1{
2 "cells": [
3  {
4   "cell_type": "code",
5   "execution_count": 1,
6   "metadata": {},
7   "outputs": [],
8   "source": [
9    "%matplotlib inline\n",
10    "%config InlineBackend.figure_format = 'retina'\n",
11    "\n",
12    "from matplotlib import pyplot as plt\n",
13    "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter\n",
14    "import numpy as np\n",
15    "import pandas as pd\n"
16   ]
17  },
18  {
19   "cell_type": "code",
20   "execution_count": 2,
21   "metadata": {},
22   "outputs": [],
23   "source": [
24    "from lifelines.datasets import load_rossi\n",
25    "\n",
26    "\n",
27    "rossi = load_rossi()\n",
28    "\n",
29    "alpha = 1.\n",
30    "wft = WeibullAFTFitter(alpha=alpha).fit(rossi, 'week', 'arrest')\n",
31    "lnt = LogNormalAFTFitter(alpha=alpha).fit(rossi, 'week', 'arrest')\n",
32    "llt = LogLogisticAFTFitter(alpha=alpha).fit(rossi, 'week', 'arrest')"
33   ]
34  },
35  {
36   "cell_type": "code",
37   "execution_count": 3,
38   "metadata": {},
39   "outputs": [
40    {
41     "data": {
42      "text/plain": [
43       "<matplotlib.legend.Legend at 0x11e2a9f98>"
44      ]
45     },
46     "execution_count": 3,
47     "metadata": {},
48     "output_type": "execute_result"
49    },
50    {
51     "data": {
52      "image/png": 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\n",
53      "text/plain": [
54       "<Figure size 576x360 with 1 Axes>"
55      ]
56     },
57     "metadata": {
58      "image/png": {
59       "height": 317,
60       "width": 496
61      },
62      "needs_background": "light"
63     },
64     "output_type": "display_data"
65    }
66   ],
67   "source": [
68    "fig = plt.figure(figsize=(8, 5))\n",
69    "ax = fig.subplots()\n",
70    "columns = ['fin', 'age', 'race', 'wexp', 'mar', 'paro', 'prio']\n",
71    "wft.plot(columns=columns, parameter='lambda_', c='red', label='Weibull', ax=ax)\n",
72    "lnt.plot(columns=columns, parameter='mu_', c='k', label='Log-Normal', ax=ax)\n",
73    "llt.plot(columns=columns, parameter='alpha_', c='b', label='Log-Logistic', ax=ax)\n",
74    "\n",
75    "plt.legend()"
76   ]
77  },
78  {
79   "cell_type": "code",
80   "execution_count": 4,
81   "metadata": {},
82   "outputs": [
83    {
84     "name": "stdout",
85     "output_type": "stream",
86     "text": [
87      "<lifelines.WeibullAFTFitter: fitted with 432 observations, 318 censored>\n",
88      "      duration col = 'week'\n",
89      "         event col = 'arrest'\n",
90      "number of subjects = 432\n",
91      "  number of events = 114\n",
92      "    log-likelihood = -679.9166\n",
93      "  time fit was run = 2019-02-24 19:24:52 UTC\n",
94      "\n",
95      "---\n",
96      "                      coef  exp(coef)  se(coef)       z      p  -log2(p)  lower 0  upper 0\n",
97      "lambda_ fin         0.2722     1.3128    0.1380  1.9727 0.0485    4.3651   0.2722   0.2722\n",
98      "        age         0.0407     1.0416    0.0160  2.5441 0.0110    6.5121   0.0407   0.0407\n",
99      "        race       -0.2248     0.7987    0.2202 -1.0211 0.3072    1.7028  -0.2248  -0.2248\n",
100      "        wexp        0.1065     1.1124    0.1515  0.7031 0.4820    1.0529   0.1065   0.1065\n",
101      "        mar         0.3113     1.3652    0.2733  1.1390 0.2547    1.9730   0.3113   0.3113\n",
102      "        paro        0.0588     1.0606    0.1396  0.4213 0.6735    0.5702   0.0588   0.0588\n",
103      "        prio       -0.0658     0.9363    0.0209 -3.1430 0.0017    9.2242  -0.0658  -0.0658\n",
104      "        _intercept  3.9901    54.0615    0.4191  9.5208 <5e-05   68.9792   3.9901   3.9901\n",
105      "rho_    _intercept  0.3391     1.4037    0.0890  3.8091 0.0001   12.8076   0.3391   0.3391\n",
106      "---\n",
107      "Concordance = 0.6401\n",
108      "Log-likelihood ratio test = 33.4157 on 7 df, -log2(p)=15.4624\n",
