297 lines
94 KiB
Plaintext
297 lines
94 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# North"
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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": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"df = pd.read_csv('../benchmark_results/north/north_result.csv', header=None)\n",
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"df\n",
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"diff = df[1] - df[2]\n",
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"\n",
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"# Sample data\n",
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"\n",
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"# Plotting the violin plot\n",
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"sns.violinplot(data=diff, color='skyblue')\n",
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"\n",
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"# Adding labels and title\n",
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"plt.xlabel('Value')\n",
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"plt.ylabel('Density')\n",
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"plt.title('Violin Plot for North Diffs')\n",
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"\n",
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"# Displaying the plot\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Rome"
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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": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
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|
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"text/plain": [
|
|||
|
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"<Figure size 640x480 with 1 Axes>"
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|
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]
|
|||
|
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},
|
|||
|
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"metadata": {},
|
|||
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"output_type": "display_data"
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}
|
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|
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],
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|||
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"source": [
|
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|
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"df = pd.read_csv('../benchmark_results/rome/rome_result.csv', header=None)\n",
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"diff = df[1] - df[2]\n",
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"\n",
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"# Sample data\n",
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"\n",
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|
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"# Plotting the violin plot\n",
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"sns.violinplot(data=diff, color='skyblue')\n",
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"\n",
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"# Adding labels and title\n",
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"plt.xlabel('Value')\n",
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"plt.ylabel('Density')\n",
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"plt.title('Violin Plot for Rome Diffs')\n",
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"\n",
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"# Displaying the plot\n",
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"plt.show()\n"
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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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|
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"execution_count": 28,
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"metadata": {},
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"outputs": [
|
|||
|
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{
|
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|
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"data": {
|
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|
|
" 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>0</th>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>grafo10000.38.gml</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>grafo10003.40.gml</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>grafo10005.39.gml</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>grafo10006.98.gml</td>\n",
|
|||
|
|
" <td>14</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>grafo10008.42.gml</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8248</th>\n",
|
|||
|
|
" <td>grafo9994.91.gml</td>\n",
|
|||
|
|
" <td>10</td>\n",
|
|||
|
|
" <td>10</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8249</th>\n",
|
|||
|
|
" <td>grafo9995.94.gml</td>\n",
|
|||
|
|
" <td>20</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8250</th>\n",
|
|||
|
|
" <td>grafo9997.40.gml</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8251</th>\n",
|
|||
|
|
" <td>grafo9998.38.gml</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8252</th>\n",
|
|||
|
|
" <td>grafo9999.39.gml</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>8253 rows × 3 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" 0 1 2\n",
|
|||
|
|
"0 grafo10000.38.gml 3 3\n",
|
|||
|
|
"1 grafo10003.40.gml 1 1\n",
|
|||
|
|
"2 grafo10005.39.gml 3 2\n",
|
|||
|
|
"3 grafo10006.98.gml 14 11\n",
|
|||
|
|
"4 grafo10008.42.gml 4 4\n",
|
|||
|
|
"... ... .. ..\n",
|
|||
|
|
"8248 grafo9994.91.gml 10 10\n",
|
|||
|
|
"8249 grafo9995.94.gml 20 18\n",
|
|||
|
|
"8250 grafo9997.40.gml 2 2\n",
|
|||
|
|
"8251 grafo9998.38.gml 1 1\n",
|
|||
|
|
"8252 grafo9999.39.gml 2 2\n",
|
|||
|
|
"\n",
|
|||
|
|
"[8253 rows x 3 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Steinlib"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df = pd.read_csv('../benchmark_results/steinlib/steinlib_result.csv', header=None)\n",
|
|||
|
|
"diff = df[1] - df[2]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Sample data\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Plotting the violin plot\n",
|
|||
|
|
"sns.violinplot(data=diff, color='skyblue')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Adding labels and title\n",
|
|||
|
|
"plt.xlabel('Value')\n",
|
|||
|
|
"plt.ylabel('Density')\n",
|
|||
|
|
"plt.title('Violin Plot for Rome Diffs')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Displaying the plot\n",
|
|||
|
|
"plt.show()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"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": "3.10.12"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|