NOTEBOOK_ARTIGO.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgRKeVD6SyOP"
      },
      "source": [
        "## Nosso dataset :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcyLWU0D8lIa",
        "outputId": "a7a1fb26-026f-4651-f03e-403b74bf7c7e"
      },
      "outputs": [],
      "source": [
        "!wget https://github.com/otaviofcoletti/Sara_dataset/raw/main/Sara_dataset_direito.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaxb_Txv8dt6",
        "outputId": "e8bd2795-c518-411b-c4f1-1a901dbc9669"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  Sara_dataset_direito.zip\n",
            "replace Sara_dataset_direito/Situação rolo 3 da esquerda levantado index 14.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n"
          ]
        }
      ],
      "source": [
        "!unzip Sara_dataset_direito.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "uBHQ1iKU7tQK"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "from aeon.classification.convolution_based import RocketClassifier\n",
        "from aeon.classification.convolution_based import Arsenal, RocketClassifier\n",
        "\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from aeon.classification.hybrid import HIVECOTEV2\n",
        "from aeon.classification.feature_based import Catch22Classifier\n",
        "from aeon.transformations.collection.catch22 import Catch22\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RuTWzhNE7tQO"
      },
      "outputs": [],
      "source": [
        "lista_de_arquivos = os.listdir(\"Sara_dataset_direito\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "sY-TLes7Ucds"
      },
      "outputs": [],
      "source": [
        "# Function to determine the unit based on the sensor type\n",
        "def get_unit(sensor):\n",
        "    if sensor == \"Gyroscope\":\n",
        "        unit = \"(deg/s)\"\n",
        "    else:\n",
        "        unit = \"(g)\"\n",
        "    return unit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "6877hkti7tQO"
      },
      "outputs": [],
      "source": [
        "label_map = {\n",
        "        \"Situação referencia index\": 0,\n",
        "        \"Situação rolo 3 da esquerda levantado index\": 1,\n",
        "        \"Situação rolo 3 da esquerda removido index\": 2,\n",
        "        \"Situação rolo 3 da direita levantado index\": 3,\n",
        "        \"Situação rolo 3 da direita removido index\": 4,\n",
        "    }\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cf4Rn1597tQZ"
      },
      "source": [
        "# Random Forest (dados brutos) 5 classes\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# Assuming 'label_map' and 'lista_de_arquivos' are defined\n",
        "\n",
        "casos = []\n",
        "labels = []\n",
        "\n",
        "for i in lista_de_arquivos:\n",
        "    for j in label_map:\n",
        "        if j in i:\n",
        "            labels.append(label_map[j])\n",
        "            df = pd.read_csv(f\"Sara_dataset_direito/{i}\")\n",
        "            \n",
        "            # Combine Gyroscope and Accelerometer features\n",
        "            features = df[['Gyroscope X (deg/s)', 'Gyroscope Y (deg/s)', 'Gyroscope Z (deg/s)',\n",
        "                            'Accelerometer X (g)', 'Accelerometer Y (g)', 'Accelerometer Z (g)']]\n",
        "            \n",
        "            # You can add more feature engineering or preprocessing steps here\n",
        "            \n",
        "            casos.append(features)\n",
        "\n",
        "X = np.array(casos)\n",
        "y = np.array(labels)\n",
        "\n",
        "# Flatten or reshape the array to make it two-dimensional\n",
        "X = np.array(casos).reshape(len(casos), -1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Random Forest CV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "k8q7cSzn7tQc"
      },
      "outputs": [],
      "source": [
        "# loocv to automatically evaluate the performance of a random forest classifier\n",
        "cv = LeaveOneOut()\n",
        "# create model\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "# evaluate model\n",
        "pred = cross_val_predict(rf, X, y, cv=cv, n_jobs=-1)\n",
        "# report performance\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKgF6uFe7tQc",
        "outputId": "24e91221-7113-43f6-bd22-c919b1f2990b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8269230769230769\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.8894373822224078\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Convert lists to numpy arrays\n",
        "y = np.array(y)\n",
        "pred = np.array(pred)\n",
        "\n",
        "# Binarize the labels\n",
        "y_binarized = label_binarize(y, classes=np.unique(y))\n",
        "\n",
        "# Ensure pred is a 2D array with one column per class\n",
        "pred = pred.reshape(-1, 1) if pred.ndim == 1 else pred\n",
        "\n",
        "# Binarize the predicted labels\n",
        "pred_binarized = label_binarize(pred, classes=np.unique(y))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_binarized, pred_binarized, multi_class='ovr')\n",
