01_Breast_Cancer_Wisconsin_(Diagnostic)_dataset_and_model.ipynb

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      "authorship_tag": "ABX9TyPK0xlVpHfRjtTC6BXNqAdU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LucasMelvin15/machine_learning-projects/blob/main/01_Breast_Cancer_Wisconsin_(Diagnostic)_dataset_and_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Load data"
      ],
      "metadata": {
        "id": "W7veOErCAdFB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ltmOGMv3T4TT"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "#load data\n",
        "data = load_breast_cancer()\n",
        "X = data.data #features\n",
        "y = data.target #labels\n",
        "feature_names = data.feature_names\n",
        "target_names = data.target_names  # Target names (labels)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the shape\n",
        "print(\"Feature shape:\", X.shape) # 569 samples with 30 features\n",
        "print(\"Labels shape:\", y.shape) #569 labels corresponding to the number of samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9x6yq2_W2i4",
        "outputId": "5b13dfc0-0f58-4c62-8b93-d4236836cf36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature shape: (569, 30)\n",
            "Labels shape: (569,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the first features\n",
        "print(f\"first 5 features: {X[:5]} \")\n",
        "print(f\"first 5 labels\\n:{y} \")\n",
        "print(f\"first 5 feature names\\n:{feature_names[:5]} \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26OpMQJRUln1",
        "outputId": "85247545-f0b8-4f35-a210-7743ecec24be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "first 5 features: [[1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01\n",
            "  1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02\n",
            "  6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01\n",
            "  1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01\n",
            "  4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 1.326e+03 8.474e-02 7.864e-02 8.690e-02\n",
            "  7.017e-02 1.812e-01 5.667e-02 5.435e-01 7.339e-01 3.398e+00 7.408e+01\n",
            "  5.225e-03 1.308e-02 1.860e-02 1.340e-02 1.389e-02 3.532e-03 2.499e+01\n",
            "  2.341e+01 1.588e+02 1.956e+03 1.238e-01 1.866e-01 2.416e-01 1.860e-01\n",
            "  2.750e-01 8.902e-02]\n",
            " [1.969e+01 2.125e+01 1.300e+02 1.203e+03 1.096e-01 1.599e-01 1.974e-01\n",
            "  1.279e-01 2.069e-01 5.999e-02 7.456e-01 7.869e-01 4.585e+00 9.403e+01\n",
            "  6.150e-03 4.006e-02 3.832e-02 2.058e-02 2.250e-02 4.571e-03 2.357e+01\n",
            "  2.553e+01 1.525e+02 1.709e+03 1.444e-01 4.245e-01 4.504e-01 2.430e-01\n",
            "  3.613e-01 8.758e-02]\n",
            " [1.142e+01 2.038e+01 7.758e+01 3.861e+02 1.425e-01 2.839e-01 2.414e-01\n",
            "  1.052e-01 2.597e-01 9.744e-02 4.956e-01 1.156e+00 3.445e+00 2.723e+01\n",
            "  9.110e-03 7.458e-02 5.661e-02 1.867e-02 5.963e-02 9.208e-03 1.491e+01\n",
            "  2.650e+01 9.887e+01 5.677e+02 2.098e-01 8.663e-01 6.869e-01 2.575e-01\n",
            "  6.638e-01 1.730e-01]\n",
            " [2.029e+01 1.434e+01 1.351e+02 1.297e+03 1.003e-01 1.328e-01 1.980e-01\n",
            "  1.043e-01 1.809e-01 5.883e-02 7.572e-01 7.813e-01 5.438e+00 9.444e+01\n",
            "  1.149e-02 2.461e-02 5.688e-02 1.885e-02 1.756e-02 5.115e-03 2.254e+01\n",
            "  1.667e+01 1.522e+02 1.575e+03 1.374e-01 2.050e-01 4.000e-01 1.625e-01\n",
            "  2.364e-01 7.678e-02]] \n",
            "first 5 labels\n",
            ":[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0\n",
            " 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
            " 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
            " 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
            " 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
            " 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
            " 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
            " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
            " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1] \n",
            "first 5 feature names\n",
            ":['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness'] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# explore the dataset\n",
        "print(feature_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qo91cHPZV2N8",
