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.pyGitHub

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)