ahc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64f1474b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, KBinsDiscretizer\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0eaf215",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(X):\n",
    "    ct = ColumnTransformer(transformers=[\n",
    "        (\"encoder\", OneHotEncoder(sparse=False), [4, 5]),\n",
    "        (\"discretizer\", KBinsDiscretizer(n_bins=[3, 3, 3, 3], encode='onehot-dense', strategy='quantile'), [0, 1, 2, 3])\n",
    "    ], remainder=\"passthrough\")\n",
    "    X = np.array(ct.fit_transform(X))\n",
    "\n",
    "    sc = StandardScaler()\n",
    "    X = sc.fit_transform(X)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fac885a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scan(file_name):\n",
    "    dataset = pd.read_csv(file_name)\n",
    "    X = dataset.iloc[:, :-1].values\n",
    "    Y = dataset.iloc[:, -1].values\n",
    "    return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c74b7247",
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_means(X, Y):\n",
    "    cluster = KMeans(n_clusters=6, init='k-means++', random_state=42)\n",
    "    Y_pred = cluster.fit_predict(X)\n",
    "    Y_pred = adapt(Y_pred, Y)\n",
    "    result(accuracy_score(Y, Y_pred), 'K-Means')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f97102e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def result(accuracy, algorithm):\n",
    "    print(f\"{algorithm}:\")\n",
    "    print(f\"Accuracy= {accuracy}\")\n",
    "    print(\"--------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37d2af5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def agglomerative(X, Y):\n",
    "    cluster = AgglomerativeClustering(n_clusters=6, affinity='euclidean', linkage='ward')\n",
    "    Y_pred = cluster.fit_predict(X)\n",
    "    Y_pred = adapt(Y_pred, Y)\n",
    "    result(accuracy_score(Y, Y_pred), 'Hierarchical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "543d387e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_wcss(X):\n",
    "    wcss = []\n",
    "    for i in range(1, 11):\n",
    "        kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
    "        kmeans.fit(X)\n",
    "        wcss.append(kmeans.inertia_)\n",
    "    plt.plot(range(1, 11), wcss)\n",
    "    plt.title('The Elbow Method')\n",
    "    plt.xlabel('Number of clusters')\n",
    "    plt.ylabel('WCSS')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8840572f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adapt(Y_pred, Y):\n",
    "    newY_pred = np.array(range(len(Y_pred)))\n",
    "    for i in range(0, 6):\n",
    "        data = Counter(Y[Y_pred == i])\n",
    "        newY_pred[Y_pred == i] = data.most_common(1)[0][0]\n",
    "    return newY_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd0b252c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_dendrogram(X):\n",
    "    sch.dendrogram(sch.linkage(X, method='ward'))\n",
    "    plt.title('Dendrogram')\n",
    "    plt.xlabel('Clusters')\n",
    "    plt.ylabel('Euclidean distances')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87feabaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    X, Y = scan('Stars.csv')\n",
    "    X = preprocessing(X)\n",
    "\n",
    "    draw_wcss(X)\n",
    "    draw_dendrogram(X)\n",
    "\n",
    "    k_means(X, Y)\n",
    "    agglomerative(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0993d5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Means:\n",
      "Accuracy= 0.7458333333333333\n",
      "--------------------------\n",
      "Hierarchical:\n",
      "Accuracy= 0.6083333333333333\n",
      "--------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\cluster\\_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}