Nyoka is a Python library for comprehensive support of the latest PMML (PMML 4.4) standard. Using Nyoka, Data Scientists can export a large number of Machine Learning models from popular Python frameworks into PMML by either using any of the numerous included ready-to-use exporters or by creating their own exporter for specialized/individual model types by simply calling a sequence of constructors.
Besides about 500 Python classes which each cover a PMML tag and all constructor parameters/attributes as defined in the standard, Nyoka also provides an increasing number of convenience classes and functions that make the Data Scientist’s life easier for example by reading or writing any PMML file in one line of code from within your favorite Python environment.
Nyoka comes to you with the complete source code in Python, extended HTML documentation for the classes/functions, and a growing number of Jupyter Notebook tutorials that help you familiarize yourself with the way Nyoka supports you in using PMML as your favorite Data Science transport file format.
Read the documentation at Nyoka Documentation.
sklearn_pandas.CategoricalImputer( From sklearn_pandas library )
tsa.arima.model.ARIMA(In statespace form)
You can install nyoka using:
pip install --upgrade nyoka
Nyoka contains seperate exporters for each library, e.g., scikit-learn, keras, xgboost etc.
|statsmodels||StatsmodelsToPmml & ExponentialSmoothingToPmml|
The main module of Nyoka is
nyoka. To use it for your model, you need to import the specific exporter from nyoka as -
from nyoka import skl_to_pmml, lgb_to_pmml #... so on
The workflow is as follows (For example, a Decision Tree Classifier with StandardScaler) -
Pipeline object and populate it with any pre-processing steps and the model object.
from sklearn.pipeline import Pipeline from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import StandardScaler pipeline_obj = Pipeline([ ("scaler",StandardScaler()), ("model",DecisionTreeClassifier()) ])
Pipeline.fit(X,y) method to train the model.
from sklearn.dataset import load_iris iris_data = load_iris() X = iris_data.data y = iris_data.target features = iris_data.feature_names pipeline_obj.fit(X,y)
Use the specific exporter and pass the pipeline object, feature names of the training dataset, target name and expected name of the PMML to the exporter function. If target name is not given default value
target is used. Similarly, for pmml name, default value
from_lighgbm.pmml is used.
from nyoka import skl_to_pmml skl_to_pmml(pipeline=pipeline_obj,col_names=features,target_name="species",pmml_f_name="decision_tree.pmml")
import pandas as pd from statsmodels.tsa.arima_model import ARIMA from nyoka import StatsmodelsToPmml sales_data = pd.read_csv('sales-cars.csv', index_col=0, parse_dates = True) model = ARIMA(sales_data, order = (4, 1, 2)) result = model.fit() StatsmodelsToPmml(result,"Sales_cars_ARIMA.pmml")
Example jupyter notebooks can be found in
nyoka/examples. These files contain code to showcase how to use different exporters.
scikit-learn models into PMML
XGBoost model into PMML
LightGBM model into PMML
statsmodels model into PMML
Nyoka contains one submodule called
preprocessing. This module contains preprocessing classes implemented by Nyoka. Currently there is only one preprocessing class, which is
Lag is a preprocessing class implemented by Nyoka. When used inside scikit-learn's pipeline, it simply applies an
aggregationfunction for the given features of the dataset by combining
valuenumber of previous records. It takes two arguments- aggregation and value.
aggregationfunctions are - "min", "max", "sum", "avg", "median", "product" and "stddev".
To use Lag -
from nyoka.preprocessing import Lag
lag_obj = Lag(aggregation="sum", value=5) ''' This means taking previous 5 values and perform `sum`. When used inside pipeline, this will be applied to all the columns. If used inside DataFrameMapper, the it will be applied to only those columns which are inside DataFrameMapper. '''
from sklearn.pipeline import Pipeline from sklearn.tree import DecisionTreeClassifier from nyoka.preprocessing import Lag pipeline_obj = Pipeline([ ("lag",Lag(aggregation="sum",value=5)), ("model",DecisionTreeClassifier()) ])
pip uninstall nyoka
You can ask questions at:
Please note that this project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
These tools are provided as-is and without warranty or support. They do not constitute part of the Software AG product suite. Users are free to use, fork and modify them, subject to the license agreement. While Software AG welcomes contributions, we cannot guarantee to include every contribution in the master project.