def initialize_tracking_and_get_run_id(experiment_hash, experiment_class_path,
random_seed, experiment_kwargs,
db_engine):
"""Create a row in the TriageRun table with some initial info and return the created run_id
Args:
experiment_hash (str) An experiment hash that exists in the experiments table
experiment_class_path (str) The name of the experiment subclass used
random_seed (int) Random seed used to run the experiment
experiment_kwargs (dict) Any runtime Experiment keyword arguments that should be saved
db_engine (sqlalchemy.engine)
"""
# Any experiment kwargs that are types (e.g. MatrixStorageClass) can't
# be serialized, so just use the class name if so
cleaned_experiment_kwargs = {
k: (classpath(v) if isinstance(v, type) else v)
for k, v in experiment_kwargs.items()
}
run = TriageRun(
start_time=datetime.datetime.now(),
git_hash=infer_git_hash(),
triage_version=infer_triage_version(),
python_version=infer_python_version(),
run_type="experiment",
run_hash=experiment_hash,
last_updated_time=datetime.datetime.now(),
current_status=TriageRunStatus.started,
installed_libraries=infer_installed_libraries(),
platform=platform.platform(),
os_user=getpass.getuser(),
working_directory=os.getcwd(),
ec2_instance_type=infer_ec2_instance_type(),
log_location=infer_log_location(),
experiment_class_path=experiment_class_path,
random_seed=random_seed,
experiment_kwargs=cleaned_experiment_kwargs,
)
run_id = None
with scoped_session(db_engine) as session:
session.add(run)
session.commit()
run_id = run.run_id
if not run_id:
raise ValueError("Failed to retrieve run_id from saved row")
return run_id
def __init__(
self,
config,
db_engine,
project_path=None,
matrix_storage_class=CSVMatrixStore,
replace=True,
cleanup=False,
cleanup_timeout=None,
materialize_subquery_fromobjs=True,
features_ignore_cohort=False,
profile=False,
save_predictions=True,
skip_validation=False,
partial_run=False,
):
# For a partial run, skip validation and avoid cleaning up
# we'll also skip filling default config values below
if partial_run:
cleanup = False
skip_validation = True
experiment_kwargs = bind_kwargs(
self.__class__, **{
key: value
for (key, value) in locals().items()
if key not in {'db_engine', 'config', 'self'}
})
self._check_config_version(config)
self.config = config
self.config['random_seed'] = self.config.get('random_seed',
random.randint(1, 1e7))
random.seed(self.config['random_seed'])
self.project_storage = ProjectStorage(project_path)
self.model_storage_engine = ModelStorageEngine(self.project_storage)
self.matrix_storage_engine = MatrixStorageEngine(
self.project_storage, matrix_storage_class)
self.project_path = project_path
self.replace = replace
self.save_predictions = save_predictions
self.skip_validation = skip_validation
self.db_engine = db_engine
results_schema.upgrade_if_clean(dburl=self.db_engine.url)
self.features_schema_name = "features"
self.materialize_subquery_fromobjs = materialize_subquery_fromobjs
self.features_ignore_cohort = features_ignore_cohort
# only fill default values for full runs
if not partial_run:
## Defaults to sane values
self.config['temporal_config'] = fill_timechop_config_missing(
self.config, self.db_engine)
## Defaults to all the entities found in the features_aggregation's from_obj
self.config['cohort_config'] = fill_cohort_config_missing(
self.config)
## Defaults to all the feature_aggregation's prefixes
self.config[
'feature_group_definition'] = fill_feature_group_definition(
self.config)
grid_config = fill_model_grid_presets(self.config)
self.config.pop('model_grid_preset', None)
if grid_config is not None:
self.config['grid_config'] = grid_config
###################### RUBICON ######################
self.experiment_hash = save_experiment_and_get_hash(
self.config, self.db_engine)
self.run_id = initialize_tracking_and_get_run_id(
self.experiment_hash,
experiment_class_path=classpath(self.__class__),
experiment_kwargs=experiment_kwargs,
db_engine=self.db_engine)
self.initialize_components()
self.cleanup = cleanup
if self.cleanup:
logging.info(
"cleanup is set to True, so intermediate tables (labels and cohort) "
"will be removed after matrix creation and subset tables will be "
"removed after model training and testing")
else:
logging.info(
"cleanup is set to False, so intermediate tables (labels, cohort, and subsets) "
"will not be removed")
self.cleanup_timeout = (self.cleanup_timeout if cleanup_timeout is None
else cleanup_timeout)
self.profile = profile
logging.info("Generate profiling stats? (profile option): %s",
