class DataStore():
def __init__(self):
self.config = Config()
self.fs_store = FsStore()
self.dir = self.config['paths']['datasources']
self.model_interface = ModelInterface()
def get_analysis(self, name, company_id=None):
datasource_record = session.query(Datasource).filter_by(
company_id=company_id, name=name).first()
if datasource_record.analysis is None:
return None
analysis = json.loads(datasource_record.analysis)
return analysis
def start_analysis(self, name, company_id=None):
datasource_record = session.query(Datasource).filter_by(
company_id=company_id, name=name).first()
if datasource_record.analysis is not None:
return None
semaphor_record = session.query(Semaphor).filter_by(
company_id=company_id,
entity_id=datasource_record.id,
entity_type='datasource').first()
if semaphor_record is None:
semaphor_record = Semaphor(company_id=company_id,
entity_id=datasource_record.id,
entity_type='datasource',
action='write')
session.add(semaphor_record)
session.commit()
else:
return
try:
analysis = self.model_interface.analyse_dataset(
ds=self.get_datasource_obj(name,
raw=True,
company_id=company_id),
company_id=company_id)
datasource_record = session.query(Datasource).filter_by(
company_id=company_id, name=name).first()
datasource_record.analysis = json.dumps(analysis,
cls=CustomJSONEncoder)
session.commit()
except Exception as e:
log.error(e)
finally:
semaphor_record = session.query(Semaphor).filter_by(
company_id=company_id,
entity_id=datasource_record.id,
entity_type='datasource').first()
session.delete(semaphor_record)
session.commit()
def get_datasources(self, name=None, company_id=None):
datasource_arr = []
if name is not None:
datasource_record_arr = session.query(Datasource).filter_by(
company_id=company_id, name=name)
else:
datasource_record_arr = session.query(Datasource).filter_by(
company_id=company_id)
for datasource_record in datasource_record_arr:
try:
if datasource_record.data is None:
continue
datasource = json.loads(datasource_record.data)
datasource['created_at'] = datasource_record.created_at
datasource['updated_at'] = datasource_record.updated_at
datasource['name'] = datasource_record.name
datasource['id'] = datasource_record.id
datasource_arr.append(datasource)
except Exception as e:
log.error(e)
return datasource_arr
def get_data(self,
name,
where=None,
limit=None,
offset=None,
company_id=None):
offset = 0 if offset is None else offset
ds = self.get_datasource_obj(name, company_id=company_id)
if limit is not None:
# @TODO Add `offset` to the `filter` method of the datasource and get rid of `offset`
filtered_ds = ds.filter(where=where,
limit=limit + offset).iloc[offset:]
else:
filtered_ds = ds.filter(where=where)
filtered_ds = filtered_ds.where(pd.notnull(filtered_ds), None)
data = filtered_ds.to_dict(orient='records')
return {
'data': data,
'rowcount': len(ds),
'columns_names': list(data[0].keys())
}
def get_datasource(self, name, company_id=None):
datasource_arr = self.get_datasources(name, company_id=company_id)
if len(datasource_arr) == 1:
return datasource_arr[0]
# @TODO: Remove when db swithc is more stable, this should never happen, but good santiy check while this is kinda buggy
elif len(datasource_arr) > 1:
log.error('Two or more datasource with the same name, (',
len(datasource_arr), ') | Full list: ', datasource_arr)
raise Exception('Two or more datasource with the same name')
return None
def delete_datasource(self, name, company_id=None):
datasource_record = Datasource.query.filter_by(company_id=company_id,
name=name).first()
if not Config()["force_datasource_removing"]:
linked_models = Predictor.query.filter_by(
company_id=company_id,
datasource_id=datasource_record.id).all()
if linked_models:
raise Exception(
"Can't delete {} datasource because there are next models linked to it: {}"
.format(name, [model.name for model in linked_models]))
session.query(Semaphor).filter_by(company_id=company_id,
entity_id=datasource_record.id,
entity_type='datasource').delete()
session.delete(datasource_record)
session.commit()
self.fs_store.delete(f'datasource_{company_id}_{datasource_record.id}')
try:
shutil.rmtree(os.path.join(self.dir, f'{company_id}@@@@@{name}'))
except Exception:
pass
def get_vacant_name(self, base=None, company_id=None):
''' returns name of datasource, which starts from 'base' and ds with that name is not exists yet
