{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "md-000",
   "metadata": {},
   "source": "# Stellar Classification: From Data to Decisions\n\n**Gareth Grant \u00b7 Applied machine learning portfolio \u00b7 4 October 2026**\n\nHow can a reproducible classifier help organise stellar observations, and what evidence is needed before anyone relies on it?\n\nThis project classifies the **six labels in the supplied Stars.csv** using four physical measurements. It combines data auditing, visual exploration, Naive Bayes, KNN, an interpretable decision tree, and honest evaluation. The important outcome is a traceable workflow and an explanation of its limits.\n\n**Scope:** a small educational dataset, not a validated observatory product. A strong score on this file establishes performance on this file's held-out sample. It does not establish accuracy on a new telescope survey.\n\n## How to use this notebook\n\n1. Put this notebook beside `Stars.csv`, install the packages in `requirements.txt`, and select a Python kernel.\n2. Restart the kernel and run the cells from top to bottom. Outputs are already included for readers.\n3. Each code cell does one task. Read its explanation, run it, and add your own comment at `# Your note:`.\n4. Treat the final test set as locked. If you change the workflow after viewing its results, use fresh external data for a new final evaluation.\n\nThe companion HTML contains the same code, outputs, and interpretation for a portfolio website. Business and policy opportunities are proposals; measured model results and hypothetical savings are identified separately.\n\n## Reading route\n\n| Section | What you learn |\n|---|---|\n| 1\u20133 | Reproducibility, data quality, and a locked train/test split |\n| 4\u20135 | Physical interpretation, log scales, and model assumptions |\n| 6\u20138 | Pipelines, training-only tuning, and nested cross-validation |\n| 9\u201311 | Final test results, error analysis, and model explanation |\n| 12 | Input checks and candidate predictions for expert review |\n| 13\u201314 | Business, astronomy, government, and scientific limits |\n| 15\u201317 | Reusable outputs, a website case study, and learning exercises |\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-001",
   "metadata": {},
   "source": "## 1. Set up a reproducible analysis\n\nAn import makes a library available; it does not train a model. Imports are grouped by their purpose so a reader can see why each tool is here.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-002",
   "metadata": {},
   "source": "### Step 01 \u00b7 Import file and environment tools\n\nThese tools handle portable paths, checksums, saved metadata, and package versions.\n"
  },
  {
   "cell_type": "code",
   "id": "code-001",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import file and environment tools.\nfrom pathlib import Path\nfrom importlib.metadata import version\nimport hashlib\nimport json\nimport platform\nimport joblib\n\n# Your note: \n",
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-004",
   "metadata": {},
   "source": "### Step 02 \u00b7 Import data tools\n\n`pandas` handles tables; `numpy` handles numerical arrays and logarithms.\n"
  },
  {
   "cell_type": "code",
   "id": "code-002",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import data tools.\nimport numpy as np\nimport pandas as pd\n\n# Your note: \n",
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-006",
   "metadata": {},
   "source": "### Step 03 \u00b7 Import plotting tools\n\nPlots make scale differences and classification errors easier to inspect.\n"
  },
  {
   "cell_type": "code",
   "id": "code-003",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import plotting tools.\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Your note: \n",
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-008",
   "metadata": {},
   "source": "### Step 04 \u00b7 Import scientific diagnostics\n\nShapiro\u2013Wilk checks a normality assumption; a standard-normal quantile supplies a confidence-interval constant.\n"
  },
  {
   "cell_type": "code",
   "id": "code-004",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import scientific diagnostics.\nfrom scipy import stats\n\n# Your note: \n",
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-010",
   "metadata": {},
   "source": "### Step 05 \u00b7 Import splitting and validation tools\n\nA split holds back the final test; cross-validation evaluates choices inside training data.\n"
  },
  {
   "cell_type": "code",
   "id": "code-005",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import splitting and validation tools.\nfrom sklearn.model_selection import (\n    train_test_split, StratifiedKFold, GridSearchCV, cross_validate\n)\n\n# Your note: \n",
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-012",
   "metadata": {},
   "source": "### Step 06 \u00b7 Import preprocessing tools\n\nPipelines keep transformations attached to the classifier. See the official [leakage guidance](https://scikit-learn.org/stable/common_pitfalls.html).\n"
  },
  {
   "cell_type": "code",
   "id": "code-006",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import preprocessing tools.\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import FunctionTransformer, StandardScaler, label_binarize\n\n# Your note: \n",
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-014",
   "metadata": {},
   "source": "### Step 07 \u00b7 Import classifiers\n\nKeep the original Naive Bayes and KNN methods; add a no-skill benchmark and a readable tree.\n"
  },
  {
   "cell_type": "code",
   "id": "code-007",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import classifiers.\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.tree import DecisionTreeClassifier, plot_tree, export_text\n\n# Your note: \n",
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-016",
   "metadata": {},
   "source": "### Step 08 \u00b7 Import evaluation tools\n\nClassification metrics belong here; regression metrics such as RMSE and R\u00b2 do not answer this classification question.\n"
  },
  {
   "cell_type": "code",
   "id": "code-008",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Import evaluation tools.\nfrom sklearn.metrics import (\n    accuracy_score, balanced_accuracy_score, precision_score, recall_score,\n    f1_score, classification_report, confusion_matrix,\n    ConfusionMatrixDisplay, roc_curve, roc_auc_score, log_loss\n)\nfrom sklearn.inspection import permutation_importance\n\n# Your note: \n",
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-018",
   "metadata": {},
   "source": "### Step 09 \u00b7 Choose fixed run settings\n\nChoose the split and evaluation plan before inspecting model test scores. Change the seed to study reproducibility, not to hunt for the best test result.\n"
  },
  {
   "cell_type": "code",
   "id": "code-009",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Choose fixed run settings.\nRANDOM_SEED = 42\nTEST_FRACTION = 0.20\nCV_FOLDS = 5\nN_JOBS = 1\nOUTPUT_DIR = Path('outputs')\nOUTPUT_DIR.mkdir(exist_ok=True)\n\n# Your note: \n",
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-020",
   "metadata": {},
   "source": "### Step 10 \u00b7 Set clear display styles\n\nUse readable numbers and restrained colours. Colour choices affect presentation, not model fitting.\n"
  },
  {
   "cell_type": "code",
   "id": "code-010",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Set clear display styles.\npd.set_option('display.max_columns', 12)\npd.set_option('display.max_rows', 30)\npd.set_option('display.precision', 4)\nsns.set_theme(style='whitegrid', context='notebook')\nplt.rcParams.update({'figure.dpi': 110, 'savefig.dpi': 180})\nCLASS_COLORS = ['#667582', '#C7353F', '#247B73', '#16324F', '#AA752C', '#794F91']\n\n# Your note: \n",
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-022",
   "metadata": {},
   "source": "### Step 11 \u00b7 Record the software environment\n\nVersion information makes differences between computers easier to investigate.\n"
  },
  {
   "cell_type": "code",
   "id": "code-011",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Record the software environment.\npackage_versions = {\n    name: version(name)\n    for name in ['numpy', 'pandas', 'scipy', 'scikit-learn', 'matplotlib', 'seaborn', 'joblib']\n}\nprint('Python:', platform.python_version())\npd.Series(package_versions, name='version')\n\n# Your note: \n",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Python: 3.12.14\n"
    },
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "numpy            2.3.5\npandas           2.2.3\nscipy           1.17.0\nscikit-learn     1.8.0\nmatplotlib      3.10.8\nseaborn         0.13.2\njoblib           1.5.3\nName: version, dtype: object",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>version</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>numpy</th>\n      <td>2.3.5</td>\n    </tr>\n    <tr>\n      <th>pandas</th>\n      <td>2.2.3</td>\n    </tr>\n    <tr>\n      <th>scipy</th>\n      <td>1.17.0</td>\n    </tr>\n    <tr>\n      <th>scikit-learn</th>\n      <td>1.8.0</td>\n    </tr>\n    <tr>\n      <th>matplotlib</th>\n      <td>3.10.8</td>\n    </tr>\n    <tr>\n      <th>seaborn</th>\n      <td>0.13.2</td>\n    </tr>\n    <tr>\n      <th>joblib</th>\n      <td>1.5.3</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-024",
   "metadata": {},
   "source": "## 2. Understand and audit the data\n\nThe original submission cites [Deepu's Kaggle star dataset](https://www.kaggle.com/datasets/deepu1109/star-dataset). This notebook uses the **uploaded file as the authoritative input**. Its extra `Star category` column supplies human-readable labels. The file has no source identifiers, observing dates, telescope identifiers, error bars, or documented selection probabilities. We cannot reconstruct its catalogue lineage or survey completeness from these columns.\n\n### Data dictionary\n\n| Column | Meaning in this file | Role |\n|---|---|---|\n| `Temperature (K)` | Temperature in kelvin; an effective-temperature interpretation needs source confirmation | Numeric predictor |\n| `Luminosity (L/Lo)` | Luminosity relative to the Sun; bolometric/passband definition needs confirmation | Numeric predictor |\n| `Radius (R/Ro)` | Radius relative to the Sun | Numeric predictor |\n| `Absolute magnitude (Mv)` | Labelled as visual absolute magnitude; smaller values mean brighter objects in that band | Numeric predictor |\n| `Star type` | Integer class code, 0\u20135 | Target |\n| `Star category` | Text version of the target | Display only; including it in predictors would leak the answer |\n| `Star color` | Descriptive text with inconsistent spelling/case | Audited; not used in the core model |\n| `Spectral Class` | Letter class | Audited; not used in the core model |\n\nThese are the dataset's six mutually exclusive labels. They are a simplified teaching taxonomy: in physical astronomy, [red dwarfs are main-sequence stars](https://science.nasa.gov/universe/stars/types/), so these labels are not a universal, non-overlapping taxonomy. A model learns the supplied labels rather than resolving those definitions.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-025",
   "metadata": {},
   "source": "### Step 12 \u00b7 Locate the input file\n\nA relative path works on Windows, macOS, and Linux when the CSV is beside the notebook. If necessary, edit just this cell.\n"
  },
  {
   "cell_type": "code",
   "id": "code-012",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Locate the input file.\nDATA_PATH = Path('Stars.csv')\nif not DATA_PATH.is_file():\n    raise FileNotFoundError('Place Stars.csv beside this notebook, or edit DATA_PATH.')\nprint('Dataset:', DATA_PATH.name)\n\n# Your note: \n",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Dataset: Stars.csv\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-027",
   "metadata": {},
   "source": "### Step 13 \u00b7 Fingerprint the input\n\nA SHA-256 checksum identifies the exact file used. It detects byte changes; it does not establish scientific provenance.\n"
  },
  {
   "cell_type": "code",
   "id": "code-013",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Fingerprint the input.\ndata_sha256 = hashlib.sha256(DATA_PATH.read_bytes()).hexdigest()\nprint('SHA-256:', data_sha256)\n\n# Your note: \n",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "SHA-256: e26584e4be5dbec3a6aba1cca27e38ebc17df85ddc7a5c34ecd5244f92693f7e\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-029",
   "metadata": {},
   "source": "### Step 14 \u00b7 Load the original data\n\nKeep the loaded table unchanged; make a working copy for cleaning.\n"
  },
  {
   "cell_type": "code",
   "id": "code-014",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Load the original data.\nraw_df = pd.read_csv(DATA_PATH)\n\n# Your note: \n",
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-031",
   "metadata": {},
   "source": "### Step 15 \u00b7 Inspect the first five records\n\nLook at the names, units, values, and target labels before selecting predictors.\n"
  },
  {
   "cell_type": "code",
   "id": "code-015",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Inspect the first five records.\nraw_df.head()\n\n# Your note: \n",
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 15,
     "data": {
      "text/plain": "   Temperature (K)  Luminosity (L/Lo)  Radius (R/Ro)  Absolute magnitude (Mv)  \\\n0             3068             0.0024         0.1700                    16.12   \n1             3042             0.0005         0.1542                    16.60   \n2             2600             0.0003         0.1020                    18.70   \n3             2800             0.0002         0.1600                    16.65   \n4             1939             0.0001         0.1030                    20.06   \n\n   Star type Star category Star color Spectral Class  \n0          0   Brown Dwarf        Red              M  \n1          0   Brown Dwarf        Red              M  \n2          0   Brown Dwarf        Red              M  \n3          0   Brown Dwarf        Red              M  \n4          0   Brown Dwarf        Red              M  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature (K)</th>\n      <th>Luminosity (L/Lo)</th>\n      <th>Radius (R/Ro)</th>\n      <th>Absolute magnitude (Mv)</th>\n      <th>Star type</th>\n      <th>Star category</th>\n      <th>Star color</th>\n      <th>Spectral Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3068</td>\n      <td>0.0024</td>\n      <td>0.1700</td>\n      <td>16.12</td>\n      <td>0</td>\n      <td>Brown Dwarf</td>\n      <td>Red</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3042</td>\n      <td>0.0005</td>\n      <td>0.1542</td>\n      <td>16.60</td>\n      <td>0</td>\n      <td>Brown Dwarf</td>\n      <td>Red</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2600</td>\n      <td>0.0003</td>\n      <td>0.1020</td>\n      <td>18.70</td>\n      <td>0</td>\n      <td>Brown Dwarf</td>\n      <td>Red</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2800</td>\n      <td>0.0002</td>\n      <td>0.1600</td>\n      <td>16.65</td>\n      <td>0</td>\n      <td>Brown Dwarf</td>\n      <td>Red</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1939</td>\n      <td>0.0001</td>\n      <td>0.1030</td>\n      <td>20.06</td>\n      <td>0</td>\n      <td>Brown Dwarf</td>\n      <td>Red</td>\n      <td>M</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-033",
   "metadata": {},
   "source": "### Step 16 \u00b7 Count rows and columns\n\nThe unit here is a row described as a stellar record. Without source identifiers, unique physical objects cannot be verified.\n"
  },
  {
   "cell_type": "code",
   "id": "code-016",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Count rows and columns.\nprint(f'{raw_df.shape[0]} rows \u00d7 {raw_df.shape[1]} columns')\n\n# Your note: \n",
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "240 rows \u00d7 8 columns\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-035",
   "metadata": {},
   "source": "### Step 17 \u00b7 Inspect column types\n\nUnexpected text in a measurement column would need attention before modelling.\n"
  },
  {
   "cell_type": "code",
   "id": "code-017",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Inspect column types.\nraw_df.dtypes.rename('dtype')\n\n# Your note: \n",
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 17,
     "data": {
      "text/plain": "Temperature (K)              int64\nLuminosity (L/Lo)          float64\nRadius (R/Ro)              float64\nAbsolute magnitude (Mv)    float64\nStar type                    int64\nStar category               object\nStar color                  object\nSpectral Class              object\nName: dtype, dtype: object",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>dtype</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Temperature (K)</th>\n      <td>int64</td>\n    </tr>\n    <tr>\n      <th>Luminosity (L/Lo)</th>\n      <td>float64</td>\n    </tr>\n    <tr>\n      <th>Radius (R/Ro)</th>\n      <td>float64</td>\n    </tr>\n    <tr>\n      <th>Absolute magnitude (Mv)</th>\n      <td>float64</td>\n    </tr>\n    <tr>\n      <th>Star type</th>\n      <td>int64</td>\n    </tr>\n    <tr>\n      <th>Star category</th>\n      <td>object</td>\n    </tr>\n    <tr>\n      <th>Star color</th>\n      <td>object</td>\n    </tr>\n    <tr>\n      <th>Spectral Class</th>\n      <td>object</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-037",
   "metadata": {},
   "source": "### Step 18 \u00b7 Check missing values once\n\n`isna()` and `isnull()` are aliases; repeating both does not add a second check.\n\n**How to read the output:** Zero means no missing entries in that column. It does not mean the measurements are correct.\n"
  },
  {
   "cell_type": "code",
   "id": "code-018",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Check missing values once.\nraw_df.isna().sum().rename('missing_count')\n\n# Your note: \n",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "Temperature (K)            0\nLuminosity (L/Lo)          0\nRadius (R/Ro)              0\nAbsolute magnitude (Mv)    0\nStar type                  0\nStar category              0\nStar color                 0\nSpectral Class             0\nName: missing_count, dtype: int64",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>missing_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Temperature (K)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Luminosity (L/Lo)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Radius (R/Ro)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Absolute magnitude (Mv)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Star type</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Star category</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Star color</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Spectral Class</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-039",
   "metadata": {},
   "source": "### Step 19 \u00b7 Check duplicate records\n\nCount exact duplicate rows without silently deleting them. Repeated observations would require source-aware grouping.\n"
  },
  {
   "cell_type": "code",
   "id": "code-019",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Check duplicate records.\nprint('Exact duplicate rows:', int(raw_df.duplicated().sum()))\n\n# Your note: \n",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Exact duplicate rows: 0\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-041",
   "metadata": {},
   "source": "### Step 20 \u00b7 Define the class names\n\nUse the mapping observed in the uploaded file, not an assumed ordering from another copy of the dataset.\n"
  },
  {
   "cell_type": "code",
   "id": "code-020",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define the class names.\nCLASS_NAMES = {\n    0: 'Brown Dwarf', 1: 'Red Dwarf', 2: 'White Dwarf',\n    3: 'Main Sequence', 4: 'Supergiant', 5: 'Hypergiant'\n}\nCLASS_IDS = list(CLASS_NAMES)\nCLASS_LABELS = list(CLASS_NAMES.values())\n\n# Your note: \n",
   "execution_count": 20,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-043",
   "metadata": {},
   "source": "### Step 21 \u00b7 Define the numeric predictors\n\nAn explicit allow-list makes accidental inclusion of the target easier to prevent.\n"
  },
  {
   "cell_type": "code",
   "id": "code-021",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define the numeric predictors.\nNUMERIC_FEATURES = [\n    'Temperature (K)', 'Luminosity (L/Lo)',\n    'Radius (R/Ro)', 'Absolute magnitude (Mv)'\n]\nPOSITIVE_FEATURES = NUMERIC_FEATURES[:3]\n\n# Your note: \n",
   "execution_count": 21,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-045",
   "metadata": {},
   "source": "### Step 22 \u00b7 Validate the input schema\n\nFail clearly if the required fields or class codes have changed.\n"
  },
  {
   "cell_type": "code",
   "id": "code-022",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Validate the input schema.\nrequired_columns = set(NUMERIC_FEATURES + ['Star type', 'Star category', 'Star color', 'Spectral Class'])\nassert required_columns.issubset(raw_df.columns), 'Required columns are missing.'\nassert raw_df['Star type'].notna().all(), 'Missing target labels.'\nassert set(raw_df['Star type']) == set(CLASS_IDS), 'Unexpected class codes.'\nassert raw_df[NUMERIC_FEATURES].apply(pd.api.types.is_numeric_dtype).all(), 'Non-numeric predictors.'\nprint('Required columns and class codes are valid.')\n\n# Your note: \n",
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Required columns and class codes are valid.\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-047",
   "metadata": {},
   "source": "### Step 23 \u00b7 Verify every target-to-name mapping\n\nMatching class counts alone is insufficient: check that each row has the correct text label.\n"
  },
  {
   "cell_type": "code",
