4_2_Zero_Shot_Learning_Assignment_SRC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LBg-xdjvhQh2"
   },
   "source": [
    "# This code implements Zero Shot Learning (ZSL) , or learning when NO prior similar Training data has been encountered!\n",
    "\n",
    "This code uses a modified version of Shrisha Bharadwaj's work at \n",
    "https://github.com/sbharadwajj/embarrassingly-simple-zero-shot-learning\n",
    "\n",
    "The Base paper is:\n",
    "An embarrassingly simple approach to zero-shot learning http://proceedings.mlr.press/v37/romera-paredes15.pdf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "17ApZX11hQh3"
   },
   "source": [
    "# We start with the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oAI46GeVC7kL"
   },
   "source": [
    "### Initially load necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "7RcE24FLhQh3"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import scipy.io\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FnRBqKPUDDZZ"
   },
   "source": [
    "We'll be using the CUB dataset. CUB referes to Caltech UCSD Birds data set with images of 788 images from 200 different types of birds annotated with 312 attributes.\n",
    "\n",
    "You can obtain it or similar datasets (such as AWA1 or SUN) [here](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/zero-shot-learning/zero-shot-learning-the-good-the-bad-and-the-ugly/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TIHe-Xi13U_h"
   },
   "source": [
    "If you are working on Jupyter notebook, download the CUB folder and put it in the same location as the notebook file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qdyK3-4OhQh_"
   },
   "source": [
    "### From the .mat files extract all the features from resnet and the attribute splits. \n",
    "- `res101` contains features and the corresponding labels.\n",
    "- `att_splits` contains the different splits for trainval:{train, val} and test set. So the `att_splits` data stores locations of the train, validation and test spilt data only.\n",
    "- `att_splits` contains the labels corresponding to "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "_8-yqE4fhQiA"
   },
   "outputs": [],
   "source": [
    "dataset='CUB_data'\n",
    "res101 = scipy.io.loadmat('./'+dataset+'/res101.mat')\n",
    "att_splits = scipy.io.loadmat('./'+dataset+'/att_splits.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0k26nKM2hQiP"
   },
   "source": [
    "Examine the `res101` and `att_splits` dictionaries and understand their fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bZIA3fHlhQiC",
    "outputId": "45aab6ff-d885-431e-d461-2e59215c0ee7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'image_files', 'features', 'labels'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res101.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L1uKeRbThQiF",
    "outputId": "1557c5d9-4eee-407d-8f88-c2bf8c8196ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'allclasses_names', 'att', 'original_att', 'test_seen_loc', 'test_unseen_loc', 'train_loc', 'trainval_loc', 'val_loc'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "att_splits.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dih1pUMUhQiS"
   },
   "source": [
    "### Let's understand the data sets first:\n",
    "1. `res101` contains a features array $X$ of shape (`n_features`, `n_samples`) = (2048, 11788). These 11788 samples are divided to train, validation and test. The 2048 features correspond to the FC layer output from resnet 101.\n",
    "2. Each of the 11788 samples has one of `n_classes` = 200 unique labels.\n",
    "3. Each label corresponds to an animal that is further represented by a signature/category vector of dimension `n_attributes` = 312.\n",
    "4. Thus, `att_splits` contains a signature/category array of shape (`n_attributes`, `n_classes`) = (312, 200). Call this $S$.\n",
    "5. Our goal is to estimate $W = (V \\cdot S)^T$ and $\\hat{Y} = X^T \\cdot V \\cdot S = (W \\cdot X)^T$. \n",
    "6. $V$, in turn, is an array of shape (`n_features`, `n_attributes`) = (2048, 312) which we will compute later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MS9LtuVuhQiP",
    "outputId": "6a57381f-c2fa-44b2-e94b-f1eabf8f81d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the features array  = (2048, 11788)\n",
