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CSE515_MWDB_Project/Phase 2/task_9.ipynb
pranavbrkr 680fae35cd Task 7
2023-10-13 23:24:48 -07:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import numpy as np\n",
"from utils import *\n",
"import math\n",
"import heapq"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [],
"source": [
"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n",
"all_images = fd_collection.find()"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cm_fd-cp-10-semantics.json loaded\n"
]
}
],
"source": [
"selected_latent_space = valid_latent_spaces[\n",
" str(input(\"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))))\n",
"]\n",
"\n",
"selected_feature_model = valid_feature_models[\n",
" str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n",
"]\n",
"\n",
"k = int(input(\"Enter value of k: \"))\n",
"if k < 1:\n",
" raise ValueError(\"k should be a positive integer\")\n",
"\n",
"selected_dim_reduction_method = str(\n",
" input(\n",
" \"Enter dimensionality reduction method - one of \"\n",
" + str(list(valid_dim_reduction_methods.keys()))\n",
" )\n",
")\n",
"\n",
"label = int(input(\"Enter label: \"))\n",
"if label < 0 and label > 100:\n",
" raise ValueError(\"label should be between 0 and 100\")\n",
"\n",
"knum = int(input(\"Enter value of knum: \"))\n",
"if knum < 1:\n",
" raise ValueError(\"knum should be a positive integer\")\n",
"\n",
"label_rep = calculate_label_representatives(fd_collection, label, selected_feature_model)\n",
"\n",
"match selected_latent_space:\n",
" case \"\":\n",
" if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
" data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
" print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
" else:\n",
" print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n",
" case \"cp\":\n",
" if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n",
" data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n",
" print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n",
" else: \n",
" print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n",
" case _:\n",
" if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
" data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
" print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
" else:\n",
" print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [],
"source": [
"def extract_similarities_ls1_ls4(latent_space, dim_reduction, data, label, label_rep):\n",
"\n",
" match dim_reduction:\n",
"\n",
" case 'svd':\n",
" U = np.array(data[\"image-semantic\"])\n",
" S = np.array(data[\"semantics-core\"])\n",
" if len(S.shape) == 1:\n",
" S = np.diag(S)\n",
" V = np.transpose(np.array(data[\"semantic-feature\"]))\n",
"\n",
" if latent_space == \"image_sim\":\n",
" label_vectors = []\n",
" length = len(U)\n",
" for i in range(length):\n",
" if all_images[i][\"true_label\"] == label:\n",
" label_vectors.append(U[i])\n",
" label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n",
" \n",
" comparison_feature_space = np.matmul(U, S)\n",
"\n",
" if latent_space == \"image_sim\":\n",
" comparison_vector = np.matmul(label_rep, S)\n",
" else:\n",
" comparison_vector = np.matmul(np.matmul(label_rep, V), S)\n",
" \n",
" case \"nmf\":\n",
" H = np.array(data['semantic-feature'])\n",
" comparison_feature_space = W = np.array(data['image-semantic'])\n",
" if latent_space == \"image_sim\":\n",
" label_vectors = []\n",
" length = len(W)\n",
" for i in range(length):\n",
" if all_images[i][\"true_label\"] == label:\n",
" label_vectors.append(W[i])\n",
" label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n",
"\n",
" if latent_space == \"image_sim\":\n",
" comparison_vector = label_rep\n",
" else:\n",
" comparison_vector = np.matmul(label_rep, np.transpose(H))\n",
"\n",
" case \"kmeans\":\n",
" comparison_vector = []\n",
" comparison_feature_space = np.array(data[\"image-semantic\"])\n",
