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nli.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>nli</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
<style type="text/css">
/*!
*
* Twitter Bootstrap
*
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<div class=" highlight hl-ipython2"><pre><span class="c"># -*- coding: utf-8 -*-</span>
</pre></div>
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<h1 id="Natural-language-inference">Natural language inference<a class="anchor-link" href="#Natural-language-inference">¶</a></h1><p>Chris Potts, for <a href="https://web.stanford.edu/class/cs224u/">Stanford's CS224u: Natural language understanding</a></p>
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<p>In the context of NLP/NLU, Natural Language Inference (NLI) is the task of predicting the logical
relationships between words, phrases, sentences, (paragraphs, documents, ...). Such relationships
are crucial for all kinds of reasoning in natural language: arguing, debating, problem solving,
summarization, extrapolation, and so forth.</p>
<p>NLI is a great task for this course. It requires serious linguistic analysis to do well,
there are good (albeit small) publicly available data sets, and there are some natural baselines
that help with getting a model up and running, and with understanding the performance of more
sophisticated approaches.</p>
<p>NLI was also the topic of <a href="http://nlp.stanford.edu/~wcmac/papers/nli-diss.pdf">Bill's thesis</a>
(he popularized the name "NLI"), so you can forever endear yourself to him by working on it!</p>
<p>The purpose of this codebook is to introduce the problem of NLI more fully in the context of
the <a href="http://alt.qcri.org/semeval2014/task1/">SemEval 2014 semantic relatedness task</a>.
This data set is called "Sentences Involving Compositional Knowledge" or, for better or worse,
"SICK". It's <a href="http://alt.qcri.org/semeval2014/task1/index.php?id=data-and-tools">freely available from the SemEval site</a>,
but we're going to work with a parsed version created by <a href="http://stanford.edu/~sbowman/">Sam Bowman</a>
as part of <a href="https://github.com/sleepinyourhat/vector-entailment/releases/tag/W15-R1">his research on neural models of semantic composition</a>.
This data is in the subfolder <code>nli-data</code> of this Github repository.</p>
<p>This codebook explores two general approaches: a standard classifier, and
a shallow neural network using distributed representations.</p>
<p>The <a href="#Classifier-training-and-assessment">Classifier training and assessment</a> section also
serves as a general illustration of how to take advantage of the
<a href="http://scikit-learn.org/stable/">scikit-learn</a> functions for
doing feature selection, cross-validation, hyper-parameter optimization, and evaluation in the
context of multi-class classification. That code could be easily modified to work with
any classification problem.</p>
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<ol>
<li><a href="#Working-with-the-parsed-SICK-data">Working with the parsed SICK data</a><ol>
<li><a href="#Trees">Trees</a></li>
<li><a href="#Readers">Readers</a></li>
</ol>
</li>
<li><a href="#MaxEnt-classifier-approach">MaxEnt classifier approach</a><ol>
<li><a href="#Baseline-classifier-features">Baseline classifier features</a></li>
<li><a href="#Classifier-training-and-assessment">Classifier training and assessment</a></li>
<li><a href="#A-few-ideas-for-better-classifier-features">A few ideas for better classifier features</a></li>
</ol>
</li>
<li><a href="#Shallow-neural-network-approach">Shallow neural network approach</a><ol>
<li><a href="#Baseline-distributed-features">Baseline distributed features</a> </li>
<li><a href="#Output-label-vectors">Output label vectors</a> </li>
<li><a href="#Network-training-and-assessment">Network training and assessment</a></li>
<li><a href="#Potential-next-steps">Potential next steps</a></li>
</ol>
</li>
<li><a href="#Some-further-reading">Some further reading</a></li>
