From f04fbf4978c28f511692cf624b6f42a12ac7e0c9 Mon Sep 17 00:00:00 2001 From: Trevor Nogues Date: Wed, 10 Apr 2024 16:30:07 -0700 Subject: [PATCH] Initial commit for public release --- .github/dependabot.yml | 6 + .github/workflows/build.yml | 50 + .gitignore | 3 + .pre-commit-config.yaml | 12 + AUTHORS | 3 + CONTRIBUTING.md | 53 + LICENSE | 109 + README.md | 52 + SECURITY.md | 11 + examples/configs/test-config.yaml | 18 + examples/configs/train-config.yaml | 26 + examples/datasets/sample_data.jsonl | 13 + examples/datasets/sample_stoplist.txt | 5 + examples/test_model.ipynb | 89 + examples/test_model/config.json | 32 + examples/train_model.ipynb | 106 + fair_housing_guardrail/__init__.py | 2 + fair_housing_guardrail/data/__init__.py | 8 + fair_housing_guardrail/data/constants.py | 2 + fair_housing_guardrail/data/json_dataset.py | 32 + fair_housing_guardrail/utils/__init__.py | 22 + .../utils/fair_housing_classification.py | 167 + fair_housing_guardrail/utils/helper.py | 95 + fair_housing_guardrail/utils/stop_phrases.py | 61 + poetry.lock | 4656 +++++++++++++++++ pyproject.toml | 32 + tests/test_fair_housing_classification.py | 87 + 27 files changed, 5752 insertions(+) create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/build.yml create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 AUTHORS create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 README.md create mode 100644 SECURITY.md create mode 100644 examples/configs/test-config.yaml create mode 100644 examples/configs/train-config.yaml create mode 100644 examples/datasets/sample_data.jsonl create mode 100644 examples/datasets/sample_stoplist.txt create mode 100644 examples/test_model.ipynb create mode 100644 examples/test_model/config.json create mode 100644 examples/train_model.ipynb create mode 100644 fair_housing_guardrail/__init__.py create mode 100644 fair_housing_guardrail/data/__init__.py create mode 100644 fair_housing_guardrail/data/constants.py create mode 100644 fair_housing_guardrail/data/json_dataset.py create mode 100644 fair_housing_guardrail/utils/__init__.py create mode 100644 fair_housing_guardrail/utils/fair_housing_classification.py create mode 100644 fair_housing_guardrail/utils/helper.py create mode 100644 fair_housing_guardrail/utils/stop_phrases.py create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 tests/test_fair_housing_classification.py diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..6a7695c --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,6 @@ +version: 2 +updates: + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "weekly" diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 0000000..be3cbb8 --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,50 @@ +name: build + +on: [push, pull_request] + +jobs: + build: + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.9"] + runs-on: ${{ matrix.os }} + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + cache-dependency-path: pyproject.toml + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install black[jupyter] cython-lint flake8 isort pytest pytest-cov + pip install --verbose --editable . + + - name: Check Python formatting with black + run: | + black --line-length 99 --diff --color . + black --line-length=99 --check . + + - name: Check Cython formatting with cython-lint + run: | + cython-lint --max-line-length=99 --ignore=E741 . + + - name: Check import order with isort + run: | + isort --profile=black --line-length=99 --check . + + - name: Lint with flake8 + uses: py-actions/flake8@v1 + with: + max-line-length: "99" + exclude: "examples/*,fair_housing_guardrail/__init__.py" + + - name: Test with pytest + run: | + python -m pytest --pyargs tests/test_fair_housing_classification.py --cov=fair_housing_guardrail diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..db1eded --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +*.ipynb_checkpoints* +*.pyc +*_pycache_* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..fc6c4ee --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,12 @@ +repos: +- repo: https://github.com/ambv/black + rev: 23.3.0 + hooks: + - id: black +- repo: https://github.com/timothycrosley/isort + rev: '5.12.0' + hooks: + - id: isort + args: ["--profile", "black"] + additional_dependencies: + - toml diff --git a/AUTHORS b/AUTHORS new file mode 100644 index 0000000..f43113b --- /dev/null +++ b/AUTHORS @@ -0,0 +1,3 @@ +Anusha Bagalkotkar +Aveek Karmakar +Trevor Nogues \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..3a93ca7 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,53 @@ +# Contributions + +Contributions are welcome, encouraged, and appreciated! + +If you encounter any bugs while using the project, or believe there's a feature that would prove useful, feel free to [submit a new issue](https://github.com/zillow/fair-housing-guardrail/issues/new). + +All contributions, suggestions, and feedback you submitted are accepted under the [project's license](https://github.com/zillow/fair-housing-guardrail/blob/main/LICENSE). + +## Submitting an Issue + +[Issues](https://github.com/zillow/fair-housing-guardrail/issues) should be used to report problems with the package or any of its dependencies, request a new feature, or to discuss potential changes before a PR is created. + +When reporting bugs, please provide a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example), the version of the package, and the environment (e.g., Python version). + +If you find an existing Issue that addresses the problem you're having, please add your own reproducible example to the existing issue rather than creating a new one. + +## Submitting a Pull Request + +[PRs](https://github.com/zillow/fair-housing-guardrail/pulls) are always welcome and can be a quick way to get your fix or improvement merged. In general, PRs should: + +- Only fix/add the functionality in question. +- Address a single concern in the fewest number of changes possible. +- Include updated documentation. + +For changes that address core functionality or would require breaking changes (e.g., a major release), it's generally best to first open an Issue to discuss your proposed changes. + +In general, we follow the ["fork-and-pull" Git workflow](https://gist.github.com/Chaser324/ce0505fbed06b947d962) + +- [Fork](https://github.com/zillow/fair-housing-guardrail/fork) the repository to your own GitHub account +- Clone the project to your machine +- Create a branch locally with a succinct but descriptive name +- Commit changes to the branch +- Following any formatting and testing guidelines specific to this repo +- Push changes to your fork +- Open a PR in our repository + +## Setting Up Your Environment + +To contribute to the `fair-housing-guardrail` source code, start by forking and then cloning the repository (i.e. `git clone git@github.com:YourUsername/fair-housing-guardrail.git`) + +Once inside the repository, to build and install the necessary dependencies, run: + +```cmd +poetry install +``` + +## Testing Your Changes + +To execute unit tests from the `fair-housing-guardrail` repository, run: + +```cmd +python -m pytest --pyargs tests/test_fair_housing_classification.py --cov=fair_housing_guardrail +``` diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..4fe436e --- /dev/null +++ b/LICENSE @@ -0,0 +1,109 @@ +AI PUBS OPEN RAIL-S LICENSE +Version 0.1, March 2, 2023 + +http://licenses.ai/ + + Section I: PREAMBLE + +This OpenRAIL-S License is generally applicable to any Source Code to be licensed with responsible use restrictions. + +The “Open” nomenclature indicates that the licensed Source Code is freely accessible to downstream and other users. 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For clarity, the term “Source Code” as used in this License includes any and all Derivatives of such Source Code. + +(j) “Third Parties” means individuals or legal entities that are not under common control with Licensor or You. + +(k) "You" (or "Your") means an individual or legal entity exercising permissions granted by this License. + +Section II: INTELLECTUAL PROPERTY RIGHTS + +Both copyright and patent grants may apply to the Source Code. The Source Code is subject to additional terms as described in Section III, which shall govern the use of the Source Code even in the event Section II is held unenforceable. + +2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly performƒ, sublicense, and distribute the Source Code. + +3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Source Code where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Source Code to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Source Code and/or a Contribution incorporated within the Source Code constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Source Code shall terminate as of the date such litigation is asserted or filed. + +Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION + +4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Source Code thereof in any medium, with or without modifications, provided that You meet the following conditions: + +(a) Use-based restrictions in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Source Code, and You shall give notice to subsequent users You Distribute to, that the Source Code is subject to paragraph 5. + +(b) You must give any Third Party recipients of the Source Code a copy of this License; + +(c) You must cause any modified files to carry prominent notices stating that You changed the files; + +(d) You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Source Code. + +You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or distribution of Your modifications, or for any Derivatives, provided Your use, reproduction, and distribution of the Source Code otherwise complies with the conditions stated in this License. + +5. Use-based restrictions. The restrictions set forth in Attachment A are considered use-based restrictions. Therefore You cannot use the Source Code in violation of such restrictions. You may use the Source Code subject to this License, including only for lawful purposes and in accordance with the License. You shall require all of Your users and licensees who use the Source Code to comply with the terms of this paragraph 5. + +Section IV: OTHER PROVISIONS + +7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Source Code in violation of this License, or update the Source Code through electronic means.. + +8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors. + +9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Source Code (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Source Code, and assume any risks associated with Your exercise of permissions under this License. + +10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Source Code (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. + +11. Accepting Warranty or Additional Liability. While redistributing the Source Code, You may choose to charge a fee in exchange for support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. + +12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein. + +END OF TERMS AND CONDITIONS + +Attachment A + +USE RESTRICTIONS +You agree not to use the Source Code or its Derivatives in any of the following ways: + + 1. Legal: + +(a) In any way that violates any applicable national, federal, state, local or international law or regulation. + + 