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cleanup (#2)
* cleanup * update workflows * precommit * fixes
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‎.github/workflows/release.yml‎

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name: Package Release
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on:
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release:
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types: [created]
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jobs:
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deploy_osx:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: ["3.7", "3.8", "3.9", "3.10"]
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os: [macos-latest]
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steps:
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- uses: actions/checkout@v2
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with:
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submodules: true
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- name: Set up Python
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uses: actions/setup-python@v1
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with:
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python-version: ${{ matrix.python-version }}
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- name: Build and publish
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env:
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TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
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TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
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run: ${GITHUB_WORKSPACE}/.github/workflows/scripts/release_osx.sh
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deploy_linux:
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strategy:
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matrix:
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python-version:
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- cp37-cp37m
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- cp38-cp38
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- cp39-cp39
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- cp310-cp310
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runs-on: ubuntu-latest
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container: quay.io/pypa/manylinux2014_x86_64
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steps:
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- uses: actions/checkout@v1
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with:
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submodules: true
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- name: Set target Python version PATH
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run: |
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echo "/opt/python/${{ matrix.python-version }}/bin" >> $GITHUB_PATH
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- name: Build and publish
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env:
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TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
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TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
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run: ${GITHUB_WORKSPACE}/.github/workflows/scripts/release_linux.sh
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deploy_windows:
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runs-on: windows-latest
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strategy:
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matrix:
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python-version: ["3.7", "3.8", "3.9", "3.10"]
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steps:
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- uses: actions/checkout@v2
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with:
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submodules: true
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v1
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with:
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python-version: ${{ matrix.python-version }}
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- name: Build and publish
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env:
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TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
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TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
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run: |
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../../.github/workflows/scripts/release_windows.bat

‎.github/workflows/scripts/release_linux.sh‎

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#!/bin/bash
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set -e
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yum makecache -y
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yum install centos-release-scl -y
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yum-config-manager --enable rhel-server-rhscl-7-rpms
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yum install llvm-toolset-7.0 python3 python3-devel -y
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# Python
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python3 -m pip install --upgrade pip
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python3 -m pip install setuptools wheel twine auditwheel
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# Publish
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python3 -m pip wheel . -w dist/ --no-deps
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twine upload --verbose --skip-existing dist/*

‎.github/workflows/scripts/release_osx.sh‎

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#!/bin/sh
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export MACOSX_DEPLOYMENT_TARGET=10.14
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python -m pip install --upgrade pip
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pip install setuptools wheel twine auditwheel
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python3 setup.py build bdist_wheel --plat-name macosx_10_14_x86_64 --dist-dir wheel
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twine upload --skip-existing wheel/*

‎.github/workflows/scripts/release_windows.bat‎

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echo on
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python -m pip install --upgrade pip
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pip install setuptools wheel twine auditwheel
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pip wheel . -w wheel/ --no-deps
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twine upload --skip-existing wheel/*

‎.github/workflows/test_decaf.yml‎

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name: DECAF
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name: Tests
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on:
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push:
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branches: [main, release]
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pull_request:
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types: [opened, synchronize, reopened]
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schedule:
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- cron: '6 5 * * 3'
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jobs:
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Linter:
@@ -34,7 +37,7 @@ jobs:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8, 3.9]
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python-version: ["3.7", "3.8", "3.9", "3.10"]
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os: [macos-latest, ubuntu-latest, windows-latest]
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steps:
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- uses: actions/checkout@v2

‎.pre-commit-config.yaml‎

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@@ -1,7 +1,7 @@
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exclude: 'setup.py|^docs'
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repos:
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- repo: git://github.com/pre-commit/pre-commit-hooks
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v3.4.0
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hooks:
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- id: trailing-whitespace
@@ -24,34 +24,18 @@ repos:
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- id: isort
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- repo: https://github.com/psf/black
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rev: 21.4b0
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rev: 22.3.0
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hooks:
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- id: black
3030
language_version: python3
31-
- repo: https://gitlab.com/pycqa/flake8
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- repo: https://github.com/pycqa/flake8
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rev: 3.9.1
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hooks:
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- id: flake8
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args: [
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"--max-line-length=340",
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"--max-line-length=440",
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"--extend-ignore=E203,W503"
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]
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v0.812
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hooks:
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- id: mypy
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args: [
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"--ignore-missing-imports",
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"--scripts-are-modules",
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"--disallow-incomplete-defs",
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"--no-implicit-optional",
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"--warn-unused-ignores",
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"--warn-redundant-casts",
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"--strict-equality",
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"--warn-unreachable",
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"--disallow-untyped-defs",
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"--disallow-untyped-calls",
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]
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- repo: local
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hooks:
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- id: flynt

‎decaf/DECAF.py‎

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@@ -10,14 +10,16 @@
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import decaf.logger as log
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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1315

