{ "cells": [ { "cell_type": "markdown", "id": "560a0699-b258-4d85-a984-785ebfacdbd7", "metadata": {}, "source": [ "# Cox (semiparametric proportional hazard)" ] }, { "cell_type": "markdown", "id": "bf8ce873-7445-4f10-a1f9-aa9a44593851", "metadata": {}, "source": [ "This demonstration requires 2 optional dependencies : [pandas](https://pypi.org/project/pandas/) and [lifelines](https://pypi.org/project/lifelines/). Install them if you wish to reproduce examples outputs. Otherwise, below examples may help you to understand how you can use Cox model with assetlife." ] }, { "cell_type": "markdown", "id": "dc178d4a7653235a", "metadata": {}, "source": [ "### Loading data" ] }, { "cell_type": "markdown", "id": "49c5c2fdc80b3b2a", "metadata": {}, "source": [ "Let's use a [lifelines](https://pypi.org/project/lifelines/) dataset." ] }, { "cell_type": "code", "execution_count": 1, "id": "9345ce34-5550-4eb0-9362-9f08c607ca64", "metadata": {}, "outputs": [], "source": [ "import lifelines\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "c43a4b58a2972191", "metadata": {}, "outputs": [], "source": [ "df = lifelines.datasets.load_canadian_senators()" ] }, { "cell_type": "markdown", "id": "04142090-c0c2-44af-b47b-452a2e3e01a9", "metadata": {}, "source": [ "Clean up the dataset" ] }, { "cell_type": "code", "execution_count": 3, "id": "25d7e066-fec3-4d78-bd6c-d5432558ffcb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameProvince / Territoryreasondiff_daysobserved
0Abbott, John Joseph CaldwellquebecDeath2363True
1Adams, MichaelnewbrunswickDeath1090True
2Adams, WillienorthwestterritoriesRetirement11766True
3Aikins, James CoxontarioResignation5333True
4Aikins, James CoxontarioDeath3133True
\n", "
" ], "text/plain": [ " Name Province / Territory reason diff_days \\\n", "0 Abbott, John Joseph Caldwell quebec Death 2363 \n", "1 Adams, Michael newbrunswick Death 1090 \n", "2 Adams, Willie northwestterritories Retirement 11766 \n", "3 Aikins, James Cox ontario Resignation 5333 \n", "4 Aikins, James Cox ontario Death 3133 \n", "\n", " observed \n", "0 True \n", "1 True \n", "2 True \n", "3 True \n", "4 True " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = df[[\"Name\", \"Province / Territory\", \"reason\", \"diff_days\", \"observed\"]]\n", "df = df[df[\"reason\"] != \"Appointment declined\"]\n", "# standardize text (removing white spaces, non alphanumeric characters and put in lower case)\n", "df[\"Province / Territory\"] = (\n", " df[\"Province / Territory\"].str.replace(\"\\W+\", \"\", regex=True).str.lower()\n", ")\n", "display(df.head())" ] }, { "cell_type": "code", "execution_count": 4, "id": "f3292125-7173-4977-acd8-a1f4923c8fec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Province / Territory\n", "quebec 246\n", "ontario 242\n", "novascotia 98\n", "newbrunswick 93\n", "britishcolumbia 44\n", "manitoba 44\n", "alberta 43\n", "princeedwardisland 39\n", "saskatchewan 32\n", "newfoundlandandlabrador 30\n", "northwestterritories 7\n", "yukon 3\n", "westernprovincesdivision 2\n", "maritimesdivision 2\n", "quebecdivision 2\n", "ontariodivision 2\n", "nunavut 1\n", "Name: count, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Province / Territory\"].value_counts()" ] }, { "cell_type": "markdown", "id": "141d1d5b-423a-4262-8817-aade4e74f319", "metadata": {}, "source": [ "Dummy encoding of \"Province / Territory\" covariates" ] }, { "cell_type": "code", "execution_count": 5, "id": "c003398a-387c-4e7b-9cce-9b0836f56045", "metadata": {}, "outputs": [], "source": [ "# dummy encoding of \"Province / Territory\"\n", "df = pd.get_dummies(df, columns=[\"Province / Territory\"], prefix=\"covar\", dtype=int)\n", "# remove