Tracker requests tailing (Pref Flip)

Tracker requests tailing (Pref Flip).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/anaconda2/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning:\n",
      "\n",
      "pylab import has clobbered these variables: ['rand', 'cbrt', 'rank', 'cosh', 'hypot', 'array', 'tan', 'size', 'isnan', 'randn', 'log', 'floor', 'sum', 'sqrt', 'degrees', 'split', 'rint', 'log10', 'sin', 'log2', 'cos', 'ceil', 'broadcast', 'sinh', 'repeat', 'trunc', 'expm1', 'tanh', 'radians', 'exp', 'log1p', 'mean']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.plotly as py\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import ttest_ind\n",
    "from collections import defaultdict as dd\n",
    "\n",
    "import datetime as DT\n",
    "\n",
    "from pyspark.sql import Row\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import *\n",
    "import itertools\n",
    "\n",
    "from moztelemetry.dataset import Dataset\n",
    "from moztelemetry import get_pings_properties\n",
    "\n",
    "from scipy.stats import mannwhitneyu\n",
    "from __future__ import division\n",
    "\n",
    "py_max = __builtin__.max\n",
    "py_map = __builtin__.map\n",
    "\n",
    "%pylab inline\n",
    "\n",
    "## these ones pull the data\n",
    "#\n",
    "def recursive_get(d, keys):\n",
    "    if len(keys) == 1:\n",
    "        return d.get(keys[0],{})\n",
    "    return recursive_get(d.get(keys[0],{}), keys[1:])\n",
    "\n",
    "def extract_probes(p):\n",
    "    branch = p.get(\"environment\",{}).get(\"experiments\",{}).get(EXPERIMENT_SLUG, {}).get(\"branch\", \"warning\")\n",
    "\n",
    "    output = []\n",
    "    for path, probe_names in experiment_probes.iteritems():\n",
    "        for probe_name in probe_names:\n",
    "            probe = recursive_get(p, path.split(\"/\")).get(probe_name, {})\n",
    "            #take all entries in histogram right now. this is probably problematic. inspect more\n",
    "            for k,v in probe.get(\"values\",{}).iteritems():\n",
    "                if v <= sys.maxint:\n",
    "                    output.extend([{\"probe\": probe_name, \"branch\": branch, \"val\": float(k)}] * int(v))                \n",
    "    return output\n",
    "\n",
    "def exptPings(slug, samp, start= DT.date.today().strftime(\"%Y%m%d\"), end= DT.date.today().strftime(\"%Y%m%d\")):\n",
    "    # returns a spark df with values from probe dictionary by branch and nothing else, default start/end dates are today\n",
    "    cohorts = Dataset.from_source(\"telemetry-cohorts\")\n",
    "    main_pings = cohorts.where(submissionDate = lambda x: x >= start and x <= end).where(experimentId= slug).where(docType= \"main\").records(sc, sample= samp)\n",
    "    #main_pings.cache() this was in ilana's ognb, mreid advised to remove it for the exception I was hitting\n",
    "    \n",
    "    probe_dicts = main_pings.flatMap(extract_probes)\n",
    "    return sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))\n",
    "\n",
    "## these ones do the analysis\n",
    "#\n",
    "\n",
