def fake_trained_model(db_engine,
train_matrix_uuid="efgh",
train_end_time=datetime.datetime(2016, 1, 1)):
"""Creates and stores a trivial trained model and training matrix
Args:
db_engine (sqlalchemy.engine)
Returns:
(int) model id for database retrieval
"""
session = sessionmaker(db_engine)()
session.merge(Matrix(matrix_uuid=train_matrix_uuid))
# Create the fake trained model and store in db
trained_model = MockTrainedModel()
db_model = Model(
model_hash="abcd",
train_matrix_uuid=train_matrix_uuid,
train_end_time=train_end_time,
)
session.add(db_model)
session.commit()
model_id = db_model.model_id
session.close()
return trained_model, model_id
def __init__(self, matrix_type, matrix_uuid, label_count, db_engine, init_labels=None, metadata_overrides=None, matrix=None):
base_metadata = {
'feature_start_time': datetime.date(2014, 1, 1),
'end_time': datetime.date(2015, 1, 1),
'as_of_date_frequency': '1y',
'matrix_id': 'some_matrix',
'label_name': 'label',
'label_timespan': '3month',
'indices': ['entity_id'],
'matrix_type': matrix_type
}
metadata_overrides = metadata_overrides or {}
base_metadata.update(metadata_overrides)
if matrix is None:
matrix = pandas.DataFrame.from_dict({
'entity_id': [1, 2],
'feature_one': [3, 4],
'feature_two': [5, 6],
'label': [7, 8]
}).set_index('entity_id')
if init_labels is None:
init_labels = []
self.matrix = matrix
self.metadata = base_metadata
self.label_count = label_count
self.init_labels = init_labels
self.matrix_uuid = matrix_uuid
session = sessionmaker(db_engine)()
session.add(Matrix(matrix_uuid=matrix_uuid))
def __init__(
self,
matrix_type,
matrix_uuid,
label_count,
db_engine,
init_labels=None,
metadata_overrides=None,
matrix=None,
init_as_of_dates=None,
):
base_metadata = {
"feature_start_time": datetime.date(2014, 1, 1),
"end_time": datetime.date(2015, 1, 1),
"as_of_date_frequency": "1y",
"matrix_id": "some_matrix",
"label_name": "label",
"label_timespan": "3month",
"indices": MatrixStore.indices,
"matrix_type": matrix_type,
"as_of_times":
[datetime.date(2014, 10, 1),
datetime.date(2014, 7, 1)],
}
metadata_overrides = metadata_overrides or {}
base_metadata.update(metadata_overrides)
if matrix is None:
matrix = pd.DataFrame.from_dict({
"entity_id": [1, 2],
"as_of_date":
[pd.Timestamp(2014, 10, 1),
pd.Timestamp(2014, 7, 1)],
"feature_one": [3, 4],
"feature_two": [5, 6],
"label": [7, 8],
}).set_index(MatrixStore.indices)
if init_labels is None:
init_labels = []
labels = matrix.pop("label")
self.matrix_label_tuple = matrix, labels
self.metadata = base_metadata
self.label_count = label_count
self.init_labels = pd.Series(init_labels, dtype="float64")
self.matrix_uuid = matrix_uuid
self.init_as_of_dates = init_as_of_dates or []
session = sessionmaker(db_engine)()
session.add(Matrix(matrix_uuid=matrix_uuid))
session.commit()
def fake_trained_model(db_engine, train_matrix_uuid='efgh'):
"""Creates and stores a trivial trained model and training matrix
Args:
db_engine (sqlalchemy.engine)
Returns:
(int) model id for database retrieval
"""
session = sessionmaker(db_engine)()
session.merge(Matrix(matrix_uuid=train_matrix_uuid))
# Create the fake trained model and store in db
trained_model = MockTrainedModel()
db_model = Model(model_hash='abcd', train_matrix_uuid=train_matrix_uuid)
session.add(db_model)
session.commit()
return trained_model, db_model.model_id
def __init__(
self,
matrix_type,
matrix_uuid,
label_count,
db_engine,
init_labels=None,
metadata_overrides=None,
matrix=None,
):
base_metadata = {
"feature_start_time": datetime.date(2014, 1, 1),
"end_time": datetime.date(2015, 1, 1),
"as_of_date_frequency": "1y",
"matrix_id": "some_matrix",
"label_name": "label",
"label_timespan": "3month",
"indices": ["entity_id"],
"matrix_type": matrix_type,
}
metadata_overrides = metadata_overrides or {}
base_metadata.update(metadata_overrides)
if matrix is None:
matrix = pandas.DataFrame.from_dict({
"entity_id": [1, 2],
"feature_one": [3, 4],
"feature_two": [5, 6],
"label": [7, 8],
}).set_index("entity_id")
if init_labels is None:
init_labels = []
self.matrix = matrix
self.metadata = base_metadata
self.label_count = label_count
self.init_labels = init_labels
self.matrix_uuid = matrix_uuid
session = sessionmaker(db_engine)()
session.add(Matrix(matrix_uuid=matrix_uuid))