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Update exp related and pytorch_nn

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This commit is contained in:
Jactus
2020-11-09 16:42:21 +08:00
parent 9a826eefa3
commit 853410c16e
6 changed files with 297 additions and 157 deletions
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61
qlib/workflow/expm.py
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@@ -9,7 +9,7 @@ from .exp import MLflowExperiment
from .recorder import MLflowRecorder
from ..log import get_module_logger
logger = get_module_logger("workflow", "WARNING")
logger = get_module_logger("workflow", "INFO")
class ExpManager:
@@ -20,7 +20,7 @@ class ExpManager:
def __init__(self):
self.uri = None
self.active_recorder = None # only one recorder can running each time
self.active_experiment = None # only one experiment can running each time
self.experiments = dict() # store the experiment name --> Experiment object
def start_exp(self, experiment_name=None, uri=None, **kwargs):
@@ -39,7 +39,7 @@ class ExpManager:
controls whether run is nested in parent run.
Returns
An object wrapped by context manager.
An active recorder.
"""
raise NotImplementedError(f"Please implement the `start_exp` method.")
@@ -73,11 +73,14 @@ class ExpManager:
Returns
-------
A pandas.DataFrame of runs.
A pandas.DataFrame of records, where each metric, parameter, and tag
are expanded into their own columns named metrics.*, params.*, and tags.*
respectively. For records that don't have a particular metric, parameter, or tag, their
value will be (NumPy) Nan, None, or None respectively.
"""
raise NotImplementedError(f"Please implement the `search_records` method.")
def __create_exp(self, experiment_name, artifact_location=None):
def create_exp(self, experiment_name, artifact_location=None):
"""
Create an experiment.
@@ -133,19 +136,6 @@ class ExpManager:
"""
return self.uri
def get_recorder(self):
"""
Get the current active Recorder.
Parameters
----------
Returns
-------
An Recorder object.
"""
return self.active_recorder
class MLflowExpManager(ExpManager):
"""
@@ -158,26 +148,27 @@ class MLflowExpManager(ExpManager):
def start_exp(self, experiment_name=None, uri=None):
# create experiment
experiment = self.__create_exp(experiment_name, uri)
# set up recorder
recorder = experiment.create_recorder()
self.active_recorder = recorder
experiment = self.create_exp(experiment_name, uri)
# set up active experiment
self.active_experiment = experiment
# store the experiment
self.experiments[experiment_name] = experiment
# start the experiment
self.active_experiment.start()
return self.active_recorder.start_run(experiment_id=experiment.id)
return self.active_experiment
def end_exp(self):
if self.active_recorder is not None:
self.active_recorder.end_run()
self.active_recorder = None
def end_exp(self, status):
if self.active_experiment is not None:
self.active_experiment.end(status)
self.active_experiment = None
def __create_exp(self, experiment_name=None, uri=None):
def create_exp(self, experiment_name=None, uri=None):
# init experiment
experiment = MLflowExperiment()
# set the tracking uri
if uri is None:
logger.warning(
logger.info(
"No tracking URI is provided. The default tracking URI is set as `mlruns` under the working directory."
)
else:
@@ -185,7 +176,7 @@ class MLflowExpManager(ExpManager):
mlflow.set_tracking_uri(self.uri)
# start the experiment
if experiment_name is None:
logger.warning("No experiment name provided. The default experiment name is set as `experiment`.")
logger.info("No experiment name provided. The default experiment name is set as `experiment`.")
experiment_id = mlflow.create_experiment("experiment")
# set the active experiment
mlflow.set_experiment("experiment")
@@ -216,17 +207,19 @@ class MLflowExpManager(ExpManager):
return mlflow.search_runs(experiment_ids, filter_string, run_view_type, max_results, order_by)
def get_exp(self, experiment_id=None, experiment_name=None):
assert (
experiment_id is not None or experiment_name is not None
), "Please provide at least one of the experiment id or name to retrieve an experiment."
if experiment_name is not None:
return self.experiments[experiment_name]
elif experiment_id is not None:
for name in self.experiments:
if self.experiments[name].id == experiment_id:
return self.experiments[name]
elif self.active_experiment is None:
raise Exception('No valid active experiment exists. Please make sure experiment manager is running.')
else:
raise Exception("No valid experiment is found. Please make sure the id and name are correctly given.")
logger.info(
"No experiment id or name is given. Return the current active experiment."
)
return self.active_experiment
def delete_exp(self, experiment_id):
mlflow.delete_experiment(experiment_id)