将模型导出到 PMML

Question

我有标记数据、耦合分类变量和两个二元目标变量。

例如

header；

column_1,column_2,column_3,column_4,target_1,target_1

如何将其导出到 PMML？我发现的唯一例子是无监督数据

import pandas

iris_df = pandas.read_csv("Iris.csv")

from sklearn2pmml import PMMLPipeline
from sklearn2pmml.decoration import ContinuousDomain
from sklearn_pandas import DataFrameMapper
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LogisticRegression

iris_pipeline = PMMLPipeline([
    ("mapper", DataFrameMapper([
        (["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"], [ContinuousDomain(), Imputer()])
    ])),
    ("pca", PCA(n_components = 3)),
    ("selector", SelectKBest(k = 2)),
    ("classifier", LogisticRegression())
])
iris_pipeline.fit(iris_df, iris_df["Species"])

from sklearn2pmml import sklearn2pmml

sklearn2pmml(iris_pipeline, "LogisticRegressionIris.pmml", with_repr = True)

Answer 1

所提供的示例是关于监督分类的 - Pipeline#fit(X, y) 方法的 y 参数是标签。

您的案例如下所示：

pipeline = PMMLPipeline(
  ("mapper", DataFrameMapper([
    (feature_column, LabelBinarizer()) for feature_column in ["column_1", "column_2", "column_3", "column_4"] 
  ])),
  ("classifier", LogisticClassification())
)
pipeline.fit(df, df["target_1"])

将模型导出到 PMML

export model to PMML

python

pmml

sklearn-pandas