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https://github.com/luxfhe/ml-sdk.git
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zeekay
bde2e2531b
Machine Learning with FHE - fork of Zama Concrete-ML - Built-in FHE-friendly ML models - scikit-learn compatible API - PyTorch model conversion - Quantization-aware training
97 lines
4.1 KiB
Python
97 lines
4.1 KiB
Python
"""Public API for encrypted data-frames."""
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from collections.abc import Hashable
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from pathlib import Path
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from typing import Optional, Sequence, Tuple, Union
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from .client_engine import ClientEngine
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from .dataframe import EncryptedDataFrame
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def load_encrypted_dataframe(path: Union[Path, str]) -> EncryptedDataFrame:
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"""Load a serialized encrypted data-frame.
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Args:
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path (Union[Path, str]): The path to consider for loading the serialized encrypted
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data-frame.
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Returns:
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EncryptedDataFrame: The loaded encrypted data-frame.
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"""
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return EncryptedDataFrame.load(path)
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# pylint: disable-next=too-many-arguments, invalid-name
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def merge(
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left_encrypted: EncryptedDataFrame,
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right_encrypted: EncryptedDataFrame,
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how: str = "left",
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on: Optional[str] = None, # pylint: disable=invalid-name
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left_on: Optional[Union[Hashable, Sequence[Hashable]]] = None,
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right_on: Optional[Union[Hashable, Sequence[Hashable]]] = None,
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left_index: bool = False,
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right_index: bool = False,
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sort: bool = False,
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suffixes: Tuple[Optional[str], Optional[str]] = ("_x", "_y"),
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copy: Optional[bool] = None,
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indicator: Union[bool, str] = False,
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validate: Optional[str] = None,
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) -> EncryptedDataFrame:
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"""Merge two encrypted data-frames in FHE using Pandas parameters.
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Note that for now, only a left and right join is implemented. Additionally, only some Pandas
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parameters are supported, and joining on multiple columns is not available.
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Pandas documentation for version 2.0 can be found here:
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https://pandas.pydata.org/pandas-docs/version/2.0/reference/api/pandas.DataFrame.merge.html
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Args:
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left_encrypted (EncryptedDataFrame): The left encrypted data-frame.
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right_encrypted (EncryptedDataFrame): The right encrypted data-frame.
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how (str): Type of merge to be performed, one of {'left', 'right'}.
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* left: use only keys from left frame, similar to a SQL left outer join;
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preserve key order.
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* right: use only keys from right frame, similar to a SQL right outer join;
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preserve key order.
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on (Optional[str]): Column name to join on. These must be found in both DataFrames. If
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it is None then this defaults to the intersection of the columns in both DataFrames.
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Default to None.
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left_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported, please
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keep the default value. Default to None.
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right_on (Optional[Union[Hashable, Sequence[Hashable]]]): Currently not supported,
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please keep the default value. Default to None.
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left_index (bool): Currently not supported, please keep the default value. Default to
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False.
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right_index (bool): Currently not supported, please keep the default value. Default
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to False.
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sort (bool): Currently not supported, please keep the default value. Default to False.
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suffixes (Tuple[Optional[str], Optional[str]]): A length-2 sequence where each element
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is optionally a string indicating the suffix to add to overlapping column names in
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`left` and `right` respectively. Pass a value of `None` instead of a string to
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indicate that the column name from `left` or `right` should be left as-is, with no
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suffix. At least one of the values must not be None.. Default to ("_x", "_y").
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copy (Optional[bool]): Currently not supported, please keep the default value. Default
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to None.
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indicator (Union[bool, str]): Currently not supported, please keep the default value.
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Default to False.
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validate (Optional[str]): Currently not supported, please keep the default value.
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Default to None.
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Returns:
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EncryptedDataFrame: The joined encrypted data-frame.
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"""
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return left_encrypted.merge(
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right_encrypted,
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how=how,
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on=on,
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left_on=left_on,
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right_on=right_on,
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left_index=left_index,
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right_index=right_index,
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sort=sort,
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suffixes=suffixes,
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copy=copy,
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indicator=indicator,
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validate=validate,
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)
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