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PavanShetty3 2023-10-13 16:47:10 -07:00
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commit e2f9bd4fed
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Phase 2/task_0.ipynb
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Phase 2/task_3.ipynb
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"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 11,
"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": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -13,7 +32,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -22,36 +41,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Applying lda on the fc_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n" "Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n",
"(209, 10, 10, 3, 3)\n"
] ]
}, },
{ {
"ename": "KeyboardInterrupt", "ename": "ValueError",
"evalue": "", "evalue": "array must have ndim <= 2",
"output_type": "error", "output_type": "error",
"traceback": [ "traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\task_3.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mk should be a positive integer\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m selected_dim_reduction_method \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39minput\u001b[39m(\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEnter dimensionality reduction method - one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mlist\u001b[39m(valid_dim_reduction_methods\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m )\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m extract_latent_semantics(\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m fd_collection,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m k,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m selected_feature_model,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m selected_dim_reduction_method,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m top_images\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_3.ipynb#W2sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m )\n", "\u001b[1;32m/Users/pavanrathnakarshetty/Documents/Multimedia and Web Databases/Phase 2/CSE515_MWDB_Project/Phase 2/task_3.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mk should be a positive integer\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m selected_dim_reduction_method \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39minput\u001b[39m(\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEnter dimensionality reduction method - one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mlist\u001b[39m(valid_dim_reduction_methods\u001b[39m.\u001b[39mkeys()))\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m )\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m )\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m extract_latent_semantics(\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m fd_collection,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m k,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m selected_feature_model,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m selected_dim_reduction_method,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m top_images\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_3.ipynb#W3sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m )\n",
"File \u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\utils.py:674\u001b[0m, in \u001b[0;36mextract_latent_semantics\u001b[1;34m(fd_collection, k, feature_model, dim_reduction_method, top_images)\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[39m# unsupervised LDA to extract topics (Latent Dirichlet Allocation)\u001b[39;00m\n\u001b[0;32m 670\u001b[0m \u001b[39m# Note: LDA takes a bit of time\u001b[39;00m\n\u001b[0;32m 671\u001b[0m \u001b[39mcase\u001b[39;00m \u001b[39m3\u001b[39m:\n\u001b[0;32m 672\u001b[0m \u001b[39m# LDA requires non-negative input data\u001b[39;00m\n\u001b[0;32m 673\u001b[0m \u001b[39m# so shift the input by subtracting the smallest value\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m min_value \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mmin(feature_vectors)\n\u001b[0;32m 675\u001b[0m feature_vectors_shifted \u001b[39m=\u001b[39m feature_vectors \u001b[39m-\u001b[39m min_value\n\u001b[0;32m 677\u001b[0m model \u001b[39m=\u001b[39m LatentDirichletAllocation(n_components\u001b[39m=\u001b[39mk, learning_method\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39monline\u001b[39m\u001b[39m\"\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m)\n", "File \u001b[0;32m~/Documents/Multimedia and Web Databases/Phase 2/CSE515_MWDB_Project/Phase 2/utils.py:865\u001b[0m, in \u001b[0;36mextract_latent_semantics\u001b[0;34m(fd_collection, k, feature_model, dim_reduction_method, sim_matrix, top_images, fn_prefix)\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[39mmatch\u001b[39;00m valid_dim_reduction_methods[dim_reduction_method]:\n\u001b[1;32m 862\u001b[0m \u001b[39m# singular value decomposition\u001b[39;00m\n\u001b[1;32m 863\u001b[0m \u001b[39m# sparse version of SVD to get only k singular values\u001b[39;00m\n\u001b[1;32m 864\u001b[0m \u001b[39mcase\u001b[39;00m \u001b[39m1\u001b[39m:\n\u001b[0;32m--> 865\u001b[0m U, S, V_T \u001b[39m=\u001b[39m svds(feature_vectors, k\u001b[39m=\u001b[39;49mk)\n\u001b[1;32m 867\u001b[0m all_latent_semantics \u001b[39m=\u001b[39m {\n\u001b[1;32m 868\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mimage-semantic\u001b[39m\u001b[39m\"\u001b[39m: U\u001b[39m.\u001b[39mtolist(),\n\u001b[1;32m 869\u001b[0m \u001b[39m\"\u001b[39m\u001b[39msemantics-core\u001b[39m\u001b[39m\"\u001b[39m: S\u001b[39m.\u001b[39mtolist(),\n\u001b[1;32m 870\u001b[0m \u001b[39m\"\u001b[39m\u001b[39msemantic-feature\u001b[39m\u001b[39m\"\u001b[39m: V_T\u001b[39m.\u001b[39mtolist(),\n\u001b[1;32m 871\u001b[0m }\n\u001b[1;32m 873\u001b[0m \u001b[39m# for each latent semantic, sort imageID-weight pairs by weights in descending order\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\base.py:1151\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1144\u001b[0m estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[0;32m 1146\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[0;32m 1147\u001b[0m skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[0;32m 1148\u001b[0m prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1149\u001b[0m )\n\u001b[0;32m 1150\u001b[0m ):\n\u001b[1;32m-> 1151\u001b[0m \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/scipy/sparse/linalg/_eigen/_svds.py:443\u001b[0m, in \u001b[0;36msvds\u001b[0;34m(A, k, ncv, tol, which, v0, maxiter, return_singular_vectors, solver, random_state, options)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msvds\u001b[39m(A, k\u001b[39m=\u001b[39m\u001b[39m6\u001b[39m, ncv\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, tol\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, which\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mLM\u001b[39m\u001b[39m'\u001b[39m, v0\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 105\u001b[0m maxiter\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, return_singular_vectors\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 106\u001b[0m solver\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39marpack\u001b[39m\u001b[39m'\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, options\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m 107\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 108\u001b[0m \u001b[39m Partial singular value decomposition of a sparse matrix.\u001b[39;00m\n\u001b[1;32m 109\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 441\u001b[0m \n\u001b[1;32m 442\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 443\u001b[0m args \u001b[39m=\u001b[39m _iv(A, k, ncv, tol, which, v0, maxiter, return_singular_vectors,\n\u001b[1;32m 444\u001b[0m solver, random_state)\n\u001b[1;32m 445\u001b[0m (A, k, ncv, tol, which, v0, maxiter,\n\u001b[1;32m 446\u001b[0m return_singular_vectors, solver, random_state) \u001b[39m=\u001b[39m args\n\u001b[1;32m 448\u001b[0m largest \u001b[39m=\u001b[39m (which \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mLM\u001b[39m\u001b[39m'\u001b[39m)\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:665\u001b[0m, in \u001b[0;36mLatentDirichletAllocation.fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[39mif\u001b[39;00m learning_method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39monline\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 664\u001b[0m \u001b[39mfor\u001b[39;00m idx_slice \u001b[39min\u001b[39;00m gen_batches(n_samples, batch_size):\n\u001b[1;32m--> 665\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_em_step(\n\u001b[0;32m 666\u001b[0m X[idx_slice, :],\n\u001b[0;32m 667\u001b[0m total_samples\u001b[39m=\u001b[39;49mn_samples,\n\u001b[0;32m 668\u001b[0m batch_update\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m 669\u001b[0m parallel\u001b[39m=\u001b[39;49mparallel,\n\u001b[0;32m 670\u001b[0m )\n\u001b[0;32m 671\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 672\u001b[0m \u001b[39m# batch update\u001b[39;00m\n\u001b[0;32m 673\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_em_step(\n\u001b[0;32m 674\u001b[0m X, total_samples\u001b[39m=\u001b[39mn_samples, batch_update\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, parallel\u001b[39m=\u001b[39mparallel\n\u001b[0;32m 