{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "tp6-000",
   "metadata": {},
   "source": [
    "# Practical 6 — Kernel SVM: features, model selection and prediction\n",
    "\n",
    "Objectives: connect explicit features and kernels, evaluate Gram matrices, visualize the interaction of C and gamma, select the whole pipeline with CV, reconstruct predictions and distinguish fitting flexibility from generalization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tp6-001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:10.530444Z",
     "iopub.status.busy": "2026-10-05T19:23:10.530092Z",
     "iopub.status.idle": "2026-10-05T19:23:11.188754Z",
     "shell.execute_reply": "2026-10-05T19:23:11.188148Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer, make_blobs, make_circles\n",
    "from sklearn.dummy import DummyClassifier, DummyRegressor\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.metrics import (accuracy_score, confusion_matrix, recall_score,\n",
    "                             roc_auc_score, root_mean_squared_error)\n",
    "from sklearn.metrics.pairwise import rbf_kernel\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC, SVR, OneClassSVM\n",
    "\n",
    "SEED = 42\n",
    "BLUE, RED = \"#2474b5\", \"#e30613\"\n",
    "plt.rcParams.update({\"figure.figsize\": (7, 4), \"font.size\": 11})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "tp6-002",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.190416Z",
     "iopub.status.busy": "2026-10-05T19:23:11.190260Z",
     "iopub.status.idle": "2026-10-05T19:23:11.193962Z",
     "shell.execute_reply": "2026-10-05T19:23:11.193563Z"
    }
   },
   "outputs": [],
   "source": [
    "def boundary_plot(ax, estimator, X, y, supports=None, levels=(0,)):\n",
    "    low, high = X.min(axis=0) - .6, X.max(axis=0) + .6\n",
    "    xx, yy = np.meshgrid(np.linspace(low[0], high[0], 150),\n",
    "                         np.linspace(low[1], high[1], 150))\n",
    "    grid = np.column_stack((xx.ravel(), yy.ravel()))\n",
    "    z = estimator.decision_function(grid).reshape(xx.shape)\n",
    "    ax.contourf(xx, yy, z, levels=[-np.inf, 0, np.inf], colors=[BLUE, RED], alpha=.1)\n",
    "    visible = [v for v in levels if z.min() < v < z.max()]\n",
    "    if visible:\n",
    "        ax.contour(xx, yy, z, levels=visible, colors=\"black\",\n",
    "                   linestyles=[\"-\" if v == 0 else \"--\" for v in visible])\n",
    "    for label, color in ((-1, BLUE), (1, RED)):\n",
    "        ax.scatter(*X[y == label].T, color=color, s=20, label=f\"y={label:+d}\")\n",
    "    if supports is not None:\n",
    "        ax.scatter(*supports.T, s=80, facecolors=\"none\", edgecolors=\"black\")\n",
    "    ax.set(xlabel=\"$x_1$\", ylabel=\"$x_2$\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-003",
   "metadata": {},
   "source": [
    "## 1. A nonlinear classification problem — 10 min\n",
    "\n",
    "Generate noisy concentric circles using `make_circles` and split raw inputs into development/test subsets. Plot development observations only. Fit a scaled linear SVM on development data and inspect its boundary. Why is a straight boundary a poor representation for this problem?\n",
    "\n",
    "All boundary plots in the core use **development observations**. They illustrate geometry and never replace CV or test evaluation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "tp6-004",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.195123Z",
     "iopub.status.busy": "2026-10-05T19:23:11.195040Z",
     "iopub.status.idle": "2026-10-05T19:23:11.198072Z",
     "shell.execute_reply": "2026-10-05T19:23:11.197643Z"
    }
   },
   "outputs": [],
   "source": [
    "X, labels = make_circles(n_samples=400, factor=.4, noise=.18, random_state=SEED)\n",
    "y = 2*labels-1\n",
