sklearn-pmml-model 0.0.18

A library to parse PMML models into Scikit-learn estimators.

sklearn-pmml-model

A Python library that provides import functionality to all major estimator classes of the popular machine learning library scikit-learn using PMML.

Installation

The easiest way is to use pip:

$ pip install sklearn-pmml-model

Status

This library is in beta, and currently not all models are supported. The library currently does support the following models:

Model	Classification	Regression	Categorical features
Decision Trees	✅	✅	✅1
Random Forests	✅	✅	✅1
Gradient Boosting	✅	✅	✅1
Linear Regression	✅	✅	✅3
Ridge	✅2	✅	✅3
Lasso	✅2	✅	✅3
ElasticNet	✅2	✅	✅
Gaussian Naive Bayes	✅		✅3
Support Vector Machines	✅	✅	✅3

_{¹ Categorical feature support using slightly modified internals, based on scikit-learn#12866.}

_{² These models differ only in training characteristics, the resulting model is of the same form. Classification is supported using PMMLLogisticRegression for regression models and PMMLRidgeClassifier for general regression models.}

_{³ By one-hot encoding categorical features automatically.}

---

The following part of the specification is covered:

• Array (including typed variants)
• SparseArray (including typed variants)
  • Indices
  • Entries (including typed variants)
• DataDictionary
  • DataField (continuous, categorical, ordinal)
    • Value
    • Interval
• TransformationDictionary / LocalTransformations
  • DerivedField
• TreeModel
  • SimplePredicate
  • SimpleSetPredicate
• Segmentation ('majorityVote' for Random Forests, 'modelChain' and 'sum' for Gradient Boosting)
• Regression
  • RegressionTable
    • NumericPredictor
    • CategoricalPredictor
• GeneralRegressionModel (only linear models)
  • PPMatrix
    • PPCell
  • ParamMatrix
    • PCell
• NaiveBayesModel
  • BayesInputs
    • BayesInput
      • TargetValueStats
        • TargetValueStat
          • GaussianDistribution
      • PairCounts
        • TargetValueCounts
          • TargetValueCount
• SupportVectorMachineModel
  • LinearKernelType
  • PolynomialKernelType
  • RadialBasisKernelType
  • SigmoidKernelType
  • VectorDictionary
    • VectorFields
    • VectorInstance
  • SupportVectorMachine
    • SupportVectors
      • SupportVector
    • Coefficients
      • Coefficient

Example

A minimal working example is shown below:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn_pmml_model.ensemble import PMMLForestClassifier

# Prepare data
iris = load_iris()
X = pd.DataFrame(iris.data)
X.columns = np.array(iris.feature_names)
y = pd.Series(np.array(iris.target_names)[iris.target])
y.name = "Class"
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.33, random_state=123)

clf = PMMLForestClassifier(pmml="models/randomForest.pmml")
clf.predict(Xte)
clf.score(Xte, yte)

More examples can be found in the subsequent packages: tree, ensemble, linear_model and naive_bayes.

Benchmark

Depending on the data set and model, sklearn-pmml-model is between 5 and a 1000 times faster than competing libraries, by leveraging the optimization and industry-tested robustness of sklearn. Source code for this benchmark can be found in the corresponding jupyter notebook.

Running times (load + predict, in seconds)

		Linear model	Naive Bayes	Decision tree	Random Forest	Gradient boosting
Wine	PyPMML	0.773291	0.77384	0.777425	0.895204	0.902355
	sklearn-pmml-model	0.005813	0.006357	0.002693	0.108882	0.121823
Breast cancer	PyPMML	3.849855	3.878448	3.83623	4.16358	4.13766
	sklearn-pmml-model	0.015723	0.011278	0.002807	0.146234	0.044016

Improvement

		Linear model	Naive Bayes	Decision tree	Random Forest	Gradient boosting
Wine	Improvement	133×	122×	289×	8×	7×
Breast cancer	Improvement	245×	344×	1,367×	28×	94×

Development

Prerequisites

Tests can be run using Py.test. Grab a local copy of the source:

$ git clone http://github.com/iamDecode/sklearn-pmml-model
$ cd sklearn-pmml-model

create a virtual environment and activating it:

$ python3 -m venv venv
$ source venv/bin/activate

and install the dependencies:

$ pip install -r requirements.txt

The final step is to build the Cython extensions:

$ python setup.py build_ext --inplace

Testing

You can execute tests with py.test by running:

$ python setup.py pytest

Contributing

Feel free to make a contribution. Please read CONTRIBUTING.md for more details.

License

This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.