   "id": "code-023",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Verify every target-to-name mapping.\nexpected_names = raw_df['Star type'].map(CLASS_NAMES)\nlabel_mismatches = raw_df['Star category'].ne(expected_names)\nassert not label_mismatches.any(), 'Star category disagrees with Star type.'\nprint('All numeric class codes match their text labels.')\n\n# Your note: \n",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "All numeric class codes match their text labels.\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-049",
   "metadata": {},
   "source": "### Step 24 \u00b7 Check numerical validity\n\nTemperature, luminosity, and radius must be positive for logarithms. Absolute magnitude may legitimately be negative.\n"
  },
  {
   "cell_type": "code",
   "id": "code-024",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Check numerical validity.\nassert np.isfinite(raw_df[NUMERIC_FEATURES].to_numpy()).all(), 'Missing or non-finite measurements.'\nassert raw_df[POSITIVE_FEATURES].gt(0).all().all(), 'Non-positive physical measurements.'\nprint('Predictors are finite; log-transform inputs are positive.')\n\n# Your note: \n",
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Predictors are finite; log-transform inputs are positive.\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-051",
   "metadata": {},
   "source": "### Step 25 \u00b7 Create a working copy\n\nKeeping the input intact helps compare cleaning choices and reproduce the audit.\n"
  },
  {
   "cell_type": "code",
   "id": "code-025",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Create a working copy.\ndf = raw_df.copy(deep=True)\n\n# Your note: \n",
   "execution_count": 25,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-053",
   "metadata": {},
   "source": "### Step 26 \u00b7 Standardise colour spelling\n\nThis is deterministic text cleaning. No information is learned from validation or test records.\n"
  },
  {
   "cell_type": "code",
   "id": "code-026",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Standardise colour spelling.\ndf['Star color'] = (\n    df['Star color'].str.strip().str.lower()\n    .str.replace(r'[-\\s]+', ' ', regex=True)\n)\n\n# Your note: \n",
   "execution_count": 26,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-055",
   "metadata": {},
   "source": "### Step 27 \u00b7 Standardise spectral text\n\nRemove spaces and normalise case, while preserving the original file in raw_df.\n"
  },
  {
   "cell_type": "code",
   "id": "code-027",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Standardise spectral text.\ndf['Spectral Class'] = df['Spectral Class'].str.strip().str.upper()\n\n# Your note: \n",
   "execution_count": 27,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-057",
   "metadata": {},
   "source": "### Step 28 \u00b7 Summarise the colour cleaning\n\nCase, hyphen, and space variants can be merged; distinct descriptive terms are not automatically treated as synonyms.\n"
  },
  {
   "cell_type": "code",
   "id": "code-028",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Summarise the colour cleaning.\nprint('Original colour categories:', raw_df['Star color'].nunique())\nprint('Cleaned colour categories:', df['Star color'].nunique())\ndf['Star color'].value_counts().rename('records')\n\n# Your note: \n",
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Original colour categories: 19\nCleaned colour categories: 12\n"
    },
    {
     "output_type": "execute_result",
     "execution_count": 28,
     "data": {
      "text/plain": "Star color\nred                   112\nblue                   56\nblue white             41\nwhite                  10\nyellow white            8\nyellowish white         3\nyellowish               3\nwhitish                 2\norange                  2\npale yellow orange      1\nwhite yellow            1\norange red              1\nName: records, dtype: int64",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>records</th>\n    </tr>\n    <tr>\n      <th>Star color</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>red</th>\n      <td>112</td>\n    </tr>\n    <tr>\n      <th>blue</th>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>blue white</th>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>white</th>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>yellow white</th>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>yellowish white</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>yellowish</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>whitish</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>orange</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>pale yellow orange</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>white yellow</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>orange red</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-059",
   "metadata": {},
   "source": "### Step 29 \u00b7 Summarise the class balance\n\nA balanced teaching sample is useful for comparisons but does not measure how common these classes are in the sky.\n"
  },
  {
   "cell_type": "code",
   "id": "code-029",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Summarise the class balance.\nclass_counts = df['Star type'].value_counts().sort_index()\npd.DataFrame({'class': CLASS_LABELS, 'records': class_counts.to_numpy(),\n              'sample_share': class_counts.to_numpy() / len(df)})\n\n# Your note: \n",
   "execution_count": 29,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 29,
     "data": {
      "text/plain": "           class  records  sample_share\n0    Brown Dwarf       40        0.1667\n1      Red Dwarf       40        0.1667\n2    White Dwarf       40        0.1667\n3  Main Sequence       40        0.1667\n4     Supergiant       40        0.1667\n5     Hypergiant       40        0.1667",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>class</th>\n      <th>records</th>\n      <th>sample_share</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brown Dwarf</td>\n      <td>40</td>\n      <td>0.1667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Red Dwarf</td>\n      <td>40</td>\n      <td>0.1667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>White Dwarf</td>\n      <td>40</td>\n      <td>0.1667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Main Sequence</td>\n      <td>40</td>\n      <td>0.1667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Supergiant</td>\n      <td>40</td>\n      <td>0.1667</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Hypergiant</td>\n      <td>40</td>\n      <td>0.1667</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-061",
   "metadata": {},
   "source": "**Audit interpretation.** The supplied file contains 240 rows, no missing entries, no exact duplicate rows, and 40 rows in each class. The text target agrees with the integer target row by row. Cleaning reduces spelling variants in colour descriptions. These checks establish internal consistency, not representativeness or observational accuracy.\n\n**Why use only four numeric predictors?** This keeps the core tutorial focused and preserves the original model question. Colour and spectral class may be useful additional inputs, but their availability and how their labels were derived need scrutiny. They are not inherently unusable for machine learning. Nominal colour strings should not be turned into arbitrary ordinal numbers and treated as meaningful distances.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-062",
   "metadata": {},
   "source": "## 3. Lock the final test set before exploration\n\nAll learned preprocessing, model tuning, and model-family selection use training records. The final holdout is used once, after the family is selected. Basic schema and file-quality checks above do not choose predictive settings.\n\nWe use an **80/20 stratified split**: 192 training records and 48 test records, with eight test records per class. The same records evaluate every model. The earlier submission's test fractions were different for the two methods and were chosen after looking at scores; that comparison cannot establish which algorithm is generally better.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-063",
   "metadata": {},
   "source": "### Step 30 \u00b7 Separate predictors from labels\n\nStar category and Star type are excluded from X by construction.\n"
  },
  {
   "cell_type": "code",
   "id": "code-030",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Separate predictors from labels.\nX = df[NUMERIC_FEATURES].copy()\ny = df['Star type'].copy()\n\n# Your note: \n",
   "execution_count": 30,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-065",
   "metadata": {},
   "source": "### Step 31 \u00b7 Create the shared stratified split\n\n`stratify=y` retains class proportions. A fixed seed makes the split reproducible.\n"
  },
  {
   "cell_type": "code",
   "id": "code-031",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Create the shared stratified split.\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=TEST_FRACTION,\n    stratify=y, random_state=RANDOM_SEED\n)\n\n# Your note: \n",
   "execution_count": 31,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-067",
   "metadata": {},
   "source": "### Step 32 \u00b7 Verify that records do not cross the split\n\nDisjoint row indices and duplicate checks reduce obvious contamination. Missing source IDs prevent a stronger object-level guarantee.\n"
  },
  {
   "cell_type": "code",
   "id": "code-032",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Verify that records do not cross the split.\nassert set(X_train.index).isdisjoint(X_test.index)\nassert len(X_train) + len(X_test) == len(df)\nassert not X.duplicated().any(), 'Identical feature rows need grouped splitting.'\nprint(f'Training: {len(X_train)} | Final test: {len(X_test)}')\n\n# Your note: \n",
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Training: 192 | Final test: 48\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-069",
   "metadata": {},
   "source": "### Step 33 \u00b7 Check the split balance\n\nThis checks the evaluation design, not model scores.\n"
  },
  {
   "cell_type": "code",
   "id": "code-033",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Check the split balance.\nsplit_counts = pd.DataFrame({\n    'training': y_train.value_counts().sort_index(),\n    'final_test': y_test.value_counts().sort_index()\n}).rename(index=CLASS_NAMES)\nsplit_counts\n\n# Your note: \n",
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 33,
     "data": {
      "text/plain": "               training  final_test\nStar type                          \nBrown Dwarf          32           8\nRed Dwarf            32           8\nWhite Dwarf          32           8\nMain Sequence        32           8\nSupergiant           32           8\nHypergiant           32           8",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>training</th>\n      <th>final_test</th>\n    </tr>\n    <tr>\n      <th>Star type</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Brown Dwarf</th>\n      <td>32</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Red Dwarf</th>\n      <td>32</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>White Dwarf</th>\n      <td>32</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Main Sequence</th>\n      <td>32</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Supergiant</th>\n      <td>32</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Hypergiant</th>\n      <td>32</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-071",
   "metadata": {},
   "source": "### Step 34 \u00b7 Create a training-only exploration table\n\nOnly training records appear in the next plots and assumption checks.\n"
  },
  {
   "cell_type": "code",
   "id": "code-034",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Create a training-only exploration table.\ntrain_df = df.loc[X_train.index].copy()\n\n# Your note: \n",
   "execution_count": 34,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-073",
   "metadata": {},
   "source": "## 4. Explore the training data\n\nLook for structure, unit differences, and potential quality issues. Do not automatically delete extreme values: a very large radius may be the defining feature of a giant class.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-074",
   "metadata": {},
   "source": "### Step 35 \u00b7 Summarise measurement ranges\n\nLarge differences in units can distort KNN distances. Use this table to understand the measurements, not to rank class codes as numbers.\n"
  },
  {
   "cell_type": "code",
   "id": "code-035",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Summarise measurement ranges.\ntrain_df[NUMERIC_FEATURES].describe().T\n\n# Your note: \n",
   "execution_count": 35,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 35,
     "data": {
      "text/plain": "                         count         mean          std         min  \\\nTemperature (K)          192.0   10032.3229    8980.2271  1.9390e+03   \nLuminosity (L/Lo)        192.0  100312.5229  170626.7787  8.0000e-05   \nRadius (R/Ro)            192.0     235.1710     512.1845  8.4000e-03   \nAbsolute magnitude (Mv)  192.0       4.3586      10.4728 -1.1920e+01   \n\n                               25%        50%          75%        max  \nTemperature (K)          3360.0000  5443.5000   14794.5000   40000.00  \nLuminosity (L/Lo)           0.0009     0.0705  185000.0000  849420.00  \nRadius (R/Ro)               0.1027     0.7325      48.7500    1948.50  \nAbsolute magnitude (Mv)    -6.2412     8.3130      13.5118      20.06  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Temperature (K)</th>\n      <td>192.0</td>\n      <td>10032.3229</td>\n      <td>8980.2271</td>\n      <td>1.9390e+03</td>\n      <td>3360.0000</td>\n      <td>5443.5000</td>\n      <td>14794.5000</td>\n      <td>40000.00</td>\n    </tr>\n    <tr>\n      <th>Luminosity (L/Lo)</th>\n      <td>192.0</td>\n      <td>100312.5229</td>\n      <td>170626.7787</td>\n      <td>8.0000e-05</td>\n      <td>0.0009</td>\n      <td>0.0705</td>\n      <td>185000.0000</td>\n      <td>849420.00</td>\n    </tr>\n    <tr>\n      <th>Radius (R/Ro)</th>\n      <td>192.0</td>\n      <td>235.1710</td>\n      <td>512.1845</td>\n      <td>8.4000e-03</td>\n      <td>0.1027</td>\n      <td>0.7325</td>\n      <td>48.7500</td>\n      <td>1948.50</td>\n    </tr>\n    <tr>\n      <th>Absolute magnitude (Mv)</th>\n      <td>192.0</td>\n      <td>4.3586</td>\n      <td>10.4728</td>\n      <td>-1.1920e+01</td>\n      <td>-6.2412</td>\n      <td>8.3130</td>\n      <td>13.5118</td>\n      <td>20.06</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-076",
   "metadata": {},
   "source": "### Step 36 \u00b7 Plot training class counts\n\nThe named labels make the chart easier to read than unexplained numeric codes.\n"
  },
  {
   "cell_type": "code",
   "id": "code-036",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Plot training class counts.\nfig, ax = plt.subplots(figsize=(9, 4))\nax.bar(CLASS_LABELS, split_counts['training'], color=CLASS_COLORS)\nax.set(title='Training records per dataset class', ylabel='Records', xlabel='Dataset class')\nax.tick_params(axis='x', rotation=25)\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '01_class_balance.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 36,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 990x440 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-078",
   "metadata": {},
   "source": "### Step 37 \u00b7 Compare typical values within classes\n\nMedians are easier to interpret when the raw numerical distributions are strongly skewed.\n"
  },
  {
   "cell_type": "code",
   "id": "code-037",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Compare typical values within classes.\ntrain_df.groupby('Star category')[NUMERIC_FEATURES].median().reindex(CLASS_LABELS)\n\n# Your note: \n",
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 37,
     "data": {
      "text/plain": "               Temperature (K)  Luminosity (L/Lo)  Radius (R/Ro)  \\\nStar category                                                      \nBrown Dwarf             2940.0             0.0005         0.1060   \nRed Dwarf               3280.5             0.0032         0.3145   \nWhite Dwarf            13165.0             0.0008         0.0105   \nMain Sequence          11674.0           680.5000         5.8880   \nSupergiant             12821.0        233000.0000        51.5000   \nHypergiant              3655.0        234500.0000      1352.5000   \n\n               Absolute magnitude (Mv)  \nStar category                           \nBrown Dwarf                    17.0300  \nRed Dwarf                      12.3650  \nWhite Dwarf                    12.2300  \nMain Sequence                  -0.5500  \nSupergiant                     -6.2425  \nHypergiant                    -10.1600  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature (K)</th>\n      <th>Luminosity (L/Lo)</th>\n      <th>Radius (R/Ro)</th>\n      <th>Absolute magnitude (Mv)</th>\n    </tr>\n    <tr>\n      <th>Star category</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Brown Dwarf</th>\n      <td>2940.0</td>\n      <td>0.0005</td>\n      <td>0.1060</td>\n      <td>17.0300</td>\n    </tr>\n    <tr>\n      <th>Red Dwarf</th>\n      <td>3280.5</td>\n      <td>0.0032</td>\n      <td>0.3145</td>\n      <td>12.3650</td>\n    </tr>\n    <tr>\n      <th>White Dwarf</th>\n      <td>13165.0</td>\n      <td>0.0008</td>\n      <td>0.0105</td>\n      <td>12.2300</td>\n    </tr>\n    <tr>\n      <th>Main Sequence</th>\n      <td>11674.0</td>\n      <td>680.5000</td>\n      <td>5.8880</td>\n      <td>-0.5500</td>\n    </tr>\n    <tr>\n      <th>Supergiant</th>\n      <td>12821.0</td>\n      <td>233000.0000</td>\n      <td>51.5000</td>\n      <td>-6.2425</td>\n    </tr>\n    <tr>\n      <th>Hypergiant</th>\n      <td>3655.0</td>\n      <td>234500.0000</td>\n      <td>1352.5000</td>\n      <td>-10.1600</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-080",
   "metadata": {},
   "source": "### Step 38 \u00b7 Plot a Hertzsprung\u2013Russell style diagram\n\nUse a log luminosity axis and put hotter objects on the left. This is a diagram of the supplied measurements, not an independent astrophysical validation.\n"
  },
  {
   "cell_type": "code",
   "id": "code-038",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Plot a Hertzsprung\u2013Russell style diagram.\nfig, ax = plt.subplots(figsize=(9, 6))\nfor class_id, color in zip(CLASS_IDS, CLASS_COLORS):\n    group = train_df[train_df['Star type'].eq(class_id)]\n    ax.scatter(group['Temperature (K)'], group['Luminosity (L/Lo)'],\n               label=CLASS_NAMES[class_id], color=color, s=38, alpha=0.8)\nax.set_xscale('log')\nax.set_yscale('log')\nax.invert_xaxis()\nax.set(title='Training sample: temperature and luminosity', xlabel='Temperature (K; hotter \u2190)',\n       ylabel='Luminosity / solar luminosity (log scale)')\nax.legend(loc='center left', bbox_to_anchor=(1.02, 0.5))\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '02_hr_diagram.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 38,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 990x660 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-082",
   "metadata": {},
   "source": "### Step 39 \u00b7 Plot temperature against visual magnitude\n\nBoth axes follow the usual diagram orientation: hotter to the left and visually brighter toward the top.\n"
  },
  {
   "cell_type": "code",
   "id": "code-039",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Plot temperature against visual magnitude.\nfig, ax = plt.subplots(figsize=(9, 6))\nsns.scatterplot(data=train_df, x='Temperature (K)', y='Absolute magnitude (Mv)',\n                hue='Star category', hue_order=CLASS_LABELS, palette=CLASS_COLORS, ax=ax)\nax.set_xscale('log')\nax.invert_xaxis()\nax.invert_yaxis()\nax.set(title='Training sample: temperature and visual absolute magnitude')\nax.legend(loc='center left', bbox_to_anchor=(1.02, 0.5))\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '03_temperature_magnitude.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 39,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 990x660 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-084",
   "metadata": {},
   "source": "### Step 40 \u00b7 Inspect feature relationships\n\nSpearman correlation compares rank relationships. A full-sample correlation does not by itself test Naive Bayes conditional independence.\n"
  },
  {
   "cell_type": "code",
   "id": "code-040",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Inspect feature relationships.\ntraining_correlation = X_train.corr(method='spearman')\nfig, ax = plt.subplots(figsize=(8, 6))\nsns.heatmap(training_correlation, annot=True, fmt='.2f', cmap='coolwarm',\n            vmin=-1, vmax=1, ax=ax)\nax.set_title('Training predictors: Spearman correlation')\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '04_feature_correlation.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 40,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 880x660 with 2 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-086",
   "metadata": {},
   "source": "**Interpretation.** The training plots show separation between several supplied classes. Radius, luminosity, and magnitude contain strong class structure; temperature alone does not describe all of it. Apparent separability is encouraging for this sample, but it may reflect how a curated dataset was constructed. Correlated physical features are a reason to question the Naive Bayes independence assumption.\n\nVisual absolute magnitude (`Mv`) is already a logarithmic brightness measure for a band. It is not automatically equal to bolometric magnitude. A colour-based correction would be needed to compare it directly with total emitted luminosity.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-087",
   "metadata": {},
