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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-03-24T13:27:06.474663Z",
"start_time": "2024-03-24T13:27:06.433992Z"
}
},
"outputs": [
{
"data": {
"text/plain": " Model Accuracy Precision Recall F1 Score Evaluation Time Overall Score\n0 Dummy 51 59 54 52 50 50\n0 Dummy2 52 60 55 53 51 51",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Model</th>\n <th>Accuracy</th>\n <th>Precision</th>\n <th>Recall</th>\n <th>F1 Score</th>\n <th>Evaluation Time</th>\n <th>Overall Score</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Dummy</td>\n <td>51</td>\n <td>59</td>\n <td>54</td>\n <td>52</td>\n <td>50</td>\n <td>50</td>\n </tr>\n <tr>\n <th>0</th>\n <td>Dummy2</td>\n <td>52</td>\n <td>60</td>\n <td>55</td>\n <td>53</td>\n <td>51</td>\n <td>51</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Build a dataframe with Model, Accuracy, Precision, Recall, F1 Score, Evaluation Time, Overall Score\n",
"\n",
"model_results = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score', 'Evaluation Time', 'Overall Score'])\n",
"\n",
"# Add dummy data using concat\n",
"model_results = pd.concat([model_results, pd.DataFrame([['Dummy', 51, 59, 54, 52, 50, 50]], columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score', 'Evaluation Time', 'Overall Score'])])\n",
"\n",
"# add more dummy data\n",
"model_results = pd.concat([model_results, pd.DataFrame([['Dummy2', 52, 60, 55, 53, 51, 51]], columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score', 'Evaluation Time', 'Overall Score'])])\n",
"\n",
"model_results"
]
},
{
"cell_type": "code",
"outputs": [],
"source": [
"# Save the model results to a csv file\n",
"model_results.to_csv('CQI_Leaderboard.csv', index=False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-03-24T13:27:07.540305Z",
"start_time": "2024-03-24T13:27:07.535417Z"
}
},
"id": "d6d288e1af91dd1d",
"execution_count": 6
},
{
"cell_type": "code",
"outputs": [],
"source": [],
"metadata": {
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"id": "f164c55726b7cbaf"
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"display_name": "Python 3",
"language": "python",
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"file_extension": ".py",
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"name": "python",
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