683 lines
56 KiB
Plaintext
683 lines
56 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f027fe48",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib as mpl\n",
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d3f5ae86",
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"metadata": {},
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"source": [
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"# Render Confusion Matrices\n",
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"\n",
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"DIGITs generates confusion matrix tables, need to render as is, not generate using scikit-learn's func"
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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": 2,
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"id": "753e7bc3",
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"metadata": {},
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"outputs": [],
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"source": [
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"frame = pd.read_csv('architecture-investigations/fc/3-layers/1024/conf.csv', index_col=0)\n",
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"accuracy_col = frame.pop('Per-class accuracy')"
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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": 3,
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"id": "6250e9b7",
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"metadata": {},
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"outputs": [
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" <th></th>\n",
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" <th>AM General Hummer SUV 2000</th>\n",
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" <th>Acura RL Sedan 2012</th>\n",
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" <th>Acura TL Sedan 2012</th>\n",
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" <th>Acura TL Type-S 2008</th>\n",
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" <th>Acura TSX Sedan 2012</th>\n",
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" <th>Aston Martin V8 Vantage Convertible 2012</th>\n",
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" <th>Toyota Camry Sedan 2012</th>\n",
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" <th>Toyota Corolla Sedan 2012</th>\n",
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" <th>Toyota 4Runner SUV 2012</th>\n",
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" <th>Volkswagen Golf Hatchback 2012</th>\n",
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||
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|
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||
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||
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|
||
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|
||
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|
||
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|
||
"\n",
|
||
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|
||
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|
||
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|
||
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||
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||
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|
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||
"Acura RL Sedan 2012 0 ... \n",
|
||
"Acura TL Sedan 2012 0 ... \n",
|
||
"Acura TL Type-S 2008 0 ... \n",
|
||
"Acura TSX Sedan 2012 0 ... \n",
|
||
"... ... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 ... \n",
|
||
"Volvo C30 Hatchback 2012 0 ... \n",
|
||
"Volvo 240 Sedan 1993 0 ... \n",
|
||
"Volvo XC90 SUV 2007 0 ... \n",
|
||
"smart fortwo Convertible 2012 0 ... \n",
|
||
"\n",
|
||
" Toyota Camry Sedan 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Toyota Corolla Sedan 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Toyota 4Runner SUV 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Volkswagen Golf Hatchback 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Volkswagen Golf Hatchback 1991 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Volkswagen Beetle Hatchback 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 5 \n",
|
||
"Volvo C30 Hatchback 2012 1 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Volvo C30 Hatchback 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 4 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Volvo 240 Sedan 1993 Volvo XC90 SUV 2007 \\\n",
|
||
"AM General Hummer SUV 2000 0 0 \n",
|
||
"Acura RL Sedan 2012 0 0 \n",
|
||
"Acura TL Sedan 2012 0 0 \n",
|
||
"Acura TL Type-S 2008 0 0 \n",
|
||
"Acura TSX Sedan 2012 0 0 \n",
|
||
"... ... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 0 \n",
|
||
"Volvo 240 Sedan 1993 10 0 \n",
|
||
"Volvo XC90 SUV 2007 0 7 \n",
|
||
"smart fortwo Convertible 2012 0 0 \n",
|
||
"\n",
|
||
" smart fortwo Convertible 2012 \n",
|
||
"AM General Hummer SUV 2000 1 \n",
|
||
"Acura RL Sedan 2012 0 \n",
|
||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
|
||
"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
|
||
"smart fortwo Convertible 2012 10 \n",
|
||
"\n",
|
||
"[196 rows x 196 columns]"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"frame"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "af1f4956",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 864x720 with 0 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<Figure size 288x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(12, 10)\n",
|
||
"# , dpi=400\n",
|
||
" )\n",
|
||
"\n",
|
||
"plt.matshow(frame)\n",
|
||
"\n",
|
||
"plt.title('Confusion matrix')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "cde63a06",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x720 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(12, 10)\n",
|
||
"# , dpi=400\n",
|
||
" )\n",
|
||
"sns.heatmap(frame, xticklabels=False, yticklabels=False)\n",
|
||
"\n",
|
||
"plt.title('Confusion matrix')\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.9.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|