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Update repr for Experiment & Recorder

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D-X-Y
2021-03-04 05:02:56 +00:00
parent 229a39d0d3
commit 592db903b3
5 changed files with 13 additions and 7 deletions
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6
qlib/workflow/__init__.py
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@@ -38,8 +38,10 @@ class QlibRecorder:
recorder_name : str recorder_name : str
name of the recorder under the experiment one wants to start. name of the recorder under the experiment one wants to start.
uri : str uri : str
the tracking uri of the experiment, where all the artifacts/metrics etc. will be stored. The tracking uri of the experiment, where all the artifacts/metrics etc. will be stored.
The default uri are set in the qlib.config. The default uri are set in the qlib.config. Note that this uri argument will not change the one defined in the config file.
Therefore, the next time when user call this function in the same experiment,
they have to also specify this argument with the same value. Otherwise, inconsistent uri may occur.
""" """
run = self.start_exp(experiment_name, recorder_name, uri) run = self.start_exp(experiment_name, recorder_name, uri)
try: try:

4
qlib/workflow/exp.py
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@@ -11,7 +11,7 @@ from ..log import get_module_logger
logger = get_module_logger("workflow", "INFO") logger = get_module_logger("workflow", "INFO")
class Experiment: class Experiment(object):
""" """
This is the `Experiment` class for each experiment being run. The API is designed similar to mlflow. This is the `Experiment` class for each experiment being run. The API is designed similar to mlflow.
(The link: https://mlflow.org/docs/latest/python_api/mlflow.html) (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)
@@ -23,7 +23,7 @@ class Experiment:
self.active_recorder = None # only one recorder can running each time self.active_recorder = None # only one recorder can running each time
def __repr__(self): def __repr__(self):
return str(self.info) return "{name}(info={info})".format(name=self.__class__.__name__, info=self.info)
def __str__(self): def __str__(self):
return str(self.info) return str(self.info)

2
qlib/workflow/expm.py
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@@ -14,7 +14,7 @@ from ..log import get_module_logger
logger = get_module_logger("workflow", "INFO") logger = get_module_logger("workflow", "INFO")
class ExpManager: class ExpManager(object):
""" """
This is the `ExpManager` class for managing experiments. The API is designed similar to mlflow. This is the `ExpManager` class for managing experiments. The API is designed similar to mlflow.
(The link: https://mlflow.org/docs/latest/python_api/mlflow.html) (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

4
qlib/workflow/recorder.py
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@@ -11,7 +11,7 @@ from ..log import get_module_logger
logger = get_module_logger("workflow", "INFO") logger = get_module_logger("workflow", "INFO")
class Recorder: class Recorder(object):
""" """
This is the `Recorder` class for logging the experiments. The API is designed similar to mlflow. This is the `Recorder` class for logging the experiments. The API is designed similar to mlflow.
(The link: https://mlflow.org/docs/latest/python_api/mlflow.html) (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)
@@ -34,7 +34,7 @@ class Recorder:
self.status = Recorder.STATUS_S self.status = Recorder.STATUS_S
def __repr__(self): def __repr__(self):
return str(self.info) return "{name}(info={info})".format(name=self.__class__.__name__, info=self.info)
def __str__(self): def __str__(self):
return str(self.info) return str(self.info)

4
tests/test_all_pipeline.py
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@@ -110,7 +110,10 @@ def train():
# model initiaiton # model initiaiton
model = init_instance_by_config(task["model"]) model = init_instance_by_config(task["model"])
print(model)
dataset = init_instance_by_config(task["dataset"]) dataset = init_instance_by_config(task["dataset"])
print(dataset)
print(R)
# start exp # start exp
with R.start(experiment_name="workflow"): with R.start(experiment_name="workflow"):
@@ -119,6 +122,7 @@ def train():
# prediction # prediction
recorder = R.get_recorder() recorder = R.get_recorder()
print(recorder)
rid = recorder.id rid = recorder.id
sr = SignalRecord(model, dataset, recorder) sr = SignalRecord(model, dataset, recorder)
sr.generate() sr.generate()