NiaPy.runner.Runner.__init__() - Code Metrics - Inspection of "Upgrade runner" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#206)

by Grega

created 2019-04-25 10:51 UTC

NiaPy.runner.Runner.init() A

↳ Parent: NiaPy.runner

Complexity

Conditions

Size

Total Lines	21
Code Lines	7

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	7
nop	8
dl	0
loc	21
rs	10
c	0
b	0
f	0

How to fix Many Parameters

"""Implementation of Runner utility class."""

import datetime
import json
import os
import logging

import xlsxwriter
from numpy import (
    amin,
    median,
    amax,
    mean,
    std
)


from NiaPy.algorithms import AlgorithmUtility

logging.basicConfig()
logger = logging.getLogger('NiaPy.runner.Runner')
logger.setLevel('INFO')

__all__ = ["Runner"]


class Runner:
    r"""Runner utility feature.

    Feature which enables running multiple algorithms with multiple benchmarks.
    It also support exporting results in various formats (e.g. LaTeX, Excel, JSON)

    Attributes:
            D (int): Dimension of problem
            NP (int): Population size
            nFES (int): Number of function evaluations
            nRuns (int): Number of repetitions
            useAlgorithms (list of Algorithm): List of algorithms to run
            useBenchmarks (list of Benchmarks): List of benchmarks to run

    Returns:
            results (Dict[str, Dict]): Returns the results.

    """

    def __init__(self, D=10, nFES=1000000, nGEN=100000, nRuns=1, useAlgorithms='ArtificialBeeColonyAlgorithm', useBenchmarks='Ackley', **kwargs):
        r"""Initialize Runner.

        **__init__(self, D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, ...)**

        Arguments:
                D (int): Dimension of problem
                nFES (int): Number of function evaluations
                nGEN (int): Number of generations
                nRuns (int): Number of repetitions
                useAlgorithms (list of Algorithm): List of algorithms to run
                useBenchmarks (list of Benchmarks): List of benchmarks to run

        """

        self.D = D
        self.nFES = nFES
        self.nRuns = nRuns
        self.useAlgorithms = useAlgorithms
        self.useBenchmarks = useBenchmarks
        self.results = {}

    def benchmark_factory(self, name):
        r"""Create optimization task.

        Args:
                name (str): Benchmark name.

        Returns:
                Task: Optimization task to use.

        """

        from NiaPy.task import StoppingTask, OptimizationType
        return StoppingTask(D=self.D, nFES=self.nFES, optType=OptimizationType.MINIMIZATION, benchmark=name)

    @classmethod
    def __create_export_dir(cls):
        r"""Create export directory if not already createed."""
        if not os.path.exists("export"):
            os.makedirs("export")

    @classmethod
    def __generate_export_name(cls, extension):
        r"""Generate export file name.

        Args:
                extension (str): File format.

        Returns:

        """

        return "export/" + str(datetime.datetime.now()).replace(":", ".") + "." + extension

    def __export_to_log(self):
        r"""Print the results to terminal."""

        print(self.results)

    def __export_to_json(self):
        r"""Export the results in the JSON form.

        See Also:
                * :func:`NiaPy.Runner.__createExportDir`

        """

        self.__create_export_dir()
        with open(self.__generate_export_name("json"), "w") as outFile:
            json.dump(self.results, outFile)
            logger.info("Export to JSON completed!")

    def __export_to_xlsx(self):
        r"""Export the results in the xlsx form.

        See Also:
                :func:`NiaPy.Runner.__generateExportName`

        """

        self.__create_export_dir()
        workbook = xlsxwriter.Workbook(self.__generate_export_name("xlsx"))
        worksheet = workbook.add_worksheet()
        row, col, nRuns = 0, 0, 0

        for alg in self.results:
            _, col = worksheet.write(row, col, alg), col + 1
            for bench in self.results[alg]:
                worksheet.write(row, col, bench)
                nRuns = len(self.results[alg][bench])
                for i in range(len(self.results[alg][bench])):
                    _, row = worksheet.write(row, col, self.results[alg][bench][i]), row + 1
                row, col = row - len(self.results[alg][bench]), col + 1
            row, col = row + 1 + nRuns, col - 1 + len(self.results[alg])

        workbook.close()
        logger.info("Export to XLSX completed!")

    def __export_to_latex(self):
        r"""Export the results in the form of latex table.

