docs/dataset.html

<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.10.0" />
<title>ktrain.dataset API documentation</title>
<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.dataset</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .imports import *


class Dataset:
    &#34;&#34;&#34;
    ```
    Base class for custom datasets in ktrain.

    If subclass of Dataset implements a method to to_tfdataset
    that converts the data to a tf.Dataset, then this will be
    invoked by Learner instances just prior to training so
    fit() will train using a tf.Dataset representation of your data.
    Sequence methods such as __get_item__ and __len__
    must still be implemented.

    The signature of to_tfdataset is as follows:

    def to_tfdataset(self, train=True)

    See ktrain.text.preprocess.TransformerDataset as an example.
    ```
    &#34;&#34;&#34;

    # required: used by ktrain.core.Learner instances
    def nsamples(self):
        raise NotImplemented

    # required: used by ktrain.core.Learner instances
    def get_y(self):
        raise NotImplemented

    # optional: to modify dataset between epochs (e.g., shuffle)
    def on_epoch_end(self):
        pass

    # optional
    def ondisk(self):
        &#34;&#34;&#34;
        ```
        Is data being read from disk like with DirectoryIterators?
        ```
        &#34;&#34;&#34;
        return False

    # optional: used only if invoking *_classifier functions
    def xshape(self):
        &#34;&#34;&#34;
        ```
        shape of X
        Examples:
            for images: input_shape
            for text: (n_example, sequence_length)
        ```
        &#34;&#34;&#34;
        raise NotImplemented

    # optional: used only if invoking *_classifier functions
    def nclasses(self):
        &#34;&#34;&#34;
        ```
        Number of classes
        For classification problems: this is the number of labels
        Not used for regression problems
        ```
        &#34;&#34;&#34;
        raise NotImplemented


class TFDataset(Dataset):
    &#34;&#34;&#34;
    ```
    Wrapper for tf.data.Datasets
    ```
    &#34;&#34;&#34;

    def __init__(self, tfdataset, n, y):
        &#34;&#34;&#34;
        ```
        Args:
          tfdataset(tf.data.Dataset):  a tf.Dataset instance
          n(int): number of examples in dataset (cardinality, which can&#39;t reliably be extracted from tf.data.Datasets)
          y(np.ndarray): y values for each example - should be in the format expected by your moddel (e.g., 1-hot-encoded)
        ```
        &#34;&#34;&#34;
        if not isinstance(tfdataset, tf.data.Dataset):
            raise ValueError(
                &#34;tfdataset must be a fully-configured tf.data.Dataset with batch_size, etc. set appropriately&#34;
            )
        self.tfdataset = tfdataset
        self.bs = next(tfdataset.as_numpy_iterator())[-1].shape[
            0
        ]  # extract batch_size from tfdataset
        self.n = n
        self.y = y

    @property
    def batch_size(self):
        return self.bs

    @batch_size.setter
    def batch_size(self, value):
        if value != self.bs:
            warnings.warn(
                &#34;batch_size parameter is ignored, as pre-configured batch_size of tf.data.Dataset is used&#34;
            )

    def nsamples(self):
        return self.n

    def get_y(self):
        return self.y

    def to_tfdataset(self, train=True):
        return self.tfdataset


class SequenceDataset(Dataset, keras.utils.Sequence):
    &#34;&#34;&#34;
    ```
    Base class for custom datasets in ktrain.

    If subclass of Dataset implements a method to to_tfdataset
    that converts the data to a tf.Dataset, then this will be
    invoked by Learner instances just prior to training so
    fit() will train using a tf.Dataset representation of your data.
    Sequence methods such as __get_item__ and __len__
    must still be implemented.

    The signature of to_tfdataset is as follows:

    def to_tfdataset(self, training=True)

    See ktrain.text.preprocess.TransformerDataset as an example.
    ```
    &#34;&#34;&#34;

    def __init__(self, batch_size=32):
        self.batch_size = batch_size

    # required by keras.utils.Sequence instances
    def __len__(self):
        raise NotImplemented

    # required by keras.utils.Sequence instances
    def __getitem__(self, idx):
        raise NotImplemented

        return False


class MultiArrayDataset(SequenceDataset):
    def __init__(self, x, y, batch_size=32, shuffle=True):
        # error checks
        err = False
        if type(x) == np.ndarray:
            if len(x.shape) != 2:
                err = True
        elif type(x) == list:
            for d in x:
                if type(d) != np.ndarray or len(d.shape) != 2:
                    err = True
                    break
        else:
            err = True
        if err:
            raise ValueError(&#34;x must be a 2d numpy array or a list of 2d numpy arrays&#34;)
        if type(y) != np.ndarray:
            raise ValueError(&#34;y must be a numpy array&#34;)
        if type(x) == np.ndarray:
            x = [x]

