{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11"
    },
    "colab": {
      "name": "week-17-exercise.ipynb"
    },
    "course_exercise": {
      "id": "W17",
      "week": "17",
      "hour": null,
      "date": "2026-12-28",
      "title": "Package evidence and defend its scope",
      "minutes": 40,
      "optional": false,
      "worksheet": "notes/exercises/week-17-exercise.html",
      "notebook": "notebooks/exercises/week-17-exercise.ipynb",
      "colab": "https://colab.research.google.com/github/CHjonnyYu/MLES-Fall-2026/blob/main/notebooks/exercises/week-17-exercise.ipynb",
      "guided_notebook": "notebooks/week-17-practice.ipynb",
      "execution_status": "New exercise extension not executed in this authoring run"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "# W17 Exercise: Package evidence and defend its scope\n\nMachine Learning in Engineering Science | NCKU / LAiMM | Fall 2026\n\n**Date:** 2026-12-28. **Exercise time:** 40 minutes within the class/practice allocation, not extra time after class.\n\n**Classroom formative practice. No new weekly graded submission is created.**\n\n## How to work independently\n1. In Colab, save a copy to your Drive and use a CPU runtime. In Jupyter, open this notebook and select a Python kernel.\n2. Run the complete worked walkthrough below from top to bottom. Read every Markdown checkpoint; the walkthrough is included so no previous notebook state is needed.\n3. At the Independent Exercise section, write each prediction in the response cell before running its experiment.\n4. Run the starter experiment, make the requested controlled change, and record the actual result. Edit only the declared factor; preserve split rules and preprocessing.\n5. Explain one mechanism and one limitation. Export the evidence JSON and discuss it with a partner.\n\nThe core exercises need no paid API, private dataset, or GPU. The agent units use clearly labeled offline simulators, not real LLM calls.\n\n**Reproducibility boundary:** the original guided notebooks were previously executed; the new extension cells have not yet been execution-tested in this revision. Report unexpected behavior rather than claiming a check passed.\n"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "# Week 17: Final project reproducibility and oral rehearsal\n\nMachine Learning in Engineering Science | NCKU + LAiMM | Fall 2026\n\n**Purpose:** a fully worked, self-paced in-class practice with an optional extension. Weeks 11 and 18 are optional examination-readiness materials. These notebooks do not create weekly graded assignments.\n\n**Before running:** choose a Python 3 kernel in Jupyter or a CPU runtime in Colab. Run every cell from top to bottom with Shift+Enter. Setup installs missing free packages. No API key or external dataset download is required.\n\n**Learning target:** complete the calculation, inspect intermediate values, and explain why the protocol supports a limited claim.\n\n**Time:** about 25-45 minutes including reading, prediction, execution, and explain-back.\n",
      "id": "cell-000"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## 1. Set up and record the environment\n\nKeep the seed fixed for the first run. Record actual outputs and package versions rather than copying a claimed performance number.\n",
      "id": "cell-001"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "metadata": {},
      "source": "import sys, subprocess, importlib.util, platform, json, random, hashlib\nfrom pathlib import Path\nrequired = {\"numpy\": \"numpy\", \"matplotlib\": \"matplotlib\", \"sklearn\": \"scikit-learn\"}\nmissing = [package for module, package in required.items() if importlib.util.find_spec(module) is None]\nif missing:\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", *missing])\nimport numpy as np\nimport matplotlib.pyplot as plt\nSEED = 2026\nrandom.seed(SEED)\nnp.random.seed(SEED)\nrng = np.random.default_rng(SEED)\nARTIFACTS = Path(\"artifacts\")\nARTIFACTS.mkdir(exist_ok=True)\nprint(\"Python:\", platform.python_version(), \"NumPy:\", np.__version__, \"Seed:\", SEED)\n",
      "id": "cell-002"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "**Checkpoint.** The environment cell must finish before any analysis cell. Restart and run all if a variable is missing.\n",
      "id": "cell-003"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## 2. Package an independently interpretable result\n\nCreate a small original artifact and an explicit claim. A result card should distinguish measured behavior from broader ambitions.\n",
