從類訪問SQLalchemy

Question

我正在做一個項目，我需要創建一個具有連接到數據庫並從中獲取數據（我使用SQLite作爲後端）的方法的類實例。 我已經有了一些flask-sqlalchemy的經驗，但是當談到純粹的SQLAlchemy時，我迷失了方向。 概念如下： 用戶創建DataSet的實例，並將路徑作爲__init__參數傳遞給數據庫。如果數據庫已經存在，我只想連接它並執行查詢，如果不存在，我想使用模型創建一個新的查詢。但我無法理解如何去做。

這裏的DataSet代碼：

from os.path import normcase, split, join, isfile 
from sqlalchemy import create_engine 
from sqlalchemy.orm import sessionmaker 
import errors 
import trainset 
import testset 


class DataSet: 
    def __init__(self, path_to_set, path_to_db, train_set=False, path_to_labels=None, label_dict=None, 
       custom_name=None): 
     self.__path_to_set = path_to_set 
     self.__label_dict = label_dict 

     if custom_name is None: 
      dbpath = join(path_to_db, 'train.db') 
      if train_set is False: 
       dbpath = join(path_to_db, 'test.db') 
     else: 
      dbpath = join(path_to_db, custom_name) 
     if isfile(dbpath): 
      self.__prepopulated = True 
     else: 
      self.__prepopulated = False 
     self.__dbpath = dbpath 

     if train_set is True and path_to_labels is None: 
      raise errors.InsufficientData('labels', 'specified') 
     if train_set is True and not isfile(path_to_labels): 
      raise errors.InsufficientData('labels', 'found at specified path', path_to_labels) 

    def prepopulate(self): 
     engine = create_engine('sqlite:////' + self.__dbpath) 
     self.__prepopulated = True 

這裏的trainset代碼：

from sqlalchemy.ext.declarative import declarative_base 
from sqlalchemy import Column, String, PickleType, Integer, MetaData 

Base = declarative_base() 
metadata = MetaData() 


class TrainSet(Base): 
    __tablename__ = 'train set' 
    id = Column(Integer, primary_key=True) 
    real_id = Column(String(60)) 
    path = Column(String(120)) 
    labels = Column(PickleType) 
    features = Column(PickleType) 

這裏的testset代碼：

from sqlalchemy.ext.declarative import declarative_base 
from sqlalchemy import Column, String, PickleType, Integer, MetaData 

Base = declarative_base() 
metadata = MetaData() 


class TestSet(Base): 
    __tablename__ = 'test set' 
    id = Column(Integer, primary_key=True) 
    real_id = Column(String(60)) 
    path = Column(String(120)) 
    features = Column(PickleType) 

因此，如果用戶創建時通過train_set=TrueDataSet ins我想創建一個使用TrainSet模型的數據庫，否則創建一個TestSet數據庫。我希望在prepopulate方法中發生這種情況，但是，我不明白該怎麼做 - 文檔調用了這個：Base.metadata.create_all(engine)，但我失去了將代碼放在哪裏。

Answer 1

首先保存參數train_set：

class DataSet: 
    def __init__(self, path_to_set, path_to_db, train_set=False, path_to_labels=None, label_dict=None, 
       custom_name=None): 
     self._train_set = train_set 
     # ... 

然後，在prepopulate使用它來創建正確的模型（S）：

def prepopulate(self): 
    engine = create_engine('sqlite:////' + self.__dbpath) 
    if self._train_set: 
     trainset.Base.create_all(engine) 
    else: 
     testset.Base.create_all(engine) 
    self.__prepopulated = True 

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一兩件事：不要前綴你的「私人」變量帶雙下劃線。請參閱PEP 8 -- Style Guide for Python Code以供參考。