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feat: Docker mcp-core deployment + Smithery one-click install (Path B)
Make pipeline torch-optional so mcp-core Docker (1.52GB, no torch) works: - 6 files: wrap `import torch` in try/except, guard cuda checks - Lazy OllamaClient import (requests not in core deps) - Remove dead distutils/sympy imports (Python 3.12 compat) - Fix Dockerfiles: healthcheck start-period, remove LaTeX from mcp-full - Add .dockerignore (5.98GB → 1.52GB image) - Fix smithery.yaml: PYTHONPATH, llmModel config - Clean requirements_mcp_core.txt - Add GitHub Actions CI: tests + Docker build + smoke test All 4 tools verified in container: discover, inspect_graph, diagnose_data, run_algorithm. Host tests: 48 passed. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎.dockerignore‎

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# Version control
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.git
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.gitmodules
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# Virtual environments
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.venv
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venv
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env
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# IDE / editor
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.vscode
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.idea
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*.swp
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*.swo
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# Data files (users provide via CSV)
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data/
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output/
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# Build artifacts
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__pycache__
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*.pyc
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*.pyo
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*.egg-info
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dist/
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build/
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*.whl
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# Docs and plans (not needed at runtime)
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docs/
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*.md
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!README.md
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# CI/CD configs
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.github/
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.claude/
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# Misc
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.env
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.env.*
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*.log
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.pytest_cache
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.coverage
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htmlcov/

‎.github/workflows/mcp-ci.yml‎

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name: MCP CI
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on:
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push:
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branches: [main, feat/mcp-*]
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paths:
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- 'causal_copilot/mcp/**'
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- 'causal_discovery/**'
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- 'tests/test_mcp*.py'
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- 'Dockerfile.mcp-*'
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- 'requirements_mcp_core.txt'
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- '.github/workflows/mcp-ci.yml'
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pull_request:
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branches: [main]
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paths:
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- 'causal_copilot/mcp/**'
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- 'causal_discovery/**'
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- 'tests/test_mcp*.py'
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- 'Dockerfile.mcp-*'
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- 'requirements_mcp_core.txt'
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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with:
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submodules: recursive
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- uses: actions/setup-python@v5
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with:
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python-version: '3.12'
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cache: pip
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- name: Install dependencies
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run: |
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pip install -r requirements_mcp_core.txt
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pip install pytest
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- name: Run MCP unit tests
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env:
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PYTHONPATH: ${{ github.workspace }}:${{ github.workspace }}/externals:${{ github.workspace }}/externals/causal-learn
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run: |
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pytest tests/test_mcp.py tests/test_mcp_golden.py tests/test_mcp_integration.py tests/test_mcp_resources.py -x -q
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docker:
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runs-on: ubuntu-latest
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needs: test
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steps:
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- uses: actions/checkout@v4
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with:
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submodules: recursive
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- name: Build mcp-core image
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run: docker build -f Dockerfile.mcp-core -t causal-copilot-mcp:core .
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- name: Verify image starts
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run: |
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docker run -d --name mcp-test -p 8000:8000 causal-copilot-mcp:core
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# Poll until MCP server responds (up to 60s)
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for i in $(seq 1 12); do
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SESSION_ID=$(curl -sf -D /dev/stderr \
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-X POST http://localhost:8000/mcp \
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-H 'Content-Type: application/json' \
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-H 'Accept: application/json, text/event-stream' \
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-d '{"jsonrpc":"2.0","id":0,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"ci","version":"0.1"}}}' \
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2>&1 | grep -oP 'mcp-session-id: \K\S+') && break || sleep 5
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done
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echo "Session: $SESSION_ID"
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test -n "$SESSION_ID" || (docker logs mcp-test && exit 1)
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- name: Smoke test discover tool
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run: |
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SESSION_ID=$(curl -sf -D /dev/stderr \
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-X POST http://localhost:8000/mcp \
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-H 'Content-Type: application/json' \
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-H 'Accept: application/json, text/event-stream' \
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-d '{"jsonrpc":"2.0","id":0,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"ci","version":"0.1"}}}' \
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2>&1 | grep -oP 'mcp-session-id: \K\S+')
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RESP=$(curl -sf -X POST http://localhost:8000/mcp \
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-H 'Content-Type: application/json' \
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-H 'Accept: application/json, text/event-stream' \
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-H "Mcp-Session-Id: $SESSION_ID" \
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-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"discover","arguments":{"csv_data":"A,B,C\n1,2,3\n2,4,5\n3,6,8\n4,8,10\n5,10,13\n6,12,15\n7,14,18\n8,16,20\n9,18,23\n10,20,25\n11,22,27\n12,24,30","algorithm":"PC","seed":42}}}')
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echo "$RESP"
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echo "$RESP" | grep -q '"ok"' || (echo "FAIL: discover did not return ok" && exit 1)
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- name: Cleanup
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if: always()
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run: docker rm -f mcp-test 2>/dev/null || true