'''
if base is None:
base = 'datasource'
datasources = session.query(
Datasource.name).filter_by(company_id=company_id).all()
datasources_names = [x[0] for x in datasources]
if base not in datasources_names:
return base
for i in range(1, 1000):
candidate = f'{base}_{i}'
if candidate not in datasources_names:
return candidate
raise Exception(
f"Can not find appropriate name for datasource '{base}'")
def create_datasource(self,
source_type,
source,
file_path=None,
company_id=None,
ds_meta_dir=None):
datasource_controller = DatasourceController()
if source_type == 'file':
source = os.path.join(ds_meta_dir, source)
shutil.move(file_path, source)
ds = FileDS(source)
creation_info = {'class': 'FileDS', 'args': [source], 'kwargs': {}}
elif datasource_controller.get_db_integration(source_type,
company_id) is not None:
integration = datasource_controller.get_db_integration(
source_type, company_id)
ds_class_map = {
'clickhouse': ClickhouseDS,
'mariadb': MariaDS,
'mysql': MySqlDS,
'singlestore': MySqlDS,
'postgres': PostgresDS,
'cockroachdb': PostgresDS,
'mssql': MSSQLDS,
'mongodb': MongoDS,
'snowflake': SnowflakeDS,
'athena': AthenaDS,
'cassandra': CassandraDS,
'scylladb': ScyllaDS,
'trinodb': TrinoDS
}
try:
dsClass = ds_class_map[integration['type']]
except KeyError:
raise KeyError(
f"Unknown DS type: {source_type}, type is {integration['type']}"
)
if dsClass is None:
raise Exception(
f"Unsupported datasource: {source_type}, type is {integration['type']}, please install required dependencies!"
)
if integration['type'] in ['clickhouse']:
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'user': integration['user'],
'password': integration['password'],
'host': integration['host'],
'port': integration['port']
}
}
ds = dsClass(**creation_info['kwargs'])
elif integration['type'] in [
'mssql', 'postgres', 'cockroachdb', 'mariadb', 'mysql',
'singlestore', 'cassandra', 'scylladb'
]:
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'user': integration['user'],
'password': integration['password'],
'host': integration['host'],
'port': integration['port']
}
}
kwargs = creation_info['kwargs']
integration_folder_name = f'integration_files_{company_id}_{integration["id"]}'
if integration['type'] in ('mysql', 'mariadb'):
kwargs['ssl'] = integration.get('ssl')
kwargs['ssl_ca'] = integration.get('ssl_ca')
kwargs['ssl_cert'] = integration.get('ssl_cert')
kwargs['ssl_key'] = integration.get('ssl_key')
for key in ['ssl_ca', 'ssl_cert', 'ssl_key']:
if isinstance(kwargs[key],
str) and len(kwargs[key]) > 0:
kwargs[key] = os.path.join(
self.integrations_dir, integration_folder_name,
kwargs[key])
elif integration['type'] in ('cassandra', 'scylla'):
kwargs['secure_connect_bundle'] = integration.get(
'secure_connect_bundle')
if (isinstance(kwargs['secure_connect_bundle'], str)
and len(kwargs['secure_connect_bundle']) > 0):
kwargs['secure_connect_bundle'] = os.path.join(
self.integrations_dir, integration_folder_name,
kwargs['secure_connect_bundle'])
if 'database' in integration:
kwargs['database'] = integration['database']
if 'database' in source:
kwargs['database'] = source['database']
ds = dsClass(**kwargs)
elif integration['type'] == 'snowflake':
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query':
source['query'].replace('"', "'"),
'schema':
source.get('schema', integration['schema']),
'warehouse':
source.get('warehouse', integration['warehouse']),
'database':
source.get('database', integration['database']),
'host':
integration['host'],
'password':
integration['password'],
'user':
integration['user'],
'account':
integration['account']
}
}
ds = dsClass(**creation_info['kwargs'])
elif integration['type'] == 'mongodb':
if isinstance(source['find'], str):
source['find'] = json.loads(source['find'])
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'database': source['database'],
'collection': source['collection'],
'query': source['find'],
'user': integration['user'],
'password': integration['password'],
'host': integration['host'],
'port': integration['port']
}
}
ds = dsClass(**creation_info['kwargs'])
elif integration['type'] == 'athena':
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'staging_dir': source['staging_dir'],
'database': source['database'],
'access_key': source['access_key'],
