taar/analysis/TAARExperimentV2Retention.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark.sql.functions as F\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import pyspark.sql.types as st\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from IPython.display import Markdown\n",
    "\n",
    "seaborn.set_style(\"whitegrid\")\n",
    "sc.setLogLevel(\"INFO\")\n",
    "udf = F.udf\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PERIODS = {}\n",
    "N_WEEKS = 12\n",
    "for i in range(1, N_WEEKS + 1):\n",
    "    PERIODS[i] = {\n",
    "        'start': i * 7,\n",
    "        'end': i * 7 + 6\n",
    "    }\n",
    "\n",
    "\n",
    "def date_plus_x_days(date, x):\n",
    "    \"\"\"\n",
    "    Returns a string date x days away from <date>\n",
    "    \n",
    "    Params:\n",
    "    date (str): date in %Y%m%s format\n",
    "    x (int) number of days to add to <date> (can be negative)\n",
    "    \n",
    "    >>> date_plus_x_days(\"20180101\", 1)\n",
    "    \"20180102\"\n",
    "    \n",
    "    >>> date_plus_x_days(\"20180510\", -9)\n",
    "    \"20180501\"\n",
    "    \"\"\"\n",
    "    new_date = dt.datetime.strptime(date, '%Y%m%d') + dt.timedelta(days=x)\n",
    "    return new_date.strftime('%Y%m%d')\n",
    "\n",
    "\n",
    "def date_diff(d1, d2, fmt='%Y%m%d'):\n",
    "    \"\"\"\n",
    "    Returns days elapsed from d2 to d1 as an integer\n",
    "\n",
    "    Params:\n",
    "    d1 (str)\n",
    "    d2 (str)\n",
    "    fmt (str): format of d1 and d2 (must be the same)\n",
    "\n",
    "    >>> date_diff('20170205', '20170201')\n",
    "    4\n",
    "\n",
    "    >>> date_diff('20170201', '20170205)\n",
    "    -4\n",
    "    \"\"\"\n",
    "    try:\n",
    "        return (pd.to_datetime(d1, format=fmt) - \n",
    "                pd.to_datetime(d2, format=fmt)).days\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "\n",
    "@udf(returnType=st.IntegerType())\n",
    "def get_period(anchor, submission_date_s3):\n",
    "    \"\"\"\n",
    "    Given an anchor and a submission_date_s3,\n",
    "    returns what period a ping belongs to. This \n",
    "    is a spark UDF (see decoration).\n",
    "\n",
    "    Params:\n",
    "    anchor (col): anchor date\n",
    "    submission_date_s3 (col): a ping's submission_date to s3\n",
    "\n",
    "    Global:\n",
    "    PERIODS (dict): defined globally based on n-week method\n",
    "\n",
    "    Returns an integer indicating the retention period\n",
    "    \"\"\"\n",
    "    if anchor is not None:\n",
    "        diff = date_diff(submission_date_s3, anchor)\n",
    "        if diff >= 7: # exclude first 7 days\n",
    "            for period in sorted(PERIODS):\n",
    "                if diff <= PERIODS[period]['end']:\n",
    "                    return period\n",
    "                \n",
    "def get_retention(data):\n",
    "    branch_counts = (\n",
    "        data\n",
    "        .groupby(\"branch\")\n",
    "        .agg(F.countDistinct(\"client_id\").alias(\"total_clients\"))\n",
    "        )\n",
    "\n",
    "    weekly_counts = (\n",
    "        data\n",
    "        .groupby(\"period\", \"branch\")\n",
    "        .agg(F.countDistinct(\"client_id\").alias(\"n_week_clients\"))\n",
    "    )\n",
    "\n",
    "    retention_by_branch = (\n",
    "        weekly_counts\n",
    "        .join(branch_counts, on='branch')\n",
    "        .withColumn(\"retention\", F.col(\"n_week_clients\") / F.col(\"total_clients\"))\n",
    "    )\n",
    "    \n",
    "    ret_df = retention_by_branch.toPandas()\n",
    "    ret_df.fillna(0, inplace=True)\n",
    "    \n",
    "    return ret_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in cleaned experiment data, generated from [this notebook](https://github.com/mozilla/taar/blob/master/analysis/TAARExperimentV2Analysis.ipynb). Filter to clients that loaded the discopane (this is for the control group, since we have already cross referenced the TAAR logs. Clients who did not load the discopane never saw the control, serving as noise in the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "S3_PATH = \"s3://net-mozaws-prod-us-west-2-pipeline-analysis/taarv2/cleaned_data/\"\n",
    "clean_data = sqlContext.read.parquet(S3_PATH).filter(\"discopane_loaded = true\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-------------------------+\n",
      "|       branch|count(DISTINCT client_id)|\n",
      "+-------------+-------------------------+\n",
      "|ensemble-taar|                   108381|\n",
      "|      control|                   139913|\n",
      "|  linear-taar|                   108071|\n",
      "+-------------+-------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clean_data.groupby(\"branch\").agg(F.countDistinct(\"client_id\")).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab the min and max submission dates for filtering `main_summary`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20180312 20180423\n"
     ]
    }
   ],
   "source": [
    "min_date = clean_data.select(F.min('submission_date_s3').alias('min_d')).collect()[0].min_d\n",
    "max_date = clean_data.select(F.max('submission_date_s3').alias('max_d')).collect()[0].max_d\n",
