Lifelines-sklearn-adapter-predicts-the-expectation.ipynb

lifelines-sklearn-adapter-predicts-the-expectation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lifelines-sklearn-adapter-predicts-the-expectation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPc82HOXEGYVhL8hKHO93lu",
      "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/gist/alonsosilvaallende/81a55cbe798ce5a33fd838af99a00a25/lifelines-sklearn-adapter-predicts-the-expectation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_IujgLW_1Ia"
      },
      "source": [
        "!pip install --quiet lifelines"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufiyPcyx_52n"
      },
      "source": [
        "from lifelines.utils.sklearn_adapter import sklearn_adapter\n",
        "\n",
        "from lifelines import WeibullAFTFitter\n",
        "from lifelines.datasets import load_rossi"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1wEfK1_AFPZ"
      },
      "source": [
        "X = load_rossi().drop('week', axis=1)\n",
        "Y = load_rossi().pop('week')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3lbnorYAJaA"
      },
      "source": [
        "WeibullAFTRegression = sklearn_adapter(WeibullAFTFitter, event_col='arrest')\n",
        "sk_waft = WeibullAFTRegression(penalizer=1e-5)\n",
        "sk_waft.fit(X, Y)\n",
        "pred_sk_waft = sk_waft.predict(X)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ez2TJ27BJIO"
      },
      "source": [
        "waftf = WeibullAFTFitter(penalizer=1e-5)\n",
        "X['week'] = Y\n",
        "waftf.fit(X, duration_col='week', event_col='arrest')\n",
        "pred_waft = waftf.predict_expectation(X)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QO71Mv5qCCz6",
        "outputId": "8304d2a6-570d-440f-d830-d165c28a95a1"
      },
      "source": [
        "all(pred_sk_waft == pred_waft)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDgHNSZvC6NL"
      },
      "source": [
        "pred_waft_median = waftf.predict_median(X)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-5fPR45CwAO",
        "outputId": "1c494dcb-9b2b-4878-beaa-26c67e1a5c22"
      },
      "source": [
        "all(pred_sk_waft == pred_waft_median)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}