def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['OptimalPSI'])
logger.info("Initiating Optimal Psi")
t = OptimalPSI(
model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores,
cutoff=100
)
t.alerts.alert("Beginning the optimal psi process")
t.database_wipe()
t.get_best_psi()
t.calculate_weights()
t.make_temp_ensemble_preds()
t.calculate_in_sample_rmse()
t.calculate_in_sample_trend()
t.determine_submodel_ids()
t.delete_predictions()
t.return_submodel_labels()
t.submodel_pv()
t.write_submodel_covariates()
t.save_outputs()
logger.info(f"Finished finding optimal psi: {t.pickled_outputs['model_pv']['best_psi']}")
t.alerts.alert("Finished with optimal psi")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['InputData'])
logger.info("Initiating the knockout generation.")
t = ModelTask(model_version_id=args.model_version_id,
db_connection=args.db_connection,
old_covariates_mvid=args.old_covariates_mvid,
debug_mode=args.debug_mode,
cores=args.cores,
step_id=STEP_IDS['InputData'],
make_inputs=False,
make_ko=False)
logger.info("Finished with input creation.")
t.alerts.alert("Done creating inputs.")
logger.info("Updating job metadata parameters.")
with open(t.model_paths.JOB_METADATA, 'r') as json_file:
logger.info("Reading inputs json.")
inputs_info = json.load(json_file)
logger.info(f"{inputs_info}")
inputs_info.update(inspect_all_inputs(t.model_metadata))
with open(t.model_paths.JOB_METADATA, 'w') as outfile:
logger.info("Writing inputs json.")
logger.info(f"{inputs_info}")
json.dump(inputs_info, outfile)
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['GPRDraws'])
ne.set_num_threads(1)
logger.info("Initiating GPR draws.")
t = GPRDraws(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores)
t.alerts.alert("Creating GPR draws.")
t.make_draws()
t.save_outputs()
t.alerts.alert("Done creating GPR draws.")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['ApplySpacetimeSmoothing'])
logger.info("Initiating spacetime smoothing.")
t = ApplySpacetimeSmoothing(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores)
t.alerts.alert("Initiating spacetime smoothing")
t.apply_spacetime_smoothing()
t.save_outputs()
t.alerts.alert("Done with spacetime smoothing")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['ApplyGPSmoothing'])
logger.info("Initiating GPR smoothing.")
ne.set_num_threads(1)
t = ApplyGPSmoothing(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores)
t.alerts.alert("Applying GP Smoothing")
t.apply_gp_smoothing()
t.save_outputs()
t.alerts.alert("Done with GP Smoothing")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=str(STEP_IDS['CovariateSelection']) +
f"_{args.outcome}")
logger.info("Initiating Covariate Selection")
t = CovariateSelection(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores,
outcome=args.outcome)
t.launch_covariate_selection()
logger.info(f"Finished with Covariate Selection for {args.outcome}")
t.alerts.alert(f"Finished with covariate selection for {args.outcome}")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['LinearModelBuilds'])
logger.info("Initiating linear model builds")
t = LinearModelBuilds(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores)
t.launch_linear_model_builds()
t.save_response_list()
t.save_outputs()
logger.info("Finished with Linear Model Builds")
t.alerts.alert("Finished with linear model builds")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['Diagnostics'])
logger.info("Initiating model diagnostics.")
t = Diagnostics(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores)
t.alerts.alert("Making diagnostics for modeler")
t.get_table_diagnostics()
t.save_tables()
t.get_plot_diagnostics()
t.alerts.alert("Done making diagnostics for modeler")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['GenerateKnockouts'])
logger.info("Initiating the knockout generation.")
t = ModelTask(model_version_id=args.model_version_id,
db_connection=args.db_connection,
old_covariates_mvid=args.old_covariates_mvid,
debug_mode=args.debug_mode,
cores=args.cores,
step_id=STEP_IDS['GenerateKnockouts'],
make_inputs=False,
make_ko=True)
t.alerts.alert("Done creating knockouts.")
logger.info("Finished with knockout creation.")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['WriteResults'])
logger.info("Initiating writing results.")