        "outputId": "557d6ae0-7eac-413c-9f7c-a1e67e68278f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
            " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
            " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
            " 'smoothness error' 'compactness error' 'concavity error'\n",
            " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
            " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
            " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
            " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing the data"
      ],
      "metadata": {
        "id": "cwQPSoXxAjDl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make DataFrame of circle data\n",
        "import pandas as pd\n",
        "df = pd.DataFrame({\n",
        "    feature_names[0]: X[:, 0],\n",
        "    feature_names[1]: X[:, 1],\n",
        "    \"Label\": y\n",
        "})\n",
        "df[\"Label\"] = df[\"Label\"].map(lambda x: target_names[x])\n",
        "\n",
        "# Display the first 10 rows of the DataFrame\n",
        "print(df.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gw4D82BMZnuh",
        "outputId": "96984a23-3b8d-4998-d347-3a82d2848d7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   mean radius  mean texture      Label\n",
            "0        17.99         10.38  malignant\n",
            "1        20.57         17.77  malignant\n",
            "2        19.69         21.25  malignant\n",
            "3        11.42         20.38  malignant\n",
            "4        20.29         14.34  malignant\n",
            "5        12.45         15.70  malignant\n",
            "6        18.25         19.98  malignant\n",
            "7        13.71         20.83  malignant\n",
            "8        13.00         21.82  malignant\n",
            "9        12.46         24.04  malignant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(list(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OwrhcgRaLbH",
        "outputId": "88009192-ef14-440b-c9df-552df3b0b871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.target_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUy3tgF2ahQ8",
        "outputId": "2ca5ad51-3229-4997-e738-4f85a6a9f4b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['malignant' 'benign']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create DataFrame with all features\n",
        "df_breast_cancer = pd.DataFrame(X, columns=feature_names)\n",
        "\n",
        "df_breast_cancer[\"Label\"] = y\n",
        "df_breast_cancer[\"Label\"] = df_breast_cancer[\"Label\"].map(lambda x: target_names[x])\n",
        "print(df_breast_cancer.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_Ln68wnbAdv",
        "outputId": "a1c54e29-dfd2-4455-a99a-58ee22bcfe13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
            "0        17.99         10.38          122.80     1001.0          0.11840   \n",
            "1        20.57         17.77          132.90     1326.0          0.08474   \n",
            "2        19.69         21.25          130.00     1203.0          0.10960   \n",
            "3        11.42         20.38           77.58      386.1          0.14250   \n",
            "4        20.29         14.34          135.10     1297.0          0.10030   \n",
            "5        12.45         15.70           82.57      477.1          0.12780   \n",
            "6        18.25         19.98          119.60     1040.0          0.09463   \n",
            "7        13.71         20.83           90.20      577.9          0.11890   \n",
            "8        13.00         21.82           87.50      519.8          0.12730   \n",
            "9        12.46         24.04           83.97      475.9          0.11860   \n",
            "\n",
            "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
            "0           0.27760         0.30010              0.14710         0.2419   \n",
            "1           0.07864         0.08690              0.07017         0.1812   \n",
            "2           0.15990         0.19740              0.12790         0.2069   \n",
            "3           0.28390         0.24140              0.10520         0.2597   \n",
            "4           0.13280         0.19800              0.10430         0.1809   \n",
            "5           0.17000         0.15780              0.08089         0.2087   \n",
            "6           0.10900         0.11270              0.07400         0.1794   \n",
            "7           0.16450         0.09366              0.05985         0.2196   \n",
