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No1-boxplot.ipynb
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{
"cells": [
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"cell_type": "code",
"execution_count": 1,
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"outputs": [],
"source": [
"import plotly.express as px\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
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"source": [
"def boxplot(output_name, network, y_min=None, y_max=None):\n",
" df_dice = pd.read_csv(f\"outputs/{network}/{output_name}/infer_dice_class.csv\")\n",
" df_iou = pd.read_csv(f\"outputs/{network}/{output_name}/infer_iou_class.csv\")\n",
"\n",
" df_dice['type'] = 'dice'\n",
" df_iou['type'] = 'iou'\n",
"\n",
" df_combined = pd.concat([df_dice, df_iou])\n",
" df_combined.reset_index(drop=True, inplace=True)\n",
" df_final = pd.melt(df_combined, id_vars=['type'], var_name='classes', value_name='scores')\n",
" df_final.sort_values(['type', 'classes'], inplace=True)\n",
" df_final.reset_index(drop=True, inplace=True)\n",
"\n",
" fig = px.box(df_final, x=\"classes\", y=\"scores\", color=\"type\")\n",
" fig.update_traces(quartilemethod=\"exclusive\")\n",
" fig.update_layout(\n",
" width=1000, \n",
" height=500,\n",
" font=dict(\n",
" size=18 \n",
" )\n",
" )\n",
" if y_min is not None and y_max is not None:\n",
" fig.update_yaxes(range=[y_min, y_max])\n",
" fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VHSCDD $(256 \\times 256)$"
]
},
{
"cell_type": "code",
"execution_count": 8,
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