109      "<lifelines.LogNormalAFTFitter: fitted with 432 observations, 318 censored>\n",
110      "      duration col = 'week'\n",
111      "         event col = 'arrest'\n",
112      "number of subjects = 432\n",
113      "  number of events = 114\n",
114      "    log-likelihood = -683.2346\n",
115      "  time fit was run = 2019-02-24 19:24:52 UTC\n",
116      "\n",
117      "---\n",
118      "                     coef  exp(coef)  se(coef)       z      p  -log2(p)  lower 0  upper 0\n",
119      "mu_    fin         0.3429     1.4090    0.1641  2.0895 0.0367    4.7696   0.3429   0.3429\n",
120      "       age         0.0272     1.0276    0.0158  1.7265 0.0843    3.5690   0.0272   0.0272\n",
121      "       race       -0.3631     0.6955    0.2647 -1.3719 0.1701    2.5557  -0.3631  -0.3631\n",
122      "       wexp        0.2681     1.3075    0.1789  1.4988 0.1339    2.9006   0.2681   0.2681\n",
123      "       mar         0.4603     1.5846    0.2951  1.5596 0.1188    3.0729   0.4603   0.4603\n",
124      "       paro        0.0558     1.0574    0.1691  0.3302 0.7413    0.4320   0.0558   0.0558\n",
125      "       prio       -0.0655     0.9366    0.0271 -2.4185 0.0156    6.0039  -0.0655  -0.0655\n",
126      "       _intercept  4.2677    71.3545    0.4617  9.2435 <5e-05   65.1846   4.2677   4.2677\n",
127      "sigma_ _intercept  0.2582     1.2946    0.0764  3.3778 0.0007   10.4182   0.2582   0.2582\n",
128      "---\n",
129      "Concordance = 0.6452\n",
130      "Log-likelihood ratio test = 29.3516 on 7 df, -log2(p)=12.9680\n",
131      "<lifelines.LogLogisticAFTFitter: fitted with 432 observations, 318 censored>\n",
132      "      duration col = 'week'\n",
133      "         event col = 'arrest'\n",
134      "number of subjects = 432\n",
135      "  number of events = 114\n",
136      "    log-likelihood = -679.9384\n",
137      "  time fit was run = 2019-02-24 19:24:52 UTC\n",
138      "\n",
139      "---\n",
140      "                     coef  exp(coef)  se(coef)       z      p  -log2(p)  lower 0  upper 0\n",
141      "alpha_ fin         0.2889     1.3349    0.1456  1.9842 0.0472    4.4042   0.2889   0.2889\n",
142      "       age         0.0364     1.0370    0.0156  2.3354 0.0195    5.6785   0.0364   0.0364\n",
143      "       race       -0.2791     0.7564    0.2297 -1.2155 0.2242    2.1573  -0.2791  -0.2791\n",
144      "       wexp        0.1784     1.1953    0.1572  1.1351 0.2563    1.9639   0.1784   0.1784\n",
145      "       mar         0.3473     1.4152    0.2697  1.2879 0.1978    2.3380   0.3473   0.3473\n",
146      "       paro        0.0508     1.0521    0.1496  0.3396 0.7342    0.4458   0.0508   0.0508\n",
147      "       prio       -0.0692     0.9332    0.0227 -3.0421 0.0023    8.7336  -0.0692  -0.0692\n",
148      "       _intercept  3.9183    50.3141    0.4274  9.1671 <5e-05   64.1572   3.9183   3.9183\n",
149      "beta_  _intercept  0.4352     1.5453    0.0864  5.0362 <5e-05   21.0058   0.4352   0.4352\n",
150      "---\n",
151      "Concordance = 0.6438\n",
152      "Log-likelihood ratio test = 33.4721 on 7 df, -log2(p)=15.4974\n"
153     ]
154    }
155   ],
156   "source": [
157    "wft.print_summary(4)\n",
158    "lnt.print_summary(4)\n",
159    "llt.print_summary(4)"
160   ]
161  },
162  {
163   "cell_type": "code",
164   "execution_count": 8,
165   "metadata": {},
166   "outputs": [
167    {
168     "data": {
169      "text/plain": [
170       "<matplotlib.axes._subplots.AxesSubplot at 0x11f067d68>"
171      ]
172     },
173     "execution_count": 8,
174     "metadata": {},
175     "output_type": "execute_result"
176    },
177    {
178     "data": {
179      "image/png": 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\n",
180      "text/plain": [
181       "<Figure size 432x288 with 1 Axes>"
182      ]
183     },
184     "metadata": {
