modified: docs/dsm_api.html

modified: dsm/dsm_api.py
autonlab · Jan 4, 2021 · d60789d · d60789d
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diff --git a/docs/dsm_api.html b/docs/dsm_api.html
@@ -115,6 +115,33 @@ <h2 id="parameters">Parameters</h2>
 <dd>random seed that determines how the validation set is chosen.</dd>
 </dl></div>
 </dd>
+<dt id="dsm.dsm_api.DeepSurvivalMachines.compute_nll"><code class="name flex">
+<span>def <span class="ident">compute_nll</span></span>(<span>self, x, t, e)</span>
+</code></dt>
+<dd>
+<p class="inheritance">
+<em>Inherited from:</em>
+<code><a title="dsm.dsm_api.DSMBase" href="#dsm.dsm_api.DSMBase">DSMBase</a></code>.<code><a title="dsm.dsm_api.DSMBase.compute_nll" href="#dsm.dsm_api.DSMBase.compute_nll">compute_nll</a></code>
+</p>
+<div class="desc"><p>This function computes the negative log likelihood of the given data.
+In case of competing risks, the negative log likelihoods are summed over
+the different events' type.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the input features, <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</dd>
+<dt><strong><code>t</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the event/censoring times, <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>.</dd>
+<dt><strong><code>e</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the event/censoring indicators, <span><span class="MathJax_Preview"> \delta </span><script type="math/tex"> \delta </script></span>.
+<span><span class="MathJax_Preview"> \delta = r </span><script type="math/tex"> \delta = r </script></span> means the event r took place.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>float</code></dt>
+<dd>Negative log likelihood.</dd>
+</dl></div>
+</dd>
 <dt id="dsm.dsm_api.DeepSurvivalMachines.predict_mean"><code class="name flex">
 <span>def <span class="ident">predict_mean</span></span>(<span>self, x, risk=1)</span>
 </code></dt>
@@ -231,6 +258,33 @@ <h2 id="parameters">Parameters</h2>
 <dd>random seed that determines how the validation set is chosen.</dd>
 </dl></div>
 </dd>
+<dt id="dsm.dsm_api.DeepRecurrentSurvivalMachines.compute_nll"><code class="name flex">
+<span>def <span class="ident">compute_nll</span></span>(<span>self, x, t, e)</span>
+</code></dt>
+<dd>
+<p class="inheritance">
+<em>Inherited from:</em>
+<code><a title="dsm.dsm_api.DSMBase" href="#dsm.dsm_api.DSMBase">DSMBase</a></code>.<code><a title="dsm.dsm_api.DSMBase.compute_nll" href="#dsm.dsm_api.DSMBase.compute_nll">compute_nll</a></code>
+</p>
+<div class="desc"><p>This function computes the negative log likelihood of the given data.
+In case of competing risks, the negative log likelihoods are summed over
+the different events' type.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the input features, <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</dd>
+<dt><strong><code>t</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the event/censoring times, <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>.</dd>
+<dt><strong><code>e</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the event/censoring indicators, <span><span class="MathJax_Preview"> \delta </span><script type="math/tex"> \delta </script></span>.
+<span><span class="MathJax_Preview"> \delta = r </span><script type="math/tex"> \delta = r </script></span> means the event r took place.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>float</code></dt>
+<dd>Negative log likelihood.</dd>
+</dl></div>
+</dd>
 <dt id="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_mean"><code class="name flex">
 <span>def <span class="ident">predict_mean</span></span>(<span>self, x, risk=1)</span>
 </code></dt>
@@ -347,6 +401,33 @@ <h2 id="parameters">Parameters</h2>
 <dd>random seed that determines how the validation set is chosen.</dd>
 </dl></div>
 </dd>
+<dt id="dsm.dsm_api.DeepConvolutionalSurvivalMachines.compute_nll"><code class="name flex">
+<span>def <span class="ident">compute_nll</span></span>(<span>self, x, t, e)</span>
+</code></dt>
+<dd>
+<p class="inheritance">
+<em>Inherited from:</em>
+<code><a title="dsm.dsm_api.DSMBase" href="#dsm.dsm_api.DSMBase">DSMBase</a></code>.<code><a title="dsm.dsm_api.DSMBase.compute_nll" href="#dsm.dsm_api.DSMBase.compute_nll">compute_nll</a></code>
+</p>
+<div class="desc"><p>This function computes the negative log likelihood of the given data.
+In case of competing risks, the negative log likelihoods are summed over
+the different events' type.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the input features, <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</dd>
+<dt><strong><code>t</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the event/censoring times, <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>.</dd>
+<dt><strong><code>e</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the event/censoring indicators, <span><span class="MathJax_Preview"> \delta </span><script type="math/tex"> \delta </script></span>.
+<span><span class="MathJax_Preview"> \delta = r </span><script type="math/tex"> \delta = r </script></span> means the event r took place.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>float</code></dt>
+<dd>Negative log likelihood.</dd>
+</dl></div>
+</dd>
 <dt id="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_mean"><code class="name flex">
 <span>def <span class="ident">predict_mean</span></span>(<span>self, x, risk=1)</span>
 </code></dt>
@@ -437,6 +518,7 @@ <h1>Index</h1>
 <h4><code><a title="dsm.dsm_api.DeepSurvivalMachines" href="#dsm.dsm_api.DeepSurvivalMachines">DeepSurvivalMachines</a></code></h4>
 <ul class="">
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.fit" href="#dsm.dsm_api.DeepSurvivalMachines.fit">fit</a></code></li>
+<li><code><a title="dsm.dsm_api.DeepSurvivalMachines.compute_nll" href="#dsm.dsm_api.DeepSurvivalMachines.compute_nll">compute_nll</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.predict_mean" href="#dsm.dsm_api.DeepSurvivalMachines.predict_mean">predict_mean</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepSurvivalMachines.predict_risk">predict_risk</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepSurvivalMachines.predict_survival">predict_survival</a></code></li>
@@ -446,6 +528,7 @@ <h4><code><a title="dsm.dsm_api.DeepSurvivalMachines" href="#dsm.dsm_api.DeepSur
 <h4><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines">DeepRecurrentSurvivalMachines</a></code></h4>
 <ul class="">
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.fit" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.fit">fit</a></code></li>
+<li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.compute_nll" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.compute_nll">compute_nll</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_mean" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_mean">predict_mean</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_risk">predict_risk</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_survival">predict_survival</a></code></li>
@@ -455,6 +538,7 @@ <h4><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_ap
 <h4><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines">DeepConvolutionalSurvivalMachines</a></code></h4>
 <ul class="">
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.fit" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.fit">fit</a></code></li>
+<li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.compute_nll" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.compute_nll">compute_nll</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_mean" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_mean">predict_mean</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_risk">predict_risk</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_survival">predict_survival</a></code></li>

