{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e8eef48",
   "metadata": {},
   "source": [
    "# Lesson 22: unsupervised learning demonstration\n",
    "\n",
    "This notebook demonstrates key concepts and tools for training unsupervised models.\n",
    "\n",
    "1. Clustering\n",
    "    - Kmeans\n",
    "    - Hierarchical\n",
    "    - DBSCAN\n",
    "    - Clustering evaluation\n",
    "    - Determining cluster number\n",
    "\n",
    "2. Dimensionality reduction\n",
    "    - PCA\n",
    "    - Feature Agglomeration\n",
    "    - t-SNE\n",
    "\n",
    "3. Anomaly detection\n",
    "4. Association rule learning\n",
    "\n",
    "\n",
    "## Notebook set up\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "463812a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.patches import Patch\n",
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "from sklearn.datasets import make_blobs, fetch_kddcup99\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import FeatureAgglomeration\n",
    "from sklearn.metrics import silhouette_score, precision_score, recall_score, f1_score\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import OneClassSVM\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7012bb8b",
   "metadata": {},
   "source": [
    "### Dataset\n",
    "\n",
    "The notebook uses a synthetic dataset of 1,000 points generated by `make_blobs`, which places samples around a fixed number of cluster centres in 2D feature space. Using synthetic data makes it easy to visually verify that each algorithm has found the correct groupings. The same dataset is reused across the clustering and anomaly detection sections; a noisier 10-feature version is derived from it for the dimensionality reduction section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60a2f9f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.972185</td>\n",
       "      <td>0.046031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.652719</td>\n",
       "      <td>0.999023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.227944</td>\n",
       "      <td>0.217025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.083584</td>\n",
       "      <td>0.128545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.608505</td>\n",
       "      <td>-0.250835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   feature_0  feature_1\n",
       "0  -0.972185   0.046031\n",
       "1   0.652719   0.999023\n",
       "2  -1.227944   0.217025\n",
       "3  -1.083584   0.128545\n",
       "4  -1.608505  -0.250835"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of clusters\n",
    "n_clusters = 3\n",
    "\n",
    "# Generate synthetic dataset\n",
    "data = make_blobs(\n",
    "    n_samples=1000,\n",
    "    n_features=2,\n",
    "    centers=n_clusters,\n",
    "    cluster_std=0.6,\n",
    "    random_state=0\n",
    ")\n",
    "\n",
    "# Create dataframe with feature columns\n",
    "df = pd.DataFrame(data[0], columns=[f'feature_{i}' for i in range(data[0].shape[1])])\n",
    "\n",
    "# Standard scale the features\n",
    "scaler = StandardScaler()\n",
    "df[df.columns] = scaler.fit_transform(df[df.columns])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a0343ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Input data')\n",
    "plt.scatter(df['feature_0'], df['feature_1'], s=5, c='black')\n",
    "plt.xlabel('feature 0')\n",
    "plt.ylabel('feature 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa06f29",
   "metadata": {},
   "source": [
    "## 1. Clustering\n",
    "\n",
    "### 1.1. KMeans\n",
    "\n",
    "KMeans partitions data into k clusters by minimizing within-cluster variance. Each cluster is represented by its centroid.\n",
    "\n",
    "A **centroid** is the average position of all points in a cluster: the point you would land on if you averaged every feature across every member of the cluster. For cluster $C_k$ with $|C_k|$ points:\n",
    "\n",
    "$$\\mu_k = \\frac{1}{|C_k|} \\sum_{i \\in C_k} x_i$$\n",
    "\n",
    "For example, if a cluster contains points $(1, 2)$, $(3, 4)$ and $(5, 6)$, its centroid is $\\left(\\frac{1+3+5}{3},\\, \\frac{2+4+6}{3}\\right) = (3, 4)$.\n",
    "\n",
    "- Choose a value for k (the number of clusters).\n",
    "- Place k centroids at random positions in the feature space.\n",
    "- Assign each point to the nearest centroid.\n",
    "- Recalculate each centroid as the mean of its assigned points.\n",
    "- Repeat the assignment and update steps until centroids stop moving.\n",
    "\n",
    "Scikit-learn [KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db77210f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans - Silhouette: 0.652\n"
     ]
    }
   ],
   "source": [
    "# Fit KMeans clustering\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=315)\n",
    "kmeans_labels = kmeans.fit_predict(df)\n",
    "\n",
    "# Calculate metrics\n",
    "kmeans_silhouette = silhouette_score(df, kmeans_labels)\n",
    "\n",
    "print(f'KMeans - Silhouette: {kmeans_silhouette:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1adac4",
   "metadata": {},
   "source": [
    "### 1.2. Hierarchical clustering\n",
    "\n",
    "Hierarchical clustering builds a tree of clusters by merging clusters based on distance metrics.\n",
    "\n",
    "- Start with every point as its own cluster.\n",
    "- Find the two clusters closest to each other using a chosen linkage metric (e.g. Ward).\n",
    "- Merge those two clusters into one.\n",
    "- Repeat until the desired number of clusters remains.\n",
    "- The result can be visualized as a dendrogram showing the merge order.\n",
    "\n",
    "**Linkage metrics** determine how the distance between two clusters is calculated:\n",
    "\n",
    "| Linkage | Distance between clusters |\n",
    "|---------|--------------------------|\n",
    "| Single | Shortest distance between any two points across the clusters |\n",
    "| Complete | Longest distance between any two points across the clusters |\n",
    "| Average | Mean distance between all pairs of points across the clusters |\n",
    "| Ward | Increase in total within-cluster variance from merging |\n",
    "\n",
    "**Ward linkage** merges the pair of clusters that causes the smallest increase in total within-cluster variance. This tends to produce compact, evenly sized clusters and is the most commonly used linkage method.\n",
    "\n",
    "Scikit-learn [Agglomerative Clustering](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8697878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hierarchical - Silhouette: 0.648\n"
     ]
    }
   ],
   "source": [
    "# Fit hierarchical clustering\n",
    "hierarchical = AgglomerativeClustering(n_clusters=n_clusters)\n",
    "hierarchical_labels = hierarchical.fit_predict(df)\n",
    "\n",
    "# Calculate metrics\n",
    "hierarchical_silhouette = silhouette_score(df, hierarchical_labels)\n",
    "\n",
    "print(f'Hierarchical - Silhouette: {hierarchical_silhouette:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f472d779",
   "metadata": {},
   "source": [
    "### 1.3. DBSCAN\n",
    "\n",
    "DBSCAN groups points that are closely packed together, marking points in low-density regions as outliers. It does not require specifying the number of clusters.\n",
    "\n",
    "- Choose a radius `eps` and a minimum neighbor count `min_samples`.\n",
    "- Label a point as a **core point** if at least `min_samples` other points fall within radius `eps` of it.\n",
    "- A neighbor of a core point is called **reachable**. Reachability chains: if B is within `eps` of core point A, and C is within `eps` of core point B, then A, B and C all belong to the same cluster, even if A and C are far apart.\n",
    "- Grow each cluster by following these chains until no new reachable points remain.\n",
    "- Points that are not within `eps` of any core point are labeled as noise (outliers).\n",
    "\n",
    "The number of clusters is determined by how many separate chains exist in the data, not by how many points each chain contains.\n",
    "\n",
    "| Parameter | Effect |\n",
    "|-----------|--------|\n",
    "| `eps` | Larger values extend chains further, merging more points into the same cluster |\n",
    "| `min_samples` | Higher values require denser cores, which can break weak chains into noise |\n",
    "\n",
    "Scikit-learn [DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e226745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBSCAN found 3 clusters and 72 noise points\n",
      "DBSCAN - Silhouette: 0.683\n"
     ]
    }
   ],
   "source": [
    "# Fit DBSCAN clustering\n",
    "eps = 0.16\n",
    "dbscan = DBSCAN(eps=eps)\n",
    "dbscan_labels = dbscan.fit_predict(df)\n",
    "\n",
    "# Count clusters (excluding noise points labeled as -1)\n",
    "n_clusters = len(set(dbscan_labels)) - (1 if -1 in dbscan_labels else 0)\n",
    "n_noise = list(dbscan_labels).count(-1)\n",
    "\n",
    "print(f'DBSCAN found {n_clusters} clusters and {n_noise} noise points')\n",
    "\n",
    "# Calculate metrics (excluding noise points)\n",
    "mask = dbscan_labels != -1\n",
    "dbscan_silhouette = silhouette_score(df[mask], dbscan_labels[mask])\n",
    "\n",
    "print(f'DBSCAN - Silhouette: {dbscan_silhouette:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b4ad2f",
   "metadata": {},
   "source": [
    "### 1.4. Clustering evaluation\n",
    "\n",
    "#### 1.4.1. Silhouette score\n",
    "\n",
    "The silhouette score measures how similar a data point is to its own cluster compared to other clusters. For each point $i$:\n",
    "\n",
    "$$s(i) = \\frac{b(i) - a(i)}{\\max(a(i),\\, b(i))}$$\n",
    "\n",
    "Where $a(i)$ is the mean distance from point $i$ to all other points in its own cluster, and $b(i)$ is the mean distance from point $i$ to all points in the nearest other cluster.\n",
    "\n",
    "The overall score is the mean of $s(i)$ across all points. It ranges from -1 to 1: a high value means points are well matched to their own cluster and far from others; a score near 0 indicates overlapping clusters; negative values suggest a point may be in the wrong cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f571b44",
   "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>Method</th>\n",
       "      <th>Silhouette</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KMeans</td>\n",
       "      <td>0.652014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hierarchical</td>\n",
       "      <td>0.647966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DBSCAN</td>\n",
       "      <td>0.682974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Method  Silhouette\n",
       "0        KMeans    0.652014\n",
       "1  Hierarchical    0.647966\n",
       "2        DBSCAN    0.682974"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create comparison dataframe\n",
    "clustering_results = pd.DataFrame({\n",
    "    'Method': ['KMeans', 'Hierarchical', 'DBSCAN'],\n",
    "    'Silhouette': [kmeans_silhouette, hierarchical_silhouette, dbscan_silhouette]\n",
    "})\n",
    "\n",
    "clustering_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd04cae2",
   "metadata": {},
   "source": [
    "#### 1.4.2. Scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25afd924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 3\n",
    "nrows = 1\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*3, nrows*3))\n",
    "\n",
    "fig.suptitle('Clustering results comparison')\n",
    "fig.supxlabel('feature 0')\n",
    "fig.supylabel('feature 1')\n",
    "\n",
    "# KMeans\n",
    "axes[0].scatter(df['feature_0'], df['feature_1'], c=kmeans_labels, s = 10, edgecolors='black', linewidth=0.5)\n",
    "axes[0].set_title(f'KMeans (k={n_clusters})')\n",
    "\n",
    "# Hierarchical\n",
    "axes[1].scatter(df['feature_0'], df['feature_1'], c=hierarchical_labels, s = 10, edgecolors='black', linewidth=0.5)\n",
    "axes[1].set_title(f'Hierarchical (k={n_clusters})')\n",
    "\n",
    "# DBSCAN\n",
    "axes[2].scatter(df['feature_0'], df['feature_1'], c=dbscan_labels, s = 10, edgecolors='black', linewidth=0.5)\n",
    "axes[2].set_title(f'DBSCAN (eps={eps})')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e537e277",
   "metadata": {},
   "source": [
    "#### 1.4.3. Dendrogram (hierarchical clustering only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0621429a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create dendrogram\n",
    "linkage_matrix = linkage(df.iloc[:100])\n",
    "\n",
    "plt.figure(figsize=(10, 4.8))\n",
    "dendrogram(linkage_matrix)\n",
    "plt.title('Hierarchical clustering dendrogram\\n(n=100 samples)')\n",
    "plt.xlabel('Sample index')\n",
    "plt.ylabel('Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c77b47d",
   "metadata": {},
   "source": [
    "### 1.5. Determining cluster number\n",
    "\n",
    "#### 1.5.1. Elbow method\n",
    "\n",
    "The elbow method helps determine the optimal number of clusters by plotting the within-cluster sum of squares (inertia) for different values of k. The optimal k is at the 'elbow' where the rate of decrease sharply changes.\n",
    "\n",
    "Inertia is the total squared distance from each point to its cluster centroid:\n",
    "\n",
    "$$\\text{Inertia} = \\sum_{k=1}^{K} \\sum_{i \\in C_k} \\|x_i - \\mu_k\\|^2$$\n",
    "\n",
