NOTEBOOK_SERIES.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "OgRKeVD6SyOP"
      },
      "source": [
        "## Nosso dataset :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcyLWU0D8lIa",
        "outputId": "a7a1fb26-026f-4651-f03e-403b74bf7c7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-12-05 14:35:27--  https://github.com/otaviofcoletti/Sara_dataset/raw/main/Sara_dataset_direito.zip\n",
            "Resolvendo github.com (github.com)... 20.201.28.151\n",
            "Conectando-se a github.com (github.com)|20.201.28.151|:443... conectado.\n",
            "A requisição HTTP foi enviada, aguardando resposta... 302 Found\n",
            "Localização: https://raw.githubusercontent.com/otaviofcoletti/Sara_dataset/main/Sara_dataset_direito.zip [redirecionando]\n",
            "--2023-12-05 14:35:29--  https://raw.githubusercontent.com/otaviofcoletti/Sara_dataset/main/Sara_dataset_direito.zip\n",
            "Resolvendo raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8003::154, 2606:50c0:8001::154, ...\n",
            "Conectando-se a raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... conectado.\n",
            "A requisição HTTP foi enviada, aguardando resposta... 200 OK\n",
            "Tamanho: 4760818 (4,5M) [application/zip]\n",
            "Salvando em: ‘Sara_dataset_direito.zip.1’\n",
            "\n",
            "Sara_dataset_direit 100%[===================>]   4,54M  19,2MB/s    em 0,2s    \n",
            "\n",
            "2023-12-05 14:35:29 (19,2 MB/s) - ‘Sara_dataset_direito.zip.1’ salvo [4760818/4760818]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://github.com/otaviofcoletti/Sara_dataset/raw/main/Sara_dataset_direito.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "uBHQ1iKU7tQK"
      },
      "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"
          ]
        }
      ],
      "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",
        "\n",
        "import plotly.express as px\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "from aeon.classification.feature_based import Catch22Classifier\n",
        "from aeon.classification.hybrid import HIVECOTEV2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "RuTWzhNE7tQO"
      },
      "outputs": [],
      "source": [
        "lista_de_arquivos = os.listdir(\"Sara_dataset_direito\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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": 7,
      "metadata": {
        "id": "6877hkti7tQO"
      },
      "outputs": [],
      "source": [
        "label_map = {\n",
        "        \"Situação referencia index\": 'Normal',\n",
        "        \"Situação rolo 3 da esquerda levantado index\": 'Left Up',\n",
        "        \"Situação rolo 3 da esquerda removido index\": 'Left Un.',\n",
        "        \"Situação rolo 3 da direita levantado index\": 'Right Up',\n",
        "        \"Situação rolo 3 da direita removido index\": 'Right Down',\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def roc_score(y_teste, predictions):\n",
        "    \n",
        "    predictions = np.array(predictions)\n",
        "    y_teste = np.array(y_teste)\n",
        "\n",
        "    # Binarize the labels\n",
        "    y_binarized = label_binarize(y_teste, classes=np.unique(y_teste))\n",
        "\n",
        "    # Ensure pred is a 2D array with one column per class\n",
        "    predictions = predictions.reshape(-1, 1) if predictions.ndim == 1 else predictions\n",
        "\n",
        "    # Binarize the predicted labels\n",
        "    pred_binarized = label_binarize(predictions, classes=np.unique(y_teste))\n",
        "\n",
        "    # Calculate ROC AUC score\n",
        "    roc_auc = roc_auc_score(y_binarized, pred_binarized, multi_class='ovr')\n",
        "\n",
        "    return roc_auc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_cm(y_teste, predictions, file_name):\n",
        "    conf_matrix = confusion_matrix(y_teste, predictions)\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(f\"{file_name}.jpeg\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "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",
        "            casos.append(features)\n",
        "\n",
        "X = np.array(casos)\n",
        "y = np.array(labels)\n",
        "\n",
        "\n",
        "X = np.array(casos).reshape(len(casos), -1)\n",
        "\n",
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cf4Rn1597tQZ"
      },
      "source": [
        "# Random Forest (dados brutos)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Initialize and train the Random Forest model\n",
        "random_forest = RandomForestClassifier()\n",
        "random_forest.fit(X_treino, y_treino)\n",
        "predictions = random_forest.predict(X_teste)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7435897435897436\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_teste, predictions)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
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                    "scene": {
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                    "shapedefaults": {
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                    "ternary": {
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                    "title": {
                      "x": 0.05
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                    "xaxis": {
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                      "ticks": "",
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                      "ticks": "",
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                },
                "title": {
                  "text": "Confusion Matrix"
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                "width": 800,
                "xaxis": {
                  "anchor": "y",
                  "constrain": "domain",
                  "domain": [
                    0,
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                  "nticks": 5,
                  "scaleanchor": "y",
                  "title": {
                    "text": "Predicted"
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                },
                "yaxis": {
                  "anchor": "x",
                  "autorange": "reversed",
                  "constrain": "domain",
                  "domain": [
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                  "nticks": 5,
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                  }
                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "plot_cm(y_teste, predictions,'random_forest')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.860995273394467\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "print(f'ROC AUC Score: {roc_score(y_teste, predictions)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hszspNHC7tQX"
      },
      "source": [
        "# Catch22 + Isolation Forest 2 classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "-O4pNiYZgSBe"
      },
      "outputs": [],
      "source": [
        "from aeon.transformations.collection.catch22 import Catch22"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "clf = Catch22Classifier()\n",
        "\n",
        "clf.fit(X_treino, y_treino)\n",
        "predictions = clf.predict(X_teste)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7435897435897436\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_teste, predictions)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.8691392127884064\n"
          ]
        }
      ],
      "source": [
        "print(f'ROC AUC Score: {roc_score(y_teste, predictions)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
