tutorial107_7_steps_to_DL.ipynb

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      "name": "tutorial107_7_steps_to_DL.ipynb",
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/bnsreenu/python_for_image_processing_APEER/blob/master/tutorial107_7_steps_to_DL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKjA3kIsCfw3"
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      "source": [
        "https://youtu.be/AxVSKE-Jb0I"
      ]
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    {
      "cell_type": "markdown",
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        "id": "GStqCcwvYMCE"
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      "source": [
        "**7 steps to building your deep learning model**\n",
        "\n",
        "**1. Load data and understand the problem to be solved:** Plot data, is it balanced, do you have enough data, do you need image augmentation, is it large data that doesn't fit memory, etc.\n",
        "\n",
        "**2. Cleanup and preprocess data:** Remove null for structured data, extract features if not provided, select/drop appropriate features, balance classes, scale/normalize data, etc.\n",
        "\n",
        "**3. Get data ready for training:** Reshape data and define X_train, X_test, X_val, y_train, y_test, y_val, define augmentation\n",
        "\n",
        "**4. Define the model:** How many layers, appropriate activation functions, do you need to add dropout, appropriate optimizer, loss, metrics, use transfer learning?\n",
        "\n",
        "**5. Train the model:** Ensure enough validation data for good accuracy checks during training. \n",
        "\n",
        "**6. Re-train, if necessary:** Check accuracy on test data, understand learning curves and continue training the model, if needed. \n",
        "\n",
        "**7. Save the model for future use:**  \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ex9OhzR8XS4d"
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      "source": [
        "\"\"\"\n",
        "\n",
        "Dataset information:\n",
        "https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)\n",
        "## 'data.frame':    569 obs. of  31 variables:\n",
        "##  $ diagnosis              : Factor w/ 2 levels \"Benign\",\"Malignant\": 2 2 2 2 2 2 2 2 2 2 ...\n",
        "##  $ radius_mean            : num  18 20.6 19.7 11.4 20.3 ...\n",
        "##  $ texture_mean           : num  10.4 17.8 21.2 20.4 14.3 ...\n",
        "##  $ perimeter_mean         : num  122.8 132.9 130 77.6 135.1 ...\n",
        "##  $ area_mean              : num  1001 1326 1203 386 1297 ...\n",
        "##  $ smoothness_mean        : num  0.1184 0.0847 0.1096 0.1425 0.1003 ...\n",
        "##  $ compactness_mean       : num  0.2776 0.0786 0.1599 0.2839 0.1328 ...\n",
        "##  $ concavity_mean         : num  0.3001 0.0869 0.1974 0.2414 0.198 ...\n",
        "##  $ concave.points_mean    : num  0.1471 0.0702 0.1279 0.1052 0.1043 ...\n",
        "##  $ symmetry_mean          : num  0.242 0.181 0.207 0.26 0.181 ...\n",
        "##  $ fractal_dimension_mean : num  0.0787 0.0567 0.06 0.0974 0.0588 ...\n",
        "##  $ radius_se              : num  1.095 0.543 0.746 0.496 0.757 ...\n",
        "##  $ texture_se             : num  0.905 0.734 0.787 1.156 0.781 ...\n",
        "##  $ perimeter_se           : num  8.59 3.4 4.58 3.44 5.44 ...\n",
        "##  $ area_se                : num  153.4 74.1 94 27.2 94.4 ...\n",
        "##  $ smoothness_se          : num  0.0064 0.00522 0.00615 0.00911 0.01149 ...\n",
        "##  $ compactness_se         : num  0.049 0.0131 0.0401 0.0746 0.0246 ...\n",
        "##  $ concavity_se           : num  0.0537 0.0186 0.0383 0.0566 0.0569 ...\n",
        "##  $ concave.points_se      : num  0.0159 0.0134 0.0206 0.0187 0.0188 ...\n",
        "##  $ symmetry_se            : num  0.03 0.0139 0.0225 0.0596 0.0176 ...\n",
        "##  $ fractal_dimension_se   : num  0.00619 0.00353 0.00457 0.00921 0.00511 ...\n",
        "##  $ radius_worst           : num  25.4 25 23.6 14.9 22.5 ...\n",
        "##  $ texture_worst          : num  17.3 23.4 25.5 26.5 16.7 ...\n",
        "##  $ perimeter_worst        : num  184.6 158.8 152.5 98.9 152.2 ...\n",
        "##  $ area_worst             : num  2019 1956 1709 568 1575 ...\n",
        "##  $ smoothness_worst       : num  0.162 0.124 0.144 0.21 0.137 ...\n",
        "##  $ compactness_worst      : num  0.666 0.187 0.424 0.866 0.205 ...\n",
        "##  $ concavity_worst        : num  0.712 0.242 0.45 0.687 0.4 ...\n",
        "##  $ concave.points_worst   : num  0.265 0.186 0.243 0.258 0.163 ...\n",
        "##  $ symmetry_worst         : num  0.46 0.275 0.361 0.664 0.236 ...\n",
        "##  $ fractal_dimension_worst: num  0.1189 0.089 0.0876 0.173 0.0768 ...\n",
        "\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RDWadhFlQe1"
      },
      "source": [
        "**1. Load data and understand the problem to be solved:** Plot data, is it balanced, do you have enough data, do you need image augmentation, is it large data that doesn't fit memory, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvcZWwuGYCJO"
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KA-qsHWMYMfm"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/data/wisconsin_breast_cancer_dataset.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFWoZEZXma7_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70150e4f-8c21-4b4e-ae5b-94ce26451076"
      },
      "source": [
        "print(df.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['ID', 'Diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
            "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
            "       'concave.points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
            "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
            "       'compactness_se ', 'concavity_se ', 'concave.points_se', 'symmetry_se',\n",
            "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
            "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
            "       'compactness_worst', 'concavity_worst', 'concave.points_worst',\n",
            "       'symmetry_worst', 'fractal_dimension_worst'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2THPyFpYUtw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58c10752-b007-4055-853b-c59cd3d904f0"
      },
      "source": [
        "print(df.describe().T)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                         count          mean  ...           75%           max\n",
            "ID                       569.0  3.037183e+07  ...  8.813129e+06  9.113205e+08\n",
            "radius_mean              569.0  1.412729e+01  ...  1.578000e+01  2.811000e+01\n",
            "texture_mean             569.0  1.928965e+01  ...  2.180000e+01  3.928000e+01\n",
            "perimeter_mean           569.0  9.196903e+01  ...  1.041000e+02  1.885000e+02\n",
            "area_mean                569.0  6.548891e+02  ...  7.827000e+02  2.501000e+03\n",
            "smoothness_mean          569.0  9.636028e-02  ...  1.053000e-01  1.634000e-01\n",
            "compactness_mean         569.0  1.043410e-01  ...  1.304000e-01  3.454000e-01\n",
            "concavity_mean           569.0  8.879932e-02  ...  1.307000e-01  4.268000e-01\n",
            "concave.points_mean      569.0  4.891915e-02  ...  7.400000e-02  2.012000e-01\n",
            "symmetry_mean            569.0  1.811619e-01  ...  1.957000e-01  3.040000e-01\n",
            "fractal_dimension_mean   569.0  6.279761e-02  ...  6.612000e-02  9.744000e-02\n",
            "radius_se                569.0  4.051721e-01  ...  4.789000e-01  2.873000e+00\n",
            "texture_se               569.0  1.216853e+00  ...  1.474000e+00  4.885000e+00\n",
            "perimeter_se             569.0  2.866059e+00  ...  3.357000e+00  2.198000e+01\n",
            "area_se                  569.0  4.033708e+01  ...  4.519000e+01  5.422000e+02\n",
            "smoothness_se            569.0  7.040979e-03  ...  8.146000e-03  3.113000e-02\n",
            "compactness_se           569.0  2.547814e-02  ...  3.245000e-02  1.354000e-01\n",
            "concavity_se             569.0  3.189372e-02  ...  4.205000e-02  3.960000e-01\n",
            "concave.points_se        569.0  1.179614e-02  ...  1.471000e-02  5.279000e-02\n",