    "Where $x_i$ is a point in cluster $C_k$ and $\\mu_k$ is the centroid of that cluster. Inertia always decreases as k increases, so the goal is to find the point where adding more clusters gives diminishing returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fea851f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate inertia for different numbers of clusters\n",
    "k_range = range(1, 11)\n",
    "inertias = []\n",
    "\n",
    "for k in k_range:\n",
    "    kmeans_temp = KMeans(n_clusters=k, random_state=315)\n",
    "    kmeans_temp.fit(df)\n",
    "    inertias.append(kmeans_temp.inertia_)\n",
    "\n",
    "# Plot elbow curve\n",
    "plt.plot(k_range, inertias, marker='o', color='black')\n",
    "plt.title('Elbow method for optimal k')\n",
    "plt.xlabel('Number of clusters (k)')\n",
    "plt.ylabel('Within cluster sum of squares')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0f3389",
   "metadata": {},
   "source": [
    "#### 1.5.2. Silhouette analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "698ef25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate silhouette score for different numbers of clusters\n",
    "k_range = range(2, 11)\n",
    "silhouette_scores = []\n",
    "\n",
    "for k in k_range:\n",
    "\n",
    "    kmeans_temp = KMeans(n_clusters=k, random_state=315)\n",
    "    labels = kmeans_temp.fit_predict(df)\n",
    "    silhouette_scores.append(silhouette_score(df, labels))\n",
    "\n",
    "# Plot silhouette curve\n",
    "plt.plot(k_range, silhouette_scores, marker='o', color='black')\n",
    "plt.title('Silhouette score plot for optimal k')\n",
    "plt.xlabel('Number of clusters (k)')\n",
    "plt.ylabel('Silhouette score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78cb3b9b",
   "metadata": {},
   "source": [
    "## 2. Dimensionality reduction\n",
    "\n",
    "### 2.1. High dimensional dataset\n",
    "\n",
    "To demonstrate dimensionality reduction techniques, we will 'hide' the cluster signal from our two original features in a larger set of noisy features. Then we will apply dimensionality reduction to try and recover the original signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98478389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape: (1000, 2)\n",
      "High dimensional dataset shape: (1000, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_0_noise_0</th>\n",
       "      <th>feature_0_noise_1</th>\n",
       "      <th>feature_0_noise_2</th>\n",
       "      <th>feature_0_noise_3</th>\n",
       "      <th>feature_0_noise_4</th>\n",
       "      <th>feature_1_noise_0</th>\n",
       "      <th>feature_1_noise_1</th>\n",
       "      <th>feature_1_noise_2</th>\n",
       "      <th>feature_1_noise_3</th>\n",
       "      <th>feature_1_noise_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.851596</td>\n",
       "      <td>-0.974346</td>\n",
       "      <td>-1.648465</td>\n",
       "      <td>-0.292339</td>\n",
       "      <td>-1.077955</td>\n",
       "      <td>-0.854047</td>\n",
       "      <td>-0.811732</td>\n",
       "      <td>0.978294</td>\n",
       "      <td>0.633805</td>\n",
       "      <td>-0.520205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.423278</td>\n",
       "      <td>1.154441</td>\n",
       "      <td>0.412045</td>\n",
       "      <td>-0.427351</td>\n",
       "      <td>0.477508</td>\n",
       "      <td>-0.146290</td>\n",
       "      <td>0.588432</td>\n",
       "      <td>0.864924</td>\n",
       "      <td>2.014352</td>\n",
       "      <td>1.792080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.725746</td>\n",
       "      <td>-0.602883</td>\n",
       "      <td>-2.173106</td>\n",
       "      <td>-1.386890</td>\n",
       "      <td>-1.195081</td>\n",
       "      <td>0.156708</td>\n",
       "      <td>-0.173057</td>\n",
       "      <td>1.079776</td>\n",
       "      <td>0.407480</td>\n",
       "      <td>0.473172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.074254</td>\n",
       "      <td>-1.644813</td>\n",
       "      <td>-1.103451</td>\n",
       "      <td>-0.700618</td>\n",
       "      <td>-1.241018</td>\n",
       "      <td>0.158105</td>\n",
       "      <td>-1.026380</td>\n",
       "      <td>0.998867</td>\n",
       "      <td>0.700117</td>\n",
       "      <td>1.183262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.482179</td>\n",
       "      <td>-1.250161</td>\n",
       "      <td>-1.010450</td>\n",
       "      <td>-2.699164</td>\n",
       "      <td>-2.450510</td>\n",
       "      <td>0.077572</td>\n",
       "      <td>0.838238</td>\n",
       "      <td>-1.325050</td>\n",
       "      <td>-0.006412</td>\n",
       "      <td>-1.080975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_0_noise_0  feature_0_noise_1  feature_0_noise_2  feature_0_noise_3  \\\n",
       "0          -0.851596          -0.974346          -1.648465          -0.292339   \n",
       "1          -0.423278           1.154441           0.412045          -0.427351   \n",
       "2          -1.725746          -0.602883          -2.173106          -1.386890   \n",
       "3          -2.074254          -1.644813          -1.103451          -0.700618   \n",
       "4          -1.482179          -1.250161          -1.010450          -2.699164   \n",
       "\n",
       "   feature_0_noise_4  feature_1_noise_0  feature_1_noise_1  feature_1_noise_2  \\\n",
       "0          -1.077955          -0.854047          -0.811732           0.978294   \n",
       "1           0.477508          -0.146290           0.588432           0.864924   \n",
       "2          -1.195081           0.156708          -0.173057           1.079776   \n",
       "3          -1.241018           0.158105          -1.026380           0.998867   \n",
       "4          -2.450510           0.077572           0.838238          -1.325050   \n",
       "\n",
       "   feature_1_noise_3  feature_1_noise_4  \n",
       "0           0.633805          -0.520205  \n",
       "1           2.014352           1.792080  \n",
       "2           0.407480           0.473172  \n",
       "3           0.700117           1.183262  \n",
       "4          -0.006412          -1.080975  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create high dimensional dataset by multiplying each feature with random noise\n",
    "# a number of times, results in a set of features that contain the cluster signal\n",
    "# hidden among noisy features\n",
    "n_features = 10\n",
    "noise_range = (-1.25, 1.25)\n",
    "noise_features = {}\n",
    "\n",
    "for feature in df.columns:\n",
    "\n",
    "    for j in range(n_features // len(df.columns)):\n",
    "\n",
    "        noise = np.random.uniform(low=noise_range[0], high=noise_range[1], size=len(df))\n",
    "        noise_features[f'{feature}_noise_{j}'] = df[feature] + noise\n",
    "\n",
    "# Create new dataframe with 10 features\n",
    "noise_df = pd.DataFrame(noise_features)\n",
    "\n",
    "print(f'Original dataset shape: {df.shape}')\n",
    "print(f'High dimensional dataset shape: {noise_df.shape}')\n",
    "noise_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1345cdd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x275 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(6, 2.75))\n",
    "\n",
    "fig.suptitle('Original feature distributions')\n",
    "fig.supxlabel('Value')\n",
    "fig.supylabel('Frequency')\n",
    "\n",
    "# Original data\n",
    "for i, feature in enumerate(df.columns):\n",
    "    axs[i].set_title(f'{feature}')\n",
    "    axs[i].hist(df[feature], bins=30, color='black')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "94bfcc34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 5, figsize=(12, 5))\n",
    "axs = axs.flatten()\n",
    "\n",
    "fig.suptitle('Derived noisy feature distributions')\n",
    "fig.supxlabel('Value')\n",
    "fig.supylabel('Frequency')\n",
    "\n",
    "# Original data\n",
    "for i, feature in enumerate(noise_df.columns):\n",
    "    axs[i].set_title(f'{feature}')\n",
    "    axs[i].hist(noise_df[feature], bins=30, color='black')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dbc26e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_features = 2\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d09f9bd",
   "metadata": {},
   "source": [
    "### 2.2. PCA\n",
    "\n",
    "Principal Component Analysis finds new axes (principal components) that capture the most variance in the data. Each component is a linear combination of the original features, ordered so the first component explains the most variance, the second the next most, and so on.\n",
    "\n",
    "**Covariance matrix**\n",
    "\n",
    "For a 2-feature dataset, the covariance matrix summarises how each pair of features varies together:\n",
    "\n",
    "$$\\Sigma = \\begin{bmatrix} \\text{Var}(x_1) & \\text{Cov}(x_1, x_2) \\\\ \\text{Cov}(x_1, x_2) & \\text{Var}(x_2) \\end{bmatrix}$$\n",
    "\n",
    "For example, if both features have variance 2 and covariance 1:\n",
    "\n",
    "$$\\Sigma = \\begin{bmatrix} 2 & 1 \\\\ 1 & 2 \\end{bmatrix}$$\n",
    "\n",
    "**Eigenvectors and eigenvalues**\n",
    "\n",
    "The $k$-th principal component is the eigenvector $v_k$ satisfying:\n",
    "\n",
    "$$\\Sigma v_k = \\lambda_k v_k$$\n",
    "\n",
    "Where $\\lambda_k$ is the eigenvalue: the variance explained by that component. For the example above, solving gives $\\lambda_1 = 3$ and $v_1 = [0.71,\\, 0.71]^T$. We can verify:\n",
    "\n",
    "$$\\begin{bmatrix} 2 & 1 \\\\ 1 & 2 \\end{bmatrix} \\begin{bmatrix} 0.71 \\\\ 0.71 \\end{bmatrix} = \\begin{bmatrix} 2.12 \\\\ 2.12 \\end{bmatrix} = 3 \\times \\begin{bmatrix} 0.71 \\\\ 0.71 \\end{bmatrix}$$\n",
    "\n",
    "The eigenvector $[0.71, 0.71]^T$ has equal values in both dimensions, which means it points equally in the $x_1$ and $x_2$ directions at 45° from either axis.\n",
    "\n",
    "A point $x$ is projected onto the top $k$ components to get its reduced representation:\n",
    "\n",
    "$$z = V_k^T (x - \\bar{x})$$\n",
    "\n",
    "Where $V_k$ is the matrix of the top $k$ eigenvectors and $\\bar{x}$ is the feature mean.\n",
    "\n",
    "- Centre the data by subtracting the mean of each feature.\n",
    "- Compute the covariance matrix of the centred features.\n",
    "- Find its eigenvectors and eigenvalues.\n",
    "- Order components by decreasing eigenvalue.\n",
    "- Project the data onto the top $k$ components.\n",
    "\n",
    "Scikit-learn [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c97f3753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate cumulative explained variance across a range of component numbers\n",
    "comp_range = range(1, 11)\n",
    "cumulative_variance = []\n",
    "silhouette_scores = []\n",
    "\n",
    "for n_comp in comp_range:\n",
    "\n",
    "    pca_temp = PCA(n_components=n_comp, random_state=315)\n",
    "    pca_temp.fit(noise_df)\n",
    "    cumulative_variance.append(np.sum(pca_temp.explained_variance_ratio_))\n",
    "\n",
    "    kmeans_temp = KMeans(n_clusters=3, random_state=315)\n",
    "    kmeans_labels_temp = kmeans_temp.fit_predict(pca_temp.transform(noise_df))\n",
    "    silhouette_scores.append(silhouette_score(noise_df, kmeans_labels_temp))\n",
    "\n",
    "# Plot cumulative explained variance\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "fig.suptitle('PCA component number selection')\n",
    "fig.supxlabel('Number of components')\n",
    "\n",
    "axs[0].set_title('Explained variance by components')\n",
    "axs[0].plot(comp_range, cumulative_variance, marker='o', color='black')\n",
    "axs[0].set_ylabel('Cumulative explained variance ratio')\n",
    "axs[0].set_xticks(comp_range)\n",
    "\n",
    "axs[1].set_title('Silhouette score by components')\n",
    "axs[1].plot(comp_range, silhouette_scores, marker='o', color='black')\n",
    "axs[1].set_ylabel('Silhouette score')\n",
    "axs[1].set_xticks(comp_range)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c1a2cb8b",
   "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>pca0</th>\n",
       "      <th>pca1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.330440</td>\n",
       "      <td>-1.720132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.160788</td>\n",
       "      <td>2.026709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.812456</td>\n",
       "      <td>-1.599557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.718501</td>\n",
       "      <td>-1.421573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.347373</td>\n",
       "      <td>-3.289073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pca0      pca1\n",
       "0 -1.330440 -1.720132\n",
       "1 -1.160788  2.026709\n",
       "2 -2.812456 -1.599557\n",
       "3 -2.718501 -1.421573\n",
       "4 -2.347373 -3.289073"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply PCA\n",
    "pca = PCA(n_components=output_features, random_state=315)\n",
    "X_pca = pca.fit_transform(noise_df)\n",
    "\n",
    "pca_df = pd.DataFrame(X_pca, columns=pca.get_feature_names_out())\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4218186f",
   "metadata": {},
   "source": [
    "### 2.3. Feature agglomeration\n",
    "\n",
    "Feature agglomeration applies hierarchical clustering to the features (columns) rather than the samples (rows). Features that behave similarly across samples are grouped together and replaced by a single aggregate, reducing the number of dimensions while preserving as much structure as possible.\n",