185      "image/png": {
186       "height": 250,
187       "width": 380
188      },
189      "needs_background": "light"
190     },
191     "output_type": "display_data"
192    }
193   ],
194   "source": [
195    "ax = llt.predict_cumulative_hazard(llt._norm_mean.to_frame().T).plot()\n",
196    "wft.predict_cumulative_hazard(llt._norm_mean.to_frame().T).plot(ax=ax)\n",
197    "lnt.predict_cumulative_hazard(llt._norm_mean.to_frame().T).plot(ax=ax)"
198   ]
199  },
200  {
201   "cell_type": "code",
202   "execution_count": 9,
203   "metadata": {},
204   "outputs": [],
205   "source": [
206    "from lifelines.datasets import load_regression_dataset\n",
207    "\n",
208    "\n",
209    "df = load_regression_dataset()\n",
210    "\n",
211    "alpha = 1.\n",
212    "wft = WeibullAFTFitter(alpha=alpha).fit(df, 'T', 'E')\n",
213    "lnt = LogNormalAFTFitter(alpha=alpha).fit(df, 'T', 'E')\n",
214    "llt = LogLogisticAFTFitter(alpha=alpha).fit(df, 'T', 'E')"
215   ]
216  },
217  {
218   "cell_type": "code",
219   "execution_count": 13,
220   "metadata": {},
221   "outputs": [
222    {
223     "data": {
224      "text/plain": [
225       "<matplotlib.legend.Legend at 0x11f886048>"
226      ]
227     },
228     "execution_count": 13,
229     "metadata": {},
230     "output_type": "execute_result"
231    },
232    {
233     "data": {
234      "image/png": 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\n",
235      "text/plain": [
236       "<Figure size 432x288 with 1 Axes>"
237      ]
238     },
239     "metadata": {
240      "image/png": {
241       "height": 250,
242       "width": 364
243      },
244      "needs_background": "light"
245     },
246     "output_type": "display_data"
247    }
248   ],
249   "source": [
250    "ax = llt.predict_cumulative_hazard(llt._norm_mean.to_frame(name='Log-Logistic').T).plot()\n",
251    "wft.predict_cumulative_hazard(llt._norm_mean.to_frame(name='Log-Normal').T).plot(ax=ax)\n",
252    "lnt.predict_cumulative_hazard(llt._norm_mean.to_frame(name='Weibull').T).plot(ax=ax)\n",
253    "plt.legend()"
254   ]
255  },
256  {
257   "cell_type": "code",
258   "execution_count": 15,
259   "metadata": {},
260   "outputs": [
261    {
262     "data": {
263      "text/plain": [
264       "<matplotlib.legend.Legend at 0x11fdf59e8>"
265      ]
266     },
267     "execution_count": 15,
268     "metadata": {},
269     "output_type": "execute_result"
270    },
271    {
272     "data": {
273      "image/png": 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\n",
274      "text/plain": [
275       "<Figure size 576x360 with 1 Axes>"
276      ]
277     },
278     "metadata": {
279      "image/png": {
280       "height": 317,
281       "width": 492
282      },
283      "needs_background": "light"
284     },
285     "output_type": "display_data"
286    }
287   ],
288   "source": [
289    "fig = plt.figure(figsize=(8, 5))\n",
290    "ax = fig.subplots()\n",
291    "columns = ['var1', 'var2', 'var3']\n",
292    "wft.plot(columns=columns, parameter='lambda_', c='red', label='Weibull', ax=ax)\n",
293    "lnt.plot(columns=columns, parameter='mu_', c='k', label='Log-Normal', ax=ax)\n",
294    "llt.plot(columns=columns, parameter='alpha_', c='b', label='Log-Logistic', ax=ax)\n",
295    "\n",
296    "plt.legend()"
297   ]
298  },
299  {
300   "cell_type": "code",
301   "execution_count": 16,
302   "metadata": {},
303   "outputs": [
304    {
305     "name": "stdout",
306     "output_type": "stream",
307     "text": [
308      "<lifelines.WeibullAFTFitter: fitted with 200 observations, 11 censored>\n",
309      "      duration col = 'T'\n",
310      "         event col = 'E'\n",
311      "number of subjects = 200\n",
312      "  number of events = 189\n",
313      "    log-likelihood = -504.4834\n",
314      "  time fit was run = 2019-02-24 19:25:43 UTC\n",
315      "\n",