        See Also:
                :func:`NiaPy.Runner.__createExportDir`
                :func:`NiaPy.Runner.__generateExportName`

        """

        self.__create_export_dir()

        metrics = ["Best", "Median", "Worst", "Mean", "Std."]

        def only_upper(s):
            return "".join(c for c in s if c.isupper())

        with open(self.__generate_export_name("tex"), "a") as outFile:
            outFile.write("\\documentclass{article}\n")
            outFile.write("\\usepackage[utf8]{inputenc}\n")
            outFile.write("\\usepackage{siunitx}\n")
            outFile.write("\\sisetup{\n")
            outFile.write("round-mode=places,round-precision=3}\n")
            outFile.write("\\begin{document}\n")
            outFile.write("\\begin{table}[h]\n")
            outFile.write("\\centering\n")
            begin_tabular = "\\begin{tabular}{cc"
            for alg in self.results:
                for _i in range(len(self.results[alg])):
                    begin_tabular += "S"
                firstLine = "   &"
                for benchmark in self.results[alg].keys():
                    firstLine += "  &   \\multicolumn{1}{c}{\\textbf{" + benchmark + "}}"
                firstLine += " \\\\"
                break
            begin_tabular += "}\n"
            outFile.write(begin_tabular)
            outFile.write("\\hline\n")
            outFile.write(firstLine + "\n")

            outFile.write("\\hline\n")
            for alg in self.results:
                for metric in metrics:
                    line = ""
                    if metric != "Worst":
                        line += "   &   " + metric
                    else:
                        shortAlg = ""
                        if alg.endswith("Algorithm"):
                            shortAlg = only_upper(alg[:-9])
                        else:
                            shortAlg = only_upper(alg)
                        line += "\\textbf{" + shortAlg + "} &   " + metric
                        for benchmark in self.results[alg]:
                            if metric == "Best":
                                line += "   &   " + str(amin(self.results[alg][benchmark]))
                            elif metric == "Median":
                                line += "   &   " + str(median(self.results[alg][benchmark]))
                            elif metric == "Worst":
                                line += "   &   " + str(amax(self.results[alg][benchmark]))
                            elif metric == "Mean":
                                line += "   &   " + str(mean(self.results[alg][benchmark]))
                            else:
                                line += "   &   " + str(std(self.results[alg][benchmark]))
                        line += "   \\\\"
                        outFile.write(line + "\n")
                    outFile.write("\\hline\n")
                outFile.write("\\end{tabular}\n")
                outFile.write("\\end{table}\n")
                outFile.write("\\end{document}")
        logger.info("Export to Latex completed!")

    def run(self, export="log", verbose=False):
        """Execute runner.

        Arguments:
                export (str): Takes export type (e.g. log, json, xlsx, latex) (default: "log")
                verbose (bool: Switch for verbose logging (default: {False})

        Raises:
                TypeError: Raises TypeError if export type is not supported

        Returns:
                dict: Returns dictionary of results

        See Also:
                * :func:`NiaPy.Runner.useAlgorithms`
                * :func:`NiaPy.Runner.useBenchmarks`
                * :func:`NiaPy.Runner.__algorithmFactory`

        """

        for alg in self.useAlgorithms:
            if not isinstance(alg, "".__class__):
                alg_name = str(type(alg).__name__)
            else:
                alg_name = alg

            self.results[alg_name] = {}

            if verbose:
                logger.info("Running %s...", alg_name)

            for bench in self.useBenchmarks:
                if not isinstance(bench, "".__class__):
                    bench_name = str(type(bench).__name__)
                else:
                    bench_name = bench

                if verbose:
                    logger.info("Running %s algorithm on %s benchmark...", alg_name, bench_name)

                benchmark_stopping_task = self.benchmark_factory(bench)
                self.results[alg_name][bench_name] = []
                for _ in range(self.nRuns):
                    algorithm = AlgorithmUtility().get_algorithm(alg)
                    self.results[alg_name][bench_name].append(algorithm.run(benchmark_stopping_task))
            if verbose:
                logger.info("---------------------------------------------------")
        if export == "log":
            self.__export_to_log()
        elif export == "json":
            self.__export_to_json()
        elif export == "xlsx":
            self.__export_to_xlsx()
        elif export == "latex":
            self.__export_to_latex()
        else:
            raise TypeError("Passed export type is not supported!")


