{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "tp3-00",
   "metadata": {},
   "source": [
    "# Practical 3 — Linear regression and regularization\n",
    "\n",
    "In this practical, you will connect the equations of ordinary least squares to their numerical implementation, then compare OLS, Ridge and Lasso on a scientific regression problem.\n",
    "\n",
    "By the end of the session, you should be able to:\n",
    "\n",
    "- solve a small OLS problem from the normal equations;\n",
    "- build OLS, Ridge and Lasso estimators with scikit-learn;\n",
    "- explain why scaling matters for regularized linear models;\n",
    "- select a regularization strength without using the test set;\n",
    "- read Ridge and Lasso coefficient paths;\n",
    "- compare regression models against a simple baseline;\n",
    "- distinguish predictive coefficients from causal effects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tp3-01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.base import clone\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.linear_model import Lasso, LinearRegression, Ridge\n",
    "from sklearn.metrics import (\n",
    "    mean_absolute_error,\n",
    "    root_mean_squared_error,\n",
    "    r2_score,\n",
    ")\n",
    "from sklearn.model_selection import (\n",
    "    GridSearchCV,\n",
    "    KFold,\n",
    "    cross_val_score,\n",
    "    train_test_split,\n",
    ")\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "RANDOM_STATE = 42\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-02",
   "metadata": {},
   "source": [
    "## 1. OLS from the normal equations\n",
    "\n",
    "A laboratory instrument measures a signal $x_i$ and must predict a reference concentration $y_i$. We use the affine model\n",
    "\n",
    "$$\n",
    "\\widehat y_i = w x_i + b.\n",
    "$$\n",
    "\n",
    "To estimate both parameters with the matrix formulation, augment the design matrix with a column of ones:\n",
    "\n",
    "$$\n",
    "X_{\\mathrm{aug}}\n",
    "=\n",
    "\\begin{bmatrix}\n",
    "x_1 & 1\\\\\n",
    "\\vdots & \\vdots\\\\\n",
    "x_n & 1\n",
    "\\end{bmatrix},\n",
    "\\qquad\n",
    "\\boldsymbol\\theta=\n",
    "\\begin{bmatrix}w\\\\b\\end{bmatrix}.\n",
    "$$\n",
    "\n",
    "What are the dimensions of $X_{\\mathrm{aug}}$, $X_{\\mathrm{aug}}^\\top X_{\\mathrm{aug}}$ and $\\boldsymbol\\theta$ for the six observations below?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "tp3-03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_calibration = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])\n",
    "y_calibration = np.array([1.5, 2.0, 2.9, 3.3, 4.2, 4.6])\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.scatter(x_calibration, y_calibration, color=\"tab:blue\")\n",
    "plt.xlabel(\"Measured signal\")\n",
    "plt.ylabel(\"Reference concentration\")\n",
    "plt.title(\"Calibration observations\")\n",
    "plt.grid(alpha=0.25)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "tp3-04",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: build X_aug with the measurement column followed by a column of ones.\n",
    "# Expected shape: (6, 2)\n",
    "\n",
    "# TODO: compute A = X_aug.T @ X_aug and c = X_aug.T @ y_calibration.\n",
    "# What are the shapes of A and c?\n",
    "\n",
    "# TODO: solve A @ theta = c with np.linalg.solve.\n",
    "# Extract w_manual and b_manual from theta.\n",
    "\n",
    "# TODO: compute y_manual and residuals_manual.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "tp3-05",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_aug / A / c shapes: (6, 2) (2, 2) (2,)\n",
      "w = 1.2857, b = 0.8333\n",
      "X_aug.T @ residuals = [-2.22044605e-16  8.88178420e-16]\n",
      "Orthogonality verified: True\n"
     ]
    }
   ],
   "source": [
    "X_aug = np.column_stack((x_calibration, np.ones_like(x_calibration)))\n",
    "A = X_aug.T @ X_aug\n",
    "c = X_aug.T @ y_calibration\n",
    "theta = np.linalg.solve(A, c) # cette fonction a déjà été utilisée en M8 et ananum. Eviter d'utiliser np.linalg.inv pour résoudre le système linéaire.\n",
