visualizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import json\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.platform import flags\n",
    "import numpy as np\n",
    "from model import Model\n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "from data_helpers import split_sentence, clean_str, get_vocab\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (16, 12)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# reset tf.flag\n",
    "tf.app.flags.FLAGS = flags._FlagValues()\n",
    "tf.app.flags._global_parser = argparse.ArgumentParser()\n",
    "\n",
    "flags = tf.app.flags\n",
    "flags.DEFINE_integer('batch_size', 1, 'Batch size')\n",
    "flags.DEFINE_integer('epochs', 100, 'epochs')\n",
    "flags.DEFINE_integer('rnn_size', 300, 'RNN unit size')\n",
    "flags.DEFINE_integer('word_attention_size', 300, 'Word level attention unit size')\n",
    "flags.DEFINE_integer('sent_attention_size', 300, 'Sentence level attention unit size')\n",
    "flags.DEFINE_integer('char_embedding_size', 300, 'Embedding dimension')\n",
    "flags.DEFINE_string('checkpoint_dir', 'checkpoint', 'Directory name to save the checkpoints [checkpoint]')\n",
    "flags.DEFINE_integer('vocab_size', 6790, 'vocabulary size')\n",
    "flags.DEFINE_float('keep_prob', 0.5, 'Dropout keep prob')\n",
    "flags.DEFINE_integer('document_size', 30, 'document size')\n",
    "flags.DEFINE_integer('sentence_size', 50, 'sentence size')\n",
    "flags.DEFINE_float('grad_clip', 5.0, 'grad clip')\n",
    "FLAGS = flags.FLAGS\n",
    "\n",
    "checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'model.ckpt')\n",
    "\n",
    "# load pre-trained char embedding\n",
    "char_emb = np.load('./data/emb.npy')\n",
    "\n",
    "with tf.variable_scope('model'):\n",
    "    test_model = Model(FLAGS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pos_doc = \"\"\"作者的觀點獨特，語言犀利，深刻的總結了男人與女人之間的是非恩怨，把男人與女人在生活中積累的宿怨一語道破---原來非本質問題，而是雙方本能。此書不足之處乃後面章節雷同，越看越有寡味之感，但是還是要感謝作者帶我們遊覽一次男人、女人的本能世界！呵呵，建議讀者可以看一看，有則改之，無則加勉！！\"\"\"\n",
    "neg_doc = \"\"\"書太小了 拿到手裡很是失望本以為會多麼精緻~~~真的是適合小孩看啊可很小的孩子的又看不懂還不是大人給小孩子講所以應該精緻一點才好價錢還是不太值啊\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Documents\n",
    "=====\n",
    "\n",
    "### Positive:\n",
    "作者的觀點獨特，語言犀利，深刻的總結了男人與女人之間的是非恩怨，把男人與女人在生活中積累的宿怨一語道破---原來非本質問題，而是雙方本能。此書不足之處乃後面章節雷同，越看越有寡味之感，但是還是要感謝作者帶我們遊覽一次男人、女人的本能世界！呵呵，建議讀者可以看一看，有則改之，無則加勉！！\n",
    "\n",
    "### Negative:\n",
    "書太小了 拿到手裡很是失望本以為會多麼精緻~~~真的是適合小孩看啊可很小的孩子的又看不懂還不是大人給小孩子講所以應該精緻一點才好價錢還是不太值啊\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "作者的觀點獨特\n",
      "語言犀利\n",
      "深刻的總結了男人與女人之間的是非恩怨\n",
      "把男人與女人在生活中積累的宿怨一語道破\n",
      "原來非本質問題\n",
      "而是雙方本能\n",
      "此書不足之處乃後面章節雷同\n",
      "越看越有寡味之感\n",
      "但是還是要感謝作者帶我們遊覽一次男人\n",
      "女人的本能世界\n",
      "呵呵\n",
      "建議讀者可以看一看\n",
      "有則改之\n",
      "無則加勉 \n",
      "\n",
      "書太小了\n",
      "拿到手裡很是失望本以為會多麼精緻\n",
      "真的是適合小孩看啊可很小的孩子的又看不懂還不是大人給小孩子講所以應該精緻一點才好價錢還是不太值啊 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "vocab, _ = get_vocab('./data/vocab.pkl')\n",
    "max_doc_len = FLAGS.document_size\n",
    "max_sent_len = FLAGS.sentence_size\n",
    "\n",
    "def vectorize(doc):\n",
    "    doc_sents = split_sentence(clean_str(doc))\n",
    "    document_length = len(doc_sents)\n",
    "    sentence_lengths = np.zeros((max_doc_len,), dtype=np.int64)\n",
    "    data = np.ones((max_doc_len * max_sent_len,), dtype=np.int64) # 1 = PAD\n",
    "    doc_len = min(document_length, max_doc_len)\n",
    "\n",
    "    for j in range(doc_len):\n",
    "        sent = doc_sents[j]\n",
    "        actual_len = len(sent)\n",
    "        pos = j * max_sent_len\n",
    "        sent_len = min(actual_len, max_sent_len)\n",
    "        # sentence_lengths\n",
    "        sentence_lengths[j] = sent_len\n",
    "        # dataset\n",
    "        data[pos:pos+sent_len] = [vocab.get(sent[k], 0) for k in range(sent_len)]\n",
    "    \n",
    "    return data, document_length, sentence_lengths, doc_sents\n",
    "\n",
    "pos_data = vectorize(pos_doc)\n",
    "neg_data = vectorize(neg_doc)\n",
    "\n",
    "print('\\n'.join(pos_data[-1]), '\\n')\n",
    "print('\\n'.join(neg_data[-1]), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from checkpoint/model.ckpt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from checkpoint/model.ckpt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos_doc:  1 neg_doc: 0\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.Saver()\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, checkpoint_path)\n",
    "    \n",
    "    sess.run([], feed_dict={test_model.embedding: char_emb})\n",
