Set n_init explicitly when creating KMeans or MiniBatchKMeans, to avoid warning
Aurélien Geron
parent
9b2c0e81c8
commit
1cf75d217b
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@ -230,7 +230,7 @@
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"mapping = {}\n",
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"for class_id in np.unique(y):\n",
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" mode, _ = stats.mode(y_pred[y==class_id])\n",
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" mapping[mode[0]] = class_id\n",
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" mapping[mode] = class_id\n",
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"\n",
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"y_pred = np.array([mapping[cluster_id] for cluster_id in y_pred])\n",
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"\n",
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@ -309,10 +309,17 @@
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" random_state=7)\n",
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"\n",
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"k = 5\n",
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"kmeans = KMeans(n_clusters=k, random_state=42)\n",
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"kmeans = KMeans(n_clusters=k, n_init=10, random_state=42)\n",
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"y_pred = kmeans.fit_predict(X)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note: Throughout this notebook, when `n_init` was not set when creating a `KMeans` estimator, I explicitly set it to `n_init=10` to avoid a warning about the fact that the default value for this hyperparameter will change from 10 to `\"auto\"` in Scikit-Learn 1.4."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -1169,10 +1176,17 @@
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"source": [
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"from sklearn.cluster import MiniBatchKMeans\n",
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"\n",
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"minibatch_kmeans = MiniBatchKMeans(n_clusters=5, random_state=42)\n",
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"minibatch_kmeans = MiniBatchKMeans(n_clusters=5, n_init=3, random_state=42)\n",
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"minibatch_kmeans.fit(X)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note: Throughout this notebook, when `n_init` was not set when creating a `MiniBatchKMeans` estimator, I explicitly set it to `n_init=3` to avoid a warning about the fact that the default value for this hyperparameter will change from 3 to `\"auto\"` in Scikit-Learn 1.4."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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@ -1215,7 +1229,7 @@
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"source": [
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"from sklearn.datasets import fetch_openml\n",
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"\n",
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"mnist = fetch_openml('mnist_784', as_frame=False)"
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"mnist = fetch_openml('mnist_784', as_frame=False, parser=\"auto\")"
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]
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},
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{
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@ -1275,7 +1289,7 @@
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"from sklearn.cluster import MiniBatchKMeans\n",
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"\n",
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"minibatch_kmeans = MiniBatchKMeans(n_clusters=10, batch_size=10,\n",
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" random_state=42)\n",
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" n_init=3, random_state=42)\n",
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"minibatch_kmeans.fit(X_memmap)"
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]
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},
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@ -1320,8 +1334,8 @@
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"times = np.empty((max_k, 2))\n",
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"inertias = np.empty((max_k, 2))\n",
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"for k in range(1, max_k + 1):\n",
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" kmeans_ = KMeans(n_clusters=k, algorithm=\"full\", random_state=42)\n",
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" minibatch_kmeans = MiniBatchKMeans(n_clusters=k, random_state=42)\n",
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" kmeans_ = KMeans(n_clusters=k, algorithm=\"lloyd\", n_init=10, random_state=42)\n",
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" minibatch_kmeans = MiniBatchKMeans(n_clusters=k, n_init=10, random_state=42)\n",
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" print(f\"\\r{k}/{max_k}\", end=\"\") # \\r returns to the start of line\n",
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" times[k - 1, 0] = timeit(\"kmeans_.fit(X)\", number=10, globals=globals())\n",
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" times[k - 1, 1] = timeit(\"minibatch_kmeans.fit(X)\", number=10,\n",
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@ -1387,8 +1401,8 @@
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"source": [
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"# extra code – this cell generates and saves Figure 9–7\n",
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"\n",
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"kmeans_k3 = KMeans(n_clusters=3, random_state=42)\n",
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"kmeans_k8 = KMeans(n_clusters=8, random_state=42)\n",
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"kmeans_k3 = KMeans(n_clusters=3, n_init=10, random_state=42)\n",
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"kmeans_k8 = KMeans(n_clusters=8, n_init=10, random_state=42)\n",
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"\n",
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"plot_clusterer_comparison(kmeans_k3, kmeans_k8, X, \"$k=3$\", \"$k=8$\")\n",
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"save_fig(\"bad_n_clusters_plot\")\n",
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@ -1470,7 +1484,7 @@
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"source": [
