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<div class="doc doc-object doc-module">
<a id="newsclassifier.data"></a>
<div class="doc doc-contents first">
<div class="doc doc-children">
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.data.clean_text" class="doc doc-heading">
<code class="highlight language-python"><span class="n">clean_text</span><span class="p">(</span><span class="n">text</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Clean text (lower, puntuations removal, blank space removal).</p>
<details class="quote">
<summary> <code>newsclassifier\data.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">55</span>
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<span class="normal">70</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">clean_text</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Clean text (lower, puntuations removal, blank space removal)."""</span>
<span class="c1"># lower case the text</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Cleaning input text."</span><span class="p">)</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="c1"># necessary to do before as stopwords are in lower case</span>
<span class="c1"># remove stopwords</span>
<span class="n">stp_pattern</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s2">"\b("</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">"|"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">Cfg</span><span class="o">.</span><span class="n">STOPWORDS</span><span class="p">)</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">")\b\s*"</span><span class="p">)</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">stp_pattern</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">""</span><span class="p">,</span> <span class="n">text</span><span class="p">)</span>
<span class="c1"># custom cleaning</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="c1"># remove space at start or end if any</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">" +"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">,</span> <span class="n">text</span><span class="p">)</span> <span class="c1"># remove extra spaces</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">"[^A-Za-z0-9]+"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">,</span> <span class="n">text</span><span class="p">)</span> <span class="c1"># remove characters that are not alphanumeric</span>
<span class="k">return</span> <span class="n">text</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.data.collate" class="doc doc-heading">
<code class="highlight language-python"><span class="n">collate</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Collate and modify the input dictionary to have the same sequence length for a particular input batch.</p>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Parameters:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>inputs</code></b>
(<code>dict</code>)
–
<div class="doc-md-description">
<p>A dictionary containing input tensors with varying sequence lengths.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Returns:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>modified_inputs</code></b>( <code>dict</code>
) –
<div class="doc-md-description">
<p>A modified dictionary with input tensors trimmed to have the same sequence length.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary> <code>newsclassifier\data.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">175</span>
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<span class="w"> </span><span class="sd">"""Collate and modify the input dictionary to have the same sequence length for a particular input batch.</span>
<span class="sd"> Args:</span>
<span class="sd"> inputs (dict): A dictionary containing input tensors with varying sequence lengths.</span>
<span class="sd"> Returns:</span>
<span class="sd"> modified_inputs (dict): A modified dictionary with input tensors trimmed to have the same sequence length.</span>
<span class="sd"> """</span>
<span class="n">max_len</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="s2">"input_ids"</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">][:,</span> <span class="p">:</span><span class="n">max_len</span><span class="p">]</span>
<span class="k">return</span> <span class="n">inputs</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.data.data_split" class="doc doc-heading">
<code class="highlight language-python"><span class="n">data_split</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">split_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">stratify_on_target</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_dfs</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Split data into train and test sets.</p>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Parameters:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>df</code></b>
(<code><span title="pandas.DataFrame">DataFrame</span></code>)
–
<div class="doc-md-description">
<p>Data to be split.</p>
</div>
</li>
<li>
<b><code>split_size</code></b>
(<code>float</code>, default:
<code>0.2</code>
)
–
<div class="doc-md-description">
<p>train-test split ratio (test ratio).</p>
</div>
</li>
<li>
<b><code>stratify_on_target</code></b>
(<code>bool</code>, default:
<code>True</code>
)
–
<div class="doc-md-description">
<p>Whether to do stratify split on target.</p>
</div>
</li>
<li>
<b><code>target_sep</code></b>
(<code>bool</code>)
–
<div class="doc-md-description">
<p>Whether to do target setting for train and test sets.</p>
</div>
</li>
<li>
<b><code>save_dfs</code></b>
(<code>bool</code>, default:
<code>False</code>
)
–
<div class="doc-md-description">
<p>Whether to save dataset splits in artifacts.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Returns:</th>
<td class="field-body">
<ul class="first simple">
<li>
–
<div class="doc-md-description">
<p>train-test splits (with/without target setting)</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary> <code>newsclassifier\data.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 99</span>
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<span class="normal">132</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">data_split</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">split_size</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">stratify_on_target</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">save_dfs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Split data into train and test sets.</span>
<span class="sd"> Args:</span>
<span class="sd"> df (pd.DataFrame): Data to be split.</span>
<span class="sd"> split_size (float): train-test split ratio (test ratio).</span>
<span class="sd"> stratify_on_target (bool): Whether to do stratify split on target.</span>
