Skip to content

Instantly share code, notes, and snippets.

@alonsosilvaallende

alonsosilvaallende/untitled25.ipynb

Created February 16, 2022 14:12
Show Gist options
  • Save alonsosilvaallende/89954323b9acb178f2fed7cbbe56391b to your computer and use it in GitHub Desktop.
Save alonsosilvaallende/89954323b9acb178f2fed7cbbe56391b to your computer and use it in GitHub Desktop.
Download ZIP
Untitled25.ipynb
Display the source blob
Display the rendered blob
Raw
untitled25.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled25.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyNlFIjwbBLXg6WyofJxLUVg",
"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/89954323b9acb178f2fed7cbbe56391b/untitled25.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "ALAV8_c8tRjL"
},
"outputs": [],
"source": [
"!pip install -q scikit-survival"
]
},
{
"cell_type": "code",
"source": [
"import sksurv\n",
"sksurv.__version__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "eK0n3so81idr",
"outputId": "2de00a47-f4c0-4dda-edb0-ba284f46baf0"
},
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'0.17.0'"
]
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"pharmacoSmoking = sm.datasets.get_rdataset(\"pharmacoSmoking\", \"asaur\")\n",
"data = pharmacoSmoking.data\n",
"data = data.drop(columns=[\"id\",\"ageGroup2\",\"ageGroup4\"]) # Drop redundant information and ids\n",
"data.head(3)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 178
},
"id": "tToD8R-dtden",
"outputId": "e90ddeea-a1be-4357-d536-95dafd136a3c"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
" <div id=\"df-b99d3495-b817-4d4a-9a7c-e84c4a164452\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ttr</th>\n",
" <th>relapse</th>\n",
" <th>grp</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>employment</th>\n",
" <th>yearsSmoking</th>\n",
" <th>levelSmoking</th>\n",
" <th>priorAttempts</th>\n",
" <th>longestNoSmoke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>182</td>\n",
" <td>0</td>\n",
" <td>patchOnly</td>\n",
" <td>36</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>ft</td>\n",
" <td>26</td>\n",
" <td>heavy</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>patchOnly</td>\n",
" <td>41</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>27</td>\n",
" <td>heavy</td>\n",
" <td>3</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>combination</td>\n",
" <td>25</td>\n",
" <td>Female</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>12</td>\n",
" <td>heavy</td>\n",
" <td>3</td>\n",
" <td>21</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b99d3495-b817-4d4a-9a7c-e84c4a164452')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-b99d3495-b817-4d4a-9a7c-e84c4a164452 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-b99d3495-b817-4d4a-9a7c-e84c4a164452');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
" ttr relapse grp ... levelSmoking priorAttempts longestNoSmoke\n",
"0 182 0 patchOnly ... heavy 0 0\n",
"1 14 1 patchOnly ... heavy 3 90\n",
"2 5 1 combination ... heavy 3 21\n",
"\n",
"[3 rows x 11 columns]"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"from sksurv.datasets import get_x_y\n",
"\n",
"X, y = get_x_y(data, attr_labels=[\"relapse\", \"ttr\"], pos_label=True)"
],
"metadata": {
"id": "6Fj-HZgftnG0"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for c in X.columns:\n",
" if X[c].dtype.kind not in ['i', 'f']:\n",
" X[c] = X[c].astype(\"category\")"
],
"metadata": {
"id": "evjHOcuutqh0"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sksurv.preprocessing import OneHotEncoder\n",
"from sksurv.ensemble import RandomSurvivalForest\n",
"from sksurv.metrics import integrated_brier_score\n",
"\n",
"def get_ibs(seed, times):\n",
" X_trn, X_val, y_trn, y_val = train_test_split(X, y, random_state=seed)\n",
" print(f\"Minimum training time: {y_trn['ttr'].min()}\")\n",
" print(f\"Maximum training time: {y_trn['ttr'].max()}\")\n",
" print(f\"Minimum validation time: {y_val['ttr'].min()}\")\n",
" print(f\"Maximum validation time: {y_val['ttr'].max()}\")\n",
" enc = OneHotEncoder()\n",
" scaler = StandardScaler()\n",
" X_trn = enc.fit_transform(X_trn)\n",
" X_trn = pd.DataFrame(scaler.fit_transform(X_trn), columns=X_trn.columns)\n",
" X_val = enc.transform(X_val)\n",
" X_val = pd.DataFrame(scaler.transform(X_val), columns=X_val.columns)\n",
" rsf = RandomSurvivalForest(random_state=42)\n",
" rsf.fit(X_trn, y_trn)\n",
" survs = rsf.predict_survival_function(X_val)\n",
" preds = np.asarray([[fn(t) for t in times] for fn in survs])\n",
" return integrated_brier_score(y_trn, y_val, preds, times)"
],
"metadata": {
"id": "ZVoepAnVtutM"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"get_ibs(0, np.arange(0,170))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Hx6LHbg_wmSd",
"outputId": "c3bab9ab-13e4-4bab-cc7a-c49419335d6a"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Minimum training time: 0.0\n",
"Maximum training time: 182.0\n",
"Minimum validation time: 0.0\n",
"Maximum validation time: 182.0\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.21497683508486282"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"get_ibs(0,np.arange(0,180))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 380
},
"id": "FbaIV83zuhLT",
"outputId": "a38d5ac7-5421-49ff-aac8-8d84a831a048"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Minimum training time: 0.0\n",
"Maximum training time: 182.0\n",
"Minimum validation time: 0.0\n",
"Maximum validation time: 182.0\n"