            "8           0.19320         0.18590              0.09353         0.2350   \n",
            "9           0.23960         0.22730              0.08543         0.2030   \n",
            "\n",
            "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
            "0                 0.07871  ...          17.33           184.60      2019.0   \n",
            "1                 0.05667  ...          23.41           158.80      1956.0   \n",
            "2                 0.05999  ...          25.53           152.50      1709.0   \n",
            "3                 0.09744  ...          26.50            98.87       567.7   \n",
            "4                 0.05883  ...          16.67           152.20      1575.0   \n",
            "5                 0.07613  ...          23.75           103.40       741.6   \n",
            "6                 0.05742  ...          27.66           153.20      1606.0   \n",
            "7                 0.07451  ...          28.14           110.60       897.0   \n",
            "8                 0.07389  ...          30.73           106.20       739.3   \n",
            "9                 0.08243  ...          40.68            97.65       711.4   \n",
            "\n",
            "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
            "0            0.1622             0.6656           0.7119                0.2654   \n",
            "1            0.1238             0.1866           0.2416                0.1860   \n",
            "2            0.1444             0.4245           0.4504                0.2430   \n",
            "3            0.2098             0.8663           0.6869                0.2575   \n",
            "4            0.1374             0.2050           0.4000                0.1625   \n",
            "5            0.1791             0.5249           0.5355                0.1741   \n",
            "6            0.1442             0.2576           0.3784                0.1932   \n",
            "7            0.1654             0.3682           0.2678                0.1556   \n",
            "8            0.1703             0.5401           0.5390                0.2060   \n",
            "9            0.1853             1.0580           1.1050                0.2210   \n",
            "\n",
            "   worst symmetry  worst fractal dimension      Label  \n",
            "0          0.4601                  0.11890  malignant  \n",
            "1          0.2750                  0.08902  malignant  \n",
            "2          0.3613                  0.08758  malignant  \n",
            "3          0.6638                  0.17300  malignant  \n",
            "4          0.2364                  0.07678  malignant  \n",
            "5          0.3985                  0.12440  malignant  \n",
            "6          0.3063                  0.08368  malignant  \n",
            "7          0.3196                  0.11510  malignant  \n",
            "8          0.4378                  0.10720  malignant  \n",
            "9          0.4366                  0.20750  malignant  \n",
            "\n",
            "[10 rows x 31 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the distribution of different labels\n",
        "print(df_breast_cancer[\"Label\"].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYfgmsofrF_G",
        "outputId": "881a08f1-1931-4d6b-fb55-b5ac3e473c57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label\n",
            "benign       357\n",
            "malignant    212\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Check the distribution of different labels\n",
        "label_counts = df_breast_cancer[\"Label\"].value_counts()\n",
        "\n",
        "# Create a bar chart\n",
        "plt.figure(figsize=(8, 6))\n",
        "label_counts.plot(kind='bar', color=['#FF9999', '#66B3FF'])\n",
        "plt.title('Distribution of Breast Cancer Labels')\n",
        "plt.xlabel('Label')\n",
        "plt.ylabel('Count')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "UNya8YcKrWL2",
        "outputId": "e09ff9f7-3bfe-4383-de6b-2128428ff218"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Input shapes and output shapes"
      ],
      "metadata": {
        "id": "NhuOqCC2tHqD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2V8GSFJ9tBBx",
        "outputId": "a7fb7e0c-f54d-40e3-ccd9-84f87b58c7f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((569, 30), (569,))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#view the first feature and labels\n",