        "\n",
        "print(f'ROC AUC Score: {roc_auc}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
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                      "ticks": "",
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                },
                "width": 800,
                "xaxis": {
                  "anchor": "y",
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                  "domain": [
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                  "scaleanchor": "y",
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                    "text": "Predicted"
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                },
                "yaxis": {
                  "anchor": "x",
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                    "text": "Actual"
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          },
          "metadata": {},
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      "source": [
        "conf_matrix = confusion_matrix(y, pred)\n",
        "\n",
        "class_names = ['Right Up', 'Right Down', 'Left Un.', 'Normal', 'Left Up']\n",
        "\n",
        "\n",
        "# Create a DataFrame from the confusion matrix\n",
        "confusion_df = pd.DataFrame(conf_matrix,\n",
        "                             columns=[f'{class_names[i]}' for i in range(conf_matrix.shape[1])],\n",
        "                             index=[f'{class_names[i]}' for i in range(conf_matrix.shape[0])])\n",
        "\n",
        "\n",
        "# Plot the heatmap\n",
        "heatmap = px.imshow(confusion_df, labels=dict(x=\"Predicted\", y=\"Actual\", color=\"Count\"), text_auto=True)\n",
        "\n",
        "heatmap.update_layout(title='Confusion Matrix',\n",
        "                      xaxis_nticks=5, yaxis_nticks=5,\n",
        "                      font=dict(size=14, family='Courier New, monospace'),\n",
        "                      width=800, height=800)\n",
        "\n",
        "heatmap.show()\n",
        "\n",
        "heatmap.write_image('random_forest.pdf')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Mini Rocket"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n",
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n",
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n",
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n",
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n",
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n",
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n",
            "/home/otavio/.local/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "cv = LeaveOneOut()\n",
        "# create model\n",
        "multi_r = Arsenal(rocket_transform=\"minirocket\")\n",
        "\n",
        "# evaluate model\n",
        "pred = cross_val_predict(multi_r, np.array(X), np.array(y), cv=cv, n_jobs=-1)\n",
        "# report performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9038461538461539\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.9388660475047317\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Convert lists to numpy arrays\n",
        "y = np.array(y)\n",
        "pred = np.array(pred)\n",
        "\n",
        "# Binarize the labels\n",
        "y_binarized = label_binarize(y, classes=np.unique(y))\n",
        "\n",
        "# Ensure pred is a 2D array with one column per class\n",
        "pred = pred.reshape(-1, 1) if pred.ndim == 1 else pred\n",
        "\n",
        "# Binarize the predicted labels\n",
        "pred_binarized = label_binarize(pred, classes=np.unique(y))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_binarized, pred_binarized, multi_class='ovr')\n",
        "\n",
        "print(f'ROC AUC Score: {roc_auc}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
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                      "baxis": {
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                      "bgcolor": "#E5ECF6",
                      "caxis": {
                        "gridcolor": "white",
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                        "ticks": ""
                      }
                    },
                    "title": {
                      "x": 0.05
                    },
                    "xaxis": {
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                      "linecolor": "white",
                      "ticks": "",
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                    },
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                "width": 800,
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                    "text": "Predicted"
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                },
                "yaxis": {
                  "anchor": "x",
                  "autorange": "reversed",
                  "constrain": "domain",
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                    "text": "Actual"
                  }
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          },
          "metadata": {},
