Add parser='auto' to fetch_openml(), and normalized_stress=False to MDS(), to avoid warnings

08_dimensionality_reduction.ipynb

@@ -775,7 +775,7 @@
    "source": [
     "from sklearn.datasets import fetch_openml\n",
     "\n",
-    "mnist = fetch_openml('mnist_784', as_frame=False)\n",
+    "mnist = fetch_openml('mnist_784', as_frame=False, parser=\"auto\")\n",
     "X_train, y_train = mnist.data[:60_000], mnist.target[:60_000]\n",
     "X_test, y_test = mnist.data[60_000:], mnist.target[60_000:]\n",
     "\n",
@@ -785,6 +785,13 @@
     "d = np.argmax(cumsum >= 0.95) + 1  # d equals 154"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: I added `parser=\"auto\"` when calling `fetch_openml()` to avoid a warning about the fact that the default value for that parameter will change in the future (it's irrelevant in this case). Please see the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html) for more details."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 21,
@@ -1388,6 +1395,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: I added `normalized_stress=False` below to avoid a warning about the fact that the default value for that hyperparameter will change in the future. Please see the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html) for more details."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 46,
@@ -1396,7 +1410,7 @@
    "source": [
     "from sklearn.manifold import MDS\n",
     "\n",
-    "mds = MDS(n_components=2, random_state=42)\n",
+    "mds = MDS(n_components=2, normalized_stress=False, random_state=42)\n",
     "X_reduced_mds = mds.fit_transform(X_swiss)"
    ]
   },
@@ -2418,7 +2432,8 @@
     }
    ],
    "source": [
-    "%time X_mds_reduced = MDS(n_components=2, random_state=42).fit_transform(X_sample)\n",
+    "%time X_mds_reduced = MDS(n_components=2, normalized_stress=False,\n",
+    "                          random_state=42).fit_transform(X_sample)\n",
     "plot_digits(X_mds_reduced, y_sample)\n",
     "plt.show()"
    ]
@@ -2464,8 +2479,10 @@
     }
    ],
    "source": [
-    "pca_mds = make_pipeline(PCA(n_components=0.95, random_state=42),\n",
-    "                        MDS(n_components=2, random_state=42))\n",
+    "pca_mds = make_pipeline(\n",
+    "    PCA(n_components=0.95, random_state=42),\n",
+    "    MDS(n_components=2, normalized_stress=False, random_state=42)\n",
+    ")\n",
     "\n",
     "%time X_pca_mds_reduced = pca_mds.fit_transform(X_sample)\n",
     "plot_digits(X_pca_mds_reduced, y_sample)\n",
@@ -2552,7 +2569,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2566,7 +2583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,