[ci skip] DOC Add parameters description for make_synthetic_competing…

…_weibull() (#67) 36071a4

_sources/auto_examples/plot_01_survival_analysis.rst.txt

@@ -1090,7 +1090,7 @@ doesn't depend on the patient features.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.501 seconds)
+   **Total running time of the script:** (0 minutes 9.918 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_01_survival_analysis.py:

_sources/auto_examples/plot_02_marginal_cumulative_incidence_estimation.rst.txt

@@ -277,17 +277,17 @@ theoretical CIFs:
 
  .. code-block:: none
 
-    Integrated theoretical any event survival curve in 0.676 s
+    Integrated theoretical any event survival curve in 0.643 s
     /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
       warnings.warn(
-    SurvivalBoost fit: 4.093 s
-    SurvivalBoost prediction: 2.934 s
-    Integrated theoretical cumulative incidence curve for event 1 in 2.995 s
-    Aalen-Johansen for event 1 fit in 5.076 s
-    Integrated theoretical cumulative incidence curve for event 2 in 5.178 s
-    Aalen-Johansen for event 2 fit in 5.082 s
-    Integrated theoretical cumulative incidence curve for event 3 in 5.163 s
-    Aalen-Johansen for event 3 fit in 5.051 s
+    SurvivalBoost fit: 4.280 s
+    SurvivalBoost prediction: 3.026 s
+    Integrated theoretical cumulative incidence curve for event 1 in 3.086 s
+    Aalen-Johansen for event 1 fit in 5.279 s
+    Integrated theoretical cumulative incidence curve for event 2 in 5.378 s
+    Aalen-Johansen for event 2 fit in 5.403 s
+    Integrated theoretical cumulative incidence curve for event 3 in 5.485 s
+    Aalen-Johansen for event 3 fit in 5.278 s
 
 
 
@@ -330,17 +330,17 @@ of censoring.
 
  .. code-block:: none
 
-    Integrated theoretical any event survival curve in 0.589 s
+    Integrated theoretical any event survival curve in 0.586 s
     /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
       warnings.warn(
-    SurvivalBoost fit: 4.198 s
-    SurvivalBoost prediction: 2.943 s
-    Integrated theoretical cumulative incidence curve for event 1 in 3.003 s
-    Aalen-Johansen for event 1 fit in 5.047 s
-    Integrated theoretical cumulative incidence curve for event 2 in 5.143 s
-    Aalen-Johansen for event 2 fit in 5.012 s
-    Integrated theoretical cumulative incidence curve for event 3 in 5.097 s
-    Aalen-Johansen for event 3 fit in 5.057 s
+    SurvivalBoost fit: 4.237 s
+    SurvivalBoost prediction: 2.978 s
+    Integrated theoretical cumulative incidence curve for event 1 in 3.038 s
+    Aalen-Johansen for event 1 fit in 5.367 s
+    Integrated theoretical cumulative incidence curve for event 2 in 5.465 s
+    Aalen-Johansen for event 2 fit in 5.302 s
+    Integrated theoretical cumulative incidence curve for event 3 in 5.383 s
+    Aalen-Johansen for event 3 fit in 5.286 s
 
 
 
@@ -364,7 +364,7 @@ the large time horizons:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 46.561 seconds)
+   **Total running time of the script:** (0 minutes 48.462 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_02_marginal_cumulative_incidence_estimation.py:

auto_examples/plot_01_survival_analysis.html

@@ -1231,7 +1231,7 @@ <h2>Survival model evaluation<a class="headerlink" href="#survival-model-evaluat
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Concordance index for SurvivalBoost: 0.67
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.501 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.918 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-01-survival-analysis-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/a6916f06450964ef8d10eb5f311100d1/plot_01_survival_analysis.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_01_survival_analysis.ipynb</span></code></a></p>

