class DataStore():
def __init__(self):
self.config = Config()
self.fs_store = FsSotre()
self.company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
self.dir = self.config.paths['datasources']
self.mindsdb_native = NativeInterface()
def get_analysis(self, name):
datasource_record = session.query(Datasource).filter_by(
company_id=self.company_id, name=name).first()
if datasource_record.analysis is None:
datasource_record.analysis = json.dumps(
self.mindsdb_native.analyse_dataset(
self.get_datasource_obj(name)))
session.commit()
analysis = json.loads(datasource_record.analysis)
return analysis
def get_datasources(self, name=None):
datasource_arr = []
if name is not None:
datasource_record_arr = session.query(Datasource).filter_by(
company_id=self.company_id, name=name)
else:
datasource_record_arr = session.query(Datasource).filter_by(
company_id=self.company_id)
for datasource_record in datasource_record_arr:
try:
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):
offset = 0 if offset is None else offset
ds = self.get_datasource_obj(name)
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': filtered_ds.columns
}
def get_datasource(self, name):
datasource_arr = self.get_datasources(name)
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):
datasource_record = Datasource.query.filter_by(
company_id=self.company_id, name=name).first()
id = datasource_record.id
session.delete(datasource_record)
session.commit()
self.fs_store.delete(
f'datasource_{self.company_id}_{datasource_record.id}')
try:
shutil.rmtree(os.path.join(self.dir, name))
except Exception:
pass
def save_datasource(self, name, source_type, source, file_path=None):
datasource_record = Datasource(company_id=self.company_id, name=name)
if source_type == 'file' and (file_path is None):
raise Exception(
'`file_path` argument required when source_type == "file"')
ds_meta_dir = os.path.join(self.dir, name)
os.mkdir(ds_meta_dir)
session.add(datasource_record)
session.commit()
datasource_record = session.query(Datasource).filter_by(
company_id=self.company_id, name=name).first()
try:
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 source_type in self.config['integrations']:
integration = self.config['integrations'][source_type]
ds_class_map = {
'clickhouse': ClickhouseDS,
'mariadb': MariaDS,
'mysql': MySqlDS,
'postgres': PostgresDS,
'mssql': MSSQLDS,
'mongodb': MongoDS,
'snowflake': SnowflakeDS
}
try:
dsClass = ds_class_map[integration['type']]
except KeyError:
raise KeyError(
f"Unknown DS type: {source_type}, type is {integration['type']}"
)
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', 'mariadb', 'mysql'
]:
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'user': integration['user'],
'password': integration['password'],
'host': integration['host'],
'port': integration['port']
}
}
if 'database' in integration:
creation_info['kwargs']['database'] = integration[
'database']
if 'database' in source:
creation_info['kwargs']['database'] = source[
'database']
ds = dsClass(**creation_info['kwargs'])
elif integration['type'] == 'snowflake':
creation_info = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'schema': source['schema'],
'warehouse': source['warehouse'],
'database': source['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'])
else:
# This probably only happens for urls
ds = FileDS(source)
creation_info = {
'class': 'FileDS',
'args': [source],
'kwargs': {}
}
df = ds.df
if '' in df.columns or len(df.columns) != len(set(df.columns)):
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':
len(df),
'columns': [dict(name=x) for x in list(df.keys())]
})
self.fs_store.put(
name, f'datasource_{self.company_id}_{datasource_record.id}',
self.dir)
except Exception:
if os.path.isdir(ds_meta_dir):
shutil.rmtree(ds_meta_dir)
raise
session.commit()
return self.get_datasource_obj(name, raw=True), name
def get_datasource_obj(self, name, raw=False):
try:
datasource_record = session.query(Datasource).filter_by(
company_id=self.company_id, name=name).first()
self.fs_store.get(
name, f'datasource_{self.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'\n{e}\n')
return None
class ModelController():
def __init__(self, ray_based):
self.config = Config()
self.fs_store = FsSotre()
self.company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
self.dbw = DatabaseWrapper()
self.predictor_cache = {}
self.ray_based = ray_based
def _pack(self, obj):
if self.ray_based:
return obj
return xmlrpc.client.Binary(pickle.dumps(obj))
def _invalidate_cached_predictors(self):
from mindsdb_datasources import FileDS, ClickhouseDS, MariaDS, MySqlDS, PostgresDS, MSSQLDS, MongoDS, SnowflakeDS, AthenaDS
import mindsdb_native
from mindsdb_native import F
from mindsdb_native.libs.constants.mindsdb import DATA_SUBTYPES
from mindsdb.interfaces.storage.db import session, Predictor
# @TODO: Cache will become stale if the respective NativeInterface 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, mode='write'):
from mindsdb.interfaces.storage.db import session, Semaphor
while True:
semaphor_record = session.query(Semaphor).filter_by(company_id=self.company_id, 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(company_id=self.company_id, entity_id=id, entity_type='predictor', action=mode)
session.add(semaphor_record)
session.commit()
return True
except Excpetion as e:
pass
time.sleep(1)
def _unlock_predictor(self, id):
from mindsdb.interfaces.storage.db import session, Semaphor
semaphor_record = session.query(Semaphor).filter_by(company_id=self.company_id, 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='write'):
try:
self._lock_predictor(mode)
yield True
finally:
self._unlock_predictor(id)
def _setup_for_creation(self, name):
from mindsdb_datasources import FileDS, ClickhouseDS, MariaDS, MySqlDS, PostgresDS, MSSQLDS, MongoDS, SnowflakeDS, AthenaDS
import mindsdb_native
from mindsdb_native import F
from mindsdb_native.libs.constants.mindsdb import DATA_SUBTYPES
from mindsdb.interfaces.storage.db import session, Predictor
if name in self.predictor_cache:
del self.predictor_cache[name]
# Here for no particular reason, because we want to run this sometimes but not too often
self._invalidate_cached_predictors()
predictor_dir = Path(self.config.paths['predictors']).joinpath(name)
create_directory(predictor_dir)
predictor_record = Predictor(company_id=self.company_id, name=name, is_custom=False)
session.add(predictor_record)
session.commit()
def _try_outdate_db_status(self, predictor_record):
from mindsdb_native import __version__ as native_version
from mindsdb import __version__ as mindsdb_version
from mindsdb.interfaces.storage.db import session
if predictor_record.update_status == 'update_failed':
return predictor_record
if predictor_record.native_version != native_version:
predictor_record.update_status = 'available'
if predictor_record.mindsdb_version != mindsdb_version:
predictor_record.update_status = 'available'
session.commit()
return predictor_record
def _update_db_status(self, predictor_record):
from mindsdb_native import __version__ as native_version
from mindsdb import __version__ as mindsdb_version
from mindsdb.interfaces.storage.db import session
predictor_record.native_version = native_version
predictor_record.mindsdb_version = mindsdb_version
predictor_record.update_status = 'up_to_date'
session.commit()
return predictor_record
def create(self, name):
from mindsdb_datasources import FileDS, ClickhouseDS, MariaDS, MySqlDS, PostgresDS, MSSQLDS, MongoDS, SnowflakeDS, AthenaDS
import mindsdb_native
from mindsdb_native import F
from mindsdb_native.libs.constants.mindsdb import DATA_SUBTYPES
from mindsdb.interfaces.storage.db import session, Predictor
self._setup_for_creation(name)
predictor = mindsdb_native.Predictor(name=name, run_env={'trigger': 'mindsdb'})
return predictor
def learn(self, name, from_data, to_predict, datasource_id, kwargs={}):
from mindsdb.interfaces.model.learn_process import LearnProcess, run_learn
create_process_mark('learn')
join_learn_process = kwargs.get('join_learn_process', False)
if 'join_learn_process' in kwargs:
del kwargs['join_learn_process']
self._setup_for_creation(name)
if self.ray_based:
run_learn(name, from_data, to_predict, kwargs, datasource_id)
else:
p = LearnProcess(name, from_data, to_predict, kwargs, datasource_id)
p.start()
if join_learn_process is True:
p.join()
if p.exitcode != 0:
delete_process_mark('learn')
raise Exception('Learning process failed !')