    "\n",
    "- Treat each feature as a point in sample space.\n",
    "- Compute pairwise similarities between features.\n",
    "- Merge the most similar pair of features into one cluster using Ward linkage (see section 1.2).\n",
    "- Repeat until the desired number of feature groups remains.\n",
    "- Replace each group with a single representative value (mean of the group).\n",
    "\n",
    "Scikit-learn [FeatureAgglomeration](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9cbac51e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate silhouette score across a range of agglomeration cluster numbers\n",
    "cluster_range = range(1, 11)\n",
    "silhouette_scores = []\n",
    "\n",
    "for n_clusters in cluster_range:\n",
    "\n",
    "    agg_temp = FeatureAgglomeration(n_clusters=n_clusters)\n",
    "    agg_temp.fit(noise_df)\n",
    "\n",
    "    kmeans_temp = KMeans(n_clusters=3, random_state=315)\n",
    "    kmeans_labels_temp = kmeans_temp.fit_predict(agg_temp.transform(noise_df))\n",
    "    silhouette_scores.append(silhouette_score(noise_df, kmeans_labels_temp))\n",
    "\n",
    "# Plot cumulative explained variance\n",
    "plt.figure(figsize=(4, 3.5))\n",
    "plt.title('Feature agglomeration cluster number selection')\n",
    "plt.plot(cluster_range, silhouette_scores, marker='o', color='black')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Silhouette score')\n",
    "plt.xticks(cluster_range)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c6ce6166",
   "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>FA1</th>\n",
       "      <th>FA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.968940</td>\n",
       "      <td>-0.114777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.238673</td>\n",
       "      <td>1.022700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.416741</td>\n",
       "      <td>0.388816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.352831</td>\n",
       "      <td>0.402794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.778493</td>\n",
       "      <td>-0.299325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        FA1       FA2\n",
       "0 -0.968940 -0.114777\n",
       "1  0.238673  1.022700\n",
       "2 -1.416741  0.388816\n",
       "3 -1.352831  0.402794\n",
       "4 -1.778493 -0.299325"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply Feature Agglomeration\n",
    "fa = FeatureAgglomeration(n_clusters=output_features)\n",
    "X_fa = fa.fit_transform(noise_df)\n",
    "\n",
    "# Create FA dataframe\n",
    "fa_df = pd.DataFrame(X_fa, columns=[f'FA{i+1}' for i in range(output_features)])\n",
    "fa_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d0c14a",
   "metadata": {},
   "source": [
    "### 2.4. Truncated SVD\n",
    "\n",
    "Truncated SVD decomposes the data matrix into three matrices and keeps only the top $k$ components of each, similar to PCA but without centring the data first. This makes it suitable for sparse matrices where centring would destroy sparsity.\n",
    "\n",
    "Any matrix $X$ with $n$ rows (observations) and $p$ columns (features) can be written as:\n",
    "\n",
    "$$X = U \\Sigma V^T$$\n",
    "\n",
    "| Matrix | Shape | What it represents |\n",
    "|--------|-------|--------------------|\n",
    "| $X$ | $n \\times p$ | The original data: $n$ observations, each with $p$ features |\n",
    "| $U$ | $n \\times n$ | One row per observation; each column is a left singular vector describing how observations relate to a component |\n",
    "| $\\Sigma$ | $n \\times p$ | Diagonal matrix of singular values, ordered largest to smallest |\n",
    "| $V^T$ | $p \\times p$ | One column per feature (transposed: one row per feature); each row is a right singular vector describing how features relate to a component |\n",
    "\n",
    "**Singular values** are the values $\\sigma_1 \\geq \\sigma_2 \\geq \\cdots \\geq 0$ along the single main diagonal of $\\Sigma$, with zeros everywhere else:\n",
    "\n",
    "$$\\Sigma = \\begin{bmatrix} \\sigma_1 & 0 & \\cdots \\\\ 0 & \\sigma_2 & \\cdots \\\\ \\vdots & \\vdots & \\ddots \\end{bmatrix}$$\n",
    "\n",
    "Each singular value measures how much of the data's total variation is captured by that component: a large value means that component explains a lot of structure, a small one means it captures mostly noise.\n",
    "\n",
    "**Singular vectors** come in pairs. For the $i$-th component:\n",
    "- $\\sigma_i$ is a **scalar**: the single value at position $(i, i)$ in $\\Sigma$.\n",
    "- $v_i$ is **column $i$ of $V$** (equivalently, **row $i$ of $V^T$**): a $p$-element vector, one entry per feature, pointing in the direction of the $i$-th component in feature space.\n",
    "- $u_i$ is **column $i$ of $U$**: an $n$-element vector, one entry per observation, giving each observation's score on the $i$-th component.\n",
    "\n",
    "Together they satisfy:\n",
    "\n",
    "$$X v_i = \\sigma_i u_i \\qquad X^T u_i = \\sigma_i v_i$$\n",
    "\n",
    "This is the SVD analogue of the eigenvector equation from PCA: instead of one vector rotating into a scaled version of itself, a pair of vectors map back and forth through the data matrix.\n",
    "\n",
    "**Truncated version:** keep only the top $k$ components, so the three matrices shrink accordingly:\n",
    "\n",
    "| Matrix | Full shape | Truncated shape |\n",
    "|--------|------------|-----------------|\n",
    "| $U_k$ | $n \\times n$ | $n \\times k$ |\n",
    "| $\\Sigma_k$ | $n \\times p$ | $k \\times k$ |\n",
    "| $V_k^T$ | $p \\times p$ | $k \\times p$ |\n",
    "\n",
    "$$X \\approx U_k \\Sigma_k V_k^T$$\n",
    "\n",
    "- Decompose $X$ into $U$, $\\Sigma$, and $V^T$.\n",
    "- Keep the $k$ largest singular values and their corresponding vectors.\n",
    "- Project the data onto the reduced space: $Z = X V_k$, giving an $n \\times k$ output.\n",
    "\n",
    "Scikit-learn [TruncatedSVD](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b3eb7040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate silhouette score across a range of agglomeration cluster numbers\n",
    "cluster_range = range(1, 11)\n",
    "silhouette_scores = []\n",
    "\n",
    "for n_clusters in cluster_range:\n",
    "\n",
    "    svd_temp = TruncatedSVD(n_components=n_clusters)\n",
    "    svd_temp.fit(noise_df)\n",
    "\n",
    "    kmeans_temp = KMeans(n_clusters=3, random_state=315)\n",
    "    kmeans_labels_temp = kmeans_temp.fit_predict(svd_temp.transform(noise_df))\n",
    "    silhouette_scores.append(silhouette_score(noise_df, kmeans_labels_temp))\n",
    "\n",
    "# Plot cumulative explained variance\n",
    "plt.figure(figsize=(4, 3.5))\n",
    "plt.title('TruncatedSVD cluster number selection')\n",
    "plt.plot(cluster_range, silhouette_scores, marker='o', color='black')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Silhouette score')\n",
    "plt.xticks(cluster_range)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c4ff242e",
   "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>SVD1</th>\n",
       "      <th>SVD2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.350897</td>\n",
       "      <td>-1.738110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.180939</td>\n",
       "      <td>2.008584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.832965</td>\n",
       "      <td>-1.617512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.739050</td>\n",
       "      <td>-1.439651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.367994</td>\n",
       "      <td>-3.306970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SVD1      SVD2\n",
       "0 -1.350897 -1.738110\n",
       "1 -1.180939  2.008584\n",
       "2 -2.832965 -1.617512\n",
       "3 -2.739050 -1.439651\n",
       "4 -2.367994 -3.306970"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply Truncated SVD\n",
    "svd = TruncatedSVD(n_components=output_features)\n",
    "X_svd = svd.fit_transform(noise_df)\n",
    "\n",
    "# Create SVD dataframe\n",
    "svd_df = pd.DataFrame(X_svd, columns=[f'SVD{i+1}' for i in range(output_features)])\n",
    "svd_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b539a53",
   "metadata": {},
   "source": [
    "### 2.5. t-SNE\n",
    "\n",
    "t-SNE is a nonlinear dimensionality reduction technique designed for visualization. It preserves local structure by modelling pairwise similarities between points as probabilities, then finding a low-dimensional layout that matches those probabilities as closely as possible.\n",
    "\n",
    "- Compute pairwise similarity between all points in the high-dimensional space using a Gaussian kernel: nearby points get high similarity, distant points get low similarity.\n",
    "- Initialize points randomly in the low-dimensional space.\n",
    "- Compute pairwise similarity in the low-dimensional space.\n",
    "- Move points iteratively until the low-dimensional similarity distribution matches the high-dimensional one as closely as possible.\n",
    "\n",
    "**Note:** t-SNE is primarily a visualization tool. The axes have no interpretable meaning, and distances between clusters in the output are not reliable for quantitative analysis.\n",
    "\n",
    "Scikit-learn [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0290f664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate silhouette score across a range of component numbers\n",
    "components_range = range(1, 11)\n",
    "silhouette_scores = []\n",
    "\n",
    "for n_components in components_range:\n",
    "\n",
    "    tsne_temp = TSNE(n_components=n_components, method='exact', random_state=315)\n",
    "    kmeans_temp = KMeans(n_clusters=n_clusters, random_state=315)\n",
    "    kmeans_labels_temp = kmeans_temp.fit_predict(tsne_temp.fit_transform(noise_df))\n",
    "    silhouette_scores.append(silhouette_score(noise_df, kmeans_labels_temp))\n",
    "\n",
    "# Plot cumulative explained variance\n",
    "plt.figure(figsize=(4, 3.5))\n",
    "plt.title('t-SNE cluster number selection')\n",
    "plt.plot(components_range, silhouette_scores, marker='o', color='black')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Silhouette score')\n",
    "plt.xticks(components_range)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d12a83f",
   "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>TSNE1</th>\n",
       "      <th>TSNE2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-35.256859</td>\n",
       "      <td>-15.693397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.653823</td>\n",
       "      <td>13.309549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-37.350857</td>\n",
       "      <td>-1.786574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-34.544605</td>\n",
       "      <td>-10.192158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-42.284966</td>\n",
       "      <td>-12.237552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TSNE1      TSNE2\n",
       "0 -35.256859 -15.693397\n",
       "1  -0.653823  13.309549\n",
       "2 -37.350857  -1.786574\n",
       "3 -34.544605 -10.192158\n",
       "4 -42.284966 -12.237552"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply t-SNE\n",
    "tsne = TSNE(n_components=output_features, method='exact', random_state=315)\n",
    "X_tsne = tsne.fit_transform(noise_df)\n",
    "\n",
    "# Create t-SNE dataframe\n",
    "tsne_df = pd.DataFrame(X_tsne, columns=[f'TSNE{i+1}' for i in range(output_features)])\n",
    "tsne_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bcc57e",
   "metadata": {},
   "source": [
    "### 2.6. Dimensionality reduction comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e73cd0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 3\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*3, nrows*3))\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Input signal\n",
    "axes[0].set_title('Hidden signal')\n",
    "axes[0].scatter(df['feature_0'], df['feature_1'], c='black', s=2)\n",
    "axes[0].set_xlabel('Feature 0')\n",
    "axes[0].set_ylabel('Feature 1')\n",
    "\n",
    "# Input features\n",
    "axes[1].set_title('First two input features')\n",
    "axes[1].scatter(noise_df['feature_0_noise_0'], noise_df['feature_1_noise_0'], c='black', s=2)\n",
    "axes[1].set_xlabel('Feature 0 + noise')\n",
    "axes[1].set_ylabel('Feature 1 + noise')\n",
    "\n",
    "# PCA\n",
    "axes[2].set_title('PCA')\n",
    "axes[2].scatter(X_pca[:, 0], X_pca[:, 1], c='black', s=2)\n",
    "axes[2].set_xlabel('PC1')\n",
    "axes[2].set_ylabel('PC2')\n",
    "\n",
    "# SVD\n",
    "axes[3].set_title('SVD')\n",
    "axes[3].scatter(X_svd[:, 0], X_svd[:, 1], c='black', s=2)\n",
    "axes[3].set_xlabel('SVD1')\n",
    "axes[3].set_ylabel('SVD2')\n",
    "\n",
    "# Feature Agglomeration\n",
    "axes[4].set_title('Feature agglomeration')\n",
    "axes[4].scatter(X_fa[:, 0], X_fa[:, 1], c='black', s=2)\n",
    "axes[4].set_xlabel('FA1')\n",
    "axes[4].set_ylabel('FA2')\n",
    "\n",
    "# t-SNE\n",
    "axes[5].set_title('t-SNE')\n",
    "axes[5].scatter(X_tsne[:, 0], X_tsne[:, 1], c='black', s=2)\n",
    "axes[5].set_xlabel('TSNE1')\n",
    "axes[5].set_ylabel('TSNE2')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc23668a",
   "metadata": {},
   "source": [
