From 93e89434abd1ff99cddd33f393a261d25c4df6ca Mon Sep 17 00:00:00 2001 From: PrivacyGo-PETPlatform Date: Mon, 1 Jul 2024 20:13:37 +0800 Subject: [PATCH] feat: version 0.1.0 --- .coveragerc | 7 + .gitignore | 10 + .pre-commit-config.yaml | 39 + .pylintrc | 558 +++++++++++ .style.yapf | 397 ++++++++ CODE_OF_CONDUCT.md | 127 +++ CONTRIBUTING.md | 61 ++ LICENSE | 201 ++++ README.md | 85 ++ docs/user_guide/Federated_Leiden.md | 124 +++ docs/user_guide/Horizontal_SecureBoost.md | 290 ++++++ docs/user_guide/PSI.md | 76 ++ examples/data/breast_hetero_mini_client.csv | 570 ++++++++++++ .../data/breast_hetero_mini_client.parquet | Bin 0 -> 21062 bytes examples/data/breast_hetero_mini_server.csv | 570 ++++++++++++ .../data/breast_hetero_mini_server.parquet | Bin 0 -> 20463 bytes examples/data/iris_binary_mini_client.csv | 31 + examples/data/iris_binary_mini_server.csv | 31 + examples/data/karate_club_local_nodes1.json | 19 + examples/data/karate_club_local_nodes2.json | 19 + examples/data/karate_club_user_weight1.json | 176 ++++ examples/data/karate_club_user_weight2.json | 190 ++++ examples/data/students_reg_mini_client.csv | 26 + examples/data/students_reg_mini_server.csv | 26 + examples/leiden.py | 64 ++ examples/psi.py | 63 ++ images/framework.jpeg | Bin 0 -> 195096 bytes images/graph.png | Bin 0 -> 126550 bytes images/secureboost_inference.jpeg | Bin 0 -> 89496 bytes images/secureboost_train.jpeg | Bin 0 -> 140175 bytes petml/__init__.py | 21 + petml/fl/__init__.py | 13 + petml/fl/base.py | 27 + petml/fl/boosting/__init__.py | 15 + petml/fl/boosting/decision_tree.py | 347 +++++++ petml/fl/boosting/loss.py | 133 +++ petml/fl/boosting/metric.py | 59 ++ petml/fl/boosting/xgb_model.py | 879 ++++++++++++++++++ petml/fl/graph/__init__.py | 19 + petml/fl/graph/leiden/__init__.py | 13 + petml/fl/graph/leiden/base.py | 172 ++++ petml/fl/graph/leiden/leiden.py | 577 ++++++++++++ petml/fl/graph/leiden/utils.py | 42 + petml/fl/preprocessing/__init__.py | 15 + petml/fl/preprocessing/psi.py | 63 ++ petml/infra/__init__.py | 13 + petml/infra/abc/__init__.py | 17 + petml/infra/abc/engine.py | 28 + petml/infra/abc/network.py | 63 ++ petml/infra/abc/storage.py | 28 + petml/infra/common/__init__.py | 13 + petml/infra/common/log_utils.py | 52 ++ petml/infra/engine/__init__.py | 13 + petml/infra/engine/cipher_engine/__init__.py | 15 + .../engine/cipher_engine/cipher_engine.py | 38 + petml/infra/network/__init__.py | 16 + petml/infra/network/cluster_def.py | 46 + petml/infra/network/network_factory.py | 34 + petml/infra/network/petnetNetwork/__init__.py | 13 + petml/infra/network/petnetNetwork/network.py | 88 ++ petml/infra/storage/__init__.py | 13 + petml/infra/storage/graph_storage/__init__.py | 15 + .../storage/graph_storage/json_storage.py | 71 ++ .../infra/storage/tabular_storage/__init__.py | 16 + petml/infra/storage/tabular_storage/base.py | 32 + .../storage/tabular_storage/csv_storage.py | 87 ++ .../tabular_storage/parquet_storage.py | 60 ++ petml/operators/__init__.py | 17 + petml/operators/boosting/__init__.py | 15 + petml/operators/boosting/xgb_model.py | 321 +++++++ petml/operators/graph/__init__.py | 15 + petml/operators/graph/leiden.py | 79 ++ petml/operators/operator_base.py | 59 ++ petml/operators/preprocessing/__init__.py | 15 + petml/operators/preprocessing/psi.py | 77 ++ petml/version.py | 15 + pytest.ini | 6 + requirements.txt | 7 + setup.py | 58 ++ tests/__init__.py | 0 tests/configs.py | 18 + tests/conftest.py | 30 + tests/fl/__init__.py | 0 tests/fl/graph/test_leiden.py | 109 +++ tests/fl/preprocessing/test_psi.py | 41 + tests/infra/__init__.py | 13 + tests/infra/common/__init__.py | 13 + tests/infra/common/test_log.py | 25 + tests/infra/engine/__init__.py | 13 + tests/infra/engine/test_cipher_engine.py | 86 ++ tests/infra/network/__init__.py | 13 + tests/infra/network/test_petnet.py | 46 + tests/infra/storage/__init__.py | 13 + tests/infra/storage/test_tabular.py | 33 + tests/operators/__init__.py | 13 + tests/operators/boosting/__init__.py | 13 + tests/operators/boosting/test_xgb.py | 217 +++++ tests/operators/graph/__init__.py | 13 + tests/operators/graph/test_leiden.py | 63 ++ tests/operators/preprocessing/__init__.py | 13 + tests/operators/preprocessing/test_psi.py | 61 ++ tests/process.py | 38 + tests/utils.py | 40 + 103 files changed, 8534 insertions(+) create mode 100644 .coveragerc create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 .pylintrc create mode 100644 .style.yapf create mode 100644 CODE_OF_CONDUCT.md create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 README.md create mode 100644 docs/user_guide/Federated_Leiden.md create mode 100644 docs/user_guide/Horizontal_SecureBoost.md create mode 100644 docs/user_guide/PSI.md create mode 100644 examples/data/breast_hetero_mini_client.csv create mode 100644 examples/data/breast_hetero_mini_client.parquet create mode 100644 examples/data/breast_hetero_mini_server.csv create mode 100644 examples/data/breast_hetero_mini_server.parquet create mode 100644 examples/data/iris_binary_mini_client.csv create mode 100644 examples/data/iris_binary_mini_server.csv create mode 100644 examples/data/karate_club_local_nodes1.json create mode 100644 examples/data/karate_club_local_nodes2.json create mode 100644 examples/data/karate_club_user_weight1.json create mode 100644 examples/data/karate_club_user_weight2.json create mode 100644 examples/data/students_reg_mini_client.csv create mode 100644 examples/data/students_reg_mini_server.csv create mode 100644 examples/leiden.py create mode 100644 examples/psi.py create mode 100644 images/framework.jpeg create mode 100644 images/graph.png create mode 100644 images/secureboost_inference.jpeg create mode 100644 images/secureboost_train.jpeg create mode 100644 petml/__init__.py create mode 100644 petml/fl/__init__.py create mode 100644 petml/fl/base.py create mode 100644 petml/fl/boosting/__init__.py create mode 100644 petml/fl/boosting/decision_tree.py create mode 100644 petml/fl/boosting/loss.py create mode 100644 petml/fl/boosting/metric.py create mode 100644 petml/fl/boosting/xgb_model.py create mode 100644 petml/fl/graph/__init__.py create mode 100644 petml/fl/graph/leiden/__init__.py create mode 100644 petml/fl/graph/leiden/base.py create mode 100644 petml/fl/graph/leiden/leiden.py create mode 100644 petml/fl/graph/leiden/utils.py create mode 100644 petml/fl/preprocessing/__init__.py create mode 100644 petml/fl/preprocessing/psi.py create mode 100644 petml/infra/__init__.py create mode 100644 petml/infra/abc/__init__.py create mode 100644 petml/infra/abc/engine.py create mode 100644 petml/infra/abc/network.py create mode 100644 petml/infra/abc/storage.py create mode 100644 petml/infra/common/__init__.py create mode 100644 petml/infra/common/log_utils.py create mode 100644 petml/infra/engine/__init__.py create mode 100644 petml/infra/engine/cipher_engine/__init__.py create mode 100644 petml/infra/engine/cipher_engine/cipher_engine.py create mode 100644 petml/infra/network/__init__.py create mode 100644 petml/infra/network/cluster_def.py create mode 100644 petml/infra/network/network_factory.py create mode 100644 petml/infra/network/petnetNetwork/__init__.py create mode 100644 petml/infra/network/petnetNetwork/network.py create mode 100644 petml/infra/storage/__init__.py create mode 100644 petml/infra/storage/graph_storage/__init__.py create mode 100644 petml/infra/storage/graph_storage/json_storage.py create mode 100644 petml/infra/storage/tabular_storage/__init__.py create mode 100644 petml/infra/storage/tabular_storage/base.py create mode 100644 petml/infra/storage/tabular_storage/csv_storage.py create mode 100644 petml/infra/storage/tabular_storage/parquet_storage.py create mode 100644 petml/operators/__init__.py create mode 100644 petml/operators/boosting/__init__.py create mode 100644 petml/operators/boosting/xgb_model.py create mode 100644 petml/operators/graph/__init__.py create mode 100644 petml/operators/graph/leiden.py create mode 100644 petml/operators/operator_base.py create mode 100644 petml/operators/preprocessing/__init__.py create mode 100644 petml/operators/preprocessing/psi.py create mode 100644 petml/version.py create mode 100644 pytest.ini create mode 100644 requirements.txt create mode 100644 setup.py create mode 100644 tests/__init__.py create mode 100644 tests/configs.py create mode 100644 tests/conftest.py create mode 100644 tests/fl/__init__.py create mode 100644 tests/fl/graph/test_leiden.py create mode 100644 tests/fl/preprocessing/test_psi.py create mode 100644 tests/infra/__init__.py create mode 100644 tests/infra/common/__init__.py create mode 100644 tests/infra/common/test_log.py create mode 100644 tests/infra/engine/__init__.py create mode 100644 tests/infra/engine/test_cipher_engine.py create mode 100644 tests/infra/network/__init__.py create mode 100644 tests/infra/network/test_petnet.py create mode 100644 tests/infra/storage/__init__.py create mode 100644 tests/infra/storage/test_tabular.py create mode 100644 tests/operators/__init__.py create mode 100644 tests/operators/boosting/__init__.py create mode 100644 tests/operators/boosting/test_xgb.py create mode 100644 tests/operators/graph/__init__.py create mode 100644 tests/operators/graph/test_leiden.py create mode 100644 tests/operators/preprocessing/__init__.py create mode 100644 tests/operators/preprocessing/test_psi.py create mode 100644 tests/process.py create mode 100644 tests/utils.py diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..d67edf0 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,7 @@ +[run] +concurrency=multiprocessing +include = + petml/* +omit = + tests/* + petml/infra/abc/* diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e56d500 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +### Python template +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +*.DS_Store + +# ide +.idea/* +venv/* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..98c27a5 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,39 @@ +default_language_version: + # force all unspecified python hooks to run python3 + python: python3 +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-merge-conflict + - id: check-symlinks + - id: check-yaml + - id: end-of-file-fixer + - id: forbid-submodules + - id: mixed-line-ending + - id: trailing-whitespace + - repo: https://github.com/google/yapf/ + rev: v0.40.2 + hooks: + - id: yapf + name: yapf + language: python + entry: yapf + args: ["--style=.style.yapf", "-i"] + types: [python] + - repo: https://github.com/pylint-dev/pylint/ + rev: v3.0.2 + hooks: + - id: pylint + name: pylint + entry: pylint + language: python + types: [python] + args: + [ + "-rn", # Only display messages + "-sn", # Don't display the score + "--rcfile=.pylintrc" + ] diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 0000000..7903bc6 --- /dev/null +++ b/.pylintrc @@ -0,0 +1,558 @@ +[MAIN] + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Files or directories to be skipped. They should be base names, not +# paths. +ignore=CVS + +# Add files or directories matching the regex patterns to the ignore-list. The +# regex matches against paths and can be in Posix or Windows format. +ignore-paths= + +# Files or directories matching the regex patterns are skipped. The regex +# matches against base names, not paths. +ignore-patterns=^\.# + +# Pickle collected data for later comparisons. +persistent=yes + +# List of plugins (as comma separated values of python modules names) to load, +# usually to register additional checkers. +load-plugins= + pylint.extensions.check_elif, + pylint.extensions.bad_builtin, + pylint.extensions.docparams, + pylint.extensions.for_any_all, + pylint.extensions.set_membership, + pylint.extensions.code_style, + pylint.extensions.overlapping_exceptions, + pylint.extensions.typing, + pylint.extensions.redefined_variable_type, + pylint.extensions.comparison_placement, + pylint.extensions.broad_try_clause, + pylint.extensions.dict_init_mutate, + pylint.extensions.consider_refactoring_into_while_condition, + +# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# number of processors available to use. +jobs=1 + +# When enabled, pylint would attempt to guess common misconfiguration and emit +# user-friendly hints instead of false-positive error messages. +suggestion-mode=yes + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code +extension-pkg-allow-list= + +# Minimum supported python version +py-version = 3.8.0 + +# Control the amount of potential inferred values when inferring a single +# object. This can help the performance when dealing with large functions or +# complex, nested conditions. +limit-inference-results=100 + +# Specify a score threshold under which the program will exit with error. +fail-under=10.0 + +# Return non-zero exit code if any of these messages/categories are detected, +# even if score is above --fail-under value. Syntax same as enable. Messages +# specified are enabled, while categories only check already-enabled messages. +fail-on= + +# Clear in-memory caches upon conclusion of linting. Useful if running pylint in +# a server-like mode. +clear-cache-post-run=no + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED +# confidence= + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +enable= + use-symbolic-message-instead, + useless-suppression, + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once).You can also use "--disable=all" to +# disable everything first and then re-enable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use"--disable=all --enable=classes +# --disable=W" + +disable= + attribute-defined-outside-init, + invalid-name, + missing-docstring, + protected-access, + too-few-public-methods, + # handled by black + format, + # We anticipate #3512 where it will become optional + fixme, + consider-using-assignment-expr, + logging-fstring-interpolation, + unspecified-encoding, + redefined-variable-type, + too-many-locals, + too-many-instance-attributes, + too-many-public-methods, + import-error, + consider-using-alias, + consider-alternative-union-syntax, + arguments-differ, + + +[REPORTS] + +# Set the output format. Available formats are text, parseable, colorized, msvs +# (visual studio) and html. You can also give a reporter class, eg +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages +reports=no + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables 'fatal', 'error', 'warning', 'refactor', 'convention' +# and 'info', which contain the number of messages in each category, as +# well as 'statement', which is the total number of statements analyzed. This +# score is used by the global evaluation report (RP0004). +evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details +#msg-template= + +# Activate the evaluation score. +score=yes + + +[LOGGING] + +# Logging modules to check that the string format arguments are in logging +# function parameter format +logging-modules=logging + +# The type of string formatting that logging methods do. `old` means using % +# formatting, `new` is for `{}` formatting. +logging-format-style=old + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=FIXME,XXX,TODO + +# Regular expression of note tags to take in consideration. +#notes-rgx= + + +[SIMILARITIES] + +# Minimum lines number of a similarity. +min-similarity-lines=6 + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=yes + +# Signatures are removed from the similarity computation +ignore-signatures=yes + + +[VARIABLES] + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid defining new builtins when possible. +additional-builtins= + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_,_cb + +# Tells whether unused global variables should be treated as a violation. +allow-global-unused-variables=yes + +# List of names allowed to shadow builtins +allowed-redefined-builtins= + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io + + +[FORMAT] + +# Maximum number of characters on a single line. +max-line-length=120 + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + +# Allow the body of a class to be on the same line as the declaration if body +# contains single statement. +single-line-class-stmt=no + +# Maximum number of lines in a module +max-module-lines=2000 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + + +[BASIC] + +# Good variable names which should always be accepted, separated by a comma +good-names=i,j,k,ex,Run,_ + +# Good variable names regexes, separated by a comma. If names match any regex, +# they will always be accepted +good-names-rgxs= + +# Bad variable names which should always be refused, separated by a comma +bad-names=foo,bar,baz,toto,tutu,tata + +# Bad variable names regexes, separated by a comma. If names match any regex, +# they will always be refused +bad-names-rgxs= + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Include a hint for the correct naming format with invalid-name +include-naming-hint=no + +# Naming style matching correct function names. +function-naming-style=snake_case + +# Regular expression matching correct function names +function-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming style matching correct variable names. +variable-naming-style=snake_case + +# Regular expression matching correct variable names +variable-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming style matching correct constant names. +const-naming-style=UPPER_CASE + +# Regular expression matching correct constant names +const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$ + +# Naming style matching correct attribute names. +attr-naming-style=snake_case + +# Regular expression matching correct attribute names +attr-rgx=[a-z_][a-z0-9_]{2,}$ + +# Naming style matching correct argument names. +argument-naming-style=snake_case + +# Regular expression matching correct argument names +argument-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming style matching correct class attribute names. +class-attribute-naming-style=any + +# Regular expression matching correct class attribute names +class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$ + +# Naming style matching correct class constant names. +class-const-naming-style=UPPER_CASE + +# Regular expression matching correct class constant names. Overrides class- +# const-naming-style. +#class-const-rgx= + +# Naming style matching correct inline iteration names. +inlinevar-naming-style=any + +# Regular expression matching correct inline iteration names +inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$ + +# Naming style matching correct class names. +class-naming-style=PascalCase + +# Regular expression matching correct class names +class-rgx=[A-Z_][a-zA-Z0-9]+$ + + +# Naming style matching correct module names. +module-naming-style=snake_case + +# Regular expression matching correct module names +module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ + + +# Naming style matching correct method names. +method-naming-style=snake_case + +# Regular expression matching correct method names +method-rgx=[a-z_][a-z0-9_]{2,}$ + +# Regular expression matching correct type variable names +#typevar-rgx= + +# Regular expression which should only match function or class names that do +# not require a docstring. Use ^(?!__init__$)_ to also check __init__. +no-docstring-rgx=__.*__ + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=-1 + +# List of decorators that define properties, such as abc.abstractproperty. +property-classes=abc.abstractproperty + + +[TYPECHECK] + +# Regex pattern to define which classes are considered mixins if ignore-mixin- +# members is set to 'yes' +mixin-class-rgx=.*MixIn + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis). It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=SQLObject, optparse.Values, thread._local, _thread._local + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members=REQUEST,acl_users,aq_parent,argparse.Namespace + +# List of decorators that create context managers from functions, such as +# contextlib.contextmanager. +contextmanager-decorators=contextlib.contextmanager + +# Tells whether to warn about missing members when the owner of the attribute +# is inferred to be None. +ignore-none=yes + +# This flag controls whether pylint should warn about no-member and similar +# checks whenever an opaque object is returned when inferring. The inference +# can return multiple potential results while evaluating a Python object, but +# some branches might not be evaluated, which results in partial inference. In +# that case, it might be useful to still emit no-member and other checks for +# the rest of the inferred objects. +ignore-on-opaque-inference=yes + +# Show a hint with possible names when a member name was not found. The aspect +# of finding the hint is based on edit distance. +missing-member-hint=yes + +# The minimum edit distance a name should have in order to be considered a +# similar match for a missing member name. +missing-member-hint-distance=1 + +# The total number of similar names that should be taken in consideration when +# showing a hint for a missing member. +missing-member-max-choices=1 + +[SPELLING] + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# List of comma separated words that should be considered directives if they +# appear and the beginning of a comment and should not be checked. +spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:,pragma:,# noinspection + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file=.pyenchant_pylint_custom_dict.txt + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + +# Limits count of emitted suggestions for spelling mistakes. +max-spelling-suggestions=2 + + +[DESIGN] + +# Maximum number of arguments for function / method +max-args = 9 + +# Maximum number of locals for function / method body +max-locals = 19 + +# Maximum number of return / yield for function / method body +max-returns=11 + +# Maximum number of branch for function / method body +max-branches = 20 + +# Maximum number of statements in function / method body +max-statements = 50 + +# Maximum number of attributes for a class (see R0902). +max-attributes=11 + +# Maximum number of statements in a try-block +max-try-statements = 7 + +[CLASSES] + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__,__new__,setUp,__post_init__ + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=mcs + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict,_fields,_replace,_source,_make + +# Warn about protected attribute access inside special methods +check-protected-access-in-special-methods=no + +[IMPORTS] + +# List of modules that can be imported at any level, not just the top level +# one. +allow-any-import-level= + +# Allow wildcard imports from modules that define __all__. +allow-wildcard-with-all=no + +# Allow explicit reexports by alias from a package __init__. +allow-reexport-from-package=no + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + +# Deprecated modules which should not be used, separated by a comma +deprecated-modules=regsub,TERMIOS,Bastion,rexec + +# Create a graph of every (i.e. internal and external) dependencies in the +# given file (report RP0402 must not be disabled) +import-graph= + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled) +ext-import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled) +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant + +# Couples of modules and preferred modules, separated by a comma. +preferred-modules= + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "Exception" +overgeneral-exceptions=builtins.Exception + + +[TYPING] + +# Set to ``no`` if the app / library does **NOT** need to support runtime +# introspection of type annotations. If you use type annotations +# **exclusively** for type checking of an application, you're probably fine. +# For libraries, evaluate if some users what to access the type hints at +# runtime first, e.g., through ``typing.get_type_hints``. Applies to Python +# versions 3.7 - 3.9 +runtime-typing = no + + +[DEPRECATED_BUILTINS] + +# List of builtins function names that should not be used, separated by a comma +bad-functions=map,input + + +[REFACTORING] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + +# Complete name of functions that never returns. When checking for +# inconsistent-return-statements if a never returning function is called then +# it will be considered as an explicit return statement and no message will be +# printed. +never-returning-functions=sys.exit,argparse.parse_error + + +[STRING] + +# This flag controls whether inconsistent-quotes generates a warning when the +# character used as a quote delimiter is used inconsistently within a module. +check-quote-consistency=no + +# This flag controls whether the implicit-str-concat should generate a warning +# on implicit string concatenation in sequences defined over several lines. +check-str-concat-over-line-jumps=no + + +[CODE_STYLE] + +# Max line length for which to sill emit suggestions. Used to prevent optional +# suggestions which would get split by a code formatter (e.g., black). Will +# default to the setting for ``max-line-length``. +#max-line-length-suggestions= diff --git a/.style.yapf b/.style.yapf new file mode 100644 index 0000000..ecc2642 --- /dev/null +++ b/.style.yapf @@ -0,0 +1,397 @@ +[style] +# Align closing bracket with visual indentation. +align_closing_bracket_with_visual_indent=False + +# Allow dictionary keys to exist on multiple lines. For example: +# +# x = { +# ('this is the first element of a tuple', +# 'this is the second element of a tuple'): +# value, +# } +allow_multiline_dictionary_keys=False + +# Allow lambdas to be formatted on more than one line. +allow_multiline_lambdas=False + +# Allow splitting before a default / named assignment in an argument list. +allow_split_before_default_or_named_assigns=True + +# Allow splits before the dictionary value. +allow_split_before_dict_value=True + +# Let spacing indicate operator precedence. For example: +# +# a = 1 * 2 + 3 / 4 +# b = 1 / 2 - 3 * 4 +# c = (1 + 2) * (3 - 4) +# d = (1 - 2) / (3 + 4) +# e = 1 * 2 - 3 +# f = 1 + 2 + 3 + 4 +# +# will be formatted as follows to indicate precedence: +# +# a = 1*2 + 3/4 +# b = 1/2 - 3*4 +# c = (1+2) * (3-4) +# d = (1-2) / (3+4) +# e = 1*2 - 3 +# f = 1 + 2 + 3 + 4 +# +arithmetic_precedence_indication=False + +# Number of blank lines surrounding top-level function and class +# definitions. +blank_lines_around_top_level_definition=2 + +# Number of blank lines between top-level imports and variable +# definitions. +blank_lines_between_top_level_imports_and_variables=1 + +# Insert a blank line before a class-level docstring. +blank_line_before_class_docstring=False + +# Insert a blank line before a module docstring. +blank_line_before_module_docstring=False + +# Insert a blank line before a 'def' or 'class' immediately nested +# within another 'def' or 'class'. For example: +# +# class Foo: +# # <------ this blank line +# def method(): +# ... +blank_line_before_nested_class_or_def=True + +# Do not split consecutive brackets. Only relevant when +# dedent_closing_brackets is set. For example: +# +# call_func_that_takes_a_dict( +# { +# 'key1': 'value1', +# 'key2': 'value2', +# } +# ) +# +# would reformat to: +# +# call_func_that_takes_a_dict({ +# 'key1': 'value1', +# 'key2': 'value2', +# }) +coalesce_brackets=False + +# The column limit. +column_limit=120 + +# The style for continuation alignment. Possible values are: +# +# - SPACE: Use spaces for continuation alignment. This is default behavior. +# - FIXED: Use fixed number (CONTINUATION_INDENT_WIDTH) of columns +# (ie: CONTINUATION_INDENT_WIDTH/INDENT_WIDTH tabs or +# CONTINUATION_INDENT_WIDTH spaces) for continuation alignment. +# - VALIGN-RIGHT: Vertically align continuation lines to multiple of +# INDENT_WIDTH columns. Slightly right (one tab or a few spaces) if +# cannot vertically align continuation lines with indent characters. +continuation_align_style=SPACE + +# Indent width used for line continuations. +continuation_indent_width=4 + +# Put closing brackets on a separate line, dedented, if the bracketed +# expression can't fit in a single line. Applies to all kinds of brackets, +# including function definitions and calls. For example: +# +# config = { +# 'key1': 'value1', +# 'key2': 'value2', +# } # <--- this bracket is dedented and on a separate line +# +# time_series = self.remote_client.query_entity_counters( +# entity='dev3246.region1', +# key='dns.query_latency_tcp', +# transform=Transformation.AVERAGE(window=timedelta(seconds=60)), +# start_ts=now()-timedelta(days=3), +# end_ts=now(), +# ) # <--- this bracket is dedented and on a separate line +dedent_closing_brackets=False + +# Disable the heuristic which places each list element on a separate line +# if the list is comma-terminated. +disable_ending_comma_heuristic=False + +# Place each dictionary entry onto its own line. +each_dict_entry_on_separate_line=True + +# Require multiline dictionary even if it would normally fit on one line. +# For example: +# +# config = { +# 'key1': 'value1' +# } +force_multiline_dict=False + +# The regex for an i18n comment. The presence of this comment stops +# reformatting of that line, because the comments are required to be +# next to the string they translate. +i18n_comment=#\..* + +# The i18n function call names. The presence of this function stops +# reformattting on that line, because the string it has cannot be moved +# away from the i18n comment. +i18n_function_call=N_, _ + +# Indent blank lines. +indent_blank_lines=False + +# Put closing brackets on a separate line, indented, if the bracketed +# expression can't fit in a single line. Applies to all kinds of brackets, +# including function definitions and calls. For example: +# +# config = { +# 'key1': 'value1', +# 'key2': 'value2', +# } # <--- this bracket is indented and on a separate line +# +# time_series = self.remote_client.query_entity_counters( +# entity='dev3246.region1', +# key='dns.query_latency_tcp', +# transform=Transformation.AVERAGE(window=timedelta(seconds=60)), +# start_ts=now()-timedelta(days=3), +# end_ts=now(), +# ) # <--- this bracket is indented and on a separate line +indent_closing_brackets=False + +# Indent the dictionary value if it cannot fit on the same line as the +# dictionary key. For example: +# +# config = { +# 'key1': +# 'value1', +# 'key2': value1 + +# value2, +# } +indent_dictionary_value=True + +# The number of columns to use for indentation. +indent_width=4 + +# Join short lines into one line. E.g., single line 'if' statements. +join_multiple_lines=False + +# Do not include spaces around selected binary operators. For example: +# +# 1 + 2 * 3 - 4 / 5 +# +# will be formatted as follows when configured with "*,/": +# +# 1 + 2*3 - 4/5 +no_spaces_around_selected_binary_operators= + +# Use spaces around default or named assigns. +spaces_around_default_or_named_assign=False + +# Adds a space after the opening '{' and before the ending '}' dict delimiters. +# +# {1: 2} +# +# will be formatted as: +# +# { 1: 2 } +spaces_around_dict_delimiters=False + +# Adds a space after the opening '[' and before the ending ']' list delimiters. +# +# [1, 2] +# +# will be formatted as: +# +# [ 1, 2 ] +spaces_around_list_delimiters=False + +# Use spaces around the power operator. +spaces_around_power_operator=False + +# Use spaces around the subscript / slice operator. For example: +# +# my_list[1 : 10 : 2] +spaces_around_subscript_colon=False + +# Adds a space after the opening '(' and before the ending ')' tuple delimiters. +# +# (1, 2, 3) +# +# will be formatted as: +# +# ( 1, 2, 3 ) +spaces_around_tuple_delimiters=False + +# The number of spaces required before a trailing comment. +# This can be a single value (representing the number of spaces +# before each trailing comment) or list of values (representing +# alignment column values; trailing comments within a block will +# be aligned to the first column value that is greater than the maximum +# line length within the block). For example: +# +# With spaces_before_comment=5: +# +# 1 + 1 # Adding values +# +# will be formatted as: +# +# 1 + 1 # Adding values <-- 5 spaces between the end of the statement and comment +# +# With spaces_before_comment=15, 20: +# +# 1 + 1 # Adding values +# two + two # More adding +# +# longer_statement # This is a longer statement +# short # This is a shorter statement +# +# a_very_long_statement_that_extends_beyond_the_final_column # Comment +# short # This is a shorter statement +# +# will be formatted as: +# +# 1 + 1 # Adding values <-- end of line comments in block aligned to col 15 +# two + two # More adding +# +# longer_statement # This is a longer statement <-- end of line comments in block aligned to col 20 +# short # This is a shorter statement +# +# a_very_long_statement_that_extends_beyond_the_final_column # Comment <-- the end of line comments are aligned based on the line length +# short # This is a shorter statement +# +spaces_before_comment=2 + +# Insert a space between the ending comma and closing bracket of a list, +# etc. +space_between_ending_comma_and_closing_bracket=False + +# Use spaces inside brackets, braces, and parentheses. For example: +# +# method_call( 1 ) +# my_dict[ 3 ][ 1 ][ get_index( *args, **kwargs ) ] +# my_set = { 1, 2, 3 } +space_inside_brackets=False + +# Split before arguments +split_all_comma_separated_values=False + +# Split before arguments, but do not split all subexpressions recursively +# (unless needed). +split_all_top_level_comma_separated_values=False + +# Split before arguments if the argument list is terminated by a +# comma. +split_arguments_when_comma_terminated=False + +# Set to True to prefer splitting before '+', '-', '*', '/', '//', or '@' +# rather than after. +split_before_arithmetic_operator=False + +# Set to True to prefer splitting before '&', '|' or '^' rather than +# after. +split_before_bitwise_operator=False + +# Split before the closing bracket if a list or dict literal doesn't fit on +# a single line. +split_before_closing_bracket=True + +# Split before a dictionary or set generator (comp_for). For example, note +# the split before the 'for': +# +# foo = { +# variable: 'Hello world, have a nice day!' +# for variable in bar if variable != 42 +# } +split_before_dict_set_generator=False + +# Split before the '.' if we need to split a longer expression: +# +# foo = ('This is a really long string: {}, {}, {}, {}'.format(a, b, c, d)) +# +# would reformat to something like: +# +# foo = ('This is a really long string: {}, {}, {}, {}' +# .format(a, b, c, d)) +split_before_dot=False + +# Split after the opening paren which surrounds an expression if it doesn't +# fit on a single line. +split_before_expression_after_opening_paren=False + +# If an argument / parameter list is going to be split, then split before +# the first argument. +split_before_first_argument=False + +# Set to True to prefer splitting before 'and' or 'or' rather than +# after. +split_before_logical_operator=False + +# Split named assignments onto individual lines. +split_before_named_assigns=True + +# Set to True to split list comprehensions and generators that have +# non-trivial expressions and multiple clauses before each of these +# clauses. For example: +# +# result = [ +# a_long_var + 100 for a_long_var in xrange(1000) +# if a_long_var % 10] +# +# would reformat to something like: +# +# result = [ +# a_long_var + 100 +# for a_long_var in xrange(1000) +# if a_long_var % 10] +split_complex_comprehension=True + +# The penalty for splitting right after the opening bracket. +split_penalty_after_opening_bracket=300 + +# The penalty for splitting the line after a unary operator. +split_penalty_after_unary_operator=10000 + +# The penalty of splitting the line around the '+', '-', '*', '/', '//', +# ``%``, and '@' operators. +split_penalty_arithmetic_operator=300 + +# The penalty for splitting right before an if expression. +split_penalty_before_if_expr=0 + +# The penalty of splitting the line around the '&', '|', and '^' +# operators. +split_penalty_bitwise_operator=300 + +# The penalty for splitting a list comprehension or generator +# expression. +split_penalty_comprehension=2100 + +# The penalty for characters over the column limit. +split_penalty_excess_character=7000 + +# The penalty incurred by adding a line split to the unwrapped line. The +# more line splits added the higher the penalty. +split_penalty_for_added_line_split=30 + +# The penalty of splitting a list of "import as" names. For example: +# +# from a_very_long_or_indented_module_name_yada_yad import (long_argument_1, +# long_argument_2, +# long_argument_3) +# +# would reformat to something like: +# +# from a_very_long_or_indented_module_name_yada_yad import ( +# long_argument_1, long_argument_2, long_argument_3) +split_penalty_import_names=0 + +# The penalty of splitting the line around the 'and' and 'or' +# operators. +split_penalty_logical_operator=300 + +# Use the Tab character for indentation. +use_tabs=False diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..41dd9eb --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,127 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..7f4f0d1 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,61 @@ +# Contributing to PETML + +Thank you for investing your time in contributing to PETML! + +Read our [Code of Coduct](./CODE_OF_CONDUCT.md) to keep our community approachable and respectable. + +This guide details how to use issues and pull requests to improve PETML. + +## General Guidelines + +### Pull Requests + +Make sure to keep Pull Requests small and functional to make them easier to review, understand, and look up in commit history. This repository uses "Squash and Commit" to keep our history clean and make it easier to revert changes based on PR. + +Adding the appropriate documentation, unit tests and e2e tests as part of a feature is the responsibility of the feature owner, whether it is done in the same Pull Request or not. + +Pull Requests should follow the "subject: message" format, where the subject describes what part of the code is being modified. + +Refer to the template for more information on what goes into a PR description. + +### Design Docs + +A contributor proposes a design with a PR on the repository to allow for revisions and discussions. If a design needs to be discussed before formulating a document for it, make use of Google doc and GitHub issue to involve the community on the discussion. + +### GitHub Issues + +GitHub Issues are used to file bugs, work items, and feature requests with actionable items/issues (Please refer to the "Reporting Bugs/Feature Requests" section below for more information). + +### Reporting Bugs/Feature Requests + +We welcome you to use the GitHub issue tracker to report bugs or suggest features that have actionable items/issues (as opposed to introducing a feature request on GitHub Discussions). + +When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already reported the issue. Please try to include as much information as you can. Details like these are incredibly useful: + +- A reproducible test case or series of steps +- The version of the code being used +- Any modifications you've made relevant to the bug +- Anything unusual about your environment or deployment + +## Contributing via Pull Requests + +### Find interesting issue + +If you spot a problem with the problem, [search if an issue already exists](https://github.com/tiktok-privacy-innovation/PETML/issues). If a related issue doesn't exist, you can open a new issue using [issue template](https://github.com/tiktok-privacy-innovation/PETML/issues/new/choose). + +### Solve an issue + +Please check `DEVELOPMENT.md` in sub folder to get familar with running and testing codes. + +### Open a Pull request. + +When you're done making the changes, open a pull request and fill PR template so we can better review your PR. The template helps reviewers understand your changes and the purpose of your pull request. + +Don't forget to link PR to issue if you are solving one. + +If you run into any merge issues, checkout this [git tutorial](https://lab.github.com/githubtraining/managing-merge-conflicts) to help you resolve merge conflicts and other issues. + + +## Finding contributions to work on + +Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' and 'good first issue' issues are a great place to start. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..b09cd78 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ +Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000..ee950f5 --- /dev/null +++ b/README.md @@ -0,0 +1,85 @@ +# PETML +Privacy-Enhance-Technology Machine learning (PETML) is an open-source framework facilitates data collaboration among +multi-parties while ensuring data security and privacy. It allows users to smoothly construct and develop federated +learning algorithms or privacy-preserving machine learning. PETML supports the ability to construct an algorithm +workflow that integrates components such as data processing, model training, and model prediction, thereby offering +a comprehensive suite for algorithm development requirements. PETML leverages the capability of +[PETAce](https://github.com/tiktok-privacy-innovation/PETAce) to deliver the foundational secure computing protocol, +thereby facilitating secure computational operations. + + +![framework](./images/framework.jpeg) +The above figure illustrates the framework of PETML. The infra layer encapsulates fundamental components like MPC, Network and storage, +offering the foundational capabilities required for the algorithm layer. This algorithm layer is responsible for developing +the core algorithms. Finally, the operation layer serves as an interface with the platform layer, supplying the essential +algorithmic unit components for utilization by the platform or users. + +## Getting Started + +### Requirements: +|System|Toolchain| +|------|-------| +|Linux/Debian|Python (==3.9), pip(>=23.3.1)| + +## User installation + +PETAce must be installed first. Please refer to [PETAce](https://github.com/tiktok-privacy-innovation/PETAce) to build petace.whl package + +``` +pip install peatce.whl +``` + +Secondly, the user is required to utilize pip to install the dependencies enumerated in the requirements + +``` +pip install -r requirements.txt +``` + +Upon successful installation of the dependencies, PETML can be installed using + +``` +python3 setup.py install +``` + +## Quick Start + +We provide an example to help you quickly use PETML. Below is a quick case about federated Leiden algorithm. +> Note: If you wish to experiment with your own dataset or adjust any parameters, simply modify the corresponding +value in the configuration dictionary in examples/leiden.py. For more detailed information on the parameters, +please refer to the provided documents in the algorithm list section. Please remember to verify the consistency of +the data features provided by the two parties. If not, it is recommended to initially perform a Private Set Intersection (PSI) operation. + +``` +python3 examples/leiden.py -p party_a +python3 examples/leiden.py -p party_b +``` + + +## Algorithm list + +Federated preprocessing: PSI +Federated Machine Learning Algorithms: GBDT, graph clustering +Model Evaluation: Binary｜Regression evaluation + +| | Operator Name | Documentation | +|---------|:--------------------------------------------------------------------------------------------------------------------------------------------:|-----------------------------------------------------------------------| +| PSI | preprocessing.PSITransform | [PSI](./docs/user_guide/PSI.md) | +| Leiden | graph.LeidenTransform | [Federated Leiden](./docs/user_guide/Federated_Leiden.md) | +| XGBoost | boosting. XGBoostClassifierFit
boosting.XGBoostClassifierPredict
boosting.XGBoostRegressorFit
