roc_auc.ipynb

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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/npradaschnor/Pima-Indians-Diabetes-Dataset/master/diabetes.csv')\n",
        "\n",
        "data.head()"
      ],
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('Outcome', axis=1)\n",
        "y = data['Outcome']\n"
      ],
      "metadata": {
        "id": "5U1KE9VT2Qs9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)\n"
      ],
      "metadata": {
        "id": "2K8z56hz2UQz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "I9u5sqY12Wda",
        "outputId": "7ecd8ca3-ddcc-476d-87db-a800fbae3f0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_scores = model.predict_proba(X_test)[:,1]\n"
      ],
      "metadata": {
        "id": "VhhVdDoQ2Ycy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "js_2UcRE8BpL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eK3Pq4MH2afn",
        "outputId": "686df575-605f-45c7-ca04-21054b1fbc4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.2758503 , 0.18842069, 0.11448981, 0.16368795, 0.4715251 ,\n",
              "       0.44170194, 0.01545458, 0.6605503 , 0.54136875, 0.77717331,\n",
              "       0.25601769, 0.89728056, 0.33614441, 0.30308996, 0.08199125,\n",
              "       0.38084702, 0.13934778, 0.07482402, 0.86665798, 0.5613247 ,\n",
              "       0.20835836, 0.07736815, 0.53982239, 0.09456509, 0.53967271,\n",
              "       0.88554708, 0.1243389 , 0.03014779, 0.25152025, 0.11579709,\n",
              "       0.91109793, 0.8706549 , 0.76531008, 0.83743011, 0.6165898 ,\n",
              "       0.68350024, 0.96806648, 0.24353318, 0.51154452, 0.73599347,\n",
              "       0.06983745, 0.59378797, 0.58351287, 0.32753119, 0.02760688,\n",
              "       0.50239856, 0.63926574, 0.22539934, 0.36059905, 0.95624662,\n",
              "       0.04890383, 0.66102112, 0.81174894, 0.24555107, 0.09320651,\n",
              "       0.04152459, 0.77799846, 0.00570139, 0.40866977, 0.75690546,\n",
              "       0.7413723 , 0.35188999, 0.19230059, 0.20514197, 0.0768586 ,\n",
              "       0.6270294 , 0.050907  , 0.73293779, 0.03690838, 0.71583657,\n",
              "       0.67525971, 0.07016837, 0.18115086, 0.11426867, 0.09119426,\n",
              "       0.51835688, 0.16355232, 0.13695503, 0.13173048, 0.2341894 ,\n",
              "       0.6556617 , 0.1468054 , 0.06144124, 0.37496999, 0.25806706,\n",
              "       0.83587578, 0.90265268, 0.30266343, 0.12370914, 0.08538068,\n",
              "       0.06547828, 0.23718831, 0.00417029, 0.55038102, 0.51677531,\n",
              "       0.64959804, 0.36889176, 0.12823988, 0.60576647, 0.08216269,\n",
              "       0.72400009, 0.06248435, 0.77818672, 0.50287874, 0.64210666,\n",
              "       0.21914482, 0.25727444, 0.73553291, 0.12705332, 0.53432103,\n",
              "       0.0987172 , 0.31806545, 0.01994664, 0.73542507, 0.17451194,\n",
              "       0.3389918 , 0.77362628, 0.21918291, 0.06360321, 0.58429026,\n",
              "       0.0585205 , 0.29639387, 0.2408059 , 0.07700151, 0.26486643,\n",
              "       0.42037419, 0.0404956 , 0.87107584, 0.97118111, 0.72966335,\n",
              "       0.70244958, 0.85198486, 0.08457216, 0.42031934, 0.81228644,\n",
              "       0.11171403, 0.15441586, 0.85444161, 0.80094282, 0.01302516,\n",
              "       0.0948891 , 0.03977236, 0.20215332, 0.37668596, 0.12403437,\n",
              "       0.29125775, 0.14051793, 0.02145003, 0.43005226, 0.75090476,\n",
              "       0.11865543, 0.48561671, 0.22505638, 0.2000772 ])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_scores)"
      ],
      "metadata": {
        "id": "ATYHy-5i2eoC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "thresholds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQFx782_7UcR",