    "### 2.7. A note about LDA\n",
    "\n",
    "LDA (linear discriminant analysis) is a supervised algorithm, so it doesn't really fit in this lesson - but if you have labels and want to do dimensionality reduction, it can be a great option!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1fc8c2aa",
   "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>lineardiscriminantanalysis0</th>\n",
       "      <th>lineardiscriminantanalysis1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.178466</td>\n",
       "      <td>-1.450777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.965496</td>\n",
       "      <td>1.921375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.532027</td>\n",
       "      <td>-1.333087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.498680</td>\n",
       "      <td>-1.282677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.288654</td>\n",
       "      <td>-3.010180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lineardiscriminantanalysis0  lineardiscriminantanalysis1\n",
       "0                     1.178466                    -1.450777\n",
       "1                     0.965496                     1.921375\n",
       "2                     2.532027                    -1.333087\n",
       "3                     2.498680                    -1.282677\n",
       "4                     2.288654                    -3.010180"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "lda_df = noise_df.copy()\n",
    "lda_df['label'] = kmeans_labels\n",
    "\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "lda_features = lda.fit_transform(lda_df.drop(columns='label'), lda_df['label'])\n",
    "lda_df = pd.DataFrame(lda_features, columns=lda.get_feature_names_out())\n",
    "lda_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c98c9ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 3\n",
    "nrows = 1\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*3, nrows*3))\n",
    "\n",
    "# Input signal\n",
    "axes[0].set_title('Hidden signal')\n",
    "axes[0].scatter(df['feature_0'], df['feature_1'], c='black', s=2)\n",
    "axes[0].set_xlabel('Feature 0')\n",
    "axes[0].set_ylabel('Feature 1')\n",
    "\n",
    "# Input features\n",
    "axes[1].set_title('First two input features')\n",
    "axes[1].scatter(noise_df['feature_0_noise_0'], noise_df['feature_1_noise_0'], c='black', s=2)\n",
    "axes[1].set_xlabel('Feature 0 + noise')\n",
    "axes[1].set_ylabel('Feature 1 + noise')\n",
    "\n",
    "# LDA\n",
    "axes[2].set_title('LDA')\n",
    "axes[2].scatter(lda_df.values[:, 0], lda_df.values[:, 1], c='black', s=2)\n",
    "axes[2].set_xlabel('LDA1')\n",
    "axes[2].set_ylabel('LDA2')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31e993a",
   "metadata": {},
   "source": [
    "## 3. Anomaly detection\n",
    "\n",
    "### 3.1. Dataset\n",
    "\n",
    "This section uses the [KDD Cup 1999 network intrusion dataset](https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html), a benchmark dataset built from simulated network traffic. Each row is one network connection described by 41 features: 38 numerical (e.g. duration, bytes transferred, number of failed login attempts) and 3 categorical (protocol type, network service, and connection flag). Connections are labeled either `normal` or as one of several attack types.\n",
    "\n",
    "Although labels exist, we treat this as an **unsupervised** problem: all four models are trained on features only, with no access to labels. This mirrors the real-world scenario where some labeled historical anomalies may exist for evaluation, but the model must detect new anomalous network traffic without supervision. Labels are revealed only in section 3.6 to evaluate results.\n",
    "\n",
    "The dataset is subsampled to 2,000 connections (1,800 normal, 200 attacks) to keep runtimes manageable across all methods. The `contamination` parameter is set to 0.10 to match the known anomaly rate of the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "607cb0fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 2000 samples, 118 features\n",
      "True anomaly rate: 10.0%\n"
     ]
    },
    {
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       "      <th>land</th>\n",
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       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>...</th>\n",
       "      <th>flag_b'REJ'</th>\n",
       "      <th>flag_b'RSTO'</th>\n",
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       "      <th>flag_b'RSTR'</th>\n",
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       "      <th>flag_b'S3'</th>\n",
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       "      <th>flag_b'SH'</th>\n",
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      "text/plain": [
       "   duration  src_bytes  dst_bytes  land  wrong_fragment  urgent       hot  \\\n",
       "0 -0.147968  -0.029682  -0.187378   0.0       -0.022366     0.0 -0.052208   \n",
       "1 -0.147968  -0.029214   1.058024   0.0       -0.022366     0.0 -0.052208   \n",
       "2 -0.147968  -0.029764  -0.074959   0.0       -0.022366     0.0 -0.052208   \n",
       "3 -0.147968  -0.027850   0.452908   0.0       -0.022366     0.0 -0.052208   \n",
       "4 -0.147968  -0.028786   1.795656   0.0       -0.022366     0.0 -0.052208   \n",
       "\n",
       "   num_failed_logins  logged_in  num_compromised  ...  flag_b'REJ'  \\\n",
       "0                0.0   0.739453        -0.038759  ...    -0.219586   \n",
       "1                0.0   0.739453        -0.038759  ...    -0.219586   \n",
       "2                0.0   0.739453        -0.038759  ...    -0.219586   \n",
       "3                0.0   0.739453        -0.038759  ...    -0.219586   \n",
       "4                0.0   0.739453        -0.038759  ...    -0.219586   \n",
       "\n",
       "   flag_b'RSTO'  flag_b'RSTOS0'  flag_b'RSTR'  flag_b'S0'  flag_b'S1'  \\\n",
       "0     -0.044766             0.0           0.0   -0.149983         0.0   \n",
       "1     -0.044766             0.0           0.0   -0.149983         0.0   \n",
       "2     -0.044766             0.0           0.0   -0.149983         0.0   \n",
       "3     -0.044766             0.0           0.0   -0.149983         0.0   \n",
       "4     -0.044766             0.0           0.0   -0.149983         0.0   \n",
       "\n",
       "   flag_b'S2'  flag_b'S3'  flag_b'SF'  flag_b'SH'  \n",
       "0         0.0         0.0    0.274352         0.0  \n",
       "1         0.0         0.0    0.274352         0.0  \n",
       "2         0.0         0.0    0.274352         0.0  \n",
       "3         0.0         0.0    0.274352         0.0  \n",
       "4         0.0         0.0    0.274352         0.0  \n",
       "\n",
       "[5 rows x 118 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdd = fetch_kddcup99(percent10=True, random_state=315)\n",
    "\n",
    "kdd_df = pd.DataFrame(kdd.data, columns=kdd.feature_names)\n",
    "kdd_df['label'] = kdd.target\n",
    "\n",
    "# Binarize labels before modifying the dataframe\n",
    "y_all = (kdd_df.pop('label') != b'normal.').astype(int)\n",
    "\n",
    "# Cast numerical features; one-hot encode the 3 categorical columns\n",
    "cat_features = ['protocol_type', 'service', 'flag']\n",
    "num_features = [c for c in kdd.feature_names if c not in cat_features]\n",
    "kdd_df[num_features] = kdd_df[num_features].apply(pd.to_numeric, errors='coerce')\n",
    "kdd_df = pd.get_dummies(kdd_df, columns=cat_features)\n",
    "\n",
    "# Subsample: 1800 normal + 200 attack to give a 10% anomaly rate\n",
    "normal_idx = y_all[y_all == 0].sample(1800, random_state=315).index\n",
    "attack_idx = y_all[y_all == 1].sample(200, random_state=315).index\n",
    "sample_idx = normal_idx.union(attack_idx)\n",
    "\n",
    "X = kdd_df.loc[sample_idx].reset_index(drop=True)\n",
    "y_true = y_all.loc[sample_idx].reset_index(drop=True)\n",
    "\n",
    "# Scale features, labels are set aside and not used during training\n",
    "scaler = StandardScaler()\n",
    "X_scaled = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "\n",
    "contamination = 0.1\n",
    "\n",
    "print(f'Dataset: {X_scaled.shape[0]} samples, {X_scaled.shape[1]} features')\n",
    "print(f'True anomaly rate: {y_true.mean():.1%}')\n",
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d0ebdb35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = pd.Series({'Normal': (y_true == 0).sum(), 'Attack': (y_true == 1).sum()})\n",
    "\n",
    "counts.plot(kind='bar', color=['#1f77b4', '#ff7f0e'], figsize=(4, 3))\n",
    "plt.title('Class distribution in sample')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36d880b",
   "metadata": {},
   "source": [
    "### 3.2. Robust covariance (elliptic envelope)\n",
    "\n",
    "Robust covariance estimation assumes the inlier data follows a multivariate Gaussian distribution. It fits a robust estimate of the covariance matrix and uses the Mahalanobis distance to measure how far each point is from the data center. Points beyond a threshold are flagged as anomalies.\n",
    "\n",
    "- Estimate the center and covariance of the data using a method that down-weights likely outliers.\n",
    "- Compute the Mahalanobis distance from each point to the center: $d(x) = \\sqrt{(x - \\mu)^T \\Sigma^{-1} (x - \\mu)}$.\n",
    "- Flag points whose distance exceeds the threshold set by `contamination`.\n",
    "\n",
    "Key hyperparameter: `contamination` (expected fraction of anomalies).\n",
    "\n",
    "Note: the covariance matrix must be full rank. One-hot encoded columns are linearly dependent (each group of indicator columns sums to 1 across every row), which makes the covariance matrix singular regardless of sample size. PCA is applied first to produce orthogonal components that guarantee full rank. EllipticEnvelope also assumes inlier data is approximately Gaussian and does not perform well on skewed or multi-modal distributions.\n",
    "\n",
    "Scikit-learn [EllipticEnvelope](https://scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "530ba70e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elliptic Envelope: 200 anomalies detected (10.0%)\n"
     ]
    }
   ],
   "source": [
    "# One-hot encoded columns are linearly dependent (each group sums to 1), making the\n",
    "# covariance matrix singular; PCA produces orthogonal components that guarantee full rank\n",
    "pca_ee = PCA(n_components=20, random_state=315)\n",
    "X_elliptic = pca_ee.fit_transform(X_scaled)\n",
    "\n",
    "# support_fraction=0.75 stabilizes the MCD covariance estimator on this dataset\n",
    "elliptic = EllipticEnvelope(contamination=contamination, support_fraction=0.75, random_state=315)\n",
    "elliptic_labels = elliptic.fit_predict(X_elliptic)\n",
    "\n",
    "n_outliers = (elliptic_labels == -1).sum()\n",
    "print(f'Elliptic Envelope: {n_outliers} anomalies detected ({n_outliers / len(elliptic_labels):.1%})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd186935",
   "metadata": {},
   "source": [
    "### 3.3. One-class SVM\n",
    "\n",
    "One-class SVM extends the support vector machine idea to the unsupervised setting. Rather than separating two classes, it finds the smallest region in a kernel-transformed feature space that encloses most of the training data. Points that fall outside this region are labeled as anomalies.\n",
    "\n",
    "- Map the data into a high-dimensional kernel space.\n",
    "- Find the smallest hypersphere that encloses a fraction $(1 - \\nu)$ of the data.\n",
    "- Points outside the sphere are labeled as anomalies.\n",
    "\n",
    "Key hyperparameters: `nu` (upper bound on the fraction of anomalies), `gamma` (kernel width for RBF).\n",
    "\n",
    "Note: runtime grows roughly as $O(n^2)$; does not scale well to large datasets.\n",
    "\n",
    "Scikit-learn [OneClassSVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "728c91d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-Class SVM: 193 anomalies detected (9.7%)\n"
     ]
    }
   ],
   "source": [
    "ocsvm = OneClassSVM(nu=contamination, gamma='scale')\n",
    "ocsvm_labels = ocsvm.fit_predict(X_scaled)\n",
    "\n",
    "n_outliers = (ocsvm_labels == -1).sum()\n",
    "print(f'One-Class SVM: {n_outliers} anomalies detected ({n_outliers / len(ocsvm_labels):.1%})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0b46d5",
   "metadata": {},
   "source": [
    "### 3.4. Isolation Forest\n",
    "\n",
    "Isolation Forest exploits the fact that anomalies are rare and different: they are easier to isolate with random splits than normal points. The model builds many random decision trees and measures the average depth at which each point is isolated. Anomalies, being unusual, require fewer splits and have shorter average path lengths.\n",
    "\n",
    "- Build many random trees by recursively selecting a random feature and a random split value.\n",
    "- For each point, record the depth at which it is isolated in each tree.\n",
    "- Average the isolation depth across all trees; a short average depth indicates an anomaly.\n",
    "\n",
    "Key hyperparameters: `n_estimators` (number of trees), `contamination`.\n",
    "\n",
    "Scikit-learn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5626ea27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isolation Forest: 200 anomalies detected (10.0%)\n"
     ]
    }