316      "---\n",
317      "                      coef  exp(coef)  se(coef)       z      p  -log2(p)  lower 0  upper 0\n",
318      "lambda_ var1       -0.0823     0.9210    0.0239 -3.4454 0.0006   10.7764  -0.0823  -0.0823\n",
319      "        var2       -0.0155     0.9846    0.0275 -0.5628 0.5736    0.8020  -0.0155  -0.0155\n",
320      "        var3       -0.0812     0.9220    0.0244 -3.3267 0.0009   10.1522  -0.0812  -0.0812\n",
321      "        _intercept  2.5315    12.5722    0.0495 51.1170 <5e-05       inf   2.5315   2.5315\n",
322      "rho_    _intercept  1.0931     2.9836    0.0543 20.1196 <5e-05  296.6589   1.0931   1.0931\n",
323      "---\n",
324      "Concordance = 0.5798\n",
325      "Log-likelihood ratio test = 19.7251 on 3 df, -log2(p)=12.3351\n",
326      "<lifelines.LogNormalAFTFitter: fitted with 200 observations, 11 censored>\n",
327      "      duration col = 'T'\n",
328      "         event col = 'E'\n",
329      "number of subjects = 200\n",
330      "  number of events = 189\n",
331      "    log-likelihood = -502.1517\n",
332      "  time fit was run = 2019-02-24 19:25:43 UTC\n",
333      "\n",
334      "---\n",
335      "                     coef  exp(coef)  se(coef)        z      p  -log2(p)  lower 0  upper 0\n",
336      "mu_    var1       -0.0984     0.9063    0.0292  -3.3740 0.0007   10.3987  -0.0984  -0.0984\n",
337      "       var2       -0.0182     0.9820    0.0301  -0.6043 0.5456    0.8740  -0.0182  -0.0182\n",
338      "       var3       -0.0523     0.9491    0.0287  -1.8211 0.0686    3.8659  -0.0523  -0.0523\n",
339      "       _intercept  2.3419    10.4006    0.0545  43.0037 <5e-05       inf   2.3419   2.3419\n",
340      "sigma_ _intercept -0.9711     0.3786    0.0515 -18.8691 <5e-05  261.3990  -0.9711  -0.9711\n",
341      "---\n",
342      "Concordance = 0.5810\n",
343      "Log-likelihood ratio test = 15.9125 on 3 df, -log2(p)=9.7248\n",
344      "<lifelines.LogLogisticAFTFitter: fitted with 200 observations, 11 censored>\n",
345      "      duration col = 'T'\n",
346      "         event col = 'E'\n",
347      "number of subjects = 200\n",
348      "  number of events = 189\n",
349      "    log-likelihood = -502.3751\n",
350      "  time fit was run = 2019-02-24 19:25:43 UTC\n",
351      "\n",
352      "---\n",
353      "                     coef  exp(coef)  se(coef)       z      p  -log2(p)  lower 0  upper 0\n",
354      "alpha_ var1       -0.1110     0.8950    0.0315 -3.5283 0.0004   11.2236  -0.1110  -0.1110\n",
355      "       var2       -0.0287     0.9717    0.0294 -0.9764 0.3289    1.6044  -0.0287  -0.0287\n",
356      "       var3       -0.0422     0.9587    0.0274 -1.5375 0.1242    3.0096  -0.0422  -0.0422\n",
357      "       _intercept  2.3675    10.6712    0.0548 43.2327 <5e-05       inf   2.3675   2.3675\n",
358      "beta_  _intercept  1.5405     4.6670    0.0604 25.5086 <5e-05  474.3736   1.5405   1.5405\n",
359      "---\n",
360      "Concordance = 0.5800\n",
361      "Log-likelihood ratio test = 15.9749 on 3 df, -log2(p)=9.7673\n"
362     ]
363    }
364   ],
365   "source": [
366    "wft.print_summary(4)\n",
367    "lnt.print_summary(4)\n",
368    "llt.print_summary(4)"
369   ]
370  },
371  {
372   "cell_type": "code",
373   "execution_count": null,
374   "metadata": {},
375   "outputs": [],
376   "source": []
377  }
378 ],
379 "metadata": {
380  "kernelspec": {
381   "display_name": "Python 3",
382   "language": "python",
383   "name": "python3"
384  },
385  "language_info": {
386   "codemirror_mode": {
387    "name": "ipython",
388    "version": 3
389   },
390   "file_extension": ".py",
391   "mimetype": "text/x-python",
392   "name": "python",
393   "nbconvert_exporter": "python",
394   "pygments_lexer": "ipython3",
395   "version": "3.7.2"
396  }
397 },
398 "nbformat": 4,
399 "nbformat_minor": 2
400}
401