        "X_sample = X[0]\n",
        "y_sample = y[0]\n",
        "\n",
        "print(f\"the values for one sample of x: {X_sample} and for y: {y_sample}\")\n",
        "print(f\"the values for one sample of x: {X_sample.shape} and for y: {y_sample.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SaNAho_AtZ_m",
        "outputId": "b254da3c-37e2-4f27-d6b5-1b5fb6bba4a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the values for one sample of x: [1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01\n",
            " 1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02\n",
            " 6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01\n",
            " 1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01\n",
            " 4.601e-01 1.189e-01] and for y: 0\n",
            "the values for one sample of x: (30,) and for y: ()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.1 Turn data into tensors"
      ],
      "metadata": {
        "id": "En0oudetuhnW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn data into tensors\n",
        "\n",
        "import torch\n",
        "X = torch.from_numpy(X).type(torch.float)\n",
        "y = torch.from_numpy(y).type(torch.float)\n",
        "\n",
        "# View the first five samples\n",
        "X[:5], y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujSPRgpKug0F",
        "outputId": "8eef6051-8391-4100-db92-3fbd12c604a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[1.7990e+01, 1.0380e+01, 1.2280e+02, 1.0010e+03, 1.1840e-01, 2.7760e-01,\n",
              "          3.0010e-01, 1.4710e-01, 2.4190e-01, 7.8710e-02, 1.0950e+00, 9.0530e-01,\n",
              "          8.5890e+00, 1.5340e+02, 6.3990e-03, 4.9040e-02, 5.3730e-02, 1.5870e-02,\n",
              "          3.0030e-02, 6.1930e-03, 2.5380e+01, 1.7330e+01, 1.8460e+02, 2.0190e+03,\n",
              "          1.6220e-01, 6.6560e-01, 7.1190e-01, 2.6540e-01, 4.6010e-01, 1.1890e-01],\n",
              "         [2.0570e+01, 1.7770e+01, 1.3290e+02, 1.3260e+03, 8.4740e-02, 7.8640e-02,\n",
              "          8.6900e-02, 7.0170e-02, 1.8120e-01, 5.6670e-02, 5.4350e-01, 7.3390e-01,\n",
              "          3.3980e+00, 7.4080e+01, 5.2250e-03, 1.3080e-02, 1.8600e-02, 1.3400e-02,\n",
              "          1.3890e-02, 3.5320e-03, 2.4990e+01, 2.3410e+01, 1.5880e+02, 1.9560e+03,\n",
              "          1.2380e-01, 1.8660e-01, 2.4160e-01, 1.8600e-01, 2.7500e-01, 8.9020e-02],\n",
              "         [1.9690e+01, 2.1250e+01, 1.3000e+02, 1.2030e+03, 1.0960e-01, 1.5990e-01,\n",
              "          1.9740e-01, 1.2790e-01, 2.0690e-01, 5.9990e-02, 7.4560e-01, 7.8690e-01,\n",
              "          4.5850e+00, 9.4030e+01, 6.1500e-03, 4.0060e-02, 3.8320e-02, 2.0580e-02,\n",
              "          2.2500e-02, 4.5710e-03, 2.3570e+01, 2.5530e+01, 1.5250e+02, 1.7090e+03,\n",
              "          1.4440e-01, 4.2450e-01, 4.5040e-01, 2.4300e-01, 3.6130e-01, 8.7580e-02],\n",
              "         [1.1420e+01, 2.0380e+01, 7.7580e+01, 3.8610e+02, 1.4250e-01, 2.8390e-01,\n",
              "          2.4140e-01, 1.0520e-01, 2.5970e-01, 9.7440e-02, 4.9560e-01, 1.1560e+00,\n",
              "          3.4450e+00, 2.7230e+01, 9.1100e-03, 7.4580e-02, 5.6610e-02, 1.8670e-02,\n",
              "          5.9630e-02, 9.2080e-03, 1.4910e+01, 2.6500e+01, 9.8870e+01, 5.6770e+02,\n",
              "          2.0980e-01, 8.6630e-01, 6.8690e-01, 2.5750e-01, 6.6380e-01, 1.7300e-01],\n",
              "         [2.0290e+01, 1.4340e+01, 1.3510e+02, 1.2970e+03, 1.0030e-01, 1.3280e-01,\n",
              "          1.9800e-01, 1.0430e-01, 1.8090e-01, 5.8830e-02, 7.5720e-01, 7.8130e-01,\n",
              "          5.4380e+00, 9.4440e+01, 1.1490e-02, 2.4610e-02, 5.6880e-02, 1.8850e-02,\n",
              "          1.7560e-02, 5.1150e-03, 2.2540e+01, 1.6670e+01, 1.5220e+02, 1.5750e+03,\n",
              "          1.3740e-01, 2.0500e-01, 4.0000e-01, 1.6250e-01, 2.3640e-01, 7.6780e-02]]),\n",
              " tensor([0., 0., 0., 0., 0.]))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.dtype, y.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfbOu8kkutg3",
        "outputId": "ff3d983e-bbce-49e2-d5c0-9fb618fdb490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.float32, torch.float32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split into training and test sets"
      ],
      "metadata": {
        "id": "9VdarUhOGIVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into train and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y,\n",