'secret_key': source['secret_key'],
'region_name': source['region_name']
}
}
ds = dsClass(**creation_info['kwargs'])
elif integration['type'] == 'trinodb':
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'user': integration['user'],
'password': integration['password'],
'host': integration['host'],
'port': integration['port'],
'schema': integration['schema'],
'catalog': integration['catalog']
}
}
ds = dsClass(**creation_info['kwargs'])
else:
# This probably only happens for urls
ds = FileDS(source)
creation_info = {'class': 'FileDS', 'args': [source], 'kwargs': {}}
return ds, creation_info
def save_datasource(self,
name,
source_type,
source,
file_path=None,
company_id=None):
if source_type == 'file' and (file_path is None):
raise Exception(
'`file_path` argument required when source_type == "file"')
datasource_record = session.query(Datasource).filter_by(
company_id=company_id, name=name).first()
while datasource_record is not None:
raise Exception(f'Datasource with name {name} already exists')
try:
datasource_record = Datasource(
company_id=company_id,
name=name,
datasources_version=mindsdb_datasources.__version__,
mindsdb_version=mindsdb_version)
session.add(datasource_record)
session.commit()
ds_meta_dir = os.path.join(self.dir, f'{company_id}@@@@@{name}')
os.mkdir(ds_meta_dir)
ds, creation_info = self.create_datasource(source_type, source,
file_path, company_id,
ds_meta_dir)
if hasattr(ds, 'get_columns') and hasattr(ds, 'get_row_count'):
try:
column_names = ds.get_columns()
row_count = ds.get_row_count()
except Exception:
df = ds.df
column_names = list(df.keys())
row_count = len(df)
else:
df = ds.df
column_names = list(df.keys())
row_count = len(df)
if '' in column_names or len(column_names) != len(
set(column_names)):
shutil.rmtree(ds_meta_dir)
raise Exception(
'Each column in datasource must have unique non-empty name'
)
datasource_record.creation_info = json.dumps(creation_info)
datasource_record.data = json.dumps({
'source_type':
source_type,
'source':
source,
'row_count':
row_count,
'columns': [dict(name=x) for x in column_names]
})
self.fs_store.put(
f'{company_id}@@@@@{name}',
f'datasource_{company_id}_{datasource_record.id}', self.dir)
session.commit()
except Exception as e:
log.error(f'Error creating datasource {name}, exception: {e}')
try:
self.delete_datasource(name, company_id=company_id)
except Exception:
pass
raise e
return self.get_datasource_obj(name, raw=True, company_id=company_id)
def get_datasource_obj(self,
name=None,
id=None,
raw=False,
company_id=None):
try:
if name is not None:
datasource_record = session.query(Datasource).filter_by(
company_id=company_id, name=name).first()
else:
datasource_record = session.query(Datasource).filter_by(
company_id=company_id, id=id).first()
self.fs_store.get(
f'{company_id}@@@@@{name}',
f'datasource_{company_id}_{datasource_record.id}', self.dir)
creation_info = json.loads(datasource_record.creation_info)
if raw:
return creation_info
else:
return eval(creation_info['class'])(*creation_info['args'],
**creation_info['kwargs'])
except Exception as e:
log.error(f'Error getting datasource {name}, exception: {e}')
return None
class ModelController():
config: Config
fs_store: FsStore
predictor_cache: Dict[str, Dict[str, Union[Any]]]
ray_based: bool
def __init__(self, ray_based: bool) -> None:
self.config = Config()
self.fs_store = FsStore()
self.predictor_cache = {}
self.ray_based = ray_based
def _invalidate_cached_predictors(self) -> None:
# @TODO: Cache will become stale if the respective ModelInterface is not invoked yet a bunch of predictors remained cached, no matter where we invoke it. In practice shouldn't be a big issue though
for predictor_name in list(self.predictor_cache.keys()):
if (datetime.datetime.now() -
self.predictor_cache[predictor_name]['created']
).total_seconds() > 1200:
del self.predictor_cache[predictor_name]
def _lock_predictor(self, id: int, mode: str) -> None:
from mindsdb.interfaces.storage.db import session, Semaphor
while True:
semaphor_record = session.query(Semaphor).filter_by(
entity_id=id, entity_type='predictor').first()
if semaphor_record is not None:
if mode == 'read' and semaphor_record.action == 'read':
return True
try:
semaphor_record = Semaphor(entity_id=id,
entity_type='predictor',
action=mode)
session.add(semaphor_record)