self.profile)
def __init__(
self,
config,
db_engine,
project_path=None,
matrix_storage_class=CSVMatrixStore,
replace=True,
cleanup=False,
cleanup_timeout=None,
materialize_subquery_fromobjs=True,
features_ignore_cohort=False,
additional_bigtrain_classnames=None,
profile=False,
save_predictions=True,
skip_validation=False,
partial_run=False,
):
# For a partial run, skip validation and avoid cleaning up
# we'll also skip filling default config values below
if partial_run:
cleanup = False
skip_validation = True
experiment_kwargs = bind_kwargs(
self.__class__,
**{
key: value
for (key, value) in locals().items()
if key not in {"db_engine", "config", "self"}
},
)
self._check_config_version(config)
self.config = config
if self.config.get("cohort_config") is not None:
self.config["cohort_config"] = load_query_if_needed(
self.config["cohort_config"]
)
if self.config.get("label_config") is not None:
self.config["label_config"] = load_query_if_needed(
self.config["label_config"]
)
self.project_storage = ProjectStorage(project_path)
self.model_storage_engine = ModelStorageEngine(self.project_storage)
self.matrix_storage_engine = MatrixStorageEngine(
self.project_storage, matrix_storage_class
)
self.project_path = project_path
logger.verbose(
f"Matrices and trained models will be saved in {self.project_path}"
)
self.replace = replace
if self.replace:
logger.notice(
f"Replace flag is set to true. Matrices, models, "
"evaluations and predictions (if they exist) will be replaced"
)
self.save_predictions = save_predictions
if not self.save_predictions:
logger.notice(
f"Save predictions flag is set to false. "
"Individual predictions won't be stored in the predictions "
"table. This will decrease both the running time "
"of an experiment and also decrease the space needed in the db"
)
self.skip_validation = skip_validation
if self.skip_validation:
logger.notice(
f"Warning: Skip validation flag is set to true. "
"The experiment config file specified won't be validated. "
"This will reduce (a little) the running time of the experiment, "
"but has some potential risks, e.g. the experiment could fail"
"after some time due to some misconfiguration. Proceed with care."
)
self.db_engine = db_engine
results_schema.upgrade_if_clean(dburl=self.db_engine.url)
self.features_schema_name = "features"
self.materialize_subquery_fromobjs = materialize_subquery_fromobjs
if not self.materialize_subquery_fromobjs:
logger.notice(
"Materialize from_objs is set to false. "
"The from_objs will be calculated on the fly every time."
)
self.features_ignore_cohort = features_ignore_cohort
if self.features_ignore_cohort:
logger.notice(
"Features will be calculated for all the entities "
"(i.e. ignoring cohort) this setting will have the effect "
"that more db space will be used, but potentially could save "
"time if you are running several similar experiments with "
"different cohorts."
)
self.additional_bigtrain_classnames = additional_bigtrain_classnames
# only fill default values for full runs
if not partial_run:
## Defaults to sane values
self.config["temporal_config"] = fill_timechop_config_missing(
self.config, self.db_engine
)
## Defaults to all the entities found in the features_aggregation's from_obj
self.config["cohort_config"] = fill_cohort_config_missing(self.config)
## Defaults to all the feature_aggregation's prefixes
self.config["feature_group_definition"] = fill_feature_group_definition(
self.config
)
grid_config = fill_model_grid_presets(self.config)
self.config.pop("model_grid_preset", None)
if grid_config is not None:
self.config["grid_config"] = grid_config
if not self.config.get("random_seed", None):
logger.notice(
"Random seed not specified. A random seed will be provided. "
"This could have interesting side effects, "
"e.g. new models per model group are trained, "
"tested and evaluated everytime that you run this experiment configuration"
)
self.random_seed = self.config.pop("random_seed", random.randint(1, 1e7))
logger.verbose(
f"Using random seed [{self.random_seed}] for running the experiment"
)
random.seed(self.random_seed)
###################### RUBICON ######################
self.experiment_hash = save_experiment_and_get_hash(self.config, self.db_engine)
logger.debug(f"Experiment hash [{self.experiment_hash}] assigned")
self.run_id = initialize_tracking_and_get_run_id(
self.experiment_hash,
experiment_class_path=classpath(self.__class__),
random_seed=self.random_seed,
experiment_kwargs=experiment_kwargs,
db_engine=self.db_engine,
)
logger.debug(f"Experiment run id [{self.run_id}] assigned")
self.initialize_components()
self.cleanup = cleanup
if self.cleanup:
logger.notice(
"Cleanup is set to true, so intermediate tables (labels and cohort) "
"will be removed after matrix creation and subset tables will be "
"removed after model training and testing"
)
self.cleanup_timeout = (
self.cleanup_timeout if cleanup_timeout is None else cleanup_timeout
)
self.profile = profile
if self.profile:
logger.spam("Profiling will be stored using cProfile")
def __init__(
self,
config,
db_engine,
project_path=None,
matrix_storage_class=CSVMatrixStore,
replace=True,
cleanup=False,
cleanup_timeout=None,
materialize_subquery_fromobjs=True,
features_ignore_cohort=False,
profile=False,
save_predictions=True,
skip_validation=False,
):
experiment_kwargs = bind_kwargs(
self.__class__, **{
key: value
for (key, value) in locals().items()
if key not in {'db_engine', 'config', 'self'}
})
self._check_config_version(config)
self.config = config
random.seed(config['random_seed'])
self.project_storage = ProjectStorage(project_path)
self.model_storage_engine = ModelStorageEngine(self.project_storage)
self.matrix_storage_engine = MatrixStorageEngine(
self.project_storage, matrix_storage_class)
self.project_path = project_path
self.replace = replace
self.save_predictions = save_predictions
self.skip_validation = skip_validation
self.db_engine = db_engine
results_schema.upgrade_if_clean(dburl=self.db_engine.url)
self.features_schema_name = "features"
self.materialize_subquery_fromobjs = materialize_subquery_fromobjs
self.features_ignore_cohort = features_ignore_cohort
self.experiment_hash = save_experiment_and_get_hash(
self.config, self.db_engine)
self.run_id = initialize_tracking_and_get_run_id(
self.experiment_hash,
experiment_class_path=classpath(self.__class__),
experiment_kwargs=experiment_kwargs,
db_engine=self.db_engine)
self.initialize_components()
self.cleanup = cleanup
if self.cleanup:
logging.info(
"cleanup is set to True, so intermediate tables (labels and cohort) "
"will be removed after matrix creation and subset tables will be "
"removed after model training and testing")
else:
logging.info(
"cleanup is set to False, so intermediate tables (labels, cohort, and subsets) "
"will not be removed")
self.cleanup_timeout = (self.cleanup_timeout if cleanup_timeout is None
else cleanup_timeout)
self.profile = profile
logging.info("Generate profiling stats? (profile option): %s",
self.profile)
def retrain(self, prediction_date):
"""Retrain a model by going back one split from prediction_date, so the as_of_date for training would be (prediction_date - training_label_timespan)
Args:
prediction_date(str)
"""
# Retrain config and hash
retrain_config = {
"model_group_id": self.model_group_id,
"prediction_date": prediction_date,
"test_label_timespan": self.test_label_timespan,
"test_duration": self.test_duration,
}
self.retrain_hash = save_retrain_and_get_hash(retrain_config,
self.db_engine)
with get_for_update(self.db_engine, Retrain,
self.retrain_hash) as retrain:
retrain.prediction_date = prediction_date
# Timechop
prediction_date = dt_from_str(prediction_date)
temporal_config = self.get_temporal_config_for_retrain(prediction_date)
timechopper = Timechop(**temporal_config)
chops = timechopper.chop_time()
assert len(chops) == 1
chops_train_matrix = chops[0]['train_matrix']
as_of_date = datetime.strftime(chops_train_matrix['last_as_of_time'],
"%Y-%m-%d")
retrain_definition = {
'first_as_of_time':
chops_train_matrix['first_as_of_time'],
'last_as_of_time':
chops_train_matrix['last_as_of_time'],
'matrix_info_end_time':
chops_train_matrix['matrix_info_end_time'],
'as_of_times': [as_of_date],
'training_label_timespan':
chops_train_matrix['training_label_timespan'],
'max_training_history':
chops_train_matrix['max_training_history'],
'training_as_of_date_frequency':
chops_train_matrix['training_as_of_date_frequency'],
}
# Set ExperimentRun
run = TriageRun(
start_time=datetime.now(),
git_hash=infer_git_hash(),
triage_version=infer_triage_version(),
python_version=infer_python_version(),
run_type="retrain",
run_hash=self.retrain_hash,
last_updated_time=datetime.now(),
current_status=TriageRunStatus.started,
installed_libraries=infer_installed_libraries(),
platform=platform.platform(),
os_user=getpass.getuser(),
working_directory=os.getcwd(),
ec2_instance_type=infer_ec2_instance_type(),
log_location=infer_log_location(),
experiment_class_path=classpath(self.__class__),
random_seed=retrieve_experiment_seed_from_run_id(
