486 lines
737 KiB
Plaintext
486 lines
737 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": 3,
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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 1400x1000 with 6 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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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Define data\n",
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"bottle_data = {\n",
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" \"ResNet50\": {\n",
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" \"1,3,5 shots normal\": [0.5892857142857143, 0.7321428571428571, 0.75],\n",
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" \"inbalanced - more good shots\": [0.75, 0.732, 0.696, 0.696],\n",
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" \"2 ways only detect if faulty or not\": [0.8395, 0.8315, 0.8031],\n",
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" \"inbalance 2 way\": [0.8031, 0.81893, 0.8336, 0.8031],\n",
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" \"only faulty class detect\": [0.7638, 0.7428, 0.787]\n",
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" },\n",
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" \"P>M>F\": {\n",
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" \"1,3,5 shots normal\": [0.67910401, 0.71710526, 0.78860294],\n",
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" \"inbalanced - more good shots\": [0.78768382, 0.78860294, 0.75827206, 0.74356618],\n",
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" \"2 ways only detect if faulty or not\": [0.86422306, 0.93201754, 0.93933824],\n",
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" \"inbalance 2 way\": [0.92371324, 0.87867647, 0.86397059, 0.87775735],\n",
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" \"only faulty class detect\": [0.57380952, 0.76705653, 0.84191176]\n",
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" },\n",
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" \"CAML\": {\n",
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" \"1,3,5 shots normal\": [0.40740741, 0.39726027, 0.30769231],\n",
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" \"2 ways only detect if faulty or not\": [0.79012346, 0.84415584, 0.87671233],\n",
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" \"only faulty class detect\": [0.58064516, 0.51785714, 0.52]\n",
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" }\n",
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"}\n",
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"\n",
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"cable_data = {\n",
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" \"ResNet50\": {\n",
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" \"1,3,5 shots normal\": [0.21808, 0.43815, 0.4321478],\n",
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" \"inbalanced - more good shots\": [0.4321478, 0.432986, 0.42340, 0.4464635],\n",
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" \"2 ways only detect if faulty or not\": [0.8592, 0.8772, 0.8495],\n",
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" \"inbalance 2 way\": [0.8495, 0.8180, 0.7460, 0.6846],\n",
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" \"only faulty class detect\": [0.240, 0.4740, 0.4805]\n",
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" },\n",
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" \"P>M>F\": {\n",
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" \"1,3,5 shots normal\": [0.25199021, 0.44388328, 0.46975059],\n",
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" \"inbalanced - more good shots\": [0.50425859, 0.48023277, 0.43118282, 0.41842534],\n",
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" \"2 ways only detect if faulty or not\": [0.79263485, 0.8707712, 0.86756514],\n",
