pmml

问题 Is there a standard (or available) way to export a gbm model in R? PMML would work, but when I I try to use the pmml library, perhaps incorrectly, I get an error: For example, my code looks similar to this: library("gbm") library("pmml") model <- gbm( formula, data = my.data, distribution = "adaboost", n.trees = 450, n.minobsinnode = 10, interaction.depth = 4, shrinkage=0.05, verbose=TRUE) export <- pmml(model) # and then export to xml And the error I get is: Error in UseMethod("pmml") : no

问题 Is there a standard (or available) way to export a gbm model in R? PMML would work, but when I I try to use the pmml library, perhaps incorrectly, I get an error: For example, my code looks similar to this: library("gbm") library("pmml") model <- gbm( formula, data = my.data, distribution = "adaboost", n.trees = 450, n.minobsinnode = 10, interaction.depth = 4, shrinkage=0.05, verbose=TRUE) export <- pmml(model) # and then export to xml And the error I get is: Error in UseMethod("pmml") : no

问题 I know that it's possible to export models as PMML with Spark-MLlib , but what about Spark-ML ? Is it possible to convert LinearRegressionModel from org.apache.spark.ml.regression to a LinearRegressionModel from org.apache.spark.mllib.regression to be able to invoke the toPMML() method? 回答1: You can convert Spark ML pipelines to PMML using the JPMML-SparkML library: StructType schema = dataFrame.schema() PipelineModel pipelineModel = pipeline.fit(dataFrame); org.dmg.pmml.PMML pmml = org.jpmml

问题 I know that it's possible to export models as PMML with Spark-MLlib , but what about Spark-ML ? Is it possible to convert LinearRegressionModel from org.apache.spark.ml.regression to a LinearRegressionModel from org.apache.spark.mllib.regression to be able to invoke the toPMML() method? 回答1: You can convert Spark ML pipelines to PMML using the JPMML-SparkML library: StructType schema = dataFrame.schema() PipelineModel pipelineModel = pipeline.fit(dataFrame); org.dmg.pmml.PMML pmml = org.jpmml

问题 I am working with Maven and I am getting errors with compiling and building my project. It is the jpmml-project to evaluate some pmml file. Now I got this error: Rule 0: org.apache.maven.plugins.enforcer.RequireMavenVersion failed with message: Detected Maven Version: 3.0.5 is not in the allowed range 3.2. Check the link to see the image: https://www.dropbox.com/s/3r9d8g8l4r1zctp/maven_error.png?dl=0 Please help! I do not have too much experience with Java and Maven. 回答1: Your pom.xml

问题 So this is a slightly insane idea that I've been kicking around. I'm thinking of building a classifier for (a specialized subset of) some web content, and then supplying it to users in the form of a chrome extension. (So: user goes to web page, clicks button on chrome extension, extension applies pre-trained model to generate prediction, presents result to user.) But I'm cheap, and I don't want to run a server to receive data from the chrome extension, generate a prediction, and send it back

问题 i have build a python file based on randomforestclassifier and made a PMML model, now we need to use this PMML in Java to classify the data into 2 catagories..but this is new to me and i don't know how to handle the java part.. 回答1: Google: pmml java Second Link is https://github.com/jpmml/jpmml-evaluator There you have a library with examples. So try it and come back if you have problems. 回答2: Another choice is PMML4S that is implemented in Scala, but you are free to use it by Scala or Java

问题 I have a randomforest model that I am trying to convert into a pmml. I could fit the model properly, as it doesn't throw any errors: test_pipeline = PMMLPipeline([("rforest", RandomForestClassifier())]) test_pipeline.fit(trainX, trainY) CPU times: user 1.18 s, sys: 61.6 ms, total: 1.24 s Wall time: 1.25 s However I get an error when I try to call the sklearn2pmml method: sklearn2pmml(test_pipeline, "DecisionTreeIris.pmml", with_repr = True) Error: ---------------------------------------------

问题 Short question : is there a PMML interface for Java, Python or any other major programming language that supports SVM models? Background : I have trained an SVM model in R using the ksvm function from the kernlab package. Now I would like to use this model in a different piece of software. To export it from R, I think I could use PMML (Predictive Model Markup Language) via the pmml package for R. However, I can't find any library for Java, Python, or basically any language that allows for

问题 There seem to be a few options for exporting PMML models out of scikit-learn, such as sklearn2pmml, but a lot less information going in the other direction. My case is an XGboost model previously built in R, and saved to PMML using r2pmml, that I would like to use in Python. Scikit normally uses pickle to save/load models, but is it also possible to import models into scikit-learn using PMML? 回答1: You can't connect different specialized representations (such as R and Scikit-Learn native data