word embedding intuition (wevi).ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This is a simple demonstration of how word embedding works in tensorflow 2.0. The implementation is directly based on this tutorial paper:\n",
    "- [word2vec Parameter Learning Explained](https://arxiv.org/abs/1411.2738)\n",
    "- and its online demo [wevi](https://ronxin.github.io/wevi/)\n",
    "\n",
    "which are based on these two pioneering works:\n",
    "- [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781)\n",
    "- [Distributed Representations of Words and Phrases and their Compositionality](https://arxiv.org/abs/1310.4546)\n",
    "\n",
    "The [wevi](https://ronxin.github.io/wevi/) demo has already given an amazing visualization of word embedding. This project tries to replicate its result using Tensorflow 2.x, which is more practical, and whose code can be reused in the future work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Environment\n",
    "The code is tested under these packages:\n",
    "- Python 3.6\n",
    "- numpy 1.17.2\n",
    "- sklearn 0.21.3\n",
    "- matplotlib 3.1.1\n",
    "- tensorflow 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0 - Prerequisite\n",
    "The words/vocabulary/symbols are tokenized and converted into one-hot vector, before starting any of these embedding process.\n",
    "\n",
    "Here are a list of imports and functions needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Check tf version is 2.x: 2.1.0\n"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "\n",
    "%matplotlib inline\n",
    "print('Check tf version is 2.x:', tf.__version__)\n",
    "\n",
    "from nn import autoencoder_1hid, train, plot_enc_2d, nHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 - Simple Autoencoder\n",
    "The simpliest Autoencoder is a 3 layer full connection neural network, with the input size same as the output. Training data and labels are the same, i.e., an Autoencoder tries to copy its input to its output. The value of hidden layer is then the encoding of the input.\n",
    "\n",
    "Corresponding to the \"self-loop\" in wevi demostration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 - Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Data and labels are the same:\n [['one-hot'], ['encoding'], ['is'], ['not'], ['one-hot'], ['vectorization']]\n"
    }
   ],
   "source": [
    "# unrelated words\n",
    "vocab = ['Alpha', 'Bravo', 'Charlie', 'Delta','Echo']\n",
    "vocab = ['one-hot', 'encoding', 'is', 'not', 'one-hot', 'vectorization']\n",
    "vocab = [[vocab[i]] for i in range(len(vocab))]\n",
    "print(\"Data and labels are the same:\\n\", vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "After Onehot, dataset is a sparse matrix with shape: (6, 5)\n[[0. 0. 0. 1. 0.]\n [1. 0. 0. 0. 0.]\n [0. 1. 0. 0. 0.]\n [0. 0. 1. 0. 0.]\n [0. 0. 0. 1. 0.]\n [0. 0. 0. 0. 1.]]\n"
    }
   ],
   "source": [
    "# one-hot encoding\n",
    "ohot = OneHotEncoder(dtype='float32')\n",
    "X_vec = ohot.fit_transform(vocab)\n",
    "Y_vec = X_vec.copy()\n",
    "print('After Onehot, dataset is a sparse matrix with shape:', X_vec.shape)\n",
    "print(X_vec.toarray())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 - Model, Training and Evaluation\n",
    "\n",
    "The hidden layer has no activation, meaning that it's a linear transformation. The result proves it's effective enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nencoder (Dense)              (None, 2)                 12        \n_________________________________________________________________\ndecoder (Dense)              (None, 5)                 15        \n=================================================================\nTotal params: 27\nTrainable params: 27\nNon-trainable params: 0\n_________________________________________________________________\n"
    }
   ],
   "source": [
    "model = autoencoder_1hid(n_input=5, n_hidden=2)\n",