1			"""Implementation of Runner utility class."""
2
3			import datetime
4			import json
5			import os
6			import logging
7
8			import xlsxwriter
9			from numpy import (
10			amin,
11			median,
12			amax,
13			mean,
14			std
15			)
16
17
18			from NiaPy.algorithms import AlgorithmUtility
19
20			logging.basicConfig()
21			logger = logging.getLogger('NiaPy.runner.Runner')
22			logger.setLevel('INFO')
23
24			__all__ = ["Runner"]
25
26
27			class Runner:
28			r"""Runner utility feature.
29
30			Feature which enables running multiple algorithms with multiple benchmarks.
31			It also support exporting results in various formats (e.g. LaTeX, Excel, JSON)
32
33			Attributes:
34			D (int): Dimension of problem
35			NP (int): Population size
36			nFES (int): Number of function evaluations
37			nRuns (int): Number of repetitions
38			useAlgorithms (list of Algorithm): List of algorithms to run
39			useBenchmarks (list of Benchmarks): List of benchmarks to run
40
41			Returns:
42			results (Dict[str, Dict]): Returns the results.
43
44			"""
45
46			def __init__(self, D=10, nFES=1000000, nGEN=100000, nRuns=1, useAlgorithms='ArtificialBeeColonyAlgorithm', useBenchmarks='Ackley', **kwargs):
47			r"""Initialize Runner.
48
49			__init__(self, D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, ...)
50
51			Arguments:
52			D (int): Dimension of problem
53			nFES (int): Number of function evaluations
54			nGEN (int): Number of generations
55			nRuns (int): Number of repetitions
56			useAlgorithms (list of Algorithm): List of algorithms to run
57			useBenchmarks (list of Benchmarks): List of benchmarks to run
58
59			"""
60
61			self.D = D
62			self.nFES = nFES
63			self.nRuns = nRuns
64			self.useAlgorithms = useAlgorithms
65			self.useBenchmarks = useBenchmarks
66			self.results = {}
67
68			def benchmark_factory(self, name):
69			r"""Create optimization task.
70
71			Args:
72			name (str): Benchmark name.
73
74			Returns:
75			Task: Optimization task to use.
76
77			"""
78
79			from NiaPy.task import StoppingTask, OptimizationType
80			return StoppingTask(D=self.D, nFES=self.nFES, optType=OptimizationType.MINIMIZATION, benchmark=name)
81
82			@classmethod
83			def __create_export_dir(cls):
84			r"""Create export directory if not already createed."""
85			if not os.path.exists("export"):
86			os.makedirs("export")
87
88			@classmethod
89			def __generate_export_name(cls, extension):
90			r"""Generate export file name.
91
92			Args:
93			extension (str): File format.
94
95			Returns:
96
97			"""
98
99			return "export/" + str(datetime.datetime.now()).replace(":", ".") + "." + extension
100
101			def __export_to_log(self):
102			r"""Print the results to terminal."""
103
104			print(self.results)
105
106			def __export_to_json(self):
107			r"""Export the results in the JSON form.
108
109			See Also:
110			* :func:`NiaPy.Runner.__createExportDir`
111
112			"""
113
114			self.__create_export_dir()
115			with open(self.__generate_export_name("json"), "w") as outFile:
116			json.dump(self.results, outFile)
117			logger.info("Export to JSON completed!")
118
119			def __export_to_xlsx(self):
120			r"""Export the results in the xlsx form.
121
122			See Also:
123			:func:`NiaPy.Runner.__generateExportName`
124
125			"""
126
127			self.__create_export_dir()
128			workbook = xlsxwriter.Workbook(self.__generate_export_name("xlsx"))
129			worksheet = workbook.add_worksheet()
130			row, col, nRuns = 0, 0, 0
131
132			for alg in self.results:
133			_, col = worksheet.write(row, col, alg), col + 1
134			for bench in self.results[alg]:
135			worksheet.write(row, col, bench)
136			nRuns = len(self.results[alg][bench])
137			for i in range(len(self.results[alg][bench])):
138			_, row = worksheet.write(row, col, self.results[alg][bench][i]), row + 1
139			row, col = row - len(self.results[alg][bench]), col + 1
140			row, col = row + 1 + nRuns, col - 1 + len(self.results[alg])
141
142			workbook.close()
143			logger.info("Export to XLSX completed!")
144
145			def __export_to_latex(self):
146			r"""Export the results in the form of latex table.
147
148			See Also:
149			:func:`NiaPy.Runner.__createExportDir`
150			:func:`NiaPy.Runner.__generateExportName`
151
152			"""
153
154			self.__create_export_dir()
155
156			metrics = ["Best", "Median", "Worst", "Mean", "Std."]
