02_Regression&NN/Visdom_Tutorial.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visdom Tutorial\n",
    "\n",
    "- text\n",
    "- image\n",
    "- scatter plot\n",
    "- bar plot\n",
    "- Contour & Surface Plot\n",
    "- Line Plot\n",
    "- Pie Chart Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from visdom import Visdom\n",
    "import numpy as np\n",
    "import math\n",
    "import os.path\n",
    "\n",
    "viz = Visdom()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "textwindow = viz.text(\"Hello Pytorch\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_window = viz.image(\n",
    "    np.random.rand(3,256,256),\n",
    "    opts=dict(\n",
    "        title = \"random\",\n",
    "        caption = \"random noise\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "images_window = viz.images(\n",
    "    np.random.rand(10,3,64,64),\n",
    "    opts=dict(\n",
    "        title = \"random\",\n",
    "        caption = \"random noise\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.random.rand(100)\n",
    "scatter_window=viz.scatter(\n",
    "            X=np.random.rand(100, 2),\n",
    "            Y=(Y+1.5).astype(int),\n",
    "            opts=dict(\n",
    "                legend=['Apples', 'Pears'],\n",
    "                xtickmin=0,\n",
    "                xtickmax=2,\n",
    "                xtickstep=0.5,\n",
    "                ytickmin=0,\n",
    "                ytickmax=2,\n",
    "                ytickstep=0.5,\n",
    "                markersymbol='cross-thin-open',\n",
    "            ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316cc680a'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.updateTrace(\n",
    "    X=np.random.rand(50),\n",
    "    Y=np.random.rand(50),\n",
    "    win=scatter_window,\n",
    "    name='bananas',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316d38a70'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.scatter(\n",
    "    X=np.random.rand(100, 3),\n",
    "    Y=(Y + 1.5).astype(int),\n",
    "    opts=dict(\n",
    "        legend=['Men', 'Women'],\n",
    "        markersize=5,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316d62f2e'"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "viz.bar(X=np.random.rand(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316d9ae36'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.bar(\n",
    "    X=np.abs(np.random.rand(5, 3)),\n",
    "    opts=dict(\n",
    "        stacked=True,\n",
    "        legend=['Facebook', 'Google', 'Twitter'],\n",
    "        rownames=['2012', '2013', '2014', '2015', '2016']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unstacked Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316dce2f2'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "viz.bar(\n",
    "    X=np.random.rand(10, 3),\n",
    "    opts=dict(\n",
    "        stacked=False,\n",
    "        legend=['The Netherlands', 'France', 'United States']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contour Map & Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.tile(np.arange(1, 101), (100, 1))\n",
    "y = x.transpose()\n",
    "X = np.exp((((x - 50) ** 2) + ((y - 50) ** 2)) / -(20.0 ** 2))\n",
    "\n",
    "# contour\n",
    "viz.contour(X=X, opts=dict(colormap='Viridis'))\n",
    "\n",
    "# surface\n",
    "viz.surf(X=X, opts=dict(colormap='Hot'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316ece316'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.line(Y=np.random.rand(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316f085e4'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# line updates\n",
    "win = viz.line(\n",
    "    X=np.column_stack((np.arange(0, 10), np.arange(0, 10))),\n",
    "    Y=np.column_stack((np.linspace(5, 10, 10), np.linspace(5, 10, 10) + 5)),\n",
    ")\n",
    "\n",
    "viz.line(\n",
    "    X=np.column_stack((np.arange(10, 20), np.arange(10, 20))),\n",
    "    Y=np.column_stack((np.linspace(5, 10, 10), np.linspace(5, 10, 10) + 5)),\n",
    "    win=win,\n",
    "    update='append'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pane_3595f316f541a6'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pie chart\n",
    "X = np.asarray([19, 26, 55])\n",
    "viz.pie(\n",
    "    X=X,\n",
    "    opts=dict(legend=['Residential', 'Non-Residential', 'Utility'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With PyTorch Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PyTorch tensor\n",
    "try:\n",
    "    import torch\n",
    "    viz.line(Y=torch.Tensor([[0., 0.], [1., 1.]]))\n",
    "except ImportError:\n",
    "    print('Skipped PyTorch example')"
   ]
  }
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