<span class="sd"> target_sep (bool): Whether to do target setting for train and test sets.</span>
<span class="sd"> save_dfs (bool): Whether to save dataset splits in artifacts.</span>
<span class="sd"> Returns:</span>
<span class="sd"> train-test splits (with/without target setting)</span>
<span class="sd"> """</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Splitting Data."</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">if</span> <span class="n">stratify_on_target</span><span class="p">:</span>
<span class="n">stra</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Category"</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">stra</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="n">split_size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">stra</span><span class="p">)</span>
<span class="n">train_ds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="n">test_ds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="k">if</span> <span class="n">save_dfs</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Saving and storing data splits."</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">Cfg</span><span class="o">.</span><span class="n">preprocessed_data_path</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">train</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">Cfg</span><span class="o">.</span><span class="n">preprocessed_data_path</span><span class="p">,</span> <span class="s2">"train.csv"</span><span class="p">))</span>
<span class="n">test</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">Cfg</span><span class="o">.</span><span class="n">preprocessed_data_path</span><span class="p">,</span> <span class="s2">"test.csv"</span><span class="p">))</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="k">return</span> <span class="n">train_ds</span><span class="p">,</span> <span class="n">test_ds</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.data.load_dataset" class="doc doc-heading">
<code class="highlight language-python"><span class="n">load_dataset</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">print_i</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>load data from source into a Pandas DataFrame.</p>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Parameters:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>filepath</code></b>
(<code>str</code>)
–
<div class="doc-md-description">
<p>file location.</p>
</div>
</li>
<li>
<b><code>print_i</code></b>
(<code>int</code>, default:
<code>0</code>
)
–
<div class="doc-md-description">
<p>Print number of instances.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Returns:</th>
<td class="field-body">
<ul class="first simple">
<li>
<code><span title="pandas.DataFrame">DataFrame</span></code>
–
<div class="doc-md-description">
<p>pd.DataFrame: Pandas DataFrame of the data.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary> <code>newsclassifier\data.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">17</span>
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<span class="normal">31</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">load_dataset</span><span class="p">(</span><span class="n">filepath</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">print_i</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""load data from source into a Pandas DataFrame.</span>
<span class="sd"> Args:</span>
<span class="sd"> filepath (str): file location.</span>
<span class="sd"> print_i (int): Print number of instances.</span>
<span class="sd"> Returns:</span>
<span class="sd"> pd.DataFrame: Pandas DataFrame of the data.</span>
<span class="sd"> """</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Loading Data."</span><span class="p">)</span>
<span class="n">df</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">filepath</span><span class="p">)</span>
<span class="k">if</span> <span class="n">print_i</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">print_i</span><span class="p">),</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.data.prepare_data" class="doc doc-heading">
<code class="highlight language-python"><span class="n">prepare_data</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Separate headlines instance and feature selection.</p>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Parameters:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>df</code></b>
(<code><span title="pandas.DataFrame">DataFrame</span></code>)
–
<div class="doc-md-description">
<p>original dataframe.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Returns:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>df</code></b>( <code><span title="pandas.DataFrame">DataFrame</span></code>
) –
<div class="doc-md-description">
<p>new dataframe with appropriate features.</p>
</div>
</li>
<li>
<b><code>headlines_df</code></b>( <code><span title="pandas.DataFrame">DataFrame</span></code>
) –
<div class="doc-md-description">
<p>dataframe cintaining "headlines" category instances.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary> <code>newsclassifier\data.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">34</span>
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<span class="normal">52</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">prepare_data</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">"""Separate headlines instance and feature selection.</span>
<span class="sd"> Args:</span>
<span class="sd"> df: original dataframe.</span>
<span class="sd"> Returns:</span>
<span class="sd"> df: new dataframe with appropriate features.</span>
<span class="sd"> headlines_df: dataframe cintaining "headlines" category instances.</span>
<span class="sd"> """</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Preparing Data."</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">"Title"</span><span class="p">,</span> <span class="s2">"Category"</span><span class="p">]]</span>
<span class="n">df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">"Title"</span><span class="p">:</span> <span class="s2">"Text"</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="p">,</span> <span class="n">headlines_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">"Category"</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">"Headlines"</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">"Category"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Headlines"</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span><span class="p">,</span> <span class="n">headlines_df</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.data.prepare_input" class="doc doc-heading">
