zkcook

This package is designed to provide functionality that facilitates the transition from ML algorithms to ZKML. Its two main functionalities are:

Serialization: saving a trained ML model in a specific format to be interpretable by other programs.
model-complexity-reducer (mcr): Given a model and a training dataset, transform the model and the data to obtain a lighter representation that maximizes the tradeoff between performance and complexity.

It's important to note that although the main goal is the transition from ML to ZKML, mcr can be useful in other contexts, such as:

The model's weight needs to be minimal, for example for mobile applications.
Minimal inference times are required for low latency applications.
We want to check if we have created an overly complex model and a simpler one would give us the same performance (or even better).
The number of steps required to perform the inference must be less than X (as is currently constrained by the ZKML paradigm).

Installation

Install from PyPi

For the latest release:

pip install giza-zkcook

Installing from source

Clone the repository and install it with pip:

    git clone git@github.com:gizatechxyz/zkcook.git
    cd zkcook
    pip install .

Serialization

To see in more detail how this tool works, check out this tutorial.

To run it:

from giza.zkcook import serialize_model

serialize_model(YOUR_TRAINED_MODEL, "OUTPUT_PATH/MODEL_NAME.json")

mcr

To see in more detail how this tool works, check out this tutorial.

To run it:

model, transformer = mcr(model = MY_MODEL,
                         X_train = X_train,
                         y_train = y_train,
                         X_eval = X_test,
                         y_eval = y_test,
                         eval_metric = 'rmse',
                         transform_features = True)

Supported Models

Model	status
XGBRegressor	✅
XGBClassifier	✅
LGBMRegressor	✅
LGBMClassifier	✅
Logistic Regression	⏳
GARCH	⏳