From d2d6250c90da3852610fd21e4343ad21083cc7db Mon Sep 17 00:00:00 2001 From: Roman <56846628+RomanBredehoft@users.noreply.github.com> Date: Tue, 2 Apr 2024 10:18:15 +0200 Subject: [PATCH] feat: encrypted data-frame --- .github/workflows/continuous-integration.yaml | 4 +- .github/workflows/refresh-one-notebook.yaml | 2 + .gitignore | 3 + ci/aws_ami_build_component.yaml | 2 +- deps_licenses/licenses_linux_user.txt | 40 +- deps_licenses/licenses_linux_user.txt.md5 | 2 +- deps_licenses/licenses_mac_intel_user.txt | 42 +- deps_licenses/licenses_mac_intel_user.txt.md5 | 2 +- deps_licenses/licenses_mac_silicon_user.txt | 43 +- .../licenses_mac_silicon_user.txt.md5 | 2 +- poetry.lock | 1792 +++++++++-------- pyproject.toml | 3 +- script/make_utils/pytest_pypi_cml.sh | 2 +- src/concrete/ml/pandas/__init__.py | 94 + .../ml/pandas/_client_server_files/client.zip | 3 + .../ml/pandas/_client_server_files/server.zip | 3 + src/concrete/ml/pandas/_development.py | 180 ++ src/concrete/ml/pandas/_operators.py | 374 ++++ src/concrete/ml/pandas/_processing.py | 321 +++ src/concrete/ml/pandas/_utils.py | 190 ++ src/concrete/ml/pandas/client_engine.py | 85 + src/concrete/ml/pandas/dataframe.py | 340 ++++ src/concrete/ml/pytest/utils.py | 64 + tests/pandas/test_pandas.py | 572 ++++++ use_case_examples/dataframe/.gitignore | 5 + .../dataframe/client_1/df_left.csv | 10 + .../dataframe/client_2/df_right.csv | 4 + .../dataframe/encrypted_pandas.ipynb | 939 +++++++++ 28 files changed, 4191 insertions(+), 932 deletions(-) create mode 100644 src/concrete/ml/pandas/__init__.py create mode 100644 src/concrete/ml/pandas/_client_server_files/client.zip create mode 100644 src/concrete/ml/pandas/_client_server_files/server.zip create mode 100644 src/concrete/ml/pandas/_development.py create mode 100644 src/concrete/ml/pandas/_operators.py create mode 100644 src/concrete/ml/pandas/_processing.py create mode 100644 src/concrete/ml/pandas/_utils.py create mode 100644 src/concrete/ml/pandas/client_engine.py create mode 100644 src/concrete/ml/pandas/dataframe.py create mode 100644 tests/pandas/test_pandas.py create mode 100644 use_case_examples/dataframe/.gitignore create mode 100644 use_case_examples/dataframe/client_1/df_left.csv create mode 100644 use_case_examples/dataframe/client_2/df_right.csv create mode 100644 use_case_examples/dataframe/encrypted_pandas.ipynb diff --git a/.github/workflows/continuous-integration.yaml b/.github/workflows/continuous-integration.yaml index c97905efe..e2aa27bca 100644 --- a/.github/workflows/continuous-integration.yaml +++ b/.github/workflows/continuous-integration.yaml @@ -388,7 +388,7 @@ jobs: # benchmarks and use cases - name: Pull LFS test files run: | - git lfs pull --include "tests/data" --exclude "" + git lfs pull --include "tests/data/**, src/concrete/ml/pandas/_client_server_files/**" --exclude "" - name: Set up Python ${{ matrix.python_version }} uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c @@ -1036,7 +1036,7 @@ jobs: # benchmarks and use cases - name: Pull LFS test files run: | - git lfs pull --include "tests/data" --exclude "" + git lfs pull --include "tests/data/**, src/concrete/ml/pandas/_client_server_files/**" --exclude "" - name: Set up Python ${{ matrix.python_version }} uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c diff --git a/.github/workflows/refresh-one-notebook.yaml b/.github/workflows/refresh-one-notebook.yaml index 963611826..5a1e4a8e9 100644 --- a/.github/workflows/refresh-one-notebook.yaml +++ b/.github/workflows/refresh-one-notebook.yaml @@ -16,6 +16,7 @@ on: - DecisionTreeClassifier \n - DecisionTreeRegressor \n - Deployment \n + - encrypted_pandas \n - ExperimentPrivacyTreePaper \n - FromImageNetToCifar \n - FullyConnectedNeuralNetwork \n @@ -60,6 +61,7 @@ env: DecisionTreeClassifier: "docs/advanced_examples/DecisionTreeClassifier.ipynb" DecisionTreeRegressor: "docs/advanced_examples/DecisionTreeRegressor.ipynb" Deployment: "docs/advanced_examples/Deployment.ipynb" + encrypted_pandas: "use_case_examples/dataframe/encrypted_pandas.ipynb" ExperimentPrivacyTreePaper: "docs/advanced_examples/ExperimentPrivacyTreePaper.ipynb" FromImageNetToCifar: "use_case_examples/cifar/cifar_brevitas_finetuning/FromImageNetToCifar.ipynb" FullyConnectedNeuralNetwork: "docs/advanced_examples/FullyConnectedNeuralNetwork.ipynb" diff --git a/.gitignore b/.gitignore index fb4a4b5c7..569ec60b8 100644 --- a/.gitignore +++ b/.gitignore @@ -96,6 +96,9 @@ ipython_config.py # VScode .vscode +# macOS +.DS_Store + # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies diff --git a/ci/aws_ami_build_component.yaml b/ci/aws_ami_build_component.yaml index 3abf7f641..084aeadfd 100644 --- a/ci/aws_ami_build_component.yaml +++ b/ci/aws_ami_build_component.yaml @@ -77,7 +77,7 @@ phases: - apt install -y git-lfs - git lfs install - source venv/bin/activate - - python -m pip install pytest==7.1.1 pandas==1.3.0 tensorflow==2.12.0 tf2onnx==1.13.0 torchvision==0.14.1 + - python -m pip install pytest==7.1.1 pandas==2.0.3 tensorflow==2.12.0 tf2onnx==1.13.0 torchvision==0.14.1 # We disable tests for test_deploy file because the instance does not have AWS CLI setup - name: RunTests diff --git a/deps_licenses/licenses_linux_user.txt b/deps_licenses/licenses_linux_user.txt index 9cb2c7ae2..075358087 100644 --- a/deps_licenses/licenses_linux_user.txt +++ b/deps_licenses/licenses_linux_user.txt @@ -2,8 +2,8 @@ Name, Version, License GitPython, 3.1.41, BSD License PyYAML, 6.0.1, MIT License anyio, 3.7.1, MIT License -boto3, 1.34.38, Apache Software License -botocore, 1.34.38, Apache Software License +boto3, 1.34.72, Apache Software License +botocore, 1.34.72, Apache Software License brevitas, 0.8.0, UNKNOWN certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0) charset-normalizer, 3.3.2, MIT License @@ -14,16 +14,16 @@ dependencies, 2.0.1, BSD License dill, 0.3.8, BSD License exceptiongroup, 1.2.0, MIT License fastapi, 0.103.2, MIT License -filelock, 3.13.1, The Unlicense (Unlicense) -flatbuffers, 23.5.26, Apache Software License -fsspec, 2024.2.0, BSD License +filelock, 3.13.3, The Unlicense (Unlicense) +flatbuffers, 24.3.25, Apache Software License +fsspec, 2024.3.1, BSD License gitdb, 4.0.11, BSD License h11, 0.14.0, MIT License -huggingface-hub, 0.20.3, Apache Software License +huggingface-hub, 0.22.1, Apache Software License humanfriendly, 10.0, MIT License hummingbird-ml, 0.4.8, MIT License idna, 3.6, BSD License -importlib-resources, 6.1.1, Apache Software License +importlib_resources, 6.4.0, Apache Software License iniconfig, 2.0.0, MIT License jmespath, 1.0.1, MIT License joblib, 1.3.2, BSD License @@ -40,18 +40,20 @@ onnxconverter-common, 1.13.0, MIT License onnxmltools, 1.11.0, Apache Software License onnxoptimizer, 0.3.10, Apache License v2.0 onnxruntime, 1.13.1, MIT License -packaging, 23.2, Apache Software License; BSD License +packaging, 24.0, Apache Software License; BSD License +pandas, 2.0.3, BSD License pluggy, 1.4.0, MIT License protobuf, 3.20.3, BSD-3-Clause psutil, 5.9.8, BSD License pydantic, 1.10.14, MIT License pytest, 7.4.1, MIT License pytest-json-report, 1.5.0, MIT -pytest-metadata, 3.1.0, Mozilla Public License 2.0 (MPL 2.0) -python-dateutil, 2.8.2, Apache Software License; BSD License +pytest-metadata, 3.1.1, Mozilla Public License 2.0 (MPL 2.0) +python-dateutil, 2.9.0.post0, Apache Software License; BSD License +pytz, 2024.1, MIT License regex, 2023.12.25, Apache Software License requests, 2.31.0, Apache Software License -s3transfer, 0.10.0, Apache Software License +s3transfer, 0.10.1, Apache Software License safetensors, 0.4.2, Apache Software License scikit-learn, 1.1.3, BSD License scipy, 1.10.1, BSD License @@ -60,18 +62,20 @@ skl2onnx, 1.12, Apache Software License skops, 0.5.0, MIT skorch, 0.11.0, new BSD 3-Clause smmap, 5.0.1, BSD License -sniffio, 1.3.0, Apache Software License; MIT License +sniffio, 1.3.1, Apache Software License; MIT License starlette, 0.27.0, BSD License sympy, 1.12, BSD License tabulate, 0.8.10, MIT License -threadpoolctl, 3.2.0, BSD License -tokenizers, 0.15.1, Apache Software License +threadpoolctl, 3.4.0, BSD License +tokenizers, 0.15.2, Apache Software License tomli, 2.0.1, MIT License torch, 1.13.1, BSD License -tqdm, 4.66.1, MIT License; Mozilla Public License 2.0 (MPL 2.0) -transformers, 4.37.2, Apache Software License +tqdm, 4.66.2, MIT License; Mozilla Public License 2.0 (MPL 2.0) +transformers, 4.39.1, Apache Software License typing_extensions, 4.5.0, Python Software Foundation License -urllib3, 2.0.7, MIT License +tzdata, 2024.1, Apache Software License +urllib3, 1.26.18, MIT License uvicorn, 0.21.1, BSD License xgboost, 1.6.2, Apache Software License -z3-solver, 4.12.5.0, MIT License +z3-solver, 4.13.0.0, MIT License +zipp, 3.18.1, MIT License diff --git a/deps_licenses/licenses_linux_user.txt.md5 b/deps_licenses/licenses_linux_user.txt.md5 index 72f5ec802..60fdcedbe 100644 --- a/deps_licenses/licenses_linux_user.txt.md5 +++ b/deps_licenses/licenses_linux_user.txt.md5 @@ -1 +1 @@ -2e50e53583434ebc1c2a34c502c81875 +8de2e8c13fe9a1fe80d9cce43dee7493 diff --git a/deps_licenses/licenses_mac_intel_user.txt b/deps_licenses/licenses_mac_intel_user.txt index cb3aa0ead..f084604e7 100644 --- a/deps_licenses/licenses_mac_intel_user.txt +++ b/deps_licenses/licenses_mac_intel_user.txt @@ -2,31 +2,32 @@ Name, Version, License GitPython, 3.1.41, BSD License PyYAML, 6.0.1, MIT License anyio, 3.7.1, MIT License -boto3, 1.34.38, Apache Software License -botocore, 1.34.38, Apache Software License +boto3, 1.34.72, Apache Software License +botocore, 1.34.72, Apache Software License brevitas, 0.8.0, UNKNOWN certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0) charset-normalizer, 3.3.2, MIT License click, 8.1.7, BSD License coloredlogs, 15.0.1, MIT License -concrete-python, 2.5.1, BSD-3-Clause +concrete-python, 2024.3.27, BSD-3-Clause dependencies, 2.0.1, BSD License dill, 0.3.8, BSD License exceptiongroup, 1.2.0, MIT License fastapi, 0.103.2, MIT License -filelock, 3.13.1, The Unlicense (Unlicense) -flatbuffers, 23.5.26, Apache Software License -fsspec, 2024.2.0, BSD License +filelock, 3.13.3, The Unlicense (Unlicense) +flatbuffers, 24.3.25, Apache Software License +fsspec, 2024.3.1, BSD License gitdb, 4.0.11, BSD License h11, 0.14.0, MIT License -huggingface-hub, 0.20.3, Apache Software License +huggingface-hub, 0.22.1, Apache Software License humanfriendly, 10.0, MIT License hummingbird-ml, 0.4.8, MIT License idna, 3.6, BSD License -importlib-resources, 6.1.1, Apache Software License +importlib_resources, 6.4.0, Apache Software License iniconfig, 2.0.0, MIT License jmespath, 1.0.1, MIT License joblib, 1.3.2, BSD License +jsonpickle, 3.0.3, BSD License mpmath, 1.3.0, BSD License networkx, 3.1, BSD License numpy, 1.23.5, BSD License @@ -35,18 +36,20 @@ onnxconverter-common, 1.13.0, MIT License onnxmltools, 1.11.0, Apache Software License onnxoptimizer, 0.3.10, Apache License v2.0 onnxruntime, 1.13.1, MIT License -packaging, 23.2, Apache Software License; BSD License +packaging, 24.0, Apache Software License; BSD License +pandas, 2.0.3, BSD License pluggy, 1.4.0, MIT License protobuf, 3.20.3, BSD-3-Clause psutil, 5.9.8, BSD License pydantic, 1.10.14, MIT License pytest, 7.4.1, MIT License pytest-json-report, 1.5.0, MIT -pytest-metadata, 3.1.0, Mozilla Public License 2.0 (MPL 2.0) -python-dateutil, 2.8.2, Apache Software License; BSD License +pytest-metadata, 3.1.1, Mozilla Public License 2.0 (MPL 2.0) +python-dateutil, 2.9.0.post0, Apache Software License; BSD License +pytz, 2024.1, MIT License regex, 2023.12.25, Apache Software License requests, 2.31.0, Apache Software License -s3transfer, 0.10.0, Apache Software License +s3transfer, 0.10.1, Apache Software License safetensors, 0.4.2, Apache Software License scikit-learn, 1.1.3, BSD License scipy, 1.10.1, BSD License @@ -55,19 +58,20 @@ skl2onnx, 1.12, Apache Software License skops, 0.5.0, MIT skorch, 0.11.0, new BSD 3-Clause smmap, 5.0.1, BSD License -sniffio, 1.3.0, Apache Software License; MIT License +sniffio, 1.3.1, Apache Software License; MIT License starlette, 0.27.0, BSD License sympy, 1.12, BSD License tabulate, 0.8.10, MIT License -threadpoolctl, 3.2.0, BSD License -tokenizers, 0.15.1, Apache Software License +threadpoolctl, 3.4.0, BSD License +tokenizers, 0.15.2, Apache Software License tomli, 2.0.1, MIT License torch, 1.13.1, BSD License -tqdm, 4.66.1, MIT License; Mozilla Public License 2.0 (MPL 2.0) -transformers, 4.37.2, Apache Software License +tqdm, 4.66.2, MIT License; Mozilla Public License 2.0 (MPL 2.0) +transformers, 4.39.1, Apache Software License typing_extensions, 4.5.0, Python Software Foundation License +tzdata, 2024.1, Apache Software License urllib3, 1.26.18, MIT License uvicorn, 0.21.1, BSD License xgboost, 1.6.2, Apache Software License -z3-solver, 4.12.5.0, MIT License -zipp, 3.17.0, MIT License +z3-solver, 4.13.0.0, MIT License +zipp, 3.18.1, MIT License diff --git a/deps_licenses/licenses_mac_intel_user.txt.md5 b/deps_licenses/licenses_mac_intel_user.txt.md5 index f858663d7..60fdcedbe 100644 --- a/deps_licenses/licenses_mac_intel_user.txt.md5 +++ b/deps_licenses/licenses_mac_intel_user.txt.md5 @@ -1 +1 @@ -a923947bfb17b658ab8efe61d5cafe96 +8de2e8c13fe9a1fe80d9cce43dee7493 diff --git a/deps_licenses/licenses_mac_silicon_user.txt b/deps_licenses/licenses_mac_silicon_user.txt index cb3aa0ead..90eb7760e 100644 --- a/deps_licenses/licenses_mac_silicon_user.txt +++ b/deps_licenses/licenses_mac_silicon_user.txt @@ -2,31 +2,32 @@ Name, Version, License GitPython, 3.1.41, BSD License PyYAML, 6.0.1, MIT License anyio, 