675\u001b[0m )\n", "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/scipy/sparse/linalg/_eigen/_svds.py:36\u001b[0m, in \u001b[0;36m_iv\u001b[0;34m(A, k, ncv, tol, which, v0, maxiter, return_singular, solver, random_state)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39msolver must be one of \u001b[39m\u001b[39m{\u001b[39;00msolvers\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 35\u001b[0m \u001b[39m# input validation/standardization for `A`\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m A \u001b[39m=\u001b[39m aslinearoperator(A) \u001b[39m# this takes care of some input validation\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (np\u001b[39m.\u001b[39missubdtype(A\u001b[39m.\u001b[39mdtype, np\u001b[39m.\u001b[39mcomplexfloating)\n\u001b[1;32m 38\u001b[0m \u001b[39mor\u001b[39;00m np\u001b[39m.\u001b[39missubdtype(A\u001b[39m.\u001b[39mdtype, np\u001b[39m.\u001b[39mfloating)):\n\u001b[1;32m 39\u001b[0m message \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m`A` must be of floating or complex floating data type.\u001b[39m\u001b[39m\"\u001b[39m\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:524\u001b[0m, in \u001b[0;36mLatentDirichletAllocation._em_step\u001b[1;34m(self, X, total_samples, batch_update, parallel)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"EM update for 1 iteration.\u001b[39;00m\n\u001b[0;32m 498\u001b[0m \n\u001b[0;32m 499\u001b[0m \u001b[39mupdate `_component` by batch VB or online VB.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 520\u001b[0m \u001b[39m Unnormalized document topic distribution.\u001b[39;00m\n\u001b[0;32m 521\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 523\u001b[0m \u001b[39m# E-step\u001b[39;00m\n\u001b[1;32m--> 524\u001b[0m _, suff_stats \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_e_step(\n\u001b[0;32m 525\u001b[0m X, cal_sstats\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, random_init\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, parallel\u001b[39m=\u001b[39;49mparallel\n\u001b[0;32m 526\u001b[0m )\n\u001b[0;32m 528\u001b[0m \u001b[39m# M-step\u001b[39;00m\n\u001b[0;32m 529\u001b[0m \u001b[39mif\u001b[39;00m batch_update:\n", "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/scipy/sparse/linalg/_interface.py:871\u001b[0m, in \u001b[0;36maslinearoperator\u001b[0;34m(A)\u001b[0m\n\u001b[1;32m 869\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(A, np\u001b[39m.\u001b[39mndarray) \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(A, np\u001b[39m.\u001b[39mmatrix):\n\u001b[1;32m 870\u001b[0m \u001b[39mif\u001b[39;00m A\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[0;32m--> 871\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39marray must have ndim <= 2\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 872\u001b[0m A \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39matleast_2d(np\u001b[39m.\u001b[39masarray(A))\n\u001b[1;32m 873\u001b[0m \u001b[39mreturn\u001b[39;00m MatrixLinearOperator(A)\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:467\u001b[0m, in \u001b[0;36mLatentDirichletAllocation._e_step\u001b[1;34m(self, X, cal_sstats, random_init, parallel)\u001b[0m\n\u001b[0;32m 465\u001b[0m \u001b[39mif\u001b[39;00m parallel \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 466\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, verbose\u001b[39m=\u001b[39m\u001b[39mmax\u001b[39m(\u001b[39m0\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m))\n\u001b[1;32m--> 467\u001b[0m results \u001b[39m=\u001b[39m parallel(\n\u001b[0;32m 468\u001b[0m delayed(_update_doc_distribution)(\n\u001b[0;32m 469\u001b[0m X[idx_slice, :],\n\u001b[0;32m 470\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mexp_dirichlet_component_,\n\u001b[0;32m 471\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdoc_topic_prior_,\n\u001b[0;32m 472\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_doc_update_iter,\n\u001b[0;32m 473\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmean_change_tol,\n\u001b[0;32m 474\u001b[0m cal_sstats,\n\u001b[0;32m 475\u001b[0m random_state,\n\u001b[0;32m 476\u001b[0m )\n\u001b[0;32m 477\u001b[0m \u001b[39mfor\u001b[39;49;00m idx_slice \u001b[39min\u001b[39;49;00m gen_even_slices(X\u001b[39m.\u001b[39;49mshape[\u001b[39m0\u001b[39;49m], n_jobs)\n\u001b[0;32m 478\u001b[0m )\n\u001b[0;32m 480\u001b[0m \u001b[39m# merge result\u001b[39;00m\n\u001b[0;32m 481\u001b[0m doc_topics, sstats_list \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mresults)\n", "\u001b[0;31mValueError\u001b[0m: array must have ndim <= 2"
