ee046202_tutorial_06_dim_reduction_tsne.ipynb

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    "# <img src=\"https://img.icons8.com/dusk/64/000000/artificial-intelligence.png\" style=\"height:50px;display:inline\"> EE 046202 - Technion - Unsupervised Learning & Data Analysis\n",
    "---\n",
    "\n",
    "#### <a href=\"https://taldatech.github.io\">Tal Daniel</a>\n",
    "\n",
    "## Tutorial 06 - Dimensionality Reduction - t-SNE\n",
    "---"
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    "### <img src=\"https://img.icons8.com/bubbles/50/000000/checklist.png\" style=\"height:50px;display:inline\"> Agenda\n",
    "---\n",
    "* [Motivation and Introduction](#-Motivation-&-Introduction)\n",
    "* [Stochastic Neighbor Embedding (SNE)](#-Stochstic-Neighbor-Embedding-(SNE))\n",
    "* [t-Distributed Stochastic Neighbor Embedding (t-SNE)](#-t-Distributed-Stochastic-Neighbor-Embedding-(t-SNE))\n",
    "    * [Symetric SNE](#-Symmetric-SNE)\n",
    "    * [The Crowding Problem](#-The-Crowding-Problem)\n",
    "    * [Student t-distribution](#-Student-t-Distribution---Mismatched-Tails-can-Compensate-for-Mismatched-Dimensionalities)\n",
    "    * [Algorithm and Optimization](#-t-SNE-Optimization-&-Algorithm)\n",
    "    * [Weaknesses](#-t-SNE-Weaknesses)\n",
    "* [Examples](#-Example---The-Wisconsin-Breast-Cancer-Dataset)\n",
    "* [Recommended Videos](#-Recommended-Videos)\n",
    "* [Credits](#-Credits)"
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    "# imports for the tutorial\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.stats import t\n",
    "%matplotlib notebook"
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    "### <img src=\"https://img.icons8.com/cotton/50/000000/party-baloons.png\" style=\"height:50px;display:inline\"> Motivation & Introduction\n",
    "---\n",
    "* We want to have a dimensionality reduction method that may help us understand the data, and there is no better way to \"explain\" data than seeing it visually!\n",
    "* **t-SNE** is a method to visualize *high-dimensional* data, which is easier to **optimize** than many other methods.\n",
    "* One of its most important traits is that it mitigates **the crowding problem** which we will discuss, but many other dimensionality reduction algorithms suffer from.\n",
    "    * In short, it is the tendency of dimensionality reduction algorithms to crowd points together in the center of the map."
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    "* It looks really (!) good. Here, have a look at the result on the MNIST dataset (digits, 0-9): <img src=\"./assets/tut_xx_tsne_mnist.png\" style=\"height:350px\">"
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    "* The goal of dimensionality reduction is to **preserve as much of the significant structure of the high-dimensional data as possible in the low-dimensional map**.\n",
    "    * For example, recall that **PCA** is a *linear* method that focuses on keeping the low-dimensional representations of dissimilar points far apart. It is very common to *chain* together PCA and t-SNE to achieve good results (first perform PCA and then apply t-SNE).\n",
    "    * On the other hand, high-dimensional data that lies *on* or *near* a low-dimensional, **non-linear manifold**, it is usually more important to keep the low-dimensional representations of very similar datapoints close together, which is usually **not possible with linear mapping**."
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    "* So what is the problem with other techniques like **Sammon, SNE, Isomap, LLE, Laplacian Eigenmaps** and etc...?\n",
    "    * Despite performing really well on *artificial* datasets, they are often not very successful at visualizing real, high-dimensional data.\n",
    "    * Most of them are not capable of retaining both the **local** and the **global** structure of the data in a single map (while t-SNE does well!).\n",
    "* t-SNE is based (how surprising...) on SNE which we introduce shortly."
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    "* Formulation:\n",
    "    * High-dimensional dataset: $\\mathcal{X} = \\{x_1, x_2, ..., x_n\\} $ (total of $n$ points)\n",
    "    * Low-dimensional dataset: $\\mathcal{Y} = \\{y_1, y_2, ..., y_n\\} $\n",
    "    * The goal is to find mapping $\\mathcal{X} \\rightarrow \\mathcal{Y}$"
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    "* Not convinced yet? look at this: <img src=\"./assets/tut_xx_mnist_anim.gif\" style=\"height:300px\">\n",
    "    * Source: <a href=\"https://ai.googleblog.com/2018/06/realtime-tsne-visualizations-with.html\">Google AI Blog</a>"
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    "### <img src=\"https://img.icons8.com/dusk/64/000000/ruler.png\" style=\"height:50px;display:inline\"> Stochastic Neighbor Embedding (SNE)\n",
    "---\n",
    "We will now introduce the base algorithm of t-SNE, called Stochastic Neighbor Embedding (SNE), and present how it works.\n",
    "\n",
    "* SNE starts by converting Euclidean distances between datapoints into **conditional probabilities** that represent similarities.\n",
    "    * The **similarity** of datapoint $x_j$ to datapoint $x_i$ is denoted $p_{j|i}$, that is, the conditional probability that $x_i$ would pick $x_j$ as its neighbor, *if* neighbors were picked in proportion to their probability density under a **Gaussian** centered (its mean) at $x_i$.\n",
    "    * As expected, for nearby datapoints this probability would be **high** whereas for widely separated datapoints it would be very **small**."
