MicroMLGen library for Python
A Python library to export scikit-learn models into C format with a single line of code
Micromlgen is a Python library to export scikit-learn models into C format with a single line of code.
As an Arduino maker, you can then run that model on your microcontroller to perform Machine Learning in your own project.
Installation
Micromlgen is available on PyPi, so you can use pip to install.
pip install -U micromlgen
Or you can clone the Github repo
git clone https://github.com/eloquentarduino/micromlgen
Supported models
Micromlgen can port to plain C many types of models:
- DecisionTree
- RandomForest
- GaussianNB
- SVC
- OneClassSVM
- XGBoost
- Relevant Vector Machines
- SEFR
How to use in Python
Micromlgen package has a single method called port(): it accepts one of the above classifiers
as input and returns the C code you need to import in your Arduino sketch.
from micromlgen import port
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
if __name__ == '__main__':
X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(n_estimators=10).fit(X, y)
c_code = port(clf)
with open('classifier.h', 'w') as file:
file.write(c_code)
How to use in Arduino sketch
classifier.h contains all the code you need to perform classification in your sketch.
You have to include the file and instantiate the classifier.
#include "classifier.h"
Eloquent::ML::Port::RandomForest clf;
void setup() {
Serial.begin(115200);
}
void loop() {
float irisSample[4] = {6.2, 2.8, 4.8, 1.8};
Serial.print("Predicted class (you should see '2'): ");
Serial.println(clf.predict(irisSample));
delay(1000);
}
Configuration
The port() method accepts 2 optional configurations:
-
classname (str): the name of the exported class. If not provided, the classifier's class name will be used -
classmap (dict): if supplied, the exported class will also have apredictLabel()method
What is a classmap?
A classmap is a mapping from class numbers to class names. It is used to give a meaningful description to the classifier's predictions.
from micromlgen import port
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
if __name__ == '__main__':
X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(n_estimators=10).fit(X, y)
classmap = {
0: 'Setosa',
1: 'Versicolor',
2: 'Virginica'
}
c_code = port(clf, classmap=classmap)
with open('classifier.h', 'w') as file:
file.write(c_code)
Now, instead of printing the class number in your Arduino sketch, you can print the class label.
#include "classifier.h"
Eloquent::ML::Port::RandomForest clf;
void setup() {
Serial.begin(115200);
}
void loop() {
float irisSample[4] = {6.2, 2.8, 4.8, 1.8};
Serial.print("Predicted label (you should see 'Virginica'): ");
Serial.println(clf.predictLabel(irisSample));
delay(1000);
}
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