1416
class TraceExpm(torch.autograd.Function):
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@staticmethod
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def forward(ctx: Any, input: torch.Tensor) -> torch.Tensor:
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# detach so we can cast to NumPy
1820
E = slin.expm(input.detach().numpy())
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f = np.trace(E)
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E = torch.from_numpy(E)
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E = torch.from_numpy(E).to(DEVICE)
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ctx.save_for_backward(E)
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return torch.as_tensor(f, dtype=input.dtype)
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@@ -183,8 +185,8 @@ def __init__(
183185
h_dim=h_dim,
184186
use_mask=use_mask,
185187
dag_seed=dag_seed,
186-
)
187-
self.discriminator = Discriminator(x_dim=self.x_dim, h_dim=h_dim)
188+
).to(DEVICE)
189+
self.discriminator = Discriminator(x_dim=self.x_dim, h_dim=h_dim).to(DEVICE)
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189191
self.dag_seed = dag_seed
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216218
)[0]
217219
W[i] = torch.sum(torch.abs(gradients), axis=0)
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219-
h = trace_expm(W ** 2) - self.hparams.x_dim
221+
h = trace_expm(W**2) - self.hparams.x_dim
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221223
return 0.5 * self.hparams.rho * h * h + self.hparams.alpha * h
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@@ -277,10 +279,10 @@ def get_W(self) -> torch.Tensor:
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278280
def dag_loss(self) -> torch.Tensor:
279281
W = self.get_W()
280-
h = trace_expm(W ** 2) - self.x_dim
282+
h = trace_expm(W**2) - self.x_dim
281283
l1_loss = torch.norm(W, 1)
282284
return (
283-
0.5 * self.hparams.rho * h ** 2
285+
0.5 * self.hparams.rho * h**2
284286
+ self.hparams.alpha * h
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+ self.hparams.l1_W * l1_loss
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)

‎decaf/version.py‎

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@@ -1 +1 @@
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__version__ = "0.1.1"
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__version__ = "0.1.2"

‎requirements.txt‎

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loguru
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networkx == 2.*
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numpy == 1.19.*
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networkx>=2.0
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numpy>=1.19
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pandas
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pytorch-lightning == 1.4.*
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pytorch-lightning>=1.4
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scipy
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sklearn
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torch == 1.9.*
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torchtext == 0.10.*
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torch>=1.9
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torchtext>=0.10.*
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xgboost

‎tests/test_decaf.py‎

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import networkx as nx
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import numpy as np
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import pandas as pd
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import pytest
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import pytorch_lightning as pl
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import torch
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from sklearn.metrics import precision_score, recall_score, roc_auc_score
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from utils import gen_data_nonlinear, load_adult
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from xgboost import XGBClassifier
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from utils import gen_data_nonlinear
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from decaf import DECAF, DataModule
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7268
dummy_dm.dims[0],
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dag_seed=seed,
7470
)
75-
trainer = pl.Trainer(max_epochs=2, logger=False)
71+
trainer = pl.Trainer(max_epochs=100, logger=True)
7672

7773
trainer.fit(model, dummy_dm)
7874

@@ -84,53 +80,3 @@ def test_sanity_generate() -> None:
8480
.numpy()
8581
)
8682
assert synth_data.shape[0] == 10
87-
88-
89-
@pytest.mark.parametrize("X,y", [load_adult()])
90-
@pytest.mark.slow
91-
def test_run_experiments(X: pd.DataFrame, y: pd.DataFrame) -> None:
92-
baseline_clf = XGBClassifier().fit(X, y)
93-
y_pred = baseline_clf.predict(X)
94-
95-
print(
96-
"baseline scores",
97-
precision_score(y, y_pred),
98-
recall_score(y, y_pred),
99-
roc_auc_score(y, y_pred),
100-
)
101-
102-
dm = DataModule(X)
103-
104-
model = DECAF(
105-
dm.dims[0],
106-
use_mask=True,
107-
grad_dag_loss=False,
108-
lambda_privacy=0,
109-
lambda_gp=10,
110-
weight_decay=1e-2,
111-
l1_g=0,
112-
p_gen=-1,
113-
batch_size=100,
114-
)
115-
trainer = pl.Trainer(max_epochs=10, logger=False)
116-
trainer.fit(model, dm)
117-
118-
X_synth = (
119-
model.gen_synthetic(
120-
dm.dataset.x,
121-
gen_order=model.get_gen_order(),
122-
)
123-
.detach()
124-
.numpy()
125-
)
126-
y_synth = baseline_clf.predict(X_synth)
127-
128-
synth_clf = XGBClassifier().fit(X_synth, y_synth)
129-
y_pred = synth_clf.predict(X_synth)
130-
131-
print(
132-
"synth scores",
133-
precision_score(y_synth, y_pred),
134-
recall_score(y_synth, y_pred),
135-
roc_auc_score(y_synth, y_pred),
136-
)

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