我希望在sklearn中训练模型并通过PMML将其导出到其他环境(使用https://github.com/jpmml/sklearn2pmml)
我能够从普通(k-Nearest Neighbors)模型(没有GridSearch)生成PMML,但是使用GridSearch获得以下错误 -
错误有意义(因为GridSearchCV不返回PMMLPipeline),但是正在寻找关于如何将优化(w / GridSearch)模型导出到PMML的想法(例如,有没有办法在PMMLPipeline中包含GridSearch)。
以下代码 - 任何想法的TIA。
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import GridSearchCV
knn_pipe = PMMLPipeline([
("regressor", KNeighborsRegressor())
])
param_grid = {"regressor__n_neighbors": [3, 2,10],
"regressor__weights": ["uniform","distance"],
"regressor__algorithm": ["auto", "ball_tree", "kd_tree"]}
cv = GridSearchCV(knn_pipe, param_grid=param_grid)
print(train.drop('y',axis=1).shape)
cv.fit(X,Y)
best_parameters = cv.best_estimator_.get_params()
print("best parameter = {}".format(best_parameters))
from sklearn2pmml import sklearn2pmml
sklearn2pmml(cv, "kNNMercedes.pmml", with_repr = True)
['regressor__algorithm', 'regressor__n_neighbors', 'regressor__metric',
'regressor__leaf_size', 'regressor', 'regressor__p',
'regressor__metric_params', 'steps', 'regressor__n_jobs',
'regressor__weights']
(4209, 365)
best parameter = {'regressor__algorithm': 'auto', 'regressor__n_neighbors':
10, 'regressor__metric': 'minkowski', 'regressor__leaf_size': 30, 'regressor':
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=10, p=2,
weights='distance'), 'regressor__p': 2, 'regressor__metric_params': None, 'steps': [('regressor', KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=10, p=2,
weights='distance'))], 'regressor__n_jobs': 1, 'regressor__weights':
'distance'}
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-38-d548c1bff799> in <module>()
30
31 from sklearn2pmml import sklearn2pmml
---> 32 sklearn2pmml(cv, "kNNMercedes.pmml", with_repr = True)
33
34 print("yeay PMML!")
/Users/venuv/.local/lib/python2.7/site-packages/sklearn2pmml/__init__.pyc in
sklearn2pmml(pipeline, pmml, user_classpath, with_repr, debug)
125 print("sklearn2pmml: ", __version__)
126 if(not isinstance(pipeline, PMMLPipeline)):
--> 127 raise TypeError("The pipeline object is not an
instance of " + PMMLPipeline.__name__)
128 cmd = ["java", "-cp", os.pathsep.join(_package_classpath() + user_classpath), "org.jpmml.sklearn.Main"]
129 dumps = []
TypeError: The pipeline object is not an instance of PMMLPipeline
答案 0 :(得分:0)
解决方案是在拟合的PMMLPipeline
实例上构建GridSearchCV
实例:
pipeline = PMMLPipeline([
("best_estimator", cv.best_estimator_)
])
sklearn2pmml(pipeline, "pipeline.pmml")
User @ vivek-kumar已经在JPMML-SkLearn项目中报告了这个问题,并在那里收到了一些额外的评论。见jpmml/jpmml-sklearn#42