        "                                                    test_size=0.2, # 20% test, 80% train\n",
        "                                                    random_state=42) # make the random split reproducible\n",
        "\n",
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdGkPiQ0uv1l",
        "outputId": "5e48b3c5-fe54-403a-f4d2-0bba044fd6b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(455, 114, 455, 114)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "X_train = torch.from_numpy(np.array(X)).type(torch.float) #handle the data and type\n",
        "y_train = torch.from_numpy(np.array(y)).type(torch.float)\n",
        "\n",
        "X_train[:5], y_train[:5]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRo7qX9OJjFY",
        "outputId": "d97213f8-86f7-4f47-eeca-d94b7eaf211c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[1.7990e+01, 1.0380e+01, 1.2280e+02, 1.0010e+03, 1.1840e-01, 2.7760e-01,\n",
              "          3.0010e-01, 1.4710e-01, 2.4190e-01, 7.8710e-02, 1.0950e+00, 9.0530e-01,\n",
              "          8.5890e+00, 1.5340e+02, 6.3990e-03, 4.9040e-02, 5.3730e-02, 1.5870e-02,\n",
              "          3.0030e-02, 6.1930e-03, 2.5380e+01, 1.7330e+01, 1.8460e+02, 2.0190e+03,\n",
              "          1.6220e-01, 6.6560e-01, 7.1190e-01, 2.6540e-01, 4.6010e-01, 1.1890e-01],\n",
              "         [2.0570e+01, 1.7770e+01, 1.3290e+02, 1.3260e+03, 8.4740e-02, 7.8640e-02,\n",
              "          8.6900e-02, 7.0170e-02, 1.8120e-01, 5.6670e-02, 5.4350e-01, 7.3390e-01,\n",
              "          3.3980e+00, 7.4080e+01, 5.2250e-03, 1.3080e-02, 1.8600e-02, 1.3400e-02,\n",
              "          1.3890e-02, 3.5320e-03, 2.4990e+01, 2.3410e+01, 1.5880e+02, 1.9560e+03,\n",
              "          1.2380e-01, 1.8660e-01, 2.4160e-01, 1.8600e-01, 2.7500e-01, 8.9020e-02],\n",
              "         [1.9690e+01, 2.1250e+01, 1.3000e+02, 1.2030e+03, 1.0960e-01, 1.5990e-01,\n",
              "          1.9740e-01, 1.2790e-01, 2.0690e-01, 5.9990e-02, 7.4560e-01, 7.8690e-01,\n",
              "          4.5850e+00, 9.4030e+01, 6.1500e-03, 4.0060e-02, 3.8320e-02, 2.0580e-02,\n",
              "          2.2500e-02, 4.5710e-03, 2.3570e+01, 2.5530e+01, 1.5250e+02, 1.7090e+03,\n",
              "          1.4440e-01, 4.2450e-01, 4.5040e-01, 2.4300e-01, 3.6130e-01, 8.7580e-02],\n",
              "         [1.1420e+01, 2.0380e+01, 7.7580e+01, 3.8610e+02, 1.4250e-01, 2.8390e-01,\n",
              "          2.4140e-01, 1.0520e-01, 2.5970e-01, 9.7440e-02, 4.9560e-01, 1.1560e+00,\n",
              "          3.4450e+00, 2.7230e+01, 9.1100e-03, 7.4580e-02, 5.6610e-02, 1.8670e-02,\n",
              "          5.9630e-02, 9.2080e-03, 1.4910e+01, 2.6500e+01, 9.8870e+01, 5.6770e+02,\n",
              "          2.0980e-01, 8.6630e-01, 6.8690e-01, 2.5750e-01, 6.6380e-01, 1.7300e-01],\n",
              "         [2.0290e+01, 1.4340e+01, 1.3510e+02, 1.2970e+03, 1.0030e-01, 1.3280e-01,\n",
              "          1.9800e-01, 1.0430e-01, 1.8090e-01, 5.8830e-02, 7.5720e-01, 7.8130e-01,\n",
              "          5.4380e+00, 9.4440e+01, 1.1490e-02, 2.4610e-02, 5.6880e-02, 1.8850e-02,\n",
              "          1.7560e-02, 5.1150e-03, 2.2540e+01, 1.6670e+01, 1.5220e+02, 1.5750e+03,\n",
              "          1.3740e-01, 2.0500e-01, 4.0000e-01, 1.6250e-01, 2.3640e-01, 7.6780e-02]]),\n",
              " tensor([0., 0., 0., 0., 0.]))"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.dtype, y_train.dtype,X_test.dtype, y_test.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xVjdgXALs4y",
        "outputId": "3a129acc-9161-4571-e718-7468b6fb8a7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.float32, torch.float32, torch.float32, torch.float32)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_train), type(y_train), type(X_test), type(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrbTyZiuL6A3",
        "outputId": "05deefa5-fa46-41cb-f41a-5cb5a751fb2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Building a model"
      ],
      "metadata": {
        "id": "wwkRgETKu_pE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# set up device agnostic code\n",
        "device = \"cuda\" if torch.cuda.is_available() else 'cpu'\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "9BoXl7DEu70n",
        "outputId": "7f0b4526-f43d-4f18-fd38-1e31366c3084"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cpu'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Construct a model class that subclasses nn.Module\n",