    "X_dev, X_test, y_dev, y_test = train_test_split(X,y,test_size=.25,stratify=y,random_state=SEED)\n",
    "cv = StratifiedKFold(5, shuffle=True, random_state=SEED)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "tp6-005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.199282Z",
     "iopub.status.busy": "2026-10-05T19:23:11.199179Z",
     "iopub.status.idle": "2026-10-05T19:23:11.200750Z",
     "shell.execute_reply": "2026-10-05T19:23:11.200345Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: plot X_dev by class, then fit a scaled linear SVC(C=1).\n",
    "# Use boundary_plot in raw coordinates with the fitted pipeline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "tp6-006",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.201807Z",
     "iopub.status.busy": "2026-10-05T19:23:11.201695Z",
     "iopub.status.idle": "2026-10-05T19:23:11.441846Z",
     "shell.execute_reply": "2026-10-05T19:23:11.441301Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_reference = Pipeline([(\"scale\",StandardScaler()),(\"model\",SVC(kernel=\"linear\",C=1))]).fit(X_dev,y_dev)\n",
    "fig,ax=plt.subplots()\n",
    "boundary_plot(ax,linear_reference,X_dev,y_dev)\n",
    "ax.set_title(\"Linear reference on development observations\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-007",
   "metadata": {},
   "source": [
    "## 2. Explicit features, implicit product — 15 min\n",
    "\n",
    "Implement $\\phi(x)=(x_1^2,\\sqrt2 x_1x_2,x_2^2)$. For $x=(1,2)$ and $x'=(3,1)$, compare $\\phi(x)^T\\phi(x')$ with $(x^Tx')^2$.\n",
    "\n",
    "On development inputs scaled **once**, compare `SVC(kernel=\"linear\")` on phi features with `SVC(kernel=\"poly\", degree=2, gamma=1, coef0=0)` on the scaled inputs. Use C=1 and tol=1e-8. Do not standardize phi again: that would change the kernel being compared. These are identity checks; later CV uses fold-specific pipelines.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "tp6-008",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.443177Z",
     "iopub.status.busy": "2026-10-05T19:23:11.443081Z",
     "iopub.status.idle": "2026-10-05T19:23:11.444776Z",
     "shell.execute_reply": "2026-10-05T19:23:11.444403Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: implement phi(X), accepting an (n,2) array.\n",
    "# TODO: verify the lecture product and full Gram identity.\n",
    "# TODO: fit the explicit/implicit models and compare development decision scores.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "tp6-009",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.446206Z",
     "iopub.status.busy": "2026-10-05T19:23:11.446111Z",
     "iopub.status.idle": "2026-10-05T19:23:11.453133Z",
     "shell.execute_reply": "2026-10-05T19:23:11.452755Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explicit: 25.0 implicit: 25.0\n",
      "Explicit and implicit models agree.\n"
     ]
    }
   ],
   "source": [
    "def phi(X):\n",
    "    X = np.atleast_2d(X)\n",
    "    return np.column_stack((X[:,0]**2, np.sqrt(2)*X[:,0]*X[:,1], X[:,1]**2))\n",
    "x,xp=np.array([1.,2.]),np.array([3.,1.])\n",
    "product=(phi(x) @ phi(xp).T)[0,0]\n",
    "print(\"Explicit:\",product,\"implicit:\",(x @ xp)**2)\n",
    "assert np.isclose(product,25)\n",
    "feature_scaler=StandardScaler().fit(X_dev)\n",
    "Z_dev=feature_scaler.transform(X_dev)\n",
    "assert np.allclose(phi(Z_dev) @ phi(Z_dev).T,(Z_dev @ Z_dev.T)**2)\n",
    "explicit=SVC(kernel=\"linear\",C=1,tol=1e-8).fit(phi(Z_dev),y_dev)\n",
    "implicit=SVC(kernel=\"poly\",degree=2,gamma=1,coef0=0,C=1,tol=1e-8).fit(Z_dev,y_dev)\n",
    "assert np.allclose(explicit.decision_function(phi(Z_dev)),implicit.decision_function(Z_dev),atol=1e-4)\n",
    "print(\"Explicit and implicit models agree.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-010",
   "metadata": {},
   "source": [
    "## 3. C and gamma: inspect flexibility — 15 min\n",