" S = np.array(data[\"semantic-feature\"])\n",
"\n",
" if latent_space == \"image_sim\":\n",
" sim_matrix = np.array(data[\"sim-matrix\"])\n",
" label_vectors = []\n",
" length = len(sim_matrix)\n",
" for i in range(length):\n",
" if all_images[i][\"true_label\"] == label:\n",
" label_vectors.append(sim_matrix[i])\n",
" label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n",
"\n",
"\n",
" for centroid in S:\n",
" comparison_vector.append(math.dist(label_rep, centroid))\n",
"\n",
" n = len(comparison_feature_space)\n",
"\n",
" distances = []\n",
" for i in range(n):\n",
" if all_images[i][\"true_label\"] != label:\n",
" distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
"\n",
" distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n",
"\n",
" similar_labels = []\n",
" unique_labels = set()\n",
"\n",
" for img in distances:\n",
" if img['label'] not in unique_labels:\n",
" similar_labels.append(img)\n",
" unique_labels.add(img[\"label\"])\n",
"\n",
" if len(similar_labels) == knum:\n",
" break\n",
"\n",
"\n",
" for x in similar_labels:\n",
" print(x)"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [],
"source": [
"def extract_similarities_ls2(data, label):\n",
"\n",
" LS = np.array(data[\"label-semantic\"])\n",
" S = np.array(data[\"semantics-core\"])\n",
"\n",
" if len(S.shape) == 1:\n",
" S = np.diag(S)\n",
"\n",
" comparison_feature_space = np.matmul(LS, S)\n",
" comparison_vector = comparison_feature_space[label]\n",
"\n",
" distances = []\n",
"\n",
" n = len(comparison_feature_space)\n",
" for i in range(n):\n",
" if i != label:\n",
" distances.append({\"label\": i, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
" \n",
" distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n",
"\n",
" for x in distances:\n",
" print(x)"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [],
"source": [
"def extract_similarities_ls3(dim_reduction, data, label):\n",
"\n",
" match dim_reduction:\n",
"\n",
" case 'svd':\n",
" U = np.array(data[\"image-semantic\"])\n",
" S = np.array(data[\"semantics-core\"])\n",
" V = np.transpose(np.array(data[\"semantic-feature\"]))\n",
"\n",
" comparison_feature_space = np.matmul(U, S)\n",
" comparison_vector = comparison_feature_space[label]\n",
" \n",
" case \"nmf\":\n",
" comparison_feature_space = np.array(data['image-semantic'])\n",
" comparison_vector = comparison_feature_space[label]\n",
"\n",
" case \"kmeans\":\n",
" comparison_feature_space = np.array(data[\"image-semantic\"])\n",
" comparison_vector = comparison_feature_space[label]\n",
"\n",
" n = len(comparison_feature_space)\n",
" distances = []\n",
" for i in range(n):\n",
" if i != label:\n",
" distances.append({\"label\": i, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
"\n",
" distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n",
"\n",
" for x in distances:\n",
" print(x)"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'label': 2, 'distance': 0.9999999999999999}\n",
"{'label': 4, 'distance': 0.9999999999999999}\n",
"{'label': 6, 'distance': 0.9999999999999999}\n",
"{'label': 7, 'distance': 0.9999999999999999}\n",
"{'label': 8, 'distance': 0.9999999999999999}\n",
"{'label': 9, 'distance': 0.9999999999999999}\n",
"{'label': 10, 'distance': 0.9999999999999999}\n",
"{'label': 11, 'distance': 0.9999999999999999}\n",
"{'label': 13, 'distance': 0.9999999999999999}\n",
"{'label': 14, 'distance': 0.9999999999999999}\n"
]
}
],
"source": [
"match selected_latent_space:\n",
"\n",
" case \"\" | \"image_sim\":\n",
" \n",
" extract_similarities_ls1_ls4(selected_latent_space, selected_dim_reduction_method, data, label, label_rep)\n",
"\n",
" case \"label_sim\":\n",
"\n",
" extract_similarities_ls3(selected_dim_reduction_method, data, label)\n",
"\n",
" case \"cp\":\n",
"\n",
" extract_similarities_ls2(data, label)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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