    "\n",
    "w_manual, b_manual = theta\n",
    "y_manual = X_aug @ theta\n",
    "residuals_manual = y_calibration - y_manual\n",
    "\n",
    "print(\"X_aug / A / c shapes:\", X_aug.shape, A.shape, c.shape)\n",
    "print(f\"w = {w_manual:.4f}, b = {b_manual:.4f}\")\n",
    "print(\"X_aug.T @ residuals =\", X_aug.T @ residuals_manual)\n",
    "print(\n",
    "    \"Orthogonality verified:\",\n",
    "    np.allclose(X_aug.T @ residuals_manual, 0.0),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "tp3-06",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# Read the LinearRegression documentation, then fit the same affine model.\n",
    "# TODO: reshape x_calibration as a two-dimensional design matrix.\n",
    "# TODO: instantiate and fit LinearRegression.\n",
    "# TODO: compare coef_, intercept_ and predictions with the manual solution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "tp3-07",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manual solution:       w=1.2857, b=0.8333\n",
      "scikit-learn solution: w=1.2857, b=0.8333\n",
      "Same predictions: True\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_calibration = x_calibration.reshape(-1, 1)\n",
    "calibration_model = LinearRegression()\n",
    "calibration_model.fit(X_calibration, y_calibration)\n",
    "y_sklearn = calibration_model.predict(X_calibration)\n",
    "\n",
    "print(f\"Manual solution:       w={w_manual:.4f}, b={b_manual:.4f}\")\n",
    "print(\n",
    "    \"scikit-learn solution: \"\n",
    "    f\"w={calibration_model.coef_[0]:.4f}, \"\n",
    "    f\"b={calibration_model.intercept_:.4f}\"\n",
    ")\n",
    "print(\"Same predictions:\", np.allclose(y_manual, y_sklearn))\n",
    "\n",
    "x_line = np.linspace(x_calibration.min(), x_calibration.max(), 100)\n",
    "y_line = calibration_model.predict(x_line.reshape(-1, 1))\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.scatter(x_calibration, y_calibration, label=\"observations\")\n",
    "plt.plot(x_line, y_line, color=\"tab:red\", label=\"OLS prediction\")\n",
    "plt.vlines(\n",
    "    x_calibration,\n",
    "    y_sklearn,\n",
    "    y_calibration,\n",
    "    color=\"tab:gray\",\n",
    "    linestyles=\"--\",\n",
    "    label=\"residuals\",\n",
    ")\n",
    "plt.xlabel(\"Measured signal\")\n",
    "plt.ylabel(\"Reference concentration\")\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.25)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-08",
   "metadata": {},
   "source": [
    "## 2. Scientific dataset and independent test set\n",
    "\n",
    "We now use scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html). Each row describes one patient through ten baseline clinical measurements. The continuous target is a quantitative measure of disease progression one year after baseline.\n",
    "\n",
    "We load the data with `scaled=False` to retain the original feature units. This differs from the default `scaled=True`, which returns centered and scaled features (see the documentation).\n",
    "\n",
    "*Disclaimer* : This is a **predictive exercise**. The fitted coefficients do not establish causal effects and must not be interpreted as medical recommendations.\n",
    "\n",
    "1. Is this a regression or classification problem?\n",
    "2. What is one observation?\n",
    "3. Which data must remain untouched during model selection?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "tp3-09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Development shape: (331, 10) (331,)\n",
      "Test shape: (111, 10) (111,)\n"
     ]
    }
   ],
   "source": [
    "diabetes = load_diabetes(scaled=False)\n",
    "X = diabetes.data\n",
    "y = diabetes.target\n",
    "feature_names = np.asarray(diabetes.feature_names)\n",
    "\n",
    "X_dev, X_test, y_dev, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.25,\n",
    "    random_state=RANDOM_STATE,\n",
    ")\n",
    "\n",
    "print(\"Development shape:\", X_dev.shape, y_dev.shape)\n",