    "\n",
    "For RBF, $k(x,x')=e^{-\\gamma\\|x-x'\\|^2}$. Fit six scaled pipelines on development data with C in `[.1,100]` and gamma in `[.1,1,20]`. Plot the zero-score boundaries and count supports.\n",
    "\n",
    "Which settings produce smooth boundaries or small islands? Can you infer test accuracy just from these plots? Explain why gamma is not another name for C.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tp6-011",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.454205Z",
     "iopub.status.busy": "2026-10-05T19:23:11.454127Z",
     "iopub.status.idle": "2026-10-05T19:23:11.455800Z",
     "shell.execute_reply": "2026-10-05T19:23:11.455453Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "C_values=[.1,100]\n",
    "gamma_values=[.1,1,20]\n",
    "# TODO: fit the 6 pipelines and call boundary_plot in a 2x3 figure.\n",
    "# Keep X_test and y_test unused.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "tp6-012",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:11.456788Z",
     "iopub.status.busy": "2026-10-05T19:23:11.456635Z",
     "iopub.status.idle": "2026-10-05T19:23:12.020806Z",
     "shell.execute_reply": "2026-10-05T19:23:12.020367Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(2,3,figsize=(13,7))\n",
    "for row,C in enumerate(C_values):\n",
    "    for col,gamma in enumerate(gamma_values):\n",
    "        model=Pipeline([(\"scale\",StandardScaler()),(\"model\",SVC(kernel=\"rbf\",C=C,gamma=gamma))]).fit(X_dev,y_dev)\n",
    "        boundary_plot(axes[row,col],model,X_dev,y_dev)\n",
    "        axes[row,col].set_title(f\"C={C:g}, gamma={gamma:g}, SV={len(model.named_steps['model'].support_)}\")\n",
    "plt.tight_layout();plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-013",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "source": [
    "Gamma changes the locality of similarities and the representation; C weights margin violations. Very local boundaries can fit noise, but their predictive quality must be measured on held-out observations. A plot of training geometry is not a proof of overfitting or underfitting.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-014",
   "metadata": {},
   "source": [
    "## 4. Select kernel and parameters together — 25 min\n",
    "\n",
    "Build a scaled SVC pipeline. Use a **list of parameter dictionaries** so each kernel gets only relevant parameters:\n",
    "\n",
    "- linear: C in `[.1,1,10]`;\n",
    "- homogeneous polynomial: degree in `[2,3]`, gamma=1, coef0=0, C in `[.1,1,10]`;\n",
    "- RBF: C in `[.1,1,10,100]`, gamma in `[.1,1,10,20]`.\n",
    "\n",
    "Use five-fold stratified CV on development data, ROC-AUC and `return_train_score=True`. Display the best result for each kernel family, plot mean CV AUC over the RBF grid and compare train/CV scores. Select the best procedure without looking at test results. If a grid optimum is on an edge, explain how you would extend the search on development data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "tp6-015",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.022674Z",
     "iopub.status.busy": "2026-10-05T19:23:12.022555Z",
     "iopub.status.idle": "2026-10-05T19:23:12.024416Z",
     "shell.execute_reply": "2026-10-05T19:23:12.023978Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: define kernel_pipeline and param_grid (list of 3 dictionaries).\n",
    "# TODO: run search=GridSearchCV(...), fit on X_dev,y_dev and inspect cv_results_.\n",
    "# TODO: report the best row for each family and plot RBF mean CV AUC as a heatmap.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "tp6-016",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.025563Z",
     "iopub.status.busy": "2026-10-05T19:23:12.025481Z",
     "iopub.status.idle": "2026-10-05T19:23:12.633244Z",