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-8702b8f1e249>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ibs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m180\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-6-70c030061f69>\u001b[0m in \u001b[0;36mget_ibs\u001b[0;34m(seed, times)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mrsf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_trn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0msurvs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrsf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_survival_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msurvs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mintegrated_brier_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-6-70c030061f69>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mrsf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_trn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0msurvs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrsf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_survival_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msurvs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mintegrated_brier_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-6-70c030061f69>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mrsf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_trn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0msurvs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrsf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_survival_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msurvs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mintegrated_brier_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sksurv/functions.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m raise ValueError(\n\u001b[0;32m---> 67\u001b[0;31m \"x must be within [%f; %f]\" % (self.x[0], self.x[-1]))\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearchsorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mnot_exact\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: x must be within [0.000000; 170.000000]"
]
}
]
},
{
"cell_type": "code",
"source": [
"get_ibs(1, np.arange(0,170))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 380
},
"id": "pOA1saaA0cic",
"outputId": "ea029fe2-2104-478c-cefe-ff2aeb89cb40"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Minimum training time: 0.0\n",
"Maximum training time: 182.0\n",
"Minimum validation time: 4.0\n",
"Maximum validation time: 182.0\n"
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-e5a48495f626>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ibs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m170\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-6-70c030061f69>\u001b[0m in \u001b[0;36mget_ibs\u001b[0;34m(seed, times)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0msurvs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrsf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_survival_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msurvs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mintegrated_brier_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sksurv/metrics.py\u001b[0m in \u001b[0;36mintegrated_brier_score\u001b[0;34m(survival_train, survival_test, estimate, times)\u001b[0m\n\u001b[1;32m 734\u001b[0m \"\"\"\n\u001b[1;32m 735\u001b[0m \u001b[0;31m# Computing the brier scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 736\u001b[0;31m \u001b[0mtimes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbrier_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbrier_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurvival_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msurvival_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 738\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sksurv/metrics.py\u001b[0m in \u001b[0;36mbrier_score\u001b[0;34m(survival_train, survival_test, estimate, times)\u001b[0m\n\u001b[1;32m 613\u001b[0m \"\"\"\n\u001b[1;32m 614\u001b[0m \u001b[0mtest_event\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_y_survival\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurvival_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 615\u001b[0;31m \u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_estimate_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 616\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mestimate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[0mestimate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sksurv/metrics.py\u001b[0m in \u001b[0;36m_check_estimate_2d\u001b[0;34m(estimate, test_time, time_points)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_estimate_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0mestimate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m \u001b[0mtime_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_times\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 80\u001b[0m \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sksurv/metrics.py\u001b[0m in \u001b[0;36m_check_times\u001b[0;34m(test_time, times)\u001b[0m\n\u001b[1;32m 70\u001b[0m raise ValueError(\n\u001b[1;32m 71\u001b[0m 'all times must be within follow-up time of test data: [{}; {}['.format(\n\u001b[0;32m---> 72\u001b[0;31m test_time.min(), test_time.max()))\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: all times must be within follow-up time of test data: [4.0; 182.0["
]
}
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment