train_gcn_on_wikidata_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0. Import stuff\n",
    "\n",
    "1. Load model:  GCN + LSTM\n",
    "    - a) The GCN class and functions it uses\n",
    "    - b) Load the model\n",
    " \n",
    "2. Load the data sets\n",
    "    - that have been downloaded with the other notebook\n",
    "    - a) training set\n",
    "    - b) test set\n",
    "        - The test set should have the same amount of KG context as in the training set\n",
    "        \n",
    "3. Training\n",
    "    - a) Training function\n",
    "    - b) Run the training\n",
    "    - c) Plot the losses from training \n",
    "\n",
    "4. Testing\n",
    "    - a) Test function\n",
    "    - b) Call the function and display / save results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import requests\n",
    "import numpy as np\n",
    "\n",
    "from gensim.models import KeyedVectors\n",
    "import logging\n",
    "\n",
    "from gensim import utils\n",
    "import nltk\n",
    "\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import shutil\n",
    "import smart_open\n",
    "import sys \n",
    "\n",
    "from sys import platform\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import json\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "TINY = 1e-6\n",
    "ONE = tf.constant(1.)\n",
    "NAMESPACE = 'gcn_qa'\n",
    "forbidden_weight = 1.\n",
    "_weight_for_positive_matches = 1.\n",
    "_rw = 1e-1\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "class GCN_QA(object):\n",
    "    _nodes_vocab_size = 300\n",
    "    _question_vocab_size = 300\n",
    "    _question_vector_size = 150\n",
    "    _types_size = 3\n",
    "    _mask_size = 200\n",
    "    _types_proj_size = 5\n",
    "    _word_proj_size = 50\n",
    "    _word_proj_size_for_rnn = 50\n",
    "    _word_proj_size_for_item = 50\n",
    "    _internal_proj_size = 250\n",
    "    _hidden_layer1_size = 250\n",
    "    _hidden_layer2_size = 250\n",
    "    _output_size = 2\n",
    "\n",
    "    _memory_dim = 100\n",
    "    _stack_dimension = 2\n",
    "\n",
    "    def __init__(self, dropout=1.0):\n",
    "        tf.compat.v1.reset_default_graph()\n",
    "        with tf.compat.v1.variable_scope(NAMESPACE):\n",
    "            config = tf.compat.v1.ConfigProto(allow_soft_placement=True)\n",
    "            self.sess = tf.compat.v1.Session(config=config)\n",
    "\n",
    "            # Input variables\n",
    "            self.node_X = tf.compat.v1.placeholder(tf.float32, shape=(None, None, self._nodes_vocab_size), name='node_X')\n",
    "            self.types = tf.compat.v1.placeholder(tf.float32, shape=(None, None, self._types_size), name='types')\n",
    "            self.Wt = tf.Variable(tf.random.uniform([self._types_size,\n",
    "                                                     self._types_proj_size], -_rw, _rw))\n",
    "            self.bt = tf.Variable(tf.random.uniform([self._types_proj_size], -_rw, _rw))\n",
    "            #problem tf.nn\n",
    "            \n",
    "            self.types_projection = lambda x: tf.nn.relu(tf.matmul(x, self.Wt) + self.bt)\n",
    "            self.types_internal = tf.map_fn(self.types_projection, self.types)\n",
    "            self.question_vectors_fw = tf.compat.v1.placeholder(tf.float32, shape=(None, None, self._question_vocab_size),\n",
    "                                                      name='question_vectors_inp_fw')\n",
    "            self.question_vectors_bw = tf.compat.v1.placeholder(tf.float32, shape=(None, None, self._question_vocab_size),\n",
    "                                                      name='question_vectors_inp_nw')\n",
    "            self.question_mask = tf.compat.v1.placeholder(tf.float32, shape=(None, None, self._mask_size),\n",
    "                                                name='question_mask')\n",
    "\n",
    "            # The question is pre-processed by a bi-GRU\n",
    "            self.Wq = tf.Variable(tf.random.uniform([self._question_vocab_size,\n",
    "                                                     self._word_proj_size_for_rnn], -_rw, _rw))\n",
    "            self.bq = tf.Variable(tf.random.uniform([self._word_proj_size_for_rnn], -_rw, _rw))\n",
    "            self.internal_projection = lambda x: tf.nn.relu(tf.matmul(x, self.Wq) + self.bq)\n",