     "shell.execute_reply": "2026-10-05T19:23:12.632808Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>params</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'model__C': 1, 'model__kernel': 'linear'}</td>\n",
       "      <td>0.519153</td>\n",
       "      <td>0.424222</td>\n",
       "      <td>0.031618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'model__C': 1, 'model__coef0': 0, 'model__deg...</td>\n",
       "      <td>0.989292</td>\n",
       "      <td>0.989556</td>\n",
       "      <td>0.008794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>{'model__C': 1, 'model__gamma': 0.1, 'model__k...</td>\n",
       "      <td>0.990056</td>\n",
       "      <td>0.991111</td>\n",
       "      <td>0.008374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               params  mean_train_score  \\\n",
       "1          {'model__C': 1, 'model__kernel': 'linear'}          0.519153   \n",
       "5   {'model__C': 1, 'model__coef0': 0, 'model__deg...          0.989292   \n",
       "13  {'model__C': 1, 'model__gamma': 0.1, 'model__k...          0.990056   \n",
       "\n",
       "    mean_test_score  std_test_score  \n",
       "1          0.424222        0.031618  \n",
       "5          0.989556        0.008794  \n",
       "13         0.991111        0.008374  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frozen selection: {'model__C': 1, 'model__gamma': 0.1, 'model__kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "kernel_pipeline=Pipeline([(\"scale\",StandardScaler()),(\"model\",SVC())])\n",
    "param_grid=[\n",
    "    {\"model__kernel\":[\"linear\"],\"model__C\":[.1,1,10]},\n",
    "    {\"model__kernel\":[\"poly\"],\"model__C\":[.1,1,10],\"model__degree\":[2,3],\"model__gamma\":[1],\"model__coef0\":[0]},\n",
    "    {\"model__kernel\":[\"rbf\"],\"model__C\":[.1,1,10,100],\"model__gamma\":[.1,1,10,20]},\n",
    "]\n",
    "search=GridSearchCV(kernel_pipeline,param_grid,cv=cv,scoring=\"roc_auc\",return_train_score=True,n_jobs=1)\n",
    "search.fit(X_dev,y_dev)\n",
    "results=pd.DataFrame(search.cv_results_)\n",
    "best_by_kernel=results.loc[results.groupby(\"param_model__kernel\")[\"mean_test_score\"].idxmax()]\n",
    "display(best_by_kernel[[\"params\",\"mean_train_score\",\"mean_test_score\",\"std_test_score\"]])\n",
    "rbf_rows=results[results.param_model__kernel==\"rbf\"]\n",
    "heat=rbf_rows.pivot(index=\"param_model__C\",columns=\"param_model__gamma\",values=\"mean_test_score\")\n",
    "fig,ax=plt.subplots()\n",
    "im=ax.imshow(heat.values,vmin=.5,vmax=1,cmap=\"Blues\",aspect=\"auto\")\n",
    "ax.set(xticks=np.arange(len(heat.columns)),xticklabels=heat.columns,\n",
    "       yticks=np.arange(len(heat.index)),yticklabels=heat.index,xlabel=\"gamma\",ylabel=\"C\",title=\"Mean CV ROC-AUC\")\n",
    "for (i,j),value in np.ndenumerate(heat.values): ax.text(j,i,f\"{value:.3f}\",ha=\"center\",va=\"center\")\n",
    "fig.colorbar(im,ax=ax);plt.tight_layout();plt.show()\n",
    "print(\"Frozen selection:\",search.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-017",
   "metadata": {},
   "source": [
    "## 5. One final test evaluation — 10 min\n",
    "\n",
    "Freeze the selected procedure and its zero decision threshold. Evaluate once on X_test,y_test: ROC-AUC, accuracy, positive-class recall and confusion matrix. Compare with the fixed linear reference from Section 1, without choosing a new procedure from the test scores. Explain the distinction between a score and a probability.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "tp6-018",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.634394Z",
     "iopub.status.busy": "2026-10-05T19:23:12.634305Z",
     "iopub.status.idle": "2026-10-05T19:23:12.635894Z",
     "shell.execute_reply": "2026-10-05T19:23:12.635558Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: evaluate search.best_estimator_ and linear_reference once on test data.\n",
