Eval_FashionMNIST_torchlike.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZ01MTdMXeie",
        "outputId": "961a630a-f9f5-409c-de9c-0b66290bd91f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Convolutional-KANs'...\n",
            "remote: Enumerating objects: 2277, done.\u001b[K\n",
            "remote: Counting objects: 100% (307/307), done.\u001b[K\n",
            "remote: Compressing objects: 100% (153/153), done.\u001b[K\n",
            "remote: Total 2277 (delta 211), reused 233 (delta 154), pack-reused 1970 (from 1)\u001b[K\n",
            "Receiving objects: 100% (2277/2277), 43.32 MiB | 15.64 MiB/s, done.\n",
            "Resolving deltas: 100% (1231/1231), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone -b torch_style_convs https://github.com/AntonioTepsich/Convolutional-KANs.git\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3AJyxeIYMdm",
        "outputId": "e90d61f4-de9a-4648-acd8-a134c19ec8d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Convolutional-KANs\n"
          ]
        }
      ],
      "source": [
        "%cd Convolutional-KANs/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "990YySiz_jci",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59ba26aa-8fd8-4a7d-9719-d9cf578c6509"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "vFCniTxBYNtU"
      },
      "outputs": [],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.datasets import FashionMNIST\n",
        "from torch.utils.data import DataLoader\n",
        "from architectures_28x28.KKAN import *\n",
        "from architectures_28x28.CKAN_BN import CKAN_BN\n",
        "from architectures_28x28.KANConvs_MLP import *\n",
        "from architectures_28x28.KANConvs_MLP_2 import *\n",
        "from architectures_28x28.SimpleModels import *\n",
        "from architectures_28x28.conv_and_kan import *\n",
        "\n",
        "from evaluations import *\n",
        "from generic_train import train_model_generic"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "id": "fgwWk4uy-4k3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "638a6263-8f27-4f15-bda0-afd78e5abcdd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "-8-FaHKnYzqt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63931138-4bd5-4638-8224-b2296ff97a49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26.4M/26.4M [00:10<00:00, 2.41MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29.5k/29.5k [00:00<00:00, 212kB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4.42M/4.42M [00:01<00:00, 3.93MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5.15k/5.15k [00:00<00:00, 19.6MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "# Transformaciones\n",
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "# Cargar MNIST y filtrar por dos clases\n",
        "mnist_train = FashionMNIST(root='./data', train=True, download=True, transform=transform)\n",
        "\n",
        "mnist_test = FashionMNIST(root='./data', train=False, download=True, transform=transform)\n",
        "\n",
        "# DataLoader\n",
        "train_loader = DataLoader(mnist_train, batch_size=128, shuffle=True)\n",
        "test_loader = DataLoader(mnist_test, batch_size=64, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "EZffErM19NIt"
      },
      "outputs": [],
      "source": [
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "path = \"/content/drive/MyDrive/KANs/models/FashionMNIST_torchlike\"\n",
        "path_res = \"/content/drive/MyDrive/KANs/results/FashionMNIST_torchlike\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate models"
      ],
      "metadata": {
        "id": "FRqgzDSVJsaF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRaAnxdjjzh5",
        "outputId": "4582cd1c-68af-43f4-d250-6c05d989c836"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def join_path(name,pa):\n",
        "  print(os.path.join(pa,name+\".pt\"))\n",
        "  return os.path.join(pa,name+\".pt\")\n",
        "models_listed = os.listdir(path)\n",
        "import json\n",
        "\n",
        "\n",
        "\n",
        "models = []\n",
        "\n",
        "for m in models_listed:\n",
        "    # Open and read the JSON file\n",