<li><a href="#Homework-4">Homework 4</a></li>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">re</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">csv</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">sklearn</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s">"scikit-learn version 0.16.* is required</span><span class="se">\n</span><span class="s">"</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">__version__</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span> <span class="o">!=</span> <span class="s">'0.16'</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s">"scikit-learn version 0.16.* is required. You're at </span><span class="si">%s</span><span class="s">.</span><span class="se">\n</span><span class="s">"</span> <span class="o">%</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction</span> <span class="kn">import</span> <span class="n">DictVectorizer</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">SelectFpr</span><span class="p">,</span> <span class="n">chi2</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="kn">from</span> <span class="nn">distributedwordreps</span> <span class="kn">import</span> <span class="n">build</span><span class="p">,</span> <span class="n">ShallowNeuralNetwork</span>
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<h2 id="Working-with-the-parsed-SICK-data">Working with the parsed SICK data<a class="anchor-link" href="#Working-with-the-parsed-SICK-data">¶</a></h2>
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<p>Our parsed version of the SICK data contains triples like this (tab-separated fields):</p>
<p><code>ENTAILMENT ( ( A child ) ( is ( playing ( in ( a yard ) ) ) ) ) ( ( A child ) ( is playing ) )</code></p>
<p><code>NEUTRAL ( ( A child ) ( is ( playing ( in ( a yard ) ) ) ) ) ( ( A child ) ( is ( wearing ( blue jeans ) ) ) )</code></p>
<p><code>CONTRADICTION ( ( A child ) ( is ( playing ) ) ) ( ( A child ) ( is sleeping ) )</code></p>
<p>The brackets encode a label-free constituency structure of each sentence. The three labels on the left
are the classes that we want to learn to predict. We'll frequently need access to them, so let's define
them as a list:</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">LABELS</span> <span class="o">=</span> <span class="p">[</span><span class="s">'ENTAILMENT'</span><span class="p">,</span> <span class="s">'CONTRADICTION'</span><span class="p">,</span> <span class="s">'NEUTRAL'</span><span class="p">]</span>
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<h3 id="Trees">Trees<a class="anchor-link" href="#Trees">¶</a></h3>
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<p>The baseline models that I define here ignore all of the tree structure, but you'll likely want
to take advantage of it. So the following function can be used to turn bracketed strings like
the above into tuples of tuples encoding the syntactic structure.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">WORD_RE</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="s">r"([^ \(\)]+)"</span><span class="p">,</span> <span class="n">re</span><span class="o">.</span><span class="n">UNICODE</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">str2tree</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="sd">"""Turns labeled bracketing s into a tree structure (tuple of tuples)"""</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">WORD_RE</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s">r'"\1",'</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">")"</span><span class="p">,</span> <span class="s">"),"</span><span class="p">)</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s">","</span><span class="p">)</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s">","</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">eval</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
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<p>Here's an example:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span> <span class="c"># Prevent this example from loading on import of this module.</span>
<span class="k">print</span> <span class="n">str2tree</span><span class="p">(</span><span class="s">"( ( A child ) ( is ( playing ( in ( a yard ) ) ) ) )"</span><span class="p">)</span>
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<pre>(('A', 'child'), ('is', ('playing', ('in', ('a', 'yard')))))
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<p>For baseline models, we often want just the words, also called terminal nodes or <em>leaves</em>.
This function gives us access to them as a list:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">leaves</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="sd">"""Returns all of the words (terminal nodes) in tree t"""</span>
<span class="n">words</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">t</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">words</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">words</span> <span class="o">+=</span> <span class="n">leaves</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">words</span>
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<p>An example:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span> <span class="c"># Prevent this example from loading on import of this module.</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">str2tree</span><span class="p">(</span><span class="s">"( ( A child ) ( is ( playing ( in ( a yard ) ) ) ) )"</span><span class="p">)</span>
<span class="k">print</span> <span class="n">leaves</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
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<pre>['A', 'child', 'is', 'playing', 'in', 'a', 'yard']
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<h3 id="Readers">Readers<a class="anchor-link" href="#Readers">¶</a></h3>
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<p>To make it easy to run through the corpus, let's define general readers for the SICK
data. The general function for this yields triples consisting of the label, the left
tree, and the right tree, as parsed by <code>str2tree</code>:</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">data_dir</span> <span class="o">=</span> <span class="s">'nli-data/'</span>
<span class="k">def</span> <span class="nf">sick_reader</span><span class="p">(</span><span class="n">src_filename</span><span class="p">):</span>
<span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">csv</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="nb">file</span><span class="p">(</span><span class="n">src_filename</span><span class="p">),</span> <span class="n">delimiter</span><span class="o">=</span><span class="s">"</span><span class="se">\t</span><span class="s">"</span><span class="p">):</span>
<span class="n">label</span><span class="p">,</span> <span class="n">t1</span><span class="p">,</span> <span class="n">t2</span> <span class="o">=</span> <span class="n">example</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">label</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s">'%'</span><span class="p">):</span> <span class="c"># Some files use leading % for comments. </span>
<span class="k">yield</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">str2tree</span><span class="p">(</span><span class="n">t1</span><span class="p">),</span> <span class="n">str2tree</span><span class="p">(</span><span class="n">t2</span><span class="p">))</span>
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<p>Now we define separate readers for the training and development data:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">sick_train_reader</span><span class="p">():</span>
<span class="k">return</span> <span class="n">sick_reader</span><span class="p">(</span><span class="n">src_filename</span><span class="o">=</span><span class="n">data_dir</span><span class="o">+</span><span class="s">"SICK_train_parsed.txt"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">sick_dev_reader</span><span class="p">():</span>
<span class="k">return</span> <span class="n">sick_reader</span><span class="p">(</span><span class="n">src_filename</span><span class="o">=</span><span class="n">data_dir</span><span class="o">+</span><span class="s">"SICK_trial_parsed.txt"</span><span class="p">)</span>
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<p>Eventually, we'll want a test-set reader. As Bill discussed in class, though,
__we swear on our honor as scholars that we won't use this data until
system development is complete and we are ready to conduct our final
assessment__!</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">sick_test_reader</span><span class="p">():</span>
<span class="k">return</span> <span class="n">sick_reader</span><span class="p">(</span><span class="n">src_filename</span><span class="o">=</span><span class="n">data_dir</span><span class="o">+</span><span class="s">"SICK_test_parsed.txt"</span><span class="p">)</span>
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<h2 id="MaxEnt-classifier-approach">MaxEnt classifier approach<a class="anchor-link" href="#MaxEnt-classifier-approach">¶</a></h2>
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<h3 id="Baseline-classifier-features">Baseline classifier features<a class="anchor-link" href="#Baseline-classifier-features">¶</a></h3>
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<p>The first baseline we define is the <em>word overlap</em> baseline. It simply uses as
features the words that appear in both sentences.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">word_overlap_features</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span>
<span class="n">overlap</span> <span class="o">=</span> <span class="p">[</span><span class="n">w1</span> <span class="k">for</span> <span class="n">w1</span> <span class="ow">in</span> <span class="n">leaves</span><span class="p">(</span><span class="n">t1</span><span class="p">)</span> <span class="k">if</span> <span class="n">w1</span> <span class="ow">in</span> <span class="n">leaves</span><span class="p">(</span><span class="n">t2</span><span class="p">)]</span>
<span class="k">return</span> <span class="n">Counter</span><span class="p">(</span><span class="n">overlap</span><span class="p">)</span>
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<p>Another popular baseline is to use as features the full cross-product of
words from both sentences:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">word_cross_product_features</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span>
<span class="k">return</span> <span class="n">Counter</span><span class="p">([(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span> <span class="k">for</span> <span class="n">w1</span><span class="p">,</span> <span class="n">w2</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="n">leaves</span><span class="p">(</span><span class="n">t1</span><span class="p">),</span> <span class="n">leaves</span><span class="p">(</span><span class="n">t2</span><span class="p">))])</span>
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<p>Both of these feature functions return count dictionaries mapping feature names to
the number of times they occur in the data. This is the representation we'll work
with throughout; scikit-learn will handle the further processing it needs to build
linear classifiers.</p>
<p>Naturally, you can do better than these feature functions!
Both of these feature classes might be useful even in a more advanced model, though.</p>
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<h3 id="Classifier-training-and-assessment">Classifier training and assessment<a class="anchor-link" href="#Classifier-training-and-assessment">¶</a></h3>
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<p>The first step in training a classifier is using a feature function like the one above
to turn the data into a list of <em>training instances</em>: feature representations and their
associated labels:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">featurizer</span><span class="p">(</span><span class="n">reader</span><span class="o">=</span><span class="n">sick_train_reader</span><span class="p">,</span> <span class="n">feature_function</span><span class="o">=</span><span class="n">word_overlap_features</span><span class="p">):</span>
<span class="sd">"""Map the data in reader to a list of features according to feature_function,</span>
<span class="sd"> and create the gold label vector."""</span>
<span class="n">feats</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">split_index</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">t1</span><span class="p">,</span> <span class="n">t2</span> <span class="ow">in</span> <span class="n">reader</span><span class="p">():</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">feature_function</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">)</span>
<span class="n">feats</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">feats</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
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<p>The main work of training a model is done by the following <code>train_classifier</code> method. At a high-level,
this function is doing a few things, with the goal of finding the right model (in the
class of models we're exploring) for the data we have to train on. The major steps:</p>
<ol>
<li>Use scikit-learn to turn the feature dictionaries into a big sparse matrix.</li>
<li>Optionally use a bit of statistical testing to select features that seem discriminating. </li>
<li>Use cross-validation within the training data to try to find the right regularization scheme.</li>
<li>Train using the best-looking regularization scheme found in the previous step.</li>
<li>Report cross-valided F1 scores to provide a sense for how we're doing.</li>
<li>Return the fitted model plus the objects we need to align with future evaluation data.</li>
</ol>
<p>Part of the thinking behind this approach is that one can work as follows:</p>
<ul>
<li>By and large, you check models just by training them and seeing how they do in cross-validation.</li>
<li>Only ccasionally, and only with good reason, do you check how you're doing on the dev set. If this is done only rarely, then it will help prevent you from over-fitting to quirks of this data or the training data.</li>
<li>At the very end, one runs the test. </li>
<li>The final paper can report, for the final model,<ul>
<li>Mean cross-validation values on the training data with standard errors</li>
<li>Dev set performance</li>
<li>Test set performance</li>
</ul>
</li>
</ul>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">train_classifier</span><span class="p">(</span>
<span class="n">reader</span><span class="o">=</span><span class="n">sick_train_reader</span><span class="p">,</span>
<span class="n">feature_function</span><span class="o">=</span><span class="n">word_overlap_features</span><span class="p">,</span>
<span class="n">feature_selector</span><span class="o">=</span><span class="n">SelectFpr</span><span class="p">(</span><span class="n">chi2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.05</span><span class="p">),</span> <span class="c"># Use None to stop feature selection</span>
<span class="n">cv</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="c"># Number of folds used in cross-validation</span>
<span class="n">priorlims</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mf">3.1</span><span class="p">,</span> <span class="o">.</span><span class="mi">1</span><span class="p">)):</span> <span class="c"># regularization priors to explore (we expect something around 1)</span>
<span class="c"># Featurize the data:</span>
<span class="n">feats</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">featurizer</span><span class="p">(</span><span class="n">reader</span><span class="o">=</span><span class="n">reader</span><span class="p">,</span> <span class="n">feature_function</span><span class="o">=</span><span class="n">feature_function</span><span class="p">)</span>
<span class="c"># Map the count dictionaries to a sparse feature matrix:</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">DictVectorizer</span><span class="p">(</span><span class="n">sparse</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">feats</span><span class="p">)</span>
<span class="c">##### FEATURE SELECTION </span>
<span class="c"># (An optional step; not always productive). By default, we select all</span>
<span class="c"># the features that pass the chi2 test of association with the</span>
<span class="c"># class labels at p < 0.05. sklearn.feature_selection has other</span>
<span class="c"># methods that are worth trying. I've seen particularly good results</span>
<span class="c"># with the model-based methods, which require some changes to the</span>
<span class="c"># current code.</span>
<span class="n">feat_matrix</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">if</span> <span class="n">feature_selector</span><span class="p">:</span>
<span class="n">feat_matrix</span> <span class="o">=</span> <span class="n">feature_selector</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">feat_matrix</span> <span class="o">=</span> <span class="n">X</span>
<span class="c">##### HYPER-PARAMETER SEARCH</span>
<span class="c"># Define the basic model to use for parameter search:</span>
<span class="n">searchmod</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">fit_intercept</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">intercept_scaling</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c"># Parameters to grid-search over:</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s">'C'</span><span class="p">:</span><span class="n">priorlims</span><span class="p">,</span> <span class="s">'penalty'</span><span class="p">:[</span><span class="s">'l1'</span><span class="p">,</span><span class="s">'l2'</span><span class="p">]}</span>
<span class="c"># Cross-validation grid search to find the best hyper-parameters: </span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">searchmod</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">feat_matrix</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">best_params_</span>
<span class="c"># Establish the model we want using the parameters obtained from the search:</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">fit_intercept</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">intercept_scaling</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s">'C'</span><span class="p">],</span> <span class="n">penalty</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s">'penalty'</span><span class="p">])</span>
<span class="c">##### ASSESSMENT </span>
<span class="c"># Cross-validation of our favored model; for other summaries, use different</span>
<span class="c"># values for scoring: http://scikit-learn.org/dev/modules/model_evaluation.html</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">feat_matrix</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s">"f1_macro"</span><span class="p">)</span>
<span class="k">print</span> <span class="s">'Best model'</span><span class="p">,</span> <span class="n">mod</span>
<span class="k">print</span> <span class="s">'</span><span class="si">%s</span><span class="s"> features selected out of </span><span class="si">%s</span><span class="s"> total'</span> <span class="o">%</span> <span class="p">(</span><span class="n">feat_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="k">print</span> <span class="s">'F1 mean: </span><span class="si">%0.2f</span><span class="s"> (+/- </span><span class="si">%0.2f</span><span class="s">)'</span> <span class="o">%</span> <span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">scores</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
<span class="c"># TRAIN OUR MODEL:</span>
<span class="n">mod</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">feat_matrix</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="c"># Return the trained model along with the objects we need to</span>
<span class="c"># featurize test data in a way that aligns with our training</span>
<span class="c"># matrix:</span>
<span class="k">return</span> <span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">feature_selector</span><span class="p">,</span> <span class="n">feature_function</span><span class="p">)</span>
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<p>Now we can train either of our baseline models. Here's the run for the word-overlap one:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span> <span class="c"># Prevent this example from loading on import of this module.</span>
<span class="n">overlapmodel</span> <span class="o">=</span> <span class="n">train_classifier</span><span class="p">(</span><span class="n">feature_function</span><span class="o">=</span><span class="n">word_overlap_features</span><span class="p">)</span>
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<pre>Best model LogisticRegression(C=2.8000000000000003, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, max_iter=100,
multi_class='ovr', penalty='l2', random_state=None,
solver='liblinear', tol=0.0001, verbose=0)
203 features selected out of 1397 total
F1 mean: 0.47 (+/- 0.03)
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<p>The following code assess the output of <code>train_classifier</code> on new data. The default is to do this
on the dev set, but this same code would be used to evaluate the final model on the test set.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">evaluate_trained_classifier</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">sick_dev_reader</span><span class="p">):</span>
