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Index of abalone
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02 Dec 1996 114 Index
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12 Jun 1996 191873 abalone.data
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12 Jun 1996 4319 abalone.names
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 54,
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"id": "e377bc9b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.cluster import KMeans, MiniBatchKMeans\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.metrics import silhouette_score\n",
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"\n",
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"from pathlib import Path\n"
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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": 55,
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"id": "d7275d15",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"WindowsPath('c:/Users/Admin/Documents/lecture ass shit/3/ml2/lw2')"
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]
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},
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"execution_count": 55,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Path.cwd()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "40f028ad",
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"metadata": {},
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"source": [
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"# game\n",
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"\n",
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"## Abalone\n"
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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": 56,
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"id": "4de18adb",
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"metadata": {},
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"outputs": [],
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"source": [
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"abalone_file = Path.cwd() / \"abalone/abalone.data\"\n",
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"\n",
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"df = pd.read_csv(abalone_file, header=None)\n",
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"df.columns = [\n",
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" \"Sex\",\n",
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" \"Length\",\n",
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" \"Diameter\",\n",
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" \"Height\",\n",
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" \"Whole\",\n",
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" \"Shucked\",\n",
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" \"Viscera\",\n",
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" \"Shell\",\n",
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" \"Rings\",\n",
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"]\n",
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"\n",
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"X = pd.get_dummies(df.drop(columns=[\"Rings\"]), drop_first=True)\n",
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"\n",
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"X = StandardScaler().fit_transform(X)\n"
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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": 57,
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"id": "50b3eb52",
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"metadata": {},
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"outputs": [],
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"source": [
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"ks = range(2, 11)\n",
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"\n",
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"inertias = []\n",
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"silhouettes = []\n",
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"labels_per_k = {}\n",
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"\n",
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"for k in ks:\n",
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" km = KMeans(\n",
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" n_clusters=k,\n",
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" init=\"k-means++\", # ⭐ best practice\n",
