From f0d92eaadf32df6ae6621901c9d82f8af08801b0 Mon Sep 17 00:00:00 2001 From: Dan Saattrup Nielsen Date: Thu, 17 Oct 2024 12:58:27 +0200 Subject: [PATCH] feat: Add notebook that converts raw data to HF --- makefile | 48 +- notebook/convert_sheet_to_hf.ipynb | 332 +++ poetry.lock | 3986 +++++++++++++++++++--------- pyproject.toml | 8 + src/scripts/fix_dot_env_file.py | 24 - 5 files changed, 3142 insertions(+), 1256 deletions(-) create mode 100644 notebook/convert_sheet_to_hf.ipynb diff --git a/makefile b/makefile index b7ef4d0..2e776d7 100644 --- a/makefile +++ b/makefile @@ -37,14 +37,11 @@ help: install: ## Install dependencies @echo "Installing the 'foqa' project..." @$(MAKE) --quiet install-brew - @$(MAKE) --quiet install-gpg - @$(MAKE) --quiet generate-gpg-key @$(MAKE) --quiet install-pipx @$(MAKE) --quiet install-poetry @$(MAKE) --quiet install-dependencies @$(MAKE) --quiet setup-environment-variables @$(MAKE) --quiet setup-git - @$(MAKE) --quiet add-repo-to-git @echo "Installed the 'foqa' project. You can now activate your virtual environment with 'source .venv/bin/activate'." @echo "Note that this is a Poetry project. Use 'poetry add ' to install new dependencies and 'poetry remove ' to remove them." @@ -54,30 +51,6 @@ install-brew: echo "Installed Homebrew."; \ fi -install-gpg: - @if [ "$(shell which gpg)" = "" ] || [ "$(shell which gpg-agent)" = "" ]; then \ - uname=$$(uname); \ - case $${uname} in \ - (*Linux*) distro=$$(lsb_release -i | sed 's/Distributor ID:\t//'); \ - case $${distro} in \ - (*Ubuntu*) installCmd='apt-get update && apt-get install -y gnupg gpg-agent'; ;; \ - (*CentOS*) installCmd='yum install -y gnupg gpg-agent'; ;; \ - (*) echo 'Could not automatically install gnupg for the $${distro} distribution. Please manually install gnupg and try again.'; exit 2; ;; \ - esac; ;; \ - (*Darwin*) installCmd='brew install gnupg pinentry-mac'; ;; \ - (*) echo 'Could not automatically install gnupg. Please manually install gnupg and try again.'; exit 2; ;; \ - esac; \ - "$${installCmd}"; \ - echo "Installed gnupg."; \ - fi - -generate-gpg-key: - @if [ "$(shell gpg --list-secret-keys --keyid-format=long | grep sec | sed -E 's/.*\/([^ ]+).*/\1/')" = "" ]; then \ - echo "Generating a new GPG key - please follow the prompts."; \ - gpg --full-generate-key; \ - echo "Generated a new GPG key. Remember to register it to Github at https://github.com/settings/gpg/new, where you add the key generated by running 'gpg --armor --export '"; \ - fi - install-pipx: @if [ "$(shell which pipx)" = "" ]; then \ uname=$$(uname); \ @@ -99,7 +72,7 @@ install-poetry: fi install-dependencies: - @poetry env use python3.11 && poetry install + @poetry env use python3.11 && poetry install -E notebook setup-environment-variables: @poetry run python src/scripts/fix_dot_env_file.py @@ -112,27 +85,8 @@ setup-git: @git init @git config --local user.name ${GIT_NAME} @git config --local user.email ${GIT_EMAIL} - @if [ ${GPG_KEY_ID} = "" ]; then \ - echo "No GPG key ID specified. Skipping GPG signing."; \ - git config --local commit.gpgsign false; \ - else \ - git config --local commit.gpgsign true; \ - git config --local user.signingkey ${GPG_KEY_ID}; \ - echo "Signed with GPG key ID ${GPG_KEY_ID}."; \ - fi @poetry run pre-commit install -add-repo-to-git: - @export GPG_TTY=$(tty) - @gpgconf --kill gpg-agent - @if [ ! "$(shell git status --short)" = "" ] && [ "$(shell git log --all)" = "" ]; then \ - git add .; \ - git commit --quiet -m "Initial commit"; \ - fi - @if [ "$(shell git remote)" = "" ]; then \ - git remote add origin git@github.com:alexandrainst/foqa.git; \ - fi - docs: ## Generate documentation @poetry run pdoc --docformat google src/foqa -o docs @echo "Saved documentation." diff --git a/notebook/convert_sheet_to_hf.ipynb b/notebook/convert_sheet_to_hf.ipynb new file mode 100644 index 0000000..69836f9 --- /dev/null +++ b/notebook/convert_sheet_to_hf.