    "print(\"Test shape:\", X_test.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "tp3-10",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# Use development data only in this section.\n",
    "# TODO: print the mean, standard deviation, minimum and maximum\n",
    "# of each feature with its name.\n",
    "\n",
    "# TODO: draw a boxplot of the development features.\n",
    "# TODO: draw a histogram of y_dev.\n",
    "\n",
    "# Why would coefficient penalties be unfair across variables\n",
    "# expressed on very different scales?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "tp3-11",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " feature       mean        std        min        max\n",
      "     age      48.80      12.77      19.00      79.00\n",
      "     sex       1.47       0.50       1.00       2.00\n",
      "     bmi      26.59       4.37      18.10      41.30\n",
      "      bp      95.02      13.89      62.00     133.00\n",
      "      s1     189.55      34.93     110.00     301.00\n",
      "      s2     115.47      30.80      41.60     242.40\n",
      "      s3      49.63      12.77      22.00      99.00\n",
      "      s4       4.09       1.31       2.00       9.09\n",
      "      s5       4.66       0.53       3.26       6.11\n",
      "      s6      92.05      11.45      58.00     124.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge and Lasso penalize coefficient magnitudes. Those magnitudes depend on the units used for each feature, so the features should be standardized consistently.\n"
     ]
    }
   ],
   "source": [
    "feature_statistics = np.column_stack(\n",
    "    (\n",
    "        X_dev.mean(axis=0),\n",
    "        X_dev.std(axis=0),\n",
    "        X_dev.min(axis=0),\n",
    "        X_dev.max(axis=0),\n",
    "    )\n",
    ")\n",
    "\n",
    "print(f\"{'feature':>8} {'mean':>10} {'std':>10} {'min':>10} {'max':>10}\")\n",
    "for name, values in zip(feature_names, feature_statistics):\n",
    "    print(\n",
    "        f\"{name:>8} {values[0]:10.2f} {values[1]:10.2f} \"\n",
    "        f\"{values[2]:10.2f} {values[3]:10.2f}\"\n",
    "    )\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n",
    "axes[0].boxplot(X_dev, tick_labels=feature_names)\n",
    "axes[0].tick_params(axis=\"x\", rotation=45)\n",
    "axes[0].set_title(\"Development feature distributions\")\n",
    "axes[0].set_ylabel(\"Original units\")\n",
    "\n",
    "axes[1].hist(y_dev, bins=20, edgecolor=\"black\")\n",
    "axes[1].set_title(\"Development target distribution\")\n",
    "axes[1].set_xlabel(\"Disease progression target\")\n",
    "axes[1].set_ylabel(\"Count\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\n",
    "    \"Ridge and Lasso penalize coefficient magnitudes. \"\n",
    "    \"Those magnitudes depend on the units used for each feature, \"\n",
    "    \"so the features should be standardized consistently.\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-12",
   "metadata": {},
   "source": [
    "## 3. OLS reference model and residuals\n",
    "\n",
    "Feature scaling does not change OLS predictions when the model includes an intercept, although it changes the numerical coefficients. We nevertheless use the same preprocessing structure for all three models so that their coefficients are expressed on comparable standardized features.\n",
    "\n",
    "Documentation: [`Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html), and [`LinearRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "tp3-13",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: create a Pipeline containing StandardScaler and LinearRegression.\n",
    "# Fit it on X_dev, y_dev only.\n",
    "# Compute development predictions and report MAE, RMSE and R².\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "tp3-14",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Development MAE:  44.055\n",
      "Development RMSE: 53.919\n",
      "Development R²:   0.519\n"
     ]
    }
   ],
   "source": [
    "ols_pipeline = Pipeline([\n",
    "    (\"scale\", StandardScaler()),\n",
    "    (\"regressor\", LinearRegression()),\n",
    "])\n",
    "ols_pipeline.fit(X_dev, y_dev)\n",
