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---
title: collab
keywords: fastai
sidebar: home_sidebar
summary: "Application to collaborative filtering"
---
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<h2 id="Collaborative-filtering">Collaborative filtering<a class="anchor-link" href="#Collaborative-filtering">¶</a></h2>
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<p>This package contains all the necessary functions to quickly train a model for a collaborative filtering task. Let's start by importing all we'll need.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">fastai.collab</span> <span class="k">import</span> <span class="o">*</span>
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<h2 id="Overview">Overview<a class="anchor-link" href="#Overview">¶</a></h2>
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<p>Collaborative filtering is when you're tasked to predict how much a user is going to like a certain item. The fastai library contains a <a href="/collab.html#CollabFilteringDataset"><code>CollabFilteringDataset</code></a> class that will help you create datasets suitable for training, and a function <code>get_colab_learner</code> to build a simple model directly from a ratings table. Let's first see how we can get started before delving into the documentation.</p>
<p>For this example, we'll use a small subset of the <a href="https://grouplens.org/datasets/movielens/">MovieLens</a> dataset to predict the rating a user would give a particular movie (from 0 to 5). The dataset comes in the form of a csv file where each line is a rating of a movie by a given person.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">ML_SAMPLE</span><span class="p">)</span>
<span class="n">ratings</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'ratings.csv'</span><span class="p">)</span>
<span class="n">ratings</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
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.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
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<th></th>
<th>userId</th>
<th>movieId</th>
<th>rating</th>
<th>timestamp</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>73</td>
<td>1097</td>
<td>4.0</td>
<td>1255504951</td>
</tr>
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<th>1</th>
<td>561</td>
<td>924</td>
<td>3.5</td>
<td>1172695223</td>
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<th>2</th>
<td>157</td>
<td>260</td>
<td>3.5</td>
<td>1291598691</td>
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<tr>
<th>3</th>
<td>358</td>
<td>1210</td>
<td>5.0</td>
<td>957481884</td>
</tr>
<tr>
<th>4</th>
<td>130</td>
<td>316</td>
<td>2.0</td>
<td>1138999234</td>
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</tbody>
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<p>We'll first turn the <code>userId</code> and <code>movieId</code> columns in category codes, so that we can replace them with their codes when it's time to feed them to an <code>Embedding</code> layer. This step would be even more important if our csv had names of users, or names of items in it. To do it, we simply have to call a <a href="/collab.html#CollabDataBunch"><code>CollabDataBunch</code></a> factory method.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">CollabDataBunch</span><span class="o">.</span><span class="n">from_df</span><span class="p">(</span><span class="n">ratings</span><span class="p">)</span>
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<p>Now that this step is done, we can directly create a <a href="/basic_train.html#Learner"><code>Learner</code></a> object:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">collab_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">n_factors</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">y_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span><span class="mf">5.</span><span class="p">))</span>
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<p>And then immediately begin training</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mf">5e-3</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
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Total time: 00:09 <p><table style='width:300px; margin-bottom:10px'>
<tr>
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
</tr>
<tr>
<th>1</th>
<th>2.427430</th>
<th>1.999472</th>
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<th>2</th>
<th>1.116335</th>
<th>0.663345</th>
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<th>3</th>
<th>0.736155</th>
<th>0.636640</th>
</tr>
<tr>
<th>4</th>
<th>0.612827</th>
<th>0.626773</th>
</tr>
<tr>
<th>5</th>
<th>0.565003</th>
<th>0.626336</th>
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<h2 id="CollabDataBunch" class="doc_header"><code>class</code> <code>CollabDataBunch</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L52" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabDataBunch-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>CollabDataBunch</code>(<strong><code>train_dl</code></strong>:<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader"><code>DataLoader</code></a>, <strong><code>valid_dl</code></strong>:<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader"><code>DataLoader</code></a>, <strong><code>fix_dl</code></strong>:<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader"><code>DataLoader</code></a>=<strong><em><code>None</code></em></strong>, <strong><code>test_dl</code></strong>:<code>Optional</code>[<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader"><code>DataLoader</code></a>]=<strong><em><code>None</code></em></strong>, <strong><code>device</code></strong>:<a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch-device"><code>device</code></a>=<strong><em><code>None</code></em></strong>, <strong><code>dl_tfms</code></strong>:<code>Optional</code>[<code>Collection</code>[<code>Callable</code>]]=<strong><em><code>None</code></em></strong>, <strong><code>path</code></strong>:<code>PathOrStr</code>=<strong><em><code>'.'</code></em></strong>, <strong><code>collate_fn</code></strong>:<code>Callable</code>=<strong><em><code>'data_collate'</code></em></strong>, <strong><code>no_check</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>) :: <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a></p>
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<div class="collapse" id="CollabDataBunch-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabDataBunch-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>CollabDataBunch</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Base <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> for collaborative filtering.</p>
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<p>The init function shouldn't be called directly (as it's the one of a basic <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a>), instead, you'll want to use the following factory method.</p>
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<h4 id="CollabDataBunch.from_df" class="doc_header"><code>from_df</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L54" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabDataBunch-from_df-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>from_df</code>(<strong><code>ratings</code></strong>:<code>DataFrame</code>, <strong><code>valid_pct</code></strong>:<code>float</code>=<strong><em><code>0.2</code></em></strong>, <strong><code>user_name</code></strong>:<code>Optional</code>[<code>str</code>]=<strong><em><code>None</code></em></strong>, <strong><code>item_name</code></strong>:<code>Optional</code>[<code>str</code>]=<strong><em><code>None</code></em></strong>, <strong><code>rating_name</code></strong>:<code>Optional</code>[<code>str</code>]=<strong><em><code>None</code></em></strong>, <strong><code>test</code></strong>:<code>DataFrame</code>=<strong><em><code>None</code></em></strong>, <strong><code>seed</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>path</code></strong>:<code>PathOrStr</code>=<strong><em><code>'.'</code></em></strong>, <strong><code>bs</code></strong>:<code>int</code>=<strong><em><code>64</code></em></strong>, <strong><code>val_bs</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>num_workers</code></strong>:<code>int</code>=<strong><em><code>4</code></em></strong>, <strong><code>dl_tfms</code></strong>:<code>Optional</code>[<code>Collection</code>[<code>Callable</code>]]=<strong><em><code>None</code></em></strong>, <strong><code>device</code></strong>:<a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch-device"><code>device</code></a>=<strong><em><code>None</code></em></strong>, <strong><code>collate_fn</code></strong>:<code>Callable</code>=<strong><em><code>'data_collate'</code></em></strong>, <strong><code>no_check</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>) → <code>CollabDataBunch</code></p>
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<div class="collapse" id="CollabDataBunch-from_df-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabDataBunch-from_df-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>from_df</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Create a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> suitable for collaborative filtering from <code>ratings</code>.</p>
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<p>Take a <code>ratings</code> dataframe and splits it randomly for train and test following <code>pct_val</code> (unless it's None). <code>user_name</code>, <code>item_name</code> and <code>rating_name</code> give the names of the corresponding columns (defaults to the first, the second and the third column). Optionally a <code>test</code> dataframe can be passed an a <code>seed</code> for the separation between training and validation set. The <code>kwargs</code> will be passed to <a href="/basic_data.html#DataBunch.create"><code>DataBunch.create</code></a>.</p>
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<h2 id="Model-and-Learner">Model and <a href="/basic_train.html#Learner"><code>Learner</code></a><a class="anchor-link" href="#Model-and-Learner">¶</a></h2>
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<h3 id="CollabLearner" class="doc_header"><code>class</code> <code>CollabLearner</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L70" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabLearner-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>CollabLearner</code>(<strong><code>data</code></strong>:<a href="/basic_data.html#DataBunch"><code>DataBunch</code></a>, <strong><code>model</code></strong>:<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Module</code></a>, <strong><code>opt_func</code></strong>:<code>Callable</code>=<strong><em><code>'Adam'</code></em></strong>, <strong><code>loss_func</code></strong>:<code>Callable</code>=<strong><em><code>None</code></em></strong>, <strong><code>metrics</code></strong>:<code>Collection</code>[<code>Callable</code>]=<strong><em><code>None</code></em></strong>, <strong><code>true_wd</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>bn_wd</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>wd</code></strong>:<code>Floats</code>=<strong><em><code>0.01</code></em></strong>, <strong><code>train_bn</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>path</code></strong>:<code>str</code>=<strong><em><code>None</code></em></strong>, <strong><code>model_dir</code></strong>:<code>PathOrStr</code>=<strong><em><code>'models'</code></em></strong>, <strong><code>callback_fns</code></strong>:<code>Collection</code>[<code>Callable</code>]=<strong><em><code>None</code></em></strong>, <strong><code>callbacks</code></strong>:<code>Collection</code>[<a href="/callback.html#Callback"><code>Callback</code></a>]=<strong><em><code><factory></code></em></strong>, <strong><code>layer_groups</code></strong>:<code>ModuleList</code>=<strong><em><code>None</code></em></strong>, <strong><code>add_time</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>silent</code></strong>:<code>bool</code>=<strong><em><code>None</code></em></strong>) :: <a href="/basic_train.html#Learner"><code>Learner</code></a></p>
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<div class="collapse" id="CollabLearner-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabLearner-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>CollabLearner</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p><a href="/basic_train.html#Learner"><code>Learner</code></a> suitable for collaborative filtering.</p>
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<p>This is a subclass of <a href="/basic_train.html#Learner"><code>Learner</code></a> that just introduces helper functions to analyze results, the initialization is the same as a regular <a href="/basic_train.html#Learner"><code>Learner</code></a>.</p>
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<h4 id="CollabLearner.bias" class="doc_header"><code>bias</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L84" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabLearner-bias-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>bias</code>(<strong><code>arr</code></strong>:<code>Collection</code>[<code>T_co</code>], <strong><code>is_item</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>)</p>
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<div class="collapse" id="CollabLearner-bias-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabLearner-bias-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>bias</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Bias for item or user (based on <code>is_item</code>) for all in <code>arr</code>. (Set model to <code>cpu</code> and no grad.)</p>
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<h4 id="CollabLearner.get_idx" class="doc_header"><code>get_idx</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L72" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabLearner-get_idx-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>get_idx</code>(<strong><code>arr</code></strong>:<code>Collection</code>[<code>T_co</code>], <strong><code>is_item</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>)</p>
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<div class="collapse" id="CollabLearner-get_idx-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabLearner-get_idx-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>get_idx</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Fetch item or user (based on <code>is_item</code>) for all in <code>arr</code>. (Set model to <code>cpu</code> and no grad.)</p>
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<h4 id="CollabLearner.weight" class="doc_header"><code>weight</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L91" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabLearner-weight-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>weight</code>(<strong><code>arr</code></strong>:<code>Collection</code>[<code>T_co</code>], <strong><code>is_item</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>)</p>
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<div class="collapse" id="CollabLearner-weight-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabLearner-weight-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>weight</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Bias for item or user (based on <code>is_item</code>) for all in <code>arr</code>. (Set model to <code>cpu</code> and no grad.)</p>
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<h3 id="EmbeddingDotBias" class="doc_header"><code>class</code> <code>EmbeddingDotBias</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L37" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#EmbeddingDotBias-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>EmbeddingDotBias</code>(<strong><code>n_factors</code></strong>:<code>int</code>, <strong><code>n_users</code></strong>:<code>int</code>, <strong><code>n_items</code></strong>:<code>int</code>, <strong><code>y_range</code></strong>:<code>Point</code>=<strong><em><code>None</code></em></strong>) :: <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Module</code></a></p>
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<div class="collapse" id="EmbeddingDotBias-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#EmbeddingDotBias-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>EmbeddingDotBias</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Base dot model for collaborative filtering.</p>
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<p>Creates a simple model with <code>Embedding</code> weights and biases for <code>n_users</code> and <code>n_items</code>, with <code>n_factors</code> latent factors. Takes the dot product of the embeddings and adds the bias, then if <code>y_range</code> is specified, feed the result to a sigmoid rescaled to go from <code>y_range[0]</code> to <code>y_range[1]</code>.</p>
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<h3 id="EmbeddingNN" class="doc_header"><code>class</code> <code>EmbeddingNN</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L27" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#EmbeddingNN-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>EmbeddingNN</code>(<strong><code>emb_szs</code></strong>:<code>ListSizes</code>, <strong><code>layers</code></strong>:<code>Collection</code>[<code>int</code>]=<strong><em><code>None</code></em></strong>, <strong><code>ps</code></strong>:<code>Collection</code>[<code>float</code>]=<strong><em><code>None</code></em></strong>, <strong><code>emb_drop</code></strong>:<code>float</code>=<strong><em><code>0.0</code></em></strong>, <strong><code>y_range</code></strong>:<code>OptRange</code>=<strong><em><code>None</code></em></strong>, <strong><code>use_bn</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>bn_final</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>) :: <a href="/tabular.models.html#TabularModel"><code>TabularModel</code></a></p>
