class MLModelInputs(db.Model, SerialializableModel):
__tablename__ = 'MLModelInputs'
model_id = db.Column(db.Integer,
ForeignKey('MLModel.model_id'),
primary_key=True,
index=True)
model_inputs = db.Column(db.Text)
class MLModel(db.Model, SerialializableModel):
"""
The SQL DeclarativeMeta model responsible for storing a model's metadata
Parameters
----------
model_id: int
The model's unique identifier
model_name: str
Model name, usually defined with instantiating a Learner class
major : int
Major version of the learner
minor: int
Minor version of the learner
patch: int
Patch version of the learner
version_dir: str
Directory where the models will be stored inside TensorFlow serving and on-disk
created_at: dateteime.datetime
When the model was created
model_type: str
The model type usually either regression or classification
"""
__tablename__ = 'MLModel'
model_id = db.Column(db.Integer, index=True, primary_key=True)
model_name = db.Column(db.Text)
major = db.Column(db.Integer)
minor = db.Column(db.Integer)
patch = db.Column(db.Integer)
version_dir = db.Column(db.String)
created_at = db.Column(db.DateTime, default=datetime.datetime.utcnow())
model_type = db.Column(db.String)
parameters = db.Column(db.String)
class ModelScores(db.Model, SerialializableModel):
"""Scores of the model
Parameters
----------
model_id: int
The model's unique identifier
scoring_fn: str
The name of the scoring function
score: float
The cross-validation score associated with the scoring function and the model id
"""
__tablename__ = 'ModelScores'
id = db.Column(db.Integer, primary_key=True, index=True)
model_id = db.Column(db.Integer,
ForeignKey('MLModel.model_id'),
primary_key=False,
index=True)
scoring_fn = db.Column(db.Text)
score = db.Column(db.Float)
class ActiveModel(db.Model, SerialializableModel):
"""
Specifies the currently active model
"""
__tablename__ = 'ActiveModel'
model_id = db.Column(db.Integer, index=True, primary_key=True)