    "###### get the bins we use for the histogram for a probe by looking at all branches\n",
    "def get_bins(probe_name, logscale=False):\n",
    "    \n",
    "    all_branches = [r.val for r in df.where(df.probe == probe_name).collect()]\n",
    "\n",
    "    #remove top 0.5%, bottom 0.5% for easy outlier\n",
    "    trim = int(len(all_branches)/200.0)\n",
    "    all_branches_trimmed = sorted(all_branches)\n",
    "    all_branches_trimmed = all_branches_trimmed[trim:-1*trim]\n",
    "    \n",
    "    if logscale:\n",
    "        if all_branches_trimmed[0] < 1:\n",
    "            all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)\n",
    "        return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))\n",
    "    \n",
    "    n,b = np.histogram(all_branches_trimmed,10)\n",
    "    return b\n",
    "\n",
    "# get values for branch of experiment for pref, and trim off outliers\n",
    "def get_vals(pref_name, branch, samp, seed=None):\n",
    "    x_vals = [r.val for r in df.where(df.probe == pref_name)\\\n",
    "                               .where(df.branch == branch)\\\n",
    "                               .sample(False, samp, seed).collect()] \n",
    "\n",
    "    trim = int(len(x_vals)/200.0)\n",
    "    x_trimmed = sorted(x_vals)[trim:-1*trim]\n",
    "    return x_trimmed\n",
    "\n",
    "def median(lst):\n",
    "    return lst[(len(lst))/2]\n",
    "\n",
    "# return (pval, direction) if significant p value for mannwhitneyu vs control\n",
    "def test_unequal(branch_vals, control_vals, p_threshold=.05):\n",
    "    \n",
    "    try:\n",
    "        r = mannwhitneyu(branch_vals, control_vals)\n",
    "    except:\n",
    "        return None, None\n",
    "    \n",
    "    prefix = \"\"\n",
    "    if r.pvalue < p_threshold:\n",
    "        prefix = \"***\"\n",
    "    \n",
    "    if median(branch_vals) > median(control_vals):\n",
    "        return (prefix + str(r.pvalue), \"> control\")\n",
    "   \n",
    "    return (prefix + str(r.pvalue), \"< control\")\n",
    "\n",
    "# return (proceed bool, reason)\n",
    "def can_chart_pref(pref_name):\n",
    "    n = df.where(df.probe == pref_name).count()\n",
    "    if n==0:\n",
    "        return (False, \"0 entries for pref %s\"%pref_name)\n",
    "    elif n>100000000:\n",
    "        return (False, \"%i values for pref %s\"%(n,pref_name))\n",
    "    return (True, None)   \n",
    "\n",
    "# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean\n",
    "def chart_pref(pref_name, logscale, samp):\n",
    "   \n",
    "    sig_branches = []    \n",
    "    fig, axarr = plt.subplots(n_branches, 1, sharex=True, sharey= 'col')\n",
    "\n",
    "    b = get_bins(pref_name, logscale)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    control_vals = get_vals(pref_name, \"Control\", samp, 666)\n",
    "    \n",
    "    for i in range(n_branches):\n",
    "        \n",
    "        if branches[i] == \"Control\":\n",
    "            x = control_vals\n",
    "            \n",
    "        else:\n",
    "            x = get_vals(pref_name, branches[i], samp, 666) \n",
