{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from task2_utils import *\n",
"warnings.filterwarnings('ignore')\n",
"# interactive plot\n",
"%matplotlib widget"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"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": 6,
"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": 7,
"metadata": {},
"outputs": [],
"source": [
"class DBSCAN:\n",
" \"\"\"DBSCAN\"\"\"\n",
" def __init__(\n",
" self, label, data, distance_measure, eps, min_samples, data_sim_matrix=None\n",
" ):\n",
" self.label = label\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",
"\n",
" self.clusters = np.zeros(self.num_images) # 0 represents unclassified points\n",
" self.core_points = []\n",
"\n",
" def dbscan(self):\n",
" \"\"\"Fit DBSCAN\"\"\"\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",
" \"\"\"Get neighbors within threshold\"\"\"\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",
" \"\"\"Assign labels to cluster and grow borders\"\"\"\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 is 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 get_core_points(self, label_img_ids):\n",
" \"\"\"Find core points (after clustering!)\"\"\"\n",
" for i in range(self.num_images):\n",
" if self.clusters[i] == -1:\n",
" continue # Skip noise points\n",
"\n",
" neighbors = self.region_query(i)\n",
" if len(neighbors) < self.min_samples:\n",
" continue # not a core point\n",
" else:\n",
" self.core_points.append(label_img_ids[i])\n",
"\n",
" def display_cluster_stats(self):\n",
" \"\"\"Display cluster counts and noise point count (after clustering!)\"\"\"\n",
" cluster_counts = np.unique(self.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\")\n",
"\n",
" def mds_scatter_clusters(self):\n",
" \"\"\"Visualize clusters as point clouds in 2-D space (after clustering!)\"\"\"\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",
" 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",
" marker=('o' if label != -1 else '*')\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.savefig(f\"Plots/DBSCAN_MDS_Label_{self.label}.png\")\n",
" plt.show()\n",
"\n",
" def group_image_clusters(self, image_data):\n",
" \"\"\"Visualize clusters as point clouds in 2-D space, and display image thumbnails at cluster centroids (after clustering!)\"\"\"\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, 6))\n",
" for label in set(self.clusters):\n",
" cluster_points = mds_components[self.clusters == label]\n",
" plt.scatter(cluster_points[:, 0],\n",
" cluster_points[:, 1],\n",
" label=f\"{(f'Cluster {int(label)}') if label != -1 else 'Noise points'}\",\n",
" marker=('o' if label != -1 else '*'),\n",
" zorder=1,\n",
" )\n",
" \n",
" if label == -1:\n",
" continue\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(\"2-D MDS space with image thumbnails at centroids\")\n",
" plt.xlabel(\"MDS component 1\")\n",
" plt.ylabel(\"MDS component 2\")\n",
" ax = plt.gca()\n",
" ax.margins(0.05)\n",
" ax.set_aspect(0.75 / ax.get_data_ratio())\n",
" plt.legend()\n",
" plt.savefig(f\"Plots/DBSCAN_MDS_Label_{self.label}_with_images.png\")\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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 = euclidean_distance_measure\n",
"# selected_distance_measure = feature_distance_matches[selected_feature_model]\n",
"selected_c = 10"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clustering label 100 ...\r"
]
}
],
"source": [
"best_models = []\n",
"for label in range(NUM_LABELS):\n",
"# for label in [0, 1]:\n",
" print(\"Clustering label\", label, \"...\", end=\"\\r\")\n",
"\n",
" # get image features\n",
" label_ids = [\n",
" label_img[\"image_id\"] for label_img in fd_collection.find({\"true_label\": label})\n",
" ]\n",
" label_fds = np.array(\n",
" [\n",
" np.array(img_fds[selected_feature_model]).flatten()\n",
" for img_fds in fd_collection.find({\"true_label\": label})\n",
" ]\n",
" )\n",
"\n",
" label_dbscan_results = (np.zeros(label_fds.shape[0]), 0, 0)\n",
" label_min_noise = label_fds.shape[0]\n",
" label_min_cluster_diff = np.inf\n",
"\n",
" label_img_sim_matrix = calculate_image_similarity(\n",
" label_fds, selected_distance_measure\n",
" )\n",
"\n",
" # decrementally try min_samples, starting from twice the desired no. of clusters\n",
" for cur_min_samples in range(selected_c, 1, -1):\n",
" # find range of epsilon to try, by checking all from mean to min knn distance\n",
" # k is current min_samples\n",
" max_eps, min_eps = avgandmin_knn_distance(label_img_sim_matrix, cur_min_samples)\n",
"\n",
" # try epsilon values\n",
" for cur_eps in np.linspace(min_eps, max_eps, num=100):\n",
" label_dbscan = DBSCAN(\n",
" label,\n",
" label_fds,\n",
" selected_distance_measure,\n",
" cur_eps,\n",
" cur_min_samples,\n",
" label_img_sim_matrix,\n",
" )\n",
"\n",
" clusters = label_dbscan.dbscan()\n",
"\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(\n",
" cluster_counts[0], cluster_counts[1]\n",
" )\n",
" )\n",
"\n",
" if cluster_counts_dict.get(-1) is not None:\n",
" noise_pts = cluster_counts_dict.get(-1)\n",
" else:\n",
" noise_pts = 0\n",
" cluster_diff = abs(len(cluster_counts_dict.keys() - {-1}) - selected_c)\n",
"\n",
" # store only most desirable clustering: as close as possible to c clusters, and then minimum noise\n",
" if cluster_diff < label_min_cluster_diff or (\n",
" cluster_diff == label_min_cluster_diff and noise_pts <= label_min_noise\n",
" ):\n",
" # print(\n",
" # \"Better clustering:\",\n",
" # label_dbscan_results[1],\n",
" # \"->\",\n",
" # cur_eps,\n",
" # \"\\t\",\n",
" # label_dbscan_results[2],\n",
" # \"->\",\n",
" # cur_min_samples,\n",
" # )\n",
" # print(\n",
" # \"Noise improvement:\",\n",
" # label_min_noise,\n",
" # \"->\",\n",
" # noise_pts,\n",
" # \"\\tCluster count improvement:\",\n",
" # label_min_cluster_diff,\n",
" # \"->\",\n",
" # cluster_diff,\n",
" # )\n",
" label_dbscan_results = (clusters, cur_eps, cur_min_samples)\n",
" label_min_noise = noise_pts\n",
" label_min_cluster_diff = cluster_diff\n",
"\n",
" best_label_dbscan = DBSCAN(\n",
" label,\n",
" label_fds,\n",
" selected_distance_measure,\n",
" label_dbscan_results[1],\n",
" label_dbscan_results[2],\n",
" label_img_sim_matrix,\n",
" )\n",
" best_label_dbscan.clusters = label_dbscan_results[0]\n",
" best_label_dbscan.get_core_points(label_ids)\n",
"\n",
" # store best clustering\n",
" best_models.append(best_label_dbscan)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interpretation"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Label: 0\n",
"Clusters: {-1.0: 138, 1.0: 2, 2.0: 2, 3.0: 3, 4.0: 46, 5.0: 3, 6.0: 4, 7.0: 4, 8.0: 5, 9.0: 3, 10.0: 8}\n",
"No. of clusters: 10\n",
"Noise points: 138\n",
"Core points: 80\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27e8475ee1334628b9dda022fceeae47",
"version_major": 2,
"version_minor": 0
},
"image/png": 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