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" n_init=10,\n",
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" random_state=42,\n",
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" )\n",
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" labels = km.fit_predict(X)\n",
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"\n",
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" inertias.append(km.inertia_) # WCSS\n",
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" silhouettes.append(silhouette_score(X, labels))\n",
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" labels_per_k[k] = labels"
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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": 58,
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"id": "c70bfa21",
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"metadata": {},
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"outputs": [],
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"source": [
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"best_k = ks[np.argmax(silhouettes)]\n",
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"best_labels = labels_per_k[best_k]"
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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": 59,
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"id": "926a255e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.plot(ks, inertias, marker=\"o\")\n",
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"plt.xlabel(\"k\")\n",
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"plt.ylabel(\"Inertia (WCSS)\")\n",
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"plt.title(\"Elbow Method\")\n",
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"plt.show()"
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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": 60,
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"id": "7b8c1750",
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"metadata": {},
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"outputs": [
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{
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"data": {
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||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(ks, silhouettes, marker=\"o\")\n",
|
||||
"plt.xlabel(\"k\")\n",
|
||||
"plt.ylabel(\"Silhouette Score\")\n",
|
||||
"plt.title(\"Silhouette Analysis\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"id": "acab8d95",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Xp = PCA(n_components=2).fit_transform(X)\n",
|
||||
"\n",
|
||||
"plt.scatter(Xp[:, 0], Xp[:, 1], c=best_labels, s=5)\n",
|
||||
"plt.title(f\"K-means Clustering (k={best_k})\")\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3841acb2",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Covertype\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"id": "92b692bc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"covtype_file = Path.cwd() / \"covertype/covtype.data\"\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(covtype_file, header=None)\n",
|
||||
"df = df.sample(20000, random_state=42) # safe size\n",
|
||||
"X = df.iloc[:, :-1].values\n",
|
||||
"X = StandardScaler().fit_transform(X)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"id": "e6bbfcb7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ks = range(2, 9)\n",
|
||||
"\n",
|
||||
"inertias = []\n",
|
||||
"silhouettes = []\n",
|
||||
"labels_per_k = {}\n",
|
||||
"\n",
|
||||
"for k in ks:\n",
|
||||
" km = MiniBatchKMeans(\n",
|
||||
" n_clusters=k, init=\"k-means++\", batch_size=2048, n_init=5, random_state=42\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" labels = km.fit_predict(X)\n",
|
||||
"\n",
|
||||
" inertias.append(km.inertia_)\n",
|
||||
" silhouettes.append(silhouette_score(X, labels))\n",
|
||||
" labels_per_k[k] = labels\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"id": "da938557",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(ks, inertias, marker=\"o\")\n",
|
||||
"plt.xlabel(\"k\")\n",
|
||||
"plt.ylabel(\"Inertia (WCSS)\")\n",
|
||||
"plt.title(\"Elbow Method – Covertype\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"id": "0070102e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(ks, silhouettes, marker=\"o\")\n",
|
||||
"plt.xlabel(\"k\")\n",
|
||||
"plt.ylabel(\"Silhouette Score\")\n",
|
||||
