dask

distributed-dashboard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:27:14.395297Z",
     "start_time": "2018-09-19T03:27:14.372880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=Arvo:400,700,400italic' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=PT+Mono' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=Shadows+Into+Light' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=Philosopher:400,700,400italic,700italic' rel='stylesheet' type='text/css'>\n",
       "\n",
       "<style>\n",
       "\n",
       "@font-face {\n",
       "    font-family: \"Computer Modern\";\n",
       "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
       "}\n",
       "\n",
       "\n",
       "/* Formatting for header cells */\n",
       ".text_cell_render h1 {\n",
       "    font-family: 'Philosopher', sans-serif;\n",
       "    font-weight: 400;\n",
       "    font-size: 2.2em;\n",
       "    line-height: 100%;\n",
       "    color: rgb(0, 80, 120);\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.1em;\n",
       "    display: block;\n",
       "}\t\n",
       ".text_cell_render h2 {\n",
       "    font-family: 'Philosopher', serif;\n",
       "    font-weight: 400;\n",
       "    font-size: 1.9em;\n",
       "    line-height: 100%;\n",
       "    color: rgb(200,100,0);\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.1em;\n",
       "    display: block;\n",
       "}\t\n",
       "\n",
       ".text_cell_render h3 {\n",
       "    font-family: 'Philosopher', serif;\n",
       "    margin-top:12px;\n",
       "    margin-bottom: 3px;\n",
       "    font-style: italic;\n",
       "    color: rgb(94,127,192);\n",
       "}\n",
       "\n",
       ".text_cell_render h4 {\n",
       "    font-family: 'Philosopher', serif;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 {\n",
       "    font-family: 'Alegreya Sans', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 16pt;\n",
       "    color: grey;\n",
       "    font-style: italic;\n",
       "    margin-bottom: .1em;\n",
       "    margin-top: 0.1em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 {\n",
       "    font-family: 'PT Mono', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 10pt;\n",
       "    color: grey;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       ".CodeMirror{\n",
       "        font-family: \"PT Mono\";\n",
       "        font-size: 100%;\n",
       "}\n",
       "\n",
       "</style>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "css_file = './css/style.css'\n",
    "HTML(open(css_file, 'r').read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 集群初始化，dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:27:24.167249Z",
     "start_time": "2018-09-19T03:27:18.316787Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Client</h3>\n",
       "<ul>\n",
       "  <li><b>Scheduler: </b>tcp://172.17.0.2:39877\n",
       "  <li><b>Dashboard: </b><a href='http://172.17.0.2:8787/status' target='_blank'>http://172.17.0.2:8787/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Cluster</h3>\n",
       "<ul>\n",
       "  <li><b>Workers: </b>16</li>\n",
       "  <li><b>Cores: </b>16</li>\n",
       "  <li><b>Memory: </b>46.04 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://172.17.0.2:39877' processes=16 cores=16>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import distributed\n",
    "c = distributed.LocalCluster(ip=\"\")\n",
    "from dask.distributed import Client\n",
    "# Setup a local cluster.\n",
    "# By default this sets up 1 worker per core\n",
    "client = Client(c)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T02:40:19.039511Z",
     "start_time": "2018-09-19T02:40:19.029164Z"
    }
   },
   "outputs": [],
   "source": [
    "from dask import delayed,compute\n",
    "from time import sleep\n",
    "@delayed\n",
    "def inc(x):\n",
    "    sleep(1)\n",
    "    return x + 1\n",
    "@delayed\n",
    "def double(x):\n",
    "    sleep(1)\n",
    "    return x + 2\n",
    "@delayed\n",
    "def add(x, y):\n",
    "    sleep(1)\n",
    "    return x + y\n",
    "data = [1, 2, 3, 4, 5]\n",
    "output = []\n",
    "for x in data:\n",
    "    a = inc(x)\n",
    "    b = double(x)\n",
    "    c = add(a, b)\n",
    "    output.append(c)\n",
    "total = delayed(sum)(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T02:41:57.403798Z",
     "start_time": "2018-09-19T02:41:55.267903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T02:41:21.524344Z",
     "start_time": "2018-09-19T02:41:20.681165Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:27:41.056089Z",
     "start_time": "2018-09-19T03:27:37.171582Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from qpython import  qconnection\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:27:43.312400Z",
     "start_time": "2018-09-19T03:27:41.061397Z"
    }
   },
   "outputs": [],
   "source": [
    "zport=8009\n",
    "zhost='10.0.18.159'\n",