self.db_engine, self.triage_run_id),
)
run_id = None
with scoped_session(self.db_engine) as session:
session.add(run)
session.commit()
run_id = run.run_id
if not run_id:
raise ValueError("Failed to retrieve run_id from saved row")
# set ModelTrainer's run_id and experiment_hash for Retrain run
self.model_trainer.run_id = run_id
self.model_trainer.experiment_hash = self.retrain_hash
# 1. Generate all labels
self.generate_all_labels(as_of_date)
record_labels_table_name(run_id, self.db_engine,
self.labels_table_name)
# 2. Generate cohort
cohort_table_name = f"triage_production.cohort_{self.experiment_config['cohort_config']['name']}_retrain"
self.generate_entity_date_table(as_of_date, cohort_table_name)
record_cohort_table_name(run_id, self.db_engine, cohort_table_name)
# 3. Generate feature aggregations
collate_aggregations = self.get_collate_aggregations(
as_of_date, cohort_table_name)
feature_aggregation_table_tasks = self.feature_generator.generate_all_table_tasks(
collate_aggregations, task_type='aggregation')
self.feature_generator.process_table_tasks(
feature_aggregation_table_tasks)
# 4. Reconstruct feature disctionary from feature_names and generate imputation
reconstructed_feature_dict, imputation_table_tasks = self.get_feature_dict_and_imputation_task(
collate_aggregations,
self.model_group_info['model_id_last_split'],
)
feature_group_creator = FeatureGroupCreator(
self.experiment_config['feature_group_definition'])
feature_group_mixer = FeatureGroupMixer(["all"])
feature_group_dict = feature_group_mixer.generate(
feature_group_creator.subsets(reconstructed_feature_dict))[0]
self.feature_generator.process_table_tasks(imputation_table_tasks)
# 5. Build new matrix
db_config = {
"features_schema_name": "triage_production",
"labels_schema_name": "public",
"cohort_table_name": cohort_table_name,
"labels_table_name": self.labels_table_name,
}
record_matrix_building_started(run_id, self.db_engine)
matrix_builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=self.matrix_storage_engine,
engine=self.db_engine,
experiment_hash=None,
replace=True,
)
new_matrix_metadata = Planner.make_metadata(
matrix_definition=retrain_definition,
feature_dictionary=feature_group_dict,
label_name=self.label_name,
label_type='binary',
cohort_name=self.cohort_name,
matrix_type='train',
feature_start_time=dt_from_str(self.feature_start_time),
user_metadata=self.user_metadata,
)
new_matrix_metadata['matrix_id'] = "_".join([
self.label_name,
'binary',
str(as_of_date),
'retrain',
])
matrix_uuid = filename_friendly_hash(new_matrix_metadata)
matrix_builder.build_matrix(
as_of_times=[as_of_date],
label_name=self.label_name,
label_type='binary',
feature_dictionary=feature_group_dict,
matrix_metadata=new_matrix_metadata,
matrix_uuid=matrix_uuid,
matrix_type="train",
)
retrain_model_comment = 'retrain_' + str(datetime.now())
misc_db_parameters = {
'train_end_time': dt_from_str(as_of_date),
'test': False,
'train_matrix_uuid': matrix_uuid,
'training_label_timespan': self.training_label_timespan,
'model_comment': retrain_model_comment,
}
# get the random seed from the last split
last_split_train_matrix_uuid, last_split_matrix_metadata = train_matrix_info_from_model_id(
self.db_engine,
model_id=self.model_group_info['model_id_last_split'])
random_seed = self.model_trainer.get_or_generate_random_seed(
model_group_id=self.model_group_id,
matrix_metadata=last_split_matrix_metadata,
train_matrix_uuid=last_split_train_matrix_uuid)
# create retrain model hash
retrain_model_hash = self.model_trainer._model_hash(
self.matrix_storage_engine.get_store(matrix_uuid).metadata,
class_path=self.model_group_info['model_type'],
parameters=self.model_group_info['hyperparameters'],
random_seed=random_seed,
)
associate_models_with_retrain(self.retrain_hash,
(retrain_model_hash, ), self.db_engine)
record_model_building_started(run_id, self.db_engine)
retrain_model_id = self.model_trainer.process_train_task(
matrix_store=self.matrix_storage_engine.get_store(matrix_uuid),
class_path=self.model_group_info['model_type'],
parameters=self.model_group_info['hyperparameters'],
model_hash=retrain_model_hash,
misc_db_parameters=misc_db_parameters,
random_seed=random_seed,
retrain=True,
model_group_id=self.model_group_id)
self.retrain_model_hash = retrieve_model_hash_from_id(
self.db_engine, retrain_model_id)
self.retrain_matrix_uuid = matrix_uuid
self.retrain_model_id = retrain_model_id
return {
'retrain_model_comment': retrain_model_comment,
'retrain_model_id': retrain_model_id
}
