examples/ml/federated_learning/federated_utils.py

# From: https://github.com/adap/flower/tree/9ee473152f2fde2a5b05b99829db27a607dc46ec/examples/sklearn-logreg-mnist
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from typing import List, Tuple, Union

import numpy as np
import openml
from sklearn.linear_model import LogisticRegression

XY = Tuple[np.ndarray, np.ndarray]
Dataset = Tuple[XY, XY]
LogRegParams = Union[XY, Tuple[np.ndarray]]
XYList = List[XY]


def get_model_parameters(model: LogisticRegression) -> LogRegParams:
    """Returns the paramters of a sklearn LogisticRegression model."""
    if model.fit_intercept:
        params = [
            model.coef_,
            model.intercept_,
        ]
    else:
        params = [
            model.coef_,
        ]
    return params


def set_model_params(model: LogisticRegression, params: LogRegParams) -> LogisticRegression:
    """Sets the parameters of a sklearn LogisticRegression model."""
    model.coef_ = params[0]
    if model.fit_intercept:
        model.intercept_ = params[1]
    return model


def set_initial_params(model: LogisticRegression):
    """Sets initial parameters as zeros Required since model params are uninitialized
    until model.fit is called.

    But server asks for initial parameters from clients at launch. Refer to
    sklearn.linear_model.LogisticRegression documentation for more information.
    """
    n_classes = 10  # MNIST has 10 classes
    n_features = 784  # Number of features in dataset
    model.classes_ = np.array([i for i in range(10)])

    model.coef_ = np.zeros((n_classes, n_features))
    if model.fit_intercept:
        model.intercept_ = np.zeros((n_classes,))


def load_mnist() -> Dataset:
    """Loads the MNIST dataset using OpenML.

    OpenML dataset link: https://www.openml.org/d/554
    """
    mnist_openml = openml.datasets.get_dataset(554)
    Xy, _, _, _ = mnist_openml.get_data(dataset_format="array")
    X = Xy[:, :-1]  # the last column contains labels
    y = Xy[:, -1]
    # First 60000 samples consist of the train set
    x_train, y_train = X[:60000], y[:60000]
    x_test, y_test = X[60000:], y[60000:]
    return (x_train, y_train), (x_test, y_test)


def shuffle(X: np.ndarray, y: np.ndarray) -> XY:
    """Shuffle X and y."""
    rng = np.random.default_rng()
    idx = rng.permutation(len(X))
    return X[idx], y[idx]


def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList:
    """Split X and y into a number of partitions."""
    return list(zip(np.array_split(X, num_partitions), np.array_split(y, num_partitions)))