3.7.1, MIT License -boto3, 1.34.38, Apache Software License -botocore, 1.34.38, Apache Software License +boto3, 1.34.72, Apache Software License +botocore, 1.34.72, Apache Software License brevitas, 0.8.0, UNKNOWN certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0) charset-normalizer, 3.3.2, MIT License click, 8.1.7, BSD License coloredlogs, 15.0.1, MIT License -concrete-python, 2.5.1, BSD-3-Clause +concrete-python, 2024.3.27, BSD-3-Clause dependencies, 2.0.1, BSD License dill, 0.3.8, BSD License exceptiongroup, 1.2.0, MIT License fastapi, 0.103.2, MIT License -filelock, 3.13.1, The Unlicense (Unlicense) -flatbuffers, 23.5.26, Apache Software License -fsspec, 2024.2.0, BSD License +filelock, 3.13.3, The Unlicense (Unlicense) +flatbuffers, 24.3.25, Apache Software License +fsspec, 2024.3.1, BSD License gitdb, 4.0.11, BSD License h11, 0.14.0, MIT License -huggingface-hub, 0.20.3, Apache Software License +huggingface-hub, 0.22.1, Apache Software License humanfriendly, 10.0, MIT License hummingbird-ml, 0.4.8, MIT License idna, 3.6, BSD License -importlib-resources, 6.1.1, Apache Software License +importlib_resources, 6.4.0, Apache Software License iniconfig, 2.0.0, MIT License jmespath, 1.0.1, MIT License joblib, 1.3.2, BSD License +jsonpickle, 3.0.3, BSD License mpmath, 1.3.0, BSD License networkx, 3.1, BSD License numpy, 1.23.5, BSD License @@ -35,18 +36,20 @@ onnxconverter-common, 1.13.0, MIT License onnxmltools, 1.11.0, Apache Software License onnxoptimizer, 0.3.10, Apache License v2.0 onnxruntime, 1.13.1, MIT License -packaging, 23.2, Apache Software License; BSD License +packaging, 24.0, Apache Software License; BSD License +pandas, 2.0.3, BSD License pluggy, 1.4.0, MIT License protobuf, 3.20.3, BSD-3-Clause psutil, 5.9.8, BSD License pydantic, 1.10.14, MIT License pytest, 7.4.1, MIT License pytest-json-report, 1.5.0, MIT -pytest-metadata, 3.1.0, Mozilla Public License 2.0 (MPL 2.0) -python-dateutil, 2.8.2, Apache Software License; BSD License +pytest-metadata, 3.1.1, Mozilla Public License 2.0 (MPL 2.0) +python-dateutil, 2.9.0.post0, Apache Software License; BSD License +pytz, 2024.1, MIT License regex, 2023.12.25, Apache Software License requests, 2.31.0, Apache Software License -s3transfer, 0.10.0, Apache Software License +s3transfer, 0.10.1, Apache Software License safetensors, 0.4.2, Apache Software License scikit-learn, 1.1.3, BSD License scipy, 1.10.1, BSD License @@ -55,19 +58,19 @@ skl2onnx, 1.12, Apache Software License skops, 0.5.0, MIT skorch, 0.11.0, new BSD 3-Clause smmap, 5.0.1, BSD License -sniffio, 1.3.0, Apache Software License; MIT License +sniffio, 1.3.1, Apache Software License; MIT License starlette, 0.27.0, BSD License sympy, 1.12, BSD License tabulate, 0.8.10, MIT License -threadpoolctl, 3.2.0, BSD License -tokenizers, 0.15.1, Apache Software License +threadpoolctl, 3.4.0, BSD License +tokenizers, 0.15.2, Apache Software License tomli, 2.0.1, MIT License torch, 1.13.1, BSD License -tqdm, 4.66.1, MIT License; Mozilla Public License 2.0 (MPL 2.0) -transformers, 4.37.2, Apache Software License +tqdm, 4.66.2, MIT License; Mozilla Public License 2.0 (MPL 2.0) +transformers, 4.39.1, Apache Software License typing_extensions, 4.5.0, Python Software Foundation License -urllib3, 1.26.18, MIT License +tzdata, 2024.1, Apache Software License +urllib3, 2.2.1, MIT License uvicorn, 0.21.1, BSD License xgboost, 1.6.2, Apache Software License -z3-solver, 4.12.5.0, MIT License -zipp, 3.17.0, MIT License +z3-solver, 4.13.0.0, MIT License diff --git a/deps_licenses/licenses_mac_silicon_user.txt.md5 b/deps_licenses/licenses_mac_silicon_user.txt.md5 index f858663d7..60fdcedbe 100644 --- a/deps_licenses/licenses_mac_silicon_user.txt.md5 +++ b/deps_licenses/licenses_mac_silicon_user.txt.md5 @@ -1 +1 @@ -a923947bfb17b658ab8efe61d5cafe96 +8de2e8c13fe9a1fe80d9cce43dee7493 diff --git a/poetry.lock b/poetry.lock index 6eef4a8e9..c8becd7b4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "absl-py" @@ -464,17 +464,17 @@ files = [ [[package]] name = "boto3" -version = "1.34.38" +version = "1.34.72" description = "The AWS SDK for Python" optional = false -python-versions = ">= 3.8" +python-versions = ">=3.8" files = [ - {file = "boto3-1.34.38-py3-none-any.whl", hash = "sha256:7c70c6ceb2706c7fad6466a5de174fe6d0d6f5f8f1e052bfaad9cbe4e53b64cd"}, - {file = "boto3-1.34.38.tar.gz", hash = "sha256:e74fbad79bc921a74a9a276ef9f38e1e31153f76690fe9bc5ec790007de36572"}, + {file = "boto3-1.34.72-py3-none-any.whl", hash = "sha256:a33585ef0d811ee0dffd92a96108344997a3059262c57349be0761d7885f6ae7"}, + {file = "boto3-1.34.72.tar.gz", hash = "sha256:cbfabd99c113bbb1708c2892e864b6dd739593b97a76fbb2e090a7d965b63b82"}, ] [package.dependencies] -botocore = ">=1.34.38,<1.35.0" +botocore = ">=1.34.72,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -483,13 +483,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.38" +version = "1.34.72" description = "Low-level, data-driven core of boto 3." optional = false -python-versions = ">= 3.8" +python-versions = ">=3.8" files = [ - {file = "botocore-1.34.38-py3-none-any.whl", hash = "sha256:773e49f5bf596191e796b2a15096ff381e61778cbe7c982b381bb9f6bfe5fef3"}, - {file = "botocore-1.34.38.tar.gz", hash = "sha256:da9754a8e1798706427ede9c9c0a55263bd8e57f217c021807b2946eb4a0c2d8"}, + {file = "botocore-1.34.72-py3-none-any.whl", hash = "sha256:a6b92735a73c19a7e540d77320420da3af3f32c91fa661c738c0b8c9f912d782"}, + {file = "botocore-1.34.72.tar.gz", hash = "sha256:342edb6f91d5839e790411822fc39f9c712c87cdaa7f3b1999f50b1ca16c4a14"}, ] [package.dependencies] @@ -497,7 +497,7 @@ jmespath = ">=0.7.1,<2.0.0" python-dateutil = ">=2.1,<3.0.0" urllib3 = [ {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""}, - {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""}, + {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}, ] [package.extras] @@ -556,13 +556,13 @@ redis = ["redis (>=2.10.5)"] [[package]] name = "cachetools" -version = "5.3.2" +version = "5.3.3" description = "Extensible memoizing collections and decorators" optional = false python-versions = ">=3.7" files = [ - {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"}, - {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"}, + {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"}, + {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"}, ] [[package]] @@ -797,13 +797,13 @@ cron = ["capturer (>=2.4)"] [[package]] name = "comm" -version = "0.2.1" +version = "0.2.2" description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." optional = 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Additionally, only some Pandas + parameters are supported, and joining on multiple columns is not available. + + Pandas documentation for version 2.0 can be found here: + https://pandas.pydata.org/pandas-docs/version/2.0/reference/api/pandas.DataFrame.merge.html + + Args: + left_encrypted (EncryptedDataFrame): The left encrypted data-frame. + right_encrypted (EncryptedDataFrame): The right encrypted data-frame. + how (str): Type of merge to be performed, one of {'left', 'right'}. + * left: use only keys from left frame, similar to a SQL left outer join; + preserve key order. + * right: use only keys from right frame, similar to a SQL right outer join; + preserve key order. + on (Optional[str]): Column name to join on. These must be found in both DataFrames. If + it is None then this defaults to the intersection of the columns in both DataFrames. + Default to None. + left_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported, please + keep the default value. Default to None. + right_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported, + please keep the default value. Default to None. + left_index (bool): Currently not supported, please keep the default value. Default to + False. + right_index (bool): Currently not supported, please keep the default value. Default + to False. + sort (bool): Currently not supported, please keep the default value. Default to False. + suffixes (Tuple[Optional[str], Optional[str]]): A length-2 sequence where each element + is optionally a string indicating the suffix to add to overlapping column names in + `left` and `right` respectively. Pass a value of `None` instead of a string to + indicate that the column name from `left` or `right` should be left as-is, with no + suffix. At least one of the values must not be None.. Default to ("_x", "_y"). + copy (Optional[bool]): Currently not supported, please keep the default value. Default + to None. + indicator (Union[bool, str]): Currently not supported, please keep the default value. + Default to False. + validate (Optional[str]): Currently not supported, please keep the default value. + Default to None. + + Returns: + EncryptedDataFrame: The joined encrypted data-frame. + """ + return left_encrypted.merge( + right_encrypted, + how=how, + on=on, + left_on=left_on, + right_on=right_on, + left_index=left_index, + right_index=right_index, + sort=sort, + suffixes=suffixes, + copy=copy, + indicator=indicator, + validate=validate, + ) diff --git a/src/concrete/ml/pandas/_client_server_files/client.zip b/src/concrete/ml/pandas/_client_server_files/client.zip new file mode 100644 index 000000000..c9f16e4bb --- /dev/null +++ b/src/concrete/ml/pandas/_client_server_files/client.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d14e44dbfe554b89c11ce379220a73b2d122c206e8d1d3d8c740a3186bed0b1 +size 594 diff --git a/src/concrete/ml/pandas/_client_server_files/server.zip b/src/concrete/ml/pandas/_client_server_files/server.zip new file mode 100644 index 000000000..6b380919e --- /dev/null +++ b/src/concrete/ml/pandas/_client_server_files/server.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76426d292f2c77a1ab04e082e8269a716b80e9d5668ec306521c2c4e82aead51 +size 1382 diff --git a/src/concrete/ml/pandas/_development.py b/src/concrete/ml/pandas/_development.py new file mode 100644 index 000000000..01e5f755d --- /dev/null +++ b/src/concrete/ml/pandas/_development.py @@ -0,0 +1,180 @@ +"""Define development methods for generating client/server files.""" +import itertools +from functools import partial +from pathlib import Path +from typing import Dict, List, Tuple, Union + +from concrete.fhe.tracing import Tracer + +from concrete import fhe + +script_dir = Path(__file__).parent + +# The paths where to find and save the client/server files +CLIENT_SERVER_DIR = script_dir / "_client_server_files" +CLIENT_PATH = CLIENT_SERVER_DIR / "client.zip" +SERVER_PATH = CLIENT_SERVER_DIR / "server.zip" + +N_BITS_PANDAS = 4 + + +def identity_pbs(value: Union[Tracer, int]) -> Union[Tracer, int]: + """Define an identity TLU. + + Args: + value (Union[Tracer, int]): The value on which to apply the identity. + + Returns: + Union[Tracer, int]: The input value. + """ + return fhe.univariate(lambda x: x)(value) + + +@fhe.compiler( + {"val_1": "encrypted", "val_2": "encrypted", "left_key": "encrypted", "right_key": "encrypted"} +) +def left_right_join_to_compile( + val_1: Union[Tracer, int], + val_2: Union[Tracer, int], + left_key: Union[Tracer, int], + right_key: Union[Tracer, int], +) -> Union[Tracer, int]: + """Define the atomic function to consider for running a left/right join in FHE. + + This function is going to be composed with itself as part of the encrypted merge algorithm, + which is explained in the '_operators.py' file. Here, the function takes two keys and two + values, all encrypted: + * left_key and right_key, one for each data-frame + * val_1, which will ultimately become the value to insert in the output data-frame once the + composition loop is done. It is therefore either representing a 0 (most of the time) or + a value from one of the input data-frame (only once during the composition loop). + * val_2, the value to add to val_1 if both keys match. As said just above, this value + should actually only be added once during the composition loop, as the keys are expected + to be unique in both data-frames + + Args: + val_1 (Union[Tracer, int]): The value used for accumulating the sum. + val_2 (Union[Tracer, int]): The value to add if the keys match. + left_key (Union[Tracer, int]): The left data-frame's encrypted key to consider. + right_key (Union[Tracer, int]): The right data-frame's encrypted key to consider. + + Returns: + Union[Tracer, int]): The new accumulated sum. + """ + condition = left_key == right_key + + sum_on_condition = val_1 + (val_2 * condition) + + # Adding an identity TLU is necessary here, else the function won't compile in FHE + sum_with_tlu = identity_pbs(sum_on_condition) + + return sum_with_tlu + + +def get_left_right_join_max_value(n_bits: int) -> int: + """Get the maximum value allowed in the data-frames for the left/right join operator. + + Args: + n_bits (int): The maximum number of bits allowed. + + Returns: + int: The maximum value allowed. + """ + return 2**n_bits - 1 + + +def get_left_right_join_inputset(n_bits: int) -> List: + """Generate the input-set to use for compiling the left/right join operator. + + Args: + n_bits (int): The maximum number of bits allowed for generating the input-set's values. + + Returns: + List: The input-set. + """ + # Build the circuit using at most 'n_bits' bits. This value defines : + # - the maximum integer value allowed in the all data-frames + # - the maximum number of rows allowed in all data-frames, assuming that the column on which to + # merge contains unique integers that start at value 1 + high = get_left_right_join_max_value(n_bits) + + # Note that any column can include NaN values, which are currently represented by 0. This means + # the input-set needs to consider 0 although pre-processing requires data-frame to provide + # integers values greater or equal to 1 + inputset = list(itertools.product([0, high], [0, high], [0, high], [0, high])) + + return inputset + + +# Store the configuration functions and parameters to their associated operator +PANDAS_OPS_TO_CIRCUIT_CONFIG = { + "left_right_join": { + "get_inputset": partial(get_left_right_join_inputset, n_bits=N_BITS_PANDAS), + "to_compile": left_right_join_to_compile, + "encrypt_config": { + "n": 4, + "pos": 1, + }, + } +} + + +def get_encrypt_config() -> Dict: + """Get the configuration parameters to use when encrypting the input values. + + Configuration parameters for encryption include the total number of inputs used in the FHE + circuit as well as the input position to consider when encrypting. + + Returns: + Dict: The configuration parameters for encryption. + """ + return PANDAS_OPS_TO_CIRCUIT_CONFIG["left_right_join"]["encrypt_config"] + + +# Allow 0 values once NaN values are not represented by it anymore +# FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 +def get_min_max_allowed() -> Tuple[int, int]: + """Get the minimum and maximum value allowed in the data-frames. + + Returns: + Tuple[int, int]: The minimum and maximum value allowed. + """ + return (1, get_left_right_join_max_value(N_BITS_PANDAS)) + + +def save_client_server(client_path: Path = CLIENT_PATH, server_path: Path = SERVER_PATH): + """Build the FHE circuit for all supported operators and save the client/server files. + + Note that this function is not made public as the files are built and saved only once directly + in the source. + + Args: + client_path (Path): The path where to save the client file. Default to CLIENT_PATH. + server_path (Path): The path where to save the client file. Default to SERVER_PATH. + """ + client_path, server_path = Path(client_path), Path(server_path) + + client_path.parent.mkdir(parents=True, exist_ok=True) + server_path.parent.mkdir(parents=True, exist_ok=True) + + config = PANDAS_OPS_TO_CIRCUIT_CONFIG["left_right_join"] + + # Get the input-set and circuit generating functions + inputset = config["get_inputset"]() + cp_func = config["to_compile"] + + # Compile the circuit and allow it to be composable with itself + merge_circuit = cp_func.compile(inputset, composable=True) + + # Save the client and server files using the MLIR + merge_circuit.client.save(client_path) + merge_circuit.server.save(server_path, via_mlir=True) + + +def load_server() -> fhe.Server: + """Load the server to use for executing operators on encrypted data-frames. + + Returns: + fhe.Server: The loaded server. + """ + return fhe.Server.load(SERVER_PATH) diff --git a/src/concrete/ml/pandas/_operators.py b/src/concrete/ml/pandas/_operators.py new file mode 100644 index 000000000..4e8db613a --- /dev/null +++ b/src/concrete/ml/pandas/_operators.py @@ -0,0 +1,374 @@ +"""Implement Pandas operators in FHE using encrypted data-frames.""" +from typing import Any, Dict, Hashable, List, Optional, Sequence, Tuple, Union + +import numpy +import pandas +from concrete.fhe import Server +from pandas.core.reshape.merge import _MergeOperation + +# List of Pandas parameters per operator that are not currently supported +UNSUPPORTED_PANDAS_PARAMETERS = { + "merge": { + "left_on": None, + "right_on": None, + "left_index": False, + "right_index": False, + "sort": False, + "copy": None, + "indicator": False, + "validate": None, + }, +} + + +def check_parameter_is_supported(parameter: Any, parameter_name: str, operator: str): + """Check that the given Pandas parameter is supported by the Concrete ML operator. + + Args: + parameter (Any): The Pandas parameter to consider. + parameter_name (str): The Pandas parameter's name. + operator (str): The Concrete ML operator to check. + + Raises: + ValueError: If the parameter is not supported by the operator. + """ + default_parameter = UNSUPPORTED_PANDAS_PARAMETERS[operator].get(parameter_name, None) + + if parameter is not default_parameter: + raise ValueError( + f"Parameter '{parameter_name}' is not currently supported. Got {parameter}." + ) + + +def check_dtype_of_selected_column_for_merge(left_encrypted, right_encrypted, selected_column: str): + """Check that the selected column dtype matches between the two encrypted data-frames. + + Args: + left_encrypted (EncryptedDataFrame): The left encrypted data-frame. + right_encrypted (EncryptedDataFrame): The right encrypted data-frame. + selected_column (str): The selected column name, common to both encrypted data-frames. + + Raises: + ValueError: If both dtypes do not match. + ValueError: If both dtypes represent floating point values. + ValueError: If both dtypes represent string values but the mappings do not match. + """ + # Get the column selected for the merge in both data-frames + selected_column_left, selected_column_right = ( + left_encrypted.dtype_mappings[selected_column], + right_encrypted.dtype_mappings[selected_column], + ) + + # Get the columns' initial dtype + dtype_left, dtype_right = numpy.dtype(selected_column_left["dtype"]), numpy.dtype( + selected_column_right["dtype"] + ) + + # If both columns' dtype match, check that they are supported + if dtype_left == dtype_right: + + # If the columns contain floating points, merging is not allowed + if numpy.issubdtype(dtype_left, numpy.floating): + raise ValueError( + f"Column '{selected_column}' cannot be selected for merging both data-frames " + f"because it has a floating dtype ({dtype_left})" + ) + + # If the columns contain strings, make sure the mappings match + if dtype_left == "object": + str_mapping_left = selected_column_left["str_to_int"] + str_mapping_right = selected_column_right["str_to_int"] + + # Avoid sending string mappings to server, instead use and check hashes + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + if str_mapping_left != str_mapping_right: + raise ValueError( + f"Mappings for string values in both common column '{selected_column}' do " + "not match." + ) + else: + raise ValueError( + f"Dtypes of both common column '{selected_column}' do not match. Got {dtype_left} " + f"(left) and {dtype_right} (right)." + ) + + +# pylint: disable-next=invalid-name +def encrypted_left_right_join( + left_encrypted, + right_encrypted, + server: Server, + how: str, + on: Optional[str], +) -> numpy.ndarray: + """Compute a left/right join in FHE between two encrypted data-frames using Pandas parameters. + + Note that for now, only a left and right join is implemented. Additionally, only some Pandas + parameters are supported, and joining on multiple columns is not available. + + The algorithm benefits from Concrete Python's composability feature. The idea is that for loops + are done in the clear, meaning positional indexes are not encrypte and only the data is. In the + case of a left merge, we need to select the encrypted value from the right data-frame for a + given (left) row and (right) column position. In order to do that, a for loop goes through + the right rows and runs the FHE circuit in a composable manner. The goal is to basically + multiply the right column values with a mask which contains a single 1 at the row position where + the left and right key matches, and then sum everything to retrieve the selected value. The + main benefit of using composability instead of a dict mult and sum is that it does not require + to know the number of columns and rows at compilation time. More details can be found in the + '_development.py' file. + + Args: + left_encrypted (EncryptedDataFrame): The left encrypted data-frame. + right_encrypted (EncryptedDataFrame): The right encrypted data-frame. + server (Server): The Concrete server to use for running the computations in FHE. + how (str): Type of merge to be performed, one of {'left', 'right'}. + * left: use only keys from left frame, similar to a SQL left outer join; + preserve key order. + * right: use only keys from right frame, similar to a SQL right outer join; + preserve key order. + on (Optional[str]): Column name to join on. These must be found in both DataFrames. If it is + None then this defaults to the intersection of the columns in both DataFrames. + + Returns: + numpy.ndarray: The values representing the joined encrypted data-frame. + """ + allowed_how = ["left", "right"] + assert how in allowed_how, f"Parameter 'how' must be in {allowed_how}. Got {how}." + + # In case of a right merge, swap the input data-frames + if how == "right": + left_encrypted, right_encrypted = right_encrypted, left_encrypted + + joined_rows = [] + + # Retrieve the left and right column's position on which keys to merge + left_key_column_position = left_encrypted.column_names_to_position[on] + right_key_column_position = right_encrypted.column_names_to_position[on] + + # Retrieve the number of useful rows and columns + n_rows_left = left_encrypted.encrypted_values.shape[0] + n_columns_right = right_encrypted.encrypted_values.shape[1] + n_rows_right = right_encrypted.encrypted_values.shape[0] + + # Loop over the left data frame's number of rows (which will become the joined data frame's + # number of rows) + for i_left in range(n_rows_left): + + # For left merge, all left values are exactly equal to the left data-frame + array_joined_i_left = left_encrypted.encrypted_values[i_left, :] + + # In case of a right merge, remove the column containing the keys on which to merge. This + # avoid unnecessary FHE computations as the output keys will exactly match the one contained + # in the (initial) left data-frame. The reason why this is needed only for the right merge + # is because, in Pandas, this selected column is always kept on the output data-frame's + # left side. The column is manually inserted back at the end of this function + if how == "right": + array_joined_i_left = numpy.delete( + array_joined_i_left, left_key_column_position, axis=0 + ) + + left_row_to_join = array_joined_i_left.tolist() + + # Retrieve the left data frame's key to merge on + left_key = left_encrypted.encrypted_values[i_left, left_key_column_position] + + right_row_to_join = [] + + # Loop over the right data-frame's number of columns + for j_right in range(n_columns_right): + + # Skip the right's index column + if j_right == right_key_column_position: + continue + + # Default value is NaN + right_value_to_join = right_encrypted.encrypted_nan + + # Loop over the right data-frame's number of rows in order to check if one row's key + # matches the on-going left key + for i_right in range(n_rows_right): + + # Retrieve the right data-frame's value to sum if both keys match + value_to_put_right = right_encrypted.encrypted_values[i_right, j_right] + + # Retrieve the right data frame's key to merge on + right_key = right_encrypted.encrypted_values[i_right, right_key_column_position] + + merge_inputs = (right_value_to_join, value_to_put_right, left_key, right_key) + + # Run the FHE execution: + # - on the first iteration, this is applied on a 0 (representing a NaN) and the + # right data-frame's value + # - on the following iterations, this is applied between the previous accumulated + # value and the right data-frame's value. + # Basically, if both keys match, the function adds the accumulated value with the + # right data-frame's value. If they don't, it just adds 0 to the accumulated value. + # In practice, keys only match once throughout this very loop as keys are assumed to + # be unique on both data-frames. + right_value_to_join = server.run( + *merge_inputs, evaluation_keys=left_encrypted.evaluation_keys + ) + + right_row_to_join.append(right_value_to_join) + + # In case of a right merge, since data-frames wee initially swapped, swap back the values + # when re-building the joined data-frame + if how == "right": + joined_row = right_row_to_join + left_row_to_join + else: + joined_row = left_row_to_join + right_row_to_join + + joined_rows.append(joined_row) + + array_joined = numpy.array(joined_rows) + + # In case of a right merge, as mentioned above, the column containing the right keys needs to be + # manually re-inserted. This avoids unnecessary FHE computations + if how == "right": + array_joined = numpy.hstack( + ( + array_joined[:, :right_key_column_position], + left_encrypted.encrypted_values[ + :, left_key_column_position : left_key_column_position + 1 + ], + array_joined[:, right_key_column_position:], + ), + ) + + return array_joined + + +# pylint: disable-next=too-many-arguments, invalid-name +def encrypted_merge( + left_encrypted, + right_encrypted, + server: Server, + how: str = "left", + on: Optional[str] = None, + left_on: Optional[Union[Hashable, Sequence[Hashable]]] = None, + right_on: Optional[Union[Hashable, Sequence[Hashable]]] = None, + left_index: bool = False, + right_index: bool = False, + sort: bool = False, + suffixes: Tuple[Optional[str], Optional[str]] = ("_x", "_y"), + copy: Optional[bool] = None, + indicator: Union[bool, str] = False, + validate: Optional[str] = None, +) -> Tuple[numpy.ndarray, List[str], Dict]: + """Merge two encrypted data-frames in FHE using Pandas parameters. + + Note that for now, only a left and right join is implemented. Additionally, only some Pandas + parameters are supported, and joining on multiple columns is not available. + + Pandas documentation for version 2.0 can be found here: + https://pandas.pydata.org/pandas-docs/version/2.0/reference/api/pandas.DataFrame.merge.html + + Args: + left_encrypted (EncryptedDataFrame): The left encrypted data-frame. + right_encrypted (EncryptedDataFrame): The right encrypted data-frame. + server (Server): The Concrete server to use for running the computations in FHE. + how (str): Type of merge to be performed, one of {'left', 'right'}. + * left: use only keys from left frame, similar to a SQL left outer join; + preserve key order. + * right: use only keys from right frame, similar to a SQL right outer join; + preserve key order. + on (Optional[str]): Column name to join on. These must be found in both DataFrames. If it is + None then this defaults to the intersection of the columns in both DataFrames. Default + to None. + left_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported, please + keep the default value. Default to None. + right_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported, please + keep the default value. Default to None. + left_index (bool): Currently not supported, please keep the default value. Default to False. + right_index (bool): Currently not supported, please keep the default value. Default + to False. + sort (bool): Currently not supported, please keep the default value. Default to False. + suffixes (Tuple[Optional[str], Optional[str]]): A length-2 sequence where each element is + optionally a string indicating the suffix to add to overlapping column names in `left` + and `right` respectively. Pass a value of `None` instead of a string to indicate that + the column name from `left` or `right` should be left as-is, with no suffix. At least + one of the values must not be None.. Default to ("_x", "_y"). + copy (Optional[bool]): Currently not supported, please keep the default value. Default to + None. + indicator (Union[bool, str]): Currently not supported, please keep the default value. + Default to False. + validate (Optional[str]): Currently not supported, please keep the default value. Default + to None. + + Raises: + ValueError: If the merge is expected to be done on multiple columns. + NotImplementedError: If parameter 'how' is set to anything else than one + of {'left', 'right'}. + + Returns: + Tuple[numpy.ndarray, List[str], Dict]: The values representing the joined encrypted + data-frame, the associated columns as well as the mappings needed for mapping the + integers back to their initial string values. + """ + # Implement other merge types + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + if how not in ["left", "right"]: + raise NotImplementedError(f"Merge type '{how}' is not currently implemented.") + + # Support relevant pandas parameters + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + for parameter, parameter_name in [ + (left_on, "left_on"), + (right_on, "right_on"), + (left_index, "left_index"), + (right_index, "right_index"), + (sort, "sort"), + (copy, "copy"), + (indicator, "indicator"), + (validate, "validate"), + ]: + check_parameter_is_supported(parameter, parameter_name, "merge") + + # Build empty Pandas data-frames based on the encrypted data-frames' column names + empty_df_left = pandas.DataFrame(index=range(1), columns=left_encrypted.column_names) + empty_df_right = pandas.DataFrame(index=range(1), columns=right_encrypted.column_names) + + # Check that the merge is valid using Pandas' underlying merge operator. This step allows us not + # to re-implement validation steps for Pandas parameters + # Additionally, it has the benefit of being able to retrieve useful attributes, like the + # expected output column names or the column name(s) to merge on in case 'on=None' + empty_merge_op = _MergeOperation( + empty_df_left, + empty_df_right, + how=how, + on=on, + left_on=left_on, + right_on=right_on, + left_index=left_index, + right_index=right_index, + sort=sort, + suffixes=suffixes, + indicator=indicator, + validate=validate, + ) + + # Retrieve the expected joined column names + empty_df_joined = empty_merge_op.get_result() + joined_column_names = list(empty_df_joined.columns) + + # Support multi-column merge + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + if len(empty_merge_op.join_names) != 1: + raise ValueError("Merging on 0 or several columns is not currently available.") + + # Retrieve the common column name on which to merge + selected_column = empty_merge_op.join_names[0] + + # Check that the merge is allowed + check_dtype_of_selected_column_for_merge(left_encrypted, right_encrypted, selected_column) + + # Join the mappings in order to recover strings and floats in post-processing (client side) + joined_dtype_mappings = {**left_encrypted.dtype_mappings, **right_encrypted.dtype_mappings} + + # Add a way to ensure that 'selected_column' only contains unique values in both data-frames + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + joined_array = encrypted_left_right_join( + left_encrypted, right_encrypted, server, how, selected_column + ) + + return joined_array, joined_column_names, joined_dtype_mappings diff --git a/src/concrete/ml/pandas/_processing.py b/src/concrete/ml/pandas/_processing.py new file mode 100644 index 000000000..056b3c966 --- /dev/null +++ b/src/concrete/ml/pandas/_processing.py @@ -0,0 +1,321 @@ +"""Define pre-processing and post-processing steps for encrypted data-frames.""" +import copy +from collections import defaultdict +from typing import Dict, List, Tuple + +import numpy +import pandas + +from concrete.ml.pandas._development import get_min_max_allowed +from concrete.ml.quantization.quantizers import STABILITY_CONST + + +def compute_scale_zero_point(column: pandas.Series, q_min: int, q_max: int) -> Tuple[float, float]: + """Compute the scale and zero point to use for quantizing / de-quantizing the given column. + + Note that the scale and zero point are computed so that values are quantized uniformly from + range [column.min(), column.max()] (float) to range [q_min, q_max] (int). + + Args: + column (pandas.Series): The column to consider. + q_min (int): The minimum quantized value to consider. + q_max (int): The maximum quantized value to consider. + + Returns: + Tuple[float, float]: The scale and zero-point. + """ + values_min, values_max = column.min(), column.max() + + # If there si a single float value in the column, the scale and zero-point need to be handled + # differently + if values_max - values_min < STABILITY_CONST: + + # If this single float value is 0, make sure it is not quantized to 0 + if numpy.abs(values_max) < STABILITY_CONST: + scale = 1.0 + zero_point = -q_min + + # Else, quantize it to 1 + else: + scale = 1 / values_max + zero_point = 0 + + else: + scale = (q_max - q_min) / (values_max - values_min) + + # Zero-point must be rounded once NaN values are not represented by 0 anymore + # The issue is that we currently need to avoid quantized values to reach 0, but having a + # round here + in the 'quant' method can make this happen. + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + zero_point = values_min * scale - q_min + + return scale, zero_point + + +def quant(x: pandas.Series, scale: float, zero_point: float) -> pandas.Series: + """Quantize the column. + + Args: + x (pandas.Series): The column to quantize. + scale (float): The scale to consider. + zero_point (float): The zero-point to consider. + + Returns: + pandas.Series: The quantized column. + """ + return numpy.round(scale * x - zero_point) + + +def dequant( + q_x: pandas.Series, scale: float, zero_point: float, dtype: numpy.dtype +) -> pandas.Series: + """De-quantize the column. + + Args: + q_x (pandas.Series): The column to de-quantize. + scale (float): The scale to consider. + zero_point (float): The zero-point to consider. + dtype (numpy.dtype): The dtype to use for casting the de-quantized value. + + Returns: + pandas.Series: The de-quantized column. + """ + x = (q_x + zero_point) / scale + + return x.astype(dtype) + + +# Provide a way for users to pass string mappings +# FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 +def pre_process_dtypes(pandas_dataframe: pandas.DataFrame) -> Tuple[pandas.DataFrame, Dict]: + """Pre-process the Pandas data-frame and check that input dtypes and ranges are supported. + + Currently, three input dtypes are supported : integers (within a specific range), floating + points and objects (strings only, with a maximum amount of unique values). Additionally, NaN + values are supported. + + Args: + pandas_dataframe (pandas.DataFrame): The Pandas data-frame to pre-process. + + Raises: + ValueError: If the values of a column with an integer dtype are out of bounds. + ValueError: If the amount of unique values found in a column with strings exceeds the + maximum allowed. + ValueError: If a column with an 'object' dtype contains other values than strings and NaNs. + ValueError: If a column has a dtype that is not supported. + + Returns: + Tuple[pandas.DataFrame, Dict]: The pre-processed Pandas data-frame, as well as the mappings + to use for recovering float and string values in post-processing. + """ + pandas_dataframe = copy.copy(pandas_dataframe) + + dtype_mappings: Dict[str, Dict] = defaultdict(dict) + + # Get the minimum and maximum quantized values allowed in an encrypted data-frame + q_min, q_max = get_min_max_allowed() + + # Avoid sending column names to server, instead use hashes + # FIXME : https://github.com/zama-ai/concrete-ml-internal/issues/4342 + for column_name in pandas_dataframe.columns: + column = pandas_dataframe[column_name] + column_dtype = column.dtype + + # Store the initial dtype, in order to cast in post-processing + dtype_mappings[column_name]["dtype"] = str(column_dtype) + + # If the column contains integers, make sure they are not out of bounds + if numpy.issubdtype(column_dtype, numpy.integer): + out_of_bounds = (column < q_min).any() or (column > q_max).any() + + if out_of_bounds: + raise ValueError( + f"Column '{column_name}' (dtype={column_dtype}) contains values that are out " + f"of bounds. Expected values to be in interval [min={q_min}, max={q_max}], but " + f"found [min={column.min()}, max={column.max()}]." + ) + + # If the column contains floats, quantize the values + elif numpy.issubdtype(column_dtype, numpy.floating): + scale, zero_point = compute_scale_zero_point(column, q_min, q_max) + + q_column = quant(column, scale, zero_point) + + pandas_dataframe[column_name] = q_column + + # Store the scale and zero point for de-quantization in post-processing + dtype_mappings[column_name]["scale"] = scale + dtype_mappings[column_name]["zero_point"] = zero_point + + # If the column contains objects, make sure it is only made of strings or NaN values + elif column_dtype == "object": + is_str = column.apply(lambda x: isinstance(x, str) or not pandas.notna(x)).all() + + if is_str: + + # Build a mapping between the unique strings values and integers + str_to_int = { + str_value: i + 1 for i, str_value in enumerate(column.dropna().unique()) + } + + # Make sure the number of unique values do not goes over the maximum integer value + # allowed in an encrypted data-frame + n_unique_values = max(str_to_int.values()) + if n_unique_values > q_max: + raise ValueError( + f"Column '{column_name}' (dtype={column_dtype}) contains too many unique " + f"values. Expected {q_max} unique values at most, got {n_unique_values}." + ) + + q_column = column.map(str_to_int) + + pandas_dataframe[column_name] = q_column + + # Store the mapping in order to recover the initial values in post-processing + # Avoid sending string mappings to server, instead use and check hashes + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + dtype_mappings[column_name]["str_to_int"] = str_to_int + + else: + raise ValueError( + f"Column '{column_name}' (dtype={column_dtype}) contains non-string values, " + "which is not currently supported." + ) + + else: + raise ValueError( + f"Column '{column_name}' has dtype '{column_dtype}', which is not currently " + "supported." + ) + + return pandas_dataframe, dtype_mappings + + +def pre_process_from_pandas(pandas_dataframe: pandas.DataFrame) -> Tuple[numpy.ndarray, Dict]: + """Pre-process the Pandas data-frame. + + Args: + pandas_dataframe (pandas.DataFrame): The Pandas data-frame to pre-process. + + Raises: + ValueError: If the data-frame's index has not been reset (meaning the index is not a + RangeIndex object). + + Returns: + Tuple[numpy.ndarray, Dict]: The pre-processed values that can be encrypted, as well as the + mappings to use for recovering float and string values in post-processing. + + """ + # Support Index of Pandas data-frames + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + if not isinstance(pandas_dataframe.index, pandas.RangeIndex): + raise ValueError( + "The data-frame's index has not been reset. Please make sure to not put relevant data " + "in the index and instead store it in a dedicated column. Encrypted data-frames do not " + "currently support any index-based operations." + ) + + # Check that values are supported and build the mappings + q_pandas_dataframe, dtype_mappings = pre_process_dtypes(pandas_dataframe) + + # Replace NaN values with 0 + # Remove this once NaN values are not represented by 0 anymore + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + q_pandas_dataframe.fillna(0, inplace=True) + + q_array = q_pandas_dataframe.to_numpy(dtype=numpy.int64) + + return q_array, dtype_mappings + + +def post_process_dtypes( + pandas_dataframe: pandas.DataFrame, dtype_mappings: Dict +) -> pandas.DataFrame: + """Post-process the pandas data-frame. + + Args: + pandas_dataframe (pandas.DataFrame): The Pandas data-frame to post-process. + dtype_mappings (Dict): The mappings to consider for recovering float and string values. + + Raises: + ValueError: If one of the column has an unsupported dtype. + + Returns: + pandas.DataFrame: The post-processed