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" \"inbalance 2 way\": [0.86966158, 0.80142425, 0.80961366, 0.66028834],\n",
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" \"only faulty class detect\": [0.24383256, 0.43800505, 0.51304563]\n",
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" },\n",
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" \"CAML\": {\n",
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" \"1,3,5 shots normal\": [0.24031008, 0.19834711, 0.15929204],\n",
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" \"2 ways only detect if faulty or not\": [0.57364341, 0.54545455, 0.59292035],\n",
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" \"only faulty class detect\": [0.12962963, 0.36363636, 0.58823529]\n",
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" }\n",
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"}\n",
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"# Prepare the data\n",
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"measurement_types = [\n",
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" \"1,3,5 shots normal\",\n",
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" \"inbalanced - more good shots\",\n",
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" \"2 ways only detect if faulty or not\",\n",
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" \"inbalance 2 way\",\n",
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" \"only faulty class detect\"\n",
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"]\n",
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"\n",
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"models = [\"ResNet50\", \"P>M>F\", \"CAML\"]\n",
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"\n",
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"# Create subplots\n",
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"fig, axes = plt.subplots(3, 2, figsize=(14, 10))\n",
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"axes = axes.flatten()\n",
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"\n",
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"# Loop through each measurement type\n",
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"for i, measurement in enumerate(measurement_types):\n",
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" ax = axes[i]\n",
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" for model in models:\n",
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" # Get the bottle and cable data for the current measurement and model\n",
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" bottle_accuracy = bottle_data[model].get(measurement, [])\n",
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" cable_accuracy = cable_data[model].get(measurement, [])\n",
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" \n",
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" # Plot both bottle and cable data\n",
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" ax.plot(bottle_accuracy, marker='o', label=f'{model} - Bottle', linestyle='-')\n",
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" \n",
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" ax.set_title(measurement)\n",
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" ax.set_xlabel(\"Shots / Samples\")\n",
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" ax.set_ylabel(\"Accuracy\")\n",
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" ax.legend()\n",
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" ax.grid(True)\n",
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"\n",
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"# Adjust layout\n",
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"plt.tight_layout()\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": "code",
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"execution_count": 2,
