def train_with_sigana(uri_path: str = None):
"""train model followed by SigAnaRecord
Returns
-------
pred_score: pandas.DataFrame
predict scores
performance: dict
model performance
"""
model = init_instance_by_config(CSI300_GBDT_TASK["model"])
dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])
# start exp
with R.start(experiment_name="workflow_with_sigana", uri=uri_path):
R.log_params(**flatten_dict(CSI300_GBDT_TASK))
model.fit(dataset)
recorder = R.get_recorder()
sr = SignalRecord(model, dataset, recorder)
sr.generate()
pred_score = sr.load("pred.pkl")
# predict and calculate ic and ric
sar = SigAnaRecord(recorder)
sar.generate()
ic = sar.load("ic.pkl")
ric = sar.load("ric.pkl")
uri_path = R.get_uri()
return pred_score, {"ic": ic, "ric": ric}, uri_path
def train_with_sigana():
"""train model followed by SigAnaRecord
Returns
-------
pred_score: pandas.DataFrame
predict scores
performance: dict
model performance
"""
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
# start exp
with R.start(experiment_name="workflow_with_sigana"):
R.log_params(**flatten_dict(task))
model.fit(dataset)
# predict and calculate ic and ric
recorder = R.get_recorder()
sar = SigAnaRecord(recorder, model=model, dataset=dataset)
sar.generate()
ic = sar.load(sar.get_path("ic.pkl"))
ric = sar.load(sar.get_path("ric.pkl"))
pred_score = sar.load("pred.pkl")
smr = SignalMseRecord(recorder)
smr.generate()
uri_path = R.get_uri()
return pred_score, {"ic": ic, "ric": ric}, uri_path
def fake_experiment():
"""A fake experiment workflow to test uri
Returns
-------
pass_or_not_for_default_uri: bool
pass_or_not_for_current_uri: bool
temporary_exp_dir: str
"""
# start exp
default_uri = R.get_uri()
current_uri = "file:./temp-test-exp-mag"
with R.start(experiment_name="fake_workflow_for_expm", uri=current_uri):
R.log_params(**flatten_dict(CSI300_GBDT_TASK))
current_uri_to_check = R.get_uri()
default_uri_to_check = R.get_uri()
return default_uri == default_uri_to_check, current_uri == current_uri_to_check, current_uri
def train_mse():
model = init_instance_by_config(CSI300_GBDT_TASK["model"])
dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])
with R.start(experiment_name="workflow"):
R.log_params(**flatten_dict(CSI300_GBDT_TASK))
model.fit(dataset)
recorder = R.get_recorder()
sr = SignalMseRecord(recorder, model=model, dataset=dataset)
sr.generate()
uri = R.get_uri()
return uri
def train_multiseg():
model = init_instance_by_config(CSI300_GBDT_TASK["model"])
dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])
with R.start(experiment_name="workflow"):
R.log_params(**flatten_dict(CSI300_GBDT_TASK))
model.fit(dataset)
recorder = R.get_recorder()
sr = MultiSegRecord(model, dataset, recorder)
sr.generate(dict(valid="valid", test="test"), True)
uri = R.get_uri()
return uri