data-frame. + """ + pandas_dataframe = copy.copy(pandas_dataframe) + + for column_name in pandas_dataframe.columns: + q_column = pandas_dataframe[column_name] + + # Retrieve the column's initial dtype + initial_column_dtype = numpy.dtype(dtype_mappings[column_name]["dtype"]) + + # If the column contained integers, cast the values to the initial dtype, unless it contains + # some NaN values (as they are represented with a float dtype) + if numpy.issubdtype(initial_column_dtype, numpy.integer): + if not q_column.isna().any(): + pandas_dataframe[column_name] = q_column.astype(initial_column_dtype) + + # If the column contained floats, de-quantize the values using the stored scale and + # zero-point + elif numpy.issubdtype(initial_column_dtype, numpy.floating): + scale = dtype_mappings[column_name]["scale"] + zero_point = dtype_mappings[column_name]["zero_point"] + + column = dequant(q_column, scale, zero_point, dtype=initial_column_dtype) + + pandas_dataframe[column_name] = column + + # If the column contained objects (strings), revert the mapping and retrieve the initial + # values + elif initial_column_dtype == "object": + + # Revert the mapping from integers to strings + string_to_int = dtype_mappings[column_name]["str_to_int"] + int_to_string = {v: k for k, v in string_to_int.items()} + + # Apply the reverted string mapping + column = q_column.map(int_to_string) + + pandas_dataframe[column_name] = column + + # Else, the data might have been modified since pre-processing + else: + raise ValueError( + f"Column '{column_name}' has dtype '{initial_column_dtype}', which is unexpected " + "and thus not supported." + ) + + return pandas_dataframe + + +def post_process_to_pandas( + clear_array: numpy.ndarray, output_column_names: List[str], dtype_mappings: Dict +) -> pandas.DataFrame: + """Post-process the decrypted values and use them to build a Pandas data-frame. + + Args: + clear_array (numpy.ndarray): The values to consider. + output_column_names (List[str]): The column names to consider when building the Pandas + data-frame. + dtype_mappings (Dict): The mapping to use for recovering float and string values. + + Returns: + pandas.DataFrame: The output Pandas data-frame. + """ + # Replace 0 values by NaN + # Remove this once NaN values are not represented by 0 anymore + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + clear_array_0_to_nan = numpy.where(clear_array == 0, numpy.nan, clear_array) + + # Build the joined Pandas data-frame using the de-serialized encrypted data-frame + pandas_dataframe = pandas.DataFrame( + clear_array_0_to_nan, + columns=output_column_names, + ) + + # Post-process the values to match Pandas' output + pandas_dataframe = post_process_dtypes(pandas_dataframe, dtype_mappings) + + return pandas_dataframe diff --git a/src/concrete/ml/pandas/_utils.py b/src/concrete/ml/pandas/_utils.py new file mode 100644 index 000000000..766066bf6 --- /dev/null +++ b/src/concrete/ml/pandas/_utils.py @@ -0,0 +1,190 @@ +"""Define utility functions for encrypted data-frames.""" +import functools +from typing import List, Optional, Tuple, Union + +import numpy + +from concrete import fhe + + +def encrypt_value( + value: Optional[Union[int, numpy.ndarray, List]], client: fhe.Client, n: int, pos: int +) -> Optional[Union[fhe.Value, Tuple[Optional[fhe.Value], ...]]]: + """Encrypt a value using a Concrete client and the given configuration parameters. + + Args: + value (Optional[Union[int, numpy.ndarray, List]]): The value(s) to encrypt. + client (fhe.Client): The client to use for encryption. + n (int): The total number of inputs the client's circuit considers. + pos (int): The input's position to consider when encrypting it. + + Returns: + Optional[Union[fhe.Value, Tuple[Optional[fhe.Value], ...]]]: A 'n'-tuple containing the + encrypted value at position 'pos' and None elsewhere. + """ + # Build the input to use for encrypting the value + # In Concrete Python, if the underlying circuit asks for 4 inputs but we only want to encrypt + # a value using the 2nd input, we need to provide (None, value, None, None) to tne '.encrypt' + clear_inputs = [None] * n + clear_inputs[pos] = value # type: ignore[assignment] + + encrypted_outputs = client.encrypt(*clear_inputs) + + # Similarly, using the above example, the output becomes (None, encrypted_output, None, None) + encrypted_output = encrypted_outputs[pos] + + return encrypted_output + + +def decrypt_value( + value: fhe.Value, client: fhe.Client +) -> Optional[Union[int, numpy.ndarray, Tuple[Optional[Union[int, numpy.ndarray]], ...]]]: + """Decrypt an FHE value using a Concrete client. + + Args: + value (fhe.Value): The FHE value(s) to decrypt. + client (fhe.Client): The client to use for decryption. + + Returns: + Optional[Union[int, numpy.ndarray, Tuple[Optional[Union[int, numpy.ndarray]], ...]]]: The + decrypted value(s). + """ + return client.decrypt(value) + + +def encrypt_elementwise( + array: numpy.ndarray, client: fhe.Client, n: int, pos: int +) -> numpy.ndarray: + """Encrypt an array element-wise. + + Arguments: + array (numpy.ndarray): The array whose values to encrypt. + client (fhe.Client): The client to use for encryption. + n (int): The total number of inputs the client's circuit considers. + pos (int): The input's position to consider when encrypting it. + + Returns: + numpy.ndarray: An array containing encrypted values only. + """ + encrypt_func = functools.partial(encrypt_value, client=client, n=n, pos=pos) + return numpy.vectorize(encrypt_func)(array) + + +def decrypt_elementwise(array: numpy.ndarray, client: fhe.Client) -> numpy.ndarray: + """Decrypt an array element-wise. + + Args: + array (numpy.ndarray): The array whose values to decrypt. + client (fhe.Client): The client to use for decryption. + + Returns: + numpy.ndarray: An array containing decrypted values only. + """ + decrypt_func = functools.partial(decrypt_value, client=client) + return numpy.vectorize(decrypt_func)(array) + + +def serialize_value(encrypted_value: fhe.Value) -> str: + """Serialize an FHE value into a string of hexadecimal numbers. + + Args: + encrypted_value (fhe.Value): The FHE value to serialize. + + Returns: + str: The serialized FHE value as a string of hexadecimal numbers. + """ + return encrypted_value.serialize().hex() + + +def deserialize_value(serialized_value: str) -> fhe.Value: + """Deserialize a string of hexadecimal numbers into an FHE value. + + Args: + serialized_value (str): The string to deserialize. + + Returns: + str: The deserialized FHE value. + """ + + return fhe.Value.deserialize(bytes.fromhex(serialized_value)) + + +def serialize_elementwise(array: numpy.ndarray) -> numpy.ndarray: + """Serialize an array made of encrypted values element-wise. + + Args: + array (numpy.ndarray): The array to serialize. + + Returns: + numpy.ndarray: An array containing serialized encrypted values only. + """ + return numpy.vectorize(serialize_value, otypes=[object])(array) + + +def deserialize_elementwise(array: numpy.ndarray) -> numpy.ndarray: + """Deserialize an array made of serialized encrypted values element-wise. + + Args: + array (numpy.ndarray): The array to deserialize. + + Returns: + numpy.ndarray: An array containing deserialized encrypted values only. + """ + return numpy.vectorize(deserialize_value)(array) + + +def serialize_evaluation_keys(evaluation_keys: fhe.EvaluationKeys) -> str: + """Serialize the evaluation keys into a string of hexadecimal numbers. + + Args: + evaluation_keys (fhe.EvaluationKeys): The evaluation keys to serialize. + + Returns: + str: The serialized evaluation keys as a string of hexadecimal numbers. + """ + return serialize_value(evaluation_keys) + + +def deserialize_evaluation_keys(serialized_evaluation_keys: str) -> fhe.EvaluationKeys: + """Deserialize the evaluation keys. + + Args: + serialized_evaluation_keys (str): The evaluation keys to deserialize. + + Returns: + fhe.EvaluationKeys: The deserialized evaluation keys. + """ + return fhe.EvaluationKeys.deserialize(bytes.fromhex(serialized_evaluation_keys)) + + +def slice_hex_str(hex_str: str, n: int = 10) -> str: + """Extract the n hexadecimal numbers found in the middle of the given string. + + This method is used for printing a small part of encrypted values, serialized as + hexadecimal numbers. Dots are added before and after the sliced string. + + Args: + hex_str (str): A string made of hexadecimal numbers. + n (int): The amount of characters to extract. Default to 10. + + Returns: + str: The extracted numbers, with dots before and after. + """ + # Get the string's middle index + start_index = len(hex_str) // 2 + assert start_index + n < len(hex_str) + + return ".." + hex_str[start_index : start_index + n] + ".." + + +def get_serialized_representation_elementwise(array: numpy.ndarray) -> numpy.ndarray: + """Get a representation of serialized values stored in an array. + + Args: + array (numpy.ndarray): The array to consider, made of serialized values represented as a + string of hexadecimal numbers. + + Returns: + numpy.ndarray: An array containing the serialized values' representations. + """ + return numpy.vectorize(slice_hex_str)(array) diff --git a/src/concrete/ml/pandas/client_engine.py b/src/concrete/ml/pandas/client_engine.py new file mode 100644 index 000000000..37e625119 --- /dev/null +++ b/src/concrete/ml/pandas/client_engine.py @@ -0,0 +1,85 @@ +"""Define the framework used for managing keys (encrypt, decrypt) for encrypted data-frames.""" +from pathlib import Path +from typing import Optional, Union + +import pandas + +from concrete import fhe +from concrete.ml.pandas._development import CLIENT_PATH, get_encrypt_config +from concrete.ml.pandas._processing import post_process_to_pandas, pre_process_from_pandas +from concrete.ml.pandas._utils import decrypt_elementwise, encrypt_elementwise, encrypt_value +from concrete.ml.pandas.dataframe import EncryptedDataFrame + +CURRENT_API_VERSION = 1 + + +class ClientEngine: + """Define a framework that manages keys.""" + + def __init__(self, keygen: bool = True, keys_path: Optional[Union[Path, str]] = None): + self.client = fhe.Client.load(CLIENT_PATH) + + if keygen: + self.keygen(keys_path=keys_path) + + def keygen(self, keys_path: Optional[Union[Path, str]] = None): + """Generate the keys. + + Args: + keys_path (Optional[Union[Path, str]]): The path where to save the keys. Note that if + some keys already exist in that path, the client will use them instead of generating + new ones. Default to None. + """ + # If a path is given and some keys are found in it, load them. Else, generate new keys + if keys_path is not None: + self.client.keys.load_if_exists_generate_and_save_otherwise(keys_path) + else: + self.client.keygen(True) + + def encrypt_from_pandas(self, pandas_dataframe: pandas.DataFrame) -> EncryptedDataFrame: + """Encrypt a Pandas data-frame using the loaded client. + + Args: + pandas_dataframe (DataFrame): The Pandas data-frame to encrypt. + + Returns: + EncryptedDataFrame: The encrypted data-frame. + """ + pandas_array, dtype_mappings = pre_process_from_pandas(pandas_dataframe) + + # Inputs need to be encrypted element-wise in order to be able to use a composable circuit + # Once multi-operator is supported, better handle encryption configuration parameters + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + encrypted_values = encrypt_elementwise(pandas_array, self.client, **get_encrypt_config()) + + # Encrypt a 0 in order to represent NaN values + # Remove this once NaN values are not represented by 0 anymore + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + encrypted_nan = encrypt_value(0, self.client, **get_encrypt_config()) + + return EncryptedDataFrame( + encrypted_values, + encrypted_nan, + self.client.evaluation_keys, + pandas_dataframe.columns, + dtype_mappings, + CURRENT_API_VERSION, + ) + + def decrypt_to_pandas(self, encrypted_dataframe: EncryptedDataFrame) -> pandas.DataFrame: + """Decrypt an encrypted data-frame using the loaded client and return a Pandas data-frame. + + Args: + encrypted_dataframe (EncryptedDataFrame): The encrypted data-frame to decrypt. + + Returns: + pandas.DataFrame: The Pandas data-frame built on the decrypted values. + """ + # Inputs need to be decrypted element-wise in order to be able to use a composable circuit + clear_array = decrypt_elementwise(encrypted_dataframe.encrypted_values, self.client) + + pandas_dataframe = post_process_to_pandas( + clear_array, encrypted_dataframe.column_names, encrypted_dataframe.dtype_mappings + ) + + return pandas_dataframe diff --git a/src/concrete/ml/pandas/dataframe.py b/src/concrete/ml/pandas/dataframe.py new file mode 100644 index 000000000..2c158a656 --- /dev/null +++ b/src/concrete/ml/pandas/dataframe.py @@ -0,0 +1,340 @@ +"""Define the encrypted data-frame framework.""" +import json +from pathlib import Path +from typing import Dict, Hashable, List, Optional, Sequence, Tuple, Union + +import numpy +import pandas +from pandas.io.formats.format import get_dataframe_repr_params + +from concrete import fhe +from concrete.ml.pandas._development import load_server +from concrete.ml.pandas._operators import encrypted_merge +from concrete.ml.pandas._utils import ( + deserialize_elementwise, + deserialize_evaluation_keys, + deserialize_value, + get_serialized_representation_elementwise, + serialize_elementwise, + serialize_evaluation_keys, + serialize_value, +) + +_SERVER = load_server() + + +class EncryptedDataFrame: + """Define an encrypted data-frame framework that supports Pandas operators and parameters.""" + + def __init__( + self, + encrypted_values: numpy.ndarray, + encrypted_nan: fhe.Value, + evaluation_keys: fhe.EvaluationKeys, + column_names: List[str], + dtype_mappings: Dict, + api_version: int, + ): + self._encrypted_values = encrypted_values + self._encrypted_nan = encrypted_nan + self._evaluation_keys = evaluation_keys + + self._column_names = list(column_names) + self._column_names_to_position = {name: index for index, name in enumerate(column_names)} + self._dtype_mappings = dtype_mappings + self._api_version = api_version + + # Generate and store the Pandas representation at initialization in order to avoid having + # to serialize values each time it is needed + self._pandas_repr = self._get_pandas_repr() + + @property + def encrypted_values(self) -> numpy.ndarray: + """Get the encrypted values. + + Returns: + numpy.ndarray: The array containing all encrypted values. + """ + return self._encrypted_values + + @property + def encrypted_nan(self) -> fhe.Value: + """Get the encrypted value representing a NaN. + + Returns: + fhe.Value: The encrypted representation of a NaN. + """ + return self._encrypted_nan + + @property + def evaluation_keys(self) -> fhe.EvaluationKeys: + """Get the evaluation keys. + + Returns: + fhe.EvaluationKeys: The evaluation keys. + """ + return self._evaluation_keys + + @property + def column_names(self) -> List[str]: + """Get the data-frame's column names in order. + + Returns: + List[str]: The data-frame's column names in order. + """ + return self._column_names + + @property + def column_names_to_position(self) -> Dict[str, int]: + """Get the mapping between each column's name and its index position. + + Returns: + Dict[str, int]: Mapping between column names and their position. + """ + return self._column_names_to_position + + @property + def dtype_mappings(self) -> Dict: + """Get the mappings for non-integer dtypes used in pre and post-processing. + + Returns: + Dict: The mappings for non-integers dtypes. + """ + return self._dtype_mappings + + @property + def api_version(self) -> int: + """Get the API version used when instantiating this instance. + + Returns: + int: The data-frame's API version. + """ + return self._api_version + + def _get_pandas_repr(self) -> pandas.DataFrame: + """Get the Pandas data-frame representing this encrypted data-frame when printing it. + + Returns: + pandas.DataFrame: The encrypted data-frame's Pandas representation. + """ + + # Encrypted values needs to be serialized in order to be displayed + encrypted_values = serialize_elementwise(self._encrypted_values) + + # Serialized encrypted values are very long, so we need to only display part of it + encrypted_values_repr = get_serialized_representation_elementwise(encrypted_values) + + # Display the representation as a Pandas data-frame + pandas_repr = pandas.DataFrame(encrypted_values_repr, columns=self._column_names) + + return pandas_repr + + def get_schema(self) -> pandas.DataFrame: + """Get the encrypted data-frame's scheme. + + The scheme can include column names, dtypes or dtype mappings. It is displayed as a Pandas + data-frame for better readability. + + Returns: + pandas.DataFrame: The encrypted data-frame's scheme. + """ + pandas_repr = pandas.DataFrame(self._dtype_mappings, columns=self._column_names) + return pandas_repr + + # pylint: disable-next=invalid-repr-returned + def __repr__(self) -> str: + """Represent the encrypted data-frame as a string. + + Returns: + str: The encrypted data-frame's string representation. + """ + # Retrieve Pandas' repr parameters and use them to convert the encrypted data-frame's repr + # to string + repr_params = get_dataframe_repr_params() + pandas_repr_str = self._pandas_repr.to_string(index=False, **repr_params) + + assert isinstance(pandas_repr_str, str) + return pandas_repr_str + + def _repr_html_(self) -> str: + """Represent the encrypted data-frame as a string for HTML. + + This is used for better displaying the data-frame in Jupyter notebooks. + + Returns: + str: The encrypted data-frame's string representation for HTML. + """ + return self._pandas_repr.to_html(index=False) + + # pylint: disable-next=too-many-arguments, invalid-name + def merge( + self, + other, + how: str = "left", + on: Optional[str] = None, + left_on: Optional[Union[Hashable, Sequence[Hashable]]] = None, + right_on: Optional[Union[Hashable, Sequence[Hashable]]] = None, + left_index: bool = False, + right_index: bool = False, + sort: bool = False, + suffixes: Tuple[Optional[str], Optional[str]] = ("_x", "_y"), + copy: Optional[bool] = None, + indicator: Union[bool, str] = False, + validate: Optional[str] = None, + ): + """Merge two encrypted data-frames in FHE using Pandas parameters. + + Note that for now, only a left and right join is implemented. Additionally, only some Pandas + parameters are supported, and joining on multiple columns is not available. + + Pandas documentation for version 2.0 can be found here: + https://pandas.pydata.org/pandas-docs/version/2.0/reference/api/pandas.DataFrame.merge.html + + Args: + other (EncryptedDataFrame): The other encrypted data-frame. + how (str): Type of merge to be performed, one of {'left', 'right'}. + * left: use only keys from left frame, similar to a SQL left outer join; + preserve key order. + * right: use only keys from right frame, similar to a SQL right outer join; + preserve key order. + on (Optional[str]): Column name to join on. These must be found in both DataFrames. If + it is None then this defaults to the intersection of the columns in both DataFrames. + Default to None. + left_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported, please + keep the default value. Default to None. + right_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported, + please keep the default value. Default to None. + left_index (bool): Currently not supported, please keep the default value. Default to + False. + right_index (bool): Currently not supported, please keep the default value. Default + to False. + sort (bool): Currently not supported, please keep the default value. Default to False. + suffixes (Tuple[Optional[str], Optional[str]]): A length-2 sequence where each element + is optionally a string indicating the suffix to add to overlapping column names in + `left` and `right` respectively. Pass a value of `None` instead of a string to + indicate that the column name from `left` or `right` should be left as-is, with no + suffix. At least one of the values must not be None.. Default to ("_x", "_y"). + copy (Optional[bool]): Currently not supported, please keep the default value. Default + to None. + indicator (Union[bool, str]): Currently not supported, please keep the default value. + Default to False. + validate (Optional[str]): Currently not supported, please keep the default value. + Default to None. + + Returns: + EncryptedDataFrame: The joined encrypted data-frame. + """ + joined_array, joined_column_names, joined_dtype_mappings = encrypted_merge( + self, + other, + _SERVER, + how=how, + on=on, + left_on=left_on, + right_on=right_on, + left_index=left_index, + right_index=right_index, + sort=sort, + suffixes=suffixes, + copy=copy, + indicator=indicator, + validate=validate, + ) + + # Once multi-operator is supported, make sure to provide relevant keys and objects + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + joined_df = EncryptedDataFrame( + joined_array, + self._encrypted_nan, + self._evaluation_keys, + joined_column_names, + joined_dtype_mappings, + self._api_version, + ) + + return joined_df + + def _to_dict(self) -> Dict: + """Serialize the encrypted data-frame as a dictionary. + + Returns: + Dict: The serialized data-frame. + """ + # Serialize encrypted values element-wise + encrypted_values = serialize_elementwise(self._encrypted_values) + encrypted_nan = serialize_value(self._encrypted_nan) + + evaluation_keys = serialize_evaluation_keys(self._evaluation_keys) + + # Avoid sending column names and string mappings to server, instead use hashes + # FIXME : https://github.com/zama-ai/concrete-ml-internal/issues/4342 + # Additionally, Numpy arrays are not serializable using JSON so we need to convert them + # to lists + output_dict = { + "encrypted_values": encrypted_values.tolist(), + "encrypted_nan": encrypted_nan, + "evaluation_keys": evaluation_keys, + "column_names": self._column_names, + "dtype_mappings": self._dtype_mappings, + "api_version": self._api_version, + } + + return output_dict + + @classmethod + def _from_dict(cls, dict_to_load: Dict): + """Load a serialized encrypted data-frame from a dictionary. + + Args: + dict_to_load (Dict): The serialized encrypted data-frame. + + Returns: + EncryptedDataFrame: The loaded encrypted data-frame. + """ + # Deserialize encrypted values element-wise + encrypted_values = deserialize_elementwise(dict_to_load["encrypted_values"]) + encrypted_nan = deserialize_value(dict_to_load["encrypted_nan"]) + + evaluation_keys = deserialize_evaluation_keys(dict_to_load["evaluation_keys"]) + + column_names = dict_to_load["column_names"] + dtype_mappings = dict_to_load["dtype_mappings"] + api_version = dict_to_load["api_version"] + + return cls( + encrypted_values, + encrypted_nan, + evaluation_keys, + column_names, + dtype_mappings, + api_version, + ) + + def save(self, path: Union[Path, str]): + """Save the encrypted data-frame on disk. + + Args: + path (Union[Path, str]): The path where to save the encrypted data-frame. + """ + path = Path(path) + + encrypted_df_dict = self._to_dict() + with path.open("w", encoding="utf-8") as file: + json.dump(encrypted_df_dict, file) + + @classmethod + def load(cls, path: Union[Path, str]): + """Load an encrypted data-frame from disk. + + Args: + path (Union[Path, str]): The path where to load the encrypted data-frame. + + Returns: + EncryptedDataFrame: The loaded encrypted data-frame. + """ + path = Path(path) + + with path.open("r", encoding="utf-8") as file: + encrypted_df_dict = json.load(file) + + return cls._from_dict(encrypted_df_dict) diff --git a/src/concrete/ml/pytest/utils.py b/src/concrete/ml/pytest/utils.py index bf1fb86a0..6ab70488e 100644 --- a/src/concrete/ml/pytest/utils.py +++ b/src/concrete/ml/pytest/utils.py @@ -1,10 +1,12 @@ """Common functions or lists for test files, which can't be put in fixtures.""" +import copy import io from functools import partial from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Type, Union import numpy +import pandas import pytest import torch from numpy.random import RandomState @@ -689,3 +691,65 @@ def get_random_samples(x: numpy.ndarray, n_sample: int) -> numpy.ndarray: random_rows_indices = numpy.random.choice(x.shape[0], size=n_sample, replace=False) return x[random_rows_indices] + + +def pandas_dataframe_are_equal( + df_1: pandas.DataFrame, + df_2: pandas.DataFrame, + float_rtol: float = 1.0e-5, + float_atol: float = 1.0e-8, + equal_nan: bool = False, +): + """Determine if both data-frames are identical. + + Args: + df_1 (pandas.DataFrame): The first data-frame to consider. + df_2 (pandas.DataFrame): The second data-frame to consider. + float_rtol (float): Numpy's relative tolerance parameter to use when comparing columns with + floating point values. Default to 1.e-5. + float_atol (float): Numpy's absolute tolerance parameter to use when comparing columns with + floating point values. Default to 1.e-8. + equal_nan (bool): Whether to compare NaN values as equal. Default to False. + + Returns: + Bool: Wether both data-frames are equal. + """ + df_1 = copy.copy(df_1) + df_2 = copy.copy(df_2) + + # Select columns with floating point values + float_columns = df_1.select_dtypes(include="float").columns + + # Check if the float columns contain the same values + float_equal = numpy.isclose( + df_1[float_columns], + df_2[float_columns], + rtol=float_rtol, + atol=float_atol, + equal_nan=equal_nan, + ).all() + + # Select other columns (integers, objects, ...) + non_float_columns = df_1.select_dtypes(exclude="float").columns + + # In case NaN values must be considered equal, replace them by a custom placeholder before + # comparing the data-frames + if equal_nan: + placeholder = "" + + # Make sure this placeholder does not already exist in the data-frames + assert ( + not df_1[non_float_columns].isin([placeholder]).any().any() + or not df_2[non_float_columns].isin([placeholder]).any().any() + ), ( + f"The placeholder value '{placeholder}' already exists in the string columns and thus " + "cannot be used for comparing the data-frames." + ) + + df_1 = df_1[non_float_columns].fillna(placeholder) + df_2 = df_2[non_float_columns].fillna(placeholder) + + # Check if non-float columns contain the same values + string_equal = df_1.eq(df_2).all().all() + + return float_equal and string_equal diff --git a/tests/pandas/test_pandas.py b/tests/pandas/test_pandas.py new file mode 100644 index 000000000..805359e84 --- /dev/null +++ b/tests/pandas/test_pandas.py @@ -0,0 +1,572 @@ +"""Tests the encrypted data-frame API abd its coherence with Pandas""" +import re +import shutil +import tempfile +from pathlib import Path +from typing import List, Optional, Sequence, Tuple, Union + +import numpy +import pandas +import pytest +from concrete.fhe.compilation.specs import ClientSpecs + +import concrete.ml.pandas +from concrete.ml.pandas import ClientEngine, load_encrypted_dataframe +from concrete.ml.pandas._development import CLIENT_PATH, get_min_max_allowed, save_client_server +from concrete.ml.pytest.utils import pandas_dataframe_are_equal + + +def generate_pandas_dataframe( + dtype: str = "mixed", + feat_name: str = "feat", + n_features: int = 1, + index_name: Optional[str] = None, + indexes: Optional[Union[int, List]] = None, + index_position: int = 0, + include_nan: bool = True, + float_min: float = -10.0, + float_max: float = 10.0, +) -> pandas.DataFrame: + """Generate a Pandas data-frame. + + Note that in this case, the index is not the Pandas' index but rather a dedicated column. + + Args: + dtype (str): The dtype to consider when generating the data-frame, one of + ["int", "float", "str", "mixed"]: + * "int": generates n_features feature(s) made of integers in the allowed range + * "float": generates n_features feature(s) made of floating points + * "str": generates n_features feature(s) made of strings picked from a fixed list + * "mixed": generates 3*n_features features, n_features for each of the above dtypes + Default to "mixed". + feat_name (str): The features' base name to consider. Default to "feat". + n_features (int): The number of features to use per dtype. Default to 1. + index_name (Optional[str]): The index's name. Default to None ("index"). + indexes (Optional[Union[int, List]]): Custom indexes to consider. Default to None (5 rows, + indexed from 1 to 5). + index_position (int): The index's column position in the data-frame. Default to 0. + include_nan (bool): If NaN values should be put in the data-frame. If True, they are + inserted in the first row. Default to True. + float_min (float): The minimum float value to use for defining the range of values allowed + when generating the float column. + float_max (float): The maximum float value to use for defining the range of values allowed + when generating the float column. + + Returns: + pandas.DataFrame: The generated Pandas data-frame. + """ + if indexes is None: + indexes = 5 + + allowed_dtype = ["int", "float", "str", "mixed"] + assert dtype in allowed_dtype, f"Parameter 'dtype' must be in {allowed_dtype}. Got {dtype}." + assert isinstance( + indexes, (int, list) + ), f"Parameter 'indexes' must either be an int or a list. Got {type(indexes)}" + assert not ( + include_nan and dtype == "int" + ), "NaN values cannot be included when testing integers values" + + # Make sure 0 is not included in the index + # Remove this once NaN values are not represented by 0 anymore + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + if isinstance(indexes, int): + indexes = list(range(1, indexes + 1)) + + if index_name is None: + index_name = "index" + + columns = {} + + # Add a column with integer values + if dtype in ["int", "mixed"]: + low, high = get_min_max_allowed() + + for i in range(1, n_features + 1): + columns[f"{feat_name}_int_{i}"] = list( + numpy.random.randint(low=low, high=high, size=(len(indexes),)) + ) + + # Add a column with float values (including NaN or not) + if dtype in ["float", "mixed"]: + for i in range(1, n_features + 1): + column_name = f"{feat_name}_float_{i}" + columns[column_name] = list( + numpy.random.uniform(low=float_min, high=float_max, size=(len(indexes),)) + ) + + if include_nan: + columns[column_name][0] = numpy.nan + + # Add a column with string values (including NaN or not) + if dtype in ["str", "mixed"]: + str_values = ["apple", "orange", "watermelon", "cherry", "banana"] + + for i in range(1, n_features + 1): + column_name = f"{feat_name}_str_{i}" + columns[column_name] = list(numpy.random.choice(str_values, size=(len(indexes),))) + + if include_nan: + columns[column_name][0] = numpy.nan + + pandas_dataframe = pandas.DataFrame(columns) + + assert index_position < len(pandas_dataframe.columns), ( + "Parameter 'index_position' should not be greater than the number of features. Got " + f"{index_position=} for {len(pandas_dataframe.columns)} features." + ) + + # Insert the column on which to merge at the given position + pandas_dataframe.insert(index_position, index_name, indexes) + + return pandas_dataframe + + +def get_two_encrypted_dataframes( + feat_names: Optional[Sequence] = None, + indexes_left: Optional[Union[int, List]] = None, + indexes_right: Optional[Union[int, List]] = None, + **data_kwargs, +) -> Tuple[pandas.DataFrame, pandas.DataFrame]: + """Generated two Pandas data-frame. + + Args: + feat_names (Optional[Sequence]): The features' base name to consider for both data-frame. + Default to None (("left", "right")). + indexes_left (Optional[Union[int, List]]): Custom indexes to consider for the first + data-frame. Default to None. + indexes_right (Optional[Union[int, List]]): Custom indexes to consider for the second + data-frame. Default to None. + + Returns: + Tuple[pandas.DataFrame, pandas.DataFrame]: The two generated Pandas data-frame. + """ + with tempfile.TemporaryDirectory() as temp_dir: + keys_path = Path(temp_dir) / "keys" + + client_1 = ClientEngine(keys_path=keys_path) + client_2 = ClientEngine(keys_path=keys_path) + + if feat_names is None: + feat_names = ("left", "right") + + pandas_df_left = generate_pandas_dataframe( + feat_name=feat_names[0], indexes=indexes_left, **data_kwargs + ) + pandas_df_right = generate_pandas_dataframe( + feat_name=feat_names[1], indexes=indexes_right, **data_kwargs + ) + + encrypted_df_left = client_1.encrypt_from_pandas(pandas_df_left) + encrypted_df_right = client_2.encrypt_from_pandas(pandas_df_right) + + return encrypted_df_left, encrypted_df_right + + +@pytest.mark.parametrize("as_method", [True, False]) +@pytest.mark.parametrize("how", ["left", "right"]) +@pytest.mark.parametrize("selected_column", ["index", None]) +def test_merge(as_method, how, selected_column): + """Test that the encrypted merge operator is equivalent to Pandas' merge.""" + pandas_kwargs = {"how": how, "on": selected_column} + + with tempfile.TemporaryDirectory() as temp_dir: + keys_path = Path(temp_dir) / "keys" + + client_1 = ClientEngine(keys_path=keys_path) + client_2 = ClientEngine(keys_path=keys_path) + + pandas_df_left = generate_pandas_dataframe( + feat_name="left", index_name=selected_column, indexes=[1, 2, 3, 4], index_position=2 + ) + pandas_df_right = generate_pandas_dataframe( + feat_name="right", index_name=selected_column, indexes=[2, 3], index_position=1 + ) + + encrypted_df_left = client_1.encrypt_from_pandas(pandas_df_left) + encrypted_df_right = client_2.encrypt_from_pandas(pandas_df_right) + + # If we test the '.merge' method + if as_method: + pandas_joined_df = pandas_df_left.merge(pandas_df_right, **pandas_kwargs) + encrypted_df_joined = encrypted_df_left.merge(encrypted_df_right, **pandas_kwargs) + + else: + pandas_joined_df = pandas.merge(pandas_df_left, pandas_df_right, **pandas_kwargs) + encrypted_df_joined = concrete.ml.pandas.merge( + encrypted_df_left, encrypted_df_right, **pandas_kwargs + ) + + clear_df_joined_1 = client_1.decrypt_to_pandas(encrypted_df_joined) + clear_df_joined_2 = client_2.decrypt_to_pandas(encrypted_df_joined) + + assert pandas_dataframe_are_equal( + clear_df_joined_1, clear_df_joined_2, equal_nan=True + ), "Joined encrypted data-frames decrypted by different clients are not equal." + + # Improve the test to avoid risk of flaky + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + assert pandas_dataframe_are_equal( + clear_df_joined_1, pandas_joined_df, float_atol=1, equal_nan=True + ), "Joined encrypted data-frame does not match Pandas' joined data-frame." + + +@pytest.mark.parametrize("dtype", ["int", "float", "str", "mixed"]) +def test_pre_post_processing(dtype): + """Test pre-processing and post-processing steps.""" + include_nan = dtype != "int" + + client = ClientEngine() + + pandas_df = generate_pandas_dataframe(dtype=dtype, include_nan=include_nan) + + encrypted_df = client.encrypt_from_pandas(pandas_df) + + clear_df = client.decrypt_to_pandas(encrypted_df) + + # Improve the test to avoid risk of flaky + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + assert pandas_dataframe_are_equal( + pandas_df, clear_df, float_atol=1, equal_nan=include_nan + ), "Processed encrypted data-frame does not match Pandas' initial data-frame." + + +@pytest.mark.parametrize("float_min_max", [0.0, 1.0]) +def test_quantization_corner_cases(float_min_max): + """Test quantization process for corner cases. + + This test makes sure that the pre-process and post-process steps properly handle columns with + single float values (0 or else), as the quantization process handle these differently. + """ + + client = ClientEngine() + + pandas_df = generate_pandas_dataframe( + dtype="float", float_min=float_min_max, float_max=float_min_max + ) + + encrypted_df = client.encrypt_from_pandas(pandas_df) + + clear_df = client.decrypt_to_pandas(encrypted_df) + + # Improve the test to avoid risk of flaky + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + assert pandas_dataframe_are_equal( + pandas_df, clear_df, float_atol=1, equal_nan=True + ), "Processed encrypted data-frame does not match Pandas' initial data-frame." + + +def test_save_load(): + """Test saving and loading an encrypted data-frame.""" + client = ClientEngine() + + pandas_df = generate_pandas_dataframe() + + encrypted_df = client.encrypt_from_pandas(pandas_df) + + with tempfile.TemporaryDirectory() as temp_dir: + enc_df_path = Path(temp_dir) / "encrypted_dataframe" + + encrypted_df.save(enc_df_path) + + loaded_encrypted_df = load_encrypted_dataframe(enc_df_path) + + assert ( + encrypted_df.api_version == loaded_encrypted_df.api_version + ), "API versions between initial and loaded encrypted data-frame do not match." + + assert ( + encrypted_df.column_names == loaded_encrypted_df.column_names + ), "Column names between initial and loaded encrypted data-frame do not match." + + assert ( + encrypted_df.column_names_to_position == loaded_encrypted_df.column_names_to_position + ), "Column name mappings between initial and loaded encrypted data-frame do not match." + + assert ( + encrypted_df.dtype_mappings == loaded_encrypted_df.dtype_mappings + ), "Dtype mappings between initial and loaded encrypted data-frame do not match." + + loaded_clear_df = client.decrypt_to_pandas(loaded_encrypted_df) + + # Improve the test to avoid risk of flaky + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 + assert pandas_dataframe_are_equal( + loaded_clear_df, pandas_df, float_atol=1, equal_nan=True + ), "Loaded encrypted data-frame does not match the initial encrypted data-frame." + + +def check_invalid_merge_parameters(): + """Check that unsupported or invalid parameters for merge raise the correct errors.""" + encrypted_df_left, encrypted_df_right = get_two_encrypted_dataframes() + + unsupported_pandas_parameters_and_values = [ + ("left_on", "index"), + ("right_on", "index"), + ("left_index", True), + ("right_index", True), + ("sort", True), + ("copy", True), + ("indicator", True), + ("validate", "1:1"), + ] + + for parameter, unsupported_value in unsupported_pandas_parameters_and_values: + with pytest.raises( + ValueError, + match=f"Parameter '{parameter}' is not currently supported. Got {unsupported_value}.", + ): + encrypted_df_left.merge( + encrypted_df_right, + **{parameter: unsupported_value}, + ) + + for how in ["outer", "inner", "cross"]: + with pytest.raises( + NotImplementedError, + match=re.escape(f"Merge type '{how}' is not currently implemented."), + ): + encrypted_df_left.merge( + encrypted_df_right, + how=how, + ) + + +def check_no_multi_columns_merge(): + """Check that trying to merge on several columns raise the correct error.""" + encrypted_df_left, encrypted_df_right = get_two_encrypted_dataframes(feat_names=("", "")) + + with pytest.raises( + ValueError, + match="Merging on 0 or several columns is not currently available.", + ): + encrypted_df_left.merge(encrypted_df_right) + + +def check_column_coherence(): + """Check that merging data-frames with unsupported scheme raise the correct errors.""" + index_name = "index" + + # Test when a selected column has a different dtype than the other one + encrypted_df_left, encrypted_df_right = get_two_encrypted_dataframes( + index_name=index_name, indexes_left=[1, 2], indexes_right=[1.3, 7.3] + ) + + with pytest.raises( + ValueError, + match=re.escape( + f"Dtypes of both common column '{index_name}' do not match. Got int64 (left) and " + "float64 (right)." + ), + ): + encrypted_df_left.merge(encrypted_df_right) + + # Test when both selected columns have a float dtype + encrypted_df_left, encrypted_df_right = get_two_encrypted_dataframes( + index_name=index_name, indexes_left=[1.3, 7.3], indexes_right=[1.3, 7.3] + ) + + with pytest.raises( + ValueError, + match=re.escape( + f"Column '{index_name}' cannot be selected for merging both data-frames because it has " + f"a floating dtype (float64)" + ), + ): + encrypted_df_left.merge(encrypted_df_right) + + # Test when both selected columns have a object dtype (string) but with different string + # mappings + encrypted_df_left, encrypted_df_right = get_two_encrypted_dataframes( + index_name=index_name, + indexes_left=["cherry", "watermelon"], + indexes_right=["orange", "watermelon"], + ) + + with pytest.raises( + ValueError, + match=re.escape( + f"Mappings for string values in both common column '{index_name}' do not match." + ), + ): + encrypted_df_left.merge(encrypted_df_right) + + +def check_unsupported_input_values(): + """Check that initializing a data-frame with unsupported inputs raise the correct errors.""" + client = ClientEngine() + + # Test with integer values that are out of bound + indexes_high_integers = [73, 100] + pandas_df = generate_pandas_dataframe(indexes=indexes_high_integers) + + with pytest.raises( + ValueError, + match=".* contains values that are out of bounds. Expected values to be in interval.