        "    model = torch.load(os.path.join(path,m), map_location=torch.device(device))\n",
        "    models.append(model)\n",
        "    print(model.name)\n"
      ],
      "metadata": {
        "id": "etTTyFf8JtNn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67eae3d6-ebd5-45d0-b627-395b4a0ca265"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-ff6bce4b2ceb>:13: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model = torch.load(os.path.join(path,m), map_location=torch.device(device))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conv & KAN (Medium) (gs = 10)\n",
            "CNN  (Medium, but with more convs)\n",
            "Conv & KAN (Medium) (gs = 20)\n",
            "KKAN (Medium) (gs = 10)\n",
            "KANC MLP (Small) (gs = 20)\n",
            "KANC MLP (Big) (gs = 10)\n",
            "Conv & KAN (Small) (gs = 10)\n",
            "KANC MLP (Medium) (gs = 10)\n",
            "CNN (Small)\n",
            "KANC MLP (Small) (gs = 10)\n",
            "KKAN (Small) (gs = 10)\n",
            "KANC MLP (Medium) (gs = 20)\n",
            "Conv & KAN (Small) (gs = 20)\n",
            "CNN (Medium)\n",
            "CNN (Big)\n",
            "KKAN (Small) (gs = 20)\n",
            "KANC MLP (Big) (gs = 20)\n",
            "KKAN (Medium) (gs = 20)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n"
      ],
      "metadata": {
        "id": "6TjTeMbEdFtp"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def highlight_max(s):\n",
        "    is_max = s == s.max()\n",
        "    return ['font-weight: bold' if v else '' for v in is_max]\n",
        "\n",
        "\n",
        "def final_plots(models, test_loader, criterion, device, use_time=False):\n",
        "    accs = []\n",
        "    precisions = []\n",
        "    recalls = []\n",
        "    f1s = []\n",
        "    params_counts = []\n",
        "    times = []\n",
        "    tflops_per_sec = []\n",
        "    contar = 0\n",
        "\n",
        "    # Define color map and dictionary to store colors for models with \"10\" and \"20\" variants\n",
        "    color_map = plt.get_cmap('tab20')(np.linspace(0, 1, len(models)))\n",
        "    color_dict = {}\n",
        "    fig, ax = plt.subplots(figsize=(12, 8))  # Increase figure size\n",
        "\n",
        "    if use_time:\n",
        "        with open(os.path.join(path_res, \"epoch_times.json\"), 'r') as file:\n",
        "            times_dict = json.load(file)\n",
        "\n",
        "    models_used = []\n",
        "    models.sort(key=lambda x: x.name, reverse=False)\n",
        "\n",
        "    for i, model in enumerate(models):\n",
        "\n",
        "        # Test model and collect metrics\n",
        "        test_loss, accuracy, precision, recall, f1 = test(model, device, test_loader, criterion)\n",
        "        accs.append(accuracy)\n",
        "        precisions.append(precision)\n",
        "        recalls.append(recall)\n",
        "        f1s.append(f1)\n",
        "        params_counts.append(count_parameters(model))\n",
        "\n",
        "        if use_time:\n",
        "            times.append(times_dict.get(model.name, np.nan))\n",
        "        else:\n",
        "            times.append(np.nan)\n",
        "\n",
        "        # Check for \"10\" or \"20\" in model names\n",
        "        base_name = model.name.replace(\"10\", \"\").replace(\"20\", \"\")\n",
        "        if base_name not in color_dict:\n",
        "            color_dict[base_name] = color_map[i]  # Assign new color to this base model name\n",
        "\n",
        "        # Select marker based on model name\n",
        "        marker = 'x' if '10' in model.name else 'o' if '20' in model.name else 's'\n",
        "        ax.scatter(count_parameters(model), accuracy*100, label=model.name, color=color_dict[base_name], marker=marker,s=65)\n",
        "\n",
        "        models_used.append(model)\n",
        "        contar += 1\n",
        "\n",
        "    # Plot setup\n",
        "    #ax.set_title(\"Number of Parameters vs Accuracy\", fontsize=16)\n",
        "    ax.set_xlabel(\"Number of Parameters\", fontsize=12)\n",