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      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# HIVECOTE"
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      "execution_count": 21,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create model\n",
        "hc2 = HIVECOTEV2(time_limit_in_minutes=0.2)\n",
        "\n",
        "hc2.fit(X_treino, y_treino)\n",
        "predictions = hc2.predict(X_teste)\n",
        "\n"
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    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
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          "name": "stdout",
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          "text": [
            "Accuracy: 0.7435897435897436\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_teste, predictions)\n",
        "print(\"Accuracy:\", accuracy)"
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    },
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                "xaxis": {
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                  "constrain": "domain",
                  "domain": [
                    0,
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                  "scaleanchor": "y",
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                    "text": "Predicted"
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                },
                "yaxis": {
                  "anchor": "x",
                  "autorange": "reversed",
                  "constrain": "domain",
                  "domain": [
                    0,
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                    "text": "Actual"
                  }
                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "plot_cm(y_teste, predictions,'hivecote2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.8433501683501683\n"
          ]
        }
      ],
      "source": [
        "print(f'ROC AUC Score: {roc_score(y_teste, predictions)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Time Series Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pyts.datasets import load_gunpoint\n",
        "from pyts.classification import TimeSeriesForest\n",
        "\n",
        "clf = TimeSeriesForest(random_state=1)\n",
        "clf.fit(X_treino, y_treino)\n",
        "predictions = clf.predict(X_teste)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8205128205128205\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_teste, predictions)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.9037878787878789\n"
          ]
        }
      ],
      "source": [
        "print(f'ROC AUC Score: {roc_score(y_teste, predictions)}')"
      ]
    },
    {
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      "execution_count": 28,
      "metadata": {},
      "outputs": [
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        "plot_cm(y_teste, predictions,'ts_forest')\n"
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    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Multirocket Arsenal "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "multi_r = Arsenal(rocket_transform=\"multirocket\")\n",
        "multi_r.fit(X_treino, y_treino)\n",
        "predictions = multi_r.predict(X_teste)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9230769230769231\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_teste, predictions)\n",
        "print(\"Accuracy:\", accuracy)"
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    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.965909090909091\n"
          ]
        }
      ],
      "source": [
        "print(f'ROC AUC Score: {roc_score(y_teste, predictions)}')"
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    },
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      "source": [
        "plot_cm(y_teste, predictions,'multirocket')"
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    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# MiniRocket Arsenal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [],
      "source": [
        "multi_r = Arsenal(rocket_transform=\"minirocket\")\n",
        "multi_r.fit(X_treino, y_treino)\n",
        "predictions = multi_r.predict(X_teste)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8974358974358975\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_teste, predictions)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.9545454545454545\n"
          ]
        }
      ],
      "source": [
        "print(f'ROC AUC Score: {roc_score(y_teste, predictions)}')"
      ]
    },
    {
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      "source": [
        "plot_cm(y_teste, predictions,'minirocket')"
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    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Cross Val Mini rocket"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "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"
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      ],
      "source": [
        "from sklearn.model_selection import cross_val_predict, KFold\n",
        "from sklearn.metrics import r2_score\n",
        "import numpy as np\n",
        "\n",
        "# Assuming X and y are your feature and target matrices, respectively\n",
        "\n",
        "# Set the number of folds (e.g., k=5 for 5-fold cross-validation)\n",
        "k_folds = 2\n",
        "\n",
        "# Create KFold cross-validator\n",
        "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "# Create Arsenal model with multirocket transform\n",
        "multi_r = Arsenal(rocket_transform=\"minirocket\")\n",
        "\n",
        "# Perform k-fold cross-validation\n",
        "pred = cross_val_predict(multi_r, np.array(X), np.array(y), cv=kf, n_jobs=-1)\n",
        "\n"
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    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8589743589743589\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.9101907113705184\n"
          ]
        }
      ],
      "source": [
        "print(f'ROC AUC Score: {roc_score(y, pred)}')"
      ]
    },
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      "source": [
        "plot_cm(y, pred,'minirocket_cross_val')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Cross Val MultiRocket"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "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"
          ]
        }
      ],
      "source": [
        "\n",
        "from sklearn.model_selection import cross_val_predict, KFold\n",
        "from sklearn.metrics import r2_score\n",
        "import numpy as np\n",
        "\n",
        "# Assuming X and y are your feature and target matrices, respectively\n",
        "\n",
        "# Set the number of folds (e.g., k=5 for 5-fold cross-validation)\n",
        "k_folds = 10\n",
        "\n",
        "# Create KFold cross-validator\n",
        "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "# Create Arsenal model with multirocket transform\n",
        "multi_r = Arsenal(rocket_transform=\"multirocket\")\n",
        "\n",
        "# Perform k-fold cross-validation\n",
        "pred = cross_val_predict(multi_r, np.array(X), np.array(y), cv=kf, n_jobs=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9166666666666666\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y, pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.9468363427921\n"
          ]
        }
      ],
      "source": [
        "print(f'ROC AUC Score: {roc_score(y, pred)}')"
      ]
    },
    {
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