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from datasets import Dataset, DatasetDict, load_dataset\n", + "\n", + "plt.style.use(\"ggplot\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "annotated_data_path = Path.cwd().parent / \"data\" / \"raw\" / \"foqa.csv\"\n", + "assert annotated_data_path.exists()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "raw_dataset_dict = load_dataset(\"alexandrainst/foqa\", \"all-samples\")\n", + "raw_df = pd.concat(\n", + " [raw_dataset_dict[split].to_pandas() for split in raw_dataset_dict.keys()]\n", + ")\n", + "raw_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "context_lengths = raw_df.original_context.str.len().sort_values()\n", + "context_lengths.hist(bins=\"auto\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "def extract_shorter_context(answer_dict: dict, context: str) -> str:\n", + " \"\"\"Extracts a shorter context containing the answer.\n", + "\n", + " This ensures that the number of characters in the context is\n", + " at most 5000 characters.\n", + "\n", + " Args:\n", + " answer_dict:\n", + " The answer dictionary, with keys 'text' and 'answer_start'.\n", + " context:\n", + " The context.\n", + "\n", + " Returns:\n", + " The shortened context.\n", + "\n", + " Raises:\n", + " RuntimeError:\n", + " If it wasn't possible to shorten the context.\n", + " \"\"\"\n", + " # If the context is short enough already then we don't have to shorten it\n", + " if len(context) < 5000:\n", + " return context\n", + "\n", + " answer = answer_dict[\"text\"][0]\n", + "\n", + " paragraphs = [p for p in context.split(\"\\n\\n\") if p]\n", + " paragraph_answer_idx = next(\n", + " idx for idx, paragraph in enumerate(paragraphs) if answer in paragraph\n", + " )\n", + " single_paragraph_is_short_enough = len(paragraphs[paragraph_answer_idx]) < 5000\n", + " assert answer in paragraphs[paragraph_answer_idx]\n", + "\n", + " # If the paragraph containing the answer is short enough, then we identify all the\n", + " # possible longest contexts with the answer, by including paragraphs above and below that\n", + " # paragraph. We then select the context at random from these.\n", + " if single_paragraph_is_short_enough:\n", + " all_valid_contexts = set()\n", + " for start_paragraph_idx in range(paragraph_answer_idx, -1, -1):\n", + " best_candidate_context = paragraphs[paragraph_answer_idx]\n", + " for end_paragraph_idx in range(paragraph_answer_idx, len(paragraphs)):\n", + " candidate_context = \"\\n\\n\".join(\n", + " paragraphs[start_paragraph_idx : end_paragraph_idx + 1]\n", + " )\n", + " if candidate_context and len(candidate_context) < 5000:\n", + " best_candidate_context = candidate_context\n", + " else:\n", + " break\n", + " all_valid_contexts.add(best_candidate_context)\n", + " assert len(all_valid_contexts) > 0\n", + " context = random.choice(list(all_valid_contexts))\n", + " return context\n", + "\n", + " # Otherwise, we start splitting up the paragraph containing the answer into lines\n", + " lines = paragraphs[paragraph_answer_idx].split(\"\\n\")\n", + " line_answer_idx = next(idx for idx, line in enumerate(lines) if answer in line)\n", + " assert