Andromeda_ImgVersion.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Andromeda with Image Data in Jupyter \n",
    "\n",
    "\n",
    "### Pre-trained Deep Learning Model for Feature Extraction\n",
    "**ResNet18 Feature Extractor** extracts features from images at the last convolutional layer, apply average pooling on each feature map, then we got 512 features for each image.\n",
    "\n",
    "\n",
    "### Interactive Inverse Dimension Reduction \n",
    "\n",
    "This notebook implements interactive dimension reduction (DR) for exploratory analysis of high-dimensional data.\n",
    "It uses a Multi-Dimensional Scaling (MDS) algorithm with a weighted distance metric. It enables both forward and inverse DR interaction. \n",
    "\n",
    "**MDS** projects high-dimensional data to a 2D scatterplot. A **weighted distance function** with user-specified weights on each dimension enables alternative projections that emphasize different dimensions. An **Inverse-DR** algorithm learns distance function weights for  user-constructed layouts of the data points.\n",
    "\n",
    "\n",
    "### CNN Feature Interpretability  \n",
    "\n",
    "We use **Visual Back Propagation** method to back propagate each feature map, then apply the learned weights from Inverse DR model to the back propogated feature maps, add the final maps together to get the final attention.\n",
    "\n",
    "\n",
    "### Instructions:\n",
    "\n",
    "1. Change the variable name of the first file **ImgFolder** to your own image folder path\n",
    "1. Click \"Kernel\" in the menu bar --> then click \"Restart Kernel and Run All\"\n",
    "1. Proceed to the interactive plots near the bottom\n",
    "1. There are three kinds of interactions:\n",
    "    1. **Projection interaction:** Drag images in the projection plot to form user-defined clusters, then click Learn to see learned weights, and click Copy to see the updated projection plot.\n",
    "    1. **Print Activation Map:** Click the \"Print features\" button at the bottom of the notebook to see which part of the original image is focused by the underlying model to form user-defined image groups.\n",
    "1. Be patient, its interactive matplotlib in python and Jupyter!\n",
    "\n",
    "\n",
    "Based on: \n",
    "1. *Self JZ, Dowling M, Wenskovitch J, Crandell I, Wang M, House L, Leman S, North C. Observation-Level and Parametric Interaction for High-Dimensional Data Analysis. ACM Transactions on Interactive Intelligent Systems.  8(2), 2018.* https://infovis.cs.vt.edu/sites/default/files/observation-level-parametric_first_look_version.pdf\n",
    "2. *Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Larry Jackel, Urs Muller, Karol Zieba. VisualBackProp: efficient visualization of CNNs.* https://arxiv.org/abs/1611.05418v3\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "### interactive notebook format is required for the interactive plot\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from math import isnan\n",
    "import random\n",
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "import cv2\n",
    "from skimage.transform import resize\n",
    "import csv\n",
    "from functools import partial\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import MDS\n",
    "import sklearn.metrics.pairwise\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
    "from matplotlib.patches import FancyBboxPatch\n",
    "from matplotlib.widgets import Slider, Button\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, Layout, Button, GridBox, ButtonStyle\n",
    "from IPython.display import display, clear_output, Image\n",
    "\n",
    "import ipyplot\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision.transforms import ToTensor\n",
    "from torchvision import transforms\n",
    "\n",
    "## see VisualBackProp.py\n",
    "import VisualBackProp as VBP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Pre-process Data\n",
    "\n",
    "Change the **imgFolder** to load a image dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image folder structure should be arranged in standard pytorch data loading format i.e: <br>\n",
    "* root/dog/xxx.png\n",
    "* root/dog/xxy.png\n",
    "* root/dog/xxz.png\n",
    "\n",
    "\n",
    "* root/cat/123.png\n",
    "* root/cat/nsdf3.png\n",
    "* root/cat/asd932_.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## settings\n",
    "# imgFolder = '/users/huiminhan/Desktop/Lab/InfoVis/Datasets/PokemonData/'\n",
    "imgFolder = '/users/huiminhan/Desktop/Lab/InfoVis/VIS2022/Andromeda_IMG/imgData/'\n",
    "# imgFolder = './Images/animal_sample/'\n",
    "sampleSizePerCat = 5  #sample size for each image category subfolder\n",
    "imgDisplaySize = 0.4  #default value for image display size, can be interactively adjusted in the UI\n",
    "total_img = 100  # maximun total number of images to display on the UI\n",
    "folderName = True  #whether the imgFolder has subfolder for each category. e.g. the fish dataset\n",
    "load_weights_from_file = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_path(imgFolder, sampleSizePerCat, folderName, max_img_num=50):\n",
    "    \"\"\"    \n",
    "    @parameters:\n",
    "        imgFolder[str]: path for the image folder. e.g. \"/root/\" \n",
    "        sampleSizePerCat[int]: the number of samples from each subfolder. e.g. 10 images each from \"root/cat/\"\n",
    "                          and \"root/dog/\"\n",
    "        folderName[boolean]: if structure is: root/cat/123.png then True, if root/123.png then False.\n",
    "        max_img_num[int]: upper limit for TOTAL image samples\n",
    "    @return[dict]: a dictionary with image index (used in later dataframe as index as well)\n",
    "             as key and full path of the image as the value\n",
    "    \"\"\"\n",
    "    imgIdx_path = {}\n",
    "    totalImg = 0\n",
    "    for (dirpath, dirnames, filenames) in os.walk(imgFolder):\n",
    "        sampleCount = 0\n",
    "        for filename in filenames:\n",
    "            if filename.lower().endswith('jpg') or filename.lower().endswith(\n",
    "                    'jpeg') or filename.lower().endswith('png'):\n",
    "                path = dirpath + '/' + filename\n",
    "                pattern = extractIdx_pattern(path, folderName)\n",
    "                imgIdx_path[pattern] = path\n",
    "                sampleCount += 1\n",
    "                totalImg += 1\n",
    "            if sampleCount == sampleSizePerCat or totalImg == max_img_num:\n",
    "                break\n",
    "        if totalImg == max_img_num:\n",
    "            break\n",
    "    return imgIdx_path\n",
    "\n",
    "\n",
    "def extractIdx_pattern(path, folderName):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        path[str]: single image path. e.g. \"/root/cat/cat_01.png\"\n",
    "        folderName[boolean]: if structure is: root/cat/123.png then True, if root/123.png then False.\n",
    "    @return[sts]: given a path(string) of image, extract image index from the path, return the image index string\n",
    "    \"\"\"\n",
    "    if folderName:\n",
    "        pattern = path.split('/')[-2] + '/' + path.split('/')[-1].split(\n",
    "            '.')[-2]\n",
    "    else:\n",
    "        pattern = path.split('/')[-1].split('.')[-2]\n",
    "    return pattern\n",
    "\n",
    "\n",
    "def data_loader(imgIdx_path):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        imgIdx_path[dict]: a dict get from get_path function storing {image index: full path of the image}\n",
    "    @return: image loader\n",
    "    \"\"\"\n",
    "\n",
    "    dataset = VBP.FilenameDataset(imgIdx_path.values())\n",
    "    loader = DataLoader(dataset)\n",
    "    if loader:\n",
    "        print(\"{} images loaded\".format(len(loader)))\n",
    "        return loader\n",
    "    else:\n",
    "        print(\"Invalid path\")\n",
    "        return\n",
    "\n",
    "\n",
    "def feature_extractor(model, loader, imgIdx_path):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        model[neuron network]: model used to extract features\n",
    "        loader: image loader returned from the data_loader function\n",
    "    @return[dataframe]: a dataframe of extracted features indexing by image index(get from extractIdx_pattern function)\n",
    "    \"\"\"\n",
    "    features = []\n",
    "    for i, img in zip(range(len(loader)), loader):\n",
    "        with torch.no_grad():\n",
    "            x, vis, target_feature_map = model(img)\n",
    "            features.append(x)\n",
    "        index = []\n",
    "        for path in imgIdx_path.values():\n",
    "            index.append(extractIdx_pattern(path, folderName))\n",
    "    df = pd.DataFrame(features,\n",
    "                      columns=[str(i) for i in range(1, 513)],\n",
    "                      index=index)\n",
    "    df.index.name = 'Image'\n",
    "    return df\n",
    "\n",
    "\n",
    "def df_preprocess(df_image, normalize=True):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        df_image[dataframe]: image features dataframe\n",
    "    @return[dataframe]: preprocessed dataframe\n",
    "    \"\"\"\n",
    "    df_image.sort_index(inplace=True)\n",
    "    df_numeric = df_image.select_dtypes(include='number').drop_duplicates(\n",
    "    )  #'int32' or 'int64' or 'float32' or 'float64'\n",
    "    df_category = df_image.select_dtypes(\n",
    "        exclude='number').drop_duplicates()  #'object'\n",
    "    ### Z-score normalization\n",
    "    if normalize:\n",
    "        normalized_df = (df_numeric - df_numeric.mean()) / df_numeric.std(\n",
    "        )  # do not normalize animal dataset, all columns are 0-100 scale\n",
    "        return normalized_df\n",
    "    return df_numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 images loaded\n"
     ]
    }
   ],
   "source": [
    "imageIndex_path_dict = get_path(imgFolder,\n",
    "                                sampleSizePerCat,\n",
    "                                folderName=folderName,\n",
    "                                max_img_num=total_img)\n",
    "img_loader = data_loader(imageIndex_path_dict)\n",
    "## using the same model in backprop file to make sure\n",
    "## the forward and backward process happens to the same network\n",
    "model = VBP.resnet18(pretrained=True).eval()\n",
    "model_bp = VBP.ResnetVisualizer(model.eval(), weight_list=torch.ones([512]))\n",
    "df = feature_extractor(model_bp, img_loader, imageIndex_path_dict)\n",
    "#################################\n",
    "# the name of the dataframe must be normalized_df, used as global variable later\n",
    "normalized_df = df_preprocess(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "                               1         2         3         4         5  \\\n",
       "Image                                                                      \n",
       "disease/2103_disease_3 -0.372807 -0.082484  3.352167 -0.353830 -1.432034   \n",
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       "disease/2105_disease_8  0.069650 -1.075414 -0.089063 -0.127408  0.925271   \n",
       "\n",
       "                               6         7         8         9        10  ...  \\\n",
       "Image                                                                     ...   \n",
       "disease/2103_disease_3 -0.770890 -0.668322 -0.937907  0.801034  0.598544  ...   \n",
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       "disease/2104_disease_3  0.996074 -0.656462  0.474668 -0.494435 -0.441069  ...   \n",
       "disease/2105_disease_8 -0.680414  0.965028  2.152237 -0.265552 -0.121427  ...   \n",
       "\n",
       "                             503       504       505       506       507  \\\n",
       "Image                                                                      \n",
       "disease/2103_disease_3 -0.222895  1.921587 -0.272119 -1.416607  0.390536   \n",
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       "\n",
       "                             508       509       510       511       512  \n",
       "Image                                                                     \n",
       "disease/2103_disease_3  2.571049 -0.902310 -1.120271 -0.852895 -1.099126  \n",
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       "disease/2104_disease_2 -0.031475  0.292431 -0.521255  1.097767  0.278055  \n",
       "disease/2104_disease_3  1.647321  0.857314 -1.009613  0.944106  0.800429  \n",
       "disease/2105_disease_8 -0.814661 -0.494337 -0.195748 -0.242221 -1.261730  \n",
       "\n",
       "[5 rows x 512 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Dimension Reduction Model:  Weighted MDS\n",
    "\n",
    "For DR, we use the Multi-Dimensional Scaling (MDS) algorithm on a weighted data space. **Dimension weights** are applied to the high-dimensional (HD) data.  Weights are normalized to sum to 1, so as to normalize the HD distances to roughly constant size space independent of p.\n",
    "\n",
    "The **distance function for the high-dimensional (HD) data** is L1 manhattan distance. L1 is good for general purpose use with multi-dimensional quantitative datasets. \n",
    "\n",
    "The **distance function for the 2D projected points** is L2 Euclidean distance, which makes sense for human percpetion in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance_matrix_HD(dataHDw):\n",
    "    \"\"\"\n",
    "    Compute the distance matrix for the weighted high-dimensional data using L1 distance function.\n",
    "    Input HD data should already be weighted.\n",
    "    \n",
    "    @parameters:\n",
