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dataset.html
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dataset.html
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<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.ner.dataset</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ...dataset import SequenceDataset
from ...imports import *
class NERSequence(SequenceDataset):
def __init__(self, x, y, batch_size=1, p=None):
self.x = x
self.y = y
self.batch_size = batch_size
self.p = p
self.prepare_called = False
def prepare(self):
if self.p is not None and not self.prepare_called:
self.x, self.y = self.p.fix_tokenization(self.x, self.y)
self.prepare_called = True
return
def __getitem__(self, idx):
batch_x = self.x[idx * self.batch_size : (idx + 1) * self.batch_size]
batch_y = self.y[idx * self.batch_size : (idx + 1) * self.batch_size]
return self.p.transform(batch_x, batch_y)
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def get_lengths(self, idx):
x_true, y_true = self[idx]
lengths = []
for y in np.argmax(y_true, -1):
try:
i = list(y).index(0)
except ValueError:
i = len(y)
lengths.append(i)
return lengths
def nsamples(self):
return len(self.x)
def get_y(self):
return self.y
def xshape(self):
return (len(self.x), self[0][0][0].shape[1])
def nclasses(self):
return len(self.p._label_vocab._id2token)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.ner.dataset.NERSequence"><code class="flex name class">
<span>class <span class="ident">NERSequence</span></span>
<span>(</span><span>x, y, batch_size=1, p=None)</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 NERSequence(SequenceDataset):
def __init__(self, x, y, batch_size=1, p=None):
self.x = x
self.y = y
self.batch_size = batch_size
self.p = p
self.prepare_called = False
def prepare(self):
if self.p is not None and not self.prepare_called:
self.x, self.y = self.p.fix_tokenization(self.x, self.y)
self.prepare_called = True
return
def __getitem__(self, idx):
batch_x = self.x[idx * self.batch_size : (idx + 1) * self.batch_size]
batch_y = self.y[idx * self.batch_size : (idx + 1) * self.batch_size]
return self.p.transform(batch_x, batch_y)
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def get_lengths(self, idx):
x_true, y_true = self[idx]
lengths = []
for y in np.argmax(y_true, -1):
try:
i = list(y).index(0)
except ValueError:
i = len(y)
lengths.append(i)
return lengths
def nsamples(self):
return len(self.x)
def get_y(self):
return self.y
def xshape(self):
return (len(self.x), self[0][0][0].shape[1])
def nclasses(self):
return len(self.p._label_vocab._id2token)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.dataset.SequenceDataset" href="../../dataset.html#ktrain.dataset.SequenceDataset">SequenceDataset</a></li>
<li><a title="ktrain.dataset.Dataset" href="../../dataset.html#ktrain.dataset.Dataset">Dataset</a></li>
<li>keras.utils.data_utils.Sequence</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.ner.dataset.NERSequence.get_lengths"><code class="name flex">
<span>def <span class="ident">get_lengths</span></span>(<span>self, idx)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_lengths(self, idx):
x_true, y_true = self[idx]
lengths = []
for y in np.argmax(y_true, -1):
try:
i = list(y).index(0)
except ValueError:
i = len(y)
lengths.append(i)
return lengths</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.dataset.NERSequence.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.text.ner.dataset.NERSequence.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 len(self.x)</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.dataset.NERSequence.prepare"><code class="name flex">
<span>def <span class="ident">prepare</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 prepare(self):
if self.p is not None and not self.prepare_called:
self.x, self.y = self.p.fix_tokenization(self.x, self.y)
self.prepare_called = True
return</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.dataset.SequenceDataset" href="../../dataset.html#ktrain.dataset.SequenceDataset">SequenceDataset</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.dataset.SequenceDataset.nclasses" href="../../dataset.html#ktrain.dataset.Dataset.nclasses">nclasses</a></code></li>
<li><code><a title="ktrain.dataset.SequenceDataset.ondisk" href="../../dataset.html#ktrain.dataset.Dataset.ondisk">ondisk</a></code></li>
<li><code><a title="ktrain.dataset.SequenceDataset.xshape" href="../../dataset.html#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.text.ner" href="index.html">ktrain.text.ner</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.ner.dataset.NERSequence" href="#ktrain.text.ner.dataset.NERSequence">NERSequence</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.ner.dataset.NERSequence.get_lengths" href="#ktrain.text.ner.dataset.NERSequence.get_lengths">get_lengths</a></code></li>
<li><code><a title="ktrain.text.ner.dataset.NERSequence.get_y" href="#ktrain.text.ner.dataset.NERSequence.get_y">get_y</a></code></li>
<li><code><a title="ktrain.text.ner.dataset.NERSequence.nsamples" href="#ktrain.text.ner.dataset.NERSequence.nsamples">nsamples</a></code></li>
<li><code><a title="ktrain.text.ner.dataset.NERSequence.prepare" href="#ktrain.text.ner.dataset.NERSequence.prepare">prepare</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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