t = WriteResults(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores)
t.database_wipe()
t.read_draws()
t.aggregate_draws()
t.get_full_envelope()
t.write_submodel_means()
t.write_model_mean()
t.create_global_table()
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['Email'])
logger.info("Initiating modeler email.")
t = Email(
model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores
)
t.alerts.alert("Creating the email to send to modelers")
t.create_email_body()
t.create_email_text()
t.send_email()
t.alerts.alert("Check your email! :)")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['ReadSpacetimeModels'])
logger.info("Initiating reading spacetime models.")
t = ReadModels(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores,
step_id=STEP_IDS['ReadSpacetimeModels'])
t.alerts.alert("Reading spacetime models")
t.read_models(input_json_path=t.model_paths.JSON_FILES['spacetime'],
output_object_name='st_models_linear')
t.pickled_outputs['st_models_linear'].delete_mixed_model_parameters()
t.save_outputs()
t.alerts.alert("Done reading spacetime models")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['LinearPV'])
logger.info("Initiating Linear Model PV.")
t = PV(model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores,
step_id=STEP_IDS['LinearPV'])
t.alerts.alert(
"Calculating submodel predictive validity for linear models.")
t.calculate_submodel_pv(input_object_name='linear_models_linear',
output_object_prefix='linear_models')
t.save_outputs()
t.alerts.alert(
"Done calculating submodel predictive validity for linear models.")
def main():
args = get_step_args()
setup_logging(model_version_id=args.model_version_id,
step_id=STEP_IDS['EnsemblePredictions'])
ne.set_num_threads(1)
logger.info("Initiating calculate coverage.")
t = EnsemblePredictions(
model_version_id=args.model_version_id,
db_connection=args.db_connection,
debug_mode=args.debug_mode,
old_covariates_mvid=args.old_covariates_mvid,
cores=args.cores
)
t.alerts.alert("Ensemble-ing all the predictions!")
t.calculate_oos_coverage()
t.calculate_is_coverage()
t.write_pv()
t.add_draws_to_df()
t.write_draws()
t.save_outputs()
t.alerts.alert("Done with ensemble-ing the predictions.")
def main():
args = get_args()
model_version_id = args.model_version_id
db_connection = args.db_connection
old_covariates_mvid = args.old_covariates_mvid
debug_mode = args.debug_mode
additional_resources = args.step_resources
additional_resources = json.loads(additional_resources.replace('^', '"'))
branch = log_branch(model_version_id, db_connection)
log_directory = setup_logging(model_version_id, step_id=None)
alerts = ModelerAlert(model_version_id=model_version_id,
db_connection=db_connection)
alerts.alert(f"This model is running on branch {branch}")
alerts.alert(f"You can find your logs here {log_directory}")
wf_0 = CODEmWorkflow(name=f'cod_{model_version_id}_inputs',
description=f'cod_{model_version_id}_cov_ko',
resume=True,
reset_running_jobs=True)
wf_1 = CODEmWorkflow(name=f'cod_{model_version_id}_inputs',
description=f'cod_{model_version_id}_cov_ko',
resume=True,
reset_running_jobs=True)
wf_2 = CODEmWorkflow(name=f'cod_{model_version_id}_ensemble',
description=f'cod_{model_version_id}_ensemble',
resume=True,
reset_running_jobs=True)
step_generator = StepTaskGenerator(
model_version_id=model_version_id,
db_connection=db_connection,
old_covariates_mvid=old_covariates_mvid,
debug_mode=debug_mode,
additional_resources=additional_resources)
model_parameters = get_model_parameters(model_version_id=model_version_id,
db_connection=db_connection,
update=True)
paths = ModelPaths(model_version_id=model_version_id,
acause=model_parameters['acause'])
old_model_version = ModelPaths(model_version_id=old_covariates_mvid,
acause=model_parameters['acause'])
logger.info(f"Saving the job parameters to {paths.JOB_METADATA}.")
if os.path.isfile(paths.JOB_METADATA):
with open(paths.JOB_METADATA, 'r') as json_file:
logger.info("Reading inputs json.")
