def _write_model_to_db(self, class_path, parameters, feature_names,
model_hash, trained_model, model_group_id,
misc_db_parameters):
"""Writes model and feature importance data to a database
Will overwrite the data of any previous versions
(any existing model that shares a hash)
Args:
class_path (string) A full classpath to the model class
parameters (dict) hyperparameters to give to the model constructor
feature_names (list) feature names in order given to model
model_hash (string) a unique id for the model
trained_model (object) a trained model object
misc_db_parameters (dict) params to pass through to the database
"""
saved_model_id = retrieve_model_id_from_hash(self.db_engine,
model_hash)
if saved_model_id:
# logging.warning('deleting existing model %s', existing_model.model_id)
# existing_model.delete(session)
# session.commit()
logging.warning('model meta data already stored %s',
saved_model_id)
return saved_model_id
session = self.sessionmaker()
model = Model(model_hash=model_hash,
model_type=class_path,
model_parameters=parameters,
model_group_id=model_group_id,
experiment_hash=self.experiment_hash,
**misc_db_parameters)
session.add(model)
feature_importance = get_feature_importances(trained_model)
temp_df = pandas.DataFrame({'feature_importance': feature_importance})
features_index = temp_df.index.tolist()
rankings_abs = temp_df['feature_importance'].rank(method='dense',
ascending=False)
rankings_pct = temp_df['feature_importance'].rank(method='dense',
ascending=False,
pct=True)
for feature_index, importance, rank_abs, rank_pct in zip(
features_index, feature_importance, rankings_abs,
rankings_pct):
feature_importance = FeatureImportance(
model=model,
feature_importance=round(float(importance), 10),
feature=feature_names[feature_index],
rank_abs=int(rank_abs),
rank_pct=round(float(rank_pct), 10))
session.add(feature_importance)
session.commit()
model_id = model.model_id
session.close()
return model_id
def _write_model_to_db(
self,
class_path,
parameters,
feature_names,
model_hash,
trained_model,
model_group_id,
misc_db_parameters
):
"""Writes model and feature importance data to a database
Will overwrite the data of any previous versions
(any existing model that shares a hash)
If the replace flag on the object is set, the existing version of the model
will have its non-unique attributes (e.g. timestamps) updated,
and feature importances fully replaced.
If the replace flag on the object is not set, the existing model metadata
and feature importances will be used.
Args:
class_path (string) A full classpath to the model class
parameters (dict) hyperparameters to give to the model constructor
feature_names (list) feature names in order given to model
model_hash (string) a unique id for the model
trained_model (object) a trained model object
misc_db_parameters (dict) params to pass through to the database
"""
model_id = retrieve_model_id_from_hash(self.db_engine, model_hash)
if model_id and not self.replace:
logging.info(
'Metadata for model_id %s found in database. Reusing model metadata.',
model_id
)
return model_id
else:
model = Model(
model_hash=model_hash,
model_type=class_path,
model_parameters=parameters,
model_group_id=model_group_id,
experiment_hash=self.experiment_hash,
**misc_db_parameters
)
session = self.sessionmaker()
if model_id:
logging.info('Found model id %s, updating non-unique attributes', model_id)
model.model_id = model_id
session.merge(model)
session.commit()
else:
session.add(model)
session.commit()
model_id = model.model_id
logging.info('Added new model id %s', model_id)
session.close()
logging.info('Saving feature importances for model_id %s', model_id)
self._save_feature_importances(
model_id,
get_feature_importances(trained_model),
feature_names
)
logging.info('Done saving feature importances for model_id %s', model_id)
return model_id
def test_correct_feature_importances_for_rf(trained_models):
feature_importances = get_feature_importances(trained_models['RF'])
assert feature_importances.shape == (30, )
def test_correct_feature_importances_for_lr(trained_models):
feature_importances = get_feature_importances(trained_models['LR'])
## It returns the intercept, too
assert feature_importances.shape == (30, )
def test_throwing_warning_if_lr(trained_models):
with pytest.warns(UserWarning):
get_feature_importances(trained_models['LR'])
def test_correct_feature_importances_for_dummy(trained_models):
feature_importances = get_feature_importances(trained_models['Dummy'])
assert feature_importances is None
def test_correct_feature_importances_for_svc_wo_linear_kernel(trained_models):
feature_importances = get_feature_importances(
trained_models['SVC_wo_linear_kernel'])
assert feature_importances is None
def test_throwing_warning_if_SVC_wo_linear_kernel(trained_models):
with pytest.warns(UserWarning):
get_feature_importances(trained_models['SVC_wo_linear_kernel'])
def test_throwing_warning_if_dummyclassifier(trained_models):
with pytest.warns(UserWarning):
get_feature_importances(trained_models['Dummy'])