        "class BreastCancerModel0(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        # 2. Create 2 nn.Linear layers capable of handling X and y input and output shapes\n",
        "        self.layer_1 = nn.Linear(in_features=30, out_features=5) # takes in 2 features (X), produces 5 features\n",
        "        self.layer_2 = nn.Linear(in_features=5, out_features=1) # takes in 5 features, produces 1 feature (y)\n",
        "\n",
        "    # 3. Define a forward method containing the forward pass computation\n",
        "    def forward(self, x):\n",
        "       return self.layer_2(self.layer_1(x))\n"
      ],
      "metadata": {
        "id": "Zo66ONAgvmPX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Create an instance of the model and send it to target device\n",
        "model_0 = BreastCancerModel0().to(device)\n",
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnTYy8BCM4vK",
        "outputId": "ccd208d8-e7c3-4c81-f954-19c54f81d6e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BreastCancerModel0(\n",
              "  (layer_1): Linear(in_features=30, out_features=5, bias=True)\n",
              "  (layer_2): Linear(in_features=5, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afrTCT6xNi5b",
        "outputId": "c206c226-a404-4775-c1da-4cefcdff539c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('layer_1.weight',\n",
              "              tensor([[ 0.1396,  0.1515, -0.0428,  0.1677, -0.0400,  0.0368, -0.0889,  0.1072,\n",
              "                        0.1609, -0.1339,  0.1587,  0.0342,  0.1349,  0.0247,  0.0880, -0.0258,\n",
              "                        0.1407,  0.0270, -0.0852,  0.0465, -0.0841, -0.0214, -0.0742,  0.1211,\n",
              "                       -0.1441, -0.0842, -0.0516, -0.1098,  0.0172, -0.1803],\n",
              "                      [ 0.1649, -0.1551,  0.1410,  0.0304, -0.0593,  0.1128,  0.0285,  0.1475,\n",
              "                        0.0200, -0.0576,  0.0491, -0.0495,  0.0768,  0.1630,  0.1055, -0.0798,\n",
              "                        0.1054,  0.0327,  0.0927, -0.1113, -0.1807, -0.0705, -0.1400,  0.1498,\n",
              "                        0.0526,  0.0756,  0.0577, -0.0032,  0.1429, -0.1297],\n",
              "                      [ 0.0115, -0.1246,  0.0563, -0.0629,  0.0559, -0.0380,  0.1514, -0.1082,\n",
              "                       -0.1089, -0.1089,  0.1642,  0.0608,  0.1757, -0.1507, -0.1811, -0.1428,\n",
              "                       -0.1228,  0.0739,  0.0654,  0.1517, -0.0943, -0.1245,  0.0969, -0.0738,\n",
              "                        0.1108, -0.0433,  0.1044, -0.1419, -0.0921,  0.0557],\n",
              "                      [ 0.0386, -0.0465,  0.1088,  0.1241, -0.1324, -0.0975,  0.1672, -0.0616,\n",
              "                       -0.0647, -0.1767, -0.1046,  0.0456, -0.0241, -0.1325,  0.0043, -0.1247,\n",
              "                       -0.1549, -0.1005, -0.1598, -0.1163,  0.1825,  0.0345,  0.0563, -0.1703,\n",
              "                       -0.1199, -0.0608,  0.0285, -0.1607, -0.0787, -0.1093],\n",
              "                      [ 0.0005, -0.0679, -0.0127, -0.1237, -0.1253, -0.1065, -0.0625, -0.1441,\n",
              "                        0.1531, -0.0362,  0.1571,  0.0569, -0.1546,  0.1263, -0.0502, -0.0700,\n",
              "                       -0.1515, -0.1815,  0.0522, -0.0399,  0.0711, -0.1498,  0.1355, -0.1340,\n",
              "                       -0.0315,  0.0381,  0.0943,  0.1474,  0.1663, -0.1448]])),\n",
              "             ('layer_1.bias',\n",
              "              tensor([ 0.0459, -0.0785, -0.0200, -0.1367,  0.1663])),\n",
              "             ('layer_2.weight',\n",
              "              tensor([[-0.3282,  0.2390,  0.1572,  0.1453, -0.2418]])),\n",
              "             ('layer_2.bias', tensor([0.4065]))])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make predictions\n",
        "with an untrained model"
      ],
      "metadata": {
        "id": "RUpdS6btBdf6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Make predictions with the model\n",
        "\n",
        "with torch.inference_mode():\n",
        "  untrained_preds = model_0(X_test)\n",
        "\n",
        "print(f\"Length of predictions: {len(untrained_preds)}, Shape: {untrained_preds.shape}\")\n",
        "print(f\"Length of test samples: {len(X_test)}, Shape: {X_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WrHmMR73v1Ut",
        "outputId": "e973ed95-ccd6-4b91-9867-389ad5955e6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of predictions: 114, Shape: torch.Size([114, 1])\n",
