babi_visualization_UT_act_pondertime.ipynb

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     "text": [
      "/home/mdehgha1/bin/anaconda3/envs/tf/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import cm\n",
    "\n",
    "from tensor2tensor import problems\n",
    "from tensor2tensor import models\n",
    "from tensor2tensor.bin import t2t_decoder  # To register the hparams set\n",
    "from tensor2tensor.utils import registry\n",
    "from tensor2tensor.utils import trainer_lib\n",
    "from tensor2tensor.data_generators import babi_qa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FtqR6jyebAJ6"
   },
   "source": [
    "## HParams"
   ]
  },
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    "outputId": "b2782784-4fb3-4cb4-e49a-510194f1a740"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/cns/lu-d/home/dehghani/babi/output_visualization/babi_qa_sentence_single_task3_10k/babi_r_transformer/r_transformer_act_step_position_timing_tiny/\n"
     ]
    }
   ],
   "source": [
    "# HParams\n",
    "babi_task_id = 'qa3'\n",
    "subset = \"1k\"\n",
    "problem_name = 'babi_qa_sentence_task' + babi_task_id.replace(\"qa\", \"\") + \"_\" + subset\n",
    "model_name = \"babi_r_transformer\"\n",
    "hparams_set = \"r_transformer_act_step_position_timing_tiny\"\n",
    "\n",
    "data_dir = '~/babi/data/' + problem_name \n",
    "\n",
    "# PUT THE MODEL YOU WANT TO LOAD HERE!\n",
    "CHECKPOINT = '~/babi/output/' + problem_name+ '/' + model_name +  '/' + hparams_set + '/'\n",
    "print(CHECKPOINT)"
   ]
  },
  {
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   "execution_count": 5,
   "metadata": {
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      "startup": false,
      "wait_interval": 0
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     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3023,
     "status": "ok",
     "timestamp": 1526592477890,
     "user": {
      "displayName": "Mostafa Dehghani",
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      "userId": "117544073113991621344"
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     "user_tz": -120
    },
    "id": "7c6JxW_bCu0w",
    "outputId": "4a07afc9-ccba-4e50-f470-367e617aaf29"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130\n",
      "7\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "_TASKS = {\n",
    "      'qa1': 'qa1_single-supporting-fact',\n",
    "      'qa2': 'qa2_two-supporting-facts',\n",
    "      'qa3': 'qa3_three-supporting-facts',\n",
    "      'qa4': 'qa4_two-arg-relations',\n",
    "      'qa5': 'qa5_three-arg-relations',\n",
    "      'qa6': 'qa6_yes-no-questions',\n",
    "      'qa7': 'qa7_counting',\n",
    "      'qa8': 'qa8_lists-sets',\n",
    "      'qa9': 'qa9_simple-negation',\n",
    "      'qa10': 'qa10_indefinite-knowledge',\n",
    "      'qa11': 'qa11_basic-coreference',\n",
    "      'qa12': 'qa12_conjunction',\n",
    "      'qa13': 'qa13_compound-coreference',\n",
    "      'qa14': 'qa14_time-reasoning',\n",
    "      'qa15': 'qa15_basic-deduction',\n",
    "      'qa16': 'qa16_basic-induction',\n",
    "      'qa17': 'qa17_positional-reasoning',\n",
    "      'qa18': 'qa18_size-reasoning',\n",
    "      'qa19': 'qa19_path-finding',\n",
    "      'qa20': 'qa20_agents-motivations'\n",
    "  }\n",
    "\n",
    "meta_data_filename = _TASKS[babi_task_id] + '-meta_data.json'\n",
    "metadata_path = os.path.join(data_dir, meta_data_filename)\n",
    "\n",
    "\n",
    "FLAGS = tf.flags.FLAGS\n",
    "FLAGS.data_dir = data_dir\n",
    "\n",
    "truncated_story_length = 130 if babi_task_id == 'qa3' else 70\n",
    "\n",
    "with tf.gfile.GFile(metadata_path, mode='r') as f:\n",
    "  metadata = json.load(f)\n",
    "max_story_length = metadata['max_story_length']\n",
    "max_sentence_length = metadata['max_sentence_length']\n",
    "max_question_length = metadata['max_question_length']\n",
    "\n",
    "print(max_story_length)\n",
    "print(max_sentence_length)\n",
    "print(max_question_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
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   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "class bAbiACTVisualizer(object):\n",
    "  \"\"\"Helper object for creating act visualizations.\"\"\"\n",
    "\n",
    "  def __init__(\n",
    "      self, hparams_set, model_name, data_dir, problem_name, beam_size=1):\n",
    "    story, question, targets, samples, ponder_time = build_model(\n",
    "        hparams_set, model_name, data_dir, problem_name, beam_size=beam_size)\n",
    "\n",
    "    # Fetch the problem\n",
    "    babi_problem = problems.problem(problem_name)\n",
    "    encoders = babi_problem.feature_encoders(data_dir)\n",
    "\n",
    "    self.story = story\n",
    "    self.question = question\n",
    "    self.targets = targets\n",
    "    self.ponder_time = ponder_time\n",
    "    self.samples = samples\n",
    "    self.encoders = encoders\n",
    "\n",
    "  def encode(self, story_str, question_str):\n",
    "    \"\"\"Input str to features dict, ready for inference.\"\"\"\n",
    "    \n",
    "    story_str = babi_qa._normalize_string(story_str)\n",
    "    question_str = babi_qa._normalize_string(question_str)\n",
    "    story = story_str.strip().split('.')\n",
    "    story = [self.encoders[babi_qa.FeatureNames.STORY].encode(sentence) \n",
    "                       for sentence in story[-truncated_story_length:]]\n",
    "    question = self.encoders[babi_qa.FeatureNames.QUESTION].encode(question_str)\n",
    "     \n",
    "    for sentence in story:\n",
    "      for _ in range(max_sentence_length - len(sentence)):\n",
    "        sentence.append(babi_qa.PAD)\n",
    "      assert len(sentence) == max_sentence_length\n",
    "\n",
    "    for _ in range(max_story_length - len(story)):\n",
    "      story.append([babi_qa.PAD for _ in range(max_sentence_length)])\n",
    "\n",
    "    for _ in range(max_question_length - len(question)):\n",
    "      question.append(babi_qa.PAD)\n",
    "\n",
    "    assert len(story) == max_story_length\n",
    "    assert len(question) == max_question_length   \n",
    "\n",
    "    story_flat = [token_id for sentence in story for token_id in sentence]\n",
    "    \n",
    "    batch_story = np.reshape(np.array(story_flat), \n",
    "                             [1, max_story_length, max_sentence_length, 1])\n",
    "    batch_question = np.reshape(np.array(question), \n",
    "                         [1, 1, max_question_length, 1])\n",
    "    return batch_story, batch_question\n",
    "\n",
    "  def decode_story(self, integers):\n",
    "    \"\"\"List of ints to str.\"\"\"\n",
    "    integers = np.squeeze(integers).tolist()\n",
    "    story = []\n",
    "    for sent in integers:\n",
    "      sent_decoded = self.encoders[babi_qa.FeatureNames.STORY].decode_list(sent)\n",
    "      sent_decoded.append('.')\n",
    "      story.append(sent_decoded)\n",
    "    return story\n",
    "  \n",
    "  def decode_question(self, integers):\n",
    "    \"\"\"List of ints to str.\"\"\"\n",
    "    integers = np.squeeze(integers).tolist()\n",
    "    return self.encoders[babi_qa.FeatureNames.QUESTION].decode_list(integers)\n",
    "  \n",
    "  def decode_targets(self, integers):\n",
    "    \"\"\"List of ints to str.\"\"\"\n",
    "    integers = np.squeeze(integers).tolist()\n",
    "    return self.encoders[\"targets\"].decode([integers])\n",
    "\n",
    "  def get_vis_data_from_string(self, sess, story_str, question_str):\n",
    "    \"\"\"Constructs the data needed for visualizing ponder_time.\n",
    "\n",
    "    Args:\n",
    "      sess: A tf.Session object.\n",
    "      input_string: The input setence to be visulized.\n",
    "\n",
    "    Returns:\n",
    "      Tuple of (\n",
    "          output_string: The answer\n",
    "          input_list: Tokenized input sentence.\n",
    "          output_list: Tokenized answer.\n",
    "          ponder_time: ponder_time matrices;\n",
    "          )\n",
    "    \"\"\"\n",
    "    encoded_story, encoded_question = self.encode(story_str, question_str)\n",
    "\n",
    "    # Run inference graph to get the label.\n",
    "    out = sess.run(self.samples, {\n",
    "        self.story: encoded_story,\n",
    "        self.question: encoded_question,\n",
    "    })\n",
    "\n",
    "    # Run the decoded answer through the training graph to get the\n",
    "    # ponder_time tensors.\n",
    "    ponder_time = sess.run(self.ponder_time, {\n",
    "        self.story: encoded_story,\n",
    "        self.question: encoded_question,\n",
    "        self.targets: np.reshape(out, [1, -1, 1, 1]),\n",
    "    })\n",
    "    \n",
    "    output = self.decode_targets(out)\n",
    "    story_list = self.decode_story(encoded_story)\n",
    "    question_list = self.decode_question(encoded_question)\n",
    "    \n",
    "    return story_list, question_list, output, ponder_time\n",
    "\n",
    "\n",
    "def build_model(hparams_set, model_name, data_dir, problem_name, beam_size=1):\n",
    "  \"\"\"Build the graph required to featch the ponder_times.\n",
    "\n",
    "  Args:\n",
    "    hparams_set: HParams set to build the model with.\n",
    "    model_name: Name of model.\n",
    "    data_dir: Path to directory contatining training data.\n",
    "    problem_name: Name of problem.\n",
    "    beam_size: (Optional) Number of beams to use when decoding a traslation.\n",
    "        If set to 1 (default) then greedy decoding is used.\n",
    "\n",
    "  Returns:\n",
    "    Tuple of (\n",
    "        inputs: Input placeholder to feed in ids.\n",
    "        targets: Targets placeholder to feed to th when fetching\n",
    "            ponder_time.\n",
    "        samples: Tensor representing the ids of the translation.\n",
    "        ponder_time: Tensors representing the ponder_time.\n",
    "    )\n",
    "  \"\"\"\n",
    "  hparams = trainer_lib.create_hparams(\n",
    "      hparams_set, data_dir=data_dir, problem_name=problem_name)\n",
    "  babi_model = registry.model(model_name)(\n",
    "      hparams, tf.estimator.ModeKeys.EVAL)\n",
    "          \n",
    "  story = tf.placeholder(tf.int32, shape=(\n",
    "      1, max_story_length, max_sentence_length, 1), \n",
    "                            name=babi_qa.FeatureNames.STORY)\n",
    "  question = tf.placeholder(tf.int32, shape=(\n",
    "      1, 1, max_question_length, 1), \n",
    "                            name=babi_qa.FeatureNames.QUESTION)\n",
    "  targets = tf.placeholder(tf.int32, shape=(1, 1, 1, 1), name='targets')\n",
    "  \n",
    "  babi_model({\n",
    "      babi_qa.FeatureNames.STORY: story,\n",
    "      babi_qa.FeatureNames.QUESTION: question,\n",
    "      'targets': targets,\n",
    "  })\n",
    "\n",
    "  # Must be called after building the training graph, so that the dict will\n",
    "  # have been filled with the ponder_time tensors. BUT before creating the\n",
    "  # interence graph otherwise the dict will be filled with tensors from\n",
    "  # inside a tf.while_loop from decoding and are marked unfetchable.\n",
    "  ponder_time = get_ponder_mats(babi_model)\n",
    "\n",
    "  with tf.variable_scope(tf.get_variable_scope(), reuse=True):\n",
    "    samples = babi_model.infer({\n",
    "       babi_qa.FeatureNames.STORY: story,\n",
    "       babi_qa.FeatureNames.QUESTION: question,\n",
    "    }, beam_size=beam_size)['outputs']\n",
    "\n",
    "  return story, question, targets, samples, ponder_time\n",
    "\n",
    "\n",
    "def get_ponder_mats(babi_model):\n",
    "  \"\"\"Get's the tensors representing the ponder_time from a build model.\n",
    "\n",
    "  The ponder_time are stored in a dict on the Transformer object while building\n",
    "  the graph.\n",
    "\n",
    "  Args:\n",
    "    babi_model: Transformer object to fetch the ponder_time from.\n",
    "\n",
    "  Returns:\n",
    "  Tuple of ponder_time matrices\n",
    "  \"\"\"\n",
    "#   print([n.name for n in tf.get_default_graph().as_graph_def().node])\n",
    "  attention_tensor_name = \"babi_r_transformer/parallel_0_5/babi_r_transformer/body/encoder/r_transformer_act/while/self_attention/multihead_attention/dot_product_attention/attention_weights\"\n",
    "  ponder_time_tensor_name = \"babi_r_transformer/parallel_0_5/babi_r_transformer/body/enc_ponder_times:0\"\n",
    "  ponder_time =  tf.get_default_graph().get_tensor_by_name(ponder_time_tensor_name)\n",
    "\n",
    "\n",
    "  return ponder_time"
   ]
  },
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      "startup": false,
      "wait_interval": 0
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    "colab_type": "code",
    "collapsed": true,
    "id": "X9fnUy-e7sSx"
   },
   "outputs": [],
   "source": [
    "ponder_visualizer = bAbiACTVisualizer(hparams_set, model_name, data_dir, problem_name, beam_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    "colab_type": "code",
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    "id": "Zv8YmyJubAKD"
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   "source": [
    "tf.Variable(0, dtype=tf.int64, trainable=False, name='global_step')\n",
    "\n",
    "sess = tf.train.MonitoredTrainingSession(\n",
    "    checkpoint_dir=CHECKPOINT,\n",
    "    save_summaries_secs=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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     "autoexec": {
      "startup": false,
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    "colab_type": "code",
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    "id": "nT7GG8tmbAKH"
   },
   "outputs": [],
   "source": [
    "if babi_task_id == 'qa1':\n",
    "#   input_story = \"John travelled to the hallway.Mary journeyed to the bathroom.\"\n",
    "#   input_question = \"Where is John?\" #hallway\n",
    "  \n",
    "  input_story = \"John travelled to the hallway.Mary journeyed to the bathroom.Daniel went back to the bathroom.John moved to the bedroom.\"\n",
    "  input_question = \"Where is Mary?\" #bathroom\n",
    "\n",
    "elif babi_task_id == 'qa2':\n",
    "  input_story = \"Mary got the milk there.John moved to the bedroom.Sandra went back to the kitchen.Mary travelled to the hallway.\"\n",
    "  input_question = \"Where is the milk?\" #hallway\n",
    "  \n",
    "#   input_story = \"Mary got the milk there.John moved to the bedroom.Sandra went back to the kitchen.Mary travelled to the hallway.John got the football there.John went to the hallway.\"\n",
    "#   input_question = \"Where is the football?\" #hallway\n",
    "\n",
    "elif babi_task_id == 'qa3':\n",
    "  input_story = \"Mary got the milk.John moved to the bedroom.Daniel journeyed to the office.John grabbed the apple there.John got the football.John journeyed to the garden.Mary left the milk.John left the football.Daniel moved to the garden.Daniel grabbed the football.Mary moved to the hallway.Mary went to the kitchen.John put down the apple there.John picked up the apple.Sandra moved to the hallway.Daniel left the football there.Daniel took the football.John travelled to the kitchen.Daniel dropped the football.John dropped the apple.John grabbed the apple.John went to the office.Sandra went back to the bedroom.Sandra took the milk.John journeyed to the bathroom.John travelled to the office.Sandra left the milk.Mary went to the bedroom.Mary moved to the office.John travelled to the hallway.Sandra moved to the garden.Mary moved to the kitchen.Daniel took the football.Mary journeyed to the bedroom.Mary grabbed the milk there.Mary discarded the milk.John went to the garden.John discarded the apple there.\"\n",
    "  input_question = \"Where was the apple before the bathroom?\" #office\n",
    "  \n",
    "#   input_story = \"Mary got the milk.John moved to the bedroom.Daniel journeyed to the office.John grabbed the apple there.John got the football.John journeyed to the garden.Mary left the milk.John left the football.Daniel moved to the garden.Daniel grabbed the football.Mary moved to the hallway.Mary went to the kitchen.John put down the apple there.John picked up the apple.Sandra moved to the hallway.Daniel left the football there.Daniel took the football.John travelled to the kitchen.Daniel dropped the football.John dropped the apple.John grabbed the apple.John went to the office.Sandra went back to the bedroom.Sandra took the milk.John journeyed to the bathroom.John travelled to the office.Sandra left the milk.Mary went to the bedroom.Mary moved to the office.John travelled to the hallway.Sandra moved to the garden.Mary moved to the kitchen.Daniel took the football.Mary journeyed to the bedroom.Mary grabbed the milk there.Mary discarded the milk.John went to the garden.John discarded the apple there.Sandra travelled to the bedroom.Daniel moved to the bathroom.\"\n",
    "#   input_question = \"Where was the apple before the hallway?\" #office\n",
    " \n"
   ]
  },
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     },
     "height": 734
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 100,
     "status": "ok",
     "timestamp": 1526592535253,
     "user": {
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    },
    "id": "_-MWft638jMz",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary got the milk .\n",
      "John moved to the bedroom .\n",
      "Daniel journeyed to the office .\n",
      "John grabbed the apple there .\n",
      "John got the football .\n",
      "John journeyed to the garden .\n",