auto_examples/plot_02_marginal_cumulative_incidence_estimation.html

@@ -564,17 +564,17 @@ <h2>CIFs estimated on uncensored data<a class="headerlink" href="#cifs-estimated
 <span class="p">)</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_001.png" srcset="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_001.png" alt="Cause-specific cumulative incidence functions (0.0% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Integrated theoretical any event survival curve in 0.676 s
+<img src="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_001.png" srcset="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_001.png" alt="Cause-specific cumulative incidence functions (0.0% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Integrated theoretical any event survival curve in 0.643 s
 /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: &#39;force_all_finite&#39; was renamed to &#39;ensure_all_finite&#39; in 1.6 and will be removed in 1.8.
   warnings.warn(
-SurvivalBoost fit: 4.093 s
-SurvivalBoost prediction: 2.934 s
-Integrated theoretical cumulative incidence curve for event 1 in 2.995 s
-Aalen-Johansen for event 1 fit in 5.076 s
-Integrated theoretical cumulative incidence curve for event 2 in 5.178 s
-Aalen-Johansen for event 2 fit in 5.082 s
-Integrated theoretical cumulative incidence curve for event 3 in 5.163 s
-Aalen-Johansen for event 3 fit in 5.051 s
+SurvivalBoost fit: 4.280 s
+SurvivalBoost prediction: 3.026 s
+Integrated theoretical cumulative incidence curve for event 1 in 3.086 s
+Aalen-Johansen for event 1 fit in 5.279 s
+Integrated theoretical cumulative incidence curve for event 2 in 5.378 s
+Aalen-Johansen for event 2 fit in 5.403 s
+Integrated theoretical cumulative incidence curve for event 3 in 5.485 s
+Aalen-Johansen for event 3 fit in 5.278 s
 </pre></div>
 </div>
 </section>
@@ -597,17 +597,17 @@ <h2>CIFs estimated on censored data<a class="headerlink" href="#cifs-estimated-o
 <span class="n">plot_cumulative_incidence_functions</span><span class="p">(</span><a href="../generated/hazardous.SurvivalBoost.html#hazardous.SurvivalBoost" title="hazardous.SurvivalBoost" class="sphx-glr-backref-module-hazardous sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">survival_boost</span></a><span class="o">=</span><a href="../generated/hazardous.SurvivalBoost.html#hazardous.SurvivalBoost" title="hazardous.SurvivalBoost" class="sphx-glr-backref-module-hazardous sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">survival_boost</span></a><span class="p">,</span> <span class="n">aj</span><span class="o">=</span><span class="n">aj</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_censored</span><span class="p">)</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_002.png" srcset="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_002.png" alt="Cause-specific cumulative incidence functions (40.4% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Integrated theoretical any event survival curve in 0.589 s
+<img src="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_002.png" srcset="../_images/sphx_glr_plot_02_marginal_cumulative_incidence_estimation_002.png" alt="Cause-specific cumulative incidence functions (40.4% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Integrated theoretical any event survival curve in 0.586 s
 /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: &#39;force_all_finite&#39; was renamed to &#39;ensure_all_finite&#39; in 1.6 and will be removed in 1.8.
   warnings.warn(
-SurvivalBoost fit: 4.198 s
-SurvivalBoost prediction: 2.943 s
-Integrated theoretical cumulative incidence curve for event 1 in 3.003 s
-Aalen-Johansen for event 1 fit in 5.047 s
-Integrated theoretical cumulative incidence curve for event 2 in 5.143 s
-Aalen-Johansen for event 2 fit in 5.012 s
-Integrated theoretical cumulative incidence curve for event 3 in 5.097 s
-Aalen-Johansen for event 3 fit in 5.057 s
+SurvivalBoost fit: 4.237 s
+SurvivalBoost prediction: 2.978 s
+Integrated theoretical cumulative incidence curve for event 1 in 3.038 s
+Aalen-Johansen for event 1 fit in 5.367 s
+Integrated theoretical cumulative incidence curve for event 2 in 5.465 s
+Aalen-Johansen for event 2 fit in 5.302 s
+Integrated theoretical cumulative incidence curve for event 3 in 5.383 s
+Aalen-Johansen for event 3 fit in 5.286 s
 </pre></div>
 </div>
 <p>Note that the Aalen-Johansen estimator is unbiased and empirically recovers
@@ -622,7 +622,7 @@ <h2>CIFs estimated on censored data<a class="headerlink" href="#cifs-estimated-o
 <p>Alternatively, we could try to enable a monotonicity constraint at training
 time, however, in practice this often causes a sever over-estimation bias for
 the large time horizons:</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 46.561 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 48.462 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-02-marginal-cumulative-incidence-estimation-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/8da6be5df74b4f584c69dbcd5de4f948/plot_02_marginal_cumulative_incidence_estimation.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_02_marginal_cumulative_incidence_estimation.ipynb</span></code></a></p>