    "# TODO: report the selected model's confusion matrix with labels [-1,+1].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "tp6-019",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.636918Z",
     "iopub.status.busy": "2026-10-05T19:23:12.636844Z",
     "iopub.status.idle": "2026-10-05T19:23:12.647021Z",
     "shell.execute_reply": "2026-10-05T19:23:12.646496Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>AUC</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>recall +1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Selected kernel SVM</td>\n",
       "      <td>0.9964</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fixed linear reference</td>\n",
       "      <td>0.4292</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    model     AUC  accuracy  recall +1\n",
       "0     Selected kernel SVM  0.9964      0.97       0.96\n",
       "1  Fixed linear reference  0.4292      0.53       0.78"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected-model confusion matrix, labels [-1,+1]:\n",
      "[[49  1]\n",
      " [ 2 48]]\n"
     ]
    }
   ],
   "source": [
    "rows=[]\n",
    "for name,model in [(\"Selected kernel SVM\",search.best_estimator_), (\"Fixed linear reference\",linear_reference)]:\n",
    "    pred=model.predict(X_test)\n",
    "    rows.append({\"model\":name,\"AUC\":roc_auc_score(y_test,model.decision_function(X_test)),\n",
    "                 \"accuracy\":accuracy_score(y_test,pred),\"recall +1\":recall_score(y_test,pred,pos_label=1)})\n",
    "display(pd.DataFrame(rows))\n",
    "print(\"Selected-model confusion matrix, labels [-1,+1]:\")\n",
    "print(confusion_matrix(y_test,search.predict(X_test),labels=[-1,1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-020",
   "metadata": {},
   "source": [
    "## 6. Gram matrices and manual prediction — 15 min\n",
    "\n",
    "Use a **separate fixed RBF demonstration model** (C=1, gamma=.5) on scaled development inputs. It is not a second model-selection experiment.\n",
    "\n",
    "1. Compute its training Gram matrix and verify symmetry and nonnegative eigenvalues up to tolerance (`-1e-8`).\n",
    "2. Reconstruct scores for three new query inputs using support vectors and signed dual coefficients. Explain the dimensions.\n",
    "3. Fit `SVC(kernel=\"precomputed\")` on the full training Gram matrix and compare query scores. Predict expects all training columns, in their original order.\n",
    "4. For $K=\\begin{pmatrix}1&2\\\\2&1\\end{pmatrix}$ and $c=(1,-1)$, compute $c^TKc$. Why are positive entries insufficient?\n",
    "\n",
    "A numerical check on one Gram matrix is not a proof of kernel validity on all finite sets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "tp6-021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.648226Z",
     "iopub.status.busy": "2026-10-05T19:23:12.648051Z",
     "iopub.status.idle": "2026-10-05T19:23:12.650216Z",
     "shell.execute_reply": "2026-10-05T19:23:12.649706Z"
    }
   },
   "outputs": [],
   "source": [
    "queries_raw=np.array([[0.,0.],[.7,.7],[-1.3,.1]])\n",
    "gamma=.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "tp6-022",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.651182Z",
     "iopub.status.busy": "2026-10-05T19:23:12.651095Z",
     "iopub.status.idle": "2026-10-05T19:23:12.652750Z",
     "shell.execute_reply": "2026-10-05T19:23:12.652371Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO: fit a scaler on X_dev, transform development and query inputs.\n",
    "# TODO: fit fixed_rbf, compute K_train and K_query_support, reconstruct scores.\n",
    "# TODO: compare with a precomputed model using K_query_train.\n",
    "# TODO: evaluate the invalid 2x2 example.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "tp6-023",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.653789Z",
