{
 "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-06-practice.ipynb",
   "provenance": []
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Week 06: Unsupervised learning and representation\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. Generate unlabeled structure\n\nThe model receives only X. Generating labels are retained solely for optional teaching comparisons and never passed into fitting.\n",
   "id": "cell-004"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "from sklearn.datasets import make_blobs\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\nX,hidden_labels=make_blobs(n_samples=360,n_features=5,centers=3,cluster_std=1.4,random_state=SEED)\nXtr,Xte=train_test_split(X,test_size=.25,random_state=SEED)\nscaler=StandardScaler().fit(Xtr)\nZtr,Zte=scaler.transform(Xtr),scaler.transform(Xte)\n",
   "id": "cell-005"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** No target is available to the clustering algorithm. Preprocessing still learns statistics and must fit training only.\n",
   "id": "cell-006"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 3. Fit PCA and inspect retained variation\n\nPCA chooses orthogonal directions of maximum training variance. Two components simplify plotting but may discard useful information.\n",
   "id": "cell-007"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "pca=PCA(n_components=2).fit(Ztr)\nP=pca.transform(Zte)\nprint(\"Explained variance ratios:\",pca.explained_variance_ratio_)\nplt.scatter(P[:,0],P[:,1]); plt.xlabel(\"PC1\"); plt.ylabel(\"PC2\"); plt.show()\n",
   "id": "cell-008"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** Explained variance is not prediction accuracy or a guarantee of physically meaningful factors.\n",
   "id": "cell-009"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 4. Cluster in the original standardized space\n\nK-means finds geometric groups under a chosen distance and k. Labels are arbitrary IDs; cluster 0 is not an engineering diagnosis.\n",
   "id": "cell-010"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "kmeans=KMeans(n_clusters=3,n_init=10,random_state=SEED).fit(Ztr)\ngroups=kmeans.predict(Zte)\nplt.scatter(P[:,0],P[:,1],c=groups,cmap=\"viridis\"); plt.xlabel(\"PC1\"); plt.ylabel(\"PC2\"); plt.show()\nsummary={\"test_silhouette\":float(silhouette_score(Zte,groups)),\n         \"retained_training_variance\":float(pca.explained_variance_ratio_.sum())}\nprint(summary)\n",
   "id": "cell-011"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** A high silhouette supports separation in this representation, not a causal or semantic interpretation of clusters.\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\":6,\"seed\":SEED,\"numpy\":np.__version__,\"python\":platform.python_version(),\"result\":summary}\n(ARTIFACTS/\"week-06-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"
  }
 ]
}