    "        \n",
    "        if logscale: #always assume 0 as lowest val for now\n",
    "            x_trans = py_map(lambda d: d+1, x)\n",
    "            ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)\n",
    "            \n",
    "        else: \n",
    "            axarr[i].hist(x, bins=b)\n",
    "            \n",
    "        axarr[i].set_title(branches[i])\n",
    "        \n",
    "        if branches[i] == \"Control\": continue\n",
    "        \n",
    "        print \"len(branch_vals) = \" + str(len(x)) + \", len(control_vals) = \" + str(len(control_vals))\n",
    "        if len(x) != 0 | len(control_vals) != 0:\n",
    "#            p, direction = test_unequal(x, control_vals)\n",
    "#            if p is not None and p.startswith(\"***\"):\n",
    "#                print branches[i], p #, direction\n",
    "            print pref_name + \" (logscale=\" + str(logscale) + \")\" + \":\"\n",
    "        else:\n",
    "            print pref_name + \" branch with no values\"\n",
    "            continue\n",
    "            \n",
    "    plt.show()\n",
    "    \n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "\n",
    "def chart_ecdfs(pref_name, samp, *percentiles):\n",
    "    \n",
    "    legend = []\n",
    "    \n",
    "    for i in range(n_branches):\n",
    "        legend_entry = \"\"\n",
    "        v = get_vals(pref_name, branches[i], samp, 666)\n",
    "        if len(v) == 0: continue\n",
    "        cdf = ECDF(v)\n",
    "        curr_plot = plt.plot(cdf.x, cdf.y)\n",
    "        curr_color = curr_plot[0].get_color()\n",
    "        legend_entry += branches[i]\n",
    "        for pct in percentiles:\n",
    "            p = np.percentile(np.array(v), pct)\n",
    "            plt.scatter(p, pct/100.0, facecolors = \"none\", \\\n",
    "                     edgecolors = curr_color, label=\"_nolegend_\")\n",
    "            legend_entry += \", \" + str(pct) + \"th percentile=\" + str(p)\n",
    "            \n",
    "        legend.append(legend_entry)\n",
    "        \n",
    "    plt.legend(legend, bbox_to_anchor=[1, .5], loc='center left')\n",
    "    plt.show()\n",
    "    \n",
    "EXPERIMENT_SLUG = \"pref-flip-tracker-request-beta-1434379\"\n",
    "branches = ['Control', 'Variant']\n",
    "n_branches = len(branches)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching 31377.13393MB in 518 files...\n",
      "len(branch_vals) = 8735881, len(control_vals) = 8664681\n",
      "TIME_TO_NON_BLANK_PAINT_MS (logscale=True):\n"
     ]
    },
    {
     "data": {
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Hjmk3yO52IvAw8LcAST6U5LtJ/jHJo0n+V5I3dX6h4zm1TyX590l+RPOaumM2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\n3Q4BcftAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f523784cdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8sgHHr51DUkoKbtv9DY2+vNohERERERER0WOAiQl6aIn3UtB/fQ8gQwukOwGZWtRK6a52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\nNvR9993Xr7/+ei8uLi5X7oYbbvD27dt7enp6dEi9yuqsbD0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5251239410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_probes = {}\n",