        "outputId": "f19b5aa9-4e10-4b3d-d151-5c09f9c3d35d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.97118111, 0.97118111, 0.96806648, 0.95624662, 0.90265268,\n",
              "       0.89728056, 0.77818672, 0.77717331, 0.73293779, 0.72400009,\n",
              "       0.68350024, 0.67525971, 0.66102112, 0.6605503 , 0.6556617 ,\n",
              "       0.64959804, 0.63926574, 0.6165898 , 0.60576647, 0.58351287,\n",
              "       0.55038102, 0.53967271, 0.53432103, 0.50287874, 0.50239856,\n",
              "       0.42037419, 0.42031934, 0.40866977, 0.38084702, 0.3389918 ,\n",
              "       0.32753119, 0.30308996, 0.30266343, 0.25727444, 0.2408059 ,\n",
              "       0.22539934, 0.22505638, 0.20835836, 0.20514197, 0.16355232,\n",
              "       0.14051793, 0.12823988, 0.12705332, 0.11448981, 0.11426867,\n",
              "       0.04152459, 0.0404956 , 0.00417029])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# Generate a trace for ROC curve\n",
        "trace0 = go.Scatter(\n",
        "    x=fpr,\n",
        "    y=tpr,\n",
        "    mode='lines',\n",
        "    name='ROC curve'\n",
        ")\n",
        "\n",
        "# Only label every nth point to avoid cluttering\n",
        "n = 10\n",
        "indices = np.arange(len(thresholds)) % n == 0  # Choose indices where index mod n is 0\n",
        "\n",
        "trace1 = go.Scatter(\n",
        "    x=fpr[indices],\n",
        "    y=tpr[indices],\n",
        "    mode='markers+text',\n",
        "    name='Threshold points',\n",
        "    text=[f\"Thr={thr:.2f}\" for thr in thresholds[indices]],\n",
        "    textposition='top center'\n",
        ")\n",
        "\n",
        "\n",
        "# Diagonal line\n",
        "trace2 = go.Scatter(\n",
        "    x=[0, 1],\n",
        "    y=[0, 1],\n",
        "    mode='lines',\n",
        "    name='Random (Area = 0.5)',\n",
        "    line=dict(dash='dash')\n",
        ")\n",
        "\n",
        "data = [trace0, trace1, trace2]\n",
        "\n",
        "# Define layout with square aspect ratio\n",
        "layout = go.Layout(\n",
        "    title='Receiver Operating Characteristic',\n",
        "    xaxis=dict(title='False Positive Rate'),\n",
        "    yaxis=dict(title='True Positive Rate'),\n",
        "    autosize=False,\n",
        "    width=800,\n",
        "    height=800,\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "# Define figure and add data\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "\n",
        "# Show figure\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "x2qGYOIu22hn",
        "outputId": "4b4c178e-bfe2-4935-ffcb-ea70b2ec1d19"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Assume that fpr, tpr, thresholds have already been calculated\n",
        "optimal_idx = np.argmax(tpr - fpr)\n",
        "optimal_threshold = thresholds[optimal_idx]\n",
        "print(\"Optimal threshold is:\", optimal_threshold)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2EIlMNGl25mi",
        "outputId": "e3641ccc-d8fc-4ddb-8695-7f1f8c3e6f51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal threshold is: 0.5503810234218872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "# Assuming fpr, tpr, thresholds are already calculated as before\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
        "\n",
        "# Calculate the AUC (Area Under the Curve)\n",
        "roc_auc = roc_auc_score(y_test, y_scores)\n",
        "\n",
        "# Generate a trace for ROC curve\n",
        "trace0 = go.Scatter(\n",
        "    x=fpr,\n",
        "    y=tpr,\n",
        "    mode='lines',\n",
        "    name=f'ROC curve (Area = {roc_auc:.2f})'\n",
        ")\n",
        "\n",
        "# Only label every nth point to avoid cluttering\n",
        "n = 10\n",
        "indices = np.arange(len(thresholds)) % n == 0  # Choose indices where index mod n is 0\n",
        "\n",
        "trace1 = go.Scatter(\n",
        "    x=fpr[indices],\n",
        "    y=tpr[indices],\n",
        "    mode='markers+text',\n",
