danaderp
/
ds4se
mirror of https://github.com/WM-SEMERU/ds4se.git


  
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            ---

title: Experimenting Neural Unsupervised Approaches for Software Information Retrieval [w2v]

keywords: fastai
sidebar: home_sidebar

summary: "Just Paper. Full Experimentation. This module is dedicated to experiment with word2vec. Consider to Copy the entire notebook for a new and separeted empirical evaluation. "
---
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<p>This copy is for Cisco purposes. It was adapted to process private github data from cisco.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">ds4se.mining.ir</span> <span class="k">import</span> <span class="o">*</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">prg</span> <span class="k">import</span> <span class="n">prg</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">ds4se</span> <span class="k">as</span> <span class="nn">ds</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> : </span><span class="si">%(levelname)s</span><span class="s1"> : </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">ERROR</span><span class="p">)</span>
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<h1 id="Artifacts-Similarity-with-BasicSequenceVectorization">Artifacts Similarity with BasicSequenceVectorization<a class="anchor-link" href="#Artifacts-Similarity-with-BasicSequenceVectorization">&#182;</a></h1><p>We test diferent similarities based on <a href="https://www.kdnuggets.com/2017/08/comparing-distance-measurements-python-scipy.html">blog</a> and <a href="https://www.kdnuggets.com/2019/01/comparison-text-distance-metrics.html">blog2</a></p>

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<h2 id="Experients-Set-up">Experients Set-up<a class="anchor-link" href="#Experients-Set-up">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path_data</span> <span class="o">=</span> <span class="s1">&#39;../dvc-ds4se/&#39;</span> <span class="c1">#dataset path</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Experiments 1.1.2 &lt;&lt;-- word2vec</span>
<span class="n">path_model_prefix</span> <span class="o">=</span> <span class="n">path_data</span><span class="o">+</span><span class="s1">&#39;models/bpe/sentencepiece/wiki_py_java_bpe_32k&#39;</span>
<span class="n">path_to_trained_model</span> <span class="o">=</span> <span class="n">path_data</span><span class="o">+</span><span class="s1">&#39;/models/wv/bpe32k/[word2vec-Py-Java-SK-500-20E-32k-1593748814.350487].model&#39;</span>
<span class="k">def</span> <span class="nf">sacp_params</span><span class="p">():</span>
        <span class="k">return</span> <span class="p">{</span>
        <span class="s2">&quot;vectorizationType&quot;</span><span class="p">:</span> <span class="n">VectorizationType</span><span class="o">.</span><span class="n">word2vec</span><span class="p">,</span>
        <span class="s2">&quot;linkType&quot;</span><span class="p">:</span> <span class="n">LinkType</span><span class="o">.</span><span class="n">issue2src</span><span class="p">,</span>
        <span class="s2">&quot;system&quot;</span><span class="p">:</span> <span class="s1">&#39;sacp-python-common&#39;</span><span class="p">,</span>
        <span class="s2">&quot;path_to_trained_model&quot;</span><span class="p">:</span> <span class="n">path_to_trained_model</span><span class="p">,</span>
        <span class="s2">&quot;source_type&quot;</span><span class="p">:</span> <span class="n">SoftwareArtifacts</span><span class="o">.</span><span class="n">PR</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
        <span class="s2">&quot;target_type&quot;</span><span class="p">:</span> <span class="n">SoftwareArtifacts</span><span class="o">.</span><span class="n">PY</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
        <span class="s2">&quot;system_path_config&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;system_path&quot;</span><span class="p">:</span> <span class="s1">&#39;/tf/data/cisco/sacp_data/[sacp-python-common-all-corpus-1609224778.517111].csv&#39;</span><span class="p">,</span>
            <span class="s2">&quot;sep&quot;</span><span class="p">:</span> <span class="s1">&#39;~&#39;</span><span class="p">,</span>
            <span class="s2">&quot;names&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;ids&#39;</span><span class="p">,</span><span class="s1">&#39;bpe32k&#39;</span><span class="p">],</span>
            <span class="s2">&quot;prep&quot;</span><span class="p">:</span> <span class="n">Preprocessing</span><span class="o">.</span><span class="n">bpe</span>
        <span class="p">},</span>
        <span class="s2">&quot;path_mappings&quot;</span><span class="p">:</span> <span class="s2">&quot;/tf/data/cisco/sacp_data/sacp-pr-mappings.csv&quot;</span><span class="p">,</span>
        <span class="s2">&quot;saving_path&quot;</span><span class="p">:</span> <span class="n">path_data</span> <span class="o">+</span> <span class="s1">&#39;metrics/traceability/experiments1.1.x/&#39;</span><span class="p">,</span>
        <span class="s2">&quot;names&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Source&#39;</span><span class="p">,</span><span class="s1">&#39;Target&#39;</span><span class="p">,</span><span class="s1">&#39;Linked?&#39;</span><span class="p">],</span>
        <span class="s2">&quot;model_prefix&quot;</span><span class="p">:</span> <span class="n">path_model_prefix</span>

        <span class="p">}</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">parameters</span> <span class="o">=</span> <span class="n">sacp_params</span><span class="p">()</span>
<span class="n">parameters</span>
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<pre>{&#39;vectorizationType&#39;: &lt;VectorizationType.word2vec: 1&gt;,
 &#39;linkType&#39;: &lt;LinkType.issue2src: 3&gt;,
 &#39;system&#39;: &#39;sacp-python-common&#39;,
 &#39;path_to_trained_model&#39;: &#39;../dvc-ds4se//models/wv/bpe32k/[word2vec-Py-Java-SK-500-20E-32k-1593748814.350487].model&#39;,
 &#39;source_type&#39;: &#39;pr&#39;,
 &#39;target_type&#39;: &#39;py&#39;,
 &#39;system_path_config&#39;: {&#39;system_path&#39;: &#39;/tf/data/cisco/sacp_data/[sacp-python-common-all-corpus-1609224778.517111].csv&#39;,
  &#39;sep&#39;: &#39;~&#39;,
  &#39;names&#39;: [&#39;ids&#39;, &#39;bpe32k&#39;],
  &#39;prep&#39;: &lt;Preprocessing.bpe: 2&gt;},
 &#39;path_mappings&#39;: &#39;/tf/data/cisco/sacp_data/sacp-pr-mappings.csv&#39;,
 &#39;saving_path&#39;: &#39;../dvc-ds4se/metrics/traceability/experiments1.1.x/&#39;,
 &#39;names&#39;: [&#39;Source&#39;, &#39;Target&#39;, &#39;Linked?&#39;],
 &#39;model_prefix&#39;: &#39;../dvc-ds4se/models/bpe/sentencepiece/wiki_py_java_bpe_32k&#39;}</pre>
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<h1 id="Artifacts-Similarity-with-Word2Vec">Artifacts Similarity with Word2Vec<a class="anchor-link" href="#Artifacts-Similarity-with-Word2Vec">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#[step 1]Creating the Vectorization Class</span>
<span class="n">word2vec</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">mining</span><span class="o">.</span><span class="n">ir</span><span class="o">.</span><span class="n">Word2VecSeqVect</span><span class="p">(</span> <span class="n">params</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">logging</span> <span class="o">=</span> <span class="n">logging</span> <span class="p">)</span>
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<pre>2021-01-26 03:00:08,973 : INFO : adding document #0 to Dictionary(0 unique tokens: [])
2021-01-26 03:00:09,051 : INFO : built Dictionary(3010 unique tokens: [&#39;28&#39;, &#39;29&#39;, &#39;3&#39;, &#39;4)&#39;, &#39;7&#39;]...) from 362 documents (total 171602 corpus positions)
2021-01-26 03:00:09,290 : INFO : Ignored vocab by BPE{&#39;γ&#39;, &#39;@&#39;, &#39;```&#39;, &#39;^&#39;, &#39;`&#39;, &#39;\r\n\r\n@&#39;, &#39;\\&#39;, &#39;\r\n\r\n&#39;, &#39;\t&#39;, &#39;\r\n&#39;, &#39;\r\n\r\n\r\n&#39;}
2021-01-26 03:00:09,292 : INFO : bpe preprocessing documents, dictionary, and vocab for the test corpus
2021-01-26 03:00:09,294 : INFO : loading Word2Vec object from ../dvc-ds4se//models/wv/bpe32k/[word2vec-Py-Java-SK-500-20E-32k-1593748814.350487].model
2021-01-26 03:00:10,536 : INFO : loading wv recursively from ../dvc-ds4se//models/wv/bpe32k/[word2vec-Py-Java-SK-500-20E-32k-1593748814.350487].model.wv.* with mmap=None
2021-01-26 03:00:10,539 : INFO : setting ignored attribute vectors_norm to None
2021-01-26 03:00:10,542 : INFO : loading vocabulary recursively from ../dvc-ds4se//models/wv/bpe32k/[word2vec-Py-Java-SK-500-20E-32k-1593748814.350487].model.vocabulary.* with mmap=None
2021-01-26 03:00:10,544 : INFO : loading trainables recursively from ../dvc-ds4se//models/wv/bpe32k/[word2vec-Py-Java-SK-500-20E-32k-1593748814.350487].model.trainables.* with mmap=None
2021-01-26 03:00:10,546 : INFO : setting ignored attribute cum_table to None
2021-01-26 03:00:10,549 : INFO : loaded ../dvc-ds4se//models/wv/bpe32k/[word2vec-Py-Java-SK-500-20E-32k-1593748814.350487].model
2021-01-26 03:00:10,589 : INFO : precomputing L2-norms of word weight vectors
2021-01-26 03:00:10,613 : INFO : constructing a sparse term similarity matrix using &lt;gensim.models.keyedvectors.WordEmbeddingSimilarityIndex object at 0x7fa07e0670f0&gt;
2021-01-26 03:00:10,616 : INFO : iterating over columns in dictionary order
2021-01-26 03:00:10,620 : INFO : PROGRESS: at 0.03% columns (1 / 3010, 0.033223% density, 0.033223% projected density)
2021-01-26 03:00:18,202 : INFO : PROGRESS: at 33.26% columns (1001 / 3010, 0.810101% density, 2.369292% projected density)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#[step 2]NonGroundTruth Computation</span>
<span class="n">metric_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">DistanceMetric</span><span class="o">.</span><span class="n">WMD</span><span class="p">,</span><span class="n">DistanceMetric</span><span class="o">.</span><span class="n">SCM</span><span class="p">,</span><span class="n">EntropyMetric</span><span class="o">.</span><span class="n">MSI_I</span><span class="p">,</span><span class="n">EntropyMetric</span><span class="o">.</span><span class="n">MI</span><span class="p">]</span>