"plt.title(\"Silhouette Analysis – Covertype\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"id": "c312a24a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_k = ks[np.argmax(silhouettes)]\n",
|
||||
"best_labels = labels_per_k[best_k]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "aecfb074",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# community\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"id": "bc26af01",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"community_data = Path.cwd() / \"communities+and+crime/communities.data\"\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(community_data, header=None, na_values=\"?\")\n",
|
||||
"df = df.iloc[:, 5:]\n",
|
||||
"\n",
|
||||
"df = df.apply(pd.to_numeric)\n",
|
||||
"\n",
|
||||
"df = df.fillna(df.mean())\n",
|
||||
"\n",
|
||||
"X = df.iloc[:, :-1].values\n",
|
||||
"X = StandardScaler().fit_transform(X)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "153c0809",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## PCA variance explained\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"id": "a3e10a92",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pca = PCA()\n",
|
||||
"pca.fit(X)\n",
|
||||
"\n",
|
||||
"plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
|
||||
"plt.xlabel(\"Number of components\")\n",
|
||||
"plt.ylabel(\"Cumulative explained variance\")\n",
|
||||
"plt.title(\"PCA Variance Explained – Communities & Crime\")\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a7acc3d1",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## PCA to 2D (no clustering yet)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"id": "69a1249a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Xp = PCA(n_components=2).fit_transform(X)\n",
|
||||
"\n",
|
||||
"plt.scatter(Xp[:, 0], Xp[:, 1], s=5)\n",
|
||||
"plt.title(\"PCA Projection (2D) – No Clustering\")\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0790bd24",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## clustering on ORIGINAL space (metrics only)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"id": "f41491b4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ks = range(2, 8)\n",
|
||||
"sil_orig = []\n",
|
||||
"\n",
|
||||
"for k in ks:\n",
|
||||
" km = KMeans(n_clusters=k, init=\"k-means++\", n_init=10, random_state=42)\n",
|
||||
" labels = km.fit_predict(X)\n",
|
||||
" sil_orig.append(silhouette_score(X, labels))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c2c74e3b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## clustering on PCA space (2D)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 71,
|
||||
"id": "873caac5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sil_pca = []\n",
|
||||
"labels_pca = {}\n",
|
||||
"\n",
|
||||
"for k in ks:\n",
|
||||
" km = KMeans(n_clusters=k, init=\"k-means++\", n_init=10, random_state=42)\n",
|
||||
" labels = km.fit_predict(Xp)\n",
|
||||
" sil_pca.append(silhouette_score(Xp, labels))\n",
|
||||
" labels_pca[k] = labels\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b5bea4fa",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## silhouette comparison plot\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 72,
|
||||
"id": "6846b929",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(ks, sil_orig, marker=\"o\", label=\"Original space\")\n",
|
||||
"plt.plot(ks, sil_pca, marker=\"o\", label=\"PCA 2D\")\n",
|
||||
"plt.xlabel(\"k\")\n",
|
||||
"plt.ylabel(\"Silhouette Score\")\n",
|
||||
"plt.title(\"Clustering Quality: Before vs After PCA\")\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9ac244f5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## PCA cluster visualization (best k)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 73,
|
||||
"id": "fc244f0c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"best_k = ks[np.argmax(sil_pca)]\n",
|
||||
"\n",
|
||||
"plt.scatter(Xp[:, 0], Xp[:, 1], c=labels_pca[best_k], s=5)\n",
|
||||
"plt.title(f\"K-means on PCA space (k={best_k})\")\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "711bd1d5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## random subspace clustering\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 74,
|
||||
"id": "813e9b68",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rng = np.random.default_rng(42)\n",
|
||||
"idx = rng.choice(X.shape[1], size=30, replace=False)\n",
|
||||
"\n",
|
||||
"X_sub = X[:, idx]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"id": "6aa93bec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sil_sub = []\n",
|
||||
"\n",
|
||||
"for k in ks:\n",
|
||||