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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": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/GU6VOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB7IUlEQVR4nO3dd3gU1eLG8e+mJ6RAAqRACKETQgfpRXovIuWKUixX5Soi2FCRovdiV9QfqChFRKlSVESCSBEQaaEF6RBKINQkQEhCMr8/BlZCCgkkbLK8n+c5D+zsmZmzu7B5c+acMxbDMAxEREREpNBzsHUDRERERCRvKNiJiIiI2AkFOxERERE7oWAnIiIiYicU7ERERETshIKdiIiIiJ1QsBMRERGxEwp2IiIiInZCwU5ERETETijYiUi+mTZtGhaLxVqcnJwoXbo0gwcP5vjx43d8/JUrV1qPPW3atEzrtGrVCovFQtmyZe/4fABly5ZN95puLBcvXsyTc+S1MWPGYLFYOHPmzC3rtmzZkpYtW+Z/o0QkXzjZugEiYv+mTp1KlSpVSExMZPXq1YwfP55Vq1axY8cOihQpcsfH9/Ly4uuvv2bQoEHpth86dIiVK1fi7e19x+e4UZMmTXj//fczbPfw8MjT84iI5JaCnYjku/DwcOrVqwfA/fffT2pqKm+++SYLFy6kf//+JCYmcubMGYKDg2/r+H379uWrr75i3759VKxY0bp9ypQplCpViurVqxMVFZVun3379lGhQgUsFkuuz1e0aFEaNmx4W20VEclPuhQrInfd9VB05MgRAE6dOkVISAjNmzdn0qRJObpkeKO2bdsSHBzMlClTrNvS0tKYPn06AwcOxMEh41fdE088QUhICC+99BJbt269g1eTf7777jsaNWqEp6cnnp6e1KpVi6+//tr6fEREBN27d6d06dK4ublRoUIFnnzyySzfv6NHj/LAAw/g7e2Nj48PDz/8MKdPn75lO5KTk3nrrbeoUqUKrq6ulChRgsGDB+doXxG5uxTsROSu279/PwAlSpQAIDg4mAULFlCqVClefPFFAgMD6dSpEzNmzCAhIeGWx3NwcGDQoEF88803pKamArBs2TKOHTvG4MGDM91n4sSJPPzww8yfP586depQtWpVxo0bx759+255PsMwuHr1arqSlpaW05efI2+88Qb9+/cnKCiIadOmsWDBAgYOHGgNwwAHDhygUaNGTJo0iWXLlvHGG2+wYcMGmjZtSkpKSoZj9uzZkwoVKjBv3jzGjBnDwoULad++faZ1r0tLS6N79+68/fbbPPTQQ/z888+8/fbbRERE0LJlSxITE/P0dYvIHTJERPLJ1KlTDcD4888/jZSUFCMhIcH46aefjBIlShheXl7GyZMnM+xz6dIlY/bs2UbPnj0NNzc3w93d3ejdu7fxww8/GFeuXElX9/fffzcAY+7cucbBgwcNi8Vi/PTTT4ZhGEbv3r2Nli1bGoZhGJ07dzZCQkKybOdff/1lDB8+3ChdurQBGHXr1jU++OAD49ixYxnqhoSEGECG8tprr93BO5XewYMHDUdHR6N///453ictLc1ISUkxjhw5YgDGokWLrM+NHj3aAIznn38+3T4zZ840AOPbb7+1bmvRooXRokUL6+Pvv//eAIz58+en23fjxo0GYEycODGXr05E8pN67EQk3zVs2BBnZ2e8vLzo0qULAQEB/PLLL/j7+2eo6+HhQZ8+ffjhhx84deoUX3zxBZcuXaJPnz74+/uzdu3aTM8RGhpKy5YtmTJlCmfPnmXRokU8+uijOWpf/fr1+eCDD4iOjmb16tU0bNiQd955hzJlyjBixIgM9Zs2bcrGjRvTlSFDhmR7jtTU1Bz38EVERJCamsp//vOfbI8ZGxvLU089RXBwME5OTjg7OxMSEgLA7t27M9Tv379/usd9+vTBycmJ33//Pctz/PTTTxQtWpSuXbuma3+tWrUICAhg5cqV2bZRRO4uTZ4QkXz3zTffULVqVZycnPD39ycwMDBH+126dIkLFy4QFxdHamoqXl5euLi4ZFn/scceY/DgwXz44Ye4u7vz4IMP5qqdKSkpxMXFceHCBRITE3FxccHLyytDPR8fH+tkkJwqX758usuoo0ePZsyYMZnWvT52rXTp0lkeLy0tjXbt2nHixAlGjRpF9erVKVKkCGlpaTRs2DDTS6QBAQHpHjs5OeHn58fZs2ezPM+pU6e4cOFClu97bsdDikj+UrATkXxXtWrVHAehM2fOMH/+fGbNmsXq1aspVqwYvXr14r///S/NmzfPdhbrAw88wH/+8x/efvttnnjiCdzd3W95vqtXr/Lbb78xe/ZsFixYQEJCAq1atWLChAn06tUrz5ZK+fHHH0lKSrI+DgoKyrLu9bGHx44dy3Km8M6dO9m2bRvTpk1j4MCB1u3Xxy9m5uTJk5QqVcr6+OrVq5w9exY/P78s9ylevDh+fn4sXbo00+czC74iYjsKdiJic8nJyXz77bfMnj2bFStW4OrqSrdu3Vi0aBHt27fH2dk5R8dxd3fnjTfeYPXq1Tz99NPZ1l2/fj3Tp09n/vz5nDlzhoYNGzJ27Fj69u2b6SXiO1W9evUc123Xrh2Ojo5MmjSJRo0aZVrnesB1dXVNt/2LL77I8rgzZ86kbt261sdz5szh6tWr2S5I3KVLF2bNmkVqaioNGjTI8WsQEdtQsBMRmztx4gRPPfUU7du355tvvqF79+63vdjv8OHDGT58+C3rjRw5kjNnzjBs2DAeeughQkNDb+t8+aFs2bK8+uqrvPnmmyQmJvKvf/0LHx8foqKiOHPmDGPHjqVKlSqUL1+eV155BcMw8PX15ccffyQiIiLL4/7www84OTnRtm1bdu3axahRo6hZsyZ9+vTJcp9+/foxc+ZMOnXqxHPPPcd9992Hs7Mzx44d4/fff6d79+707NkzP94GEbkNCnYiYnOBgYGcPHkSX1/fu3bO77//Psdj/Wxh3LhxVKxYkU8//ZT+/fvj5ORExYoVGTp0KADOzs78+OOPPPfcczz55JM4OTnRpk0bli9fTpkyZTI95g8//MCYMWOYNGkSFouFrl278vHHH2c7btHR0ZHFixczYcIEZsyYwfjx4623hmvRokWueiJFJP9ZDMMwbN0IEREREblzWu5ERERExE4o2ImIiIjYCQU7ERERETuhYCciIiJiJxTsREREROyEgp2IiIiInbjn1rFLS0vjxIkTeHl5ZXtrIhEREZGCwDAMEhISCAoKwsEh+z65ey7YnThxIst7L4qIiIgUVEePHqV06dLZ1rnngt31G1YfPXr0ljf3TklJYdmyZbRr1y7H96oU29HnVXjosxKRe11uvgfj4+MJDg62Zpjs3HPB7vrlV29v7xwFOw8PD7y9vfXDpxDQ51V46LMSkXvd7XwP5mQImSZPiIiIiNgJBTsRERERO6FgJyIiImIn7rkxdiJSuKWlpZGcnGzrZoiI3JGUlBScnJxISkrCwcEBR0fHPDmugp2IFBrJyckcOnSItLQ0WzdFROSOGIZBQEAA0dHRWCwWihYtSkBAwB2vsatgJyKFgmEYxMTE4OjoSHBw8C0X6RQRKcjS0tK4ePEiRYoU4cqVK8TGxgIQGBh4R8dVsBORQuHq1atcvnyZoKAgPDw8bN0cEZE7cn1Yibu7O0WKFAEgNjaWkiVL3tFlWf3KKyKFQmpqKgAuLi42bomISN67/gtrSkrKHR1HwU5EChXd41lE7FFefbcp2ImIiIjYCQW7rKSlYjnyB6XOrcdy5A9IS7V1i0TkHmGxWFi4cOEdHWPMmDHUqlUrT9qTnw4fPozFYiEyMjLbenv27CEgIICEhIS707BCLKfvaVZatmzJsGHD8rRNOVG/fn1++OGHu35ee6Ngl5moxfBxOE7f9qDekUk4fdsDPg43t4tIoZaaZrD+wFkWRR5n/YGzpKYZ+Xq+QYMG0aNHj3w9x73gtdde4z//+U+OboIutnc7v1iMGjWKV155JU+WMzp16hSDBg2yTrbq0KED+/btu+PjFgYKdjeLWgxzBkD8ifTb42PM7Qp