<span class="sd">"""Evaluate model, the output of train_classifier, on the data in reader."""</span>
<span class="n">mod</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">feature_selector</span><span class="p">,</span> <span class="n">feature_function</span> <span class="o">=</span> <span class="n">model</span>
<span class="n">feats</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">featurizer</span><span class="p">(</span><span class="n">reader</span><span class="o">=</span><span class="n">reader</span><span class="p">,</span> <span class="n">feature_function</span><span class="o">=</span><span class="n">feature_function</span><span class="p">)</span>
<span class="n">feat_matrix</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">feats</span><span class="p">)</span>
<span class="k">if</span> <span class="n">feature_selector</span><span class="p">:</span>
<span class="n">feat_matrix</span> <span class="o">=</span> <span class="n">feature_selector</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">feat_matrix</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">feat_matrix</span><span class="p">)</span>
<span class="k">return</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
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<p>Let's see how we do on the training data as well as on the development data:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span> <span class="c"># Prevent this example from loading on import of this module.</span>
<span class="k">for</span> <span class="n">readername</span><span class="p">,</span> <span class="n">reader</span> <span class="ow">in</span> <span class="p">((</span><span class="s">'Train'</span><span class="p">,</span> <span class="n">sick_train_reader</span><span class="p">),</span> <span class="p">(</span><span class="s">'Dev'</span><span class="p">,</span> <span class="n">sick_dev_reader</span><span class="p">)):</span>
<span class="k">print</span> <span class="s">"======================================================================"</span>
<span class="k">print</span> <span class="n">readername</span>
<span class="k">print</span> <span class="n">evaluate_trained_classifier</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">overlapmodel</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">reader</span><span class="p">)</span>
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<pre>======================================================================
Train
precision recall f1-score support
CONTRADICTION 0.66 0.39 0.49 665
ENTAILMENT 0.60 0.37 0.46 1299
NEUTRAL 0.70 0.92 0.80 2536
avg / total 0.67 0.68 0.65 4500
======================================================================
Dev
precision recall f1-score support
CONTRADICTION 0.43 0.22 0.29 74
ENTAILMENT 0.39 0.28 0.32 144
NEUTRAL 0.64 0.82 0.72 282
avg / total 0.54 0.58 0.54 500
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<p>The <code>word_cross_product_features</code> model achieves better results, since it has more information, but it takes
a while to train --- and look at how substantially the feature space is affected by
feature selection!</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span> <span class="c"># Prevent this example from loading on import of this module.</span>
<span class="n">crossmodel</span> <span class="o">=</span> <span class="n">train_classifier</span><span class="p">(</span><span class="n">feature_function</span><span class="o">=</span><span class="n">word_cross_product_features</span><span class="p">)</span>
<span class="k">for</span> <span class="n">readername</span><span class="p">,</span> <span class="n">reader</span> <span class="ow">in</span> <span class="p">((</span><span class="s">'Train'</span><span class="p">,</span> <span class="n">sick_train_reader</span><span class="p">),</span> <span class="p">(</span><span class="s">'Dev'</span><span class="p">,</span> <span class="n">sick_dev_reader</span><span class="p">)):</span>
<span class="k">print</span> <span class="s">"======================================================================"</span>
<span class="k">print</span> <span class="n">readername</span>
<span class="k">print</span> <span class="n">evaluate_trained_classifier</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">crossmodel</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">reader</span><span class="p">)</span>
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<pre>Best model LogisticRegression(C=1.3000000000000003, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, max_iter=100,
multi_class='ovr', penalty='l2', random_state=None,
solver='liblinear', tol=0.0001, verbose=0)
3680 features selected out of 89038 total
F1 mean: 0.69 (+/- 0.07)
======================================================================
Train
precision recall f1-score support
CONTRADICTION 0.98 0.94 0.96 665
ENTAILMENT 0.90 0.80 0.85 1299
NEUTRAL 0.89 0.95 0.92 2536
avg / total 0.91 0.91 0.91 4500
======================================================================
Dev