            "Length of test samples: 114, Shape: torch.Size([114, 30])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"first 10 predictions: \\n{torch.round(untrained_preds[:10])}\")\n",
        "print(f\"first 10 labels: \\n{y_test[:10]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20BjLLbFN1EU",
        "outputId": "4cf9898e-feb4-44f4-8ff7-608ce6ca26de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "first 10 predictions: \n",
            "tensor([[ -8.],\n",
            "        [-25.],\n",
            "        [-15.],\n",
            "        [ -6.],\n",
            "        [ -5.],\n",
            "        [-26.],\n",
            "        [-32.],\n",
            "        [-17.],\n",
            "        [ -7.],\n",
            "        [ -9.]])\n",
            "first 10 labels: \n",
            "tensor([1., 0., 0., 1., 1., 0., 0., 0., 1., 1.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## set up a loss function and optimizer for training"
      ],
      "metadata": {
        "id": "WCuFbLjuBwTR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up a loss function and optimizer\n",
        "\n",
        "# Create a loss function\n",
        "loss_fn = nn.BCEWithLogitsLoss()\n",
        "\n",
        "# Create an optimizer\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(),\n",
        "                            lr=0.001)"
      ],
      "metadata": {
        "id": "nwutOLVYx0X0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an evaluation metric to calculate accuracy\n",
        "#compares the predictions to ground truth labels\n",
        "def accuracy_fn(y_true, y_pred):\n",
        "  correct = torch.eq(y_true, y_pred).sum().item()\n",
        "  acc = (correct/len(y_pred)) * 100\n",
        "  return acc"
      ],
      "metadata": {
        "id": "U1a0Q08ROrND"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make predictions on an untrained model"
      ],
      "metadata": {
        "id": "8_POp7NiPrMX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#view the first outputs of the forward pass on the test data\n",
        "model_0.eval()\n",
        "with  torch.inference_mode():\n",
        "  y_logits = model_0(X_test)[:5]\n",
        "y_logits"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qABai-tCN-tz",
        "outputId": "105d694d-6fdd-47b9-913f-9ff1d4154367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-1.5092e-02],\n",
              "        [-1.7502e+01],\n",
              "        [-6.3829e+00],\n",
              "        [ 4.5275e+00],\n",
              "        [ 3.7352e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwHaSOn0OHME",
        "outputId": "10553c23-fcc6-45bc-ff0a-ace8d96b7e9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1., 0., 0., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_probs = torch.sigmoid(y_logits)\n",
        "y_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nse4wiujOOet",
        "outputId": "f9c04c33-8b0f-4c15-a335-46f3405ae950"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[4.9623e-01],\n",
              "        [2.5062e-08],\n",
              "        [1.6874e-03],\n",
              "        [9.8931e-01],\n",
              "        [9.7669e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find the predicted labels\n",
        "y_preds = torch.round(y_pred_probs)\n",
        "\n",
        "#in full (logits -> predicted probabilities -> predicted lables)\n",
        "y_pred_labels = torch.round(torch.sigmoid(model_0(X_test)[:5]))\n",
        "\n",
        "#check for equality\n",
        "print(torch.eq(y_preds.squeeze(), y_pred_labels.squeeze()))\n",
        "\n",
        "#squeeze removes all dimensions of size 1\n",
        "y_preds.squeeze()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1365-xOOTmT",
        "outputId": "4d3b9418-bcef-4455-c8bc-1c942d2a51e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([True, True, True, True, True])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0., 0., 0., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.Create testing and training loops"
      ],
      "metadata": {
        "id": "uGu08C5YyrYP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#set a random seed\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# set the number of epochs\n",
        "epochs = 300\n",
        "\n",
        "#Building a training loop\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  ### Training\n",
        "  model_0.train()\n",
        "\n",