session.commit()
return True
except Exception:
pass
time.sleep(1)
def _unlock_predictor(self, id: int) -> None:
from mindsdb.interfaces.storage.db import session, Semaphor
semaphor_record = session.query(Semaphor).filter_by(
entity_id=id, entity_type='predictor').first()
if semaphor_record is not None:
session.delete(semaphor_record)
session.commit()
@contextmanager
def _lock_context(self, id, mode: str):
try:
self._lock_predictor(id, mode)
yield True
finally:
self._unlock_predictor(id)
def _get_from_data_df(self, from_data: dict) -> DataFrame:
ds_cls = getattr(mindsdb_datasources, from_data['class'])
ds = ds_cls(*from_data['args'], **from_data['kwargs'])
return ds.df
def _unpack_old_args(
self,
from_data: dict,
kwargs: dict,
to_predict: Optional[Union[str, list]] = None
) -> Tuple[pd.DataFrame, ProblemDefinition, bool]:
problem_definition = kwargs or {}
if isinstance(to_predict, str):
problem_definition['target'] = to_predict
elif isinstance(to_predict, list) and len(to_predict) == 1:
problem_definition['target'] = to_predict[0]
elif problem_definition.get('target') is None:
raise Exception(
f"Predict target must be 'str' or 'list' with 1 element. Got: {to_predict}"
)
join_learn_process = kwargs.get('join_learn_process', False)
if 'join_learn_process' in kwargs:
del kwargs['join_learn_process']
# Adapt kwargs to problem definition
if 'timeseries_settings' in kwargs:
problem_definition['timeseries_settings'] = kwargs[
'timeseries_settings']
if 'stop_training_in_x_seconds' in kwargs:
problem_definition['time_aim'] = kwargs[
'stop_training_in_x_seconds']
if kwargs.get('ignore_columns') is not None:
problem_definition['ignore_features'] = kwargs['ignore_columns']
if (problem_definition.get('ignore_features') is not None
and isinstance(problem_definition['ignore_features'],
list) is False):
problem_definition['ignore_features'] = [
problem_definition['ignore_features']
]
df = self._get_from_data_df(from_data)
return df, problem_definition, join_learn_process
@mark_process(name='learn')
def learn(self,
name: str,
from_data: dict,
to_predict: str,
datasource_id: int,
kwargs: dict,
company_id: int,
delete_ds_on_fail: Optional[bool] = False) -> None:
predictor_record = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=name).first()
if predictor_record is not None:
raise Exception('Predictor name must be unique.')
df, problem_definition, join_learn_process = self._unpack_old_args(
from_data, kwargs, to_predict)
problem_definition = ProblemDefinition.from_dict(problem_definition)
predictor_record = db.Predictor(
company_id=company_id,
name=name,
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()
predictor_id = predictor_record.id
p = LearnProcess(df, problem_definition, predictor_id,
delete_ds_on_fail)
p.start()
if join_learn_process:
p.join()
if not IS_PY36:
p.close()
db.session.refresh(predictor_record)
data = {}
if predictor_record.update_status == 'available':
data['status'] = 'complete'
elif predictor_record.json_ai is None and predictor_record.code is None:
data['status'] = 'generating'
elif predictor_record.data is None:
data['status'] = 'editable'
elif 'training_log' in predictor_record.data:
data['status'] = 'training'
elif 'error' not in predictor_record.data:
data['status'] = 'complete'
else:
data['status'] = 'error'
@mark_process(name='predict')
def predict(self, name: str, when_data: Union[dict, list, pd.DataFrame],
pred_format: str, company_id: int):
original_name = name
name = f'{company_id}@@@@@{name}'
predictor_record = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=original_name).first()
assert predictor_record is not None
predictor_data = self.get_model_data(name, company_id)
fs_name = f'predictor_{company_id}_{predictor_record.id}'
if (name in self.predictor_cache
and self.predictor_cache[name]['updated_at'] !=
predictor_record.updated_at):
del self.predictor_cache[name]
if name not in self.predictor_cache:
# Clear the cache entirely if we have less than 1.2 GB left
if psutil.virtual_memory().available < 1.2 * pow(10, 9):
self.predictor_cache = {}
if predictor_data['status'] == 'complete':
self.fs_store.get(fs_name, fs_name,
self.config['paths']['predictors'])
self.predictor_cache[name] = {
'predictor':
lightwood.predictor_from_state(
os.path.join(self.config['paths']['predictors'],
fs_name), predictor_record.code),