    "        dataHDw[pd.df or np.array]: weighted high-dimensional data\n",
    "    @return[array]: distance matrix for input weighted high-dimensional data\n",
    "    \"\"\"\n",
    "    dist_matrix = sklearn.metrics.pairwise.manhattan_distances(dataHDw)\n",
    "    return dist_matrix\n",
    "\n",
    "\n",
    "def distance_matrix_2D(data2D):\n",
    "    \"\"\"\n",
    "    Compute the distance matrix for 2D projected data using L2 distance function.\n",
    "    \n",
    "    @parameters: \n",
    "        data2D[pd.df or np.array]: projected 2D data\n",
    "    @return[np.array]: distance matrix for 2D input data\n",
    "    \"\"\"\n",
    "    dist_matrix = sklearn.metrics.pairwise.euclidean_distances(data2D)\n",
    "    return dist_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MDS** projects the weighted high-dimensional data to 2D. Tune the algorithm's parameters for performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stress(distHD, dist2D):\n",
    "    \"\"\"\n",
    "    Calculate the MDS stress metric between HD and 2D distances.\n",
    "    @parameters: \n",
    "        distHD[np.array]: distance matrix for high-dimensional data\n",
    "        dist2D[np.array]: distance matrix for 2D data\n",
    "    @return[float]: stress value\n",
    "    \"\"\"\n",
    "    s = ((distHD - dist2D)**\n",
    "         2).sum() / (distHD**2).sum()  # numpy, eliminate sqrt for efficiency\n",
    "    return s\n",
    "\n",
    "\n",
    "def compute_mds(dataHDw):\n",
    "    \"\"\"\n",
    "    apply MDS to high-dimensional data to get 2D data\n",
    "    @parameters:\n",
    "        dataHDw[pd.df or np.array]: weighted high-dimensional data\n",
    "    @return[dataframe]: a dataframe of 2D data \n",
    "    \"\"\"\n",
    "    distHD = distance_matrix_HD(dataHDw)\n",
    "    ### Adjust these parameters for performance/accuracy tradeoff\n",
    "    mds = sklearn.manifold.MDS(n_components=2,\n",
    "                               dissimilarity='precomputed',\n",
    "                               n_init=10,\n",
    "                               max_iter=1000,\n",
    "                               random_state=3)\n",
    "    # Reduction algorithm happens here:  data2D is n*2 matrix\n",
    "    data2D = mds.fit_transform(distHD)\n",
    "\n",
    "    ### Rotate the resulting 2D projection to make it more consistent across multiple runs.\n",
    "    ### Set the 1st PC to the y axis, plot looks better to spread data vertically with horizontal text labels\n",
    "    pca = sklearn.decomposition.PCA(n_components=2)\n",
    "    data2D = pca.fit_transform(data2D)\n",
    "    data2D = pd.DataFrame(data2D, columns=['y', 'x'], index=dataHDw.index)\n",
    "\n",
    "    data2D.stress_value = stress(distHD, distance_matrix_2D(data2D))\n",
    "    return data2D\n",
    "\n",
    "\n",
    "def dimension_reduction(dataHD, wts):  # dataHD, wts -> data2D (pandas)\n",
    "    \"\"\"\n",
    "    apply weights to high-dimensional data then apply MDS to get 2D data\n",
    "    @parameters:\n",
    "        dataHD[pd.df or np.array]: original high-dimensional data\n",
    "    @return[dataframe]: a dataframe of projected 2D data\n",
    "    \"\"\"\n",
    "    ### Normalize the weights to sum to 1\n",
    "    wts = wts / wts.sum()\n",
    "\n",
    "    ### Apply weights to the HD data\n",
    "    dataHDw = dataHD * wts\n",
    "\n",
    "    ### DR algorithm\n",
    "    data2D = compute_mds(dataHDw)\n",
    "\n",
    "    ### Compute row relevances as:  data dot weights\n",
    "    ### High relevance means large values in upweighted dimensions\n",
    "    #     data2D['relevance'] = dataHDw.sum(axis=1)\n",
    "    return data2D\n",
    "\n",
    "\n",
    "def get_weights(min_weight, max_weight, index, load_weights_from_file=False):\n",
    "    \"\"\"\n",
    "    initialize weights for high-dimensional data\n",
    "    @parameters:\n",
    "        load_weights_from_file[boolean]: whether load weights from a saved csv file or not\n",
    "    @return[dataframe]: a dataframe of initialized weights (equal for each features if not loaded from a saved csv file)\n",
    "    \"\"\"\n",
    "    if load_weights_from_file:\n",
    "        weights = pd.read_csv(weights_path)['Weight']\n",
    "    else:\n",
    "        # 1.0/len(normalized_df.columns) # initialize to min to make the sliders easier to use.\n",
    "        weights = pd.Series(init_weight, index=index,\n",
    "                            name=\"Weight\")  # the current weight list\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized distance matrix:\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Image</th>\n",
       "      <th>disease/2103_disease_3</th>\n",
       "      <th>disease/2103_disease_5</th>\n",
       "      <th>disease/2104_disease_2</th>\n",
       "      <th>disease/2104_disease_3</th>\n",
       "      <th>disease/2105_disease_8</th>\n",
       "      <th>late/2081_late_18</th>\n",
       "      <th>late/2083_late_11</th>\n",
       "      <th>late/2086_late_14</th>\n",
       "      <th>late/2096_late_11</th>\n",
       "      <th>late/2100_late_10</th>\n",
       "      <th>ready/2028_ready_1</th>\n",
       "      <th>ready/2038_ready_15</th>\n",
       "      <th>ready/2038_ready_5</th>\n",
       "      <th>ready/2053_ready_9</th>\n",
       "      <th>ready/2059_ready_18</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Image</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>disease/2103_disease_3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.967672</td>\n",
       "      <td>1.207994</td>\n",
       "      <td>1.270031</td>\n",
       "      <td>1.301306</td>\n",
       "      <td>1.384427</td>\n",
       "      <td>1.278787</td>\n",
       "      <td>1.494939</td>\n",
       "      <td>1.323383</td>\n",
       "      <td>1.289200</td>\n",
       "      <td>1.110412</td>\n",
       "      <td>1.259681</td>\n",
       "      <td>1.205335</td>\n",
       "      <td>1.270655</td>\n",
       "      <td>1.281268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disease/2103_disease_5</th>\n",
       "      <td>0.967672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.029067</td>\n",
       "      <td>1.074484</td>\n",
       "      <td>1.175076</td>\n",
       "      <td>1.150145</td>\n",
       "      <td>1.116144</td>\n",
       "      <td>1.300742</td>\n",
       "      <td>1.190458</td>\n",
       "      <td>1.069684</td>\n",
       "      <td>0.804199</td>\n",
       "      <td>0.917987</td>\n",
       "      <td>0.834563</td>\n",
       "      <td>1.008691</td>\n",
       "      <td>1.001193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Image                   disease/2103_disease_3  disease/2103_disease_5  \\\n",
       "Image                                                                    \n",