diff --git a/dsm/dsm_api.py b/dsm/dsm_api.py
@@ -30,8 +30,9 @@
 from dsm.dsm_torch import DeepSurvivalMachinesTorch
 from dsm.dsm_torch import DeepRecurrentSurvivalMachinesTorch
 from dsm.dsm_torch import DeepConvolutionalSurvivalMachinesTorch
-from dsm.losses import predict_cdf
+
 import dsm.losses as losses
+
 from dsm.utilities import train_dsm
 from dsm.utilities import _get_padded_features, _get_padded_targets
 from dsm.utilities import _reshape_tensor_with_nans
@@ -40,8 +41,10 @@
 import numpy as np
 
 __pdoc__ = {}
-__pdoc__["DSMBase"] = False
 __pdoc__["DeepSurvivalMachines.fit"] = True
+__pdoc__["DeepSurvivalMachines._eval_nll"] = True
+__pdoc__["DeepConvolutionalSurvivalMachines._eval_nll"] = True
+__pdoc__["DSMBase"] = False
 
 
 class DSMBase():
@@ -120,9 +123,10 @@ def fit(self, x, t, e, vsize=0.15,
     self.torch_model = model.eval()
     self.fitted = True
 
-    return self
+    return self    
+
 
-  def _eval_nll(self, x, t, e):
+  def compute_nll(self, x, t, e):
     r"""This function computes the negative log likelihood of the given data.
     In case of competing risks, the negative log likelihoods are summed over
     the different events' type.
@@ -243,7 +247,7 @@ def predict_survival(self, x, t, risk=1):
     if not isinstance(t, list):
       t = [t]
     if self.fitted:
-      scores = predict_cdf(self.torch_model, x, t, risk=str(risk))
+      scores = losses.predict_cdf(self.torch_model, x, t, risk=str(risk))
       return np.exp(np.array(scores)).T
     else:
       raise Exception("The model has not been fitted yet. Please fit the " +