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      ],
      "source": [
        "conf_matrix = confusion_matrix(y, pred)\n",
        "\n",
        "class_names = ['Right Up', 'Right Down', 'Left Un.', 'Normal', 'Left Up']\n",
        "\n",
        "\n",
        "# Create a DataFrame from the confusion matrix\n",
        "confusion_df = pd.DataFrame(conf_matrix,\n",
        "                             columns=[f'{class_names[i]}' for i in range(conf_matrix.shape[1])],\n",
        "                             index=[f'{class_names[i]}' for i in range(conf_matrix.shape[0])])\n",
        "\n",
        "\n",
        "# Plot the heatmap\n",
        "heatmap = px.imshow(confusion_df, labels=dict(x=\"Predicted\", y=\"Actual\", color=\"Count\"), text_auto=True)\n",
        "\n",
        "heatmap.update_layout(\n",
        "                      xaxis_nticks=5, yaxis_nticks=5,\n",
        "                      font=dict(size=20, family='Courier New, monospace'),\n",
        "                      width=800, height=800)\n",
        "\n",
        "heatmap.show()\n",
        "\n",
        "heatmap.write_image('minirocket.pdf')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hszspNHC7tQX"
      },
      "source": [
        "# Catch22 + Isolation Forest 2 classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[1;32m/home/otavio/Área de Trabalho/Trabalho de séries/ROCKET_+_CATCH22_MULTIVARIATE.ipynb Célula 43\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/otavio/%C3%81rea%20de%20Trabalho/Trabalho%20de%20s%C3%A9ries/ROCKET_%2B_CATCH22_MULTIVARIATE.ipynb#X56sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m clf \u001b[39m=\u001b[39m Catch22Classifier()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/otavio/%C3%81rea%20de%20Trabalho/Trabalho%20de%20s%C3%A9ries/ROCKET_%2B_CATCH22_MULTIVARIATE.ipynb#X56sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39m# evaluate model\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/otavio/%C3%81rea%20de%20Trabalho/Trabalho%20de%20s%C3%A9ries/ROCKET_%2B_CATCH22_MULTIVARIATE.ipynb#X56sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m pred \u001b[39m=\u001b[39m cross_val_predict(clf, np\u001b[39m.\u001b[39;49marray(X), np\u001b[39m.\u001b[39;49marray(y), cv\u001b[39m=\u001b[39;49mcv, n_jobs\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/otavio/%C3%81rea%20de%20Trabalho/Trabalho%20de%20s%C3%A9ries/ROCKET_%2B_CATCH22_MULTIVARIATE.ipynb#X56sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39m# report performance\u001b[39;00m\n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:986\u001b[0m, in \u001b[0;36mcross_val_predict\u001b[0;34m(estimator, X, y, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method)\u001b[0m\n\u001b[1;32m    983\u001b[0m \u001b[39m# We clone the estimator to make sure that all the folds are\u001b[39;00m\n\u001b[1;32m    984\u001b[0m \u001b[39m# independent, and that it is pickle-able.\u001b[39;00m\n\u001b[1;32m    985\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, verbose\u001b[39m=\u001b[39mverbose, pre_dispatch\u001b[39m=\u001b[39mpre_dispatch)\n\u001b[0;32m--> 986\u001b[0m predictions \u001b[39m=\u001b[39m parallel(\n\u001b[1;32m    987\u001b[0m     delayed(_fit_and_predict)(\n\u001b[1;32m    988\u001b[0m         clone(estimator), X, y, train, test, verbose, fit_params, method\n\u001b[1;32m    989\u001b[0m     )\n\u001b[1;32m    990\u001b[0m     \u001b[39mfor\u001b[39;49;00m train, test \u001b[39min\u001b[39;49;00m splits\n\u001b[1;32m    991\u001b[0m )\n\u001b[1;32m    993\u001b[0m inv_test_indices \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mempty(\u001b[39mlen\u001b[39m(test_indices), dtype\u001b[39m=\u001b[39m\u001b[39mint\u001b[39m)\n\u001b[1;32m    994\u001b[0m inv_test_indices[test_indices] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39mlen\u001b[39m(test_indices))\n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/utils/parallel.py:63\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     58\u001b[0m config \u001b[39m=\u001b[39m get_config()\n\u001b[1;32m     59\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n\u001b[1;32m     60\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     61\u001b[0m     \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n\u001b[1;32m     62\u001b[0m )\n\u001b[0;32m---> 63\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(iterable_with_config)\n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/joblib/parallel.py:1098\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1095\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m   1097\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1098\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mretrieve()\n\u001b[1;32m   1099\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/joblib/parallel.py:975\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m--> 975\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39;49mget(timeout\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout))\n\u001b[1;32m    976\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    977\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/joblib/_parallel_backends.py:567\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m    565\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m    566\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 567\u001b[0m     \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39;49mresult(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m    568\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    569\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