       "disease/2103_disease_3                0.000000                0.967672   \n",
       "disease/2103_disease_5                0.967672                0.000000   \n",
       "\n",
       "Image                   disease/2104_disease_2  disease/2104_disease_3  \\\n",
       "Image                                                                    \n",
       "disease/2103_disease_3                1.207994                1.270031   \n",
       "disease/2103_disease_5                1.029067                1.074484   \n",
       "\n",
       "Image                   disease/2105_disease_8  late/2081_late_18  \\\n",
       "Image                                                               \n",
       "disease/2103_disease_3                1.301306           1.384427   \n",
       "disease/2103_disease_5                1.175076           1.150145   \n",
       "\n",
       "Image                   late/2083_late_11  late/2086_late_14  \\\n",
       "Image                                                          \n",
       "disease/2103_disease_3           1.278787           1.494939   \n",
       "disease/2103_disease_5           1.116144           1.300742   \n",
       "\n",
       "Image                   late/2096_late_11  late/2100_late_10  \\\n",
       "Image                                                          \n",
       "disease/2103_disease_3           1.323383           1.289200   \n",
       "disease/2103_disease_5           1.190458           1.069684   \n",
       "\n",
       "Image                   ready/2028_ready_1  ready/2038_ready_15  \\\n",
       "Image                                                             \n",
       "disease/2103_disease_3            1.110412             1.259681   \n",
       "disease/2103_disease_5            0.804199             0.917987   \n",
       "\n",
       "Image                   ready/2038_ready_5  ready/2053_ready_9  \\\n",
       "Image                                                            \n",
       "disease/2103_disease_3            1.205335            1.270655   \n",
       "disease/2103_disease_5            0.834563            1.008691   \n",
       "\n",
       "Image                   ready/2059_ready_18  \n",
       "Image                                        \n",
       "disease/2103_disease_3             1.281268  \n",
       "disease/2103_disease_5             1.001193  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_weight, min_weight, max_weight = 0.00001, 0.00001, 0.9999\n",
    "weights = get_weights(min_weight, max_weight, normalized_df.columns)\n",
    "df_2D = dimension_reduction(normalized_df,\n",
    "                            weights)  # the current projected data\n",
    "print(\"Initialized distance matrix:\")\n",
    "pd.DataFrame(distance_matrix_HD(normalized_df * (weights / weights.sum())),\n",
    "             columns=normalized_df.index,\n",
    "             index=normalized_df.index).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse Dimension-Reduction Learning Algorithm\n",
    "\n",
    "Computes the inverse-Dimension-Reduction: given input 2D points, compute new weights.\n",
    "Optimizes the MDS stress function that compares 2D pairwise distances (||$x_i-x_j||$) to weighted HD pairwise distances ($d_{ij}$):\n",
    "![Stress](https://wikimedia.org/api/rest_v1/media/math/render/svg/7989b3afc0d8795a78c1631c7e807f260d9cfe68)\n",
    "\n",
    "Technically, we compute the inverse weighted distance function. We shortcut the optimization by eliminating MDS from the process, and assume that the user input 2D distances are actually the desired HD distances, not the 2D distances after re-projection. Thus, given the input (HD) distances, we find weights that would produce these distances in the HD space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This method is used to propose a new weight for current column in a smart fashion\n",
    "def new_proposal(current, step, direction):\n",
    "    return np.clip(current + direction * step * random.random(), 0.00001,\n",
    "                   0.9999)\n",
    "\n",
    "\n",
    "def inverse_DR(dataHD, data2D, curWeights=None):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        dataHD[pd.df or np.array]: high-dimensional data\n",
    "        data2D[pd.df or np.array]: projected 2D data\n",
    "    @return[pd.Series]: new weights  \n",
    "    \"\"\"\n",
    "    dist2D = distance_matrix_2D(data2D)  # compute 2D distances only once\n",
    "    col_names = dataHD.columns\n",
    "    dataHD = dataHD.to_numpy()  # use numpy for efficiency\n",
    "    row, col = dataHD.shape\n",
    "\n",
    "    if curWeights == None:\n",
    "        curWeights = np.array([1.0 / col] * col)  # default weights = 1/p\n",
    "    else:\n",
    "        curWeights = curWeights.to_numpy()\n",
    "        curWeights = curWeights / curWeights.sum(\n",
    "        )  # Normalize weights to sum to 1\n",
    "    newWeights = curWeights.copy()  # re-use this array for efficiency\n",
    "\n",
    "    # Initialize state\n",
    "    flag = [0] * col  # degree of success of a weight change\n",
    "    direction = [1] * col  # direction to move a weight, pos or neg\n",
    "    step = [1.0 / col] * col  # how much to change each weight\n",
    "\n",
    "    dataHDw = dataHD * curWeights  # weighted space, re-use this array for efficiency\n",
    "    distHD = distance_matrix_HD(dataHDw)\n",
    "    curStress = stress(distHD, dist2D)\n",
    "    print('Starting stress =', curStress, 'Processing...')\n",
    "\n",
    "    MAX = 500  # default setting of the number of iterations\n",
    "\n",
    "    # Try to minorly adjust each weight to see if it reduces stress\n",
    "    for i in range(MAX):\n",
    "        for dim in range(col):\n",
    "            # Get a new weight for current column\n",
    "            nw = new_proposal(curWeights[dim], step[dim], direction[dim])\n",
    "\n",
    "            # Scale the weight list such that it sums to 1\n",
    "            s = 1.0 + nw - curWeights[dim]  # 1.0 == curWeights.sum()\n",
    "            np.true_divide(\n",
    "                curWeights, s,\n",
    "                out=newWeights)  # transfers to other array, while doing /\n",
    "            newWeights[dim] = nw / s\n",
    "\n",
    "            # Apply new weights to HD data\n",
    "            np.multiply(\n",
    "                dataHD, newWeights, out=dataHDw\n",
    "            )  # dataHDw = dataHD * newWeights; efficiently reuses dataHDw array\n",
    "            distHD = distance_matrix_HD(dataHDw)\n",
    "\n",
    "            # Get the new stress\n",
    "            newStress = stress(distHD, dist2D)\n",
    "\n",
    "            # If new stress is lower, then update weights and flag this success\n",
    "            if newStress < curStress:\n",
    "                temp = curWeights\n",
    "                curWeights = newWeights\n",
    "                newWeights = temp  # reuse the old array next iteration\n",
    "                curStress = newStress\n",
    "                flag[dim] = flag[dim] + 1\n",
    "            else:\n",
    "                flag[dim] = flag[dim] - 1\n",
    "                direction[dim] = -direction[dim]  # Reverse course\n",
    "\n",
    "            # If recent success, then speed up the step rate\n",
    "            if flag[dim] >= 5:\n",
    "                step[dim] = step[dim] * 2\n",
    "                flag[dim] = 0\n",
    "            elif flag[dim] <= -5:\n",
    "                step[dim] = step[dim] / 2\n",
    "                flag[dim] = 0\n",
    "\n",
    "    print('Solution stress =', curStress, 'Done.')\n",
    "    return pd.Series(curWeights, index=col_names, name=\"Weight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization and UI code\n",
    "\n",
    "Use these functions to create the GUI components in any cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sliders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_size_slider(imgDisplaySize=imgDisplaySize):\n",
    "    \"\"\"\n",
    "    Create image size adjust slider\n",
    "    @parameters:\n",
    "        imgDisplaySize[float]: zoom image displayed size\n",
    "    @return[widgets slider]: return the slize adjustment slider \n",
    "    \"\"\"\n",
    "\n",
    "    style = {'description_width': 'initial'}\n",
    "    size_slider = widgets.FloatSlider(\n",
    "        min=0,\n",
    "        max=1,\n",
    "        step=0.01,\n",
    "        value=imgDisplaySize,\n",
    "        description='Adjust image size',\n",
    "        style=style,\n",
    "        continuous_update=False,\n",
    "        readout_format='.5f',\n",
    "    )\n",
    "    size_slider.style.handle_color = 'lightblue'\n",
    "    return size_slider"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_checkbox(ax):\n",
    "    \"\"\"\n",
    "    @return: create the checkbox to toggle the images and titles(image index in the dataframe)  \n",
    "    \"\"\"\n",
    "    title_checkbox = widgets.Checkbox(False,\n",
    "                                      description='Toggle Titles',\n",
    "                                      indent=False,\n",
    "                                      layout=Layout(width='20%',\n",
    "                                                    height='20px'))\n",
    "    image_checkbox = widgets.Checkbox(True,\n",
    "                                      description='Toggle Images',\n",
    "                                      indent=False,\n",
    "                                      layout=Layout(width='20%',\n",
    "                                                    height='20px'))\n",
    "\n",
    "    def title_check_clicked(x):\n",
    "        image = image_checkbox.value\n",
    "        draw_plot(ax, df_2D, x, image, imgSize=size_slider.value)\n",
    "\n",
    "    interact(title_check_clicked, x=title_checkbox)\n",
    "\n",
    "    def image_clicked(x):\n",
    "        title = title_checkbox.value\n",
    "        draw_plot(ax, df_2D, title, x, imgSize=size_slider.value)\n",
    "\n",
    "    interact(image_clicked, x=image_checkbox)\n",
    "    return title_checkbox, image_checkbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Buttons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_size_slider_button(size_slider, ax):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        size_slider: \n",
    "        ax:\n",
    "    @return[button]: return image size adjust button\n",
    "    \"\"\"\n",
    "    size_apply_button = widgets.Button(\n",
    "        description='Apply Slider Size',\n",
    "        style=ButtonStyle(button_color='lightblue'))\n",
    "\n",
    "    def size_slider_button_clicked(change):\n",
    "        toggle_image = hasattr(plot_ax.dragpoint.artists[0], 'ab')\n",
    "        toggle_tittle = ax.dragpoint.artists[0].text.get_text() != ''\n",
    "        draw_plot(ax,\n",
    "                  df_2D,\n",
    "                  toggle_tittle,\n",
    "                  toggle_image,\n",
    "                  imgSize=size_slider.value)\n",
    "\n",
    "    size_apply_button.on_click(size_slider_button_clicked)\n",
    "    return size_apply_button\n",
    "\n",
    "\n",
    "## reset button\n",
    "def create_reset_buttons(ax, sliders=None):\n",
    "    \"\"\"\n",
    "    @return[button]: return reset plot button  \n",
    "    \"\"\"\n",
    "    reset_button = widgets.Button(description='Reset Plot',\n",
    "                                  style=ButtonStyle(button_color='salmon'))\n",
    "\n",
    "    def reset_button_clicked(change):\n",
    "        if sliders:\n",
    "            for s in sliders:\n",
    "                s.value = init_weight \n",
    "        global weights, df_2D, normalized_df  # Update weights and df_2D globals\n",
    "        weights = pd.Series(init_weight,\n",
    "                            index=normalized_df.columns,\n",
    "                            name=\"Weight\")\n",
    "        df_2D = dimension_reduction(normalized_df, weights)\n",
    "        toggle_image = hasattr(plot_ax.dragpoint.artists[0], 'ab')\n",
    "        toggle_tittle = ax.dragpoint.artists[0].text.get_text() != ''\n",
    "        # Redraw the plot\n",
    "        draw_plot(ax,\n",
    "                  df_2D,\n",
    "                  toggle_tittle,\n",
    "                  toggle_image,\n",
    "                  imgSize=size_slider.value)\n",
    "\n",
    "    reset_button.on_click(reset_button_clicked)\n",
    "\n",
    "    return reset_button\n",
    "\n",
    "\n",
    "## Visual Back Prop button\n",
    "def create_visual_explainations(ax):\n",
    "    \"\"\"\n",
    "    @return[button]: return button and plot Visual Backprop map\n",
    "    \"\"\"\n",
    "\n",
    "    print_button = widgets.Button(description='Visual Explainations')\n",
    "    print_output = widgets.Output()\n",
    "\n",
    "    def transformer():\n",
    "        \"\"\"\n",
    "        transformer for image dataset, using the ImageNet mean and std since Resnet18 is pre-trained on ImageNet\n",
    "        \"\"\"\n",
    "        transformer = transforms.Compose([\n",
    "            transforms.Resize((224, 224)),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "        ])\n",
    "        return transformer\n",
    "\n",
    "    def plot_VBP_map(loader, model_bp):\n",
    "        if len(loader) > 0:\n",
    "            # set number of columns (use 3 to demonstrate the change)\n",
    "            ncols = 2\n",
    "            # calculate number of rows\n",
    "            nrows = len(loader)\n",
    "\n",
    "            plt.figure(figsize=(6, 10))\n",
    "            for i, img in zip(range(len(loader)), loader):\n",
    "                with torch.no_grad():\n",
    "                    x, vis, feature_map = model_bp(img)\n",
    "                vis = vis[0].numpy().transpose(1, 2, 0)[:, :, 0]\n",
    "                vis = np.interp(vis, [vis.min(), vis.max()], [0, 1])\n",
    "                img = img[0].numpy().transpose(1, 2, 0)\n",
    "                img = np.interp(img, [img.min(), img.max()], [0, 1])\n",
    "                plt.subplot(nrows, ncols, 2 * i + 1)\n",
    "                plt.imshow(img, interpolation='bilinear')\n",
    "                plt.axis('off')\n",
    "                plt.subplot(nrows, ncols, 2 * (i + 1))\n",
    "                plt.imshow(vis, cmap='viridis', interpolation='bilinear')\n",
    "                plt.axis('off')\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "        else:\n",
    "            print('Select points in the plot to see details here')\n",
    "\n",
    "    def feature_button_clicked(change):\n",
    "        print_output.clear_output()\n",
    "        # Get list of selected points and print their source data values\n",
    "        c_index = [c.index for c in ax.circles if c.selected]\n",
    "        s_index = list(df.iloc[c_index, :].index)\n",
    "        weights_list = torch.tensor(np.float32(weights))\n",
    "        files = []\n",
    "        for i in s_index:\n",
    "            files.append(imageIndex_path_dict[i])\n",
    "\n",
    "        ## this transformer is for pre-trained model on ImageNet dataset\n",
    "        transform = transformer()\n",
    "        ds = VBP.FilenameDataset(files, transform)\n",
    "        loader = DataLoader(ds)\n",
    "        global model_bp\n",
    "        with print_output:\n",
    "            plot_VBP_map(loader, model_bp)\n",
    "\n",
    "    print_button.on_click(feature_button_clicked)\n",
    "    display(print_button)\n",
    "    display(print_output)\n",
    "    return print_button, print_output\n",
    "\n",
    "\n",
    "## inverse DR button\n",
    "def create_inverse_button(ax, fig_show=False):\n",
    "    \"\"\"\n",
    "    @return[button]: return 'Learn New Weights' and 'Update Projections' buttons\n",
    "    \"\"\"\n",
    "    inverse_button = widgets.Button(\n",
    "        description='Learn New Weights',\n",
    "        style=ButtonStyle(button_color='darkseagreen'))\n",
    "    copy_button = widgets.Button(\n",
    "        description='Update Projections',\n",
    "        style=ButtonStyle(button_color='darkseagreen'))\n",
    "\n",
    "    def inverse_button_clicked(change):\n",
    "        # Check minimum number of points moved\n",
    "        n = sum([i.selected for i in ax.dragpoint.artists])\n",
    "        if n < 2:\n",
    "            print(\n",
    "                'Need to select or move at least 2 points in the plot first.')\n",
    "            return\n",
    "\n",
    "        # Get selected data points\n",
    "        data2Dnew = pd.DataFrame(\n",
    "            [c.center for c in ax.dragpoint.artists if c.selected],\n",
    "            columns=['x', 'y'],\n",
    "            index=[c.label for c in ax.dragpoint.artists if c.selected])\n",
    "        global normalized_df\n",
    "        dataHDpart = normalized_df.loc[data2Dnew.index]\n",
    "\n",
    "        # Learn new weights\n",
    "        global weights\n",
    "        weights = inverse_DR(dataHDpart, data2Dnew)\n",
    "\n",
    "    inverse_button.on_click(inverse_button_clicked)\n",
    "\n",
    "    def copy_button_clicked(change):\n",
    "\n",
    "        global df_2D, size_slider, normalized_df\n",
    "\n",
    "        df_2D = dimension_reduction(normalized_df, weights)\n",
    "        toggle_image = hasattr(plot_ax.dragpoint.artists[0], 'ab')\n",
    "        toggle_tittle = ax.dragpoint.artists[0].text.get_text() != ''\n",
    "\n",
    "        draw_plot(ax, df_2D, toggle_tittle, toggle_image, size_slider.value)\n",
    "\n",
    "    copy_button.on_click(copy_button_clicked)\n",
    "\n",
    "    if fig_show:\n",
    "        fig, ax = plt.subplots(\n",
    "            figsize=(5, 7))  # reserve a fig for the weights bar chart\n",
    "        weights.sort_index(ascending=False).plot.barh(ax=ax1)\n",
    "        ax1.set_xlabel(\"Weight\")\n",
    "        fig.tight_layout()\n",
    "\n",
    "    return inverse_button, copy_button"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Draggable Dimension-Reduction 2D Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handles mouse drag interaction events in the plot, users can select and drag points.\n",
    "class DraggablePoints(object):\n",
    "    def __init__(self, ax, artists):\n",
    "        self.ax = ax\n",
    "        self.artists = artists\n",
    "        self.current_artist = None\n",
    "        self.last_selected = None\n",
    "        ax.selected_text.set_text('Selected: none')\n",
    "        self.offset = (0, 0)\n",
    "        # Set up mouse listeners\n",
    "        ax.figure.canvas.mpl_connect('pick_event', self.on_pick)\n",
    "        ax.figure.canvas.mpl_connect('motion_notify_event', self.on_motion)\n",
    "        ax.figure.canvas.mpl_connect('button_release_event', self.on_release)\n",
    "\n",
    "    def on_pick(self, event):\n",
    "        # When point is clicked on (mouse down), select it and start the drag\n",
    "        if self.current_artist is None:  # clicking on overlapped points sends multiple events\n",
    "            self.last_selected = event.artist.index  # event.ind\n",
    "            self.current_artist = event.artist\n",
    "            event.artist.selected = True\n",
    "            event.artist.savecolor = event.artist.get_facecolor()\n",
    "            event.artist.set_facecolor('green')\n",
    "            #event.artist.set_alpha(1.0)\n",
    "            self.ax.selected_text.set_text(\"Selected: \" + event.artist.label)\n",
    "            x0, y0 = event.artist.center\n",
    "            self.offset = (x0 -\n",
    "                           event.mouseevent.xdata), (y0 -\n",
    "                                                     event.mouseevent.ydata)\n",
    "\n",
    "    def on_motion(self, event):\n",
    "        # When dragging, check if point is selected and valid mouse coordinates\n",
    "        if (self.current_artist\n",
    "                is not None) and (event.xdata is not None) and (event.ydata\n",
    "                                                                is not None):\n",
    "            # Drag the point and its text label\n",
    "            dx, dy = self.offset\n",
    "            self.current_artist.center = x0, y0 = event.xdata + dx, event.ydata + dy\n",
    "            self.current_artist.text.set_position(\n",
    "                (x0 + self.current_artist.radius, y0))\n",
    "            if self.current_artist.ab:\n",
    "                self.current_artist.ab.xybox = (x0, y0)\n",
    "\n",
    "    def on_release(self, event):\n",
    "        # When mouse is released, stop the drag\n",
    "        self.current_artist = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def create_plot(data2D, title=False):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        data2D[pd.df or np.array]: projected 2D data\n",
    "        title[boolean]: toggle tittle or not \n",
    "    @return[AxesSubplot]: plotting axes\n",
    "    \"\"\"\n",
    "    # Initialize DR plot figure\n",
    "    fig, ax = plt.subplots(figsize=(10, 10), dpi=80)\n",
    "    ax.selected_text = ax.figure.text(0,\n",
    "                                      0.005,\n",
    "                                      'Selected: none',\n",
    "                                      wrap=True,\n",
    "                                      color='green')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.figure.tight_layout()\n",
    "\n",
    "    # Store state data:\n",
    "    ax.dragpoint = None\n",
    "    draw_plot(ax, data2D, title)\n",
    "\n",
    "    return ax\n",
    "\n",
    "\n",
    "def image_preprocessing(imgIndex):\n",
    "    \"\"\"\n",
    "    segment images if background is black\n",
    "    @parameters:\n",
    "        imgIndex[str]: image index\n",
    "    @return[np.array(image)]: processed image\n",
    "    \"\"\"\n",
    "\n",
    "    global imageIndex_path_dict\n",
    "    path = imageIndex_path_dict[imgIndex]\n",
    "    up_width = 224\n",
    "    up_height = 224\n",
    "    up_points = (up_width, up_height)\n",
    "    src = cv2.imread(path, 1)\n",
    "    src = cv2.resize(src, up_points, interpolation=cv2.INTER_LINEAR)\n",
    "    tmp = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\n",
    "    _, alpha = cv2.threshold(tmp, 0, 255, cv2.THRESH_BINARY)\n",
    "    b, g, r = cv2.split(src)\n",
    "    rgba = [r, g, b, alpha]\n",
    "    processed_img = cv2.merge(rgba, 4)\n",
    "\n",
    "    return processed_img\n",
    "\n",
    "\n",
    "def draw_plot(ax, data2D, title=False, image=True, imgSize=imgDisplaySize):\n",
    "    \"\"\"\n",
    "    @parameters:\n",
    "        ax[AxesSubplot]: plot ax\n",
    "        data2D[dataframe]: projected 2D dataframe\n",
    "        title[boolean]: whether toggle title or not\n",
    "        image[boolean]: whether toggle image or not\n",
    "        imgSize[float]: zoom parameter of OffsetImage, determine size of displaying images\n",
    "    \"\"\"\n",
    "    labels_df = data2D.index.to_series().apply(lambda x: x.split('/')[0])\n",
    "    data2D['label'] = labels_df\n",
    "    label_list = list(set(data2D['label']))\n",
    "    conversion_dict = {\n",
    "        k: v\n",
    "        for k, v in zip(label_list, range(len(label_list)))\n",
    "    }\n",
    "    data2D['label_num'] = data2D['label'].replace(conversion_dict)\n",
    "    ax.clear()\n",
    "    wid = max(data2D.x.max() - data2D.x.min(),\n",
    "              data2D.y.max() - data2D.y.min())  # max range of x,y axes\n",
    "    if not image:\n",
    "        cnorm = mpl.colors.Normalize(vmin=data2D.label_num.min(),\n",
    "                                     vmax=data2D.label_num.max())\n",
    "\n",
    "        ax.circles = data2D.apply(axis=1,\n",
    "                                  func=lambda row: mpl.patches.Circle(\n",
    "                                      xy=(row.x, row.y),\n",
    "                                      radius=wid / 70,\n",
    "                                      alpha=0.95,\n",
    "                                      label=row.label_num,\n",
    "                                      color=plt.cm.Set3(cnorm(row.label_num)),\n",
    "                                      picker=True))\n",
    "        for i, c in enumerate(ax.circles):\n",
    "            # Store state data:\n",
    "            c.index, c.label, c.selected = i, data2D.index[i], False\n",
    "            # Draw circles and text labels in plot\n",
    "            ax.add_patch(c)\n",
    "            if title:\n",
    "                c.text = ax.text(c.center[0] + c.radius,\n",
    "                                 c.center[1],\n",
    "                                 c.label,\n",
    "                                 color='black')\n",
    "            else:\n",
    "                c.text = ax.text(c.center[0] + c.radius,\n",
    "                                 c.center[1],\n",
    "                                 \"\",\n",
    "                                 color='none')\n",
    "    else:\n",
    "        ax.circles = []\n",
    "        for x0, y0, index in zip(data2D.x, data2D.y, data2D.index):\n",
    "            ax.circles.append(\n",
    "                mpl.patches.Circle(xy=(x0, y0),\n",
    "                                   radius=wid * imgSize / 3,\n",
    "                                   alpha=0.5,\n",
    "                                   color='none',\n",
    "                                   picker=True))\n",
    "\n",
    "        for i, c in enumerate(ax.circles):\n",
    "            # Store state data:\n",
    "            c.index, c.label, c.selected = i, data2D.index[i], False\n",
    "            # Draw circles and text labels in plot\n",
    "            ax.add_patch(c)\n",
    "            processed_img = image_preprocessing(c.label)\n",
    "            img = OffsetImage(processed_img, zoom=imgSize)\n",
    "            c.ab = AnnotationBbox(img, (c.center[0], c.center[1]),\n",
    "                                  frameon=False)\n",
    "            ax.add_artist(c.ab)\n",
    "            if title:\n",
    "                c.text = ax.text(c.center[0] + c.radius,\n",
    "                                 c.center[1],\n",
    "                                 c.label,\n",
    "                                 color='black')\n",
    "            else:\n",
    "                c.text = ax.text(c.center[0] + c.radius,\n",
    "                                 c.center[1],\n",
    "                                 \"\",\n",
    "                                 color='none')\n",
    "\n",
    "    # Make plot circles draggable\n",
    "    ax.dragpoint = DraggablePoints(ax, ax.circles)\n",
    "    # Clean up the plot\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.axis('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimension Reduction Plot\n",
    "This shows the HD data in 2D form, such that **proximity == similarity**, based on the current slider weights.  Distances between points in the plot approximately reflect their distances in the weighted HD data.  Thus points near each other have similar HD data values in the up-weighted dimensions, and points far away have very different HD data values in those dimensions.\n",
    "\n",
    "Points can be **selected** to highlight in Green and view their details below.  Points can be **dragged** to specify a new projection for learning weights, see below. To reset the plot and clear the selections, click the **Reset** button above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inverse Dimension Reduction\n",
    "After selecting and/or dragging some points in the DR plot, click the **Learn** button to machine learn new dimension weights that would produce a plot with similar pairwise distances as your plot.  **Only the Green selected points** in the plot are considered when learning new weights. You must select or move at least two points to specify new desired distances. Use this to create your own clusters, and find out what makes some data points similar to or different from others.  The **learned weights** are shown in a bar chart below.\n",
    "\n",
    "To see the effects of the learned weights, click the **Copy** button to apply the learned weights to the sliders and  make a new DR plot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cell_style": "center",
    "scrolled": false
   },
   "outputs": [
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       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c51aefecf354c03940b06cfd9bebb95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatSlider(value=0.4, continuous_update=False, description='Adjust image size', max=1.0, reado…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a071df46a2e4431a8b003b38b348c7d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Button(description='Learn New Weights', style=ButtonStyle(button_color='darkseagreen')), Button…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ax = create_plot(df_2D)\n",
    "size_slider = create_size_slider()\n",
    "size_apply_button = create_size_slider_button(size_slider, plot_ax)\n",
    "learn_new_weights_button, update_projections_button = create_inverse_button(\n",
    "    plot_ax)\n",
    "display(widgets.HBox([size_slider, size_apply_button]))\n",
    "display(\n",
    "    widgets.HBox([\n",
    "        learn_new_weights_button, update_projections_button,\n",
    "        create_reset_buttons(plot_ax)\n",
    "    ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1719a09f16c94c36948fa3de6b82625b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Checkbox(value=False, description='Toggle Titles', indent=False, layout=Layout(height='2…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28dcf319e64b461d8bb18fc8cf7e1f39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Checkbox(value=True, description='Toggle Images', indent=False, layout=Layout(height='20…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "point_attribute = create_checkbox(plot_ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Image Saliency Map\n",
    "Click the **Visual Explainations** button below to display the weighted mask captured by the underlying model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b926a77c3eb445e92c8387e3a7b9764",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Visual Explainations', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75b930a3dfee4cadbf681c70edf0c753",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attention = create_visual_explainations(plot_ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "045fa8eb92b84f4e9976ee9ed11ebab8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Visual Explainations', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fe7727d07ed4a8f8ba4f8f84f9f2df4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attention = create_visual_explainations(plot_ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sampler(data_2D, n_sample):\n",
    "    print('Initial s score is: ',\n",
    "          silhouette_score(data_2D[['x', 'y']], data_2D['label']))\n",
    "    df_sample = data_2D.copy()\n",
    "    df_sample['label'] = df_sample.index.str.split('/').str[:1].str.join('')\n",
    "    selected_points = df_sample.groupby('label').apply(\n",
    "        lambda group: group.sample(n_sample))\n",
    "    selected_points.index = selected_points.index.droplevel(0)\n",
    "    return selected_points\n",
    "\n",
    "\n",
    "def InteractionSimulator(sample_data_2D):\n",
    "    data_HD = normalized_df.loc[sample_data_2D.index]\n",
    "    data_2D = pd.get_dummies(sample_data_2D['label'])\n",
    "    #     display(data_2D)\n",
    "    #     display(pd.DataFrame(distance_matrix_2D(data_2D)*100,index=data_2D.index,columns=data_2D.index,dtype=float))\n",
    "    wts = inverse_DR(data_HD, data_2D)\n",
    "    return wts\n",
    "\n",
    "\n",
    "def EvaluationSimulator(n_control_points, output_file_mark=\"test\"):\n",
    "    s_score_list = []\n",
    "    wts_list = []\n",
    "    for i in tqdm(range(1, n_control_points + 1)):\n",
    "        sample_df = Sampler(df_2D, n_sample=i)\n",
    "        wts = InteractionSimulator(sample_df)\n",
    "        wts.to_csv('./WeightsDirectory/' + str(output_file_mark) +\n",
    "                   '_iteration_' + str(i) + '.csv',\n",
    "                   header=True)\n",
    "        updated_df_2D = dimension_reduction(normalized_df, weights)\n",
    "        updated_df_2D['label'] = updated_df_2D.index.str.split(\n",
    "            '/').str[:1].str.join('')\n",
    "        s_score = silhouette_score(updated_df_2D[['x', 'y']],\n",
    "                                   updated_df_2D['label'])\n",
    "        print(\"Updated s score after dragging: \", s_score)\n",
    "        s_score_list.append(s_score)\n",
    "        wts_list.append(wts)\n",
    "    return s_score_list, wts_list\n",
    "\n",
    "\n",
    "def create_load_weights_from_file(n_control_points, output_file_mark='animal'):\n",
    "    load_button = widgets.Button(description=\"Load weights from file\")\n",
    "\n",
    "    def apply_button_clicked(change):\n",
    "        # Use the slider values to re compute the DR and redraw the plot\n",
    "        global weights, df_2D, imageIndex_path_dict\n",
    "        paths = imageIndex_path_dict.values()\n",
    "        weights = pd.read_csv('./WeightsDirectory/' + str(output_file_mark) +\n",
    "                              '_iteration_' + str(n_control_points) +\n",
    "                              '.csv')['Weight']\n",
    "        weights.index = normalized_df.columns\n",
    "        df_2D = dimension_reduction(normalized_df, weights)\n",
    "        draw_plot(plot_ax, df_2D)\n",
    "\n",
    "    load_button.on_click(apply_button_clicked)\n",
    "    display(load_button)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## the following code is for simulation engine\n",
    "#EvaluationSimulator(3, 'animal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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