Using FELT Algorithms

We provide algorithms that you can use to train scikit-learn models and perform data analytics on CSV data. The general workflow of using the FELT application is described in the following:
Complete Getting Started
If you need an algorithm for your specific use case, see the following section:
Creating Custom Algorithms
Supported models
We are extending the list of supported models. If you request a certain model, we will try to prioritize adding it to our application.
Issues · FELT-Labs/feltlabs.py
GitHub
Here you can create issue - requesting model type
Scikit-learn models
Regression:
​Linear Regression​
​Ridge Regression​
​Lasso Regression​
​Elastic-Net​
​LARS Lasso​
Classification:
​SGD Classifier​
​Logistic Regression​
Clustering:
​Nearest Centroid Classifier​
Neural Networks:
​MLP Classifier​
​MLP Regressor​
Data analytics
Mean
Sum
Variance
Standard deviation
TensorFlow models
Coming soon
Using Final Models
FELT is using custom format (based on JSON) for storing and exchanging the models. When you finish the training, you will download your final model file (e.g. final-model-House Prices.json).
In order to use it, you have to install the FELT python library using pip (it requires Python 3.9 or newer, the Python 3.9 is recommended):
pip install feltlabs
Then you can load the model using feltlabs.model.load_model(model_path) function. This function will take the path of the model file as an argument and return the model object. Right now, we support two types of models: federated learning and federated analytics. The behaviour of each is slightly different.
Federated Learning - Scikit-learn Model
When using the federated learning option and importing the model using load_model(...) function, the function returns the model, which can be used as a standard scikit-learn model object. The model can then be used for prediction using the function model.predict(data). You can check the following code for sample usage:
Federated Analytics Models
Similarly to federated learning models, these models can be loaded using load_model(...) a function. This time you don't have to pass any data to the model, and you can obtain calculated value (of sum, mean, variance, or std) using the model.predict(None) function. See the example below:
from feltlabs.model import load_model
​
# Load model
model = load_model("final-model-mean.json")
# Call predict function without any input
mean = model.predict(None)
print(mean)
# This will print the value of mean calculated by the model
Converting FELT format to Python Pickle format
You can convert the FELT model format into a standard pickle file. This file will then contain a pickled object of scikit-learn model. FELT library provides an easy command for that. After installing feltlabs.py library, you can run:
felt-export --input "final-model-House Prices.json" --output "model.pkl"
Then you can use the created file as follows:
import pickle
​
with open('model.pkl', 'rb') as f:
 model = pickle.load(object, f)
 
# See the above code example for data definition
model.predict(data)

Last modified 26d ago