    "    \n",
    "    def forward(data, document_length, sentence_lengths):\n",
    "        pred, attention = sess.run([test_model.pred, test_model.char_attentions], feed_dict={\n",
    "            test_model.inputs: data.reshape((1, FLAGS.document_size, FLAGS.sentence_size)),\n",
    "            test_model.sentence_lengths: [sentence_lengths],\n",
    "            test_model.document_lengths: [document_length],\n",
    "            test_model.is_training: False\n",
    "        })\n",
    "        return pred[0], attention\n",
    "    \n",
    "    pos_result, pos_attention = forward(pos_data[0], pos_data[1], pos_data[2])\n",
    "    neg_result, neg_attention = forward(neg_data[0], neg_data[1], neg_data[2])\n",
    "\n",
    "print('pos_doc: ', pos_result, 'neg_doc:', neg_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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[0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164], [0.019999999552965164]]];\n",
       "           window.pos_doc=['作者的觀點獨特', '語言犀利', '深刻的總結了男人與女人之間的是非恩怨', '把男人與女人在生活中積累的宿怨一語道破', '原來非本質問題', '而是雙方本能', '此書不足之處乃後面章節雷同', '越看越有寡味之感', '但是還是要感謝作者帶我們遊覽一次男人', '女人的本能世界', '呵呵', '建議讀者可以看一看', '有則改之', '無則加勉'];\n",
       "           window.neg_doc=['書太小了', '拿到手裡很是失望本以為會多麼精緻', '真的是適合小孩看啊可很小的孩子的又看不懂還不是大人給小孩子講所以應該精緻一點才好價錢還是不太值啊'];\n",
       "           "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Javascript\n",
    "\n",
    "#runs arbitrary javascript, client-side\n",
    "Javascript(\"\"\"\n",
    "           window.pos_attention={};\n",
    "           window.neg_attention={};\n",
    "           window.pos_doc={};\n",
    "           window.neg_doc={};\n",
    "           \"\"\".format(json.dumps(pos_attention.tolist()),\n",
    "                      json.dumps(neg_attention.tolist()),\n",
    "                      pos_data[-1],\n",
    "                      neg_data[-1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "function visualizer (doc, attention) {\n",
       "    const elements = attention\n",
       "        .filter((row, i) => i < doc.length)\n",
       "        .map((row, i) => {\n",
       "            const sent = doc[i];\n",
       "            const elems = row\n",
       "                .filter((col, j) => j < sent.length)\n",
       "                .map((col, j) => {\n",
       "                    const char = sent[j];\n",
       "                    const weight = col * 10;\n",
       "                    return '<span style=\"background-color: rgba(255, 50, 50, ' + weight + ');\">' + char + '</span>';\n",
       "                })\n",
       "                .join('');\n",
       "            return '<p>' + elems + '</p>';\n",
       "        });\n",
       "\n",
       "    return elements.join('\\n');\n",
       "}\n",
       "\n",
       "const visualized_pos_doc = visualizer(pos_doc, pos_attention)\n",
       "element.append('<div style=\"float:left; width:50%\"><h1>Positive Document with attetion</h1>' + visualized_pos_doc + '</div>')\n",
       "\n",
       "const visualized_neg_doc = visualizer(neg_doc, neg_attention)\n",
       "element.append('<div><h1>Negative Document with attetion</h1>' + visualized_neg_doc + '</div>')"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "\n",
    "function visualizer (doc, attention) {\n",
    "    const elements = attention\n",
    "        .filter((row, i) => i < doc.length)\n",
    "        .map((row, i) => {\n",
    "            const sent = doc[i];\n",
    "            const elems = row\n",
    "                .filter((col, j) => j < sent.length)\n",
    "                .map((col, j) => {\n",
    "                    const char = sent[j];\n",
    "                    const weight = col * 10;\n",
    "                    return '<span style=\"background-color: rgba(255, 50, 50, ' + weight + ');\">' + char + '</span>';\n",
    "                })\n",
    "                .join('');\n",
    "            return '<p>' + elems + '</p>';\n",
    "        });\n",
    "\n",
    "    return elements.join('\\n');\n",
    "}\n",
    "\n",
    "const visualized_pos_doc = visualizer(pos_doc, pos_attention)\n",
    "element.append('<div style=\"float:left; width:50%\"><h1>Positive Document with attention</h1>' + visualized_pos_doc + '</div>')\n",
    "\n",
    "const visualized_neg_doc = visualizer(neg_doc, neg_attention)\n",
    "element.append('<div><h1>Negative Document with attention</h1>' + visualized_neg_doc + '</div>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (ENV)",
   "language": "python",
   "name": "env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}