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Create subplots\n",
|
||
|
|
"fig, ax = plt.subplots()\n",
|
||
|
|
"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"model = \"P>M>F\"\n",
|
||
|
|
"data = cable_data\n",
|
||
|
|
"imgclass = \"cable\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot both bottle and cable data\n",
|
||
|
|
"ax.plot([5,10,15, 30], data[model].get(\"inbalanced - more good shots\", []), marker='o', label=f'Inbalanced (more good shots) - 9 way', linestyle='-')\n",
|
||
|
|
"ax.plot([5,10,15, 30], data[model].get(\"inbalance 2 way\", []), marker='o', label=f'faulty or not - 2 way', linestyle='-')\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set_title(f'{model} - {imgclass}')\n",
|
||
|
|
"ax.set_xlabel(\"Shots per class\")\n",
|
||
|
|
"ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set(xlim=(4.5, 30.5), xticks=[5,10,15, 30])\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig(f\"{model}-{imgclass}-inbalanced.png\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Create subplots\n",
|
||
|
|
"fig, ax = plt.subplots()\n",
|
||
|
|
"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"model = \"P>M>F\"\n",
|
||
|
|
"data = bottle_data\n",
|
||
|
|
"imgclass = \"bottle\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot both bottle and cable data\n",
|
||
|
|
"ax.plot([1,3,5], data[model].get(\"1,3,5 shots normal\", []), marker='o', label=f'Normal all classes - 4 way', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[model].get(\"2 ways only detect if faulty or not\", []), marker='o', label=f'faulty or not - 2 way', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[model].get(\"only faulty class detect\", []), marker='o', label=f'faulty classes - 3 way', linestyle='-')\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set_title(f'{model} - {imgclass}')\n",
|
||
|
|
"ax.set_xlabel(\"Shots per class\")\n",
|
||
|
|
"ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set(xlim=(0.75, 5.25), xticks=[1,3,5])\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig(f\"{model}-{imgclass}.png\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "ValueError",
|
||
|
|
"evalue": "x and y must have same first dimension, but have shapes (3,) and (4,)",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"Cell \u001b[0;32mIn[2], line 10\u001b[0m\n\u001b[1;32m 7\u001b[0m imgclass \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbottle\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Plot both bottle and cable data\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mResNet50\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minbalanced - more good shots\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mResNet50\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinestyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m-\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot([\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m], data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mP>M>F\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minbalanced - more good shots\u001b[39m\u001b[38;5;124m\"\u001b[39m, []), marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mP>M>F\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 12\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot([\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m], data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCAML\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minbalanced - more good shots\u001b[39m\u001b[38;5;124m\"\u001b[39m, []), marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCAML\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