'updated_at':
predictor_record.updated_at,
'created':
datetime.datetime.now(),
'code':
predictor_record.code,
'pickle':
str(
os.path.join(self.config['paths']['predictors'],
fs_name))
}
else:
raise Exception(
f'Trying to predict using predictor {original_name} with status: {predictor_data["status"]}. Error is: {predictor_data.get("error", "unknown")}'
)
if isinstance(when_data,
dict) and 'kwargs' in when_data and 'args' in when_data:
ds_cls = getattr(mindsdb_datasources, when_data['class'])
df = ds_cls(*when_data['args'], **when_data['kwargs']).df
else:
if isinstance(when_data, dict):
when_data = [when_data]
df = pd.DataFrame(when_data)
predictions = self.predictor_cache[name]['predictor'].predict(df)
predictions = predictions.to_dict(orient='records')
# Bellow is useful for debugging caching and storage issues
# del self.predictor_cache[name]
target = predictor_record.to_predict[0]
if pred_format in ('explain', 'dict', 'dict&explain'):
explain_arr = []
dict_arr = []
for i, row in enumerate(predictions):
explain_arr.append({
target: {
'predicted_value': row['prediction'],
'confidence': row.get('confidence', None),
'confidence_lower_bound': row.get('lower', None),
'confidence_upper_bound': row.get('upper', None),
'anomaly': row.get('anomaly', None),
'truth': row.get('truth', None)
}
})
td = {'predicted_value': row['prediction']}
for col in df.columns:
if col in row:
td[col] = row[col]
elif f'order_{col}' in row:
td[col] = row[f'order_{col}']
elif f'group_{col}' in row:
td[col] = row[f'group_{col}']
else:
orginal_index = row.get('original_index')
if orginal_index is None:
log.warning('original_index is None')
orginal_index = i
td[col] = df.iloc[orginal_index][col]
dict_arr.append({target: td})
if pred_format == 'explain':
return explain_arr
elif pred_format == 'dict':
return dict_arr
elif pred_format == 'dict&explain':
return dict_arr, explain_arr
# New format -- Try switching to this in 2-3 months for speed, for now above is ok
else:
return predictions
@mark_process(name='analyse')
def analyse_dataset(self, ds: dict,
company_id: int) -> lightwood.DataAnalysis:
ds_cls = getattr(mindsdb_datasources, ds['class'])
df = ds_cls(*ds['args'], **ds['kwargs']).df
analysis = lightwood.analyze_dataset(df)
return analysis.to_dict() # type: ignore
def get_model_data(self, name, company_id: int):
if '@@@@@' in name:
sn = name.split('@@@@@')
assert len(sn) < 3 # security
name = sn[1]
original_name = name
name = f'{company_id}@@@@@{name}'
predictor_record = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=original_name).first()
assert predictor_record is not None
linked_db_ds = db.session.query(db.Datasource).filter_by(
company_id=company_id, id=predictor_record.datasource_id).first()
data = deepcopy(predictor_record.data)
data['dtype_dict'] = predictor_record.dtype_dict
data['created_at'] = str(
parse_datetime(str(predictor_record.created_at).split('.')[0]))
data['updated_at'] = str(
parse_datetime(str(predictor_record.updated_at).split('.')[0]))
data['predict'] = predictor_record.to_predict[0]
data['update'] = predictor_record.update_status
data['mindsdb_version'] = predictor_record.mindsdb_version
data['name'] = predictor_record.name
data['code'] = predictor_record.code
data['json_ai'] = predictor_record.json_ai
data['data_source_name'] = linked_db_ds.name if linked_db_ds else None
data['problem_definition'] = predictor_record.learn_args
# assume older models are complete, only temporary
if 'error' in predictor_record.data:
data['status'] = 'error'
elif predictor_record.update_status == 'available':
data['status'] = 'complete'
elif predictor_record.json_ai is None and predictor_record.code is None:
data['status'] = 'generating'
elif predictor_record.data is None:
data['status'] = 'editable'
elif 'training_log' in predictor_record.data:
data['status'] = 'training'
elif 'error' not in predictor_record.data:
data['status'] = 'complete'
else:
data['status'] = 'error'
if data.get('accuracies', None) is not None:
if len(data['accuracies']) > 0:
data['accuracy'] = float(
np.mean(list(data['accuracies'].values())))
return data
def get_model_description(self, name: str, company_id: int):
"""
Similar to `get_model_data` but meant to be seen directly by the user, rather than parsed by something like the Studio predictor view.