   ],
   "source": [
    "iso_forest = IsolationForest(contamination=contamination, random_state=315)\n",
    "iso_forest_labels = iso_forest.fit_predict(X_scaled)\n",
    "\n",
    "n_outliers = (iso_forest_labels == -1).sum()\n",
    "print(f'Isolation Forest: {n_outliers} anomalies detected ({n_outliers / len(iso_forest_labels):.1%})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7acddabf",
   "metadata": {},
   "source": [
    "### 3.5. Local Outlier Factor\n",
    "\n",
    "Local Outlier Factor detects anomalies by comparing each point's local density to the density of its neighbors. A point in a much sparser region than its neighbors has a high local outlier factor and is flagged as an anomaly. Because it uses local density, it works well when anomalies only stand out relative to their immediate neighborhood.\n",
    "\n",
    "- For each point, find its $k$ nearest neighbors.\n",
    "- Estimate local density as the inverse of the mean reachability distance to those neighbors.\n",
    "- Compare each point's density to its neighbors' densities; a ratio much less than 1 indicates an anomaly.\n",
    "\n",
    "Key hyperparameters: `n_neighbors` (neighborhood size), `contamination`.\n",
    "\n",
    "Scikit-learn [LocalOutlierFactor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f893cfc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local Outlier Factor: 200 anomalies detected (10.0%)\n"
     ]
    }
   ],
   "source": [
    "# n_neighbors=35 avoids the duplicate-value warning common in KDD Cup 99\n",
    "lof = LocalOutlierFactor(n_neighbors=100, contamination=contamination)\n",
    "lof_labels = lof.fit_predict(X_scaled)\n",
    "\n",
    "n_outliers = (lof_labels == -1).sum()\n",
    "print(f'Local Outlier Factor: {n_outliers} anomalies detected ({n_outliers / len(lof_labels):.1%})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ecff1bf",
   "metadata": {},
   "source": [
    "### 3.6. Anomaly detection comparison\n",
    "\n",
    "Now that the models have been trained without labels, we can reveal the true labels from the KDD Cup 99 dataset and evaluate how well each detector found the real attacks. The data is projected to 2D with PCA for visualization; precision, recall, and F1 are computed against the true labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6a8f66c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Project to 2D for visualization\n",
    "pca_vis = PCA(n_components=2, random_state=315)\n",
    "X_vis = pca_vis.fit_transform(X_scaled)\n",
    "\n",
    "# Convert sklearn's -1/1 output to 0/1 to compare against y_true\n",
    "def to_binary(labels):\n",
    "    return (np.array(labels) == -1).astype(int)\n",
    "\n",
    "colors = {0: '#1f77b4', 1: '#ff7f0e'}\n",
    "\n",
    "ncols = 3\n",
    "nrows = 2\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols * 3.5, nrows * 3.5))\n",
    "axes = axes.flatten()\n",
    "\n",
    "model_data = [\n",
    "    ('True labels', y_true.values, False),\n",
    "    ('Elliptic Envelope', to_binary(elliptic_labels), True),\n",
    "    ('One-Class SVM', to_binary(ocsvm_labels), True),\n",
    "    ('Isolation Forest', to_binary(iso_forest_labels), True),\n",
    "    ('Local Outlier Factor', to_binary(lof_labels), True),\n",
    "]\n",
    "\n",
    "for i, (name, labels, show_metrics) in enumerate(model_data):\n",
    "    axes[i].scatter(X_vis[:, 0], X_vis[:, 1], c=[colors[l] for l in labels], s=3, alpha=0.5)\n",
    "    if show_metrics:\n",
    "        p = precision_score(y_true, labels, zero_division=0)\n",
    "        r = recall_score(y_true, labels, zero_division=0)\n",
    "        f = f1_score(y_true, labels, zero_division=0)\n",
    "        axes[i].set_title(f'{name}\\nP={p:.2f}  R={r:.2f}  F1={f:.2f}')\n",
    "    else:\n",
    "        axes[i].set_title(name)\n",
    "    axes[i].set_xlabel('PC1')\n",
    "    axes[i].set_ylabel('PC2')\n",
    "\n",
    "axes[5].set_visible(False)\n",
    "\n",
    "legend_elements = [Patch(facecolor='#1f77b4', label='Normal'), Patch(facecolor='#ff7f0e', label='Attack')]\n",
    "fig.legend(handles=legend_elements, loc='lower right', bbox_to_anchor=(0.97, 0.07))\n",
    "\n",
    "fig.suptitle('Anomaly detection results vs. true labels (PCA projection)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736d664f",
   "metadata": {},
   "source": [
    "## 4. Association rule learning\n",
    "\n",
    "### 4.1. Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "735a5c95",
   "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>item_1</th>\n",
       "      <th>item_2</th>\n",
       "      <th>item_3</th>\n",
       "      <th>item_4</th>\n",
       "      <th>item_5</th>\n",
       "      <th>item_6</th>\n",
       "      <th>item_7</th>\n",
       "      <th>item_8</th>\n",
       "      <th>item_9</th>\n",
       "      <th>item_10</th>\n",
       "      <th>item_11</th>\n",
       "      <th>item_12</th>\n",
       "      <th>item_13</th>\n",
       "      <th>item_14</th>\n",
       "      <th>item_15</th>\n",
       "      <th>item_16</th>\n",
       "      <th>item_17</th>\n",
       "      <th>item_18</th>\n",
       "      <th>item_19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>burgers</td>\n",
       "      <td>meatballs</td>\n",
       "      <td>eggs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chutney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>turkey</td>\n",
       "      <td>avocado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mineral water</td>\n",
       "      <td>milk</td>\n",
       "      <td>energy bar</td>\n",
       "      <td>whole wheat rice</td>\n",
       "      <td>green tea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>low fat yogurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           item_1     item_2      item_3            item_4     item_5 item_6  \\\n",
       "0         burgers  meatballs        eggs               NaN        NaN    NaN   \n",
       "1         chutney        NaN         NaN               NaN        NaN    NaN   \n",
       "2          turkey    avocado         NaN               NaN        NaN    NaN   \n",
       "3   mineral water       milk  energy bar  whole wheat rice  green tea    NaN   \n",
       "4  low fat yogurt        NaN         NaN               NaN        NaN    NaN   \n",
       "\n",
       "  item_7 item_8 item_9 item_10 item_11 item_12 item_13 item_14 item_15  \\\n",
       "0    NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1    NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2    NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3    NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4    NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "  item_16 item_17 item_18 item_19  \n",
       "0     NaN     NaN     NaN     NaN  \n",
       "1     NaN     NaN     NaN     NaN  \n",
       "2     NaN     NaN     NaN     NaN  \n",
       "3     NaN     NaN     NaN     NaN  \n",
       "4     NaN     NaN     NaN     NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_df = pd.read_csv('https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/market_basket.csv')\n",
    "basket_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "74dc3d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 7500 entries, 0 to 7499\n",
      "Data columns (total 19 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   item_1   7500 non-null   str  \n",
      " 1   item_2   5746 non-null   str  \n",
      " 2   item_3   4388 non-null   str  \n",
      " 3   item_4   3344 non-null   str  \n",
      " 4   item_5   2528 non-null   str  \n",
      " 5   item_6   1863 non-null   str  \n",
      " 6   item_7   1368 non-null   str  \n",
      " 7   item_8   980 non-null    str  \n",
      " 8   item_9   653 non-null    str  \n",
      " 9   item_10  394 non-null    str  \n",
      " 10  item_11  255 non-null    str  \n",
      " 11  item_12  153 non-null    str  \n",
      " 12  item_13  86 non-null     str  \n",
      " 13  item_14  46 non-null     str  \n",
      " 14  item_15  24 non-null     str  \n",
      " 15  item_16  7 non-null      str  \n",
      " 16  item_17  3 non-null      str  \n",
      " 17  item_18  3 non-null      str  \n",
      " 18  item_19  2 non-null      str  \n",
      "dtypes: str(19)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "basket_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e31a1745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['burgers', 'meatballs', 'eggs'],\n",
       " ['chutney'],\n",
       " ['turkey', 'avocado'],\n",
       " ['mineral water', 'milk', 'energy bar', 'whole wheat rice', 'green tea'],\n",
       " ['low fat yogurt']]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket = basket_df.apply(lambda row: row.dropna().tolist(), axis=1).tolist()\n",
    "basket[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c66f7ae",
   "metadata": {},
   "source": [
    "### 4.2. Encode items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9d0194af",
   "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>asparagus</th>\n",
       "      <th>almonds</th>\n",
       "      <th>antioxydant juice</th>\n",
       "      <th>asparagus</th>\n",
       "      <th>avocado</th>\n",
       "      <th>babies food</th>\n",
       "      <th>bacon</th>\n",
       "      <th>barbecue sauce</th>\n",
       "      <th>black tea</th>\n",
       "      <th>blueberries</th>\n",
       "      <th>...</th>\n",
       "      <th>turkey</th>\n",
       "      <th>vegetables mix</th>\n",
       "      <th>water spray</th>\n",
       "      <th>white wine</th>\n",
       "      <th>whole weat flour</th>\n",
       "      <th>whole wheat pasta</th>\n",
       "      <th>whole wheat rice</th>\n",
       "      <th>yams</th>\n",
       "      <th>yogurt cake</th>\n",
       "      <th>zucchini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    asparagus  almonds  antioxydant juice  asparagus  avocado  babies food  \\\n",
       "0       False    False              False      False    False        False   \n",
       "1       False    False              False      False    False        False   \n",
       "2       False    False              False      False     True        False   \n",
       "3       False    False              False      False    False        False   \n",
       "4       False    False              False      False    False        False   \n",
       "\n",
       "   bacon  barbecue sauce  black tea  blueberries  ...  turkey  vegetables mix  \\\n",
       "0  False           False      False        False  ...   False           False   \n",
       "1  False           False      False        False  ...   False           False   \n",
       "2  False           False      False        False  ...    True           False   \n",
       "3  False           False      False        False  ...   False           False   \n",
       "4  False           False      False        False  ...   False           False   \n",
       "\n",
       "   water spray  white wine  whole weat flour  whole wheat pasta  \\\n",
       "0        False       False             False              False   \n",
       "1        False       False             False              False   \n",
       "2        False       False             False              False   \n",
       "3        False       False             False              False   \n",
       "4        False       False             False              False   \n",
       "\n",
       "   whole wheat rice   yams  yogurt cake  zucchini  \n",
       "0             False  False        False     False  \n",
       "1             False  False        False     False  \n",
       "2             False  False        False     False  \n",
       "3              True  False        False     False  \n",
       "4             False  False        False     False  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = TransactionEncoder()\n",
    "encoder.fit(basket)\n",
    "transactions = encoder.transform(basket)\n",
    "basket_df = pd.DataFrame(transactions, columns=encoder.columns_)\n",
    "basket_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8714d8d8",
   "metadata": {},
   "source": [
    "### 4.3. Find association rules with Apriori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "798c9668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of frequent itemsets found: 259\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.238267</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.179733</td>\n",
       "      <td>frozenset({eggs})</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.174133</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.170933</td>\n",
       "      <td>frozenset({french fries})</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.163867</td>\n",
       "      <td>frozenset({chocolate})</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                    itemsets\n",
       "0  0.238267  frozenset({mineral water})\n",
       "1  0.179733           frozenset({eggs})\n",
       "2  0.174133      frozenset({spaghetti})\n",
       "3  0.170933   frozenset({french fries})\n",
       "4  0.163867      frozenset({chocolate})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(basket_df, min_support=0.01, use_colnames=True)\n",
    "frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)\n",
    "frequent_itemsets.reset_index(drop=True, inplace=True)\n",