            "File \u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py:453\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m    451\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 453\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    455\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m    456\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n",
            "File \u001b[0;32m/usr/lib/python3.10/threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    319\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m         waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m    321\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    322\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# loocv to automatically evaluate the performance of a random forest classifier\n",
        "\n",
        "cv = LeaveOneOut()\n",
        "# create model\n",
        "clf = Catch22Classifier()\n",
        "\n",
        "# evaluate model\n",
        "pred = cross_val_predict(clf, np.array(X), np.array(y), cv=cv, n_jobs=-1)\n",
        "# report performance\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y = np.array(y)\n",
        "pred = np.array(pred)\n",
        "\n",
        "# Binarize the labels\n",
        "y_binarized = label_binarize(y, classes=np.unique(y))\n",
        "\n",
        "# Ensure pred is a 2D array with one column per class\n",
        "pred = pred.reshape(-1, 1) if pred.ndim == 1 else pred\n",
        "\n",
        "# Binarize the predicted labels\n",
        "pred_binarized = label_binarize(pred, classes=np.unique(y))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_binarized, pred_binarized, multi_class='ovr')\n",
        "\n",
        "print(f'ROC AUC Score: {roc_auc}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "conf_matrix = confusion_matrix(y, pred)\n",
        "\n",
        "class_names = ['Right Up', 'Right Down', 'Left Un.', 'Normal', 'Left Up']\n",
        "\n",
        "\n",
        "# Create a DataFrame from the confusion matrix\n",
        "confusion_df = pd.DataFrame(conf_matrix,\n",
        "                             columns=[f'{class_names[i]}' for i in range(conf_matrix.shape[1])],\n",
        "                             index=[f'{class_names[i]}' for i in range(conf_matrix.shape[0])])\n",
        "\n",
        "\n",
        "# Plot the heatmap\n",
        "heatmap = px.imshow(confusion_df, labels=dict(x=\"Predicted\", y=\"Actual\", color=\"Count\"), text_auto=True)\n",
        "\n",
        "heatmap.update_layout(title='Confusion Matrix',\n",
        "                      xaxis_nticks=5, yaxis_nticks=5,\n",
        "                      font=dict(size=14, family='Courier New, monospace'),\n",
        "                      width=800, height=800)\n",
        "\n",
        "heatmap.show()\n",
        "\n",
        "heatmap.write_html('catch22.html')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# HIVECOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "cv = LeaveOneOut()\n",
        "# create model\n",
        "hc2 = HIVECOTEV2(time_limit_in_minutes=0.2)\n",
        "\n",
        "# evaluate model\n",
        "pred = cross_val_predict(hc2, np.array(X), np.array(y), cv=cv, n_jobs=-1)\n",
        "# report performance\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y = np.array(y)\n",
        "pred = np.array(pred)\n",
        "\n",
        "# Binarize the labels\n",
        "y_binarized = label_binarize(y, classes=np.unique(y))\n",
        "\n",
        "# Ensure pred is a 2D array with one column per class\n",
        "pred = pred.reshape(-1, 1) if pred.ndim == 1 else pred\n",
        "\n",
        "# Binarize the predicted labels\n",
        "pred_binarized = label_binarize(pred, classes=np.unique(y))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_binarized, pred_binarized, multi_class='ovr')\n",
        "\n",
        "print(f'ROC AUC Score: {roc_auc}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "conf_matrix = confusion_matrix(y, pred)\n",
        "\n",
        "class_names = ['Right Up', 'Right Down', 'Left Un.', 'Normal', 'Left Up']\n",
        "\n",
        "\n",
        "# Create a DataFrame from the confusion matrix\n",
        "confusion_df = pd.DataFrame(conf_matrix,\n",
        "                             columns=[f'{class_names[i]}' for i in range(conf_matrix.shape[1])],\n",
        "                             index=[f'{class_names[i]}' for i in range(conf_matrix.shape[0])])\n",
        "\n",
        "\n",
        "# Plot the heatmap\n",
        "heatmap = px.imshow(confusion_df, labels=dict(x=\"Predicted\", y=\"Actual\", color=\"Count\"), text_auto=True)\n",
        "\n",