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"File \u001b[0;32m/usr/lib/python3.13/site-packages/matplotlib/axes/_axes.py:1777\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1535\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1536\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1776\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1777\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1778\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
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"File \u001b[0;32m/usr/lib/python3.13/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m/usr/lib/python3.13/site-packages/matplotlib/axes/_base.py:494\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 491\u001b[0m axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m 493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 494\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
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"\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (3,) and (4,)"
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]
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},
|
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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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"# Create subplots\n",
|
||
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"fig, ax = plt.subplots()\n",
|
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"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"model = \"P>M>F\"\n",
|
||
|
|
"data = bottle_data\n",
|
||
|
|
"imgclass = \"bottle\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot both bottle and cable data\n",
|
||
|
|
"ax.plot([1,3,5], data[\"ResNet50\"].get(\"1,3,5 shots normal\", []), marker='o', label=f'ResNet50', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[\"P>M>F\"].get(\"1,3,5 shots normal\", []), marker='o', label=f'P>M>F', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[\"CAML\"].get(\"1,3,5 shots normal\", []), marker='o', label=f'CAML', linestyle='-')\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set_title(f'{model} - {imgclass}')\n",
|
||
|
|
"ax.set_xlabel(\"Shots per class\")\n",
|
||
|
|
"ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set(xlim=(0.75, 5.25), xticks=[1,3,5])\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig(f\"{model}-{imgclass}.png\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1400x1000 with 6 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Create subplots\n",
|
||
|
|
"fig, axes = plt.subplots(3, 2, figsize=(14, 10))\n",
|
||
|
|
"axes = axes.flatten()\n",
|
||
|
|
"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"for i, measurement in enumerate(measurement_types):\n",
|
||
|
|
" ax = axes[i]\n",
|
||
|
|
" for model in models:\n",
|
||
|
|
" # Get the bottle and cable data for the current measurement and model\n",
|
||
|
|
" bottle_accuracy = bottle_data[model].get(measurement, [])\n",
|
||
|
|
" cable_accuracy = cable_data[model].get(measurement, [])\n",
|
||
|
|
" \n",
|
||
|
|
" # Plot both bottle and cable data\n",
|
||
|
|
" ax.plot(cable_accuracy, marker='o', label=f'{model} - Cable', linestyle='-')\n",
|
||
|
|
" \n",
|
||
|
|
" ax.set_title(measurement)\n",
|
||
|
|
" ax.set_xlabel(\"Shots / Samples\")\n",
|
||
|
|
" ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
" ax.legend()\n",
|
||
|
|
" ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Create subplots\n",
|
||
|
|
"fig, ax = plt.subplots()\n",
|
||
|
|
"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"model = \"P>M>F\"\n",
|
||
|
|
"data = cable_data\n",
|
||
|
|
"imgclass = \"cable\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot both bottle and cable data\n",
|
||
|
|