    "model.summary()\n",
    "# print out the init values\n",
    "# print('\\nPredicting output with random init weights:')\n",
    "# print(model.predict(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training process** uses mean square error and gradient descent. No fancy early-stopping or other gimmicks here.\n",
    "\n",
    "Note:\n",
    "- ```model(X)``` and ```model.predict(X)``` are different, one outputs tensor and the other np.array.\n",
    "- modify ```learning_rate``` and ```epochs``` to change the duration of training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
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     "text": "Epoch:100, loss = 0.04545\nEpoch:200, loss = 0.00414\nEpoch:300, loss = 0.00122\nEpoch:400, loss = 0.00065\nEpoch:500, loss = 0.00043\nEpoch:600, loss = 0.00032\nEpoch:700, loss = 0.00025\nEpoch:800, loss = 0.00020\nEpoch:900, loss = 0.00017\nEpoch:1000, loss = 0.00015\n\nPlotting the loss value:\n"
    },
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "train(model, X_vec, Y_vec, n_epochs=1000, learning_rate=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now input the training data X and see the output. After converting back to labels, they match exactly with input (A,B,C,D,E)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Output for the vocabulary:\n[[5.8960782e-06 5.1224893e-03 9.0697734e-03 9.8580176e-01 1.6920448e-07]\n [9.7037238e-01 2.6259961e-04 1.4595020e-02 1.6375969e-05 1.4753611e-02]\n [4.1723836e-04 9.7583950e-01 1.2438126e-04 1.1746077e-02 1.1872766e-02]\n [1.3678976e-02 3.5712335e-05 9.7439981e-01 1.1883797e-02 1.6261807e-06]\n [5.8960782e-06 5.1224893e-03 9.0697734e-03 9.8580176e-01 1.6920448e-07]\n [1.4823257e-02 4.8303232e-03 9.0583023e-07 8.0862215e-08 9.8034543e-01]]\nTransform back to labels:\n[['one-hot']\n ['encoding']\n ['is']\n ['not']\n ['one-hot']\n ['vectorization']]\n"
    }
   ],
   "source": [
    "# Evaluation \n",
    "print('Output for the vocabulary:')\n",
    "print(model.predict(X_vec.toarray()))\n",
    "print('Transform back to labels:')\n",
    "print(ohot.inverse_transform(model.predict(X_vec.toarray())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 - Visualize Encoding\n",
    "Here we build another model that is identical to the left half of the neural network. Its output will be the encoding of given word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The encoding for all 5 words:\n[[ 2.2184117   1.8567868 ]\n [-2.1387587  -0.11875032]\n [ 1.6799577  -1.1405529 ]\n [-0.8832802   2.436826  ]\n [ 2.2184117   1.8567868 ]\n [-1.0492346  -2.8677301 ]]\n"
    }
   ],
   "source": [
    "# encoding model\n",
    "enc_model = tf.keras.Model(inputs=model.input,\n",
    "                           outputs=model.get_layer('encoder').output)\n",
    "X_enc = enc_model.predict(X_vec.toarray())\n",
    "print(\"The encoding for all 5 words:\")\n",
    "print(X_enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[['one-hot'], ['encoding'], ['is'], ['not'], ['one-hot'], ['vectorization']]"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The 2D encoding of A,B,C,D,E are fairly split apart.\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(-3.5, 2.8)"
     },
     "metadata": {},
     "execution_count": 21
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# plotting\n",
    "vocab_disp = [word[0] for word in vocab]\n",
    "plt.figure(figsize=(8, 5))\n",
    "plot_enc_2d(X_enc, vocab_disp, offset=(0.05,0))\n",
    "print('The 2D encoding of A,B,C,D,E are fairly split apart.')\n",
    "plt.xlabel('dimension 1', fontsize=14)\n",
    "plt.ylabel('dimension 2', fontsize=14)\n",
    "plt.xlim([-2.5, 3])\n",
    "plt.ylim([-3.5, 2.8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 - AutoEncoder with context\n",
    "Still in AutoEncoder form, but the values of input & output is not the same, but have some relation.\n",
