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Expand Up @@ -76,16 +76,16 @@ <h2 id="example">Example</h2>
&gt;&gt;&gt; model = DeepSurvivalMachines()
&gt;&gt;&gt; model.fit(x, t, e)
</code></pre></div>
<h3>Ancestors</h3>
<ul class="hlist">
<li>dsm.dsm_api.DSMBase</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="dsm.dsm_api.DeepSurvivalMachines.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x, t, e, vsize=0.15, iters=1, learning_rate=0.001, batch_size=100, elbo=True, optimizer='Adam', random_state=100)</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.fit" href="#dsm.dsm_api.DSMBase.fit">fit</a></code>
</p>
<div class="desc"><p>This method is used to train an instance of the DSM model.</p>
<h2 id="parameters">Parameters</h2>
<dl>
Expand Down Expand Up @@ -123,8 +123,21 @@ <h2 id="parameters">Parameters</h2>
<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.predict_risk" href="#dsm.dsm_api.DSMBase.predict_risk">predict_risk</a></code>
</p>
<div class="desc inherited"><p>Returns the estimated risk of an event occuring before time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T\leq t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T\leq t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span> …</p></div>
<div class="desc"><p>Returns the estimated risk of an event occuring before time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T\leq t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T\leq t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</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>list</code> or <code>float</code></dt>
<dd>a list or float of the times at which survival probability is
to be computed</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>np.array</code></dt>
<dd>numpy array of the risks at each time in t.</dd>
</dl></div>
</dd>
<dt id="dsm.dsm_api.DeepSurvivalMachines.predict_survival"><code class="name flex">
<span>def <span class="ident">predict_survival</span></span>(<span>self, x, t)</span>
Expand All @@ -134,8 +147,21 @@ <h2 id="parameters">Parameters</h2>
<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.predict_survival" href="#dsm.dsm_api.DSMBase.predict_survival">predict_survival</a></code>
</p>
<div class="desc inherited"><p>Returns the estimated survival probability at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T &gt; t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T > t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span> …</p></div>
<div class="desc"><p>Returns the estimated survival probability at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T &gt; t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T > t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</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>list</code> or <code>float</code></dt>
<dd>a list or float of the times at which survival probability is
to be computed</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>np.array</code></dt>
<dd>numpy array of the survival probabilites at each time in t.</dd>
</dl></div>
</dd>
</dl>
</dd>
Expand All @@ -146,20 +172,16 @@ <h2 id="parameters">Parameters</h2>
<dd>
<div class="desc"><p>The Deep Recurrent Survival Machines model to handle data with
time-dependent covariates.</p></div>
<h3>Ancestors</h3>
<ul class="hlist">
<li>dsm.dsm_api.DSMBase</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines">DeepConvolutionalSurvivalMachines</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="dsm.dsm_api.DeepRecurrentSurvivalMachines.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x, t, e, vsize=0.15, iters=1, learning_rate=0.001, batch_size=100, elbo=True, optimizer='Adam', random_state=100)</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.fit" href="#dsm.dsm_api.DSMBase.fit">fit</a></code>
</p>
<div class="desc"><p>This method is used to train an instance of the DSM model.</p>
<h2 id="parameters">Parameters</h2>
<dl>
Expand Down Expand Up @@ -197,8 +219,21 @@ <h2 id="parameters">Parameters</h2>
<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.predict_risk" href="#dsm.dsm_api.DSMBase.predict_risk">predict_risk</a></code>
</p>
<div class="desc inherited"><p>Returns the estimated risk of an event occuring before time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T\leq t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T\leq t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span> …</p></div>
<div class="desc"><p>Returns the estimated risk of an event occuring before time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T\leq t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T\leq t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</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>list</code> or <code>float</code></dt>
<dd>a list or float of the times at which survival probability is
to be computed</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>np.array</code></dt>
<dd>numpy array of the risks at each time in t.</dd>
</dl></div>
</dd>
<dt id="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_survival"><code class="name flex">
<span>def <span class="ident">predict_survival</span></span>(<span>self, x, t)</span>
Expand All @@ -208,8 +243,21 @@ <h2 id="parameters">Parameters</h2>
<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.predict_survival" href="#dsm.dsm_api.DSMBase.predict_survival">predict_survival</a></code>
</p>
<div class="desc inherited"><p>Returns the estimated survival probability at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T &gt; t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T > t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span> …</p></div>
<div class="desc"><p>Returns the estimated survival probability at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T &gt; t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T > t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</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>list</code> or <code>float</code></dt>
<dd>a list or float of the times at which survival probability is
to be computed</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>np.array</code></dt>
<dd>numpy array of the survival probabilites at each time in t.</dd>
</dl></div>
</dd>
</dl>
</dd>
Expand All @@ -221,11 +269,6 @@ <h2 id="parameters">Parameters</h2>
<div class="desc"><div class="admonition warning">
<p class="admonition-title">Warning:&ensp;Not Implemented</p>
</div></div>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines">DeepRecurrentSurvivalMachines</a></li>
<li>dsm.dsm_api.DSMBase</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="dsm.dsm_api.DeepConvolutionalSurvivalMachines.fit"><code class="name flex">
Expand All @@ -236,7 +279,34 @@ <h3>Methods</h3>
<em>Inherited from:</em>
<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines">DeepRecurrentSurvivalMachines</a></code>.<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.fit" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.fit">fit</a></code>
</p>
<div class="desc inherited"><p>This method is used to train an instance of the DSM model …</p></div>
<div class="desc"><p>This method is used to train an instance of the DSM model.</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 = 1 </span><script type="math/tex"> \delta = 1 </script></span> means the event took place.</dd>
<dt><strong><code>vsize</code></strong> :&ensp;<code>float</code></dt>
<dd>Amount of data to set aside as the validation set.</dd>
<dt><strong><code>iters</code></strong> :&ensp;<code>int</code></dt>
<dd>The maximum number of training iterations on the training dataset.</dd>
<dt><strong><code>learning_rate</code></strong> :&ensp;<code>float</code></dt>
<dd>The learning rate for the <code>Adam</code> optimizer.</dd>
<dt><strong><code>batch_size</code></strong> :&ensp;<code>int</code></dt>
<dd>learning is performed on mini-batches of input data. this parameter
specifies the size of each mini-batch.</dd>
<dt><strong><code>elbo</code></strong> :&ensp;<code>bool</code></dt>
<dd>Whether to use the Evidence Lower Bound for optimization.