boosting.XGBoostRegressorPredict | [Horizontal SecureBoost](./docs/user_guide/Horizontal_SecureBoost.md) | + +More to come. + +## Contribution +Please check [Contributing](CONTRIBUTING.md) for more details. + +## Code of Conduct + +Please check [Code of Conduct](CODE_OF_CONDUCT.md) for more details. + +## License + +This project is licensed under the [Apache-2.0 License](LICENSE). + +## Disclaimer +This software is not an officially supported product of TikTok. It is provided as-is, without any guarantees or +warranties, whether express or implied. diff --git a/docs/user_guide/Federated_Leiden.md b/docs/user_guide/Federated_Leiden.md new file mode 100644 index 0000000..dd70dca --- /dev/null +++ b/docs/user_guide/Federated_Leiden.md @@ -0,0 +1,124 @@ +# Federated Leiden + +## Introduction +Community detection algorithms aim to identify groups of nodes in a network that are more densely connected than the +rest of the network. One such algorithm is the Leiden algorithm, which is a refinement of the Louvain algorithm. + +In the cross-party scenario, the network is distributed among multiple parties, each responsible for handling a specific +portion of the global graph. The federated Leiden algorithm enables community detection across parties while protecting +data privacy. + +## Leiden Algorithm +The problem of community detection requires the partition of a graph into communities of densely connected vertices, with +the vertices belonging to different communities being only sparsely connected. The Leiden algorithm is based on the Louvain +algorithm. It maximizes modularity (see Eq(1) in [[1]](#reference)) by joining vertices into communities when it increases +modularity, which is conducted in two iteratively repeating phases: **Move nodes** and **Aggregate nodes**. More details +of Leiden can be found in [[1]](#reference). + +## Details of Federated Leiden +### Setting +#### Scenario setting +Our proposal is designed for two parties (A & B), to jointly build a secure Leiden model. Under this scenario, each party +can only observe a small subgraph of the network. +![graph](./../../images/graph.png) +From the diagram above, the entire network $G$ is composed of sub-networks $G_A$ and $G_B$ from two parties. We refer +to the vertices that connect sub-networks as '**ghost vertices**', while the remaining ones are termed '**local vertices**'. +Edges that link vertices within the same party are established in plaintext locally. While, edges that link vertices across +different parties exist in the form of secret shares, distributed within the two involved parties. For vertices belonging +to party B, party A only knows the de-identified vertex information. The same applies to party A. + +#### Security setting +To ensure a secure two-party computation, we will employ the MPC protocol of [PETAce](https://github.com/tiktok-privacy-innovation/PETAce) +in a semi-honest adversarial environment. PETAce provides computation protocols under two-party settings. The protocols include: +- Secure 2-party Addition: $[z]=[x]+[y]$ +- Secure 2-party Multiplication: $[z]=[x]\cdot[y]$ +- Secure 2-party Division: $[z]=[x]/[y]$ +- Secure 2-party Argmax: $[z]=argmax([x])$ + +### Algorithm Details +The Federated Leiden algorithm is also conducted in two iteratively repeating phases. We will use MPC to do calculation +in both phases when the vertex is ghost vertex. + +**Move nodes:** +The algorithm behaves differently based on the parties involved in the vertices. If all vertices belong to the same party, +the computation is executed in plaintext, as outlined in [[1]](#reference)). On the other hand, if vertices come from different +parties, the computation of modularity is carried out using MPC. Lastly, the secure two-party argmax function is used to decide the +direction of movement for the node that maximizes the value of modularity. + +**Aggregate nodes:** +We call a vertex **cross-party** if it is aggregated from vertices of different parties. Similar to the step in the Move nodes +phase, if all vertices belong to the same party, the computation is executed in plaintext. If vertices from multiple parties +are involved, secure 2-party addition is used to aggregate values derived from the edges within the network. + +We are preparing a detailed whitepaper that will provide in-depth information about the algorithm. Please stay tuned if you are interested. + + +## Config + +### Module Name +``` +petml.operators.graph.LeidenTansform +``` + +### Model Parameters +| Name | Type | Description | Default | +|-----------|------|-------------------------------------|---------| +| ```max_move``` | int | The limit times of local node move | 20 | +| ```max_merge``` | int | The limit times of merge meta nodes | 10 | + +### Input +| Name | File Type | Description | +|--------------|-----------|--------------------------------| +| user_weights | json | The weight of user-user weight | +| local_nodes | json | The local nodes of each party | + +### Output + +| Name | File Type | Description | +|---------|-----------|--------------------------------| +| cluster | csv | The result of Leiden algorithm | + +### Examples +``` + config = { + "common": { + "max_move": 20, + "max_merge": 10, + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "user_weights": "examples/data/karate_club_user_weight1.json", + "local_nodes": "examples/data/karate_club_local_nodes1.json", + }, + "outputs": { + "cluster": "tmp/server/cluster.csv" + } + }, + "party_b": { + "inputs": { + "user_weights": "examples/data/karate_club_user_weight2.json", + "local_nodes": "examples/data/karate_club_local_nodes2.json", + }, + "outputs": { + "cluster": "tmp/client/cluster.csv" + } + } +} + +#if run the code in party a, the party should be 'party_a' and vice versa +operator = petml.operators.graph.LeidenTansform(party) +operator.run(config_map) +``` + +## Reference +[1] [From Louvain to Leiden: guaranteeing well-connected communities](https://www.nature.com/articles/s41598-019-41695-z.pdf) diff --git a/docs/user_guide/Horizontal_SecureBoost.md b/docs/user_guide/Horizontal_SecureBoost.md new file mode 100644 index 0000000..e3b0a3a --- /dev/null +++ b/docs/user_guide/Horizontal_SecureBoost.md @@ -0,0 +1,290 @@ +# Horizontal SecureBoost + +## Introduction +PETML implements **secure gradient descent decision tree model** training and inference using **secure multi-party computation**. +It currently supports training for regression problems and binary classification problems within two parties. + +## XGBoost Algorithm +Gradient Boosting Decision Tree (GBDT) is a widely used statistical model for classification and regression problems. +We sketch the key steps of XGBoost below and refer readers to the paper [[1]](#references) for details. + + +According to [[1]](#references), the steps of building one tree are: + +- Step 1: Based on the loss function, the first derivative $g_i$ and the second derivative $h_i$ for each sample are +calculated by ground truth label and current predicted values. Taking logistic regression as an example, +$g_i= sigmoid(y_{pred_i}) - y_{truth_i}$$ and $$h_i = sigmoid(y_{pred_i})*(1 - sigmoid(y_{pred_i}))$. + +- Step 2: The sample that yields the largest gain is selected to perform the split in the current node. +This is achieved by enumerating all samples under each feature. (Please refer to the Eq (7) in the paper [[1]](#references).) + +- Step 3: Upon identifying the split points, the data is split into two parts, which are then allocated to the left and +right subtrees respectively. The training continues to the next level, repeating Step 2 until the conditions for tree growth +termination are fulfilled. + +- Step 4: The weight of the leaf node is determined by the samples that fall into it. It is computed using the leaf weight +formula Eq (5) in [[1]](#references). + + +## Secure-XGBoost Algorithm +To ensure no sample information is leaked, we use secure two-party computation protocol to implement the addition, +multiplication, comparison, and other necessary operations in the training and inference algorithms of GBDT. +The split feature, split points, and leaf weight values in the tree models are saved in the format of secure shares to +ensure that no information is leaked. We implement Secure-XGBoost using [PETAce](https://github.com/tiktok-privacy-innovation/PETAce), +which provides implementations of the underlying MPC protocols. + +In short, our secure-XGBoost algorithm replaces the plaintext operations of XGBoost sketched above with their corresponding +secure two-party computation versions, except Step 3. In Step 3, data splitting could lead to uneven data sizes in the left +and right subtrees depending on the input data distribution. To remove such leakage, we use all samples to calculate the gain +at every split node in each layer of the tree. + +To achieve this, we use an additional secret sharing vector which has the same length as samples and can be regarded as +the index array of the training samples, denoted as $V$. The initial value for all elements in $V$ at the root node +will be set to 1. After identifying the optimal split point through gain calculation, we employ secret sharing comparisons +with secure 2-party selection protocol to identify samples that are larger than the split point. Then, based on the index, +we locate the corresponding positions of these samples in the $$V$$ and set them to 0. Here, we have the new index array +$V_{left}$ for left subtree. The same steps are repeated for the right subtree. During the subtree calculations, +we multiply $g$ and $h$ with the $V$ by secure 2-party multiplication. + +The growth of the tree stops when it reaches the maximum depth set by the user. Note that different datasets may result +in different tree structures. To prevent potential attacks based on the structure of the tree, we construct a full binary +tree. If the tree cannot find the optimal split in the current node, it will use the split information from its parent node +to ensure accurate results. + +The key steps of building one secure tree are: +- Step 1: The first derivative $g_i$ and the second derivative $h_i$ for each sample are computed using the secret sharing-based +MPC protocol. Using logistic regression as an example, secure 2-party operations such as sigmoid, multiplication, and +subtraction are used. +- Step 2: The formula employed for the computation of gain incorporates secure 2-party addition, multiplication, and division at each iteration. +- Step 3: As discussed above. +- Step 4: The formula employed for the computation of leaf weight incorporates secure 2-party addition, and division. + +![Training process](./../../images/secureboost_train.jpeg) +
Figure 1: Tree-building process
+ +Assume we have a dataset with only one feature $f_1$. The tree identifies the optimal +split value in the first layer by enumerating all samples and obtaining a result with the split value 0.5. Using the secure +two-party comparison protocol, we can derive two index arrays, $V_{left} = [1,1,1,1,1,0,0,0]$ and $V_{right} =[0,0,0,0,0,1,1,1]$. +In the second layer of the left node, we multiply all samples with $V_{left}$ to identify the samples that fall into the left node +and repeat the progress in the first layer to find the optimal split value. The tree is constructed by repeating this process. + +## Model Secure-Inference +To mitigate the information leakage, all inference samples have to do secret comparisons with all decision nodes in each +tree. By sum of all the edges in the tree from the root to that node by using a secure 2-party addition protocol, we can +generate a secret vector $S$ that represents all paths to the leaf nodes as in Figure 2. Then, comparing secret vector +$S$ with the depth of the tree using a secure 2-party comparison protocol, we get the inference result corresponding to the sample. + +![Training process](./../../images/secureboost_inference.jpeg) +
Figure 2: Model inferencing process
+Assume we have an inference sample with the feature $f_1=4$. This value will be +compared with all decision nodes in the tree using the secure comparison protocol. Summing up the output of the comparison +via secure 2-party addition across all root-to-leave paths results in the vector$[0,1,1,2,1,2,2,3]$. By comparing this +result with the depth (=3) of the tree, we obtain the result indicator array $[0,0,0,0,0,0,0,1]$, namely the inferencing +result is the last leave node. + +## Classifier Config +### Training +#### Module Name +``` +petml.operators.boosting.XGBoostClassifierFit +``` + +#### Module Parameters +| Name | Type | Description | Default | +|-------------------------|-------|-----------------------------------------------------------------------------------------------------------------------------------|----------| +| ```min_split_loss``` | float | The minimum number of gain to split an internal node | 1e-5 | +| ```learning_rate``` | float | Learning rate shrinks the contribution of each tree by ```learning_rate``` | 0.1 | +| ```n_estimators``` | int | The number of boosting stages to perform | 100 | +| ```base_score``` | float | Initial value of $y_hat$ | 0.5 | +| ```max_depth``` | int | Maximum depth of the tree | 3 | +| ```reg_alpha``` | float | L1 regularization term on weights | 0. | +| ```reg_lambda``` | float | L2 regularization term on weights | 1. | +| ```min_child_samples``` | int | The minimum number of samples required to be at a leaf node | 1 | +| ```min_child_weight``` | float | The minimum sum of instance weight (hessian) needed in a child | 0.5 | +| ```test_size``` | float | Size of eval dataset of input data | 0.3 | +| ```eval_epochs``` | int | Calculating the evaluation metric after every certain number of epochs | 10 | +| ```eval_threshold``` | float | Regard the instances with eval prediction value larger than threshold as positive instances, and the others as negative instances | 0.5 | +| ```objective``` | str | The loss function to be optimized | logitraw | + + +#### Input +| Name | File Type | Description | +| --- |-----------| --- | +| train_data | csv | The training dataset | + +#### Output + +| Name | File Type | Description | +| --- |-----------| --- | +| model_path | pkl | The trained model | + +#### Examples +``` +config = { + "common": { + "objective": "logitraw", + "n_estimators": 10, + "max_depth": 3, + "reg_lambda": 1, + "reg_alpha": 0.0, + "min_child_weight": 0.1, + "base_score": 0.5, + "learning_rate": 0.1, + "network_mode": "petnet", + "network_scheme": "socket", + "label_name": "label", + "test_size": 0.3, + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "train_data": "data0.csv", + }, + "outputs": { + "model_path": "model_name0.pkl" + } + }, + "party_b": { + "inputs": { + "train_data": "data1.csv", + }, + "outputs": { + "model_path": "model_name1.pkl" + } + } +} + +#if run the code in party a, the party should be 'party_a' and vice versa +operator = petml.operator.boosting.XGBoostClassifierFit(party) +operator.run(config_map) +``` + +### Inference +#### Module Name +``` +petml.operators.boosting.XGBoostClassifierPredict +``` + +#### Input +| Name | File Type | Description | +|--------------|-----------|-----------------------| +| predict_data | csv | The inference dataset | +| model_path | pkl | The trained model | + + +#### Output + +| Name | File Type | Description | +|--------------------|-----------|-------------------------| +| inference_res_path | csv | The result of inference | + + +#### Examples +``` +config = { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "predict_data": "data0.csv", + "model_path": "model_name0.pkl" + }, + "outputs": { + "inference_res_path": "predict0.csv" + } +}, +"party_b": { + "inputs": { + "predict_data": "data1.csv", + "model_path": "model_name1.pkl" + }, + "outputs": { + "inference_res_path": "predict1.csv" + } +} +} + +#if run the code in party a, the party should be 'party_a' and vice versa +operator = petml.operator.boosting.XGBoostClassifierPredict(party) +operator.run(config_map) +``` + +## Classifier Config +### Training +#### Module Name +``` +petml.operators.boosting.XGBoostRegressorFit +``` +#### Module Parameters +| Name | Type | Description | Default | +|-------------------------|-------|-----------------------------------------------------------------------------------------------------------------------------------|--------------| +| ```min_split_loss``` | float | The minimum number of gain to split an internal node | 1e-5 | +| ```learning_rate``` | float | Learning rate shrinks the contribution of each tree by ```learning_rate``` | 0.1 | +| ```n_estimators``` | int | The number of boosting stages to perform | 100 | +| ```base_score``` | float | Initial value of $y_hat$ | 0.5 | +| ```max_depth``` | int | Maximum depth of the tree | 3 | +| ```reg_alpha``` | float | L1 regularization term on weights | 0. | +| ```reg_lambda``` | float | L2 regularization term on weights | 1. | +| ```min_child_samples``` | int | The minimum number of samples required to be at a leaf node | 1 | +| ```min_child_weight``` | float | The minimum sum of instance weight (hessian) needed in a child | 1 | +| ```test_size``` | float | Size of eval dataset of input data | 0.3 | +| ```eval_epochs``` | int | Calculating the evaluation metric after every certain number of epochs | 10 | +| ```eval_threshold``` | float | Regard the instances with eval prediction value larger than threshold as positive instances, and the others as negative instances | 0.5 | +| ```objective``` | str | The loss function to be optimized | squarederror | + +#### Input +| Name | File Type | Description | +|------------|-----------|----------------------| +| train_data | csv | The training dataset | + +#### Output + +| Name | File Type | Description | +|------------|-----------|-------------------| +| model_path | pkl | The trained model | + +#### Examples +Refer to the examples in classifier training config + +### Inference +#### Module Name +``` +petml.operators.boosting.XGBoostRegressorPredict +``` + +#### Input +| Name | File Type | Description | +|--------------|-----------|-----------------------| +| predict_data | csv | The inference dataset | +| model_path | pkl | The trained model | + + +#### Output + +| Name | File Type | Description | +|--------------------|-----------|-------------------------| +| inference_res_path | csv | The result of inference | + + +#### Examples +Refer to the examples in classifier inference config + +## References +[1] [XGBoost: A Scalable Tree Boosting System](https://arxiv.org/abs/1603.02754) diff --git a/docs/user_guide/PSI.md b/docs/user_guide/PSI.md new file mode 100644 index 0000000..db3b275 --- /dev/null +++ b/docs/user_guide/PSI.md @@ -0,0 +1,76 @@ +# PSI + +## Introduction +In federated learning, a secure and dependable method is required to find any intersection in features or IDs between +datasets from two parties. The intersection data will then be used to update the model. We focus on the setting where +two parties (e.g., A & B) hold vertically partitioned data. They need to align their data before training the model. + +Private Set Intersection (PSI) protocol is well-suited to solve this problem. Our implementation relies on the elliptic +curve Diffie-Hellman PSI (ECDH-PSI) provided by [PETAce](https://github.com/tiktok-privacy-innovation/PETAce). +(More detailed information is available in the PETAce documentation.) +To improve user experience, we encapsulated the PSI function in PETAce, presenting it as an intersection module. +Users can simply upload a CSV file along with the names of the columns to be aligned, enabling them to obtain their +aligned data effortlessly. + +## Config + +### Module Name +``` +petml.operators.preprocessing.PSITransform +``` + +### Model Parameters +| Name | Type | Description | Default | +|-------------------|------|----------------------------------|---------| +| ```column_name``` | str | The column used to implement PSI | | + +### Input +| Name | File Type | Description | +|------|-----------|------------------| +| data | csv | The data for PSI | + +### Output + +| Name | File Type | Description | +|--------------|-----------|--------------------------| +| intersection | csv | The intersection of data | + +### Examples +``` +config = { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "column_name": "id", + "inputs": { + "data": "examples/data/breast_hetero_mini_server.csv", + }, + "outputs": { + "data": "tmp/server/data.csv" + } + }, + "party_b": { + "column_name": "id", + "inputs": { + "data": "examples/data/breast_hetero_mini_client.csv", + }, + "outputs": { + "data": "tmp/client/data.csv" + } + } +} + +#if run the code in party a, the party should be 'party_a' and vice versa +operator = petml.operators.preprocessing.PSITransform(party) +operator.run(config_map) +``` diff --git a/examples/data/breast_hetero_mini_client.csv b/examples/data/breast_hetero_mini_client.csv new file mode 100644 index 0000000..51eda0b 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z$X0qP_GS+hLCMJ-eE@neR%RJ82xY9fb7;YphTATa`H*qEqIu%dJOr5U4bp{r2Xl>K zcF^)>-Vp)pt`+2UAmeX{C>*HW?{7sa6SITavX=}CqG}E=`+PsSJch(OeZuvY67KF;3%PR1Fx% z$c<5inS;WyP(m)N(EZ4F)=;~wAQ!xEd5ol#;K&1*eS;QtATH7Zm|;`n>zmGx@xaRG zmEHBskH1%^%a(s_nj`HRT z4MipIX`!>$mvs`Rj}5u5U=6aLhz!I7pbSq_*sJnOE!#~pH~!+zsQd`!Ku{fb5$CgD(&=}xOVR??c8_ z9@f<^1iRmN9AS`R?Q!eN-KOc#fy_fea>D+GfqDejh8hhXIj-Aa>(a&Y()#ulL5kjo zi|%zobqQ57c$f@DnQ3)ZO?j<#psHWVg63`)E!8a&=&D$k8;59-prISkQ_(Sb`Y$GZ gocZ_n{Y%wO|6b4kI-LFQHT(CP{WE(2@@w*c0C8+DF8}}l literal 0 HcmV?d00001 diff --git a/petml/__init__.py b/petml/__init__.py new file mode 100644 index 0000000..6ce6a1b --- /dev/null +++ b/petml/__init__.py @@ -0,0 +1,21 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .version import __version__ +from . import operators + +__all__ = [ + "operators", + "__version__", +] diff --git a/petml/fl/__init__.py b/petml/fl/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/petml/fl/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/petml/fl/base.py b/petml/fl/base.py new file mode 100644 index 0000000..388885f --- /dev/null +++ b/petml/fl/base.py @@ -0,0 +1,27 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractmethod + +from petml.infra.common.log_utils import LoggerFactory + + +class FlBase(ABC): + + def __init__(self): + self.logger = LoggerFactory.get_logger(self.__class__.__name__) + + @abstractmethod + def set_infra(self, **kwargs): + pass diff --git a/petml/fl/boosting/__init__.py b/petml/fl/boosting/__init__.py new file mode 100644 index 0000000..7aba90d --- /dev/null +++ b/petml/fl/boosting/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .xgb_model import XGBoostClassifier, XGBoostRegressor diff --git a/petml/fl/boosting/decision_tree.py b/petml/fl/boosting/decision_tree.py new file mode 100644 index 0000000..c8a3b4d --- /dev/null +++ b/petml/fl/boosting/decision_tree.py @@ -0,0 +1,347 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +from petace.securenumpy import SecureArray +import petace.securenumpy as snp + + +class MPCTreeNode: + """ + Class for secure decision tree node + + Attributes + ---------- + is_leaf : Bool + Flag to check the node is leaf or not + + leaf_weight : float + Leaf weight value in secret share format of the current node + + split_feat : SecureArray + Best split feature in secret share format in current node + + split_val : SecureArray + Pplit value of the best split feature in secret share format in current node + + left_child : MPCTreeNode + Record the information of the left node of the current node. + + right_child : MPCTreeNode + Record the information of the right node of the current node. + """ + + def __init__(self, + is_leaf: bool = False, + leaf_weight: float = None, + split_feat: SecureArray = None, + split_val: SecureArray = None, + left_child: "MPCTreeNode" = None, + right_child: "MPCTreeNode" = None): + self.is_leaf = is_leaf + self.leaf_weight = leaf_weight + self.split_feat = split_feat + self.split_val = split_val + self.left_child = left_child + self.right_child = right_child + + +class MPCTree: + """ + Tree class + + Parameters + ---------- + reg_alpha : float, default=0. + L1 regularization term on weights + - values must be in the range `[0.0, inf)`. + + reg_lambda : float, default=1.0 + L2 regularization term on weights + Values must be in the range `[0.0, inf)`. + + min_child_samples : int, default=1 + The minimum number of samples required to be at a leaf node. + A split point at any depth will only be considered if it leaves at + least ``min_samples_leaf`` training samples in each of the left and + right branches. This may have the effect of smoothing the model, + especially in regression. + Values must be in the range `[1, inf)`. + + min_child_weight : float, default=1.0 + Minimum sum of instance weight (hessian) needed in a child. If the tree partition step results + in a leaf node with the sum of instance weight less than min_child_weight, then the building + process will give up further partitioning. + Values must be in the range `(0, inf)`. + + min_split_loss: float, default=1e-5 + The minimum number of gain to split an internal node: + Values must be in the range `[0.0, inf) + + max_depth : int or None, default=3 + Maximum depth of the tree. + If int, values must be in the range `[1, inf)`. + + columns: SecureArray + Encrypted column names in the training data to prevent leakage during training. + + Attributes + ---------- + root: MPCTreeNode + The root node of the tree + + """ + + def __init__(self, + reg_alpha: float = None, + reg_lambda: float = None, + min_child_weight: float = None, + min_child_samples: int = None, + min_split_loss: float = None, + max_depth: int = None, + columns: SecureArray = None): + + self.columns = columns + self.root = None + self.reg_alpha = reg_alpha + self.reg_lambda = reg_lambda + self.min_child_weight = min_child_weight + self.min_child_samples = min_child_samples + self.min_split_loss = min_split_loss + self.max_depth = max_depth + + def _calc_threshold(self, gsum): + """clip the value of gain""" + res = snp.where(gsum > self.reg_alpha, gsum - self.reg_alpha, + snp.where(gsum < -1. * self.reg_alpha, gsum + self.reg_alpha, snp.zeros(1))) + return res[0] + + def _calc_leaf_weight(self, best_gradient, best_hessian): + weight = -self._calc_threshold(best_gradient) / (best_hessian + self.reg_lambda) + return weight + + def _calc_split_gain(self, left_sum_grads, right_sum_grads, left_sum_hess, right_sum_hess): + + left_sum_grads_threshold = self._calc_threshold(left_sum_grads) + right_sum_grads_threshold = self._calc_threshold(right_sum_grads) + sum_grads_threshold = self._calc_threshold((left_sum_grads + right_sum_grads)) + + gain = left_sum_grads_threshold * left_sum_grads_threshold / ( + left_sum_hess + self.reg_lambda) + right_sum_grads_threshold * right_sum_grads_threshold / ( + right_sum_hess + self.reg_lambda) - sum_grads_threshold * sum_grads_threshold / ( + left_sum_hess + right_sum_hess + self.reg_lambda) + + return gain + + def _find_best_split(self, X, grads, hess, sum_grads, sum_hess, index_array): + """find the best split feature and value in current node""" + max_gain, best_feat, best_split_val, best_feat_idx = snp.zeros(1), snp.zeros(1), snp.zeros(1), snp.zeros(1) + + for feat_idx, item in enumerate(self.columns): + x = X[:, feat_idx] + filter_x = snp.reshape(x, (-1, 1)) * index_array + cur_feat_data = snp.hstack((filter_x, grads, hess)) + sorted_cur_feat_data = cur_feat_data.quick_sort_by_column(0) + left_sum_grads, left_sum_hess = np.array([0.]), np.array([0.]) + + for i in range(sorted_cur_feat_data.shape[0] - 1): + x_i = sorted_cur_feat_data[i, 0] + x_i_next = sorted_cur_feat_data[i + 1, 0] + grads_i = sorted_cur_feat_data[i, 1] + hess_i = sorted_cur_feat_data[i, 2] + left_sum_grads += grads_i + right_sum_grads = sum_grads - left_sum_grads + left_sum_hess += hess_i + right_sum_hess = sum_hess - left_sum_hess + + same_val_flag = snp.where( + snp.reshape(x_i, (-1,)) == snp.reshape(x_i_next, (-1,)), snp.zeros(1), snp.ones(1)) + + if (self.min_child_samples and (i + 1 < self.min_child_samples or + (len(x) - i - 1) < self.min_child_samples)): + continue + + gain = self._calc_split_gain(left_sum_grads, right_sum_grads, left_sum_hess, right_sum_hess) + gain = gain * same_val_flag[0] + if self.min_child_weight: + left_hess_flag = snp.where(left_sum_hess < self.min_child_weight, snp.zeros(left_sum_hess.shape), + snp.ones(left_sum_hess.shape)) + right_hess_flag = snp.where(right_sum_hess < self.min_child_weight, snp.zeros(right_sum_hess.shape), + snp.ones(right_sum_hess.shape)) + + gain = gain * left_hess_flag[0] * right_hess_flag[0] + + gain_condition = gain > max_gain + max_gain = snp.where(gain_condition, gain, max_gain) + best_feat = snp.where(gain_condition, snp.reshape(item, (-1,)), best_feat) + best_split_val = snp.where(gain_condition, snp.reshape(0.5 * (x_i + x_i_next), (-1,)), best_split_val) + best_feat_idx = snp.where(gain_condition, snp.ones(1) * feat_idx, best_feat_idx) + + best_feat = snp.where(max_gain < self.min_split_loss, snp.zeros(1), best_feat) + return best_feat, best_split_val, best_feat_idx + + def _create_leaf_node(self, grads, hess): + is_leaf = True + grads_sum = snp.reshape(snp.sum(grads), (-1,)) + hess_sum = snp.reshape(snp.sum(hess), (-1,)) + leaf_weight = self._calc_leaf_weight(grads_sum, hess_sum) + return MPCTreeNode(is_leaf=is_leaf, leaf_weight=leaf_weight) + + def build_tree(self, + X: SecureArray, + grads: SecureArray, + hess: SecureArray, + index_array: SecureArray, + current_depth: int, + last_layer_feat: SecureArray = None, + last_layer_feat_val: SecureArray = None, + last_layer_feat_idx: SecureArray = None): + """ + Build the tree node recursively. Will only stop when it reaches the maximum depth. + When the current node cannot find the optimal split point, it will use the value + of its parent node as a substitute. + + Parameters + ---------- + X : SecureArray + train data + + grads: SecureArray + Secret sharing data of first order derivative + + hess: SecureArray + Secret sharing data of second order derivative + + index_array: array or SecureArray + An array to record the sample is in current node or not + + current_depth: int + current depth of the node + + last_layer_feat: SecureArray or None + Best split feature in secret share format in its parent node + + last_layer_feat_val: SecureArray or None + Split value of the best feature in secret share format in its parent node + + last_layer_feat_idx: SecureArray or None + Best feature idx in secret share format in its parent node + + Returns + ---------- + sub_tre: MPCTreeNode + Trained tree + """ + cur_grads = grads * index_array + cur_hess = hess * index_array + + if self.max_depth - 1 < current_depth: + return self._create_leaf_node(cur_grads, cur_hess) + + sum_grads, sum_hess = snp.reshape(snp.sum(cur_grads), (-1,)), snp.reshape(snp.sum(cur_hess), (-1,)) + if current_depth == 0: + cur_child_samples = np.sum(index_array) + child_samples_flag = np.where(cur_child_samples < self.min_child_samples, np.zeros(1), np.ones(1)) + else: + cur_child_samples = snp.reshape(snp.sum(index_array), (-1,)) + child_samples_flag = snp.where(cur_child_samples < self.min_child_samples, + snp.zeros(cur_child_samples.shape), snp.ones(cur_child_samples.shape)) + + child_weight_flag = snp.where(sum_hess < self.min_child_weight, snp.zeros(sum_hess.shape), + snp.ones(sum_hess.shape)) + + best_feat, best_split_val, best_feat_idx = self._find_best_split( + X, cur_grads * child_weight_flag[0] * child_samples_flag[0], + cur_hess * child_weight_flag[0] * child_samples_flag[0], sum_grads, sum_hess, index_array) + + check_flag = best_feat * child_weight_flag * child_samples_flag + flag_compare_res = check_flag == 0 + + if current_depth == 0: + best_feat = snp.where(flag_compare_res, snp.reshape(self.columns[0], (1,)), best_feat) + best_split_val = snp.where(flag_compare_res, snp.min(X[:, 0]), best_split_val) + best_feat_idx = snp.where(flag_compare_res, snp.zeros(1), best_feat_idx) + else: + best_feat = snp.where(flag_compare_res, last_layer_feat, best_feat) + best_split_val = snp.where(flag_compare_res, last_layer_feat_val, best_split_val) + best_feat_idx = snp.where(flag_compare_res, last_layer_feat_idx, best_feat_idx) + + col_array = np.arange(self.columns.shape[0]) + col_array_matrix = np.tile(col_array, X.shape[0]).reshape(X.shape[0], -1) + get_fidx = snp.where(col_array_matrix == best_feat_idx[0], snp.ones(col_array_matrix.shape), + snp.zeros(col_array_matrix.shape)) + filter_x = X * get_fidx + x = snp.sum(filter_x, axis=1) + x = snp.reshape(x, (-1, 1)) + left_idx = snp.where(x < best_split_val[0], snp.ones(x.shape), snp.zeros(x.shape)) + right_idx = snp.where(x >= best_split_val[0], snp.ones(x.shape), snp.zeros(x.shape)) + left_idx = snp.reshape(left_idx, (-1, 1)) + right_idx = snp.reshape(right_idx, (-1, 1)) + + left_tree = self.build_tree(X, grads, hess, left_idx * index_array, current_depth + 1, best_feat, + best_split_val, best_feat_idx) + right_tree = self.build_tree(X, grads, hess, right_idx * index_array, current_depth + 1, best_feat, + best_split_val, best_feat_idx) + + sub_tree = MPCTreeNode(is_leaf=False, + leaf_weight=None, + split_feat=best_feat, + split_val=best_split_val, + left_child=left_tree, + right_child=right_tree) + + return sub_tree + + def predict_proba(self, tree_node: MPCTreeNode, data: SecureArray, columns: SecureArray, + last_layer_flag: SecureArray): + """ + Predict probabilities. Each piece of data will be compared with all nodes of the tree to ensure that + data information is not leaked. + + Parameters + ---------- + tree_node : MPCTreeNode + Node in tree + + data: SecureArray + Secret sharing of predict data + + columns: array or SecureArray + Columns in data + + last_layer_flag: SecureArray + Check the data is in current node or not + + Returns + ---------- + sub_tre: MPCTreeNode + Trained tree + """ + leaf_values = [] + if tree_node.is_leaf: + weight_flag = snp.where(last_layer_flag == self.max_depth, snp.ones(1), snp.zeros(1)) + leaf_values.append(tree_node.leaf_weight * weight_flag) + return leaf_values + + get_fidx = snp.where(columns == tree_node.split_feat[0], snp.ones(columns.shape), snp.zeros(columns.shape)) + + cur_col_data = data * get_fidx + cur_col_data = snp.sum(cur_col_data, axis=1) + + left_flag = snp.where(cur_col_data < tree_node.split_val, snp.ones(1), snp.zeros(1)) + right_flag = snp.where(cur_col_data >= tree_node.split_val, snp.ones(1), snp.zeros(1)) + leaf_values.extend(self.predict_proba(tree_node.left_child, data, columns, left_flag + last_layer_flag)) + leaf_values.extend(self.predict_proba(tree_node.right_child, data, columns, right_flag + last_layer_flag)) + + return leaf_values diff --git a/petml/fl/boosting/loss.py b/petml/fl/boosting/loss.py new file mode 100644 index 0000000..a6ad1c5 --- /dev/null +++ b/petml/fl/boosting/loss.py @@ -0,0 +1,133 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Union + +import numpy as np +from petace.securenumpy import SecureArray +import petace.securenumpy as snp +import petace.secureml as sml + + +class LogisticLoss: + """ + class for calculate logistic loss function + """ + + def _sigmoid(self, y_pred: np.ndarray): + """ + Implemented sigmoid equation + + Parameters + ---------- + y_pred : array, shape = (n_samples, ) + Predicted labels + + + Returns + ------- + values : float + """ + return 1.0 / (1.0 + np.exp(-y_pred) + 1e-16) + + def grad(self, y_pred: Union[SecureArray, np.ndarray], label: Union[SecureArray, np.ndarray]): + """ + First order derivative of the logistic loss function + + Parameters + ---------- + y_pred : array or SecureArray, shape = (n_samples, 1) + Predicted labels + + label : array-like, shape = (n_samples,1) + Ground truth (correct) labels + + Returns + ------- + values : array or SecureArray, shape = (n_samples, 1) + """ + if isinstance(y_pred, np.ndarray): + y_pred = self._sigmoid(y_pred) + return np.array(y_pred - label).reshape(-1, 1) + + y_pred = sml.sigmoid(y_pred) + return snp.reshape(y_pred - label, (-1, 1)) + + def hess(self, y_pred: Union[SecureArray, np.ndarray]): + """ + Second order derivative of the logistic loss function + + Parameters + ---------- + y_pred : array or SecureArray, shape = (n_samples, 1) + Predicted labels + + Returns + ------- + values : array or SecureArray, shape = (n_samples, 1) + """ + if isinstance(y_pred, np.ndarray): + y_pred = self._sigmoid(y_pred) + return np.array([max(i * (1.0 - i), 1e-16) for i in y_pred]).reshape(-1, 1) + + y_pred = sml.sigmoid(y_pred) + return snp.reshape(y_pred * (snp.ones(y_pred.shape) - y_pred), (-1, 1)) + + +class SquareLoss: + """ + class for calculate square loss function + """ + + def __init__(self): + pass + + def grad(self, y_pred: Union[SecureArray, np.ndarray], label: Union[SecureArray, np.ndarray]): + """ + First order derivative of the square loss function + + Parameters + ---------- + y_pred : array or SecureArray, shape = (n_samples, 1) + Predicted labels + + label : array-like, shape = (n_samples,1) + Ground truth (correct) labels + + Returns + ------- + values : array or SecureArray, shape = (n_samples, 1) + """ + if isinstance(y_pred, np.ndarray): + return np.reshape(y_pred - label, (-1, 1)) + + return y_pred - label + + def hess(self, y_pred: Union[SecureArray, np.ndarray]): + """ + Second order derivative of the square loss function + + Parameters + ---------- + y_pred : array or SecureArray, shape = (n_samples, 1) + Predicted labels + + Returns + ------- + values : array or SecureArray, shape = (n_samples, 1) + """ + if isinstance(y_pred, np.ndarray): + return np.ones_like(y_pred).reshape(-1, 1) + + return snp.ones(y_pred.shape) diff --git a/petml/fl/boosting/metric.py b/petml/fl/boosting/metric.py new file mode 100644 index 0000000..7b817bb --- /dev/null +++ b/petml/fl/boosting/metric.py @@ -0,0 +1,59 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from petace.securenumpy import SecureArray +import petace.securenumpy as snp + + +def error(y_pred: SecureArray, label: SecureArray): + """ + Binary classification error rate. It is calculated as #(wrong cases)/#(all cases). + + Parameters + ---------- + y_pred : SecureArray, shape = (n_samples, 1) + Predicted labels, as returned by a classifier's + predict method. + + label : SecureArray, shape = (n_samples, 1) + Ground truth (correct) labels + + Returns + ------- + error : float + """ + wrong_cond = snp.where(y_pred != label, snp.ones(shape=y_pred.shape), snp.zeros(shape=y_pred.shape)) + return sum(wrong_cond) / len(y_pred) + + +def mean_absolute_error(y_pred: SecureArray, label: SecureArray): + """ + Implementation of mean absolute error. + + Parameters + ---------- + y_pred : SecureArray, shape = (n_samples, 1) + Predicted probabilities, as returned by a classifier's + predict_proba method. + + label : SecureArray, shape = (n_samples, 1) + Ground truth (correct) labels + + Returns + ------- + error : float + """ + + absolut_error = snp.where(y_pred > label, y_pred - label, label - y_pred) + return snp.sum(absolut_error) / len(y_pred) diff --git a/petml/fl/boosting/xgb_model.py b/petml/fl/boosting/xgb_model.py new file mode 100644 index 0000000..d4c4077 --- /dev/null +++ b/petml/fl/boosting/xgb_model.py @@ -0,0 +1,879 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import hashlib +import pickle +import time + +import numpy as np +import pandas as pd +from petace.securenumpy import SecureArray +import petace.securenumpy as snp +from sklearn.model_selection import train_test_split + +from petml.fl.base import FlBase +from .decision_tree import MPCTree +from .loss import LogisticLoss, SquareLoss +from .metric import error, mean_absolute_error + + +class BaseXGB(FlBase): + """Base class for XGBoost""" + + def __init__(self, + min_split_loss: float, + learning_rate: float, + base_score: float, + max_depth: int, + min_child_samples: int, + min_child_weight: float, + reg_alpha: float = 0., + reg_lambda: float = 1.0, + label_name: str = 'label', + test_size: float = 0.): + super().__init__() + self.min_split_loss = min_split_loss + self.learning_rate = learning_rate + self.base_score = base_score + self.max_depth = max_depth + self.reg_alpha = reg_alpha + self.reg_lambda = reg_lambda + self.min_child_samples = min_child_samples + self.min_child_weight = min_child_weight + self.label_name = label_name + self.test_size = test_size + + self.party_id = None + self.peer_party = None + self._federation = None + self._mpc_engine = None + self.do_eval = False + + def set_infra(self, party_id, federation, mpc_engine): + self.party_id = party_id + self.peer_party = 1 - party_id + self._federation = federation + self._mpc_engine = mpc_engine + snp.set_vm(self._mpc_engine.engine) + + @staticmethod + def _set_hash_column(columns): + """ + Transform string column to numerical hashed values + + Parameters + ---------- + columns: array-like + Columns in train dataset + + Returns + ------- + hashed_column: array + Numerical hashed values of columns + """ + hash_set = set() + hashed_column = [] + for col in columns: + hash_value = hashlib.sha256(col.encode()).digest() + hash_float_value = int.from_bytes(hash_value[:8], 'big') % (2**16) + while hash_float_value in hash_set: + hash_float_value = (hash_float_value + 1) % (2**16) + hash_set.add(hash_float_value) + hashed_column.append(hash_float_value) + hashed_column = np.array(hashed_column) + return hashed_column + + def prepare_train_data(self, data): + """ + prepare_train_data + + Parameters + ---------- + data: DataFrame + Train data + + Returns + ------- + train_x_cipher: SecureArray, shape=(n_samples, k) + Secret sharing train data which concat data from two parties + + train_y: array + local true label + + train_y_cipher: SecureArray, shape=(n_samples, ) + Secret sharing data which concat train label from two parties + + eval_x_cipher: SecureArray + Secret sharing eval data which concat data from two parties + + eval_y: SecureArray, shape=(n_samples, ) + Secret sharing data which concat eval label from two parties + + train_column_cipher_party0: SecureArray, shape=(k, ) + Secret sharing data of columns + + hashed_column: array + Numerical hashed values of columns + """ + x = data[[col for col in data.columns if col != self.label_name]] + y = data['label'] + + columns = x.columns.tolist() + hashed_column = self._set_hash_column(columns) + + if self.test_size > 0: + self.do_eval = True + train_x, eval_x, train_y, eval_y = train_test_split(x.values, + y.values, + test_size=self.test_size, + random_state=42) + else: + train_x = x.reset_index(drop='True').values + train_y = y.values + eval_x = None + eval_y = None + + if self.party_id == 0: + train_x_party0 = train_x + train_x_party1 = None + train_y_party0 = train_y.reshape(-1, 1) + train_y_party1 = None + eval_x_party0 = eval_x + eval_x_party1 = None + eval_y_party0 = eval_y + eval_y_party1 = None + train_column_party0 = hashed_column + train_column_party1 = None + + else: + train_x_party0 = None + train_x_party1 = train_x + train_y_party0 = None + train_y_party1 = train_y.reshape(-1, 1) + eval_x_party0 = None + eval_x_party1 = eval_x + eval_y_party0 = None + eval_y_party1 = eval_y + train_column_party0 = None + train_column_party1 = hashed_column + + train_x_cipher_party0 = snp.array(train_x_party0, party=0) + train_x_cipher_party1 = snp.array(train_x_party1, party=1) + train_y_cipher_party0 = snp.array(train_y_party0, party=0) + train_y_cipher_party1 = snp.array(train_y_party1, party=1) + train_column_cipher_party0 = snp.array(train_column_party0, party=0) + train_column_cipher_party1 = snp.array(train_column_party1, party=1) + + column_check = snp.where(train_column_cipher_party0 == train_column_cipher_party1, + snp.ones(train_column_cipher_party0.shape), + snp.zeros(train_column_cipher_party0.shape)) + column_check_lens = snp.sum(column_check) + column_check_lens = column_check_lens.reveal_to(0) + + if column_check_lens.size > 0 and column_check_lens != len(hashed_column): + raise ValueError("Columns in the two parties have different order or values") + + train_x_cipher = snp.vstack((train_x_cipher_party0, train_x_cipher_party1)) + train_y_cipher = snp.vstack((train_y_cipher_party0, train_y_cipher_party1)) + eval_x_cipher = None + eval_y_cipher = None + + if self.do_eval: + eval_x_cipher_party0 = snp.array(eval_x_party0, party=0) + eval_x_cipher_party1 = snp.array(eval_x_party1, party=1) + eval_y_cipher_party0 = snp.array(eval_y_party0, party=0) + eval_y_cipher_party1 = snp.array(eval_y_party1, party=1) + eval_x_cipher = snp.vstack((eval_x_cipher_party0, eval_x_cipher_party1)) + eval_y_cipher = snp.hstack((eval_y_cipher_party0, eval_y_cipher_party1)) + eval_y_cipher = snp.reshape(eval_y_cipher, (-1, 1)) + + return train_x_cipher, train_y, train_y_cipher, eval_x_cipher, \ + eval_y_cipher, train_column_cipher_party0, hashed_column + + def prepare_predict_data(self, data): + """ + + Parameters + ---------- + data: DataFrame + Predict data + + Returns + ------- + predict_x_cipher: SecureArray, shape=(n_samples, k) + Secret sharing train data which concat data from two parties + + hashed_column: array + Numerical hashed values of columns + """ + x = data[[x for x in data.columns if x != self.label_name]] + + columns = x.columns.tolist() + hashed_column = self._set_hash_column(columns) + + if self.party_id == 0: + predict_x_party0 = x.values + predict_x_party1 = None + predict_column_party0 = hashed_column + predict_column_party1 = None + + else: + predict_x_party0 = None + predict_x_party1 = x.values + predict_column_party0 = None + predict_column_party1 = hashed_column + + predict_x_cipher_party0 = snp.array(predict_x_party0, party=0) + predict_x_cipher_party1 = snp.array(predict_x_party1, party=1) + predict_column_cipher_party0 = snp.array(predict_column_party0, party=0) + predict_column_cipher_party1 = snp.array(predict_column_party1, party=1) + predict_x_cipher = snp.vstack((predict_x_cipher_party0, predict_x_cipher_party1)) + + if predict_x_cipher.shape[0] > x.shape[0]: + column_check = snp.where(predict_column_cipher_party0 == predict_column_cipher_party1, + snp.ones(predict_column_cipher_party0.shape), + snp.zeros(predict_column_cipher_party0.shape)) + column_check_lens = snp.sum(column_check) + column_check_lens = column_check_lens.reveal_to(0) + + if column_check_lens.size > 0 and column_check_lens != len(hashed_column): + raise ValueError("Columns in the two parties have different order or values") + + return predict_x_cipher, hashed_column + + def calc_gradient_local(self, loss_func, y): + """ + Calculate first and second order derivative in the first round + + Parameters + ---------- + loss_func: loss class + y: array + Ground true label + + Returns + ------- + grads_cipher: SecureArray, shape=(n_samples, ) + Secret sharing data of first order derivative + + hess_cipher: SecureArray, shape=(n_samples, ) + Secret sharing data of second order derivative + """ + grads = loss_func.grad(np.array([self.base_score] * y.shape[0]), y) + hess = loss_func.hess(np.array([self.base_score] * y.shape[0])) + + if self.party_id == 0: + grads_party0 = grads + grads_party1 = None + hess_party0 = hess + hess_party1 = None + + else: + grads_party0 = None + grads_party1 = grads + hess_party0 = None + hess_party1 = hess + + grads_cipher_party0 = snp.array(grads_party0, party=0) + grads_cipher_party1 = snp.array(grads_party1, party=1) + hess_cipher_party0 = snp.array(hess_party0, party=0) + hess_cipher_party1 = snp.array(hess_party1, party=1) + grads_cipher = snp.vstack((grads_cipher_party0, grads_cipher_party1)) + hess_cipher = snp.vstack((hess_cipher_party0, hess_cipher_party1)) + + return grads_cipher, hess_cipher + + def transform_one_tree(self, train_x, train_y, train_y_cipher, eval_x_cipher, y_hat, eval_y_hat, column_cipher, + hashed_column, loss_func, num_t): + """ + Build one decision tree + + Parameters + ---------- + train_x: SecureArray, shape=(n_samples, k) + Secret sharing train data which concat data from two parties + train_y: array + local true label + train_y_cipher: SecureArray, shape=(n_samples, ) + Secret sharing data which concat train label from two parties + eval_x_cipher: SecureArray + Secret sharing eval data which concat data from two parties + y_hat: SecureArray, shape=(n_samples, ) + predict train y of last epoch + eval_y_hat: SecureArray + predict eval y of last epoch + column_cipher: SecureArray, shape=(k,) + Secret sharing data of columns + hashed_column: array + Numerical hashed values of columns + loss_func: loss class + num_t: int + current epoch + + + Returns + ------- + tree: MPCTree class + Trained tree in current epoch + y_hat: SecureArray + Predict train y in current epoch + eval_y_hat: SecureArray + Predict eval y in current epoch + """ + + index_array = np.ones((train_x.shape[0], 1)) + + if num_t == 0: + grads, hess = self.calc_gradient_local(loss_func, train_y) + else: + grads = loss_func.grad(y_hat, train_y_cipher) + hess = loss_func.hess(y_hat) + + tree = MPCTree(reg_alpha=self.reg_alpha, + reg_lambda=self.reg_lambda, + min_child_weight=self.min_child_weight, + columns=column_cipher, + min_child_samples=self.min_child_samples, + min_split_loss=self.min_split_loss, + max_depth=self.max_depth) + + tree.root = tree.build_tree(train_x, grads, hess, index_array, current_depth=0) + + y_preds = snp.zeros(shape=(train_x.shape[0], 1)) + for i in range(train_x.shape[0]): + leaf_values = tree.predict_proba(tree.root, train_x[i, :], hashed_column, 0) + y_preds[i] = sum(leaf_values) + + y_hat += self.learning_rate * y_preds + + if self.do_eval: + eval_y_preds = snp.zeros(shape=(eval_x_cipher.shape[0], 1)) + for i in range(eval_x_cipher.shape[0]): + leaf_values = tree.predict_proba(tree.root, eval_x_cipher[i, :], hashed_column, 0) + eval_y_preds[i] = sum(leaf_values) + eval_y_hat += self.learning_rate * eval_y_preds + + return tree, y_hat, eval_y_hat + + def save_tree_from_ss_to_numpy(self, trees): + for tree in trees: + tree.columns = tree.columns.to_share().astype(np.int64) + self._save_tree_from_ss_to_numpy(tree.root) + + def _save_tree_from_ss_to_numpy(self, tree_node): + """Convert secure object to numerical value""" + if tree_node.is_leaf: + tree_node.leaf_weight = tree_node.leaf_weight.to_share().astype(np.int64) + return + + tree_node.split_feat = tree_node.split_feat.to_share().astype(np.int64) + tree_node.split_val = tree_node.split_val.to_share().astype(np.int64) + self._save_tree_from_ss_to_numpy(tree_node.left_child) + self._save_tree_from_ss_to_numpy(tree_node.right_child) + + def load_tree_from_numpy_to_ss(self, trees): + for tree in trees: + tree.columns = snp.fromshare(tree.columns, np.float64) + self._load_tree_from_numpy_to_ss(tree.root) + + def _load_tree_from_numpy_to_ss(self, tree_node): + """Convert to secure object from numerical value""" + if tree_node.is_leaf: + tree_node.leaf_weight = snp.fromshare(tree_node.leaf_weight, np.float64) + return + + tree_node.split_feat = snp.fromshare(tree_node.split_feat, np.float64) + tree_node.split_val = snp.fromshare(tree_node.split_val, np.float64) + self._load_tree_from_numpy_to_ss(tree_node.left_child) + self._load_tree_from_numpy_to_ss(tree_node.right_child) + + +class XGBoostClassifier(BaseXGB): + """ + XGBoosting for classification + + Parameters + ---------- + min_split_loss: float, default=1e-5 + The minimum number of gain to split an internal node: + + learning_rate : float, default=0.1 + Learning rate shrinks the contribution of each tree by `learning_rate`. + There is a trade-off between learning_rate and n_estimators. + Values must be in the range `[0.0, inf)`. + + n_estimators : int, default=100 + The number of boosting stages to perform. Gradient boosting + is fairly robust to over-fitting so a large number usually + results in better performance. + Values must be in the range `[1, inf)`. + + base_score : float, default=0.5 + Initial value of y hat + Values must be in the range `[0.0, 1)`. + + max_depth : int or None, default=3 + Maximum depth of a tree. + If int, values must be in the range `[1, inf)`. + + reg_alpha : float, default=0. + L1 regularization term on weights + - values must be in the range `[0.0, inf)`. + + reg_lambda : float, default=1.0 + L2 regularization term on weights + Values must be in the range `[0.0, inf)`. + + min_child_samples : int, default=1 + The minimum number of samples required to be at a leaf node. + A split point at any depth will only be considered if it leaves at + least ``min_samples_leaf`` training samples in each of the left and + right branches. This may have the effect of smoothing the model, + especially in regression. + + Values must be in the range `[1, inf)`. + + min_child_weight : float, default=1.0 + Minimum sum of instance weight (hessian) needed in a child. If the tree partition step results + in a leaf node with the sum of instance weight less than min_child_weight, then the building + process will give up further partitioning. + Values must be in the range `(0, inf)`. + + test_size : float, default=0.0 + Size of eval dataset of input data + Values must be in the range `(0, 1)`. + + eval_epochs : int, default=10 + Calculate the evaluation metric after every certain number of epochs. + Values must be in the range `(1, inf)`. + + eval_threshold : float, default=0.5 + Regard the instances with eval prediction value larger than threshold + as positive instances, and the others as negative instances + + objective : {'logitraw', 'logistic'}, default='logitraw' + The loss function to be optimized. Only support logistic + + """ + + def __init__( + self, + min_split_loss: float = 1e-5, + learning_rate: float = 0.1, + n_estimators: int = 100, + base_score: float = 0.5, + max_depth: int = 3, + reg_alpha: float = 0., + reg_lambda: float = 1.0, + min_child_samples: int = 1, + min_child_weight: float = 1., + label_name: str = 'label', + test_size: float = 0., + eval_epochs: int = 10, + eval_threshold: float = 0.5, + objective: str = 'logitraw', + ): + super().__init__(min_split_loss=min_split_loss, + learning_rate=learning_rate, + base_score=base_score, + max_depth=max_depth, + min_child_samples=min_child_samples, + min_child_weight=min_child_weight, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + label_name=label_name, + test_size=test_size) + + self.n_estimators = n_estimators + self.eval_epochs = eval_epochs + self.objective = objective + self.eval_threshold = eval_threshold + self.trees = [] + self.loss_func = LogisticLoss() + + def fit(self, data: pd.DataFrame) -> None: + """ + Fit the model + + Parameters + ---------- + data : DataFrame + Train data + """ + train_x_cipher, train_y, train_y_cipher, eval_x_cipher, eval_y_cipher, column_cipher, \ + hashed_column = self.prepare_train_data(data) + + if self.objective == 'logistic': + self.base_score = 0.0 + self.loss_func = LogisticLoss() + elif self.objective == 'logitraw': + self.loss_func = LogisticLoss() + else: + raise ValueError("Only support logistic loss when apply XGBoostClassifier") + + y_hat = np.full((train_x_cipher.shape[0], 1), self.base_score) + if self.do_eval: + eval_y_hat = np.full((eval_x_cipher.shape[0], 1), self.base_score) + else: + eval_y_hat = None + for num_t in range(self.n_estimators): + start_time = time.time() + self.logger.info(f"fitting tree {num_t + 1}...") + tree, y_hat, eval_y_hat = self.transform_one_tree(train_x_cipher, train_y, train_y_cipher, eval_x_cipher, + y_hat, eval_y_hat, column_cipher, hashed_column, + self.loss_func, num_t) + self.trees.append(tree) + self.logger.info(f"tree {num_t + 1} fit done!, time: {time.time() - start_time}") + + if self.do_eval and (num_t + 1) % self.eval_epochs == 0: + eval_predict_label = snp.where(eval_y_hat > self.eval_threshold, snp.ones(eval_y_hat.shape), + snp.zeros(eval_y_hat.shape)) + errors = error(eval_predict_label, eval_y_cipher) + plain_eval_error0 = errors.reveal_to(0) + plain_eval_error1 = errors.reveal_to(1) + if self.party_id == 0: + self.logger.info(f"eval error in {num_t + 1}: {plain_eval_error0}") + else: + self.logger.info(f"eval error in {num_t + 1}: {plain_eval_error1}") + + self.logger.info("Finished training") + + def predict_proba(self, data: pd.DataFrame) -> SecureArray: + """ + Predict probabilities. + + Parameters + ---------- + data : DataFrame + Predict data + + Returns + ------- + proba : array of shape (n_samples, 1) + Probabilities values + """ + predict_x, hashed_column = self.prepare_predict_data(data) + if len(predict_x) == 0: + raise ValueError('Predict data in empty!') + + y_pred = [self.base_score] * predict_x.shape[0] + + for tree in self.trees: + for i in range(predict_x.shape[0]): + leaf_array = tree.predict_proba(tree.root, predict_x[i, :], hashed_column, 0) + y_pred[i] = y_pred[i] + sum(leaf_array) * self.learning_rate + + return y_pred + + def predict(self, data: pd.DataFrame) -> pd.Series: + """ + Predict label. + + Parameters + ---------- + data : DataFrame + Predict data + + Returns + ---------- + predict_proba: Series + Probabilities values of classifier + """ + predict_proba_cipher = self.predict_proba(data) + if self.party_id == 0: + data_length_in_party0 = len(data) + else: + data_length_in_party0 = len(predict_proba_cipher) - len(data) + + predict_proba0 = [i.reveal_to(0) for i in predict_proba_cipher[:data_length_in_party0]] + predict_proba1 = [i.reveal_to(1) for i in predict_proba_cipher[data_length_in_party0:]] + if self.party_id == 0: + predict_proba = predict_proba0 + else: + predict_proba = predict_proba1 + predict_proba = np.array(predict_proba).reshape(-1,) + + return pd.Series(predict_proba) + + def save_model(self, model_path: str) -> None: + """ + Save trained model. + + Parameters + ---------- + model_path: string + File path of the saved model + """ + self.save_tree_from_ss_to_numpy(self.trees) + try: + self._federation = None + self._mpc_engine = None + with open(model_path, 'wb') as f: + pickle.dump(self, f) + self.logger.info("Save model success") + except pickle.PickleError as e: + self.logger.error(f"Save model file. err={e}") + + def load_model(self, model_path: str) -> None: + """ + Load model. + + Parameters + ---------- + model_path: string + File path of the saved model + + Returns + ------- + loadobj: model + Saved XGboost model + """ + try: + with open(model_path, 'rb') as f: + load_obj = pickle.load(f) + + self.learning_rate = load_obj.learning_rate + self.base_score = load_obj.base_score + self.trees = load_obj.trees + self.load_tree_from_numpy_to_ss(self.trees) + self.logger.info("Load model success") + except pickle.PickleError as e: + self.logger.error(f"Load model fail. err={e}") + + +class XGBoostRegressor(BaseXGB): + """ + XGBoosting for regression + + Parameters + ---------- + min_split_loss: float, default=1e-5 + The minimum number of gain to split an internal node: + + learning_rate : float, default=0.1 + Learning rate shrinks the contribution of each tree by `learning_rate`. + There is a trade-off between learning_rate and n_estimators. + Values must be in the range `[0.0, inf)`. + + n_estimators : int, default=100 + The number of boosting stages to perform. Gradient boosting + is fairly robust to over-fitting so a large number usually + results in better performance. + Values must be in the range `[1, inf)`. + + base_score : float, default=0.5 + Initial value of y hat + Values must be in the range `[0.0, 1)`. + + max_depth : int or None, default=3 + Maximum depth of the Tree. + If int, values must be in the range `[1, inf)`. + + reg_alpha : float, default=0. + L1 regularization term on weights + - values must be in the range `[0.0, inf)`. + + reg_lambda : float, default=1.0 + L2 regularization term on weights + Values must be in the range `[0.0, inf)`. + + min_child_samples : int, default=1 + The minimum number of samples required to be at a leaf node. + A split point at any depth will only be considered if it leaves at + least ``min_samples_leaf`` training samples in each of the left and + right branches. This may have the effect of smoothing the model, + especially in regression. + + Values must be in the range `[1, inf)`. + + min_child_weight : float, default=1.0 + The minimum number of weights required to split an internal node: + Values must be in the range `(0, inf)`. + + test_size : float, default=0.0 + Size of eval dataset of input data + Values must be in the range `(0, 1)`. + + eval_epochs : int, default=10 + Calculate the evaluation metric after every certain number of epochs. + Values must be in the range `(1, inf)`. + + objective : {'squarederror', 'logistic'}, default='squarederror' + The loss function to be optimized. Only support logistic and squarederror + + """ + + def __init__( + self, + min_split_loss: float = 1e-5, + learning_rate: float = 0.1, + n_estimators: int = 100, + base_score: float = 0.5, + max_depth: int = 3, + reg_alpha: float = 0., + reg_lambda: float = 1.0, + min_child_samples: int = 1, + min_child_weight: float = 1, + label_name: str = 'label', + test_size: float = 0., + eval_epochs: int = 0, + objective: str = 'squarederror', + ): + super().__init__(min_split_loss=min_split_loss, + learning_rate=learning_rate, + base_score=base_score, + max_depth=max_depth, + min_child_samples=min_child_samples, + min_child_weight=min_child_weight, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + label_name=label_name, + test_size=test_size) + + self.n_estimators = n_estimators + self.eval_epochs = eval_epochs + self.objective = objective + self.trees = [] + self.loss_func = LogisticLoss() + + def fit(self, data: pd.DataFrame) -> None: + """ + Fit the model + + Parameters + ---------- + data : DataFrame + Train data + + """ + train_x_cipher, train_y, train_y_cipher, eval_x_cipher, eval_y_cipher, column_cipher, \ + hashed_column = self.prepare_train_data(data) + + if self.objective == 'logistic': + self.base_score = 0.0 + self.loss_func = LogisticLoss() + elif self.objective == 'squarederror': + self.loss_func = SquareLoss() + else: + raise ValueError("Only support logistic loss and squarederror when apply XGBoostRegressor") + + y_hat = np.full((train_x_cipher.shape[0], 1), self.base_score) + if self.do_eval: + eval_y_hat = np.full((eval_x_cipher.shape[0], 1), self.base_score) + else: + eval_y_hat = None + for num_t in range(self.n_estimators): + start_time = time.time() + self.logger.info(f"fitting tree {num_t + 1}...") + tree, y_hat, eval_y_hat = self.transform_one_tree(train_x_cipher, train_y, train_y_cipher, eval_x_cipher, + y_hat, eval_y_hat, column_cipher, hashed_column, + self.loss_func, num_t) + self.trees.append(tree) + self.logger.info(f"tree {num_t + 1} fit done!, time: {time.time() - start_time}") + + if self.do_eval and (num_t + 1) % self.eval_epochs == 0: + errors = mean_absolute_error(eval_y_hat, eval_y_cipher) + plain_eval_error0 = errors.reveal_to(0) + plain_eval_error1 = errors.reveal_to(1) + if self.party_id == 0: + self.logger.info(f"eval error in {num_t + 1}: {plain_eval_error0}") + else: + self.logger.info(f"eval error in {num_t + 1}: {plain_eval_error1}") + + self.logger.info("Finished training") + + def predict(self, data: pd.DataFrame) -> pd.Series: + """ + Predict label. + + Parameters + ---------- + data : DataFrame + Predict data + + Return + ---------- + predict_values: Series + Predict value of regression + """ + predict_x, hashed_column = self.prepare_predict_data(data) + if len(predict_x) == 0: + raise ValueError('Predict data is empty!') + + y_pred = [self.base_score] * predict_x.shape[0] + + for tree in self.trees: + for i in range(predict_x.shape[0]): + leaf_array = tree.predict_proba(tree.root, predict_x[i, :], hashed_column, 0) + y_pred[i] = y_pred[i] + sum(leaf_array) * self.learning_rate + + if self.party_id == 0: + data_length_in_party0 = len(data) + else: + data_length_in_party0 = len(y_pred) - len(data) + + predict_values0 = [i.reveal_to(0) for i in y_pred[:data_length_in_party0]] + predict_values1 = [i.reveal_to(1) for i in y_pred[data_length_in_party0:]] + + if self.party_id == 0: + predict_values = predict_values0 + else: + predict_values = predict_values1 + predict_values = np.array(predict_values).reshape(-1,) + return pd.Series(predict_values) + + def save_model(self, model_path: str) -> None: + """ + Save trained model. + + Parameters + ---------- + model_path: string + File path of the saved model + """ + self.save_tree_from_ss_to_numpy(self.trees) + try: + self._federation = None + self._mpc_engine = None + with open(model_path, 'wb') as f: + pickle.dump(self, f) + self.logger.info("Save model success") + except pickle.PickleError as e: + self.logger.error(f"Save model file. err={e}") + + def load_model(self, model_path: str) -> None: + """ + Load model. + + Parameters + ---------- + model_path: string + File path of the saved model + + Returns + ------- + loadobj: model + Saved XGboost model + """ + try: + with open(model_path, 'rb') as f: + load_obj = pickle.load(f) + + self.learning_rate = load_obj.learning_rate + self.base_score = load_obj.base_score + self.trees = load_obj.trees + self.load_tree_from_numpy_to_ss(self.trees) + self.logger.info("Load model success") + except pickle.PickleError as e: + self.logger.error(f"Load model fail. err={e}") diff --git a/petml/fl/graph/__init__.py b/petml/fl/graph/__init__.py new file mode 100644 index 0000000..d63907a --- /dev/null +++ b/petml/fl/graph/__init__.py @@ -0,0 +1,19 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .leiden.leiden import Leiden + +__all__ = [ + 'Leiden', +] diff --git a/petml/fl/graph/leiden/__init__.py b/petml/fl/graph/leiden/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/petml/fl/graph/leiden/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/petml/fl/graph/leiden/base.py b/petml/fl/graph/leiden/base.py new file mode 100644 index 0000000..39cc5f5 --- /dev/null +++ b/petml/fl/graph/leiden/base.py @@ -0,0 +1,172 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Auxiliary classes for Leiden algorithm. +The Louvain algorithm starts from a singleton partition, with each vertex being assigned to its own community, +and repeatedly moves vertices into new communities with positive modularity gains until the modularity reaches the maximum. + +Terms: +- nodes: The node id belong to origin graph. +- vertex: The node belong to Aggrated Graph, A vertex may contain a set of nodes. +During the iterative process of the algorithm, similar vertexs can gradually combine together to form one larger vertex. +- community: A community may contain a set of Vertex. +""" + +import collections +import copy +import typing as t + + +class Vertex: + """ + Vertex class for Leiden algorithm. + + Parameters + ---------- + vid : int or str + Vertex id. + + cid : int or str + Vommunity id. + + nodes : set + The node id of the original graph it contains, when merged into a vertex, + records which original nodes it is composed of. + + degree : int + Degree of the vertex. + + is_ghost : bool + Whether the vertex is a ghost vertex. Ghost nodes indicate that some information exists + on the opposite node. Ghost nodes exist on both sides simultaneously + + is_cross_merge : bool + Used to represent merged cross-border supernodes: where some nodes are on one side + and some nodes are on the other side. + Due to the fact that cross-border supernodes belong to both the server and client side, + we defines cross-border supernodes to only perform local move judgment on the server side. + However, some neighbors of cross-border supernodes are invisible to the server, so it is necessary + for the client side to calculate the Q values of some neighbor nodes. + """ + + def __init__(self, + vid: t.Union[int, str], + cid: t.Union[int, str] = None, + nodes: t.Set[t.Union[int, str]] = None, + degree: int = 0, + is_ghost: bool = False, + is_cross_merge: bool = False): + + self.vid = vid + if cid is None: + cid = vid + self.cid = cid + self.nodes = nodes or set() + self.degree = degree + self.is_ghost = is_ghost + self.is_cross_merge = is_cross_merge + + def __repr__(self): + return f""" +Vertex( + vid : {self.vid} + cid : {self.cid} + nodes : {self.nodes} + degree : {self.degree} + is_ghost : {self.is_ghost} + is_cross_merge : {self.is_cross_merge} +) +""" + + def merge(self, other: "Vertex"): + """Merge other vertex into self. + """ + self.nodes.update(other.nodes) + self.degree += other.degree + self.is_ghost = self.is_ghost or other.is_ghost + self.is_cross_merge = self.is_cross_merge or other.is_cross_merge + + +class Community: + """ + Community class for Leiden algorithm. + + Parameters + ---------- + cid : int or str + Community id. + + vertexs : set + The vertex it contains. + + weight : float + The weight of the community. + + is_ghost : bool + Whether the community is a ghost community. + """ + + def __init__(self, + cid: t.Union[int, str], + vertexs: t.Set[t.Union[int, str]] = None, + weight: float = 0, + is_ghost: bool = False): + self.cid = cid + self.vertexs = vertexs or set() + self.weight = weight + self.is_ghost = is_ghost + + def __repr__(self): + return f""" +Community( + cid : {self.cid} + vertexs : {self.vertexs} + weight : {self.weight} + is_ghost : {self.is_ghost} +) +""" + + def remove_vertex(self, vid: t.Union[int, str], degree: float): + self.vertexs.remove(vid) + self.weight -= degree + + def add_vertex(self, vid: t.Union[int, str], degree: float): + if vid not in self.vertexs: + self.vertexs.add(vid) + self.weight += degree + + +class Graph: + """ + Graph class for Leiden algorithm. + + We use a dict to store the graph. The key is the node id, and the value is a dict, + the key is the neighbor node id, and the value is the weight. + """ + + def __init__(self) -> None: + self.data = collections.defaultdict(dict) + + def from_dict(self, d: dict): + self.data = copy.deepcopy(d) + + def neighbors(self, node_id: t.Union[int, str]) -> t.Iterable: + return self.data[node_id].keys() + + def weight(self, node_id1: t.Union[int, str], node_id2: t.Union[int, str]) -> float: + if node_id1 is None or node_id2 is None: + return 0 + return self.data[node_id1].get(node_id2, 0) + + def update_weight(self, node_id1: t.Union[int, str], node_id2: t.Union[int, str], weight: float): + self.data[node_id1][node_id2] = weight diff --git a/petml/fl/graph/leiden/leiden.py b/petml/fl/graph/leiden/leiden.py new file mode 100644 index 0000000..33719e4 --- /dev/null +++ b/petml/fl/graph/leiden/leiden.py @@ -0,0 +1,577 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time +import copy +import json +import random + +import numpy as np +import pandas as pd +import petace.securenumpy as snp + +from petml.fl.base import FlBase +from .base import Vertex, Community, Graph +from .utils import Cache, Message + + +class Leiden(FlBase): + """ + Leiden alogrithm. + + Parameters + ---------------- + max_move : int + The limit times of local node move. + + max_merge : int + The limit times of merge meta nodes. + """ + + def __init__(self, max_move: int = 10, max_merge: int = 10): + super().__init__() + self.max_move = max_move + self.max_merge = max_merge + + self.vertex_table: dict = {} + self.community_table: dict = {} + self.local_vertexs: dict = {} + self.G_vertex_vertex = Graph() + self.G_vertex_community = Graph() + self.total_weight: float = 0 + + self.cache = Cache() + self.first_loop = True + self.next_visit_sequence = set() + + def set_infra(self, party_id, federation, mpc_engine): + self.party_id = party_id + self.peer_party = 1 - party_id + self._federation = federation + self._mpc_engine = mpc_engine + + def send(self, state, data=None): + message = Message(state, data) + self._federation.remote(json.dumps(message.dumps()).encode()) + + def recv(self) -> Message: + return Message(**json.loads(self._federation.get().decode())) + + def __init_attr(self, V, G): + self.local_vertexs = V + self.G_vertex_vertex.from_dict(G) + self.G_vertex_community.from_dict(G) + + # init vertex_table + for vid, adj_info in G.items(): + for adj_vid, weight in adj_info.items(): + if adj_vid > vid: + self.total_weight += weight + is_ghost = False + if vid not in V or adj_vid not in V: + is_ghost = True + self.__update_vertex_table(V, vid, weight, is_ghost) + self.