Auto_Sklearn.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/padmapraba/AutoML-Packages/blob/main/Auto_Sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lr_L5nwIYEo",
        "outputId": "fbf30fbc-d0c2-4c92-aca4-672bfb86cf6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "build-essential is already the newest version (12.4ubuntu1).\n",
            "swig is already the newest version (3.0.12-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 37 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!apt-get install build-essential swig "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5p8HIy57HmT2",
        "outputId": "f4f15831-7232-4d92-f412-360af4bc4d04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: auto-sklearn in /usr/local/lib/python3.7/dist-packages (0.14.2)\n",
            "Requirement already satisfied: scikit-learn<0.25.0,>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (0.24.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (57.4.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.1.0)\n",
            "Requirement already satisfied: liac-arff in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (2.5.0)\n",
            "Requirement already satisfied: threadpoolctl in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (3.0.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (3.13)\n",
            "Requirement already satisfied: ConfigSpace<0.5,>=0.4.14 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (0.4.20)\n",
            "Requirement already satisfied: smac>=0.14 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.1.1)\n",
            "Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.1.5)\n",
            "Requirement already satisfied: pynisher>=0.6.3 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (0.6.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (3.10.0.2)\n",
            "Requirement already satisfied: dask<2021.07 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (2021.6.2)\n",
            "Requirement already satisfied: distributed<2021.07,>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (2021.6.2)\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.19.5)\n",
            "Requirement already satisfied: pyrfr<0.9,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (0.8.2)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.7.1)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from ConfigSpace<0.5,>=0.4.14->auto-sklearn) (0.29.24)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (from ConfigSpace<0.5,>=0.4.14->auto-sklearn) (3.0.6)\n",
            "Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (2.0.0)\n",
            "Requirement already satisfied: partd>=0.3.10 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (1.2.0)\n",
            "Requirement already satisfied: fsspec>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (2021.11.1)\n",
            "Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (0.11.2)\n",
            "Requirement already satisfied: psutil>=5.0 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (5.4.8)\n",
            "Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (2.4.0)\n",
            "Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (7.1.2)\n",
            "Requirement already satisfied: zict>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (2.0.0)\n",
            "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (1.7.0)\n",
            "Requirement already satisfied: tornado>=5 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (5.1.1)\n",
            "Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (1.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->auto-sklearn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->auto-sklearn) (2018.9)\n",
            "Requirement already satisfied: locket in /usr/local/lib/python3.7/dist-packages (from partd>=0.3.10->dask<2021.07->auto-sklearn) (0.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0->auto-sklearn) (1.15.0)\n",
            "Requirement already satisfied: emcee>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from smac>=0.14->auto-sklearn) (3.1.1)\n",
            "Requirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed<2021.07,>=2.2.0->auto-sklearn) (1.0.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install auto-sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uLeF6s-yIFff",
        "outputId": "5660ef16-9f47-4d60-c41f-71e434127adf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: scipy==1.7.1 in /usr/local/lib/python3.7/dist-packages (1.7.1)\n",
            "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /usr/local/lib/python3.7/dist-packages (from scipy==1.7.1) (1.19.5)\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade scipy==1.7.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UrsSOQ8DHxkM"
      },
      "outputs": [],
      "source": [
        "import sklearn.metrics\n",
        "from sklearn.metrics import roc_auc_score, roc_curve\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.datasets import make_moons, make_circles, make_classification\n",
        "import numpy as np\n",
        "\n",
        "import autosklearn.classification\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYD_WO5sJ7lw"
      },
      "source": [
        "# Circles: Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dEdqeAy4N50R"
      },
      "outputs": [],
      "source": [
        "# Circles dataset\n",
        "circles_X, circles_y = sklearn.datasets.make_circles(noise=0.2, random_state=1, factor=0.5)\n",
        "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(circles_X,circles_y,random_state=31)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5fcpMYHJ--8"
      },
      "source": [
        "# Circles: Build and fit a classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "idM8uLbqJ4Dv",
        "outputId": "dd4d5289-631d-476a-9802-f03c97da6f83"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5f9e16ad0>"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "circle = autosklearn.classification.AutoSklearnClassifier()\n",
