class DataSourceModel(BaseModel):
_id = UUIDType(required=True)
file_name = StringType(required=True)
file_path = StringType(required=True)
file_sep = StringType()
pickle_file_path = StringType()
content_type = StringType(required=True)
file_name_md5 = StringType()
file_size = StringType()
uploaded_at = DateTimeType(default=datetime.datetime.now())
samples_count = IntType(default=0)
predictors_count = IntType(default=0)
nominal_predictors_count = IntType(default=0)
numeric_predictors_count = IntType(default=0)
class_count = IntType(default=0)
missing_values = FloatType(default=.0)
status = StringType(default='check')
predictor_values = ListType(StringType())
predictor_details = ListType(DictType(BaseType))
predictor_ids = ListType(StringType())
predictor_target_name = StringType()
MONGO_COLLECTION = 'datasources'
class WorkflowModel(BaseModel):
_id = UUIDType(required=True)
name = StringType()
created_at = DateTimeType(default=datetime.datetime.now())
processing_started_at = DateTimeType()
processing_ended_at = DateTimeType()
training_data = DictType(BaseType, {})
preprocessing = ListType(StringType, default=list())
model_processing = DictType(BaseType, default={'type': 'supervised'})
validation = DictType(BaseType, default={'type': 'fold', 'value': 3})
trained_models = DictType(BaseType, {})
fi_booster = StringType()
validation_details = DictType(BaseType, default={})
state = StringType(default='new')
MONGO_COLLECTION = 'workflows'