    "print min_date, max_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in `main_summary`, filtered to the min date of the experiment, and 42 days beyond its compleition to allow for 6-week Retention Analysis. We then join `main_summary` with the experiment data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms = (\n",
    "    sqlContext.read.option(\"mergeSchema\", True)\n",
    "     .parquet(\"s3://telemetry-parquet/main_summary/v4\")\n",
    "     .filter(\"submission_date_s3 >= '{}'\".format(min_date))\n",
    "     .filter(\"submission_date_s3 <= '{}'\".format(date_plus_x_days(max_date, 7*N_WEEKS)))\n",
    "     .filter(\"normalized_channel = 'release'\")\n",
    "     .filter(\"app_name = 'Firefox'\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a client's enrollment date is determined by their first appearance in the experiment\n",
    "enrollment_dates = (\n",
    "    clean_data.groupby(\"client_id\", \"branch\")\n",
    "     .agg(F.min('submission_date_s3')\n",
    "          .alias(\"enrollment_date\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# join main_summary to exp data\n",
    "joined = enrollment_dates.join(ms.select(\"submission_date_s3\", \"client_id\"), on=\"client_id\", how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# verify join contains same number of distinct clients as the experiment data.\n",
    "# this also initializes our cache\n",
    "jc = joined.select(\"client_id\").distinct().count()\n",
    "cc = clean_data.select(\"client_id\").distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc - cc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Retention Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform 12-week retention analysis based on [this example](https://docs.telemetry.mozilla.org/cookbooks/retention.html). [1-12]-Week Retention are additionally included since we can get them at a low cost. We expand out to 12-week retention to better validate the data since the TAAR branches exhibit suspiciously similar retention values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = (\n",
    "    joined.withColumn(\"period\", get_period(\"enrollment_date\", \"submission_date_s3\"))\n",
    "          .filter(\"enrollment_date <= '{}'\".format(max_date))\n",
    ").distinct().cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18640155"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_df  = get_retention(joined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_df.to_csv(\"taar_v2_retention.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write to s3 since this job is quite expensive and should only be run once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 1.7 KiB/1.7 KiB (22.2 KiB/s) with 1 file(s) remaining\r",
      "upload: ./taar_v2_retention.csv to s3://net-mozaws-prod-us-west-2-pipeline-analysis/taarv2/taar_v2_retention.csv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "aws s3 cp taar_v2_retention.csv s3://net-mozaws-prod-us-west-2-pipeline-analysis/taarv2/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load processed Retention Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section loads the data generated above without having to the re-run the entire notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 1.7 KiB/1.7 KiB (29.9 KiB/s) with 1 file(s) remaining\r",
      "download: s3://net-mozaws-prod-us-west-2-pipeline-analysis/taarv2/taar_v2_retention.csv to ./taar_v2_retention.csv\n"
     ]
    }
   ],
   "source": [
    "%%bash \n",
    "aws s3 cp s3://net-mozaws-prod-us-west-2-pipeline-analysis/taarv2/taar_v2_retention.csv ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_df = pd.read_csv(\"taar_v2_retention.csv\")\n",
    "ret_df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y597wdVgiIiIiIlIG7dq1i6SkJG6//fYcz4mnpaXRvHlzwElsmzZtmrUvOjoa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\nGeglSZIkSaqh/w+8Au0iYgSqKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a591d7a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (12, 6)\n",
    "fig, ax = plt.subplots()\n",
    "for group, data in ret_df.groupby(\"branch\"):\n",
    "    (data.sort_values(\"period\")\n",
    "         .plot(x='period', \n",
    "               y='retention', \n",
    "               ax=ax, \n",
    "               label=group))\n",
    "plt.ylabel(\"Retention\")\n",
    "plt.xlabel(\"Week (period)\")\n",
    "plt.title(\"1-12 Week Retention by Branch\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>branch</th>\n",
       "      <th>period</th>\n",
       "      <th>n_week_clients</th>\n",
       "      <th>total_clients</th>\n",
       "      <th>retention</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>control</td>\n",
       "      <td>6.0</td>\n",
       "      <td>83578</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.597357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65909</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.608123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65698</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.607915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>control</td>\n",
       "      <td>7.0</td>\n",
       "      <td>80556</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.575758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63699</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.587732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63363</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.586309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>control</td>\n",