     "iopub.status.busy": "2026-10-05T19:23:12.653712Z",
     "iopub.status.idle": "2026-10-05T19:23:12.816055Z",
     "shell.execute_reply": "2026-10-05T19:23:12.815813Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (300, 300) query-support: (3, 78) query-training: (3, 300)\n",
      "Query scores: [ 3.27191408 -1.01441914 -2.03104194]\n",
      "Invalid quadratic form: -2.0\n"
     ]
    }
   ],
   "source": [
    "scaler=StandardScaler().fit(X_dev)\n",
    "Z=scaler.transform(X_dev)\n",
    "Q=scaler.transform(queries_raw)\n",
    "fixed_rbf=SVC(kernel=\"rbf\",C=1,gamma=gamma,tol=1e-9).fit(Z,y_dev)\n",
    "K_train=rbf_kernel(Z,gamma=gamma)\n",
    "assert np.allclose(K_train,K_train.T)\n",
    "assert np.linalg.eigvalsh(K_train).min()>-1e-8\n",
    "K_query_support=rbf_kernel(Q,fixed_rbf.support_vectors_,gamma=gamma)\n",
    "manual=K_query_support @ fixed_rbf.dual_coef_[0]+fixed_rbf.intercept_[0]\n",
    "assert np.allclose(manual,fixed_rbf.decision_function(Q))\n",
    "precomputed=SVC(kernel=\"precomputed\",C=1,tol=1e-9).fit(K_train,y_dev)\n",
    "K_query_train=rbf_kernel(Q,Z,gamma=gamma)\n",
    "assert np.allclose(manual,precomputed.decision_function(K_query_train),atol=1e-5)\n",
    "print(\"Training:\",K_train.shape,\"query-support:\",K_query_support.shape,\"query-training:\",K_query_train.shape)\n",
    "print(\"Query scores:\",manual)\n",
    "K_bad=np.array([[1.,2.],[2.,1.]])\n",
    "c=np.array([1.,-1.])\n",
    "print(\"Invalid quadratic form:\",c @ K_bad @ c)\n",
    "assert c @ K_bad @ c == -2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-024",
   "metadata": {
    "tags": [
     "correction"
    ]
   },
   "source": [
    "Training uses n by n products; manual prediction uses m by n_SV products. The precomputed API uses m by n training columns. A symmetric matrix with positive entries can still have a negative eigenvalue; the counterexample gives -2. Changing the kernel changes the training optimization, so coefficients must be fitted again.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-025",
   "metadata": {
    "tags": [
     "optional"
    ]
   },
   "source": [
    "## Optional A — RBF novelty detection after TP5\n",
    "\n",
    "Generate the same positive sensor reference cluster as TP5. Fit a scaled RBF One-Class SVM with gamma=.5, nu=.1 on references only. Predict the same three measurements. Plot the acceptance region and compare the high-valued point with the linear detector's unbounded half-space. Why can centered scaling be used with a distance-based RBF kernel?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "tp6-026",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.818679Z",
     "iopub.status.busy": "2026-10-05T19:23:12.818573Z",
     "iopub.status.idle": "2026-10-05T19:23:12.821409Z",
     "shell.execute_reply": "2026-10-05T19:23:12.821065Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "rng=np.random.default_rng(SEED+1)\n",
    "X_reference=rng.normal(loc=[3.,3.],scale=[.25,.25],size=(100,2))\n",
    "X_sensor_new=np.array([[3.,3.],[.2,.2],[8.,8.]])\n",
    "# TODO: fit the RBF detector and compare its three scores and flags with TP5.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "tp6-027",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.823532Z",
     "iopub.status.busy": "2026-10-05T19:23:12.823339Z",
     "iopub.status.idle": "2026-10-05T19:23:12.920834Z",
     "shell.execute_reply": "2026-10-05T19:23:12.920340Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "detector=Pipeline([(\"scale\",StandardScaler()),(\"model\",OneClassSVM(kernel=\"rbf\",gamma=.5,nu=.1))]).fit(X_reference)\n",
    "display(pd.DataFrame({\"x1\":X_sensor_new[:,0],\"x2\":X_sensor_new[:,1],\n",
    "                      \"score\":detector.decision_function(X_sensor_new),\"flag\":detector.predict(X_sensor_new)}))\n",