   "source": "## 5. Examine transformations and assumptions\n\nLogarithms compress several orders of magnitude: luminosities of 0.001, 1, and 1,000 become \u22123, 0, and 3 in base 10. Temperature, luminosity, and radius are positive, so all three are log-transformed. Magnitude is kept as supplied. Standardisation then gives each transformed feature a comparable numerical scale for KNN.\n\nGaussian Naive Bayes models a normal distribution for **each feature within each class**, and assumes conditional independence. A log transform can reduce skew without guaranteeing either assumption. The official [Naive Bayes documentation](https://scikit-learn.org/stable/modules/naive_bayes.html) also cautions that its probability estimates can be poor.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-088",
   "metadata": {},
   "source": "### Step 41 \u00b7 Create transformed training values for diagnostics\n\nThis deterministic transform is only for exploration here. The model pipeline will repeat the same transform automatically.\n"
  },
  {
   "cell_type": "code",
   "id": "code-041",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Create transformed training values for diagnostics.\nlog_train = X_train.copy()\nlog_train[POSITIVE_FEATURES] = np.log10(log_train[POSITIVE_FEATURES])\n\n# Your note: \n",
   "execution_count": 41,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-090",
   "metadata": {},
   "source": "### Step 42 \u00b7 Compare transformed distributions\n\nPlot one panel per feature so a reader can compare classes without an oversized pair plot.\n"
  },
  {
   "cell_type": "code",
   "id": "code-042",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Compare transformed distributions.\nplot_df = log_train.assign(category=y_train.map(CLASS_NAMES))\nfig, axes = plt.subplots(2, 2, figsize=(12, 8))\nfor feature, ax in zip(NUMERIC_FEATURES, axes.flat):\n    sns.boxplot(data=plot_df, x='category', y=feature, order=CLASS_LABELS,\n                color='#B8C2CC', ax=ax)\n    ax.set_xlabel('')\n    ax.set_ylabel(('log10 ' if feature in POSITIVE_FEATURES else '') + feature)\n    ax.tick_params(axis='x', rotation=35)\nfig.suptitle('Training distributions after the chosen transformation', y=1.02)\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '05_transformed_distributions.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 42,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 1320x880 with 4 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-092",
   "metadata": {},
   "source": "### Step 43 \u00b7 Check normality within each class\n\nRun 24 training-only Shapiro\u2013Wilk tests, one for each feature/class combination. See [SciPy documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html).\n"
  },
  {
   "cell_type": "code",
   "id": "code-043",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Check normality within each class.\nnormality_rows = []\nfor class_id in CLASS_IDS:\n    for feature in NUMERIC_FEATURES:\n        values = log_train.loc[y_train.eq(class_id), feature]\n        statistic, p_value = stats.shapiro(values)\n        normality_rows.append({'class': CLASS_NAMES[class_id], 'feature': feature,\n                               'n': len(values), 'W': statistic, 'p_value': p_value})\nnormality_table = pd.DataFrame(normality_rows)\n\n# Your note: \n",
   "execution_count": 43,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-094",
   "metadata": {},
   "source": "### Step 44 \u00b7 Account for the 24 normality checks\n\nBonferroni adjustment is conservative and controls the family-wise error rate. These are diagnostics, not a gate used to choose a test split.\n\n**How to read the output:** A small adjusted p-value is evidence against normality. A large p-value is insufficient evidence to reject it, not proof of a normal distribution.\n"
  },
  {
   "cell_type": "code",
   "id": "code-044",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Account for the 24 normality checks.\nnormality_table['p_bonferroni'] = (normality_table['p_value'] * len(normality_table)).clip(upper=1)\nnormality_table['evidence_against_normality'] = normality_table['p_bonferroni'].lt(0.05)\nnormality_table.round(4)\n\n# Your note: \n",
   "execution_count": 44,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 44,
     "data": {
      "text/plain": "            class                  feature   n       W  p_value  p_bonferroni  \\\n0     Brown Dwarf          Temperature (K)  32  0.8893   0.0033        0.0804   \n1     Brown Dwarf        Luminosity (L/Lo)  32  0.9621   0.3133        1.0000   \n2     Brown Dwarf            Radius (R/Ro)  32  0.9372   0.0623        1.0000   \n3     Brown Dwarf  Absolute magnitude (Mv)  32  0.8666   0.0010        0.0233   \n4       Red Dwarf          Temperature (K)  32  0.9328   0.0469        1.0000   \n5       Red Dwarf        Luminosity (L/Lo)  32  0.9868   0.9563        1.0000   \n6       Red Dwarf            Radius (R/Ro)  32  0.9749   0.6441        1.0000   \n7       Red Dwarf  Absolute magnitude (Mv)  32  0.9580   0.2426        1.0000   \n8     White Dwarf          Temperature (K)  32  0.9489   0.1343        1.0000   \n9     White Dwarf        Luminosity (L/Lo)  32  0.8889   0.0033        0.0784   \n10    White Dwarf            Radius (R/Ro)  32  0.9548   0.1967        1.0000   \n11    White Dwarf  Absolute magnitude (Mv)  32  0.9508   0.1514        1.0000   \n12  Main Sequence          Temperature (K)  32  0.9449   0.1029        1.0000   \n13  Main Sequence        Luminosity (L/Lo)  32  0.9276   0.0335        0.8039   \n14  Main Sequence            Radius (R/Ro)  32  0.8001   0.0000        0.0010   \n15  Main Sequence  Absolute magnitude (Mv)  32  0.9030   0.0074        0.1776   \n16     Supergiant          Temperature (K)  32  0.9008   0.0065        0.1561   \n17     Supergiant        Luminosity (L/Lo)  32  0.9510   0.1541        1.0000   \n18     Supergiant            Radius (R/Ro)  32  0.9185   0.0189        0.4531   \n19     Supergiant  Absolute magnitude (Mv)  32  0.9539   0.1855        1.0000   \n20     Hypergiant          Temperature (K)  32  0.7207   0.0000        0.0000   \n21     Hypergiant        Luminosity (L/Lo)  32  0.9799   0.7955        1.0000   \n22     Hypergiant            Radius (R/Ro)  32  0.9416   0.0830        1.0000   \n23     Hypergiant  Absolute magnitude (Mv)  32  0.8738   0.0014        0.0342   \n\n    evidence_against_normality  \n0                        False  \n1                        False  \n2                        False  \n3                         True  \n4                        False  \n5                        False  \n6                        False  \n7                        False  \n8                        False  \n9                        False  \n10                       False  \n11                       False  \n12                       False  \n13                       False  \n14                        True  \n15                       False  \n16                       False  \n17                       False  \n18                       False  \n19                       False  \n20                        True  \n21                       False  \n22                       False  \n23                        True  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>class</th>\n      <th>feature</th>\n      <th>n</th>\n      <th>W</th>\n      <th>p_value</th>\n      <th>p_bonferroni</th>\n      <th>evidence_against_normality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brown Dwarf</td>\n      <td>Temperature (K)</td>\n      <td>32</td>\n      <td>0.8893</td>\n      <td>0.0033</td>\n      <td>0.0804</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brown Dwarf</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>32</td>\n      <td>0.9621</td>\n      <td>0.3133</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Brown Dwarf</td>\n      <td>Radius (R/Ro)</td>\n      <td>32</td>\n      <td>0.9372</td>\n      <td>0.0623</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Brown Dwarf</td>\n      <td>Absolute magnitude (Mv)</td>\n      <td>32</td>\n      <td>0.8666</td>\n      <td>0.0010</td>\n      <td>0.0233</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Red Dwarf</td>\n      <td>Temperature (K)</td>\n      <td>32</td>\n      <td>0.9328</td>\n      <td>0.0469</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Red Dwarf</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>32</td>\n      <td>0.9868</td>\n      <td>0.9563</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Red Dwarf</td>\n      <td>Radius (R/Ro)</td>\n      <td>32</td>\n      <td>0.9749</td>\n      <td>0.6441</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Red Dwarf</td>\n      <td>Absolute magnitude (Mv)</td>\n      <td>32</td>\n      <td>0.9580</td>\n      <td>0.2426</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>White Dwarf</td>\n      <td>Temperature (K)</td>\n      <td>32</td>\n      <td>0.9489</td>\n      <td>0.1343</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>White Dwarf</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>32</td>\n      <td>0.8889</td>\n      <td>0.0033</td>\n      <td>0.0784</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>White Dwarf</td>\n      <td>Radius (R/Ro)</td>\n      <td>32</td>\n      <td>0.9548</td>\n      <td>0.1967</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>White Dwarf</td>\n      <td>Absolute magnitude (Mv)</td>\n      <td>32</td>\n      <td>0.9508</td>\n      <td>0.1514</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Main Sequence</td>\n      <td>Temperature (K)</td>\n      <td>32</td>\n      <td>0.9449</td>\n      <td>0.1029</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Main Sequence</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>32</td>\n      <td>0.9276</td>\n      <td>0.0335</td>\n      <td>0.8039</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Main Sequence</td>\n      <td>Radius (R/Ro)</td>\n      <td>32</td>\n      <td>0.8001</td>\n      <td>0.0000</td>\n      <td>0.0010</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Main Sequence</td>\n      <td>Absolute magnitude (Mv)</td>\n      <td>32</td>\n      <td>0.9030</td>\n      <td>0.0074</td>\n      <td>0.1776</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Supergiant</td>\n      <td>Temperature (K)</td>\n      <td>32</td>\n      <td>0.9008</td>\n      <td>0.0065</td>\n      <td>0.1561</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Supergiant</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>32</td>\n      <td>0.9510</td>\n      <td>0.1541</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Supergiant</td>\n      <td>Radius (R/Ro)</td>\n      <td>32</td>\n      <td>0.9185</td>\n      <td>0.0189</td>\n      <td>0.4531</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Supergiant</td>\n      <td>Absolute magnitude (Mv)</td>\n      <td>32</td>\n      <td>0.9539</td>\n      <td>0.1855</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Hypergiant</td>\n      <td>Temperature (K)</td>\n      <td>32</td>\n      <td>0.7207</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Hypergiant</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>32</td>\n      <td>0.9799</td>\n      <td>0.7955</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Hypergiant</td>\n      <td>Radius (R/Ro)</td>\n      <td>32</td>\n      <td>0.9416</td>\n      <td>0.0830</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Hypergiant</td>\n      <td>Absolute magnitude (Mv)</td>\n      <td>32</td>\n      <td>0.8738</td>\n      <td>0.0014</td>\n      <td>0.0342</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-096",
   "metadata": {},
   "source": "### Step 45 \u00b7 Check relationships within classes\n\nA maximum absolute within-class Spearman correlation highlights pairs that deserve attention. Low correlation cannot prove independence.\n"
  },
  {
   "cell_type": "code",
   "id": "code-045",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Check relationships within classes.\nconditional_rows = []\nfor class_id in CLASS_IDS:\n    corr = log_train.loc[y_train.eq(class_id)].corr(method='spearman').abs()\n    pairs = corr.where(np.triu(np.ones(corr.shape), k=1).astype(bool)).stack()\n    pair = pairs.idxmax()\n    conditional_rows.append({'class': CLASS_NAMES[class_id],\n                             'feature_1': pair[0], 'feature_2': pair[1],\n                             'absolute_spearman': pairs.max()})\npd.DataFrame(conditional_rows).round(3)\n\n# Your note: \n",
   "execution_count": 45,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 45,
     "data": {
      "text/plain": "           class          feature_1                feature_2  \\\n0    Brown Dwarf    Temperature (K)        Luminosity (L/Lo)   \n1      Red Dwarf    Temperature (K)            Radius (R/Ro)   \n2    White Dwarf    Temperature (K)  Absolute magnitude (Mv)   \n3  Main Sequence    Temperature (K)        Luminosity (L/Lo)   \n4     Supergiant  Luminosity (L/Lo)            Radius (R/Ro)   \n5     Hypergiant    Temperature (K)        Luminosity (L/Lo)   \n\n   absolute_spearman  \n0              0.366  \n1              0.556  \n2              0.834  \n3              0.995  \n4              0.235  \n5              0.571  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>class</th>\n      <th>feature_1</th>\n      <th>feature_2</th>\n      <th>absolute_spearman</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brown Dwarf</td>\n      <td>Temperature (K)</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>0.366</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Red Dwarf</td>\n      <td>Temperature (K)</td>\n      <td>Radius (R/Ro)</td>\n      <td>0.556</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>White Dwarf</td>\n      <td>Temperature (K)</td>\n      <td>Absolute magnitude (Mv)</td>\n      <td>0.834</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Main Sequence</td>\n      <td>Temperature (K)</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>0.995</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Supergiant</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>Radius (R/Ro)</td>\n      <td>0.235</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Hypergiant</td>\n      <td>Temperature (K)</td>\n      <td>Luminosity (L/Lo)</td>\n      <td>0.571</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-098",
   "metadata": {},
   "source": "### Step 46 \u00b7 Run an optional physical consistency diagnostic\n\nAssuming bolometric luminosity and effective temperature, the Stefan\u2013Boltzmann relation gives L/L\u2609 \u2248 (R/R\u2609)\u00b2(T/5772 K)\u2074. The temperature scale follows [IAU nominal solar constants](https://arxiv.org/abs/1605.09788). Definitions and uncertainties must be confirmed before treating deviations as errors.\n\n**How to read the output:** Zero indicates agreement under those definitions; +1 means 10\u00d7 the implied luminosity. Large deviations flag questions about metadata, not automatic row deletion.\n"
  },
  {
   "cell_type": "code",
   "id": "code-046",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Run an optional physical consistency diagnostic.\nimplied_luminosity = (\n    X_train['Radius (R/Ro)'] ** 2 * (X_train['Temperature (K)'] / 5772.0) ** 4\n)\nphysics_residual = np.log10(X_train['Luminosity (L/Lo)'] / implied_luminosity)\nphysics_audit = pd.DataFrame({'class': y_train.map(CLASS_NAMES),\n                             'log10_observed_over_implied_L': physics_residual})\nphysics_audit.groupby('class').agg(['median', 'min', 'max']).reindex(CLASS_LABELS).round(3)\n\n# Your note: \n",
   "execution_count": 46,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 46,
     "data": {
      "text/plain": "              log10_observed_over_implied_L              \n                                     median    min    max\nclass                                                    \nBrown Dwarf                          -0.268 -0.902  0.318\nRed Dwarf                            -0.327 -2.023  0.399\nWhite Dwarf                          -0.704 -1.487  0.353\nMain Sequence                        -0.006 -0.449  0.877\nSupergiant                            0.607 -0.860  3.854\nHypergiant                           -0.322 -4.113  0.108",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">log10_observed_over_implied_L</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>median</th>\n      <th>min</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>class</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Brown Dwarf</th>\n      <td>-0.268</td>\n      <td>-0.902</td>\n      <td>0.318</td>\n    </tr>\n    <tr>\n      <th>Red Dwarf</th>\n      <td>-0.327</td>\n      <td>-2.023</td>\n      <td>0.399</td>\n    </tr>\n    <tr>\n      <th>White Dwarf</th>\n      <td>-0.704</td>\n      <td>-1.487</td>\n      <td>0.353</td>\n    </tr>\n    <tr>\n      <th>Main Sequence</th>\n      <td>-0.006</td>\n      <td>-0.449</td>\n      <td>0.877</td>\n    </tr>\n    <tr>\n      <th>Supergiant</th>\n      <td>0.607</td>\n      <td>-0.860</td>\n      <td>3.854</td>\n    </tr>\n    <tr>\n      <th>Hypergiant</th>\n      <td>-0.322</td>\n      <td>-4.113</td>\n      <td>0.108</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-100",
   "metadata": {},
   "source": "**What this changes in the interpretation.** Complete cells and high classification accuracy can coexist with questionable physical metadata. Retain the records for this labelled-data exercise, document the assumptions, and ask a domain specialist to verify catalogue provenance before making scientific claims. The classifier is not learning a complete physical theory of stellar evolution.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-101",
   "metadata": {},
   "source": "## 6. Build simple, reusable pipelines\n\n| Model | Preprocessing | Why it is included |\n|---|---|---|\n| Most-frequent baseline | None | Quantifies what a rule with no feature knowledge achieves |\n| Gaussian Naive Bayes | Log positive physical features, then standardise | Retains the original probabilistic model and tests a simple distributional approach |\n| KNN | The same log transform and standardisation | Learns from proximity; feature scale matters |\n| Decision tree | Raw numeric measurements | A comparison with readable thresholds and fewer distributional assumptions |\n\nDifferent algorithms may need different preprocessing while still using the same records and evaluation design. Every learned transformation is fitted inside its training fold. The earlier `scaler.fit_transform(X)` discarded the transformed array, so subsequent models still received raw values.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-102",
   "metadata": {},
   "source": "### Step 47 \u00b7 Define the log transformer\n\nThe first three columns are transformed; the remaining magnitude column passes through. The returned transformer can be cloned and saved.\n"
  },
  {
   "cell_type": "code",
   "id": "code-047",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define the log transformer.\ndef make_log_preprocessor():\n    return ColumnTransformer(\n        [('log10', FunctionTransformer(np.log10, feature_names_out='one-to-one'), POSITIVE_FEATURES)],\n        remainder='passthrough', verbose_feature_names_out=False\n    )\n\n# Your note: \n",
   "execution_count": 47,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-104",
   "metadata": {},
   "source": "### Step 48 \u00b7 Build the Naive Bayes pipeline\n\nScaling is attached to the model. It will be learned again within each cross-validation training fold.\n"
  },
  {
   "cell_type": "code",
   "id": "code-048",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Build the Naive Bayes pipeline.\nnb_pipeline = Pipeline([\n    ('log_features', make_log_preprocessor()),\n    ('scale', StandardScaler()),\n    ('classifier', GaussianNB())\n])\n\n# Your note: \n",
   "execution_count": 48,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-106",
   "metadata": {},
   "source": "### Step 49 \u00b7 Build the KNN pipeline\n\nThe pipeline applies the same numerical treatment to training and prediction inputs.\n"
  },
  {
   "cell_type": "code",
   "id": "code-049",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Build the KNN pipeline.\nknn_pipeline = Pipeline([\n    ('log_features', make_log_preprocessor()),\n    ('scale', StandardScaler()),\n    ('classifier', KNeighborsClassifier())\n])\n\n# Your note: \n",
   "execution_count": 49,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-108",
   "metadata": {},
   "source": "### Step 50 \u00b7 Build the decision tree pipeline\n\nTrees can split the raw measurements directly. Depth and leaf size are tuned later to limit unnecessary complexity.\n"
  },
  {
   "cell_type": "code",
   "id": "code-050",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Build the decision tree pipeline.\ntree_pipeline = Pipeline([\n    ('classifier', DecisionTreeClassifier(random_state=RANDOM_SEED))\n])\n\n# Your note: \n",
   "execution_count": 50,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-110",
   "metadata": {},
   "source": "### Step 51 \u00b7 Build the no-skill baseline\n\nAll training class counts are equal, so the most-frequent rule chooses one tied class. Its test accuracy should be 1/6, or 16.67%.\n"
  },
  {
   "cell_type": "code",
   "id": "code-051",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Build the no-skill baseline.\nbaseline = DummyClassifier(strategy='most_frequent')\n\n# Your note: \n",
   "execution_count": 51,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-112",
   "metadata": {},
   "source": "### Step 52 \u00b7 Show what log transformation and scaling do\n\nFit this demonstration on training records only. This illustration is separate from the models, whose own scalers stay inside their pipelines.\n\n**How to read the output:** Means are near zero. StandardScaler uses population variance; pandas std uses sample variance, so the displayed standard deviations are slightly above one.\n"
  },
  {
   "cell_type": "code",
   "id": "code-052",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Show what log transformation and scaling do.\ndemo_preprocessor = Pipeline([('log_features', make_log_preprocessor()), ('scale', StandardScaler())])\nscaled_training = demo_preprocessor.fit_transform(X_train)\npd.DataFrame(scaled_training, columns=NUMERIC_FEATURES).agg(['mean', 'std']).round(3)\n\n# Your note: \n",