" km = KMeans(n_clusters=k, init=\"k-means++\", n_init=10, random_state=42)\n",
|
||||
" labels = km.fit_predict(X_sub)\n",
|
||||
" sil_sub.append(silhouette_score(X_sub, labels))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ab44c9fb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## final comparison plot\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"id": "1fb883f8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(ks, sil_orig, label=\"Original\")\n",
|
||||
"plt.plot(ks, sil_pca, label=\"PCA 2D\")\n",
|
||||
"plt.plot(ks, sil_sub, label=\"Random Subspace\")\n",
|
||||
"plt.xlabel(\"k\")\n",
|
||||
"plt.ylabel(\"Silhouette Score\")\n",
|
||||
"plt.title(\"Subspace Clustering Comparison\")\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "ml2",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.14.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
+372
@@ -0,0 +1,372 @@
|
||||
```python
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.cluster import KMeans, MiniBatchKMeans
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.metrics import silhouette_score
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
Path.cwd()
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
WindowsPath('c:/Users/Admin/Documents/lecture ass shit/3/ml2/lw2')
|
||||
|
||||
|
||||
|
||||
# game
|
||||
|
||||
## Abalone
|
||||
|
||||
|
||||
|
||||
```python
|
||||
abalone_file = Path.cwd() / "abalone/abalone.data"
|
||||
|
||||
df = pd.read_csv(abalone_file, header=None)
|
||||
df.columns = [
|
||||
"Sex",
|
||||
"Length",
|
||||
"Diameter",
|
||||
"Height",
|
||||
"Whole",
|
||||
"Shucked",
|
||||
"Viscera",
|
||||
"Shell",
|
||||
"Rings",
|
||||
]
|
||||
|
||||
X = pd.get_dummies(df.drop(columns=["Rings"]), drop_first=True)
|
||||
|
||||
X = StandardScaler().fit_transform(X)
|
||||
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
ks = range(2, 11)
|
||||
|
||||
inertias = []
|
||||
silhouettes = []
|
||||
labels_per_k = {}
|
||||
|
||||
for k in ks:
|
||||
km = KMeans(
|
||||
n_clusters=k,
|
||||
init="k-means++", # ⭐ best practice
|
||||
n_init=10,
|
||||
random_state=42,
|
||||
)
|
||||
labels = km.fit_predict(X)
|
||||
|
||||
inertias.append(km.inertia_) # WCSS
|
||||
silhouettes.append(silhouette_score(X, labels))
|
||||
labels_per_k[k] = labels
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
best_k = ks[np.argmax(silhouettes)]
|
||||
best_labels = labels_per_k[best_k]
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
plt.plot(ks, inertias, marker="o")
|
||||
plt.xlabel("k")
|
||||
plt.ylabel("Inertia (WCSS)")
|
||||
plt.title("Elbow Method")
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
plt.plot(ks, silhouettes, marker="o")
|
||||
plt.xlabel("k")
|
||||
plt.ylabel("Silhouette Score")
|
||||
plt.title("Silhouette Analysis")
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
Xp = PCA(n_components=2).fit_transform(X)
|
||||
|
||||
plt.scatter(Xp[:, 0], Xp[:, 1], c=best_labels, s=5)
|
||||
plt.title(f"K-means Clustering (k={best_k})")
|
||||
plt.show()
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Covertype
|
||||
|
||||
|
||||
|
||||
```python
|
||||
covtype_file = Path.cwd() / "covertype/covtype.data"
|
||||
|
||||
df = pd.read_csv(covtype_file, header=None)
|
||||
df = df.sample(20000, random_state=42) # safe size
|
||||
X = df.iloc[:, :-1].values
|
||||
X = StandardScaler().fit_transform(X)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
ks = range(2, 9)
|
||||
|
||||
inertias = []
|
||||
silhouettes = []
|
||||
labels_per_k = {}
|
||||
|
||||
for k in ks:
|
||||
km = MiniBatchKMeans(
|
||||
n_clusters=k, init="k-means++", batch_size=2048, n_init=5, random_state=42
|
||||
)
|
||||
|
||||
labels = km.fit_predict(X)
|
||||
|
||||
inertias.append(km.inertia_)
|
||||
silhouettes.append(silhouette_score(X, labels))
|
||||
labels_per_k[k] = labels
|
||||
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
plt.plot(ks, inertias, marker="o")
|
||||
plt.xlabel("k")
|
||||
plt.ylabel("Inertia (WCSS)")
|
||||
plt.title("Elbow Method – Covertype")
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
plt.plot(ks, silhouettes, marker="o")
|
||||
plt.xlabel("k")
|
||||
plt.ylabel("Silhouette Score")
|
||||
plt.title("Silhouette Analysis – Covertype")
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
best_k = ks[np.argmax(silhouettes)]
|
||||
best_labels = labels_per_k[best_k]
|
||||
```
|
||||
|
||||
# community
|
||||
|
||||
|
||||
|
||||
```python
|
||||
community_data = Path.cwd() / "communities+and+crime/communities.data"
|
||||
|
||||