‎Dockerfile.mcp-core‎

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# Dockerfile.mcp-core — Lightweight MCP server (~800MB)
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# No torch, no LaTeX. Algorithms that don't need torch.
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# No torch, no LaTeX. PC, GES, FCI, DirectLiNGAM, etc. work.
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# LLM pipeline falls back to rule-based offline mode.
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FROM python:3.12-slim AS base
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WORKDIR /app
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# System deps
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# System deps (graphviz needed by causal-learn graph rendering)
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential graphviz git && \
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rm -rf /var/lib/apt/lists/*
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RUN useradd -m mcp && chown -R mcp:mcp /app
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USER mcp
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HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"
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# Healthcheck with startup grace period
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HEALTHCHECK --interval=30s --timeout=10s --start-period=15s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1
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EXPOSE 8000
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ENTRYPOINT ["python", "-m", "causal_copilot.mcp", "--http"]

‎Dockerfile.mcp-full‎

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# Dockerfile.mcp-full — Full MCP server (~4GB)
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# All algorithms including torch-based + LaTeX reports.
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# Dockerfile.mcp-full — Full MCP server with all algorithms (~3GB)
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# Includes torch-based algorithms (NOTEARS, GOLEM, etc.).
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# No LaTeX — MCP server doesn't generate PDF reports.
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FROM pytorch/pytorch:2.2.2-cuda11.8-cudnn8-runtime AS base
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WORKDIR /app
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# System deps
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential graphviz git \
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texlive-latex-base texlive-latex-extra texlive-fonts-recommended && \
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build-essential graphviz git && \
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rm -rf /var/lib/apt/lists/*
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# Python deps
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# Python deps (full algorithm suite)
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COPY requirements_cpu.txt .
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RUN pip install --no-cache-dir -r requirements_cpu.txt
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RUN pip install --no-cache-dir "fastmcp>=3.0"
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# Application
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COPY . .
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RUN useradd -m mcp && chown -R mcp:mcp /app
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USER mcp
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HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"
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# Healthcheck with startup grace period
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HEALTHCHECK --interval=30s --timeout=10s --start-period=15s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1
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EXPOSE 8000
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ENTRYPOINT ["python", "-m", "causal_copilot.mcp", "--http"]

‎causal_discovery/filter.py‎

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import json
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import os
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import torch
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try:
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import torch
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except ImportError:
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torch = None
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from utils.logger import logger
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from llm import LLMClient
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"[COLUMNS]": ', '.join(global_state.user_data.processed_data.columns),
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"[STATISTICS_DESC]": global_state.statistics.description,
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"[ALGO_CONTEXT]": algo_context,
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"[CUDA_WARNING]": "Current machine supports CUDA, some algorithms can be accelerated by GPU if needed." if torch.cuda.is_available() else "\nCurrent machine doesn't support CUDA, do not choose any GPU-powered algorithms.",
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"[CUDA_WARNING]": "Current machine supports CUDA, some algorithms can be accelerated by GPU if needed." if (torch is not None and torch.cuda.is_available()) else "\nCurrent machine doesn't support CUDA, do not choose any GPU-powered algorithms.",
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"[TOP_K]": str(TOP_K),
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"[ACCEPT_CPDAG]": "The user accepts the output graph including undirected edges/undeterministic directions (CPDAG/PAG)" if global_state.user_data.accept_CPDAG else "The user does not accept the output graph including undirected edges/undeterministic directions (CPDAG/PAG), so the output graph should be a DAG."
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}