answer in lines[line_answer_idx]\n", + "\n", + " # Again, we do the same as we did for the paragraphs, just for lines instead\n", + " all_valid_contexts = set()\n", + " for start_line_idx in range(line_answer_idx, -1, -1):\n", + " best_candidate_context = \"\"\n", + " for end_line_idx in range(line_answer_idx, len(lines)):\n", + " candidate_context = \"\\n\".join(lines[start_line_idx : end_line_idx + 1])\n", + " if candidate_context and len(candidate_context) < 5000:\n", + " best_candidate_context = candidate_context\n", + " else:\n", + " break\n", + " all_valid_contexts.add(best_candidate_context)\n", + " assert len(all_valid_contexts) > 0\n", + " return random.choice(list(all_valid_contexts))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "def update_answer_start(context: str, answer_dict: dict) -> dict:\n", + " \"\"\"Updates the start of the answers.\n", + "\n", + " Args:\n", + " context:\n", + " The context where the answer appears.\n", + " answer_dict:\n", + " The original answer dictionary, with keys 'text' and 'answer_start'.\n", + "\n", + " Returns:\n", + " A dictionary with keys 'text' and 'answer_start', each being\n", + " lists with a single element.\n", + " \"\"\"\n", + " answer = answer_dict[\"text\"][0]\n", + " answer_start = context.index(answer)\n", + " assert context[answer_start : answer_start + len(answer)] == answer\n", + " return dict(text=[answer], answer_start=[answer_start])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "if \"original_context\" not in raw_df.columns:\n", + " raw_df.rename(columns=dict(context=\"original_context\"), inplace=True)\n", + "raw_df[\"context\"] = raw_df.apply(\n", + " lambda x: extract_shorter_context(\n", + " answer_dict=x.answers, context=x.original_context\n", + " ),\n", + " axis=1,\n", + ")\n", + "raw_df[\"answers\"] = raw_df.apply(\n", + " lambda x: update_answer_start(context=x.context, answer_dict=x.answers), axis=1\n", + ")\n", + "shorter_context_lengths = raw_df.context.str.len().sort_values()\n", + "shorter_context_lengths.hist(bins=\"auto\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "annotated_df = pd.read_csv(annotated_data_path)\n", + "annotated_df.columns = [\n", + " \"id\",\n", + " \"title\",\n", + " \"context\",\n", + " \"question\",\n", + " \"deleteme\",\n", + " \"validation\",\n", + " \"answers\",\n", + "]\n", + "annotated_df.drop(columns=[\"deleteme\", \"answers\"], inplace=True)\n", + "annotated_df[\"answers\"] = raw_df.answers\n", + "annotated_df[\"context\"] = raw_df.context\n", + "annotated_df = annotated_df[raw_df.drop(columns=\"original_context\").columns]\n", + "annotated_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "correct_texts = [\"correct\", \"corrected\"]\n", + "correct_df = annotated_df.query(\"validation in @correct_texts\")\n", + "correct_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "test_df = correct_df.query('validation == \"corrected\"')\n", + "num_test_samples_missing = 1024 - len(test_df)\n", + "test_df = pd.concat(\n", + " [\n", + " test_df,\n", + " correct_df.loc[\n", + " [idx for idx in correct_df.index if idx not in test_df.index]\n", + " ].sample(n=num_test_samples_missing),\n", + " ]\n", + ")\n", + "val_df = correct_df.loc[\n", + " [idx for idx in correct_df.index if idx not in test_df.index]\n", + "].sample(n=128)\n", + "train_df = correct_df.loc[\n", + " [\n", + " idx\n", + " for idx in correct_df.index\n", + " if idx not in test_df.index and idx not in val_df.index\n", + " ]\n", + "]\n", + "len(train_df), len(val_df), len(test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "train = Dataset.from_pandas(train_df, preserve_index=False)\n", + "val = Dataset.from_pandas(val_df, preserve_index=False)\n", + "test = Dataset.from_pandas(test_df, preserve_index=False)\n", + "DatasetDict(dict(train=train, val=val, test=test)).push_to_hub(\n", + " \"alexandrainst/foqa\", \"default\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "incorrect_texts = [\"incorrect\", \"incorrect-answer\"]\n", + "incorrect_df = annotated_df.query(\"validation in @incorrect_texts\").dropna()\n", + "\n", + "wrong_corrected_df = raw_df.drop(columns=\"original_context\").loc[\n", + " annotated_df.query('validation == \"corrected\"').index.tolist()\n", + "]\n", + "wrong_corrected_df.validation = annotated_df.loc[\n", + " wrong_corrected_df.index.tolist()\n", + "].validation\n", + "\n", + "incorrect_df = pd.concat(objs=[incorrect_df, wrong_corrected_df])\n", + "incorrect_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "Dataset.from_pandas(incorrect_df, preserve_index=False).push_to_hub(\n", + " \"alexandrainst/foqa\", \"incorrect-samples\"\n", + ")\n", + "Dataset.from_pandas(raw_df, preserve_index=False).push_to_hub(\n", + " \"alexandrainst/foqa\", \"all-samples\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff 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[metadata] lock-version = "2.0" python-versions = ">=3.11,<3.13" -content-hash = "01b75b10ad80c168972c884b10283076ca419dfa1c44baf6f5788572929ec473" +content-hash = "8efe3f90d5f0555680fcc470410312d1e9cfaba3dda635973344ef49a6f6ba35" diff --git a/pyproject.toml b/pyproject.toml index cbac038..5e64b28 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,14 @@ openai = "^1.21.2" python-dotenv = "^1.0.1" pandas = "^2.2.2" datasets = "^2.18.0" +jupyterlab = {version = "^4.2.5", optional = true} +ipywidgets = {version = "^8.1.5", optional = true} + +[tool.poetry.extras] +notebook = [ + "jupyterlab", + "ipywidgets", +] [build-system] requires = [ diff --git a/src/scripts/fix_dot_env_file.py b/src/scripts/fix_dot_env_file.py index c43dbc2..9b1078c 100644 --- a/src/scripts/fix_dot_env_file.py +++ b/src/scripts/fix_dot_env_file.py @@ -4,16 +4,12 @@ python src/scripts/fix_dot_env_file.py [--non-interactive] """ -import subprocess from pathlib import Path import click # List of all the environment variables that are desired DESIRED_ENVIRONMENT_VARIABLES = dict( - GPG_KEY_ID="Enter GPG key ID or leave empty if you do not want to use it. Type " - "`gpg --list-secret-keys --keyid-format=long | grep sec | sed -E " - "'s/.*\/([^ ]+).*/\\1/'` to see your key ID:\n> ", # noqa GIT_NAME="Enter your full name, to be shown in Git commits:\n> ", GIT_EMAIL="Enter your email, as registered on your Github account:\n> ", ) @@ -63,30 +59,10 @@ def fix_dot_env_file(non_interactive: bool) -> None: with env_path.open("a") as f: for env_var in env_vars_missing: value = "" - if env_var in name_and_email_vars: value = name_and_email_vars[env_var] - elif env_var == "GPG_KEY_ID": - gpg = subprocess.Popen( - ["gpg", "--list-secret-keys", "--keyid-format=long"], - stdout=subprocess.PIPE, - ) - grep = subprocess.Popen( - ["grep", "sec"], stdin=gpg.stdout, stdout=subprocess.PIPE - ) - value = ( - subprocess.check_output( - ["sed", "-E", "s/.*\\/([^ ]+).*/\\1/"], stdin=grep.stdout - ) - .decode() - .strip("\n") - ) - gpg.wait() - grep.wait() - if value == "" and not non_interactive: value = input(DESIRED_ENVIRONMENT_VARIABLES[env_var]) - f.write(f'{env_var}="{value}"\n') # Remove the name and email file