df = pd.read_csv(community_data, header=None, na_values="?")
|
||||
df = df.iloc[:, 5:]
|
||||
|
||||
df = df.apply(pd.to_numeric)
|
||||
|
||||
df = df.fillna(df.mean())
|
||||
|
||||
X = df.iloc[:, :-1].values
|
||||
X = StandardScaler().fit_transform(X)
|
||||
|
||||
```
|
||||
|
||||
## PCA variance explained
|
||||
|
||||
|
||||
|
||||
```python
|
||||
pca = PCA()
|
||||
pca.fit(X)
|
||||
|
||||
plt.plot(np.cumsum(pca.explained_variance_ratio_))
|
||||
plt.xlabel("Number of components")
|
||||
plt.ylabel("Cumulative explained variance")
|
||||
plt.title("PCA Variance Explained – Communities & Crime")
|
||||
plt.show()
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## PCA to 2D (no clustering yet)
|
||||
|
||||
|
||||
|
||||
```python
|
||||
Xp = PCA(n_components=2).fit_transform(X)
|
||||
|
||||
plt.scatter(Xp[:, 0], Xp[:, 1], s=5)
|
||||
plt.title("PCA Projection (2D) – No Clustering")
|
||||
plt.show()
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## clustering on ORIGINAL space (metrics only)
|
||||
|
||||
|
||||
|
||||
```python
|
||||
ks = range(2, 8)
|
||||
sil_orig = []
|
||||
|
||||
for k in ks:
|
||||
km = KMeans(n_clusters=k, init="k-means++", n_init=10, random_state=42)
|
||||
labels = km.fit_predict(X)
|
||||
sil_orig.append(silhouette_score(X, labels))
|
||||
|
||||
```
|
||||
|
||||
## clustering on PCA space (2D)
|
||||
|
||||
|
||||
|
||||
```python
|
||||
sil_pca = []
|
||||
labels_pca = {}
|
||||
|
||||
for k in ks:
|
||||
km = KMeans(n_clusters=k, init="k-means++", n_init=10, random_state=42)
|
||||
labels = km.fit_predict(Xp)
|
||||
sil_pca.append(silhouette_score(Xp, labels))
|
||||
labels_pca[k] = labels
|
||||
|
||||
```
|
||||
|
||||
## silhouette comparison plot
|
||||
|
||||
|
||||
|
||||
```python
|
||||
plt.plot(ks, sil_orig, marker="o", label="Original space")
|
||||
plt.plot(ks, sil_pca, marker="o", label="PCA 2D")
|
||||
plt.xlabel("k")
|
||||
plt.ylabel("Silhouette Score")
|
||||
plt.title("Clustering Quality: Before vs After PCA")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## PCA cluster visualization (best k)
|
||||
|
||||
|
||||
|
||||
```python
|
||||
best_k = ks[np.argmax(sil_pca)]
|
||||
|
||||
plt.scatter(Xp[:, 0], Xp[:, 1], c=labels_pca[best_k], s=5)
|
||||
plt.title(f"K-means on PCA space (k={best_k})")
|
||||
plt.show()
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## random subspace clustering
|
||||
|
||||
|
||||
|
||||
```python
|
||||
rng = np.random.default_rng(42)
|
||||
idx = rng.choice(X.shape[1], size=30, replace=False)
|
||||
|
||||
X_sub = X[:, idx]
|
||||
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
sil_sub = []
|
||||
|
||||
for k in ks:
|
||||
km = KMeans(n_clusters=k, init="k-means++", n_init=10, random_state=42)
|
||||
labels = km.fit_predict(X_sub)
|
||||
sil_sub.append(silhouette_score(X_sub, labels))
|
||||
|
||||
```
|
||||
|
||||
## final comparison plot
|
||||
|
||||
|
||||
|
||||
```python
|
||||
plt.plot(ks, sil_orig, label="Original")
|
||||
plt.plot(ks, sil_pca, label="PCA 2D")
|
||||
plt.plot(ks, sil_sub, label="Random Subspace")
|
||||
plt.xlabel("k")
|
||||
plt.ylabel("Silhouette Score")
|
||||
plt.title("Subspace Clustering Comparison")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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+257
@@ -0,0 +1,257 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "6b4e3004",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Silhouette (high-D): 0.11498283339035163\n",
|
||||
"Silhouette (PCA 2D): 0.3707790707560126\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Silhouette (random subspace): 0.20570999740812154\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Jupyter Notebook: K-means & Subspace Clustering (Plots Only)\n",
|
||||
"# Focus: generating figures for the report (elbow, silhouette, PCA plots, comparisons)\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"from sklearn.cluster import KMeans, DBSCAN\n",
|
||||
"from sklearn.metrics import silhouette_score\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"\n",
|
||||
"plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
|
||||
"\n",
|
||||
"# -----------------------------\n",
|
||||
"# Helpers\n",
|
||||
"# -----------------------------\n",
|
||||
"\n",
|
||||
"def load_uci_csv(url, header=None):\n",
|
||||
" return pd.read_csv(url, header=header)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def preprocess_numeric(df):\n",
|
||||
" X = df.select_dtypes(include=[np.number])\n",
|
||||
" X = SimpleImputer(strategy=\"median\").fit_transform(X)\n",
|
||||
