framework/output: save classifiers in Result

Save classifiers at Result as well as Metric level. Reason: when
processing output, one might want to filter complete results, as well as
individual metrics. While it is in theory possible to get the
classifiers for a job by simply extracting the common classifiers
between all metrics, this fails when there are no metrics generated for
a job (note that one might still want to process the output in this
case, e.g. for the artifacts).

wa/framework/execution.py

@@ -131,7 +131,8 @@ class ExecutionContext(object):
         if not self.job_queue:
             raise RuntimeError('No jobs to run')
         self.current_job = self.job_queue.pop(0)
-        self.current_job.output = init_job_output(self.run_output, self.current_job)
+        job_output = init_job_output(self.run_output, self.current_job)
+        self.current_job.set_output(job_output)
         self.update_job_state(self.current_job)
         self.tm.start()
         return self.current_job

wa/framework/job.py

@@ -1,4 +1,5 @@
 import logging
+from copy import copy
 from datetime import datetime
 
 from wa.framework import pluginloader, signal, instruments
@@ -56,6 +57,10 @@ class Job(object):
         else:
             self.workload = self._workload_cache[self.id]
 
+    def set_output(self, output):
+        output.classifiers = copy(self.classifiers)
+        self.output = output
+
     def initialize(self, context):
         self.logger.info('Initializing job {} [{}]'.format(self.id, self.iteration))
         with signal.wrap('WORKLOAD_INITIALIZED', self, context):

wa/framework/output.py

@@ -1,6 +1,7 @@
 import logging
 import os
 import shutil
+from collections import OrderedDict
 from copy import copy
 from datetime import datetime
 
@@ -58,6 +59,19 @@ class Output(object):
             return []
         return self.result.artifacts
 
+    @property
+    def classifiers(self):
+        if self.result is None:
+            return OrderedDict()
+        return self.result.classifiers
+
+    @classifiers.setter
+    def classifiers(self, value):
+        if self.result is None:
+            msg ='Attempting to set classifiers before output has been set'
+            raise RuntimeError(msg)
+        self.result.classifiers = value
+
     def __init__(self, path):
         self.basepath = path
         self.result = None
@@ -240,6 +254,7 @@ class Result(object):
         instance.metrics = [Metric.from_pod(m) for m in pod['metrics']]
         instance.artifacts = [Artifact.from_pod(a) for a in pod['artifacts']]
         instance.events = [Event.from_pod(e) for e in pod['events']]
+        instance.classifiers = pod.get('classifiers', OrderedDict())
         return instance
 
     def __init__(self):
@@ -248,6 +263,7 @@ class Result(object):
         self.metrics = []
         self.artifacts = []
         self.events = []
+        self.classifiers = OrderedDict()
 
     def add_metric(self, name, value, units=None, lower_is_better=False,
                    classifiers=None):
@@ -282,6 +298,7 @@ class Result(object):
             metrics=[m.to_pod() for m in self.metrics],
             artifacts=[a.to_pod() for a in self.artifacts],
             events=[e.to_pod() for e in self.events],
+            classifiers=copy(self.classifiers),
         )