            "symmetry_se              569.0  2.054230e-02  ...  2.348000e-02  7.895000e-02\n",
            "fractal_dimension_se     569.0  3.794904e-03  ...  4.558000e-03  2.984000e-02\n",
            "radius_worst             569.0  1.626919e+01  ...  1.879000e+01  3.604000e+01\n",
            "texture_worst            569.0  2.567722e+01  ...  2.972000e+01  4.954000e+01\n",
            "perimeter_worst          569.0  1.072612e+02  ...  1.254000e+02  2.512000e+02\n",
            "area_worst               569.0  8.805831e+02  ...  1.084000e+03  4.254000e+03\n",
            "smoothness_worst         569.0  1.323686e-01  ...  1.460000e-01  2.226000e-01\n",
            "compactness_worst        569.0  2.542650e-01  ...  3.391000e-01  1.058000e+00\n",
            "concavity_worst          569.0  2.721885e-01  ...  3.829000e-01  1.252000e+00\n",
            "concave.points_worst     569.0  1.146062e-01  ...  1.614000e-01  2.910000e-01\n",
            "symmetry_worst           569.0  2.900756e-01  ...  3.179000e-01  6.638000e-01\n",
            "fractal_dimension_worst  569.0  8.394582e-02  ...  9.208000e-02  2.075000e-01\n",
            "\n",
            "[31 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-ZYTxkGZDdy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "1580b8a4-c8e2-43f7-d5a6-a59c2119736a"
      },
      "source": [
        "#Understand the data \n",
        "sns.countplot(x=\"Diagnosis\", data=df) #M - malignant   B - benign"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d183d6610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uYinIOzZNuwV9J/82570lT8Re/uGRViy1UDimqtoCbJl2HyeiJNuranbafUiH829z8Sy100f7gTVjy6tbTZK0CJZaKHwaWJfk2UmeClwKbJtyT5K0bCyp00dVdTDJa4H/CawArq2qPVNuaznxtJyWKv82F0mqato9SJKWiKV2+kiSNEWGgiSpMxSWuSSV5L+OLZ+UZC7JR6fZlwSQ5FCSnUk+l+QzSX502j2d6JbUhWZNxd8Cz0tyalV9E3gJ3gaspeObVbUBIMlLgd8GfnK6LZ3YPFIQwI3Av2jzlwEfmGIv0pF8H/DgtJs40RkKgtE7pi5NcgrwfOD2KfcjzTu1nT76PPBfgN+cdkMnOk8fiaralWQto6OEG6fbjfQdxk8fvQi4PsnzynvpB+ORguZtA96Bp460RFXVpxi9GG9m2r2cyDxS0Lxrga9V1Z1JLph2M9LhkjyH0ZsOHph2LycyQ0EAVNU+4N3T7kM6zKlJdrb5AJuq6tA0GzrR+ZoLSVLnNQVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaClq2xN3DuaW/hfEOSp7R1s0mmeovuUuhBy4+3pGrZSvJwVT29zT8L+CPg1qp6y3Q7k6bHIwUJqKoDwGbgtRm5YP47JZKcl+RTST6b5JNJfrDVvyfJDUnuSvLhJLcnmW3rHk5yZTsCuS3JWa2+NsnHkuxKcnOSs1v9FUl2t/Efb7XxHn6yHdXsbH08Y/H/K2k5MBSkpqq+yOg1Cs86bNXngZ+oqnOBNwP/odV/GXiwqtYD/x744bFtvhe4rapeAHwc+MVWfw+wtaqeD7yfR58ifzPw0jb+pxdo798CV7SXw/0E8M3H/YtKR2EoSMf2TOBPkuwGrgKe2+o/zui141TVbmDX2DbfBua/vW4HsLbNv4jRaSqA97V9ANwKXJfkFxkF0+FuBd6V5N8Ap1XVwe/yd5IWZChITZJ/BBwCDhy26jeBW6rqecBPAadMsLu/G3u98yGO8Z6xqvol4N8Ba4AdSb7/sPVvB/41cCpwa3s5nPSEMxQkIMkM8J+B31/gXf3P5NGvKP2FsfqtwCVt+/XAD03woz4JXNrm/yXwf9r251TV7VX1ZmCOUTiM93dOVd1ZVb8DfBowFDQIQ0HL2fy3eu0B/hL4C+BtC4z7j8BvJ/ks3/l//H8AzCS5C/gtYA/w0DF+5q8Ar06yC3gl8LpW/90kd7ZTVJ8EPnfYdq9vF6J3AX8H/PnEv6V0HLwlVXqckqwATq6qbyU5h1Gw/GBVfXvKrUmPm9+nID1+3wPckuRkRu/6/2UDQU92HilIkjqvKUiSOkNBktQZCpKkzlCQJHWGgiSp+/+eDLWB4o3S3gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wPg1OZQmFpx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "23dc2431-98d2-42bd-e513-a332e1be0847"
      },
      "source": [
        "fig, ax =plt.subplots(1,3, figsize=(15, 5))\n",
        "sns.histplot(df['radius_mean'], ax=ax[0])\n",
        "sns.histplot(df['area_mean'], ax=ax[1])\n",
        "sns.histplot(df['smoothness_se'], ax=ax[2])\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VolDCF03okML",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "faab42ca-98d0-4127-becd-912f034132b9"
      },
      "source": [
        "#Look for strong correlations so you can drop certain highly correlated features. \n",
        "fig, ax =plt.subplots(2,3, figsize=(15, 10))\n",
        "sns.lineplot(x=df['radius_mean'], y=df['perimeter_mean'], ax=ax[0,0])\n",
        "sns.lineplot(x=df['radius_mean'], y=df['area_mean'], ax=ax[0,1])\n",
        "sns.lineplot(x=df['radius_mean'], y=df['smoothness_mean'], ax=ax[0,2])\n",
        "sns.lineplot(x=df['radius_mean'], y=df['compactness_mean'], ax=ax[1,0])\n",
        "sns.lineplot(x=df['radius_mean'], y=df['concavity_mean'], ax=ax[1,1])\n",