<span class="c1">#metric_list = [EntropyMetric.MSI_I,EntropyMetric.MI]</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">ComputeDistanceArtifacts</span><span class="p">(</span> <span class="n">sampling</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">samples</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">metric_list</span> <span class="o">=</span> <span class="n">metric_list</span> <span class="p">)</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">df_nonground_link</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">word2vec</span><span class="o">.</span><span class="n">df_nonground_link</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">word2vec</span><span class="o">.</span><span class="n">df_nonground_link</span><span class="p">[</span><span class="s1">&#39;Target&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#[step 3]Saving Non-GroundTruth Links</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">SaveLinks</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Loading Non-GroundTruth Links (change the timestamp with the assigned in the previous step)</span>
<span class="n">df_nonglinks</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">mining</span><span class="o">.</span><span class="n">ir</span><span class="o">.</span><span class="n">LoadLinks</span><span class="p">(</span><span class="n">timestamp</span><span class="o">=</span><span class="mf">1610579170.341825</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span> <span class="n">logging</span><span class="o">=</span><span class="n">logging</span><span class="p">)</span>
<span class="n">df_nonglinks</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">word2vec</span><span class="o">.</span><span class="n">df_nonground_link</span> <span class="o">=</span> <span class="n">df_nonglinks</span> <span class="c1"># Only to load links from file</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#[step 4]GroundTruthMatching Testing</span>
<span class="c1">#TODO change the path for a param</span>
<span class="n">path_to_ground_truth</span> <span class="o">=</span>  <span class="n">parameters</span><span class="p">[</span><span class="s1">&#39;path_mappings&#39;</span><span class="p">]</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">MatchWithGroundTruth</span><span class="p">(</span><span class="n">path_to_ground_truth</span><span class="p">,</span> <span class="n">semeru_format</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">df_ground_link</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">word2vec</span><span class="o">.</span><span class="n">df_ground_link</span><span class="p">[</span><span class="n">word2vec</span><span class="o">.</span><span class="n">df_ground_link</span> <span class="p">[</span><span class="s1">&#39;Linked?&#39;</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">word2vec</span><span class="o">.</span><span class="n">df_ground_link</span><span class="p">[</span><span class="s1">&#39;Source&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
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<h2 id="4.1-Only-SACP">4.1 Only SACP<a class="anchor-link" href="#4.1-Only-SACP">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#[step 4.1]GroundTruthMatching Testing For CISCO Mappings</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">MatchWithGroundTruth</span><span class="p">(</span><span class="n">from_mappings</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">df_ground_link</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#[optional]GroundTruth Direct Processing</span>
<span class="n">ground_links</span> <span class="o">=</span> <span class="n">word2vec</span><span class="o">.</span><span class="n">ground_truth_processing</span><span class="p">(</span><span class="n">path_to_ground_truth</span><span class="p">)</span>
<span class="n">ground_links</span> <span class="c1"># A tuple</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#[step 5]Saving GroundTruth Links</span>
<span class="n">word2vec</span><span class="o">.</span><span class="n">SaveLinks</span><span class="p">(</span><span class="n">grtruth</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Loading Non-GroundTruth Links (change the timestamp with the assigned in the previous step)</span>
<span class="n">df_glinks</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">mining</span><span class="o">.</span><span class="n">ir</span><span class="o">.</span><span class="n">LoadLinks</span><span class="p">(</span><span class="n">timestamp</span><span class="o">=</span><span class="mf">1610579318.97542</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span><span class="n">grtruth</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">logging</span><span class="o">=</span><span class="n">logging</span><span class="p">)</span>
<span class="n">df_glinks</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<h3 id="Generating-Documentation">Generating Documentation<a class="anchor-link" href="#Generating-Documentation">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span> nbdev_build_docs #&lt;-------- <span class="o">[</span>Activate when stable<span class="o">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span> nbdev_build_lib
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nbdev.export</span> <span class="k">import</span> <span class="n">notebook2script</span>
<span class="n">notebook2script</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="ch">#! pip install -e .</span>
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