def test_hdf_matrix(self):
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
ensure_db(engine)
create_schemas(engine=engine,
features_tables=features_tables,
labels=labels,
states=states)
with get_matrix_storage_engine() as matrix_storage_engine:
matrix_storage_engine.matrix_storage_class = HDFMatrixStore
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
engine=engine)
uuid = metta.generate_uuid(self.good_metadata)
builder.build_matrix(
as_of_times=self.good_dates,
label_name='booking',
label_type='binary',
feature_dictionary=self.good_feature_dictionary,
matrix_metadata=self.good_metadata,
matrix_uuid=uuid,
matrix_type='test')
assert len(matrix_storage_engine.get_store(uuid).matrix) == 5
def test_test_matrix(self):
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
ensure_db(engine)
create_schemas(
engine=engine,
features_tables=features_tables,
labels=labels,
states=states,
)
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
experiment_hash=experiment_hash,
engine=engine,
)
uuid = filename_friendly_hash(self.good_metadata)
builder.build_matrix(
as_of_times=self.good_dates,
label_name="booking",
label_type="binary",
feature_dictionary=self.good_feature_dictionary,
matrix_metadata=self.good_metadata,
matrix_uuid=uuid,
matrix_type="test",
)
assert len(matrix_storage_engine.get_store(uuid).design_matrix) == 5
def test_nullcheck(self):
f0_dict = {(r[0], r[1]): r for r in features0_pre}
f1_dict = {(r[0], r[1]): r for r in features1_pre}
features0 = sorted(f0_dict.values(), key=lambda x: (x[1], x[0]))
features1 = sorted(f1_dict.values(), key=lambda x: (x[1], x[0]))
features_tables = [features0, features1]
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
create_schemas(
engine=engine,
features_tables=features_tables,
labels=labels,
states=states,
)
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0),
datetime.datetime(2016, 3, 1, 0, 0),
]
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
experiment_hash=experiment_hash,
engine=engine,
)
feature_dictionary = {
"features0": ["f1", "f2"],
"features1": ["f3", "f4"],
}
matrix_metadata = {
"matrix_id": "hi",
"state": "active",
"label_name": "booking",
"end_time": datetime.datetime(2016, 3, 1, 0, 0),
"feature_start_time": datetime.datetime(2016, 1, 1, 0, 0),
"label_timespan": "1 month",
"test_duration": "1 month",
"indices": ["entity_id", "as_of_date"],
}
uuid = filename_friendly_hash(matrix_metadata)
with self.assertRaises(ValueError):
builder.build_matrix(
as_of_times=dates,
label_name="booking",
label_type="binary",
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type="test",
)
def test_replace_false_rerun(self):
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
ensure_db(engine)
create_schemas(engine=engine,
features_tables=features_tables,
labels=labels,
states=states)
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0),
datetime.datetime(2016, 3, 1, 0, 0)
]
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
engine=engine,
replace=False)
feature_dictionary = {
'features0': ['f1', 'f2'],
'features1': ['f3', 'f4'],
}
matrix_metadata = {
'matrix_id': 'hi',
'state': 'state_one AND state_two',
'label_name': 'booking',
'end_time': datetime.datetime(2016, 3, 1, 0, 0),
'feature_start_time': datetime.datetime(2016, 1, 1, 0, 0),
'label_timespan': '1 month',
'test_duration': '1 month',
'indices': ['entity_id', 'as_of_date'],
}
uuid = metta.generate_uuid(matrix_metadata)
builder.build_matrix(as_of_times=dates,
label_name='booking',
label_type='binary',
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type='test')
assert len(matrix_storage_engine.get_store(uuid).matrix) == 5
# rerun
builder.make_entity_date_table = Mock()
builder.build_matrix(as_of_times=dates,
label_name='booking',
label_type='binary',
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type='test')
assert not builder.make_entity_date_table.called
def test_replace_true_rerun(self):
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
ensure_db(engine)
create_schemas(
engine=engine,
features_tables=features_tables,
labels=labels,
states=states,
)
matrix_metadata = matrix_metadata_creator(state="active",
test_duration="1month",
label_name="booking")
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0),
datetime.datetime(2016, 3, 1, 0, 0),
]
feature_dictionary = {
"features0": ["f1", "f2"],
"features1": ["f3", "f4"]
}
uuid = filename_friendly_hash(matrix_metadata)
build_args = dict(
as_of_times=dates,
label_name="booking",
label_type="binary",
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type="test",
)
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
experiment_hash=experiment_hash,
engine=engine,
replace=True,
)
builder.build_matrix(**build_args)
assert len(
matrix_storage_engine.get_store(uuid).design_matrix) == 5
assert builder.sessionmaker().query(Matrix).get(uuid)
# rerun
builder.build_matrix(**build_args)
assert len(
matrix_storage_engine.get_store(uuid).design_matrix) == 5
assert builder.sessionmaker().query(Matrix).get(uuid)
def test_nullcheck(self):
f0_dict = {(r[0], r[1]): r for r in features0_pre}
f1_dict = {(r[0], r[1]): r for r in features1_pre}
features0 = sorted(f0_dict.values(), key=lambda x: (x[1], x[0]))
features1 = sorted(f1_dict.values(), key=lambda x: (x[1], x[0]))
features_tables = [features0, features1]
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
create_schemas(engine=engine,
features_tables=features_tables,
labels=labels,
states=states)
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0),
datetime.datetime(2016, 3, 1, 0, 0)
]
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
engine=engine)
feature_dictionary = {
'features0': ['f1', 'f2'],
'features1': ['f3', 'f4'],
}
matrix_metadata = {
'matrix_id': 'hi',