*", + ): + client.encrypt_from_pandas(pandas_df) + + # Test with string values that contains too many unique values + indexes_str = list(map(str, list(range(100)))) + pandas_df = generate_pandas_dataframe(indexes=indexes_str) + + with pytest.raises(ValueError, match=".* contains too many unique values.*"): + client.encrypt_from_pandas(pandas_df) + + # Test with object dtype that contains non-string values + indexes_object_non_str = [object(), object()] + pandas_df = generate_pandas_dataframe(indexes=indexes_object_non_str) + + with pytest.raises( + ValueError, + match=".* contains non-string values, which is not currently supported.*", + ): + client.encrypt_from_pandas(pandas_df) + + # Test with values of unsupported dtype + indexes_unsupported_dtype = [1 + 2j, -3 - 4j] + pandas_df = generate_pandas_dataframe(indexes=indexes_unsupported_dtype) + + with pytest.raises( + ValueError, + match=".* has dtype 'complex128', which is not currently supported.", + ): + client.encrypt_from_pandas(pandas_df) + + # Test with a data-frame that contains an Pandas index with possible relevant information in it + indexes_not_range = [1, 3] + pandas_df = generate_pandas_dataframe(indexes=indexes_not_range) + pandas_df.set_index("index", inplace=True) + + with pytest.raises( + ValueError, + match=( + "The data-frame's index has not been reset. Please make sure to not put relevant data " + "in the index and instead store it in a dedicated column. Encrypted data-frames do not " + "currently support any index-based operations." + ), + ): + client.encrypt_from_pandas(pandas_df) + + +def check_post_processing_coherence(): + """Check post-processing a data-frame with unsupported scheme raise the correct errors.""" + index_name = "index" + + client = ClientEngine() + + pandas_df = generate_pandas_dataframe(index_name=index_name) + + encrypted_df = client.encrypt_from_pandas(pandas_df) + + wrong_dtype = "complex128" + encrypted_df.dtype_mappings[index_name]["dtype"] = wrong_dtype + + with pytest.raises( + ValueError, + match=re.escape( + f"Column '{index_name}' has dtype '{wrong_dtype}', which is unexpected and thus not " + "supported." + ), + ): + client.decrypt_to_pandas(encrypted_df) + + +def test_error_raises(): + """Check that expected errors are properly raised.""" + check_invalid_merge_parameters() + check_no_multi_columns_merge() + check_column_coherence() + check_unsupported_input_values() + check_post_processing_coherence() + + +def deserialize_client_file(client_path: Union[Path, str]) -> ClientSpecs: + """Deserialize a Concrete client file. + + Args: + client_path (Union[Path, str]): The path to the client file. + + Returns: + ClientSpecs: The ClientSpecs object used for instantiating a Client object. + """ + with tempfile.TemporaryDirectory() as temp_dir: + output_dir_path = Path(temp_dir) + + shutil.unpack_archive(client_path, output_dir_path, "zip") + + with (output_dir_path / "client.specs.json").open("rb") as f: + client_specs = ClientSpecs.deserialize(f.read()) + + return client_specs + + +def concrete_client_files_are_equal( + client_path_1: Union[Path, str], client_path_2: Union[Path, str] +) -> bool: + """Deserialize and compare two Concrete client files. + + Args: + client_path_1 (Union[Path, str]): The path to the first client file. + client_path_2 (Union[Path, str]): The path to the second client file. + + Returns: + bool: If both client files are equal. + """ + client_path_1, client_path_2 = Path(client_path_1), Path(client_path_2) + + assert client_path_1.is_file(), f"Path '{client_path_1}' is not a file." + assert client_path_2.is_file(), f"Path '{client_path_2}' is not a file." + + client_specs_1 = deserialize_client_file(client_path_1) + client_specs_2 = deserialize_client_file(client_path_2) + + return client_specs_1 == client_specs_2 + + +# Improve this test if Concrete Python provides an official way to check such compatibility +# FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4342 +def test_parameter_sets(): + """Test if new generated parameter sets (client.zip) are equal to the ones stored in source.""" + with tempfile.TemporaryDirectory() as temp_dir: + client_path = Path(temp_dir) / "client.zip" + server_path = Path(temp_dir) / "server.zip" + + save_client_server(client_path=client_path, server_path=server_path) + + assert concrete_client_files_are_equal( + client_path, CLIENT_PATH + ), "The new generated client file is not equal to the one stored in source." + + +def test_print_and_repr(): + """Test that print, repr and get_schema properly work.""" + pandas_df = pandas.DataFrame( + {"index": [1, 2], "A": [9, 3], "B": [-5.2, 2.9], "C": ["orange", "watermelon"]} + ) + + client = ClientEngine() + + encrypted_df = client.encrypt_from_pandas(pandas_df) + + # Because values are encrypted and this cannot be seeded, we are currently not able to make sure + # the print and repr are matching an expected result + print(encrypted_df) + repr(encrypted_df) + encrypted_df._repr_html_() # pylint: disable=protected-access + + expected_schema = pandas.DataFrame( + { + "index": ["int64", numpy.nan, numpy.nan, numpy.nan], + "A": ["int64", numpy.nan, numpy.nan, numpy.nan], + "B": ["float64", 1.7283950617283952, -9.987654320987655, numpy.nan], + "C": ["object", numpy.nan, numpy.nan, {"orange": 1, "watermelon": 2}], + }, + index=["dtype", "scale", "zero_point", "str_to_int"], + ) + + schema = encrypted_df.get_schema() + + assert pandas_dataframe_are_equal( + expected_schema, schema, equal_nan=True + ), "Expected and retrieved schemas do not match." diff --git a/use_case_examples/dataframe/.gitignore b/use_case_examples/dataframe/.gitignore new file mode 100644 index 000000000..8b6a62be9 --- /dev/null +++ b/use_case_examples/dataframe/.gitignore @@ -0,0 +1,5 @@ +client_1/* +!client_1/df_left.csv + +client_2/* +!client_2/df_right.csv diff --git a/use_case_examples/dataframe/client_1/df_left.csv b/use_case_examples/dataframe/client_1/df_left.csv new file mode 100644 index 000000000..3e065dc65 --- /dev/null +++ b/use_case_examples/dataframe/client_1/df_left.csv @@ -0,0 +1,10 @@ +index,total_bill,tip,sex,smoker +1,12.54,2.5,Male,No +2,11.17,1.5,Female,No +3,20.29,2.75,Female,No +4,14.07,2.5,Male,No +5,15.69,3.0,Male,Yes +6,18.29,3.0,Male,No +7,16.93,3.07,Female,No +8,24.27,2.03,Male,Yes +9,8.77,2.0,Male,No diff --git a/use_case_examples/dataframe/client_2/df_right.csv b/use_case_examples/dataframe/client_2/df_right.csv new file mode 100644 index 000000000..0b95a0560 --- /dev/null +++ b/use_case_examples/dataframe/client_2/df_right.csv @@ -0,0 +1,4 @@ +index,day,time,size +2,Thur,Lunch,2 +5,Sat,Dinner,3 +9,Sun,Dinner,2 diff --git a/use_case_examples/dataframe/encrypted_pandas.ipynb b/use_case_examples/dataframe/encrypted_pandas.ipynb new file mode 100644 index 000000000..35d7e4bfc --- /dev/null +++ b/use_case_examples/dataframe/encrypted_pandas.ipynb @@ -0,0 +1,939 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "from pathlib import Path\n", + "from tempfile import TemporaryDirectory\n", + "\n", + "import numpy\n", + "import pandas\n", + "\n", + "from concrete.ml.pandas import ClientEngine, load_encrypted_dataframe\n", + "from concrete.ml.pytest.utils import pandas_dataframe_are_equal\n", + "\n", + "numpy.random.seed(0)\n", + "\n", + "# pylint: disable=pointless-statement, consider-using-with" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Client 1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Path directory for client and server files\n", + "CLIENT_1_DIR = Path(\"client_1\")\n", + "CLIENT_2_DIR = Path(\"client_2\")\n", + "\n", + "# Pandas kwargs\n", + "HOW = \"left\"\n", + "ON = \"index\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " index total_bill tip sex \\\n", + "dtype int64 float64 float64 object \n", + "scale NaN 0.903226 8.917197 NaN \n", + "zero_point NaN 6.92129 12.375796 NaN \n", + "str_to_int NaN NaN NaN {'Male': 1, 'Female': 2} \n", + "\n", + " smoker \n", + "dtype object \n", + "scale NaN \n", + "zero_point NaN \n", + "str_to_int {'No': 1, 'Yes': 2} " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_left_enc.get_schema()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_left_enc_path = client_1_temp_path / \"df_left_enc\"\n", + "df_left_enc.save(df_left_enc_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Client 2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexdaytimesize
02ThurLunch2
15SatDinner3
29SunDinner2
\n", + "
" + ], + "text/plain": [ + " index day time size\n", + "0 2 Thur Lunch 2\n", + "1 5 Sat Dinner 3\n", + "2 9 Sun Dinner 2" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_right = pandas.read_csv(CLIENT_2_DIR / \"df_right.csv\")\n", + "\n", + "df_right" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clients need to share private keys" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "client_2_temp_dir = TemporaryDirectory(dir=str(CLIENT_2_DIR))\n", + "client_2_temp_path = Path(client_2_temp_dir.name)\n", + "\n", + "client_2_keys_path = client_2_temp_path / \"keys\"\n", + "\n", + "shutil.copy2(client_1_keys_path, client_2_keys_path);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "client_2 = ClientEngine(keys_path=client_2_keys_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_right_enc = client_2.encrypt_from_pandas(df_right)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexdaytimesize
..48d4814937....dd6b288e52....497a80e2dd....41f496fe3a..
..0a19fbfc58....047a92f5bc....7f7a6f1167....5ca8e5edfc..
..79c726effe....6835b68ece....4ae3bca370....f4eb2bde07..
" + ], + "text/plain": [ + " index day time size\n", + "..48d4814937.. ..dd6b288e52.. ..497a80e2dd.. ..41f496fe3a..\n", + "..0a19fbfc58.. ..047a92f5bc.. ..7f7a6f1167.. ..5ca8e5edfc..\n", + "..79c726effe.. ..6835b68ece.. ..4ae3bca370.. ..f4eb2bde07.." + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_right_enc" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "df_right_enc_path = client_2_temp_path / \"df_right_enc\"\n", + "df_right_enc.save(df_right_enc_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Server" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df_left_enc = load_encrypted_dataframe(df_left_enc_path)\n", + "df_right_enc = load_encrypted_dataframe(df_right_enc_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df_joined_enc_server = df_left_enc.merge(df_right_enc, how=HOW, on=ON)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both clients are able decrypt the result" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "df_joined_enc_server_path = client_1_temp_path / \"df_joined_enc\"\n", + "\n", + "df_joined_enc_server.save(df_joined_enc_server_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Client" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df_joined_enc = load_encrypted_dataframe(df_joined_enc_server_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "df_joined_cml = client_1.decrypt_to_pandas(df_joined_enc)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indextotal_billtipsexsmokerdaytimesize
0112.0914292.509286MaleNoNaNNaNNaN
1210.9842861.500000FemaleNoThurLunch2.0
2319.8414292.733571FemaleNoNaNNaNNaN
3414.3057142.509286MaleNoNaNNaNNaN
4515.4128572.957857MaleYesSatDinner3.0
5618.7342862.957857MaleNoNaNNaNNaN
6716.5200003.070000FemaleNoNaNNaNNaN
7824.2700002.060714MaleYesNaNNaNNaN
898.7700001.948571MaleNoSunDinner2.0
\n", + "
" + ], + "text/plain": [ + " index total_bill tip sex smoker day time size\n", + "0 1 12.091429 2.509286 Male No NaN NaN NaN\n", + "1 2 10.984286 1.500000 Female No Thur Lunch 2.0\n", + "2 3 19.841429 2.733571 Female No NaN NaN NaN\n", + "3 4 14.305714 2.509286 Male No NaN NaN NaN\n", + "4 5 15.412857 2.957857 Male Yes Sat Dinner 3.0\n", + "5 6 18.734286 2.957857 Male No NaN NaN NaN\n", + "6 7 16.520000 3.070000 Female No NaN NaN NaN\n", + "7 8 24.270000 2.060714 Male Yes NaN NaN NaN\n", + "8 9 8.770000 1.948571 Male No Sun Dinner 2.0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_joined_cml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Concrete ML vs Pandas comparison\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indextotal_billtipsexsmokerdaytimesize
0112.542.50MaleNoNaNNaNNaN
1211.171.50FemaleNoThurLunch2.0
2320.292.75FemaleNoNaNNaNNaN
3414.072.50MaleNoNaNNaNNaN
4515.693.00MaleYesSatDinner3.0
5618.293.00MaleNoNaNNaNNaN
6716.933.07FemaleNoNaNNaNNaN
7824.272.03MaleYesNaNNaNNaN
898.772.00MaleNoSunDinner2.0
\n", + "
" + ], + "text/plain": [ + " index total_bill tip sex smoker day time size\n", + "0 1 12.54 2.50 Male No NaN NaN NaN\n", + "1 2 11.17 1.50 Female No Thur Lunch 2.0\n", + "2 3 20.29 2.75 Female No NaN NaN NaN\n", + "3 4 14.07 2.50 Male No NaN NaN NaN\n", + "4 5 15.69 3.00 Male Yes Sat Dinner 3.0\n", + "5 6 18.29 3.00 Male No NaN NaN NaN\n", + "6 7 16.93 3.07 Female No NaN NaN NaN\n", + "7 8 24.27 2.03 Male Yes NaN NaN NaN\n", + "8 9 8.77 2.00 Male No Sun Dinner 2.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute the left-joined data-frame using Pandas\n", + "df_joined_pandas = pandas.merge(df_left, df_right, on=ON, how=HOW)\n", + "\n", + "df_joined_pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Concrete ML data-frame is equal to Pandas data-frame: True \n", + "\n" + ] + } + ], + "source": [ + "# Compte the joined Pandas data-frame to the Concrete ML result\n", + "df_are_equal = pandas_dataframe_are_equal(\n", + " df_joined_pandas, df_joined_cml, float_rtol=0.1, equal_nan=True\n", + ")\n", + "\n", + "print(\"Concrete ML data-frame is equal to Pandas data-frame:\", df_are_equal, \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "client_1_temp_dir.cleanup()\n", + "client_2_temp_dir.cleanup()" + ] + } + ], + "metadata": { + "execution": { + "timeout": 10800 + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}