<code class="highlight language-python"><span class="n">prepare_input</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">text</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Tokenize and prepare the input text using the provided tokenizer.</p>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Parameters:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>tokenizer</code></b>
(<code><span title="transformers.RobertaTokenizer">RobertaTokenizer</span></code>)
–
<div class="doc-md-description">
<p>The Roberta tokenizer to encode the input.</p>
</div>
</li>
<li>
<b><code>text</code></b>
(<code>str</code>)
–
<div class="doc-md-description">
<p>The input text to be tokenized.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Returns:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>inputs</code></b>( <code>dict</code>
) –
<div class="doc-md-description">
<p>A dictionary containing the tokenized input with keys such as 'input_ids',
'attention_mask', etc.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary> <code>newsclassifier\data.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">135</span>
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<span class="normal">157</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">prepare_input</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">:</span> <span class="n">RobertaTokenizer</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">Dict</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Tokenize and prepare the input text using the provided tokenizer.</span>
<span class="sd"> Args:</span>
<span class="sd"> tokenizer (RobertaTokenizer): The Roberta tokenizer to encode the input.</span>
<span class="sd"> text (str): The input text to be tokenized.</span>
<span class="sd"> Returns:</span>
<span class="sd"> inputs (dict): A dictionary containing the tokenized input with keys such as 'input_ids',</span>
<span class="sd"> 'attention_mask', etc.</span>
<span class="sd"> """</span>
<span class="n">logger</span><span class="p">(</span><span class="s2">"Tokenizing input text."</span><span class="p">)</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode_plus</span><span class="p">(</span>
<span class="n">text</span><span class="p">,</span>
<span class="n">return_tensors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">add_special_tokens</span><span class="o">=</span><span class="n">Cfg</span><span class="o">.</span><span class="n">add_special_tokens</span><span class="p">,</span>
<span class="n">max_length</span><span class="o">=</span><span class="n">Cfg</span><span class="o">.</span><span class="n">max_len</span><span class="p">,</span>
<span class="n">pad_to_max_length</span><span class="o">=</span><span class="n">Cfg</span><span class="o">.</span><span class="n">pad_to_max_length</span><span class="p">,</span>
<span class="n">truncation</span><span class="o">=</span><span class="n">Cfg</span><span class="o">.</span><span class="n">truncation</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="k">return</span> <span class="n">inputs</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.data.preprocess" class="doc doc-heading">
<code class="highlight language-python"><span class="n">preprocess</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Preprocess the data.</p>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Parameters:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>df</code></b>
(<code><span title="pandas.DataFrame">DataFrame</span></code>)
–
<div class="doc-md-description">
<p>Dataframe on which the preprocessing steps need to be performed.</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<table class="field-list">
<colgroup>
<col class="field-name" />
<col class="field-body" />
</colgroup>
<tbody valign="top">
<tr class="field">
<th class="field-name">Returns:</th>
<td class="field-body">
<ul class="first simple">
<li>
<b><code>df</code></b>( <code><span title="pandas.DataFrame">DataFrame</span></code>
) –
<div class="doc-md-description">
<p>Preprocessed Data.</p>
</div>
</li>
<li>
<b><code>class_to_index</code></b>( <code><span title="pandas.DataFrame">DataFrame</span></code>
) –
<div class="doc-md-description">
<p>class labels to indices mapping</p>
</div>
</li>
<li>
<b><code>class_to_index</code></b>( <code><span title="typing.Dict">Dict</span></code>
) –
<div class="doc-md-description">
<p>indices to class labels mapping</p>
</div>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary> <code>newsclassifier\data.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">73</span>
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<span class="normal">96</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Dict</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">"""Preprocess the data.</span>
<span class="sd"> Args:</span>
<span class="sd"> df: Dataframe on which the preprocessing steps need to be performed.</span>
<span class="sd"> Returns:</span>
<span class="sd"> df: Preprocessed Data.</span>
<span class="sd"> class_to_index: class labels to indices mapping</span>
<span class="sd"> class_to_index: indices to class labels mapping</span>
<span class="sd"> """</span>
<span class="n">df</span><span class="p">,</span> <span class="n">headlines_df</span> <span class="o">=</span> <span class="n">prepare_data</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">cats</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Category"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">class_to_index</span> <span class="o">=</span> <span class="p">{</span><span class="n">tag</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cats</span><span class="p">)}</span>
<span class="n">index_to_class</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">class_to_index</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="n">df</span><span class="p">[</span><span class="s2">"Text"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Text"</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">clean_text</span><span class="p">)</span> <span class="c1"># clean text</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">"Text"</span><span class="p">,</span> <span class="s2">"Category"</span><span class="p">]]</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">df</span><span class="p">[</span><span class="s2">"Category"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Category"</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">class_to_index</span><span class="p">)</span> <span class="c1"># label encoding</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span><span class="p">,</span> <span class="n">headlines_df</span><span class="p">,</span> <span class="n">class_to_index</span><span class="p">,</span> <span class="n">index_to_class</span>
</code></pre></div></td></tr></table></div>
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