---

title: Session (torch) Datasets


keywords: fastai
sidebar: home_sidebar

summary: "Session-based recommendation datasets in PyTorch Dataset format."
description: "Session-based recommendation datasets in PyTorch Dataset format."
nb_path: "nbs/datasets/torch/session.ipynb"
---
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<h2 id="Dataset" class="doc_header"><code>class</code> <code>Dataset</code><a href="https://github.com/RecoHut-Projects/recohut/tree/master/recohut/datasets/torch/session.py#L18" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>Dataset</code>(<strong>*<code>args</code></strong>, <strong>**<code>kwds</code></strong>) :: <a href="/recohut/session.html#Dataset"><code>Dataset</code></a></p>
</blockquote>
<p>An abstract class representing a :class:<a href="/recohut/session.html#Dataset"><code>Dataset</code></a>.</p>
<p>All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:<code>__getitem__</code>, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
:meth:<code>__len__</code>, which is expected to return the size of the dataset by many
:class:<code>~torch.utils.data.Sampler</code> implementations and the default options
of :class:<code>~torch.utils.data.DataLoader</code>.</p>
<p>.. note::
  :class:<code>~torch.utils.data.DataLoader</code> by default constructs a index
  sampler that yields integral indices.  To make it work with a map-style
  dataset with non-integral indices/keys, a custom sampler must be provided.</p>

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<h2 id="YoochooseDataset" class="doc_header"><code>class</code> <code>YoochooseDataset</code><a href="https://github.com/RecoHut-Projects/recohut/tree/master/recohut/datasets/torch/session.py#L182" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>YoochooseDataset</code>(<strong>*<code>args</code></strong>, <strong>**<code>kwds</code></strong>) :: <a href="/recohut/session.html#Dataset"><code>Dataset</code></a></p>
</blockquote>
<p>An abstract class representing a :class:<a href="/recohut/session.html#Dataset"><code>Dataset</code></a>.</p>
<p>All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:<code>__getitem__</code>, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
:meth:<code>__len__</code>, which is expected to return the size of the dataset by many
:class:<code>~torch.utils.data.Sampler</code> implementations and the default options
of :class:<code>~torch.utils.data.DataLoader</code>.</p>
<p>.. note::
  :class:<code>~torch.utils.data.DataLoader</code> by default constructs a index
  sampler that yields integral indices.  To make it work with a map-style
  dataset with non-integral indices/keys, a custom sampler must be provided.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">YoochooseDataset</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;/content/yoochoose&#39;</span><span class="p">,</span> <span class="n">maxlen</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>

<span class="n">sampler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">samples</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
<span class="n">samples</span>
</pre></div>

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<pre>Using existing file yoochoose.csv
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<pre>Avg length: 10.0
Maximum length: 10
Number of sessions: 80183
Number of items: 12936
Number of actions: 406979
Average length of sessions: 5.075627003230111
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<h2 id="NowplayingDataset" class="doc_header"><code>class</code> <code>NowplayingDataset</code><a href="https://github.com/RecoHut-Projects/recohut/tree/master/recohut/datasets/torch/session.py#L190" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>NowplayingDataset</code>(<strong>*<code>args</code></strong>, <strong>**<code>kwds</code></strong>) :: <a href="/recohut/session.html#Dataset"><code>Dataset</code></a></p>
</blockquote>
<p>An abstract class representing a :class:<a href="/recohut/session.html#Dataset"><code>Dataset</code></a>.</p>
<p>All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:<code>__getitem__</code>, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
:meth:<code>__len__</code>, which is expected to return the size of the dataset by many
:class:<code>~torch.utils.data.Sampler</code> implementations and the default options
of :class:<code>~torch.utils.data.DataLoader</code>.</p>
<p>.. note::
  :class:<code>~torch.utils.data.DataLoader</code> by default constructs a index
  sampler that yields integral indices.  To make it work with a map-style
  dataset with non-integral indices/keys, a custom sampler must be provided.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">NowplayingDataset</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;/content/nowplaying&#39;</span><span class="p">,</span> <span class="n">maxlen</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>

