{
 "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\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\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60a2f9f5",
   "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>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",
       "</table>\n",
       "</div>"
      ],
      "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",
    "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 either merging clusters based on distance metrics.\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",
    "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. It ranges from -1 to 1, where a high value indicates that the point is well matched to its own cluster and poorly matched to neighboring clusters. A score near 0 indicates overlapping clusters, and negative values suggest the point may be assigned to 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."
   ]
  },
  {
   "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"
   ]
  },
  {
   "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",
       "    }\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>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>-1.250746</td>\n",
       "      <td>-0.493683</td>\n",
       "      <td>-1.283210</td>\n",
       "      <td>-1.250444</td>\n",
       "      <td>-0.285173</td>\n",
       "      <td>-0.868342</td>\n",
       "      <td>-1.069531</td>\n",
       "      <td>-0.641797</td>\n",
       "      <td>0.320179</td>\n",
       "      <td>0.356259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.165642</td>\n",
       "      <td>-0.232848</td>\n",
       "      <td>0.911428</td>\n",
       "      <td>1.318942</td>\n",
       "      <td>0.877343</td>\n",
       "      <td>0.089246</td>\n",
       "      <td>0.570787</td>\n",
       "      <td>0.894398</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>1.029452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.336438</td>\n",
       "      <td>-0.605475</td>\n",
       "      <td>-0.610190</td>\n",
       "      <td>-1.160542</td>\n",
       "      <td>-2.105449</td>\n",
       "      <td>-0.377287</td>\n",
       "      <td>-0.682313</td>\n",
       "      <td>0.811085</td>\n",
       "      <td>0.112980</td>\n",
       "      <td>1.164129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.928589</td>\n",
       "      <td>-0.852942</td>\n",
       "      <td>0.040630</td>\n",
       "      <td>0.060772</td>\n",
       "      <td>-1.903721</td>\n",
       "      <td>0.505711</td>\n",
       "      <td>-0.470205</td>\n",
       "      <td>-0.303466</td>\n",
       "      <td>0.978760</td>\n",
       "      <td>0.450839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.111359</td>\n",
       "      <td>-1.928010</td>\n",
       "      <td>-2.283240</td>\n",
       "      <td>-0.717552</td>\n",
       "      <td>-1.497510</td>\n",
       "      <td>0.441594</td>\n",
       "      <td>-0.446113</td>\n",
       "      <td>-1.475098</td>\n",
       "      <td>0.512449</td>\n",
       "      <td>-0.830396</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          -1.250746          -0.493683          -1.283210          -1.250444   \n",
       "1           0.165642          -0.232848           0.911428           1.318942   \n",
       "2          -1.336438          -0.605475          -0.610190          -1.160542   \n",
       "3          -1.928589          -0.852942           0.040630           0.060772   \n",
       "4          -2.111359          -1.928010          -2.283240          -0.717552   \n",
       "\n",
       "   feature_0_noise_4  feature_1_noise_0  feature_1_noise_1  feature_1_noise_2  \\\n",
       "0          -0.285173          -0.868342          -1.069531          -0.641797   \n",
       "1           0.877343           0.089246           0.570787           0.894398   \n",
       "2          -2.105449          -0.377287          -0.682313           0.811085   \n",
       "3          -1.903721           0.505711          -0.470205          -0.303466   \n",
       "4          -1.497510           0.441594          -0.446113          -1.475098   \n",
       "\n",
       "   feature_1_noise_3  feature_1_noise_4  \n",
       "0           0.320179           0.356259  \n",
       "1           0.000170           1.029452  \n",
       "2           0.112980           1.164129  \n",
       "3           0.978760           0.450839  \n",
       "4           0.512449          -0.830396  "
      ]
     },
     "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 transforms data to a new coordinate system where the greatest variance lies on the first coordinate (first principal component).\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>-0.850866</td>\n",
       "      <td>-2.010835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.187233</td>\n",
       "      <td>1.816915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.162281</td>\n",
       "      <td>-1.462003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.819822</td>\n",
       "      <td>-1.055760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.144221</td>\n",
       "      <td>-3.255350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pca0      pca1\n",
       "0 -0.850866 -2.010835\n",
       "1  0.187233  1.816915\n",
       "2 -2.162281 -1.462003\n",
       "3 -1.819822 -1.055760\n",
       "4 -2.144221 -3.255350"
      ]
     },
     "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 uses hierarchical clustering to group similar features together, reducing dimensionality while preserving the structure of the data.\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>-0.380646</td>\n",
       "      <td>-0.912651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.516811</td>\n",
       "      <td>0.608101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.205719</td>\n",
       "      <td>-1.163619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.232328</td>\n",
       "      <td>-0.916770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.359513</td>\n",
       "      <td>-1.707534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        FA1       FA2\n",
       "0 -0.380646 -0.912651\n",
       "1  0.516811  0.608101\n",
       "2  0.205719 -1.163619\n",
       "3  0.232328 -0.916770\n",
       "4 -0.359513 -1.707534"
      ]
     },
     "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. TruncatedSVD"
   ]
  },
  {
   "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>-0.877775</td>\n",
       "      <td>-2.034454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.161245</td>\n",
       "      <td>1.793759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.189309</td>\n",
       "      <td>-1.485840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.846827</td>\n",
       "      <td>-1.079749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.171189</td>\n",
       "      <td>-3.278483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SVD1      SVD2\n",
       "0 -0.877775 -2.034454\n",
       "1  0.161245  1.793759\n",
       "2 -2.189309 -1.485840\n",
       "3 -1.846827 -1.079749\n",
       "4 -2.171189 -3.278483"
      ]
     },
     "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-Distributed Stochastic Neighbor Embedding is a nonlinear dimensionality reduction technique particularly well-suited for visualizing high-dimensional data.\n",
    "\n",
    "Scikit-learn [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>-21.813768</td>\n",
       "      <td>-22.895798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.330935</td>\n",
       "      <td>25.935995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-23.466413</td>\n",
       "      <td>-8.033711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-23.869495</td>\n",
       "      <td>-3.444280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-42.131195</td>\n",
       "      <td>-25.176720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TSNE1      TSNE2\n",
       "0 -21.813768 -22.895798\n",
       "1   4.330935  25.935995\n",
       "2 -23.466413  -8.033711\n",
       "3 -23.869495  -3.444280\n",
       "4 -42.131195 -25.176720"
      ]
     },
     "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 algorithms, 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>0.839965</td>\n",
       "      <td>-1.858448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.317839</td>\n",
       "      <td>1.717841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.188981</td>\n",
       "      <td>-1.316268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.819160</td>\n",
       "      <td>-0.833337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.104161</td>\n",
       "      <td>-2.896294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lineardiscriminantanalysis0  lineardiscriminantanalysis1\n",
       "0                     0.839965                    -1.858448\n",
       "1                    -0.317839                     1.717841\n",
       "2                     2.188981                    -1.316268\n",
       "3                     1.819160                    -0.833337\n",
       "4                     2.104161                    -2.896294"
      ]
     },
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "607cb0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add random uniform noise points to original clusters\n",
    "num_noise_points = 10\n",
    "noise_points = {}\n",
    "\n",
    "for feature in df.columns:\n",
    "\n",
    "    noise_points[feature] = np.random.uniform(\n",
    "        low=df[feature].mean() - df[feature].std() * 5,\n",
    "        high=df[feature].mean() + df[feature].std() * 5,\n",
    "        size=num_noise_points\n",
    "    )\n",
    "\n",
    "outlier_df = pd.concat([df, pd.DataFrame(noise_points)], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d0ebdb35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Data with outliers')\n",
    "plt.scatter(outlier_df['feature_0'], outlier_df['feature_1'], s=5, c='black')\n",
    "plt.xlabel('feature 0')\n",
    "plt.ylabel('feature 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36d880b",
   "metadata": {},
   "source": [
    "### 3.2. Robust covariance (elliptic envelope)\n",
    "\n",
    "Robust covariance estimation assumes that the data is Gaussian and learns an ellipse that contains most of the normal data points.\n",
    "\n",
    "Scikit-learn [EllipticEnvelope](https://scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "530ba70e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elliptic Envelope found 11 outliers and 999 inliers\n"
