{
 "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-11-practice.ipynb",
   "provenance": []
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Week 11: Midterm written and oral readiness\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. Compute metrics from counts\n\nPrepare for a written question: 18 true positives, 2 false negatives, 8 false positives, and 72 true negatives. Compute by hand before running.\n",
   "id": "cell-004"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "tp,fn,fp,tn=18,2,8,72\nsummary={\"recall\":tp/(tp+fn),\"precision\":tp/(tp+fp),\n         \"specificity\":tn/(tn+fp),\"accuracy\":(tp+tn)/(tp+fn+fp+tn)}\nprint(summary)\nassert np.isclose(summary[\"recall\"],.9)\n",
   "id": "cell-005"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** Explain why precision is not recall. Recall is 0.9; the denominators refer to different populations.\n",
   "id": "cell-006"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 3. Audit a timeline for leakage\n\nA forecast at index t may use indices strictly below t. Write and inspect a counterexample with one future feature.\n",
   "id": "cell-007"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "origin=100\ngood=np.arange(76,100); bad=np.arange(77,101)\nprint(\"Safe history:\",bool((good<origin).all()))\nprint(\"Unsafe history:\",bool((bad<origin).all()))\nassert good.max()<origin and not (bad.max()<origin)\n",
   "id": "cell-008"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** The unsafe window contains index 100, the target time. Identify the error before discussing model accuracy.\n",
   "id": "cell-009"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 4. Rehearse an oral evidence chain\n\nA model report must state data, split, baseline, metric, limitation, and reproducibility. This schema supports a rehearsal and does not replace either examination.\n",
   "id": "cell-010"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "oral_card={\"task\":\"rolling one-step forecast\",\"split\":\"chronological by target index\",\n           \"baseline\":\"last observed value\",\"metric\":\"held-out MAE\",\n           \"limitation\":\"synthetic signal only\",\"reproduction\":\"seed, versions, data hash, boundaries\"}\nfor field,value in oral_card.items(): print(field+\": \"+value)\nsummary[\"oral_card\"]=oral_card\n",
   "id": "cell-011"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** Explain each field without reading code. This is optional exam preparation, not a new graded assignment.\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\":11,\"seed\":SEED,\"numpy\":np.__version__,\"python\":platform.python_version(),\"result\":summary}\n(ARTIFACTS/\"week-11-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"
  }
 ]
}