   "execution_count": 52,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 52,
     "data": {
      "text/plain": "      Temperature (K)  Luminosity (L/Lo)  Radius (R/Ro)  \\\nmean            0.000             -0.000         -0.000   \nstd             1.003              1.003          1.003   \n\n      Absolute magnitude (Mv)  \nmean                    0.000  \nstd                     1.003  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature (K)</th>\n      <th>Luminosity (L/Lo)</th>\n      <th>Radius (R/Ro)</th>\n      <th>Absolute magnitude (Mv)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>mean</th>\n      <td>0.000</td>\n      <td>-0.000</td>\n      <td>-0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.003</td>\n      <td>1.003</td>\n      <td>1.003</td>\n      <td>1.003</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-114",
   "metadata": {},
   "source": "## 7. Tune settings using training data\n\n**Primary selection metric: macro F1.** Compute F1 for each class and average equally. This makes weak performance on one class visible rather than letting common classes dominate. Accuracy and balanced accuracy provide additional views; they equal one another on this equally balanced test sample.\n\nPrecision asks how often a predicted class is correct. Recall asks how much of an actual class is recovered. The per-class F1 score balances precision and recall. Macro F1 is the mean of the six class F1 scores; it is not the F1 of the averaged precision and recall.\n\nThe grids below are deliberately small and specified before final test evaluation. Cross-validation search scores are useful for tuning but optimistic as an estimate of the selected configuration's performance. The next section adds nested validation.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-115",
   "metadata": {},
   "source": "### Step 53 \u00b7 Define common validation folds\n\nAll searches receive the same stratified, shuffled folds and the same fixed seed.\n"
  },
  {
   "cell_type": "code",
   "id": "code-053",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define common validation folds.\ntuning_cv = StratifiedKFold(n_splits=CV_FOLDS, shuffle=True, random_state=RANDOM_SEED)\n\n# Your note: \n",
   "execution_count": 53,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-117",
   "metadata": {},
   "source": "### Step 54 \u00b7 Define Naive Bayes candidate settings\n\nvar_smoothing stabilises small variances. Candidate values are set in advance rather than chosen from the final holdout.\n"
  },
  {
   "cell_type": "code",
   "id": "code-054",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define Naive Bayes candidate settings.\nnb_grid = {'classifier__var_smoothing': [1e-11, 1e-9, 1e-7, 1e-5, 1e-3]}\n\n# Your note: \n",
   "execution_count": 54,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-119",
   "metadata": {},
   "source": "### Step 55 \u00b7 Define KNN candidate settings\n\nTune neighbour count and weighting; Euclidean distance is fixed to keep the lesson focused.\n"
  },
  {
   "cell_type": "code",
   "id": "code-055",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define KNN candidate settings.\nknn_grid = {\n    'classifier__n_neighbors': [1, 3, 5, 7, 9, 11],\n    'classifier__weights': ['uniform', 'distance']\n}\n\n# Your note: \n",
   "execution_count": 55,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-121",
   "metadata": {},
   "source": "### Step 56 \u00b7 Define tree candidate settings\n\nDepth controls the number of splits; leaf size stops a terminal rule from relying on too few records.\n"
  },
  {
   "cell_type": "code",
   "id": "code-056",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define tree candidate settings.\ntree_grid = {\n    'classifier__max_depth': [2, 3, 4, 5, None],\n    'classifier__min_samples_leaf': [1, 2, 4]\n}\n\n# Your note: \n",
   "execution_count": 56,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-123",
   "metadata": {},
   "source": "### Step 57 \u00b7 Create one search factory\n\nKeep refit and scoring consistent. refit trains the best configuration on all training records, never on the final holdout.\n"
  },
  {
   "cell_type": "code",
   "id": "code-057",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Create one search factory.\ndef make_search(pipeline, grid, cv):\n    return GridSearchCV(\n        pipeline, grid, cv=cv, scoring={'macro_f1': 'f1_macro', 'accuracy': 'accuracy'},\n        refit='macro_f1', return_train_score=True, n_jobs=N_JOBS, error_score='raise'\n    )\n\n# Your note: \n",
   "execution_count": 57,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-125",
   "metadata": {},
   "source": "### Step 58 \u00b7 Tune Naive Bayes\n\nOnly X_train and y_train enter the search.\n"
  },
  {
   "cell_type": "code",
   "id": "code-058",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Tune Naive Bayes.\nnb_search = make_search(nb_pipeline, nb_grid, tuning_cv)\nnb_search.fit(X_train, y_train)\nprint('Best settings:', nb_search.best_params_)\n\n# Your note: \n",
   "execution_count": 58,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Best settings: {'classifier__var_smoothing': 0.001}\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-127",
   "metadata": {},
   "source": "### Step 59 \u00b7 Tune KNN\n\nThe scaler is fitted separately for every candidate in every training fold.\n"
  },
  {
   "cell_type": "code",
   "id": "code-059",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Tune KNN.\nknn_search = make_search(knn_pipeline, knn_grid, tuning_cv)\nknn_search.fit(X_train, y_train)\nprint('Best settings:', knn_search.best_params_)\n\n# Your note: \n",
   "execution_count": 59,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Best settings: {'classifier__n_neighbors': 3, 'classifier__weights': 'uniform'}\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-129",
   "metadata": {},
   "source": "### Step 60 \u00b7 Tune the tree\n\nThe tree receives exactly the same training records and validation folds as the other methods.\n"
  },
  {
   "cell_type": "code",
   "id": "code-060",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Tune the tree.\ntree_search = make_search(tree_pipeline, tree_grid, tuning_cv)\ntree_search.fit(X_train, y_train)\nprint('Best settings:', tree_search.best_params_)\n\n# Your note: \n",
   "execution_count": 60,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Best settings: {'classifier__max_depth': 5, 'classifier__min_samples_leaf': 1}\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-131",
   "metadata": {},
   "source": "### Step 61 \u00b7 Collect training search results\n\nDisplay tuning performance and the training-to-validation gap. Fold standard deviation measures fold variation; it is not a confidence interval.\n"
  },
  {
   "cell_type": "code",
   "id": "code-061",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Collect training search results.\nsearches = {'Gaussian Naive Bayes': nb_search, 'KNN': knn_search, 'Decision tree': tree_search}\nsearch_rows = []\nfor name, search in searches.items():\n    i = search.best_index_\n    search_rows.append({'model': name, 'CV_macro_F1': search.best_score_,\n                        'CV_fold_SD': search.cv_results_['std_test_macro_f1'][i],\n                        'training_macro_F1': search.cv_results_['mean_train_macro_f1'][i],\n                        'best_parameters': str(search.best_params_)})\nsearch_summary = pd.DataFrame(search_rows).set_index('model')\nsearch_summary.round(4)\n\n# Your note: \n",
   "execution_count": 61,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 61,
     "data": {
      "text/plain": "                      CV_macro_F1  CV_fold_SD  training_macro_F1  \\\nmodel                                                              \nGaussian Naive Bayes       1.0000      0.0000             0.9974   \nKNN                        0.9949      0.0103             0.9987   \nDecision tree              1.0000      0.0000             1.0000   \n\n                                                        best_parameters  \nmodel                                                                    \nGaussian Naive Bayes               {'classifier__var_smoothing': 0.001}  \nKNN                   {'classifier__n_neighbors': 3, 'classifier__we...  \nDecision tree         {'classifier__max_depth': 5, 'classifier__min_...  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CV_macro_F1</th>\n      <th>CV_fold_SD</th>\n      <th>training_macro_F1</th>\n      <th>best_parameters</th>\n    </tr>\n    <tr>\n      <th>model</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Gaussian Naive Bayes</th>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9974</td>\n      <td>{'classifier__var_smoothing': 0.001}</td>\n    </tr>\n    <tr>\n      <th>KNN</th>\n      <td>0.9949</td>\n      <td>0.0103</td>\n      <td>0.9987</td>\n      <td>{'classifier__n_neighbors': 3, 'classifier__weights': 'uniform'}</td>\n    </tr>\n    <tr>\n      <th>Decision tree</th>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>1.0000</td>\n      <td>{'classifier__max_depth': 5, 'classifier__min_samples_leaf': 1}</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-133",
   "metadata": {},
   "source": "### Step 62 \u00b7 Inspect KNN tuning\n\nA chart makes the effect of neighbour count visible. The values still come from training cross-validation.\n"
  },
  {
   "cell_type": "code",
   "id": "code-062",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Inspect KNN tuning.\nknn_cv_table = pd.DataFrame(knn_search.cv_results_)\nfig, ax = plt.subplots(figsize=(8, 4))\nfor weight, group in knn_cv_table.groupby('param_classifier__weights'):\n    ax.errorbar(group['param_classifier__n_neighbors'].astype(int), group['mean_test_macro_f1'],\n                yerr=group['std_test_macro_f1'], marker='o', capsize=3, label=weight)\nax.set(title='KNN: training CV macro F1', xlabel='Number of neighbours', ylabel='Macro F1')\nax.legend(title='Weighting')\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '06_knn_tuning.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 62,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 880x440 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-135",
   "metadata": {},
   "source": "## 8. Add a stronger model-selection check\n\nThis is the advanced part; the underlying idea is simple. **Inner folds tune settings; outer folds assess that tuning procedure on different training records.** The final test is still untouched. See the official [nested cross-validation example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html).\n\nUse the mean outer-fold macro F1 to choose a model family. These are five overlapping-training-fold estimates, not five independent experiments. After choosing a family, its previously fitted five-fold search provides the final settings. Family selection itself still introduces optimism into the winning nested mean, so the shared holdout remains the final estimate. No statistical superiority is inferred from small score differences.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-136",
   "metadata": {},
   "source": "### Step 63 \u00b7 Define inner and outer folds\n\nNested CV stays entirely within the 192 training records. Three inner folds keep this small tutorial quick.\n"
  },
  {
   "cell_type": "code",
   "id": "code-063",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define inner and outer folds.\ninner_cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=RANDOM_SEED)\nouter_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_SEED + 1)\n\n# Your note: \n",
   "execution_count": 63,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-138",
   "metadata": {},
   "source": "### Step 64 \u00b7 Evaluate the Naive Bayes tuning procedure\n\nEach outer fold runs a fresh inner search. The outer validation fold never enters that search.\n"
  },
  {
   "cell_type": "code",
   "id": "code-064",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Evaluate the Naive Bayes tuning procedure.\nnb_nested = cross_validate(\n    make_search(nb_pipeline, nb_grid, inner_cv), X_train, y_train,\n    cv=outer_cv, scoring={'macro_f1': 'f1_macro', 'accuracy': 'accuracy'},\n    n_jobs=N_JOBS, error_score='raise'\n)\n\n# Your note: \n",
   "execution_count": 64,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-140",
   "metadata": {},
   "source": "### Step 65 \u00b7 Evaluate the KNN tuning procedure\n\nUse the same outer folds for a fair family comparison.\n"
  },
  {
   "cell_type": "code",
   "id": "code-065",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Evaluate the KNN tuning procedure.\nknn_nested = cross_validate(\n    make_search(knn_pipeline, knn_grid, inner_cv), X_train, y_train,\n    cv=outer_cv, scoring={'macro_f1': 'f1_macro', 'accuracy': 'accuracy'},\n    n_jobs=N_JOBS, error_score='raise'\n)\n\n# Your note: \n",
   "execution_count": 65,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-142",
   "metadata": {},
   "source": "### Step 66 \u00b7 Evaluate the tree tuning procedure\n\nThe complexity choices are retuned within each outer training fold.\n"
  },
  {
   "cell_type": "code",
   "id": "code-066",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Evaluate the tree tuning procedure.\ntree_nested = cross_validate(\n    make_search(tree_pipeline, tree_grid, inner_cv), X_train, y_train,\n    cv=outer_cv, scoring={'macro_f1': 'f1_macro', 'accuracy': 'accuracy'},\n    n_jobs=N_JOBS, error_score='raise'\n)\n\n# Your note: \n",
   "execution_count": 66,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-144",
   "metadata": {},
   "source": "### Step 67 \u00b7 Compare nested validation results\n\nKeep fold scores so the summary can be audited.\n"
  },
  {
   "cell_type": "code",
   "id": "code-067",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Compare nested validation results.\nnested_results = {'Gaussian Naive Bayes': nb_nested, 'KNN': knn_nested, 'Decision tree': tree_nested}\nnested_fold_scores = pd.DataFrame({name: result['test_macro_f1'] for name, result in nested_results.items()})\nnested_summary = pd.DataFrame({\n    'nested_macro_F1': nested_fold_scores.mean(),\n    'outer_fold_SD': nested_fold_scores.std(ddof=1),\n    'nested_accuracy': {name: result['test_accuracy'].mean() for name, result in nested_results.items()}\n})\nnested_summary.index.name = 'model'\nnested_summary.sort_values('nested_macro_F1', ascending=False).round(4)\n\n# Your note: \n",
   "execution_count": 67,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 67,
     "data": {
      "text/plain": "                      nested_macro_F1  outer_fold_SD  nested_accuracy\nmodel                                                                \nDecision tree                  0.9944         0.0125           0.9947\nGaussian Naive Bayes           0.9849         0.0229           0.9846\nKNN                            0.9793         0.0339           0.9795",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nested_macro_F1</th>\n      <th>outer_fold_SD</th>\n      <th>nested_accuracy</th>\n    </tr>\n    <tr>\n      <th>model</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Decision tree</th>\n      <td>0.9944</td>\n      <td>0.0125</td>\n      <td>0.9947</td>\n    </tr>\n    <tr>\n      <th>Gaussian Naive Bayes</th>\n      <td>0.9849</td>\n      <td>0.0229</td>\n      <td>0.9846</td>\n    </tr>\n    <tr>\n      <th>KNN</th>\n      <td>0.9793</td>\n      <td>0.0339</td>\n      <td>0.9795</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-146",
   "metadata": {},
   "source": "### Step 68 \u00b7 Select the family before opening the holdout\n\nChoose by training nested macro F1. An exact tie is resolved by the fixed order: Naive Bayes, KNN, then tree.\n"
  },
  {
   "cell_type": "code",
   "id": "code-068",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Select the family before opening the holdout.\nwinner_name = nested_summary['nested_macro_F1'].idxmax()\nselected_model = searches[winner_name].best_estimator_\nprint('Selected family:', winner_name)\nprint('Selected parameters:', searches[winner_name].best_params_)\n\n# Your note: \n",
   "execution_count": 68,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Selected family: Decision tree\nSelected parameters: {'classifier__max_depth': 5, 'classifier__min_samples_leaf': 1}\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-148",
   "metadata": {},
   "source": "## 9. Evaluate the locked final test\n\nAll models below are already fitted on training data, apart from the baseline which is fitted in the next cell. The model family is fixed. Comparing holdout scores is descriptive and does not reopen model selection. Every metric is computed from that model's own predictions on the **same 48 records**.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-149",
   "metadata": {},
   "source": "### Step 69 \u00b7 Fit the baseline on training labels\n\nThe baseline establishes how much useful signal the fitted models add.\n"
  },
  {
   "cell_type": "code",
   "id": "code-069",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Fit the baseline on training labels.\nbaseline.fit(X_train, y_train)\n\n# Your note: \n",
   "execution_count": 69,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 69,
     "data": {
      "text/plain": "DummyClassifier(strategy='most_frequent')"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-151",
   "metadata": {},
   "source": "### Step 70 \u00b7 Collect fitted models\n\nStore the selected family and all comparators without refitting on test records.\n"
  },
  {
   "cell_type": "code",
   "id": "code-070",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Collect fitted models.\nfitted_models = {'Most-frequent baseline': baseline}\nfitted_models.update({name: search.best_estimator_ for name, search in searches.items()})\n\n# Your note: \n",
   "execution_count": 70,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-153",
   "metadata": {},
   "source": "### Step 71 \u00b7 Generate final test predictions\n\nOne prediction array per model prevents accidental reuse of another model's F1 score.\n"
  },
  {
   "cell_type": "code",
   "id": "code-071",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Generate final test predictions.\ntest_predictions = {name: model.predict(X_test) for name, model in fitted_models.items()}\n\n# Your note: \n",
   "execution_count": 71,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-155",
   "metadata": {},
   "source": "### Step 72 \u00b7 Define classification metrics\n\nThe class list is explicit, and zero_division handles a baseline that never predicts five of the classes.\n"
  },
  {
   "cell_type": "code",
   "id": "code-072",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define classification metrics.\ndef classification_metrics(actual, predicted):\n    return {\n        'accuracy': accuracy_score(actual, predicted),\n        'balanced_accuracy': balanced_accuracy_score(actual, predicted),\n        'macro_precision': precision_score(actual, predicted, labels=CLASS_IDS, average='macro', zero_division=0),\n        'macro_recall': recall_score(actual, predicted, labels=CLASS_IDS, average='macro', zero_division=0),\n        'macro_F1': f1_score(actual, predicted, labels=CLASS_IDS, average='macro', zero_division=0)\n    }\n\n# Your note: \n",
   "execution_count": 72,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-157",
   "metadata": {},
   "source": "### Step 73 \u00b7 Compare the shared holdout metrics\n\nRows stay in the predefined order; the selected family remains the one chosen by nested training CV.\n"
  },
  {
   "cell_type": "code",
   "id": "code-073",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Compare the shared holdout metrics.\ntest_metrics = pd.DataFrame({\n    name: classification_metrics(y_test, predicted)\n    for name, predicted in test_predictions.items()\n}).T\ntest_metrics.index.name = 'model'\ntest_metrics['selected_before_test'] = test_metrics.index == winner_name\ntest_metrics.round(4)\n\n# Your note: \n",
   "execution_count": 73,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 73,
     "data": {
      "text/plain": "                        accuracy  balanced_accuracy  macro_precision  \\\nmodel                                                                  \nMost-frequent baseline    0.1667             0.1667           0.0278   \nGaussian Naive Bayes      0.9792             0.9792           0.9815   \nKNN                       1.0000             1.0000           1.0000   \nDecision tree             1.0000             1.0000           1.0000   \n\n                        macro_recall  macro_F1  selected_before_test  \nmodel                                                                 \nMost-frequent baseline        0.1667    0.0476                 False  \nGaussian Naive Bayes          0.9792    0.9791                 False  \nKNN                           1.0000    1.0000                 False  \nDecision tree                 1.0000    1.0000                  True  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>accuracy</th>\n      <th>balanced_accuracy</th>\n      <th>macro_precision</th>\n      <th>macro_recall</th>\n      <th>macro_F1</th>\n      <th>selected_before_test</th>\n    </tr>\n    <tr>\n      <th>model</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Most-frequent baseline</th>\n      <td>0.1667</td>\n      <td>0.1667</td>\n      <td>0.0278</td>\n      <td>0.1667</td>\n      <td>0.0476</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>Gaussian Naive Bayes</th>\n      <td>0.9792</td>\n      <td>0.9792</td>\n      <td>0.9815</td>\n      <td>0.9792</td>\n      <td>0.9791</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>KNN</th>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>Decision tree</th>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>1.0000</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-159",
   "metadata": {},
   "source": "### Step 74 \u00b7 Plot model accuracy and macro F1\n\nThis comparison exposes weaknesses that a single accuracy figure can hide.\n"
  },
  {
   "cell_type": "code",