" X = StandardScaler().fit_transform(X)\n",
|
||||
" return X\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def kmeans_experiment(X, k_range, title_prefix=\"\"):\n",
|
||||
" inertia = []\n",
|
||||
" silhouette = []\n",
|
||||
"\n",
|
||||
" for k in k_range:\n",
|
||||
" km = KMeans(n_clusters=k, init=\"k-means++\", n_init=10, random_state=42)\n",
|
||||
" labels = km.fit_predict(X)\n",
|
||||
" inertia.append(km.inertia_)\n",
|
||||
" silhouette.append(silhouette_score(X, labels))\n",
|
||||
"\n",
|
||||
" # Elbow\n",
|
||||
" plt.plot(k_range, inertia, marker=\"o\")\n",
|
||||
" plt.xlabel(\"k\")\n",
|
||||
" plt.ylabel(\"Inertia\")\n",
|
||||
" plt.title(f\"{title_prefix} – Elbow\")\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
" # Silhouette\n",
|
||||
" plt.plot(k_range, silhouette, marker=\"o\")\n",
|
||||
" plt.xlabel(\"k\")\n",
|
||||
" plt.ylabel(\"Silhouette score\")\n",
|
||||
" plt.title(f\"{title_prefix} – Silhouette\")\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# =============================\n",
|
||||
"# PART 1 — K-MEANS (UCI)\n",
|
||||
"# =============================\n",
|
||||
"\n",
|
||||
"# ---- Abalone ----\n",
|
||||
"abalone_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\"\n",
|
||||
"abalone = load_uci_csv(abalone_url)\n",
|
||||
"X_abalone = preprocess_numeric(abalone)\n",
|
||||
"\n",
|
||||
"k_range = range(2, 11)\n",
|
||||
"kmeans_experiment(X_abalone, k_range, \"Abalone\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ---- Covertype (sampled, very large) ----\n",
|
||||
"covertype_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz\"\n",
|
||||
"covertype = pd.read_csv(covertype_url, header=None, compression=\"gzip\")\n",
|
||||
"\n",
|
||||
"# sample for speed\n",
|
||||
"covertype_sample = covertype.sample(n=20000, random_state=42)\n",
|
||||
"X_covertype = preprocess_numeric(covertype_sample)\n",
|
||||
"\n",
|
||||
"k_range = range(2, 15)\n",
|
||||
"kmeans_experiment(X_covertype, k_range, \"Covertype (sampled)\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# =============================\n",
|
||||
"# PART 2 — SUBSPACE CLUSTERING\n",
|
||||
"# =============================\n",
|
||||
"\n",
|
||||
"# ---- Communities and Crime (>100 features) ----\n",
|
||||
"crime_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/communities/communities.data\"\n",
|
||||
"crime = load_uci_csv(crime_url)\n",
|
||||
"\n",
|
||||
"X_crime = preprocess_numeric(crime)\n",
|
||||
"\n",
|
||||
"# ---- PCA 2D ----\n",
|
||||
"pca2 = PCA(n_components=2)\n",
|
||||
"X_pca2 = pca2.fit_transform(X_crime)\n",
|
||||
"\n",
|
||||
"plt.scatter(X_pca2[:, 0], X_pca2[:, 1], s=5)\n",
|
||||
"plt.title(\"Communities & Crime – PCA 2D\")\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"# ---- PCA 3D ----\n",
|
||||
"pca3 = PCA(n_components=3)\n",
|
||||
"X_pca3 = pca3.fit_transform(X_crime)\n",
|
||||
"\n",
|
||||
"fig = plt.figure()\n",
|
||||
"ax = fig.add_subplot(111, projection=\"3d\")\n",
|
||||
"ax.scatter(X_pca3[:, 0], X_pca3[:, 1], X_pca3[:, 2], s=5)\n",
|
||||
"ax.set_title(\"Communities & Crime – PCA 3D\")\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ---- Clustering BEFORE PCA (high-D) ----\n",
|
||||
"km_hd = KMeans(n_clusters=4, n_init=10, random_state=42)\n",
|
||||
"labels_hd = km_hd.fit_predict(X_crime)\n",
|
||||
"sil_hd = silhouette_score(X_crime, labels_hd)\n",
|
||||
"\n",
|
||||
"# ---- Clustering AFTER PCA (2D) ----\n",
|
||||
"km_2d = KMeans(n_clusters=4, n_init=10, random_state=42)\n",
|
||||
"labels_2d = km_2d.fit_predict(X_pca2)\n",
|
||||
"sil_2d = silhouette_score(X_pca2, labels_2d)\n",
|
||||
"\n",
|
||||
"print(\"Silhouette (high-D):\", sil_hd)\n",
|
||||
"print(\"Silhouette (PCA 2D):\", sil_2d)\n",
|
||||
"\n",
|
||||
"plt.scatter(X_pca2[:, 0], X_pca2[:, 1], c=labels_2d, s=5)\n",
|
||||
"plt.title(\"K-means on PCA 2D\")\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ---- Random subspace clustering ----\n",
|
||||
"np.random.seed(42)\n",
|
||||
"subspace_idx = np.random.choice(X_crime.shape[1], size=20, replace=False)\n",
|
||||
"X_sub = X_crime[:, subspace_idx]\n",
|
||||
"\n",
|
||||
"km_sub = KMeans(n_clusters=4, n_init=10, random_state=42)\n",
|
||||
"labels_sub = km_sub.fit_predict(X_sub)\n",
|
||||
"sil_sub = silhouette_score(X_sub, labels_sub)\n",
|
||||
"\n",
|
||||
"print(\"Silhouette (random subspace):\", sil_sub)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "ml2",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.14.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in new issue
Block a user