    "y_dev_ols = ols_pipeline.predict(X_dev)\n",
    "\n",
    "print(f\"Development MAE:  {mean_absolute_error(y_dev, y_dev_ols):.3f}\")\n",
    "print(f\"Development RMSE: {root_mean_squared_error(y_dev, y_dev_ols):.3f}\")\n",
    "print(f\"Development R²:   {r2_score(y_dev, y_dev_ols):.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "tp3-15",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: compute the development residuals y_dev - y_dev_ols.\n",
    "# Plot them against the fitted values and add a horizontal line at zero.\n",
    "# Look for systematic structure, changes in spread and isolated residuals.\n",
    "# Why is this not a final estimate of generalization performance?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "tp3-16",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The plot may reveal patterns worth investigating, but visual inspection does not prove all model assumptions. These are training residuals, so they are not an independent generalization estimate.\n"
     ]
    }
   ],
   "source": [
    "residuals_dev = y_dev - y_dev_ols\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.scatter(y_dev_ols, residuals_dev, alpha=0.7)\n",
    "plt.axhline(0.0, color=\"black\", linestyle=\"--\")\n",
    "plt.xlabel(\"Fitted value\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.title(\"OLS residuals on development data\")\n",
    "plt.grid(alpha=0.2)\n",
    "plt.show()\n",
    "\n",
    "print(\n",
    "    \"The plot may reveal patterns worth investigating, but visual inspection \"\n",
    "    \"does not prove all model assumptions. These are training residuals, so \"\n",
    "    \"they are not an independent generalization estimate.\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-17",
   "metadata": {},
   "source": [
    "## 4. Ridge and Lasso pipelines\n",
    "\n",
    "- **Ridge** shrinks coefficients and is often stable when predictors are correlated.\n",
    "- **Lasso** also shrinks coefficients and may set some of them exactly to zero.\n",
    "- Both penalties act on coefficient magnitudes, so the features must be scaled consistently.\n",
    "\n",
    "Documentation: [`Ridge`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) and [`Lasso`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tp3-18",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: build ridge_pipeline and lasso_pipeline.\n",
    "# Each pipeline must contain a fresh StandardScaler followed by a regressor.\n",
    "# Start with alpha=1.0 and use a sufficiently large max_iter for Lasso. \n",
    "# What does alpha represent?\n",
    "\n",
    "# Fit one temporary pipeline on development data.\n",
    "# Check that its scaler.mean_ equals X_dev.mean(axis=0) (not statistics estimated from the complete dataset). \n",
    "\n",
    "# Why must the scaler remain inside the object passed to cross-validation?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tp3-19",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaler fitted on development data: True\n",
      "Largest difference from full-data means: 0.7851362250686975\n",
      "Pipeline steps: {'scale': StandardScaler(), 'regressor': Ridge()}\n"
     ]
    }
   ],
   "source": [
    "ridge_pipeline = Pipeline([\n",
    "    (\"scale\", StandardScaler()),\n",
    "    (\"regressor\", Ridge(alpha=1.0)),\n",
    "])\n",
    "lasso_pipeline = Pipeline([\n",
    "    (\"scale\", StandardScaler()),\n",
    "    (\"regressor\", Lasso(alpha=1.0, max_iter=50_000)),\n",
    "])\n",
    "\n",
    "ridge_pipeline.fit(X_dev, y_dev)\n",
    "fitted_scaler = ridge_pipeline.named_steps[\"scale\"]\n",
    "\n",
    "print(\n",
    "    \"Scaler fitted on development data:\",\n",
    "    np.allclose(fitted_scaler.mean_, X_dev.mean(axis=0)),\n",
    ")\n",
    "\n",
    "print(\"Pipeline steps:\", ridge_pipeline.named_steps)\n",
    "\n",
    "# During cross-validation, Pipeline refits the scaler on each training fold.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-20",
   "metadata": {},
   "source": [
    "## 5. Select the regularization strengths\n",
    "\n",
