{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"text-align: right;\"> &#9989; Put your name here</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day 11.5 Pre-Class Assignment: More SVMs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals for today's pre-class assignment\n",
    "\n",
    "\n",
    "1. Observing non-linear separability (`sklearn.datasets.make_circles`)\n",
    "1. Graphing data in 3D\n",
    "1. Explaining how a RBF kernel works like a nearest neighbors model\n",
    "\n",
    "<img src=\"https://sebastianraschka.com/images/blog/2014/kernel_pca/2014-09-14-kernel_pca_72_0.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports for the day\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.datasets import make_circles\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 1. Non linear data\n",
    "\n",
    "It's all well and good to want to have linearly separable data to do our work, but that is simply not the case many times. Look at the data in the figure above. Not very linear indeed. We can find a way to make it linearly separable, but we will do that in-class.\n",
    "\n",
    "### 1.1 `sklearn.datasets.make_circles` data generators\n",
    "\n",
    "Sticking with the  <a href=\"https://scikit-learn.org/stable/datasets/index.html\"> sklearn.datasets page </a> and in particuar <a href=\"https://scikit-learn.org/stable/datasets/index.html#generated-datasets\"> Section 7.4 </a> we see <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html\"> `make_circles` </a> which allows us to make data such as found in the picture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles), please create an example data set with two \"blobs\", a 2-class data set. It should:\n",
    "- have 100 samples\n",
    "- two classes\n",
    "- set the `random_state` value so that you can generate the same \"blobs\" every time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your answer here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. Graph the circles\n",
    "\n",
    "It is hard to see what we got. \n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Do this - Graph the result of the `make_circles` operation. Color all the elements of one class one color and the elements of the other class the other color. \n",
    "\n",
    "\n",
    "These are notes we used from last class but perhaps they are still helpful here:\n",
    "\n",
    "- you can create a list/array with color assignments for each data element based on their class\n",
    "- you can use any of the various color maps matplotlib provides\n",
    "- play with the `random_state` value and see if you can get some \"blobs\" that are relatively \"close\" to each other. It should be the case that if you use the same `random_state` value each time that you get the same distribution of data. Remember, \"random\" means \"relatively random\" here. That is, predicting the next value in a sequence should be (statistically speaking) random. However, start with the same seed value (which is what `random_state` is for this process) you get the exact same sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your answer here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 3. Adding a dimension to linearly seperate the data\n",
    "\n",
    "It's hard to see how we could separate these two classes given their overlap, and that is likely true in the 2D format we are looking. But what if we added a 3rd dimension based on the features of the data that we have? That is, can we use the existing X and Y values to make a new dimension that would that help linearly seperate the data?\n",
    "\n",
    "*As you learned in the last class, creating these new dimensions from the data is what an SVM does. We will try to do this manually.*\n",
    "\n",
    "First, let remind ourselves of 3D plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Making 3D scatter plot\n",
    "\n",
    "To setup a 3D scatter plot, you need the following steps (found in this <a href=\"https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html#scatter-plots\"> tutorial </a>). We can generate some random values and try it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'simple 3D scatter plot')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.randint(-5, 5, 100)\n",
    "y = np.random.randint(-5, 5, 100)\n",
    "z = np.random.randint(-5, 5, 100)\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(figsize = (10, 7))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    " \n",
    "# Creating plot\n",
    "ax.scatter3D(x, y, z, color = \"blue\")\n",
    "plt.title(\"simple 3D scatter plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=8 color=\"#009600\">&#9998;</font> Do this - Plot your circle data in 3D. Because we don't have a third/$z$ dimension you can just pass a fixed value for $z$. In fact, you can just use a single number. As always, mark the two classes in colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Trying to make the data linearly seperable\n",
    "\n",
    "Let's try to separate these circles of data using the existing values. We can try to add a third dimension to the data, based on the existing values. This is called a \"transfomation\" of the data.\n",
    "\n",
    "What might be a procedure to do that? Think about it for a moment, look online if you like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What does that transformation/dimension look like?\n",
    "1. Give the dimensions that you tried.\n",
    "1. Do they help with separation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=8 color=\"#009600\">&#9998;</font> Answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 4. Alternative Kernels for SVM\n",
    "\n",
    "The linear kernel is one of a number of possible kernels that an SVM can use. One common choice for nonlinear data is the Radial Basis Function (RBF) Kernel. The video below conceptually describes how the RBF works like a nearest neighbors model. More details on the RBF, which is beyond the scope of our course are available online."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"360\"\n",
       "            src=\"https://www.youtube.com/embed/efR1C6CvhmE?start=1049\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f8f68390f10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "from datetime import timedelta\n",
    "start=int(timedelta(hours=0, minutes=17, seconds=29).total_seconds())\n",
    "YouTubeVideo(\"efR1C6CvhmE\",width=640,height=360,start=start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** How does a the RBF work like a nearest-neighbors model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=8 color=\"#009600\">&#9998;</font> Answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "# Assignment wrap-up\n",
    "\n",
    "Please fill out the form that appears when you run the code below.  **You must completely fill this out in order to receive credit for the assignment!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<iframe \n",
       "\tsrc=\"https://cmse.msu.edu/cmse202-pc-survey\" \n",
       "\twidth=\"800px\" \n",
       "\theight=\"600px\" \n",
       "\tframeborder=\"0\" \n",
       "\tmarginheight=\"0\" \n",
       "\tmarginwidth=\"0\">\n",
       "\tLoading...\n",
       "</iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML(\n",
    "\"\"\"\n",
    "<iframe \n",
    "\tsrc=\"https://cmse.msu.edu/cmse202-pc-survey\" \n",
    "\twidth=\"800px\" \n",
    "\theight=\"600px\" \n",
    "\tframeborder=\"0\" \n",
    "\tmarginheight=\"0\" \n",
    "\tmarginwidth=\"0\">\n",
    "\tLoading...\n",
    "</iframe>\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------\n",
    "### Congratulations, you're done with your pre-class assignment!\n",
    "\n",
    "Now, you just need to submit this assignment by uploading it to the course <a href=\"https://d2l.msu.edu/\">Desire2Learn</a> web page for today's submission folder (Don't forget to add your name in the first cell)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#169; Copyright Michigan State University Board of Trustees"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
