def edit_json_ai(self, name: str, json_ai: dict, company_id=None):
predictor_record = db.session.query(db.Predictor).filter_by(company_id=company_id, name=name).first()
assert predictor_record is not None
json_ai = lightwood.JsonAI.from_dict(json_ai)
predictor_record.code = lightwood.code_from_json_ai(json_ai)
predictor_record.json_ai = json_ai.to_dict()
db.session.commit()
def run_generate(df: DataFrame, problem_definition: ProblemDefinition,
predictor_id: int) -> int:
json_ai = lightwood.json_ai_from_problem(df, problem_definition)
code = lightwood.code_from_json_ai(json_ai)
predictor_record = Predictor.query.with_for_update().get(predictor_id)
predictor_record.json_ai = json_ai.to_dict()
predictor_record.code = code
db.session.commit()
def run_update(name: str, company_id: int):
original_name = name
name = f'{company_id}@@@@@{name}'
fs_store = FsStore()
config = Config()
data_store = DataStoreWrapper(DataStore(), company_id)
try:
predictor_record = Predictor.query.filter_by(company_id=company_id, name=original_name).first()
assert predictor_record is not None
predictor_record.update_status = 'updating'
session.commit()
ds = data_store.get_datasource_obj(None, raw=False, id=predictor_record.datasource_id)
df = ds.df
problem_definition = predictor_record.learn_args
problem_definition['target'] = predictor_record.to_predict[0]
if 'join_learn_process' in problem_definition:
del problem_definition['join_learn_process']
# Adapt kwargs to problem definition
if 'timeseries_settings' in problem_definition:
problem_definition['timeseries_settings'] = problem_definition['timeseries_settings']
if 'stop_training_in_x_seconds' in problem_definition:
problem_definition['time_aim'] = problem_definition['stop_training_in_x_seconds']
json_ai = lightwood.json_ai_from_problem(df, problem_definition)
predictor_record.json_ai = json_ai.to_dict()
predictor_record.code = lightwood.code_from_json_ai(json_ai)
predictor_record.data = {'training_log': 'training'}
session.commit()
predictor: lightwood.PredictorInterface = lightwood.predictor_from_code(predictor_record.code)
predictor.learn(df)
fs_name = f'predictor_{predictor_record.company_id}_{predictor_record.id}'
pickle_path = os.path.join(config['paths']['predictors'], fs_name)
predictor.save(pickle_path)
fs_store.put(fs_name, fs_name, config['paths']['predictors'])
predictor_record.data = predictor.model_analysis.to_dict() # type: ignore
session.commit()
predictor_record.lightwood_version = lightwood.__version__
predictor_record.mindsdb_version = mindsdb_version
predictor_record.update_status = 'up_to_date'
session.commit()
except Exception as e:
log.error(e)
predictor_record.update_status = 'update_failed' # type: ignore
session.commit()
return str(e)
def run_generate(df: DataFrame,
problem_definition: ProblemDefinition,
predictor_id: int,
json_ai_override: dict = None) -> int:
json_ai = lightwood.json_ai_from_problem(df, problem_definition)
if json_ai_override is None:
json_ai_override = {}
json_ai_override = brack_to_mod(json_ai_override)
json_ai = json_ai.to_dict()
rep_recur(json_ai, json_ai_override)
json_ai = JsonAI.from_dict(json_ai)
code = lightwood.code_from_json_ai(json_ai)
predictor_record = Predictor.query.with_for_update().get(predictor_id)
predictor_record.json_ai = json_ai.to_dict()
predictor_record.code = code
db.session.commit()
def run_generate(df: DataFrame, problem_definition: ProblemDefinition, name: str, company_id: int, datasource_id: int) -> int:
json_ai = lightwood.json_ai_from_problem(df, problem_definition)
code = lightwood.code_from_json_ai(json_ai)
predictor_record = db.Predictor(
company_id=company_id,
name=name,
json_ai=json_ai.to_dict(),
code=code,
datasource_id=datasource_id,
mindsdb_version=mindsdb_version,
lightwood_version=lightwood_version,
to_predict=[problem_definition.target],
learn_args=problem_definition.to_dict(),
data={'name': name}
)
db.session.add(predictor_record)
db.session.commit()
def code_from_json_ai(self, json_ai: dict, company_id=None):
json_ai = lightwood.JsonAI.from_dict(json_ai)
code = lightwood.code_from_json_ai(json_ai)
return code
