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@polinabee
Created December 21, 2020 23:02
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MST clustering from a group project assignment
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deterministic Models and Optimization: Clustering methods, K-Means¶\n",
"By Polina, Livia, Buelent and Antonios"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from numpy import random\n",
"import pandas as pd\n",
"from sklearn.neighbors import DistanceMetric\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import math\n",
"import numpy.random as rd\n",
"from python_algorithms.basic import union_find"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Defining the euclidean distances"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the first step, we need to define euclidean distances. "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"def euclidean(v1, v2):\n",
" return sum((p-q)**2 for p, q in zip(v1, v2)) ** .5"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def euclidean_distance(df):\n",
" df = df.to_numpy()\n",
" weight_matrix = np.zeros(shape = (len(df),len(df)))\n",
" i = 0\n",
" for point_x in df:\n",
" j = 0\n",
" for point_y in df:\n",
" dist = np.linalg.norm(point_x - point_y)\n",
" weight_matrix[i][j] = dist\n",
" j += 1\n",
" i +=1\n",
" return weight_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Defining the dunn index"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"# define dunn index since sklearn package is out of date\n",
"\n",
"def δ(ck, cl):\n",
" values = np.ones([len(ck), len(cl)])\n",
" \n",
" for i in range(0, len(ck)):\n",
" for j in range(0, len(cl)):\n",
" values[i, j] = np.linalg.norm(ck[i]-cl[j])\n",
" \n",
" return np.min(values)\n",
"\n",
"def Δ(ci):\n",
" values = np.zeros([len(ci), len(ci)])\n",
" \n",
" for i in range(0, len(ci)):\n",
" for j in range(0, len(ci)):\n",
" values[i, j] = np.linalg.norm(ci[i]-ci[j])\n",
" \n",
" return np.max(values)\n",
"\n",
"def dunn(k_list):\n",
" δs = np.ones([len(k_list), len(k_list)])\n",
" Δs = np.zeros([len(k_list), 1])\n",
" l_range = list(range(0, len(k_list)))\n",
" \n",
" for k in l_range:\n",
" for l in (l_range[0:k]+l_range[k+1:]):\n",
" δs[k, l] = δ(k_list[k], k_list[l])\n",
" \n",
" Δs[k] = Δ(k_list[k])\n",
"\n",
" di = np.min(δs)/np.max(Δs)\n",
" return di"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MST_Clustering\n",
"This is the MST clustering algorithm if the dimension of the inputs are 2. For the Thyroid dataset we will need to adjust it a tiny bit and do it for 5 dimensions. "
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
"class Node_2d:\n",
" def __init__(self, point, p):\n",
" self.x = point[0]\n",
" self.y = point[1]\n",
" self.parent = p\n",
" self.rank = 0 # number of neighbours\n",
"\n",
"\n",
"class Node_5d:\n",
" def __init__(self, point, p):\n",
" self.x = point[0]\n",
" self.y = point[1]\n",
" self.z = point[2]\n",
" self.w = point[3]\n",
" self.q = point[4]\n",
" self.parent = p\n",
" self.rank = 0 # number of neighbours\n",
" \n",
" \n",
"class Edge:\n",
" def __init__(self, u, v, w):\n",
" self.u = u #endpoint of the edge\n",
" self.v = v #the other endpoint of the edge\n",
" self.weight = w #comes from eucledian distance\n",
" \n",
"#to find the parent of the given node\n",
"def Find(i, nodes):\n",
" if (i != nodes[i].parent) :\n",
" nodes[i].parent = Find(nodes[i].parent, nodes)\n",
" return nodes[i].parent\n",
"\n",
"#to unite the two edges\n",
"def Union(u, v, nodes):\n",
" r1 = Find(u, nodes) #parent of first point\n",
" r2 = Find(v, nodes) #parent of second point\n",
" if (r1 != r2):\n",
" if (nodes[r1].rank > nodes[r2].rank):\n",
" nodes[r2].parent = r1\n",
" else:\n",
" nodes[r1].parent = r2\n",
" if (nodes[r1].rank == nodes[r2].rank):\n",
" nodes[r2].rank += 1\n",
" \n",
"# MST Kruskal algorithm for 2d data\n",
"def mst_kruskal_2d(df,k):\n",
" n = df.shape[0]\n",
" nodes = []\n",
" for i in range(n):\n",
" nodes.append(Node_2d(df.iloc[i], i))\n",
" \n",