covariate columns corresponding to low count province\n", "covar_cols = [col for col in df.columns if \"covar\" in col]\n", "province_count = df[covar_cols].sum()\n", "province_with_low_count = province_count[province_count < 10]\n", "df = df.drop(columns=province_with_low_count.index)" ] }, { "cell_type": "code", "execution_count": 6, "id": "c936f4a2-33df-4c94-9546-8291c46eee47", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['Name', 'reason', 'diff_days', 'observed', 'covar_alberta',\n", " 'covar_britishcolumbia', 'covar_manitoba', 'covar_newbrunswick',\n", " 'covar_newfoundlandandlabrador', 'covar_novascotia', 'covar_ontario',\n", " 'covar_princeedwardisland', 'covar_quebec', 'covar_saskatchewan'],\n", " dtype='object')\n" ] } ], "source": [ "print(df.columns)" ] }, { "cell_type": "markdown", "id": "edd09506cf6ec942", "metadata": {}, "source": [ "### Fitting" ] }, { "cell_type": "markdown", "id": "834746ac-407f-4593-9173-e061757fdd14", "metadata": {}, "source": [ "Lifelines version" ] }, { "cell_type": "code", "execution_count": 7, "id": "a94bda3d2ac9a025", "metadata": { "ExecuteTime": { "end_time": "2026-02-27T13:50:16.592449Z", "start_time": "2026-02-27T13:50:16.417480Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "covariate\n", "covar_alberta 0.345928\n", "covar_britishcolumbia 0.173319\n", "covar_manitoba 0.292570\n", "covar_newbrunswick 0.193955\n", "covar_newfoundlandandlabrador 0.444993\n", "covar_novascotia 0.266590\n", "covar_ontario 0.319706\n", "covar_princeedwardisland 0.397880\n", "covar_quebec 0.344936\n", "covar_saskatchewan -0.049557\n", "Name: coef, dtype: float64\n" ] } ], "source": [ "from lifelines.fitters.coxph_fitter import CoxPHFitter\n", "\n", "# Lifelines model fit\n", "\n", "covar_cols = [col for col in df.columns if \"covar\" in col]\n", "ll_model = CoxPHFitter()\n", "ll_model.fit(\n", " df=df,\n", " duration_col=\"diff_days\",\n", " event_col=\"observed\",\n", " formula=\"~ \" + \" + \".join(covar_cols),\n", ")\n", "print(ll_model.params_)" ] }, { "cell_type": "code", "execution_count": 8, "id": "7ad0d134-2103-49ea-bac1-5fbb31710c08", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Lifelines sf\n", "max_offset = 0\n", "\n", "X = df.filter(regex=\"covar\").iloc[:2]\n", "\n", "sf_lifelines = ll_model.predict_survival_function(\n", " X=X,\n", " times=range(\n", " df[\"diff_days\"].min().astype(int),\n", " df[\"diff_days\"].max().astype(int) + max_offset,\n", " ),\n", ")\n", "\n", "sf_lifelines.columns = X.index\n", "sf_lifelines.plot(xlabel=\"time\", ylabel=\"sf\")" ] }, { "cell_type": "markdown", "id": "913c06f0-2d2b-49aa-bcc9-de420d3e01a6", "metadata": {}, "source": [ "assetlife version " ] }, { "cell_type": "code", "execution_count": 9, "id": "8f0742e4-ebf1-4d90-9f88-6d37f4700e4e", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "from assetlife.lifetime_models import SemiParametricProportionalHazard\n", "\n", "cox_model = SemiParametricProportionalHazard(\n", " df[\"diff_days\"].to_numpy(),\n", " covar=np.unstack(df.filter(regex=\"covar\").to_numpy(), axis=-1),\n", " event=df[\"observed\"].to_numpy(dtype=bool),\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "id": "2f78e90c-b1d4-4f2e-9ee3-dc74aa0740ae", "metadata": {}, "outputs": [], "source": [ "timeline, sf_values, se = cox_model.sf(*np.unstack(X.values, axis=-1))" ] }, { "cell_type": "code", "execution_count": 11, "id": "e98ff78d-a015-4c28-b5d3-72a8dfd1cb27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(timeline, sf_values[0])\n", "plt.plot(timeline, sf_values[1])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0rc1" } }, "nbformat": 4, "nbformat_minor": 5 }