    "with qconnection.QConnection(host=zhost,port=zport,pandas=True) as q:\n",
    "    rawdata = q.sync('alldata_week')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:27:43.327028Z",
     "start_time": "2018-09-19T03:27:43.317491Z"
    }
   },
   "outputs": [],
   "source": [
    "from dask import delayed,compute\n",
    "def winsorize(df, factors, extend=3):\n",
    "    for factor in factors:\n",
    "        q1,q3 = df[factor].quantile([0.25,0.75])\n",
    "        dist =  q3-q1\n",
    "        mask1=df[factor]> q3 + extend*dist\n",
    "        df.loc[mask1,factor]= q3 + extend*dist\n",
    "        mask2=df[factor]< q1 - extend*dist\n",
    "        df.loc[mask2,factor]= q1 - extend*dist\n",
    "    return df\n",
    "@delayed\n",
    "def purify_onday(df):\n",
    "    dfc = df.copy()\n",
    "    dfc = dfc[['date','secucode','hy','size','tov']]\n",
    "    dfc = winsorize(dfc,['size','tov'])\n",
    "    y=dfc['tov']\n",
    "    dummy=pd.get_dummies(dfc['hy'],prefix='sector')\n",
    "    x=pd.concat([dummy,dfc[['size']]],axis=1)\n",
    "    dfc['tov'+'_purify']=sm.OLS(y, x, hasconst=False, missing='drop').fit().resid\n",
    "    return dfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:27:44.422803Z",
     "start_time": "2018-09-19T03:27:43.333234Z"
    }
   },
   "outputs": [],
   "source": [
    "reu = []\n",
    "for x in rawdata.date.unique():\n",
    "    pp = rawdata.query('date==@x')\n",
    "    pp2 = purify_onday(pp)\n",
    "    reu.append(pp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:38:57.393659Z",
     "start_time": "2018-09-19T03:38:49.655670Z"
    }
   },
   "outputs": [],
   "source": [
    "result = compute(*reu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:39:11.071681Z",
     "start_time": "2018-09-19T03:39:01.471764Z"
    }
   },
   "outputs": [],
   "source": [
    "result = compute(*reu,scheduler='threading')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:37:40.941175Z",
     "start_time": "2018-09-19T03:37:25.568358Z"
    }
   },
   "outputs": [],
   "source": [
    "result = compute(*reu,scheduler='processes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:38:18.185685Z",
     "start_time": "2018-09-19T03:37:54.537161Z"
    }
   },
   "outputs": [],
   "source": [
    "result = compute(*reu,scheduler='sync')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:44:38.228443Z",
     "start_time": "2018-09-19T03:44:38.206653Z"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot concatenate object of type \"<class 'dask.delayed.Delayed'>\"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-460ef79634f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    223\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m                        copy=copy, sort=sort)\n\u001b[0m\u001b[1;32m    226\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    284\u001b[0m                        \u001b[0;34m' only pd.Series, pd.DataFrame, and pd.Panel'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m                        ' (deprecated) objs are valid'.format(type(obj)))\n\u001b[0;32m--> 286\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0;31m# consolidate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot concatenate object of type \"<class 'dask.delayed.Delayed'>\"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid"
     ]
    }
   ],
   "source": [
    "(pd.concat(reu)).visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-19T03:40:52.264119Z",
     "start_time": "2018-09-19T03:40:51.901800Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'visualize'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-2ba6da2dd479>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'visualize'"
     ]
    }
   ],
   "source": [
    "reu.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def winsorize(df, factors, extend=3):\n",
    "    for factor in factors:\n",
    "        q1,q3 = df[factor].quantile([0.25,0.75])\n",
    "        dist =  q3-q1\n",
    "        mask1=df[factor]> q3 + extend*dist\n",
    "        df.loc[mask1,factor]= q3 + extend*dist\n",
    "        mask2=df[factor]< q1 - extend*dist\n",
    "        df.loc[mask2,factor]= q1 - extend*dist\n",
    "    return df\n",
    "def purify_d(testdata,facd):\n",
    "    testdata=winsorize(testdata,['size',facd])\n",
    "    y=testdata[facd]\n",
    "    dummy=pd.get_dummies(testdata['hy'],prefix='sector')\n",
    "    x=pd.concat([dummy,testdata[['size']]],axis=1)\n",
    "    testdata[facd+'_purify']=sm.OLS(y, x, hasconst=False, missing='drop').fit().resid\n",
    "    return testdata\n",
    "def purify_fac(fac,dataraw):\n",
    "    df = dataraw[['date','secucode','hy','size',fac]].set_index(['date','secucode']).dropna()\n",
    "    df=df[['hy','size',fac]]\n",
    "    df=df.groupby('date').apply(purify_d,(fac))\n",
    "    return df[fac+'_purify']\n"
   ]
  }
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需要安装bokeh 0.13以上的版本，才能显示出dashboard

conda install bokeh=0.13