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    "* Mathematically: $$ p_{j|i} = \\frac{e^{-|| x_i - x_j ||^2 / 2\\sigma_i^2}}{\\sum_{k \\neq i} e^{-|| x_i - x_k ||^2 / 2\\sigma_i^2}} $$\n",
    "    * $\\sigma_i$ is the variance of the Gaussian that is centererd on datapoint $x_i$\n",
    "    * $p_{i|i} = 0$, as we are interested only in the pairwise relation.\n",
    "* Similarly, for the **low-dimensional** counterparts, $y_i$ and $y_j$ we denote the conditional probability: $$q_{j|i} = \\frac{e^{-|| y_i - y_j ||^2}}{\\sum_{k \\neq i} e^{-|| y_i - y_k ||^2 }} $$\n",
    "    * We set the *variance* of the Gaussian to $\\frac{1}{\\sqrt{2}}$ which results in $2\\cdot (\\frac{1}{\\sqrt{2}})^2 = 1$ (in case you were wondering where did the $\\sigma$ go).\n",
    "        * Why $\\frac{1}{\\sqrt{2}}$ ? setting to another value only changes the scale in the final map. Using the **same variance** for each datapoint we lose some property of the data (as it has $\\sigma_i$ in the high-dimensional space).\n",
    "        * As before, $q_{i|i} = 0 $\n",
    "    * **Intuition** - if the mapping $\\mathcal{X} \\rightarrow \\mathcal{Y}$ correctly models the similarity between datapoints, then $p_{j|i}$ and $q_{j|i}$ will be equal."
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    "* **Choosing** $\\sigma_i$:\n",
    "    * In **dense** regions - a smaller value of $\\sigma_i$ is usually more appropriate than in **sparser** regions.\n",
    "    * SNE performs **binary search** for the value of $\\sigma_i$ that produces a $P_i$ with a *fixed perplexity* that is specified by the *user*. The perplexity is defined as: $$ Perp(P_i) = 2^{H(P_i)} $$\n",
    "        * $H(P_i)$ is the **Shannon entropy** of $P_i$ measured in bits $$ H(P_i) = -\\sum_j p_{j|i}\\log_2 p_{j|i} $$\n",
    "            * Intuition for perplexity - **a smooth measure of the effective number of neighbors**.\n",
    "            * Typical values are between 5 and 50 (SNE is fairly robust for this hyper-parameter)."
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    "* The **goal** of SNE is to find a mapping that minimizes the mismatches between $p_{j|i}$ and $q_{j|i}$.\n",
    "    * How do we measure \"distance between probabilities\"? As you probably have guessed, **KL-divergence** comes to the rescue!\n",
    "* SNE minimizes the **sum of Kullback-Leibler** divergences over all datapoints using a **gradient descent method**. We denote the cost function $C$: $$ C = \\sum_i KL(P_i || Q_i) = \\sum_i \\sum_{j\\neq i} p_{j|i} \\log \\frac{p_{j|i}}{q_{j|i}} $$\n",
    "    * $P_i$ - the conditional probability distribution over all other *datapoints* given datapoint $x_i$.\n",
    "    * $Q_i$ - the conditional probability distribution over all other *map points* given point $y_i$."
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    "* KL-divergence is **not a metric** as it is **not symmetric**, and many errors are due to the fact the distances are not weighted equally.\n",
    "    * There is a **large cost** for using **widely separated map points** to represent nearby datapoints (using a small $q_{j|i}$ to model a large $p_{j|i}$), but only a **small cost** for using **nearby map points** to represent widely separated datapoints (using a large $q_{j|i}$ to model a small $p_{j|i}$).\n",
    "        * In other words, SNE's lost function focuses on retaining the **local** structure of the data in the map (which is made possible thanks to this asymmetry)."
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    "#### <img src=\"https://img.icons8.com/dusk/64/000000/task.png\" style=\"height:30px;display:inline\"> Exercise - Gradient of SNE's Cost Function\n",
    "---\n",
    "The minimization of the cost function $C$ is performed using gradient descent. Calculate the gradient of $C$ w.r.t. $y_i$."
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    "#### <img src=\"https://img.icons8.com/dusk/64/000000/idea.png\" style=\"height:30px;display:inline\"> Solution\n",
    "---\n",
    "The derivative calculation is not so trivial, so we will break it up to parts:\n",
    "* We denote the following:\n",
    "$$ q_{j|i} = \\frac{e^{-|| y_i - y_j||^2}}{\\sum_{k \\neq i}e^{-|| y_i - y_k||^2}} = \\frac{E_{ij}}{\\sum_{k \\neq i} E_{ik}} =  \\frac{E_{ij}}{Z_i} $$\n",
    "    * Notice that $E_{ij} = E_{ji}$ (because of the $||\\cdot||^2$)\n",
    "* We express $C$ as follows: $$ C = \\sum_{i, j \\neq i}p_{j|i} \\log \\frac{p_{j|i}}{q_{j|i}} =\\sum_{i, j \\neq i}p_{j|i}\\log p_{j|i} - p_{j|i} \\log q_{j|i} =  \\sum_{i, j \\neq i}p_{j|i}\\log p_{j|i} - p_{j|i} \\log E_{ij} + p_{j|i} \\log Z_i $$\n",
    "    * Notice that we got 3 expressions, and notice that the first one ($p_{j|i}\\log p_{j|i}$) **does not depend on** $y_i$ and thus we can work only on the other two.\n",
    "    \n",
    "We will now derive the derivative for the 2 parts separately and we **change the indices from $i,j$ to $k, l$ to avoid confusion**."