      "id": "cell-004"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "metadata": {},
      "source": "signal=rng.normal(size=100)\ncard={\"task\":\"mean of a generated normal sample\",\"seed\":SEED,\"n\":100,\n      \"observed_mean\":float(signal.mean()),\"units\":\"dimensionless synthetic\",\n      \"limitation\":\"one random sample; no real engineering measurement\"}\n",
      "id": "cell-005"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "**Checkpoint.** The sample mean will not generally equal zero exactly; zero is the population expectation.\n",
      "id": "cell-006"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## 3. Verify data identity\n\nSave the array with a hash based on its bytes and reload it. This checks artifact integrity, not scientific validity.\n",
      "id": "cell-007"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "metadata": {},
      "source": "path=ARTIFACTS/\"final-synthetic.npy\"; np.save(path,signal)\ndigest=hashlib.sha256(path.read_bytes()).hexdigest()\nrestored=np.load(path)\nassert np.array_equal(signal,restored)\ncard[\"file_sha256\"]=digest\nprint(card)\n",
      "id": "cell-008"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "**Checkpoint.** A classmate needs both the artifact and its interpretation. An unlabeled array is insufficient.\n",
      "id": "cell-009"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## 4. Prepare three oral questions\n\nWrite claim/evidence/limitation answers and identify what would change your conclusion. This is optional rehearsal.\n",
      "id": "cell-010"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "metadata": {},
      "source": "questions=[\"What exactly was measured?\",\"Can another person rerun it?\",\"What would falsify the claim?\"]\nsummary={\"result_card\":card,\"oral_questions\":questions,\"AI_use\":\"Disclose any AI assistance and the checks you performed.\"}\nfor question in questions: print(question)\n",
      "id": "cell-011"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "**Checkpoint.** Replace the toy card with your project evidence before the final examination.\n",
      "id": "cell-012"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## 6. Preserve your evidence\n\nExport a JSON record with a seed, package version, and measured result. Download the artifacts directory before leaving Colab; runtime storage is temporary.\n",
      "id": "cell-013"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "metadata": {},
      "source": "record={\"week\":17,\"seed\":SEED,\"numpy\":np.__version__,\"python\":platform.python_version(),\"result\":summary}\n(ARTIFACTS/\"week-17-evidence.json\").write_text(json.dumps(record,indent=2))\nprint(json.dumps(record,indent=2))\n",
      "id": "cell-014"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "**Checkpoint.** The evidence file must match the result you actually obtained. A saved screenshot alone does not permit rerunning the calculation.\n",
      "id": "cell-015"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## 7. Try one change and explain it\n1. Predict the effect of one change before editing code. Change one parameter, input, or boundary condition only.\n2. Rerun all cells in a fresh kernel. Keep the original evidence record and compare the observed difference.\n3. Explain the result using the mechanism, not just the final number.\n4. Name one assumption that limits the claim, and a new test that could challenge it.\n\n**Troubleshooting:** if installation fails, restore network access; if a variable is undefined, run earlier cells; if your output differs, compare package versions, seed, and data construction. Do not repeatedly edit code until a preferred score appears.\n\n**Completion criterion:** one executed notebook, one evidence JSON, and a brief claim/evidence/limitation statement.\n\n### Reference documentation\n- [NumPy user guide](https://numpy.org/doc/stable/user/)\n- [scikit-learn user guide](https://scikit-learn.org/stable/user_guide.html)\n- [Course repository](https://github.com/CHjonnyYu/MLES-Fall-2026)\n",
      "id": "cell-016"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## Independent Exercise: W17\n\nComplete the three tasks below after the walkthrough. The starter cells produce observations, not your written interpretation. Fill the response fields in the next cell. Save separate records for the original run and any controlled change."