delete_process_mark('learn')
return 0
def predict(self, name, pred_format, when_data=None, kwargs={}):
from mindsdb_datasources import FileDS, ClickhouseDS, MariaDS, MySqlDS, PostgresDS, MSSQLDS, MongoDS, SnowflakeDS, AthenaDS
import mindsdb_native
from mindsdb.interfaces.storage.db import session, Predictor
create_process_mark('predict')
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 = {}
predictor_record = Predictor.query.filter_by(company_id=self.company_id, name=name, is_custom=False).first()
if predictor_record.data['status'] == 'complete':
self.fs_store.get(name, f'predictor_{self.company_id}_{predictor_record.id}', self.config['paths']['predictors'])
self.predictor_cache[name] = {
'predictor': mindsdb_native.Predictor(name=name, run_env={'trigger': 'mindsdb'}),
'created': datetime.datetime.now()
}
if isinstance(when_data, dict) and 'kwargs' in when_data and 'args' in when_data:
data_source = getattr(mindsdb_datasources, when_data['class'])(*when_data['args'], **when_data['kwargs'])
else:
# @TODO: Replace with Datasource
try:
data_source = pd.DataFrame(when_data)
except Exception:
data_source = when_data
predictions = self.predictor_cache[name]['predictor'].predict(
when_data=when_data,
**kwargs
)
if pred_format == 'explain' or pred_format == 'new_explain':
predictions = [p.explain() for p in predictions]
elif pred_format == 'dict':
predictions = [p.as_dict() for p in predictions]
elif pred_format == 'dict&explain':
predictions = [[p.as_dict() for p in predictions], [p.explain() for p in predictions]]
else:
delete_process_mark('predict')
raise Exception(f'Unkown predictions format: {pred_format}')
delete_process_mark('predict')
return self._pack(predictions)
def analyse_dataset(self, ds):
from mindsdb_datasources import FileDS, ClickhouseDS, MariaDS, MySqlDS, PostgresDS, MSSQLDS, MongoDS, SnowflakeDS, AthenaDS
from mindsdb_native import F
create_process_mark('analyse')
ds = eval(ds['class'])(*ds['args'], **ds['kwargs'])
analysis = F.analyse_dataset(ds)
delete_process_mark('analyse')
return self._pack(analysis)
def get_model_data(self, name, db_fix=True):
from mindsdb_native import F
from mindsdb_native.libs.constants.mindsdb import DATA_SUBTYPES
from mindsdb.interfaces.storage.db import session, Predictor
predictor_record = Predictor.query.filter_by(company_id=self.company_id, name=name, is_custom=False).first()
predictor_record = self._try_outdate_db_status(predictor_record)
model = predictor_record.data
if model is None or model['status'] == 'training':
try:
self.fs_store.get(name, f'predictor_{self.company_id}_{predictor_record.id}', self.config['paths']['predictors'])
new_model_data = mindsdb_native.F.get_model_data(name)
except Exception:
new_model_data = None
if predictor_record.data is None or (new_model_data is not None and len(new_model_data) > len(predictor_record.data)):
predictor_record.data = new_model_data
model = new_model_data
session.commit()
# Make some corrections for databases not to break when dealing with empty columns
if db_fix:
data_analysis = model['data_analysis_v2']
for column in model['columns']:
analysis = data_analysis.get(column)
if isinstance(analysis, dict) and (len(analysis) == 0 or analysis.get('empty', {}).get('is_empty', False)):
data_analysis[column]['typing'] = {
'data_subtype': DATA_SUBTYPES.INT
}
model['created_at'] = str(parse_datetime(str(predictor_record.created_at).split('.')[0]))
model['updated_at'] = str(parse_datetime(str(predictor_record.updated_at).split('.')[0]))
model['predict'] = predictor_record.to_predict
model['update'] = predictor_record.update_status
return self._pack(model)
def get_models(self):
from mindsdb.interfaces.storage.db import session, Predictor
models = []
predictor_records = Predictor.query.filter_by(company_id=self.company_id, is_custom=False)
predictor_names = [
x.name for x in predictor_records
]
for model_name in predictor_names:
try:
if self.ray_based:
model_data = self.get_model_data(model_name, db_fix=False)
else:
bin = self.get_model_data(model_name, db_fix=False)
model_data = pickle.loads(bin.data)
reduced_model_data = {}
for k in ['name', 'version', 'is_active', 'predict', 'status', 'current_phase', 'accuracy', 'data_source', 'update']:
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)
except Exception as e:
log.error(f"Can't list data for model: '{model_name}' when calling `get_models(), error: {e}`")
return self._pack(models)
def delete_model(self, name):
from mindsdb_native import F
from mindsdb_native.libs.constants.mindsdb import DATA_SUBTYPES
from mindsdb.interfaces.storage.db import session, Predictor
predictor_record = Predictor.query.filter_by(company_id=self.company_id, name=name, is_custom=False).first()
id = predictor_record.id
session.delete(predictor_record)
session.commit()
F.delete_model(name)
self.dbw.unregister_predictor(name)
self.fs_store.delete(f'predictor_{self.company_id}_{id}')
return 0
def update_model(self, name):
from mindsdb_native import F
from mindsdb_worker.updater.update_model import update_model
from mindsdb.interfaces.storage.db import session, Predictor
from mindsdb.interfaces.datastore.datastore import DataStore
try:
predictor_record = Predictor.query.filter_by(company_id=self.company_id, name=name, is_custom=False).first()
predictor_record.update_status = 'updating'
session.commit()
update_model(name, self.delete_model, F.delete_model, self.learn, self._lock_context, self.company_id, self.config['paths']['predictors'], predictor_record, self.fs_store, DataStore())
predictor_record = self._update_db_status(predictor_record)
except Exception as e:
log.error(e)
predictor_record.update_status = 'update_failed'
session.commit()
return str(e)
class CustomModels():
def __init__(self):
self.config = Config()
self.fs_store = FsSotre()
self.company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
self.dbw = DatabaseWrapper()
self.storage_dir = self.config['paths']['custom_models']
os.makedirs(self.storage_dir, exist_ok=True)
self.model_cache = {}
self.mindsdb_native = NativeInterface()
self.dbw = DatabaseWrapper()
def _dir(self, name):
return str(os.path.join(self.storage_dir, name))
def _internal_load(self, name):
self.fs_store.get(name, f'custom_model_{self.company_id}_{name}',
self.storage_dir)
sys.path.insert(0, self._dir(name))
module = __import__(name)
try:
model = module.Model.load(
os.path.join(self._dir(name), 'model.pickle'))
except Exception as e:
model = module.Model()
model.initialize_column_types()
if hasattr(model, 'setup'):
model.setup()
self.model_cache[name] = model
return model
def learn(self, name, from_data, to_predict, datasource_id, kwargs={}):
model_data = self.get_model_data(name)
model_data['status'] = 'training'
self.save_model_data(name, model_data)
to_predict = to_predict if isinstance(to_predict,
list) else [to_predict]
data_source = getattr(mindsdb_datasources,
from_data['class'])(*from_data['args'],
**from_data['kwargs'])
data_frame = data_source.df
model = self._internal_load(name)
model.to_predict = to_predict
model_data = self.get_model_data(name)
model_data['predict'] = model.to_predict
self.save_model_data(name, model_data)
data_analysis = self.mindsdb_native.analyse_dataset(
data_source)['data_analysis_v2']
model_data = self.get_model_data(name)
model_data['data_analysis_v2'] = data_analysis
self.save_model_data(name, model_data)
model.fit(data_frame, to_predict, data_analysis, kwargs)
model.save(os.path.join(self._dir(name), 'model.pickle'))
self.model_cache[name] = model
model_data = self.get_model_data(name)
model_data['status'] = 'completed'
model_data['columns'] = list(data_analysis.keys())
self.save_model_data(name, model_data)
self.fs_store.put(name, f'custom_model_{self.company_id}_{name}',
self.storage_dir)
self.dbw.unregister_predictor(name)
self.dbw.register_predictors([self.get_model_data(name)])
def predict(self, name, when_data=None, from_data=None, kwargs=None):
self.fs_store.get(name, f'custom_model_{self.company_id}_{name}',
self.storage_dir)
if kwargs is None:
kwargs = {}
if from_data is not None:
if isinstance(from_data, dict):
data_source = getattr(mindsdb_datasources, from_data['class'])(
*from_data['args'], **from_data['kwargs'])
# assume that particular instance of any DataSource class is provided
else:
data_source = from_data
data_frame = data_source.df
elif when_data is not None:
if isinstance(when_data, dict):
for k in when_data:
when_data[k] = [when_data[k]]
data_frame = pd.DataFrame(when_data)
else:
data_frame = pd.DataFrame(when_data)
model = self._internal_load(name)
predictions = model.predict(data_frame, kwargs)
