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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.graph.learner</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..core import GenLearner
from ..imports import *


class NodeClassLearner(GenLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for node classification
    Main parameters are:

    model (Model): A compiled instance of keras.engine.training.Model
    train_data (Iterator): a Iterator instance for training set
    val_data (Iterator):   A Iterator instance for validation set
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Typically over-ridden by Learner subclasses.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None
        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
            # print(obs)
        return

    def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
        &#34;&#34;&#34;
        ```
        Prints output of layer with index &lt;layer_id&gt; to help debug models.
        Uses first example (example_id=0) from training set, by default.
        ```
        &#34;&#34;&#34;
        raise Exception(
            &#34;currently_unsupported: layer_output method is not yet supported for &#34;
            + &#34;graph neural networks in ktrain&#34;
        )


class LinkPredLearner(GenLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for link prediction
    Main parameters are:

    model (Model): A compiled instance of keras.engine.training.Model
    train_data (Iterator): a Iterator instance for training set
    val_data (Iterator):   A Iterator instance for validation set
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Typically over-ridden by Learner subclasses.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None
        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
            # print(obs)
        return

    def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
        &#34;&#34;&#34;
        ```
        Prints output of layer with index &lt;layer_id&gt; to help debug models.
        Uses first example (example_id=0) from training set, by default.
        ```
        &#34;&#34;&#34;
        raise Exception(
            &#34;currently_unsupported: layer_output method is not yet supported for &#34;
            + &#34;graph neural networks in ktrain&#34;
        )</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.graph.learner.LinkPredLearner"><code class="flex name class">
<span>class <span class="ident">LinkPredLearner</span></span>
<span>(</span><span>model, train_data=None, val_data=None, batch_size=32, eval_batch_size=32, workers=1, use_multiprocessing=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Main class used to tune and train Keras models for link prediction
Main parameters are:

model (Model): A compiled instance of keras.engine.training.Model
train_data (Iterator): a Iterator instance for training set
val_data (Iterator):   A Iterator instance for validation set
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class LinkPredLearner(GenLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for link prediction
    Main parameters are:

    model (Model): A compiled instance of keras.engine.training.Model
    train_data (Iterator): a Iterator instance for training set
    val_data (Iterator):   A Iterator instance for validation set
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Typically over-ridden by Learner subclasses.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None
        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
            # print(obs)
        return

    def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
        &#34;&#34;&#34;
        ```
        Prints output of layer with index &lt;layer_id&gt; to help debug models.
        Uses first example (example_id=0) from training set, by default.
        ```
        &#34;&#34;&#34;
        raise Exception(
            &#34;currently_unsupported: layer_output method is not yet supported for &#34;
            + &#34;graph neural networks in ktrain&#34;
        )</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></li>
<li><a title="ktrain.core.Learner" href="../core.html#ktrain.core.Learner">Learner</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.graph.learner.LinkPredLearner.layer_output"><code class="name flex">
<span>def <span class="ident">layer_output</span></span>(<span>self, layer_id, example_id=0, batch_id=0, use_val=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Prints output of layer with index &lt;layer_id&gt; to help debug models.
Uses first example (example_id=0) from training set, by default.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
    &#34;&#34;&#34;
    ```
    Prints output of layer with index &lt;layer_id&gt; to help debug models.
    Uses first example (example_id=0) from training set, by default.
    ```
    &#34;&#34;&#34;
    raise Exception(
        &#34;currently_unsupported: layer_output method is not yet supported for &#34;
        + &#34;graph neural networks in ktrain&#34;
    )</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.core.GenLearner.autofit" href="../core.html#ktrain.core.Learner.autofit">autofit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.evaluate" href="../core.html#ktrain.core.Learner.evaluate">evaluate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit" href="../core.html#ktrain.core.GenLearner.fit">fit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit_onecycle" href="../core.html#ktrain.core.Learner.fit_onecycle">fit_onecycle</a></code></li>
<li><code><a title="ktrain.core.GenLearner.freeze" href="../core.html#ktrain.core.Learner.freeze">freeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.get_weight_decay" href="../core.html#ktrain.core.Learner.get_weight_decay">get_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.load_model" href="../core.html#ktrain.core.Learner.load_model">load_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_estimate" href="../core.html#ktrain.core.Learner.lr_estimate">lr_estimate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_find" href="../core.html#ktrain.core.Learner.lr_find">lr_find</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_plot" href="../core.html#ktrain.core.Learner.lr_plot">lr_plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.plot" href="../core.html#ktrain.core.Learner.plot">plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.predict" href="../core.html#ktrain.core.Learner.predict">predict</a></code></li>
<li><code><a title="ktrain.core.GenLearner.print_layers" href="../core.html#ktrain.core.Learner.print_layers">print_layers</a></code></li>
<li><code><a title="ktrain.core.GenLearner.reset_weights" href="../core.html#ktrain.core.Learner.reset_weights">reset_weights</a></code></li>
<li><code><a title="ktrain.core.GenLearner.save_model" href="../core.html#ktrain.core.Learner.save_model">save_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_model" href="../core.html#ktrain.core.Learner.set_model">set_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_weight_decay" href="../core.html#ktrain.core.Learner.set_weight_decay">set_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.top_losses" href="../core.html#ktrain.core.Learner.top_losses">top_losses</a></code></li>
<li><code><a title="ktrain.core.GenLearner.unfreeze" href="../core.html#ktrain.core.Learner.unfreeze">unfreeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.validate" href="../core.html#ktrain.core.Learner.validate">validate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.view_top_losses" href="../core.html#ktrain.core.GenLearner.view_top_losses">view_top_losses</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="ktrain.graph.learner.NodeClassLearner"><code class="flex name class">
<span>class <span class="ident">NodeClassLearner</span></span>
<span>(</span><span>model, train_data=None, val_data=None, batch_size=32, eval_batch_size=32, workers=1, use_multiprocessing=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Main class used to tune and train Keras models for node classification
Main parameters are:

model (Model): A compiled instance of keras.engine.training.Model
train_data (Iterator): a Iterator instance for training set
val_data (Iterator):   A Iterator instance for validation set
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class NodeClassLearner(GenLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for node classification
    Main parameters are:

    model (Model): A compiled instance of keras.engine.training.Model
    train_data (Iterator): a Iterator instance for training set
    val_data (Iterator):   A Iterator instance for validation set
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Typically over-ridden by Learner subclasses.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None
        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
            # print(obs)
        return

    def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
        &#34;&#34;&#34;
        ```
        Prints output of layer with index &lt;layer_id&gt; to help debug models.
        Uses first example (example_id=0) from training set, by default.
        ```
        &#34;&#34;&#34;
        raise Exception(
            &#34;currently_unsupported: layer_output method is not yet supported for &#34;
            + &#34;graph neural networks in ktrain&#34;
        )</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></li>
<li><a title="ktrain.core.Learner" href="../core.html#ktrain.core.Learner">Learner</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.graph.learner.NodeClassLearner.layer_output"><code class="name flex">
<span>def <span class="ident">layer_output</span></span>(<span>self, layer_id, example_id=0, batch_id=0, use_val=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Prints output of layer with index &lt;layer_id&gt; to help debug models.
Uses first example (example_id=0) from training set, by default.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
    &#34;&#34;&#34;
    ```
    Prints output of layer with index &lt;layer_id&gt; to help debug models.
    Uses first example (example_id=0) from training set, by default.
    ```
    &#34;&#34;&#34;
    raise Exception(
        &#34;currently_unsupported: layer_output method is not yet supported for &#34;
        + &#34;graph neural networks in ktrain&#34;
    )</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.core.GenLearner.autofit" href="../core.html#ktrain.core.Learner.autofit">autofit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.evaluate" href="../core.html#ktrain.core.Learner.evaluate">evaluate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit" href="../core.html#ktrain.core.GenLearner.fit">fit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit_onecycle" href="../core.html#ktrain.core.Learner.fit_onecycle">fit_onecycle</a></code></li>
<li><code><a title="ktrain.core.GenLearner.freeze" href="../core.html#ktrain.core.Learner.freeze">freeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.get_weight_decay" href="../core.html#ktrain.core.Learner.get_weight_decay">get_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.load_model" href="../core.html#ktrain.core.Learner.load_model">load_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_estimate" href="../core.html#ktrain.core.Learner.lr_estimate">lr_estimate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_find" href="../core.html#ktrain.core.Learner.lr_find">lr_find</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_plot" href="../core.html#ktrain.core.Learner.lr_plot">lr_plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.plot" href="../core.html#ktrain.core.Learner.plot">plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.predict" href="../core.html#ktrain.core.Learner.predict">predict</a></code></li>
<li><code><a title="ktrain.core.GenLearner.print_layers" href="../core.html#ktrain.core.Learner.print_layers">print_layers</a></code></li>
<li><code><a title="ktrain.core.GenLearner.reset_weights" href="../core.html#ktrain.core.Learner.reset_weights">reset_weights</a></code></li>
<li><code><a title="ktrain.core.GenLearner.save_model" href="../core.html#ktrain.core.Learner.save_model">save_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_model" href="../core.html#ktrain.core.Learner.set_model">set_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_weight_decay" href="../core.html#ktrain.core.Learner.set_weight_decay">set_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.top_losses" href="../core.html#ktrain.core.Learner.top_losses">top_losses</a></code></li>
<li><code><a title="ktrain.core.GenLearner.unfreeze" href="../core.html#ktrain.core.Learner.unfreeze">unfreeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.validate" href="../core.html#ktrain.core.Learner.validate">validate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.view_top_losses" href="../core.html#ktrain.core.GenLearner.view_top_losses">view_top_losses</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
</article>
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<h4><code><a title="ktrain.graph.learner.LinkPredLearner" href="#ktrain.graph.learner.LinkPredLearner">LinkPredLearner</a></code></h4>
<ul class="">
<li><code><a title="ktrain.graph.learner.LinkPredLearner.layer_output" href="#ktrain.graph.learner.LinkPredLearner.layer_output">layer_output</a></code></li>
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<h4><code><a title="ktrain.graph.learner.NodeClassLearner" href="#ktrain.graph.learner.NodeClassLearner">NodeClassLearner</a></code></h4>
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<li><code><a title="ktrain.graph.learner.NodeClassLearner.layer_output" href="#ktrain.graph.learner.NodeClassLearner.layer_output">layer_output</a></code></li>
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