nyoka 4.1.0

A Python library to export Machine Learning/ Deep Learning models into PMML

Nyoka

Overview

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 and Deep 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.

List of libraries and models supported by Nyoka :

Scikit-Learn (version <= 0.20.3):

Click to expand!

Models -

• LinearRegression
• LogisticRegression
• RidgeClassifier
• SGDClassifier
• LinearDiscriminantAnalysis
• LinearSVC
• LinearSVR
• DecisionTreeClassifier
• DecisionTreeRegressor
• SVC
• SVR
• OneClassSVM
• GaussianNB
• RandomForestRegressor
• RandomForestClassifier
• GradientBoostingRegressor
• GradientBoostingClassifier
• IsolationForest
• MLPClassifier
• MLPRegressor
• KNNClassifier
• KNNRegressor
• KMeans

Pre-Processing -

• StandardScaler
• MinMaxScaler
• RobustScaler
• MaxAbsScaler
• TfidfVectorizer
• CountVectorizer
• LabelEncoder
• Imputer
• Binarizer
• PolynomialFeatures
• PCA
• LabelBinarizer
• OneHotEncoder
• CategoricalImputer

Keras (version 2.2.4):

Click to expand!

Models -

• Mobilenet
• VGG
• DenseNet
• Inception
• ResNet
• Xception

Object Detection Model:

• Keras-RetinaNet

LightGBM:

Click to expand!

Models -

• LGBMClassifier
• LGBMRegressor

XGBoost:

Click to expand!

Models -

• XGBClassifier
• XGBRegressor

Statsmodels:

Click to expand!

Models -

• ARIMA
• SARIMAX
• ExponentialSmoothing

Prerequisites

• Python >= 3.6

Dependencies

nyoka requires:

• lxml

Installation

You can install nyoka using:

pip install --upgrade nyoka

Usage

Nyoka contains seperate exporters for each library, e.g., scikit-learn, keras, xgboost etc.

library	exporter
scikit-learn	skl_to_pmml
xgboost	xgboost_to_pmml
lightgbm	lgbm_to_pmml
keras	KerasToPmml
statsmodels	ArimaToPmml & ExponentialSmoothingToPmml
retinanet	RetinanetToPmml

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

Note -

• If scikit-learn, xgboost and lightgbm model is used then the model should be used inside sklearn's Pipeline. The workflow is as follows -
  • Create scikit-learn's Pipeline object and populate it with any preprocessing steps and the model object.
  • Call Pipeline.fit(X,y) method to train the model.
  • 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_sklearn.pmml/from_xgboost.pmml/from_lighgbm.pmml is used.
• For Keras and Statsmodels, the fitted model needs to be passed to the exporter.

Demo is provided below

Nyoka to export scikit-learn models:

Exporting a Support Vector Classifier pipeline object into PMML

import pandas as pd
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

iris = datasets.load_iris()
irisd = pd.DataFrame(iris.data,columns=iris.feature_names)
irisd['Species'] = iris.target
features = irisd.columns.drop('Species')
target = 'Species'

pipeline_obj = Pipeline([
    ('scaler', StandardScaler()),
    ('svm',SVC())
])
pipeline_obj.fit(irisd[features],irisd[target])

from nyoka import skl_to_pmml
skl_to_pmml(pipeline_obj,features,target,"svc_pmml.pmml")

Nyoka to export xgboost models:

Exporting a XGBoost model into PMML

from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import xgboost as xgb

boston = datasets.load_boston()
y = boston['target']
X = boston['data']
xgb_model = xgb.XGBRegressor()

pipeline_obj = Pipeline([
    ("scaling", StandardScaler()),
    ("model", XGBRegressor())
])

pipeline_obj.fit(X, y)

from nyoka import xgboost_to_pmml
xgboost_to_pmml(pipeline_obj, boston.feature_names, 'target', "xgb_pmml.pmml")

Nyoka to export lightGBM models:

Exporting a LGBM model into PMML

import pandas as pd
from sklearn import datasets
from sklearn.pipeline import Pipeline
from lightgbm import LGBMClassifier


iris = datasets.load_iris()
irisd = pd.DataFrame(iris.data,columns=iris.feature_names)
irisd['Species'] = iris.target
features = irisd.columns.drop('Species')
target = 'Species'

pipeline_obj = Pipeline([
    ('lgbmc',LGBMClassifier())
])
pipeline_obj.fit(irisd[features],irisd[target])

from nyoka import lgb_to_pmml
lgb_to_pmml(pipeline_obj,features,target,"lgbmc_pmml.pmml")

Nyoka to export keras models:

Exporting a Mobilenet model into PMML

from keras import applications
from keras.layers import Flatten, Dense
from keras.models import Model

model = applications.MobileNet(weights='imagenet', include_top=False,input_shape = (224, 224,3))
activType='sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
model_final = Model(inputs =model.input, outputs = predictions,name='predictions')

from nyoka import KerasToPmml
cnn_pmml = KerasToPmml(model_final,dataSet='image',predictedClasses=['cats','dogs'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)

Exporting user given python script with keras model

from keras import applications
from keras.layers import Flatten, Dense
from keras.models import Model
model = applications.MobileNet(weights='imagenet', include_top=False,input_shape = (224, 224,3))
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation='sigmoid')(x)
model_final = Model(inputs =model.input, outputs = predictions,name='predictions')
script_content = open("preprocess.py",'r').read()
pmml_obj=KerasToPmml(model_final,
            dataSet='image',
            predictedClasses=['cat','dog'],
            script_args = {
                "content" : script_content,
                "def_name" : "getBase64EncodedString",
                "return_type" : "string",
                "encode":True
            }
        )
pmml_obj.export(open("script_with_keras_encoded.pmml",'w'),0)

Nyoka to export object detection model

Exporting RetinaNet to PMML

from keras_retinanet.models import load_model
from nyoka import RetinanetToPmml
model = load_model('resnet50_coco_best_v2.1.0.h5', backbone_name='resnet50')
backbone = 'resnet'
RetinanetToPmml(
    model,
    input_shape=(224,224,3),
    input_format="image",
    backbone_name=backbone,
    pmml_file_name="retinanet_with_coco_.pmml"
)

Nyoka to export statsmodels model

Exporting Non Seasonal ARIMA to PMML

import pandas as pd
import numpy as np
from statsmodels.tsa.arima_model import ARIMA
from nyoka import ArimaToPMML

def parser(x):
    return pd.datetime.strptime(x,'%Y-%m')

sales_data = pd.read_csv('sales-cars.csv', index_col=0, parse_dates = [0], date_parser = parser)
model = ARIMA(sales_data, order = (9, 2, 0))
result = model.fit()

pmml_f_name = 'non_seasonal_car_sales.pmml'
ArimaToPMML(results_obj = result,pmml_file_name = pmml_f_name)

Exporting Seasonal ARIMA to PMML

import pandas as pd
from nyoka import ArimaToPMML
from statsmodels.tsa.statespace.sarimax import SARIMAX
data=pd.read_csv("JohnsonJohnsonWithDate.csv")
data['index']=pd.to_datetime(data['index'], format='%Y-%m-%d')
data.set_index(['index'], inplace=True)

mod = SARIMAX(data,order=(1,0,0),seasonal_order=(1,0,0, 4))
result = mod.fit()

ArimaToPMML(results, 'jnj_seasonal_arima.pmml')

More in Nyoka

Nyoka contains one submodule called preprocessing. This module contains preprocessing classes implemented by Nyoka. Currently there is only one preprocessing class, which is Lag.

What is Lag? When to use it?

Lag is a preprocessing class implemented by Nyoka. When used inside scikit-learn's pipeline, it simply applies an aggregation function for the given features of the dataset by combining value number of previous records. It takes two arguments- aggregation and value.

The valid aggregation functions are - "min", "max", "sum", "avg", "median", "product" and "stddev".

To use Lag -

• Import it from nyoka as from nyoka.preprocessing import Lag
• Create an instance of Lag as Lag(aggregation="sum", value=5)
  • This means, take 5 previous values for the given fields and perform summation.
• Use this object inside scikit-learn's pipeline to train.

Uninstallation

pip uninstall nyoka

Support

You can ask questions at:

• Stack Overflow by tagging your questions with #pmml, #nyoka
• You can also post bug reports in GitHub issues