        "circle.fit(X_train, y_train)\n",
        "\n",
        "circle.cv_results_  # returns dictionary with keys as col headers and values as columns\n",
        "\n",
        "# returns a dataframe containing the models performance over time\n",
        "circle.performance_over_time_.plot(\n",
        "    x='Timestamp',\n",
        "    kind='line',\n",
        "    legend=True,\n",
        "    title='Auto-sklearn accuracy over time',\n",
        "    grid=True,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KxHoRVTBFyas"
      },
      "outputs": [],
      "source": [
        "# gives the name of the dataset, the metric used and the best validation score obtained\n",
        "circle.sprint_statistics()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "753AxVyDFztP"
      },
      "outputs": [],
      "source": [
        "# display all models created\n",
        "circle.show_models()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfL3Pi1EQCOH",
        "outputId": "9cbe6928-80c5-49e4-9943-83f04f193120"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score: 0.88\n"
          ]
        }
      ],
      "source": [
        "pred_prob_circle = circle.predict_proba(X_test)\n",
        "\n",
        "predictions = circle.predict(X_test)\n",
        "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_test, predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QsaIKou4b5hi"
      },
      "outputs": [],
      "source": [
        "# ROC curve for best circle model\n",
        "fpr_circ, tpr_circ, tresh_circ = roc_curve(y_test, pred_prob_circle[:,1],pos_label =1)\n",
        "\n",
        "# AUC Score\n",
        "auc_score_circ = roc_auc_score(y_test, pred_prob_circle[:,1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XBfrKujKGxb"
      },
      "source": [
        "# Circles: View the models found by auto-sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XhheAflwKChb"
      },
      "outputs": [],
      "source": [
        "print(circle.leaderboard())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J3HLbzGmKPgN"
      },
      "outputs": [],
      "source": [
        "#print(cls.show_models())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7k06t19NKn3w",
        "outputId": "f89722f3-2e85-440d-fd8a-1470b7a5b208"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score: 0.88\n"
          ]
        }
      ],
      "source": [
        "predictions = circle.predict(X_test)\n",
        "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_test, predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGwOHrE0a7TT"
      },
      "source": [
        "# Moon Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJWHqN8XKwU9",
        "outputId": "dc546d30-3d05-4a41-ca5e-9423b641a27b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score: 0.8\n"
          ]
        }
      ],
      "source": [
        "# Moons dataset\n",
        "moon_X, moon_y = sklearn.datasets.make_moons(noise=0.3, random_state=0)\n",
        "X_moon_train, X_moon_test, y_moon_train, y_moon_test = sklearn.model_selection.train_test_split(moon_X,moon_y,random_state=31)\n",
        "\n",
        "# \n",
        "moon = autosklearn.classification.AutoSklearnClassifier()\n",
        "moon.fit(X_moon_train, y_moon_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lI2hyU7LvSDn",
        "outputId": "bbfd43e2-4613-4b8b-b535-44ca939dc361"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score: 0.8\n"
          ]
        }
      ],
      "source": [
        "pred_prob_moon = moon.predict_proba(X_moon_test)\n",
        "\n",
        "# ROC and AUC curve for best moon model\n",
        "fpr_moon, tpr_moon, tresh_moon = roc_curve(y_moon_test, pred_prob_moon[:,1],pos_label =1)\n",
        "auc_score_moon = roc_auc_score(y_moon_test, pred_prob_moon[:,1])\n",
        "\n",
        "# \n",
        "pred = moon.predict(X_moon_test)\n",
        "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_moon_test, pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQos68nUGKjX",
        "outputId": "b2cb3242-fa8e-4ef7-f03b-f1e7113a8503"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "          rank  ensemble_weight           type  cost  duration\n",
            "model_id                                                      \n",
            "2            1             0.04  random_forest  0.04  1.910229\n",
            "204          2             0.02  random_forest  0.04  1.758544\n",
            "202          3             0.02  random_forest  0.04  2.161933\n",
            "198          4             0.04  random_forest  0.04  1.920479\n",
            "195          5             0.04  random_forest  0.04  2.202078\n",
            "186          6             0.04  random_forest  0.04  2.079329\n",
            "174          7             0.02  random_forest  0.04  2.122981\n",
            "164          8             0.04  random_forest  0.04  2.162659\n",
            "162          9             0.02  random_forest  0.04  2.081647\n",
            "150         10             0.02  random_forest  0.04  2.129200\n",
            "136         11             0.02  random_forest  0.04  2.247468\n",
            "128         12             0.02  random_forest  0.04  1.922426\n",
            "127         13             0.04  random_forest  0.04  1.881563\n",
            "125         14             0.04  random_forest  0.04  1.978313\n",
            "122         15             0.04  random_forest  0.04  2.063214\n",
            "209         16             0.02  random_forest  0.04  1.940006\n",
            "114         17             0.02  random_forest  0.04  1.940048\n",
            "104         18             0.02  random_forest  0.04  2.165172\n",
            "102         19             0.02  random_forest  0.04  1.947763\n",
            "97          20             0.02  random_forest  0.04  2.138222\n",
            "95          21             0.04  random_forest  0.04  2.153119\n",