        "sns.lineplot(x=df['radius_mean'], y=df['symmetry_mean'], ax=ax[1,2])\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLT2ghMRqWs_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd8a973a-f888-416f-cc12-9fa0efc7af7a"
      },
      "source": [
        "corr = df.corr()\n",
        "corr_unstacked = corr.unstack()\n",
        "corr_unstacked_sorted = corr_unstacked.sort_values(kind=\"quicksort\", ascending=False)\n",
        "corr_df=pd.Series.to_frame(corr_unstacked_sorted, name='correlation')  #Convert Series to DataFrame and name correlation column accordingly. \n",
        "high_corr_features =  corr_df[corr_df.correlation != 1]  #Remove all 1s that correspond to self correlation\n",
        "print(high_corr_features.head(30))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                 correlation\n",
            "radius_mean     perimeter_mean      0.997855\n",
            "perimeter_mean  radius_mean         0.997855\n",
            "radius_worst    perimeter_worst     0.993708\n",
            "perimeter_worst radius_worst        0.993708\n",
            "area_mean       radius_mean         0.987357\n",
            "radius_mean     area_mean           0.987357\n",
            "perimeter_mean  area_mean           0.986507\n",
            "area_mean       perimeter_mean      0.986507\n",
            "area_worst      radius_worst        0.984015\n",
            "radius_worst    area_worst          0.984015\n",
            "area_worst      perimeter_worst     0.977578\n",
            "perimeter_worst area_worst          0.977578\n",
            "perimeter_se    radius_se           0.972794\n",
            "radius_se       perimeter_se        0.972794\n",
            "perimeter_worst perimeter_mean      0.970387\n",
            "perimeter_mean  perimeter_worst     0.970387\n",
            "radius_worst    radius_mean         0.969539\n",
            "radius_mean     radius_worst        0.969539\n",
            "perimeter_mean  radius_worst        0.969476\n",
            "radius_worst    perimeter_mean      0.969476\n",
            "radius_mean     perimeter_worst     0.965137\n",
            "perimeter_worst radius_mean         0.965137\n",
            "area_mean       radius_worst        0.962746\n",
            "radius_worst    area_mean           0.962746\n",
            "area_mean       area_worst          0.959213\n",
            "area_worst      area_mean           0.959213\n",
            "area_mean       perimeter_worst     0.959120\n",
            "perimeter_worst area_mean           0.959120\n",
            "area_se         radius_se           0.951830\n",
            "radius_se       area_se             0.951830\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xFruSbnWC1b_"
      },
      "source": [
        "**2. Cleanup and preprocess data:** Remove null for structured data, extract features if not provided, select/drop appropriate features, balance classes, scale/normalize data, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yR0W0G3XBR_f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6833cc27-33ba-4b92-9a66-75064c715c52"
      },
      "source": [
        "#Drop features that are highly correlated with another feature. \n",
        "#In this case, let us just use Radius and drop area & perimeter.\n",
        "cols = [c for c in df.columns if c.lower()[:4] != 'area']  #Drop all column names starting with 'area'\n",
        "df=df[cols]  #Update the dataframe with new columns\n",
        "cols = [c for c in df.columns if c.lower()[:9] != 'perimeter'] #Drop all column names starting with 'perimeter'\n",
        "df=df[cols]  #Update the dataframe with new columns\n",
        "print(df.columns)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['ID', 'Diagnosis', 'radius_mean', 'texture_mean', 'smoothness_mean',\n",
            "       'compactness_mean', 'concavity_mean', 'concave.points_mean',\n",
            "       'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se',\n",
            "       'smoothness_se', 'compactness_se ', 'concavity_se ',\n",
            "       'concave.points_se', 'symmetry_se', 'fractal_dimension_se',\n",
            "       'radius_worst', 'texture_worst', 'smoothness_worst',\n",