    "Use a five-fold shuffled `KFold` on the development set. RMSE is the primary selection metric and the final test set remains untouched.\n",
    "\n",
    "We explore logarithmic grids because useful regularization strengths may span several orders of magnitude:\n",
    "\n",
    "```python\n",
    "ridge_alphas = np.logspace(-4, 4, 41)\n",
    "lasso_alphas = np.logspace(-4, 1, 41)\n",
    "```\n",
    "\n",
    "scikit-learn maximizes scores. The search therefore uses `scoring=\"neg_root_mean_squared_error\"` (RTFM). Negate the reported score to recover RMSE.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "tp3-21",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "ridge_alphas = np.logspace(-4, 4, 41)\n",
    "lasso_alphas = np.logspace(-4, 1, 41)\n",
    "\n",
    "# TODO: instantiate KFold with five folds, shuffling and RANDOM_STATE.\n",
    "# TODO: create one GridSearchCV for each pipeline.\n",
    "# Address alpha with the parameter name regressor__alpha.\n",
    "# TODO: fit both searches on development data only.\n",
    "# Print each selected alpha and its positive cross-validated RMSE.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "tp3-22",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge: alpha=1.585, CV RMSE=56.069\n",
      "Lasso: alpha=0.1, CV RMSE=56.056\n",
      "Lasso iterations: 175\n"
     ]
    }
   ],
   "source": [
    "cv = KFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
    "\n",
    "ridge_search = GridSearchCV(\n",
    "    ridge_pipeline,\n",
    "    param_grid={\"regressor__alpha\": ridge_alphas},\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    cv=cv,\n",
    ")\n",
    "lasso_search = GridSearchCV(\n",
    "    lasso_pipeline,\n",
    "    param_grid={\"regressor__alpha\": lasso_alphas},\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    cv=cv,\n",
    ")\n",
    "\n",
    "ridge_search.fit(X_dev, y_dev)\n",
    "lasso_search.fit(X_dev, y_dev)\n",
    "\n",
    "print(\n",
    "    f\"Ridge: alpha={ridge_search.best_params_['regressor__alpha']:.4g}, \"\n",
    "    f\"CV RMSE={-ridge_search.best_score_:.3f}\"\n",
    ")\n",
    "print(\n",
    "    f\"Lasso: alpha={lasso_search.best_params_['regressor__alpha']:.4g}, \"\n",
    "    f\"CV RMSE={-lasso_search.best_score_:.3f}\"\n",
    ")\n",
    "print(\n",
    "    \"Lasso iterations:\",\n",
    "    lasso_search.best_estimator_.named_steps[\"regressor\"].n_iter_,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "tp3-23",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: evaluate a mean DummyRegressor and the OLS pipeline with\n",
    "# cross_val_score, using the same cv object and scoring metric.\n",
    "# Compare their mean RMSE with the best Ridge and Lasso validation RMSE.\n",
    "# Why must all models use the same folds?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "tp3-24",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean baseline: CV RMSE = 78.142\n",
      "          OLS: CV RMSE = 56.119\n",
      "        Ridge: CV RMSE = 56.069\n",
      "        Lasso: CV RMSE = 56.056\n",
      "Small numerical differences should not automatically be interpreted as meaningful improvements.\n"
     ]
    }
   ],
   "source": [
    "dummy = DummyRegressor(strategy=\"mean\")\n",
    "\n",
    "dummy_scores = cross_val_score(\n",
    "    dummy,\n",
    "    X_dev,\n",
    "    y_dev,\n",
    "    cv=cv,\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    ")\n",
    "ols_scores = cross_val_score(\n",
    "    ols_pipeline,\n",
    "    X_dev,\n",
    "    y_dev,\n",
    "    cv=cv,\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    ")\n",
    "\n",
    "cv_rmse = {\n",
    "    \"Mean baseline\": -dummy_scores.mean(),\n",
    "    \"OLS\": -ols_scores.mean(),\n",
    "    \"Ridge\": -ridge_search.best_score_,\n",
    "    \"Lasso\": -lasso_search.best_score_,\n",
    "}\n",
    "for name, value in cv_rmse.items():\n",
    "    print(f\"{name:>13}: CV RMSE = {value:.3f}\")\n",
    "\n",
    "print(\n",
    "    \"Small numerical differences should not automatically be interpreted \"\n",
