{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "\n",
    "***\n",
    "***\n",
    "# Graphlab 安装与使用\n",
    "***\n",
    "***\n",
    "\n",
    "王成军\n",
    "\n",
    "wangchengjun@nju.edu.cn\n",
    "\n",
    "计算传播网 http://computational-communication.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Problem\n",
    "\n",
    "只有低版本的anaconda才可以安装，强行安装还会破坏掉anaconda的jupyter notebook中的kernel，排除使用anaconda运行graphlab的方案。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Register for Academic Use of GraphLab Create\n",
    "\n",
    "https://turi.com/download/academic.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 查邮件\n",
    "\n",
    "https://turi.com/download/install-graphlab-create.html?email=wangchengjun%40nju.edu.cn&key=4972-65DF-8E02-816C-AB15-021C-EC1B-0367&utm_medium=email&utm_source=transactional&utm_campaign=beta_registration_confirmation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Renew Academic License for GraphLab Create\n",
    "\n",
    "https://turi.com/download/renew.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### License Renewal Confirmation\n",
    "Your academic license for GraphLab Create has been renewed. Please restart GraphLab Create while connected to the internet.\n",
    "\n",
    "\n",
    "Email: wangchengjun@nju.edu.cn\n",
    "\n",
    "Expiration Date: 03-13-2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Python 2.7.x\n",
    "\n",
    "GraphLab Create installation requires a Python 2.7.x environment and pip version >= 7 and Anaconda2 v4.0.0 (64-bit). IPython Notebook is recommended."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# To install a different version of Python without overwriting the current version\n",
    "\n",
    "https://conda.io/docs/user-guide/tasks/manage-python.html\n",
    "\n",
    "Creating a new environment and install the second Python version into it. To create the new environment for Python 2.7, in your Terminal window or an Anaconda Prompt, run:\n",
    "> conda create -n py27 python=2.7 anaconda\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "## Activate the new environment ** 切换到新环境**\n",
    "\n",
    "- linux/Mac下需要使用: `source activate py27`\n",
    "- windows需要使用: `activate py27`\n",
    "\n",
    "**退出环境: `source deactivate py27`\n",
    "也可以使用** `activate root`切回root环境\n",
    "\n",
    "3. [Verify that the new environment is your current environment.](https://conda.io/docs/user-guide/tasks/manage-environments.html#determine-current-env)\n",
    "4. To verify that the current environment uses the new Python version, in your Terminal window or an Anaconda Prompt, run: `python --version`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-04T03:47:23.334875Z",
     "start_time": "2019-03-04T03:47:23.211768Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 2.7.14 :: Anaconda, Inc.\r\n"
     ]
    }
   ],
   "source": [
    "! python --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Install your licensed copy of GraphLab Create\n",
    "pip install --upgrade --no-cache-dir https://get.graphlab.com/GraphLab-Create/2.1/your registered email address here/your product key here/GraphLab-Create-License.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "# Open a terminal and input:\n",
    "\n",
    "pip install --upgrade --no-cache-dir https://get.graphlab.com/GraphLab-Create/2.1/wangchengjun@nju.edu.cn/4972-65DF-8E02-816C-AB15-021C-EC1B-0367/GraphLab-Create-License.tar.gz\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
    }
   },
   "source": [
    "# Error \n",
    "Could not find a version that satisfies the requirement graphlab-create>=2.1 (from GraphLab-Create-License==2.1) (from versions: )\n",
    "No matching distribution found for graphlab-create>=2.1 (from GraphLab-Create-License==2.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "# 使用方法\n",
    "\n",
    "https://turi.com/learn/userguide/\n",
    "\n",
    "GraphLab Create is a Python package that allows programmers to perform end-to-end large-scale data analysis and data product development.\n",
    "\n",
    "- Data ingestion and cleaning with SFrames. SFrame is an efficient disk-based tabular data structure that is not limited by RAM. This lets you scale your analysis and data processing to handle terabytes of data, even on your laptop.\n",
    "\n",
    "- Data exploration and visualization with GraphLab Canvas. GraphLab Canvas is a browser-based interactive GUI that allows you to explore tabular data, summary plots and statistics.\n",
    "\n",
    "- Network analysis with SGraph. SGraph is a disk-based graph data structure that stores vertices and edges in SFrames.\n",
    "\n",
    "- Predictive model development with machine learning toolkits. GraphLab Create includes several toolkits for quick prototyping with fast, scalable algorithms.\n",
    "\n",
    "- Production automation with data pipelines. Data pipelines allow you to assemble reusable code tasks into jobs and automatically run them on common execution environments (e.g. Amazon Web Services, Hadoop)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-04T03:50:55.741270Z",