            "       'compactness_worst', 'concavity_worst', 'concave.points_worst',\n",
            "       'symmetry_worst', 'fractal_dimension_worst'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnjyJZThYqPg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37417ae1-652f-4d1f-f090-6825c1b22bb9"
      },
      "source": [
        "print(df.isnull().sum())\n",
        "#df = df.dropna()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID                         0\n",
            "Diagnosis                  0\n",
            "radius_mean                0\n",
            "texture_mean               0\n",
            "smoothness_mean            0\n",
            "compactness_mean           0\n",
            "concavity_mean             0\n",
            "concave.points_mean        0\n",
            "symmetry_mean              0\n",
            "fractal_dimension_mean     0\n",
            "radius_se                  0\n",
            "texture_se                 0\n",
            "smoothness_se              0\n",
            "compactness_se             0\n",
            "concavity_se               0\n",
            "concave.points_se          0\n",
            "symmetry_se                0\n",
            "fractal_dimension_se       0\n",
            "radius_worst               0\n",
            "texture_worst              0\n",
            "smoothness_worst           0\n",
            "compactness_worst          0\n",
            "concavity_worst            0\n",
            "concave.points_worst       0\n",
            "symmetry_worst             0\n",
            "fractal_dimension_worst    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EUWeZDgY0NA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7da8fc9c-caa5-42aa-e126-509eae3ceba1"
      },
      "source": [
        "#Rename Dataset to Label to make it easy to understand\n",
        "df = df.rename(columns={'Diagnosis':'Label'})\n",
        "print(df.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID                           int64\n",
            "Label                       object\n",
            "radius_mean                float64\n",
            "texture_mean               float64\n",
            "smoothness_mean            float64\n",
            "compactness_mean           float64\n",
            "concavity_mean             float64\n",
            "concave.points_mean        float64\n",
            "symmetry_mean              float64\n",
            "fractal_dimension_mean     float64\n",
            "radius_se                  float64\n",
            "texture_se                 float64\n",
            "smoothness_se              float64\n",
            "compactness_se             float64\n",
            "concavity_se               float64\n",
            "concave.points_se          float64\n",
            "symmetry_se                float64\n",
            "fractal_dimension_se       float64\n",
            "radius_worst               float64\n",
            "texture_worst              float64\n",
            "smoothness_worst           float64\n",
            "compactness_worst          float64\n",
            "concavity_worst            float64\n",
            "concave.points_worst       float64\n",
            "symmetry_worst             float64\n",
            "fractal_dimension_worst    float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHf0pr6mZ-sS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9372bbab-5519-4719-d9d8-329879f7c18a"
      },
      "source": [
        "####### Replace categorical values with numbers########\n",
        "print(\"Distribution of data: \", df['Label'].value_counts())\n",
        "\n",
        "#Define the dependent variable that needs to be predicted (labels)\n",
        "y = df[\"Label\"].values\n",
        "print(\"Labels before encoding are: \", np.unique(y))\n",
        "\n",
        "# Encoding categorical data from text (B and M) to integers (0 and 1)\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder = LabelEncoder()\n",
        "Y = labelencoder.fit_transform(y) # M=1 and B=0\n",