157
158			def only_upper(s):
159			return "".join(c for c in s if c.isupper())
160
161			with open(self.__generate_export_name("tex"), "a") as outFile:
162			outFile.write("\\documentclass{article}\n")
163			outFile.write("\\usepackage[utf8]{inputenc}\n")
164			outFile.write("\\usepackage{siunitx}\n")
165			outFile.write("\\sisetup{\n")
166			outFile.write("round-mode=places,round-precision=3}\n")
167			outFile.write("\\begin{document}\n")
168			outFile.write("\\begin{table}[h]\n")
169			outFile.write("\\centering\n")
170			begin_tabular = "\\begin{tabular}{cc"
171			for alg in self.results:
172			for _i in range(len(self.results[alg])):
173			begin_tabular += "S"
174			firstLine = " &"
175			for benchmark in self.results[alg].keys():
176			firstLine += " & \\multicolumn{1}{c}{\\textbf{" + benchmark + "}}"
177			firstLine += " \\\\"
178			break
179			begin_tabular += "}\n"
180			outFile.write(begin_tabular)
181			outFile.write("\\hline\n")
182			outFile.write(firstLine + "\n")
			0 ignored issues – show introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report The variable `firstLine` does not seem to be defined for all execution paths. Loading history...
183			outFile.write("\\hline\n")
184			for alg in self.results:
185			for metric in metrics:
186			line = ""
187			if metric != "Worst":
188			line += " & " + metric
189			else:
190			shortAlg = ""
191			if alg.endswith("Algorithm"):
192			shortAlg = only_upper(alg[:-9])
193			else:
194			shortAlg = only_upper(alg)
195			line += "\\textbf{" + shortAlg + "} & " + metric
196			for benchmark in self.results[alg]:
197			if metric == "Best":
198			line += " & " + str(amin(self.results[alg][benchmark]))
199			elif metric == "Median":
200			line += " & " + str(median(self.results[alg][benchmark]))
201			elif metric == "Worst":
202			line += " & " + str(amax(self.results[alg][benchmark]))
203			elif metric == "Mean":
204			line += " & " + str(mean(self.results[alg][benchmark]))
205			else:
206			line += " & " + str(std(self.results[alg][benchmark]))
207			line += " \\\\"
208			outFile.write(line + "\n")
209			outFile.write("\\hline\n")
210			outFile.write("\\end{tabular}\n")
211			outFile.write("\\end{table}\n")
212			outFile.write("\\end{document}")
213			logger.info("Export to Latex completed!")
214
215			def run(self, export="log", verbose=False):
216			"""Execute runner.
217
218			Arguments:
219			export (str): Takes export type (e.g. log, json, xlsx, latex) (default: "log")
220			verbose (bool: Switch for verbose logging (default: {False})
221
222			Raises:
223			TypeError: Raises TypeError if export type is not supported
224
225			Returns:
226			dict: Returns dictionary of results
227
228			See Also:
229			* :func:`NiaPy.Runner.useAlgorithms`
230			* :func:`NiaPy.Runner.useBenchmarks`
231			* :func:`NiaPy.Runner.__algorithmFactory`
232
233			"""
234
235			for alg in self.useAlgorithms:
236			if not isinstance(alg, "".__class__):
237			alg_name = str(type(alg).__name__)
238			else:
239			alg_name = alg
240
241			self.results[alg_name] = {}
242
243			if verbose:
244			logger.info("Running %s...", alg_name)
245
246			for bench in self.useBenchmarks:
247			if not isinstance(bench, "".__class__):
248			bench_name = str(type(bench).__name__)
249			else:
250			bench_name = bench
251
252			if verbose:
253			logger.info("Running %s algorithm on %s benchmark...", alg_name, bench_name)
254
255			benchmark_stopping_task = self.benchmark_factory(bench)
256			self.results[alg_name][bench_name] = []
257			for _ in range(self.nRuns):
258			algorithm = AlgorithmUtility().get_algorithm(alg)
259			self.results[alg_name][bench_name].append(algorithm.run(benchmark_stopping_task))
260			if verbose:
261			logger.info("---------------------------------------------------")
262			if export == "log":
263			self.__export_to_log()
264			elif export == "json":
265			self.__export_to_json()
266			elif export == "xlsx":
267			self.__export_to_xlsx()
268			elif export == "latex":
269			self.__export_to_latex()
270			else:
271			raise TypeError("Passed export type is not supported!")
272

NiaOrg / NiaPy

Pull Request — master (#206)

NiaPy.runner.Runner.__init__() A

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Duplication

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NiaPy.runner.Runner.init() A