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Pavel Reich
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pymc-devs#2899: added cores=1
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‎docs/source/notebooks/survival_analysis_weibull.ipynb

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},
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"cell_type": "code",
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@@ -58,7 +58,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
@@ -99,76 +99,76 @@
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>3542</td>\n",
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" <td>61</td>\n",
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" <td>M</td>\n",
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" <td>1997</td>\n",
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" <td>1.690</td>\n",
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" <td>1.69</td>\n",
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" <td>8</td>\n",
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" <td>1.1</td>\n",
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" <td>5401</td>\n",
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" <td>59</td>\n",
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" <td>F</td>\n",
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" <td>1996</td>\n",
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" <td>1.40</td>\n",
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" <td>1.34</td>\n",
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" <td>5</td>\n",
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" <td>1.0</td>\n",
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" <td>0</td>\n",
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" <td>4526</td>\n",
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" <td>4655</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>4770</td>\n",
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" <td>57</td>\n",
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" <td>F</td>\n",
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" <td>1998</td>\n",
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" <td>1.260</td>\n",
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" <td>1.17</td>\n",
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" <td>4</td>\n",
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" <td>0.9</td>\n",
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" <td>771</td>\n",
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" <td>75</td>\n",
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" <td>M</td>\n",
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" <td>1996</td>\n",
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" <td>3.23</td>\n",
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" <td>3.61</td>\n",
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" <td>10</td>\n",
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" <td>1.3</td>\n",
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" <td>0</td>\n",
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" <td>4089</td>\n",
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" <td>4698</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3374</td>\n",
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" <td>66</td>\n",
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" <td>1502</td>\n",
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" <td>71</td>\n",
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" <td>F</td>\n",
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" <td>1996</td>\n",
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" <td>1.010</td>\n",
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" <td>1.34</td>\n",
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" <td>1.16</td>\n",
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" <td>1.29</td>\n",
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" <td>4</td>\n",
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" <td>0.8</td>\n",
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" <td>0.9</td>\n",
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" <td>0</td>\n",
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" <td>4837</td>\n",
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" <td>0</td>\n",
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" <td>2615</td>\n",
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" <td>1</td>\n",
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" <td>Digestive</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>7867</td>\n",
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" <td>53</td>\n",
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" <td>4107</td>\n",
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" <td>61</td>\n",
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" <td>F</td>\n",
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" <td>1997</td>\n",
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" <td>0.705</td>\n",
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" <td>1.25</td>\n",
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" <td>2</td>\n",
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" <td>0.8</td>\n",
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" <td>2.72</td>\n",
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" <td>2.51</td>\n",
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" <td>10</td>\n",
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" <td>0.9</td>\n",
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" <td>0</td>\n",
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" <td>4547</td>\n",
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" <td>4458</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>7674</td>\n",
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" <td>50</td>\n",
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" <td>5355</td>\n",
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" <td>55</td>\n",
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" <td>F</td>\n",
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" <td>1996</td>\n",
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" <td>0.793</td>\n",
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" <td>1997</td>\n",
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" <td>1.20</td>\n",
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" <td>2</td>\n",
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" <td>1.19</td>\n",
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" <td>4</td>\n",
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" <td>0.8</td>\n",
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" <td>0</td>\n",
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" <td>4761</td>\n",
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" <td>4514</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
@@ -178,21 +178,21 @@
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],
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"text/plain": [
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" index age sex sample.yr kappa lambda flc.grp creatinine mgus t \\\n",
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"0 3542 61 M 1997 1.690 1.69 8 1.1 0 4526 \n",
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"1 4770 57 F 1998 1.260 1.17 4 0.9 0 4089 \n",
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"2 3374 66 F 1996 1.010 1.34 4 0.8 0 2615 \n",
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"3 7867 53 F 1997 0.705 1.25 2 0.8 0 4547 \n",
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"4 7674 50 F 1996 0.793 1.20 2 0.8 0 4761 \n",
181+
"0 5401 59 F 1996 1.40 1.34 5 1.0 0 4655 \n",
182+
"1 771 75 M 1996 3.23 3.61 10 1.3 0 4698 \n",
183+
"2 1502 71 F 1996 1.16 1.29 4 0.9 0 4837 \n",
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"3 4107 61 F 1997 2.72 2.51 10 0.9 0 4458 \n",
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"4 5355 55 F 1997 1.20 1.19 4 0.8 0 4514 \n",
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"\n",
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" event chapter \n",
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"0 0 NaN \n",
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"1 0 NaN \n",
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"2 1 Digestive \n",
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"3 0 NaN \n",
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"4 0 NaN "
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" event chapter \n",
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"0 0 NaN \n",
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"1 0 NaN \n",
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"2 0 NaN \n",
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"3 0 NaN \n",
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"4 0 NaN "
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]
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},
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -251,29 +251,13 @@
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"Auto-assigning NUTS sampler...\n",
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"INFO:pymc3:Auto-assigning NUTS sampler...\n",
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"Initializing NUTS using adapt_diag...\n",
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"INFO:pymc3:Initializing NUTS using adapt_diag...\n",
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"Multiprocess sampling (4 chains in 4 jobs)\n",
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"INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n",
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"NUTS: [tau_raw_log__, tau_s_raw_log__, mu, a0, b0]\n",
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"INFO:pymc3:NUTS: [tau_raw_log__, tau_s_raw_log__, mu, a0, b0]\n",
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" 0%| | 0/2000 [00:00<?, ?it/s]"
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"INFO:pymc3:Initializing NUTS using adapt_diag...\n"
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]
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}
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],
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"source": [
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"with weibull:\n",
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" trace = pm.sample(1000, tune=1000, init='adapt_diag',threads=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pm.traceplot(trace, \n",
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" varnames=['mu','alpha', 'beta'], \n",
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" lines=dict(mu=mu_stan, alpha=alpha_stan, beta=beta_stan));"
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" trace = pm.sample(1000, tune=1000, init='adapt_diag',cores=1)"
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]
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},
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{

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