        "print(\"Labels after encoding are: \", np.unique(Y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Distribution of data:  B    357\n",
            "M    212\n",
            "Name: Label, dtype: int64\n",
            "Labels before encoding are:  ['B' 'M']\n",
            "Labels after encoding are:  [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4yc60BVajj9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f070501-2259-4556-f821-0138ce019adb"
      },
      "source": [
        "#Define x and normalize / scale values\n",
        "\n",
        "#Define the independent variables. Drop label and ID, and normalize other data\n",
        "X = df.drop(labels = [\"Label\", \"ID\"], axis=1) \n",
        "print(X.describe().T) #Needs scaling"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                         count       mean  ...        75%       max\n",
            "radius_mean              569.0  14.127292  ...  15.780000  28.11000\n",
            "texture_mean             569.0  19.289649  ...  21.800000  39.28000\n",
            "smoothness_mean          569.0   0.096360  ...   0.105300   0.16340\n",
            "compactness_mean         569.0   0.104341  ...   0.130400   0.34540\n",
            "concavity_mean           569.0   0.088799  ...   0.130700   0.42680\n",
            "concave.points_mean      569.0   0.048919  ...   0.074000   0.20120\n",
            "symmetry_mean            569.0   0.181162  ...   0.195700   0.30400\n",
            "fractal_dimension_mean   569.0   0.062798  ...   0.066120   0.09744\n",
            "radius_se                569.0   0.405172  ...   0.478900   2.87300\n",
            "texture_se               569.0   1.216853  ...   1.474000   4.88500\n",
            "smoothness_se            569.0   0.007041  ...   0.008146   0.03113\n",
            "compactness_se           569.0   0.025478  ...   0.032450   0.13540\n",
            "concavity_se             569.0   0.031894  ...   0.042050   0.39600\n",
            "concave.points_se        569.0   0.011796  ...   0.014710   0.05279\n",
            "symmetry_se              569.0   0.020542  ...   0.023480   0.07895\n",
            "fractal_dimension_se     569.0   0.003795  ...   0.004558   0.02984\n",
            "radius_worst             569.0  16.269190  ...  18.790000  36.04000\n",
            "texture_worst            569.0  25.677223  ...  29.720000  49.54000\n",
            "smoothness_worst         569.0   0.132369  ...   0.146000   0.22260\n",
            "compactness_worst        569.0   0.254265  ...   0.339100   1.05800\n",
            "concavity_worst          569.0   0.272188  ...   0.382900   1.25200\n",
            "concave.points_worst     569.0   0.114606  ...   0.161400   0.29100\n",
            "symmetry_worst           569.0   0.290076  ...   0.317900   0.66380\n",
            "fractal_dimension_worst  569.0   0.083946  ...   0.092080   0.20750\n",
            "\n",
            "[24 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFVtqnTHbCS9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fccd7f0-e480-42f7-83d3-5598fbd6fece"
      },
      "source": [
        "#Scale / normalize the values to bring them to similar range\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X)\n",
        "X = scaler.transform(X)\n",
        "print(X)  #Scaled values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.52103744 0.0226581  0.59375282 ... 0.91202749 0.59846245 0.41886396]\n",
            " [0.64314449 0.27257355 0.28987993 ... 0.63917526 0.23358959 0.22287813]\n",
            " [0.60149557 0.3902604  0.51430893 ... 0.83505155 0.40370589 0.21343303]\n",
            " ...\n",
            " [0.45525108 0.62123774 0.28816467 ... 0.48728522 0.12872068 0.1519087 ]\n",
            " [0.64456434 0.66351031 0.58833619 ... 0.91065292 0.49714173 0.45231536]\n",
            " [0.03686876 0.50152181 0.         ... 0.         0.25744136 0.10068215]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbaE9yw5DIby"
      },
      "source": [
        "**3. Get data ready for training:** Reshape data and define X_train, X_test, X_val, y_train, y_test, y_val, define augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjCYDT5kbUTz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46e66a75-acb6-4a5e-a1ba-fb14dfd27493"
