def __init__(
self,
config,
db_engine,
model_storage_class=FSModelStorageEngine,
project_path=None,
replace=True,
cleanup=False,
cleanup_timeout=None,
):
self._check_config_version(config)
self.config = config
if isinstance(db_engine, Engine):
logging.warning(
'Raw, unserializable SQLAlchemy engine passed. URL will be used, other options may be lost in multi-process environments'
)
self.db_engine = create_engine(db_engine.url)
else:
self.db_engine = db_engine
if model_storage_class:
self.model_storage_engine = model_storage_class(
project_path=project_path)
self.matrix_store_class = CSVMatrixStore # can't be configurable until Architect obeys
self.project_path = project_path
self.replace = replace
upgrade_db(db_engine=self.db_engine)
self.features_schema_name = 'features'
if project_path:
self.matrices_directory = os.path.join(self.project_path,
'matrices')
if not os.path.exists(self.matrices_directory):
os.makedirs(self.matrices_directory)
self.experiment_hash = save_experiment_and_get_hash(
self.config, self.db_engine)
self.labels_table_name = 'labels_{}'.format(self.experiment_hash)
self.initialize_components()
self.cleanup = cleanup
if self.cleanup:
logging.info(
'cleanup is set to True, so intermediate tables (labels and states) will be removed after matrix creation'
)
else:
logging.info(
'cleanup is set to False, so intermediate tables (labels and states) will not be removed after matrix creation'
)
self.cleanup_timeout = (self.cleanup_timeout if cleanup_timeout is None
else cleanup_timeout)
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,
):
self._check_config_version(config)
self.config = 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
self.replace = replace
self.save_predictions = save_predictions
self.db_engine = db_engine
upgrade_db(db_engine=self.db_engine)
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.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,
):
self._check_config_version(config)
self.config = config
if isinstance(db_engine, Engine):
logging.warning(
"Raw, unserializable SQLAlchemy engine passed. "
"URL will be used, other options may be lost in multi-process environments"
)
self.db_engine = create_engine(db_engine.url)
else:
self.db_engine = db_engine
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
upgrade_db(db_engine=self.db_engine)
self.features_schema_name = "features"
self.experiment_hash = save_experiment_and_get_hash(
self.config, self.db_engine)
self.labels_table_name = "labels_{}".format(self.experiment_hash)
self.initialize_components()
self.cleanup = cleanup
if self.cleanup:
logging.info(
"cleanup is set to True, so intermediate tables (labels and states) "
"will be removed after matrix creation")
else:
logging.info(
"cleanup is set to False, so intermediate tables (labels and states) "
"will not be removed after matrix creation")
self.cleanup_timeout = (self.cleanup_timeout if cleanup_timeout is None
else cleanup_timeout)
def upgrade(self, args):
"""Upgrade triage results database"""
upgrade_db(args.dbfile)
def upgrade(self, args):
"""Upgrade triage results database"""
upgrade_db(revision=args.revision, dburl=self.root.db_url)
def predict_forward_with_existed_model(db_engine, project_path, model_id,
as_of_date):
"""Predict forward given model_id and as_of_date and store the prediction in database
Args:
db_engine (sqlalchemy.db.engine)
project_storage (catwalk.storage.ProjectStorage)
model_id (int) The id of a given model in the database
as_of_date (string) a date string like "YYYY-MM-DD"
"""
logger.spam("In PREDICT LIST................")
upgrade_db(db_engine=db_engine)
project_storage = ProjectStorage(project_path)
matrix_storage_engine = project_storage.matrix_storage_engine()
# 1. Get feature and cohort config from database
(train_matrix_uuid,
matrix_metadata) = train_matrix_info_from_model_id(db_engine, model_id)
experiment_config = experiment_config_from_model_id(db_engine, model_id)
# 2. Generate cohort
cohort_table_name = f"triage_production.cohort_{experiment_config['cohort_config']['name']}"
cohort_table_generator = EntityDateTableGenerator(
db_engine=db_engine,
query=experiment_config['cohort_config']['query'],
entity_date_table_name=cohort_table_name)
cohort_table_generator.generate_entity_date_table(
as_of_dates=[dt_from_str(as_of_date)])
# 3. Generate feature aggregations
feature_generator = FeatureGenerator(
db_engine=db_engine,
features_schema_name="triage_production",
feature_start_time=experiment_config['temporal_config']
['feature_start_time'],
)
collate_aggregations = feature_generator.aggregations(
feature_aggregation_config=experiment_config['feature_aggregations'],
feature_dates=[as_of_date],
state_table=cohort_table_name)
feature_generator.process_table_tasks(
feature_generator.generate_all_table_tasks(collate_aggregations,
task_type='aggregation'))
# 4. Reconstruct feature disctionary from feature_names and generate imputation
reconstructed_feature_dict = FeatureGroup()
imputation_table_tasks = OrderedDict()
for aggregation in collate_aggregations:
feature_group, feature_names = get_feature_names(
aggregation, matrix_metadata)
reconstructed_feature_dict[feature_group] = feature_names
# Make sure that the features imputed in training should also be imputed in production
features_imputed_in_train = get_feature_needs_imputation_in_train(