    "\n",
    "Corresponding to the \"fruit and juice\" in wevi demostration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 - Dataset\n",
    "This time vocabulary is different from training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "vocabulary: [['apple'], ['drink'], ['eat'], ['juice'], ['milk'], ['orange'], ['rice'], ['water']]\n"
    }
   ],
   "source": [
    "# related words\n",
    "vocab = ['apple', 'drink', 'eat', 'juice', 'milk', 'orange', 'rice', 'water']\n",
    "vocab = [[_] for _ in vocab]    # sk.OneHotEncoder requires shape (n,1), not (n, )\n",
    "print('vocabulary:', vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "dataset: 12 samples.\ncontext  \t target\n['eat'] \t ['apple']\n['eat'] \t ['orange']\n['eat'] \t ['rice']\n['drink'] \t ['juice']\n['drink'] \t ['milk']\n['drink'] \t ['water']\n['orange'] \t ['juice']\n['apple'] \t ['juice']\n['rice'] \t ['milk']\n['milk'] \t ['drink']\n['water'] \t ['drink']\n['juice'] \t ['drink']\n"
    }
   ],
   "source": [
    "# context-target dataset, copied from wevi\n",
    "dataset = 'eat|apple,eat|orange,eat|rice,drink|juice,drink|milk,drink|water,orange|juice,\\\n",
    "apple|juice,rice|milk,milk|drink,water|drink,juice|drink'\n",
    "dataset = dataset.split(',')\n",
    "dataset = [pair.split('|') for pair in dataset]\n",
    "X,Y = [[pair[0]] for pair in dataset], [[pair[1]] for pair in dataset]\n",
    "\n",
    "print('dataset: {} samples.'.format(len(X)))\n",
    "print('context  \\t target')\n",
    "for x_, y_ in zip(X,Y):\n",
    "    print(x_, '\\t', y_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "shape of onehot: (12, 8) (12, 8)\n"
    }
   ],
   "source": [
    "# Onehot Encoding\n",
    "ohot = OneHotEncoder(dtype='float32')\n",
    "ohot.fit(vocab) \n",
    "X_vec = ohot.transform(X)\n",
    "Y_vec = ohot.transform(Y)\n",
    "print('shape of onehot:', X_vec.shape, Y_vec.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 - Model, Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"sequential_11\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nencoder (Dense)              (None, 2)                 18        \n_________________________________________________________________\ndecoder (Dense)              (None, 8)                 24        \n=================================================================\nTotal params: 42\nTrainable params: 42\nNon-trainable params: 0\n_________________________________________________________________\n"
    }
   ],
   "source": [
    "model = autoencoder_1hid(n_input=8, n_hidden=2)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch:100, loss = 0.09125\nEpoch:200, loss = 0.07064\nEpoch:300, loss = 0.05464\n\nPlotting the loss value:\n"
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "train(model, X_vec, Y_vec, n_epochs=300, learning_rate=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now input the training data X and see the output. After converting back to labels, we notice some are not matching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Result after training:\ninput   \t ground truth\t prediction\n['eat'] \t ['apple'] \t ['drink']\t*\n['eat'] \t ['orange'] \t ['drink']\t*\n['eat'] \t ['rice'] \t ['drink']\t*\n['drink'] \t ['juice'] \t ['milk']\t*\n['drink'] \t ['milk'] \t ['milk']\t\n['drink'] \t ['water'] \t ['milk']\t*\n['orange'] \t ['juice'] \t ['juice']\t\n['apple'] \t ['juice'] \t ['juice']\t\n['rice'] \t ['milk'] \t ['milk']\t\n['milk'] \t ['drink'] \t ['drink']\t\n['water'] \t ['drink'] \t ['drink']\t\n['juice'] \t ['drink'] \t ['drink']\t\n"
    }
   ],
   "source": [
    "print('Result after training:')\n",
    "print('input   \\t ground truth\\t prediction')\n",
    "for x_, y_, y_pred in zip(X, Y, ohot.inverse_transform(model.predict(X_vec.toarray()))):\n",
    "    print(x_, '\\t', y_,'\\t',y_pred, end='\\t')\n",
    "    if y_ == y_pred: print()\n",
    "    else: print('*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is because some words have 1-to-3 relationships (eat - apple/orange/rice) that cannot be fully interpreted by top-1 prediction. Now take a look at detailed predictions of a single word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The prediction scores for the word \"eat\":\n\n--output--\t--score--\n['apple'] \t0.0768\t\n['drink'] \t0.2080\t*\n['eat'] \t0.0433\t\n['juice'] \t0.1935\t*\n['milk'] \t0.1798\t*\n['orange'] \t0.0883\t\n['rice'] \t0.0866\t\n['water'] \t0.1237\t\n\nTherefore, the network successfully models 1-to-many relations.\n"
    }
   ],
   "source": [
    "word = 'eat'   # change this\n",
    "\n",
    "print('The prediction scores for the word \"{}\":\\n'.format(word))\n",
    "print('--output--\\t--score--')\n",
    "scores = model.predict(ohot.transform([[word]]).toarray())[0]\n",
    "for label_, score_ in zip(vocab, scores):\n",
    "    print(label_, '\\t{:.4f}'.format(score_), end='\\t')\n",
    "    if score_ > 1.0/8: print('*')\n",
    "    else: print()\n",
    "print('\\nTherefore, the network successfully models 1-to-many relations.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 - Visualize Encoding\n",
    "Here we build another model that is identical to the left half of the neural network. Its output will be the encoding of given word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Encoding of all 8 words: (8, 2)\n['apple'] \t [1.3270669 0.9897884]\n['drink'] \t [ 0.840496  -0.7916093]\n['eat'] \t [ 0.1982154  -0.16265078]\n['juice'] \t [-0.7655994   0.55419576]\n['milk'] \t [-0.95338565  0.7293177 ]\n['orange'] \t [0.9599685 1.2742213]\n['rice'] \t [-1.1261125 -1.4088839]\n['water'] \t [-1.0861223  0.8332627]\n"
    }
   ],
   "source": [
    "# encoding model\n",
    "enc_model = tf.keras.Model(inputs=model.input,\n",
    "                           outputs=model.get_layer('encoder').output)\n",
    "vocab_enc = enc_model.predict(ohot.transform(vocab).toarray())\n",
    "print('Encoding of all 8 words:', vocab_enc.shape)\n",
    "for word, enc in zip(vocab, vocab_enc):\n",
    "    print(word, '\\t', enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "This 2D encoding shows word relationship.\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "vocab_disp = [word[0] for word in vocab]\n",
    "plt.figure(figsize=(8,5))\n",
    "plot_enc_2d(vocab_enc, vocab_disp, offset=[0.03,0.01])\n",
    "plt.xlim([-1.4,1.8])\n",
    "plt.ylim([-1.7,1.6])\n",
    "plt.xlabel('dimension 1', fontsize=16)\n",
    "plt.ylabel('dimension 2', fontsize=16)\n",
    "print('This 2D encoding shows word relationship.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 - Skip-gram model\n",
    "In skip-gram, input and output still have same size, but now allow output to have more 1's in the \"one-hot\" vector."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 - Dataset\n",
    "the dataset is generated by context word, e.g.:\n",
    "- \"drink apple juice\" => target \"drink\", context \"apple\" \"juice\"\n",
    "\n",
    "Since we are dealing with multiple labels, \"n-hot\" is needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "vocabulary: ['apple', 'drink', 'eat', 'juice', 'milk', 'orange', 'rice', 'water']\ndataset: 12 samples.\n--target--\t --context--\n['apple'] \t ['drink', 'juice']\n['orange'] \t ['eat', 'apple']\n['rice'] \t ['drink', 'juice']\n['juice'] \t ['drink', 'milk']\n['milk'] \t ['drink', 'rice']\n['water'] \t ['drink', 'milk']\n['juice'] \t ['orange', 'apple']\n['juice'] \t ['apple', 'drink']\n['milk'] \t ['rice', 'drink']\n['drink'] \t ['milk', 'water']\n['drink'] \t ['water', 'juice']\n['drink'] \t ['juice', 'water']\n"
    }
   ],
   "source": [
    "# context / target, copied from wevi\n",
    "vocab = ['apple', 'drink', 'eat', 'juice', 'milk', 'orange', 'rice', 'water']\n",
    "print('vocabulary:', vocab)\n",
    "vocab = [[_] for _ in vocab]    # sk.OneHotEncoder requires shape (n,1), not (n, )\n",
    "\n",
    "dataset = 'apple|drink^juice,orange|eat^apple,rice|drink^juice,\\\n",
    "juice|drink^milk,milk|drink^rice,water|drink^milk,juice|orange^apple,\\\n",
    "juice|apple^drink,milk|rice^drink,drink|milk^water,drink|water^juice,\\\n",
    "drink|juice^water'\n",
    "dataset = dataset.split(',')\n",
    "dataset = [pair.split('|') for pair in dataset]\n",
    "X = [[pair[0]] for pair in dataset]\n",
    "Y = [pair[1].split('^') for pair in dataset]\n",
    "\n",
    "print('dataset: {} samples.'.format(len(X)))\n",
    "print('--target--\\t --context--')\n",
    "for x_, y_ in zip(X,Y):\n",
    "    print(x_, '\\t', y_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "shape of encoded input/output: (12, 8) (12, 8)\n"
    }
   ],
   "source": [
    "# N-hot Encoding\n",
    "ohot = OneHotEncoder(dtype='float32', handle_unknown='ignore')\n",
    "ohot.fit(vocab)\n",
    "nhot = nHotEncoder(ohot)\n",
    "X_vec = nhot.transform(X)\n",
    "Y_vec = nhot.transform(Y)\n",
    "print('shape of encoded input/output:', X_vec.shape, Y_vec.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[['drink', 'juice'],\n ['apple', 'eat'],\n ['drink', 'juice'],\n ['drink', 'milk'],\n ['drink', 'rice'],\n ['drink', 'milk'],\n ['apple', 'orange'],\n ['apple', 'drink'],\n ['drink', 'rice'],\n ['milk', 'water'],\n ['juice', 'water'],\n ['juice', 'water']]"
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "source": [
    "nhot.inverse_transform(Y_vec,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 - Model, Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"sequential_4\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nencoder (Dense)              (None, 2)                 18        \n_________________________________________________________________\ndecoder (Dense)              (None, 8)                 24        \n=================================================================\nTotal params: 42\nTrainable params: 42\nNon-trainable params: 0\n_________________________________________________________________\n"
    }
   ],
   "source": [
    "model = autoencoder_1hid(n_input=8, n_hidden=2)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch:100, loss = 0.14210\nEpoch:200, loss = 0.12280\nEpoch:300, loss = 0.11310\nEpoch:400, loss = 0.11084\nEpoch:500, loss = 0.10813\nEpoch:600, loss = 0.10343\nEpoch:700, loss = 0.10058\nEpoch:800, loss = 0.09920\nEpoch:900, loss = 0.09836\nEpoch:1000, loss = 0.09754\n\nPlotting the loss value:\n"
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "train(model, X_vec, Y_vec, n_epochs=1000, learning_rate=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the loss value at the very end is significantly larger than before. Because each target word is not strictly matching to 1, 2 or any n context words. The training data always have 1-to-2 matching, which is never achievable, and not realistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Result after training:\ninput   \t ground truth\t\t prediction\n['apple'] \t ['drink', 'juice'] \t ['juice', 'drink']\t\n['orange'] \t ['eat', 'apple'] \t ['apple', 'eat']\t\n['rice'] \t ['drink', 'juice'] \t ['juice', 'drink']\t\n['juice'] \t ['drink', 'milk'] \t ['drink', 'apple']\t*\n['milk'] \t ['drink', 'rice'] \t ['drink', 'rice']\t\n['water'] \t ['drink', 'milk'] \t ['milk', 'drink']\t\n['juice'] \t ['orange', 'apple'] \t ['drink', 'apple']\t*\n['juice'] \t ['apple', 'drink'] \t ['drink', 'apple']\t\n['milk'] \t ['rice', 'drink'] \t ['drink', 'rice']\t\n['drink'] \t ['milk', 'water'] \t ['water', 'juice']\t*\n['drink'] \t ['water', 'juice'] \t ['water', 'juice']\t\n['drink'] \t ['juice', 'water'] \t ['water', 'juice']\t\n"
    }
   ],
   "source": [
    "# evaluation\n",
    "print('Result after training:')\n",
    "print('input   \\t ground truth\\t\\t prediction')\n",