" edges = []\n",
" \n",
" for i in range(n):\n",
" point_a=df.iloc[i]\n",
" for j in range(i+1, n):\n",
" point_b=df.iloc[j]\n",
" edges.append(Edge(i, j, euclidean(point_a,point_b)))\n",
" \n",
" edges = sorted(edges, key=lambda edge: edge.weight) #Sort the edges \n",
" num_edges_added = 0\n",
" \n",
" for edge in edges: #to iterate through the sorted edges list\n",
" if (num_edges_added <= n-k):\n",
" if Find(edge.u, nodes) != Find(edge.v, nodes):\n",
" num_edges_added += 1\n",
" Union(edge.u, edge.v, nodes)\n",
" else:\n",
" pass\n",
" #if (num_edges_added > n-k):\n",
" # pass\n",
" output = []\n",
" for element in nodes:\n",
" output.append((element.x,element.y,element.parent))\n",
" \n",
" output = pd.DataFrame(output, columns = [df.columns[0],df.columns[1],\"label\"])\n",
" return output\n",
"\n",
"\n",
"# MST Kruskal algorithm for 5d data\n",
"def mst_kruskal_5d(df,k):\n",
" n = df.shape[0]\n",
" nodes = []\n",
" for i in range(n):\n",
" nodes.append(Node_5d(df.iloc[i], i))\n",
" \n",
" edges = []\n",
" \n",
" for i in range(n):\n",
" point_a=df.iloc[i]\n",
" for j in range(i+1, n):\n",
" point_b=df.iloc[j]\n",
" edges.append(Edge(i, j, euclidean(point_a,point_b)))\n",
" \n",
" edges = sorted(edges, key=lambda edge: edge.weight) #Sort the edges \n",
" num_edges_added = 0\n",
" \n",
" for edge in edges: #to iterate through the sorted edges list\n",
" if (num_edges_added <= n-k):\n",
" if Find(edge.u, nodes) != Find(edge.v, nodes):\n",
" num_edges_added += 1\n",
" Union(edge.u, edge.v, nodes)\n",
" else:\n",
" pass\n",
" #if (num_edges_added > n-k):\n",
" # pass\n",
" output = []\n",
" for element in nodes:\n",
" output.append((element.x,element.y,element.z, element.w, element.q, element.parent))\n",
" \n",
" output = pd.DataFrame(output, columns = [df.columns[0],df.columns[1],df.columns[2],df.columns[3],df.columns[4],\"label\"])\n",
" return output\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Applying the MST algorithm "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Synthetic part"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-100-0c3e24eac52a>:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" synthetic = pd.read_csv('synthetic.txt', sep=\" \", header=None)\n"
]
}
],
"source": [
"synthetic = pd.read_csv('synthetic.txt', sep=\" \", header=None)\n",
"synthetic.columns = [\"a\", \"b\"]"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"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>a</th>\n",
" <th>b</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>664159</td>\n",
" <td>550946</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>665845</td>\n",
" <td>557965</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>597173</td>\n",
" <td>575538</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>618600</td>\n",
" <td>551446</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>635690</td>\n",
" <td>608046</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4995</th>\n",
" <td>665426</td>\n",
" <td>853940</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4996</th>\n",
" <td>691827</td>\n",
" <td>863963</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4997</th>\n",
" <td>650661</td>\n",
" <td>861267</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4998</th>\n",
" <td>599647</td>\n",
" <td>858702</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4999</th>\n",
" <td>684091</td>\n",
" <td>842566</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5000 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" a b label\n",
"0 664159 550946 2451\n",
"1 665845 557965 2451\n",
"2 597173 575538 2451\n",
"3 618600 551446 2451\n",
"4 635690 608046 2451\n",
"... ... ... ...\n",
"4995 665426 853940 4641\n",
"4996 691827 863963 4641\n",
"4997 650661 861267 4641\n",
"4998 599647 858702 4641\n",
"4999 684091 842566 4641\n",
"\n",
"[5000 rows x 3 columns]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"synthetic_cluster = mst_kruskal_2d(synthetic, 15)\n",