    "print(f'Number of frequent itemsets found: {len(frequent_itemsets)}')\n",
    "frequent_itemsets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f9f07128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of association rules found: 162\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>frozenset({ground beef, eggs})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.010133</td>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>frozenset({ground beef, milk})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
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       "      <td>1.532756</td>\n",
       "      <td>0.538177</td>\n",
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       "      <td>0.347580</td>\n",
       "      <td>0.274738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>frozenset({chocolate, ground beef})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.023067</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.010933</td>\n",
       "      <td>0.473988</td>\n",
       "      <td>1.989319</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005437</td>\n",
       "      <td>1.448130</td>\n",
       "      <td>0.509058</td>\n",
       "      <td>0.043663</td>\n",
       "      <td>0.309454</td>\n",
       "      <td>0.259938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>frozenset({frozen vegetables, milk})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>0.468927</td>\n",
       "      <td>1.968075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005444</td>\n",
       "      <td>1.434328</td>\n",
       "      <td>0.503778</td>\n",
       "      <td>0.044125</td>\n",
       "      <td>0.302809</td>\n",
       "      <td>0.257687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>frozenset({soup})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.050533</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.023067</td>\n",
       "      <td>0.456464</td>\n",
       "      <td>1.915771</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011026</td>\n",
       "      <td>1.401441</td>\n",
       "      <td>0.503458</td>\n",
       "      <td>0.086804</td>\n",
       "      <td>0.286449</td>\n",
       "      <td>0.276637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>frozenset({spaghetti, pancakes})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.025200</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.011467</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>1.909736</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005462</td>\n",
       "      <td>1.397744</td>\n",
       "      <td>0.488682</td>\n",
       "      <td>0.045503</td>\n",
       "      <td>0.284561</td>\n",
       "      <td>0.251576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>frozenset({spaghetti, olive oil})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.022933</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>0.447674</td>\n",
       "      <td>1.878880</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004802</td>\n",
       "      <td>1.379138</td>\n",
       "      <td>0.478747</td>\n",
       "      <td>0.040914</td>\n",
       "      <td>0.274910</td>\n",
       "      <td>0.245382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>frozenset({spaghetti, milk})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.035467</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.015733</td>\n",
       "      <td>0.443609</td>\n",
       "      <td>1.861817</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007283</td>\n",
       "      <td>1.369061</td>\n",
       "      <td>0.479911</td>\n",
       "      <td>0.060982</td>\n",
       "      <td>0.269572</td>\n",
       "      <td>0.254821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>frozenset({chocolate, milk})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.032133</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.435685</td>\n",
       "      <td>1.828559</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006344</td>\n",
       "      <td>1.349836</td>\n",
       "      <td>0.468165</td>\n",
       "      <td>0.054602</td>\n",
       "      <td>0.259169</td>\n",
       "      <td>0.247221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>frozenset({ground beef, spaghetti})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.039200</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>0.435374</td>\n",
       "      <td>1.827256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007727</td>\n",
       "      <td>1.349094</td>\n",
       "      <td>0.471202</td>\n",
       "      <td>0.065540</td>\n",
       "      <td>0.258762</td>\n",
       "      <td>0.253501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>frozenset({spaghetti, frozen vegetables})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.027867</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.430622</td>\n",
       "      <td>1.807311</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005360</td>\n",
       "      <td>1.337834</td>\n",
       "      <td>0.459496</td>\n",
       "      <td>0.047219</td>\n",
       "      <td>0.252523</td>\n",
       "      <td>0.240493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>frozenset({eggs, milk})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.013067</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>1.780536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005728</td>\n",
       "      <td>1.323011</td>\n",
       "      <td>0.452302</td>\n",
       "      <td>0.051042</td>\n",
       "      <td>0.244148</td>\n",
       "      <td>0.239541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>frozenset({olive oil})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.065733</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.027467</td>\n",
       "      <td>0.417850</td>\n",
       "      <td>1.753707</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011805</td>\n",
       "      <td>1.308483</td>\n",
       "      <td>0.460018</td>\n",
       "      <td>0.099325</td>\n",
       "      <td>0.235756</td>\n",
       "      <td>0.266563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>frozenset({ground beef, mineral water})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.040933</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>0.416938</td>\n",
       "      <td>2.394361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009939</td>\n",
       "      <td>1.416431</td>\n",
       "      <td>0.607207</td>\n",
       "      <td>0.086195</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.257474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>frozenset({ground beef})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.098267</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.040933</td>\n",
       "      <td>0.416554</td>\n",
       "      <td>1.748266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017520</td>\n",
       "      <td>1.305576</td>\n",
       "      <td>0.474647</td>\n",
       "      <td>0.138475</td>\n",
       "      <td>0.234054</td>\n",
       "      <td>0.294175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>frozenset({chocolate, eggs})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.013467</td>\n",
       "      <td>0.405622</td>\n",
       "      <td>1.702389</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>1.281565</td>\n",
       "      <td>0.426758</td>\n",
       "      <td>0.052196</td>\n",
       "      <td>0.219704</td>\n",
       "      <td>0.231071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>frozenset({chocolate, spaghetti})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.039200</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.015867</td>\n",
       "      <td>0.404762</td>\n",
       "      <td>1.698777</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006527</td>\n",
       "      <td>1.279712</td>\n",
       "      <td>0.428124</td>\n",
       "      <td>0.060652</td>\n",
       "      <td>0.218574</td>\n",
       "      <td>0.235677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>frozenset({salmon})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.042400</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.016933</td>\n",
       "      <td>0.399371</td>\n",
       "      <td>1.676152</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006831</td>\n",
       "      <td>1.268226</td>\n",
       "      <td>0.421257</td>\n",
       "      <td>0.064206</td>\n",
       "      <td>0.211497</td>\n",
       "      <td>0.235220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>frozenset({cereals})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.025733</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>0.398964</td>\n",
       "      <td>1.674442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004135</td>\n",
       "      <td>1.267367</td>\n",
       "      <td>0.413425</td>\n",
       "      <td>0.040462</td>\n",
       "      <td>0.210962</td>\n",
       "      <td>0.221026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>frozenset({ground beef})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.098267</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.039200</td>\n",
       "      <td>0.398915</td>\n",
       "      <td>2.290857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022088</td>\n",
       "      <td>1.373959</td>\n",
       "      <td>0.624888</td>\n",
       "      <td>0.168096</td>\n",
       "      <td>0.272176</td>\n",
       "      <td>0.312015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>frozenset({cooking oil})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.051067</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.020133</td>\n",
       "      <td>0.394256</td>\n",
       "      <td>1.654683</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007966</td>\n",
       "      <td>1.257517</td>\n",
       "      <td>0.416947</td>\n",
       "      <td>0.074789</td>\n",
       "      <td>0.204782</td>\n",
       "      <td>0.239378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>frozenset({spaghetti, eggs})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.036533</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.014267</td>\n",
       "      <td>0.390511</td>\n",
       "      <td>1.638966</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005562</td>\n",
       "      <td>1.249790</td>\n",
       "      <td>0.404642</td>\n",
       "      <td>0.054759</td>\n",
       "      <td>0.199866</td>\n",
       "      <td>0.225194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>frozenset({red wine})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.028133</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.010933</td>\n",
       "      <td>0.388626</td>\n",
       "      <td>1.631053</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004230</td>\n",
       "      <td>1.245936</td>\n",
       "      <td>0.398099</td>\n",
       "      <td>0.042797</td>\n",
       "      <td>0.197391</td>\n",
       "      <td>0.217256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>frozenset({chicken})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.022800</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>1.594852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>1.228602</td>\n",
       "      <td>0.396790</td>\n",
       "      <td>0.082769</td>\n",
       "      <td>0.186067</td>\n",
       "      <td>0.237846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>frozenset({frozen vegetables})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.095333</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.035733</td>\n",
       "      <td>0.374825</td>\n",
       "      <td>1.573133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013019</td>\n",
       "      <td>1.218433</td>\n",
       "      <td>0.402718</td>\n",
       "      <td>0.119964</td>\n",
       "      <td>0.179273</td>\n",
       "      <td>0.262399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>frozenset({olive oil, mineral water})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.027467</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>0.373786</td>\n",
       "      <td>2.146553</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005484</td>\n",
       "      <td>1.318826</td>\n",
       "      <td>0.549222</td>\n",
       "      <td>0.053659</td>\n",
       "      <td>0.241750</td>\n",
       "      <td>0.216373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>frozenset({milk})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.129600</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.048000</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>1.554436</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017121</td>\n",
       "      <td>1.209812</td>\n",
       "      <td>0.409789</td>\n",
       "      <td>0.150063</td>\n",
       "      <td>0.173425</td>\n",
       "      <td>0.285913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>frozenset({french fries, spaghetti})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.027600</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.367150</td>\n",
       "      <td>1.540920</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003557</td>\n",
       "      <td>1.203655</td>\n",
       "      <td>0.361000</td>\n",
       "      <td>0.039625</td>\n",
