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 CustomModels():
def __init__(self, config):
self.config = config
self.dbw = DatabaseWrapper(self.config)
self.storage_dir = os.path.join(config['storage_dir'], 'misc')
os.makedirs(self.storage_dir, exist_ok=True)
self.model_cache = {}
self.mindsdb_native = NativeInterface(self.config)
self.dbw = DatabaseWrapper(self.config)
def _dir(self, name):
return str(os.path.join(self.storage_dir, 'custom_model_' + name))
def _internal_load(self, name):
# Caching (2 lines bellow), currently disabled due to multiprocessing cache invalidation issues
#if name in self.model_cache:
# return self.model_cache[name]
# "Proper" model loading (3 lines bellow), currently disabled due to pickling issues
#spec = importlib.util.spec_from_file_location(name, self._dir(name) + '/model.py')
#module = importlib.util.module_from_spec(spec)
#spec.loader.exec_module(module)
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, 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_native,
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'
self.save_model_data(name, model_data)
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):
if kwargs is None:
kwargs = {}
if from_data is not None:
if isinstance(from_data, dict):
data_source = getattr(mindsdb_native, 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):
with open(os.path.join(self._dir(name), 'metadata.json'), 'r') as fp:
return json.load(fp)
def save_model_data(self, name, data):
with open(os.path.join(self._dir(name), 'metadata.json'), 'w') as fp:
json.dump(data, fp)
def get_models(self):
models = []
for model_dir in os.listdir(self.storage_dir):
if 'custom_model_' in model_dir:
name = model_dir.replace('custom_model_', '')
try:
models.append(self.get_model_data(name))
except:
print(f'Model {name} not found !')
return models
def delete_model(self, name):
shutil.rmtree(self._dir(name))
self.dbw.unregister_predictor(name)
def rename_model(self, name, new_name):
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'))
self.dbw.register_predictors([self.get_model_data(new_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
})
with open(os.path.join(self._dir(name), '__init__.py'), 'w') as fp:
fp.write('')
if trained_status == 'trained':
self.dbw.register_predictors([self.get_model_data(name)])
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_native,
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_native, 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 DataStore():
def __init__(self, config):
self.config = config
self.dir = config.paths['datasources']
self.mindsdb_native = NativeInterface(config)
def get_analysis(self, ds):
return self.mindsdb_native.analyse_dataset(self.get_datasource_obj(ds))
def get_datasources(self):
datasource_arr = []
for ds_name in os.listdir(self.dir):
try:
with open(os.path.join(self.dir, ds_name, 'metadata.json'),
'r') as fp:
try:
datasource = json.load(fp)
datasource['created_at'] = parse_dt(
datasource['created_at'].split('.')[0])
datasource['updated_at'] = parse_dt(
datasource['updated_at'].split('.')[0])
datasource_arr.append(datasource)
except Exception as e:
print(e)
except Exception as e:
print(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)
data = filtered_ds.to_dict(orient='records')
return {
'data': data,
'rowcount': len(ds),
'columns_names': filtered_ds.columns
}
def get_datasource(self, name):
for ds in self.get_datasources():
if ds['name'] == name:
return ds
return None
def delete_datasource(self, name):
shutil.rmtree(os.path.join(self.dir, name))
def save_datasource(self, name, source_type, source, file_path=None):
if source_type == 'file' and (file_path is None):
raise Exception(
'`file_path` argument required when source_type == "file"')
for i in range(1, 1000):
if name in [x['name'] for x in self.get_datasources()]:
previous_index = i - 1
name = name.replace(f'__{previous_index}__', '')
name = f'{name}__{i}__'
else:
break
ds_meta_dir = os.path.join(self.dir, name)
os.mkdir(ds_meta_dir)
try:
if source_type == 'file':
source = os.path.join(ds_meta_dir, source)
shutil.move(file_path, source)
ds = FileDS(source)
picklable = {'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']:
picklable = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'user': integration['user'],
'password': integration['password'],
'host': integration['host'],
'port': integration['port']
}
}
ds = dsClass(**picklable['kwargs'])
elif integration['type'] in [
'mssql', 'postgres', 'mariadb', 'mysql'
]:
picklable = {
'class': dsClass.__name__,
'args': [],
'kwargs': {
'query': source['query'],
'user': integration['user'],
'password': integration['password'],
'host': integration['host'],
'port': integration['port']
}
}
if 'database' in integration:
picklable['kwargs']['database'] = integration[
'database']
if 'database' in source:
picklable['kwargs']['database'] = source['database']
ds = dsClass(**picklable['kwargs'])
elif integration['type'] == 'snowflake':
picklable = {
'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(**picklable['kwargs'])
elif integration['type'] == 'mongodb':
if isinstance(source['find'], str):
source['find'] = json.loads(source['find'])
picklable = {
'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(**picklable['kwargs'])
else:
# This probably only happens for urls
ds = FileDS(source)
picklable = {'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 name')
with open(os.path.join(ds_meta_dir, 'ds.pickle'), 'wb') as fp:
pickle.dump(picklable, fp)
with open(os.path.join(ds_meta_dir, 'metadata.json'), 'w') as fp:
meta = {
'name': name,
'source_type': source_type,
'source': source,
'created_at': str(datetime.datetime.now()).split('.')[0],
'updated_at': str(datetime.datetime.now()).split('.')[0],
'row_count': len(df),
'columns': [dict(name=x) for x in list(df.keys())]
}
json.dump(meta, fp, indent=4, sort_keys=True)
with open(os.path.join(ds_meta_dir, 'versions.json'), 'wt') as fp:
json.dump(self.config.versions, fp, indent=4, sort_keys=True)
except Exception:
if os.path.isdir(ds_meta_dir):
shutil.rmtree(ds_meta_dir)
raise
return self.get_datasource_obj(name, raw=True), name
def get_datasource_obj(self, name, raw=False):
ds_meta_dir = os.path.join(self.dir, name)
ds = None
try:
with open(os.path.join(ds_meta_dir, 'ds.pickle'), 'rb') as fp:
picklable = pickle.load(fp)
if raw:
return picklable
try:
ds = eval(picklable['class'])(*picklable['args'],
**picklable['kwargs'])
except Exception:
ds = picklable
return ds
except Exception as e:
print(f'\n{e}\n')
return None