    "experiment_probes[\"payload/processes/content/histograms\"] =  [\"TIME_TO_NON_BLANK_PAINT_MS\"]\n",
    "probe_names = list(itertools.chain(*experiment_probes.values()))\n",
    "\n",
    "df = exptPings(EXPERIMENT_SLUG, 0.1, \"20180220\", \"20180305\")\n",
    "\n",
    "for p in probe_names:\n",
    "    chart_pref(p, True, 1.0)\n",
    "    chart_ecdfs(p, 1.0, 50, 95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching 31377.13393MB in 518 files...\n",
      "len(branch_vals) = 12996347, len(control_vals) = 13099774\n",
      "TIME_TO_DOM_INTERACTIVE_MS (logscale=True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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skiRJA87AJknPUpI5SbYm+dBs90XS7sXAJmmXleTLSTYn2XeKmkuTPJbkBTPU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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04b3697dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IiIgeQUxMUI6ciz6Hi7cv4o52Ea7KB4d+bKF3SERERERERPQIY2KCsu16bDTKTikHUUbA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\nv18JJCYAAAAAAFVIotcB4NDZ53bJp2SvwwAAAAAAIGa0mIgjBdqjxOI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04cd0aa410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_probes = {}\n",
    "experiment_probes[\"payload/processes/content/histograms\"] =  [\"TIME_TO_DOM_INTERACTIVE_MS\"]\n",
    "probe_names = list(itertools.chain(*experiment_probes.values()))\n",
    "\n",
    "df = exptPings(EXPERIMENT_SLUG, 0.1, \"20180220\", \"20180305\")\n",
    "\n",
    "for p in probe_names:\n",
    "    chart_pref(p, True, 1.0)\n",
    "    chart_ecdfs(p, 1.0, 50, 95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching 31377.13393MB in 518 files...\n",
      "len(branch_vals) = 10891346, len(control_vals) = 10961694\n",
      "TIME_TO_LOAD_EVENT_START_MS (logscale=True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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PTZ87JE0EvgLcXknfzczMzGplV+ewjQECeBNA0oeAccATpUBErAWeBY5Oh44kKxTzmZXA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\n/wH5VauaJW5fTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f525142f310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZKb8DaAjUrBCMKiUDnR0eERERERERPaCY7CCn+afCQKDmDzZlpQ2lEVS+pJMiIiIiIiIi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\nmTNHF1xwQbHzCIW7jtatW6d33nkn5NoIJzc3Vw8//LDatm0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5251239410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_probes = {}\n",
    "experiment_probes[\"payload/processes/content/histograms\"] =  [\"TIME_TO_LOAD_EVENT_START_MS\"]\n",
    "probe_names = list(itertools.chain(*experiment_probes.values()))\n",
    "\n",
    "df = exptPings(EXPERIMENT_SLUG, 0.1, \"20180220\", \"20180305\")\n",
    "\n",
    "for p in probe_names:\n",
    "    chart_pref(p, True, 1.0)\n",
    "    chart_ecdfs(p, 1.0, 50, 95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching 16033.39773MB in 259 files...\n",
      "len(branch_vals) = 7339844, len(control_vals) = 7043928\n",
      "TIME_TO_DOM_CONTENT_LOADED_START_MS (logscale=True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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OasZuZmZmVi/b+wzbGCCAtwAkfRQYCzxZDkTEKuA54Ih06DCyQjGfWQZ05zKTgZXlYi15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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc39e3d7c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ExAQ9kKNxe6A43kaPqoPhYGOHUe37ah0SERERERERPYaYmKAiuXsXuHkTSEkG9LrKWD/s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\n6+Z1jTa9Dh06pNatXLlynvWefvrpQsX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piJ2AUAr3HMjPz9ewYcP01FNPRZf23LVrV4Xn\nUSY7Ozt6fyrOOuusuJ4l3bt3V/369bVixYqUHj98+PC4XyWHDBmim2++Wa+//rpGjBihefPmacWK\nFbrnnnviZpN3d51++ukVfqk1M40YMSJu3/r16/X+++9rzJgxlfZEcXe98soruvLKK1VcXBx3rjPP\nPFPTpk3TwoUL1bNnz+j+a665Jq6bd58+feTuWrFiRXTIxoGYNm2azEwXXXRRXD5HHXWUjj32WM2e\nPVu33XabpPB8D7HDVipTfr6NnJwcde7cWccee6zOPvtsffPNN5o4caIGDRqk9957T23atJGkKifq\nTHbNTJkyRXl5eSnll+zajI3ZFzfeeKNGjhypa6+9VrfddpuKior0v//7v/rqq68qHHPChAlxjx08\neLDatWuncePG6ZVXXtGFF6Y2QX1Zz4AyderUUc+ePVN+bfTp0yduUs3WrVvrvPPO01//+ldJ+3fN\nXnXVVRWGFkyfPl35+fk6++yzK83lD3/4g77zne+oXbt2cec5/fTT5e6aPXt23PCCA31fSGb+/Pkp\nvy/k5+enNLxMUrRnWUZGhq6//npNnDhRd9xxR3RYy5gxY6IrF1V2HS5ZskSLFi3S2LFjk54v1WEc\npaWlmjp1qnr27Bk3UWtlUn0dHYz5dyhMpLms7e2UGeLPDACofv/a+C/l/7pid8+Dbf7189XjqB7J\nA1Nw2WWX6eGHH9aUKVN0++23a82aNXrvvfd08803V+gC/Oqrr2r8+PFatGhRtHuulHjiwbIvYcns\n2rVL9957r5577jl9+eWX0UnczCxhl+FWrVrF3S6bzf1gLmlamSuuuEK33nqrZs6cGS1MlBUCYtuj\nzO7du/dp9Y3yz00Kr16Q6nMrXwgyM7Vr1y66NOSyZcskKeEH97LlNXfs2BG3DGXbtm3j4pYvXy5J\nVS6xt27dOm3btk0FBQV64oknEp6r/EoT1f13XbZsmUpKSnTssccmzCd2voS2bdtWeN6pGDRokHJz\nc6PzikjSeeedp/bt2+vnP/95dLhRTk5OwutFCl8zibq8lz2HefPm6ZZbbkmpe36yazM2Zl/cdNNN\nWrNmjSZOnKjf/e53MjP16tVLt912m+67776k1/wtt9yicePGaebMmSkXJhK9Npo0aaKlS5em9PhE\nRdIOHTpo+vTp2rRpk/bs2bPP12yi97jly5cnnEw41tKlS7Vs2bK4IWFVnedA3xeSKWvDVN4XGjdu\nXKFgnYp7771Xmzdv1oQJEzR+/HiZmQYOHKjhw4frN7/5TaXXzO9///uU54x48cUX1a1bt6QF6lmz\nZmndunXReTmSqa7XUSJ8Y01nNXuCXwBADdOpWSfNv37+ITnPwdKjRw916tRJhYWFuv3226MTzpX/\nIDh79mwNGjRIZ5xxhp566inl5eUpKytLv/nNb+K+iJVJ9YPaDTfcoClTpmj06NH63ve+p9zcXJmZ\nLr74YpWWllaIr2xZtrKCRnVr1apV3GSbZb0G1q5dW2Hiw7Vr16pv374pH7u6n1tZez7yyCOVLslY\n/u+2Px+4y84zbNiwSr+knXjiiXG3D8Vzz8zMjP5CXl7sfBU7duzQ9u3bkx4zMzNTRxxxhKTwl7tZ\ns2bpmWeeiYs54ogj1Lt3b82ZMye676ijjtLevXu1ZcuWuDkq9uzZoy1btujoo49OeL59+ZImSc2a\nNVNWVpbWrl1b4b61a9fKzPZ7/pXx48dr7Nix+uc//6lGjRqpa9eu0V+1k/VwKfuCG/s6SuZQvTb2\n5Zrd3y+jpaWl6tatmx588MGE+Zefj+dwel8oKipK+e/WvHnzaAGtTp06mjRpksaPH6+lS5cqLy9P\n7dq10+DBg5WRkZGwYCiFJ6js2rVrhdU3ynvrrbe0evXqCj3dEpk8ebIyMzPjeuNVJfY9vry1a9fG\nPc8DRWEijS1eLOnAe/8BAJCSeln1DlpPhkPpsssu05133qmPP/5YhYWFat++fYWJvqZPn6769evr\nr3/9a9wH5aeffvqAzj1t2jRdc801cZMr7tq1K+UJ1hI5WB8Sy3N3rVy5Mjo5nyR169ZN7q558+ap\nW7du0f1ffPGF1q1bF500rjrzKlP+12N31/Lly9WrVy9JinZbzs3N3a9fPWOP8cknn1Qak5eXp/r1\n66u0tHS/z5PIgbRfu3btoj0mkvXmmTBhgu69996kxzzuuOP0+eefSwoPcZEU7Zoeq6ioSMXFxdHb\nZdfJvHnz4pYD/fDDD+XucddRrMLCQnXo0CFuYsmqZGRkqGvXrpo3b16F+z788EO1b9/+gJa4bNSo\nUdxrYebMmWrdunXSIVxbt27V5s2b43oMHOrXhiR99tlnatiwoZo2baqSkpKDcs22a9euytdGWcxn\nn32W0nCcVB3oa0NK7X3hnXfeUf/+/VPK54svvqhQZGvevLmaN28uKfxaeeedd3TyyScnvA7nzJmj\nlStXVhgOlMjkyZOVkZGhSy65pMq4PXv26JVXXtH3v//9hD1WEjnmmGPUtGnThK+juXPnVvp63R8s\nF5rG9rb6mw5SzxoAANLWZZddJnfXnXfeqYULFyb8xTAjI0OhUCjui9eKFSv0pz/96YDOnZGRUaFn\nxCOPPHJAvwbWr18/bmm+/RE71rrM448/rs2bN2vgwIHRfSeccILat2+vX//613E5FxQUKCMjI66r\netkQiQPNrTLPPfecduzYEb394osvasOGDdHx7r169VKbNm30wAMPJFx1ItGyseW1aNFCvXv31qRJ\nk7RmzZqEMRkZGRo0aJCmTp2qJUuWJD1Pql+qDuTvetFFF8nMKp2FP/ZX4KuvvlozZ85Muj3//PPR\nx7Rv315mppdeeinuuKtWrdKcOXPiignf//73lZubq4KCgrjYJ598Ug0bNoy7vsqUrSJQ1TCBL774\nIlooKXPxxRfrgw8+0KJFi6L7Pv30U7399tuVrvyxPyZPnqyPPvooOsRJCn8JjL0ey9x1113Rrvxl\nqvu18d5772nx4sXR2ytXrtSf//xnnXXWWZIO3jV70UUXaf78+XrttdcqzWXw4MH6z3/+U6F3jRQu\nyu7PvB8H0n778r5QNsdEsu1vf/tb0i/+EyZM0FdffaVbb7014f1TpkxRKBTS0KFDqzxOUVGRpk+f\nrtNOOy1pD6BXX31V27Ztq3KFjxUrVsQtVSyF5xh69dVX43pNvPHGG1qxYsVBfR0dkh4TZnaTpNsk\n5UlaJOnH7v6PFB53sqS3JH3s7jXvJ5iAlTZephbZ/x10GgAAHNbatGmj3r17a8aMGZV2FT/nnHP0\n2GOPacCAARo6dKjWrl2rgoICdezYUf/85z/3+9znnnuunnnmGTVo0EAdO3bU3//+d7399tsJl46r\nrFhRfn9+fr5++9vfavz48WrXrp3y8vKiQypatmwZXaKuMiUlJWrdurWGDBmi448/XtnZ2Xr77bej\nE6Zde+21cfEPPPCALrzwQg0YMEBDhgzRwoULVVBQoBtvvDHu1+P8/Hy5u37605/qBz/4gbKysnTB\nBRcoM/PgfBxt1KiR+vTpo+HDh+vLL7/Uo48+qs6dO+vqq6+WFJ7sdNKkSTr33HN1/PHHa/jw4Tr6\n6KO1Zs0azZo1S0ceeWTCYTnlPf744+rbt6+6d++u66+/Xm3atNGKFSv05ptvRn9VvP/++/XOO++o\nV69euu6669S5c2dt2rRJ8+bN07vvvqt169ZFj5dqESo/P1/Tp0/XmDFjlJ+fr9zc3ConGYzVvn17\n3XXXXbrzzju1fPlynX/++WrQoIFWrFihV155RT/+8Y81cuRISfs3x0SLFi00bNgwPffcc+rfv78G\nDRqkLVu2qKCgQEVFRbr99tujsfXq1dPdd9+tm2++WZdccon69++v2bNna+rUqbr//vvj5rsoUzaM\no6ovaUOHDtWHH36ooqKi6L4f/ehHmjRpkgYOHKhbb71VoVBIEydOVMuWLXXzzTfHPf7yyy/XlClT\ntHr16kqHk0jhYV0TJkxQ//791bRpU82ZM0fPP/+8zjvvPN10003RuNWrV6tXr14aOnSoOnbsKHfX\nX/7yF73xxhs6//zz4/527du3V8OGDVVQUKDs7GzVr19fvXv3Tji/wv7o2rWrzjzzTP34xz9WZmam\nCgoKlJmZqV/84hfRmINxzY4dO1bTpk3ThRdeqKuvvlrdu3fX119/rRkzZuh3v/udunTpouHDh+vl\nl1/Wddddp5kzZ6p3794qLi7WkiVL9PLLL2v27Nk64YQT9un5VfbeUtmSsrH578v7wv7OMfH8889r\nxowZ6tOnj+rXr68333xT06dP14gRI3TuuedWiC8pKdHLL7+sk08+Oek18Nprr2nz5s0pLSc6efJk\n1atXTxdccEGlMaeeemqFfyPuuOOOaPFj5MiR2rp1qx588EF1795dV1xxRdLzpizZsh0HukkaImm3\npCsldZL0tKRNkpoleVwjScsk/UXSgiri0nYZswOln+b6ueMfTB4IAKiVavtyobEKCgo8FAr5SSed\nVGnMpEmTvEOHDp6Tk+Ndu3b1F154ocKyjMXFxR4KhfyWW26p8Piy+8aPHx/dt2XLFr/qqqu8efPm\nnpub6+eee64vW7bMW7Vq5ddff33cuUOhkC9atCjumOWXvnN3X7t2rZ9zzjmem5vroVAobunQJk2a\neN++fZO2x3XXXeddu3b1Ro0aeXZ2tnfs2NHvuOOOuKVCY73yyivevXt3z8nJ8WOOOcbvuusuLykp\nqRB39913e8uWLT0zMzO6pF5VbVa+HRIpa4Np06b57bff7nl5eV6/fn2/4IILfPXq1RXiP/roI7/w\nwgu9WbNmnpOT423btvWhQ4f622+/HY254447PBQK+datWxO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc3bb136790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_probes = {}\n",
    "experiment_probes[\"payload/processes/content/histograms\"] =  [\"TIME_TO_DOM_CONTENT_LOADED_START_MS\"]\n",
    "probe_names = list(itertools.chain(*experiment_probes.values()))\n",
    "\n",
    "df = exptPings(EXPERIMENT_SLUG, 0.05, \"20180220\", \"20180305\")\n",
    "\n",
    "for p in probe_names:\n",
    "    chart_pref(p, True, 1.0)\n",
    "    chart_ecdfs(p, 1.0, 50, 95)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

