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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.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 TransformerDataset(SequenceDataset):
"""
```
Wrapper for Transformer datasets.
```
"""
def __init__(self, x, y, batch_size=1):
if type(x) not in [list, np.ndarray]:
raise ValueError("x must be list or np.ndarray")
if type(y) not in [list, np.ndarray]:
raise ValueError("y must be list or np.ndarray")
if type(x) == list:
x = np.array(x)
if type(y) == list:
y = np.array(y)
self.x = x
self.y = y
self.batch_size = batch_size
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 (batch_x, batch_y)
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def to_tfdataset(self, train=True):
"""
```
convert transformer features to tf.Dataset
```
"""
if train:
shuffle = True
repeat = True
else:
shuffle = False
repeat = False
if len(self.y.shape) == 1:
yshape = []
ytype = tf.float32
else:
yshape = [None]
ytype = tf.int64
def gen():
for idx, data in enumerate(self.x):
yield (
{
"input_ids": data[0],
"attention_mask": data[1],
"token_type_ids": data[2],
},
self.y[idx],
)
tfdataset = tf.data.Dataset.from_generator(
gen,
(
{
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
},
ytype,
),
(
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
},
tf.TensorShape(yshape),
),
)
if shuffle:
tfdataset = tfdataset.shuffle(self.x.shape[0])
tfdataset = tfdataset.batch(self.batch_size)
if repeat:
tfdataset = tfdataset.repeat(-1)
return tfdataset
def get_y(self):
return self.y
def nsamples(self):
return len(self.x)
def nclasses(self):
return self.y.shape[1]
def xshape(self):
return (len(self.x), self.x[0].shape[1])</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.dataset.TransformerDataset"><code class="flex name class">
<span>class <span class="ident">TransformerDataset</span></span>
<span>(</span><span>x, y, batch_size=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Wrapper for Transformer datasets.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TransformerDataset(SequenceDataset):
"""
```
Wrapper for Transformer datasets.
```
"""
def __init__(self, x, y, batch_size=1):
if type(x) not in [list, np.ndarray]:
raise ValueError("x must be list or np.ndarray")
if type(y) not in [list, np.ndarray]:
raise ValueError("y must be list or np.ndarray")
if type(x) == list:
x = np.array(x)
if type(y) == list:
y = np.array(y)
self.x = x
self.y = y
self.batch_size = batch_size
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 (batch_x, batch_y)
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def to_tfdataset(self, train=True):
"""
```
convert transformer features to tf.Dataset
```
"""
if train:
shuffle = True
repeat = True
else:
shuffle = False
repeat = False
if len(self.y.shape) == 1:
yshape = []
ytype = tf.float32
else:
yshape = [None]
ytype = tf.int64
def gen():
for idx, data in enumerate(self.x):
yield (
{
"input_ids": data[0],
"attention_mask": data[1],
"token_type_ids": data[2],
},
self.y[idx],
)
tfdataset = tf.data.Dataset.from_generator(
gen,
(
{
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
},
ytype,
),
(
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
},
tf.TensorShape(yshape),
),
)
if shuffle:
tfdataset = tfdataset.shuffle(self.x.shape[0])
tfdataset = tfdataset.batch(self.batch_size)
if repeat:
tfdataset = tfdataset.repeat(-1)
return tfdataset
def get_y(self):
return self.y
def nsamples(self):
return len(self.x)
def nclasses(self):
return self.y.shape[1]
def xshape(self):
return (len(self.x), self.x[0].shape[1])</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>tensorflow.python.keras.utils.data_utils.Sequence</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.dataset.TransformerDataset.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.dataset.TransformerDataset.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.dataset.TransformerDataset.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"><pre><code>convert transformer features to tf.Dataset
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def to_tfdataset(self, train=True):
"""
```
convert transformer features to tf.Dataset
```
"""
if train:
shuffle = True
repeat = True
else:
shuffle = False
repeat = False
if len(self.y.shape) == 1:
yshape = []
ytype = tf.float32
else:
yshape = [None]
ytype = tf.int64
def gen():
for idx, data in enumerate(self.x):
yield (
{
"input_ids": data[0],
"attention_mask": data[1],
"token_type_ids": data[2],
},
self.y[idx],
)
tfdataset = tf.data.Dataset.from_generator(
gen,
(
{
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
},
ytype,
),
(
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
},
tf.TensorShape(yshape),
),
)
if shuffle:
tfdataset = tfdataset.shuffle(self.x.shape[0])
tfdataset = tfdataset.batch(self.batch_size)
if repeat:
tfdataset = tfdataset.repeat(-1)
return tfdataset</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" href="index.html">ktrain.text</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.dataset.TransformerDataset" href="#ktrain.text.dataset.TransformerDataset">TransformerDataset</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.dataset.TransformerDataset.get_y" href="#ktrain.text.dataset.TransformerDataset.get_y">get_y</a></code></li>
<li><code><a title="ktrain.text.dataset.TransformerDataset.nsamples" href="#ktrain.text.dataset.TransformerDataset.nsamples">nsamples</a></code></li>
<li><code><a title="ktrain.text.dataset.TransformerDataset.to_tfdataset" href="#ktrain.text.dataset.TransformerDataset.to_tfdataset">to_tfdataset</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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