generated/hazardous.data.make_synthetic_competing_weibull.html

@@ -390,17 +390,58 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 different range of shape and scale parameters.</p>
 <p>Then we sample event durations for each event type from the corresponding
 Weibull distribution parametrized by the sampled shape and scale
-parameters.</p>
-<p>The shape and scale parameters are returned as features. For each
-individual, the event type with the shortest duration is kept as the target
-event (competing risks setting) and its event identifier and duration are
-returned as the target dataframe.</p>
-<p>A fraction of the individuals are censored by sampling a censoring time
-from a Weibull distribution with shape 1 and scale equal to the mean
-duration of the target event times the <code class="docutils literal notranslate"><span class="pre">censoring_relative_scale</span></code>.</p>
-<p>Setting <code class="docutils literal notranslate"><span class="pre">censoring_relative_scale</span></code> to 0 or None disables censoring.
+parameters. The shape and scale parameters are returned as features.</p>
+<p>Then, we apply the same procedure to sample the duration for the censoring
+event (event = 0) if <code class="docutils literal notranslate"><span class="pre">censoring_relative_scale</span></code> is not None or 0.</p>
+<p>For each individual, the event type with the shortest duration is kept as
+the target event (competing risks setting) and its event identifier and
+duration are returned as the target dataframe.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>n_events: int, default=3</strong></dt><dd><p>Number of events of interest.</p>
+</dd>
+<dt><strong>n_samples: int, default=3000</strong></dt><dd><p>Number of individuals in the population.</p>
+</dd>
+<dt><strong>return_X_y: bool, default=False</strong></dt><dd><p>If True, returns <code class="docutils literal notranslate"><span class="pre">(data,</span> <span class="pre">target)</span></code> instead of a Bunch object.</p>
+</dd>
+<dt><strong>feature_rounding: int or None, default=2</strong></dt><dd><p>Round the feature values. If None, no rounding will be applied.</p>
+</dd>
+<dt><strong>target_rounding: int or None, default=1</strong></dt><dd><p>Round the column duration of the target. If None, no rounding will
+be applied.</p>
+</dd>
+<dt><strong>shape_ranges: tuple of shape (n_events, 2)</strong></dt><dd><p>The lower and upper boundary of the shape, <cite>n_samples</cite> shape
+values for <cite>n_events</cite> will be drawn from a uniform distribution.</p>
+</dd>
+<dt><strong>scale_ranges: tuple of shape (n_events, 2)</strong></dt><dd><p>The lower and upper boundary of the scale, <cite>n_samples</cite> scale
+values for <cite>n_events</cite> will be drawn from a uniform distribution.</p>
+</dd>
+<dt><strong>base_scale: int, default=1000</strong></dt><dd><p>Scaling parameter of the <code class="docutils literal notranslate"><span class="pre">scale_range</span></code>.</p>
+</dd>
+<dt><strong>censoring_relative_scale: float, default=1.5</strong></dt><dd><p>Relative scale of the censoring level. Individuals are censored by
+sampling a censoring time from a Weibull distribution with shape 1
+and scale equal to the mean duration of the target event times
+the <code class="docutils literal notranslate"><span class="pre">censoring_relative_scale</span></code>.
+Setting <code class="docutils literal notranslate"><span class="pre">censoring_relative_scale</span></code> to 0 or None disables censoring.
 Setting it to a small value (e.g. 0.5 instead of 1.5) will result in a
 larger fraction of censored individuals.</p>
+</dd>
+<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the randomness of the uniform time sampler.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt>(data, target): tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True</dt><dd><p>A tuple of two dataframes. The first containing a 2D array of shape
+(n_samples, n_features) with each row representing one sample
+and each column representing the features. The second dataframe
+of shape (n_samples, 2) containing the target samples. The first
+column contains the event identifier (event = 0 represents the censoring
+event) and the second column contains the duration of the target event.</p>
+</dd>
+</dl>
+</dd>
+</dl>
 </dd></dl>
 
 <div class="clearer"></div></section>