{
 "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-02-practice.ipynb",
   "provenance": []
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Week 02: Features, targets, and an honest first split\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. Define one sample and its target\n\nUse the packaged Iris teaching dataset, which needs no download. Features are four flower measurements; the target is species. This is classification, not engineering diagnosis.\n",
   "id": "cell-004"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "from sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score,confusion_matrix\ndataset=load_iris(); X,y=dataset.data,dataset.target\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.25,stratify=y,random_state=SEED)\nprint(dataset.feature_names, dataset.target_names, X_train.shape, X_test.shape)\n",
   "id": "cell-005"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** Training and test observations are distinct. Stratification approximately preserves class proportions.\n",
   "id": "cell-006"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 3. Fit preprocessing only inside the pipeline\n\nThe scaler estimates means and variances using training rows. A Pipeline applies the same learned transformation to test rows.\n",
   "id": "cell-007"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "model=make_pipeline(StandardScaler(),LogisticRegression(max_iter=300,random_state=SEED))\nmodel.fit(X_train,y_train)\npred=model.predict(X_test)\nsummary={\"test_accuracy\":float(accuracy_score(y_test,pred)),\"training_rows\":len(y_train),\"test_rows\":len(y_test)}\nprint(summary)\nassert np.allclose(model[0].mean_,X_train.mean(axis=0))\n",
   "id": "cell-008"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** Explain which values were learned during fit. Do not refit the scaler on test.\n",
   "id": "cell-009"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 4. Inspect mistakes instead of only accuracy\n\nRows of this confusion matrix are true classes and columns are predictions. Inspect any errors and relate them to overlapping feature distributions.\n",
   "id": "cell-010"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": "matrix=confusion_matrix(y_test,pred)\nplt.imshow(matrix,cmap=\"Blues\"); plt.xticks(range(3),dataset.target_names,rotation=20)\nplt.yticks(range(3),dataset.target_names); plt.xlabel(\"Predicted\"); plt.ylabel(\"True\"); plt.colorbar(); plt.show()\nprint(\"Wrong sample positions in test:\",np.flatnonzero(pred!=y_test))\n",
   "id": "cell-011"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "**Checkpoint.** A correct result may have few or no errors. State that this small test does not establish broad reliability.\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\":2,\"seed\":SEED,\"numpy\":np.__version__,\"python\":platform.python_version(),\"result\":summary}\n(ARTIFACTS/\"week-02-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"
  }
 ]
}