2
-			return val
-		return evaluate
+        return evaluate
 
 
 # we will run Particle Swarm Algorithm with on custom benchmark
 for i in range(1):
-    task = StoppingTask(D=10, nGEN=1000, optType=OptimizationType.MINIMIZATION, benchmark=MyBenchmark())
-    algo = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4)
+    task = StoppingTask(max_iters=1000, dimension=10, optimization_type=OptimizationType.MINIMIZATION,
+                        benchmark=MyBenchmark())
+    algo = ParticleSwarmAlgorithm(population_size=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4)
     best = algo.run(task=task)
     print('%s -> %s ' % (best[0], best[1]))

examples/custom_init_population.py

@@ -2,23 +2,25 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 # End of fix
 
 from niapy.algorithms.basic import ParticleSwarmAlgorithm
-from niapy.task.task import StoppingTask, OptimizationType
+from niapy.task.task import StoppingTask
 from niapy.benchmarks import Sphere
 from numpy import random as rand, apply_along_axis
 
+
 def MyInit(task, NP, rnd=rand, **kwargs):
-    pop = 0.2 + rnd.rand(NP, task.D) * task.bRange
+    pop = 0.2 + rnd.rand(NP, task.dimension) * task.range
     fpop = apply_along_axis(task.eval, 1, pop)
     return pop, fpop
 
 
 # we will run Particle Swarm Algorithm with custom Init function for 5 independent runs
 for i in range(5):
-	task = StoppingTask(D=10, nFES=1000, benchmark=Sphere())
-	algo = ParticleSwarmAlgorithm(NP=10, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, InitPopFunc=MyInit)
-	best = algo.run(task=task)
-	print(best)
+    task = StoppingTask(max_evals=1000, dimension=10, benchmark=Sphere())
+    algo = ParticleSwarmAlgorithm(population_size=10, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, initialization_function=MyInit)
+    best = algo.run(task=task)
+    print(best)

examples/log_results.py

@@ -2,18 +2,19 @@
 # This is temporary fix to import module from parent folder
 # It will be removed when package is published on PyPI
 import sys
+
 sys.path.append('../')
 # End of fix
 
-from niapy.task import StoppingTask, OptimizationType
+from niapy.task import StoppingTask
 from niapy.benchmarks import Sphere
 from niapy.algorithms.basic import DifferentialEvolution
 
 # Storing improvements during the evolutionary cycle
 for i in range(1):
-	task = StoppingTask(D=10, nFES=10000, benchmark=Sphere())
-	algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
-	best = algo.run(task)
-	evals, x_f = task.return_conv()
-	print(evals)  # print function evaluations
-	print(x_f)  # print values
+    task = StoppingTask(max_evals=10000, dimension=10, benchmark=Sphere())
+    algo = DifferentialEvolution(population_size=40, CR=0.9, F=0.5)
+    best = algo.run(task)
+    evals, x_f = task.return_conv()
+    print(evals)  # print function evaluations
+    print(x_f)  # print values