    "for x_, y_, y_pred in zip(X,\n",
    "                          Y,\n",
    "                          nhot.inverse_transform(model.predict(X_vec.toarray()),2)):\n",
    "    print(x_, '\\t', y_,'\\t',y_pred, end='\\t')\n",
    "    for val in y_pred:\n",
    "        if val not in y_:\n",
    "            print('*')\n",
    "            break\n",
    "        else:\n",
    "            pass\n",
    "    else:\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as before, the prediction cannot match ground truth perfectly, we have to look closer to each prediction.\n",
    "\n",
    "With that being said, the prediction follows language intuition. E.g. the 4th line, 'juice' predicts 'drink' and 'apple', both are reasonable; however, in the ground truth, 'milk' is most likely a noise input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The prediction scores for the word \"eat\":\n\n--output--\t--score--\n['apple'] \t0.1038\t\n['drink'] \t0.6729\t*\n['eat'] \t0.0228\t\n['juice'] \t0.1094\t\n['milk'] \t0.0117\t\n['orange'] \t0.0124\t\n['rice'] \t0.0056\t\n['water'] \t0.0612\t\n"
    }
   ],
   "source": [
    "word = 'eat'   # change this\n",
    "\n",
    "print('The prediction scores for the word \"{}\":\\n'.format(word))\n",
    "print('--output--\\t--score--')\n",
    "scores = model.predict(ohot.transform([[word]]).toarray())[0]\n",
    "for label_, score_ in zip(vocab, scores):\n",
    "    print(label_, '\\t{:.4f}'.format(score_), end='\\t')\n",
    "    if score_ > 1.0/8: print('*')\n",
    "    else: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The word 'water' have multiple relations in the training data, however it only predicts 'drink' with high accuracy.\n",
    "\n",
    "On the other hand, the word 'eat' outputs 2 dominant relations 'drink' and 'juice', the latter one is confusing.\n",
    "\n",
    "These may due to the bias of the training data (examine training samples with 'eat'), and the network follows suit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 - Visualize Encoding\n",
    "Here we build another model that is identical to the left half of the neural network. Its output will be the encoding of given word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Encoding of all 8 words: (8, 2)\n\nEncoding for the word \"eat\":\n[[-0.5388979  -0.01903719]]\n"
    }
   ],
   "source": [
    "#\n",
    "enc_model = tf.keras.Model(inputs=model.input,\n",
    "                           outputs=model.get_layer('encoder').output)\n",
    "\n",
    "vocab_enc = enc_model.predict(ohot.transform(vocab).toarray())\n",
    "print('Encoding of all 8 words:', vocab_enc.shape)\n",
    "\n",
    "word = 'eat'\n",
    "print('\\nEncoding for the word \"{}\":'.format(word))\n",
    "print(enc_model.predict(ohot.transform([[word]]).toarray()))\n",
    "# print(X_enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "This 2D encoding shows word relationship.\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "vocab_disp = [word[0] for word in vocab]\n",
    "plt.figure(figsize=(8,5))\n",
    "plot_enc_2d(vocab_enc, vocab_disp, offset=[0.05,-0.15])\n",
    "plt.xlabel('dimension 1', fontsize=16)\n",
    "plt.ylabel('dimension 2', fontsize=16)\n",
    "print('This 2D encoding shows word relationship.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, things are not that clear, e.g. 'water', 'milk', juice' are split apart, and so are 'apple' and 'orange'. This may due to lack of training data. \n",
    "\n",
    "On the good side, this result is consistant with the wevi webpage demo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CBOW and others\n",
    "CBOW is simple swap the input and output of skip-gram, and will not be shown here.\n",
    "\n",
    "For application, the gensim package provides more efficient all round solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.10-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}