      "Shape of the signature array = (312, 200)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape of the features array  = {res101['features'].shape}\")\n",
    "print(f\"Shape of the signature array = {att_splits['att'].shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-bw006lcfixc"
   },
   "source": [
    "### Verify that our data contains 200 unique labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2Q6MdY3EhQiT",
    "outputId": "6c573912-47c2-46d7-d5e7-cf3e4d3c767b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11788, 1)\n",
      "[  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
      "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
      "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
      "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
      "  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108\n",
      " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n",
      " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n",
      " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
      " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180\n",
      " 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198\n",
      " 199 200]\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "labels = res101['labels']\n",
    "print(np.shape(labels))\n",
    "print(np.unique(labels))\n",
    "print(len(np.unique(labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q_actSwNhQiV"
   },
   "source": [
    "We need the corresponding ground-truth labels/classes for each training example for all our train, val, trainval, and test sets according to the split locations provided. In this example we have used the `CUB` dataset, which has 200 unique classes overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "mLCBRvE_hQiW"
   },
   "outputs": [],
   "source": [
    "labels_train = labels[np.squeeze(att_splits['train_loc']-1)]\n",
    "labels_val = labels[np.squeeze(att_splits['val_loc']-1)]\n",
    "labels_trainval = labels[np.squeeze(att_splits['trainval_loc']-1)]\n",
    "labels_test = labels[np.squeeze(att_splits['test_unseen_loc']-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5vYrGNdahQiY",
    "outputId": "d59d6f29-efbf-41a3-c146-90fdb81630fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4702, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[197, 198,  31,  25,  22,  86,  28, 136, 190, 177,  43,  17, 194,\n",
       "         90,  97,  13,  16,  73,  18, 144,  70,   2, 109, 194,  27, 190,\n",
       "         18,  43, 106, 149, 169,  27,  47,  67,  90,  18,  75,  57, 172,\n",
       "         27,  57,  11, 134,  26, 169, 164,  97, 162,  11,   9, 199, 126,\n",
       "          8,  23,  78,  81,  99, 178,  81, 197,  60,  54,  81, 106,  30,\n",
       "        106,  17, 181, 198,  25,  27,   2,  22,  16, 103,  54,  28,  27,\n",
       "        128, 114, 114,  48,  22,  31,  13,  85,   1, 169,  15,   1,  67,\n",
       "         26,  96, 114,  60,  96,  13,  89,  20, 153]], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.shape(labels_train))\n",
    "labels_train[:100,:].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q_B3YnWZhQic"
   },
   "source": [
    "In a typical zero-shot learning scenario, there are no overlapping classes between training and testing phase, i.e the train classes are completely different from the test classes. So let us verify if there are any overlapping classes in the test and train scenario. Within each phase/split, we have `n_classes_split` unique classes. \n",
    "\n",
    "Let's verify that all of the labels in our dataset are represented in either our `trainval` or `test` splits that none of the labels in the `trainval` and `test` splits overlap, and likewise that none of the labels in the `train` and `val` splits overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VH3NvQWIhQic",
    "outputId": "5dad1fd1-c742-434a-8878-4c7b8d2462db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique training labels = 100\n",
      "Number of unique validation labels = 50\n",
      "Number of unique train/validation labels = 150\n",
      "Number of unique test labels = 50\n",
      "[45 38 37 40 46 48 52 50 51 52 32 48 45 45 42 45 52 50 48 46 44 46 47 49\n",