   "id": "code-074",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Plot model accuracy and macro F1.\nfig, ax = plt.subplots(figsize=(9, 5))\ntest_metrics[['accuracy', 'macro_F1']].plot.bar(ax=ax, color=['#16324F', '#C7353F'])\nax.set(ylim=(0, 1.07), title='Same final holdout: model comparison', ylabel='Score', xlabel='')\nax.tick_params(axis='x', rotation=18)\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '07_model_comparison.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 74,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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HjhxRnz595O3trcuXL2vTpk06f/68goKC9NZbb8nDw8PWzpEjR3TlyhX9/vvvOn36tDZt2mSbG7NKlSp688031aZNG5UoUUKS1LVrV23ZskUffvih/vrrL/Xt21dnz55VbGysNm3apA0bNujq1asKCwuz10NjKAQ+AO4r65w8Of3iaP0wWqdOHZUrV05r1qyR9M+vkzVr1pSrq6t27typdu3aqXDhwpKkJk2aaN++fVq/fr2efvrpLGONy5Urpxo1amjTpk06d+6cChcurAoVKuijjz7S+++/r2XLlmnZsmXy9PRUcnKyXF1d9eijj6pnz55ZbtkOIGf/Ht9/oy+V1i+d19+69ciRI3JyclK5cuVs27i6uurzzz/XqFGj9NFHH0m61pvv6tWrcnZ2tg3lun4S97p168rV1VUpKSn65ptv1KJFixxrOHfunJKSkvTwww/rwIEDGjp0qO1uSS4uLnr00UfVvXt3VatWjS/JuCGz2az169fLy8tLzZo1s70HfvrpJ8XGxuqLL76wfSmyTgju5uamBg0aaPPmzVq3bp369+8vR0dHjRkzRq+//rpeffVVde7cWc2aNdP48eP122+/6dVXX7W91uvWrSsPDw9FRUXp/Pnz8vHxsfVya9iwoT788ENNmTJFf/zxh6Rrd5z8d+AJANI/vdybNGmiiIgIPffcczp//ryka9eajIwMubm5qWXLllq3bp327dunMWPGyMPDQ2az2fY3uVy5ctq1a5feeecdpaWlqXr16ho2bFi2uTGvXr0qJycnVatWTaNHj9YHH3ygCRMm6Ntvv1Xx4sWVkJAgs9ms+vXrKywsTMHBwXZ5XIyGwAfAfWX9QLx//36tX7/edmEPCAiwfaD19vZWUFCQFi5cqB07dqhOnTqSrs15ULNmTe3evVsnTpywTX7ZokULTZw4UevWrdPTTz+dJUzy8vJS7dq19dtvv2n37t3y8/OTyWSSt7e3Ro8erSNHjujAgQOKjY1VQECAmjVrluVLJIDsrg95rh/ff+LECR07dkw+Pj6qXLlytiGT1vBk7969WrBggdauXavk5GR5eHgoJCREvXv3Vo0aNSRJjRs3ltls1vPPP69Ro0bpkUcekY+PjyTp7Nmzkq4NfZH+mezW2vvh9OnT2SZ9Pn/+vJYtW6ZZs2apd+/eevjhhxUaGqrPPvtMGRkZqlixYrZbwBL24EYqVqwoBwcHRUZG6sqVKypYsKB27dqlmTNnKigoyDZfj9X1vXQ2b96sFStWqFmzZurRo4fKly+vQYMGaeTIkXJ1ddXjjz+u6tWrKzo6WidOnFC5cuVsPYhCQ0MVHh6uDRs2qEuXLll6yz722GMqU6aMvvrqKzVr1sw2dILXMoB/M5vNWrp0qT788EOZTCYlJSXp2WefVd26dVW+fHmVLVtW0rUhqOXKlVN8fLxteKnJZFKBAgUkXft7HR4ersqVK2v+/Pm5DiONiYlRhQoV5OHhoZo1a2r8+PHavXu3Dh48qAsXLqhKlSpq3bq1SpYseX8egP8IAh8Aee5Gv4pv3LhRX3/9tXbt2qXChQsrOTlZ48aN0+OPP65XX33V9it//fr1tXDhQv3yyy+qU6eOzGaz3Nzc1LhxY23cuFH79u2zfTkLDAxU5cqVtWvXLtsvntYP1tcP61qzZo3atm0rDw8P2zCxwMDAbF/yMjMzbb0QAGR3fcjz559/Kjw8XL///rv++usvmc1mubu7q27duho2bJgqVqxoC18SExM1e/ZsLV68WNK193nhwoX1119/aenSpdq9e7fCw8NtvSKaNm2q1157TePHj9dbb72l999/X2XKlLFN5HjlyhVJ/3yR7tGjh/bv369PPvlEMTExevLJJ/X333/rzJkz2rhxow4ePKhHHnnENo+Xs7Oz2rZtm+XceP/jVplMJvXu3VszZ87U1q1b1aRJE02bNk0pKSl64YUXVLBgwSzbW98zLVu2lCRt3rzZFuI0bdpUn376qT7++GMNGTJE7u7uatCggaKjo7VkyRINHDjQ9jp/4oknFB4erhUrVqhLly7ZXqvVqlXTpEmT7sMjACA/c3R0VKVKlfTBBx9o1qxZOnHihJo2baq6detm2a5MmTKqVq2aVq5cqb///ltS1hC5ZcuWKly4sPbv368zZ87I19fXti4tLU1//vmnxo8fr4SEBH366ae2v/FlypRRmTJl1KZNG0Lpe4jAB0CesM7ob/3FPyfW+Q4uX76sV155RTVr1lSxYsX0+++/6+uvv1Zqaqq++uorOTs7q06dOipTpoxWrVql999/3/aBNigoSK6urtq1a5fatm1rG9rRqFEjTZ8+3faL5/W/7j/00EPy9fXV4cOHlZKSIg8Pj2w1Xt9j4d+3ngSQ1dmzZzVr1iz9/PPPiouLU/HixVWzZk1VrlxZkhQVFaW1a9eqSJEiGjlypG2/v//+W1999ZWeeOIJtW/fXo888ohtaObSpUv1xhtv6IcfflBYWJjtzkTPPfec4uPj9d133+mbb77Rxx9/bJtM2d3dXdI/Q7sCAgI0atQojRkzRosXL9bixYttc5lUqFBBYWFheuKJJ2w9hays8345ODjw/sdt6dq1q2bOnKn169fLw8NDv//+ux577DE1atQo27Ymk0lms1mFChVS3bp1tW3bNm3dulWNGjVSZmamWrVqJWdnZ3388cd66623VKVKFTk5Oem3337TwIEDbb+mW4dobd++XQkJCUwuDkAWi0WZmZnZetbejJ+fn6pXry5nZ2e9+eab2rZtm+rWrWsbfiVJhQsXVlBQkMLDw3X06FHVr1/f9rcyMzNTBQsW1IsvvqixY8fq6aefVlhYmCpVqqSLFy9q37592rJli9LS0vTss8/qoYceylYDYc+9ReADIE9c/yVp//79Sk5O1sMPP6zChQvb5u2ZPHmyjhw5ounTp2eZU6BcuXK6ePGirYt6ixYtVKpUKdWpU0e//PKL9u7da+uF4+vrqxo1atiGdVWvXl3StV8XZs6cqXXr1qlLly4ymUy2PyDFixfXp59+mu1L3vWu77EA4MaioqJsEyNbb8FaunRp21j948ePq3Xr1rbbvFrD1/Lly2vmzJkKCQmxtZWYmKjff/9d8+fPl9ls1ubNmxUaGqoKFSrYrh39+/dXXFycFi5cqOLFiysxMVEODg6qUKFClrosFosqVqyoTz/9VGfOnNGBAweUmpqqOnXq2MKonFx/vQBuR8WKFWUymbRy5Urt2bNHDg4OGjx4sG39v3u8Wn+MaNeunbZt26ZVq1apUaNGtt47zZs3l6+vr5555hlt3LhRLi4uOnLkiE6dOiVfX19bj6AFCxYoICDgtr/cATAO6/xdDg4OMplMtuvB6dOnlZaWpnLlyt30GmH9waRp06aSpC1btmjAgAFZ9jOZTPL391fJkiUVGRmpNm3aqHjx4rZjS1K3bt1UokQJjRs3TmPHjrXN1efg4KDQ0FB16dJFISEhfNa2A/5KALhj13+Q3bZtm+bPn6/IyEilp6fL09NTYWFhCgsLk6Ojo+Li4rRkyRI1btzYFvacP39eR44c0cGDB/Xnn38qLS1Nq1evVu3ateXl5aUGDRrol19+0ZIlSxQYGCiz2SwPDw81btxYY8eO1f79+22BT1BQkDw9PbVq1SpdunRJXl5eWWq1hj3/ntcDwK2zvufr1q2rmjVr6sSJE2rUqJFtLh2r8uXLq2TJkrb3/vWTNoaEhMhsNmvLli2aM2eOIiIiZDKZVKVKFVWrVk27d+/WsWPHVKFCBdsHw2LFiuntt9/WgQMHNGfOHDk5OcnDw0NxcXG2OQakf34l9PT0lL+/v/z9/bPUlZmZaftgDOQFBwcHdenSRQsWLFBcXJzMZrMGDhyobt26qW3btvL29pb0zw0LrK/pRx99VO+++67Wr1+f5Vd5s9mshx9+WOPGjdMnn3xim3z58OHD8vX1lbOzsywWCzcVAJDl82xaWpq+//57zZs3TydPnlSRIkXUuHFjDRo06KZ3nMzMzJSXl5dq1qypP/74wzZtwvWfmUuXLq3atWsrMjJSp0+ftgU+1jZdXV3Vpk0b1a1bVydPntSRI0fk4+Oj+vXrMzemnRH4ALip3EISk8kki8Wib7/9VlOmTFHJkiXVqFEj+fj4qHjx4lnGACckJKhgwYIqXLiw1q1bp4MHDyoyMlJ79uxRcnKyKlSooBdeeEHdu3e3hTVBQUEqWbKkVq5cqbfeestWQ3BwsAoWLKjIyEi1bt3aNhb4448/lo+Pj7y8vHL9w0bYA9w563uqTJkyqlq1qnbu3Kldu3ZlCXz+/vtvzZ8/XxcuXNCwYcNUtGhR25ddqz179ui9995TamqqunXrpoYNG6pKlSravHmz3n//fe3Zs0f169eXq6urpGsfRt3d3fXGG29owoQJ2r59u6pUqWK7xeuNmM1mWw8eflnEvdCjRw8tWLBANWrUUOPGjTVhwgSNGjVK8+bNU4cOHdS3b1+5ublJuvYeyszMVNGiRVWjRg3t2bNHO3bsUHBwsO19YrFYVKtWLY0aNUoXL17M0iPO2gaA/47cPtNeuHBBU6ZMUVBQkBwdHTVz5kwFBASoQYMGOnz4sJYsWaILFy5o0qRJ2eYU+3f70rUJ5Xfv3q3IyEgFBgbafiSRrg3rCg4O1vLly7V//34FBgZm+0ydmZkpb29veXt72264Avsj8AGQo+snLr1RSLJmzRpNmjRJtWvX1ssvvyx/f39bAHO9hIQEFStWTGvWrNHq1auVmpqqSpUq6ZlnnlGHDh2y/EpvVbZsWdWpU0fLli3TgQMHVKVKFUnXvmyWL19ely5dUmpqqu14oaGhtn35QAzcG9YAuHbt2po/f742b96sevXqKS4uTnv27NGGDRu0YcMG23DODRs2qHHjxrb909PTNXz4cMXHx2v06NFq1qyZ7RpTpEgRmc1m/fnnnzp//rzKly8v6Z8ho/Xq1VORIkXUsWNHXbhwIUvPodwQ8uJes/5tunjxol544QW1a9dO3377rdatW6evvvpKP/74o/r06aP27dvL29vb9npu37699uzZo8WLF2e5/bD171elSpVUqVKl+39CAO6L60Pef39uNZvNtjvwXb/u+m3PnTun2bNn6+jRo4qOjlZYWJi6detm630zaNAgrVy5UqtXr1bbtm2z/fhiZV3WunVrffjhh9q0aZMGDBhgmzfMOpS0WrVq8vDw0MqVKxUaGmo7zr/bwYOFT0EAcuTo6CgHBwclJSVpxYoVmjt3rg4cOKC0tLQs223cuFFpaWkaPHiwgoKCsoU91l8NfHx8VLZsWaWkpKhDhw5av369li1bppdeeilL2BMXF6e4uDjb/+vVqydJ+uGHH2zteXl5acaMGfruu+9s3eWtrOOZAdw666TFt8L6QbN69eqqXLmyIiIi9Pbbb+uFF17QJ598ov3796tTp05q0KCBvvzySz333HP64YcflJycLEk6ePCgLl++rNDQULVo0SJLIBMbGyvp2jxgJ0+ezHbszMxM+fv7q2LFiipWrJguX758t6cO3DVHR0d16NBBJ06c0JYtW1S2bFl9/PHHmjFjhvr166f09HR9+umn6tGjh7766ivba7tly5by8PCwTWLKlyXgv2HTpk36f+3deViVdfr48feBw74vsoMgsiqIsqOJCC6YkuaSpplO5Vg2aWbZ1ywrJ01bxjZHp8zcSktyG7cUdwRENhcEEQUVVBRFBUWWc35/8DtPoGjNTJt2v65rrhk5z3nOc841z/K5P/fnvoOCgli0aBHwY+OT5po3ETh+/DgnTpwAfrwHa7VaAgICCAwMJDU1laioKCZMmIC9vb2yvxEjRgDw/fff3/N4dAXl7ezsCA0NJSMjg3Xr1nHkyBFWrlzJsmXLqKysxMPDgx49ehAQEKBk4Io/PsnwEeIBV1NTQ11dHTY2Nj+7fk1jYyMZGRmsXLmSHTt2ANDQ0ADAo48+yvjx45UHVF9fXwBWrlzJzZs3lVmAyspKbt68SWxsLA4ODjg6OhIaGkpqaip2dnZYW1u3+MyGhgYOHTrEjBkzCA0N5c033wSalnXFxMQQEBAA/Hijs7KyatFZR0dm84X4eZp3pmttZvFu55JuWw8PD4KCgigsLKSgoIDBgwfz6KOPtljelZycrAx8XVxciI2NxcDAAH19faU2CTTVHkhNTeWf//wnDz/8MBs3bmTfvn1ERkYq3bqgaUBcWVlJY2Mjrq6utGnTRupyiT+EUaNGsX79evbs2UN0dDQNDQ34+PgwdepURo8ezeLFi9m8eTPz589n165dLFiwAGdnZw4ePPh7H7oQ4jeiu1/p6+tTX19PTk4OQIsCybpt8vPzWbp0KTt37uTWrVvY2tri5eXFuHHjiIyMRKVSYWBgQHx8PPn5+coEqO45HCAkJIROnTqRmppKbW3tz1rWNWrUKE6cOMHrr7+OsbEx165do127dsTGxtK+fXvmzJnza/084lciAR8hHmBbtmxh0qRJTJo0ifHjxys1d3QpnXdb9rR161Y+/PBDzM3NefzxxwkICMDc3Jzs7GwWL15MdXU1H3/8MQD9+vVjy5YtbNiwgbVr17aYeVCpVFhaWjJr1izi4+NJTEwkKyuL+fPnU1VVxRNPPEFNTQ2nT58mOzub3bt3o6enR9euXZVj8fLy4ssvv2z1OKWzjhD/veY1bU6ePMnhw4dxcHAgJCTkJ2fudA+kISEhrFmzhl69evHKK68orzU0NGBoaEiPHj3Yu3cv27dvJy8vj9jYWPz8/IiLi+O7777j6aefpkOHDlRVVbF//37i4uIYOXIkNjY29OjRo0WwB5qC0enp6ZSWlipFayXYI/4IAgMDgaas16lTpyoDuMbGRpydnZk2bRqPPvooxsbGylJFIcSfS/NalMuXL6ddu3YtXtdNYubl5fHaa69RUVGh1KosKSlhx44dlJaW8tJLL5GYmAjAQw89xCeffMLFixfRaDRKsEer1WJiYkJERAR5eXns2LGDfv36/eSyroSEBNzc3Fi7di1WVlbExcVJkfj7nAR8hHiA6doQFxUVcevWLWXwpHsQbT4LoKPVaikqKsLBwYFXXnmFwMBAZZtevXpRWFjIDz/8oLSItbKy4uOPPyY3N5cTJ05gZ2eHq6srV69eJS8vj88//5wlS5YQExODl5cX06dP5+233+brr79m5cqVWFtbc+3aNfT19ZXBXmuF3hoaGqT9rBD/heb1uJqrq6tj48aNfPnllxQVFWFkZIRWq6VNmzbMnDmTiIgI1Gp1q7UFdP/u1KkTbdu2Zd++fRw/fhxfX1/09PQwMDCgsbEROzs7LCwsaGhoQKVSKdecSZMm0djYyLZt20hNTcXc3Jy4uDiee+452rZtS3Bw8B3f4/LlyyxcuJCNGzdib2/P008//ev9aEL8h9RqNb179+aHH37g4MGDhIWFKRl0Ord3jRNC/Hk0v5fq6ekRFhbGrVu3OHv2LG5ubkog5vLly7z77ruUlpby/vvv07VrV6VcwooVK5g5cybz5s0jNjYWU1NT/Pz88PT05OTJk5w+fRpPT08lA16lUhETE8MXX3zB2rVr6dev308ep4GBAUFBQRLkeYDI6EmIB5i3tzchISFkZ2dz/PhxgoKCOHv2LBs2bGD//v3Y2NjQrVs34uPjsbOzA5oGcmPGjGHixInKfoqLi1m9ejXbt2+nrKwMgM2bNzNu3Dg0Gg1WVlbExsYSGxvb4vMTEhL44YcfOHbsmLIkzMvLi88//5zMzEwKCgq4cOECfn5+xMfHY2lpedfvIsEeIf47d6sLsm3bNmbPno2TkxMvvfQSrq6unD17ltWrVzN16lSmT59Onz597hi0wo8BHy8vLzp16sR3331HYWEh3t7eSvag7j0VFRUA2NvbY2hoqASC3n77bZ5++mmuXr1KSEjIHUGl22ch6+vrOXbsGB07dmTcuHEyeBZ/OKNGjSIrK6vFoE4I8eC6deuWMslxO11Gva75ye33uKKiIgYNGoSXlxcbNmxQllSdPXuW3NxcunfvTp8+fZR9aTQaRo4cye7du9mzZw8pKSkMGDAAIyMj4uLiWLJkCYcPH1YCPrrrT1BQEIGBgaSnp8vk6Z+U3ImEuE/oqvX/p3r27Mm5c+coLCykoqKCZ599luXLl3PlyhX27t3LG2+8wTPPPMP58+eV9+gCLzk5OTzzzDMMGDCA5ORkfHx8GD16NJaWlqSkpNxRP6e56upqNm3aRFlZ2R0DM7VaTXR0NGPHjuXVV19l0KBBWFpaotFoWi1cJ4Rocvz4cb7++muuX7/+s99z5swZpk6dSkpKSov9zJkzBwcHBz799FOeeeYZ+vXrx7hx43j//fe5cuUKS5YsAe4eMNJdj7p06YJarSY9PV25Hly9epWcnBymTZvG7t276dWrF3369FE6jkDTdaBdu3Z07txZaVXdvOj67Z/r4ODA0qVLWbBgAV26dPnZ31+I30pERASpqanSjliIP4G1a9fSqVMn/v3vf7f6ukqlQq1Wo6enx/Xr1zl48CCFhYXK6x4eHnTs2JGTJ09y48YNJRBz7NgxtFot4eHhwI9ZujrDhg0DmiZedeLj49FoNGRnZwM/Bpu1Wi3m5uZERERQV1fHli1bAGlw8mcjIT4h7hO6i/e9Wji2Ji4ujg8//JA9e/awcuVKrKysePnll/Hx8cHExIT33nuP5ORk5s6dy+TJk3Fzc0OlUnH+/Hnef/998vPzGTVqFHFxcQQGBmJiYkJmZiZ5eXmcPHlSaRlbUlJCTU0NFRUVlJeXK0u//P39mT59OhYWFq0ec/PCsTIbKsTdVVdX8+GHH7Jr1y4CAwMJCQkB7syE0dGdbykpKaxbt65F4PXIkSNUVFQwbdo0pQB7RUUFp06doqioCDMzMyUzUFeY/W6CgoJo27YtGRkZbNu2jaqqKjIyMsjMzOTixYv06NGDcePG3bWFuu44f6pDkdTrEkII8XvT1bAzNDRErVa32lES4Nq1a2zatInvv/+eM2fOoKenh6OjI4sWLcLGxgYjIyMiIyPJzc1l+/btJCUlASiFlYuLi1vsT3ePjIiIUF5v3i7d1dWV3NxcpeRC8+fryMhIMjMzsbCwACT78M9GAj5C3CcOHjzIrFmzGDlyJIMHD6a+vv6O+jut8fb2JjAwkH379uHg4MCsWbNaDODGjx9PVVUVKSkphIeHM2LECDQaDdu2bSMrK4vZs2czaNCgFvvUfe7+/fuVgM+GDRv47LPPsLa2pqamBhMTE/r27cuIESPw8/O7ayednzPQE0KAubk5/fr1w9raGisrK+XvuvPn8uXL2Nra3hEA0m1rb2+v/O348eNAU9bMwYMHycnJITMzk9zcXK5du4anpyePPPJIi/fc7vZlXd9//z1Tpkyhrq4Oa2trEhISGDJkiBKY+qn9CCGEEH90untWbGwsa9asUeplNnfq1CnefPNNDh8+TGBgIP3798fGxgYXFxcMDAyUZ+Lo6Gg+//xz1q9frwR8dJk9WVlZQMts14aGBurr63F2dsbGxoaqqiocHBwwMTGhZ8+erFixgpycHCXgo3tvjx496NGjx6/5s4g/MAn4CHGfqK+vJz8/n0OHDjF48GAl6FJZWanU32mNnp4e/fr14/3338fOzg5fX1/q6uqUNFM3NzceffRRdu/eTXp6OgMGDMDc3FxpFXl7N5GjR49SUVGBiYkJW7duZdSoUahUKoYOHYqFhQX6+vr4+/srN6zmxyGE+N88/PDDykOhTlpaGmPHjiUmJoYvv/xSecDTPZTq6m45Ozsr79HV1HrrrbcoKyujurqatm3b8vjjj/PII4/g5eX1s45H90Dp6+tLYGAgXbp0ISkp6Y6iy9I6XQghxP1Mt4xZd281MzPDx8dHyfDx8PBAq9Vy69YtPv/8czIyMpg0aRKJiYm4ubm1OrkZHBxMhw4dSE1NVbJ1XFxcCAoK4vDhw+zevbtFfUy1Wk1RURHnzp0jLCwMBwcHZZKnW7duLFu2TFnyLfdcoSP/TxDiPhEdHY2Pjw+5ubkkJyfz6quv4u/vz9y5c7lx48Y939utWzegaUmIrl2y7kagUqnw8fHBw8OD06dPU15eDqAM2LZv3w40BZZ2797N66+/jrW1NR06dODgwYOcPXsWACcnJ8aMGcMTTzzRYt2xrBMW4pejr6/PoUOHmDx5MgUFBUBTp6zo6Gj279/PV199RU1NDdAUJAa4efMm0JQBpBMZGQlAeXk5zz//PFu3bmXr1q1MmjSpRbDn0qVL1NXV/eRxPf7443z//fdMnz5duXY0NDQo5788eAohhLjfaLVaZYJEpVLdkZGalpZG7969mTt3rrLNxYsXSU1NJTAwkPHjx9O2bdu7Lrs2NTUlIiICrVbLjh07lNeGDx8OwIcffkhqairQ9Ayfl5fHvHnzMDExIS4uDvgxA6hbt27k5eUxcuTIX/hXEPc7yfAR4g9GV9H/9ptKTk4O169f5+LFi7z22mt4enrSv39/evbs+ZMV99u3b4+/vz9VVVWcOHECf3//FjPuhoaGWFlZUVFRobR+TEpKYs2aNSxatIhdu3Zhb29PaWkplpaWLFq0SAn0uLu7t/is5gM8WaolxI9+KstFt96+eQv11upeHThwgE2bNtGlSxe8vb0xNTXl5ZdfZubMmbz33ntYWVkxaNAg5X0mJiYYGBi0WAYWGxuLhYUF1dXVjBkzptVj2b59O+vXr+fJJ58kLCzsnu3ZdRmHDQ0NSkcS6QQihBDifqYrvAxNS6GPHTuGvb094eHhGBoa4uHhQadOnTh69CjV1dWYm5vj6uqKhYUFBQUFbN26FRMTE2UfZ8+exdPTk8DAQOV5Ozo6mkWLFrFu3Tr69u0LQJ8+fSgvL+ezzz7j2WefJTw8HGtra44ePcq5c+d49tln72ixrq+vL8/dolXyNCbEH4RuMNjaxfry5cusXLmS+vp6GhsbiYmJ4dVXX8XNzQ1TU9Of3LdaraZHjx4sWLCAtLQ0/P39ldaMenp6WFpacvbsWW7evKksD7O1teXdd99l7dq1ZGRkcOnSJRITExk6dCht2rShTZs2rX6WzOQL0TrduXHlyhVsbGzuCKA0r2d17do1SktL8fPzaxFMUavVhIeH4+7uzr59+3jkkUcwMDAgMDCQKVOmMGbMGD755BP8/f0JCAhAo9FQUVFBfX09Dg4OAEr9ryeeeIL58+czYsQIXnrpJbp06UJJSQnFxcXs3r2b7du306FDB5ycnJTj+ykS5BFCCHG/092fL168yPfff8+qVauoqKjA1NQUjUbDl19+SXBwMK6urnTu3JmvvvqK3NxcunXrhp6eHmPGjOHTTz9l4sSJ6Ovr39Fpt2vXrnz88ceYmZnRqVMnAgIC2Lt3rzIWMDc3529/+xvW1takpaVx4sQJTpw4QXBwMDNmzCA6Ovp3/HXE/UaezIT4nTSvng9Ng8G6ujq2b9/O0aNHadu2LQkJCdja2mJra8v48eN58sknmTBhAlVVVdja2io3np8TZImPj2fBggUsXbqUAQMGtCjGumfPHi5evMhjjz3WYl8BAQH4+flx7do1rK2tW/0OUnBViB/d7ZzQarX88MMPvPzyyzz99NO88MILd2x37do1tm7dyurVqykqKsLAwAA7OzseffRRnn76aSWY4uHhQYcOHdi/fz/nzp1Tum6EhoYybdo0Zs2axaxZs5g9ezZubm5cvXoVaAo0eXl5KZ87evRo1Go18+fPZ9SoUdjb22NpaUlZWRnGxsbEx8czZswY3Nzcfs2fTAghhPjN6TJqW7tnq1QqNBoN8+bNY926dURHR/PEE08o2Tvt2rVTJmE6d+7M8uXL2b59u1JC4ZFHHiEsLIzMzEyuXr1Ku3btMDQ05OLFi6xfv57U1FQ2bdrEwIEDsbCwICIigmPHjrF7927i4uKUej5PPPEEjz/+OBcuXMDFxeW3/onEA0ICPkL8TnSz+Tdv3sTExIQtW7bw3nvvUV5erswCrFq1ipkzZxIYGIinpycqlYqePXuyatUqMjMzSUxM/Nmf5+fnR2BgIPn5+bz00kv07NkTe3t7Tp06xZIlS/Dw8GDkyJEYGBi0eJ+enp4S7Gm+XEP3HYQQPwZ67nZOqFQqjIyM0NfX58SJE1y6dAl7e3vlfYcOHeKjjz6isLAQd3d3Bg4cSENDA0eOHGHevHnU19fz7LPPAmBjY0OXLl3YsmULubm5+Pj4KJ87YsQITp06xdKlS/niiy+YNm0aNjY2WFtbK+exLnBkbW3Nc889R0hICLm5uZw4cYKGhgZGjhxJv379sLGx+fV/OCGEEOI3osu00dfX/8nJ0vfee4/k5GRGjBjBpEmTWiyLbq5Dhw74+vqyZ88e5ZnewMAAT0/PFo1PdPd7tVpNWloaZ8+eVZ65IyIiWLJkCcuXLycuLq7Fs7ienp4Ee8T/RAI+QvyKdFk8rdXkAZgzZw6LFy/mnXfeYe3atbi7u/Piiy/i5OTEjh07+PLLL/nss8/47LPPlPfHxcWxYsUKcnNzSUxM/NlLqAwNDenevTsFBQXU1taybt06KioqqKqqomPHjjz33HMt2rW3RpZrCNE6lUpFdXU1W7ZswdLSkm7duikZeLpAkK+vL8HBweTl5XHy5Ens7e1RqVTU1tayefNm8vPzmTBhAtHR0Xh6eirZfwMGDGDFihX07t0bb29vAAIDA3F0dGTPnj08/PDDmJmZKZ06nnrqKc6cOcP333+Pra0tFRUV1NTUYGtr2+qxx8TEEBMTo8xW6kjBZSGEEH9UWq0WrVb7H92jmm+7Z88eCgoKCAgIwN/fXylVoNVqqaysJC0tDQ8PD5577jkl2NNaVpCbmxtdunRh+fLlHDp0SGmKcDuVSsWlS5fYtWsXQIt7cnh4OJMnTyYqKkrZtvn7hPhfyOhNiF9B8xtCazV5dAMrGxsb9PT0+PTTTwkMDOTjjz9Wtvf39+f06dPs2bOHQ4cOKZ1vQkJCcHBwIDs7m3PnzuHs7Pyzl3XFxMSwYMECvL29eeONN8jNzcXb2/uu9XiEED9PXl4er7zyCqWlpajVap577jmee+459PT0lOuBnZ0d4eHhHDhwgPz8fCIiIgAwNjYmMTGRCRMmKEUcAXJzc9mzZw/nz5/n+vXrbNu2TQn4tGvXjqCgILKysjh//jze3t7KtcPR0ZEZM2YwYsQIvv32WxoaGrCzs6OysrLVpZk6arVamf1snsknhBBC/BE0L4fQPPBy+fJljI2Nf7Ku5bVr11i2bBkrVqzgypUrGBkZUVtbi6enJxMmTGDAgAGoVCquXbtGQUEBQUFBtGnTRnnOvv2+qPt7ly5d+Oabb9i+fbsS8KmpqaG0tBSVSsXJkycpKytj//79HDp0iLFjx/Lkk08q+7G0tGTcuHG/8K8lRBMJ+AjxK9DdEM6dO8emTZvIz8/H29ub8PBwwsPDlVn0xMREFixYQEVFBS+99BL6+vrKzczc3JyePXuyc+dO9u3bR2BgIGq1GnNzc7p3786GDRs4cuQIzs7OP3tgFhISwgcffMBDDz2EsbGxMpPQPMVVCPHz6VK0bW1t8fHxUQI+n3/+OdeuXePVV19Vzk8jIyOCg4MxNTUlOzubpKQkZYZPd35XVFSwdu1aVq1aRVlZGXZ2dgQFBbF//3527drF+PHjgaaZwdDQULZv305eXp4SCIKm89nJyYkXX3yRuXPnUlVVRefOnX9WgXcJ8gghhPijaj6RmpGRwbZt2ygqKqK2thZjY2M0Gg2PPPIIsbGxLSYzdffqVatW8a9//YuwsDASExNxdnbm5s2bzJo1i//7v//Dzc2Nzp07Y25urtTHq6iowMHB4Z6dKjt27Ej79u3ZtWsXU6ZMUZZwP/nkk9TW1mJiYkJ1dTXe3t68+OKLDBgw4Df6xYSQgI8Q/7V7BUlqamr4+OOP+fbbb5WZ/c2bN6PRaBg1ahTTp08Hmlqad+zYkQMHDijtzZunpwYGBuLl5cXevXsZMWKEUlOjd+/erF69moyMDHr16kVdXR3nzp3DzMysRTHm2xkaGvLwww+3+Nt/mg4rhPiR7mHPzc2NgIAAduzYQWJiIrm5uXz11VcYGRkxevRopfudl5cX/v7+HDlyhJMnT2Jra4tGo1Gya/71r3+xcuVKQkJCeOKJJwgJCSEwMJABAwaQm5vLyZMnadeuHdBUN8De3p59+/aRmJiIiYlJi2Pq3bs3lpaWaDQa4uLiJC1cCCHEfW3//v0kJyezY8cObt68iaurKw4ODmg0GqqqqiguLiYzM5OoqCgmT55McHCwstz5+PHjfPDBB/Tu3Zu33nqrRZ06IyMjXnnlFb7++mvatm2Lvr4+7dq1o6KiglOnTrUI+LS2lMzDw0PJ8jl69ChdunTB2NiYt99+m7KyMmxsbAgPD8fDw+P3+NnEn5wEfIT4L90tSKLValm8eDFLliyhd+/eDBw4kLZt22Jtbc0777zD8uXL8fT0ZPjw4ajVauLi4jhw4AB79+6lU6dOLW4i7u7uhISEsH79ek6fPq3cnLp3746LiwvffPONktq6a9cuEhISmDhxIsbGxvc89uazFDIIFOJ/ozufOnbsiLm5OVqtlpdffpn169ezcOFCLl++zNSpUzE3N8fW1pawsDD+9a9/cezYMcLCwpTzffXq1SxfvpzBgwczadKkFrOT/v7+lJaWsmvXLiXg065dO4KDg9m3bx/l5eVKlo/unDY2NqZHjx53HKcQQghxv5kxYwarVq3C0tKSwYMH4+/vj6+vL25ubtja2qLVajl48CCzZs0iPT2dN998k++++06ZmE1OTgbgL3/5CzY2Nty4cYNz585x6tQpMjMzuXLlCtu3b+eRRx4hOjqaqKgoFi5cyKZNm4iMjFQaquiWkl2+fJny8nJ8fX0xNDSkQ4cOAGzevJkuXboA0LdvX7nvit+dTOsL8RN0F/jbVVdX88EHH7Bs2TJlG61Wy+nTp1mwYAG9e/dm3rx59OzZE29vb+zs7BgzZgy+vr6sWbOGEydOAE3BGwMDA/bt2wfQImPIzMyMzp0709DQQEZGhpJVBDBp0iQCAwNJTk5m7dq1ODo6Eh0d/ZPBHpAgjxC/hvbt2xMYGMjevXuxtbXlzTffpGvXrnz33XdMmTKFmpoa5Zw2NjYmNzeXK1euKO8vKCgAmoo062oGAFy4cIHDhw+jVquVYo8A9vb2+Pv7o6enR01NzV2PS7cfOe+FEELcb3TPvX379gWaus5Onz6dIUOGEBwcrCyN1mg0hIeH8/777+Pn50d+fj5r1qxR9lNdXY2xsTGlpaXs3LmTjz76iClTpvDCCy+wdOlSwsLCePvtt4mMjERfX58hQ4ZgbW3NqlWr+Pbbb5XJmZKSEtauXUtSUhJLly5V7rHR0dHMnTtX6agJct8VfwyS4SPET7jbxbqyspIlS5YQHBxMr169cHJyQqVSsXv3bhoaGujbty96enpcvHiRM2fOcPz4cdLT0zl+/DjQdMPw9/fH29ubDh06cOTIEYqLi/H29m5R9DkgIIB27dqxf/9+HnvsMaVTQFJSEt26daOsrIyOHTvKTUWIX0hjYyPwY/D1pzJjdK85OTnRpUsX0tPTyc7OJjg4mE8//ZQpU6aQkpLC//3f//Hqq68SEhKCt7c3eXl5lJSUKJl7ulTvwsJCoqKi0NPTo6ysjIULFyrFnA8cOMCpU6fw8vICmmYqX3jhhXt+H1myKYQQ4n6lu8dGR0djamrKoUOHuHz5Mra2ti26S+rr69PY2Ii3tzfDhw/nrbfeYsOGDfTq1Qtzc3Ps7e2pra3lo48+ory8HH19fSIjI5k9ezZ9+/bFyMhI+czGxkbc3d2ZPHkyCxYs4I033mDJkiXY2tpy8+ZNTp06hbu7e4v3ubq64urq+tv/QEL8BAn4CPH/3T7I0zl48CBGRkYEBQUBPw7+nJycGDBgADt37qS4uBgnJyegqQOXvr4+Z8+eZfny5Rw4cICsrCwqKyuxtbXl8ccfJykpiZCQEGVfcXFx5ObmsmvXLry9ve9Y1tW2bVtSUlIoLy9XAj7QVLhVN7Nxt+MXQvxndOeQRqPh2rVrWFtb/2TQR1fPq2PHjlhZWZGVlaUUhHzjjTdwdXVl6dKl1NbW8sILL9CjRw8+/fRTjh07RufOnQFISEjgo48+Yt68eZw5cwYzMzOOHj1Kfn4+n3/+OUeOHKGoqKjFNUAXCNLVKBBCCCEeNLpuWAkJCaxfv54dO3YwZMiQO+7Lumfn3r17M3fuXHJycjhz5gwdO3bEyckJU1NTNBoN77zzDoMHD271sy5cuICjoyMAw4YNIzQ0lMWLF3P69GkuXbqEo6Mj06ZNo2/fvpiZmf26X1yIX4AEfMSfWmNjIyqVCj09vVYHSxkZGTz55JNERUXx1VdfAT/ONBgZGRETE0NycjJHjhyha9euyuv6+vrMnz+f2tpaHBwcSExMZNCgQcr6Xh3dIFI3+Nu7dy9PPfVUi2OxsLBg7NixjB07loCAgLt+FxnsCfG/0S2Z3LlzJ9999x0nT56kbdu29O3blwEDBrSY/bsbHx8ffH19OXz4MGVlZTg7O+Po6MjEiRPRarUsW7aMsrIyhg4dirGxMYcOHaJfv35YW1vj5ubG1KlTSU5OJjk5mdraWry8vHjxxRfx8/O74/rRnJz/QgghHlS6Z+/+/fuzfv16UlJSGDJkyB0ZrLqiyroOlwcOHKCoqIiOHTsSEBCAu7s7dXV1dOzY8Y7PuHbtGsnJyXzzzTckJycrXbq8vb35+9//zpUrVzAzM8PQ0PDX/8JC/IIk4CP+lHQzBc0HSVVVVUyfPh1PT0+mTJkCNA3eoqOjKSgoUNJHm+vYsSNt2rQhKyuL8+fP4+TkhJubG/b29jQ2NjJ//nxiYmJaPYacnBxlZt/Pzw9PT0/S09NbLNfQCQsL+yW/vhCimdraWrZs2cLAgQNJTk7mH//4B3Z2dlhYWJCTk8O+ffu4ceMGo0ePvus+dA+jzs7OdO7cmczMTCV7R19fHzMzM1577TWlLeyCBQtoaGjg1KlTlJSUEBISAsDw4cPp378/R44cwc3NDTc3txaf0zxILYQQQvwZ6O6x3bt3x8jIiPT0dOW5/PYMXF0zk4iICA4cOMCxY8cYNGgQ/v7+jBw5kjfeeINXX32VV199FS8vL86cOUNxcTEpKSnk5eURERGh1OXR0Wq1Lbp6CXE/kYCP+FPS09OjsbGR7du3k5GRga+vL+7u7mzfvh0PDw8l4GNra0unTp1IS0sjMzOTPn36tNiPg4MDERERpKWlcfLkSZycnAgODiYgIID9+/dz+fLlOz67pKSENWvWsHDhQr799luCg4MBGDx4MGVlZS2WazQnHXaEaN29zg2tVqssd9LN/N2+7T//+U8WLlxIRkYGxcXFxMfHM3LkSHx9fcnLy+Nvf/sbX331FREREfj7+9/zWNRqNUFBQZibm3Pw4EESExOxt7dX6gxMnDgRDw8P/v73vwOQl5fHkSNHlIAPNC3TioqKUo5fF6DWZQ8KIYQQfza6e2FcXBxbtmxh3759JCUlKQEeHX19fTQaDTdu3ADAxMQEaOpcOWzYMEpKSli2bBlPPvkk1tbWqFQqrly5gouLC6NGjWLIkCF3PIvL87e4n0nAR/wppaWlMWvWLIqKiggICGDXrl3o6+tjYWFBeXk52dnZSkvFzp07Y2Jiwg8//HBHwMfY2Jjo6Gg2btxIfn4+MTExODo6MmbMGA4ePMjUqVOpqKggICCAK1eucPLkSVJTUzl9+jRDhw7FxcVF2deYMWPuecxysxGipYKCAubMmUP//v3vWIvfvH2qrqBjTU0Npqamyjb19fUYGBgQFRVFcnIyGzZsoHfv3rz11lvKNh06dGDcuHG88847ZGZm4uPjc9egiy6Y5OPjg4+PD4cOHeLs2bPY29srx2BmZsaoUaNQqVRs2bKFhIQEBg4ceM/9SZBHCCHEn53uOXjAgAFs2bKF7du3k5SUdEd2DzRN7GZnZwMQEREB/Fjr7pVXXqFv374cPXqUgoICTExM6NatG926dfuNv5EQvw0J+IgHyu3LHVqbzb9y5QpffPEFRUVFTJ48mbi4ODw9Pdm1axefffYZBQUF7Nq1Swn4BAYGEhgYSGpqKlevXm0R9VepVLi4uGBiYsKhQ4eUQm/h4eHMnj2b+fPnM3fuXNRqNWq1mtraWjp27MikSZPo06cPlpaW9zx+IcTdXb58mbS0NNzd3enfv3+LGju68/7ChQusX7+enTt3Ultbi6+vL1FRUQwcOFAJwoSEhODm5salS5fo3r078ONDo1qtpnPnzjg7O7N//34lY6c1us90cXEhNDSUJUuWcPToUYKDg+84px9//HFGjhx5z+8nQV4hhBCiie6eGBsbi76+PmlpacpzuS6Yo7vXJicnk5ubS3h4OJ06dQJa1roLDg5WMuyFeNBJwEc8UHQX82vXrlFeXo6vr69yg9AtqUhNTSU1NZXHH3+ccePGKe/t1asX9vb2jBgxgt27dzN58mQA2rRpQ5cuXcjKyiIzM5OEhAQ0Gg0ajQa1Wk1FRQU3b96ktLSUkpISpbJ/z549CQ8P5/Tp0+Tm5mJsbExUVNQ9WzbKTL4QP19QUBDvvvsuQUFBdxRUvnTpEitWrOD777+ntrYWd3d39PT02LFjB//+97/RaDQkJSWhVqsxMTEhLCyM3Nxcbt26BfzYdQuaAjidO3cmPT1dydi5F0NDQ3x8fKivrycrK4ukpCSl+KOObnlZ8+VmQgghhLg73bN3bGwsO3bsIC0tjb59+yr367KyMtatW8c///lPPDw8GDdunNLNUog/Kwn4iD+kq1evArRaz0Y3SNLNzuuyeG7evMmWLVv4+uuvOXHiBObm5jg6OpKUlMTo0aNRq9VotVouXLgAoLRZ160J1mq1dO7cmS5dupCdnc3hw4eVbUJDQ1m5ciXr1q2jS5cu2Nraoqenx40bN0hLS8PHx4eioiJyc3OJjIxUjtXCwoIOHTq06K5ze00OIcR/x8LCQlkOVV1d3eKhbvPmzSxbtozY2FgSExNp3749np6eXLlyhWeffZZPP/0ULy8vpXB6TEwMixYtIiUlhccee6zF51hbWxMVFcWmTZvumrGjo7seRUZG8uWXXxIaGnrX7l7Nl5sJIYQQ4t6aL+vasWMH27ZtIyEhQXlu37NnDwUFBURERDBx4kTJ4hECCfiIP5AbN26wefNmli9fzrFjx/jkk0/o1asX0HJp1u2DJJVKRWNjIwsWLGDx4sX4+/szZMgQZXZ97ty5XL58mUmTJqFSqSgvLwdQBoe6gZuu6FuPHj3Izs5m9+7dSsAnODiYhIQE1qxZg7m5OQMHDuTixYv88MMPHDlyhClTpvDmm29SW1tLXV1dqy0bNRoNKpVKanII8R/StUtv7bw5c+YMTz/9NKGhobzxxhsYGxsD4OjoyLvvvktCQkKL7S9duoSRkRGXLl1ix44dSsCnebF1XTagjkqlws/PjzZt2nDgwAESExPv6NjXfFsAJycnnJycfpHvL4QQQogf77FxcXGo1Wp27tzJX//6V44cOcLVq1fx9fVlwoQJJCYm0qZNm9/5aIX4Y5CAj/jDWLZsGQsWLMDf35/x48fTrl075bXmmTDnzp1j5cqVnDp1ig8++AADAwNWr17NwoUL6d+/P+PGjcPb21up0j9q1CgWLVpETEwMERERypKrvLw8evXqpWTb6AI/um45O3fu5PnnnweaunVNmDCBnJwc1qxZw5o1a4CmLl0zZswgPj6e+Pj4u87kA1KXR4j/0O3nJsD58+cxMjJS2qMaGRmhVqspLCykrKwMb29voOlh0MDAAID8/HxWrVrFli1buHr1Kg4ODtTX13PgwAFu3ryJiYkJ5ubmREZGkp+fz65du0hISFCWWwG4ubkRGhpKWloaJSUldw34CCGEEOLXo9FoMDY2xtfXl/z8fC5cuMDw4cMZOHAgXl5ev/fhCfGHIwEf8YdQWlrKunXr8PDw4IMPPsDe3r5FlkxxcTEajQYTExMmTZpEeXk5tra2nD9/HldXV9avX4+9vT0zZ85U2i9WVlZSX19PQEAA2dnZfPnll0RERBAdHQ3Ali1bGD9+PObm5srAEuDQoUPo6+tTVFTEsWPHCAgIoK6uDjc3N7766isyMzM5cuQInTp1omfPnkpGgZGREY2NjbJUS4hfiO6czMrKYv369eTk5FBdXU1sbCwTJ07E2toaGxsbEhISWLx4MYWFhUrARxfsWbt2LR999BE3btwgOjqayMhIoqKimDZtGocPH+bw4cNKB4+oqCgWL17MunXr7sgMsrKyIigoiNTUVBoaGn7DX0EIIYQQt5s7dy7Gxsa4ubn93ocixB+aBHzE70q3VKu+vp4rV67g7++vtCovLCzEz8+PwsJCHnnkEXr06EFZWRkmJibMnj0bLy8v3N3dKSkpobS0lLCwMAwMDMjOzubEiRNkZmaSlZVFeXk5JiYmyvKKoKAg4uLi2LlzJ8uWLWPs2LFK0Ka6uprU1FTatGnDxYsXSUlJISAgQBl4Ojk5MWDAAAYMGKB8h+bBIlmqJcR/5l6d6c6fP8+cOXNISUmhTZs2Si0sGxsbpYuWrq36559/Tl5eHgkJCUqwODs7m3/84x8YGxvz+uuvExISomTmREREkJOTQ2pqqhLw6dy5M4GBgezbtw9oeT7r6+vz2GOP8Ze//OXX+zGEEEIIcU+654X27dv/zkcixP1BAj7id6XLhGnfvj1OTk7k5eXx0ksvkZ6eTmVlJatXr8bHx4fw8HDS0tLw8vJi7ty5eHp6KvvQ09OjurqarKwspk2bRnp6OhUVFVhZWdGzZ08GDhzYopAywJgxYzh37hwfffQR586dIzo6mhs3brBhwwbq6+uZOXMmzz77LHv37uX555+/o7CqrvBy8xaQQoifR6vVotVq0dPTaxFUqampwczMDK1WS319PfPmzWPbtm0MHz6chx9+mHbt2rVayN3b2xtvb29ycnIoKytTUroLCwu5cOECM2bMoGfPnsr29fX1ZGRkoFKpyM7OVupuWVpa4uPjQ35+PmlpaUo2oI6ZmRlAi6VeQgghhBBC/FFJwEf87srKyli2bBnHjx9Hq9Wyb98+4uPj8ff3p02bNqjVanr27ElmZiZmZmZ4enpSX1+PWq1GpVLh4eGBra0t5eXl7N27l379+tGvXz9CQ0Pv+KyrV69iZWVFZGQkM2bMYPbs2Xz//fd8++23ALRt25YXX3yRhx56iKCgIA4fPkxxcTHe3t4tMnmk8LIQ/z1d8fL6+npSUlLYs2cP58+fx9HRkdmzZyuvbd++nc6dO/Paa6/dc5mkjY0N3bp145tvvuH48eN4eXmh1WqVzD2tVtti+71791JbW4utrS1ZWVlkZ2cTFRUFwLhx4xg7diz+/v4tisU3J+e+EEIIIYS4H0jAR/yiioqKqKysJDIyEpVKdUe3m+Z0g6nKykpOnTpFZGQk+/fvp3379syaNavFtp06dcLY2Bg9PT2uX7+OhYUFgLL/hIQEli5dytixYxk3blyrn5eSksKZM2cYM2YM0LR846uvviInJ4eSkhL8/PwICwtTto+LiyMnJ4ddu3bh7e19x6BRCHFvumVXt2fB3bp1i2XLlvH1119z4cIFPDw8sLGxIT8/n7KyMlxdXbly5Qru7u5cv36dgwcP0qZNGxoaGjAxMaGyslIp2AhNy7piYmL46quvOHz4MHFxcRgaGtK+fXscHR354osvMDQ0xNrammPHjrF8+XLGjRuHsbExH3/8cYsAjq4GECC1uIQQQgghxH1NpZVRrPiFnD9/nocffhh3d3c++eQT3N3df9b7GhsbqaiowMLCgmHDhlFZWcm2bduwtLSkvr4eAwMDLl++zHPPPce1a9f48MMPldl33bKQgoICnnjiCQwNDXnrrbeUgquXLl3i8OHD/PDDD6xZs4ZnnnmGv/3tbxgaGrbI2GlOt7yjqKiIAQMG4O3tzcaNG3/R30qIPxPdeayzbt06Xn/9dYKCghg2bBh+fn54eHhgamqqbFNXV8eqVat45513MDY2xtLSkoaGBq5du0ZDQwPu7u4kJSUxZswYLCwsOHfuHE899RSWlpbMmTOHtm3bArB8+XLee+896uvrgaYgTp8+fZg6darSse92d8vsEUIIIYQQ4n4iGT7iF2NhYUG/fv3YuXMn169fB5oGdunp6UyfPl2pf6GjizXq6+vj7OwMQExMDMuXL2fHjh0MHDhQGXRZWVnRtWtXFi5cSFFREf7+/sqyEAB/f39mzJjBzJkzef755wkNDcXW1paamhqOHTuGnp4eo0ePZuTIkUpB1+bBnubBI93rPj4+9O7dG19f3zsGrEKIJlqtlsbGxlYz+S5fvswbb7yBWq3m9ddfx87OjqtXr/Ldd99hYWHBP/7xDxwcHFrdr6GhIU888QQuLi5kZGRQWVmJp6cndnZ2nDlzhm3btvHZZ5/Rtm1bkpKSsLa2JiYmhnXr1lFcXKwEfEaOHEm3bt3YuXMn5ubmxMTE4Orq2uKzbs9ElGCPEEIIIYR4EEjAR/zXbi9camZmRpcuXfjuu++YOHEiZWVlALi4uHDp0iWlGKtuMNXaoCo+Pr5FwKd596uoqCgWLlxIXl4evXv3xsjIqMV7+/fvj4ODA+vXr6egoID8/HxMTU0ZNmwYgwYNalHo+XbNg0fw4wz/xx9//F//PkL8GahUqrsu22xoaKC6uprS0lLOnz+PnZ2dkqWjUqmwtramurqa2tpabGxsuHXrFvX19UphZo1GQ3x8PHFxccq1QHfdiYqK4q9//SuHDh0iKSkJY2NjunbtyvLly9m/fz+xsbHK9cnT05OxY8cqx9U8wAvc9fiFEEIIIYS4n8lTrviPNG+hfHvh0m+++YZ3330XgKqqKoYMGULPnj1xdXXFzc0N+DHI09DQwLlz5zh+/DhXrlxhyJAhAERGRuLh4UFmZqZSYFm39Kpdu3b4+vqSnZ3N2bNnlbo6zQM1ERERREREUFVVhUajUVow69xtGdftZIZfiJbutszp1q1bLF26lOvXrzN58uQW2TJ2dnbEx8cza9YsCgoKCAgIwM7OjoSEBObPn0/37t1p164denp6VFRUUFlZiY+PD/3792fUqFHKudr8nNXX16empob09HQAQkJCgKZz1t/fn4cffpiuXbu2KLCuo9FolOCunONCCCGEEOJBJwEf8R/RBXmuXLnC3r17KS0tJTAwkPj4eHx9ffnrX//Kvn37KC4uplevXjz00EMt3n/9+nXS0tLIy8sjIyODgoICDA0N6dGjB/b29ujp6RETE8PKlStJTU2lX79+SpDGysqKhx56iGXLllFYWIi3t/ddB23W1tbAj8tN9PT0lP8IIX5enRqNRoNGo1E64rWmpqaGL774gqtXrzJ58uQW2TL6+voEBgZiZWXFgQMHiIuLw9bWllGjRuHg4MC+ffsoKSnBzs6O0NBQampqSEtLY9asWVhZWTFgwACqq6sxNTWlqqqKkpISzp49y/79+9m6dSuDBg0iLi5O+TxnZ2c++OCDu34fOf+FEEIIIcSfiQR8xM/W2NjInj17WLJkCVlZWZiamlJXV4evry8dO3YkNDSUTp064eXlxYsvvsjx48eVgI9ucHnq1CneffddysvLCQ8PZ8aMGSQmJmJubq7UyYmPj2flypWkpKTQr1+/FssuwsLCWLhwIdnZ2fTq1esn6+rca7mJEH82/2mGS/MgaVlZGTU1NUpnLGg6r21tbenfvz8rVqwgLS2N6OjoFss93d3d6dSpE1lZWVy4cAFbW1tsbW0ZPnw4SUlJSqHmuro6DAwM2Lx5M9OmTSMrK4t+/fpRUlLCnDlzOHbsGJaWllRUVGBqasrQoUMZPXr0HbXB4M6aPEIIIYQQQvwZyRPxn8DPWcak0WjQarXKIK212f+0tDRmzpyJgYEBY8eOJSAgAGdnZ7y8vJQ26Wq1Gl9fX+zs7MjMzOSRRx7B3t4ejUaDvr4+rq6uzJkzh+Dg4BY1eDQajRK80S3rys7Oprq6GnNzc+U7BAUF8fHHHxMTEyNFlIX4GZqfy7rrQGVlJcePH8fU1BRHR0esra0xNja+470nT55k7dq1bNy4kaqqKiwsLHB3d2fSpEmEhoYq20VERLBixQrWr19PdHQ0zZs/2traEhkZyZ49eygsLCQgIEB5rXlXLl2xdF1NHwMDA6Wge9++ffHw8MDCwoLw8HBiY2PvGdCRYI8QQgghhBAS8Hmg6QZ69wr2tLbNrVu37iiIXFdXx2uvvUZ9fT2zZ88mMjLyrvtydnYmLCyMrKwsTp48qSzVgqaaHnZ2dkBTxhA0LfvQva7RaDA0NCQgIICtW7eSlpZGr169lAGrtbU1vXv3/h9+FSH+XHTnzoULF1i3bh0bNmzg/PnzGBoacvXqVbRaLdOnT2fYsGFKwPfWrVts2LCBRYsWUVtbS0BAAA4ODly5coW0tDTeeOMN3nrrLcLCwgAIDQ2lbdu2pKSkAC0DLmq1Gh8fH0xMTMjKyqJnz55YWlq26HxXWVnJmTNnyMrKYvny5fj6+jJ48GCg6ZoxbNiwOwK8zeuJCSGEEEIIIe4kAZ8HlC74cvnyZf79739z69YtHn/88TuWP+gGg8XFxaxbt47Dhw9jaWlJcHAwAwcOVIIzVVVVGBoa4u3trQzymgeGmi+hMDExITY2lq1bt3L06FEiIiJaPcbbiz7rjhvgqaeeonfv3vTo0aPVbKOfU39ECNEURF26dKlSWLlDhw706NEDS0tLXFxcKCoqwtDQkNraWuX6cPr0ab7++mtsbGx46qmn6NixI46OjkDTteLhhx9m1apVyrXA3t6esLAwkpOTycrKIjQ0FI1GAzRlFdna2mJqasrRo0cpLy/H0tKSqqoq3nnnHcrKylCr1ZSXl1NZWUmHDh2YMGEC/v7+ynfQBXuaZyK2dv0QQgghhBBC/EgCPvexewU9VCoVhw8fZujQoUDTsoqEhAS8vLxabNfY2Mgnn3zCkiVLMDExwcHBgaKiIrZu3crGjRuZOHEisbGx6OnpERISwoYNG3jxxRfx8PBArVZz8eJFKioqCAwMpH///vj4+KBSqQgODsbGxoaDBw8yePBgLC0tAVrM6rdGN4gLDg4mODj4rttJsEeIe9NqtTQ0NLBs2TLef/992rdvzwsvvEBUVBROTk73fK+rqytTp069I5Nv8+bNbNu2DYDs7GyOHDlCx44dAejatSvJycmsWbOG0NBQGhsblSCwRqPh0qVL3Lhxg+LiYvz9/WnTpg3W1tZUVlaiUqlISEggMTFRCSK1RrJ5hBBCCCGE+Pkk4HMf0mXB/FTQQxdYcXZ25vz58xw7duyOgM/69etZsGABXbt25ZlnnsHX15eGhgZWrlzJ/PnzmTdvHmFhYdjb2/PCCy9QWlpKdnY2O3bswMjICAcHByorK9m7dy/btm3j3XffJTg4GGdnZ6Kjo9m5cyebNm2iS5cuHD58mIqKCp555pmfVWPj57ZQF0K0pAsGZ2Zm8t577+Hv78/SpUuVWlvQFOxtXjurOVNTU8LDwwE4fPgwX3/9NVu2bOHmzZv4+vrSqVMn8vLyOHDggBLwCQ0NxcfHhx9++IFRo0YpGTqlpaXMnTuXrl27kpaWRkpKCt26dcPKyoqXXnqJhoYGbGxsWny+nPtCCCGEEEL87yTgcx/SBXpKSkrYtWsXw4cPb7XgamlpKaampgQGBnLz5k327t1Lz549lW2rqqpYsmQJ1tbWvPfee9ja2irvfeGFF8jPz2fXrl38+9//ZtCgQbi7u/Ptt99SWFiIgYEBzs7OXLt2jYaGBpKTk5k/fz5btmwhODgYMzMzRo4cyaZNm3jnnXewsrLi0qVLGBgYkJSUhKur609+TxnwCfHfUalU3Lx5k1WrVqHVavnb3/6GhYUFjY2N6OnpoVKpWiyLunXrFufOncPKygobGxtliWZxcTF///vfOX78OL169aJr16507tyZyspKRowYQUZGBn/5y18AcHR0ZNCgQXzyySdMmjSJPn36oFaryczMpKGhgfHjx2Nvb4+/vz8mJiYAdwSgdDV55NwXQgghhBDifycBn/vUsGHDOHToEADl5eU89dRTODo6tqibUVNTA0CHDh04f/48GRkZXLx4EXd3d6BpeVVBQQEhISGYm5sDtHj/o48+Sk5ODnv27CE+Ph57e3u0Wi1+fn7KcegGbrplGLp/Q9OM/5IlS9i+fTt6enrEx8fftZ6PEOKXpdVqycjIwMDAgA4dOgAowR5oKsR++vRpiouLyc7OprS0lKioKMaMGYNaraauro5Zs2Zx5MgRZs+eTXx8vFLjR6VSYWtrS0FBAQUFBUo2z6hRo7h27RpLly5l4cKFAAQGBjJ58mS6du1K165d73q8UpNHCCGEEEKIX5YEfO4zjY2N6Ovr4+3tzaFDh7C1tWXTpk0ATJs2DT09PWXJl7GxMXV1dfTo0YMzZ86wZs0aCgoKlIBPeXk59vb2WFtbc/36dezs7Fq839fXF1dXVwoLC5VA0O3LyC5dukROTg7z5s3D1NSU7t27t3g9MjLyjjoguu8ghPj1nDlzBnNzc9q0acPVq1dxdHRUzt/GxkZSU1OZOXMm586dQ6VSodFoqK6uZvTo0ejp6dHY2MipU6fw8/OjT58+GBkZKUvF0tPTuXz5Mubm5mRkZODv709DQwOGhoa88MILDBs2jCNHjhAQEKBcb3SaF3gXQgghhBBC/Hokb/4+o1vqoGtZ7ODgQM+ePVmxYgUbN24EfgzKnDx5koaGBqytrXnooYcASE1N5ebNmwAYGRlhYWFBWVkZN27cUD5D935PT0+0Wi1nz55VgkAlJSWsWbOGOXPm8OabbzJ16lTefPNNamtrefvtt+nUqVOrx62rFwIyky/Eb0Gr1VJbW0tNTQ11dXUtXtPX10dPT4+goCD+8Y9/kJ2dTWBgIIWFheTn5wNw7do1XFxcqK+v59SpU0BTVuDBgwdZvXo18fHxVFdXs3XrVuDHVux6enq4uLjQu3dv3N3d0Wq1NDY2Kp8twR4hhBBCCCF+G/LkfZ/RBWPCwsKwtLTkxIkTvPvuuxw/fpyZM2dib2+vZNTU19djbm5OfX09fn5+tG3blvT0dCorK3Fzc8Pd3R0/Pz+2bdtGVlYWbm5uyky/np6e8t92dnZKQKixsZGNGzeSk5ODqakpNjY2DB06lMGDB98xk9+cBHmE+G35+PhgZ2dHYWEhp0+fpkOHDi0y9GJjY4mNjVX+HR0dTX5+Punp6XTs2BErKyv69u3L3//+d15//XW6du1KbW0t+/fvx8TEhE8//ZSXX36ZyMjIVossNy8uL+e/EEIIIYQQvz3J8LkP6WbLe/XqRUNDA6Wlpbz99tuYmpoye/ZsioqKgKYBl0ajwcXFBXd3dzp16kRJSQnHjx9Hq9ViZmZGjx49MDMzY/ny5crMvm7glpyczNGjRwkLC1O6e3l5eTFjxgwWLVrE6tWrWb9+PZMmTbpnsEcI8dvT19dXgr/79u3j8uXLwI+BGGi6luiyf/r27QtAWloa0LQkdNSoUSQlJXHx4kW++OILVqxYgZOTE1OnTkWlUvH+++8zdOjQVossq1Sqn+wkKIQQQgghhPj1SMDnPqQbXA0YMACANWvW4Ovry1tvvUVxcTHvvPMOGo2GsrIyTE1NuXz5MoaGhoSEhACwf/9+bt26BTTN8g8aNIgjR47wxhtvkJyczIYNG/jggw/48MMPCQgIYMqUKS0+293dnZCQEBwdHdFqtTQ0NLQYRAoh/hh69+6NtbU1e/bsYefOnUBTIEYXNNbX18fQ0FD5O0B1dTUXLlxQ9jF37ly+/PJLvvjiC9LS0vjXv/5Fly5dlNcbGhp+q68jhBBCCCGE+A/Ikq77kG5gFhUVhampKfv37+f8+fM89NBD/OUvf2HhwoUsXryYyspKXF1dlWBMp06dcHd3Jz09nStXruDs7IyNjQ0TJ07E0NCQJUuW8Nprr2FgYEBDQwPdunXj6aefvmf2jkqlkpocQvxBhYSE8Nhjj7Fw4UI+/PBD/Pz8CAwMVJZY3bhxg5MnT7Jr1y5WrlyJp6cnU6ZMwdHRscV+2rVrR7t27YCmTn5arVbZh5z/QgghhBBC/DHJk/p9SlczIyEhgfXr17N3716GDh3KmDFjqK+v57333gOa6ng4OTkp/zsoKIjNmzdz4sQJnJ2dATA1NWXy5MmMGDGCw4cPo9VqiYyMxMbG5nf7fkKI/51areb555+nsrKS1atXM3z4cOLj4/H39+f69etUVVWRnZ3NxYsXiYiI4OmnnyYsLKzVfek6dLW2fEsIIYQQQgjxxyMBn/uULsunf//+rF+/nh07djB06FBsbGyYMGEC+fn5nD59moiICKUNsqGhIaGhoWzatImUlBQiIyMxNDRUBnIuLi64uLgon3H7TL4Q4v5jYGDAlClTiIqK4uuvv2bPnj3s3bsXtVqNkZERYWFhvP7663Tr1u2e+5F6PEIIIYQQQtxfJOBzn9INvrp3746RkRHp6elcvnwZW1tbzMzMmD17NlevXsXFxQW1Wq1kBAUEBODu7o6BgUGLLjrN6baVmXwhHgzW1tb079+fHj16UF9fT3FxMebm5vj7+7fYrrVuW0IIIYQQQoj7k0or1XbvW7rB2aRJk9iyZQtz584lKSmJxsbGu2blaDQaABnUCfEnc7dgTvMCzkIIIYQQQogHh4z672O6zBxdt67mXXjuRpe509jYKJ21hPgTuT3Yozv/9fX1JdgjhBBCCCHEA0gyfB4A9fX1BAUFAZCZmYmFhcXvfERCCCGEEEIIIYT4PUkNn/ucRqPBwMCAN998Ey8vLywsLJQizEIIIYQQQgghhPhzkgwfIYQQQgghhBBCiAeM1PB5gOgKMgshhBBCCCGEEOLPTTJ8hBBCCCGEEEIIIR4wkuEjhBBCCCGEEEII8YCRgI8QQgghhBBCCCHEA0YCPkIIIYQQQgghhBAPGAn4CCGEEEIIIYQQQjxgJOAjhBBCCCGEEEII8YCRgI8QQgghhBBCCCHEA0YCPkIIIYQQQgghhBAPGAn4CCGEEEIIIYQQQjxgJOAjhBBCCCGEEEII8YCRgI8QQgghhBBCCCHEA0YCPkIIIYQQQgghhBAPGAn4CCGEEEIIIYQQQjxgJOAjhBBCCCGEEEII8YD5f3l6Sh5/zMOaAAAAAElFTkSuQmCC",
      "text/plain": "<Figure size 990x550 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-161",
   "metadata": {},
   "source": "### Step 75 \u00b7 Report the selected model result\n\nState the denominator beside the percentage and keep the training and test estimates distinct.\n"
  },
  {
   "cell_type": "code",
   "id": "code-075",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Report the selected model result.\nselected_predictions = test_predictions[winner_name]\nselected_accuracy = test_metrics.loc[winner_name, 'accuracy']\nselected_f1 = test_metrics.loc[winner_name, 'macro_F1']\ncorrect_count = int(np.sum(selected_predictions == y_test.to_numpy()))\nprint(f'{winner_name}: {correct_count}/{len(y_test)} correct ({selected_accuracy:.2%}).')\nprint(f'Final-test macro F1: {selected_f1:.4f}.')\nprint(f'Training nested-CV macro F1: {nested_summary.loc[winner_name, \"nested_macro_F1\"]:.4f}.')\nprint('These results do not establish accuracy on a new telescope survey.')\n\n# Your note: \n",
   "execution_count": 75,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Decision tree: 48/48 correct (100.00%).\nFinal-test macro F1: 1.0000.\nTraining nested-CV macro F1: 0.9944.\nThese results do not establish accuracy on a new telescope survey.\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-163",
   "metadata": {},
   "source": "### Step 76 \u00b7 Define a Wilson accuracy interval\n\nA Wilson interval avoids a misleading zero-width uncertainty interval when all test cases are correct. It concerns aggregate accuracy under an independent, same-distribution sampling assumption.\n"
  },
  {
   "cell_type": "code",
   "id": "code-076",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define a Wilson accuracy interval.\ndef wilson_interval(correct, total, confidence=0.95):\n    z = stats.norm.ppf(0.5 + confidence / 2)\n    proportion = correct / total\n    denominator = 1 + z ** 2 / total\n    centre = (proportion + z ** 2 / (2 * total)) / denominator\n    half_width = z * np.sqrt(proportion * (1 - proportion) / total + z ** 2 / (4 * total ** 2)) / denominator\n    return centre - half_width, centre + half_width\n\n# Your note: \n",
   "execution_count": 76,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-165",
   "metadata": {},
   "source": "### Step 77 \u00b7 Quantify holdout uncertainty\n\nThere are only 48 test records and eight per class. Unknown dependence and curated sampling limit this interval's real-world interpretation.\n"
  },
  {
   "cell_type": "code",
   "id": "code-077",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Quantify holdout uncertainty.\naccuracy_low, accuracy_high = wilson_interval(correct_count, len(y_test))\nprint(f'95% Wilson interval for aggregate accuracy: {accuracy_low:.2%}\u2013{accuracy_high:.2%}')\nprint(f'One additional test error changes accuracy by {1 / len(y_test):.2%}.')\n\n# Your note: \n",
   "execution_count": 77,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "95% Wilson interval for aggregate accuracy: 92.59%\u2013100.00%\nOne additional test error changes accuracy by 2.08%.\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-167",
   "metadata": {},
   "source": "## 10. Inspect errors, ranking, and model scores\n\nA confusion matrix shows **which** classes are confused. ROC AUC evaluates ranking across one-vs-rest comparisons; it does not mean that the same percentage of records is classified correctly. Log loss evaluates the model scores assigned to the true classes. A score of 0.95 from `predict_proba` is not automatically a verified 95% chance of correctness.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-168",
   "metadata": {},
   "source": "### Step 78 \u00b7 Show all learned-model confusion matrices\n\nRows are true classes; columns are predictions. The identical label ordering enables direct comparison.\n"
  },
  {
   "cell_type": "code",
   "id": "code-078",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Show all learned-model confusion matrices.\nfig, axes = plt.subplots(1, 3, figsize=(18, 6))\nfor (name, _), ax in zip(searches.items(), axes):\n    matrix = confusion_matrix(y_test, test_predictions[name], labels=CLASS_IDS)\n    ConfusionMatrixDisplay(matrix, display_labels=CLASS_LABELS).plot(\n        ax=ax, cmap='Blues', colorbar=False, xticks_rotation=75\n    )\n    ax.set_title(name)\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '08_confusion_matrices.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 78,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 1980x660 with 3 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-170",
   "metadata": {},
   "source": "### Step 79 \u00b7 Produce the selected model classification report\n\nSupport is eight per class. A perfect class score based on eight examples is still limited evidence.\n"
  },
  {
   "cell_type": "code",
   "id": "code-079",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Produce the selected model classification report.\nreport_dict = classification_report(\n    y_test, selected_predictions, labels=CLASS_IDS, target_names=CLASS_LABELS,\n    output_dict=True, zero_division=0\n)\nclassification_report_table = pd.DataFrame(report_dict).T\nclassification_report_table.round(4)\n\n# Your note: \n",
   "execution_count": 79,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 79,
     "data": {
      "text/plain": "               precision  recall  f1-score  support\nBrown Dwarf          1.0     1.0       1.0      8.0\nRed Dwarf            1.0     1.0       1.0      8.0\nWhite Dwarf          1.0     1.0       1.0      8.0\nMain Sequence        1.0     1.0       1.0      8.0\nSupergiant           1.0     1.0       1.0      8.0\nHypergiant           1.0     1.0       1.0      8.0\naccuracy             1.0     1.0       1.0      1.0\nmacro avg            1.0     1.0       1.0     48.0\nweighted avg         1.0     1.0       1.0     48.0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>f1-score</th>\n      <th>support</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Brown Dwarf</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>Red Dwarf</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>White Dwarf</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>Main Sequence</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>Supergiant</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>Hypergiant</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>accuracy</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>macro avg</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <th>weighted avg</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>48.0</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-172",
   "metadata": {},
   "source": "### Step 80 \u00b7 Build a record-level prediction audit\n\nThese are CSV row indices, not astronomical source identifiers. Keep the true label, predicted label, and every input together.\n"
  },
  {
   "cell_type": "code",
   "id": "code-080",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Build a record-level prediction audit.\nprediction_audit = X_test.copy()\nprediction_audit.index.name = 'csv_row_index'\nprediction_audit['actual_class'] = y_test.map(CLASS_NAMES)\nprediction_audit['predicted_class'] = pd.Series(selected_predictions, index=X_test.index).map(CLASS_NAMES)\nprediction_audit['correct'] = prediction_audit['actual_class'].eq(prediction_audit['predicted_class'])\n\n# Your note: \n",
   "execution_count": 80,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-174",
   "metadata": {},
   "source": "### Step 81 \u00b7 Inspect the selected model mistakes\n\nAn empty table means no observed errors in this holdout; it does not demonstrate absence of future errors.\n"
  },
  {
   "cell_type": "code",
   "id": "code-081",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Inspect the selected model mistakes.\nmistakes = prediction_audit.loc[~prediction_audit['correct']]\nprint('Observed holdout errors:', len(mistakes))\nmistakes\n\n# Your note: \n",
   "execution_count": 81,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Observed holdout errors: 0\n"
    },
    {
     "output_type": "execute_result",
     "execution_count": 81,
     "data": {
      "text/plain": "Empty DataFrame\nColumns: [Temperature (K), Luminosity (L/Lo), Radius (R/Ro), Absolute magnitude (Mv), actual_class, predicted_class, correct]\nIndex: []",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature (K)</th>\n      <th>Luminosity (L/Lo)</th>\n      <th>Radius (R/Ro)</th>\n      <th>Absolute magnitude (Mv)</th>\n      <th>actual_class</th>\n      <th>predicted_class</th>\n      <th>correct</th>\n    </tr>\n    <tr>\n      <th>csv_row_index</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-176",
   "metadata": {},
   "source": "### Step 82 \u00b7 Compare mistakes from the learned models\n\nInspect errors even when the selected model has none. Exclude the no-skill baseline to keep the diagnostic focused.\n"
  },
  {
   "cell_type": "code",
   "id": "code-082",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Compare mistakes from the learned models.\ncomparison_errors = []\nfor name in searches:\n    for row_index, actual, predicted in zip(X_test.index, y_test, test_predictions[name]):\n        if actual != predicted:\n            comparison_errors.append({'model': name, 'csv_row_index': row_index,\n                                      'actual': CLASS_NAMES[int(actual)],\n                                      'predicted': CLASS_NAMES[int(predicted)]})\npd.DataFrame(comparison_errors, columns=['model', 'csv_row_index', 'actual', 'predicted'])\n\n# Your note: \n",
   "execution_count": 82,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 82,
     "data": {
      "text/plain": "                  model  csv_row_index         actual   predicted\n0  Gaussian Naive Bayes             30  Main Sequence  Supergiant",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>model</th>\n      <th>csv_row_index</th>\n      <th>actual</th>\n      <th>predicted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Gaussian Naive Bayes</td>\n      <td>30</td>\n      <td>Main Sequence</td>\n      <td>Supergiant</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-178",
   "metadata": {},
   "source": "### Step 83 \u00b7 Align probability columns explicitly\n\nProbability columns follow model.classes_. Reorder them to the declared class list before calculating multiclass metrics.\n"
  },
  {
   "cell_type": "code",
   "id": "code-083",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Align probability columns explicitly.\nraw_probabilities = selected_model.predict_proba(X_test)\nprobability_order = [list(selected_model.classes_).index(class_id) for class_id in CLASS_IDS]\ntest_probabilities = raw_probabilities[:, probability_order]\nassert np.allclose(test_probabilities.sum(axis=1), 1)\n\n# Your note: \n",
   "execution_count": 83,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-180",
   "metadata": {},
   "source": "### Step 84 \u00b7 Calculate ranking and probability-quality metrics\n\nAUC is descriptive on a small balanced holdout. Log loss can be poor even when accuracy is high if mistakes are overconfident.\n"
  },
  {
   "cell_type": "code",
   "id": "code-084",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Calculate ranking and probability-quality metrics.\ntest_auc = roc_auc_score(y_test, test_probabilities, labels=CLASS_IDS, multi_class='ovr', average='macro')\ntest_log_loss = log_loss(y_test, test_probabilities, labels=CLASS_IDS)\nprint(f'Macro one-vs-rest ROC AUC: {test_auc:.4f}')\nprint(f'Multiclass log loss (lower is better): {test_log_loss:.4f}')\n\n# Your note: \n",
   "execution_count": 84,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Macro one-vs-rest ROC AUC: 1.0000\nMulticlass log loss (lower is better): 0.0000\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-182",
   "metadata": {},
   "source": "### Step 85 \u00b7 Plot one-vs-rest ROC curves\n\nEach curve compares one named class with all other classes. Ties in scores receive half credit in AUC ranking interpretation.\n"
  },
  {
   "cell_type": "code",
   "id": "code-085",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Plot one-vs-rest ROC curves.\nbinarized_test = label_binarize(y_test, classes=CLASS_IDS)\nfig, ax = plt.subplots(figsize=(8, 6))\nfor i, (class_id, color) in enumerate(zip(CLASS_IDS, CLASS_COLORS)):\n    fpr, tpr, _ = roc_curve(binarized_test[:, i], test_probabilities[:, i])\n    class_auc = roc_auc_score(binarized_test[:, i], test_probabilities[:, i])\n    ax.plot(fpr, tpr, color=color, label=f'{CLASS_NAMES[class_id]} (AUC {class_auc:.3f})')\nax.plot([0, 1], [0, 1], '--', color='#667582', label='Chance ranking')\nax.set(title=f'{winner_name}: final-test one-vs-rest ROC', xlabel='False positive rate', ylabel='True positive rate')\nax.legend(loc='lower right', fontsize=9)\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '09_roc_curves.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 85,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 880x660 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-184",
   "metadata": {},
   "source": "### Step 86 \u00b7 Attach model scores to the audit table\n\nA model score is useful for diagnostic review but is not labelled as calibrated confidence.\n"
  },
  {
   "cell_type": "code",
   "id": "code-086",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Attach model scores to the audit table.\nprediction_audit['maximum_model_score'] = test_probabilities.max(axis=1)\nprediction_audit[['actual_class', 'predicted_class', 'correct', 'maximum_model_score']].head(10)\n\n# Your note: \n",
   "execution_count": 86,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 86,
     "data": {