        # set variables
        super().__init__(batch_size=batch_size)
        self.x, self.y = x, y
        self.indices = np.arange(self.x[0].shape[0])
        self.n_inputs = len(x)
        self.shuffle = shuffle

    def __len__(self):
        return math.ceil(self.x[0].shape[0] / self.batch_size)

    def __getitem__(self, idx):
        inds = self.indices[idx * self.batch_size : (idx + 1) * self.batch_size]
        batch_x = []
        for i in range(self.n_inputs):
            batch_x.append(self.x[i][inds])
        batch_y = self.y[inds]
        return tuple(batch_x), batch_y

    def nsamples(self):
        return self.x[0].shape[0]

    def get_y(self):
        return self.y

    def on_epoch_end(self):
        if self.shuffle:
            np.random.shuffle(self.indices)

    def xshape(self):
        return self.x[0].shape

    def nclasses(self):
        return self.y.shape[1]

    def ondisk(self):
        return False</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.dataset.Dataset"><code class="flex name class">
<span>class <span class="ident">Dataset</span></span>
</code></dt>
<dd>
<div class="desc"><pre><code>Base class for custom datasets in ktrain.

If subclass of Dataset implements a method to to_tfdataset
that converts the data to a tf.Dataset, then this will be
invoked by Learner instances just prior to training so
fit() will train using a tf.Dataset representation of your data.
Sequence methods such as __get_item__ and __len__
must still be implemented.

The signature of to_tfdataset is as follows:

def to_tfdataset(self, train=True)

See ktrain.text.preprocess.TransformerDataset as an example.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Dataset:
    &#34;&#34;&#34;
    ```
    Base class for custom datasets in ktrain.

    If subclass of Dataset implements a method to to_tfdataset
    that converts the data to a tf.Dataset, then this will be
    invoked by Learner instances just prior to training so
    fit() will train using a tf.Dataset representation of your data.
    Sequence methods such as __get_item__ and __len__
    must still be implemented.

    The signature of to_tfdataset is as follows:

    def to_tfdataset(self, train=True)

    See ktrain.text.preprocess.TransformerDataset as an example.
    ```
    &#34;&#34;&#34;

    # required: used by ktrain.core.Learner instances
    def nsamples(self):
        raise NotImplemented

    # required: used by ktrain.core.Learner instances
    def get_y(self):
        raise NotImplemented

    # optional: to modify dataset between epochs (e.g., shuffle)
    def on_epoch_end(self):
        pass

    # optional
    def ondisk(self):
        &#34;&#34;&#34;
        ```
        Is data being read from disk like with DirectoryIterators?
        ```
        &#34;&#34;&#34;
        return False

    # optional: used only if invoking *_classifier functions
    def xshape(self):
        &#34;&#34;&#34;
        ```
        shape of X
        Examples:
            for images: input_shape
            for text: (n_example, sequence_length)
        ```
        &#34;&#34;&#34;
        raise NotImplemented

    # optional: used only if invoking *_classifier functions
    def nclasses(self):
        &#34;&#34;&#34;
        ```
        Number of classes
        For classification problems: this is the number of labels
        Not used for regression problems
        ```
        &#34;&#34;&#34;
        raise NotImplemented</code></pre>
</details>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="ktrain.dataset.SequenceDataset" href="#ktrain.dataset.SequenceDataset">SequenceDataset</a></li>
<li><a title="ktrain.dataset.TFDataset" href="#ktrain.dataset.TFDataset">TFDataset</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.dataset.Dataset.get_y"><code class="name flex">
<span>def <span class="ident">get_y</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_y(self):
    raise NotImplemented</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.Dataset.nclasses"><code class="name flex">
<span>def <span class="ident">nclasses</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Number of classes
For classification problems: this is the number of labels
Not used for regression problems
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def nclasses(self):
    &#34;&#34;&#34;
    ```
    Number of classes
    For classification problems: this is the number of labels
    Not used for regression problems
    ```
    &#34;&#34;&#34;
    raise NotImplemented</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.Dataset.nsamples"><code class="name flex">
<span>def <span class="ident">nsamples</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def nsamples(self):
    raise NotImplemented</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.Dataset.on_epoch_end"><code class="name flex">
<span>def <span class="ident">on_epoch_end</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_epoch_end(self):
    pass</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.Dataset.ondisk"><code class="name flex">
<span>def <span class="ident">ondisk</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Is data being read from disk like with DirectoryIterators?
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def ondisk(self):
    &#34;&#34;&#34;
    ```
    Is data being read from disk like with DirectoryIterators?
    ```
    &#34;&#34;&#34;
    return False</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.Dataset.xshape"><code class="name flex">
<span>def <span class="ident">xshape</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>shape of X
Examples:
    for images: input_shape
    for text: (n_example, sequence_length)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def xshape(self):
    &#34;&#34;&#34;
    ```
    shape of X
    Examples:
        for images: input_shape
        for text: (n_example, sequence_length)
    ```
    &#34;&#34;&#34;
    raise NotImplemented</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="ktrain.dataset.MultiArrayDataset"><code class="flex name class">
<span>class <span class="ident">MultiArrayDataset</span></span>
<span>(</span><span>x, y, batch_size=32, shuffle=True)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Base class for custom datasets in ktrain.