<span class="n">sampler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">samples</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
<span class="n">samples</span>
</pre></div>

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<pre>Downloading https://github.com/RecoHut-Datasets/nowplaying/raw/v3/nowplaying.csv
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<pre>Avg length: 20.0
Maximum length: 20
Number of sessions: 113918
Number of items: 239221
Number of actions: 1184815
Average length of sessions: 10.400595164943205
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<h2 id="DigineticaDataset" class="doc_header"><code>class</code> <code>DigineticaDataset</code><a href="https://github.com/RecoHut-Projects/recohut/tree/master/recohut/datasets/torch/session.py#L198" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>DigineticaDataset</code>(<strong>*<code>args</code></strong>, <strong>**<code>kwds</code></strong>) :: <a href="/recohut/session.html#Dataset"><code>Dataset</code></a></p>
</blockquote>
<p>An abstract class representing a :class:<a href="/recohut/session.html#Dataset"><code>Dataset</code></a>.</p>
<p>All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:<code>__getitem__</code>, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
:meth:<code>__len__</code>, which is expected to return the size of the dataset by many
:class:<code>~torch.utils.data.Sampler</code> implementations and the default options
of :class:<code>~torch.utils.data.DataLoader</code>.</p>
<p>.. note::
  :class:<code>~torch.utils.data.DataLoader</code> by default constructs a index
  sampler that yields integral indices.  To make it work with a map-style
  dataset with non-integral indices/keys, a custom sampler must be provided.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">DigineticaDataset</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;/content/diginetica&#39;</span><span class="p">,</span> <span class="n">maxlen</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>

<span class="n">sampler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">samples</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
<span class="n">samples</span>
</pre></div>

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<pre>Downloading https://github.com/RecoHut-Datasets/diginetica/raw/v4/diginetica.csv
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<pre>Avg length: 8.777109003245833
Maximum length: 70
Number of sessions: 63466
Number of items: 38970
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<pre>/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray
  return array(a, dtype, copy=False, order=order)
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<pre>Number of actions: 557048
Average length of sessions: 8.777109003245833
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<h2 id="LastfmDataset" class="doc_header"><code>class</code> <code>LastfmDataset</code><a href="https://github.com/RecoHut-Projects/recohut/tree/master/recohut/datasets/torch/session.py#L206" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>LastfmDataset</code>(<strong>*<code>args</code></strong>, <strong>**<code>kwds</code></strong>) :: <a href="/recohut/session.html#Dataset"><code>Dataset</code></a></p>
</blockquote>
<p>An abstract class representing a :class:<a href="/recohut/session.html#Dataset"><code>Dataset</code></a>.</p>
<p>All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:<code>__getitem__</code>, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
:meth:<code>__len__</code>, which is expected to return the size of the dataset by many
:class:<code>~torch.utils.data.Sampler</code> implementations and the default options
of :class:<code>~torch.utils.data.DataLoader</code>.</p>
<p>.. note::
  :class:<code>~torch.utils.data.DataLoader</code> by default constructs a index
  sampler that yields integral indices.  To make it work with a map-style
  dataset with non-integral indices/keys, a custom sampler must be provided.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">LastfmDataset</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;/content/lastfm&#39;</span><span class="p">,</span> <span class="n">maxlen</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>

<span class="n">sampler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">samples</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
<span class="n">samples</span>
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<pre>Downloading https://github.com/RecoHut-Datasets/lastfm/raw/v2/last_fm.csv
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<pre>Avg length: 17.447849599510228
Maximum length: 49
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<pre>/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray
  return array(a, dtype, copy=False, order=order)
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<pre>Number of sessions: 196010
Number of items: 107391
Number of actions: 3419953
Average length of sessions: 17.447849599510228
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