    "    \"as meaningful improvements.\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-25",
   "metadata": {},
   "source": [
    "## 6. Coefficient paths and interpretation\n",
    "\n",
    "Track the standardized coefficients as the regularization strength changes. Every model below is fitted on development data. The paths help inspect model behaviour; they are not a test evaluation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "tp3-26",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "ridge_path_alphas = np.logspace(-4, 4, 50)\n",
    "lasso_path_alphas = np.logspace(-4, 2, 50)\n",
    "\n",
    "# TODO: for every Ridge alpha, fit a cloned pipeline on development data\n",
    "# and store regressor.coef_. Repeat for Lasso.\n",
    "\n",
    "# TODO: produce two plots with logarithmic alpha axes.\n",
    "# Use feature_names in the legends and label both axes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "tp3-27",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge_coefficients = []\n",
    "for alpha in ridge_path_alphas:\n",
    "    model = clone(ridge_pipeline).set_params(regressor__alpha=alpha)\n",
    "    model.fit(X_dev, y_dev)\n",
    "    ridge_coefficients.append(model.named_steps[\"regressor\"].coef_)\n",
    "\n",
    "lasso_coefficients = []\n",
    "for alpha in lasso_path_alphas:\n",
    "    model = clone(lasso_pipeline).set_params(regressor__alpha=alpha)\n",
    "    model.fit(X_dev, y_dev)\n",
    "    lasso_coefficients.append(model.named_steps[\"regressor\"].coef_)\n",
    "\n",
    "ridge_coefficients = np.asarray(ridge_coefficients)\n",
    "lasso_coefficients = np.asarray(lasso_coefficients)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "axes[0].plot(ridge_path_alphas, ridge_coefficients)\n",
    "axes[0].set_xscale(\"log\")\n",
    "axes[0].set_title(\"Ridge coefficient paths\")\n",
    "axes[0].set_xlabel(\"alpha\")\n",
    "axes[0].set_ylabel(\"Coefficient on standardized feature\")\n",
    "\n",
    "axes[1].plot(lasso_path_alphas, lasso_coefficients)\n",
    "axes[1].set_xscale(\"log\")\n",
    "axes[1].set_title(\"Lasso coefficient paths\")\n",
    "axes[1].set_xlabel(\"alpha\")\n",
    "axes[1].set_ylabel(\"Coefficient on standardized feature\")\n",
    "\n",
    "for axis in axes:\n",
    "    axis.axhline(0.0, color=\"black\", linewidth=0.8)\n",
    "    axis.grid(alpha=0.2)\n",
    "    axis.legend(feature_names, fontsize=\"small\", ncol=2)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-28",
   "metadata": {},
   "source": [
    "### Interpret the paths\n",
    "\n",
    "1. What happens to Ridge coefficients as alpha increases?\n",
    "2. Which Lasso coefficients become exactly zero?\n",
    "3. Are all coefficient trajectories necessarily monotonic?\n",
    "4. Why does a zero Lasso coefficient not prove that the corresponding variable has no scientific relevance?\n",
    "5. Why are coefficients easier to compare after standardization?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-29",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "source": [
    "### Proposed interpretation\n",
    "\n",
    "1. Ridge progressively shrinks the coefficient vector toward zero. It generally retains all variables rather than producing exact zeros.\n",
    "2. Lasso produces an increasingly sparse vector as alpha grows. The exact variables removed depend on the sample, the value of alpha and correlations among predictors.\n",
    "3. Individual trajectories are not guaranteed to be monotonic even when the overall amount of shrinkage increases.\n",
    "4. With correlated variables, Lasso may retain one representative and discard another carrying similar information. A zero predictive coefficient is not evidence that a variable has no scientific role.\n",
    "5. After standardization, one unit corresponds to one development-set standard deviation for every feature. Coefficient magnitudes then refer to comparable changes in the inputs, although they remain predictive associations rather than causal effects.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-30",
   "metadata": {},
   "source": [