inputs_info = json.load(json_file)
inputs_info.update(inspect_parameters(parameters=model_parameters))
logger.info(f"{inputs_info}")
else:
inputs_info = inspect_parameters(parameters=model_parameters)
with open(paths.JOB_METADATA, 'w') as outfile:
json.dump(inputs_info, outfile)
input_data = step_generator.generate(step_id=STEP_IDS['InputData'],
inputs_info=inputs_info)
wf_0.add_task(input_data)
try:
logger.info("Running input data workflow.")
exit_status = wf_0.run()
if exit_status:
logger.info(
f"Input data workflow returned exit status {exit_status}")
alerts.alert(
"The input data workflow failed. Please submit a ticket!")
change_model_status(model_version_id=model_version_id,
status=7,
db_connection=db_connection)
sys.exit(exit_status)
else:
logger.info("Input data workflow finished successfully.")
alerts.alert("Input data workflow finished successfully.")
except WorkflowAlreadyComplete:
logger.info("Workflow already complete for input data. Skipping.")
pass
logger.info("Reading job metadata.")
with open(paths.JOB_METADATA, 'r') as json_file:
inputs_info = json.load(json_file)
generate_knockouts = step_generator.generate(
step_id=STEP_IDS['GenerateKnockouts'],
inputs_info=inputs_info,
resource_scales={
'm_mem_free': 0.5,
'max_runtime_seconds': 0.1
})
wf_1.add_task(generate_knockouts)
if old_covariates_mvid:
logger.info(f'Using {old_covariates_mvid} for old covariates.')
alerts.alert(f"Skipping: using {old_covariates_mvid}s "
f"covariates for this models covariates instead")
subprocess.call('cp ' + old_model_version.COVARIATE_FILES['ln_rate'] +
' ' + paths.COVARIATE_FILES['ln_rate'],
shell=True)
subprocess.call('cp ' + old_model_version.COVARIATE_FILES['lt_cf'] +
' ' + paths.COVARIATE_FILES['lt_cf'],
shell=True)
else:
logger.info("Need to run covariate selection.")
covariate_selection_tasks = []
for outcome in ['ln_rate', 'lt_cf']:
covariate_selection_tasks.append(
step_generator.generate(step_id=STEP_IDS['CovariateSelection'],
inputs_info=inputs_info,
additional_args={'outcome': outcome},
resource_scales={
'm_mem_free': 1,
'max_runtime_seconds': 1
}))
wf_1.add_tasks(covariate_selection_tasks)
try:
logger.info("Running covariate selection + knockouts workflow.")
exit_status = wf_1.run()
if exit_status:
logger.info(
f"Covariate selection + knockouts returned exit status {exit_status}"
)
alerts.alert(
"The covariate selection workflow failed. Please submit a ticket!"
)
change_model_status(model_version_id=model_version_id,
status=7,
db_connection=db_connection)
sys.exit(exit_status)
else:
logger.info(
"The covariate selection + knockouts workflow was successful.")
alerts.alert(
"The covariate selection + knockouts workflow was successful.")
except WorkflowAlreadyComplete:
logger.info(
"Workflow already complete for covariate selection + knockouts. Skipping."
)
pass
if (os.path.isfile(paths.COVARIATE_FILES_NO_SELECT['ln_rate'])) and \
(os.path.isfile(paths.COVARIATE_FILES_NO_SELECT['lt_cf'])):
logger.info(
"No covariates selected for either ln_rate or lt_cf. Please"
"try different covariates.")
alerts.alert("No covariates were selected for this model. "
"Please relaunch the model with different covariates!")
raise RuntimeError("There were no covariates selected at all.")
logger.info("Updating job metadata parameters.")
with open(paths.JOB_METADATA, 'r') as json_file:
logger.info("Reading inputs after covariate selection json.")
inputs_info = json.load(json_file)
logger.info(f"{inputs_info}")
inputs_info.update(inspect_submodels(paths))
with open(paths.JOB_METADATA, 'w') as outfile:
logger.info("Writing inputs after covariate selection json.")
logger.info(f"{inputs_info}")
json.dump(inputs_info, outfile)
logger.info("Reading job metadata.")