Tracker requests tailing (Pref Flip).py

# coding: utf-8

# In[1]:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.plotly as py
from scipy.stats import chi2_contingency
from scipy.stats import ttest_ind
from collections import defaultdict as dd

import datetime as DT

from pyspark.sql import Row
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import *
import itertools

from moztelemetry.dataset import Dataset
from moztelemetry import get_pings_properties

from scipy.stats import mannwhitneyu
from __future__ import division

py_max = __builtin__.max
py_map = __builtin__.map

get_ipython().magic(u'pylab inline')

## these ones pull the data
#
def recursive_get(d, keys):
    if len(keys) == 1:
        return d.get(keys[0],{})
    return recursive_get(d.get(keys[0],{}), keys[1:])

def extract_probes(p):
    branch = p.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")

    output = []
    for path, probe_names in experiment_probes.iteritems():
        for probe_name in probe_names:
            probe = recursive_get(p, path.split("/")).get(probe_name, {})
            #take all entries in histogram right now. this is probably problematic. inspect more
            for k,v in probe.get("values",{}).iteritems():
                if v <= sys.maxint:
                    output.extend([{"probe": probe_name, "branch": branch, "val": float(k)}] * int(v))                
    return output

def exptPings(slug, samp, start= DT.date.today().strftime("%Y%m%d"), end= DT.date.today().strftime("%Y%m%d")):
    # returns a spark df with values from probe dictionary by branch and nothing else, default start/end dates are today
    cohorts = Dataset.from_source("telemetry-cohorts")
    main_pings = cohorts.where(submissionDate = lambda x: x >= start and x <= end).where(experimentId= slug).where(docType= "main").records(sc, sample= samp)
    #main_pings.cache() this was in ilana's ognb, mreid advised to remove it for the exception I was hitting
    
    probe_dicts = main_pings.flatMap(extract_probes)
    return sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))

## these ones do the analysis
#

###### get the bins we use for the histogram for a probe by looking at all branches
def get_bins(probe_name, logscale=False):
    
    all_branches = [r.val for r in df.where(df.probe == probe_name).collect()]

    #remove top 0.5%, bottom 0.5% for easy outlier
    trim = int(len(all_branches)/200.0)
    all_branches_trimmed = sorted(all_branches)
    all_branches_trimmed = all_branches_trimmed[trim:-1*trim]
    
    if logscale:
        if all_branches_trimmed[0] < 1:
            all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)
        return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))
    
    n,b = np.histogram(all_branches_trimmed,10)
    return b

# get values for branch of experiment for pref, and trim off outliers
def get_vals(pref_name, branch, samp, seed=None):
    x_vals = [r.val for r in df.where(df.probe == pref_name)                               .where(df.branch == branch)                               .sample(False, samp, seed).collect()] 

    trim = int(len(x_vals)/200.0)
    x_trimmed = sorted(x_vals)[trim:-1*trim]
    return x_trimmed

def median(lst):
    return lst[(len(lst))/2]

# return (pval, direction) if significant p value for mannwhitneyu vs control
def test_unequal(branch_vals, control_vals, p_threshold=.05):
    
    try:
        r = mannwhitneyu(branch_vals, control_vals)
    except:
        return None, None
    
    prefix = ""
    if r.pvalue < p_threshold:
        prefix = "***"
    
    if median(branch_vals) > median(control_vals):
        return (prefix + str(r.pvalue), "> control")
   
    return (prefix + str(r.pvalue), "< control")

# return (proceed bool, reason)
def can_chart_pref(pref_name):
    n = df.where(df.probe == pref_name).count()
    if n==0:
        return (False, "0 entries for pref %s"%pref_name)
    elif n>100000000:
        return (False, "%i values for pref %s"%(n,pref_name))
    return (True, None)   

# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean
def chart_pref(pref_name, logscale, samp):
   
    sig_branches = []    
    fig, axarr = plt.subplots(n_branches, 1, sharex=True, sharey= 'col')

    b = get_bins(pref_name, logscale)
    plt.tight_layout()

    control_vals = get_vals(pref_name, "Control", samp, 666)
    
    for i in range(n_branches):
        
        if branches[i] == "Control":
            x = control_vals
            
        else:
            x = get_vals(pref_name, branches[i], samp, 666) 
        
        if logscale: #always assume 0 as lowest val for now
            x_trans = py_map(lambda d: d+1, x)
            ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)
            
        else: 
            axarr[i].hist(x, bins=b)
            
        axarr[i].set_title(branches[i])
        
        if branches[i] == "Control": continue
        
        print "len(branch_vals) = " + str(len(x)) + ", len(control_vals) = " + str(len(control_vals))
        if len(x) != 0 | len(control_vals) != 0:
#            p, direction = test_unequal(x, control_vals)
#            if p is not None and p.startswith("***"):
#                print branches[i], p #, direction
            print pref_name + " (logscale=" + str(logscale) + ")" + ":"
        else:
            print pref_name + " branch with no values"
            continue
            
    plt.show()
    
from statsmodels.distributions.empirical_distribution import ECDF

def chart_ecdfs(pref_name, samp, *percentiles):
    
    legend = []
    
    for i in range(n_branches):
        legend_entry = ""
        v = get_vals(pref_name, branches[i], samp, 666)
        if len(v) == 0: continue
        cdf = ECDF(v)
        curr_plot = plt.plot(cdf.x, cdf.y)
        curr_color = curr_plot[0].get_color()
        legend_entry += branches[i]
        for pct in percentiles:
            p = np.percentile(np.array(v), pct)
            plt.scatter(p, pct/100.0, facecolors = "none",                      edgecolors = curr_color, label="_nolegend_")
            legend_entry += ", " + str(pct) + "th percentile=" + str(p)
            
        legend.append(legend_entry)
        
    plt.legend(legend, bbox_to_anchor=[1, .5], loc='center left')
    plt.show()
    
EXPERIMENT_SLUG = "pref-flip-tracker-request-beta-1434379"
branches = ['Control', 'Variant']
n_branches = len(branches)


# In[2]:

experiment_probes = {}
experiment_probes["payload/processes/content/histograms"] =  ["TIME_TO_NON_BLANK_PAINT_MS"]
probe_names = list(itertools.chain(*experiment_probes.values()))

df = exptPings(EXPERIMENT_SLUG, 0.1, "20180220", "20180305")

for p in probe_names:
    chart_pref(p, True, 1.0)
    chart_ecdfs(p, 1.0, 50, 95)


# In[2]:

experiment_probes = {}
experiment_probes["payload/processes/content/histograms"] =  ["TIME_TO_DOM_INTERACTIVE_MS"]
probe_names = list(itertools.chain(*experiment_probes.values()))

df = exptPings(EXPERIMENT_SLUG, 0.1, "20180220", "20180305")

for p in probe_names:
    chart_pref(p, True, 1.0)
    chart_ecdfs(p, 1.0, 50, 95)


# In[3]:

experiment_probes = {}
experiment_probes["payload/processes/content/histograms"] =  ["TIME_TO_LOAD_EVENT_START_MS"]
probe_names = list(itertools.chain(*experiment_probes.values()))

df = exptPings(EXPERIMENT_SLUG, 0.1, "20180220", "20180305")

for p in probe_names:
    chart_pref(p, True, 1.0)
    chart_ecdfs(p, 1.0, 50, 95)


# In[3]:

experiment_probes = {}
experiment_probes["payload/processes/content/histograms"] =  ["TIME_TO_DOM_CONTENT_LOADED_START_MS"]
probe_names = list(itertools.chain(*experiment_probes.values()))

df = exptPings(EXPERIMENT_SLUG, 0.05, "20180220", "20180305")

for p in probe_names:
    chart_pref(p, True, 1.0)
    chart_ecdfs(p, 1.0, 50, 95)