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"/Users/federicoviscioletti/Dev/gradio-lite-apps/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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"[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
"[LightGBM] [Info] Number of positive: 310, number of negative: 490\n",
"[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 519\n",
"[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n",
"[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.387500 -> initscore=-0.457833\n",
"[LightGBM] [Info] Start training from score -0.457833\n",
"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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"ename": "ValueError",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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" 1. , 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 0.98780488, 0.98780488, 0.98780488, 0.98780488,\n",
" 0.98780488, 0.98780488, 0.98780488, 0.98780488, 0.98780488, 0.97560976,\n",
" 0.97560976, 0.97560976, 0.97560976, 0.97560976, 0.97560976, 0.97560976,\n",
" 0.97560976, 0.97560976, 0.97560976, 0.97560976, 0.97560976, 0.96341463,\n",
" 0.96341463, 0.96341463, 0.96341463, 0.96341463, 0.96341463, 0.96341463,\n",
" 0.96341463, 0.96341463, 0.95121951, 0.93902439, 0.93902439, 0.92682927,\n",
" 0.92682927, 0.92682927, 0.91463415, 0.8902439 , 0.8902439 , 0.87804878,\n",
" 0.86585366, 0.85365854, 0.84146341, 0.84146341, 0.82926829, 0.81707317,\n",
" 0.80487805, 0.79268293, 0.79268293, 0.79268293, 0.7804878 , 0.7804878 ,\n",
" 0.75609756, 0.75609756, 0.74390244, 0.73170732, 0.7195122 , 0.70731707,\n",
" 0.70731707, 0.70731707, 0.69512195, 0.68292683, 0.68292683, 0.67073171,\n",
" 0.67073171, 0.67073171, 0.65853659, 0.64634146, 0.64634146, 0.64634146,\n",
" 0.63414634, 0.62195122, 0.6097561 , 0.6097561 , 0.59756098, 0.58536585,\n",
" 0.57317073, 0.56097561, 0.54878049, 0.53658537, 0.52439024, 0.51219512,\n",
" 0.5 , 0.48780488, 0.47560976, 0.46341463, 0.45121951, 0.43902439,\n",
" 0.42682927, 0.41463415, 0.40243902, 0.3902439 , 0.37804878, 0.36585366,\n",
" 0.35365854, 0.34146341, 0.32926829, 0.31707317, 0.30487805, 0.29268293,\n",
" 0.2804878 , 0.26829268, 0.25609756, 0.24390244, 0.23170732, 0.2195122 ,\n",
" 0.20731707, 0.19512195, 0.18292683, 0.17073171, 0.15853659, 0.14634146,\n",
" 0.13414634, 0.12195122, 0.1097561 , 0.09756098, 0.08536585, 0.07317073,\n",
" 0.06097561, 0.04878049, 0.03658537, 0.02439024, 0.01219512, 0. ])}],\n",
" 'layout': {'annotations': [{'showarrow': False,\n",
" 'text': 'Threshold: 0.5',\n",
" 'x': 0.5,\n",
" 'xanchor': 'left',\n",
" 'xref': 'x',\n",
" 'y': 1,\n",
" 'yanchor': 'top',\n",
" 'yref': 'y domain'}],\n",
" 'legend': {'tracegroupgap': 0},\n",
" 'shapes': [{'line': {'color': 'black', 'dash': 'dash'},\n",
" 'type': 'line',\n",
" 'x0': 0.5,\n",
" 'x1': 0.5,\n",
" 'xref': 'x',\n",
" 'y0': 0,\n",
" 'y1': 1,\n",
" 'yref': 'y domain'}],\n",
" 'template': '...',\n",
" 'title': {'text': 'Precision and Recall at Different Thresholds'},\n",
" 'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Threshold'}},\n",
" 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'precision'}}}\n",
"})"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"\n",
"plot_precision_recall(0.5)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"df = pd.read_csv(\"hf://datasets/merve/supersoaker-failures/supersoaker.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.11.9"
}
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"nbformat": 4,
"nbformat_minor": 2
}