"ax.plot([1,3,5], data[\"CAML\"].get(\"2 ways only detect if faulty or not\", []), marker='o', label=f'CAML', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[\"P>M>F\"].get(\"2 ways only detect if faulty or not\", []), marker='o', label=f'P>M>F', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[\"ResNet50\"].get(\"2 ways only detect if faulty or not\", []), marker='o', label=f'ResNet50', linestyle='-')\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set_title(f'{model} - {imgclass}')\n",
|
||
|
|
"ax.set_xlabel(\"Shots per class\")\n",
|
||
|
|
"ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set(xlim=(0.75, 5.25), xticks=[1,3,5])\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig(f\"comparison-2way-{imgclass}.png\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Create subplots\n",
|
||
|
|
"fig, ax = plt.subplots()\n",
|
||
|
|
"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"model = \"P>M>F\"\n",
|
||
|
|
"data = cable_data\n",
|
||
|
|
"imgclass = \"cable\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot both bottle and cable data\n",
|
||
|
|
"ax.plot([5, 10, 15, 30], data[\"P>M>F\"].get(\"inbalanced - more good shots\", []), marker='o', label=f'P>M>F', linestyle='-')\n",
|
||
|
|
"ax.plot([5, 10, 15, 30], data[\"ResNet50\"].get(\"inbalanced - more good shots\", []), marker='o', label=f'ResNet50', linestyle='-')\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set_title(f'Inbalanced - {imgclass}')\n",
|
||
|
|
"ax.set_xlabel(\"Shots per class\")\n",
|
||
|
|
"ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set(xlim=(4.75, 30.25), xticks=[5,10,15,30])\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig(f\"inbalanced-{imgclass}.png\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Create subplots\n",
|
||
|
|
"fig, ax = plt.subplots()\n",
|
||
|
|
"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"model = \"P>M>F\"\n",
|
||
|
|
"data = cable_data\n",
|
||
|
|
"imgclass = \"cable\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot both bottle and cable data\n",
|
||
|
|
"ax.plot([5, 10, 15, 30], data[\"P>M>F\"].get(\"inbalance 2 way\", []), marker='o', label=f'P>M>F', linestyle='-')\n",
|
||
|
|
"ax.plot([5, 10, 15, 30], data[\"ResNet50\"].get(\"inbalance 2 way\", []), marker='o', label=f'ResNet50', linestyle='-')\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set_title(f'Inbalanced 2-Way - {imgclass}')\n",
|
||
|
|
"ax.set_xlabel(\"Shots per class\")\n",
|
||
|
|
"ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set(xlim=(4.75, 30.25), xticks=[5,10,15,30])\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig(f\"inbalanced-2way-{imgclass}.png\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Create subplots\n",
|
||
|
|
"fig, ax = plt.subplots()\n",
|
||
|
|
"\n",
|
||
|
|
"# Loop through each measurement type\n",
|
||
|
|
"data = cable_data\n",
|
||
|
|
"imgclass = \"cable\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot both bottle and cable data\n",
|
||
|
|
"#only faulty class detect\n",
|
||
|
|
"#1,3,5 shots normal\n",
|
||
|
|
"ax.plot([1,3,5], data[\"CAML\"].get(\"only faulty class detect\", []), marker='o', label=f'CAML', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[\"P>M>F\"].get(\"only faulty class detect\", []), marker='o', label=f'P>M>F', linestyle='-')\n",
|
||
|
|
"ax.plot([1,3,5], data[\"ResNet50\"].get(\"only faulty class detect\", []), marker='o', label=f'ResNet50', linestyle='-')\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set_title(f'8-Way - {imgclass}')\n",
|
||
|
|
"ax.set_xlabel(\"Shots per class\")\n",
|
||
|
|
"ax.set_ylabel(\"Accuracy\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.grid(True)\n",
|
||
|
|
"\n",
|
||
|
|
"ax.set(xlim=(0.75, 5.25), xticks=[1,3,5])\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust layout\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig(f\"faultclasses-{imgclass}.png\")"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.13.1"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|