with open(paths.JOB_METADATA, 'r') as json_file:
inputs_info = json.load(json_file)
linear_model_builds = step_generator.generate(
step_id=STEP_IDS['LinearModelBuilds'],
inputs_info=inputs_info,
resource_scales={
'm_mem_free': 1,
'max_runtime_seconds': 1
})
read_linear_models = step_generator.generate(
step_id=STEP_IDS['ReadLinearModels'],
inputs_info=inputs_info,
upstream_tasks=[linear_model_builds],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
linear_pv = step_generator.generate(step_id=STEP_IDS['LinearPV'],
inputs_info=inputs_info,
upstream_tasks=[read_linear_models],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
read_spacetime_models = step_generator.generate(
step_id=STEP_IDS['ReadSpacetimeModels'],
inputs_info=inputs_info,
upstream_tasks=[linear_model_builds],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
apply_spacetime_smoothing = step_generator.generate(
step_id=STEP_IDS['ApplySpacetimeSmoothing'],
inputs_info=inputs_info,
upstream_tasks=[read_spacetime_models],
resource_scales={
'm_mem_free': 1,
'max_runtime_seconds': 0.5
})
apply_gp_smoothing = step_generator.generate(
step_id=STEP_IDS['ApplyGPSmoothing'],
inputs_info=inputs_info,
upstream_tasks=[apply_spacetime_smoothing],
resource_scales={
'm_mem_free': 1,
'max_runtime_seconds': 0.5
})
spacetime_pv = step_generator.generate(step_id=STEP_IDS['SpacetimePV'],
inputs_info=inputs_info,
upstream_tasks=[apply_gp_smoothing],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
optimal_psi = step_generator.generate(
step_id=STEP_IDS['OptimalPSI'],
inputs_info=inputs_info,
upstream_tasks=[linear_pv, spacetime_pv],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
linear_draws = step_generator.generate(step_id=STEP_IDS['LinearDraws'],
inputs_info=inputs_info,
upstream_tasks=[optimal_psi],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.5
})
gpr_draws = step_generator.generate(step_id=STEP_IDS['GPRDraws'],
inputs_info=inputs_info,
upstream_tasks=[optimal_psi],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.5
})
ensemble_predictions = step_generator.generate(
step_id=STEP_IDS['EnsemblePredictions'],
inputs_info=inputs_info,
upstream_tasks=[optimal_psi, linear_draws, gpr_draws],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
write_results = step_generator.generate(
step_id=STEP_IDS['WriteResults'],
inputs_info=inputs_info,
upstream_tasks=[ensemble_predictions],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
diagnostics = step_generator.generate(
step_id=STEP_IDS['Diagnostics'],
inputs_info=inputs_info,
upstream_tasks=[ensemble_predictions],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
email = step_generator.generate(
step_id=STEP_IDS['Email'],
inputs_info=inputs_info,
upstream_tasks=[write_results, diagnostics],
resource_scales={
'm_mem_free': 0.3,
'max_runtime_seconds': 0.25
})
wf_2.add_tasks([
linear_model_builds, read_linear_models, linear_pv, linear_draws,
read_spacetime_models, apply_spacetime_smoothing, apply_gp_smoothing,
spacetime_pv, gpr_draws, optimal_psi, ensemble_predictions,
write_results, diagnostics, email
])
alerts.alert("Initiating the ensemble workflow.")
logger.info("Running the rest of the ensemble workflow.")
exit_status = wf_2.run()
if exit_status:
logger.info(
f"The ensemble workflow failed, returning exit status {exit_status}"
)
alerts.alert("The ensemble workflow failed. Please submit a ticket!")
change_model_status(model_version_id=model_version_id,
status=7,
db_connection=db_connection)
sys.exit(exit_status)
else:
logger.info("The ensemble workflow successfully completed.")
alerts.alert("The model has successful completed!!")
logger.info(
f"Changing model version {model_version_id} to complete in the database."
)
change_model_status(model_version_id=model_version_id,
status=1,
db_connection=db_connection)
logger.info("Cleaning up files...")
cleanup_files(model_version_id=model_version_id,
acause=model_parameters['acause'])
logger.info("Finished.")