            "93          22             0.04  random_forest  0.04  1.910995\n",
            "92          23             0.04  random_forest  0.04  2.069231\n",
            "86          24             0.04  random_forest  0.04  2.124317\n",
            "82          25             0.06  random_forest  0.04  2.077400\n",
            "77          26             0.06  random_forest  0.04  2.122563\n",
            "75          27             0.02  random_forest  0.04  1.936215\n",
            "71          28             0.02  decision_tree  0.04  1.068763\n",
            "65          29             0.02  random_forest  0.04  1.907533\n",
            "43          30             0.02  random_forest  0.04  1.772992\n",
            "4           31             0.04     libsvm_svc  0.04  0.957988\n",
            "112         32             0.02  random_forest  0.04  1.935786\n",
            "217         33             0.02  random_forest  0.04  2.122976\n"
          ]
        }
      ],
      "source": [
        "print(moon.leaderboard())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVS8LOPKbAHI"
      },
      "source": [
        "# Linearly Separable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wYCGdHAIX1R7"
      },
      "outputs": [],
      "source": [
        "# linearly separable\n",
        "lin_X, lin_y = make_classification(\n",
        "    n_features=2,\n",
        "    n_redundant=0,\n",
        "    n_informative=2,\n",
        "    random_state=1,\n",
        "    n_clusters_per_class=1,\n",
        ")\n",
        "rng = np.random.RandomState(2)\n",
        "lin_X += 2 * rng.uniform(size=lin_X.shape)\n",
        "\n",
        "X_lin_train, X_lin_test, y_lin_train, y_lin_test = sklearn.model_selection.train_test_split(lin_X,lin_y,random_state=31)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6hZ39CeiT3A",
        "outputId": "09bf90a6-a46c-4f90-961e-8addef2990ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AutoSklearnClassifier(per_run_time_limit=360)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "linear = autosklearn.classification.AutoSklearnClassifier()\n",
        "linear.fit(X_lin_train, y_lin_train)\n",
        "\n",
        "# linear.cv_results_  # returns dictionary with keys as col headers and values as columns\n",
        "\n",
        "# # returns a dataframe containing the models performance over time\n",
        "# linear.performance_over_time_.plot(\n",
        "#     x='Timestamp',\n",
        "#     kind='line',\n",
        "#     legend=True,\n",
        "#     title='Auto-sklearn accuracy over time',\n",
        "#     grid=True,\n",
        "# )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LEhyEQvLinDh",
        "outputId": "72d05b6c-59b3-4231-f2df-284896fc13ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score: 1.0\n"
          ]
        }
      ],
      "source": [
        "predictions = linear.predict(X_lin_test)\n",
        "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_lin_test, predictions))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tiZCrpjIinI4"
      },
      "outputs": [],
      "source": [
        "pred_prob_lin = linear.predict_proba(X_lin_test)\n",
        "\n",
        "# ROC and AUC curve for best moon model\n",
        "fpr_lin, tpr_lin, tresh_lin = roc_curve(y_lin_test, pred_prob_lin[:,1],pos_label =1)\n",
        "auc_score_lin = roc_auc_score(y_lin_test, pred_prob_lin[:,1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3WFr-m17bHOm"
      },
      "source": [
        "# Breast Cancer Dataset "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bmT3qMhgRl68"
      },
      "outputs": [],
      "source": [
        "X, y = load_breast_cancer(return_X_y=True)\n",
        "X_cancer_train, X_cancer_test, y_cancer_train, y_cancer_test = sklearn.model_selection.train_test_split(X, y, stratify=y, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "slb11SAG8h6S",
        "outputId": "31eaf9a0-00c8-486b-9c56-013426ab2d11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score: 0.9230769230769231\n"
          ]
        }
      ],
      "source": [
        "cancer = autosklearn.classification.AutoSklearnClassifier()\n",
        "cancer.fit(X_cancer_train, y_cancer_train)\n",
        "\n",
        "predictions = cancer.predict(X_cancer_test)\n",
        "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_cancer_test, predictions))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "-MmMQlr734DE"
      },
      "outputs": [],
      "source": [
        "pred_prob_cancer = cancer.predict_proba(X_cancer_test)\n",
        "\n",
        "# ROC and AUC curve for best moon model\n",
        "fpr_cancer, tpr_cancer, tresh_cancer = roc_curve(y_cancer_test, pred_prob_cancer[:,1],pos_label =1)\n",
        "auc_score_cancer = roc_auc_score(y_cancer_test, pred_prob_cancer[:,1])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "P_ZvPHqGRjAE",
        "outputId": "5aed3749-0082-4f3d-ab9c-ed78c58399e1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn')\n",
        "\n",
        "\n",
        "# roc curve for tpr = fpr \n",
        "rand_prob= [0 for i in range(len(y_test))]\n",
        "p_fpr, p_tpr, _ = roc_curve(y_test, rand_prob,pos_label=1)\n",
        "\n",
        "\n",
        "# plot roc curves\n",
        "plt.plot(fpr_cancer, tpr_cancer, linestyle='--', color='orange',label= 'Breast Cancer sklearn')\n",
        "plt.plot(fpr_moon, tpr_moon, linestyle='--', color='green',label= 'Moons sklearn')\n",
        "plt.plot(fpr_circ, tpr_circ, linestyle='--', color='blue',label= 'Circles sklearn')\n",
        "plt.plot(fpr_lin, tpr_lin, linestyle='--', color='red',label= 'Linearly Separable sklearn')\n",
        "plt.plot(p_fpr, p_tpr, linestyle='--', color='black')\n",
        "plt.title(\"ROC Curve\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pWdoFsI2_peQ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Auto-Sklearn.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMz2zRRhWAeNH1UWdzxRlCN",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}