      },
      "source": [
        "#Split data into train and test to verify accuracy after fitting the model. \n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=42)\n",
        "print(\"Shape of training data is: \", X_train.shape)\n",
        "print(\"Shape of testing data is: \", X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of training data is:  (426, 24)\n",
            "Shape of testing data is:  (143, 24)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Tl0Ws1vDT4O"
      },
      "source": [
        "**4. Define the model:** How many layers, appropriate activation functions, do you need to add dropout, appropriate optimizer, loss, metrics, use transfer learning?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJiToUeIbmNx"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5KZ-t-Pb2-F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4120f742-8f08-453e-f6ba-2b3d380f4b1b"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(16, input_dim=X_train.shape[1], activation='relu')) \n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1)) \n",
        "model.add(Activation('sigmoid')) \n",
        " \n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                400       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 417\n",
            "Trainable params: 417\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Az6kAMF_DbQG"
      },
      "source": [
        "**5. Train the model:** Ensure enough validation data for good accuracy checks during training. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NA-F7oq7GLSX"
      },
      "source": [
        "#Add Callbacks, e.g. ModelCheckpoints, earlystopping, csvlogger.\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger\n",
        "\n",
        "#Overwrite the model each time accuracy improves. Saves a lot of space. \n",
        "#filepath=\"/content/drive/MyDrive/Colab Notebooks/saved_models/best_model.hdf5\" #File name includes epoch and validation accuracy.\n",
        "\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1)\n",
        "#This callback will stop the training when there is no improvement in\n",
        "# the validation loss for three consecutive epochs.\n",
        "\n",
        "callbacks_list = [early_stop]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhQev7Jmc3Z2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ae2ec5f-a347-45a0-f490-14edb0e0f9e2"
      },
      "source": [
        "#Fit with early stopping (and other) callback. \n",
        "history = model.fit(X_train, y_train, verbose=1, epochs=20, batch_size=64,\n",
        "                    validation_data=(X_test, y_test), callbacks=callbacks_list)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1810 - accuracy: 0.9390 - val_loss: 0.1423 - val_accuracy: 0.9580\n",
            "Epoch 2/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1843 - accuracy: 0.9366 - val_loss: 0.1408 - val_accuracy: 0.9580\n",
            "Epoch 3/20\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1753 - accuracy: 0.9437 - val_loss: 0.1393 - val_accuracy: 0.9650\n",
            "Epoch 4/20\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1775 - accuracy: 0.9343 - val_loss: 0.1384 - val_accuracy: 0.9650\n",
            "Epoch 5/20\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.1782 - accuracy: 0.9460 - val_loss: 0.1391 - val_accuracy: 0.9580\n",
            "Epoch 6/20\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.1680 - accuracy: 0.9484 - val_loss: 0.1368 - val_accuracy: 0.9580\n",
            "Epoch 7/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1594 - accuracy: 0.9531 - val_loss: 0.1357 - val_accuracy: 0.9580\n",
            "Epoch 8/20\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.1769 - accuracy: 0.9437 - val_loss: 0.1355 - val_accuracy: 0.9580\n",
            "Epoch 9/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1718 - accuracy: 0.9343 - val_loss: 0.1342 - val_accuracy: 0.9580\n",
            "Epoch 10/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1692 - accuracy: 0.9437 - val_loss: 0.1330 - val_accuracy: 0.9650\n",