        "    ax.set_ylabel(\"Accuracy (%)\", fontsize=12)\n",
        "    legend = ax.legend(loc=\"upper center\", bbox_to_anchor=(0.5, 1.15), fontsize=9, ncol=3)  # Set smaller font size and 3 columns\n",
        "    plt.grid(True)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Create DataFrame and export results\n",
        "    df = pd.DataFrame({\n",
        "        \"Test Accuracy\": accs,\n",
        "        \"Test Precision\": precisions,\n",
        "        \"Test Recall\": recalls,\n",
        "        \"Test F1 Score\": f1s,\n",
        "        \"Number of Parameters\": params_counts,\n",
        "        \"Seconds/epoch\": times,\n",
        "    }, index=[m.name for m in models_used])\n",
        "\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "2m2guVeyZAPm"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tabla = final_plots(models,test_loader,criterion,device,use_time = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "1umOMdZlZBSH",
        "outputId": "2bcda1e6-a5c0-4017-8e91-2b30f3a96b58"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabla"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "oItCiliDguLB",
        "outputId": "0c20f684-b651-4391-d027-4bd65723971f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Test Accuracy  Test Precision  \\\n",
              "CNN  (Medium, but with more convs)         0.8956        0.896023   \n",
              "CNN (Big)                                  0.8944        0.894001   \n",
              "CNN (Medium)                               0.8834        0.881956   \n",
              "CNN (Small)                                0.8710        0.870822   \n",
              "Conv & KAN (Medium) (gs = 10)              0.8792        0.878418   \n",
              "Conv & KAN (Medium) (gs = 20)              0.8790        0.880789   \n",
              "Conv & KAN (Small) (gs = 10)               0.8755        0.873701   \n",
              "Conv & KAN (Small) (gs = 20)               0.8767        0.877424   \n",
              "KANC MLP (Big) (gs = 10)                   0.8915        0.892151   \n",
              "KANC MLP (Big) (gs = 20)                   0.8911        0.890554   \n",
              "KANC MLP (Medium) (gs = 10)                0.8899        0.889723   \n",
              "KANC MLP (Medium) (gs = 20)                0.8890        0.888546   \n",
              "KANC MLP (Small) (gs = 10)                 0.8743        0.874131   \n",
              "KANC MLP (Small) (gs = 20)                 0.8815        0.880209   \n",
              "KKAN (Medium) (gs = 10)                    0.8791        0.883658   \n",
              "KKAN (Medium) (gs = 20)                    0.8856        0.885222   \n",
              "KKAN (Small) (gs = 10)                     0.8801        0.878661   \n",
              "KKAN (Small) (gs = 20)                     0.8794        0.878020   \n",
              "\n",
              "                                    Test Recall  Test F1 Score  \\\n",
              "CNN  (Medium, but with more convs)       0.8956       0.895525   \n",
              "CNN (Big)                                0.8944       0.893895   \n",
              "CNN (Medium)                             0.8834       0.882159   \n",
              "CNN (Small)                              0.8710       0.870091   \n",
              "Conv & KAN (Medium) (gs = 10)            0.8792       0.878035   \n",
              "Conv & KAN (Medium) (gs = 20)            0.8790       0.879483   \n",
              "Conv & KAN (Small) (gs = 10)             0.8755       0.874222   \n",
              "Conv & KAN (Small) (gs = 20)             0.8767       0.875915   \n",
              "KANC MLP (Big) (gs = 10)                 0.8915       0.891433   \n",
              "KANC MLP (Big) (gs = 20)                 0.8911       0.890560   \n",
              "KANC MLP (Medium) (gs = 10)              0.8899       0.889618   \n",
              "KANC MLP (Medium) (gs = 20)              0.8890       0.888265   \n",
              "KANC MLP (Small) (gs = 10)               0.8743       0.874083   \n",
              "KANC MLP (Small) (gs = 20)               0.8815       0.879433   \n",