    "            self.question_int_fw = tf.map_fn(self.internal_projection, self.question_vectors_fw)\n",
    "            self.question_int_bw = tf.map_fn(self.internal_projection, self.question_vectors_bw)\n",
    "            \n",
    "            #problem rnn\n",
    "            self.rnn_cell_fw = tf.compat.v1.nn.rnn_cell.MultiRNNCell([tf.compat.v1.nn.rnn_cell.GRUCell(self._memory_dim) for _ in range(self._stack_dimension)],\n",
    "                                                state_is_tuple=True)\n",
    "            self.rnn_cell_bw = tf.compat.v1.nn.rnn_cell.MultiRNNCell([tf.compat.v1.nn.rnn_cell.GRUCell(self._memory_dim) for _ in range(self._stack_dimension)],\n",
    "                                                state_is_tuple=True)\n",
    "            with tf.compat.v1.variable_scope('fw'):\n",
    "                #problem dynamic rnn\n",
    "                output_fw, state_fw = tf.compat.v1.nn.dynamic_rnn(self.rnn_cell_fw, self.question_int_fw, time_major=True,\n",
    "                                                        dtype=tf.float32)\n",
    "            with tf.compat.v1.variable_scope('bw'):\n",
    "                #problem dynamen rnn\n",
    "                output_bw, state_bw = tf.compat.v1.nn.dynamic_rnn(self.rnn_cell_bw, self.question_int_bw, time_major=True,\n",
    "                                                        dtype=tf.float32)\n",
    "\n",
    "            self.states = tf.concat(values=[output_fw, tf.reverse(output_bw, [0])], axis=2)\n",
    "            self.question_vector_pre = tf.reduce_mean(tf.multiply(self.question_mask, self.states), axis=0)\n",
    "            self.Wqa = tf.Variable(\n",
    "                tf.random.uniform([2 * self._memory_dim, self._question_vector_size], -_rw, _rw),\n",
    "                name='Wqa')\n",
    "            self.bqa = tf.Variable(tf.random.uniform([self._question_vector_size], -_rw, _rw), name='bqa')\n",
    "            #problem tf.nn\n",
    "            self.question_vector = tf.nn.relu(tf.matmul(self.question_vector_pre, self.Wqa) + self.bqa)\n",
    "\n",
    "            # Dense layer before gcn\n",
    "            self.Wi = tf.Variable(tf.random.uniform([self._nodes_vocab_size,\n",
    "                                                     self._word_proj_size], -_rw, _rw))\n",
    "            self.bi = tf.Variable(tf.random.uniform([self._word_proj_size], -_rw, _rw))\n",
    "            self.internal_projection2 = lambda x: tf.nn.relu(tf.matmul(x, self.Wi) + self.bi)\n",
    "            self.word_embeddings = tf.map_fn(self.internal_projection2, self.node_X)\n",
    "\n",
    "            self.inputs = tf.concat(values=[self.word_embeddings, self.types_internal], axis=2)\n",
    "            self.Wp = tf.Variable(tf.random.uniform([self._word_proj_size + self._types_proj_size,\n",
    "                                                     self._internal_proj_size], -_rw, _rw))\n",
    "            self.bp = tf.Variable(tf.random.uniform([self._internal_proj_size], -_rw, _rw))\n",
    "            self.enc_int_projection = lambda x: tf.nn.relu(tf.matmul(x, self.Wp) + self.bp)\n",
    "            self.enc_int = tf.map_fn(self.enc_int_projection, self.inputs)\n",
    "\n",
    "            # GCN part\n",
    "            self.Atilde_fw = tf.nn.dropout(tf.compat.v1.placeholder(tf.float32, shape=(None, None, None), name=\"Atilde_fw\"), 0.25)\n",
    "\n",
    "            self.X1_fw = GCN_layer_fw(self._internal_proj_size,\n",
    "                                      self._hidden_layer1_size,\n",
    "                                      self._question_vector_size,\n",
    "                                      self.enc_int,\n",
    "                                      self.Atilde_fw,\n",
    "                                      self.question_vector,\n",
    "                                      '_1')\n",
    "            #problem tf.nn\n",
    "            self.X1_fw_dropout = tf.nn.dropout(self.X1_fw, dropout)\n",
    "\n",
    "            self.X2_fw = GCN_layer_fw(self._hidden_layer1_size,\n",
    "                                      self._hidden_layer1_size,\n",
    "                                      self._question_vector_size,\n",
    "                                      self.X1_fw_dropout,\n",
    "                                      self.Atilde_fw,\n",
    "                                      self.question_vector,\n",
    "                                      '_2')\n",
    "            self.X2_fw_dropout = tf.nn.dropout(self.X2_fw, dropout)\n",
    "\n",
    "            self.X3_fw = GCN_layer_fw(self._hidden_layer1_size,\n",
    "                                      self._hidden_layer1_size,\n",
    "                                      self._question_vector_size,\n",
    "                                      self.X2_fw_dropout,\n",
    "                                      self.Atilde_fw,\n",
    "                                      self.question_vector,\n",
    "                                      '_3')\n",
    "            self.X3_fw_dropout = tf.nn.dropout(self.X3_fw, dropout)\n",
    "\n",
    "            self.X4_fw = GCN_layer_fw(self._hidden_layer1_size,\n",
    "                                      self._hidden_layer1_size,\n",
    "                                      self._question_vector_size,\n",
    "                                      self.X3_fw_dropout,\n",
    "                                      self.Atilde_fw,\n",
    "                                      self.question_vector,\n",
    "                                      '_4')\n",
    "            #tf.nn\n",
    "            self.X4_fw_dropout = tf.nn.dropout(self.X4_fw, dropout)\n",
    "            self.first_node = self.X4_fw_dropout[0]\n",
    "            self.concatenated = tf.concat(values=[self.question_vector, self.first_node], axis=1)\n",
    "\n",
    "            # Final feedforward layers\n",
    "            self.Ws1 = tf.Variable(\n",
    "                tf.random.uniform([self._question_vector_size\n",
    "                                   + self._hidden_layer1_size,\n",
    "                                   self._hidden_layer2_size], -_rw, _rw),\n",
    "                name='Ws1')\n",
    "            self.bs1 = tf.Variable(tf.random.uniform([self._hidden_layer2_size], -_rw, _rw), name='bs1')\n",
    "            #problem tf.nn\n",
    "            self.first_hidden = tf.nn.relu(tf.matmul(self.concatenated, self.Ws1) + self.bs1)\n",
    "            self.first_hidden_dropout = tf.nn.dropout(self.first_hidden, dropout)\n",
    "\n",
    "            self.Wf = tf.Variable(\n",
    "                tf.random.uniform([self._hidden_layer2_size, self._output_size], -_rw,\n",
    "                                  _rw),\n",
    "                name='Wf')\n",
    "            self.bf = tf.Variable(tf.random.uniform([self._output_size], -_rw, _rw), name='bf')\n",
    "            #problem tf.nn\n",
    "            self.outputs = tf.nn.softmax(tf.matmul(self.first_hidden_dropout, self.Wf) + self.bf)\n",
    "\n",
    "            # Loss function and training\n",
    "            self.y_ = tf.compat.v1.placeholder(tf.float32, shape=(None, self._output_size), name='y_')\n",
    "            self.outputs2 = tf.squeeze(self.outputs)\n",
    "            self.y2_ = tf.squeeze(self.y_)\n",
    "            self.one = tf.ones_like(self.outputs)\n",
    "            self.tiny = self.one * TINY\n",
    "            self.cross_entropy = (tf.reduce_mean(\n",
    "                -tf.reduce_sum(self.y_ * tf.compat.v1.log(self.outputs + self.tiny) * _weight_for_positive_matches\n",
    "                               + (self.one - self.y_) * tf.compat.v1.log(\n",
    "                    self.one - self.outputs + self.tiny))\n",
    "            ))\n",
    "\n",
    "        # Clipping the gradient\n",
    "        optimizer = tf.compat.v1.train.AdamOptimizer(1e-4)\n",
    "        gvs = optimizer.compute_gradients(self.cross_entropy)\n",
    "        capped_gvs = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gvs if var.name.find(NAMESPACE) != -1]\n",
    "        self.train_step = optimizer.apply_gradients(capped_gvs)\n",
    "        self.sess.run(tf.compat.v1.global_variables_initializer())\n",
    "\n",
    "        # Adding the summaries\n",
    "        tf.summary.scalar('cross_entropy', self.cross_entropy)\n",
    "        self.merged = tf.compat.v1.summary.merge_all(key=tf.compat.v1.GraphKeys.SUMMARIES)\n",
    "        self.train_writer = tf.compat.v1.summary.FileWriter('./train', self.sess.graph)\n",
    "\n",
    "    def _add_identity(self, A):\n",
    "        num_nodes = A.shape[0]\n",
    "        identity = np.identity(num_nodes)\n",
    "        return identity + A\n",
    "\n",
    "    def __train(self, A_fw, node_X, types, item_vector, question_vectors, question_mask, y):\n",
    "        item_vector = np.array(item_vector)\n",
    "        Atilde_fw = np.array([self._add_identity(item) for item in A_fw])\n",
    "        node_X = np.array(node_X)\n",
    "        \n",
    "        node_X = np.transpose(node_X, (1, 0, 2))\n",
    "\n",
    "        types = np.array(types)\n",
    "        types = np.transpose(types, (1, 0, 2))\n",
    "\n",
    "        question_vectors = np.array(question_vectors)\n",
    "        question_vectors_fw = np.transpose(question_vectors, (1, 0, 2))\n",
    "        question_vectors_bw = question_vectors_fw[::-1, :, :]\n",
    "\n",
    "        question_mask = np.array(question_mask)\n",
    "        question_mask = np.transpose(question_mask, (1, 0, 2))\n",
    "\n",
    "        y = np.array(y)\n",
    "\n",
    "        feed_dict = {}\n",
    "        feed_dict.update({self.node_X: node_X})\n",
    "        feed_dict.update({self.types: types})\n",
    "        feed_dict.update({self.question_vectors_fw: question_vectors_fw})\n",
    "        feed_dict.update({self.question_vectors_bw: question_vectors_bw})\n",
    "        feed_dict.update({self.question_mask: question_mask})\n",
    "        feed_dict.update({self.Atilde_fw: Atilde_fw})\n",
    "        feed_dict.update({self.y_: y})\n",
    "        loss, _,outputs2, y2 = self.sess.run(\n",
    "            [self.cross_entropy, self.train_step,  self.outputs2, self.y2_], feed_dict)\n",
    "        return loss\n",
    "\n",
    "    def train(self, data, epochs=20):\n",
    "        losses = []\n",
    "        \n",
    "#         print(type(data[0][6]))\n",
    "        for epoch in range(epochs):\n",
    "        \n",
    "            loss = self.__train([data[i][0] for i in range(len(data))],\n",
    "                                   [data[i][1] for i in range(len(data))],\n",
    "                                   [data[i][2] for i in range(len(data))],\n",
    "                                   [data[i][3] for i in range(len(data))],\n",
    "                                   [data[i][4] for i in range(len(data))],\n",
    "                                   [data[i][5] for i in range(len(data))],\n",
    "                                   [data[i][6] for i in range(len(data))])\n",
    "\n",
    "#             print(loss)\n",
    "            losses.append(loss)\n",
    "            \n",
    "            sys.stdout.flush()\n",
    "        return losses\n",
    "\n",
    "    def __predict(self, A_fw, node_X, types, item_vector, question_vectors, question_mask):\n",
    "        item_vector = np.array(item_vector)\n",
    "        Atilde_fw = np.array([self._add_identity(item) for item in A_fw])\n",
    "\n",
    "        node_X = np.array(node_X)\n",
    "        node_X = np.transpose(node_X, (1, 0, 2))\n",
    "\n",
    "        types = np.array(types)\n",
    "        types = np.transpose(types, (1, 0, 2))\n",
    "\n",
    "        question_vectors = np.array(question_vectors)\n",
    "        question_vectors_fw = np.transpose(question_vectors, (1, 0, 2))\n",
    "        question_vectors_bw = question_vectors_fw[::-1, :, :]\n",
    "\n",
    "        question_mask = np.array(question_mask)\n",
    "        question_mask = np.transpose(question_mask, (1, 0, 2))\n",
    "\n",
    "        feed_dict = {}\n",
    "        feed_dict.update({self.node_X: node_X})\n",
    "        feed_dict.update({self.types: types})\n",
    "        feed_dict.update({self.question_vectors_fw: question_vectors_fw})\n",
    "        feed_dict.update({self.question_vectors_bw: question_vectors_bw})\n",
    "        feed_dict.update({self.question_mask: question_mask})\n",
    "        feed_dict.update({self.Atilde_fw: Atilde_fw})\n",
    "\n",
    "        y_batch = self.sess.run([self.outputs2], feed_dict)\n",
    "        return y_batch\n",
    "\n",
    "    def __standardize_item(self, item):\n",
    "        if item[0] < item[1]:\n",
    "            return [0., 1.]\n",
    "        return [1., 0.]\n",
    "\n",
    "    def predict(self, A_fw, node_X, types, item_vector, question_vectors, question_mask):\n",
    "        output = self.__predict([A_fw], [node_X], [types], [item_vector], [question_vectors], [question_mask])\n",
    "        return self.__standardize_item(output[0])\n",
    "\n",
    "    # Loading and saving functions\n",
    "\n",
    "    def save(self, filename):\n",
    "        saver = tf.compat.v1.train.Saver()\n",
    "        saver.save(self.sess, filename)\n",
    "\n",
    "    def load_tensorflow(self, filename):\n",
    "        saver =tf.compat.v1.train.Saver([v for v in tf.compat.v1.global_variables() if NAMESPACE in v.name])\n",
    "        saver.restore(self.sess, filename)\n",
    "\n",
    "    @classmethod\n",
    "    def load(self, filename, dropout=0.25):\n",
    "        model = GCN_QA(dropout)\n",
    "        model.load_tensorflow(filename)\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_new_adjacency_matrix(embedding_size, attention_size, memory_dim, A, H, question_vector, name):\n",
    "    Wa = tf.Variable(tf.compat.v1.random_uniform([embedding_size + memory_dim, attention_size], -_rw, _rw),\n",
    "                     name='Wa_' + name)\n",
    "    ba = tf.Variable(tf.compat.v1.random_uniform([attention_size], -_rw, _rw), name='b0_fw' + name)\n",
    "    #problem with tf.nn\n",
    "    WHQ_projection = lambda x: tf.nn.relu(tf.matmul(tf.concat([x, question_vector], axis=1), Wa) + ba)\n",
    "    WHQ = tf.map_fn(WHQ_projection, H)\n",
    "    WHQ = tf.transpose(WHQ, perm=[1, 0, 2])\n",
    "    #problem with tf.nn\n",
    "    WHQ_squared_projection = lambda x: tf.nn.softmax(tf.matmul(x, tf.transpose(x, perm=[1, 0])))\n",
    "    WHQ_squared = tf.map_fn(WHQ_squared_projection, WHQ)\n",
    "    new_A = tf.multiply(A, WHQ_squared)\n",
    "    return new_A\n",
    "\n",
    "\n",
    "def GCN_layer_fw(embedding_size, hidden_layer1_size, memory_dim, hidden, Atilde_fw, question_vector, name):\n",
    "    new_A = compute_new_adjacency_matrix(embedding_size, 250, memory_dim, Atilde_fw, hidden, question_vector, name)\n",
    "    W0_fw = tf.Variable(tf.compat.v1.random_uniform([embedding_size, hidden_layer1_size], -_rw, _rw),\n",
    "                        name='W0_fw' + name)\n",
    "    b0_fw = tf.Variable(tf.compat.v1.random_uniform([hidden_layer1_size], -_rw, _rw), name='b0_fw' + name)\n",
    "    left_X1_projection_fw = lambda x: tf.matmul(x, W0_fw) + b0_fw\n",
    "    left_X1_fw = tf.map_fn(left_X1_projection_fw, hidden)\n",
    "    left_X1_fw = tf.transpose(left_X1_fw, perm=[1, 0, 2], name='left_X1_fw' + name)\n",
    "    #problem with tf.nn\n",
    "    X1_fw = tf.nn.relu(tf.matmul(new_A, left_X1_fw))\n",
    "    X1_fw = tf.transpose(X1_fw, perm=[1, 0, 2])\n",
    "    return X1_fw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import sys\n",
    "\n",
    "nn_model_gcn_with_attention = GCN_QA(dropout=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Store data (serialize)\n",
    "\n",
    "# pickle file, saved from running the make_training_set_wikidata notebook\n",
    "   # name has model, number of entities and number of nodes\n",
    "current_training_set =  'training_data_gcn_188_10.pickle'\n",
    "\n",
    "with open(current_training_set, 'rb') as handle:\n",
    "    training_data = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': ' fantasy novelist David Gemmell. Achilles is featured heavily in the novel The Firebrand by Marion Zimmer Bradley. The comic book hero Captain Marvel is endowed with the courage of Achilles, as well ',\n",
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       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0,\n",
       "   0.0]],\n",
       " 'answer': [0.0, 1.0]}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle file, saved from running the make_training_set_wikidata notebook\n",
    "   # name has model, number of entities and number of nodes\n",
    "    \n",
    "current_test_set =  'test_data_gcn_2000_10.pickle'\n",
    "with open(current_test_set, 'rb') as handle:\n",
    "    test_set = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_chunks(l, n):\n",
    "    return [l[i:i + n] for i in range(0, len(l), n)]\n",
    "\n",
    "def bin_data_into_buckets(data, batch_size):\n",
    "    buckets = []\n",
    "    size_to_data_dict = {}\n",
    "    for item in data:\n",
    "        seq_length = len(item['graph']['vectors'])\n",
    "        question_length = len(item['question_vectors'])\n",
    "        try:\n",
    "            size_to_data_dict[(seq_length, question_length)].append(item)\n",
    "        except:\n",
    "            size_to_data_dict[(seq_length, question_length)] = [item]\n",
    "    for key in size_to_data_dict.keys():\n",
    "        data = size_to_data_dict[key]\n",
    "        chunks = get_chunks(data, batch_size)\n",
    "        for chunk in chunks:\n",
    "            buckets.append(chunk)\n",
    "    return buckets\n",
    "    \n",
    "\n",
    "def train(data, model, saving_dir, name_prefix, epochs=20, bucket_size=10, trace_every=1):\n",
    "    \n",
    "\n",
    "    buckets = bin_data_into_buckets(data, bucket_size)\n",
    "    start_time = time.time()\n",
    "\n",
    "    losses = []\n",
    "    for i in range(epochs):\n",
    "        random_buckets = sorted(buckets, key=lambda x: random.random())\n",
    "        sys.stderr.write('--------- Epoch ' + str(i) + ' ---------\\n')\n",
    "        sys.stderr.write('time_elapsed (s): ' + str(time.time() - start_time) + '\\n')\n",
    "        \n",
    "        for bucket in random_buckets:\n",
    "            graph_bucket = []\n",
    "            \n",
    "#             try:\n",
    "            for item in bucket:\n",
    "                node_vectors = item['graph']['vectors']\n",
    "                types = item['graph']['types']\n",
    "                A_bw = item['graph']['A_bw']\n",
    "                y = item['answer']\n",
    "                item_vector = item['item_vector']\n",
    "                question_vectors = item['question_vectors']\n",
    "                question_mask = item['question_mask']\n",
    "                graph_bucket.append((A_bw, node_vectors, types, item_vector, question_vectors, question_mask, y))\n",
    "            if len(graph_bucket) > 0:\n",
    "                loss = model.train(graph_bucket, 1)\n",
    "                losses.append(loss)\n",
    "#             except Exception as e:\n",
    "#                 print('Exception caught during training: ' + str(e))\n",
    "        if i % trace_every == 0:\n",
    "            save_filename = saving_dir + name_prefix + '-' + str(i) + '.tf'\n",
    "            sys.stderr.write('Saving into ' + save_filename + '\\n')\n",
    "            model.save(save_filename)\n",
    "    return losses\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model: \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--------- Epoch 0 ---------\n",
      "time_elapsed (s): 8.988380432128906e-05\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-f267639a8fde>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0m_saving_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'data\\\\'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m losses = train(training_data,\n\u001b[0m\u001b[1;32m      7\u001b[0m       \u001b[0mnn_model_gcn_with_attention\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m       \u001b[0m_saving_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-ad9ad4bedb86>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(data, model, saving_dir, name_prefix, epochs, bucket_size, trace_every)\u001b[0m\n\u001b[1;32m     46\u001b[0m                 \u001b[0mgraph_bucket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA_bw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_vectors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem_vector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquestion_vectors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquestion_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph_bucket\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph_bucket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m                 \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;31m#             except Exception as e:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-19-9407a3f42a61>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, data, epochs)\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             loss = self.__train([data[i][0] for i in range(len(data))],\n\u001b[0m\u001b[1;32m    223\u001b[0m                                    \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                                    \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-19-9407a3f42a61>\u001b[0m in \u001b[0;36m__train\u001b[0;34m(self, A_fw, node_X, types, item_vector, question_vectors, question_mask, y)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0mfeed_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAtilde_fw\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAtilde_fw\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0mfeed_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m         loss, _,outputs2, y2 = self.sess.run(\n\u001b[0m\u001b[1;32m    213\u001b[0m             [self.cross_entropy, self.train_step,  self.outputs2, self.y2_], feed_dict)\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.8.2/lib/python3.8/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    955\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    956\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 957\u001b[0;31m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0m\u001b[1;32m    958\u001b[0m                          run_metadata_ptr)\n\u001b[1;32m    959\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.8.2/lib/python3.8/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1148\u001b[0m             \u001b[0mfeed_handles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubfeed_val\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m           \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m             \u001b[0mnp_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubfeed_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m           if (not is_tensor_handle_feed and\n",
      "\u001b[0;32m~/.pyenv/versions/3.8.2/lib/python3.8/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "n_epochs = 60\n",
    "\n",
    "print('Loading model: ')\n",
    "_saving_dir = 'data\\\\'\n",
    "\n",
    "losses = train(training_data,\n",
    "      nn_model_gcn_with_attention,\n",
    "      _saving_dir,\n",
    "      name_prefix='qa',\n",
    "      epochs=n_epochs,\n",
    "      bucket_size=10,\n",
    "      trace_every=1,\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !\n",
    "display loss for \n",
    "\n",
    "10 epochs,  25 epochs,   60 epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(data, model):\n",
    "    true_positives = 0\n",
    "    false_positives = 0\n",
    "    true_negatives = 0\n",
    "    false_negatives = 0\n",
    "    for item in data:\n",
    "        node_vectors = item['graph']['vectors']\n",
    "        types = item['graph']['types']\n",
    "        A_bw = item['graph']['A_bw']\n",
    "        expected = item['answer']\n",
    "        item_vector = item['item_vector']\n",
    "        question_vectors = item['question_vectors']\n",
    "        question_mask = item['question_mask']\n",
    "#        try:\n",
    "        prediction = model.predict(A_bw, node_vectors, types, item_vector, question_vectors, question_mask)\n",
    "        if prediction == expected and expected == _is_relevant:\n",
    "            true_positives += 1\n",
    "        if prediction == expected and expected == _is_not_relevant:\n",
    "            true_negatives += 1\n",
    "        if prediction != expected and expected == _is_relevant:\n",
    "            false_negatives += 1\n",
    "        if prediction != expected and expected == _is_not_relevant:\n",
    "            false_positives += 1\n",
    "#        except Exception as e:\n",
    "#            print('Exception caught during training: ' + str(e))\n",
    "    try:\n",
    "        precision = true_positives / (true_positives + false_positives)\n",
    "        recall = true_positives / (true_positives + false_negatives)\n",
    "        f1 = 2 * 1 / (1 / precision + 1 / recall)\n",
    "        print('precision', precision)\n",
    "        print('recall', recall)\n",
    "        print('f1', f1)\n",
    "    except:\n",
    "        print('Cannot compute precision and recall.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the test set, made with the other notebook\n",
    "\n",
    "The test set should have the same max number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "path_to_test_set = ''\n",
    "# Store data (serialize)\n",
    "with open(path_to_test_set, 'rb') as handle:\n",
    "    test_data = pickle.load(handle)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test(test_data_,   nn_model_gcn_with_attention)"
   ]
  }
 ],
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