            "Epoch 11/20\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1651 - accuracy: 0.9484 - val_loss: 0.1314 - val_accuracy: 0.9650\n",
            "Epoch 12/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1701 - accuracy: 0.9460 - val_loss: 0.1300 - val_accuracy: 0.9650\n",
            "Epoch 13/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1594 - accuracy: 0.9484 - val_loss: 0.1300 - val_accuracy: 0.9650\n",
            "Epoch 14/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1648 - accuracy: 0.9437 - val_loss: 0.1289 - val_accuracy: 0.9650\n",
            "Epoch 15/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1566 - accuracy: 0.9507 - val_loss: 0.1283 - val_accuracy: 0.9650\n",
            "Epoch 16/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1679 - accuracy: 0.9507 - val_loss: 0.1278 - val_accuracy: 0.9650\n",
            "Epoch 17/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1633 - accuracy: 0.9507 - val_loss: 0.1273 - val_accuracy: 0.9650\n",
            "Epoch 18/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1671 - accuracy: 0.9413 - val_loss: 0.1255 - val_accuracy: 0.9650\n",
            "Epoch 19/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1619 - accuracy: 0.9484 - val_loss: 0.1259 - val_accuracy: 0.9720\n",
            "Epoch 20/20\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1609 - accuracy: 0.9484 - val_loss: 0.1260 - val_accuracy: 0.9720\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPdKMDMwD7po"
      },
      "source": [
        "**6. Re-train, if necessary:** Check accuracy on test data, understand learning curves and continue training the model, if needed. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUtN6d9keAQn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "b59df2a9-9dc5-49c3-a1da-6ef5cda8d54e"
      },
      "source": [
        "#plot the training and validation accuracy and loss at each epoch\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'y', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "acc = history.history['accuracy']  #Use accuracy if acc doesn't work\n",
        "val_acc = history.history['val_accuracy']  #Use val_accuracy if acc doesn't work\n",
        "plt.plot(epochs, acc, 'y', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "lRYHH_A3eeQB",
        "outputId": "d9dd7746-e357-4ae1-a758-7ecef1564ad6"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "sns.heatmap(cm, annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c7e72a490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtAr-BICEm4X"
      },
      "source": [
        "#Re-train, if necessary. Here we are re-training for extra 50 epochs. \n",
        "#Since we are continuing the training using same model name (model), no need to save and import the model. \n",
        "history = model.fit(X_train, y_train, verbose=1, epochs=50, batch_size=64,\n",
        "                    validation_data=(X_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqLmwzw3EK5z"
      },
      "source": [
        "**7. Save the model for future use:**  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mboz_8DhENFP"
      },
      "source": [
        "model.save('/content/drive/MyDrive/Colab Notebooks/saved_models/tutorial106.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93aw-CdPFFFR"
      },
      "source": [
        "**8. Load model (in future) and use it to predict:**  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5soaCwuE-GF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "03c17ebd-55a1-47a8-d4e3-a9f69ce696eb"
      },
      "source": [
        "# Here, let us apply the loaded model to predict on our test data. \n",
        "from keras.models import load_model\n",
        "my_loaded_model = load_model('/content/drive/MyDrive/Colab Notebooks/saved_models/tutorial106.h5')\n",
        "y_pred_loaded_model = my_loaded_model.predict(X_test)\n",
        "y_pred_loaded_model = (y_pred_loaded_model > 0.5)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred_loaded_model)\n",
        "\n",
        "sns.heatmap(cm, annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c7e612f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}