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\utils\\parallel.py:65\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 60\u001b[0m config \u001b[39m=\u001b[39m get_config()\n\u001b[0;32m 61\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n\u001b[0;32m 62\u001b[0m (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m 63\u001b[0m \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n\u001b[0;32m 64\u001b[0m )\n\u001b[1;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(iterable_with_config)\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\joblib\\parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 1861\u001b[0m output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m 1862\u001b[0m \u001b[39mnext\u001b[39m(output)\n\u001b[1;32m-> 1863\u001b[0m \u001b[39mreturn\u001b[39;00m output \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_generator \u001b[39melse\u001b[39;00m \u001b[39mlist\u001b[39;49m(output)\n\u001b[0;32m 1865\u001b[0m \u001b[39m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m 1866\u001b[0m \u001b[39m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m 1867\u001b[0m \u001b[39m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m 1868\u001b[0m \u001b[39m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m 1869\u001b[0m \u001b[39m# callback.\u001b[39;00m\n\u001b[0;32m 1870\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\joblib\\parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 1790\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_batches \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 1791\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m-> 1792\u001b[0m res \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1793\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_completed_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 1794\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_progress()\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\utils\\parallel.py:127\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 125\u001b[0m config \u001b[39m=\u001b[39m {}\n\u001b[0;32m 126\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mconfig):\n\u001b[1;32m--> 127\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunction(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
"File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:144\u001b[0m, in \u001b[0;36m_update_doc_distribution\u001b[1;34m(X, exp_topic_word_distr, doc_topic_prior, max_doc_update_iter, mean_change_tol, cal_sstats, random_state)\u001b[0m\n\u001b[0;32m 140\u001b[0m last_d \u001b[39m=\u001b[39m doc_topic_d\n\u001b[0;32m 142\u001b[0m \u001b[39m# The optimal phi_{dwk} is proportional to\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \u001b[39m# exp(E[log(theta_{dk})]) * exp(E[log(beta_{dw})]).\u001b[39;00m\n\u001b[1;32m--> 144\u001b[0m norm_phi \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mdot(exp_doc_topic_d, exp_topic_word_d) \u001b[39m+\u001b[39m eps\n\u001b[0;32m 146\u001b[0m doc_topic_d \u001b[39m=\u001b[39m exp_doc_topic_d \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39mdot(cnts \u001b[39m/\u001b[39m norm_phi, exp_topic_word_d\u001b[39m.\u001b[39mT)\n\u001b[0;32m 147\u001b[0m \u001b[39m# Note: adds doc_topic_prior to doc_topic_d, in-place.\u001b[39;00m\n",
"File \u001b[1;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
] ]
} }
], ],
@ -104,7 +117,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.5" "version": "3.11.5"
} }
}, },
"nbformat": 4, "nbformat": 4,

11
Phase 2/utils.py
View File

@ -29,11 +29,14 @@ from pymongo import MongoClient
# Visualizing # Visualizing
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import certifi
def getCollection(db, collection): def getCollection(db, collection):
ca = certifi.where()
"""Load feature descriptor collection from MongoDB""" """Load feature descriptor collection from MongoDB"""
client = MongoClient("mongodb://localhost:27017") client = MongoClient("mongodb+srv://pavan:pavan123@cluster0.buq5bqh.mongodb.net/", tlsCAFile = ca)
return client[db][collection] return client[db][collection]
@ -608,11 +611,11 @@ def extract_latent_semantics(
top_img_str = "" top_img_str = ""
if top_images is not None: if top_images is not None:
top_img_str = f" (showing only top {top_images} image-weight pairs for each latent semantic)" top_img_str = f" (showing only top {top_images} image-weight pairs for each latent semantic)"
print( print(
"Applying {} on the {} space to get {} latent semantics{}...".format( "Applying {} on the {} space to get {} latent semantics{}...".format(
dim_reduction_method, feature_model, k, top_img_str dim_reduction_method, feature_model, k, top_img_str
) ))
) print(feature_vectors.shape)
displayed_latent_semantics = {} displayed_latent_semantics = {}
all_latent_semantics = {} all_latent_semantics = {}