    "xx,yy=np.meshgrid(np.linspace(1.8,4.2,160),np.linspace(1.8,4.2,160))\n",
    "z=detector.decision_function(np.column_stack((xx.ravel(),yy.ravel()))).reshape(xx.shape)\n",
    "fig,ax=plt.subplots()\n",
    "ax.contourf(xx,yy,z,levels=[-np.inf,0,np.inf],colors=[\"#fce7e9\",\"#e8f1f8\"])\n",
    "ax.contour(xx,yy,z,levels=[0],colors=\"black\")\n",
    "ax.scatter(*X_reference.T,s=15,color=BLUE)\n",
    "ax.set(xlabel=\"Sensor 1\",ylabel=\"Sensor 2\",title=\"RBF reference region (zoom on the cluster)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tp6-028",
   "metadata": {
    "tags": [
     "optional"
    ]
   },
   "source": [
    "## Optional B — Construct a simple kernel on labelled graphs\n",
    "\n",
    "A graph is represented by node labels and a list of edges. Define shared features: counts of C nodes, O nodes, C--C edges and C--O edges. Construct the kernel $k(G,G')=\\phi(G)^T\\phi(G')$ for the three tiny graphs below.\n",
    "\n",
    "Compute the Gram matrix, check PSD numerically and normalize it with its diagonal. Explain why the explicit feature-map argument proves validity beyond these three graphs. Do these counts uniquely identify a graph? No graph library is required. This is a kernel construction exercise, not a benchmark on three objects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "tp6-029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.922222Z",
     "iopub.status.busy": "2026-10-05T19:23:12.922124Z",
     "iopub.status.idle": "2026-10-05T19:23:12.924454Z",
     "shell.execute_reply": "2026-10-05T19:23:12.924031Z"
    },
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "graphs=[\n",
    "    {\"nodes\":[\"C\",\"C\",\"O\"],\"edges\":[(0,1),(1,2)]},\n",
    "    {\"nodes\":[\"C\",\"O\"],\"edges\":[(0,1)]},\n",
    "    {\"nodes\":[\"C\",\"C\",\"C\"],\"edges\":[(0,1),(1,2),(0,2)]},\n",
    "]\n",
    "# TODO: implement graph_features(graph), stack features, compute and normalize the Gram matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "tp6-030",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T19:23:12.925530Z",
     "iopub.status.busy": "2026-10-05T19:23:12.925445Z",
     "iopub.status.idle": "2026-10-05T19:23:12.933284Z",
     "shell.execute_reply": "2026-10-05T19:23:12.932786Z"
    },
    "tags": [
     "correction"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C nodes</th>\n",
       "      <th>O nodes</th>\n",
       "      <th>C-C edges</th>\n",
       "      <th>C-O edges</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
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       "    </tr>\n",
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       "</table>\n",
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       "   C nodes  O nodes  C-C edges  C-O edges\n",
       "0        2        1          1          1\n",
       "1        1        1          0          1\n",
       "2        3        0          3          0"
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   "source": [
    "def graph_features(graph):\n",
    "    nodes,edges=graph[\"nodes\"],graph[\"edges\"]\n",
    "    pairs=[tuple(sorted((nodes[i],nodes[j]))) for i,j in edges]\n",
    "    return np.array([nodes.count(\"C\"),nodes.count(\"O\"),pairs.count((\"C\",\"C\")),pairs.count((\"C\",\"O\"))])\n",
    "F=np.vstack([graph_features(g) for g in graphs])\n",
    "K_graph=F @ F.T\n",
    "norms=np.sqrt(np.diag(K_graph))\n",
    "assert np.all(norms>0)\n",
    "K_normalized=K_graph/np.outer(norms,norms)\n",
    "display(pd.DataFrame(F,columns=[\"C nodes\",\"O nodes\",\"C-C edges\",\"C-O edges\"]))\n",
    "display(pd.DataFrame(K_graph))\n",
    "display(pd.DataFrame(K_normalized))\n",
    "assert np.linalg.eigvalsh(K_graph).min()>-1e-8\n",
    "assert np.allclose(np.diag(K_normalized),1)\n"
   ]
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