If subclass of Dataset implements a method to to_tfdataset
that converts the data to a tf.Dataset, then this will be
invoked by Learner instances just prior to training so
fit() will train using a tf.Dataset representation of your data.
Sequence methods such as __get_item__ and __len__
must still be implemented.

The signature of to_tfdataset is as follows:

def to_tfdataset(self, training=True)

See ktrain.text.preprocess.TransformerDataset as an example.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MultiArrayDataset(SequenceDataset):
    def __init__(self, x, y, batch_size=32, shuffle=True):
        # error checks
        err = False
        if type(x) == np.ndarray:
            if len(x.shape) != 2:
                err = True
        elif type(x) == list:
            for d in x:
                if type(d) != np.ndarray or len(d.shape) != 2:
                    err = True
                    break
        else:
            err = True
        if err:
            raise ValueError(&#34;x must be a 2d numpy array or a list of 2d numpy arrays&#34;)
        if type(y) != np.ndarray:
            raise ValueError(&#34;y must be a numpy array&#34;)
        if type(x) == np.ndarray:
            x = [x]

        # set variables
        super().__init__(batch_size=batch_size)
        self.x, self.y = x, y
        self.indices = np.arange(self.x[0].shape[0])
        self.n_inputs = len(x)
        self.shuffle = shuffle

    def __len__(self):
        return math.ceil(self.x[0].shape[0] / self.batch_size)

    def __getitem__(self, idx):
        inds = self.indices[idx * self.batch_size : (idx + 1) * self.batch_size]
        batch_x = []
        for i in range(self.n_inputs):
            batch_x.append(self.x[i][inds])
        batch_y = self.y[inds]
        return tuple(batch_x), batch_y

    def nsamples(self):
        return self.x[0].shape[0]

    def get_y(self):
        return self.y

    def on_epoch_end(self):
        if self.shuffle:
            np.random.shuffle(self.indices)

    def xshape(self):
        return self.x[0].shape

    def nclasses(self):
        return self.y.shape[1]

    def ondisk(self):
        return False</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.dataset.SequenceDataset" href="#ktrain.dataset.SequenceDataset">SequenceDataset</a></li>
<li><a title="ktrain.dataset.Dataset" href="#ktrain.dataset.Dataset">Dataset</a></li>
<li>keras.utils.data_utils.Sequence</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.dataset.MultiArrayDataset.get_y"><code class="name flex">
<span>def <span class="ident">get_y</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_y(self):
    return self.y</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.MultiArrayDataset.nsamples"><code class="name flex">
<span>def <span class="ident">nsamples</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def nsamples(self):
    return self.x[0].shape[0]</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.MultiArrayDataset.on_epoch_end"><code class="name flex">
<span>def <span class="ident">on_epoch_end</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Method called at the end of every epoch.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_epoch_end(self):
    if self.shuffle:
        np.random.shuffle(self.indices)</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.dataset.SequenceDataset" href="#ktrain.dataset.SequenceDataset">SequenceDataset</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.dataset.SequenceDataset.nclasses" href="#ktrain.dataset.Dataset.nclasses">nclasses</a></code></li>
<li><code><a title="ktrain.dataset.SequenceDataset.ondisk" href="#ktrain.dataset.Dataset.ondisk">ondisk</a></code></li>
<li><code><a title="ktrain.dataset.SequenceDataset.xshape" href="#ktrain.dataset.Dataset.xshape">xshape</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="ktrain.dataset.SequenceDataset"><code class="flex name class">
<span>class <span class="ident">SequenceDataset</span></span>
<span>(</span><span>batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Base class for custom datasets in ktrain.