aggregation, feature_names)
features_imputed_in_production = get_feature_needs_imputation_in_production(
aggregation, db_engine)
total_impute_cols = set(features_imputed_in_production) | set(
features_imputed_in_train)
total_nonimpute_cols = set(f for f in set(feature_names)
if '_imp' not in f) - total_impute_cols
task_generator = feature_generator._generate_imp_table_tasks_for
imputation_table_tasks.update(
task_generator(aggregation,
impute_cols=list(total_impute_cols),
nonimpute_cols=list(total_nonimpute_cols)))
feature_generator.process_table_tasks(imputation_table_tasks)
# 5. Build matrix
db_config = {
"features_schema_name": "triage_production",
"labels_schema_name": "public",
"cohort_table_name": cohort_table_name,
}
matrix_builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
engine=db_engine,
experiment_hash=None,
replace=True,
)
feature_start_time = experiment_config['temporal_config'][
'feature_start_time']
label_name = experiment_config['label_config']['name']
label_type = 'binary'
cohort_name = experiment_config['cohort_config']['name']
user_metadata = experiment_config['user_metadata']
# Use timechop to get the time definition for production
temporal_config = experiment_config["temporal_config"]
temporal_config.update(
temporal_params_from_matrix_metadata(db_engine, model_id))
timechopper = Timechop(**temporal_config)
prod_definitions = timechopper.define_test_matrices(
train_test_split_time=dt_from_str(as_of_date),
test_duration=temporal_config['test_durations'][0],
test_label_timespan=temporal_config['test_label_timespans'][0])
matrix_metadata = Planner.make_metadata(
prod_definitions[-1],
reconstructed_feature_dict,
label_name,
label_type,
cohort_name,
'production',
feature_start_time,
user_metadata,
)
matrix_metadata['matrix_id'] = str(
as_of_date) + f'_model_id_{model_id}' + '_risklist'
matrix_uuid = filename_friendly_hash(matrix_metadata)
matrix_builder.build_matrix(
as_of_times=[as_of_date],
label_name=label_name,
label_type=label_type,
feature_dictionary=reconstructed_feature_dict,
matrix_metadata=matrix_metadata,
matrix_uuid=matrix_uuid,
matrix_type="production",
)
# 6. Predict the risk score for production
predictor = Predictor(
model_storage_engine=project_storage.model_storage_engine(),
db_engine=db_engine,
rank_order='best')
predictor.predict(
model_id=model_id,
matrix_store=matrix_storage_engine.get_store(matrix_uuid),
misc_db_parameters={},
train_matrix_columns=matrix_storage_engine.get_store(
train_matrix_uuid).columns())
def __init__(self, db_engine, project_path, model_group_id):
self.retrain_hash = None
self.db_engine = db_engine
upgrade_db(db_engine=self.db_engine)
self.project_storage = ProjectStorage(project_path)
self.model_group_id = model_group_id
self.model_group_info = get_model_group_info(self.db_engine,
self.model_group_id)
self.matrix_storage_engine = self.project_storage.matrix_storage_engine(
)
self.triage_run_id, self.experiment_config = experiment_config_from_model_group_id(
self.db_engine, self.model_group_id)
# This feels like it needs some refactoring since in some edge cases at least the test matrix temporal parameters
# might differ across models in the mdoel group (the training ones shouldn't), but this should probably work for
# the vast majorty of use cases...
self.experiment_config['temporal_config'].update(
temporal_params_from_matrix_metadata(
self.db_engine, self.model_group_info['model_id_last_split']))
# Since "testing" here is predicting forward to a single new date, the test_duration should always be '0day'
# (regardless of what it may have been before)
self.experiment_config['temporal_config']['test_durations'] = ['0day']
# These lists should now only contain one item (the value actually used for the last model in this group)
self.training_label_timespan = self.experiment_config[
'temporal_config']['training_label_timespans'][0]
self.test_label_timespan = self.experiment_config['temporal_config'][
'test_label_timespans'][0]
self.test_duration = self.experiment_config['temporal_config'][
'test_durations'][0]
self.feature_start_time = self.experiment_config['temporal_config'][
'feature_start_time']
self.label_name = self.experiment_config['label_config']['name']
self.cohort_name = self.experiment_config['cohort_config']['name']
self.user_metadata = self.experiment_config['user_metadata']
self.feature_dictionary_creator = FeatureDictionaryCreator(
features_schema_name='triage_production', db_engine=self.db_engine)
self.label_generator = LabelGenerator(
label_name=self.experiment_config['label_config'].get(
"name", None),
query=self.experiment_config['label_config']["query"],
replace=True,
db_engine=self.db_engine,
)
self.labels_table_name = "labels_{}_{}_production".format(
self.experiment_config['label_config'].get('name', 'default'),
filename_friendly_hash(
self.experiment_config['label_config']['query']))
self.feature_generator = FeatureGenerator(
db_engine=self.db_engine,
features_schema_name="triage_production",
feature_start_time=self.feature_start_time,
)
self.model_trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=ModelStorageEngine(self.project_storage),
db_engine=self.db_engine,
replace=True,
run_id=self.triage_run_id,
)