        "    name='Threshold points',\n",
        "    text=[f\"Thr={thr:.2f}\" for thr in thresholds[indices]],\n",
        "    textposition='top center'\n",
        ")\n",
        "\n",
        "# Diagonal line\n",
        "trace2 = go.Scatter(\n",
        "    x=[0, 1],\n",
        "    y=[0, 1],\n",
        "    mode='lines',\n",
        "    name='Random (Area = 0.5)',\n",
        "    line=dict(dash='dash')\n",
        ")\n",
        "\n",
        "data = [trace0, trace1, trace2]\n",
        "\n",
        "# Define layout with square aspect ratio\n",
        "layout = go.Layout(\n",
        "    title='Receiver Operating Characteristic',\n",
        "    xaxis=dict(title='False Positive Rate'),\n",
        "    yaxis=dict(title='True Positive Rate'),\n",
        "    autosize=False,\n",
        "    width=800,\n",
        "    height=800,\n",
        "    showlegend=True\n",
        ")\n",
        "\n",
        "# Define figure and add data\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "\n",
        "# Show figure\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "8Q1THyeK4Plv",
        "outputId": "1a7301f0-ed0a-4a95-cb8b-be30f81c50f7"
      },
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ySrSO7Ja4vDg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Assuming that X_train, X_test, y_train, y_test are already defined\n",
        "\n",
        "# SVM requires feature scaling for better performance\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Logistic Regression model\n",
        "lr_model = LogisticRegression(max_iter=1000)\n",
        "lr_model.fit(X_train, y_train)\n",
        "lr_scores = lr_model.predict_proba(X_test)[:,1]\n",
        "\n",
        "# SVM model\n",
        "svm_model = SVC(probability=True)\n",
        "svm_model.fit(X_train_scaled, y_train)\n",
        "svm_scores = svm_model.predict_proba(X_test_scaled)[:,1]\n",
        "\n",
        "# Generate ROC curve data for logistic regression model\n",
        "lr_fpr, lr_tpr, lr_thresholds = roc_curve(y_test, lr_scores)\n",
        "lr_auc = roc_auc_score(y_test, lr_scores)\n",
        "\n",
        "# Generate ROC curve data for SVM model\n",
        "svm_fpr, svm_tpr, svm_thresholds = roc_curve(y_test, svm_scores)\n",
        "svm_auc = roc_auc_score(y_test, svm_scores)\n",
        "\n",
        "# Generate a trace for the Logistic Regression ROC curve\n",
        "trace0 = go.Scatter(\n",
        "    x=lr_fpr,\n",
        "    y=lr_tpr,\n",
        "    mode='lines',\n",
        "    name=f'Logistic Regression (Area = {lr_auc:.2f})'\n",
        ")\n",
        "\n",
        "# Generate a trace for the SVM ROC curve\n",
        "trace1 = go.Scatter(\n",
        "    x=svm_fpr,\n",
        "    y=svm_tpr,\n",
        "    mode='lines',\n",
        "    name=f'SVM (Area = {svm_auc:.2f})'\n",
        ")\n",
        "\n",
        "# Diagonal line\n",
        "trace2 = go.Scatter(\n",
        "    x=[0, 1],\n",
        "    y=[0, 1],\n",
        "    mode='lines',\n",
        "    name='Random (Area = 0.5)',\n",
        "    line=dict(dash='dash')\n",
        ")\n",
        "\n",
        "data = [trace0, trace1, trace2]\n",
        "\n",
        "# Define layout with square aspect ratio\n",
        "layout = go.Layout(\n",
        "    title='Receiver Operating Characteristic',\n",
        "    xaxis=dict(title='False Positive Rate'),\n",
        "    yaxis=dict(title='True Positive Rate'),\n",
        "    autosize=False,\n",
        "    width=800,\n",
        "    height=800,\n",
        "    showlegend=True\n",
        ")\n",
        "\n",
        "# Define figure and add data\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "\n",
        "# Show figure\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "AgzwtUgc6CQ9",
        "outputId": "d580082c-bfa7-4181-b124-2368257e8233"
      },
      "execution_count": null,
      "outputs": [
        {
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