If subclass of Dataset implements a method to to_tfdataset
that converts the data to a tf.Dataset, then this will be
invoked by Learner instances just prior to training so
fit() will train using a tf.Dataset representation of your data.
Sequence methods such as __get_item__ and __len__
must still be implemented.

The signature of to_tfdataset is as follows:

def to_tfdataset(self, training=True)

See ktrain.text.preprocess.TransformerDataset as an example.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SequenceDataset(Dataset, keras.utils.Sequence):
    &#34;&#34;&#34;
    ```
    Base class for custom datasets in ktrain.

    If subclass of Dataset implements a method to to_tfdataset
    that converts the data to a tf.Dataset, then this will be
    invoked by Learner instances just prior to training so
    fit() will train using a tf.Dataset representation of your data.
    Sequence methods such as __get_item__ and __len__
    must still be implemented.

    The signature of to_tfdataset is as follows:

    def to_tfdataset(self, training=True)

    See ktrain.text.preprocess.TransformerDataset as an example.
    ```
    &#34;&#34;&#34;

    def __init__(self, batch_size=32):
        self.batch_size = batch_size

    # required by keras.utils.Sequence instances
    def __len__(self):
        raise NotImplemented

    # required by keras.utils.Sequence instances
    def __getitem__(self, idx):
        raise NotImplemented

        return False</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.dataset.Dataset" href="#ktrain.dataset.Dataset">Dataset</a></li>
<li>keras.utils.data_utils.Sequence</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="ktrain.dataset.MultiArrayDataset" href="#ktrain.dataset.MultiArrayDataset">MultiArrayDataset</a></li>
<li><a title="ktrain.graph.sg_wrappers.LinkSequenceWrapper" href="graph/sg_wrappers.html#ktrain.graph.sg_wrappers.LinkSequenceWrapper">LinkSequenceWrapper</a></li>
<li><a title="ktrain.graph.sg_wrappers.NodeSequenceWrapper" href="graph/sg_wrappers.html#ktrain.graph.sg_wrappers.NodeSequenceWrapper">NodeSequenceWrapper</a></li>
<li><a title="ktrain.tabular.dataset.TabularDataset" href="tabular/dataset.html#ktrain.tabular.dataset.TabularDataset">TabularDataset</a></li>
<li><a title="ktrain.text.dataset.TransformerDataset" href="text/dataset.html#ktrain.text.dataset.TransformerDataset">TransformerDataset</a></li>
<li><a title="ktrain.text.ner.dataset.NERSequence" href="text/ner/dataset.html#ktrain.text.ner.dataset.NERSequence">NERSequence</a></li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.dataset.Dataset" href="#ktrain.dataset.Dataset">Dataset</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.dataset.Dataset.nclasses" href="#ktrain.dataset.Dataset.nclasses">nclasses</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.ondisk" href="#ktrain.dataset.Dataset.ondisk">ondisk</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.xshape" href="#ktrain.dataset.Dataset.xshape">xshape</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="ktrain.dataset.TFDataset"><code class="flex name class">
<span>class <span class="ident">TFDataset</span></span>
<span>(</span><span>tfdataset, n, y)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Wrapper for tf.data.Datasets
</code></pre>
<pre><code>Args:
  tfdataset(tf.data.Dataset):  a tf.Dataset instance
  n(int): number of examples in dataset (cardinality, which can't reliably be extracted from tf.data.Datasets)
  y(np.ndarray): y values for each example - should be in the format expected by your moddel (e.g., 1-hot-encoded)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TFDataset(Dataset):
    &#34;&#34;&#34;
    ```
    Wrapper for tf.data.Datasets
    ```
    &#34;&#34;&#34;

    def __init__(self, tfdataset, n, y):
        &#34;&#34;&#34;
        ```
        Args:
          tfdataset(tf.data.Dataset):  a tf.Dataset instance
          n(int): number of examples in dataset (cardinality, which can&#39;t reliably be extracted from tf.data.Datasets)
          y(np.ndarray): y values for each example - should be in the format expected by your moddel (e.g., 1-hot-encoded)
        ```
        &#34;&#34;&#34;
        if not isinstance(tfdataset, tf.data.Dataset):
            raise ValueError(
                &#34;tfdataset must be a fully-configured tf.data.Dataset with batch_size, etc. set appropriately&#34;
            )
        self.tfdataset = tfdataset
        self.bs = next(tfdataset.as_numpy_iterator())[-1].shape[
            0
        ]  # extract batch_size from tfdataset
        self.n = n
        self.y = y