pred_arr = []
for i in range(len(predictions)):
pred_arr.append({})
pred_arr[-1] = {}
for col in predictions.columns:
pred_arr[-1][col] = {}
pred_arr[-1][col]['predicted_value'] = predictions[col].iloc[i]
return pred_arr
def get_model_data(self, name):
predictor_record = Predictor.query.filter_by(
company_id=self.company_id, name=name, is_custom=True).first()
return predictor_record.data
def save_model_data(self, name, data):
predictor_record = Predictor.query.filter_by(
company_id=self.company_id, name=name, is_custom=True).first()
if predictor_record is None:
predictor_record = Predictor(company_id=self.company_id,
name=name,
is_custom=True,
data=data)
session.add(predictor_record)
else:
predictor_record.data = data
session.commit()
def get_models(self):
predictor_names = [
x.name
for x in Predictor.query.filter_by(company_id=self.company_id,
is_custom=True)
]
models = []
for name in predictor_names:
models.append(self.get_model_data(name))
return models
def delete_model(self, name):
Predictor.query.filter_by(company_id=self.company_id,
name=name,
is_custom=True).delete()
session.commit()
shutil.rmtree(self._dir(name))
self.dbw.unregister_predictor(name)
self.fs_store.delete(f'custom_model_{self.company_id}_{name}')
def rename_model(self, name, new_name):
self.fs_store.get(name, f'custom_model_{self.company_id}_{name}',
self.storage_dir)
self.dbw.unregister_predictor(name)
shutil.move(self._dir(name), self._dir(new_name))
shutil.move(os.path.join(self._dir(new_name) + f'{name}.py'),
os.path.join(self._dir(new_name), f'{new_name}.py'))
predictor_record = Predictor.query.filter_by(
company_id=self.company_id, name=name, is_custom=True).first()
predictor_record.name = new_name
session.commit()
self.dbw.register_predictors([self.get_model_data(new_name)])
self.fs_store.put(name, f'custom_model_{self.company_id}_{new_name}',
self.storage_dir)
self.fs_store.delete(f'custom_model_{self.company_id}_{name}')
def export_model(self, name):
shutil.make_archive(base_name=name,
format='zip',
root_dir=self._dir(name))
return str(self._dir(name)) + '.zip'
def load_model(self, fpath, name, trained_status):
shutil.unpack_archive(fpath, self._dir(name), 'zip')
shutil.move(os.path.join(self._dir(name), 'model.py'),
os.path.join(self._dir(name), f'{name}.py'))
model = self._internal_load(name)
model.to_predict = model.to_predict if isinstance(
model.to_predict, list) else [model.to_predict]
self.save_model_data(
name, {
'name': name,
'data_analysis_v2': model.column_type_map,
'predict': model.to_predict,
'status': trained_status,
'is_custom': True,
'columns': list(model.column_type_map.keys())
})
with open(os.path.join(self._dir(name), '__init__.py'), 'w') as fp:
fp.write('')
self.fs_store.put(name, f'custom_model_{self.company_id}_{name}',
self.storage_dir)
if trained_status == 'trained':
self.dbw.register_predictors([self.get_model_data(name)])
class NativeInterface():
def __init__(self):
self.config = Config()
self.fs_store = FsSotre()
self.company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
self.dbw = DatabaseWrapper()
self.predictor_cache = {}
def _invalidate_cached_predictors(self):
# @TODO: Cache will become stale if the respective NativeInterface 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 _setup_for_creation(self, name):
if name in self.predictor_cache:
del self.predictor_cache[name]
# Here for no particular reason, because we want to run this sometimes but not too often
self._invalidate_cached_predictors()
predictor_dir = Path(self.config.paths['predictors']).joinpath(name)
create_directory(predictor_dir)
predictor_record = Predictor(company_id=self.company_id,
name=name,
is_custom=False)
session.add(predictor_record)
session.commit()
def create(self, name):
self._setup_for_creation(name)
predictor = mindsdb_native.Predictor(name=name,
run_env={'trigger': 'mindsdb'})
return predictor
def learn(self, name, from_data, to_predict, datasource_id, kwargs={}):
join_learn_process = kwargs.get('join_learn_process', False)
if 'join_learn_process' in kwargs:
del kwargs['join_learn_process']
self._setup_for_creation(name)
p = LearnProcess(name, from_data, to_predict, kwargs, datasource_id)
p.start()
if join_learn_process is True:
p.join()
if p.exitcode != 0:
raise Exception('Learning process failed !')
