mirror of
https://github.com/20kaushik02/CSE515_MWDB_Project.git
synced 2025-12-06 11:14:06 +00:00
task 4 init
This commit is contained in:
pranavbrkr
parent
878e66b2a1
commit
e569240731
@ -4561,6 +4561,67 @@
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" for i in range(pred_len):\n",
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" for i in range(pred_len):\n",
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" print(f\"Image ID: {i * 2 + 1} is similar to {predictions[i]}\")"
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" print(f\"Image ID: {i * 2 + 1} is similar to {predictions[i]}\")"
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]
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "Found input variables with inconsistent numbers of samples: [217, 178]",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 3\\task_3.ipynb Cell 7\u001b[0m line \u001b[0;36m2\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m true_labels \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m5\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m8\u001b[39m, \u001b[39m8\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m5\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m2\u001b[39m])\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m predicted_labels \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m0\u001b[39m, \u001b[39m7\u001b[39m, \u001b[39m5\u001b[39m, \u001b[39m56\u001b[39m, \u001b[39m4\u001b[39m, \u001b[39m8\u001b[39m, \u001b[39m22\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m5\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m2\u001b[39m])\n\u001b[1;32m---> <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m calculate_scores(np\u001b[39m.\u001b[39;49marray(odd_image_labels), np\u001b[39m.\u001b[39;49marray(predictions))\n",
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"\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 3\\task_3.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m true_label_subset \u001b[39m=\u001b[39m true_labels[mask_true] \u001b[39m# Subset of true labels with the current label\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m pred_label_subset \u001b[39m=\u001b[39m predicted_labels[mask_pred] \u001b[39m# Subset of predicted labels with the current label\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m precision, recall, f1, _ \u001b[39m=\u001b[39m precision_recall_fscore_support(true_label_subset, pred_label_subset, average\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mbinary\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mLabel \u001b[39m\u001b[39m{\u001b[39;00mlabel\u001b[39m}\u001b[39;00m\u001b[39m:\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%203/task_3.ipynb#X12sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mPrecision: \u001b[39m\u001b[39m{\u001b[39;00mprecision\u001b[39m:\u001b[39;00m\u001b[39m.4f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
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"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:211\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 206\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[0;32m 207\u001b[0m skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[0;32m 208\u001b[0m prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 209\u001b[0m )\n\u001b[0;32m 210\u001b[0m ):\n\u001b[1;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m 212\u001b[0m \u001b[39mexcept\u001b[39;00m InvalidParameterError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 213\u001b[0m \u001b[39m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 214\u001b[0m \u001b[39m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 215\u001b[0m \u001b[39m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[39m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 217\u001b[0m msg \u001b[39m=\u001b[39m re\u001b[39m.\u001b[39msub(\n\u001b[0;32m 218\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m\\\u001b[39m\u001b[39mw+ must be\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 219\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__qualname__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m must be\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 220\u001b[0m \u001b[39mstr\u001b[39m(e),\n\u001b[0;32m 221\u001b[0m )\n",
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"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1721\u001b[0m, in \u001b[0;36mprecision_recall_fscore_support\u001b[1;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[0;32m 1563\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Compute precision, recall, F-measure and support for each class.\u001b[39;00m\n\u001b[0;32m 1564\u001b[0m \n\u001b[0;32m 1565\u001b[0m \u001b[39mThe precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1718\u001b[0m \u001b[39m array([2, 2, 2]))\u001b[39;00m\n\u001b[0;32m 1719\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 1720\u001b[0m zero_division_value \u001b[39m=\u001b[39m _check_zero_division(zero_division)\n\u001b[1;32m-> 1721\u001b[0m labels \u001b[39m=\u001b[39m _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n\u001b[0;32m 1723\u001b[0m \u001b[39m# Calculate tp_sum, pred_sum, true_sum ###\u001b[39;00m\n\u001b[0;32m 1724\u001b[0m samplewise \u001b[39m=\u001b[39m average \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msamples\u001b[39m\u001b[39m\"\u001b[39m\n",