        "heatmap.update_layout(title='Confusion Matrix',\n",
        "                      xaxis_nticks=5, yaxis_nticks=5,\n",
        "                      font=dict(size=14, family='Courier New, monospace'),\n",
        "                      width=800, height=800)\n",
        "\n",
        "heatmap.show()\n",
        "\n",
        "heatmap.write_html('hivecote2.html')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Time Series Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pyts.datasets import load_gunpoint\n",
        "from pyts.classification import TimeSeriesForest\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(np.array(X), np.array(y), test_size=0.25, random_state=42)\n",
        "clf = TimeSeriesForest(random_state=1)\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "clf.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cv = LeaveOneOut()\n",
        "# create model\n",
        "clf = TimeSeriesForest()\n",
        "\n",
        "# evaluate model\n",
        "pred = cross_val_predict(clf, np.array(X), np.array(y), cv=cv, n_jobs=-1)\n",
        "# report performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y = np.array(y)\n",
        "pred = np.array(pred)\n",
        "\n",
        "# Binarize the labels\n",
        "y_binarized = label_binarize(y, classes=np.unique(y))\n",
        "\n",
        "# Ensure pred is a 2D array with one column per class\n",
        "pred = pred.reshape(-1, 1) if pred.ndim == 1 else pred\n",
        "\n",
        "# Binarize the predicted labels\n",
        "pred_binarized = label_binarize(pred, classes=np.unique(y))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_binarized, pred_binarized, multi_class='ovr')\n",
        "\n",
        "print(f'ROC AUC Score: {roc_auc}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "conf_matrix = confusion_matrix(y, pred)\n",
        "\n",
        "class_names = ['Right Up', 'Right Down', 'Left Un.', 'Normal', 'Left Up']\n",
        "\n",
        "\n",
        "# Create a DataFrame from the confusion matrix\n",
        "confusion_df = pd.DataFrame(conf_matrix,\n",
        "                             columns=[f'{class_names[i]}' for i in range(conf_matrix.shape[1])],\n",
        "                             index=[f'{class_names[i]}' for i in range(conf_matrix.shape[0])])\n",
        "\n",
        "\n",
        "# Plot the heatmap\n",
        "heatmap = px.imshow(confusion_df, labels=dict(x=\"Predicted\", y=\"Actual\", color=\"Count\"), text_auto=True)\n",
        "\n",
        "heatmap.update_layout(title='Confusion Matrix',\n",
        "                      xaxis_nticks=5, yaxis_nticks=5,\n",
        "                      font=dict(size=14, family='Courier New, monospace'),\n",
        "                      width=800, height=800)\n",
        "\n",
        "heatmap.show()\n",
        "\n",
        "heatmap.write_html('ts_forest.html')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Rocket Multirocket "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cv = LeaveOneOut()\n",
        "# create model\n",
        "multi_r = Arsenal(rocket_transform=\"multirocket\")\n",
        "\n",
        "# evaluate model\n",
        "pred = cross_val_predict(multi_r, np.array(X), np.array(y), cv=cv, n_jobs=-1)\n",
        "# report performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y = np.array(y)\n",
        "pred = np.array(pred)\n",
        "\n",
        "# Binarize the labels\n",
        "y_binarized = label_binarize(y, classes=np.unique(y))\n",
        "\n",
        "# Ensure pred is a 2D array with one column per class\n",
        "pred = pred.reshape(-1, 1) if pred.ndim == 1 else pred\n",
        "\n",
        "# Binarize the predicted labels\n",
        "pred_binarized = label_binarize(pred, classes=np.unique(y))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_binarized, pred_binarized, multi_class='ovr')\n",
        "\n",
        "print(f'ROC AUC Score: {roc_auc}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "conf_matrix = confusion_matrix(y, pred)\n",
        "\n",
        "class_names = ['Right Up', 'Right Down', 'Left Un.', 'Normal', 'Left Up']\n",
        "\n",
        "\n",
        "# Create a DataFrame from the confusion matrix\n",
        "confusion_df = pd.DataFrame(conf_matrix,\n",
        "                             columns=[f'{class_names[i]}' for i in range(conf_matrix.shape[1])],\n",
        "                             index=[f'{class_names[i]}' for i in range(conf_matrix.shape[0])])\n",
        "\n",
        "\n",
        "# Plot the heatmap\n",
        "heatmap = px.imshow(confusion_df, labels=dict(x=\"Predicted\", y=\"Actual\", color=\"Count\"), text_auto=True)\n",
        "\n",
        "heatmap.update_layout(title='Confusion Matrix',\n",
        "                      xaxis_nticks=5, yaxis_nticks=5,\n",
        "                      font=dict(size=14, family='Courier New, monospace'),\n",
        "                      width=800, height=800)\n",
        "\n",
        "heatmap.show()\n",
        "\n",
        "heatmap.write_html('multirocket.html')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}