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<div class="collapse" id="EmbeddingNN-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#EmbeddingNN-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>EmbeddingNN</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Subclass <a href="/tabular.models.html#TabularModel"><code>TabularModel</code></a> to create a NN suitable for collaborative filtering.</p>
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<p><code>emb_szs</code> will overwrite the default and <code>kwargs</code> are passed to <a href="/tabular.models.html#TabularModel"><code>TabularModel</code></a>.</p>
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<h4 id="collab_learner" class="doc_header"><code>collab_learner</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L98" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#collab_learner-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>collab_learner</code>(<strong><code>data</code></strong>, <strong><code>n_factors</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>use_nn</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>emb_szs</code></strong>:<code>Dict</code>[<code>str</code>, <code>int</code>]=<strong><em><code>None</code></em></strong>, <strong><code>layers</code></strong>:<code>Collection</code>[<code>int</code>]=<strong><em><code>None</code></em></strong>, <strong><code>ps</code></strong>:<code>Collection</code>[<code>float</code>]=<strong><em><code>None</code></em></strong>, <strong><code>emb_drop</code></strong>:<code>float</code>=<strong><em><code>0.0</code></em></strong>, <strong><code>y_range</code></strong>:<code>OptRange</code>=<strong><em><code>None</code></em></strong>, <strong><code>use_bn</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>bn_final</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong>**<code>learn_kwargs</code></strong>) → <a href="/basic_train.html#Learner"><code>Learner</code></a></p>
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<div class="collapse" id="collab_learner-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#collab_learner-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>collab_learner</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Create a Learner for collaborative filtering on <code>data</code>.</p>
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<p>More specifically, binds <a href="/tabular.data.html#tabular.data"><code>data</code></a> with a model that is either an <a href="/collab.html#EmbeddingDotBias"><code>EmbeddingDotBias</code></a> with <code>n_factors</code> if <code>use_nn=False</code> or a <a href="/collab.html#EmbeddingNN"><code>EmbeddingNN</code></a> with <code>emb_szs</code> otherwise. In both cases the numbers of users and items will be inferred from the data, <code>y_range</code> can be specified in the <code>kwargs</code> and you can pass <a href="/metrics.html#metrics"><code>metrics</code></a> or <code>wd</code> to the <a href="/basic_train.html#Learner"><code>Learner</code></a> constructor.</p>
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<h2 id="Links-with-the-Data-Block-API">Links with the Data Block API<a class="anchor-link" href="#Links-with-the-Data-Block-API">¶</a></h2>
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<h3 id="CollabLine" class="doc_header"><code>class</code> <code>CollabLine</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L15" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabLine-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>CollabLine</code>(<strong><code>cats</code></strong>, <strong><code>conts</code></strong>, <strong><code>classes</code></strong>, <strong><code>names</code></strong>) :: <a href="/tabular.data.html#TabularLine"><code>TabularLine</code></a></p>
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<div class="collapse" id="CollabLine-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabLine-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>CollabLine</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div>
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<p>Subclass of <a href="/tabular.data.html#TabularLine"><code>TabularLine</code></a> for collaborative filtering.</p>
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<h3 id="CollabList" class="doc_header"><code>class</code> <code>CollabList</code><a href="https://github.com/fastai/fastai/blob/master/fastai/collab.py#L21" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CollabList-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>CollabList</code>(<strong><code>items</code></strong>:<code>Iterator</code>[<code>T_co</code>], <strong><code>cat_names</code></strong>:<code>OptStrList</code>=<strong><em><code>None</code></em></strong>, <strong><code>cont_names</code></strong>:<code>OptStrList</code>=<strong><em><code>None</code></em></strong>, <strong><code>procs</code></strong>=<strong><em><code>None</code></em></strong>, <strong>**<code>kwargs</code></strong>) → <code>TabularList</code> :: <a href="/tabular.data.html#TabularList"><code>TabularList</code></a></p>
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<div class="collapse" id="CollabList-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CollabList-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>CollabList</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div>
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<p>Subclass of <a href="/tabular.data.html#TabularList"><code>TabularList</code></a> for collaborative filtering.</p>
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