Replace .format with f-strings

04_training_linear_models.ipynb

@@ -346,7 +346,7 @@
     "    plt.xlabel(\"$x_1$\")\n",
     "    plt.axis([0, 2, 0, 15])\n",
     "    plt.grid()\n",
-    "    plt.title(r\"$\\eta = {}$\".format(eta))\n",
+    "    plt.title(fr\"$\\eta = {eta}$\")\n",
     "    return theta_path\n",
     "\n",
     "np.random.seed(42)\n",
@@ -820,7 +820,7 @@
     "        model.fit(X, y)\n",
     "        y_new_regul = model.predict(X_new)\n",
     "        plt.plot(X_new, y_new_regul, style, linewidth=2,\n",
-    "                 label=r\"$\\alpha = {}$\".format(alpha))\n",
+    "                 label=fr\"$\\alpha = {alpha}$\")\n",
     "    plt.legend(loc=\"upper left\")\n",
     "    plt.xlabel(\"$x_1$\")\n",
     "    plt.axis([0, 3, 0, 3.5])\n",
@@ -960,7 +960,7 @@
     "    ax.plot(path_N[:, 0], path_N[:, 1], \"y--\")\n",
     "    ax.plot(0, 0, \"ys\")\n",
     "    ax.plot(t1_min, t2_min, \"ys\")\n",
-    "    ax.set_title(r\"$\\ell_{}$ penalty\".format(i + 1))\n",
+    "    ax.set_title(fr\"$\\ell_{i + 1}$ penalty\")\n",
     "    ax.axis([t1a, t1b, t2a, t2b])\n",
     "    if i == 1:\n",
     "        ax.set_xlabel(r\"$\\theta_1$\")\n",