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| <main> | ||
| <article id="content"> | ||
| <header> | ||
| <h1 class="title">Module <code>dsm.contrib.dcm_api</code></h1> | ||
| </header> | ||
| <section id="section-intro"> | ||
| <div class="git-link-div"><a href="https://github.com/autonlab/DeepSurvivalMachines" class="git-link">Browse git</a></div> | ||
| </section> | ||
| <section> | ||
| </section> | ||
| <section> | ||
| </section> | ||
| <section> | ||
| </section> | ||
| <section> | ||
| <h2 class="section-title" id="header-classes">Classes</h2> | ||
| <dl> | ||
| <dt id="dsm.contrib.dcm_api.DeepCoxMixtures"><code class="flex name class"> | ||
| <span>class <span class="ident">DeepCoxMixtures</span></span> | ||
| <span>(</span><span>k=3, layers=None, distribution='Weibull', temp=1000.0, discount=1.0)</span> | ||
| </code></dt> | ||
| <dd> | ||
| <div class="desc"><p>A Deep Cox Mixture model.</p> | ||
| <p>This is the main interface to a Deep Cox Mixture model. | ||
| A model is instantiated with approporiate set of hyperparameters and | ||
| fit on numpy arrays consisting of the features, event/censoring times | ||
| and the event/censoring indicators.</p> | ||
| <p>For full details on Deep Cox Mixture, refer to the paper [1].</p> | ||
| <h2 id="references">References</h2> | ||
| <p>[1] <a href="https://arxiv.org/abs/2101.06536">Deep Cox Mixtures | ||
| for Survival Regression. Machine Learning in Health Conference (2021)</a></p> | ||
| <h2 id="parameters">Parameters</h2> | ||
| <dl> | ||
| <dt><strong><code>k</code></strong> : <code>int</code></dt> | ||
| <dd>The number of underlying Cox distributions.</dd> | ||
| <dt><strong><code>layers</code></strong> : <code>list</code></dt> | ||
| <dd>A list of integers consisting of the number of neurons in each | ||
| hidden layer.</dd> | ||
| </dl> | ||
| <h2 id="example">Example</h2> | ||
| <pre><code class="language-python-repl">>>> from dsm.contrib import DeepCoxMixtures | ||
| >>> model = DeepCoxMixtures() | ||
| >>> model.fit(x, t, e) | ||
| </code></pre></div> | ||
| <div class="git-link-div"><a href="https://github.com/autonlab/DeepSurvivalMachines" class="git-link">Browse git</a></div> | ||
| <h3>Methods</h3> | ||
| <dl> | ||
| <dt id="dsm.contrib.dcm_api.DeepCoxMixtures.fit"><code class="name flex"> | ||
| <span>def <span class="ident">fit</span></span>(<span>self, x, t, e, vsize=0.15, val_data=None, iters=1, learning_rate=0.001, batch_size=100, optimizer='Adam', random_state=100)</span> | ||
| </code></dt> | ||
| <dd> | ||
| <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> : <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> : <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> : <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> : <code>float</code></dt> | ||
| <dd>Amount of data to set aside as the validation set.</dd> | ||
| <dt><strong><code>val_data</code></strong> : <code>tuple</code></dt> | ||
| <dd>A tuple of the validation dataset. If passed vsize is ignored.</dd> | ||
| <dt><strong><code>iters</code></strong> : <code>int</code></dt> | ||
| <dd>The maximum number of training iterations on the training dataset.</dd> | ||
| <dt><strong><code>learning_rate</code></strong> : <code>float</code></dt> | ||
| <dd>The learning rate for the <code>Adam</code> optimizer.</dd> | ||
| <dt><strong><code>batch_size</code></strong> : <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>optimizer</code></strong> : <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> : <code>float</code></dt> | ||
| <dd>random seed that determines how the validation set is chosen.</dd> | ||
| </dl></div> | ||
| <div class="git-link-div"><a href="https://github.com/autonlab/DeepSurvivalMachines" class="git-link">Browse git</a></div> | ||
| </dd> | ||
| <dt id="dsm.contrib.dcm_api.DeepCoxMixtures.predict_survival"><code class="name flex"> | ||
| <span>def <span class="ident">predict_survival</span></span>(<span>self, x, t)</span> | ||
| </code></dt> | ||
| <dd> | ||
| <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 > 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> : <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> : <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> | ||
| <div class="git-link-div"><a href="https://github.com/autonlab/DeepSurvivalMachines" class="git-link">Browse git</a></div> | ||
| </dd> | ||
| </dl> | ||
| </dd> | ||
| </dl> | ||
| </section> | ||
| </article> | ||
| <nav id="sidebar"> | ||
| <img src="https://ndownloader.figshare.com/files/26367844"> | ||
| <h1>Index</h1> | ||
| <div class="toc"> | ||
| <ul></ul> | ||
| </div> | ||
| <ul id="index"> | ||
| <li><h3>Super-module</h3> | ||
| <ul> | ||
| <li><code><a title="dsm.contrib" href="index.html">dsm.contrib</a></code></li> | ||
| </ul> | ||
| </li> | ||
| <li><h3><a href="#header-classes">Classes</a></h3> | ||
| <ul> | ||
| <li> | ||
| <h4><code><a title="dsm.contrib.dcm_api.DeepCoxMixtures" href="#dsm.contrib.dcm_api.DeepCoxMixtures">DeepCoxMixtures</a></code></h4> | ||
| <ul class=""> | ||
| <li><code><a title="dsm.contrib.dcm_api.DeepCoxMixtures.fit" href="#dsm.contrib.dcm_api.DeepCoxMixtures.fit">fit</a></code></li> | ||
| <li><code><a title="dsm.contrib.dcm_api.DeepCoxMixtures.predict_survival" href="#dsm.contrib.dcm_api.DeepCoxMixtures.predict_survival">predict_survival</a></code></li> | ||
| </ul> | ||
| </li> | ||
| </ul> | ||
| </li> | ||
| </ul> | ||
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| </main> | ||
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