       "      <td>8.0</td>\n",
       "      <td>77789</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.555981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>8.0</td>\n",
       "      <td>61393</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.566455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>8.0</td>\n",
       "      <td>61164</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.565961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>control</td>\n",
       "      <td>9.0</td>\n",
       "      <td>75070</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.536548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>9.0</td>\n",
       "      <td>59350</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.547605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>9.0</td>\n",
       "      <td>59055</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.546446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>control</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72598</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.518880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57404</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.529650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57254</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.529781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>control</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70461</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.503606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55793</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.514786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55486</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.513422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>control</td>\n",
       "      <td>12.0</td>\n",
       "      <td>67862</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.485030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>12.0</td>\n",
       "      <td>54080</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.498980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>12.0</td>\n",
       "      <td>53843</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.498219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           branch  period  n_week_clients  total_clients  retention\n",
       "20        control     6.0           83578         139913   0.597357\n",
       "9   ensemble-taar     6.0           65909         108381   0.608123\n",
       "38    linear-taar     6.0           65698         108071   0.607915\n",
       "17        control     7.0           80556         139913   0.575758\n",
       "5   ensemble-taar     7.0           63699         108381   0.587732\n",
       "32    linear-taar     7.0           63363         108071   0.586309\n",
       "21        control     8.0           77789         139913   0.555981\n",
       "12  ensemble-taar     8.0           61393         108381   0.566455\n",
       "34    linear-taar     8.0           61164         108071   0.565961\n",
       "14        control     9.0           75070         139913   0.536548\n",
       "11  ensemble-taar     9.0           59350         108381   0.547605\n",
       "31    linear-taar     9.0           59055         108071   0.546446\n",
       "25        control    10.0           72598         139913   0.518880\n",
       "10  ensemble-taar    10.0           57404         108381   0.529650\n",
       "36    linear-taar    10.0           57254         108071   0.529781\n",
       "22        control    11.0           70461         139913   0.503606\n",
       "1   ensemble-taar    11.0           55793         108381   0.514786\n",
       "37    linear-taar    11.0           55486         108071   0.513422\n",
       "19        control    12.0           67862         139913   0.485030\n",
       "8   ensemble-taar    12.0           54080         108381   0.498980\n",
       "28    linear-taar    12.0           53843         108071   0.498219"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df[ret_df.period >= 6.0].sort_values([\"period\", \"branch\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate nearly identical retention lines for TAAR Branches\n",
    "\n",
    "Let's look at 6-week retention over time by each enrollment date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_over_day_retention = []\n",
    "for i in range(40):\n",
    "    d = date_plus_x_days(\"20180312\", i)\n",
    "    joinedx = joined.filter(\"enrollment_date = '{}'\".format(d))\n",
    "    ret_dfx = get_retention(joinedx)\n",
    "    week6 = ret_dfx[ret_dfx.period == 6.0]\n",
    "    for b, data in week6.groupby(\"branch\"):\n",
    "        x = {\n",
    "            'branch': b,\n",
    "            'ret': data['retention'].values[0],\n",
    "            'date': d\n",
    "        }\n",
    "        day_over_day_retention.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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/1SCNN4AYa+0fgqZNBp4GNuMGLlkO3Gyt/aF+90BERERERERk39pfRrlvhXst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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a59144450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_over_day_retention_df = pd.DataFrame(day_over_day_retention)\n",
