7.4.2 データストアへのオブジェクトの保存

oml.ds.save関数は、1つ以上のPythonオブジェクトをデータストアに保存します。

OML4Pyでは、現在のユーザーのスキーマにデータストアが作成されます。

oml.ds.saveの構文は次のとおりです。

oml.ds.save(objs, name, description=' ', grantable=None,
                      overwrite=False, append=False, compression=False)

objs引数は、name引数で指定したデータストアに保存する名前とオブジェクトのペアを含むdictです。

description引数を使用すると、データストアの情報取得時に表示される説明テキストを指定できます。descriptionパラメータは、appendパラメータとともに使用すると機能しません。

grantable引数を使用すると、データストアへの読取り権限を他のユーザーに付与できるかどうかを指定できます。

overwrite引数をTRUEに設定した場合、既存のデータストアを同じ名前の別のデータストアで置換できます。

append引数をTRUEに設定した場合、既存のデータストアにオブジェクトを追加できます。overwriteおよびappendの引数は相互に排他的です。

compressionをTrueに設定した場合、シリアライズされたPythonオブジェクトがデータストアで圧縮されます。

例7-14 データストアへのPythonオブジェクトの保存

この例では、データストアの作成を示します。

import oml
from sklearn import datasets
from sklearn import linear_model
import pandas as pd

# Load three data sets and create oml.DataFrame objects for them.
wine = datasets.load_wine()
x = pd.DataFrame(wine.data, columns = wine.feature_names)
y = pd.DataFrame(wine.target, columns = ['Class'])

# Create the database table WINE.
oml_wine = oml.create(pd.concat([x, y], axis=1), table = 'WINE')
oml_wine.columns

diabetes = datasets.load_diabetes()
x = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
y = pd.DataFrame(diabetes.target, columns=['disease_progression'])
oml_diabetes = oml.create(pd.concat([x, y], axis=1), 
                                    table = "DIABETES")
oml_diabetes.columns

boston = datasets.load_boston()
x = pd.DataFrame(boston.data, columns = boston.feature_names.tolist())
y = pd.DataFrame(boston.target, columns = ['Value'])
oml_boston = oml.create(pd.concat([x, y], axis=1), table = "BOSTON")
oml_boston.columns

# Save the wine Bunch object to the datastore directly, 
# along with the oml.DataFrame proxy object for the BOSTON table.
oml.ds.save(objs={'wine':wine, 'oml_boston':oml_boston},
            name="ds_pydata", description = "python datasets")

# Save the oml_diabetes proxy object to an existing datastore.
oml.ds.save(objs={'oml_diabetes':oml_diabetes},
                  name="ds_pydata", append=True)

# Save the oml_wine proxy object to another datastore.
oml.ds.save(objs={'oml_wine':oml_wine},
            name="ds_wine_data", description = "wine dataset")

# Create regression models using sklearn and oml.
# The regr1 linear model is a native Python object.
regr1 = linear_model.LinearRegression()
regr1.fit(boston.data, boston.target)
# The regr2 GLM model is an oml object.
regr2 = oml.glm("regression")
X = oml_boston.drop('Value')
y = oml_boston['Value']
regr2 = regr2.fit(X, y)

# Save the native Python object and the oml proxy object to a datastore
# and allow the read privilege to be granted to them.
oml.ds.save(objs={'regr1':regr1, 'regr2':regr2},
            name="ds_pymodel", grantable=True)

# Grant the read privilege to the datastore to every user.
oml.grant(name="ds_pymodel", typ="datastore", user=None)

# List the datastores to which the read privilege has been granted.
oml.ds.dir(dstype="grant")

この例のリスト

>>> import oml
>>> from sklearn import datasets
>>> from sklearn import linear_model
>>> import pandas as pd
>>>
>>> # Load three data sets and create oml.DataFrame objects for them.
>>> wine = datasets.load_wine()
>>> x = pd.DataFrame(wine.data, columns = wine.feature_names)
>>> y = pd.DataFrame(wine.target, columns = ['Class'])
>>> 
>>> # Create the database table WINE.
... oml_wine = oml.create(pd.concat([x, y], axis=1), table = 'WINE')
>>> oml_wine.columns
['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline', 'Class']
>>>
>>> diabetes = datasets.load_diabetes()
>>> x = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
>>> y = pd.DataFrame(diabetes.target, columns=['disease_progression'])
>>> oml_diabetes = oml.create(pd.concat([x, y], axis=1), 
...                           table = "DIABETES")
>>> oml_diabetes.columns
['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'disease_progression']
>>>
>>> boston = datasets.load_boston()
>>> x = pd.DataFrame(boston.data, columns = boston.feature_names.tolist())
>>> y = pd.DataFrame(boston.target, columns = ['Value'])
>>> oml_boston = oml.create(pd.concat([x, y], axis=1), table = "BOSTON")
>>> oml_boston.columns
['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'Value']
>>>
>>> # Save the wine Bunch object to the datastore directly, 
... # along with the oml.DataFrame proxy object for the BOSTON table.
... oml.ds.save(objs={'wine':wine, 'oml_boston':oml_boston},
...             name="ds_pydata", description = "python datasets")
>>>
>>> # Save the oml_diabetes proxy object to an existing datastore.                           
... oml.ds.save(objs={'oml_diabetes':oml_diabetes},
...                    name="ds_pydata", append=True)
>>>
>>> # Save the oml_wine proxy object to another datastore.
... oml.ds.save(objs={'oml_wine':oml_wine},
...             name="ds_wine_data", description = "wine dataset")
>>>
>>> # Create regression models using sklearn and oml.
... # The regr1 linear model is a native Python object.
... regr1 = linear_model.LinearRegression()          
>>> regr1.fit(boston.data, boston.target)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
>>> # The regr2 GLM model is an oml proxy object.
... regr2 = oml.glm("regression")
>>> X = oml_boston.drop('Value')
>>> y = oml_boston['Value']
>>> regr2 = regr2.fit(X, y)
>>>
>>> # Save the native Python object and the oml proxy object to a datastore
... # and allow the read privilege to be granted to them.
... oml.ds.save(objs={'regr1':regr1, 'regr2':regr2},
...             name="ds_pymodel", grantable=True)
>>>
>>> # Grant the read privilege to the ds_pymodel datastore to every user.
... oml.grant(name="ds_pymodel", typ="datastore", user=None)
>>>
>>> # List the datastores to which the read privilege has been granted.
... oml.ds.dir(dstype="grant")
  datastore_name grantee
0     ds_pymodel  PUBLIC