2. Harm and Discrimination + +(a) For the purpose of exploiting, Harming or attempting to exploit or Harm minors in any way; + +(b) To generate or disseminate false information with the purpose of Harming others; + +(c) To generate or disseminate personal identifiable information that can be used to Harm an individual; + +(d) To defame, disparage or otherwise harass others; + +(e) For any use intended to or which has the effect of Harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics + +(f) To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person belonging to that group in a manner that causes or is likely to cause that person or another person Harm + +(g) For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories. + +3. Transparency + +(a) To generate or disseminate machine-generated information or content in any medium without expressly and intelligibly disclaiming that it is machine-generated; + +(b) To impersonate or attempt to impersonate human beings for purposes of deception; + +(c) For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation. diff --git a/README.md b/README.md new file mode 100644 index 0000000..b8703cb --- /dev/null +++ b/README.md @@ -0,0 +1,52 @@ +# Fair Housing Guardrail +Fair Housing Guardrail is a tool designed to help housing providers, real estate professionals, and related organizations review compliance of textual content with fair housing and fair lending laws in the United States, specifically the federal Fair Housing Act and the Equal Credit Opportunity Act. By leveraging a combination of stop list and fine-tuned BERT-based classifier which offers low latency and ease of fine-tuning compared to LLMs, Fair Housing Guardrail identifies content that may potentially violate these laws, allowing users to make necessary adjustments to help maintain compliance. + +## Overview +For a comprehensive overview of Fair Housing Guardrail, including its features, usage instructions, and implementation details, please visit our [blog](https://www.zillow.com/tech/navigating-fair-housing-guardrails-in-llms/). + +In use cases which use an LLM, we recommend using Fair Housing Guardrail in combination with a fair housing specific prompt as we found that it yielded better coverage in violation detection and prevention. Refer to the ‘Prompt Engineering’ section [here](https://www.zillow.com/tech/navigating-fair-housing-guardrails-in-llms/) for more details. + +## Features +**Compliance Detection**: Fair Housing Guardrail utilizes advanced natural language processing techniques to identify content that may violate fair housing and fair lending laws. + +**Customizable Stop List**: The tool demonstrates incorporating a curated list of terms and phrases commonly associated with discriminatory language, helping to flag potentially non-compliant content. Users may modify the sample stop list to add phrases relevant for their scenario. + +**Customizable BERT-based Classifier**: Fair Housing Guardrail employs a fine-tuned BERT-based classifier to analyze text and determine whether it aligns with fair housing and fair lending regulations. Users can adapt this framework to a different domain by providing their specific training data with compliant and non-compliant examples. + +**Fair Housing Policy**: Specifically, the model was trained to address the risk of illegal “steering” in real estate. In the traditional brick and mortar sense, this is when a real estate agent takes into account the legally-protected characteristics of their client when determining which listings to show them. A well publicized example of this was documented in a three-year investigation by [Newsday](https://www.google.com/url?q=https://projects.newsday.com/long-island/real-estate-agents-investigation/&sa=D&source=docs&ust=1712163974278248&usg=AOvVaw3U1PPg4BEXVJm_kQ17UB5f), published in 2019. Extensive fair housing testing or “secret shopping” of real estate agents on Long Island, New York was conducted, finding that many would direct different clients to different neighborhoods, depending on the race or ethnicity of the client, and the racial and ethnic demographics of the neighborhood. + +**Fine-tuned model and labeled dataset**: Refer to ‘Contact Us’ section to request this. + + +## How It Works +Fair Housing Guardrail works by taking input text and running it through both the stop list and the fine-tuned BERT-based classifier. If the text contains any flagged terms from the stop list or if the classifier determines it to be non-compliant with the fair housing and fair lending guidelines that it was trained on, the tool will label the content accordingly, providing an opportunity to review and revise the content as necessary. + +## Usage +After cloning the repo to a local directory, you can install the necessary dependencies with poetry. + +Make sure you have Poetry installed. If not, you can install it by following the instructions at https://python-poetry.org/docs/#installation. + +Navigate to the project directory that you cloned. + +Run the following command to install the project dependencies using Poetry: + +`poetry install` + +The project includes an `examples/` folder that contains two Jupyter notebooks: + +**train_model**: This notebook uses the sample `train-config.yaml` file to load train and test datasets. Then, it runs training and plots the training and validation losses and saves the pretrained model. + +**test_model**: This notebook uses the sample `test-config.yaml` file to load a test dataset. Ensure that paths to dataset and trained model are updated in test-config.yaml. Then, it runs predictions and returns the results. + +## Contributing +See the [Contributing](https://github.com/zillow/fair-housing-guardrail/blob/main/CONTRIBUTING.md) file for instructions on how to submit a PR. + +## License +See the [License](https://github.com/zillow/fair-housing-guardrail/blob/main/LICENSE) file. + +## Disclaimer +Fair Housing Guardrail is provided for informational purposes only and should not be considered legal advice. We recognize that users will interpret fair housing and fair lending requirements based on their own understanding and risk appetite, and are responsible for ensuring compliance with all applicable laws and regulations. + +## Contact Us +If you are interested in obtaining the training data and/or trained model, kindly contact us at +[fair-housing-guardrail-oss-support@zillowgroup.com](mailto:fair-housing-guardrail-oss-support@zillowgroup.com). In your message, provide a brief paragraph outlining your intended use case and how you plan to utilize both the model and dataset. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..597c001 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,11 @@ +# Security Policy + +## Supported Versions + +| Version | Supported | +| ------- | ------------------ | +| 1.0.0 | :white_check_mark: | + +## Reporting a Vulnerability + +If you notice any security vulnerabilities, please reach out by raising a [new issue](https://github.com/zillow/fair-housing-guardrail/issues/new). diff --git a/examples/configs/test-config.yaml b/examples/configs/test-config.yaml new file mode 100644 index 0000000..b5ae2c5 --- /dev/null +++ b/examples/configs/test-config.yaml @@ -0,0 +1,18 @@ +input_data: + content_column: 'content' + label_column: 'label' + max_length: 512 + input_data_path: './datasets/sample_data.jsonl' + stop_list_path: './datasets/sample_stoplist.txt' + +input_model: + model_dir: '../examples/test_model/' + +model: + do_train: False + +output_data: + output_dir: '../output' + +fairhousing: + threshold: 0.5 diff --git a/examples/configs/train-config.yaml b/examples/configs/train-config.yaml new file mode 100644 index 0000000..7383fdb --- /dev/null +++ b/examples/configs/train-config.yaml @@ -0,0 +1,26 @@ +input_data: + content_column: 'content' + label_column: 'label' + max_length: 512 + input_data_path: './datasets/sample_data.jsonl' + stop_list_path: './datasets/sample_stoplist.txt' + +output_data: + output_dir: '../output' + model_output_dir: '../output/model' + tokenizer_output_dir: '../output/tokenizer' + +model: + do_train: True + learning_rate: 1e-5 + train_batch_size: 4 + eval_batch_size: 4 + num_train_epochs: 5 + weight_decay: 0.01 + eval_steps: 1 + logging_steps: 1 + save_steps: 1 + save_total_limit: 3 + +fairhousing: + threshold: 0.5 diff --git a/examples/datasets/sample_data.jsonl b/examples/datasets/sample_data.jsonl new file mode 100644 index 0000000..8550cf2 --- /dev/null +++ b/examples/datasets/sample_data.jsonl @@ -0,0 +1,13 @@ +{"content": "I want to find a house or apartment in Montclair, New Jersey. The house should have at least 2 bedrooms and 1.5 bathrooms and the price should be under $700,000.", "label": "compliant"} +{"content": "what is the down payment on a house", "label": "compliant"} +{"content": "Give me a selection of 10 apartments to buy with a maximum price of 500,000 USD in Miami Beach with ocean view or partial ocean view and 2 bedrooms.", "label": "compliant"} +{"content": "What is the average price per square foot for a house in Bellevue, WA?", "label": "compliant"} +{"content": "find a 5 acre lot in Texas", "label": "compliant"} +{"content":"Find me a dream house with swimming pool and at least two-car parking garage in Newport Coast, LA, sorted by price.","label":"compliant"} +{"content":"how much income needed for 250k mortgage","label":"compliant"} +{"content":"Find me multi-family properties with three units or more in Hartford County, CT that are move-in ready","label":"compliant"} +{"content": "Properties for sale in predominantly white suburbs in Seattle", "label": "non-compliant"} +{"content": "What kind of loan do you recommend for an Orthodox Jew?", "label": "non-compliant"} +{"content": "Is there a certain kind of loan that is recommended for Filipinos?", "label": "non-compliant"} +{"content": "Where can I find a Christian neighborhoods in Seattle?", "label": "non-compliant"} +{"content": "I'm interested in homes far away from black people", "label": "non-compliant"} \ No newline at end of file diff --git a/examples/datasets/sample_stoplist.txt b/examples/datasets/sample_stoplist.txt new file mode 100644 index 0000000..bef8dc5 --- /dev/null +++ b/examples/datasets/sample_stoplist.txt @@ -0,0 +1,5 @@ +no vouchers +no kids +kids not allowed +veterans not allowed +christians only \ No newline at end of file diff --git a/examples/test_model.ipynb b/examples/test_model.ipynb new file mode 100644 index 0000000..7357583 --- /dev/null +++ b/examples/test_model.ipynb @@ -0,0 +1,89 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "df3f896a-2492-4191-8de3-a46c9c78cf96", + "metadata": {}, + "outputs": [], + "source": [ + "# Add fair_housing_guardrail package to system path\n", + "import os\n", + "import sys\n", + "\n", + "dir_path = os.path.dirname(os.path.realpath(\"./\")) + \"/fair_housing_guardrail\"\n", + "\n", + "if dir_path not in sys.path:\n", + " sys.path.insert(1, dir_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98e72afc-d797-40e9-965b-1334c8c66e4e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from utils.helper import load_config, load_dataset, load_tokenizer\n", + "from utils.fair_housing_classification import FairHousingGuardrailClassification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b71487b3-0f04-4c3d-9d79-baeb4743e306", + "metadata": {}, + "outputs": [], + "source": [ + "CONFIG_FILE_PATH = \"configs/test-config.yaml\"\n", + "config = load_config(CONFIG_FILE_PATH)\n", + "tokenizer = load_tokenizer()\n", + "_, test_dataset = load_dataset(config, tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8d171e0-4dea-4afa-8fa4-94980b23a010", + "metadata": {}, + "outputs": [], + "source": [ + "fh_classifier = FairHousingGuardrailClassification(config, tokenizer, None, test_dataset)\n", + "predictions = fh_classifier.predict()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dadf1133-d935-43e6-8ff9-0c4cbdb99dbe", + "metadata": {}, + "outputs": [], + "source": [ + "predictions" + ] + } + ], + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/test_model/config.json b/examples/test_model/config.json new file mode 100644 index 0000000..0444593 --- /dev/null +++ b/examples/test_model/config.json @@ -0,0 +1,32 @@ +{ + "_name_or_path": "bert-base-uncased", + "architectures": [ + "BertForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "classifier_dropout": null, + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": "LABEL_0" + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "LABEL_0": 0 + }, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.31.0", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 30522 +} diff --git a/examples/train_model.ipynb b/examples/train_model.ipynb new file mode 100644 index 0000000..aa4b540 --- /dev/null +++ b/examples/train_model.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c6d1c643-ae34-4dd0-87db-d13522df780d", + "metadata": {}, + "outputs": [], + "source": [ + "# Add fairhousingguardrail package to system path\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "dir_path = os.path.dirname(os.path.realpath(\"./\")) + \"/fair_housing_guardrail\"\n", + "\n", + "if dir_path not in sys.path:\n", + " sys.path.insert(1, dir_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10018ffa-eaea-42d2-b7ff-9255916b21b5", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "\n", + "from data.json_dataset import JsonDataset\n", + "from utils.helper import load_config, load_dataset, load_tokenizer\n", + "from utils.fair_housing_classification import FairHousingGuardrailClassification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c2fbd5a-eabf-41ac-a555-7de88c515830", + "metadata": {}, + "outputs": [], + "source": [ + "CONFIG_FILE_PATH = \"configs/train-config.yaml\"\n", + "config = load_config(CONFIG_FILE_PATH)\n", + "tokenizer = load_tokenizer()\n", + "train_dataset, test_dataset = load_dataset(config, tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae3ca773-d14d-464a-8532-aae8a8dfcb2c", + "metadata": {}, + "outputs": [], + "source": [ + "fh_classifier = FairHousingGuardrailClassification(config, tokenizer, train_dataset, test_dataset)\n", + "train_loss, eval_loss = fh_classifier.train()\n", + "\n", + "print(\"Train Loss\")\n", + "print(train_loss)\n", + "\n", + "print(\"Validation Loss\")\n", + "print(eval_loss)\n", + "\n", + "# Plot loss\n", + "_train_loss, steps = zip(*train_loss)\n", + "_eval_loss, steps = zip(*eval_loss)\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=steps, y=_train_loss, mode=\"lines+markers\", name=\"Training Loss\"))\n", + "fig.add_trace(go.Scatter(x=steps, y=_eval_loss, mode=\"lines+markers\", name=\"Validation Loss\"))\n", + "\n", + "fig.update_layout(title=\"Loss\", xaxis_title=\"Training Steps\", yaxis_title=\"Loss\")\n", + "fig.show()\n", + "\n", + "# Save model and tokenizer\n", + "fh_classifier.trainer.save_model()\n", + "\n", + "fh_classifier.model.save_pretrained(config[\"output_data\"][\"model_output_dir\"])\n", + "fh_classifier.tokenizer.save_pretrained(config[\"output_data\"][\"tokenizer_output_dir\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myenv1", + "language": "python", + "name": "myenv1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/fair_housing_guardrail/__init__.py b/fair_housing_guardrail/__init__.py new file mode 100644 index 0000000..e114594 --- /dev/null +++ b/fair_housing_guardrail/__init__.py @@ -0,0 +1,2 @@ +from .data import * +from .utils import * diff --git a/fair_housing_guardrail/data/__init__.py b/fair_housing_guardrail/data/__init__.py new file mode 100644 index 0000000..0c8ab5b --- /dev/null +++ b/fair_housing_guardrail/data/__init__.py @@ -0,0 +1,8 @@ +from .constants import ID_TO_LABEL, LABEL_TO_ID +from .json_dataset import JsonDataset + +__all__ = [ + "ID_TO_LABEL", + "LABEL_TO_ID", + "JsonDataset", +] diff --git a/fair_housing_guardrail/data/constants.py b/fair_housing_guardrail/data/constants.py new file mode 100644 index 0000000..77bd14e --- /dev/null +++ b/fair_housing_guardrail/data/constants.py @@ -0,0 +1,2 @@ +ID_TO_LABEL = {0: "non-compliant", 1: "compliant"} +LABEL_TO_ID = {"non-compliant": 0, "compliant": 1} diff --git a/fair_housing_guardrail/data/json_dataset.py b/fair_housing_guardrail/data/json_dataset.py new file mode 100644 index 0000000..67af206 --- /dev/null +++ b/fair_housing_guardrail/data/json_dataset.py @@ -0,0 +1,32 @@ +import random + +from torch.utils.data import Dataset +from tqdm import tqdm + + +def preprocess_function(row, tokenizer, config): + tensors = tokenizer( + row[config["input_data"]["content_column"]].lower(), + truncation=True, + max_length=config["input_data"]["max_length"], + ) + return tensors + + +class JsonDataset(Dataset): + def __init__(self, df, label2id, tokenizer, config): + self.data = list() + for i in tqdm(range(len(df))): + row = df.iloc[i] + _sample = preprocess_function(row, tokenizer, config) + _sample[config["input_data"]["label_column"]] = float( + label2id[row[config["input_data"]["label_column"]]] + ) + self.data.append(_sample) + random.shuffle(self.data) + + def __len__(self): + return len(self.data) + + def __getitem__(self, index): + return self.data[index] diff --git a/fair_housing_guardrail/utils/__init__.py b/fair_housing_guardrail/utils/__init__.py new file mode 100644 index 0000000..de24a93 --- /dev/null +++ b/fair_housing_guardrail/utils/__init__.py @@ -0,0 +1,22 @@ +from .fair_housing_classification import FairHousingGuardrailClassification, SigmoidTrainer +from .helper import ( + compute_metrics, + load_config, + load_dataset, + load_model, + load_phrase_checker, + load_tokenizer, +) +from .stop_phrases import ProtectedAttributesStopWordsCheck + +__all__ = [ + "FairHousingGuardrailClassification", + "SigmoidTrainer", + "compute_metrics", + "load_config", + "load_tokenizer", + "load_model", + "load_phrase_checker", + "load_dataset", + "ProtectedAttributesStopWordsCheck", +] diff --git a/fair_housing_guardrail/utils/fair_housing_classification.py b/fair_housing_guardrail/utils/fair_housing_classification.py new file mode 100644 index 0000000..9a76288 --- /dev/null +++ b/fair_housing_guardrail/utils/fair_housing_classification.py @@ -0,0 +1,167 @@ +import logging +from typing import Dict, List, Tuple, Union + +import torch +from transformers import DataCollatorWithPadding, Trainer, TrainingArguments + +from fair_housing_guardrail.data.constants import ID_TO_LABEL +from fair_housing_guardrail.utils.helper import compute_metrics, load_model, load_phrase_checker + +logger = logging.getLogger() +logger.setLevel(logging.INFO) + + +class FairHousingGuardrailClassification: + def __init__(self, config={}, tokenizer=None, train_data=None, test_data=None): + self.config = config + self.tokenizer = tokenizer + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.model = load_model(self.config) + self.model = self.model.to(self.device) + self.phrase_checker = load_phrase_checker(self.config["input_data"]["stop_list_path"]) + self.train_dataset = train_data + self.test_dataset = test_data + self.predictions = [] + + def train(self) -> Tuple[List[Tuple[float, int]], List[Tuple[float, int]]]: + # Set training arguments from config if present otherwise set default values + config_model = self.config.get("model", {}) + training_args = TrainingArguments( + output_dir=self.config["output_data"]["output_dir"], + learning_rate=float(config_model.get("learning_rate", 1e-5)), + per_device_train_batch_size=int(config_model.get("train_batch_size", 32)), + per_device_eval_batch_size=int(config_model.get("eval_batch_size", 32)), + num_train_epochs=int(config_model.get("num_train_epochs", 1)), + weight_decay=float(config_model.get("weight_decay", 0.01)), + evaluation_strategy="steps", + eval_steps=int(config_model.get("eval_steps", 10)), + logging_steps=int(config_model.get("logging_steps", 20)), + save_strategy="steps", + save_steps=int(config_model.get("save_steps", 20)), + save_total_limit=int(config_model.get("save_total_limit", 3)), + logging_strategy="steps", + load_best_model_at_end=True, + push_to_hub=False, + group_by_length=True, + ) + self.trainer = SigmoidTrainer( + model=self.model, + args=training_args, + train_dataset=self.train_dataset, + eval_dataset=self.test_dataset, + tokenizer=self.tokenizer, + data_collator=DataCollatorWithPadding(tokenizer=self.tokenizer), + compute_metrics=compute_metrics, + ) + logger.info("Trainer built. Running training...") + self.trainer.train() + train_loss = [ + (i["loss"], i["step"]) for i in self.trainer.state.log_history if "loss" in i + ] + eval_loss = [ + (i["eval_loss"], i["step"]) for i in self.trainer.state.log_history if "eval_loss" in i + ] + return train_loss, eval_loss + + def predict(self) -> List[Dict[str, Union[str, float]]]: + """ + Populates self.predictions with Dict objects. + Each object represents the prediction results per input with the following data: + prediction: compliant or non-compliant + score: classifier score (0 if Stoplist fails) + non-compliant-text: first sentence in input to fail compliance check + (empty if compliant) + """ + self.model = self.model.eval() + input_strs = self.test_dataset[self.config["input_data"]["content_column"]].tolist() + + if "fairhousing" in self.config and "threshold" in self.config["fairhousing"]: + threshold = self.config["fairhousing"]["threshold"] + logger.info(f"Using config threshold of {threshold}.") + else: + threshold = 0.5 + logger.info("Threshold not found in config, using 0.5 as threshold.") + + # Build batch of sentences to run against classifier + # Each individual sentence is checked against model + sentences = [] + input_mapping = {} + for input_str in input_strs: + for sent in self.phrase_checker.get_sentences(input_str): + sent = sent[:-1] if (sent.endswith(".") or sent.endswith("?")) else sent + sent = sent.lower() + sentences.append(sent) + input_mapping[sent] = input_str + + feats = self.tokenizer( + sentences, + return_tensors="pt", + max_length=self.config["input_data"]["max_length"], + padding=True, + truncation=True, + ).to(self.device) + + with torch.inference_mode(): + outs = self.model(**feats) + + preds = torch.nn.functional.sigmoid(outs.logits.squeeze(1)) + # Set value of 0 or 1 to preds, based on threshold + preds_modified = torch.where( + preds > threshold, + torch.tensor(1, device=self.device), + torch.tensor(0, device=self.device), + ) + + preds = preds.cpu().numpy() + preds_modified = preds_modified.cpu().numpy() + + # Map sentence predictions back to their original input text + total_score = {} + prediction = {} + non_compliant_text = {} + for pred, pred_modified, sent in zip(preds, preds_modified, sentences): + # Set defaults if this is the first sentence of input being checked + prediction.setdefault(input_mapping[sent], "compliant") + total_score.setdefault(input_mapping[sent], []) + non_compliant_text.setdefault(input_mapping[sent], "") + if prediction.get(input_mapping[sent], "compliant") == "non-compliant": + # Input already determined to be non-compliant, skip + continue + if self.phrase_checker.check_phrase_is_stoplist_compliant(sent): + # Stoplist passed + score = pred.item() + sent_prediction = ID_TO_LABEL[pred_modified.item()] + if sent_prediction == "non-compliant": + prediction[input_mapping[sent]] = "non-compliant" + total_score[input_mapping[sent]] = [score] + non_compliant_text[input_mapping[sent]] = sent + total_score[input_mapping[sent]].append(score) + else: + # Stoplist failure + logger.info(f"Stoplist check failed for sentence: {sent}") + prediction[input_mapping[sent]] = "non-compliant" + total_score[input_mapping[sent]] = [0] + non_compliant_text[input_mapping[sent]] = sent + + # Build final prediction for each input + for input_str in input_strs: + input_prediction = { + "prediction": prediction[input_str], + "score": sum(total_score[input_str]) / len(total_score[input_str]), + "non-compliant-text": non_compliant_text[input_str], + } + self.predictions.append(input_prediction) + + return self.predictions + + +class SigmoidTrainer(Trainer): + loss_fn = torch.nn.BCEWithLogitsLoss() + + def compute_loss(self, model, inputs, return_outputs: bool = False): + outs = model(**{k: v for k, v in inputs.items() if k != "labels"}) + labels = inputs["labels"] + loss = self.loss_fn(outs.logits.squeeze(0).reshape(labels.shape, 1), labels) + if return_outputs: + return loss, outs + return loss diff --git a/fair_housing_guardrail/utils/helper.py b/fair_housing_guardrail/utils/helper.py new file mode 100644 index 0000000..827e6e9 --- /dev/null +++ b/fair_housing_guardrail/utils/helper.py @@ -0,0 +1,95 @@ +import logging +import os + +import evaluate +import pandas as pd +import torch +import yaml +from sklearn.model_selection import train_test_split +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +from fair_housing_guardrail.data.constants import LABEL_TO_ID +from fair_housing_guardrail.data.json_dataset import JsonDataset +from fair_housing_guardrail.utils.stop_phrases import ProtectedAttributesStopWordsCheck + +logger = logging.getLogger() +logger.setLevel(logging.INFO) + +accuracy = evaluate.load("accuracy") +CONFIG_DICT = {} + + +def compute_metrics(eval_pred): + predictions, labels = eval_pred + if "fairhousing" in CONFIG_DICT and "threshold" in CONFIG_DICT["fairhousing"]: + threshold = CONFIG_DICT["fairhousing"]["threshold"] + else: + logger.info("Threshold not found in config, using 0.5 as threshold") + threshold = 0.5 + preds = torch.where( + torch.nn.functional.sigmoid(torch.tensor(predictions)).reshape( + -1, + ) + > threshold, + 1, + 0, + ) + return accuracy.compute(predictions=preds, references=torch.tensor(labels, dtype=torch.int32)) + + +def load_config(yaml_file_path): + if not os.path.isfile(yaml_file_path): + raise Exception("Config yaml file not found at path" + yaml_file_path) + + with open(yaml_file_path) as f: + logger.info(f"Loading yaml config from path: {yaml_file_path}") + config = yaml.load(f, Loader=yaml.FullLoader) + logger.info(config) + global CONFIG_DICT + CONFIG_DICT = config + return config + + +def load_tokenizer(): + return AutoTokenizer.from_pretrained("bert-base-uncased") + + +def load_model(config): + if "model" in config and config["model"]["do_train"] is True: + logger.info("Train Mode: Loading bert-base-uncased") + return AutoModelForSequenceClassification.from_pretrained( + "bert-base-uncased", num_labels=1 + ) + if "input_model" not in config: + raise Exception("Error: must define either 'model' or 'input_model' in config.") + if config["input_model"]["model_dir"] is None: + raise Exception("Predict Mode: Unable to load model as model_dir is None") + + return AutoModelForSequenceClassification.from_pretrained( + config["input_model"]["model_dir"], num_labels=1 + ) + + +def load_phrase_checker(stop_list_path): + if stop_list_path is None: + raise Exception("Stop list path is None") + return ProtectedAttributesStopWordsCheck.get_instance(stop_list_path) + + +def load_dataset(config, tokenizer): + pd_data = pd.read_json(config["input_data"]["input_data_path"], orient="records", lines=True) + logger.info("Found the following columns in data: " + pd_data.columns) + + if "model" in config and config["model"]["do_train"] is True: + train_df, test_df = train_test_split(pd_data, test_size=0.5) + logger.info("Splitting dataset into train and test sets") + logger.info(f"Train count: {train_df.count()}") + logger.info(f"Test count: {test_df.count()}") + train_dataset = JsonDataset(train_df, LABEL_TO_ID, tokenizer, config) + test_dataset = JsonDataset(test_df, LABEL_TO_ID, tokenizer, config) + else: + train_dataset = None + test_dataset = pd_data + logger.info(f"Test count: {pd_data.count()}") + + return train_dataset, test_dataset diff --git a/fair_housing_guardrail/utils/stop_phrases.py b/fair_housing_guardrail/utils/stop_phrases.py new file mode 100644 index 0000000..43c0348 --- /dev/null +++ b/fair_housing_guardrail/utils/stop_phrases.py @@ -0,0 +1,61 @@ +import nltk +from nltk import ngrams +from nltk.stem import SnowballStemmer, WordNetLemmatizer +from nltk.tokenize import RegexpTokenizer + + +class ProtectedAttributesStopWordsCheck(object): + def __init__(self, denied_phrases_file): + nltk.download("wordnet") + nltk.download("omw-1.4") + nltk.download("punkt") + self.lemmatizer = WordNetLemmatizer() + self.stemmer = SnowballStemmer("english") + self.tokenizer = RegexpTokenizer(r"\w+") + self.stemmed_lemmatized_stop_list = set() + stop_list = list() + with open(denied_phrases_file, "r") as f: + for line in f: + line = line.rstrip("\n") + stop_list.append(line) + for phrase in stop_list: + tokens = self.tokenizer.tokenize(phrase) + tokens = [self.stemmer.stem(self.lemmatizer.lemmatize(token)) for token in tokens] + self.stemmed_lemmatized_stop_list.add(" ".join(tokens)) + + def check_phrase_is_stoplist_compliant(self, query): + """ + Checks entire query against phrases in stoplist file. + This is used as a check prior to classifier model to block + clearly explicit words or phrases. + """ + unigrams = self.tokenizer.tokenize(query.lower()) + unigrams = [self.stemmer.stem(self.lemmatizer.lemmatize(token)) for token in unigrams] + + # Exact match check for stoplist phrases in sentence + unigrams_str = " " + " ".join(unigrams) + " " + for phrase in self.stemmed_lemmatized_stop_list: + phrase_with_space = " " + phrase + " " + if phrase_with_space in unigrams_str: + return False + + # Unigram and bigram checks + for unigram in unigrams: + if unigram in self.stemmed_lemmatized_stop_list: + return False + bigrams = list(ngrams(unigrams, 2)) + for bigram in bigrams: + if " ".join(bigram) in self.stemmed_lemmatized_stop_list: + return False + return True + + def get_sentences(self, text): + return nltk.sent_tokenize(text) + + @classmethod + def get_instance(cls, denied_phrases_file): + if not hasattr(cls, "instance"): + cls.instance = ProtectedAttributesStopWordsCheck( + denied_phrases_file=denied_phrases_file + ) + return cls.instance diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..b465ba7 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,4656 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "accelerate" +version = "0.28.0" +description = "Accelerate" +optional = false +python-versions = ">=3.8.0" 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+1,32 @@ +[tool.poetry] +name = "fair-housing-guardrail" +version = "1.0.0" +description = "" +authors = ["Anusha Bagalkotkar ", "Aveek Karmakar ", "Trevor Nogues "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.9" +accelerate = "0.28.0" +evaluate = "0.4.1" +matplotlib = "^3.7.0" +plotly = "5.18.0" +nltk = "3.8.1" +numpy = "^1.24.0" +pandas = "^2.0" +scikit-learn = "1.4.1.post1" +torch = "2.2.2" +tqdm = "4.66.1" +transformers = "4.39.1" +pre-commit = "^3.4.0" +pytest = "^8.1.1" +urllib3 = "1.26.18" +jupyter = "^1.0.0" + + +[build-system] +requires = ["poetry_core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.black] +line-length = 110 diff --git a/tests/test_fair_housing_classification.py b/tests/test_fair_housing_classification.py new file mode 100644 index 0000000..4b9fbab --- /dev/null +++ b/tests/test_fair_housing_classification.py @@ -0,0 +1,87 @@ +import os +import shutil +from io import StringIO + +import pandas as pd + +from fair_housing_guardrail.utils.fair_housing_classification import ( + FairHousingGuardrailClassification, +) +from fair_housing_guardrail.utils.helper import load_config, load_dataset, load_tokenizer + +current_path = os.getcwd() + +# Setup train +TRAIN_CONFIG_FILE_PATH = os.path.join(current_path, "examples/configs/train-config.yaml") +train_config = load_config(TRAIN_CONFIG_FILE_PATH) +train_config["output_data"]["output_dir"] = os.path.join(current_path, "tmp/") +train_config["output_data"]["model_output_dir"] = os.path.join(current_path, "tmp/model/") +train_config["input_data"]["stop_list_path"] = os.path.join( + current_path, "examples/datasets/sample_stoplist.txt" +) +train_config["input_data"]["input_data_path"] = os.path.join( + current_path, "examples/datasets/sample_data.jsonl" +) + +# Setup test +TEST_CONFIG_FILE_PATH = os.path.join(current_path, "examples/configs/test-config.yaml") +test_config = load_config(TEST_CONFIG_FILE_PATH) +test_config["input_model"]["model_dir"] = os.path.join(current_path, "tmp/model/") +test_config["input_data"]["stop_list_path"] = os.path.join( + current_path, "examples/datasets/sample_stoplist.txt" +) +test_config["input_data"]["input_data_path"] = os.path.join( + current_path, "examples/datasets/sample_data.jsonl" +) +# Setup tokenizer +tokenizer = load_tokenizer() + + +def test_train_and_predict(): + with open(train_config["input_data"]["input_data_path"], "r") as file: + json_data = file.read() + train_dataset, test_dataset = load_dataset(train_config, tokenizer) + + instance = FairHousingGuardrailClassification( + train_config, tokenizer, train_data=train_dataset, test_data=test_dataset + ) + temp_dir = os.path.join(current_path, "tmp/") + if not os.path.exists(temp_dir): + os.mkdir(temp_dir) + + # Test train + train_loss, eval_loss = instance.train() + print(train_loss, eval_loss) + + instance.trainer.save_model() + instance.model.save_pretrained(train_config["output_data"]["model_output_dir"]) + + config_path = os.path.join(temp_dir, "config.json") + + assert os.path.exists(config_path) + assert isinstance(train_loss, list) + assert isinstance(eval_loss, list) + assert len(train_loss) > 0 + assert len(eval_loss) > 0 + + # Test predict + with open(test_config["input_data"]["input_data_path"], "r") as file: + json_data = file.read() + predict_data = pd.read_json(StringIO(json_data), orient="records", lines=True) + + run_predict(predict_data) + + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + + +def run_predict(predict_data): + instance = FairHousingGuardrailClassification(test_config, tokenizer, None, predict_data) + predictions = instance.predict() + + assert len(predictions) == 13 + assert isinstance(predictions, list) + assert isinstance(predictions[0], dict) + assert "prediction" in predictions[0] + assert "score" in predictions[0] + assert "non-compliant-text" in predictions[0]