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Change HyperParameterOptimizer spawn_task_project to spawn_project
Add HyperParameterOptimizer parallel coordinates (issues #279)
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allegroai
parent
54132e2505
commit
794d97ca7c
@ -913,7 +913,7 @@ class HyperParameterOptimizer(object):
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compute_time_limit=None, # type: Optional[float]
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auto_connect_task=True, # type: Union[bool, Task]
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always_create_task=False, # type: bool
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spawn_task_project=None, # type: Optional[str]
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spawn_project=None, # type: Optional[str]
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save_top_k_tasks_only=None, # type: Optional[int]
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**optimizer_kwargs # type: Any
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):
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@ -962,8 +962,8 @@ class HyperParameterOptimizer(object):
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- ``False`` - Use the :py:meth:`task.Task.current_task` (if exists) to report statistics.
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:param str spawn_task_project: If project name is specified, create all optimization Jobs (Tasks) in the
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specified project, instead of the original base_task_id project.
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:param str spawn_project: If project name is specified, create all optimization Jobs (Tasks) in the
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specified project instead of the original base_task_id project.
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:param int save_top_k_tasks_only: If specified and above 0, keep only the top_k performing Tasks,
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and archive the rest of the created Tasks. Default: -1 keep everything, nothing will be archived.
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@ -1063,7 +1063,7 @@ class HyperParameterOptimizer(object):
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self._experiment_completed_cb = None
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self._save_top_k_tasks_only = max(0, save_top_k_tasks_only or 0)
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self.optimizer.set_job_default_parent(
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self._task.id if self._task else None, project_name=spawn_task_project or None)
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self._task.id if self._task else None, project_name=spawn_project or None)
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self.set_time_limit(in_minutes=opts['optimization_time_limit'])
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def get_num_active_experiments(self):
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@ -1538,30 +1538,62 @@ class HyperParameterOptimizer(object):
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# update scatter plot
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task_logger.report_scatter2d(
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title='optimization', series=title,
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title='Optimization Objective', series=title,
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scatter=pairs, iteration=0, labels=labels,
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mode='markers', xaxis='job #', yaxis='objective')
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# update summary table
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job_ids = list(completed_jobs.keys())
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job_ids_sorted_by_objective = sorted(
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job_ids, key=lambda x: completed_jobs[x][0], reverse=bool(self.objective_metric.sign >= 0))
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# sort the columns except for 'objective', 'iteration'
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columns = list(sorted(set([c for k, v in completed_jobs.items() for c in v[2].keys()])))
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# add the index column (task id) and the first two columns 'objective', 'iteration' then the rest
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table_values = [['task id', 'objective', 'iteration'] + columns]
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# update summary table
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job_ids = list(completed_jobs.keys())
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job_ids_sorted_by_objective = sorted(
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job_ids, key=lambda x: completed_jobs[x][0], reverse=bool(self.objective_metric.sign >= 0))
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# sort the columns except for 'objective', 'iteration'
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columns = list(sorted(set([c for k, v in completed_jobs.items() for c in v[2].keys()])))
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# add the index column (task id) and the first two columns 'objective', 'iteration' then the rest
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table_values = [['task id', 'objective', 'iteration'] + columns]
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table_values += \
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[([job, completed_jobs[job][0], completed_jobs[job][1]] +
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[completed_jobs[job][2].get(c, '') for c in columns]) for job in job_ids_sorted_by_objective]
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task_logger.report_table(
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"summary", "job", 0, table_plot=table_values,
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extra_layout={"title": "objective: {}".format(title)})
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# upload summary as artifact
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if force:
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task = self._task or Task.current_task()
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if task:
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task.upload_artifact(name='summary', artifact_object={'table': table_values})
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table_values += \
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[([job, completed_jobs[job][0], completed_jobs[job][1]] +
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[completed_jobs[job][2].get(c, '') for c in columns]) for job in job_ids_sorted_by_objective]
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task_logger.report_table(
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"summary", "job", 0, table_plot=table_values,
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extra_layout={"title": "objective: {}".format(title)})
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# Build parallel Coordinates: convert to columns, and reorder accordingly
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if len(table_values) > 1:
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table_values_columns = [[row[i] for row in table_values] for i in range(len(table_values[0]))]
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table_values_columns = \
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[[table_values_columns[0][0]] + [c[:6]+'...' for c in table_values_columns[0][1:]]] + \
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table_values_columns[2:-1] + [[title]+table_values_columns[1][1:]]
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pcc_dims = []
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for col in table_values_columns:
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# test if all values are numbers:
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try:
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# try to cast all values to float
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values = [float(v) for v in col[1:]]
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d = dict(label=col[0], values=values)
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except (ValueError, TypeError):
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values = list(range(len(col[1:])))
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ticks = col[1:]
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d = dict(label=col[0], values=values, tickvals=values, ticktext=ticks)
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pcc_dims.append(d)
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# report parallel coordinates
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plotly_pcc = dict(
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data=[dict(
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type='parcoords',
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line=dict(colorscale='Viridis',
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reversescale=bool(self.objective_metric.sign < 0),
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color=table_values_columns[-1][1:]),
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dimensions=pcc_dims)],
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layout={})
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task_logger.report_plotly(
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title='Parallel Coordinates', series='',
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iteration=0, figure=plotly_pcc)
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# upload summary as artifact
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if force:
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task = self._task or Task.current_task()
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if task:
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task.upload_artifact(name='summary', artifact_object={'table': table_values})
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def _report_remaining_budget(self, task_logger, counter):
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# noinspection PyBroadException
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@ -86,8 +86,8 @@ an_optimizer = HyperParameterOptimizer(
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optimizer_class=aSearchStrategy,
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# Select an execution queue to schedule the experiments for execution
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execution_queue=execution_queue,
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# If specified all Tasks created by the HPO process will be under the `spawned_tasks_project` project
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spawn_task_project=None, # 'HPO spawn project',
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# If specified all Tasks created by the HPO process will be created under the `spawned_project` project
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spawn_project=None, # 'HPO spawn project',
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# If specified only the top K performing Tasks will be kept, the others will be automatically archived
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save_top_k_tasks_only=None, # 5,
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# Optional: Limit the execution time of a single experiment, in minutes.
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