Default is True.</dd>
<dt><strong><code>optimizer</code></strong> :&ensp;<code>str</code></dt>
<dd>The choice of the gradient based optimization method. One of
'Adam', 'RMSProp' or 'SGD'.</dd>
<dt><strong><code>random_state</code></strong> :&ensp;<code>float</code></dt>
<dd>random seed that determines how the validation set is chosen.</dd>
</dl></div>
</dd>
<dt id="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_risk"><code class="name flex">
<span>def <span class="ident">predict_risk</span></span>(<span>self, x, t)</span>
Expand All @@ -246,8 +316,21 @@ <h3>Methods</h3>
<em>Inherited from:</em>
<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines">DeepRecurrentSurvivalMachines</a></code>.<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_risk">predict_risk</a></code>
</p>
<div class="desc inherited"><p>Returns the estimated risk of an event occuring before time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T\leq t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T\leq t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span> …</p></div>
<div class="desc"><p>Returns the estimated risk of an event occuring before time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T\leq t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T\leq t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</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>list</code> or <code>float</code></dt>
<dd>a list or float of the times at which survival probability is
to be computed</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>np.array</code></dt>
<dd>numpy array of the risks at each time in t.</dd>
</dl></div>
</dd>
<dt id="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_survival"><code class="name flex">
<span>def <span class="ident">predict_survival</span></span>(<span>self, x, t)</span>
Expand All @@ -257,8 +340,21 @@ <h3>Methods</h3>
<em>Inherited from:</em>
<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines">DeepRecurrentSurvivalMachines</a></code>.<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_survival">predict_survival</a></code>
</p>
<div class="desc inherited"><p>Returns the estimated survival probability at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T &gt; t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T > t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span> …</p></div>
<div class="desc"><p>Returns the estimated survival probability at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T &gt; t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T > t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</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>list</code> or <code>float</code></dt>
<dd>a list or float of the times at which survival probability is
to be computed</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>np.array</code></dt>
<dd>numpy array of the survival probabilites at each time in t.</dd>
</dl></div>
</dd>
</dl>
</dd>
Expand Down
34 changes: 0 additions & 34 deletions docs/dsm_torch.html
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Expand Up @@ -112,21 +112,6 @@ <h2 id="parameters">Parameters</h2>
Default is 1.</dd>
</dl>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="dsm.dsm_torch.DeepSurvivalMachinesTorch.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="dsm.dsm_torch.DeepSurvivalMachinesTorch.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="dsm.dsm_torch.DeepSurvivalMachinesTorch.forward"><code class="name flex">
Expand Down Expand Up @@ -189,21 +174,6 @@ <h2 id="parameters">Parameters</h2>
Default is 1.</dd>
</dl>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.forward"><code class="name flex">
Expand Down Expand Up @@ -252,17 +222,13 @@ <h4><code><a title="dsm.dsm_torch.DeepSurvivalMachinesTorch" href="#dsm.dsm_torc
<ul class="">
<li><code><a title="dsm.dsm_torch.DeepSurvivalMachinesTorch.forward" href="#dsm.dsm_torch.DeepSurvivalMachinesTorch.forward">forward</a></code></li>
<li><code><a title="dsm.dsm_torch.DeepSurvivalMachinesTorch.get_shape_scale" href="#dsm.dsm_torch.DeepSurvivalMachinesTorch.get_shape_scale">get_shape_scale</a></code></li>
<li><code><a title="dsm.dsm_torch.DeepSurvivalMachinesTorch.dump_patches" href="#dsm.dsm_torch.DeepSurvivalMachinesTorch.dump_patches">dump_patches</a></code></li>
<li><code><a title="dsm.dsm_torch.DeepSurvivalMachinesTorch.training" href="#dsm.dsm_torch.DeepSurvivalMachinesTorch.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch" href="#dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch">DeepRecurrentSurvivalMachinesTorch</a></code></h4>
<ul class="">
<li><code><a title="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.forward" href="#dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.forward">forward</a></code></li>
<li><code><a title="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.get_shape_scale" href="#dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.get_shape_scale">get_shape_scale</a></code></li>
<li><code><a title="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.dump_patches" href="#dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.dump_patches">dump_patches</a></code></li>
<li><code><a title="dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.training" href="#dsm.dsm_torch.DeepRecurrentSurvivalMachinesTorch.training">training</a></code></li>
</ul>
</li>
</ul>
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