    "## 7. Freeze the models and evaluate once\n",
    "\n",
    "Before revealing the test results, write down:\n",
    "\n",
    "- the OLS, selected Ridge and selected Lasso learning procedures;\n",
    "- the selected alpha values;\n",
    "- RMSE as the primary metric and the mean-prediction baseline;\n",
    "- one limitation of this experiment.\n",
    "\n",
    "Then evaluate exactly once on `X_test`, `y_test`:\n",
    "\n",
    "- the mean-prediction baseline;\n",
    "- OLS fitted on all development data;\n",
    "- `ridge_search.best_estimator_`;\n",
    "- `lasso_search.best_estimator_`.\n",
    "\n",
    "Report MAE, RMSE and $R^2$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "tp3-31",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "def regression_metrics(y_true, y_pred):\n",
    "    # TODO: return MAE, RMSE and R² in a dictionary.\n",
    "    return {}\n",
    "\n",
    "\n",
    "# TODO: fit the baseline and OLS on all development data.\n",
    "# Ridge and Lasso were refitted on all development data by GridSearchCV.\n",
    "# Evaluate the four frozen models on the test set exactly once.\n",
    "# Print a compact aligned comparison without pandas.\n",
    "\n",
    "# TODO: report the number of nonzero coefficients in the selected Lasso.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "tp3-32",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        model        MAE       RMSE         R²\n",
      "Mean baseline     65.527     74.881     -0.014\n",
      "          OLS     41.549     53.370      0.485\n",
      "        Ridge     41.491     53.302      0.486\n",
      "        Lasso     41.485     53.282      0.487\n",
      "\n",
      "Nonzero Lasso coefficients: 9/10\n"
     ]
    }
   ],
   "source": [
    "def regression_metrics(y_true, y_pred):\n",
    "    return {\n",
    "        \"MAE\": mean_absolute_error(y_true, y_pred),\n",
    "        \"RMSE\": root_mean_squared_error(y_true, y_pred),\n",
    "        \"R2\": r2_score(y_true, y_pred),\n",
    "    }\n",
    "\n",
    "\n",
    "final_models = {\n",
    "    \"Mean baseline\": DummyRegressor(strategy=\"mean\").fit(X_dev, y_dev),\n",
    "    \"OLS\": clone(ols_pipeline).fit(X_dev, y_dev),\n",
    "    \"Ridge\": ridge_search.best_estimator_,\n",
    "    \"Lasso\": lasso_search.best_estimator_,\n",
    "}\n",
    "\n",
    "final_results = {}\n",
    "for name, model in final_models.items():\n",
    "    final_results[name] = regression_metrics(y_test, model.predict(X_test))\n",
    "\n",
    "print(f\"{'model':>13} {'MAE':>10} {'RMSE':>10} {'R²':>10}\")\n",
    "for name, metrics in final_results.items():\n",
    "    print(\n",
    "        f\"{name:>13} {metrics['MAE']:10.3f} \"\n",
    "        f\"{metrics['RMSE']:10.3f} {metrics['R2']:10.3f}\"\n",
    "    )\n",
    "\n",
    "lasso_coef = final_models[\"Lasso\"].named_steps[\"regressor\"].coef_\n",
    "nonzero_count = np.count_nonzero(np.abs(lasso_coef) > 1e-10)\n",
    "print(f\"\\nNonzero Lasso coefficients: {nonzero_count}/{lasso_coef.size}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-33",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "### Final interpretation\n",
    "\n",
    "Complete these statements in your own words.\n",
    "\n",
    "1. Ridge differs from OLS because ...\n",
    "2. Lasso differs from Ridge because ...\n",
    "3. The selected alpha was chosen without test leakage because ...\n",
    "4. The coefficient values do not prove causal effects because ...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-34",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "source": [
    "### Proposed takeaways\n",
    "\n",
    "1. Ridge minimizes squared error together with an $L_2$ penalty, whereas OLS minimizes squared error alone.\n",
    "2. Lasso uses an $L_1$ penalty and can produce exact zeros; Ridge usually retains every coefficient.\n",
    "3. The scaler and regressors were fitted inside cross-validation on development folds, and alpha was fixed before the final test evaluation.\n",
