Sklearn PMML of Python: introduction, installation and usage of sklearn PMML

A Virgo procedural ape 2020-11-13 05:52:19
sklearn pmml python introduction installation


Python And sklearn-pmml:sklearn-pmml An introduction to the 、 install 、 A detailed introduction to how to use

 

 

 

Catalog

sklearn-pmml An introduction to the

1、 classification

2、 Return to

sklearn-pmml Installation

sklearn-pmml How to use

1、 preservation GBDT The model is pmml File and load


 

 

 

 

 

sklearn-pmml An introduction to the

        A permit will SciKit-Learn The estimator is serialized to PMML The library of .PMML Output . The classifier converter can only operate on the classification output , For each category output variable “varname”,PMML The output contains the following output :- Classification of instance prediction tags “varname”- double “varname”. label ' Represents the probability of a given label . The regression model PMML Output numerical response variable as output variable . Supported models :

  • DecisionTreeClassifier
  • DecisionTreeRegressor
  • GradientBoostingClassifier
  • RandomForestClassifier

1、 classification

The classifier converter can only operate on the classification output , For each category output variable varname, PMML The output contains the following output :

  • Classification of prediction tags for instances varname
  • varname twice as much . The tag represents the probability of a given tag

 

2、 Return to

The regression model PMML Output numerical response variable as output variable

 

 

 

sklearn-pmml Installation

pip install sklearn-pmml
pip install --user -i https://pypi.tuna.tsinghua.edu.cn/simple sklearn-pmml

 

 

sklearn-pmml How to use

1、 preservation GBDT The model is pmml File and load

GBDT = GradientBoostingClassifier(random_state=123,max_depth=5,min_samples_split=10)
clf = PMMLPipeline([('vecd', DictVectorizer(sparse=False)), ('classifier', GBDT)])
vec = DictVectorizer(sparse=False)
clf.fit(X_train_dict, y_train)
y_predict = clf.predict(X_test_dict)
print(clf.score(X_test_dict, y_test))
print(classification_report(y_predict, y_test, target_names=['died', 'survivied']))
print(roc_auc_score(y_test,y_predict))
sklearn2pmml(clf, 'Model.pmml', with_repr=True, debug=True)
from pypmml import Model
model=Model.fromFile('Model.pmml')
X = titanic[['pclass', 'age', 'sex',"room"]]
ret=model.predict(X_test);
print(ret)
auc=roc_auc_score(y_test,round(ret['probability(1)']))
print(auc)

 

 

 

 

 

 

 

 

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