__update_vertex_table(V, adj_vid, weight, is_ghost) + + # init community_table + for vid, vertex in self.vertex_table.items(): + self.community_table[vid] = Community(vid, {vid}, vertex.degree, vertex.is_ghost) + + def __update_vertex_table(self, V, vid, weight, is_ghost): + if vid not in self.vertex_table: + vertex = Vertex(vid) + if vid in V: + vertex.nodes = {vid} + self.vertex_table[vid] = vertex + vertex = self.vertex_table[vid] + vertex.degree += weight + vertex.is_ghost = is_ghost or vertex.is_ghost + + def sync_total_weight(self): + self.send(MessageState.SyncTotalWeight, self.total_weight) + recdata = self.recv() + self.total_weight += recdata.data + self.logger.info('sync total_weight successfully') + + def move_nodes(self, vid, new_cid): + """move vertex vid to a new community new_cid.""" + old_cid = self.vertex_table[vid].cid + self.move_nodes_local(vid, old_cid, new_cid) + + # Ghost nodes need to change information on both sides at the same time + if self.vertex_table[vid].is_ghost: + self.send_move_node_info(vid, old_cid, new_cid) + + def move_nodes_local(self, vid, old_cid, new_cid): + """move vertex vid to a new community new_cid locally.""" + if new_cid not in self.community_table: + self.community_table[new_cid] = Community(new_cid, is_ghost=True) + + self.vertex_table[vid].cid = new_cid + self.community_table[old_cid].remove_vertex(vid, self.vertex_table[vid].degree) + self.community_table[new_cid].add_vertex(vid, self.vertex_table[vid].degree) + self.community_table[ + new_cid].is_ghost = self.community_table[new_cid].is_ghost or self.vertex_table[vid].is_ghost + + for neighbor_vid in self.G_vertex_vertex.neighbors(vid): + old_cid_weight = self.G_vertex_community.weight(neighbor_vid, old_cid) - self.G_vertex_vertex.weight( + vid, neighbor_vid) + new_cid_weight = self.G_vertex_community.weight(neighbor_vid, new_cid) + self.G_vertex_vertex.weight( + vid, neighbor_vid) + self.G_vertex_community.update_weight(neighbor_vid, old_cid, old_cid_weight) + self.G_vertex_community.update_weight(neighbor_vid, new_cid, new_cid_weight) + + if neighbor_vid in self.local_vertexs and self.vertex_table[neighbor_vid].cid != new_cid: + self.next_visit_sequence.add(neighbor_vid) + + def send_move_node_info(self, vid, old_cid, new_cid): + self.send(MessageState.SyncMoveNodeInfo, {'vid': vid, 'old_cid': old_cid, 'new_cid': new_cid}) + + def receive_move_node_info(self, info: dict): + vid = info["vid"] + old_cid = info["old_cid"] + new_cid = info["new_cid"] + self.move_nodes_local(vid, old_cid, new_cid) + + def send_MPC_find_max_direction(self, move_flag, max_Q): + self.send(MessageState.MPCFindMaxDirection) + return self.MPC_find_max_direction(True, move_flag, max_Q) + + def receive_MPC_find_max_direction(self, _): + self.MPC_find_max_direction(False) + + def send_move_node(self, vid, max_direction): + self.send(MessageState.MoveNode, {'vid': vid, "max_direction": max_direction}) + recdata = self.recv() + self.receive_move_node_info(recdata) + + def receive_move_node(self, info: dict): + vid = info["vid"] + max_direction = info["max_direction"] + self.move_nodes(vid, max_direction) + + def send_clear_data(self): + self.cache.clear() + self.send(MessageState.ClearCache) + + def receive_clear_data(self, _): + self.cache.clear() + + def sync_MPC_Q_info(self, vid, w_cids): + self.cache.vid = vid + cid = self.vertex_table[vid].cid + self.cache.cid = cid + self.cache.w_cids += w_cids + self.ask_sync_Q_info(vid, cid, w_cids) + + def ask_sync_Q_info(self, vid, cid, w_cids): + m = len(w_cids) + info = {"vid": None, "cid": None, "w_cids": [None for _ in range(m)]} + if self.vertex_table[vid].is_ghost: + info['vid'] = vid + if self.community_table[cid].is_ghost: + info['cid'] = cid + + for i, w_cid in enumerate(w_cids): + if self.community_table[w_cid].is_ghost: + info['w_cids'][i] = w_cid + + self.send(MessageState.SyncQInfo, info) + + def receive_sync_Q_info(self, info: dict): + self.cache.vid = info["vid"] + self.cache.cid = info["cid"] + self.cache.w_cids += info["w_cids"] + + def prepare_Q_data(self, vid, cid, w_cids): + weight_c1 = [] + weight_u_c1 = [] + weight_u_c0 = 0 + weight_c0 = 0 + degree_u = 0 + + if cid is not None: + weight_c0 = self.community_table[cid].weight + if vid is not None: + degree_u = self.vertex_table[vid].degree + if vid is not None and cid is not None: + weight_u_c0 = 2 * self.G_vertex_community.weight(vid, cid) + + for w_cid in w_cids: + weight_c1_tmp = 0 + weight_u_c1_tmp = 0 + if w_cid is not None: + weight_c1_tmp = self.community_table[w_cid].weight + if vid is not None: + weight_u_c1_tmp = 2 * self.G_vertex_community.weight(vid, w_cid) + weight_c1.append(weight_c1_tmp) + weight_u_c1.append(weight_u_c1_tmp) + + # to avoid some petace bug + weight_c1 = np.reshape(weight_c1, (-1, 1)) * 1.0 + weight_u_c1 = np.reshape(weight_u_c1, (-1, 1)) * 1.0 + shape = weight_c1.shape + weight_u_c0 = np.broadcast_to(weight_u_c0, shape) * 1.0 + weight_c0 = np.broadcast_to(weight_c0, shape) * 1.0 + degree_u = np.broadcast_to(degree_u, shape) * 1.0 + + return weight_u_c0, weight_u_c1, weight_c0, weight_c1, degree_u + + def calculate_and_find_maxQ(self, vid, w_cids): + """ + Consider move vertex u from community c0 to community c1, + we use Q to measure the modularity gain: + + Q = weight_c1 - weight_c0 + (degree_u * degree_c0 - degree_u * degree_u - degree_u * degree_c1) / m + + where: + - weight_u_c1: the weight of the edge between vertices u and community c1 + - weight_u_c0: the weight of the edge between vertices u and community c0 + - degree_u: the degree of vertex u + - weight_c0: the sum of the weights of edges incident to two vertices in c0 + - weight_c1: the sum of the weights of edges incident to two vertices in c1 + - m: the sum of all of the edge weights in the graph + """ + flag = True + c0 = self.vertex_table[vid].cid + weight_u_c0, weight_u_c1, weight_c0, weight_c1, degree_u = self.prepare_Q_data(vid, c0, w_cids) + Q = weight_u_c1 - weight_u_c0 + degree_u * (weight_c0 - degree_u - weight_c1) / self.total_weight + max_Q = np.max(Q) + max_direction = w_cids[np.argmax(Q)] + if max_Q < 0 or max_direction is None: + flag = False + max_Q = -1 + max_direction = None + return (flag, max_Q, max_direction) + + def judge_if_move_node(self, vid): + self.send_clear_data() + w_cid_intern = [] + w_cid_MPC = [] + move_flag = False + max_Q = -1 + max_direction = None + viewed_vertex = {self.vertex_table[vid].cid} + for w_vid in self.G_vertex_vertex.neighbors(vid): + w_cid = self.vertex_table[w_vid].cid + if w_cid not in viewed_vertex: + viewed_vertex.add(w_cid) + if self.vertex_table[vid].is_ghost or self.community_table[w_cid].is_ghost: + w_cid_MPC.append(w_cid) + else: + w_cid_intern.append(w_cid) + + if w_cid_intern: + move_flag, max_Q, max_direction = self.calculate_and_find_maxQ(vid, w_cid_intern) + + if w_cid_MPC: + self.sync_MPC_Q_info(vid, w_cid_MPC) + + # For cross-border super nodes, because there may be some neighbor servers that cannot be observed, + # it is necessary to ask clients whether there is a possibility of moving. + if self.vertex_table[vid].is_cross_merge: + self.ask_if_move_merge_node(vid) + + if len(self.cache.w_cids) > 0: + new_max_direction = self.send_MPC_find_max_direction(move_flag, max_Q) + if new_max_direction != -1: + if vid in self.vertex_table and new_max_direction in self.community_table: + self.move_nodes(vid, new_max_direction) + else: + self.send_move_node(vid, new_max_direction) + return True + + if move_flag: + self.move_nodes(vid, max_direction) + return True + return False + + def ask_if_move_merge_node(self, vid): + self.send(MessageState.IfMoveMergeNode, vid) + message = self.recv() + if message.state != MessageState.EndMoveMergeNode: + self.receive_sync_Q_info(message) + # receive end state + self.recv() + + def receive_if_move_merge_node(self, vid): + v_MPC = [] + for w_cid in self.G_vertex_community.neighbors(vid): + if self.vertex_table[vid].is_ghost or self.community_table[w_cid].is_ghost: + v_MPC.append(w_cid) + self.sync_MPC_Q_info(vid, v_MPC) + self.send(MessageState.EndMoveMergeNode) + + def build_visit_sequnce(self): + if self.first_loop: + visit_sequence = list(self.local_vertexs) + self.first_loop = False + else: + visit_sequence = list(self.next_visit_sequence) + self.next_visit_sequence = set() + random.shuffle(visit_sequence) + return visit_sequence + + def first_stage(self): + visit_sequence = self.build_visit_sequnce() + move_flag = False + for v_vid in visit_sequence: + if self.judge_if_move_node(v_vid): + move_flag = True + self.send(MessageState.FirstStageEnd, move_flag) + return move_flag + + def listen_local_move(self): + handler = { + MessageState.SyncQInfo: self.receive_sync_Q_info, + MessageState.IfMoveMergeNode: self.receive_if_move_merge_node, + MessageState.SyncMoveNodeInfo: self.receive_move_node_info, + MessageState.MPCFindMaxDirection: self.receive_MPC_find_max_direction, + MessageState.MoveNode: self.receive_move_node, + MessageState.ClearCache: self.receive_clear_data, + } + while True: + message = self.recv() + state = message.state + if state == MessageState.FirstStageEnd: + return message.data + handler[state](message.data) + continue + + def second_stage(self): + self.merge_vertex() + self.first_loop = True + + def merge_vertex(self): + """merge vertexs belong to the same community + """ + new_community_vertices = {} + new_vertex_vertex = {} + new_local_vertexs = set() + for cid, community in self.community_table.items(): + if not community.vertexs: + continue + new_vertex = Vertex(cid) + is_cross_merge = False + exist_local_vertex = False + exist_no_local_vertex = False + + for vid in community.vertexs: + new_vertex.merge(self.vertex_table[vid]) + if vid not in self.local_vertexs: + exist_no_local_vertex = True + if new_vertex.nodes: + exist_local_vertex = True + + # There are two standards for cross-border vertex: + # 1. One of the sub-vertex is a cross-border vertex + # 2. Contains both its own node and the other node + new_vertex.is_cross_merge = new_vertex.is_cross_merge or (exist_no_local_vertex and exist_local_vertex) + new_vertex.is_cross_merge = is_cross_merge + new_community = Community(cid, {cid}, new_vertex.degree, is_ghost=new_vertex.is_ghost) + new_community_vertices[cid] = new_community + new_vertex_vertex[cid] = new_vertex + + # How to determine the ownership of a node, if one of the following + # conditions exists, the node belongs to you: + # 1. this vertext is not cross_merge + # 2. this vertext is cross_merge and you are server + if (not new_vertex.is_cross_merge and new_vertex.nodes) \ + or (new_vertex.is_cross_merge and self.party_id == 0): + new_local_vertexs.add(cid) + + G = Graph() + for cid1 in new_community_vertices: + for vid in self.community_table[cid1].vertexs: + for cid2 in self.G_vertex_community.neighbors(vid): + if cid2 > cid1 and cid2 in new_community_vertices: + G.update_weight(cid1, cid2, self.G_vertex_community.weight(vid, cid2) + G.weight(cid1, cid2)) + G.update_weight(cid2, cid1, self.G_vertex_community.weight(vid, cid2) + G.weight(cid2, cid1)) + + self.community_table = new_community_vertices + self.vertex_table = new_vertex_vertex + self.G_vertex_vertex = copy.deepcopy(G) + self.G_vertex_community = copy.deepcopy(G) + self.local_vertexs = new_local_vertexs + + def get_partition(self): + partition = [] + for vid, node in self.vertex_table.items(): + for v in node.nodes: + partition.append([v, vid]) + return pd.DataFrame(partition, columns=["user_id", "cluster_id"]) + + def MPC_find_max_direction(self, launch=True, move_flag=None, max_Q=None): + self.logger.debug("Start MPC_find_max_direction") + t1 = time.time() + max_index, max_value = self.MPC_calculate_max_Q(launch) + directions = self.cache.w_cids + new_max_direction = -1 + + if launch: + max_index = int(max_index.flatten()[0]) + max_value = max_value.flatten()[0] + max_val = 0 + if max_value > max_val: + if directions[max_index] is not None: + state = MessageState.MPCEnd + data = None + else: + state = MessageState.QueryDirection + data = max_index + self.send(state, data) + response = self.recv() + + if state == MessageState.QueryDirection: + new_max_direction = response.data + else: + new_max_direction = directions[max_index] + max_val = max_value + else: + self.send(MessageState.MPCEnd) + self.recv() + + if move_flag and max_Q > max_val: + new_max_direction = -1 + else: + message = self.recv() + if message.state == MessageState.MPCEnd: + self.send(MessageState.OK) + else: + max_index = data["data"] + self.send(MessageState.OK, directions[max_index]) + t2 = time.time() + self.logger.debug(f"MPC_find_max_direction time cost: {t2-t1}") + return new_max_direction + + def MPC_calculate_max_Q(self, launch): + vid = self.cache.vid + cid = self.cache.cid + w_cids = self.cache.w_cids + weight_u_c0, weight_u_c1, weight_c0, weight_c1, degree_u = self.prepare_Q_data(vid, cid, w_cids) + self.logger.debug(f"MPC_find_max_direction data shape: {np.shape(weight_u_c0)}") + if launch: + party_id1 = self.party_id + party_id2 = self.peer_party + else: + party_id1 = self.peer_party + party_id2 = self.party_id + + weight_u_c0_priv = snp.array(weight_u_c0, party=party_id1) + snp.array(weight_u_c0, party=party_id2) + weight_u_c1_priv = snp.array(weight_u_c1, party=party_id1) + snp.array(weight_u_c1, party=party_id2) + weight_c0_priv = snp.array(weight_c0, party=party_id1) + snp.array(weight_c0, party=party_id2) + degree_u_priv = snp.array(degree_u, party=party_id1) + snp.array(degree_u, party=party_id2) + weight_c1_priv = snp.array(weight_c1, party=party_id1) + snp.array(weight_c1, party=party_id2) + + Q_rpiv = weight_u_c1_priv - weight_u_c0_priv + degree_u_priv * (weight_c0_priv - degree_u_priv - + weight_c1_priv) * (1. / self.total_weight) + + max_index, max_value = snp.argmax_and_max(Q_rpiv, axis=0) + max_index = max_index.reveal_to(party_id1) + max_value = max_value.reveal_to(party_id1) + return max_index, max_value + + def fit_server(self): + iter_phase_I = 0 + iter_phase_II = 0 + + state = TrainState.state0 + while True: + if state == TrainState.state0: + if iter_phase_I < self.max_move: + self.send(TrainState.state0) + move_flag1 = self.first_stage() + move_flag2 = self.listen_local_move() + self.logger.info(f"phase I: {iter_phase_I}") + if (move_flag1 or move_flag2): + iter_phase_I += 1 + continue + state = TrainState.state1 + + if state == TrainState.state1: + if iter_phase_I > 0 and iter_phase_II < self.max_merge: + self.send(TrainState.state1) + self.second_stage() + self.recv() + iter_phase_I = 0 + iter_phase_II += 1 + self.logger.info(f"phase II: {iter_phase_II}") + state = TrainState.state0 + continue + state = TrainState.state2 + break + + self.send(TrainState.state2) + + def fit_client(self): + while True: + state = self.recv().state + if state == TrainState.state0: + self.listen_local_move() + self.first_stage() + continue + if state == TrainState.state1: + self.second_stage() + self.send(MessageState.OK) + continue + if state == TrainState.state2: + break + + def transform(self, user_weights: dict, local_nodes: set) -> pd.DataFrame: + """ + Fit the model according to the given training data. + + Parameters + ---------- + user_weights: dict. + The weight of user-user weight. + + local_nodes : set. + The local nodes of each party. + + Returns + ------- + cluster: csv file + The result of Leiden algorithm. + """ + snp.set_vm(self._mpc_engine.engine) + self.__init_attr(local_nodes, user_weights) + self.sync_total_weight() + if self.party_id == 0: + self.fit_server() + else: + self.fit_client() + partition = self.get_partition() + return partition + + +class TrainState: + state0 = "launch local move" + state1 = "launch merge Vertex" + state2 = "save partition results" + + +class MessageState: + OK = "ok" + SyncTotalWeight = "sync_total_graph_weight" + SyncMoveNodeInfo = "sync_move_node_info" + MPCFindMaxDirection = "mpc_find_max_direction" + MoveNode = "move_node" + ClearCache = "clear_cache" + SyncQInfo = "sync_q_info" + IfMoveMergeNode = "if_move_merge_node" + EndMoveMergeNode = "end_move_merge_node" + FirstStageEnd = "first_stage_end" + MPCEnd = "mpc_end" + QueryDirection = "query_direction" diff --git a/petml/fl/graph/leiden/utils.py b/petml/fl/graph/leiden/utils.py new file mode 100644 index 0000000..8ec5346 --- /dev/null +++ b/petml/fl/graph/leiden/utils.py @@ -0,0 +1,42 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class Cache: + """Cache for the information to compute modularity. + """ + + def __init__(self): + self.vid = None + self.cid = None + self.w_cids = [] + + def clear(self): + self.vid = None + self.cid = None + self.w_cids = [] + + +class Message: + """Message for the communication between parties. + """ + + def __init__(self, state: str, data=None): + self.state = state + self.data = data + + def dumps(self): + if self.data is None: + return {"state": self.state} + return {"state": self.state, "data": self.data} diff --git a/petml/fl/preprocessing/__init__.py b/petml/fl/preprocessing/__init__.py new file mode 100644 index 0000000..98f9ba7 --- /dev/null +++ b/petml/fl/preprocessing/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .psi import PSI diff --git a/petml/fl/preprocessing/psi.py b/petml/fl/preprocessing/psi.py new file mode 100644 index 0000000..1b70c36 --- /dev/null +++ b/petml/fl/preprocessing/psi.py @@ -0,0 +1,63 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pandas as pd + +from petml.fl.base import FlBase + + +class PSI(FlBase): + """ + Private Set Intersection (PSI) model. + + Parameters + ---------------- + column_name : str + The column to PSI. + """ + + def __init__(self, column_name: str): + super().__init__() + self.column_name = column_name + + def set_infra(self, psi_engine): + self._psi_engine = psi_engine.engine + + def transform(self, data: pd.DataFrame) -> pd.DataFrame: + """ + Fit the model according to the given training data. + + Parameters + ---------- + data: pd.DataFrame + The data to PSI. + + Returns + ------- + intersection: pd.DataFrame + The intersection of the data. + """ + if data[self.column_name].duplicated().any(): + raise ValueError(f"Duplicated values in column {self.column_name}") + + id_dtype = data[self.column_name].dtype + data[self.column_name] = data[self.column_name].astype(str) # PSI engine only support string type + + intersection = self._psi_engine.process(data[self.column_name].tolist(), obtain_result=True) + self.logger.info(f"Origin data count {len(data)}, Intersection count {len(intersection)}") + + intersection_id = pd.DataFrame(intersection, columns=[self.column_name]) + intersection_df = pd.merge(intersection_id, data, how="left", on=self.column_name) + intersection_df[self.column_name] = intersection_df[self.column_name].astype(id_dtype) + return intersection_df diff --git a/petml/infra/__init__.py b/petml/infra/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/petml/infra/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/petml/infra/abc/__init__.py b/petml/infra/abc/__init__.py new file mode 100644 index 0000000..79a7833 --- /dev/null +++ b/petml/infra/abc/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .engine import EngineABC +from .network import NetworkABC +from .storage import StorageABC diff --git a/petml/infra/abc/engine.py b/petml/infra/abc/engine.py new file mode 100644 index 0000000..264156d --- /dev/null +++ b/petml/infra/abc/engine.py @@ -0,0 +1,28 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABCMeta, abstractmethod + + +class EngineABC(metaclass=ABCMeta): + _type = None + + @abstractmethod + def build(self, **kwargs): + """build the engine based on the engine_config + """ + + @property + def type(self): + return self._type diff --git a/petml/infra/abc/network.py b/petml/infra/abc/network.py new file mode 100644 index 0000000..f435b2b --- /dev/null +++ b/petml/infra/abc/network.py @@ -0,0 +1,63 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractmethod +from petml.infra.common.log_utils import LoggerFactory + + +class NetworkABC(ABC): + """NetworkABC is the base class. + """ + logger = LoggerFactory.get_logger(__name__) + net = None + + @abstractmethod + def get(self, name: str = None, party: int = None) -> bytes: + """ + Get data from party. + + Parameters + ---------- + party : int + party id + + name : str + name of the data + + Returns + ------- + data : bytes + data from party + + """ + + @abstractmethod + def remote(self, obj: bytes, name: str = None, party: int = None) -> None: + """Send obj to party named name. + + Parameters + ---------- + party : int + party id + + obj : bytes + data to send + + name : str + name of the data + """ + + @abstractmethod + def clear(self): + pass diff --git a/petml/infra/abc/storage.py b/petml/infra/abc/storage.py new file mode 100644 index 0000000..6dc27dc --- /dev/null +++ b/petml/infra/abc/storage.py @@ -0,0 +1,28 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import abstractmethod, ABC + + +class StorageABC(ABC): + + @staticmethod + @abstractmethod + def read(path: str, *args, **kwargs): + pass + + @staticmethod + @abstractmethod + def write(obj, path: str, *args, **kwargs): + pass diff --git a/petml/infra/common/__init__.py b/petml/infra/common/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/petml/infra/common/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/petml/infra/common/log_utils.py b/petml/infra/common/log_utils.py new file mode 100644 index 0000000..62373f3 --- /dev/null +++ b/petml/infra/common/log_utils.py @@ -0,0 +1,52 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + + +class LoggerFactory: + + @staticmethod + def get_logger(name: str, level: int = logging.INFO, formater: str = None): + """ + Return a logger + + Parameters + ---------- + name : str + name of logger. + + level : {logging.DEBUG, logging.INFO, logging.ERROR, logging.WARNING}, default is LOGGING.DEBUG + log level. + + formater : str + + Returns + ------- + logger + """ + if not formater: + formater = "[%(asctime)s] [%(name)s] [%(levelname)s] [%(module)s.%(funcName)s:%(lineno)d] [pid-thread:%(process)d-%(thread)d]: %(message)s" + logger = logging.getLogger(name) + logger.setLevel(level) + + formatter = logging.Formatter(formater) + + console_handler = logging.StreamHandler() + console_handler.setLevel(level) + console_handler.setFormatter(formatter) + + logger.addHandler(console_handler) + + return logger diff --git a/petml/infra/engine/__init__.py b/petml/infra/engine/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/petml/infra/engine/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/petml/infra/engine/cipher_engine/__init__.py b/petml/infra/engine/cipher_engine/__init__.py new file mode 100644 index 0000000..a778b61 --- /dev/null +++ b/petml/infra/engine/cipher_engine/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .cipher_engine import CipherEngine diff --git a/petml/infra/engine/cipher_engine/cipher_engine.py b/petml/infra/engine/cipher_engine/cipher_engine.py new file mode 100644 index 0000000..c977fdb --- /dev/null +++ b/petml/infra/engine/cipher_engine/cipher_engine.py @@ -0,0 +1,38 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from petace.duet import VM +from petace.setops.psi import PSI +from petace.setops import PSIScheme + +from petml.infra.abc.network import NetworkABC + + +class CipherEngine: + support_engine = ("mpc", "kkrt_psi", "ecdh_psi") + + def __init__(self, engine_type: str, party_id: int, net: NetworkABC): + if engine_type not in self.support_engine: + raise ValueError(f"engine_type must be in {self.support_engine}") + + if engine_type == "mpc": + self._engine = VM(net.net, party_id) + elif engine_type == "kkrt_psi": + self._engine = PSI(net.net, party_id, PSIScheme.KKRT_PSI) + elif engine_type == "ecdh_psi": + self._engine = PSI(net.net, party_id, PSIScheme.ECDH_PSI) + + @property + def engine(self): + return self._engine diff --git a/petml/infra/network/__init__.py b/petml/infra/network/__init__.py new file mode 100644 index 0000000..f2f0853 --- /dev/null +++ b/petml/infra/network/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .network_factory import NetworkFactory +from .cluster_def import ClusterDef diff --git a/petml/infra/network/cluster_def.py b/petml/infra/network/cluster_def.py new file mode 100644 index 0000000..1672ddf --- /dev/null +++ b/petml/infra/network/cluster_def.py @@ -0,0 +1,46 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class ClusterDef: + """ + The cluster config of multi parties. + + cluster_def like + { + "mode": "petnet", + "scheme": "socket" + "parties": { + 0 : {"address": ["127.0.0.1:50011"]}, + 1 : {"address": ["127.0.0.1:50012"]}, + } + "topic": "petnet_topic" + } + """ + + def __init__(self, mode: str = None, scheme: str = None, parties: dict = None, topic: str = None): + self.mode = mode + self.scheme = scheme + self.parties = parties + if scheme == "agent" and topic is None: + raise ValueError("topic must be set when scheme is agent") + self.topic = topic + + def __repr__(self): + return f""" + mode : {self.mode} + scheme : {self.scheme} + parties : {self.parties} + tpopic : {self.topic} + """ diff --git a/petml/infra/network/network_factory.py b/petml/infra/network/network_factory.py new file mode 100644 index 0000000..7de5938 --- /dev/null +++ b/petml/infra/network/network_factory.py @@ -0,0 +1,34 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Union + +from petml.infra.network.petnetNetwork.network import PetnetNetwork +from .cluster_def import ClusterDef + +FEDMAP = { + "petnet": PetnetNetwork, +} + + +class NetworkFactory: + + @staticmethod + def get_network(party_id_or_party: Union[int, str], cluster_def: ClusterDef): + if cluster_def is None: + return None + if cluster_def.mode not in FEDMAP: + raise ValueError(f"{cluster_def.mode} not support yet.") + federation = FEDMAP.get(cluster_def.mode)(party_id_or_party, cluster_def) + return federation diff --git a/petml/infra/network/petnetNetwork/__init__.py b/petml/infra/network/petnetNetwork/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/petml/infra/network/petnetNetwork/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/petml/infra/network/petnetNetwork/network.py b/petml/infra/network/petnetNetwork/network.py new file mode 100644 index 0000000..1b1fe92 --- /dev/null +++ b/petml/infra/network/petnetNetwork/network.py @@ -0,0 +1,88 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +import struct +from typing import Union + +from petace.network import NetParams, NetScheme, NetFactory + +from petml.infra.abc import NetworkABC +from ..cluster_def import ClusterDef + + +class PetnetNetwork(NetworkABC): + """Only supports two parties to synchronously send and receive data + """ + + def __init__(self, party_id_or_party: Union[int, str], cluster_def: ClusterDef): + self.party_id = party_id_or_party + self.cluster_def = cluster_def + self.channel_cache = collections.deque([]) + self.net = None + + if len(cluster_def.parties) != 2: + raise ValueError(f"PetnetNetwork only support 2-party, not {len(cluster_def['parties'])}") + self.