def predict(self, name, when_data=None, kwargs={}):
try:
original_process_title = setproctitle.getproctitle()
setproctitle.setproctitle('mindsdb_native_process')
except Exception:
pass
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 = {}
predictor_record = Predictor.query.filter_by(
company_id=self.company_id, name=name,
is_custom=False).first()
if predictor_record.data['status'] == 'complete':
self.fs_store.get(
name, f'predictor_{self.company_id}_{predictor_record.id}',
self.config['paths']['predictors'])
self.predictor_cache[name] = {
'predictor':
mindsdb_native.Predictor(name=name,
run_env={'trigger': 'mindsdb'}),
'created':
datetime.datetime.now()
}
predictions = self.predictor_cache[name]['predictor'].predict(
when_data=when_data, **kwargs)
try:
setproctitle.setproctitle(original_process_title)
except Exception:
pass
return predictions
# @TODO Move somewhere else to avoid circular import issues in the future
def analyse_dataset(self, ds):
return F.analyse_dataset(ds)
def get_model_data(self, name, db_fix=True):
predictor_record = Predictor.query.filter_by(
company_id=self.company_id, name=name, is_custom=False).first()
model = predictor_record.data
if model is None or model['status'] == 'training':
try:
self.fs_store.get(
name, f'predictor_{self.company_id}_{predictor_record.id}',
self.config['paths']['predictors'])
new_model_data = mindsdb_native.F.get_model_data(name)
except Exception:
new_model_data = None
if predictor_record.data is None or (
new_model_data is not None
and len(new_model_data) > len(predictor_record.data)):
predictor_record.data = new_model_data
model = new_model_data
session.commit()
# Make some corrections for databases not to break when dealing with empty columns
if db_fix:
data_analysis = model['data_analysis_v2']
for column in model['columns']:
analysis = data_analysis.get(column)
if isinstance(analysis,
dict) and (len(analysis) == 0 or analysis.get(
'empty', {}).get('is_empty', False)):
data_analysis[column]['typing'] = {
'data_subtype': DATA_SUBTYPES.INT
}
return model
def get_models(self):
models = []
predictor_names = [
x.name
for x in Predictor.query.filter_by(company_id=self.company_id,
is_custom=False)
]
for model_name in predictor_names:
try:
model_data = self.get_model_data(model_name, db_fix=False)
if model_data['status'] == 'training' and parse_datetime(
model_data['created_at']) < parse_datetime(
self.config['mindsdb_last_started_at']):
continue
reduced_model_data = {}
for k in [
'name', 'version', 'is_active', 'predict', 'status',
'current_phase', 'accuracy', 'data_source'
]:
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
print(
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)
except Exception as e:
print(
f"Can't list data for model: '{model_name}' when calling `get_models(), error: {e}`"
)
return models
def delete_model(self, name):
predictor_record = Predictor.query.filter_by(
company_id=self.company_id, name=name, is_custom=False).first()
id = predictor_record.id
session.delete(predictor_record)
session.commit()
F.delete_model(name)
self.dbw.unregister_predictor(name)
self.fs_store.delete(f'predictor_{self.company_id}_{id}')