      "Mary left the milk .\n",
      "John left the football .\n",
      "Daniel moved to the garden .\n",
      "Daniel grabbed the football .\n",
      "Mary moved to the hallway .\n",
      "Mary went to the kitchen .\n",
      "John put down the apple there .\n",
      "John picked up the apple .\n",
      "Sandra moved to the hallway .\n",
      "Daniel left the football there .\n",
      "Daniel took the football .\n",
      "John travelled to the kitchen .\n",
      "Daniel dropped the football .\n",
      "John dropped the apple .\n",
      "John grabbed the apple .\n",
      "John went to the office .\n",
      "Sandra went back to the bedroom .\n",
      "Sandra took the milk .\n",
      "John journeyed to the bathroom .\n",
      "John travelled to the office .\n",
      "Sandra left the milk .\n",
      "Mary went to the bedroom .\n",
      "Mary moved to the office .\n",
      "John travelled to the hallway .\n",
      "Sandra moved to the garden .\n",
      "Mary moved to the kitchen .\n",
      "Daniel took the football .\n",
      "Mary journeyed to the bedroom .\n",
      "Mary grabbed the milk there .\n",
      "Mary discarded the milk .\n",
      "John went to the garden .\n",
      "John discarded the apple there .\n",
      "Where was the apple before the bathroom ?\n",
      "office\n",
      "[1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 3.0, 1.0, 1.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0]\n"
     ]
    }
   ],
   "source": [
    "story_text, question_text, output, ponder_time = ponder_visualizer.get_vis_data_from_string(sess, input_story, input_question)\n",
    "# print(output)\n",
    "# print(story_text)\n",
    "# print(question_text)\n",
    "\n",
    "inp_text = []\n",
    "for sent in story_text:\n",
    "  inp_text.append(' '.join(sent))\n",
    "inp_text.append(' '.join(question_text))\n",
    "ponder_time = np.squeeze(np.array(ponder_time)).tolist()\n",
    "# print(ponder_time)\n",
    "def pad_remover(inp_text, ponder_time):\n",
    "  pad_sent_index = [ i for i, sent in enumerate(inp_text) if sent.startswith('<pad>')]\n",
    "  start = min(pad_sent_index)\n",
    "  end = max(pad_sent_index)\n",
    "  filtered_inp_text = inp_text[:start] + inp_text[end+1:]\n",
    "  filtered_inp_text = [sent.replace('<pad> ', '') for sent in filtered_inp_text]\n",
    "  filtered_ponder_time = ponder_time[:start] + ponder_time[end+1:] \n",
    "  return filtered_inp_text, filtered_ponder_time\n",
    "\n",
    "\n",
    "filtered_inp_text, filtered_ponder_time = pad_remover(inp_text, ponder_time)\n",
    "for sent in filtered_inp_text:\n",
    "  print(sent)\n",
    "print(output)\n",
    "print(filtered_ponder_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "height": 594
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 752,
     "status": "ok",
     "timestamp": 1526592536589,
     "user": {
      "displayName": "Mostafa Dehghani",
      "photoUrl": "//lh5.googleusercontent.com/-ovSYJK9S1U8/AAAAAAAAAAI/AAAAAAAAALw/GbZ35s0KD_I/s50-c-k-no/photo.jpg",
      "userId": "117544073113991621344"
     },
     "user_tz": -120
    },
    "id": "0rEcvtvKLIrH",
    "outputId": "84364a5e-0593-4603-8d12-8b6be31775bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3b3433910>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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evVy7do1169bxzTffaKqjIt9DU0R4IEL5z0av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\niMjI/wM8kGBBdDMN0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3b3433950>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {'input': filtered_inp_text,\n",
    "     'ponder_time': filtered_ponder_time,\n",
    "    })\n",
    "\n",
    "f_size = (10,5)\n",
    "if babi_task_id == 'qa2':\n",
    "  f_size = (15,5)\n",
    "if babi_task_id == 'qa3':\n",
    "  f_size = (25,5)\n",
    "df.plot(kind='bar', x='input', y='ponder_time', rot=90,  width=0.3, figsize=f_size, cmap='Spectral')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "last_runtime": {
    "build_target": "",
    "kind": "local"
   },
   "name": "RTransformer_bAbi_ACT_visualization.ipynb",
   "provenance": [
    {
     "file_id": "1CJmDr4hgot9ssXCcBId34Ix0DYhTwru4",
     "timestamp": 1526506288877
    },
    {
     "file_id": "1-fJHfJGATpgezFg6b81a2-G25bQzTYzk",
     "timestamp": 1520932107568
    }
   ],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}