Junior B.
b731478f5d
Set up the project skeleton per PLAN.md §4: - src/ package: identifiers, disease, drugs, scoring, provenance with pydantic schemas and confidence-tier logic (working); data-pull/compute functions stubbed per their build week - 5 starter notebooks (01-05) with PLAN-referenced steps - tests/test_scoring.py: tier-assignment tests pass; scoring reference test xfail until Week 3 - docs/: recovery_test_report, data_sources, known_limitations skeletons - pyproject.toml (requires-python >=3.11,<3.14), .gitignore, README - data/ tree preserved via .gitkeep; raw/processed/results gitignored Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
48 lines
1.2 KiB
Plaintext
48 lines
1.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 05 \u2014 Recovery test\n",
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"\n",
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"Week 4 (PLAN.md \u00a76). **Commit the pre-registered success criteria to git BEFORE running this notebook** (PLAN.md \u00a78, \u00a710).\n\nPull hydroxyurea + L-glutamine ranks and 5 negative-control ranks, compute pass/fail, examine the top 10, and fill in `docs/recovery_test_report.md`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.insert(0, '..') # import the src package from notebooks/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from src import RESULTS_DIR\n",
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"\n",
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"ranking = pd.read_csv(RESULTS_DIR / 'ranked_candidates_v1.csv')\n",
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"# Pull ground-truth + negative-control ranks; evaluate pre-registered criteria."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |