{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from task2_utils import *\n", "warnings.filterwarnings('ignore')\n", "# interactive plot\n", "%matplotlib widget" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def calculate_image_similarity(data, distance_measure):\n", " \"\"\"Object-object similarity with given distance measure\"\"\"\n", " n = data.shape[0]\n", " image_sim_matrix = np.zeros((n, n))\n", " for i in range(n):\n", " for j in range(i + 1, n):\n", " image_sim_matrix[i][j] = image_sim_matrix[j][i] = distance_measure(\n", " np.array(data[i]), np.array(data[j])\n", " )\n", " return image_sim_matrix" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def mds_projection(data_sim_matrix, n_components=2):\n", " \"\"\"MDS projection to n-D space\"\"\"\n", " n = data_sim_matrix.shape[0]\n", " # Centering matrix\n", " C = np.eye(n) - np.ones((n, n)) / n\n", " # B = -1/2 * C * D^2 * C\n", " B = -0.5 * C @ (data_sim_matrix**2) @ C\n", " # Eigen decomposition\n", " eigvals, eigvecs = np.linalg.eigh(B)\n", "\n", " # Sort eigenvalues and corresponding eigenvectors\n", " indices = np.argsort(eigvals)[::-1]\n", " eigvals = eigvals[indices]\n", " eigvecs = eigvecs[:, indices]\n", "\n", " # Take the first n_components eigenvectors\n", " components = eigvecs[:, :n_components]\n", "\n", " return components" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def avgandmin_knn_distance(data_sim_matrix, k):\n", " \"\"\"Get avg. and minimum k-th nearest neighbor distance\"\"\"\n", "\t# Sort each row of the distance matrix and extract the kth-nearest neighbor distance\n", " kth_neighbor_distances = np.sort(data_sim_matrix, axis=1)[:, k-1]\n", "\n", " # Understanding KNN distribution to figure out strategy to find epsilon range\n", " # plt.plot(np.sort(kth_neighbor_distances))\n", " # plt.show()\n", " \n", " # Calculate the average and minimum distance of the kth-nearest neighbor\n", " average_distance = np.mean(kth_neighbor_distances)\n", " minimum_distance = np.min(kth_neighbor_distances)\n", "\n", " return average_distance, minimum_distance\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def display_cluster_stats(clusters):\n", " \"\"\"Display cluster counts and noise point count\"\"\"\n", " cluster_counts = np.unique(clusters, return_counts=True)\n", " cluster_counts_dict = dict(\n", " (unique_label, unique_count)\n", " for unique_label, unique_count in zip(cluster_counts[0], cluster_counts[1])\n", " )\n", " print(\"Clusters:\", cluster_counts_dict)\n", " print(\"No. of clusters:\", len(cluster_counts_dict.keys() - {-1}))\n", " if -1 in cluster_counts_dict:\n", " print(\"Noise points:\", cluster_counts_dict[-1])\n", " else:\n", " print(\"No noise points\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "class DBSCAN:\n", " def __init__(self, data, distance_measure, eps, min_samples, data_sim_matrix=None):\n", " self.eps = eps\n", " self.min_samples = min_samples\n", "\n", " self.data = data\n", " self.distance_measure = distance_measure\n", " self.num_images = data.shape[0]\n", "\n", " self.image_sim_matrix = np.zeros((self.num_images, self.num_images))\n", " if data_sim_matrix is not None:\n", " self.image_sim_matrix = data_sim_matrix\n", " self.clusters = np.zeros(self.num_images) # 0 represents unclassified points\n", "\n", " def dbscan(self):\n", " \"\"\"DBSCAN algorithm\"\"\"\n", " # if similarities not provided/calculated already\n", " if np.array_equal(\n", " self.image_sim_matrix, np.zeros((self.num_images, self.num_images))\n", " ):\n", " calculate_image_similarity(self.data, self.distance_measure)\n", "\n", " cluster_id = 0\n", " for i in range(self.num_images):\n", " if self.clusters[i] != 0:\n", " continue # Skip already classified points\n", "\n", " neighbors = self.region_query(i)\n", " if len(neighbors) < self.min_samples:\n", " self.clusters[i] = -1 # Mark point as noise\n", " else:\n", " cluster_id += 1 # New cluster identified\n", " self.clusters[i] = cluster_id\n", " self.grow_cluster(neighbors, cluster_id)\n", "\n", " return self.clusters\n", "\n", " def region_query(self, center):\n", " distances = self.image_sim_matrix[center]\n", " return [i for i, dist in enumerate(distances) if dist < self.eps]\n", "\n", " def grow_cluster(self, neighbors, cluster_id):\n", " i = 0\n", " # check neighbors for connected components\n", " while i < len(neighbors):\n", " neighbor = neighbors[i]\n", "\n", " if self.clusters[neighbor] == -1:\n", " self.clusters[neighbor] = cluster_id # Change noise to border point\n", " elif self.clusters[neighbor] == 0:\n", " self.clusters[neighbor] = cluster_id\n", " new_neighbors = self.region_query(neighbor)\n", " # If new point could be a core point\n", " if len(new_neighbors) >= self.min_samples:\n", " neighbors += new_neighbors # add its neighbors to list of neighbors to consider\n", " i += 1\n", "\n", " def mds_scatter_clusters(self):\n", " \"\"\"Visualize clusters as point clouds in 2-D space\"\"\"\n", " # Perform MDS projection\n", " mds_components = mds_projection(self.image_sim_matrix)\n", "\n", " # Plot clusters\n", " plt.figure(figsize=(8, 6))\n", " for label in set(self.clusters):\n", " # to hide noise\n", " # if label == -1:\n", " # continue\n", " cluster_points = mds_components[self.clusters == label]\n", " plt.scatter(\n", " cluster_points[:, 0],\n", " cluster_points[:, 1],\n", " label=f\"{(f'Cluster {int(label)}') if label != -1 else 'Noise points'}\",\n", " )\n", "\n", " plt.title(\"DBSCAN clusters projected onto 2-D MDS space\")\n", " plt.xlabel(\"MDS component 1\")\n", " plt.ylabel(\"MDS component 2\")\n", " plt.legend()\n", " plt.show()\n", "\n", " def group_image_clusters(self, image_data):\n", " # Perform MDS projection\n", " mds_components = mds_projection(self.image_sim_matrix)\n", " # Scaling up to fit images inside\n", " mds_components = mds_components * 10000\n", "\n", " min_x_mds = np.min(mds_components[:, 0])\n", " min_y_mds = np.min(mds_components[:, 1])\n", " max_x_mds = np.max(mds_components[:, 0])\n", " max_y_mds = np.max(mds_components[:, 1])\n", "\n", " img_width = (max_x_mds - min_x_mds) / 10\n", " img_height = (max_y_mds - min_y_mds) / 10\n", "\n", " # Plot clusters\n", " plt.figure(figsize=(8, 8))\n", " for label in set(self.clusters):\n", " cluster_points = mds_components[self.clusters == label]\n", " plt.scatter(\n", " cluster_points[:, 0],\n", " cluster_points[:, 1],\n", " label=f\"{(f'Cluster {int(label)}') if label != -1 else 'Noise points'}\",\n", " zorder=1,\n", " )\n", "\n", " # Display image thumbnails at cluster centroids\n", " cluster_indices = np.where(self.clusters == label)[0]\n", " cluster_center = np.mean(mds_components[cluster_indices], axis=0)\n", " thumbnail_data = image_data[cluster_indices[0]].resize(\n", " (int(np.ceil(img_width)), int(np.ceil(img_height)))\n", " )\n", " im = plt.imshow(\n", " thumbnail_data,\n", " extent=(\n", " cluster_center[0] - 0.5 * img_width,\n", " cluster_center[0] + 0.5 * img_width,\n", " cluster_center[1] - 0.5 * img_height,\n", " cluster_center[1] + 0.5 * img_height,\n", " ),\n", " interpolation=\"nearest\",\n", " cmap=plt.cm.gray_r,\n", " zorder=0,\n", " )\n", "\n", " # Image border\n", " x1, x2, y1, y2 = im.get_extent()\n", " (im_border,) = plt.plot(\n", " [x1, x2, x2, x1, x1],\n", " [y1, y1, y2, y2, y1],\n", " \"-\",\n", " linewidth=2,\n", " solid_capstyle=\"butt\",\n", " zorder=0,\n", " )\n", "\n", " # Click to bring to front\n", " def region_click(event, region_area=im, region_border=im_border):\n", " if region_area.contains(event)[0]:\n", " region_border.set_zorder(2)\n", " region_area.set_zorder(2)\n", " else:\n", " region_border.set_zorder(0)\n", " region_area.set_zorder(0)\n", "\n", " im.figure.canvas.mpl_connect(\"button_press_event\", region_click)\n", "\n", " plt.title(\"DBSCAN clusters projected onto 2-D MDS space\")\n", " plt.xlabel(\"MDS component 1\")\n", " plt.ylabel(\"MDS component 2\")\n", " plt.xlim(min_x_mds, max_x_mds)\n", " plt.ylim(min_y_mds, max_y_mds)\n", " plt.legend()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# selected_feature_model = valid_feature_models[\n", "# str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n", "# ]\n", "selected_feature_model = valid_feature_models[\"avgpool\"]\n", "selected_distance_measure = feature_distance_matches[selected_feature_model]\n", "# selected_distance_measure = euclidean_distance_measure\n", "selected_c = 5" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Label: 0\n", "Epsilon: 0.10715872252338608 \tMinPts: 3\n", "Clusters: {-1.0: 94, 1.0: 15, 2.0: 94, 3.0: 3, 4.0: 4, 5.0: 8}\n", "No. of clusters: 5\n", "Noise points: 94\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c248a05637e54077be7ebe0d3c03a337", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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