"synthetic_cluster"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'MST Clustering algorithm, K = 15')"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# implementation and visualization using class data\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"\n",
"g = sns.scatterplot(data=synthetic_cluster,x='a',y='b',hue='label',alpha=0.3,s=10,palette='colorblind')\n",
"g.legend_.remove()\n",
"g.set_title('MST Clustering algorithm, K = 15')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assessing the quality "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need to assess the quality of the clustering. We will consider the two indices."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Davies Bouldin"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import davies_bouldin_score"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best score is with 1.2079539844545797 clusters :15\n"
]
}
],
"source": [
"score = davies_bouldin_score(synthetic_cluster[['a','b']], synthetic_cluster.label)\n",
"print(f'Best score is with {score} clusters :15')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dunn Index "
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [],
"source": [
"def get_dunn_2d(df):\n",
"\n",
" #mst = my_kruskal_2d(d,k)\n",
" k_list = []\n",
" for i in df.label.unique().tolist():\n",
" k_list.append(df[df.label == i].values)\n",
" return dunn(k_list)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"score_dunn_2d = get_dunn_2d(synthetic_cluster)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.3003734337888515e-06"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_dunn_2d"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Applying the algorithms to the dataset thyroid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the dataset "
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-132-53eaebe6c883>:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" thyroid = pd.read_csv('thyroid.txt', sep=\" \", header=None)\n"
]
}
],
"source": [
"thyroid = pd.read_csv('thyroid.txt', sep=\" \", header=None)\n",
"thyroid.apply(pd.to_numeric)\n",
"thyroid.columns = ['a', 'b','c','d','e']"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" <th>e</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3195023</td>\n",
" <td>3455331</td>\n",
" <td>3497964</td>\n",
" <td>3068822</td>\n",
" <td>3206710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3651455</td>\n",
" <td>3412754</td>\n",
" <td>4131996</td>\n",
" <td>3248619</td>\n",
" <td>3603214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4716462</td>\n",
" <td>4051411</td>\n",
" <td>3638860</td>\n",
" <td>3150548</td>\n",
" <td>2946503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3347167</td>\n",
" <td>2433481</td>\n",
" <td>3075276</td>\n",
" <td>3150548</td>\n",
" <td>3058020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3042879</td>\n",
" <td>2859252</td>\n",
" <td>3004828</td>\n",
" <td>3166893</td>\n",
" <td>2859768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>4031814</td>\n",
" <td>2688944</td>\n",
" <td>2863932</td>\n",
" <td>3199583</td>\n",
" <td>4297098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211</th>\n",
" <td>5629326</td>\n",
" <td>2199307</td>\n",
" <td>2441244</td>\n",
" <td>3624557</td>\n",
" <td>3652778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>212</th>\n",
" <td>2890735</td>\n",
" <td>2390904</td>\n",
" <td>2934380</td>\n",
" <td>3117858</td>\n",
" <td>3491697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>2434303</td>\n",
" <td>2305750</td>\n",
" <td>2723036</td>\n",
" <td>3264964</td>\n",
" <td>4433397</td>\n",
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" <tr>\n",
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" <td>2433481</td>\n",
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" <td>3702341</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>215 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" a b c d e\n",
"0 3195023 3455331 3497964 3068822 3206710\n",
"1 3651455 3412754 4131996 3248619 3603214\n",
"2 4716462 4051411 3638860 3150548 2946503\n",
"3 3347167 2433481 3075276 3150548 3058020\n",
"4 3042879 2859252 3004828 3166893 2859768\n",
".. ... ... ... ... ...\n",
"210 4031814 2688944 2863932 3199583 4297098\n",
"211 5629326 2199307 2441244 3624557 3652778\n",
"212 2890735 2390904 2934380 3117858 3491697\n",
"213 2434303 2305750 2723036 3264964 4433397\n",
"214 2814663 2433481 2934380 3134203 3702341\n",
"\n",
"[215 rows x 5 columns]"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"thyroid"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Davies-Bouldin score calculations\n",
"Best score is with 2 clusters :0.23289221878248303\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import davies_bouldin_score\n",
"\n",
"print('Running Davies-Bouldin score calculations')\n",
"scores = []\n",
"k = []\n",
"for n in range(2,40):\n",
" tdata = mst_kruskal_5d(thyroid, n)\n",
" k.append(n)\n",
" score = davies_bouldin_score(tdata[['a','b','c','d','e']], tdata.label)\n",
" scores.append(score)\n",
"\n",
"sns.lineplot(x=k,y=scores).set_title('Davies-Bouldin Score of K Clusters')\n",
"\n",
"print(f'Best score is with {k[scores.index(min(scores))]} clusters :{min(scores)}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing the quality measures using dunn index"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
"def get_dunn(d,k):\n",
" output = mst_kruskal_5d(d,k)\n",
" k_list = []\n",
" for i in output.label.unique().tolist():\n",
" k_list.append(output[output.label == i].values)\n",
" return dunn(k_list)"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Dunn Score calculations: \n",
"Best score is with 45 clusters :2.9267480213441506e-07\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"scores = []\n",
"k = []\n",
"print('Running Dunn Score calculations: ')\n",
"for n in range(2,50):\n",
" score = get_dunn(thyroid,n)\n",
" k.append(n)\n",
" scores.append(score)\n",
"\n",
"sns.lineplot(x=k,y=scores).set_title('Dunn Score of K Clusters')\n",
"print(f'Best score is with {k[scores.index(max(scores))]} clusters :{max(scores)}')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deterministic Models and Optimization: Clustering methods, K-Means¶\n",
"By Polina, Livia, Buelent and Antonios"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from numpy import random\n",
"import pandas as pd\n",
"from sklearn.neighbors import DistanceMetric\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import math\n",
"import numpy.random as rd\n",
"from python_algorithms.basic import union_find"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Defining the euclidean distances"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the first step, we need to define euclidean distances. "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"def euclidean(v1, v2):\n",
" return sum((p-q)**2 for p, q in zip(v1, v2)) ** .5"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def euclidean_distance(df):\n",
" df = df.to_numpy()\n",
" weight_matrix = np.zeros(shape = (len(df),len(df)))\n",
" i = 0\n",
" for point_x in df:\n",
" j = 0\n",
" for point_y in df:\n",
" dist = np.linalg.norm(point_x - point_y)\n",
" weight_matrix[i][j] = dist\n",
" j += 1\n",
" i +=1\n",
" return weight_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Defining the dunn index"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"# define dunn index since sklearn package is out of date\n",
"\n",
"def δ(ck, cl):\n",
" values = np.ones([len(ck), len(cl)])\n",
" \n",
" for i in range(0, len(ck)):\n",
" for j in range(0, len(cl)):\n",
" values[i, j] = np.linalg.norm(ck[i]-cl[j])\n",
" \n",
" return np.min(values)\n",
"\n",
"def Δ(ci):\n",
" values = np.zeros([len(ci), len(ci)])\n",
" \n",
" for i in range(0, len(ci)):\n",
" for j in range(0, len(ci)):\n",
" values[i, j] = np.linalg.norm(ci[i]-ci[j])\n",
" \n",
" return np.max(values)\n",
"\n",
"def dunn(k_list):\n",
" δs = np.ones([len(k_list), len(k_list)])\n",
" Δs = np.zeros([len(k_list), 1])\n",
" l_range = list(range(0, len(k_list)))\n",
" \n",
" for k in l_range:\n",
" for l in (l_range[0:k]+l_range[k+1:]):\n",
" δs[k, l] = δ(k_list[k], k_list[l])\n",
" \n",
" Δs[k] = Δ(k_list[k])\n",
"\n",
" di = np.min(δs)/np.max(Δs)\n",
" return di"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MST_Clustering\n",
"This is the MST clustering algorithm if the dimension of the inputs are 2. For the Thyroid dataset we will need to adjust it a tiny bit and do it for 5 dimensions. "
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
"class Node_2d:\n",
" def __init__(self, point, p):\n",
" self.x = point[0]\n",
" self.y = point[1]\n",
" self.parent = p\n",
" self.rank = 0 # number of neighbours\n",
"\n",
"\n",
"class Node_5d:\n",
" def __init__(self, point, p):\n",
" self.x = point[0]\n",
" self.y = point[1]\n",
" self.z = point[2]\n",
" self.w = point[3]\n",
" self.q = point[4]\n",
" self.parent = p\n",
" self.rank = 0 # number of neighbours\n",
" \n",
" \n",
"class Edge:\n",
" def __init__(self, u, v, w):\n",
" self.u = u #endpoint of the edge\n",
" self.v = v #the other endpoint of the edge\n",
" self.weight = w #comes from eucledian distance\n",
" \n",
"#to find the parent of the given node\n",
"def Find(i, nodes):\n",
" if (i != nodes[i].parent) :\n",
" nodes[i].parent = Find(nodes[i].parent, nodes)\n",
" return nodes[i].parent\n",
"\n",
"#to unite the two edges\n",
"def Union(u, v, nodes):\n",
" r1 = Find(u, nodes) #parent of first point\n",
" r2 = Find(v, nodes) #parent of second point\n",
" if (r1 != r2):\n",
" if (nodes[r1].rank > nodes[r2].rank):\n",
" nodes[r2].parent = r1\n",
" else:\n",
" nodes[r1].parent = r2\n",
" if (nodes[r1].rank == nodes[r2].rank):\n",
" nodes[r2].rank += 1\n",
" \n",
"# MST Kruskal algorithm for 2d data\n",
"def mst_kruskal_2d(df,k):\n",
" n = df.shape[0]\n",
" nodes = []\n",
" for i in range(n):\n",
" nodes.append(Node_2d(df.iloc[i], i))\n",
" \n",
" edges = []\n",
" \n",
" for i in range(n):\n",
" point_a=df.iloc[i]\n",
" for j in range(i+1, n):\n",
" point_b=df.iloc[j]\n",
" edges.append(Edge(i, j, euclidean(point_a,point_b)))\n",
" \n",
" edges = sorted(edges, key=lambda edge: edge.weight) #Sort the edges \n",
" num_edges_added = 0\n",
" \n",
" for edge in edges: #to iterate through the sorted edges list\n",
" if (num_edges_added <= n-k):\n",
" if Find(edge.u, nodes) != Find(edge.v, nodes):\n",
" num_edges_added += 1\n",
" Union(edge.u, edge.v, nodes)\n",
" else:\n",
" pass\n",
" #if (num_edges_added > n-k):\n",
" # pass\n",
" output = []\n",
" for element in nodes:\n",
" output.append((element.x,element.y,element.parent))\n",
" \n",
" output = pd.DataFrame(output, columns = [df.columns[0],df.columns[1],\"label\"])\n",
" return output\n",
"\n",
"\n",
"# MST Kruskal algorithm for 5d data\n",
"def mst_kruskal_5d(df,k):\n",
" n = df.shape[0]\n",
" nodes = []\n",
" for i in range(n):\n",
" nodes.append(Node_5d(df.iloc[i], i))\n",
" \n",
" edges = []\n",
" \n",
" for i in range(n):\n",
" point_a=df.iloc[i]\n",
" for j in range(i+1, n):\n",
" point_b=df.iloc[j]\n",
" edges.append(Edge(i, j, euclidean(point_a,point_b)))\n",
" \n",
" edges = sorted(edges, key=lambda edge: edge.weight) #Sort the edges \n",
" num_edges_added = 0\n",
" \n",
" for edge in edges: #to iterate through the sorted edges list\n",
" if (num_edges_added <= n-k):\n",
" if Find(edge.u, nodes) != Find(edge.v, nodes):\n",
" num_edges_added += 1\n",
" Union(edge.u, edge.v, nodes)\n",
" else:\n",
" pass\n",
" #if (num_edges_added > n-k):\n",
" # pass\n",
" output = []\n",
" for element in nodes:\n",
" output.append((element.x,element.y,element.z, element.w, element.q, element.parent))\n",
" \n",
" output = pd.DataFrame(output, columns = [df.columns[0],df.columns[1],df.columns[2],df.columns[3],df.columns[4],\"label\"])\n",
" return output\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Applying the MST algorithm "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Synthetic part"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-100-0c3e24eac52a>:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" synthetic = pd.read_csv('synthetic.txt', sep=\" \", header=None)\n"
]
}
],
"source": [
"synthetic = pd.read_csv('synthetic.txt', sep=\" \", header=None)\n",
"synthetic.columns = [\"a\", \"b\"]"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"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>a</th>\n",
" <th>b</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>664159</td>\n",
" <td>550946</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>665845</td>\n",
" <td>557965</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>597173</td>\n",
" <td>575538</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>618600</td>\n",
" <td>551446</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>635690</td>\n",
" <td>608046</td>\n",
" <td>2451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4995</th>\n",
" <td>665426</td>\n",
" <td>853940</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4996</th>\n",
" <td>691827</td>\n",
" <td>863963</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4997</th>\n",
" <td>650661</td>\n",
" <td>861267</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4998</th>\n",
" <td>599647</td>\n",
" <td>858702</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4999</th>\n",
" <td>684091</td>\n",
" <td>842566</td>\n",
" <td>4641</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5000 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" a b label\n",
"0 664159 550946 2451\n",
"1 665845 557965 2451\n",
"2 597173 575538 2451\n",
"3 618600 551446 2451\n",
"4 635690 608046 2451\n",
"... ... ... ...\n",
"4995 665426 853940 4641\n",
"4996 691827 863963 4641\n",
"4997 650661 861267 4641\n",
"4998 599647 858702 4641\n",
"4999 684091 842566 4641\n",
"\n",
"[5000 rows x 3 columns]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"synthetic_cluster = mst_kruskal_2d(synthetic, 15)\n",
"synthetic_cluster"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'MST Clustering algorithm, K = 15')"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# implementation and visualization using class data\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"\n",
"g = sns.scatterplot(data=synthetic_cluster,x='a',y='b',hue='label',alpha=0.3,s=10,palette='colorblind')\n",
"g.legend_.remove()\n",
"g.set_title('MST Clustering algorithm, K = 15')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assessing the quality "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need to assess the quality of the clustering. We will consider the two indices."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Davies Bouldin"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import davies_bouldin_score"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best score is with 1.2079539844545797 clusters :15\n"
]
}
],
"source": [
"score = davies_bouldin_score(synthetic_cluster[['a','b']], synthetic_cluster.label)\n",
"print(f'Best score is with {score} clusters :15')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dunn Index "
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [],
"source": [
"def get_dunn_2d(df):\n",
"\n",
" #mst = my_kruskal_2d(d,k)\n",
" k_list = []\n",
" for i in df.label.unique().tolist():\n",
" k_list.append(df[df.label == i].values)\n",
" return dunn(k_list)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"score_dunn_2d = get_dunn_2d(synthetic_cluster)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.3003734337888515e-06"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_dunn_2d"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Applying the algorithms to the dataset thyroid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the dataset "
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-132-53eaebe6c883>:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" thyroid = pd.read_csv('thyroid.txt', sep=\" \", header=None)\n"
]
}
],
"source": [
"thyroid = pd.read_csv('thyroid.txt', sep=\" \", header=None)\n",
"thyroid.apply(pd.to_numeric)\n",
"thyroid.columns = ['a', 'b','c','d','e']"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" <th>e</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3195023</td>\n",
" <td>3455331</td>\n",
" <td>3497964</td>\n",
" <td>3068822</td>\n",
" <td>3206710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3651455</td>\n",
" <td>3412754</td>\n",
" <td>4131996</td>\n",
" <td>3248619</td>\n",
" <td>3603214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4716462</td>\n",
" <td>4051411</td>\n",
" <td>3638860</td>\n",
" <td>3150548</td>\n",
" <td>2946503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3347167</td>\n",
" <td>2433481</td>\n",
" <td>3075276</td>\n",
" <td>3150548</td>\n",
" <td>3058020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3042879</td>\n",
" <td>2859252</td>\n",
" <td>3004828</td>\n",
" <td>3166893</td>\n",
" <td>2859768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>4031814</td>\n",
" <td>2688944</td>\n",
" <td>2863932</td>\n",
" <td>3199583</td>\n",
" <td>4297098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211</th>\n",
" <td>5629326</td>\n",
" <td>2199307</td>\n",
" <td>2441244</td>\n",
" <td>3624557</td>\n",
" <td>3652778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>212</th>\n",
" <td>2890735</td>\n",
" <td>2390904</td>\n",
" <td>2934380</td>\n",
" <td>3117858</td>\n",
" <td>3491697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>2434303</td>\n",
" <td>2305750</td>\n",
" <td>2723036</td>\n",
" <td>3264964</td>\n",
" <td>4433397</td>\n",
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" <tr>\n",
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" <td>2433481</td>\n",
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" <td>3702341</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>215 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" a b c d e\n",
"0 3195023 3455331 3497964 3068822 3206710\n",
"1 3651455 3412754 4131996 3248619 3603214\n",
"2 4716462 4051411 3638860 3150548 2946503\n",
"3 3347167 2433481 3075276 3150548 3058020\n",
"4 3042879 2859252 3004828 3166893 2859768\n",
".. ... ... ... ... ...\n",
"210 4031814 2688944 2863932 3199583 4297098\n",
"211 5629326 2199307 2441244 3624557 3652778\n",
"212 2890735 2390904 2934380 3117858 3491697\n",
"213 2434303 2305750 2723036 3264964 4433397\n",
"214 2814663 2433481 2934380 3134203 3702341\n",
"\n",
"[215 rows x 5 columns]"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"thyroid"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Davies-Bouldin score calculations\n",
"Best score is with 2 clusters :0.23289221878248303\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import davies_bouldin_score\n",
"\n",
"print('Running Davies-Bouldin score calculations')\n",
"scores = []\n",
"k = []\n",
"for n in range(2,40):\n",
" tdata = mst_kruskal_5d(thyroid, n)\n",
" k.append(n)\n",
" score = davies_bouldin_score(tdata[['a','b','c','d','e']], tdata.label)\n",
" scores.append(score)\n",
"\n",
"sns.lineplot(x=k,y=scores).set_title('Davies-Bouldin Score of K Clusters')\n",
"\n",
"print(f'Best score is with {k[scores.index(min(scores))]} clusters :{min(scores)}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing the quality measures using dunn index"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
"def get_dunn(d,k):\n",
" output = mst_kruskal_5d(d,k)\n",
" k_list = []\n",
" for i in output.label.unique().tolist():\n",
" k_list.append(output[output.label == i].values)\n",
" return dunn(k_list)"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Dunn Score calculations: \n",
"Best score is with 45 clusters :2.9267480213441506e-07\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"scores = []\n",
"k = []\n",
"print('Running Dunn Score calculations: ')\n",
"for n in range(2,50):\n",
" score = get_dunn(thyroid,n)\n",
" k.append(n)\n",
" scores.append(score)\n",
"\n",
"sns.lineplot(x=k,y=scores).set_title('Dunn Score of K Clusters')\n",
"print(f'Best score is with {k[scores.index(max(scores))]} clusters :{max(scores)}')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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