Adding classifiers to metrics and updating csv and telemetry to take advantage of them

- 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).

wlauto/core/execution.py

@@ -192,6 +192,9 @@ class ExecutionContext(object):
         self.current_job = None
         self.output_directory = self.run_output_directory
 
+    def add_metric(self, *args, **kwargs):
+        self.result.add_metric(*args, **kwargs)
+
     def add_artifact(self, name, path, kind, *args, **kwargs):
         if self.current_job is None:
             self.add_run_artifact(name, path, kind, *args, **kwargs)

wlauto/core/result.py

@@ -261,8 +261,8 @@ class IterationResult(object):
         self.metrics = []
         self.artifacts = []
 
-    def add_metric(self, name, value, units=None, lower_is_better=False):
-        self.metrics.append(Metric(name, value, units, lower_is_better))
+    def add_metric(self, name, value, units=None, lower_is_better=False, classifiers=None):
+        self.metrics.append(Metric(name, value, units, lower_is_better, classifiers))
 
     def has_metric(self, name):
         for metric in self.metrics:
@@ -300,14 +300,18 @@ class Metric(object):
                   has no units (e.g. it's a count or a standardised score).
     :param lower_is_better: Boolean flag indicating where lower values are
                             better than higher ones. Defaults to False.
+    :param classifiers: A set of key-value pairs to further classify this metric
+                        beyond current iteration (e.g. this can be used to identify
+                        sub-tests).
 
     """
 
-    def __init__(self, name, value, units=None, lower_is_better=False):
+    def __init__(self, name, value, units=None, lower_is_better=False, classifiers=None):
         self.name = name
         self.value = numeric(value)
         self.units = units
         self.lower_is_better = lower_is_better
+        self.classifiers = classifiers or {}
 
     def to_dict(self):
         return self.__dict__