sklearn2pmml | Python library for converting Scikit-Learn pipelines to PMML | Machine Learning library
kandi X-RAY | sklearn2pmml Summary
kandi X-RAY | sklearn2pmml Summary
Python library for converting Scikit-Learn pipelines to PMML
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Top functions reviewed by kandi - BETA
- Convert a sklearn pipeline to pmml
- Return the java version
- Dump object to disk
- Return a list of the classpath
- Fit the model
- Compute mask for missing values
- Count the frequencies in the given mask
- Cast X to dtype
- Make an Xgboost dataframe mapper
- Return True if dtype is a categorical
- Check if dtype is categorical
- Transform valid values in X
- Replace missing values
- Construct a tree from training data
- Return a boolean mask for the input data
- Apply function to each row of rows
- Check if a GBDDT is supported
- Transform X
- Replace invalid values
- Check if the given gbdDT is supported
- Compute the duration of the time series
- Convert X to int
- Apply the apply transformation
- Apply the step
- Apply function to data
- Compute time series
sklearn2pmml Key Features
sklearn2pmml Examples and Code Snippets
Lines of Code : 12License : Strong Copyleft (CC BY-SA 4.0)
Copymapper = DataFrameMapper(
[(d, LabelBinarizer()) for d in dummies]
)
mapper = DataFrameMapper(
[(d, LabelEncoder()) for d in dummies]
)
lm = PMMLPipeline([("mapper", mapper),
("onehot", pipeline = PMMLPipeline([
("tuned-rf", GridSearchCV(RandomForestClassifier(..), param_grid = {..}))
])
pipeline.fit(X, y)
pipeline = PMMLPipeline([
("rf", RandomForestClassifier(..))
])
gridsearch = GridSearchCfrom sklearn.preprocessing import FunctionTransformer
import numpy as np
from sklearn2pmml import make_pmml_pipeline
# fake data with 7 columns
X = np.random.rand(10,7)
n_rows = X.shape[0]
def custom_function(X):
#averiging 4 first from sklearn_pandas import DataFrameMapper
from sklearn2pmml.preprocessing import Aggregator
pipeline = PMMLPipeline([
("mapper", DataFrameMapper([
(["stiffness_1", "stiffness_2", "stiffness_3", "stiffness_4"], Aggregator(function =pipeline = PMMLPipeline([('classifier', clf)])
pipeline.fit(x, y, classifier__gbdt__verbose = 2)
pmml_pipeline = PMMLPipeline([
("regressor", XGBRegressor())
])
tuner = GridSearchCV(pmml_pipeline, ...)
tuner.fit(X, y)
sklearn2pmml(tuner.best_estimator_, "xgbregressor-pipeline.pmml")
from sklearn.linear_model import LogisticRegression
from skleanr2pmml import sklearn2pmml
from sklearn2pmml.pipeline import PMMLPipeline
logreg = LogisticRegression()
logreg.classes_ = numpy.asarray([, ])
logreg.coef_ = numpy.asarray([])
transformer = ExpressionTransformer("1 if X['Y'] < 1 else X[1]")
org.dmg.pmml.PMML pmml = loadFromFile(..)
org.dmg.pmml.Visitor mfUpdater = new org.jpmml.model.visitors.AbstractVisitor(){
@Override
public VisitorAction visit(MiningField miningField){
miningField.setInvalidValueTreatment(InvalidVpipeline = PMMLPipeline([("mapper", DataFrameMapper([
(["balanced_data.type",
"balanced_data.amount",
Community Discussions
Trending Discussions on sklearn2pmml
QUESTION
I am exporting a PMMLPipeline with a categorical string feature day_of_week as a PMML file. When I open the file in Java and list the InputFields I see that the data type of day_of_week field is double:
ANSWER
Answered 2020-May-25 at 11:27You can assist SkLearn2PMML by providing "feature type hints" using sklearn2pmml.decoration.CategoricalDomain and sklearn2pmml.decoration.ContinuousDomain decorators (see here for more details).
In the current case, you should prepend a CategoricalDomain step to the pipeline that deals with categorical features:
QUESTION
I cannot convert the following pipeline to pmml because "the number of input features is not specified".
A minimal example pipeline that reproduces the error is:
...ANSWER
Answered 2020-May-25 at 07:56What's the point of creating a single-step sklearn2pmml.pipline.PMMLPipeline based on a sklearn.pipeline.Pipeline?
Leave out this no-op, and the conversion should succeed:
QUESTION
I used sklearn2pmml to serialize my decision tree classifier to a pmml file. I used pmml4s in java to deserialize the model and use it to predict.
Iuse the code below to make a prediction over a single incoming value. This should return either 0/1/2/3/4/5/6.
...ANSWER
Answered 2020-May-07 at 08:10It is certainty of model for each class. In your case it means that it's 4 with probability 94.5% or 5 with probability 5.5% In simple case, if you want to receive value, you should pick index for the maximal value.
However you might use this probabilities to additional control logic, like thresholding when decision is ambiguous (two values with probability ~0.4, etc.)
QUESTION
I want to save my XGBoost model as pmml using sklearn2pmml. I'm using Python V3.7.3 with Sklearn 0.20.3 & sklearn2pmml V0.53.0. My data is mainly binary, with just 3 columns of continuous data, I'm running my notebook in Databricks and convert my Spark dataframe to a pandas dataframe. Code snippet below
...ANSWER
Answered 2020-Feb-21 at 15:32It is probably a XGBoost package version issue. The SkLearn2PMML package expects the label encoder (XGBClassifier._le attribute) to be a "normal" Scikit-Learn label encoder class (sklearn.preprocessing.(label|_label).LabelEncoder), but in your case it's something different (xgboost.compat.XGBoostLabelEncoder).
In which XGBOost package version was this xgboost.compat.XGBoostLabelEncoder introduced? It's either some very old, or very new thing.
In any case, please open a feature request with the JPMML-SkLearn project here to have this issue sorted out.
QUESTION
I trained a model in Python using sklearn.neural_network.MLPClassifier (0.20.3) and saved it in PMML format using sklearn2pmml (0.48.0). The saved PMML model works as expected when loaded in Java using org.jpmml:pmml-evaluator:1.4.14.
I now want to load the PMML model and make predictions in C# using the Syncfusion package:
...ANSWER
Answered 2020-Jan-23 at 11:49We have checked sample PMML file using NeuralNetworkModelEvaluator and we couldn’t reproduce the issue. Can you share your PMML file to check our side and provide you the solution sooner.
Also, we would suggest you to try the below code,
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install sklearn2pmml
You can use sklearn2pmml like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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