              "KKAN (Medium) (gs = 10)                  0.8791       0.879970   \n",
              "KKAN (Medium) (gs = 20)                  0.8856       0.885183   \n",
              "KKAN (Small) (gs = 10)                   0.8801       0.877642   \n",
              "KKAN (Small) (gs = 20)                   0.8794       0.877172   \n",
              "\n",
              "                                    Number of Parameters  Seconds/epoch  \n",
              "CNN  (Medium, but with more convs)                  6930      12.619682  \n",
              "CNN (Big)                                          26620      12.681805  \n",
              "CNN (Medium)                                        3020      12.617077  \n",
              "CNN (Small)                                         1540      13.969206  \n",
              "Conv & KAN (Medium) (gs = 10)                      38010      13.838395  \n",
              "Conv & KAN (Medium) (gs = 20)                      63010      13.595402  \n",
              "Conv & KAN (Small) (gs = 10)                       19030      13.819559  \n",
              "Conv & KAN (Small) (gs = 20)                       31530      13.526114  \n",
              "KANC MLP (Big) (gs = 10)                           33535      99.136612  \n",
              "KANC MLP (Big) (gs = 20)                           38485     147.796222  \n",
              "KANC MLP (Medium) (gs = 10)                         9935      98.643374  \n",
              "KANC MLP (Medium) (gs = 20)                        14885     147.687061  \n",
              "KANC MLP (Small) (gs = 10)                          5310      70.226353  \n",
              "KANC MLP (Small) (gs = 20)                          8010      97.570139  \n",
              "KKAN (Medium) (gs = 10)                            44925      98.551077  \n",
              "KKAN (Medium) (gs = 20)                            74875     148.520436  \n",
              "KKAN (Small) (gs = 10)                             22800      69.591632  \n",
              "KKAN (Small) (gs = 20)                             38000      98.016934  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc5432ca-5d38-42f4-ac9c-01119829dbea\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        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>Test Accuracy</th>\n",
              "      <th>Test Precision</th>\n",
              "      <th>Test Recall</th>\n",
              "      <th>Test F1 Score</th>\n",
              "      <th>Number of Parameters</th>\n",
              "      <th>Seconds/epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CNN  (Medium, but with more convs)</th>\n",
              "      <td>0.8956</td>\n",
              "      <td>0.896023</td>\n",
              "      <td>0.8956</td>\n",
              "      <td>0.895525</td>\n",
              "      <td>6930</td>\n",
              "      <td>12.619682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CNN (Big)</th>\n",
              "      <td>0.8944</td>\n",
              "      <td>0.894001</td>\n",
              "      <td>0.8944</td>\n",
              "      <td>0.893895</td>\n",
              "      <td>26620</td>\n",
              "      <td>12.681805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CNN (Medium)</th>\n",
              "      <td>0.8834</td>\n",
              "      <td>0.881956</td>\n",
              "      <td>0.8834</td>\n",
              "      <td>0.882159</td>\n",
              "      <td>3020</td>\n",
              "      <td>12.617077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CNN (Small)</th>\n",
              "      <td>0.8710</td>\n",
              "      <td>0.870822</td>\n",
              "      <td>0.8710</td>\n",
              "      <td>0.870091</td>\n",
              "      <td>1540</td>\n",
              "      <td>13.969206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Conv &amp; KAN (Medium) (gs = 10)</th>\n",
              "      <td>0.8792</td>\n",
              "      <td>0.878418</td>\n",
              "      <td>0.8792</td>\n",
              "      <td>0.878035</td>\n",
              "      <td>38010</td>\n",
              "      <td>13.838395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Conv &amp; KAN (Medium) (gs = 20)</th>\n",
              "      <td>0.8790</td>\n",
              "      <td>0.880789</td>\n",
              "      <td>0.8790</td>\n",
              "      <td>0.879483</td>\n",
              "      <td>63010</td>\n",
              "      <td>13.595402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Conv &amp; KAN (Small) (gs = 10)</th>\n",
              "      <td>0.8755</td>\n",
              "      <td>0.873701</td>\n",
              "      <td>0.8755</td>\n",
              "      <td>0.874222</td>\n",
              "      <td>19030</td>\n",
              "      <td>13.819559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Conv &amp; KAN (Small) (gs = 20)</th>\n",
              "      <td>0.8767</td>\n",
              "      <td>0.877424</td>\n",
              "      <td>0.8767</td>\n",
              "      <td>0.875915</td>\n",
              "      <td>31530</td>\n",
              "      <td>13.526114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KANC MLP (Big) (gs = 10)</th>\n",
              "      <td>0.8915</td>\n",
              "      <td>0.892151</td>\n",
              "      <td>0.8915</td>\n",
              "      <td>0.891433</td>\n",
              "      <td>33535</td>\n",
              "      <td>99.136612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KANC MLP (Big) (gs = 20)</th>\n",
              "      <td>0.8911</td>\n",
              "      <td>0.890554</td>\n",
              "      <td>0.8911</td>\n",
              "      <td>0.890560</td>\n",
              "      <td>38485</td>\n",
              "      <td>147.796222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KANC MLP (Medium) (gs = 10)</th>\n",
              "      <td>0.8899</td>\n",
              "      <td>0.889723</td>\n",
              "      <td>0.8899</td>\n",
              "      <td>0.889618</td>\n",
              "      <td>9935</td>\n",
              "      <td>98.643374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KANC MLP (Medium) (gs = 20)</th>\n",
              "      <td>0.8890</td>\n",
              "      <td>0.888546</td>\n",
              "      <td>0.8890</td>\n",
              "      <td>0.888265</td>\n",
              "      <td>14885</td>\n",
              "      <td>147.687061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KANC MLP (Small) (gs = 10)</th>\n",
              "      <td>0.8743</td>\n",
              "      <td>0.874131</td>\n",
              "      <td>0.8743</td>\n",
              "      <td>0.874083</td>\n",
              "      <td>5310</td>\n",
              "      <td>70.226353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KANC MLP (Small) (gs = 20)</th>\n",
              "      <td>0.8815</td>\n",
              "      <td>0.880209</td>\n",
              "      <td>0.8815</td>\n",
              "      <td>0.879433</td>\n",
              "      <td>8010</td>\n",
              "      <td>97.570139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KKAN (Medium) (gs = 10)</th>\n",
              "      <td>0.8791</td>\n",
              "      <td>0.883658</td>\n",
              "      <td>0.8791</td>\n",
              "      <td>0.879970</td>\n",
              "      <td>44925</td>\n",
              "      <td>98.551077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KKAN (Medium) (gs = 20)</th>\n",
              "      <td>0.8856</td>\n",
              "      <td>0.885222</td>\n",
              "      <td>0.8856</td>\n",
              "      <td>0.885183</td>\n",
              "      <td>74875</td>\n",
              "      <td>148.520436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KKAN (Small) (gs = 10)</th>\n",
              "      <td>0.8801</td>\n",
              "      <td>0.878661</td>\n",
              "      <td>0.8801</td>\n",
              "      <td>0.877642</td>\n",
              "      <td>22800</td>\n",
              "      <td>69.591632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KKAN (Small) (gs = 20)</th>\n",
              "      <td>0.8794</td>\n",
              "      <td>0.878020</td>\n",
              "      <td>0.8794</td>\n",
              "      <td>0.877172</td>\n",
              "      <td>38000</td>\n",
              "      <td>98.016934</td>\n",
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              "summary": "{\n  \"name\": \"tabla\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Test Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007283002415419518,\n        \"min\": 0.871,\n        \"max\": 0.8956,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.8956,\n          0.8944,\n          0.8915\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007418768926670805,\n        \"min\": 0.870822474647641,\n        \"max\": 0.896022626030516,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.896022626030516,\n          0.8940011809097168,\n          0.8921513060842694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00728300241541951,\n        \"min\": 0.8709999999999999,\n        \"max\": 0.8956,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.8956,\n          0.8943999999999999,\n          0.8915\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007548779809802726,\n        \"min\": 0.8700907294881572,\n        \"max\": 0.8955245590813302,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.8955245590813302,\n          0.8938947450235777,\n          0.8914331554689465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Parameters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20753,\n        \"min\": 1540,\n        \"max\": 74875,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          6930,\n          26620,\n          33535\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seconds/epoch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.93703808463123,\n        \"min\": 12.617076822999934,\n        \"max\": 148.52043588800007,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          12.61968221699999,\n          12.681805123000004,\n          99.13661243800004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aZrRNtXm06ze"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "latex_table = tabla.to_latex()\n",
        "\n",
        "from IPython.display import display, Latex\n",
        "\n",
        "display(Latex(f\"```latex\\n{latex_table}\\n```\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "6WnWcMOpeC2O",
        "outputId": "202d4026-6429-4e87-a3ab-806d6b674efa"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Latex object>"
            ],
            "text/latex": "```latex\n\\begin{tabular}{lrrrrrr}\n\\toprule\n & Test Accuracy & Test Precision & Test Recall & Test F1 Score & Number of Parameters & Seconds/epoch \\\\\n\\midrule\nConv & KAN (Medium) (gs = 10) & 0.879200 & 0.878418 & 0.879200 & 0.878035 & 38010 & 13.838395 \\\\\nCNN  (Medium, but with more convs) & 0.895600 & 0.896023 & 0.895600 & 0.895525 & 6930 & 12.619682 \\\\\nConv & KAN (Medium) (gs = 20) & 0.879000 & 0.880789 & 0.879000 & 0.879483 & 63010 & 13.595402 \\\\\nKKAN (Medium) (gs = 10) & 0.879100 & 0.883658 & 0.879100 & 0.879970 & 44925 & 98.551077 \\\\\nKANC MLP (Small) (gs = 20) & 0.881500 & 0.880209 & 0.881500 & 0.879433 & 8010 & 97.570139 \\\\\nKANC MLP (Big) (gs = 10) & 0.891500 & 0.892151 & 0.891500 & 0.891433 & 33535 & 99.136612 \\\\\nConv & KAN (Small) (gs = 10) & 0.875500 & 0.873701 & 0.875500 & 0.874222 & 19030 & 13.819559 \\\\\nKANC MLP (Medium) (gs = 10) & 0.889900 & 0.889723 & 0.889900 & 0.889618 & 9935 & 98.643374 \\\\\nCNN (Small) & 0.871000 & 0.870822 & 0.871000 & 0.870091 & 1540 & 13.969206 \\\\\nKANC MLP (Small) (gs = 10) & 0.874300 & 0.874131 & 0.874300 & 0.874083 & 5310 & 70.226353 \\\\\nKKAN (Small) (gs = 10) & 0.880100 & 0.878661 & 0.880100 & 0.877642 & 22800 & 69.591632 \\\\\nKANC MLP (Medium) (gs = 20) & 0.889000 & 0.888546 & 0.889000 & 0.888265 & 14885 & 147.687061 \\\\\nConv & KAN (Small) (gs = 20) & 0.876700 & 0.877424 & 0.876700 & 0.875915 & 31530 & 13.526114 \\\\\nCNN (Medium) & 0.883400 & 0.881956 & 0.883400 & 0.882159 & 3020 & 12.617077 \\\\\nCNN (Big) & 0.894400 & 0.894001 & 0.894400 & 0.893895 & 26620 & 12.681805 \\\\\nKKAN (Small) (gs = 20) & 0.879400 & 0.878020 & 0.879400 & 0.877172 & 38000 & 98.016934 \\\\\nKANC MLP (Big) (gs = 20) & 0.891100 & 0.890554 & 0.891100 & 0.890560 & 38485 & 147.796222 \\\\\nKKAN (Medium) (gs = 20) & 0.885600 & 0.885222 & 0.885600 & 0.885183 & 74875 & 148.520436 \\\\\n\\bottomrule\n\\end{tabular}\n\n```"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def final_plots_espanol(models, test_loader, criterion, device, use_time=False):\n",
