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* feat: add Dataset class * test: add dataset tests * feat: add meaningful error message to TaskType * feat: add to from datasest for text classification * test: add dataset fixtures * feat: implement pandas to text classification * feat: add token classification support * test: add token classification tests * test: use singlelabel_textclassification_records * chore: small improvements * refactor: switch to class implementation * chore: import in init * test: add missing tests plus minor fixes * chore: add future warning about as_pandas * chore: more integrations in the library * fix: wrong import * chore: add new test dependency * test: add test for tasktype * feat: ignore not supported columns * test: add tests for read_pandas/datasets * docs: put type hints only in description, it becomes too messy * test: improve tests * docs: curate docstrings * docs: add datasets to python reference * fix: return None's instead of empty dicts for metrics * docs: add dataset guide * docs: increase contrast * Update docs/guides/datasets_in_the_client.ipynb Co-authored-by: Daniel Vila Suero <daniel@recogn.ai> * Update docs/guides/datasets_in_the_client.ipynb Co-authored-by: Daniel Vila Suero <daniel@recogn.ai> * Update docs/guides/datasets_in_the_client.ipynb Co-authored-by: Daniel Vila Suero <daniel@recogn.ai> * Update docs/guides/datasets_in_the_client.ipynb Co-authored-by: Daniel Vila Suero <daniel@recogn.ai> * Update docs/guides/datasets_in_the_client.ipynb Co-authored-by: Daniel Vila Suero <daniel@recogn.ai> * test: add general log/load tests for all allowed input types of rb.load * fix: remove append Co-authored-by: Daniel Vila Suero <daniel@recogn.ai> (cherry picked from commit 14c8087)
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "d09c1533-97fd-43f7-83ea-f3a56edd1d5e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Datasets\n", | ||
| "\n", | ||
| "This guide showcases some features of the `Dataset` classes in the Rubrix client.\n", | ||
| "The Dataset classes are lightweight containers for Rubrix records. These classes facilitate importing from and exporting to different formats (e.g., `pandas.DataFrame`, `datasets.Dataset`) as well as sharing and versioning Rubrix datasets using the Hugging Face Hub.\n", | ||
| "\n", | ||
| "For each record type there's a corresponding Dataset class called `DatasetFor<RecordType>`.\n", | ||
| "You can look up their API in the [reference section](../reference/python/python_client.rst#module-rubrix.client.datasets)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "ae9d4c9e-24a1-4a59-8a17-3b6ac1a39c88", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Working with a Dataset\n", | ||
| "\n", | ||
| "Under the hood the Dataset classes store the records in a simple Python list.\n", | ||
| "Therefore, working with a Dataset class is not very different to working with a simple list of records:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "edbf8c7f-463d-48ee-944a-3adb57edb159", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import rubrix as rb\n", | ||
| "\n", | ||
| "# Start with a list of Rubrix records\n", | ||
| "dataset_rb = rb.DatasetForTextClassification(my_records)\n", | ||
| "\n", | ||
| "# Loop over the dataset\n", | ||
| "for record in dataset_rb:\n", | ||
| " print(record)\n", | ||
| " \n", | ||
| "# Index into the dataset\n", | ||
| "dataset_rb[0] = rb.TextClassificationRecord(inputs=\"replace record\")\n", | ||
| "\n", | ||
| "# log a dataset to the Rubrix web app\n", | ||
| "rb.log(dataset_rb, \"my_dataset\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "2d3e9fc0-8563-4727-abca-62205a4de385", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The Dataset classes do some extra checks for you, to make sure you do not mix record types when appending or indexing into a dataset. " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "df88889b-12f4-472f-bcbe-fb47be475d02", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Importing from other formats\n", | ||
| "\n", | ||
| "When you have your data in a [_pandas DataFrame_](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) or a [_datasets Dataset_](https://huggingface.co/docs/datasets/access.html), we provide some neat shortcuts to import this data into a Rubrix Dataset. \n", | ||
| "You have to make sure that the data follows the record model of a specific task, otherwise you will get validation errors. \n", | ||
| "Columns in your DataFrame/Dataset that are not supported or recognized, will simply be ignored.\n", | ||
| "\n", | ||
| "The record models of the tasks are explained in the [reference section](../reference/python/python_client.rst#module-rubrix.client.models). \n", | ||
| "\n", | ||
| "<div class=\"alert alert-info\">\n", | ||
| "\n", | ||
| "Note\n", | ||
| "\n", | ||
| "Due to it's pyarrow nature, data in a `datasets.Dataset` has to follow a slightly different model, that you can look up in the examples of the `Dataset*.from_datasets` [docstrings](../reference/python/python_client.rst#rubrix.client.datasets.DatasetForTokenClassification.from_datasets). \n", | ||
| " \n", | ||
| "</div>" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "62ca56d4-2bb5-4c77-a069-7a50ee78b415", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import rubrix as rb\n", | ||
| "\n", | ||
| "# import data from a pandas DataFrame\n", | ||
| "dataset_rb = rb.read_pandas(my_dataframe, task=\"TextClassification\")\n", | ||
| "\n", | ||
| "# import data from a datasets Dataset\n", | ||
| "dataset_rb = rb.read_datasets(my_dataset, task=\"TextClassification\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "eb290a71-ad07-496f-b167-ad80d91fa633", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Sharing via the Hugging Face Hub\n", | ||
| "\n", | ||
| "You can easily share your Rubrix dataset with your community via the Hugging Face Hub.\n", | ||
| "For this you just need to export your Rubrix Dataset to a `datasets.Dataset` and [push it to the hub](https://huggingface.co/docs/datasets/upload_dataset.html?highlight=push_to_hub#upload-from-python):" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f4d6d70b-0b91-4efb-94b6-6b7710c105c2", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import rubrix as rb\n", | ||
| "\n", | ||
| "# load your annotated dataset from the Rubrix web app\n", | ||
| "dataset_rb = rb.load(\"my_dataset\", as_pandas=False)\n", | ||
| "\n", | ||
| "# export your Rubrix Dataset to a datasets Dataset\n", | ||
| "dataset_ds = dataset_rb.to_datasets()\n", | ||
| "\n", | ||
| "# push the dataset to the Hugging Face Hub\n", | ||
| "dataset_ds.push_to_hub(\"my_dataset\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "696605dd-be87-4ae6-b367-b0cdabfaf39f", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Afterward, your community can easily access your annotated dataset and log it directly to the Rubrix web app:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "e70a4792-bc91-4d64-8465-b2bccf23502f", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from datasets import load_dataset\n", | ||
| "\n", | ||
| "# download the dataset from the Hugging Face Hub\n", | ||
| "dataset_ds = load_dataset(\"user/my_dataset\", split=\"train\")\n", | ||
| "\n", | ||
| "# read in dataset, assuming its a dataset for text classification\n", | ||
| "dataset_rb = rb.read_datasets(dataset_ds, task=\"TextClassification\")\n", | ||
| "\n", | ||
| "# log the dataset to the Rubrix web app\n", | ||
| "rb.log(dataset_rb, \"dataset_by_user\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.10" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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