      " 51 51 50 46 46 48 47 48 49 48 48 45 48 49 46 38 47 52 48 49 51 53 47 45\n",
      " 47 48 45 53 47 46 53 48 46 49 49 47 35 47 52 44 49 54 46 51 47 49 48 43\n",
      " 54 43 51 45 46 45 45 48 49 41 47 46 50 49 43 50 43 49 47 45 47 48 46 50\n",
      " 50 44 44 48 50 49 50 50 46 46 47 46 51 45 51 48 49 53 48 52 45 44 44 30\n",
      " 40 49 48 48 42 47 49 50 48 43 54 47 48 50 48 49 53 36 43 52 46 47 49 42\n",
      " 49 52 52 45 37 46]\n"
     ]
    }
   ],
   "source": [
    "train_labels_seen, train_counts = np.unique(labels_train, return_counts=True)\n",
    "print(f'Number of unique training labels = {train_labels_seen.shape[0]}')\n",
    "val_labels_unseen = np.unique(labels_val)\n",
    "print(f'Number of unique validation labels = {val_labels_unseen.shape[0]}')\n",
    "trainval_labels_seen, trainval_counts = np.unique(labels_trainval,return_counts=True)\n",
    "print(f'Number of unique train/validation labels = {trainval_labels_seen.shape[0]}')\n",
    "test_labels_unseen, test_counts = np.unique(labels_test, return_counts=True)\n",
    "print(f'Number of unique test labels = {test_labels_unseen.shape[0]}')\n",
    "print(trainval_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-euNoInthQif",
    "outputId": "fc82a647-08a5-4f3c-ba64-59a5306c4096"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of overlapping classes between train and val: 0\n",
      "Number of overlapping classes between train, val and test: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of overlapping classes between train and val:\",len(set(train_labels_seen).intersection(set(val_labels_unseen))))\n",
    "print(\"Number of overlapping classes between train, val and test:\",len(set(trainval_labels_seen).intersection(set(test_labels_unseen))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZFLI_z7yhQih"
   },
   "source": [
    "So now we have verified data in train set, validation set and test set have no overlaps!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's begin training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XWwYigQLK86b"
   },
   "source": [
    "### Task 1: Map the labels within each split to integers $\\in$ [0, `n_classes_split`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "t8uM4sqNhQih"
   },
   "outputs": [],
   "source": [
    "# Now let's assign each training/val/test label an index\n",
    "i = 0\n",
    "for labels in train_labels_seen:\n",
    "    labels_train[labels_train == labels] = i    \n",
    "    i = i+1\n",
    "#The above code maps ['labels_train' belonging to 'train_labels_seen']\n",
    "\n",
    "#Now, Do the same as above for ['labels_val' belonging to 'vals_labels_unseen'], \n",
    "#['labels_trainval' belonging to 'trainval_labels_seen']\n",
    "#['labels_test' belonging to 'test_labels_unseen']\n",
    "\n",
    "j=0 #counter for labels_val\n",
    "for labels in val_labels_unseen:\n",
    "    labels_val[labels_val == labels] = j\n",
    "    j = j + 1\n",
    "\n",
    "k=0 #counter for labels_trainval\n",
    "for labels in trainval_labels_seen:\n",
    "    labels_trainval[labels_trainval == labels] = k\n",
    "    k = k+ 1\n",
    "\n",
    "l=0 #counter for labels_test\n",
    "for labels in test_labels_unseen:\n",
    "    labels_test[labels_test == labels] = l\n",
    "    l = l +1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vHMKI2-mPNF-"
   },
   "source": [
    "If we've done this right, `i`, `j`, `k`, and `l` should equal the number of train, val, trainval, and test classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4AykT9IXzxgC",
    "outputId": "c3b526d8-2a01-4a33-ccb3-e9eabca88336"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(i, j, k, l) = (100, 50, 150, 50)\n"
     ]
    }
   ],
   "source": [
    "print(f'(i, j, k, l) = ({i}, {j}, {k}, {l})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2NDeWf2whQik"
   },
   "source": [
    "### Feature array split\n",
    "\n",
    "For each split, the features array `X` has the shape (`n_features`, `n_samples_split`). Note that `n_samples_trainval` + `n_samples_test` $\\neq$ `n_samples`. The equality would hold if we concatenated the so-called `test_seen` split with the `test_unseen` split, but if we did that, there would be an overlap between the classes represented in the `trainval` and `test` data.\n",