__init_net() + + def __init_net(self): + net_params = NetParams() + if self.cluster_def.scheme == "socket": + net_scheme = NetScheme.SOCKET + net_params.local_port = int(self.cluster_def.parties[self.party_id]["address"][0].split(":")[-1]) + net_params.remote_addr, remote_port = self.cluster_def.parties[1 - self.party_id]["address"][0].split(":") + net_params.remote_port = int(remote_port) + elif self.cluster_def.scheme == "agent": + remote_party = None + for party in self.cluster_def.parties: + if party != self.party_id: + remote_party = party + break + if remote_party is None: + raise ValueError("remote party not found") + net_scheme = NetScheme.AGENT + net_params.shared_topic = self.cluster_def.topic + net_params.remote_party = remote_party + net_params.local_agent = self.cluster_def.parties[self.party_id]["address"][0] + + else: + raise ValueError(f"mode {self.cluster_def.mode} not supported") + + self.net = NetFactory.get_instance().build(net_scheme, net_params) + + def clear(self): + pass + + def get(self, name: str = None, party: int = None) -> bytes: + ret = bytearray(4) + self.net.recv_data(ret, 4) + data_length = struct.unpack('!I', bytes(ret))[0] + + ret = bytearray(data_length) + self.net.recv_data(ret, data_length) + self.logger.debug(f"recv {name} successfully with length {data_length}") + return bytes(ret) + + def remote(self, obj: bytes, name=None, party=None): + if not isinstance(obj, bytes): + raise TypeError(f"obj must be bytes, not {type(obj)}") + data_length = len(obj) + if data_length >= 2**32: + raise ValueError(f"obj length must be less than 2**32, not {data_length}") + + length_prefix = struct.pack('!I', data_length) + self.net.send_data(length_prefix, 4) + self.logger.debug(f"send length prefix {length_prefix}") + self.net.send_data(obj, len(obj)) + self.logger.debug(f"send {name} successfully") diff --git a/petml/infra/storage/__init__.py b/petml/infra/storage/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/petml/infra/storage/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/petml/infra/storage/graph_storage/__init__.py b/petml/infra/storage/graph_storage/__init__.py new file mode 100644 index 0000000..65a992c --- /dev/null +++ b/petml/infra/storage/graph_storage/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .json_storage import JsonStorage diff --git a/petml/infra/storage/graph_storage/json_storage.py b/petml/infra/storage/graph_storage/json_storage.py new file mode 100644 index 0000000..fb89bf7 --- /dev/null +++ b/petml/infra/storage/graph_storage/json_storage.py @@ -0,0 +1,71 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations +import json +import collections +from petml.infra.abc import StorageABC + + +def keystoint(x): + # to support int key + for k in x: + if k.isdigit(): + return {int(k): v for k, v in x.items()} + break + return x + + +class JsonStorage(StorageABC): + + @staticmethod + def read(path: str) -> collections.defaultdict: + """ + read a json file to a dict + + Parameters + ---------- + path : str + path of json file + + Returns + ------- + dict + """ + with open(path, "rb") as f: + data = json.load(f, object_hook=keystoint) + if isinstance(data, list): + return data + G = collections.defaultdict(dict) + G.update(data) + return G + + @staticmethod + def write(obj: list | dict, path: str) -> None: + """ + write a dict to a json file + + Parameters + ---------- + obj : list|dict + dict to write + + path : str + path of json file + """ + if not isinstance(obj, (list, dict)): + raise TypeError(f"GraphStorage only support write list or dict, not {type(obj)}") + + with open(path, "w") as f: + json.dump(obj, f, indent=2) diff --git a/petml/infra/storage/tabular_storage/__init__.py b/petml/infra/storage/tabular_storage/__init__.py new file mode 100644 index 0000000..2d6c11a --- /dev/null +++ b/petml/infra/storage/tabular_storage/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .csv_storage import CsvStorage +from .parquet_storage import ParquetStorage diff --git a/petml/infra/storage/tabular_storage/base.py b/petml/infra/storage/tabular_storage/base.py new file mode 100644 index 0000000..bdef793 --- /dev/null +++ b/petml/infra/storage/tabular_storage/base.py @@ -0,0 +1,32 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pandas as pd +from petml.infra.abc import StorageABC + + +class TabularStorage(StorageABC): + """ + TabularStorage provides functionality to efficiently work with tabular. + + The supported file formats currently are Parquet, Feather / Arrow IPC, CSV and ORC + """ + + @staticmethod + def read(path, *args, **kwargs) -> pd.DataFrame: + pass + + @staticmethod + def write(obj: pd.DataFrame, path: str, *args, **kwargs) -> None: + pass diff --git a/petml/infra/storage/tabular_storage/csv_storage.py b/petml/infra/storage/tabular_storage/csv_storage.py new file mode 100644 index 0000000..9581685 --- /dev/null +++ b/petml/infra/storage/tabular_storage/csv_storage.py @@ -0,0 +1,87 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pandas as pd +from petml.infra.storage.tabular_storage.base import TabularStorage + + +class CsvStorage(TabularStorage): + + @staticmethod + def read(path: str, + sep: str = ',', + header: str = 'infer', + usecols: list = None, + dtype: str = None, + nrows: int = None) -> pd.DataFrame: + """ + read a csv as pd.DataFrame + + Parameters + ---------- + path : str + path of csv file + + sep : str, default ',' + Character or regex pattern to treat as the delimiter. + + header : int, Sequence of int, 'infer' or None, default 'infer' + Row number(s) containing column labels and marking the start of the data (zero-indexed). + + usecols: Sequence of Hashable or Callable, optional + Subset of columns to select, denoted either by column labels or column indices. + + dtype : dtype or dict of {Hashabledtype}, optional + Data type(s) to apply to either the whole dataset or individual columns. + + nrows : int, optional + Number of rows of file to read. Useful for reading pieces of large files. + + Returns + ------- + pandas.DataFrame + """ + return pd.read_csv(path, sep=sep, header=header, usecols=usecols, dtype=dtype, nrows=nrows) + + @staticmethod + def write(obj: pd.DataFrame, + path: str, + columns: list = None, + sep: str = ',', + header: str = True, + index: bool = False) -> None: + """ + write a DataFrame to file_path + + Parameters + ---------- + obj : pandas.DataFrame + obj to be written + + path : str + path of csv file + + sep : str, default ',' + String of length 1. Field delimiter for the output file. + + columns : sequence, optional + Columns to write. + + header : bool or list of str, default True + Write out the column names. If a list of strings is given it is assumed to be aliases for the column names. + + index : bool, default True + Write row names (index). + """ + obj.to_csv(path, columns=columns, sep=sep, header=header, index=index) diff --git a/petml/infra/storage/tabular_storage/parquet_storage.py b/petml/infra/storage/tabular_storage/parquet_storage.py new file mode 100644 index 0000000..608e0df --- /dev/null +++ b/petml/infra/storage/tabular_storage/parquet_storage.py @@ -0,0 +1,60 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pandas as pd +from petml.infra.storage.tabular_storage.base import TabularStorage + + +class ParquetStorage(TabularStorage): + + @staticmethod + def read(path: str, usecols: list = None) -> pd.DataFrame: + """ + Load a parquet object from the file path, returning a DataFrame. + + Parameters + ---------- + path : str + path of csv file + + usecols: list, default=None + If not None, only these columns will be read from the file. + + Returns + ------- + pandas.DataFrame + """ + return pd.read_parquet(path, columns=usecols) + + @staticmethod + def write(obj: pd.DataFrame, path: str, index: bool = None, partition_cols: list = None) -> None: + """ + Write a DataFrame to the binary parquet format. + + Parameters + ---------- + obj : pandas.DataFrame + obj to be written + + path : str + path of csv file + + index : bool, default None + If True, include the dataframe's index(es) in the file output. If False, they will not be written to the file. + + partition_cols : list, optional, default None + Column names by which to partition the dataset. Columns are partitioned in the order they are given. + Must be None if path is not a string. + """ + obj.to_parquet(path, index=index, partition_cols=partition_cols) diff --git a/petml/operators/__init__.py b/petml/operators/__init__.py new file mode 100644 index 0000000..e9fec1f --- /dev/null +++ b/petml/operators/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from . import graph +from . import preprocessing +from . import boosting diff --git a/petml/operators/boosting/__init__.py b/petml/operators/boosting/__init__.py new file mode 100644 index 0000000..f923ff2 --- /dev/null +++ b/petml/operators/boosting/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .xgb_model import XGBoostClassifierFit, XGBoostClassifierPredict, XGBoostRegressorFit, XGBoostRegressorPredict diff --git a/petml/operators/boosting/xgb_model.py b/petml/operators/boosting/xgb_model.py new file mode 100644 index 0000000..9240c35 --- /dev/null +++ b/petml/operators/boosting/xgb_model.py @@ -0,0 +1,321 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from pathlib import Path + +from petml.fl.boosting import XGBoostClassifier, XGBoostRegressor +from petml.infra.engine.cipher_engine import CipherEngine +from petml.infra.storage.tabular_storage import CsvStorage +from petml.operators.operator_base import OperatorBase + + +class XGBoostClassifierFit(OperatorBase): + + def _run(self, net, configs: dict) -> bool: + """ + Train a xgboost classification model base on the data. + + Expects the following configmap: + { + "common": { + "objective": "logitraw", + "n_estimators": 100, + "max_depth": 3, + "reg_lambda": 1, + "reg_alpha": 0.0, + "min_child_weight": 0.5, + "base_score": 0.5, + "learning_rate": 0.1, + "network_mode": "petnet", + "network_scheme": "socket", + "label_name": "label", + "test_size": 0.3, + "parties": { + "party_a": { + "address": ["IP_ADDRESS:50011"] + }, + "party_b": { + "address": ["IP_ADDRESS:50012"] + } + } + }, + "party_a": { + "inputs": { + "train_data": "/path/to/data.csv", + }, + "outputs": { + "model_path": "/path/to/model_name.pkl" + } + }, + "party_b": { + "inputs": { + "train_data": "/path/to/data.csv", + }, + "outputs": { + "model_path": "/path/to/model_name.pkl" + } + } + } + """ + min_split_loss = configs.get("min_split_loss", 1e-5) + learning_rate = configs.get("learning_rate", 0.1) + n_estimators = configs.get("n_estimators", 100) + base_score = configs.get("base_score", 0.5) + max_depth = configs.get("max_depth", 3) + reg_alpha = configs.get("reg_alpha", 0.) + reg_lambda = configs.get("reg_lambda", 1.0) + min_child_samples = configs.get("min_child_samples", 1) + min_child_weight = configs.get("min_child_weight", 1) + label_name = configs.get("label_name", "label") + objective = configs.get("objective", "logitraw") + test_size = configs.get("test_size", 0.3) + + # init infra + mpc_engine = CipherEngine("mpc", self.party_id, net) + + # init io + train_data = CsvStorage.read(configs["inputs"]["train_data"]) + model_path = configs["outputs"]["model_path"] + ext = Path(model_path) + if ext.suffix != '.pkl': + raise ValueError('The `model_path` should end with the `.pkl` format.') + + # construct model + model = XGBoostClassifier(min_split_loss, + learning_rate, + n_estimators, + base_score, + max_depth, + reg_alpha, + reg_lambda, + min_child_samples, + min_child_weight, + label_name, + test_size=test_size, + objective=objective) + model.set_infra(self.party_id, net, mpc_engine) + model.fit(train_data) + model.save_model(model_path) + return True + + +class XGBoostClassifierPredict(OperatorBase): + + def _run(self, net, configs: dict) -> bool: + """ + Train a xgboost classification model base on the data. + + Expects the following configmap: + { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["IP_ADDRESS:50011"] + }, + "party_b": { + "address": ["IP_ADDRESS:50012"] + } + } + }, + "party_a": { + "inputs": { + "predict_data": "/path/to/data.csv", + "model_path": "/path/to/model_name.pkl" + }, + "outputs": { + "inference_res_path": "pathto/predict_proba_value.csv" + } + }, + "party_b": { + "inputs": { + "predict_data": "/path/to/data.csv", + "model_path": "/path/to/model_name.pkl" + }, + "outputs": { + "inference_res_path": "/path/to/predict_proba_value.csv" + } + } + } + """ + # init infra + mpc_engine = CipherEngine("mpc", self.party_id, net) + + # init io + predict_data = CsvStorage.read(configs["inputs"]["predict_data"]) + model_path = configs["inputs"]["model_path"] + ext = Path(model_path) + if ext.suffix != '.pkl': + raise ValueError('The `model_path` should end with the `.pkl` format.') + inference_res_path = configs["outputs"]["inference_res_path"] + + # inference model + model = XGBoostClassifier() + model.set_infra(self.party_id, net, mpc_engine) + model.load_model(model_path) + predict_proba = model.predict(predict_data) + CsvStorage.write(predict_proba, f"{inference_res_path}", index=False) + return True + + +class XGBoostRegressorFit(OperatorBase): + + def _run(self, net, configs: dict) -> bool: + """ + Train a xgboost classification model base on the data. + + Expects the following configmap: + { + "common": { + "objective": "squarederror", + "n_estimators": 100, + "max_depth": 3, + "reg_lambda": 1, + "reg_alpha": 0.0, + "min_child_weight": 1, + "base_score": 0.5, + "learning_rate": 0.1, + "network_mode": "petnet", + "network_scheme": "socket", + "label_name": "label", + "test_size": 0.3, + "parties": { + "party_a": { + "address": ["IP_ADDRESS:50011"] + }, + "party_b": { + "address": ["IP_ADDRESS:50012"] + } + } + }, + "party_a": { + "inputs": { + "train_data": "/path/to/data.csv", + }, + "outputs": { + "model_path": "/path/to/model_name.pkl" + } + }, + "party_b": { + "inputs": { + "train_data": "/path/to/data.csv", + }, + "outputs": { + "model_path": "/path/to/model_name.pkl" + } + } + } + """ + min_split_loss = configs.get("min_split_loss", 1e-5) + learning_rate = configs.get("learning_rate", 0.1) + n_estimators = configs.get("n_estimators", 100) + base_score = configs.get("base_score", 0.5) + max_depth = configs.get("max_depth", 3) + reg_alpha = configs.get("reg_alpha", 0.) + reg_lambda = configs.get("reg_lambda", 1.0) + min_child_samples = configs.get("min_child_samples", 1) + min_child_weight = configs.get("min_child_weight", 1) + label_name = configs.get("label_name", "label") + objective = configs.get("objective", "squarederror") + test_size = configs.get("test_size", 0.3) + + # init infra + mpc_engine = CipherEngine("mpc", self.party_id, net) + + # init io + train_data = CsvStorage.read(configs["inputs"]["train_data"]) + model_path = configs["outputs"]["model_path"] + ext = Path(model_path) + if ext.suffix != '.pkl': + raise ValueError('The `model_path` should end with the `.pkl` format.') + + # construct model + model = XGBoostRegressor(min_split_loss, + learning_rate, + n_estimators, + base_score, + max_depth, + reg_alpha, + reg_lambda, + min_child_samples, + min_child_weight, + label_name, + test_size=test_size, + objective=objective) + model.set_infra(self.party_id, net, mpc_engine) + model.fit(train_data) + model.save_model(model_path) + return True + + +class XGBoostRegressorPredict(OperatorBase): + + def _run(self, net, configs: dict) -> bool: + """ + Train a xgboost classification model base on the data. + + Expects the following configmap: + { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["IP_ADDRESS:50011"] + }, + "party_b": { + "address": ["IP_ADDRESS:50012"] + } + } + }, + "party_a": { + "inputs": { + "predict_data": "/path/to/data.csv", + "model_path": "/path/to/model_name.pkl" + }, + "outputs": { + "inference_res_path": "/path/to/predict_value.csv" + } + }, + "party_b": { + "inputs": { + "predict_data": "/path/to/data.csv", + "model_path": "/path/to/model_name.pkl" + } + "outputs": { + "inference_res_path": "path/to/predict_value.csv" + } + } + } + """ + # init infra + mpc_engine = CipherEngine("mpc", self.party_id, net) + + # init io + predict_data = CsvStorage.read(configs["inputs"]["predict_data"]) + model_path = configs["inputs"]["model_path"] + ext = Path(model_path) + if ext.suffix != '.pkl': + raise ValueError('The `model_path` should end with the `.pkl` format.') + inference_res_path = configs["outputs"]["inference_res_path"] + + # inference model + model = XGBoostRegressor() + model.set_infra(self.party_id, net, mpc_engine) + model.load_model(model_path) + predict_label = model.predict(predict_data) + + CsvStorage.write(predict_label, f"{inference_res_path}", index=False) + return True diff --git a/petml/operators/graph/__init__.py b/petml/operators/graph/__init__.py new file mode 100644 index 0000000..a304a32 --- /dev/null +++ b/petml/operators/graph/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .leiden import LeidenTransform diff --git a/petml/operators/graph/leiden.py b/petml/operators/graph/leiden.py new file mode 100644 index 0000000..82f396d --- /dev/null +++ b/petml/operators/graph/leiden.py @@ -0,0 +1,79 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from petml.fl.graph import Leiden +from petml.infra.engine.cipher_engine import CipherEngine +from petml.infra.storage.tabular_storage import CsvStorage +from petml.infra.storage.graph_storage import JsonStorage +from petml.operators.operator_base import OperatorBase + + +class LeidenTransform(OperatorBase): + + def _run(self, net, configs: dict) -> bool: + """ + Leiden algorithm. + + Expects the following configmap: + { + "common": { + "max_move": 20, + "max_merge": 10, + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["IP_ADDRESS:50011"] + }, + "party_b": { + "address": ["IP_ADDRESS:50012"] + } + } + }, + "party_a": { + "inputs": { + "user_weights": "/path/to/data.json", + "local_nodes": "/path/to/data.json", + } + "outputs": { + "cluster": "/path/to/data.csv" + } + }, + "party_b": { + "inputs": { + "user_weights": "/path/to/data.json", + "local_nodes": "/path/to/data.json", + } + "outputs": { + "cluster": "/path/to/data.csv" + } + } + } + """ + mpc_engine = CipherEngine("mpc", self.party_id, net) + + max_move = configs.get("max_move", 20) + max_merge = configs.get("max_move", 10) + + # init io + user_weights = JsonStorage.read(configs["inputs"]["user_weights"]) + local_nodes = JsonStorage.read(configs["inputs"]["local_nodes"]) + + # construct model + model = Leiden(max_move, max_merge) + model.set_infra(self.party_id, net, mpc_engine) + res = model.transform(user_weights, local_nodes) + + CsvStorage.write(res, configs["outputs"]["cluster"], index=False) + return True diff --git a/petml/operators/operator_base.py b/petml/operators/operator_base.py new file mode 100644 index 0000000..5ddf758 --- /dev/null +++ b/petml/operators/operator_base.py @@ -0,0 +1,59 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from copy import deepcopy +from typing import Dict +from abc import ABC, abstractmethod + +from petml.infra.network import ClusterDef, NetworkFactory + + +class OperatorBase(ABC): + + def __init__(self, party: str, **kwargs): + self.party = party + self.party_id = None + + def _trans_parties(self, network_scheme: str, parties: dict): + """ + Transform the parties to a int party_id. + """ + parties_map = dict(zip(sorted(parties.keys()), range(len(parties)))) + self.party_id = parties_map[self.party] + if network_scheme == "agent": + new_parties = parties + else: + new_parties = {parties_map[party]: parties[party] for party in parties} + return new_parties + + def run(self, configmap: Dict, config_manager: "ConfigManager" = None): + # init infra + parties = self._trans_parties(configmap["common"]["network_scheme"], configmap["common"]["parties"]) + cluster_def = ClusterDef(configmap["common"]["network_mode"], configmap["common"]["network_scheme"], parties, + configmap["common"].get("shared_topic")) + if configmap["common"]["network_scheme"] == "agent": + net = NetworkFactory.get_network(self.party, cluster_def) + elif configmap["common"]["network_scheme"] == "socket": + net = NetworkFactory.get_network(self.party_id, cluster_def) + else: + raise ValueError(f"network scheme {configmap['common']['network_scheme']} not supported") + + configs = deepcopy(configmap["common"]) + configs.update(configmap[self.party]) + self._run(net, configs) + return True + + @abstractmethod + def _run(self, net, configs: dict): + pass diff --git a/petml/operators/preprocessing/__init__.py b/petml/operators/preprocessing/__init__.py new file mode 100644 index 0000000..1b4283b --- /dev/null +++ b/petml/operators/preprocessing/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .psi import PSITransform diff --git a/petml/operators/preprocessing/psi.py b/petml/operators/preprocessing/psi.py new file mode 100644 index 0000000..a8a9114 --- /dev/null +++ b/petml/operators/preprocessing/psi.py @@ -0,0 +1,77 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from petml.operators.operator_base import OperatorBase +from petml.fl.preprocessing import PSI +from petml.infra.engine.cipher_engine import CipherEngine +from petml.infra.storage.tabular_storage import CsvStorage + + +class PSITransform(OperatorBase): + + def _run(self, net, configs: dict) -> bool: + """ + Perform PSI transform on the data. + + Expects the following configmap: + { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["IP_ADDRESS:50011"] + }, + "party_b": { + "address": ["IP_ADDRESS:50012"] + } + } + "protocol": "kkrt" + }, + "party_a": { + "column_name" : "id" + "inputs": { + "data": "/path/to/data.csv" + } + "outputs": { + "data": "/path/to/data.csv" + } + }, + "party_b": { + "column_name" : "id" + "inputs": { + "data": "/path/to/data.csv" + } + "outputs": { + "data": "/path/to/data.csv" + } + } + } + """ + column_name = configs["column_name"] + protocol = configs.get("protocol", "kkrt") + if protocol not in ("ecdh", "kkrt"): + raise ValueError(f"Protocol {protocol} not supported") + psi_engine = CipherEngine(f"{protocol}_psi", self.party_id, net) + + # init io + data = CsvStorage.read(configs["inputs"]["data"]) + + # construct model + model = PSI(column_name) + model.set_infra(psi_engine) + res = model.transform(data) + + CsvStorage.write(res, configs["outputs"]["data"], index=False) + return True diff --git a/petml/version.py b/petml/version.py new file mode 100644 index 0000000..ebc521c --- /dev/null +++ b/petml/version.py @@ -0,0 +1,15 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__version__ = "0.1.0" diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..e373743 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,6 @@ +[pytest] +addopts = -s -v --cov=petml/ +testpaths = ./tests +python_files = test*.py +python_classes = Test* +python_functions = test* diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d2a6177 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +numpy~=1.25 +pytest~=7.3.1 +pandas~=2.0.2 +pytest-cov~=4.1.0 +pyarrow>=14.0.1 +scikit-learn>=1.5.0 +petace==0.3.0 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..f291d7f --- /dev/null +++ b/setup.py @@ -0,0 +1,58 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import re +import sys +from setuptools import find_packages, setup + + +def find_version(*filepath): + # Extract version information from filepath + with open(os.path.join('.', *filepath)) as fp: + version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", fp.read(), re.M) + if version_match: + return version_match.group(1) + print("Unable to find version string.") + sys.exit(-1) + + +def read_requirements(): + requirements = [] + _dependency_links = [] + with open('./requirements.txt') as file: + requirements = file.read().splitlines() + for r in requirements: + if r.startswith("--extra-index-url"): + requirements.remove(r) + _dependency_links.append(r) + print("Requirements: ", requirements) + print("Dependency: ", _dependency_links) + return requirements, _dependency_links + + +install_requires, dependency_links = read_requirements() + +setup( + name='petml', + version=find_version("petml", "version.py"), + license='Apache 2.0', + description='privacy preserving machine learning', + author='PrivacyGo-PETPlatform', + author_email='privacygo-petplatform@tiktok.com', + python_requires="==3.9", + packages=find_packages(exclude=('examples', 'examples.