Uses `get_model_data` to compose this, but in the future we might want to make this independent if we deprected `get_model_data`
:returns: Dictionary of the analysis (meant to be foramtted by the APIs and displayed as json/yml/whatever)
""" # noqa
model_description = {}
model_data = self.get_model_data(name, company_id)
model_description['accuracies'] = model_data['accuracies']
model_description['column_importances'] = model_data[
'column_importances']
model_description['outputs'] = [model_data['predict']]
model_description['inputs'] = [
col for col in model_data['dtype_dict']
if col not in model_description['outputs']
]
model_description['datasource'] = model_data['data_source_name']
model_description['model'] = ' --> '.join(
str(k) for k in model_data['json_ai'])
return model_description
def get_models(self, company_id: int):
models = []
for db_p in db.session.query(
db.Predictor).filter_by(company_id=company_id):
model_data = self.get_model_data(db_p.name, company_id=company_id)
reduced_model_data = {}
for k in [
'name', 'version', 'is_active', 'predict', 'status',
'current_phase', 'accuracy', 'data_source', 'update',
'data_source_name', 'mindsdb_version', 'error'
]:
reduced_model_data[k] = model_data.get(k, None)
for k in ['train_end_at', 'updated_at', 'created_at']:
reduced_model_data[k] = model_data.get(k, None)
if reduced_model_data[k] is not None:
try:
reduced_model_data[k] = parse_datetime(
str(reduced_model_data[k]).split('.')[0])
except Exception as e:
# @TODO Does this ever happen
log.error(
f'Date parsing exception while parsing: {k} in get_models: ',
e)
reduced_model_data[k] = parse_datetime(
str(reduced_model_data[k]))
models.append(reduced_model_data)
return models
def delete_model(self, name, company_id: int):
original_name = name
name = f'{company_id}@@@@@{name}'
db_p = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=original_name).first()
if db_p is None:
raise Exception(f"Predictor '{name}' does not exist")
db.session.delete(db_p)
if db_p.datasource_id is not None:
try:
dataset_record = db.Datasource.query.get(db_p.datasource_id)
if (isinstance(dataset_record.data, str) and json.loads(
dataset_record.data).get('source_type') != 'file'):
DataStore().delete_datasource(dataset_record.name,
company_id)
except Exception:
pass
db.session.commit()
DatabaseWrapper(company_id).unregister_predictor(name)
# delete from s3
self.fs_store.delete(f'predictor_{company_id}_{db_p.id}')
return 0
def rename_model(self, old_name, new_name, company_id: int):
db_p = db.session.query(db.Predictor).filter_by(company_id=company_id,
name=old_name).first()
db_p.name = new_name
db.session.commit()
dbw = DatabaseWrapper(company_id)
dbw.unregister_predictor(old_name)
dbw.register_predictors([self.get_model_data(new_name, company_id)])
@mark_process(name='learn')
def update_model(self, name: str, company_id: int):
# TODO: Add version check here once we're done debugging
predictor_record = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=name).first()
assert predictor_record is not None
predictor_record.update_status = 'updating'
db.session.commit()
p = UpdateProcess(name, company_id)
p.start()
return 'Updated in progress'
@mark_process(name='learn')
def generate_predictor(self, name: str, from_data: dict, datasource_id,
problem_definition_dict: dict,
join_learn_process: bool, company_id: int):
predictor_record = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=name).first()
if predictor_record is not None:
raise Exception('Predictor name must be unique.')
df, problem_definition, _ = self._unpack_old_args(
from_data, problem_definition_dict)
problem_definition = ProblemDefinition.from_dict(problem_definition)
predictor_record = db.Predictor(
company_id=company_id,
name=name,
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()
predictor_id = predictor_record.id
p = GenerateProcess(df, problem_definition, predictor_id)
p.start()
if join_learn_process:
p.join()
if not IS_PY36:
p.close()
db.session.refresh(predictor_record)
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 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
def edit_code(self, name: str, code: str, company_id=None):
"""Edit an existing predictor's code"""
if self.config.get('cloud', False):
raise Exception('Code editing prohibited on cloud')
predictor_record = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=name).first()
assert predictor_record is not None
lightwood.predictor_from_code(code)
predictor_record.code = code
predictor_record.json_ai = None
db.session.commit()
@mark_process(name='learn')
def fit_predictor(self, name: str, from_data: dict,
join_learn_process: bool, company_id: int) -> None:
predictor_record = db.session.query(db.Predictor).filter_by(
company_id=company_id, name=name).first()
assert predictor_record is not None
df = self._get_from_data_df(from_data)
p = FitProcess(predictor_record.id, df)
p.start()
if join_learn_process:
p.join()
if not IS_PY36:
p.close()