    "day_over_day_retention_df.date = (\n",
    "    pd.to_datetime(day_over_day_retention_df.date, format='%Y%m%d'))\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12, 6)\n",
    "fig, ax = plt.subplots()\n",
    "for group, data in day_over_day_retention_df.groupby(\"branch\"):\n",
    "    data['ma'] = data.ret.rolling(window=6).mean()\n",
    "    (data.sort_values(\"date\")\n",
    "         .plot(x='date', \n",
    "               y='ma', \n",
    "               ax=ax, \n",
    "               label=group))\n",
    "plt.ylabel(\"Retention\")\n",
    "plt.xlabel(\"Enrollment Date\")\n",
    "plt.title(\"6 Week Retention by Enrollment Date, Branch (6-Day Moving Average)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see increased variability with time, which is most certainly due to the study being front-loaded with participants. Looking at enrollment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f95ac07eb10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VQz5P30DvDL0kSZIkZZmBPgPSQD97+tBL1gFMmVTDuHHJ5w30kiRJkpRtBvoM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\nhyzoJUmSJEnKIQt6SZIkSZJy6P8DRj9vimIQGXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ad993e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(joined.groupby(\"enrollment_date\")\n",
    "       .agg(F.countDistinct(\"client_id\").alias(\"number of participants\"))\n",
    "       .sort(\"enrollment_date\")\n",
    "       .toPandas()\n",
    "       .plot(x='enrollment_date'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see most of our clients are enrolled before 2018-03-22. This is why the lines are so smooth for the first ~9 datapoints in the previous retention chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Breaking retention down into shorter segments shows that there are indeed differences between the taar branches, however they track each other rather closely and are consistently higher than the control. This is evidence that the similarities we see in the lines for the first plot are in fact the true retention values and not a data-handling issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>branch</th>\n",
       "      <th>period</th>\n",
       "      <th>n_week_clients</th>\n",
       "      <th>total_clients</th>\n",
       "      <th>retention</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ensemble-taar</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65909</td>\n",
       "      <td>108381</td>\n",
       "      <td>0.608123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>control</td>\n",
       "      <td>6.0</td>\n",
       "      <td>83578</td>\n",
       "      <td>139913</td>\n",
       "      <td>0.597357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>linear-taar</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65698</td>\n",
       "      <td>108071</td>\n",
       "      <td>0.607915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           branch  period  n_week_clients  total_clients  retention\n",
       "9   ensemble-taar     6.0           65909         108381   0.608123\n",
       "20        control     6.0           83578         139913   0.597357\n",
       "38    linear-taar     6.0           65698         108071   0.607915"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w6r = ret_df[ret_df.period==6]\n",
    "w6r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "(slinear, nlinear,\n",
    " sensemble, nensemble,\n",
    " scontrol, ncontrol) = [int(w6r[w6r.branch == b][i].values[0])\n",
    "                        for b in ('linear-taar', 'ensemble-taar', 'control')\n",
    "                        for i in ('n_week_clients', \"total_clients\")]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_effect(g1s, g2s, g1n, g2n):\n",
    "    \"\"\"\n",
    "    Extracts the observed difference and p-value \n",
    "    for two proportions\n",
    "    \n",
    "    g1s: the number of successes for group 1\n",
    "    g2s: the number of successes for group 2\n",
    "    g1n: total trials for group 1\n",
    "    g2n: total trials for group 2\n",
    "    \n",
    "    returns the effect and p-value as a tuple\n",
    "    \"\"\"\n",
    "    # use counts to form a proportion and format\n",
    "    effect = str(round((g1s*1.0 / g1n) - (g2s*1.0 / g2n), 4) * 100) + '%'\n",
    "    \n",
    "    # perform test of proportions\n",
    "    pval = sm.stats.proportions_ztest(np.array([g1s, g2s]),\n",
    "                               np.array([g1n, g2n]),\n",
    "                               value=0.05)[1]\n",
    "    return effect, pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear effect: 1.06%\n",
      "pvalue: 4.34802206697e-88\n",
      "\n",
      "ensemble effect: 1.08%\n",
      "pvalue: 2.51189892932e-87\n"
     ]
    }
   ],
   "source": [
    "le, lp = get_effect(slinear, scontrol, nlinear, ncontrol)  \n",
    "print \"linear effect: {}\\npvalue: {}\\n\".format(le, lp)\n",
    "\n",
    "ee, ep = get_effect(sensemble, scontrol, nensemble, ncontrol)  \n",
    "print \"ensemble effect: {}\\npvalue: {}\".format(ee, ep)\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "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"
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 },
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