    "### Task 2: Lets create the train, validation, trainval and test data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "AOAXjLZzhQik"
   },
   "outputs": [],
   "source": [
    "X_features = res101['features']\n",
    "train_features = X_features[:,np.squeeze(att_splits['train_loc']-1)]\n",
    "#The above extracts 'train_features' from att_splits column 'train_loc'\n",
    "#Now do the same for the other data sets:\n",
    "\n",
    "#Extract 'val_feaures' from att_splits column 'val_loc'\n",
    "val_features = X_features[:,np.squeeze(att_splits['val_loc']-1)]\n",
    "#Extract 'trainval_feaures' from att_splits column 'trainval_loc'\n",
    "trainval_features = X_features[:,np.squeeze(att_splits['trainval_loc']-1)]\n",
    "#Extract 'test_feaures' from att_splits column 'test_unseen_loc'\n",
    "test_features = X_features[:,np.squeeze(att_splits['test_unseen_loc']-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "7rnj8bZqhQin"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features for entire dataset: (2048, 11788)\n",
      "Features for train: (2048, 4702)\n",
      "Features for val: (2048, 2355)\n",
      "Features for trainval: (2048, 7057)\n",
      "Features for test: (2048, 2967)\n"
     ]
    }
   ],
   "source": [
    "print(\"Features for entire dataset:\", X_features.shape)\n",
    "print(\"Features for train:\", train_features.shape)\n",
    "print(\"Features for val:\", val_features.shape)\n",
    "print(\"Features for trainval:\", trainval_features.shape)\n",
    "print(\"Features for test:\", test_features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CKzhJdhYhQiq"
   },
   "source": [
    "### Normalize the vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "NnHDYXyNhQiq"
   },
   "outputs": [],
   "source": [
    "def normalization(vec,mean,std):\n",
    "    sol = vec - mean\n",
    "    sol1 = sol/std\n",
    "    return sol1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rpd67geQhQis"
   },
   "source": [
    "<!-- Each class in the dataset has an attribute (a) description. This vector is known as the `Signature matrix` of dimension S ∈ [0, 1]a×z. For training stage there are z classes and z' classes  for test S ∈ [0, 1]a×z'. -->\n",
    "\n",
    "### Dataset class association\n",
    "Each class in the dataset has an associated attribute vector of length `n_attributes` = 312 for `CUB`, where each component $\\in [0, 1]$. Each split of the data thus has a signature matrix $S_{\\textrm{split}}$ of shape (`n_attributes`, `n_classes_split`).\n",
    "## Task 3: Create the Signature Matrices\n",
    "\n",
    "In this task, our goal is to filter columns of signature array based on if it belongs to train, test, val etc. The columns we need should be based on the train, val, test labels.  Since the labels are starting from index 1, thus, we decrease the index of signature by 1 to apply as 0 based index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "signature = att_splits['att']\n",
    "train_sig = signature[:,(train_labels_seen)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "whj-bUEhhQit",
    "outputId": "b9c308d0-bafc-43bb-8ae1-35baf7ca7394"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "signature.shape = (312, 200)\n",
      "train_sig.shape = (312, 100)\n",
      "val_sig.shape = (312, 50)\n",
      "trainval_sig.shape = (312, 150)\n",
      "test_sig.shape = (312, 50)\n"
     ]
    }
   ],
   "source": [
    "#Signature matrix Creation\n",
    "signature = att_splits['att']\n",
    "train_sig = signature[:,(train_labels_seen)-1]\n",
    "# The above generates 'train_sig' from 'att' and 'train_labels_seen'\n",
    "#Now do the same for the other data sets:\n",
    "\n",
    "# Generate 'val_sig' from 'att' and 'val_labels_unseen'\n",
    "val_sig = signature[:,(val_labels_unseen)-1]\n",
    "# Generate 'trainval_sig' from 'att' and 'trainval_labels_seen'\n",
    "trainval_sig = signature[:,(trainval_labels_seen)-1]\n",
    "# Generate 'test_sig' from 'att' and 'test_labels_unseen'\n",
    "test_sig = signature[:,(test_labels_unseen)-1]\n",
    "\n",
    "print(f'signature.shape = {signature.shape}')\n",
    "print(f'train_sig.shape = {train_sig.shape}')\n",
    "print(f'val_sig.shape = {val_sig.shape}')\n",
