716 lines
67 KiB
Plaintext
716 lines
67 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": "34b0345b",
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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": "75b9f701",
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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": "10521209",
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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": 19,
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"id": "02c2844f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"50.0% 0.5\n",
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"25.0% 0.25\n",
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"30.0% 0.3\n",
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"100.0% 1.0\n",
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"71.43% 0.7142857142857143\n",
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"44.44% 0.4444444444444444\n",
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"62.5% 0.625\n",
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"0.0% 0.0\n",
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"50.0% 0.5\n",
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"20.0% 0.2\n",
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"\n",
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"MAP = 49.3%\n"
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]
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}
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],
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"source": [
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"normalised_array = np.ndarray((196, 196))\n",
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"for idx, row in enumerate(frame.to_numpy()):\n",
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" normalised_array[idx, :] = row / np.sum(row)\n",
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"\n",
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"# check correct accuracy\n",
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"for acc, (idx, row) in zip(accuracy_col, enumerate(normalised_array[:10])):\n",
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" print(acc, row[idx])\n",
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"\n",
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"class_acc_counter = 0\n",
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"for idx, row in enumerate(normalised_array):\n",
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" class_acc_counter += row[idx]\n",
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" \n",
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"print(f\"\\nMAP = {class_acc_counter*100/196:.4}%\")"
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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": 4,
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"id": "91132579",
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"metadata": {},
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"outputs": [
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{
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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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>Acura Integra Type R 2001</th>\n",
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" <th>Acura ZDX Hatchback 2012</th>\n",
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" <th>Aston Martin V8 Vantage Convertible 2012</th>\n",
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" <th>Aston Martin V8 Vantage Coupe 2012</th>\n",
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" <th>Aston Martin Virage Convertible 2012</th>\n",
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" <th>...</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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" <th>Volkswagen Golf Hatchback 1991</th>\n",
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" <th>Volkswagen Beetle Hatchback 2012</th>\n",
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" <th>Volvo C30 Hatchback 2012</th>\n",
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" <th>Volvo 240 Sedan 1993</th>\n",
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" <th>Volvo XC90 SUV 2007</th>\n",
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" </tr>\n",
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" <th>Acura RL Sedan 2012</th>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Acura TL Sedan 2012</th>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <th>Acura TL Type-S 2008</th>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <tr>\n",
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" <th>Acura TSX Sedan 2012</th>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>Volkswagen Beetle Hatchback 2012</th>\n",
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||
" <td>0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>0</td>\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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" <td>0</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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" <th>Volvo C30 Hatchback 2012</th>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>4</td>\n",
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" <td>0</td>\n",
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||
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||
" <td>0</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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" <th>Volvo 240 Sedan 1993</th>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>10</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Volvo XC90 SUV 2007</th>\n",
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" <td>0</td>\n",
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||
" <td>7</td>\n",
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||
" <td>0</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>smart fortwo Convertible 2012</th>\n",
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||
" <td>0</td>\n",
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||
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||
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||
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" <td>10</td>\n",
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||
" </tr>\n",
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||
" </tbody>\n",
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||
"</table>\n",
|
||
"<p>196 rows × 196 columns</p>\n",
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"</div>"
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],
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"text/plain": [
|
||
" AM General Hummer SUV 2000 \\\n",
|
||
"AM General Hummer SUV 2000 6 \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",
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||
"Volkswagen Beetle Hatchback 2012 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 \n",
|
||
"Volvo 240 Sedan 1993 0 \n",
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||
"Volvo XC90 SUV 2007 0 \n",
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||
"smart fortwo Convertible 2012 0 \n",
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||
"\n",
|
||
" Acura RL Sedan 2012 Acura TL Sedan 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 0 \n",
|
||
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|
||
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|
||
"Acura TL Type-S 2008 0 0 \n",
|
||
"Acura TSX Sedan 2012 1 0 \n",
|
||
"... ... ... \n",
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||
"Volkswagen Beetle Hatchback 2012 0 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 0 \n",
|
||
"Volvo 240 Sedan 1993 0 0 \n",
|
||
"Volvo XC90 SUV 2007 0 0 \n",
|
||
"smart fortwo Convertible 2012 0 0 \n",
|
||
"\n",
|
||
" Acura TL Type-S 2008 Acura TSX Sedan 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 0 \n",
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||
"Acura RL Sedan 2012 0 2 \n",
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||
"Acura TL Sedan 2012 0 2 \n",
|
||
"Acura TL Type-S 2008 3 0 \n",
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||
"Acura TSX Sedan 2012 0 5 \n",
|
||
"... ... ... \n",
|
||
"Volkswagen Beetle Hatchback 2012 0 0 \n",
|
||
"Volvo C30 Hatchback 2012 0 0 \n",
|
||
"Volvo 240 Sedan 1993 0 0 \n",
|
||
"Volvo XC90 SUV 2007 0 0 \n",
|
||
"smart fortwo Convertible 2012 0 0 \n",
|
||
"\n",
|
||
" Acura Integra Type R 2001 \\\n",
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||
"AM General Hummer SUV 2000 0 \n",
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||
"Acura RL Sedan 2012 0 \n",
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||
"Acura TL Sedan 2012 0 \n",
|
||
"Acura TL Type-S 2008 0 \n",
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"Acura TSX Sedan 2012 0 \n",
|
||
"... ... \n",
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"Volkswagen Beetle Hatchback 2012 0 \n",
|
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"Volvo C30 Hatchback 2012 0 \n",
|
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"Volvo 240 Sedan 1993 0 \n",
|
||
"Volvo XC90 SUV 2007 0 \n",
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||
"smart fortwo Convertible 2012 0 \n",
|
||
"\n",
|
||
" Acura ZDX Hatchback 2012 \\\n",
|
||
"AM General Hummer SUV 2000 0 \n",
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||
"Acura RL Sedan 2012 0 \n",
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||
"Acura TL Sedan 2012 0 \n",
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||
"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",
|
||
" Aston Martin V8 Vantage Convertible 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",
|
||
" Aston Martin V8 Vantage Coupe 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",
|
||
" Aston Martin Virage Convertible 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 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": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"frame"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "72eacc1e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x720 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, ax = plt.subplots(figsize=(12, 10)\n",
|
||
"# , dpi=400\n",
|
||
" )\n",
|
||
"\n",
|
||
"ax.matshow(normalised_array)\n",
|
||
"\n",
|
||
"ax.set_title('Confusion matrix')\n",
|
||
"# plt.colorbar(ax=ax)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "831a9fab",
|
||
"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(normalised_array, xticklabels=False, yticklabels=False, cmap='inferno')\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
|
||
}
|