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<!DOCTYPE html>
<html lang="en">
<head>
<title>Data Blog : Test Post</title>
<meta charset="utf-8" />
<link rel="stylesheet" href="http://fmfn.github.io/theme/css/main.css" type="text/css" />
<link href="http://fmfn.github.io/" type="application/atom+xml" rel="alternate" title="Data Blog ATOM Feed" />
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<h1><a href="http://fmfn.github.io/test-post.html" id="page-title">Test Post</a></h1>
<span id="sitename"><a href="http://fmfn.github.io" id="site-title">Data Blog </a> ⋅</span>
<time datetime="2016-07-31T20:00:00-04:00">Sun 31 July 2016</time></header>
<article>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="kn">import</span> <span class="n">LSTM</span><span class="p">,</span> <span class="n">GRU</span><span class="p">,</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">RepeatVector</span><span class="p">,</span> <span class="n">Input</span><span class="p">,</span> <span class="n">Embedding</span><span class="p">,</span> <span class="n">TimeDistributed</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span><span class="p">,</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">keras.objectives</span> <span class="kn">import</span> <span class="n">categorical_crossentropy</span><span class="p">,</span> <span class="n">sparse_categorical_crossentropy</span>
<span class="kn">import</span> <span class="nn">keras.backend</span> <span class="kn">as</span> <span class="nn">K</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span>
<span class="kn">import</span> <span class="nn">string_to_array</span>
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<h1 id="Data">Data<a class="anchor-link" href="#Data">¶</a></h1>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">X</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="s2">"../data/x_a10k_v15k.csv"</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="c1"># X = pd.read_csv("/mnt/simxiv/data/x_a1M_v150k.csv").values.astype(np.int32)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="s2">"../data/arxiv-10k.json"</span><span class="p">)</span>
<span class="c1"># df = pd.read_json("/mnt/simxiv/data/arxiv-1M.json")</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">maxlen</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">title_size</span> <span class="o">=</span> <span class="mi">16</span>
<span class="n">max_features</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">1500</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">max</span><span class="p">())</span> <span class="o">+</span> <span class="mi">1</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="o">%%</span><span class="k">time</span>
Y = []
for _, row in df.iterrows():
Y.append(string_to_array.string_to_array(row['title'], maxlen=title_size, vocab_file='10k').flatten())
Y = np.array(Y)
Y = np.expand_dims(Y, -1)
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<pre>CPU times: user 14.2 s, sys: 151 ms, total: 14.3 s
Wall time: 14.3 s
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">X</span><span class="p">[</span><span class="n">X</span> <span class="o">>=</span> <span class="n">max_features</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">Y</span><span class="p">[</span><span class="n">Y</span> <span class="o">>=</span> <span class="n">max_features</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
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<h1 id="Model">Model<a class="anchor-link" href="#Model">¶</a></h1>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">char_softmax</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<span class="k">return</span> <span class="n">K</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">categorical_crossentropy</span><span class="p">(</span><span class="n">y_true</span><span class="p">[:,</span> <span class="n">i</span><span class="p">,</span> <span class="p">:],</span> <span class="n">y_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">,</span> <span class="p">:])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">title_size</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">sparse_char_softmax</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<span class="n">steps_loss</span> <span class="o">=</span> <span class="p">[</span><span class="n">sparse_categorical_crossentropy</span><span class="p">(</span><span class="n">y_true</span><span class="p">[:,</span> <span class="n">i</span><span class="p">,</span> <span class="p">:],</span> <span class="n">y_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">,</span> <span class="p">:])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">title_size</span><span class="p">)]</span>
<span class="k">return</span> <span class="n">K</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">steps_loss</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">input_layer</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">maxlen</span><span class="p">,</span> <span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'input'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">embedding</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'embedding'</span><span class="p">)</span>
<span class="n">embedding</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Embedding</span><span class="p">(</span><span class="n">max_features</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">input_length</span><span class="o">=</span><span class="n">maxlen</span><span class="p">,</span> <span class="n">mask_zero</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"Embedding_layer"</span><span class="p">))</span>
<span class="n">embedding</span> <span class="o">=</span> <span class="n">embedding</span><span class="p">(</span><span class="n">input_layer</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">encoder</span> <span class="o">=</span> <span class="n">GRU</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'encode_rnn'</span><span class="p">)(</span><span class="n">embedding</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">decoder</span> <span class="o">=</span> <span class="n">RepeatVector</span><span class="p">(</span><span class="n">title_size</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'context_vector'</span><span class="p">)(</span><span class="n">encoder</span><span class="p">)</span>
<span class="n">decoder</span> <span class="o">=</span> <span class="n">GRU</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'decode_rnn'</span><span class="p">)(</span><span class="n">decoder</span><span class="p">)</span>
<span class="n">decoder</span> <span class="o">=</span> <span class="n">TimeDistributed</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">max_features</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'output_sequence'</span><span class="p">))(</span><span class="n">decoder</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_layer</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="n">decoder</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input (InputLayer) (None, 64) 0
____________________________________________________________________________________________________
embedding (Sequential) (None, 64, 50) 75000 input[0][0]
____________________________________________________________________________________________________
encode_rnn (GRU) (None, 25) 5700 embedding[1][0]
____________________________________________________________________________________________________
context_vector (RepeatVector) (None, 16, 25) 0 encode_rnn[0][0]
____________________________________________________________________________________________________
decode_rnn (GRU) (None, 16, 25) 3825 context_vector[0][0]
____________________________________________________________________________________________________
timedistributed_5 (TimeDistributed)(None, 16, 1500) 39000 decode_rnn[0][0]
====================================================================================================
Total params: 123525
____________________________________________________________________________________________________
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="n">sparse_char_softmax</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">'rmsprop'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">10</span><span class="p">,</span> <span class="p">:</span><span class="n">maxlen</span><span class="p">],</span> <span class="n">Y</span><span class="p">[:</span><span class="mi">10</span><span class="p">],</span> <span class="n">nb_epoch</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre><keras.callbacks.history at 0x2159b3dd0></pre>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">([</span><span class="s1">u'fivethirtyeight'</span><span class="p">,</span> <span class="s1">'dark_background'</span><span class="p">])</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">'loss'</span><span class="p">])</span>
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<pre>[<matplotlib.lines.line2d at 0x122d5ba50>]</pre>
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<div class="prompt input_prompt">In [231]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="p">:</span><span class="n">maxlen</span><span class="p">])</span>
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<div class="prompt input_prompt">In [232]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">titles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
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<div class="prompt input_prompt">In [241]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">string_to_array</span><span class="o">.</span><span class="n">array_to_string</span><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">titles</span><span class="o">.</span><span class="n">max</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="n">vocab_file</span><span class="o">=</span><span class="s1">'10k'</span><span class="p">)</span>
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<div class="output_area"><div class="prompt output_prompt">Out[241]:</div>
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<pre>'acoust weyl node from stack unkown chain'</pre>
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<div class="prompt input_prompt">In [238]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">string_to_array</span><span class="o">.</span><span class="n">array_to_string</span><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vocab_file</span><span class="o">=</span><span class="s1">'10k'</span><span class="p">)</span>
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<div class="output_area"><div class="prompt output_prompt">Out[238]:</div>
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<pre>'exact solut for optim unkown of unkown unkown and the unkown equat'</pre>
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<div class="prompt input_prompt">In [242]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">title</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">titles</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))]</span>
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<pre>u'Acoustic Weyl nodes from stacking dimerized chains'</pre>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">titles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<pre>array([611, 91, 11, 115, 1, 3, 1, 1, 5, 2, 1, 81, 0,
0, 0, 0])</pre>
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