      "text/plain": "                actual_class predicted_class  correct  maximum_model_score\ncsv_row_index                                                             \n135                Red Dwarf       Red Dwarf     True                  1.0\n152            Main Sequence   Main Sequence     True                  1.0\n73                 Red Dwarf       Red Dwarf     True                  1.0\n64               Brown Dwarf     Brown Dwarf     True                  1.0\n169               Supergiant      Supergiant     True                  1.0\n228               Supergiant      Supergiant     True                  1.0\n153            Main Sequence   Main Sequence     True                  1.0\n188              Brown Dwarf     Brown Dwarf     True                  1.0\n227               Supergiant      Supergiant     True                  1.0\n45                Supergiant      Supergiant     True                  1.0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual_class</th>\n      <th>predicted_class</th>\n      <th>correct</th>\n      <th>maximum_model_score</th>\n    </tr>\n    <tr>\n      <th>csv_row_index</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>135</th>\n      <td>Red Dwarf</td>\n      <td>Red Dwarf</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>Main Sequence</td>\n      <td>Main Sequence</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Red Dwarf</td>\n      <td>Red Dwarf</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>Brown Dwarf</td>\n      <td>Brown Dwarf</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>169</th>\n      <td>Supergiant</td>\n      <td>Supergiant</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>228</th>\n      <td>Supergiant</td>\n      <td>Supergiant</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>Main Sequence</td>\n      <td>Main Sequence</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>188</th>\n      <td>Brown Dwarf</td>\n      <td>Brown Dwarf</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>227</th>\n      <td>Supergiant</td>\n      <td>Supergiant</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Supergiant</td>\n      <td>Supergiant</td>\n      <td>True</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-186",
   "metadata": {},
   "source": "### Step 87 \u00b7 Summarise score bins\n\nThis is a coarse diagnostic with small bin counts. It does not calibrate probabilities or set an acceptance threshold.\n"
  },
  {
   "cell_type": "code",
   "id": "code-087",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Summarise score bins.\nscore_bins = pd.cut(prediction_audit['maximum_model_score'],\n                    bins=[0, 0.5, 0.8, 0.95, 1.0], include_lowest=True)\nscore_diagnostic = prediction_audit.groupby(score_bins, observed=True).agg(\n    records=('correct', 'size'), mean_score=('maximum_model_score', 'mean'),\n    observed_accuracy=('correct', 'mean')\n)\nscore_diagnostic.round(4)\n\n# Your note: \n",
   "execution_count": 87,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 87,
     "data": {
      "text/plain": "                     records  mean_score  observed_accuracy\nmaximum_model_score                                        \n(0.95, 1.0]               48         1.0                1.0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>records</th>\n      <th>mean_score</th>\n      <th>observed_accuracy</th>\n    </tr>\n    <tr>\n      <th>maximum_model_score</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>(0.95, 1.0]</th>\n      <td>48</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-188",
   "metadata": {},
   "source": "**Decision implication.** To build a review queue, validate calibration and class-specific precision/recall on an independent, representative dataset. Define review thresholds using development data and the cost of errors. The final holdout above cannot now be reused to tune that policy. For rare-object searches, recall, follow-up yield, and contamination rate may matter more than overall accuracy.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-189",
   "metadata": {},
   "source": "## 11. Explain the fitted models\n\nThe tree provides human-readable rules. Permutation importance estimates how a fitted model's score changes when a column is shuffled. Correlated predictors can share signal, so a low score drop does not establish physical irrelevance or causation.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-190",
   "metadata": {},
   "source": "### Step 88 \u00b7 Print readable tree rules\n\nThese thresholds use the raw measurement units. The rules are a comparator even if the tree was not the selected family.\n"
  },
  {
   "cell_type": "code",
   "id": "code-088",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Print readable tree rules.\nfitted_tree = tree_search.best_estimator_.named_steps['classifier']\nprint(export_text(fitted_tree, feature_names=NUMERIC_FEATURES, decimals=3))\n\n# Your note: \n",
   "execution_count": 88,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "|--- Radius (R/Ro) <= 0.046\n|   |--- class: 2\n|--- Radius (R/Ro) >  0.046\n|   |--- Radius (R/Ro) <= 0.733\n|   |   |--- Absolute magnitude (Mv) <= 15.495\n|   |   |   |--- class: 1\n|   |   |--- Absolute magnitude (Mv) >  15.495\n|   |   |   |--- class: 0\n|   |--- Radius (R/Ro) >  0.733\n|   |   |--- Absolute magnitude (Mv) <= -7.515\n|   |   |   |--- class: 5\n|   |   |--- Absolute magnitude (Mv) >  -7.515\n|   |   |   |--- Radius (R/Ro) <= 11.100\n|   |   |   |   |--- class: 3\n|   |   |   |--- Radius (R/Ro) >  11.100\n|   |   |   |   |--- class: 4\n\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-192",
   "metadata": {},
   "source": "### Step 89 \u00b7 Draw the learned tree\n\nClass codes at the leaves map to the named classes shown elsewhere in the notebook.\n"
  },
  {
   "cell_type": "code",
   "id": "code-089",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Draw the learned tree.\nfig, ax = plt.subplots(figsize=(17, 9))\nplot_tree(fitted_tree, feature_names=NUMERIC_FEATURES, class_names=CLASS_LABELS,\n          filled=True, rounded=True, impurity=False, proportion=False, ax=ax, fontsize=8)\nax.set_title('Tuned decision tree: rules learned from training records')\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '10_tree_rules.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 89,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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      "text/plain": "<Figure size 1870x990 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-194",
   "metadata": {},
   "source": "### Step 90 \u00b7 Estimate exploratory feature importance\n\nUse training records only so explanation does not guide changes using the final holdout. These in-sample importances may be optimistic.\n"
  },
  {
   "cell_type": "code",
   "id": "code-090",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Estimate exploratory feature importance.\nimportance_result = permutation_importance(\n    selected_model, X_train, y_train, scoring='f1_macro',\n    n_repeats=20, random_state=RANDOM_SEED, n_jobs=N_JOBS\n)\nimportance_table = pd.DataFrame({\n    'feature': NUMERIC_FEATURES, 'mean_F1_drop': importance_result.importances_mean,\n    'shuffle_SD': importance_result.importances_std\n}).sort_values('mean_F1_drop', ascending=False)\nimportance_table.round(4)\n\n# Your note: \n",
   "execution_count": 90,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 90,
     "data": {
      "text/plain": "                   feature  mean_F1_drop  shuffle_SD\n2            Radius (R/Ro)        0.6589      0.0352\n3  Absolute magnitude (Mv)        0.3949      0.0275\n1        Luminosity (L/Lo)        0.0000      0.0000\n0          Temperature (K)        0.0000      0.0000",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature</th>\n      <th>mean_F1_drop</th>\n      <th>shuffle_SD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Radius (R/Ro)</td>\n      <td>0.6589</td>\n      <td>0.0352</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Absolute magnitude (Mv)</td>\n      <td>0.3949</td>\n      <td>0.0275</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Luminosity (L/Lo)</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Temperature (K)</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-196",
   "metadata": {},
   "source": "### Step 91 \u00b7 Plot exploratory importance\n\nError bars show variation across random shuffles, not statistical confidence intervals.\n"
  },
  {
   "cell_type": "code",
   "id": "code-091",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Plot exploratory importance.\nfig, ax = plt.subplots(figsize=(9, 4))\nordered_importance = importance_table.sort_values('mean_F1_drop')\nax.barh(ordered_importance['feature'], ordered_importance['mean_F1_drop'],\n         xerr=ordered_importance['shuffle_SD'], color='#16324F', capsize=3)\nax.set(title=f'{winner_name}: exploratory training permutation importance',\n       xlabel='Drop in training macro F1 after shuffling')\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '11_permutation_importance.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 91,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 990x440 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-198",
   "metadata": {},
   "source": "## 12. Make candidate predictions with input checks\n\nThe original example supplied `Mv = 612`, far outside the data range, and still returned a label. A production-facing function needs units, column checks, numerical validation, and an explicit review route for unsupported inputs.\n\nThe demonstration below uses **existing held-out records**, not newly collected stars. All suggestions remain candidates for expert review. A simple min/max range check cannot detect every unusual combination of otherwise in-range measurements.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-199",
   "metadata": {},
   "source": "### Step 92 \u00b7 Define measurement validation\n\nRequire named columns, finite numerical values, and positive inputs to logarithms. Reject missing, additional, or malformed fields with a clear error.\n"
  },
  {
   "cell_type": "code",
   "id": "code-092",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define measurement validation.\ndef validate_measurements(measurements):\n    if not isinstance(measurements, pd.DataFrame):\n        raise TypeError('Supply a pandas DataFrame with named measurement columns.')\n    if not measurements.columns.is_unique or set(measurements.columns) != set(NUMERIC_FEATURES):\n        raise ValueError('Supply exactly the four named predictor columns.')\n    if measurements.empty:\n        raise ValueError('Supply at least one observation.')\n    numeric = measurements.loc[:, NUMERIC_FEATURES].apply(pd.to_numeric, errors='raise')\n    if not np.isfinite(numeric.to_numpy()).all():\n        raise ValueError('Measurements must be finite and non-missing.')\n    if not numeric[POSITIVE_FEATURES].gt(0).all().all():\n        raise ValueError('Temperature, luminosity, and radius must be positive.')\n    return numeric\n\n# Your note: \n",
   "execution_count": 92,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-201",
   "metadata": {},
   "source": "### Step 93 \u00b7 Record supported marginal ranges\n\nRange limits come only from training records. They describe the sample; they are not universal physical bounds.\n"
  },
  {
   "cell_type": "code",
   "id": "code-093",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Record supported marginal ranges.\ntraining_ranges = X_train.agg(['min', 'max'])\ntraining_ranges\n\n# Your note: \n",
   "execution_count": 93,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 93,
     "data": {
      "text/plain": "     Temperature (K)  Luminosity (L/Lo)  Radius (R/Ro)  \\\nmin             1939         8.0000e-05         0.0084   \nmax            40000         8.4942e+05      1948.5000   \n\n     Absolute magnitude (Mv)  \nmin                   -11.92  \nmax                    20.06  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature (K)</th>\n      <th>Luminosity (L/Lo)</th>\n      <th>Radius (R/Ro)</th>\n      <th>Absolute magnitude (Mv)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>min</th>\n      <td>1939</td>\n      <td>8.0000e-05</td>\n      <td>0.0084</td>\n      <td>-11.92</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>40000</td>\n      <td>8.4942e+05</td>\n      <td>1948.5000</td>\n      <td>20.06</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-203",
   "metadata": {},
   "source": "### Step 94 \u00b7 Define a candidate prediction function\n\nAbstain when any measurement falls outside training ranges. Scores are diagnostic only, and even in-range predictions require expert confirmation.\n"
  },
  {
   "cell_type": "code",
   "id": "code-094",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Define a candidate prediction function.\ndef suggest_for_review(measurements):\n    numeric = validate_measurements(measurements)\n    outside = numeric.lt(training_ranges.loc['min']) | numeric.gt(training_ranges.loc['max'])\n    supported = ~outside.any(axis=1)\n    result = pd.DataFrame(index=numeric.index)\n    result['suggested_class'] = 'Abstain'\n    result['maximum_model_score'] = np.nan\n    result['review_status'] = 'Outside training range; expert review required'\n    if supported.any():\n        inputs = numeric.loc[supported]\n        result.loc[supported, 'suggested_class'] = [CLASS_NAMES[int(c)] for c in selected_model.predict(inputs)]\n        result.loc[supported, 'maximum_model_score'] = selected_model.predict_proba(inputs).max(axis=1)\n        result.loc[supported, 'review_status'] = 'Candidate only; expert confirmation required'\n    return result\n\n# Your note: \n",
   "execution_count": 94,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-205",
   "metadata": {},
   "source": "### Step 95 \u00b7 Create demonstration inputs\n\nThese examples are sampled from the existing holdout. The example does not provide an additional independent test.\n"
  },
  {
   "cell_type": "code",
   "id": "code-095",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Create demonstration inputs.\nexample_measurements = X_test.head(3).copy()\nexample_measurements\n\n# Your note: \n",
   "execution_count": 95,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 95,
     "data": {
      "text/plain": "     Temperature (K)  Luminosity (L/Lo)  Radius (R/Ro)  \\\n135             3243             0.0023          0.730   \n152            14060          1092.0000          5.745   \n73              3150             0.0088          0.350   \n\n     Absolute magnitude (Mv)  \n135                    14.75  \n152                    -2.04  \n73                     11.94  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature (K)</th>\n      <th>Luminosity (L/Lo)</th>\n      <th>Radius (R/Ro)</th>\n      <th>Absolute magnitude (Mv)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>135</th>\n      <td>3243</td>\n      <td>0.0023</td>\n      <td>0.730</td>\n      <td>14.75</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>14060</td>\n      <td>1092.0000</td>\n      <td>5.745</td>\n      <td>-2.04</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>3150</td>\n      <td>0.0088</td>\n      <td>0.350</td>\n      <td>11.94</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-207",
   "metadata": {},
   "source": "### Step 96 \u00b7 Generate candidate suggestions\n\nPredict from named columns so feature order remains explicit.\n"
  },
  {
   "cell_type": "code",
   "id": "code-096",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Generate candidate suggestions.\nexample_suggestions = suggest_for_review(example_measurements)\nexample_suggestions\n\n# Your note: \n",
   "execution_count": 96,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 96,
     "data": {
      "text/plain": "    suggested_class  maximum_model_score  \\\n135       Red Dwarf                  1.0   \n152   Main Sequence                  1.0   \n73        Red Dwarf                  1.0   \n\n                                    review_status  \n135  Candidate only; expert confirmation required  \n152  Candidate only; expert confirmation required  \n73   Candidate only; expert confirmation required  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>suggested_class</th>\n      <th>maximum_model_score</th>\n      <th>review_status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>135</th>\n      <td>Red Dwarf</td>\n      <td>1.0</td>\n      <td>Candidate only; expert confirmation required</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>Main Sequence</td>\n      <td>1.0</td>\n      <td>Candidate only; expert confirmation required</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Red Dwarf</td>\n      <td>1.0</td>\n      <td>Candidate only; expert confirmation required</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-209",
   "metadata": {},
   "source": "### Step 97 \u00b7 Demonstrate abstention for an unsupported magnitude\n\nUse the earlier magnitude value of 612 to show how the new interface handles extrapolation.\n"
  },
  {
   "cell_type": "code",
   "id": "code-097",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Demonstrate abstention for an unsupported magnitude.\nunsupported_example = example_measurements.iloc[[0]].copy()\nunsupported_example['Absolute magnitude (Mv)'] = 612.0\nsuggest_for_review(unsupported_example)\n\n# Your note: \n",
   "execution_count": 97,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 97,
     "data": {
      "text/plain": "    suggested_class  maximum_model_score  \\\n135         Abstain                  NaN   \n\n                                      review_status  \n135  Outside training range; expert review required  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>suggested_class</th>\n      <th>maximum_model_score</th>\n      <th>review_status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>135</th>\n      <td>Abstain</td>\n      <td>NaN</td>\n      <td>Outside training range; expert review required</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-211",
   "metadata": {},
   "source": "## 13. Translate the workflow into stakeholder value\n\n### Astronomy: organise catalogues and prioritise follow-up\n\nA larger, verified version of this workflow could propose labels for survey sources, identify ambiguous records for expert inspection, and help build candidate lists for particular stellar populations. Scientifically useful evaluation would include class-specific completeness, contamination, measurement uncertainty, extinction, unresolved binaries, and differences between instruments. Rare-object follow-up would use uncertainty and scientific value, rather than automatically observing whichever class has the highest model score.\n\nThis is a plausible application of the **method**, not a validated use of this fitted model. Actual survey work uses richer inputs. For context, the [Gaia Apsis methods paper](https://arxiv.org/abs/2206.05864) describes large-scale source classification and astrophysical parameter estimation from spectra, astrometry, and photometry. It establishes that the task is relevant; it does not validate this six-class tutorial or make its labels equivalent to Gaia's.\n\n### Business: sell dependable software and services around the workflow\n\nRelevant customers could include astronomy software providers, observatories buying data-processing services, and educational technology organisations. The value would come from reducing repetitive catalogue work, documenting quality, and providing usable review tools. A credible product would pair predictions with source metadata, validation evidence, an audit trail, and a way for users to correct suggestions.\n\nThe broader portfolio value is transferable: the same discipline applies to customer segmentation, document routing, quality inspection, and other labelled-data problems, once domain-specific data and risks are validated. The star classifier itself does not analyse customers or predict commercial demand.\n\n| Audience | Proposed decision support | Evidence to collect | Useful success metric |\n|---|---|---|---|\n| Observatory analyst | Triage a large incoming catalogue | Independent instrument/source-separated labels and error bars | Expert minutes per verified source; contamination by class |\n| Telescope team | Build candidate lists for a science programme | Position, visibility, spectrum, uncertainty, and scientific priority | Useful discoveries or confirmations per observing hour |\n| Astronomy software business | Offer a documented classification/review tool | Pilot users, representative error rates, running costs | Cost per verified record; correction rate; adoption |\n| Educational platform | Teach a complete supervised-learning workflow | Student feedback and assessment of learning | Completion and improvement in modelling reasoning |\n| Public research funder | Compare reproducible analysis outputs | Provenance, external validation, staff effort, and access | Cost per verified catalogue release; reproducibility |\n\n### Government: research capacity and accountable public investment\n\nFor South Africa, the strongest connection is public astronomy infrastructure and scientific skills. [SALT's official organisation page](https://www.salt.ac.za/about/) describes an international academic/government partnership, a research facility, and an education mission. **An inference from that context** is that verified analysis tools could support research productivity, student training, and reproducible publicly funded data products.\n\nFunders would need measured project outcomes: verified catalogues released, researcher hours per product, independent scientific use, training outcomes, and maintained software. A balanced 240-row file does not estimate the Galactic population, national economic benefit, or the return on an observatory's budget. Those questions need population-aware surveys and project financial/operational data.\n\n### Auditing perspective\n\nThe checksum, fixed split, class mapping, version records, exported predictions, and documented selection rule make the analysis inspectable. An auditor could trace a reported metric back to specific CSV rows and code. Scientific provenance remains incomplete, so the audit trail proves reproducibility of this computation rather than correctness of the underlying measurements. A real service would also retain source IDs, data-access permissions, model versions, human corrections, and release approval records.\n\n### Illustrative workload scenario \u2014 not a measured saving\n\nSuppose a future, externally validated system receives 10,000 records and a manual review takes three minutes per record. If some records can be routed through a validated assisted process, staff review the remainder plus a 5% quality sample of the routed records. Assume one second of serial model processing per record. The next cells compare **gross processing effort only**. They omit integration, model development, oversight, specialist follow-up, rework, and software costs. The routing fractions are assumptions, not fractions achieved by this notebook.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-212",