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    "#### The Second Term\n",
    "---\n",
    "The second term: $$\\frac{\\partial}{\\partial y_i}\\sum_{k, l \\neq k}- p_{l|k} \\log E_{kl} $$\n",
    "* Now we ask: when is this derivative non-zero? **Only** when $k = i$ or $l =i$\n",
    "$$=\\sum_{k}- p_{k|i}\\frac{\\partial}{\\partial y_i} \\log E_{ik} - p_{i|k}\\frac{\\partial}{\\partial y_i} \\log E_{ki}$$\n",
    "* $\\frac{\\partial}{\\partial y_i} E_{ik} = E_{ik} \\cdot (-2(y_i - y_k)) \\rightarrow \\frac{\\partial}{\\partial y_i} \\log E_{ik} = \\frac{E_{ik} \\cdot (-2(y_i - y_k))}{E_{ik}} = -2(y_i - y_k) $\n",
    "$$ \\rightarrow  \\sum_{k}- p_{k|i}\\frac{\\partial}{\\partial y_i} \\log E_{ik} - p_{i|k}\\frac{\\partial}{\\partial y_i} \\log E_{ki} = $$ $$ \\sum_k -p_{k|i}(-2(y_i - y_k)) -p_{i|k} \\cdot 2 (y_k -y_i) = 2 \\sum_k (p_{k|i} + p_{i|k}) (y_i - y_k) $$"
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    "#### The Third Term\n",
    "---\n",
    "The third term: $$ \\frac{\\partial}{\\partial y_i}\\sum_{k, l \\neq k} p_{l|k} \\log Z_k $$\n",
    "* Notice that:\n",
    "    * $\\sum_{l \\neq k} p_{l|k} = 1$ (as we modeled it as probability, it is not suprising)\n",
    "    * $Z_k$ **does not depend** on $l$ (in the $\\sum$ operation), thus we can write the following:\n",
    "$$ \\frac{\\partial}{\\partial y_i}\\sum_{k, l \\neq k} p_{l|k} \\log Z_k = \\sum_{k, l \\neq k} p_{l|k} \\frac{\\partial}{\\partial y_i} \\log Z_k = \\sum_k \\frac{\\partial}{\\partial y_i} \\log Z_k \\sum_{l \\neq k} p_{l|k} =  \\sum_k \\frac{\\partial}{\\partial y_i} \\log Z_k$$"
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    "* Now we ask: when is this derivative non-zero? **Only** when $k = i$ or $m =i$. ($Z_k = \\sum_{m \\neq k} E_{km}$)\n",
    "$$ \\rightarrow \\sum_k \\frac{\\partial}{\\partial y_i} \\log Z_k = \\sum_k \\frac{1}{Z_k}\\sum_{m \\neq k} \\frac{\\partial}{\\partial y_i} E_{km} $$ $$ =\\sum_k \\frac{E_{ki}\\cdot(2(y_k - y_i))}{Z_k} + \\sum_k \\frac{E_{ik}(-2(y_i - y_k))}{Z_i} $$ $$ = 2\\sum_k(-q_{k|i} - q_{i|k})(y_i - y_k) $$"
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    "Changing the index $k \\rightarrow j$, we get in total:\n",
    "$$ \\frac{\\partial C}{\\partial y_i} = 2 \\sum_{j}(p_{j|i} - q_{j|i} + p_{i|j} - q_{i|j})(y_i - y_j) $$"
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    "* Physics intuition: the expression of the gradient may be interpreted as the resultant force created by a set of springs between the map point $y_i$ and all other map points $y_j$. The *direction* of the force is $(y_i -y_j)$. The spring between $y_i$ and $y_j$ repels or attracts the map points depending on whether the distance between the in the map is too small or too large to represent the similarities between the two high-dimensional points."
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    "#### <img src=\"https://img.icons8.com/dusk/64/000000/flow-chart.png\" style=\"height:30px;display:inline\"> SNE Algorithm Guidelines\n",
    "---\n",
    "* The **gradient descent** is initialized by sampling map points *randomly* from an isotropic Gaussian with small variance that is centered around the origin.\n",
    "* In order **speed up optimization and to avoid poor local minima**, a relatively large momentum term is added to the gradient.\n",
    "    * Or, the *current* gradient is added to an exponentially decaying sum of *previous gradient* in order to determine the changes in the coordinates of map points at each iteration of the gradient search.\n",
    "    $$ \\mathcal{Y}^{(t)} = \\mathcal{Y}^{(t-1)} + \\eta \\frac{\\partial C}{\\partial \\mathcal{Y}} + \\alpha(t) \\big( \\mathcal{Y}^{(t-1)} - \\mathcal{Y}^{(t-2)} \\big) $$\n",
    "    * $\\eta$ is the learning rate\n",
    "    * $\\alpha(t)$ is the momentum at iteration $t$"
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    "* The weakness of SNE is that in order to avoid **poor local minima**, Gaussian noise is added to the map points after each iteration, which requires some sensible tuning.\n",
    "    *  In case of high-dimensionality, far values are accumulated in a “block around”. This slippage is due to our willingness to adequately represent the neighbour’s structure.\n",
    "    \n",
    "* **The main problem with SNE**: although it constructs reasonably good visualizations, its cost function is rather difficult to optimize by a problem called **\"the crowding problem\"**. t-SNE aims to alleviate this problem. "
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    "* Results on MNIST:\n",
    "<img src=\"./assets/tut_xx_sne_mnist.png\" style=\"height:350px\">"
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    "### <img src=\"https://img.icons8.com/dusk/50/000000/t.png\" style=\"height:50px;display:inline\"> t-Distributed Stochastic Neighbor Embedding (t-SNE)\n",
    "---\n",
    "As mentioned, SNE's cost function is problematic and hard to optimize. t-SNE's cost function differs from the one used by SNE in 2 ways:\n",
    "1. **Symmetry** - it uses a symmetrized version of the SNE cost function with simpler gradients.\n",
    "2. **Student-t Distribution** - it uses a Student-t distribution instead of a Gaussian to compute the *similarity* between two points in the *low-dimensional space*. t-SNE employs a *heavy-tailed* distribution in the low-dimensional space to alleviate both the **crowding problem** and the **optimization** problems of SNE."
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    "* What is a **heavy-tailed distribution**?\n",
    "    * A heavy tailed distribution has a tail that’s heavier than an *exponential distribution*. In other words, a distribution that is heavy tailed goes to zero slower than one with heavy tails.\n",
    "    * Heavy tailed distributions tend to have many **outliers** with very high values. The heavier the tail, the larger the probability that you’ll get one or more disproportionate values in a sample."
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    "* <img src=\"./assets/tut_xx_heavy_tail.png\" style=\"height:300px\"> Source: <a href=\"https://www.statisticshowto.datasciencecentral.com/heavy-tailed-distribution/\">Data Science Central</a>"
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    "* Two important properties of heavy-tailed distributions:\n",
    "    * The Central Limit (**CLT**) Theorem doesn’t work.\n",
    "    * Some moments don’t exist, so order statistics are used instead.\n",
    "\n",
    "We will now perform in-depth analysis of t-SNE's characteristics and improvements over SNE."
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    "#### <img src=\"https://img.icons8.com/officel/80/000000/person-in-a-mirror.png\" style=\"height:30px;display:inline\"> Symmetric SNE\n",
    "---\n",
    "* Recall that in SNE we minimize the **sum of KL divergences** between the conditional probabilities $p_{j|i}$ and $q_{j|i}$. It is also possible to minimize a **single KL divergence** between a *joint probability distribution* $P$ in the high-dimensional space and a joint probability distribution $Q$ in the low-dimensional space: $$ C=KL(P||Q) = \\sum_i \\sum_{j \n",
    "\\neq i}p_{ij}\\log \\frac{p_{ij}}{q_{ij}} $$\n",
    "    * Again $p_{ii}=0$ and $q_{ii} =0$\n",
    "    * **Symmetry** - notice the property that $p_{ij} = p_{ji}, q_{ij}=q_{ji}$"
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    "* In symmetric SNE, the **pairwise similarities** in the *low-dimensional* map $q_{ij}$ are given by $$ q_{ij} = \\frac{e^{-|| y_i - y_j||^2}}{\\sum_k\\sum_{l \\neq k}e^{-||y_k-y_l||^2}} $$\n",
    "    * WAIT! How is this different from the expression for $q_{j|i}$???\n",
    "        * Look again, whereas the nominator is the same, the **denominator** is different, and includes a sum of all possible distances!\n",
    "    * Similarly, the pairwise similarities in the high-dimensional space: $$ p_{ij} = \\frac{e^{-|| x_i - x_j||^2 / 2\\sigma^2}}{\\sum_k\\sum_{l \\neq k}e^{-||x_k-x_l||^2 / 2\\sigma^2}} $$"
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    "#### <img src=\"https://img.icons8.com/dusk/64/000000/task.png\" style=\"height:30px;display:inline\"> Exercise - Symmetric SNE Weakness\n",
    "---\n",
    "Look again at the mapping in the low-dimensional and high-dimensional spaces. What will happen if the point in the high dimensional-space, $x_i$, is an **outlier** (i.e., all pairwise distances $||x_i - x_j||^2$ are large)?"
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    "#### <img src=\"https://img.icons8.com/dusk/64/000000/idea.png\" style=\"height:30px;display:inline\"> Solution\n",
    "---\n",
    "In that case, the values of $p_{ij}$ are extremely small for all $j$, so the location of its *low-dimensional* map point $y_i$ has very little effect on the cost function (remember the KL divegence form). As a result, the position of the map point is not well-determined by the positions of other map points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* So how do we solve this **outliers** problem? We redefine the joint probabilities $p_{ij}$ in the high-dimensional space to be the **symmetrized conditional probabilities**: $$ p_{ij} = \\frac{p_{j|i} + p_{i|j}}{2n} $$\n",
    "    * This ensures $\\sum_j p_{ij} > \\frac{1}{2n}$ for all datapoints $x_i$, and thus each datapoint $x_i$ makes a significant contribution to the cost function.\n",
    "    * The definition for $q_{ij}$ is **the same as before**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* The main advantage of the symmetric version of SNE is the **simpler form of its gradient**, which is faster (!) to compute: $$ \\frac{\\partial C}{\\partial y_i} = 4\\sum_j (p_{ij} -q_{ij})(y_i - y_j)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/clouds/100/000000/crowd.png\" style=\"height:30px;display:inline\"> The Crowding Problem\n",
    "---\n",
    "* **The Crowding Problem** - the area of low-dimensional (2D, 3D) map that is available to accommodate moderately (medium) distant datapoints will not be nearly large enough compared with the area available to accommodate nearby datapoints.\n",
    "    * Thus, if we wish to model the small distances accurately in the map, most of the points that are at a moderate distance from datapoint $i$ will have to be placed much too far away in the low-dimensional map.\n",
    "    * In SNE, the \"force\" that connects datapoint $i$ to each of these too-distant map points will be fairly small.\n",
    "        * The very large number of such forces collapses together the points in the center of the map and prevents gaps from forming between the natural clusters. \n",
    "    * The problem is not specific to SNE and also occurs in other local methods for dimensionality reduction such as Sammon mapping."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Imaginative explanation**: consider a set of datapoints that lie on a 2D curved manifold which is approximately linear on a small scale, and which is embedded within a higher-dimensional space. It is possible to model the small pairwise distances between datapoints fairly well in a 2D map (look at the \"Swiss Roll\" example). Now let's assume that the manifold has 10 intrinsic dimensions (that is, it can be represented with 10 variables, like the MNIST dataset which is comprised of images of 10 digits, so each digit can be represnted as a variable, e.g. a one-hot vector of size 10). There are several reasons why the pairwise distances in a 2D map cannot faithfuly model distances between poins on the ten-dimensional manifold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* For instance, in 10 dimensions, it is possible to have 11 datapoints that are evenly spaced and there is no way to model this faithfully in a 2 dimensions.\n",
    "    * <img src=\"./assets/tut_xx_crowding.png\" style=\"height:150px\">\n",
    "* There were attempts to solve this problem for SNE. The method is named UNI-SNE and it has a very complicated process of optimization. t-SNE also solves this problem as we will soon see."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/color/48/000000/tail-of-whale.png\" style=\"height:30px;display:inline\"> Student-t Distribution - Mismatched Tails can Compensate for Mismatched Dimensionalities\n",
    "---\n",
    "* Due to matching the joint probabilities of pairs of datapoints in the high-dimensional and low-dimensional spaces instead of their distances, there is a natural way to alleviate the **crowding problem** which is described in this section.\n",
    "* In the *high-dimensional* space we convert distances to probabilities using a **Gaussian** distribution. \n",
    "* In the *low-dimensional* map we can use a probability distribution that has much **heavier tails** than a Gaussian to convert distances into probabilities.\n",
    "    * **Why is this helpful?** This allows a moderate (medium) distance in the high-dimensional space to be faithfully modeled by a much **larger distance** in the map. As a result, it eliminates unwanted attractive forces between map points that represent moderately dissimilar datapoints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* In t-SNE a **Student-t** distribution with *one* degree of freedom (which turns it to a **Cauchy** distribution) is used as the heavy-tailed distribution in the low-dimensional map.\n",
    "* The joint probabilities $q_{ij}$ are defined as $$ q_{ij} = \\frac{(1 + || y_i-y_j ||^2)^{-1}}{\\sum_k \\sum_{l \\neq k}(1 + || y_k - y_l ||^2)^{-1}} $$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Why **one** degree of freedom? It has a nice property that $(1 + || y_i - y_j ||^2)^{-1}$ approaches an **inverse square law** for large pairwise distances $||y_i -y_j||$ in the low-dimensional map. This makes the map's representation of joint probabilities (almost) invariant to changes in the scale of the map for map points that are far apart.\n",
    "    * Also, it means that large clusters of points that are far apart interact the same way as individual points, so the optimization operates in the same way at all but the finest scales.\n",
    "* Theoretically, the selection of Student-t is justified due to the fact that it is closely related to the Gaussian distribution, as it is an **infinite mixture of Gaussians**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Illustration: <img src=\"./assets/tut_xx_student_t_anim.gif\" style=\"height:300px\"> Source: <a href=\"http://www.sumsar.net/blog/2013/12/t-as-a-mixture-of-normals/\">Rasmus Bååth's Research Blog </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Generally, Student-t proability density function (PDF) with $\\nu$ degrees of freedom is defined as: $$ f(x) = \\frac{\\Gamma(\\frac{\\nu + 1}{2})}{\\sqrt{\\nu \\pi} \\Gamma(\\frac{\\nu}{2})} (1+\\frac{x^2}{\\nu})^{-\\frac{\\nu + 1}{2}} $$\n",
    "    * $\\Gamma(z)$ is the Gamma function: $\\Gamma(z) = \\int_0^{\\infty}x^{z-1}e^{-x}dx $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# student-t example, look how it looks like a normal distribution\n",
    "# doc @ https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html\n",
    "\n",
    "def plot_student_t_example():\n",
    "    # calculate the moments\n",
    "    df = 2.74  # degrees of freedom, does not have to be discrete\n",
    "    df_1 = 1\n",
    "    mean, var, skew, kurt = t.stats(df, moments='mvsk')  \n",
    "    # press Shift+TAB inside the parenthesis to see argumnets description\n",
    "\n",
    "    # let's see the pdf\n",
    "    x = np.linspace(t.ppf(0.01, df), t.ppf(0.99, df), 100)  # ppf - inverse cdf at q of given r.v. (df)\n",
    "    x_1 = np.linspace(t.ppf(0.01, 1), t.ppf(0.99, 1), 100)\n",
    "\n",
    "    # plotting\n",
    "    fig = plt.figure(figsize=(8, 5))\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    ax.plot(x, t.pdf(x, df), 'r-', lw=3, alpha=0.6, label='t pdf, df=2.74')\n",
    "    ax.plot(x_1, t.pdf(x_1, df_1), 'b-', lw=3, alpha=0.6, label='t pdf, df=1')\n",
    "    ax.set_title(\"student-t pdf\")\n",
    "\n",
    "    ax.grid()\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_student_t_example()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/office/40/000000/rgb-circle-3.png\" style=\"height:30px;display:inline\"> The Gradient of t-SNE\n",
    "---\n",
    "* The gradient of the KL-divergence between P and and the Student-t joint-probablity distribution Q is given by $$ \\frac{\\partial C}{\\partial y_i} = 4 \\sum_{j \\neq i} (p_{ij} - q_{ij})(1 + || y_i - y_j ||^2)^{-1}(y_i - y_j) $$\n",
    "    * The proof is left for HW, but it uses the same tricks as the gradient for SNE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* The following shows the gradients between two low-dimensional datapoints $y_i$ and $y_j$ as a function of their pairwise Euclidean distances in the high-dimensional ($||x_i - x_j||$) and low-dimensional ($y_i - y_j$) space: <img src=\"./assets/tut_xx_tsne_grad.png\" style=\"height:300px\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Positive** values of the gradient represent an *attraction* between the low-dimensional datapoints $y_i$ and $y_j$.\n",
    "* **Negative** values of the gradient represent a *repulsion* between the low-dimensional datapoints $y_i$ and $y_j$.\n",
    "* The advantages of **t-SNE** gradient as seen above:\n",
    "    * t-SNE's gradient **strongly repels** dissimilar datapoints that are modeled by a small pairwise distance in the low-dimensional represntation. SNE has similar behaviour, but its effect is minimal compared to the strong attractions elsewhere in the gradient.\n",
    "    * Although t-SNE introduces strong repulsions between dissimilar points that are modeled by small pairwise distances, these repulsions **don't go to infinity**, which makes t-SNE different than UNI-SNE where the strength of the repulsion between very dissimilar datapoints is proportional to their pairwise distance in the low-dimensional map (ehich may cause dissimilar points to move too far away from each other)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/flow-chart.png\" style=\"height:30px;display:inline\"> t-SNE Optimization & Algorithm\n",
    "---\n",
    "* The optimization of the t-SNE cost function is much easier than the optimization of the cost functions of SNE and UNI-SNE.\n",
    "* t-SNE introduces long-range forces in the low-dimensional map that can pull back together two clusters of similar points that get separated early on the optimization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* The algorithm: <img src=\"./assets/tut_xx_tsne_algo.png\" style=\"height:300px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* The **momentum** term, $\\alpha(t)$, reduces the number of iterations requierd (and works best if it is small until the map points have become moderately well organized).\n",
    "* The above version can be sped up using *adapative learning scheme*, which gradually increases the learning rate in directions in which the gradient is stable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Tricks** to improve the algorithm:\n",
    "    * **\"Early compression\"** - forcing the map points to stay close together at the start of the optimization. When the distances between map points are small, it is easy for clusters to move through one another, which makes it easier to explore the space of possible global organizations of the data.\n",
    "        * Implementation - adding **L2-penalty** to the cost function that is proportional to the sum of squared distances of map points from the origin.\n",
    "    * **\"Early exaggeration\"** - multiplying all of the $p_{ij}$'s by, for example, 4, in the initial stages of the optimization. This means that almost all of the $q_{ij}$'s (which still add up to 1) are still too small to model their corresponding $p_{ij}$'s. This encourages the values of $q_{ij}$'s to become more focused on larger $p_{ij}$'s (i.e., closer points) which as a result causes natural clusters in the data to form tight widely separated clusters in the map (and in the process creating a lot of empty space in the map and allows cluster to move around more easily and find a good global optimization)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/40/000000/broken-link.png\" style=\"height:30px;display:inline\"> t-SNE Weaknesses\n",
    "---\n",
    "* **Applying t-SNE to large data sets**\n",
    "    * t-SNE has a computational and memory complexity that is *quadratic* in the number of datapoints (like many other visualizations techniques).\n",
    "    * This makes it almost infeasible to apply the standard version of t-SNE to data sets that contain more than 10,000 points.\n",
    "        * The trivial soultion would be to just use a subset of the data, but this approach does not make use of all the information available.\n",
    "        * There is a modification to t-SNE that uses random walk on a graph, you can read about it in the official paper (look in the \"Credits\" section)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Dimensionality reduction for other purposes** (other than *visualization*) - it is unclear how t-SNE performs on the more general task of dimensionality reduction (using t-SNE to reduce to $d>3$ dimensions).\n",
    "    * Because of the heavy tails of the Student-t distribution, it is not enough to look at the behaviour of t-SNE on $d \\leq 3$ dimensions. In high-dimensional spaces, the heavy tails comprise a relatively large portion of the probability mass under the Student-t distribution, which may lead to $d$-dimensional data representations that **don't preserve the local structure of the data as well**. In the case of $d>3$, it would be more approriate to use Student-t with more the one degree of freedom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Curse of intrinsic dimensionality** - t-SNE reduces the dimensionality of data mainly based on local properties of the data, which makes t-SNE sensitive to the curse of intrinsic dimensionality of data. In data sets with high intrinsic dimensionality and an underlying manifold that is highly varying, the local linearity assumption on the manifold that t-SNE implicitly makes  (by using Euclidean distances between near neighbors) may be violated.\n",
    "    * In other words, in complex manifolds, where even the local structure is complex, one cannot assume local linearity. In such cases, deep architectures, like autoencoders, may perform better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Non-convexity of the t-SNE cost function** - unlike other dimensionality reduction techniques such as PCA, Isomap and LLE, t-SNE's cost function is **not convex**, which requiers tuning of the optimization parameters.\n",
    "    * This fact has important consequence: **t-SNE is non-deterministic**, that is, you can run it multiple times and get a different result each time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/bubbles/50/000000/classroom.png\" style=\"height:50px;display:inline\"> Example - The Wisconsin Breast Cancer Dataset\n",
    "---\n",
    "We will now see how t-SNE works on the breast cancer dataset, both in 2D and 3D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in the dataset: 569\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>871201</td>\n",
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       "      <td>19.59</td>\n",
       "      <td>18.15</td>\n",
       "      <td>130.70</td>\n",
       "      <td>1214.0</td>\n",
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       "      <td>0.16660</td>\n",
       "      <td>0.250800</td>\n",
       "      <td>0.128600</td>\n",
       "      <td>...</td>\n",
       "      <td>26.73</td>\n",
       "      <td>26.39</td>\n",
       "      <td>174.90</td>\n",
       "      <td>2232.0</td>\n",
       "      <td>0.14380</td>\n",
       "      <td>0.38460</td>\n",
       "      <td>0.681000</td>\n",
       "      <td>0.22470</td>\n",
       "      <td>0.3643</td>\n",
       "      <td>0.09223</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>901549</td>\n",
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       "      <td>11.27</td>\n",
       "      <td>12.96</td>\n",
       "      <td>73.16</td>\n",
       "      <td>386.3</td>\n",
       "      <td>0.12370</td>\n",
       "      <td>0.11110</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>0.055500</td>\n",
       "      <td>...</td>\n",
       "      <td>12.84</td>\n",
       "      <td>20.53</td>\n",
       "      <td>84.93</td>\n",
       "      <td>476.1</td>\n",
       "      <td>0.16100</td>\n",
       "      <td>0.24290</td>\n",
       "      <td>0.224700</td>\n",
       "      <td>0.13180</td>\n",
       "      <td>0.3343</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>859465</td>\n",
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       "      <td>11.31</td>\n",
       "      <td>19.04</td>\n",
       "      <td>71.80</td>\n",
       "      <td>394.1</td>\n",
       "      <td>0.08139</td>\n",
       "      <td>0.04701</td>\n",
       "      <td>0.037090</td>\n",
       "      <td>0.022300</td>\n",
       "      <td>...</td>\n",
       "      <td>12.33</td>\n",
       "      <td>23.84</td>\n",
       "      <td>78.00</td>\n",
       "      <td>466.7</td>\n",
       "      <td>0.12900</td>\n",
       "      <td>0.09148</td>\n",
       "      <td>0.144400</td>\n",
       "      <td>0.06961</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.06641</td>\n",
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       "    <tr>\n",
       "      <td>NaN</td>\n",
       "      <td>89511502</td>\n",
       "      <td>B</td>\n",
       "      <td>12.67</td>\n",
       "      <td>17.30</td>\n",
       "      <td>81.25</td>\n",
       "      <td>489.9</td>\n",
       "      <td>0.10280</td>\n",
       "      <td>0.07664</td>\n",
       "      <td>0.031930</td>\n",
       "      <td>0.021070</td>\n",
       "      <td>...</td>\n",
       "      <td>13.71</td>\n",
       "      <td>21.10</td>\n",
       "      <td>88.70</td>\n",
       "      <td>574.4</td>\n",
       "      <td>0.13840</td>\n",
       "      <td>0.12120</td>\n",
       "      <td>0.102000</td>\n",
       "      <td>0.05602</td>\n",
       "      <td>0.2688</td>\n",
       "      <td>0.06888</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>8913</td>\n",
       "      <td>B</td>\n",
       "      <td>12.89</td>\n",
       "      <td>13.12</td>\n",
       "      <td>81.89</td>\n",
       "      <td>515.9</td>\n",
       "      <td>0.06955</td>\n",
       "      <td>0.03729</td>\n",
       "      <td>0.022600</td>\n",
       "      <td>0.011710</td>\n",
       "      <td>...</td>\n",
       "      <td>13.62</td>\n",
       "      <td>15.54</td>\n",
       "      <td>87.40</td>\n",
       "      <td>577.0</td>\n",
       "      <td>0.09616</td>\n",
       "      <td>0.11470</td>\n",
       "      <td>0.118600</td>\n",
       "      <td>0.05366</td>\n",
       "      <td>0.2309</td>\n",
       "      <td>0.06915</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>906616</td>\n",
       "      <td>B</td>\n",
       "      <td>11.61</td>\n",
       "      <td>16.02</td>\n",
       "      <td>75.46</td>\n",
       "      <td>408.2</td>\n",
       "      <td>0.10880</td>\n",
       "      <td>0.11680</td>\n",
       "      <td>0.070970</td>\n",
       "      <td>0.044970</td>\n",
       "      <td>...</td>\n",
       "      <td>12.64</td>\n",
       "      <td>19.67</td>\n",
       "      <td>81.93</td>\n",
       "      <td>475.7</td>\n",
       "      <td>0.14150</td>\n",
       "      <td>0.21700</td>\n",
       "      <td>0.230200</td>\n",
       "      <td>0.11050</td>\n",
       "      <td>0.2787</td>\n",
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       "      <td>8911230</td>\n",
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       "      <td>11.33</td>\n",
       "      <td>14.16</td>\n",
       "      <td>71.79</td>\n",
       "      <td>396.6</td>\n",
       "      <td>0.09379</td>\n",
       "      <td>0.03872</td>\n",
       "      <td>0.001487</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>...</td>\n",
       "      <td>12.20</td>\n",
       "      <td>18.99</td>\n",
       "      <td>77.37</td>\n",
       "      <td>458.0</td>\n",
       "      <td>0.12590</td>\n",
       "      <td>0.07348</td>\n",
       "      <td>0.004955</td>\n",
       "      <td>0.01111</td>\n",
       "      <td>0.2758</td>\n",
       "      <td>0.06386</td>\n",
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       "    <tr>\n",
       "      <td>NaN</td>\n",
       "      <td>85638502</td>\n",
       "      <td>M</td>\n",
       "      <td>13.17</td>\n",
       "      <td>21.81</td>\n",
       "      <td>85.42</td>\n",
       "      <td>531.5</td>\n",
       "      <td>0.09714</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.082590</td>\n",
       "      <td>0.052520</td>\n",
       "      <td>...</td>\n",
       "      <td>16.23</td>\n",
       "      <td>29.89</td>\n",
       "      <td>105.50</td>\n",
       "      <td>740.7</td>\n",
       "      <td>0.15030</td>\n",
       "      <td>0.39040</td>\n",
       "      <td>0.372800</td>\n",
       "      <td>0.16070</td>\n",
       "      <td>0.3693</td>\n",
       "      <td>0.09618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NaN</td>\n",
       "      <td>8912284</td>\n",
       "      <td>B</td>\n",
       "      <td>12.89</td>\n",
       "      <td>15.70</td>\n",
       "      <td>84.08</td>\n",
       "      <td>516.6</td>\n",
       "      <td>0.07818</td>\n",
       "      <td>0.09580</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.033900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.90</td>\n",
       "      <td>19.69</td>\n",
       "      <td>92.12</td>\n",
       "      <td>595.6</td>\n",
       "      <td>0.09926</td>\n",
       "      <td>0.23170</td>\n",
       "      <td>0.334400</td>\n",
       "      <td>0.10170</td>\n",
       "      <td>0.1999</td>\n",
       "      <td>0.07127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NaN</td>\n",
       "      <td>884689</td>\n",
       "      <td>B</td>\n",
       "      <td>11.52</td>\n",
       "      <td>14.93</td>\n",
       "      <td>73.87</td>\n",
       "      <td>406.3</td>\n",
       "      <td>0.10130</td>\n",
       "      <td>0.07808</td>\n",
       "      <td>0.043280</td>\n",
       "      <td>0.029290</td>\n",
       "      <td>...</td>\n",
       "      <td>12.65</td>\n",
       "      <td>21.19</td>\n",
       "      <td>80.88</td>\n",
       "      <td>491.8</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.15820</td>\n",
       "      <td>0.180400</td>\n",
       "      <td>0.09608</td>\n",
       "      <td>0.2664</td>\n",
       "      <td>0.07809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "NaN    871201         M        19.59         18.15          130.70     1214.0   \n",
       "NaN    901549         B        11.27         12.96           73.16      386.3   \n",
       "NaN    859465         B        11.31         19.04           71.80      394.1   \n",
       "NaN  89511502         B        12.67         17.30           81.25      489.9   \n",
       "NaN      8913         B        12.89         13.12           81.89      515.9   \n",
       "NaN    906616         B        11.61         16.02           75.46      408.2   \n",
       "NaN   8911230         B        11.33         14.16           71.79      396.6   \n",
       "NaN  85638502         M        13.17         21.81           85.42      531.5   \n",
       "NaN   8912284         B        12.89         15.70           84.08      516.6   \n",
       "NaN    884689         B        11.52         14.93           73.87      406.3   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "NaN          0.11200           0.16660        0.250800             0.128600   \n",
       "NaN          0.12370           0.11110        0.079000             0.055500   \n",
       "NaN          0.08139           0.04701        0.037090             0.022300   \n",
       "NaN          0.10280           0.07664        0.031930             0.021070   \n",
       "NaN          0.06955           0.03729        0.022600             0.011710   \n",
       "NaN          0.10880           0.11680        0.070970             0.044970   \n",
       "NaN          0.09379           0.03872        0.001487             0.003333   \n",
       "NaN          0.09714           0.10470        0.082590             0.052520   \n",
       "NaN          0.07818           0.09580        0.111500             0.033900   \n",
       "NaN          0.10130           0.07808        0.043280             0.029290   \n",
       "\n",
       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "NaN  ...         26.73          26.39           174.90      2232.0   \n",
       "NaN  ...         12.84          20.53            84.93       476.1   \n",
       "NaN  ...         12.33          23.84            78.00       466.7   \n",
       "NaN  ...         13.71          21.10            88.70       574.4   \n",
       "NaN  ...         13.62          15.54            87.40       577.0   \n",
       "NaN  ...         12.64          19.67            81.93       475.7   \n",
       "NaN  ...         12.20          18.99            77.37       458.0   \n",
       "NaN  ...         16.23          29.89           105.50       740.7   \n",
       "NaN  ...         13.90          19.69            92.12       595.6   \n",
       "NaN  ...         12.65          21.19            80.88       491.8   \n",
       "\n",
       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "NaN           0.14380            0.38460         0.681000   \n",
       "NaN           0.16100            0.24290         0.224700   \n",
       "NaN           0.12900            0.09148         0.144400   \n",
       "NaN           0.13840            0.12120         0.102000   \n",
       "NaN           0.09616            0.11470         0.118600   \n",
       "NaN           0.14150            0.21700         0.230200   \n",
       "NaN           0.12590            0.07348         0.004955   \n",
       "NaN           0.15030            0.39040         0.372800   \n",
       "NaN           0.09926            0.23170         0.334400   \n",
       "NaN           0.13890            0.15820         0.180400   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "NaN               0.22470          0.3643                  0.09223  \n",
       "NaN               0.13180          0.3343                  0.09215  \n",
       "NaN               0.06961          0.2400                  0.06641  \n",
       "NaN               0.05602          0.2688                  0.06888  \n",
       "NaN               0.05366          0.2309                  0.06915  \n",
       "NaN               0.11050          0.2787                  0.07427  \n",
       "NaN               0.01111          0.2758                  0.06386  \n",
       "NaN               0.16070          0.3693                  0.09618  \n",
       "NaN               0.10170          0.1999                  0.07127  \n",
       "NaN               0.09608          0.2664                  0.07809  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# time to see it in action, let's load the breat cancer dataset\n",
    "dataset = pd.read_csv('./datasets/cancer_dataset.csv', index_col=-1)\n",
    "# print the number of rows in the data set\n",
    "number_of_rows = len(dataset)\n",
    "print('Number of rows in the dataset: {}'.format(number_of_rows))\n",
    "# separate labels from data\n",
    "X = dataset.drop(columns=['id', 'diagnosis'])\n",
    "y = dataset['diagnosis'].values == 'M'\n",
    "## Show a sample 10 rows\n",
    "dataset.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plot_tsne(X, dim=2, perplexity=30.0, scale_data=False):\n",
    "    if dim < 2 or dim > 3:\n",
    "        print(\"OH NO :(\")\n",
    "        raise SystemError(\"2 <= dim <= 3\")\n",
    "    t_sne = TSNE(n_components=dim, perplexity=perplexity)\n",
    "    if scale_data:\n",
    "        scaler = StandardScaler()\n",
    "        X = scaler.fit_transform(X)\n",
    "    X_embedded = t_sne.fit_transform(X)\n",
    "    if dim == 2:\n",
    "        fig = plt.figure(figsize=(8,5))\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        ax.scatter(X_embedded[y,0], X_embedded[y, 1], color='r', marker='*', label='Malignant')\n",
    "        ax.scatter(X_embedded[~y,0], X_embedded[~y, 1], color='b', marker='x', label='Benign')\n",
    "        ax.grid()\n",
    "        ax.legend()\n",
    "        ax.set_title(\"2D t-SNE of the Breast Cancer Dataset\")\n",
    "    else:\n",
    "        fig = plt.figure(figsize=(8, 8))\n",
    "        ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
    "#         ax.axis('equal')\n",
    "        ax.scatter(X_embedded[y, 0], X_embedded [y, 1], X_embedded [y, 2], color='r', marker='*', label='Malignant')\n",
    "        ax.scatter(X_embedded[~y, 0], X_embedded [~y, 1], X_embedded [~y, 2], color='b', marker='x', label='Benign')\n",
    "        ax.grid()\n",
    "        ax.legend()\n",
    "        ax.set_title(\"3D t-SNE of the Breast Cancer Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's try to reduce to 2 dimensions, with scaling\n",
    "plot_tsne(X, dim=2, perplexity=30.0, scale_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's try to reduce to 2 dimensions, without scaling\n",
    "plot_tsne(X, dim=2, perplexity=30.0, scale_data=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = 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.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",
       "            /* 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",
       "            evt.data.type = \"image/png\";\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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       " * http://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             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",
       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";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.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",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's try to reduce to 3 dimensions\n",
    "%matplotlib notebook\n",
    "plot_tsne(X, dim=3, perplexity=10.0, scale_data=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/bubbles/50/000000/video-playlist.png\" style=\"height:50px;display:inline\"> Recommended Videos\n",
    "---\n",
    "#### <img src=\"https://img.icons8.com/cute-clipart/64/000000/warning-shield.png\" style=\"height:30px;display:inline\"> Warning!\n",
    "* These videos do not replace the lectures and tutorials.\n",
    "* Please use these to get a better understanding of the material, and not as an alternative to the written material.\n",
    "\n",
    "#### Video By Subject\n",
    "\n",
    "* Student's T Distribution - <a href=\"https://www.youtube.com/watch?v=32CuxWdOlow\">365 Data Science - Student's T Distribution </a>\n",
    "* t-SNE - <a href=\"https://www.youtube.com/watch?v=RJVL80Gg3lA\">GoogleTechTalks - Visualizing Data Using t-SNE</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## <img src=\"https://img.icons8.com/dusk/64/000000/prize.png\" style=\"height:50px;display:inline\"> Credits\n",
    "---\n",
    "* Visualizing Data using t-SNE, der Maaten, Hinton and Bengio - https://lvdmaaten.github.io/tsne/\n",
    "* Icons from <a href=\"https://icons8.com/\">Icon8.com</a> - https://icons8.com\n",
    "* Datasets from <a href=\"https://www.kaggle.com/\">Kaggle</a> - https://www.kaggle.com/"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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 },
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}