     ]
    }
   ],
   "source": [
    "# Fit Elliptic Envelope\n",
    "elliptic = EllipticEnvelope(contamination=0.01, random_state=315)\n",
    "elliptic_labels = elliptic.fit_predict(outlier_df)\n",
    "\n",
    "# Count outliers\n",
    "n_outliers = list(elliptic_labels).count(-1)\n",
    "n_inliers = list(elliptic_labels).count(1)\n",
    "\n",
    "print(f'Elliptic Envelope found {n_outliers} outliers and {n_inliers} inliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd186935",
   "metadata": {},
   "source": [
    "### 3.3. One-class SVM\n",
    "\n",
    "One-class SVM learns a decision boundary around the normal data points using support vector machines.\n",
    "\n",
    "Scikit-learn [OneClassSVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "728c91d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-Class SVM found 10 outliers and 1000 inliers\n"
     ]
    }
   ],
   "source": [
    "# Fit One-Class SVM\n",
    "ocsvm = OneClassSVM(nu=0.01, gamma='auto')\n",
    "ocsvm_labels = ocsvm.fit_predict(outlier_df)\n",
    "\n",
    "# Count outliers\n",
    "n_outliers = list(ocsvm_labels).count(-1)\n",
    "n_inliers = list(ocsvm_labels).count(1)\n",
    "\n",
    "print(f'One-Class SVM found {n_outliers} outliers and {n_inliers} inliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0b46d5",
   "metadata": {},
   "source": [
    "### 3.4. Isolation Forest\n",
    "\n",
    "Isolation Forest isolates anomalies by randomly selecting features and split values. Anomalies are points that have shorter paths in the tree structure.\n",
    "\n",
    "Scikit-learn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5626ea27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isolation Forest found 11 outliers and 999 inliers\n"
     ]
    }
   ],
   "source": [
    "# Fit Isolation Forest\n",
    "iso_forest = IsolationForest(contamination=0.01, random_state=315)\n",
    "iso_forest_labels = iso_forest.fit_predict(outlier_df)\n",
    "\n",
    "# Count outliers\n",
    "n_outliers = list(iso_forest_labels).count(-1)\n",
    "n_inliers = list(iso_forest_labels).count(1)\n",
    "\n",
    "print(f'Isolation Forest found {n_outliers} outliers and {n_inliers} inliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7acddabf",
   "metadata": {},
   "source": [
    "### 3.5. Local Outlier Factor\n",
    "\n",
    "Local Outlier Factor computes the local density deviation of a data point with respect to its neighbors. Points with substantially lower density than their neighbors are considered outliers.\n",
    "\n",
    "Scikit-learn [LocalOutlierFactor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f893cfc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local Outlier Factor found 11 outliers and 999 inliers\n"
     ]
    }
   ],
   "source": [
    "# Fit Local Outlier Factor\n",
    "lof = LocalOutlierFactor(contamination=0.01)\n",
    "lof_labels = lof.fit_predict(outlier_df)\n",
    "\n",
    "# Count outliers\n",
    "n_outliers = list(lof_labels).count(-1)\n",
    "n_inliers = list(lof_labels).count(1)\n",
    "\n",
    "print(f'Local Outlier Factor found {n_outliers} outliers and {n_inliers} inliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ecff1bf",
   "metadata": {},
   "source": [
    "### 3.6. Anomaly detection comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6a8f66c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x375 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define color mapping for outlier detection results\n",
    "colors = {-1:'#ff7f0e', 1:'#1f77b4'}\n",
    "\n",
    "ncols = 4\n",
    "nrows = 1\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*3.25, nrows*3.75))\n",
    "axes = axes.flatten()\n",
    "\n",
    "fig.suptitle('Anomaly detection comparison')\n",
    "fig.supxlabel('feature 0')\n",
    "fig.supylabel('feature 1')\n",
    "\n",
    "# Elliptic Envelope\n",
    "axes[0].set_title('Elliptic Envelope')\n",
    "axes[0].scatter(outlier_df['feature_0'], outlier_df['feature_1'], c=[colors[label] for label in elliptic_labels], s=10, edgecolors='black', linewidth=0.5)\n",
    "\n",
    "# One-Class SVM\n",
    "axes[1].set_title('One-Class SVM')\n",
    "axes[1].scatter(outlier_df['feature_0'], outlier_df['feature_1'], c=[colors[label] for label in ocsvm_labels], s=10, edgecolors='black', linewidth=0.5)\n",
    "\n",
    "# Isolation Forest\n",
    "axes[2].set_title('Isolation Forest')\n",
    "axes[2].scatter(outlier_df['feature_0'], outlier_df['feature_1'], c=[colors[label] for label in iso_forest_labels], s=10, edgecolors='black', linewidth=0.5)\n",
    "\n",
    "# Local Outlier Factor\n",
    "axes[3].set_title('Local Outlier Factor')\n",
    "axes[3].scatter(outlier_df['feature_0'], outlier_df['feature_1'], c=[colors[label] for label in lof_labels], s=10, edgecolors='black', linewidth=0.5)\n",
    "\n",
    "# Create custom legend\n",
    "legend_elements = [\n",
    "    Patch(facecolor='#1f77b4', label='Inlier'),\n",
    "    Patch(facecolor='#ff7f0e', label='Outlier')\n",
    "]\n",
    "fig.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=(0.5, 0.93), ncol=2)\n",
    "\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\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://gperdrizet.github.io/FSA_devops/assets/data/unit3/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.core.frame.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   object\n",