        "    accs = []\n",
        "    precisions = []\n",
        "    recalls = []\n",
        "    f1s = []\n",
        "    params_counts = []\n",
        "    times = []\n",
        "    tflops_per_sec = []\n",
        "    contar = 0\n",
        "\n",
        "    # Define color map and dictionary to store colors for models with \"10\" and \"20\" variants\n",
        "    color_map = plt.get_cmap('tab20')(np.linspace(0, 1, len(models)))\n",
        "    color_dict = {}\n",
        "    fig, ax = plt.subplots(figsize=(12, 8))  # Increase figure size\n",
        "\n",
        "    if use_time:\n",
        "        with open(os.path.join(path_res, \"epoch_times.json\"), 'r') as file:\n",
        "            times_dict = json.load(file)\n",
        "\n",
        "    models_used = []\n",
        "    models.sort(key=lambda x: x.name, reverse=False)\n",
        "    for i, model in enumerate(models):\n",
        "\n",
        "        # Test model and collect metrics\n",
        "        test_loss, accuracy, precision, recall, f1 = test(model, device, test_loader, criterion)\n",
        "        accs.append(accuracy)\n",
        "        precisions.append(precision)\n",
        "        recalls.append(recall)\n",
        "        f1s.append(f1)\n",
        "        params_counts.append(count_parameters(model))\n",
        "\n",
        "        if use_time:\n",
        "            times.append(times_dict.get(model.name, np.nan))\n",
        "        else:\n",
        "            times.append(np.nan)\n",
        "\n",
        "        # Check for \"10\" or \"20\" in model names\n",
        "        base_name = model.name.replace(\"10\", \"\").replace(\"20\", \"\")\n",
        "        if base_name not in color_dict:\n",
        "            color_dict[base_name] = color_map[i]  # Assign new color to this base model name\n",
        "\n",
        "        # Select marker based on model name\n",
        "        marker = 'x' if '10' in model.name else 'o' if '20' in model.name else 's'\n",
        "        ax.scatter(count_parameters(model), accuracy*100, label=model.name, color=color_dict[base_name], marker=marker,s=65)\n",
        "\n",
        "        models_used.append(model)\n",
        "        contar += 1\n",
        "\n",
        "    # Plot setup\n",
        "    #ax.set_title(\"Cantidad de parametros vs Accuracy\", fontsize=16)\n",
        "    ax.set_xlabel(\"Cantidad de parametros\", fontsize=12)\n",
        "    ax.set_ylabel(\"Accuracy (%)\", fontsize=12)\n",
        "    #legend = ax.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), fontsize=8)  # Set smaller font size\n",
        "    legend = ax.legend(loc=\"upper center\", bbox_to_anchor=(0.5, 1.15), fontsize=9, ncol=3)  # Set smaller font size and 3 columns\n",
        "\n",
        "    plt.grid(True)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Create DataFrame and export results\n",
        "    df = pd.DataFrame({\n",
        "        \"Test Accuracy\": accs,\n",
        "        \"Test Precision\": precisions,\n",
        "        \"Test Recall\": recalls,\n",
        "        \"Test F1 Score\": f1s,\n",
        "        \"Number of Parameters\": params_counts,\n",
        "        \"Seconds/epoch\": times,\n",
        "    }, index=[m.name for m in models_used])\n",
        "\n",
        "    df.to_csv('experiment_28x28.csv', index=False)\n",
        "\n",
        "    # Apply styling\n",
        "    return df"
      ],
      "metadata": {
        "id": "R9M6jX7jeGKu"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tabla = final_plots_espanol(models,test_loader,criterion,device,use_time = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "BfD6spN6Zlss",
        "outputId": "65b7e338-79da-46b2-818a-da49cd1c5b2b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_ww7RJFhZsy1"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}