'state': 'state_one AND state_two',
'label_name': 'booking',
'end_time': datetime.datetime(2016, 3, 1, 0, 0),
'feature_start_time': datetime.datetime(2016, 1, 1, 0, 0),
'label_timespan': '1 month',
'test_duration': '1 month',
'indices': ['entity_id', 'as_of_date'],
}
uuid = metta.generate_uuid(matrix_metadata)
with self.assertRaises(ValueError):
builder.build_matrix(as_of_times=dates,
label_name='booking',
label_type='binary',
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type='test')
def test_replace_true_rerun(self):
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
ensure_db(engine)
create_schemas(engine=engine,
features_tables=features_tables,
labels=labels,
states=states)
matrix_metadata = matrix_metadata_creator(
state='state_one and state_two', test_duration='1month')
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0),
datetime.datetime(2016, 3, 1, 0, 0)
]
feature_dictionary = {
'features0': ['f1', 'f2'],
'features1': ['f3', 'f4'],
}
uuid = metta.generate_uuid(matrix_metadata)
build_args = dict(as_of_times=dates,
label_name='booking',
label_type='binary',
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type='test')
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
engine=engine,
replace=True)
builder.build_matrix(**build_args)
assert len(matrix_storage_engine.get_store(uuid).matrix) == 5
assert builder.sessionmaker().query(Matrix).get(uuid)
# rerun
builder.build_matrix(**build_args)
assert len(matrix_storage_engine.get_store(uuid).matrix) == 5
assert builder.sessionmaker().query(Matrix).get(uuid)
def test_replace_false_rerun(self):
with testing.postgresql.Postgresql() as postgresql:
# create an engine and generate a table with fake feature data
engine = create_engine(postgresql.url())
ensure_db(engine)
create_schemas(
engine=engine,
features_tables=features_tables,
labels=labels,
states=states,
)
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0),
datetime.datetime(2016, 3, 1, 0, 0),
]
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
experiment_hash=experiment_hash,
engine=engine,
replace=False,
)
feature_dictionary = {
"features0": ["f1", "f2"],
"features1": ["f3", "f4"],
}
matrix_metadata = {
"matrix_id": "hi",
"state": "active",
"label_name": "booking",
"end_time": datetime.datetime(2016, 3, 1, 0, 0),
"feature_start_time": datetime.datetime(2016, 1, 1, 0, 0),
"label_timespan": "1 month",
"test_duration": "1 month",
"indices": ["entity_id", "as_of_date"],
}
uuid = filename_friendly_hash(matrix_metadata)
builder.build_matrix(
as_of_times=dates,
label_name="booking",
label_type="binary",
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type="test",
)
assert len(matrix_storage_engine.get_store(uuid).design_matrix) == 5
# rerun
builder.make_entity_date_table = Mock()
builder.build_matrix(
as_of_times=dates,
label_name="booking",
label_type="binary",
feature_dictionary=feature_dictionary,
matrix_metadata=matrix_metadata,
matrix_uuid=uuid,
matrix_type="test",
)
assert not builder.make_entity_date_table.called
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 predict(self, prediction_date):
"""Predict forward by creating a matrix using as_of_date = prediction_date and applying the retrain model on it
Args:
prediction_date(str)
"""
cohort_table_name = f"triage_production.cohort_{self.experiment_config['cohort_config']['name']}_predict"
# 1. Generate cohort
self.generate_entity_date_table(prediction_date, cohort_table_name)
# 2. Generate feature aggregations
collate_aggregations = self.get_collate_aggregations(
prediction_date, cohort_table_name)
self.feature_generator.process_table_tasks(
self.feature_generator.generate_all_table_tasks(
collate_aggregations, task_type='aggregation'))
# 3. 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.retrain_model_id)
self.feature_generator.process_table_tasks(imputation_table_tasks)
# 4. 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=self.matrix_storage_engine,
engine=self.db_engine,
experiment_hash=None,
replace=True,
)
# Use timechop to get the time definition for production
temporal_config = self.get_temporal_config_for_retrain(
dt_from_str(prediction_date))
timechopper = Timechop(**temporal_config)
retrain_config = get_retrain_config_from_model_id(
self.db_engine, self.retrain_model_id)
prod_definitions = timechopper.define_test_matrices(
train_test_split_time=dt_from_str(prediction_date),
test_duration=retrain_config['test_duration'],
test_label_timespan=retrain_config['test_label_timespan'])
last_split_definition = prod_definitions[-1]
matrix_metadata = Planner.make_metadata(
matrix_definition=last_split_definition,
feature_dictionary=reconstructed_feature_dict,
label_name=self.label_name,
label_type='binary',
cohort_name=self.cohort_name,
matrix_type='production',
feature_start_time=self.feature_start_time,
user_metadata=self.user_metadata,
)
matrix_metadata['matrix_id'] = str(
prediction_date
) + f'_model_id_{self.retrain_model_id}' + '_risklist'
matrix_uuid = filename_friendly_hash(matrix_metadata)
matrix_builder.build_matrix(
as_of_times=[prediction_date],
label_name=self.label_name,
label_type='binary',
feature_dictionary=reconstructed_feature_dict,
matrix_metadata=matrix_metadata,
matrix_uuid=matrix_uuid,
matrix_type="production",
)
# 5. Predict the risk score for production
predictor = Predictor(
model_storage_engine=self.project_storage.model_storage_engine(),
db_engine=self.db_engine,
rank_order='best')
predictor.predict(
model_id=self.retrain_model_id,
matrix_store=self.matrix_storage_engine.get_store(matrix_uuid),
misc_db_parameters={},
train_matrix_columns=self.matrix_storage_engine.get_store(
self.retrain_matrix_uuid).columns(),
)
self.predict_matrix_uuid = matrix_uuid
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
}