       "      <td>0.169197</td>\n",
       "      <td>0.204840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>frozenset({red wine})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.028133</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>0.364929</td>\n",
       "      <td>2.095687</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>1.300432</td>\n",
       "      <td>0.537964</td>\n",
       "      <td>0.053472</td>\n",
       "      <td>0.231025</td>\n",
       "      <td>0.211944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>frozenset({tomatoes})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.068400</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.024400</td>\n",
       "      <td>0.356725</td>\n",
       "      <td>1.497168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008103</td>\n",
       "      <td>1.184149</td>\n",
       "      <td>0.356454</td>\n",
       "      <td>0.086443</td>\n",
       "      <td>0.155512</td>\n",
       "      <td>0.229566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>frozenset({pancakes})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.095067</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.033733</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>1.489250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011082</td>\n",
       "      <td>1.180687</td>\n",
       "      <td>0.363034</td>\n",
       "      <td>0.112595</td>\n",
       "      <td>0.153035</td>\n",
       "      <td>0.248208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>frozenset({olive oil})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.065733</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.022933</td>\n",
       "      <td>0.348884</td>\n",
       "      <td>2.003547</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011487</td>\n",
       "      <td>1.268387</td>\n",
       "      <td>0.536127</td>\n",
       "      <td>0.105716</td>\n",
       "      <td>0.211597</td>\n",
       "      <td>0.240292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>frozenset({avocado})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.011467</td>\n",
       "      <td>0.345382</td>\n",
       "      <td>1.449559</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>1.163629</td>\n",
       "      <td>0.320785</td>\n",
       "      <td>0.044103</td>\n",
       "      <td>0.140620</td>\n",
       "      <td>0.196753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>frozenset({herb &amp; pepper})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.049467</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>0.345013</td>\n",
       "      <td>1.448014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005280</td>\n",
       "      <td>1.162976</td>\n",
       "      <td>0.325500</td>\n",
       "      <td>0.063054</td>\n",
       "      <td>0.140137</td>\n",
       "      <td>0.208321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>frozenset({whole wheat rice})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.058533</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.020133</td>\n",
       "      <td>0.343964</td>\n",
       "      <td>1.443608</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006187</td>\n",
       "      <td>1.161114</td>\n",
       "      <td>0.326396</td>\n",
       "      <td>0.072771</td>\n",
       "      <td>0.138758</td>\n",
       "      <td>0.214231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.059733</td>\n",
       "      <td>0.343032</td>\n",
       "      <td>1.439698</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018243</td>\n",
       "      <td>1.159468</td>\n",
       "      <td>0.369806</td>\n",
       "      <td>0.169376</td>\n",
       "      <td>0.137536</td>\n",
       "      <td>0.296866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>frozenset({chocolate, milk})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.032133</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.010933</td>\n",
       "      <td>0.340249</td>\n",
       "      <td>1.953957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005338</td>\n",
       "      <td>1.251785</td>\n",
       "      <td>0.504427</td>\n",
       "      <td>0.055973</td>\n",
       "      <td>0.201141</td>\n",
       "      <td>0.201518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>frozenset({pancakes, mineral water})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.033733</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.011467</td>\n",
       "      <td>0.339921</td>\n",
       "      <td>1.952073</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005593</td>\n",
       "      <td>1.251163</td>\n",
       "      <td>0.504751</td>\n",
       "      <td>0.058384</td>\n",
       "      <td>0.200744</td>\n",
       "      <td>0.202885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>frozenset({cake})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.081067</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.027467</td>\n",
       "      <td>0.338816</td>\n",
       "      <td>1.422002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008151</td>\n",
       "      <td>1.152074</td>\n",
       "      <td>0.322947</td>\n",
       "      <td>0.094107</td>\n",
       "      <td>0.132000</td>\n",
       "      <td>0.227046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>frozenset({frozen vegetables, mineral water})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.035733</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.335821</td>\n",
       "      <td>1.928527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005778</td>\n",
       "      <td>1.243440</td>\n",
       "      <td>0.499312</td>\n",
       "      <td>0.060647</td>\n",
       "      <td>0.195779</td>\n",
       "      <td>0.202367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>frozenset({grated cheese})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.052400</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.017467</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.398993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>1.142600</td>\n",
       "      <td>0.300971</td>\n",
       "      <td>0.063934</td>\n",
       "      <td>0.124803</td>\n",
       "      <td>0.203320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>frozenset({burgers})</td>\n",
       "      <td>frozenset({eggs})</td>\n",
       "      <td>0.087200</td>\n",
       "      <td>0.179733</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.330275</td>\n",
       "      <td>1.837585</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013127</td>\n",
       "      <td>1.224782</td>\n",
       "      <td>0.499351</td>\n",
       "      <td>0.120941</td>\n",
       "      <td>0.183528</td>\n",
       "      <td>0.245256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>frozenset({shrimp})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.071333</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.023467</td>\n",
       "      <td>0.328972</td>\n",
       "      <td>1.380688</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006470</td>\n",
       "      <td>1.135174</td>\n",
       "      <td>0.296903</td>\n",
       "      <td>0.082013</td>\n",
       "      <td>0.119077</td>\n",
       "      <td>0.213731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>frozenset({herb &amp; pepper})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.049467</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.328841</td>\n",
       "      <td>1.888444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007653</td>\n",
       "      <td>1.230508</td>\n",
       "      <td>0.494947</td>\n",
       "      <td>0.078457</td>\n",
       "      <td>0.187328</td>\n",
       "      <td>0.211128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>frozenset({milk, mineral water})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.048000</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.015733</td>\n",
       "      <td>0.327778</td>\n",
       "      <td>1.882338</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>1.228562</td>\n",
       "      <td>0.492380</td>\n",
       "      <td>0.076227</td>\n",
       "      <td>0.186040</td>\n",
       "      <td>0.209065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>frozenset({herb &amp; pepper})</td>\n",
       "      <td>frozenset({ground beef})</td>\n",
       "      <td>0.049467</td>\n",
       "      <td>0.098267</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.323450</td>\n",
       "      <td>3.291555</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011139</td>\n",
       "      <td>1.332841</td>\n",
       "      <td>0.732423</td>\n",
       "      <td>0.121457</td>\n",
       "      <td>0.249723</td>\n",
       "      <td>0.243136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>frozenset({chocolate})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.163867</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.052667</td>\n",
       "      <td>0.321400</td>\n",
       "      <td>1.348907</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013623</td>\n",
       "      <td>1.122506</td>\n",
       "      <td>0.309351</td>\n",
       "      <td>0.150706</td>\n",
       "      <td>0.109136</td>\n",
       "      <td>0.271220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>frozenset({frozen smoothie})</td>\n",
       "      <td>frozenset({mineral water})</td>\n",
       "      <td>0.063200</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.020133</td>\n",
       "      <td>0.318565</td>\n",
       "      <td>1.337012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>1.117838</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.071564</td>\n",
       "      <td>0.105416</td>\n",
       "      <td>0.201532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>frozenset({salmon})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.042400</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.013467</td>\n",
       "      <td>0.317610</td>\n",
       "      <td>1.823948</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006083</td>\n",
       "      <td>1.210256</td>\n",
       "      <td>0.471740</td>\n",
       "      <td>0.066316</td>\n",
       "      <td>0.173729</td>\n",
       "      <td>0.197473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>frozenset({chocolate, eggs})</td>\n",
       "      <td>frozenset({spaghetti})</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.174133</td>\n",
       "      <td>0.010533</td>\n",
       "      <td>0.317269</td>\n",
       "      <td>1.821989</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004752</td>\n",
       "      <td>1.209652</td>\n",
       "      <td>0.466642</td>\n",
       "      <td>0.053523</td>\n",
       "      <td>0.173316</td>\n",
       "      <td>0.188880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      antecedents                 consequents  \\\n",
       "0                  frozenset({ground beef, eggs})  frozenset({mineral water})   \n",
       "1                  frozenset({ground beef, milk})  frozenset({mineral water})   \n",
       "2             frozenset({chocolate, ground beef})  frozenset({mineral water})   \n",
       "3            frozenset({frozen vegetables, milk})  frozenset({mineral water})   \n",
       "4                               frozenset({soup})  frozenset({mineral water})   \n",
       "5                frozenset({spaghetti, pancakes})  frozenset({mineral water})   \n",
       "6               frozenset({spaghetti, olive oil})  frozenset({mineral water})   \n",
       "7                    frozenset({spaghetti, milk})  frozenset({mineral water})   \n",
       "8                    frozenset({chocolate, milk})  frozenset({mineral water})   \n",
       "9             frozenset({ground beef, spaghetti})  frozenset({mineral water})   \n",
       "10      frozenset({spaghetti, frozen vegetables})  frozenset({mineral water})   \n",
       "11                        frozenset({eggs, milk})  frozenset({mineral water})   \n",
       "12                         frozenset({olive oil})  frozenset({mineral water})   \n",
       "13        frozenset({ground beef, mineral water})      frozenset({spaghetti})   \n",
       "14                       frozenset({ground beef})  frozenset({mineral water})   \n",
       "15                   frozenset({chocolate, eggs})  frozenset({mineral water})   \n",
       "16              frozenset({chocolate, spaghetti})  frozenset({mineral water})   \n",
       "17                            frozenset({salmon})  frozenset({mineral water})   \n",
       "18                           frozenset({cereals})  frozenset({mineral water})   \n",
       "19                       frozenset({ground beef})      frozenset({spaghetti})   \n",
       "20                       frozenset({cooking oil})  frozenset({mineral water})   \n",
       "21                   frozenset({spaghetti, eggs})  frozenset({mineral water})   \n",
       "22                          frozenset({red wine})  frozenset({mineral water})   \n",
       "23                           frozenset({chicken})  frozenset({mineral water})   \n",
       "24                 frozenset({frozen vegetables})  frozenset({mineral water})   \n",
       "25          frozenset({olive oil, mineral water})      frozenset({spaghetti})   \n",
