fix code tree

datasets/README.md

@@ -1,65 +1 @@
 # Flower Datasets
-
-[![GitHub license](https://img.shields.io/github/license/adap/flower)](https://github.com/adap/flower/blob/main/LICENSE)
-[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
-![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
-![Downloads](https://pepy.tech/badge/flwr)
-[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack)
-
-Flower Datasets (`flwr-datasets`) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the `Flower Labs` team that also created Flower: A Friendly Federated Learning Framework. 
-Flower Datasets library supports:
-* **downloading datasets** - choose the dataset from Hugging Face's `datasets`,
-* **partitioning datasets** - customize the partitioning scheme,
-* **creating centralized datasets** - leave parts of the dataset unpartitioned (e.g. for centralized evaluation).
-
-Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets integrates with the following popular formats/frameworks:
-* Hugging Face,
-* PyTorch, 
-* TensorFlow, 
-* Numpy, 
-* Pandas, 
-* Jax,
-* Arrow.
-
-Create **custom partitioning schemes** or choose from the **implemented partitioning schemes**:
-* IID partitioning `IidPartitioner(num_partitions)`
-* more to come in future releases.
-
-# Installation
-
-## With pip
-
-Flower Datasets can be installed from PyPi
-
-```bash
-pip install flwr-datasets
-
-If you plan to change the type of the dataset to run the code with your ML framework, make sure to have it installed too.
-
-# Usage
-
-The Flower Datasets exposes `FederatedDataset(dataset, partitioners)` abstraction to represent the dataset needed for federated learning/analytics. It has two powerful methods that let you handle the dataset preprocessing. They are `load_partition(idx, split)` and `load_full(split)`.
-
-Here's a quick example of how to partition the MNIST dataset:
-
-
-`FederatedDataset(dataset, partitioners)` allows you specification of:
-
-* `dataset:str` - the name of the dataset.
-
-* `partitioners: Dict[str: int]` - `{split_name: str` to `number-of-partitions: int}` - partitioner that will be used with an associated split of the dataset e.g. `{"train": 100}`. It assumes by default the i.i.d. partitioning.
-
-More customization of `partitioners` is coming in future releases. 
-
-# Future release
-
-Here are a few of the things that we will work on in future releases:
-
-* Support for more datasets (especially the ones that have user id present).
-* Creation of custom `Partitioner`s.
-* More out-of-the-box `Partitioner`s.
-* Passing `Partitioner`s via `FederatedDataset`'s `partitioner` argument. 
-* Customization of the dataset splitting before the partitioning.
-* Simplification of the dataset transformation to the popular frameworks/types.
-* Creation of the synthetic data,
-* Support for Vertical FL.

datasets/flwr_datasets/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+# Copyright 2023 Adap GmbH. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,8 +13,3 @@
 # limitations under the License.
 # ==============================================================================
 """Flower Datasets main package."""
-
-
-from .federated_dataset import FederatedDataset
-
-__all__ = ["FederatedDataset"]

datasets/pyproject.toml

@@ -54,7 +54,6 @@ exclude = [
 [tool.poetry.dependencies]
 python = "^3.8"
 numpy = "^1.21.0"
-datasets = "^2.14.3"
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -63,7 +62,6 @@ docformatter = "==1.7.1"
 mypy = "==1.4.0"
 pylint = "==2.13.9"
 flake8 = "==3.9.2"
-parameterized = "==0.9.0"
 pytest = "==7.1.2"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.277"