    "4. The coefficients describe predictive associations conditional on the chosen variables and model. Confounding, correlated predictors and the observational design prevent a causal conclusion.\n",
    "\n",
    "The test results concern one finite split. A small numerical advantage on this test sample does not establish that one model is universally superior.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp3-35",
   "metadata": {
    "tags": [
     "optional"
    ]
   },
   "source": [
    "---\n",
    "\n",
    "The remainder of this practical is optional and can be completed as bonus work.\n",
    "\n",
    "---\n",
    "\n",
    "## 8. Optional — Coefficient stability under resampling\n",
    "\n",
    "Repeatedly resample the development observations, refit OLS, the selected Ridge model and the selected Lasso model, then compare the variation of their standardized coefficients.\n",
    "\n",
    "Before running the experiment, predict which model should have the most stable coefficients.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "tp3-36",
   "metadata": {
    "tags": [
     "exercise",
     "optional"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO (optional): use a fixed NumPy generator and 100 bootstrap samples.\n",
    "# For each sample, clone and fit ols_pipeline, ridge_search.best_estimator_\n",
    "# and lasso_search.best_estimator_.\n",
    "# Store the standardized coefficient vectors.\n",
    "# Compare their feature-wise standard deviations.\n",
    "\n",
    "# Do not evaluate the final test set inside this loop.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "tp3-37",
   "metadata": {
    "tags": [
     "correction",
     "optional"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  OLS: mean coefficient std = 7.467\n",
      "Ridge: mean coefficient std = 5.944\n",
      "Lasso: mean coefficient std = 6.634\n"
     ]
    }
   ],
   "source": [
    "bootstrap_rng = np.random.default_rng(RANDOM_STATE)\n",
    "bootstrap_models = {\n",
    "    \"OLS\": ols_pipeline,\n",
    "    \"Ridge\": ridge_search.best_estimator_,\n",
    "    \"Lasso\": lasso_search.best_estimator_,\n",
    "}\n",
    "bootstrap_coefficients = {\n",
    "    name: [] for name in bootstrap_models\n",
    "}\n",
    "\n",
    "for _ in range(100):\n",
    "    indices = bootstrap_rng.integers(0, len(X_dev), size=len(X_dev))\n",
    "    X_boot = X_dev[indices]\n",
    "    y_boot = y_dev[indices]\n",
    "\n",
    "    for name, model in bootstrap_models.items():\n",
    "        fitted_model = clone(model).fit(X_boot, y_boot)\n",
    "        coefficients = fitted_model.named_steps[\"regressor\"].coef_\n",
    "        bootstrap_coefficients[name].append(coefficients)\n",
    "\n",
    "coefficient_std = {\n",
    "    name: np.asarray(values).std(axis=0)\n",
    "    for name, values in bootstrap_coefficients.items()\n",
    "}\n",
    "\n",
    "positions = np.arange(len(feature_names))\n",
    "width = 0.25\n",
    "plt.figure(figsize=(12, 5))\n",
    "for offset, (name, values) in enumerate(coefficient_std.items()):\n",
    "    plt.bar(\n",
    "        positions + (offset - 1) * width,\n",
    "        values,\n",
    "        width=width,\n",
    "        label=name,\n",
    "    )\n",
    "plt.xticks(positions, feature_names)\n",
    "plt.ylabel(\"Bootstrap standard deviation of coefficient\")\n",
    "plt.title(\"Coefficient stability across bootstrap samples\")\n",
    "plt.legend()\n",
    "plt.grid(axis=\"y\", alpha=0.2)\n",
    "plt.show()\n",
    "\n",
    "for name, values in coefficient_std.items():\n",
    "    print(f\"{name:>5}: mean coefficient std = {values.mean():.3f}\")\n",
    "\n",
    "# This experiment studies coefficient stability, not test performance.\n",
    "# Ridge often reduces coefficient variability. Lasso can change which\n",
    "# member of a correlated group it selects across bootstrap samples.\n"
   ]
  }
 ],
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   "display_name": "Python (cours-ml)",
   "language": "python",
   "name": "cours-ml"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.20"
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