       "26                              frozenset({milk})  frozenset({mineral water})   \n",
       "27           frozenset({french fries, spaghetti})  frozenset({mineral water})   \n",
       "28                          frozenset({red wine})      frozenset({spaghetti})   \n",
       "29                          frozenset({tomatoes})  frozenset({mineral water})   \n",
       "30                          frozenset({pancakes})  frozenset({mineral water})   \n",
       "31                         frozenset({olive oil})      frozenset({spaghetti})   \n",
       "32                           frozenset({avocado})  frozenset({mineral water})   \n",
       "33                     frozenset({herb & pepper})  frozenset({mineral water})   \n",
       "34                  frozenset({whole wheat rice})  frozenset({mineral water})   \n",
       "35                         frozenset({spaghetti})  frozenset({mineral water})   \n",
       "36                   frozenset({chocolate, milk})      frozenset({spaghetti})   \n",
       "37           frozenset({pancakes, mineral water})      frozenset({spaghetti})   \n",
       "38                              frozenset({cake})  frozenset({mineral water})   \n",
       "39  frozenset({frozen vegetables, mineral water})      frozenset({spaghetti})   \n",
       "40                     frozenset({grated cheese})  frozenset({mineral water})   \n",
       "41                           frozenset({burgers})           frozenset({eggs})   \n",
       "42                            frozenset({shrimp})  frozenset({mineral water})   \n",
       "43                     frozenset({herb & pepper})      frozenset({spaghetti})   \n",
       "44               frozenset({milk, mineral water})      frozenset({spaghetti})   \n",
       "45                     frozenset({herb & pepper})    frozenset({ground beef})   \n",
       "46                         frozenset({chocolate})  frozenset({mineral water})   \n",
       "47                   frozenset({frozen smoothie})  frozenset({mineral water})   \n",
       "48                            frozenset({salmon})      frozenset({spaghetti})   \n",
       "49                   frozenset({chocolate, eggs})      frozenset({spaghetti})   \n",
       "\n",
       "    antecedent support  consequent support   support  confidence      lift  \\\n",
       "0             0.020000            0.238267  0.010133    0.506667  2.126469   \n",
       "1             0.022000            0.238267  0.011067    0.503030  2.111207   \n",
       "2             0.023067            0.238267  0.010933    0.473988  1.989319   \n",
       "3             0.023600            0.238267  0.011067    0.468927  1.968075   \n",
       "4             0.050533            0.238267  0.023067    0.456464  1.915771   \n",
       "5             0.025200            0.238267  0.011467    0.455026  1.909736   \n",
       "6             0.022933            0.238267  0.010267    0.447674  1.878880   \n",
       "7             0.035467            0.238267  0.015733    0.443609  1.861817   \n",
       "8             0.032133            0.238267  0.014000    0.435685  1.828559   \n",
       "9             0.039200            0.238267  0.017067    0.435374  1.827256   \n",
       "10            0.027867            0.238267  0.012000    0.430622  1.807311   \n",
       "11            0.030800            0.238267  0.013067    0.424242  1.780536   \n",
       "12            0.065733            0.238267  0.027467    0.417850  1.753707   \n",
       "13            0.040933            0.174133  0.017067    0.416938  2.394361   \n",
       "14            0.098267            0.238267  0.040933    0.416554  1.748266   \n",
       "15            0.033200            0.238267  0.013467    0.405622  1.702389   \n",
       "16            0.039200            0.238267  0.015867    0.404762  1.698777   \n",
       "17            0.042400            0.238267  0.016933    0.399371  1.676152   \n",
       "18            0.025733            0.238267  0.010267    0.398964  1.674442   \n",
       "19            0.098267            0.174133  0.039200    0.398915  2.290857   \n",
       "20            0.051067            0.238267  0.020133    0.394256  1.654683   \n",
       "21            0.036533            0.238267  0.014267    0.390511  1.638966   \n",
       "22            0.028133            0.238267  0.010933    0.388626  1.631053   \n",
       "23            0.060000            0.238267  0.022800    0.380000  1.594852   \n",
       "24            0.095333            0.238267  0.035733    0.374825  1.573133   \n",
       "25            0.027467            0.174133  0.010267    0.373786  2.146553   \n",
       "26            0.129600            0.238267  0.048000    0.370370  1.554436   \n",
       "27            0.027600            0.238267  0.010133    0.367150  1.540920   \n",
       "28            0.028133            0.174133  0.010267    0.364929  2.095687   \n",
       "29            0.068400            0.238267  0.024400    0.356725  1.497168   \n",
       "30            0.095067            0.238267  0.033733    0.354839  1.489250   \n",
       "31            0.065733            0.174133  0.022933    0.348884  2.003547   \n",
       "32            0.033200            0.238267  0.011467    0.345382  1.449559   \n",
       "33            0.049467            0.238267  0.017067    0.345013  1.448014   \n",
       "34            0.058533            0.238267  0.020133    0.343964  1.443608   \n",
       "35            0.174133            0.238267  0.059733    0.343032  1.439698   \n",
       "36            0.032133            0.174133  0.010933    0.340249  1.953957   \n",
       "37            0.033733            0.174133  0.011467    0.339921  1.952073   \n",
       "38            0.081067            0.238267  0.027467    0.338816  1.422002   \n",
       "39            0.035733            0.174133  0.012000    0.335821  1.928527   \n",
       "40            0.052400            0.238267  0.017467    0.333333  1.398993   \n",
       "41            0.087200            0.179733  0.028800    0.330275  1.837585   \n",
       "42            0.071333            0.238267  0.023467    0.328972  1.380688   \n",
       "43            0.049467            0.174133  0.016267    0.328841  1.888444   \n",
       "44            0.048000            0.174133  0.015733    0.327778  1.882338   \n",
       "45            0.049467            0.098267  0.016000    0.323450  3.291555   \n",
       "46            0.163867            0.238267  0.052667    0.321400  1.348907   \n",
       "47            0.063200            0.238267  0.020133    0.318565  1.337012   \n",
       "48            0.042400            0.174133  0.013467    0.317610  1.823948   \n",
       "49            0.033200            0.174133  0.010533    0.317269  1.821989   \n",
       "\n",
       "    representativity  leverage  conviction  zhangs_metric   jaccard  \\\n",
       "0                1.0  0.005368    1.544054       0.540548  0.040838   \n",
       "1                1.0  0.005825    1.532756       0.538177  0.044409   \n",
       "2                1.0  0.005437    1.448130       0.509058  0.043663   \n",
       "3                1.0  0.005444    1.434328       0.503778  0.044125   \n",
       "4                1.0  0.011026    1.401441       0.503458  0.086804   \n",
       "5                1.0  0.005462    1.397744       0.488682  0.045503   \n",
       "6                1.0  0.004802    1.379138       0.478747  0.040914   \n",
       "7                1.0  0.007283    1.369061       0.479911  0.060982   \n",
       "8                1.0  0.006344    1.349836       0.468165  0.054602   \n",
       "9                1.0  0.007727    1.349094       0.471202  0.065540   \n",
       "10               1.0  0.005360    1.337834       0.459496  0.047219   \n",
       "11               1.0  0.005728    1.323011       0.452302  0.051042   \n",
       "12               1.0  0.011805    1.308483       0.460018  0.099325   \n",
       "13               1.0  0.009939    1.416431       0.607207  0.086195   \n",
       "14               1.0  0.017520    1.305576       0.474647  0.138475   \n",
       "15               1.0  0.005556    1.281565       0.426758  0.052196   \n",
       "16               1.0  0.006527    1.279712       0.428124  0.060652   \n",
       "17               1.0  0.006831    1.268226       0.421257  0.064206   \n",
       "18               1.0  0.004135    1.267367       0.413425  0.040462   \n",
       "19               1.0  0.022088    1.373959       0.624888  0.168096   \n",
       "20               1.0  0.007966    1.257517       0.416947  0.074789   \n",
       "21               1.0  0.005562    1.249790       0.404642  0.054759   \n",
       "22               1.0  0.004230    1.245936       0.398099  0.042797   \n",
       "23               1.0  0.008504    1.228602       0.396790  0.082769   \n",
       "24               1.0  0.013019    1.218433       0.402718  0.119964   \n",
       "25               1.0  0.005484    1.318826       0.549222  0.053659   \n",
       "26               1.0  0.017121    1.209812       0.409789  0.150063   \n",
       "27               1.0  0.003557    1.203655       0.361000  0.039625   \n",
       "28               1.0  0.005368    1.300432       0.537964  0.053472   \n",
       "29               1.0  0.008103    1.184149       0.356454  0.086443   \n",
       "30               1.0  0.011082    1.180687       0.363034  0.112595   \n",
       "31               1.0  0.011487    1.268387       0.536127  0.105716   \n",
       "32               1.0  0.003556    1.163629       0.320785  0.044103   \n",
       "33               1.0  0.005280    1.162976       0.325500  0.063054   \n",
       "34               1.0  0.006187    1.161114       0.326396  0.072771   \n",
       "35               1.0  0.018243    1.159468       0.369806  0.169376   \n",
       "36               1.0  0.005338    1.251785       0.504427  0.055973   \n",
       "37               1.0  0.005593    1.251163       0.504751  0.058384   \n",
       "38               1.0  0.008151    1.152074       0.322947  0.094107   \n",
       "39               1.0  0.005778    1.243440       0.499312  0.060647   \n",
       "40               1.0  0.004981    1.142600       0.300971  0.063934   \n",
       "41               1.0  0.013127    1.224782       0.499351  0.120941   \n",
       "42               1.0  0.006470    1.135174       0.296903  0.082013   \n",
       "43               1.0  0.007653    1.230508       0.494947  0.078457   \n",
       "44               1.0  0.007375    1.228562       0.492380  0.076227   \n",
       "45               1.0  0.011139    1.332841       0.732423  0.121457   \n",
       "46               1.0  0.013623    1.122506       0.309351  0.150706   \n",
       "47               1.0  0.005075    1.117838       0.269069  0.071564   \n",
       "48               1.0  0.006083    1.210256       0.471740  0.066316   \n",
       "49               1.0  0.004752    1.209652       0.466642  0.053523   \n",
       "\n",
       "    certainty  kulczynski  \n",
       "0    0.352354    0.274598  \n",
       "1    0.347580    0.274738  \n",
       "2    0.309454    0.259938  \n",
       "3    0.302809    0.257687  \n",
       "4    0.286449    0.276637  \n",
       "5    0.284561    0.251576  \n",
       "6    0.274910    0.245382  \n",
       "7    0.269572    0.254821  \n",
       "8    0.259169    0.247221  \n",
       "9    0.258762    0.253501  \n",
       "10   0.252523    0.240493  \n",
       "11   0.244148    0.239541  \n",
       "12   0.235756    0.266563  \n",
       "13   0.294000    0.257474  \n",
       "14   0.234054    0.294175  \n",
       "15   0.219704    0.231071  \n",
       "16   0.218574    0.235677  \n",
       "17   0.211497    0.235220  \n",
       "18   0.210962    0.221026  \n",
       "19   0.272176    0.312015  \n",
       "20   0.204782    0.239378  \n",
       "21   0.199866    0.225194  \n",
       "22   0.197391    0.217256  \n",
       "23   0.186067    0.237846  \n",
       "24   0.179273    0.262399  \n",
       "25   0.241750    0.216373  \n",
       "26   0.173425    0.285913  \n",
       "27   0.169197    0.204840  \n",
       "28   0.231025    0.211944  \n",
       "29   0.155512    0.229566  \n",
       "30   0.153035    0.248208  \n",
       "31   0.211597    0.240292  \n",
       "32   0.140620    0.196753  \n",
       "33   0.140137    0.208321  \n",
       "34   0.138758    0.214231  \n",
       "35   0.137536    0.296866  \n",
       "36   0.201141    0.201518  \n",
       "37   0.200744    0.202885  \n",
       "38   0.132000    0.227046  \n",
       "39   0.195779    0.202367  \n",
       "40   0.124803    0.203320  \n",
       "41   0.183528    0.245256  \n",
       "42   0.119077    0.213731  \n",
       "43   0.187328    0.211128  \n",
       "44   0.186040    0.209065  \n",
       "45   0.249723    0.243136  \n",
       "46   0.109136    0.271220  \n",
       "47   0.105416    0.201532  \n",
       "48   0.173729    0.197473  \n",
       "49   0.173316    0.188880  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.2)\n",
    "rules.sort_values(by='confidence', ascending=False, inplace=True)\n",
    "rules.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print(f'Number of association rules found: {len(rules)}')\n",
    "rules.head(50)"
   ]
  }
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