        "  ###1. Forward pass\n",
        "  #model outputs raw logits\n",
        "  y_logits = model_0(X_train).squeeze()\n",
        "  y_preds = torch.round(torch.sigmoid(y_logits))\n",
        "\n",
        "  ### 2. calculate the loss\n",
        "  loss = loss_fn(y_logits,y_train) # nn.BCEWithLogitsLoss expects raw logits as input\n",
        "\n",
        "  acc = accuracy_fn(y_true=y_train,\n",
        "                     y_pred=y_preds)\n",
        "\n",
        "  ### 3.optimizer zero grad\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  ### 4. Backpropagation\n",
        "  loss.backward()\n",
        "\n",
        "  ### 5.optimizer step\n",
        "  optimizer.step()\n",
        "\n",
        "  ####testing\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    # 1. Forward pass\n",
        "    test_logits = model_0(X_test).squeeze()\n",
        "    test_preds = torch.round(torch.sigmoid(test_logits))\n",
        "\n",
        "    # calculate loss/acc\n",
        "    test_loss = loss_fn(test_logits, y_test)\n",
        "    test_acc = accuracy_fn(y_true=y_test,\n",
        "                           y_pred=test_preds)\n",
        "\n",
        "    ##Print whats happening every 10 epochs\n",
        "    if epoch % 20 == 0:\n",
        "      print(f\"Epoch:{epoch} | Loss: {loss: .5f} | Accuracy: {acc: .2f}% | Test loss:{test_loss: .5f} | Test acc:{test_acc: .2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_f9RlS7ObQ6",
        "outputId": "e48a9586-d2c4-4c9c-d571-3003dce07d53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0 | Loss:  0.19730 | Accuracy:  92.27% | Test loss: 0.12290 | Test acc: 96.49%\n",
            "Epoch:20 | Loss:  0.19662 | Accuracy:  92.27% | Test loss: 0.12146 | Test acc: 96.49%\n",
            "Epoch:40 | Loss:  0.19603 | Accuracy:  92.27% | Test loss: 0.12022 | Test acc: 96.49%\n",
            "Epoch:60 | Loss:  0.19552 | Accuracy:  92.44% | Test loss: 0.11916 | Test acc: 97.37%\n",
            "Epoch:80 | Loss:  0.19507 | Accuracy:  92.44% | Test loss: 0.11823 | Test acc: 97.37%\n",
            "Epoch:100 | Loss:  0.19466 | Accuracy:  92.44% | Test loss: 0.11742 | Test acc: 97.37%\n",
            "Epoch:120 | Loss:  0.19430 | Accuracy:  92.44% | Test loss: 0.11670 | Test acc: 97.37%\n",
            "Epoch:140 | Loss:  0.19397 | Accuracy:  92.44% | Test loss: 0.11607 | Test acc: 97.37%\n",
            "Epoch:160 | Loss:  0.19366 | Accuracy:  92.44% | Test loss: 0.11551 | Test acc: 97.37%\n",
            "Epoch:180 | Loss:  0.19356 | Accuracy:  92.27% | Test loss: 0.11532 | Test acc: 97.37%\n",
            "Epoch:200 | Loss:  0.28215 | Accuracy:  88.75% | Test loss: 0.23558 | Test acc: 91.23%\n",
            "Epoch:220 | Loss:  0.19959 | Accuracy:  92.27% | Test loss: 0.12779 | Test acc: 97.37%\n",
            "Epoch:240 | Loss:  0.19741 | Accuracy:  92.62% | Test loss: 0.12493 | Test acc: 97.37%\n",
            "Epoch:260 | Loss:  0.19655 | Accuracy:  92.44% | Test loss: 0.12298 | Test acc: 97.37%\n",
            "Epoch:280 | Loss:  0.19585 | Accuracy:  92.44% | Test loss: 0.12135 | Test acc: 97.37%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model evaluation"
      ],
      "metadata": {
        "id": "FJQxZLpwPloB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# Define the updated accuracy function\n",
        "def accuracy_fn(y_true, y_pred):\n",
        "    correct = torch.eq(y_true, y_pred).sum().item()\n",
        "    acc = (correct / len(y_pred)) * 100\n",
        "    return acc\n",
        "\n",
        "# Ensure model is in evaluation mode\n",
        "model_0.eval()\n",
        "\n",
        "# No gradient calculation needed for evaluation\n",
        "with torch.no_grad():\n",
        "    # Forward pass on the test data\n",
        "    test_logits = model_0(X_test).squeeze()\n",
        "    test_preds = torch.round(torch.sigmoid(test_logits))\n",
        "\n",
        "    # Calculate test loss\n",
        "    test_loss = loss_fn(test_logits, y_test)\n",
        "\n",
        "    # Calculate test accuracy\n",
        "    test_acc = accuracy_fn(y_true=y_test, y_pred=test_preds)\n",
        "\n",
        "    # Print evaluation results\n",
        "    print(f\"Test Loss: {test_loss:.5f}\")\n",
        "    print(f\"Test Accuracy: {test_acc:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6G-rQx2Ph50",
        "outputId": "126bd5f7-8189-4401-b51b-2bec397967bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.12005\n",
            "Test Accuracy: 97.37%\n"
          ]
        }
      ]
    }
  ]
}