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Bring your own LIWC & matplotlib dependency fix #322

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Bring your own LIWC & matplotlib dependency fix #322

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34e7e84
added BYOL feature, updated some doc strings
sundy1994 Oct 2, 2024
27c571e
add matplotlib requirements
sundy1994 Oct 17, 2024
1367090
bug fix
sundy1994 Oct 17, 2024
006ad11
bug fixes
sundy1994 Oct 18, 2024
26f32d9
Merge branch 'dev' into yuxuan/BYOL
xehu Oct 22, 2024
65140f1
update documentation
xehu Oct 22, 2024
4909be0
update regex for emojis, add liwc loading checks, add columns for cus…
sundy1994 Oct 29, 2024
8e138d8
use original message col in lexical_features() to improve readability
sundy1994 Oct 30, 2024
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3 changes: 2 additions & 1 deletion docs/build/html/features/lexical_features_v2.html
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<dl class="py function">
<dt class="sig sig-object py" id="features.lexical_features_v2.liwc_features">
<span class="sig-prename descclassname"><span class="pre">features.lexical_features_v2.</span></span><span class="sig-name descname"><span class="pre">liwc_features</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chat_df</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message_col</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">DataFrame</span></span></span><a class="headerlink" href="#features.lexical_features_v2.liwc_features" title="Link to this definition"></a></dt>
<span class="sig-prename descclassname"><span class="pre">features.lexical_features_v2.</span></span><span class="sig-name descname"><span class="pre">liwc_features</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chat_df</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message_col</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_liwc_dictionary</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">DataFrame</span></span></span><a class="headerlink" href="#features.lexical_features_v2.liwc_features" title="Link to this definition"></a></dt>
<dd><blockquote>
<div><p>This function takes in the chat level input dataframe and computes lexical features
(the number of words from a given lexicon, such as LIWC).</p>
Expand All @@ -146,6 +146,7 @@
<dd class="field-odd"><ul class="simple">
<li><p><strong>chat_df</strong> (<em>pd.DataFrame</em>) – This is a pandas dataframe of the chat level features. Should contain ‘message’ column.</p></li>
<li><p><strong>message_col</strong> (<em>str</em>) – This is a string with the name of the column containing the message / text.</p></li>
<li><p><strong>custom_liwc_dictionary</strong> (<em>dict</em>) – This is a dictionary of the user’s custom LIWC dic.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
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Expand Up @@ -449,6 +449,8 @@ <h2 id="F">F</h2>
</li>
</ul></li>
<li><a href="feature_builder.html#feature_builder.FeatureBuilder.featurize">featurize() (feature_builder.FeatureBuilder method)</a>
</li>
<li><a href="utils/check_embeddings.html#utils.check_embeddings.fix_abbreviations">fix_abbreviations() (in module utils.check_embeddings)</a>
</li>
<li><a href="features/word_mimicry.html#features.word_mimicry.function_mimicry_score">function_mimicry_score() (in module features.word_mimicry)</a>
</li>
Expand Down Expand Up @@ -621,6 +623,8 @@ <h2 id="L">L</h2>
<li><a href="utils/calculate_chat_level_features.html#utils.calculate_chat_level_features.ChatLevelFeaturesCalculator.lexical_features">lexical_features() (utils.calculate_chat_level_features.ChatLevelFeaturesCalculator method)</a>
</li>
<li><a href="features/lexical_features_v2.html#features.lexical_features_v2.liwc_features">liwc_features() (in module features.lexical_features_v2)</a>
</li>
<li><a href="utils/check_embeddings.html#utils.check_embeddings.load_liwc_dict">load_liwc_dict() (in module utils.check_embeddings)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
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8 changes: 6 additions & 2 deletions docs/build/html/utils/calculate_chat_level_features.html
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<span id="calculate-chat-level-features-module"></span><span id="chat-level-features"></span><h1>calculate_chat_level_features module<a class="headerlink" href="#module-utils.calculate_chat_level_features" title="Link to this heading"></a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="utils.calculate_chat_level_features.ChatLevelFeaturesCalculator">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">utils.calculate_chat_level_features.</span></span><span class="sig-name descname"><span class="pre">ChatLevelFeaturesCalculator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chat_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vect_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bert_sentiment_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ner_training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ner_cutoff</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conversation_id_col</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message_col</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp_col</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#utils.calculate_chat_level_features.ChatLevelFeaturesCalculator" title="Link to this definition"></a></dt>
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">utils.calculate_chat_level_features.</span></span><span class="sig-name descname"><span class="pre">ChatLevelFeaturesCalculator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chat_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vect_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bert_sentiment_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ner_training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">DataFrame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ner_cutoff</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conversation_id_col</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message_col</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp_col</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_liwc_dictionary</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#utils.calculate_chat_level_features.ChatLevelFeaturesCalculator" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Initialize variables and objects used by the ChatLevelFeaturesCalculator class.</p>
<p>This class uses various feature modules to define chat-level features. It reads input data and
Expand All @@ -107,8 +107,12 @@
<li><p><strong>chat_data</strong> (<em>pd.DataFrame</em>) – Pandas dataframe of chat-level features read from the input dataset</p></li>
<li><p><strong>vect_data</strong> (<em>pd.DataFrame</em>) – Pandas dataframe containing vector data</p></li>
<li><p><strong>bert_sentiment_data</strong> (<em>pd.DataFrame</em>) – Pandas dataframe containing BERT sentiment data</p></li>
<li><p><strong>ner_training_df</strong> (<em>pd.DataFrame</em>) – This is a pandas dataframe of training data for named entity recognition feature</p></li>
<li><p><strong>ner_training</strong> (<em>pd.DataFrame</em>) – This is a pandas dataframe of training data for named entity recognition feature</p></li>
<li><p><strong>ner_cutoff</strong> (<em>int</em>) – This is the cutoff value for the confidence of prediction for each named entity</p></li>
<li><p><strong>conversation_id_col</strong> (<em>str</em>) – A string representing the column name that should be selected as the conversation ID. Defaults to “conversation_num”.</p></li>
<li><p><strong>message_col</strong> (<em>str</em>) – A string representing the column name that should be selected as the message. Defaults to “message”.</p></li>
<li><p><strong>timestamp_col</strong> (<em>str</em>) – A string representing the column name that should be selected as the message. Defaults to “timestamp”.</p></li>
<li><p><strong>custom_liwc_dictionary</strong> (<em>dict</em>) – This is the user’s own LIWC dictionary. Defaults to empty dictionary.</p></li>
</ul>
</dd>
</dl>
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