    @property
    def batch_size(self):
        return self.bs

    @batch_size.setter
    def batch_size(self, value):
        if value != self.bs:
            warnings.warn(
                &#34;batch_size parameter is ignored, as pre-configured batch_size of tf.data.Dataset is used&#34;
            )

    def nsamples(self):
        return self.n

    def get_y(self):
        return self.y

    def to_tfdataset(self, train=True):
        return self.tfdataset</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.dataset.Dataset" href="#ktrain.dataset.Dataset">Dataset</a></li>
</ul>
<h3>Instance variables</h3>
<dl>
<dt id="ktrain.dataset.TFDataset.batch_size"><code class="name">var <span class="ident">batch_size</span></code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@property
def batch_size(self):
    return self.bs</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="ktrain.dataset.TFDataset.get_y"><code class="name flex">
<span>def <span class="ident">get_y</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_y(self):
    return self.y</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.TFDataset.nsamples"><code class="name flex">
<span>def <span class="ident">nsamples</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def nsamples(self):
    return self.n</code></pre>
</details>
</dd>
<dt id="ktrain.dataset.TFDataset.to_tfdataset"><code class="name flex">
<span>def <span class="ident">to_tfdataset</span></span>(<span>self, train=True)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def to_tfdataset(self, train=True):
    return self.tfdataset</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.dataset.Dataset" href="#ktrain.dataset.Dataset">Dataset</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.dataset.Dataset.nclasses" href="#ktrain.dataset.Dataset.nclasses">nclasses</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.ondisk" href="#ktrain.dataset.Dataset.ondisk">ondisk</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.xshape" href="#ktrain.dataset.Dataset.xshape">xshape</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain" href="index.html">ktrain</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.dataset.Dataset" href="#ktrain.dataset.Dataset">Dataset</a></code></h4>
<ul class="two-column">
<li><code><a title="ktrain.dataset.Dataset.get_y" href="#ktrain.dataset.Dataset.get_y">get_y</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.nclasses" href="#ktrain.dataset.Dataset.nclasses">nclasses</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.nsamples" href="#ktrain.dataset.Dataset.nsamples">nsamples</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.on_epoch_end" href="#ktrain.dataset.Dataset.on_epoch_end">on_epoch_end</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.ondisk" href="#ktrain.dataset.Dataset.ondisk">ondisk</a></code></li>
<li><code><a title="ktrain.dataset.Dataset.xshape" href="#ktrain.dataset.Dataset.xshape">xshape</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="ktrain.dataset.MultiArrayDataset" href="#ktrain.dataset.MultiArrayDataset">MultiArrayDataset</a></code></h4>
<ul class="">
<li><code><a title="ktrain.dataset.MultiArrayDataset.get_y" href="#ktrain.dataset.MultiArrayDataset.get_y">get_y</a></code></li>
<li><code><a title="ktrain.dataset.MultiArrayDataset.nsamples" href="#ktrain.dataset.MultiArrayDataset.nsamples">nsamples</a></code></li>
<li><code><a title="ktrain.dataset.MultiArrayDataset.on_epoch_end" href="#ktrain.dataset.MultiArrayDataset.on_epoch_end">on_epoch_end</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="ktrain.dataset.SequenceDataset" href="#ktrain.dataset.SequenceDataset">SequenceDataset</a></code></h4>
</li>
<li>
<h4><code><a title="ktrain.dataset.TFDataset" href="#ktrain.dataset.TFDataset">TFDataset</a></code></h4>
<ul class="">
<li><code><a title="ktrain.dataset.TFDataset.batch_size" href="#ktrain.dataset.TFDataset.batch_size">batch_size</a></code></li>
<li><code><a title="ktrain.dataset.TFDataset.get_y" href="#ktrain.dataset.TFDataset.get_y">get_y</a></code></li>
<li><code><a title="ktrain.dataset.TFDataset.nsamples" href="#ktrain.dataset.TFDataset.nsamples">nsamples</a></code></li>
<li><code><a title="ktrain.dataset.TFDataset.to_tfdataset" href="#ktrain.dataset.TFDataset.to_tfdataset">to_tfdataset</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
</footer>
</body>
</html>