      " 1   item_2   5746 non-null   object\n",
      " 2   item_3   4388 non-null   object\n",
      " 3   item_4   3344 non-null   object\n",
      " 4   item_5   2528 non-null   object\n",
      " 5   item_6   1863 non-null   object\n",
      " 6   item_7   1368 non-null   object\n",
      " 7   item_8   980 non-null    object\n",
      " 8   item_9   653 non-null    object\n",
      " 9   item_10  394 non-null    object\n",
      " 10  item_11  255 non-null    object\n",
      " 11  item_12  153 non-null    object\n",
      " 12  item_13  86 non-null     object\n",
      " 13  item_14  46 non-null     object\n",
      " 14  item_15  24 non-null     object\n",
      " 15  item_16  7 non-null      object\n",
      " 16  item_17  3 non-null      object\n",
      " 17  item_18  3 non-null      object\n",
      " 18  item_19  2 non-null      object\n",
      "dtypes: object(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",
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       "    }\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>(mineral water)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.179733</td>\n",
       "      <td>(eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.174133</td>\n",
       "      <td>(spaghetti)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.170933</td>\n",
       "      <td>(french fries)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.163867</td>\n",
       "      <td>(chocolate)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support         itemsets\n",
       "0  0.238267  (mineral water)\n",
       "1  0.179733           (eggs)\n",
       "2  0.174133      (spaghetti)\n",
       "3  0.170933   (french fries)\n",
       "4  0.163867      (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": 39,
   "id": "f9f07128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of association rules found: 162\n"
     ]
    },
    {
     "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>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",
       "      <th>lift</th>\n",
       "      <th>representativity</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>zhangs_metric</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>certainty</th>\n",
       "      <th>kulczynski</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(eggs, ground beef)</td>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.506667</td>\n",
       "      <td>2.126469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>1.544054</td>\n",
       "      <td>0.540548</td>\n",
       "      <td>0.040838</td>\n",
       "      <td>0.352354</td>\n",
       "      <td>0.274598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(milk, ground beef)</td>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.238267</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>0.503030</td>\n",
       "      <td>2.111207</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005825</td>\n",
       "      <td>1.532756</td>\n",
       "      <td>0.538177</td>\n",
       "      <td>0.044409</td>\n",
       "      <td>0.347580</td>\n",
       "      <td>0.274738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(chocolate, ground beef)</td>\n",
       "      <td>(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>(milk, frozen vegetables)</td>\n",
       "      <td>(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>(soup)</td>\n",
       "      <td>(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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents      consequents  antecedent support  \\\n",
       "0        (eggs, ground beef)  (mineral water)            0.020000   \n",
       "1        (milk, ground beef)  (mineral water)            0.022000   \n",
       "2   (chocolate, ground beef)  (mineral water)            0.023067   \n",
       "3  (milk, frozen vegetables)  (mineral water)            0.023600   \n",
       "4                     (soup)  (mineral water)            0.050533   \n",
       "\n",
       "   consequent support   support  confidence      lift  representativity  \\\n",
       "0            0.238267  0.010133    0.506667  2.126469               1.0   \n",
       "1            0.238267  0.011067    0.503030  2.111207               1.0   \n",
       "2            0.238267  0.010933    0.473988  1.989319               1.0   \n",
       "3            0.238267  0.011067    0.468927  1.968075               1.0   \n",
       "4            0.238267  0.023067    0.456464  1.915771               1.0   \n",
       "\n",
       "   leverage  conviction  zhangs_metric   jaccard  certainty  kulczynski  \n",
       "0  0.005368    1.544054       0.540548  0.040838   0.352354    0.274598  \n",
       "1  0.005825    1.532756       0.538177  0.044409   0.347580    0.274738  \n",
       "2  0.005437    1.448130       0.509058  0.043663   0.309454    0.259938  \n",
       "3  0.005444    1.434328       0.503778  0.044125   0.302809    0.257687  \n",
       "4  0.011026    1.401441       0.503458  0.086804   0.286449    0.276637  "
      ]
     },
     "execution_count": 39,
     "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()"
   ]
  }
 ],
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