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- Adding "classifiers" field to Metric objects. This is a dict mapping classifier names (arbitrary strings) to corresponding values for that specific metrics. This is to allow extensions to add extension-specific annotations to metric that could be handled in a generic way (e.g. by result processors). - Updating telemetry workload to add classifiers for the url and internal iteration (or "time") for a particular result. - Updating csv result processor with the option to use classifiers to add columns to results.csv (either using all classifiers found, or only for the specific ones listed).
162 lines
5.7 KiB
Python
162 lines
5.7 KiB
Python
# Copyright 2013-2015 ARM Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# pylint: disable=R0201
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"""
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This module contains a few "standard" result processors that write results to
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text files in various formats.
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"""
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import os
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import csv
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import json
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from wlauto import ResultProcessor, Parameter
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from wlauto.exceptions import ConfigError
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from wlauto.utils.types import list_of_strings
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class StandardProcessor(ResultProcessor):
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name = 'standard'
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description = """
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Creates a ``result.txt`` file for every iteration that contains metrics
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for that iteration.
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The metrics are written in ::
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metric = value [units]
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format.
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"""
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def process_iteration_result(self, result, context):
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outfile = os.path.join(context.output_directory, 'result.txt')
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with open(outfile, 'w') as wfh:
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for metric in result.metrics:
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line = '{} = {}'.format(metric.name, metric.value)
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if metric.units:
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line = ' '.join([line, metric.units])
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line += '\n'
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wfh.write(line)
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context.add_artifact('iteration_result', 'result.txt', 'export')
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class CsvReportProcessor(ResultProcessor):
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"""
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Creates a ``results.csv`` in the output directory containing results for
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all iterations in CSV format, each line containing a single metric.
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"""
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name = 'csv'
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parameters = [
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Parameter('use_all_classifiers', kind=bool, default=False,
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description="""
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If set to ``True``, this will add a column for every classifier
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that features in at least one collected metric.
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.. note:: This cannot be ``True`` if ``extra_columns`` is set.
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"""),
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Parameter('extra_columns', kind=list_of_strings,
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description="""
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List of classifiers to use as columns.
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.. note:: This cannot be set if ``use_all_classifiers`` is ``True``.
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"""),
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]
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def validate(self):
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if self.use_all_classifiers and self.extra_columns:
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raise ConfigError('extra_columns cannot be specified when use_all_classifiers is True')
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def process_run_result(self, result, context):
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if self.use_all_classifiers:
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classifiers = set([])
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for ir in result.iteration_results:
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for metric in ir.metrics:
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classifiers.update(metric.classifiers.keys())
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extra_columns = list(classifiers)
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elif self.extra_columns:
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extra_columns = self.extra_columns
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else:
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extra_columns = []
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outfile = os.path.join(context.run_output_directory, 'results.csv')
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with open(outfile, 'wb') as wfh:
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writer = csv.writer(wfh)
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writer.writerow(['id', 'workload', 'iteration', 'metric', ] +
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extra_columns + ['value', 'units'])
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for ir in result.iteration_results:
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for metric in ir.metrics:
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row = ([ir.id, ir.spec.label, ir.iteration, metric.name] +
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[str(metric.classifiers.get(c) or '') for c in extra_columns] +
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[str(metric.value), metric.units or ''])
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writer.writerow(row)
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context.add_artifact('run_result_csv', 'results.csv', 'export')
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class JsonReportProcessor(ResultProcessor):
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"""
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Creates a ``results.json`` in the output directory containing results for
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all iterations in JSON format.
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"""
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name = 'json'
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def process_run_result(self, result, context):
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outfile = os.path.join(context.run_output_directory, 'results.json')
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with open(outfile, 'wb') as wfh:
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output = []
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for result in result.iteration_results:
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output.append({
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'id': result.id,
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'workload': result.workload.name,
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'iteration': result.iteration,
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'metrics': [dict([(k, v) for k, v in m.__dict__.iteritems()
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if not k.startswith('_')])
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for m in result.metrics],
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})
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json.dump(output, wfh, indent=4)
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context.add_artifact('run_result_json', 'results.json', 'export')
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class SummaryCsvProcessor(ResultProcessor):
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"""
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Similar to csv result processor, but only contains workloads' summary metrics.
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"""
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name = 'summary_csv'
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def process_run_result(self, result, context):
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outfile = os.path.join(context.run_output_directory, 'summary.csv')
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with open(outfile, 'wb') as wfh:
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writer = csv.writer(wfh)
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writer.writerow(['id', 'workload', 'iteration', 'metric', 'value', 'units'])
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for result in result.iteration_results:
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for metric in result.metrics:
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if metric.name in result.workload.summary_metrics:
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row = [result.id, result.workload.name, result.iteration,
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metric.name, str(metric.value), metric.units or '']
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writer.writerow(row)
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context.add_artifact('run_result_summary', 'summary.csv', 'export')
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