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": "exercise_id=\"W17\"\nobservations={}\nresponses={\n    'task1_prediction':'', 'task1_explanation':'',\n    'task2_prediction':'', 'task2_explanation':'',\n    'task3_prediction':'', 'task3_explanation':'',\n    'controlled_change':'', 'limitation':'', 'ai_use_and_checks':''\n}\n# Fill these strings before and after each experiment. Use 'none' if no AI assistance was used.\nprint('Exercise record initialized:', exercise_id)"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "### Task 1: Preserve bytes and interpretation\n\nHash the saved bytes of a tiny array, restore it, and keep a schema. Explain why a hash alone is not enough to interpret an artifact.\n\n**Steps**\n1. Write your prediction in `responses['task1_prediction']` before running.\n2. Run the starter cell; read every printed quantity and retain the observation.\n3. Make the controlled comparison requested above. Record its configuration in `responses['controlled_change']`.\n4. Write a mechanism-based explanation in `responses['task1_explanation']`; do not merely repeat a score.\n\n**Checkpoint:** The restored values are [1,2,3]. Interpretation requires quantities, units, shape, and a declared source in addition to byte identity."
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": "import io\nbuffer_ex=io.BytesIO(); np.save(buffer_ex,np.array([1.,2.,3.]))\npayload_ex=buffer_ex.getvalue(); restored_ex=np.load(io.BytesIO(payload_ex))\nobservations['task1']={'sha256':hashlib.sha256(payload_ex).hexdigest(),'restored_values':restored_ex.tolist(),'schema':{'quantity':'synthetic reading','unit':'synthetic units','shape':[3]}}\nprint(observations['task1'])"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "### Task 2: Distinguish sample and population claims\n\nGenerate a finite sample from a zero-mean process. Change the seed and explain why the observed sample mean need not be exactly zero.\n\n**Steps**\n1. Write your prediction in `responses['task2_prediction']` before running.\n2. Run the starter cell; read every printed quantity and retain the observation.\n3. Make the controlled comparison requested above. Record its configuration in `responses['controlled_change']`.\n4. Write a mechanism-based explanation in `responses['task2_explanation']`; do not merely repeat a score.\n\n**Checkpoint:** Report the observed sample mean. Do not replace an observed result with the population expectation."
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": "sample_ex=np.random.default_rng(2026).normal(size=100)\nobservations['task2']={'seed':2026,'sample_size':len(sample_ex),'observed_mean':float(sample_ex.mean()),'population_expectation':0.}\nprint(observations['task2'])"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "### Task 3: Run a peer explain-back\n\nGive a partner your data/configuration record and ask them what is missing. Write one falsifiable claim and one limitation in the response cell.\n\n**Steps**\n1. Write your prediction in `responses['task3_prediction']` before running.\n2. Run the starter cell; read every printed quantity and retain the observation.\n3. Make the controlled comparison requested above. Record its configuration in `responses['controlled_change']`.\n4. Write a mechanism-based explanation in `responses['task3_explanation']`; do not merely repeat a score.\n\n**Checkpoint:** Use your project artifacts rather than treating this toy record as proof that the final project is reproducible."
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": "observations['task3']={'peer_questions':['What exactly is the claim?','Which artifact supports it?','What result would contradict it?','Which AI assistance was used and checked?']}\nprint(observations['task3'])"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "### Finish: preserve and explain\n\nFill all response strings, then run the export cell. A draft file is intentionally labeled incomplete; a complete response record is not automatically a scientifically correct result. Download the JSON before the Colab session ends. Share it with a partner for explain-back, not as an additional weekly submission.\n\n**Exit questions:** What changed? Which observation supports your explanation? What does this experiment not establish?"
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": "missing_responses=[key for key,value in responses.items() if not str(value).strip()]\nexercise_record={'exercise':exercise_id,'status':'draft' if missing_responses else 'ready for peer discussion','responses':responses,'observations':observations,'missing_responses':missing_responses,'environment':{'python':platform.python_version(),'numpy':np.__version__},'data_scope':'course-provided or synthetic teaching data'}\nexercise_folder=Path('artifacts')/'exercises'; exercise_folder.mkdir(parents=True,exist_ok=True)\nexercise_path=exercise_folder/(exercise_id+'-evidence.json')\nexercise_path.write_text(json.dumps(exercise_record,indent=2,default=lambda value:value.tolist() if hasattr(value,'tolist') else str(value)))\nprint('Saved:',exercise_path)\nprint('Status:',exercise_record['status'])\nprint('Fields still to complete:',missing_responses)"
    }
  ]
}