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"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1499\u001b[0m, in \u001b[0;36m_check_set_wise_labels\u001b[1;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[0;32m 1496\u001b[0m \u001b[39mif\u001b[39;00m average \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m average_options \u001b[39mand\u001b[39;00m average \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mbinary\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 1497\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39maverage has to be one of \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(average_options))\n\u001b[1;32m-> 1499\u001b[0m y_type, y_true, y_pred \u001b[39m=\u001b[39m _check_targets(y_true, y_pred)\n\u001b[0;32m 1500\u001b[0m \u001b[39m# Convert to Python primitive type to avoid NumPy type / Python str\u001b[39;00m\n\u001b[0;32m 1501\u001b[0m \u001b[39m# comparison. See https://github.com/numpy/numpy/issues/6784\u001b[39;00m\n\u001b[0;32m 1502\u001b[0m present_labels \u001b[39m=\u001b[39m unique_labels(y_true, y_pred)\u001b[39m.\u001b[39mtolist()\n",
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"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:84\u001b[0m, in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_targets\u001b[39m(y_true, y_pred):\n\u001b[0;32m 58\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Check that y_true and y_pred belong to the same classification task.\u001b[39;00m\n\u001b[0;32m 59\u001b[0m \n\u001b[0;32m 60\u001b[0m \u001b[39m This converts multiclass or binary types to a common shape, and raises a\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[39m y_pred : array or indicator matrix\u001b[39;00m\n\u001b[0;32m 83\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 84\u001b[0m check_consistent_length(y_true, y_pred)\n\u001b[0;32m 85\u001b[0m type_true \u001b[39m=\u001b[39m type_of_target(y_true, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my_true\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 86\u001b[0m type_pred \u001b[39m=\u001b[39m type_of_target(y_pred, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my_pred\u001b[39m\u001b[39m\"\u001b[39m)\n",
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"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:407\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m 405\u001b[0m uniques \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39munique(lengths)\n\u001b[0;32m 406\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(uniques) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m--> 407\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 408\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m 409\u001b[0m \u001b[39m%\u001b[39m [\u001b[39mint\u001b[39m(l) \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m lengths]\n\u001b[0;32m 410\u001b[0m )\n",
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"\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [217, 178]"
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]
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}
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],
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"source": [
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"def calculate_scores(true_labels, predicted_labels):\n",
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" unique_labels = np.unique(true_labels) # Get unique labels from true labels\n",
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"\n",
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" for label in unique_labels:\n",
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" if label not in np.unique(predicted_labels):\n",
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" # Skip labels that have no predictions\n",
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" print(f\"Label {label}: No predictions for this label.\")\n",
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" continue\n",
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"\n",
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" mask_true = (true_labels == label) # Mask for true labels with the current label\n",
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" mask_pred = (predicted_labels == label) # Mask for predicted labels with the current label\n",
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"\n",
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" true_label_subset = true_labels[mask_true] # Subset of true labels with the current label\n",
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" pred_label_subset = predicted_labels[mask_pred] # Subset of predicted labels with the current label\n",
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"\n",
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" precision, recall, f1, _ = precision_recall_fscore_support(true_label_subset, pred_label_subset, average='binary')\n",
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"\n",
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" print(f\"Label {label}:\")\n",
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" print(f\"Precision: {precision:.4f}\")\n",
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" print(f\"Recall: {recall:.4f}\")\n",
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" print(f\"F1 Score: {f1:.4f}\")\n",
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" print()\n",
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"\n",
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"# Example true labels and predicted labels\n",
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"true_labels = np.array([0, 1, 5, 2, 8, 8, 3, 1, 5, 0, 2])\n",
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"predicted_labels = np.array([0, 7, 5, 56, 4, 8, 22, 1, 5, 0, 2])\n",
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"\n",
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"calculate_scores(np.array(odd_image_labels), np.array(predictions))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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Phase 3/task_4.ipynb
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215
Phase 3/task_4.ipynb
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