   "metadata": {},
   "source": "### Step 98 \u00b7 Specify workload assumptions\n\nKeep business assumptions separate from scientific measurements.\n"
  },
  {
   "cell_type": "code",
   "id": "code-098",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Specify workload assumptions.\nSCENARIO_RECORDS = 10_000\nMANUAL_MINUTES = 3.0\nASSISTED_SAMPLE_RATE = 0.05\nINFERENCE_SECONDS = 1.0\nASSUMED_ROUTING_FRACTIONS = [0.0, 0.5, 0.7, 0.9]\n\n# Your note: \n",
   "execution_count": 98,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-214",
   "metadata": {},
   "source": "### Step 99 \u00b7 Calculate hypothetical processing effort\n\nReview the non-routed records and sample the routed ones. Apply processing time only when the assisted system is used.\n"
  },
  {
   "cell_type": "code",
   "id": "code-099",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Calculate hypothetical processing effort.\nmanual_hours = SCENARIO_RECORDS * MANUAL_MINUTES / 60\nscenario_rows = []\nfor fraction in ASSUMED_ROUTING_FRACTIONS:\n    reviewed_fraction = 1 - fraction + fraction * ASSISTED_SAMPLE_RATE\n    review_hours = SCENARIO_RECORDS * reviewed_fraction * MANUAL_MINUTES / 60\n    processing_hours = SCENARIO_RECORDS * INFERENCE_SECONDS / 3600 if fraction > 0 else 0\n    scenario_rows.append({'assumed_routed_share': fraction, 'human_review_hours': review_hours,\n                          'serial_processing_hours': processing_hours,\n                          'gross_effort_hours': review_hours + processing_hours,\n                          'gross_hours_avoided': manual_hours - review_hours - processing_hours})\nworkload_scenarios = pd.DataFrame(scenario_rows)\nworkload_scenarios.round(2)\n\n# Your note: \n",
   "execution_count": 99,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 99,
     "data": {
      "text/plain": "   assumed_routed_share  human_review_hours  serial_processing_hours  \\\n0                   0.0               500.0                     0.00   \n1                   0.5               262.5                     2.78   \n2                   0.7               167.5                     2.78   \n3                   0.9                72.5                     2.78   \n\n   gross_effort_hours  gross_hours_avoided  \n0              500.00                 0.00  \n1              265.28               234.72  \n2              170.28               329.72  \n3               75.28               424.72  ",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>assumed_routed_share</th>\n      <th>human_review_hours</th>\n      <th>serial_processing_hours</th>\n      <th>gross_effort_hours</th>\n      <th>gross_hours_avoided</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>500.0</td>\n      <td>0.00</td>\n      <td>500.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.5</td>\n      <td>262.5</td>\n      <td>2.78</td>\n      <td>265.28</td>\n      <td>234.72</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.7</td>\n      <td>167.5</td>\n      <td>2.78</td>\n      <td>170.28</td>\n      <td>329.72</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.9</td>\n      <td>72.5</td>\n      <td>2.78</td>\n      <td>75.28</td>\n      <td>424.72</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-216",
   "metadata": {},
   "source": "### Step 100 \u00b7 Visualise the assumed workload\n\nThe chart is a sensitivity analysis. It does not establish ROI or the quality of automatically routed labels.\n"
  },
  {
   "cell_type": "code",
   "id": "code-100",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Visualise the assumed workload.\nfig, ax = plt.subplots(figsize=(8, 4))\nax.bar(workload_scenarios['assumed_routed_share'].map(lambda x: f'{x:.0%}'),\n       workload_scenarios['gross_effort_hours'], color='#16324F')\nax.axhline(manual_hours, linestyle='--', color='#C7353F', label='Manual-only assumption')\nax.set(title='Hypothetical workload \u2014 not measured business savings',\n       xlabel='Assumed share routed through assisted processing', ylabel='Gross effort (hours)')\nax.legend()\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / '12_workload_scenario.png', bbox_inches='tight')\nplt.show()\n\n# Your note: \n",
   "execution_count": 100,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": "<Figure size 880x440 with 1 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-218",
   "metadata": {},
   "source": "## 14. State what the evidence supports\n\n| Limitation | Why it matters | Next evidence needed |\n|---|---|---|\n| 240 curated records; 48 in the final test | A few cases substantially change the reported score | A larger independent test with a justified sampling design |\n| Equal class counts | Sample prevalence does not describe the sky or a customer's incoming records | Representative prevalence and class-specific evaluation |\n| No catalogue/source IDs or instrument metadata | Row-level disjointness cannot guarantee object/instrument independence | Source-, survey-, time-, or instrument-separated splits |\n| No measurement uncertainties | Input noise and derived-parameter uncertainty are unmodelled | Uncertainty-aware features and sensitivity analysis |\n| Simplified and potentially overlapping physical categories | Dataset labels may differ from a professional classification scheme | Expert-reviewed target definitions |\n| Luminosity/temperature definitions need confirmation | A physical consistency check relies on those definitions | Original metadata, passbands, and error bars |\n| Model scores are uncalibrated | High numeric scores may still be wrong | A separate calibration/development set and external verification |\n| Marginal range check only | In-range values may form unseen or implausible combinations | Joint distribution/OOD checks and specialist rules |\n| No actual business-cost data | A workload illustration does not demonstrate realised savings | A pilot with observed effort, quality, rework, and total costs |\n\n**What this portfolio demonstrates:** readable code, careful label handling, train/test discipline, appropriate preprocessing, comparative modelling, uncertainty reporting, reproducible exports, and a connection between technical evidence and decisions.\n\n**What it does not demonstrate:** confirmed discovery of new stellar types, a deployable survey classifier, reliable class-population estimates, or a measured financial return.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-219",
   "metadata": {},
   "source": "## 15. Export the results and preserve the audit trail\n\nThe output folder contains the selected fitted pipeline, charts, tables, holdout predictions, and metadata. The saved pipeline stays fitted on the 192 training records so its predictions can reproduce the final evaluation. It is not refitted on all 240 records.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-220",
   "metadata": {},
   "source": "### Step 101 \u00b7 Export evaluation tables\n\nCSV tables support a website, further review, or a report without copying numbers by hand.\n"
  },
  {
   "cell_type": "code",
   "id": "code-101",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Export evaluation tables.\ntest_metrics.to_csv(OUTPUT_DIR / 'model_comparison.csv')\nsearch_summary.to_csv(OUTPUT_DIR / 'training_search_summary.csv')\nnested_summary.to_csv(OUTPUT_DIR / 'nested_cv_summary.csv')\nnested_fold_scores.to_csv(OUTPUT_DIR / 'nested_cv_fold_scores.csv', index_label='outer_fold')\nclassification_report_table.to_csv(OUTPUT_DIR / 'selected_classification_report.csv')\n\n# Your note: \n",
   "execution_count": 101,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-222",
   "metadata": {},
   "source": "### Step 102 \u00b7 Export the record-level audit\n\nRetain model-specific predictions for every comparator as well as the selected model's scored audit.\n"
  },
  {
   "cell_type": "code",
   "id": "code-102",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Export the record-level audit.\nprediction_audit.to_csv(OUTPUT_DIR / 'holdout_prediction_audit.csv')\nall_model_predictions = pd.DataFrame(test_predictions, index=X_test.index)\nall_model_predictions.insert(0, 'actual_class_code', y_test)\nall_model_predictions.to_csv(OUTPUT_DIR / 'all_model_holdout_predictions.csv', index_label='csv_row_index')\n\n# Your note: \n",
   "execution_count": 102,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-224",
   "metadata": {},
   "source": "### Step 103 \u00b7 Export diagnostic tables\n\nKeep assumption checks, exploratory explanations, and hypothetical workloads identifiable.\n"
  },
  {
   "cell_type": "code",
   "id": "code-103",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Export diagnostic tables.\nnormality_table.to_csv(OUTPUT_DIR / 'training_normality_diagnostics.csv', index=False)\nphysics_audit.to_csv(OUTPUT_DIR / 'training_physics_diagnostic.csv', index_label='csv_row_index')\nimportance_table.to_csv(OUTPUT_DIR / 'exploratory_training_importance.csv', index=False)\nworkload_scenarios.to_csv(OUTPUT_DIR / 'hypothetical_workload_scenarios.csv', index=False)\ntraining_ranges.to_csv(OUTPUT_DIR / 'training_input_ranges.csv')\n\n# Your note: \n",
   "execution_count": 103,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-226",
   "metadata": {},
   "source": "### Step 104 \u00b7 Save the complete fitted pipeline\n\nSaving preprocessing with the classifier prevents inconsistent prediction inputs. Only load joblib files you trust.\n"
  },
  {
   "cell_type": "code",
   "id": "code-104",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Save the complete fitted pipeline.\njoblib.dump(selected_model, OUTPUT_DIR / 'selected_stellar_pipeline.joblib')\n\n# Your note: \n",
   "execution_count": 104,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 104,
     "data": {
      "text/plain": "['outputs/selected_stellar_pipeline.joblib']"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-228",
   "metadata": {},
   "source": "### Step 105 \u00b7 Check that the saved pipeline reproduces predictions\n\nReload the file just created and compare its holdout predictions with the recorded result.\n"
  },
  {
   "cell_type": "code",
   "id": "code-105",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Check that the saved pipeline reproduces predictions.\nreloaded_model = joblib.load(OUTPUT_DIR / 'selected_stellar_pipeline.joblib')\nassert np.array_equal(reloaded_model.predict(X_test), selected_predictions)\nprint('Reloaded pipeline reproduces all selected-model holdout predictions.')\n\n# Your note: \n",
   "execution_count": 105,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Reloaded pipeline reproduces all selected-model holdout predictions.\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-230",
   "metadata": {},
   "source": "### Step 106 \u00b7 Build reproducibility metadata\n\nPreserve exact row membership, versions, settings, selection criteria, and the uncertainty interval.\n"
  },
  {
   "cell_type": "code",
   "id": "code-106",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Build reproducibility metadata.\nrun_metadata = {\n    'project': 'Stellar Classification: From Data to Decisions', 'author': 'Gareth Grant',\n    'data_file': DATA_PATH.name, 'data_sha256': data_sha256,\n    'source_url_from_original_submission': 'https://www.kaggle.com/datasets/deepu1109/star-dataset',\n    'provenance_status': 'Uploaded file verified internally; catalogue lineage and observational metadata unavailable',\n    'python_version': platform.python_version(), 'package_versions': package_versions,\n    'random_seed': RANDOM_SEED, 'test_fraction': TEST_FRACTION,\n    'training_row_indices': sorted(map(int, X_train.index)),\n    'test_row_indices': sorted(map(int, X_test.index)),\n    'predictors': NUMERIC_FEATURES, 'classes': CLASS_NAMES,\n    'selection_rule': 'Highest training nested-CV mean macro F1; fixed family-order tie break',\n    'selected_model': winner_name, 'selected_parameters': searches[winner_name].best_params_,\n    'final_test_metrics': classification_metrics(y_test, selected_predictions),\n    'correct_test_records': correct_count, 'test_records': len(y_test),\n    'wilson_accuracy_interval_95': [accuracy_low, accuracy_high],\n    'macro_ovr_auc': test_auc, 'multiclass_log_loss': test_log_loss,\n    'model_fit_scope': '192 training rows only; final test excluded'\n}\n\n# Your note: \n",
   "execution_count": 106,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "id": "md-232",
   "metadata": {},
   "source": "### Step 107 \u00b7 Save the run metadata\n\nThe metadata makes the computation reviewable; it does not fill the gaps in scientific provenance.\n"
  },
  {
   "cell_type": "code",
   "id": "code-107",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Save the run metadata.\n(OUTPUT_DIR / 'run_metadata.json').write_text(json.dumps(run_metadata, indent=2), encoding='utf-8')\nprint('Saved run_metadata.json')\n\n# Your note: \n",
   "execution_count": 107,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Saved run_metadata.json\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-234",
   "metadata": {},
   "source": "## 16. Write the result as a website case study\n\nThe next cell creates a concise summary from the computed results. This avoids manually retaining a claim when code or data change. Use the complete tutorial for readers who want to inspect the code, and the short summary for visitors assessing your skills.\n"
  },
  {
   "cell_type": "markdown",
   "id": "md-235",
   "metadata": {},
   "source": "### Step 108 \u00b7 Generate a result-grounded portfolio summary\n\nThe wording describes the method and the observed holdout, while keeping future uses separate from demonstrated results.\n"
  },
  {
   "cell_type": "code",
   "id": "code-108",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Generate a result-grounded portfolio summary.\nportfolio_summary = (\n    '# Stellar classification: a reproducible machine-learning case study\\n\\n'\n    'I rebuilt a six-class classification workflow around 240 labelled stellar records. '\n    'The analysis audits the data, visualises physical relationships, and compares Naive Bayes, '\n    'KNN, and a decision tree with a no-skill baseline. Learned preprocessing stays inside '\n    'training folds; nested cross-validation selects the model family before a shared final test.\\n\\n'\n    f'The selected {winner_name} model classified {correct_count} of {len(y_test)} held-out records '\n    f'correctly ({selected_accuracy:.2%}), with macro F1 {selected_f1:.4f}. '\n    f'The approximate 95% Wilson accuracy interval is {accuracy_low:.2%} to {accuracy_high:.2%}. '\n    'These are results on a small curated dataset and do not establish accuracy on an independent survey.\\n\\n'\n    'The project demonstrates data auditing, reproducible Python, comparative modelling, '\n    'uncertainty reporting, explainable rules, and decision-focused interpretation. '\n    'Potential extensions include catalogue triage, expert review tools, and research education. '\n    'Operational or financial claims would require representative data and a measured pilot.\\n'\n)\nprint(portfolio_summary)\n\n# Your note: \n",
   "execution_count": 108,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "# Stellar classification: a reproducible machine-learning case study\n\nI rebuilt a six-class classification workflow around 240 labelled stellar records. The analysis audits the data, visualises physical relationships, and compares Naive Bayes, KNN, and a decision tree with a no-skill baseline. Learned preprocessing stays inside training folds; nested cross-validation selects the model family before a shared final test.\n\nThe selected Decision tree model classified 48 of 48 held-out records correctly (100.00%), with macro F1 1.0000. The approximate 95% Wilson accuracy interval is 92.59% to 100.00%. These are results on a small curated dataset and do not establish accuracy on an independent survey.\n\nThe project demonstrates data auditing, reproducible Python, comparative modelling, uncertainty reporting, explainable rules, and decision-focused interpretation. Potential extensions include catalogue triage, expert review tools, and research education. Operational or financial claims would require representative data and a measured pilot.\n\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-237",
   "metadata": {},
   "source": "### Step 109 \u00b7 Save the short website summary\n\nMarkdown is easy to edit and integrate into a portfolio content system.\n"
  },
  {
   "cell_type": "code",
   "id": "code-109",
   "metadata": {
    "tags": [
     "one-task-per-cell"
    ]
   },
   "source": "# Save the short website summary.\n(OUTPUT_DIR / 'portfolio_summary.md').write_text(portfolio_summary, encoding='utf-8')\n\n# Your note: \n",
   "execution_count": 109,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 109,
     "data": {
      "text/plain": "1049"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md-239",
   "metadata": {},
   "source": "## 17. Learning exercises and future work\n\nAdd your explanation in a nearby Markdown cell or in the `# Your note:` area. Use **training data only** for these experiments. Because the current holdout results have been viewed, a genuinely new final claim would need a fresh external test.\n\n1. **Explain scaling.** Why would raw luminosity dominate an ordinary Euclidean KNN distance? Compare a deliberately unscaled KNN using training-only CV; do not claim a new final-test improvement.\n2. **Interpret one confusion.** If two classes are confused, which measurement ranges overlap? Use training records to investigate hypotheses.\n3. **Inspect the assumptions.** Why does a large Shapiro\u2013Wilk p-value not prove normality? Why does a correlation matrix not establish conditional independence?\n4. **Extend the inputs.** Add colour and spectral class through a `ColumnTransformer` with one-hot encoding for nominal text. Audit how these labels were obtained and whether they would be available at prediction time. Do not impose arbitrary integer distances on colour names.\n5. **Study feature availability.** If radius or luminosity is expensive to obtain, compare a smaller feature set in development CV. Use a nested selection procedure if choosing features from scores, and document the acquisition cost.\n6. **Improve scientific validation.** Obtain a verified catalogue with identifiers and uncertainties, align class definitions with a specialist, and split by physical source and instrument where appropriate.\n7. **Validate a review policy.** Use separate development/calibration data, define the cost of false positives and missed rare sources, and measure recall and contamination on a new test.\n8. **Measure value.** Run a small pilot, record actual review time and corrections, and compare total costs with the manual process.\n\n## References and provenance notes\n\nThe source link below was inherited from the original submission; the locally supplied CSV is the file actually analysed. Its catalogue lineage and reuse terms are not contained in the upload. Verify the original data terms before redistributing it publicly. Method and application references were checked on 4 October 2026.\n\n| Reference | Supports |\n|---|---|\n| [Kaggle: Star dataset to predict star types](https://www.kaggle.com/datasets/deepu1109/star-dataset) | Source attribution in the original notebook; exact observational provenance remains unverified |\n| [scikit-learn: Common pitfalls](https://scikit-learn.org/stable/common_pitfalls.html) | Consistent preprocessing and prevention of data leakage |\n| [scikit-learn: Naive Bayes](https://scikit-learn.org/stable/modules/naive_bayes.html) | Conditional independence, Gaussian likelihoods, and probability-estimation caveats |\n| [scikit-learn: Nearest neighbours](https://scikit-learn.org/stable/modules/neighbors.html) | Neighbour-based classification |\n| [scikit-learn: Classification metrics](https://scikit-learn.org/stable/modules/model_evaluation.html) | Accuracy, per-class metrics, macro averages, ROC AUC, and log loss |\n| [scikit-learn: Nested versus non-nested CV](https://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html) | Separation of tuning and validation |\n| [SciPy: Shapiro\u2013Wilk](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html) | The normality null hypothesis and test interpretation |\n| [NASA: Types of stars](https://science.nasa.gov/universe/stars/types/) | Real stellar categories and their relationship to main-sequence stars |\n| [Pr\u0161a et al. (2016), IAU nominal solar values](https://arxiv.org/abs/1605.09788) | Nominal solar conversion constants used in the optional physics diagnostic |\n| [Creevey et al. (2023), Gaia Apsis methods](https://arxiv.org/abs/2206.05864) | Real astronomical classification and parameter-estimation context |\n| [SALT: About the organisation](https://www.salt.ac.za/about/) | South African research infrastructure and education context |\n\n**Reproducibility checklist:** input checksum recorded; no exact duplicate rows; row-wise class mapping verified; one fixed stratified holdout; fold-specific preprocessing; training-only tuning/selection; independent metrics per model; saved predictions and settings; fitted-pipeline reload verified.\n"
  }
 ],
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  "title": "Stellar Classification: From Data to Decisions",
  "authors": [
   {
    "name": "Gareth Grant"
   }
  ],
  "portfolio": {
   "source_notebook": "PDAN 8411 Task 2",
   "one_task_per_code_cell": true,
   "execution_method": "Clean Python process; sequential cell execution with captured outputs",
   "executed_code_cells": 109
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