*', 'tests', 'tests.*')), + install_requires=install_requires, + dependency_links=dependency_links, +) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/configs.py b/tests/configs.py new file mode 100644 index 0000000..4fed464 --- /dev/null +++ b/tests/configs.py @@ -0,0 +1,18 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from petml.infra.network import ClusterDef + +parties = {0: {'address': ['127.0.0.1:50011'],}, 1: {'address': ['127.0.0.1:50012'],}} +CLUSTER_DEF = ClusterDef(mode="petnet", scheme="socket", parties=parties) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..cfb191d --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,30 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import shutil +import pytest + +sys.path.append(os.getcwd()) +sys.path.insert(0, "../") + + +@pytest.fixture(scope="session", autouse=True) +def set_tmp_path(): + os.makedirs("./tmp/client", exist_ok=True) + os.makedirs("./tmp/server", exist_ok=True) + yield + if os.path.exists("./tmp"): + shutil.rmtree("./tmp") diff --git a/tests/fl/__init__.py b/tests/fl/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/fl/graph/test_leiden.py b/tests/fl/graph/test_leiden.py new file mode 100644 index 0000000..a258ec1 --- /dev/null +++ b/tests/fl/graph/test_leiden.py @@ -0,0 +1,109 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +import pandas as pd + +from tests.configs import CLUSTER_DEF +from tests.utils import run_multi_process +from petml.fl.graph import Leiden +from petml.infra.storage.graph_storage import JsonStorage +from petml.infra.storage.tabular_storage import CsvStorage +from petml.infra.network.network_factory import NetworkFactory +from petml.infra.engine.cipher_engine import CipherEngine + + +class TestLeiden: + + def run_leiden(self, party_id, cluster_def, user_weights, local_nodes, output_path): + net = NetworkFactory.get_network(party_id, cluster_def) + mpc_engine = CipherEngine("mpc", party_id, net) + model = Leiden(max_move=20, max_merge=10) + model.set_infra( + party_id=party_id, + federation=net, + mpc_engine=mpc_engine, + ) + res = model.transform(user_weights, local_nodes) + CsvStorage.write(res, output_path, index=False) + + def test_leiden(self): + user_weights_a = JsonStorage.read("examples/data/karate_club_user_weight1.json") + user_weights_b = JsonStorage.read("examples/data/karate_club_user_weight2.json") + local_nodes_a = JsonStorage.read("examples/data/karate_club_local_nodes1.json") + local_nodes_b = JsonStorage.read("examples/data/karate_club_local_nodes2.json") + tmpfile_a = "tmp/cluster_a.csv" + tmpfile_b = "tmp/cluster_b.csv" + + expected_modularity = 0.41 + configs = [(0, CLUSTER_DEF, user_weights_a, local_nodes_a, tmpfile_a), + (1, CLUSTER_DEF, user_weights_b, local_nodes_b, tmpfile_b)] + + run_multi_process(self.run_leiden, configs) + g = load_graph("examples/data/karate_club_user_weight1.json", "examples/data/karate_club_user_weight2.json") + partition = get_partition(tmpfile_a, tmpfile_b) + assert modularity(g, partition) > expected_modularity + + +def get_partition(file1, file2): + partitions1 = [] + for file in (file1, file2): + with open(file, "r") as f: + data = f.readlines() + data = data[1:] + for line in data: + vertices = line.strip().split(',') + vid = int(vertices[0]) + cid = vertices[1] + partitions1.append([vid, cid]) + partitions1 = pd.DataFrame(partitions1, columns=["user_id", "label"]) + return partitions1 + + +def load_graph(path1, path2): + g1 = JsonStorage.read(path1) + g2 = JsonStorage.read(path2) + for n1, n1_adj in g2.items(): + for n2, w in n1_adj.items(): + if n1 not in g1[n2]: + g1[n2][n1] = 0 + if n2 not in g1[n1]: + g1[n1][n2] = 0 + g1[n1][n2] += w / 2 + g1[n2][n1] += w / 2 + return g1 + + +def modularity(G, partition): + community = collections.defaultdict(set) + m = 0 + for u, v in partition.values: + community[v].add(u) + for u in G: + for v in G[u]: + m += G[u][v] + m /= 2 + sum_in = 0 + sum_tot = 0 + for com in community.values(): + c_sum_in = 0 + c_sum_tot = 0 + for u in com: + for v in G[u]: + c_sum_tot += G[u][v] + if v in com: + c_sum_in += G[u][v] + sum_in += c_sum_in + sum_tot += c_sum_tot**2 + return sum_in / (2 * m) - sum_tot / (4 * m**2) diff --git a/tests/fl/preprocessing/test_psi.py b/tests/fl/preprocessing/test_psi.py new file mode 100644 index 0000000..3ec0025 --- /dev/null +++ b/tests/fl/preprocessing/test_psi.py @@ -0,0 +1,41 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tests.configs import CLUSTER_DEF +from tests.utils import run_multi_process +from petml.fl.preprocessing import PSI +from petml.infra.network import NetworkFactory +from petml.infra.engine.cipher_engine import CipherEngine +from petml.infra.storage.tabular_storage import CsvStorage + + +class TestPSI: + + def test_psi(self): + + def run_psi(party_id, column_name, cluster_def, data, plain_res): + net = NetworkFactory.get_network(party_id, cluster_def) + psi_engine = CipherEngine("kkrt_psi", party_id, net) + model = PSI(column_name) + model.set_infra(psi_engine=psi_engine) + res = model.transform(data) + assert set(res[column_name]) == set(plain_res) + + column_name = "id" + data_a = CsvStorage.read("examples/data/breast_hetero_mini_client.csv") + data_b = CsvStorage.read("examples/data/breast_hetero_mini_server.csv") + plain_res = set(data_a["id"]) & set(data_b["id"]) + + configs = [(0, column_name, CLUSTER_DEF, data_a, plain_res), (1, column_name, CLUSTER_DEF, data_b, plain_res)] + run_multi_process(run_psi, configs) diff --git a/tests/infra/__init__.py b/tests/infra/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/infra/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/infra/common/__init__.py b/tests/infra/common/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/infra/common/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/infra/common/test_log.py b/tests/infra/common/test_log.py new file mode 100644 index 0000000..a7d83ac --- /dev/null +++ b/tests/infra/common/test_log.py @@ -0,0 +1,25 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from petml.infra.common.log_utils import LoggerFactory + + +class TestLoggerFactory: + + def test_basic(self): + logger = LoggerFactory.get_logger("test") + logger.info("info") + logger.debug("debug") + logger.warning("warning") + logger.error("error") diff --git a/tests/infra/engine/__init__.py b/tests/infra/engine/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/infra/engine/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/infra/engine/test_cipher_engine.py b/tests/infra/engine/test_cipher_engine.py new file mode 100644 index 0000000..0ef0f1b --- /dev/null +++ b/tests/infra/engine/test_cipher_engine.py @@ -0,0 +1,86 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import multiprocessing +import numpy as np +import numpy.testing as npt +import petace.securenumpy as snp + +from petml.infra.engine.cipher_engine import CipherEngine +from petml.infra.network.network_factory import NetworkFactory +from tests.configs import CLUSTER_DEF + + +class TestCipherEngine: + + @staticmethod + def create_petace_engine(que, data, party_id, cluster_def): + net = NetworkFactory.get_network(party_id, cluster_def) + petace_engine = CipherEngine("mpc", party_id, net).engine + snp.set_vm(petace_engine) + data0 = snp.array(data, 0) + data1 = snp.array(data, 1) + res_add = data0 + data1 + res_mul = data0 * data1 + res_comp = data0 > data1 + res_add_plain = res_add.reveal_to(0) + res_mul_plian = res_mul.reveal_to(0) + res_comp_plain = res_comp.reveal_to(0) + if party_id == 0: + que.put(res_add_plain) + que.put(res_mul_plian) + que.put(res_comp_plain) + + @staticmethod + def create_psi_engine(que, data, party_id, cluster_def, protocol): + net = NetworkFactory.get_network(party_id, cluster_def) + psi_engine = CipherEngine(protocol, party_id, net).engine + intersection = psi_engine.process(data, True) + que.put(intersection) + + @staticmethod + def run_process(target, args0, args1): + p0 = multiprocessing.Process(target=target, args=args0) + p1 = multiprocessing.Process(target=target, args=args1) + p0.start() + p1.start() + p0.join() + p1.join() + + def test_petace(self): + que = multiprocessing.Queue() + data0 = np.arange(10).astype(np.float64).reshape((1, -1)) + data1 = np.arange(5, 15).astype(np.float64).reshape((1, -1)) + cluster_def = CLUSTER_DEF + args0 = (que, data0, 0, cluster_def) + args1 = (que, data1, 1, cluster_def) + self.run_process(self.create_petace_engine, args0, args1) + result = que.get() + npt.assert_equal(result, data0 + data1) + result = que.get() + npt.assert_almost_equal(result, data0 * data1, decimal=4) + result = que.get() + npt.assert_equal(result, data0 > data1) + + def test_psi(self): + for protocol in ("ecdh_psi", "kkrt_psi"): + que = multiprocessing.Queue() + data0 = [f"{i}" for i in range(10)] + data1 = [f"{i}" for i in range(5, 15)] + cluster_def = CLUSTER_DEF + args0 = (que, data0, 0, cluster_def, protocol) + args1 = (que, data1, 1, cluster_def, protocol) + self.run_process(self.create_psi_engine, args0, args1) + result = que.get() + assert (set(data0) & set(data1)) == set(result) diff --git a/tests/infra/network/__init__.py b/tests/infra/network/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/infra/network/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/infra/network/test_petnet.py b/tests/infra/network/test_petnet.py new file mode 100644 index 0000000..08d2db0 --- /dev/null +++ b/tests/infra/network/test_petnet.py @@ -0,0 +1,46 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy.testing as npt + +from petml.infra.network import NetworkFactory +from tests.process import Process +from tests.configs import CLUSTER_DEF + + +class TestSocketFederation: + + def sync_message(self, party): + message = b"hello world" + F = NetworkFactory.get_network(party, CLUSTER_DEF) + if party == 0: + F.remote(message) + else: + res = F.get() + npt.assert_equal(res, message) + + F.clear() + + def test_basic(self): + p0 = Process(target=self.sync_message, args=(0,)) + p1 = Process(target=self.sync_message, args=(1,)) + p0.start() + p1.start() + p0.join() + p1.join() + for p in (p0, p1): + if p.exception: + error, _ = p.exception + p.terminate() + raise AssertionError(error) diff --git a/tests/infra/storage/__init__.py b/tests/infra/storage/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/infra/storage/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/infra/storage/test_tabular.py b/tests/infra/storage/test_tabular.py new file mode 100644 index 0000000..8601286 --- /dev/null +++ b/tests/infra/storage/test_tabular.py @@ -0,0 +1,33 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from petml.infra.storage.tabular_storage import CsvStorage, ParquetStorage + + +class TestCsv: + + def test_read_write(self, tmp_path): + path = "examples/data/breast_hetero_mini_client.csv" + df = CsvStorage.read(path) + save_path = str(tmp_path / "res1.csv") + CsvStorage.write(df, save_path) + + +class TestParquet: + + def test_read_write(self, tmp_path): + path = "examples/data/breast_hetero_mini_client.parquet" + df = ParquetStorage.read(path) + save_path = str(tmp_path / "res1.parquet") + ParquetStorage.write(df, save_path) diff --git a/tests/operators/__init__.py b/tests/operators/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/operators/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/operators/boosting/__init__.py b/tests/operators/boosting/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/operators/boosting/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/operators/boosting/test_xgb.py b/tests/operators/boosting/test_xgb.py new file mode 100644 index 0000000..d519d40 --- /dev/null +++ b/tests/operators/boosting/test_xgb.py @@ -0,0 +1,217 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pandas as pd +from sklearn.metrics import accuracy_score, mean_squared_error + +from petml.operators.boosting import XGBoostClassifierFit, XGBoostClassifierPredict, XGBoostRegressorFit, \ + XGBoostRegressorPredict +from tests.utils import run_multi_process + + +class TestXGBoostClassifier: + + def _run_fit_classifier(self, party, config_map): + operator = XGBoostClassifierFit(party) + operator.run(config_map) + + def _run_predict_classifier(self, party, config_map): + operator = XGBoostClassifierPredict(party) + operator.run(config_map) + + def test_xgb_classifier(self): + fit_configmap = { + "common": { + "objective:": "logitraw", + "n_estimators": 1, + "max_depth": 2, + "reg_lambda": 1, + "reg_alpha": 0.0, + "base_score": 0.5, + "learning_rate": 0.1, + "min_child_weight": 0.1, + "network_mode": "petnet", + "network_scheme": "socket", + "label_name": "label", + "test_size": 0., + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "train_data": "examples/data/iris_binary_mini_server.csv", + }, + "outputs": { + "model_path": "tmp/test_binary_xgb_server.pkl" + } + }, + "party_b": { + "inputs": { + "train_data": "examples/data/iris_binary_mini_client.csv", + }, + "outputs": { + "model_path": "tmp/test_binary_xgb_client.pkl" + } + } + } + predict_configmap = { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "predict_data": "examples/data/iris_binary_mini_server.csv", + "model_path": "tmp/test_binary_xgb_server.pkl" + }, + "outputs": { + "inference_res_path": "tmp/test_binary_predict_server.csv" + } + }, + "party_b": { + "inputs": { + "predict_data": "examples/data/iris_binary_mini_client.csv", + "model_path": "tmp/test_binary_xgb_client.pkl" + }, + "outputs": { + "inference_res_path": "tmp/test_binary_predict_client.csv" + } + } + } + data1 = pd.read_csv(predict_configmap['party_a']['inputs']['predict_data']) + data2 = pd.read_csv(predict_configmap['party_b']['inputs']['predict_data']) + true_label = pd.concat([data1, data2], axis=0)['label'].values + run_multi_process(self._run_fit_classifier, [("party_a", fit_configmap), ("party_b", fit_configmap)]) + run_multi_process(self._run_predict_classifier, [("party_a", predict_configmap), + ("party_b", predict_configmap)]) + server_result_save_path = predict_configmap["party_a"]["outputs"]["inference_res_path"] + client_result_save_path = predict_configmap["party_b"]["outputs"]["inference_res_path"] + server_y_pred = pd.read_csv(f"{server_result_save_path}").values + client_y_pred = pd.read_csv(f"{client_result_save_path}").values + y_pred = np.vstack((server_y_pred, client_y_pred)) + y_pred = np.where(y_pred >= 0.5, 1, 0) + acc_value = accuracy_score(true_label, y_pred) + assert abs(acc_value - 1) < 0.01 + + +class TestXGBoostRegressor: + + def _run_fit_regressor(self, party, config_map): + operator = XGBoostRegressorFit(party) + operator.run(config_map) + + def _run_predict_regressor(self, party, config_map): + operator = XGBoostRegressorPredict(party) + operator.run(config_map) + + def test_xgb_regressor(self): + fit_configmap = { + "common": { + "objective:": "squarederror", + "n_estimators": 1, + "max_depth": 2, + "reg_lambda": 1, + "reg_alpha": 0.0, + "base_score": 0.5, + "learning_rate": 0.1, + "min_child_weight": 1, + "network_mode": "petnet", + "network_scheme": "socket", + "label_name": "label", + "test_size": 0., + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "train_data": "examples/data/students_reg_mini_server.csv", + }, + "outputs": { + "model_path": "tmp/test_reg_xgb_server.pkl" + } + }, + "party_b": { + "inputs": { + "train_data": "examples/data/students_reg_mini_client.csv", + }, + "outputs": { + "model_path": "tmp/test_reg_xgb_client.pkl" + } + } + } + predict_configmap = { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "predict_data": "examples/data/students_reg_mini_server.csv", + "model_path": "tmp/test_reg_xgb_server.pkl" + }, + "outputs": { + "inference_res_path": "tmp/test_reg_predict_server.csv" + } + }, + "party_b": { + "inputs": { + "predict_data": "examples/data/students_reg_mini_client.csv", + "model_path": "tmp/test_reg_xgb_client.pkl" + }, + "outputs": { + "inference_res_path": "tmp/test_reg_predict_client.csv" + } + } + } + data1 = pd.read_csv(predict_configmap['party_a']['inputs']['predict_data']) + data2 = pd.read_csv(predict_configmap['party_b']['inputs']['predict_data']) + true_label = pd.concat([data1, data2], axis=0)['label'].values + run_multi_process(self._run_fit_regressor, [("party_a", fit_configmap), ("party_b", fit_configmap)]) + run_multi_process(self._run_predict_regressor, [("party_a", predict_configmap), ("party_b", predict_configmap)]) + server_result_save_path = predict_configmap["party_a"]["outputs"]["inference_res_path"] + client_result_save_path = predict_configmap["party_b"]["outputs"]["inference_res_path"] + server_y_pred = pd.read_csv(f"{server_result_save_path}").values + client_y_pred = pd.read_csv(f"{client_result_save_path}").values + y_pred = np.vstack((server_y_pred, client_y_pred)) + mse_error_value = mean_squared_error(true_label, y_pred) + assert abs(mse_error_value - 122.04) < 10 diff --git a/tests/operators/graph/__init__.py b/tests/operators/graph/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/operators/graph/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/operators/graph/test_leiden.py b/tests/operators/graph/test_leiden.py new file mode 100644 index 0000000..6f7da3e --- /dev/null +++ b/tests/operators/graph/test_leiden.py @@ -0,0 +1,63 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import petml + +from tests.utils import run_multi_process + +config_map = { + "common": { + "max_move": 20, + "max_merge": 10, + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "inputs": { + "user_weights": "examples/data/karate_club_user_weight1.json", + "local_nodes": "examples/data/karate_club_local_nodes1.json", + }, + "outputs": { + "cluster": "tmp/server/cluster.csv" + } + }, + "party_b": { + "inputs": { + "user_weights": "examples/data/karate_club_user_weight2.json", + "local_nodes": "examples/data/karate_club_local_nodes2.json", + }, + "outputs": { + "cluster": "tmp/client/cluster.csv" + } + } +} + + +class TestLeidenTransform: + + def test_leiden_transform(self): + + def run_leiden(party, config_map): + operator = petml.operators.graph.LeidenTransform(party) + operator.run(config_map) + + run_multi_process(run_leiden, [("party_a", config_map), ("party_b", config_map)]) diff --git a/tests/operators/preprocessing/__init__.py b/tests/operators/preprocessing/__init__.py new file mode 100644 index 0000000..4d6166b --- /dev/null +++ b/tests/operators/preprocessing/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/operators/preprocessing/test_psi.py b/tests/operators/preprocessing/test_psi.py new file mode 100644 index 0000000..674c562 --- /dev/null +++ b/tests/operators/preprocessing/test_psi.py @@ -0,0 +1,61 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import petml + +from tests.utils import run_multi_process + +config_map = { + "common": { + "network_mode": "petnet", + "network_scheme": "socket", + "parties": { + "party_a": { + "address": ["127.0.0.1:50011"] + }, + "party_b": { + "address": ["127.0.0.1:50012"] + } + } + }, + "party_a": { + "column_name": "id", + "inputs": { + "data": "examples/data/breast_hetero_mini_server.csv", + }, + "outputs": { + "data": "tmp/server/data.csv" + } + }, + "party_b": { + "column_name": "id", + "inputs": { + "data": "examples/data/breast_hetero_mini_client.csv", + }, + "outputs": { + "data": "tmp/client/data.csv" + } + } +} + + +class TestPSITransform: + + def test_psi_transform(self): + + def run_leiden(party, config_map): + operator = petml.operators.preprocessing.PSITransform(party) + operator.run(config_map) + + run_multi_process(run_leiden, [("party_a", config_map), ("party_b", config_map)]) diff --git a/tests/process.py b/tests/process.py new file mode 100644 index 0000000..8ebb309 --- /dev/null +++ b/tests/process.py @@ -0,0 +1,38 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import multiprocessing +import traceback + + +class Process(multiprocessing.Process): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._pconn, self._cconn = multiprocessing.Pipe() + self._exception = None + + def run(self): + try: + multiprocessing.Process.run(self) + self._cconn.send(None) + except Exception as e: + tb = traceback.format_exc() + self._cconn.send((e, tb)) + + @property + def exception(self): + if self._pconn.poll(): + self._exception = self._pconn.recv() + return self._exception diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 0000000..91281b4 --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,40 @@ +# Copyright 2024 TikTok Pte. Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tests.process import Process + + +class FLTestException(Exception): + + def __init__(self, message) -> None: + self.message = message + + def __str__(self): + return f"test failed: {self.message}" + + +def run_multi_process(func, args_list): + process_list = [] + for args in args_list: + p = Process(target=func, args=args) + p.start() + process_list.append(p) + for p in process_list: + p.join() + + for p in process_list: + if p.exception: + error, _ = p.exception + p.terminate() + raise FLTestException(error)