def __init__(self, **kwargs):
"""
See :class:`~lab.reports.Report` for inherited parameters.
You can filter and modify runs for a report with
:py:class:`filters <.Report>`. For example, you can include only
a subset of algorithms or compute new attributes. If you provide
a list for *filter_algorithm*, it will be used to determine the
order of algorithms in the report.
>>> # Use a filter function to select algorithms.
>>> def only_blind_and_lmcut(run):
... return run['algorithm'] in ['blind', 'lmcut']
>>> report = PlanningReport(filter=only_blind_and_lmcut)
>>> # Use "filter_algorithm" to select and *order* algorithms.
>>> r = PlanningReport(filter_algorithm=['lmcut', 'blind'])
:py:class:`Filters <.Report>` can be very helpful so we
recommend reading up on them to use their full potential.
"""
# Set non-default options for some attributes.
attributes = tools.make_list(kwargs.get('attributes') or [])
kwargs['attributes'] = [
self._prepare_attribute(attr) for attr in attributes
]
# Remember the order of algorithms if it is given as a keyword argument filter.
self.filter_algorithm = tools.make_list(
kwargs.get('filter_algorithm', []))
Report.__init__(self, **kwargs)
def __init__(self, derived_properties=None, **kwargs):
"""
See :py:class:`Report <lab.reports.Report>` for inherited parameters.
*derived_properties* must be a function or a list of functions taking a
single argument. This argument is a list of problem runs i.e. it contains
one run-dictionary for each config in the experiment. The function is
called for every problem in the suite. A function that computes the
IPC score based on the results of the experiment is added automatically
to the *derived_properties* list and serves as an example here:
.. literalinclude:: ../downward/reports/__init__.py
:pyobject: quality
You can include only specific domains or configurations by using
:py:class:`filters <.Report>`.
If you provide a list for *filter_config* or *filter_config_nick*, it
will be used to determine the order of configurations in the report. ::
# Use a filter function.
def only_blind_and_lmcut(run):
return run['config'] in ['WORK-blind', 'WORK-lmcut']
PlanningReport(filter=only_blind_and_lmcut)
# Filter with a list and set the order of the configs.
PlanningReport(filter_config=['WORK-lmcut', 'WORK-blind'])
PlanningReport(filter_config_nick=['lmcut', 'blind'])
Tip: When you append ``_relative`` to an attribute, you will get a table
containing the attribute's values of each configuration relative to the
leftmost column.
"""
# Allow specifying a single property or a list of properties.
if hasattr(derived_properties, '__call__'):
derived_properties = [derived_properties]
self.derived_properties = derived_properties or []
# Set non-default options for some attributes.
attributes = tools.make_list(kwargs.get('attributes') or [])
kwargs['attributes'] = [self._prepare_attribute(attr) for attr in attributes]
self._handle_relative_attributes(kwargs['attributes'])
# Remember the order of the configs if it is given as a key word argument filter.
self.filter_config = tools.make_list(kwargs.get('filter_config') or [])
self.filter_config_nick = tools.make_list(kwargs.get('filter_config_nick') or [])
Report.__init__(self, **kwargs)
self.derived_properties.append(quality)
def __init__(self, **kwargs):
"""
See :class:`~lab.reports.Report` for inherited parameters.
You can filter and modify runs for a report with
:py:class:`filters <.Report>`. For example, you can include only
a subset of algorithms or compute new attributes. If you provide
a list for *filter_algorithm*, it will be used to determine the
order of algorithms in the report.
>>> # Use a filter function to select algorithms.
>>> def only_blind_and_lmcut(run):
... return run["algorithm"] in ["blind", "lmcut"]
...
>>> report = PlanningReport(filter=only_blind_and_lmcut)
>>> # Use "filter_algorithm" to select and *order* algorithms.
>>> report = PlanningReport(filter_algorithm=["lmcut", "blind"])
:py:class:`Filters <.Report>` can be very helpful so we
recommend reading up on them to use their full potential.
Subclasses can use the member variable ``problem_runs`` to access the
experiment data. It is a dictionary mapping from tasks (i.e.,
``(domain, problem)`` pairs) to the runs for that task. Each run is a
dictionary that maps from attribute names to values.
>>> class MinRuntimePerTask(PlanningReport):
... def get_text(self):
... map = {}
... for (domain, problem), runs in self.problem_runs.items():
... times = [run.get("planner_time") for run in runs]
... times = [t for t in times if t is not None]
... map[(domain, problem)] = min(times) if times else None
... return str(map)
...
"""
# Set non-default options for some attributes.
attributes = tools.make_list(kwargs.get("attributes"))
kwargs["attributes"] = [
self._prepare_attribute(attr) for attr in attributes
]
# Remember the order of algorithms if it is given as a keyword argument filter.
self.filter_algorithm = tools.make_list(kwargs.get("filter_algorithm"))
Report.__init__(self, **kwargs)
def __init__(self, **kwargs):
Report.__init__(self, **kwargs)
def __init__(self, *args, **kwargs):
Report.__init__(self, *args, **kwargs)