     "start_time": "2019-03-04T03:50:47.024232Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to wangchengjun@nju.edu.cn and will expire on March 14, 2019.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1551671450.log\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGraph({'num_edges': 1, 'num_vertices': 3})\n"
     ]
    }
   ],
   "source": [
    "from graphlab import SGraph, Vertex, Edge\n",
    "g = SGraph()\n",
    "verts = [Vertex(0, attr={'breed': 'labrador'}),\n",
    "         Vertex(1, attr={'breed': 'labrador'}),\n",
    "         Vertex(2, attr={'breed': 'vizsla'})]\n",
    "g = g.add_vertices(verts)\n",
    "g = g.add_edges(Edge(1, 2))\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-04T03:51:30.249064Z",
     "start_time": "2019-03-04T03:51:30.184980Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGraph({'num_edges': 1, 'num_vertices': 3})\n"
     ]
    }
   ],
   "source": [
    "from graphlab import SGraph, Vertex, Edge\n",
    "g = SGraph()\n",
    "verts = [Vertex(0, attr={'breed': 'labrador'}),\n",
    "         Vertex(1, attr={'breed': 'labrador'}),\n",
    "         Vertex(2, attr={'breed': 'vizsla'})]\n",
    "g = g.add_vertices(verts)\n",
    "g = g.add_edges(Edge(1, 2))\n",
    "print g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-04T03:51:39.507312Z",
     "start_time": "2019-03-04T03:51:39.304216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canvas is accessible via web browser at the URL: http://localhost:62302/index.html\n",
      "Opening Canvas in default web browser.\n"
     ]
    }
   ],
   "source": [
    "g.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-04T03:51:59.037393Z",
     "start_time": "2019-03-04T03:51:58.929048Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/datalab/github/bigdata/data/bond_edges.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/datalab/github/bigdata/data/bond_edges.csv"
      ]
     },
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     "output_type": "display_data"
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       "<pre>Parsing completed. Parsed 20 lines in 0.022952 secs.</pre>"
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       "Parsing completed. Parsed 20 lines in 0.022952 secs."
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[str,str,str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/datalab/github/bigdata/data/bond_edges.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/datalab/github/bigdata/data/bond_edges.csv"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/html": [
       "<pre>Parsing completed. Parsed 20 lines in 0.009524 secs.</pre>"
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       "Parsing completed. Parsed 20 lines in 0.009524 secs."
      ]
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGraph({'num_edges': 20, 'num_vertices': 10})\n"
     ]
    }
   ],
   "source": [
    "from graphlab import SFrame,SGraph\n",
    "edge_data = SFrame.read_csv('../data/bond_edges.csv')\n",
    "    #'https://static.turi.com/datasets/bond/bond_edges.csv')\n",
    "\n",
    "g = SGraph()\n",
    "g = g.add_edges(edge_data, src_field='src', dst_field='dst')\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-13T09:40:42.615740",
     "start_time": "2017-05-13T09:40:41.014215"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Downloading https://static.turi.com/datasets/bond/bond_vertices.csv to /var/tmp/graphlab-chengjun/1639/be40440c-c4eb-45f1-ae71-501011acc59f.csv</pre>"
      ],
      "text/plain": [
       "Downloading https://static.turi.com/datasets/bond/bond_vertices.csv to /var/tmp/graphlab-chengjun/1639/be40440c-c4eb-45f1-ae71-501011acc59f.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file https://static.turi.com/datasets/bond/bond_vertices.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file https://static.turi.com/datasets/bond/bond_vertices.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 10 lines in 0.010129 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 10 lines in 0.010129 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first line of file as \n",
      "column_type_hints=[str,str,int,int]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file https://static.turi.com/datasets/bond/bond_vertices.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file https://static.turi.com/datasets/bond/bond_vertices.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 10 lines in 0.0082 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 10 lines in 0.0082 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vertex_data = SFrame.read_csv('https://static.turi.com/datasets/bond/bond_vertices.csv')\n",
    "\n",
    "g = SGraph(vertices=vertex_data, edges=edge_data, vid_field='name',\n",
    "           src_field='src', dst_field='dst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-04T03:52:11.073515Z",
     "start_time": "2019-03-04T03:52:11.069355Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canvas is updated and available in a tab in the default browser.\n"
     ]
    }
   ],
   "source": [
    "g.show(vlabel='id', highlight=['James Bond', 'Moneypenny'], \\\n",
    "       arrows=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 阅读材料\n",
    "- https://turi.com/learn/userguide/\n"
   ]
  }
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