‎causal_discovery/hyperparameter_selector.py‎

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import json
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import torch
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try:
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import torch
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except ImportError:
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torch = None
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import causal_discovery.wrappers as wrappers
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from llm import LLMClient
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from utils.logger import logger
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hp_prompt = hp_prompt.replace("[COLUMNS]", table_columns)
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hp_prompt = hp_prompt.replace("[KNOWLEDGE_INFO]", knowledge_info)
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hp_prompt = hp_prompt.replace("[STATISTICS INFO]", global_state.statistics.description)
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hp_prompt = hp_prompt.replace("[CUDA_WARNING]", "Current machine supports CUDA, some algorithms can be accelerated by GPU if needed." if torch.cuda.is_available() else "\nCurrent machine doesn't support CUDA, do not choose any GPU-powered algorithms.")
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hp_prompt = hp_prompt.replace("[CUDA_WARNING]", "Current machine supports CUDA, some algorithms can be accelerated by GPU if needed." if (torch is not None and torch.cuda.is_available()) else "\nCurrent machine doesn't support CUDA, do not choose any GPU-powered algorithms.")
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# hp_prompt = hp_prompt.replace("[ALGORITHM_DESCRIPTION]", algorithm_optimum_reason)
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hp_prompt = hp_prompt.replace("[PRIMARY_HYPERPARAMETERS]", ', '.join(primary_params))
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hp_prompt = hp_prompt.replace("[HYPERPARAMETER_INFO]", hp_info_str)

‎causal_discovery/rerank.py‎

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import json
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import torch
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try:
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import torch
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except ImportError:
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torch = None
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from .wrappers import __all__ as all_algos
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from .hyperparameter_selector import HyperparameterSelector
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from .runtime_estimators.runtime_estimator import RuntimeEstimator
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"[DIMENSIONS]": str(global_state.user_data.processed_data.shape[1]),
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"[KNOWLEDGE_INFO]": str(global_state.user_data.knowledge_docs),
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"[STATISTICS_INFO]": global_state.statistics.description,
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"[CUDA_WARNING]": "Current machine supports CUDA, some algorithms can be accelerated by GPU if needed." if torch.cuda.is_available() else "\nCurrent machine doesn't support CUDA, do not choose any GPU-powered algorithms.",
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"[CUDA_WARNING]": "Current machine supports CUDA, some algorithms can be accelerated by GPU if needed." if (torch is not None and torch.cuda.is_available()) else "\nCurrent machine doesn't support CUDA, do not choose any GPU-powered algorithms.",
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"[ALGORITHM_CANDIDATES]": str(list(global_state.algorithm.algorithm_candidates.keys())),
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"[WAIT_TIME]": str(global_state.algorithm.waiting_minutes),
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"[TIME_INFO]": time_info,

‎causal_discovery/wrappers/cdnod.py‎

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from causal_discovery.wrappers.pc import PC
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from causal_discovery.evaluation.evaluator import GraphEvaluator
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import torch
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cuda_available = torch.cuda.is_available()
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try:
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import torch
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cuda_available = torch.cuda.is_available()
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except ImportError:
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torch = None
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cuda_available = False
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try:
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from externals.acceleration.cdnod.cdnod import accelerated_cdnod
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except ImportError:

‎causal_discovery/wrappers/var_lingam.py‎

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from causal_discovery.evaluation.evaluator import GraphEvaluator
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from causal_discovery.wrappers.utils.ts_utils import generate_stationary_linear
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import torch
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cuda_available = torch.cuda.is_available()
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try:
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import torch
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cuda_available = torch.cuda.is_available()
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except ImportError:
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torch = None
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cuda_available = False
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try:
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from culingam.varlingam import VARLiNGAM as AcVarLiNGAM
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from culingam.utils import check_array

‎global_setting/Initialize_state.py‎

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import numpy as np
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import os
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import json
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import torch
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try:
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import torch
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except ImportError:
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torch = None
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from datetime import datetime
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from global_setting.state import GlobalState
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from data.simulator.simulation import SimulationManager
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global_state.algorithm.selected_algorithm = info_extracted["selected_algorithm"]
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global_state.statistics.time_series = info_extracted["time_series"]
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# GPU availability
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global_state.statistics.gpu_available = torch.cuda.is_available()
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global_state.statistics.gpu_available = torch is not None and torch.cuda.is_available()
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189192
if info_extracted["waiting_minutes"] is not None:
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global_state.algorithm.waiting_minutes = info_extracted["waiting_minutes"]

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