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"# extra code – this cell generates and saves Figure 9–8\n",
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"\n",
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"kmeans_per_k = [KMeans(n_clusters=k, random_state=42).fit(X)\n",
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"kmeans_per_k = [KMeans(n_clusters=k, n_init=10, random_state=42).fit(X)\n",
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" for k in range(1, 10)]\n",
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"inertias = [model.inertia_ for model in kmeans_per_k]\n",
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"\n",
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@ -1724,7 +1738,7 @@
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"kmeans_good = KMeans(n_clusters=3,\n",
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" init=np.array([[-1.5, 2.5], [0.5, 0], [4, 0]]),\n",
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" n_init=1, random_state=42)\n",
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"kmeans_bad = KMeans(n_clusters=3, random_state=42)\n",
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"kmeans_bad = KMeans(n_clusters=3, n_init=10, random_state=42)\n",
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"kmeans_good.fit(X)\n",
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"kmeans_bad.fit(X)\n",
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"\n",
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@ -1805,7 +1819,7 @@
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"outputs": [],
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"source": [
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"X = image.reshape(-1, 3)\n",
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"kmeans = KMeans(n_clusters=8, random_state=42).fit(X)\n",
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"kmeans = KMeans(n_clusters=8, n_init=10, random_state=42).fit(X)\n",
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"segmented_img = kmeans.cluster_centers_[kmeans.labels_]\n",
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"segmented_img = segmented_img.reshape(image.shape)"
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]
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@ -1834,7 +1848,7 @@
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"segmented_imgs = []\n",
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"n_colors = (10, 8, 6, 4, 2)\n",
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"for n_clusters in n_colors:\n",
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" kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(X)\n",
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" kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=42).fit(X)\n",
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" segmented_img = kmeans.cluster_centers_[kmeans.labels_]\n",
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" segmented_imgs.append(segmented_img.reshape(image.shape))\n",
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"\n",
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@ -1978,7 +1992,7 @@
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"outputs": [],
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"source": [
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"k = 50\n",
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"kmeans = KMeans(n_clusters=k, random_state=42)\n",
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"kmeans = KMeans(n_clusters=k, n_init=10, random_state=42)\n",
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"X_digits_dist = kmeans.fit_transform(X_train)\n",
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"representative_digit_idx = X_digits_dist.argmin(axis=0)\n",
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"X_representative_digits = X_train[representative_digit_idx]"
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@ -2623,8 +2637,7 @@
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"source": [
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"def plot_spectral_clustering(sc, X, size, alpha, show_xlabels=True,\n",
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" show_ylabels=True):\n",
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" plt.scatter(X[:, 0], X[:, 1], marker='o', s=size, c='gray', cmap=\"Paired\",\n",
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" alpha=alpha)\n",
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" plt.scatter(X[:, 0], X[:, 1], marker='o', s=size, c='gray', alpha=alpha)\n",
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" plt.scatter(X[:, 0], X[:, 1], marker='o', s=30, c='w')\n",
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" plt.scatter(X[:, 0], X[:, 1], marker='.', s=10, c=sc.labels_, cmap=\"Paired\")\n",
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" \n",
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@ -4005,7 +4018,7 @@
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"kmeans_per_k = []\n",
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"for k in k_range:\n",
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" print(f\"k={k}\")\n",
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" kmeans = KMeans(n_clusters=k, random_state=42)\n",
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" kmeans = KMeans(n_clusters=k, n_init=10, random_state=42)\n",
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" kmeans.fit(X_train_pca)\n",
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" kmeans_per_k.append(kmeans)"
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]
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@ -6581,7 +6594,7 @@
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"\n",
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"for n_clusters in k_range:\n",
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" pipeline = make_pipeline(\n",
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" KMeans(n_clusters=n_clusters, random_state=42),\n",
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" KMeans(n_clusters=n_clusters, n_init=10, random_state=42),\n",
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" RandomForestClassifier(n_estimators=150, random_state=42)\n",
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" )\n",
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" pipeline.fit(X_train_pca, y_train)\n",
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@ -6971,7 +6984,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -6985,7 +6998,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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