    "print(f'trainval_sig.shape = {trainval_sig.shape}')\n",
    "print(f'test_sig.shape = {test_sig.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vUGSbT-whQiv"
   },
   "source": [
    "Each row in the signature matrix corresponds to an attribute, while each column corresponds to a class. \n",
    "\n",
    "For example, say the classes are `Horse` and `Zebra` and the attributes are [`wild_anima`l, `4_legged`, `herbivore`].\n",
    "\n",
    "```\n",
    " Horse      Zebra\n",
    "[0.00354613 0.        ] wild_animal\n",
    "[0.13829921 0.20209503] 4_legged\n",
    "[0.06560347 0.04155225] herbivore\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JXW87fPrZo6C"
   },
   "source": [
    "### Let's print the signature for 2 columns, features 3:6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PNcBds4_QRqQ",
    "outputId": "0dc3d8e7-9a33-4621-9013-4464484bbb5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00354613 0.        ]\n",
      " [0.13829921 0.20209503]\n",
      " [0.06560347 0.04155225]]\n"
     ]
    }
   ],
   "source": [
    "print(train_sig[3:6,:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UfTh5hG2Q1oT"
   },
   "source": [
    "Define the numbers of samples and classes for all the splits. We'll use these to set up the ground truth arrays $Y_{\\textrm{split}}$ for the appropriate splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oCwjQJxphQi0",
    "outputId": "77f9188d-8cef-45b8-ee6c-06b9b8463f76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training ground truth size = (4702, 100)\n",
      "Test Groundtruth size = (2967, 50)\n"
     ]
    }
   ],
   "source": [
    "#params for train and val set\n",
    "n_samples_train = labels_train.shape[0]\n",
    "n_samples_val = labels_val.shape[0]\n",
    "n_classes_train = len(train_labels_seen)\n",
    "n_classes_val = len(val_labels_unseen)\n",
    "print(f'Training ground truth size = ({n_samples_train}, {n_classes_train})')\n",
    "\n",
    "#params for trainval and test set\n",
    "n_samples_trainval = labels_trainval.shape[0]\n",
    "n_samples_test = labels_test.shape[0]\n",
    "n_classes_trainval = len(trainval_labels_seen)\n",
    "n_classes_test = len(test_labels_unseen)\n",
    "print(f'Test Groundtruth size = ({n_samples_test}, {n_classes_test})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O9Q8qPu1hQi3"
   },
   "source": [
    "Set up the ground truth arrays as one-hot-encoded matrices. Each row corresponds to a sample and each column corresponds to a label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "IpcTiV_0hQi3"
   },
   "outputs": [],
   "source": [
    "#ground truth for train and val set\n",
    "gt_train = np.zeros((n_samples_train, n_classes_train))\n",
    "gt_train[np.arange(n_samples_train), np.squeeze(labels_train)] = 1\n",
    "\n",
    "#ground truth for trainval and test set\n",
    "gt_trainval = np.zeros((n_samples_trainval, n_classes_trainval))\n",
    "gt_trainval[np.arange(n_samples_trainval), np.squeeze(labels_trainval)] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AnQR7gyJhQi9"
   },
   "source": [
    "For the trainval split, the features array `X` and signature array `S` are both wider than they are tall. This means that we have to introduce 2 regularization parameters to estimate the matrix `V` of shape (`n_features`, `n_attributes`). `V` is thus given by \n",
    "$V = (X \\cdot X^T + \\alpha I_{\\texttt{n_features}})^{-1} \\cdot X \\cdot Y \\cdot S^T \\cdot (S \\cdot S^T + \\gamma I_{\\texttt{n_attributes}})^{-1}$\n",
    "\n",
    "Even with the regularization parameters, you should still use pseudoinverses rather than standard matrix inverses.\n",
    "\n",
    "For details on the derrivation, please see the paper http://proceedings.mlr.press/v37/romera-paredes15.pdf\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UpRoKRHTTNPJ"
   },
   "source": [
    "Initialize $V$, mainly for illustrative purposes. No matter the split, $V$ will have the shape (`n_features`, `n_atttributes`).\n",
    "# Zero Shot Learning Begins here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KaWYKK2DhQi7",
    "outputId": "9cfc22b5-8117-4b4c-8eef-b26adcd2bc6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2048, 312)\n"
     ]
    }
   ],
   "source": [
    "#Initialization. This is where optimal hyper parameters are set!\n",
    "n_features = trainval_features.shape[0]\n",
    "n_attributes = trainval_sig.shape[0]\n",
    "V = np.zeros((n_features,n_attributes))\n",
    "\n",
    "print(V.shape)\n",
    "#Note: These hyperparameters should be changed to optiomal versions after implementing the last code cell\n",
    "gamm1 = 0\n",
    "alph1 = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fRl8JaiVamW_"
   },
   "source": [
    "## Task 4: Train for Zero Shot Learning\n",
    "Calculate $V$ for the `trainval` split according to the formula above,  using pseudoinverses rather than true inverses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "G8XIpisahQi_"
   },
   "outputs": [],
   "source": [
    "##Using the formulation for V above, compute V\n",
    "dataMat = np.matmul(trainval_features, trainval_features.transpose())\n",
    "reg_datMat = np.linalg.pinv(dataMat + (10**alph1)*np.eye(n_features))\n",
    "SMat = np.matmul(trainval_sig, trainval_sig.transpose())\n",
    "reg_SMat = np.linalg.pinv(SMat + (10**gamm1)*np.eye(n_attributes))\n",
    "XYS = np.matmul(np.matmul(trainval_features,gt_trainval),trainval_sig.transpose())\n",
    "V = np.matmul(np.matmul(reg_datMat, XYS), reg_SMat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LUd3qW1ZbDhm"
   },
   "source": [
    "Verify that $V$ is of the correct shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sEmrPBXAbHUK",
    "outputId": "c9d533d6-5ae8-4d7f-cdc6-ee7c3450ef76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V.shape == (n_features, n_attributes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For inferencing, $ \\hat{y} = \\textrm{argmax}(X_{\\texttt{test}}^T \\cdot V \\cdot S_{\\texttt{test}})$, where $S_{\\texttt{test}}$ is the signature matrix of the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H357lH7OhQjB"
   },
   "source": [
    "## Task 5: Find the predictions (y_hat=preds_1) using V computed above, and the equation above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-ABfQySihQjB",
    "outputId": "05166985-43de-46b4-84a5-d7e698047d63",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "preds_1 = np.argmax(np.matmul(np.matmul(test_features.transpose(),V),test_sig), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label= 1\n",
      "Actual Label= [32]\n"
     ]
    }
   ],
   "source": [
    "#Lets look at a sample prediction \n",
    "num=11\n",
    "print('Predicted label=',preds_1[num])\n",
    "print('Actual Label=',labels_test[num])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qbgZHpUp3U_t"
   },
   "source": [
    "Next, lets evaluate the outcome of Zero Shot Learning in terms of 1% accuracy and condusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 6: Find the confusion matrix for Zero Shot Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "eKR9GM-D3U_t",
    "outputId": "7183da1e-5081-4b31-dbfe-a8351195f1d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Confusion Matric is= (50, 50)\n",
      "The top 1% accuracy is: 51.91533906969492\n"
     ]
    }
   ],
   "source": [
    "### Create a confusion matrix between actual labels(labels_test) and predictions (preds_1)\n",
    "#####START CODE HERE##########################\n",
    "cm = confusion_matrix(labels_test, preds_1) #insert the command for confusion matrix\n",
    "##########END CODE HERE########################\n",
    "print(\"Shape of Confusion Matric is=\",np.shape(cm))\n",
    "cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "avg = sum(cm.diagonal())/len(test_labels_unseen)\n",
    "print(\"The top 1% accuracy is:\", avg*100) #This is the definition of top 1% accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "zOCEj_Zj3U_t",
    "outputId": "521af8a0-d58a-48d3-8f79-44d98b580f01"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Output visualization: The confusion Matrix\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_cm = pd.DataFrame(cm, range(cm.shape[0]), range(cm.shape[1]))\n",
    "sn.set(font_scale=1.4) # for label size\n",
    "plt.figure(figsize = (20,14))\n",
    "sn.heatmap(df_cm,annot=True, annot_kws={\"size\": 8}) # font size\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uo8-xF4gc3vc"
   },
   "source": [
    "Next, for comparative purposes, lets evaluate what would have happened if we had just used ridge regression.\n",
    "\n",
    "Let's calculate $W$ and $\\hat{y}$ using ridge regression, i.e. $W = (XX^{T}+\\alpha I)^{-1} \\cdot X \\cdot Y$, $\\hat{y} = \\arg \\max (X^{T} W)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "eoHTqJKNhQjE"
   },
   "outputs": [],
   "source": [
    "#Step 1: Lets initialize W and alpha\n",
    "W=np.zeros((n_features,gt_trainval.shape[1]))\n",
    "alpha2=alph1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "__AJWusH3U_u"
   },
   "source": [
    "## Task 7: Compute W vector using ridge regression and teh equation above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2048, 150)\n"
     ]
    }
   ],
   "source": [
    "##Using the formulation for # above, compute W\n",
    "dataMat = np.matmul(trainval_features, trainval_features.transpose())\n",
    "reg_datMat = np.linalg.pinv(dataMat + (10**alph1)*np.eye(n_features))\n",
    "\n",
    "XY = np.matmul(trainval_features,gt_trainval)\n",
    "W = np.matmul(reg_datMat, XY)\n",
    "print(W.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "65VY02aThQjI",
    "outputId": "093743fd-d7cd-4985-c26d-6ae46c4cdf01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Regression Predicted label= 4\n",
      "Actual Label= [32]\n"
     ]
    }
   ],
   "source": [
    "#Next Now lets evaluate (y_hat=preds_2) using the W and features already computed!\n",
    "##########START CODE HERE############\n",
    "preds_2 = np.argmax(np.matmul(test_features.transpose(), W), axis=1)\n",
    "#########END CODE HERE##################\n",
    "#Look at a sample prediction, same sample (num) as the ZSL example above\n",
    "print('Normal Regression Predicted label=',preds_2[num])\n",
    "print('Actual Label=',labels_test[num])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sLcWqT7shQjK",
    "outputId": "0cdc5eaa-0351-48f3-f6c5-90b189e0167b"
   },
   "source": [
    "### Step 4: Evaluation using 1 in % accuracy and confusion matrix.\n",
    "Evaluating the performance of ZSL models is tricky since the model has never previously see the exact test labels. (See example in https://github.com/cetinsamet/zero-shot-learning). Thus, based on the signature vector, the model predicts several outcomes. If top 5 outcomes are predicted then the metric is refered to as top 5 in % accuracy. Similarly if top 3 outcomes are predicted, metric is top 3 in % accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 826
    },
    "id": "B2Jl7XC7hQjM",
    "outputId": "a8c3ebfb-ab63-4c42-f01f-9e57ee6bde68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 1% accuracy is: 0.33559322033898303\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-cd7b854307f3>:5: RuntimeWarning: invalid value encountered in true_divide\n",
      "  cm2 = cm2.astype('float') / cm2.sum(axis=1)[:, np.newaxis]\n"
     ]
    }
   ],
   "source": [
    "### Create a confusion matrix between actual labels(labels_test) and predictions (preds_2)\n",
    "#####START CODE HERE##########################\n",
    "cm2 = confusion_matrix(labels_test, preds_2) #insert the command for confusion matrix\n",
    "##########END CODE HERE########################\n",
    "cm2 = cm2.astype('float') / cm2.sum(axis=1)[:, np.newaxis]\n",
    "cm2=np.nan_to_num(cm2,nan=0)\n",
    "avg2 = sum(cm2.diagonal())/len(test_labels_unseen)\n",
    "print(\"The top 1% accuracy is:\", avg2*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YjRttlQwhQjO",
    "outputId": "7647ae10-ee8d-46e1-8ea4-22509fd25316"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets visualize confusion matrix. Observe too many 0 values in the confusion matrix\n",
    "df_cm2 = pd.DataFrame(cm2, range(cm2.shape[0]), range(cm2.shape[1]))\n",
    "sn.set(font_scale=1.4) # for label size\n",
    "plt.figure(figsize = (20,14))\n",
    "sn.heatmap(df_cm2,annot=True, annot_kws={\"size\": 8}) # font size\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9BC3H-UEhQjR"
   },
   "source": [
    "### Improvement from Zero Shot\n",
    "So we see that the use of vectorization and signature matrix yields Zero Shot Learning 1% Accuracy of  52% for unseen data as opposed to <1% accuracy that can be obtained by ridge regression only.\n",
    "\n",
    "Thus, we see the given method method using signature matrix is highly useful for previously unseen data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kEFE7o9jhQjS"
   },
   "source": [
    "## Lets try some hyper-parameter tuning!\n",
    "\n",
    "Below code snippet can be used to find the best hyper-parameter using the train and val set. This can be used to set alpha1, gamma1 in the equations above. We will run the following snippet to obtain the best alpha1 and gamma1 and substitute in the zero shot learning cells (After \" Zero Shot Learning begins here!\"). Report the performance improvement by using the optimal hyper-parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 240
    },
    "id": "yrYmi_IPhQjS",
    "outputId": "7925f224-f346-48eb-cd8b-f1b0212bf70c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg: 0.18585450209673038 -3 -3\n",
      "Avg: 0.24968040478867645 -3 -2\n",
      "Avg: 0.34653258722533276 -3 -1\n",
      "Avg: 0.4093230883557413 -3 0\n",
      "Avg: 0.3892262675964993 -3 1\n",
      "Avg: 0.38704134343461916 -3 2\n",
      "Avg: 0.3531400601840633 -3 3\n",
      "Avg: 0.18585450209673038 -2 -3\n",
      "Avg: 0.24968040478867645 -2 -2\n",
      "Avg: 0.34703258722533276 -2 -1\n",
      "Avg: 0.4093230883557413 -2 0\n",
      "Avg: 0.3888341107337542 -2 1\n",
      "Avg: 0.38704134343461916 -2 2\n",
      "Avg: 0.3527052775753677 -2 3\n",
      "Avg: 0.18585450209673038 -1 -3\n",
      "Avg: 0.2501233053531576 -1 -2\n",
      "Avg: 0.34703258722533276 -1 -1\n",
      "Avg: 0.4093230883557413 -1 0\n",
      "Avg: 0.38926889334244985 -1 1\n",
      "Avg: 0.38661581151972557 -1 2\n",
      "Avg: 0.3535563414051549 -1 3\n",
      "Avg: 0.18711892762812204 0 -3\n",
      "Avg: 0.2551315135698468 0 -2\n",
      "Avg: 0.3523568358486893 0 -1\n",
      "Avg: 0.41178783716475353 0 0\n",
      "Avg: 0.39021185318408413 0 1\n",
      "Avg: 0.3882897513282071 0 2\n",
      "Avg: 0.3577092836277689 0 3\n",
      "Avg: 0.2129903057974815 1 -3\n",
      "Avg: 0.2750085113017567 1 -2\n",
      "Avg: 0.3811041180952309 1 -1\n",
      "Avg: 0.437161019212587 1 0\n",
      "Avg: 0.4167186252556304 1 1\n",
      "Avg: 0.4057926011892311 1 2\n",
      "Avg: 0.3786245732615904 1 3\n",
      "Avg: 0.2726155336080635 2 -3\n",
      "Avg: 0.3493418520832315 2 -2\n",
      "Avg: 0.44549392114624486 2 -1\n",
      "Avg: 0.4909643364371752 2 0\n",
      "Avg: 0.44736598911347614 2 1\n",
      "Avg: 0.43724886744165237 2 2\n",
      "Avg: 0.39989936234815965 2 3\n",
      "Avg: 0.35898327116750545 3 -3\n",
      "Avg: 0.42822915463141586 3 -2\n",
      "Avg: 0.5002568244155162 3 -1\n",
      "Avg: 0.5062013087372061 3 0\n",
      "Avg: 0.45656260752338473 3 1\n",
      "Avg: 0.4488064942715358 3 2\n",
      "Avg: 0.395433478692378 3 3\n",
      "Optimal Alpha= 3 Optimal Gamma= 0\n"
     ]
    }
   ],
   "source": [
    "accu = 0.10\n",
    "alph1 = 4\n",
    "gamm1 = 1\n",
    "for alpha in range(-3, 4):\n",
    "    for gamma in range(-3,4):\n",
    "        #One line solution\n",
    "        part_1 = np.linalg.pinv(np.matmul(train_features, train_features.transpose()) + (10**alpha)*np.eye(n_features))\n",
    "        part_0 = np.matmul(np.matmul(train_features,gt_train),train_sig.transpose())\n",
    "        part_2 = np.linalg.pinv(np.matmul(train_sig, train_sig.transpose()) + (10**gamma)*np.eye(n_attributes))\n",
    "\n",
    "        V = np.matmul(np.matmul(part_1,part_0),part_2)\n",
    "        #print(V)\n",
    "\n",
    "        #predictions\n",
    "        outputs = np.matmul(np.matmul(val_features.transpose(),V),val_sig)\n",
    "        preds = np.array([np.argmax(output) for output in outputs])\n",
    "\n",
    "        cm = confusion_matrix(labels_val, preds)\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        avg = sum(cm.diagonal())/len(val_labels_unseen)\n",
    "        print(\"Avg:\", avg, alpha, gamma)\n",
    "\n",
    "        if avg > accu:\n",
    "            accu = avg\n",
    "            alph1 = alpha\n",
    "            gamm1 = gamma\n",
    "print('Optimal Alpha=',alph1, 'Optimal Gamma=',gamm1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "\n",
    "Using the Optimal parameters improved the top 1% accuracy to 52 percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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