fab-5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import IPython.core.display as di\n",
    "\n",
    "# This line will hide code by default when the notebook is exported as HTML\n",
    "di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>', raw=True)\n",
    "\n",
    "# This line will add a button to toggle visibility of code blocks, for use with the HTML export version\n",
    "di.display_html('''<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>''', raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import display, HTML\n",
    "from itertools import combinations, product\n",
    "import pandas as pd\n",
    "import json\n",
    "from jarvis.brain.insights.paths.path_pipeline import PathPipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "goal_metric = {\"value\": \"aw_cost_per_conv\", \"base_metrics\": {\"numerator\": \"aw_cost\", \"denominator\":\"aw_conversions\"}, \"diff\": -1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# {\n",
    "# \t\"account_ids\":{\"adwords\":[221,223,222]},\n",
    "# \t\"start_date\":\"2017-08-10\",\n",
    "# \t\"end_date\":\"2017-08-16\",\n",
    "# \t\"goal_metric\":{\"value\":\"aw_cost_per_conv\",\"name\":\"AW: Cost / conv.\",\"visible\":true,\"currency\":true,\"precision\":2,\"ratio\":true,\"diff\":-1,\"base_metrics\":{\"numerator\":\"fb_spend+aw_cost\",\"denominator\":\"ga_transactions+af_custom_column_int_9_30day\"},\"group\":\"aw\"},\n",
    "# \t\"num_results\":10,\n",
    "# \t\"staging\":0,\n",
    "# \t\"imp_factor\":8\n",
    "# }\n",
    "# {\n",
    "#     \"account_ids\":{\"facebook\":[261,260]},\n",
    "#     \"start_date\":\"2017-07-01\",\n",
    "#     \"end_date\":\"2017-07-31\",\n",
    "#     \"goal_metric\":{\"value\":\"ga_fb_cpt\",\"name\":\"GA-FB: CPT\",\"visible\":true,\"ratio\":true,\"precision\":2,\"currency\":true,\"diff\":-1,\"base_metrics\":{\"numerator_channel\":\"ad\",\"numerator_metric\":\"spend\",\"denominator_channel\":\"analytics\",\"denominator_metric\":\"transactions\",\"numerator\":\"fb_spend\",\"denominator\":\"ga_transactions\"},\"ad_channel\":\"facebook\",\"group\":\"ga\"},\n",
    "#     \"num_results\":20,\"staging\":0,\"imp_factor\":8,\n",
    "#     \"filters\":[{\"operator\":\"$nin\",\"dimension\":\"custom_tags.Campaigns.metadata.name\",\"value\":[\"International_1\",\"AdWyze - DPA\"]},{\"operator\":\"$in\",\"dimension\":\"custom_tags.Sale Campaign.name\",\"value\":[\"non-sale\"]}],\n",
    "#     \"tag_list\":[\"Adsets\",\"Audience Types\",\"Behaviors\",\"Custom Audiences\",\"Image\",\"Interests\",\"Lookalike Types\",\"Product Category\"], \n",
    "#     \"num_trees\":1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME USED 4.360836744308472\n",
      "TIME USED 4.072947263717651\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sabby/jarvis/jarvis/brain/insights/paths/path_processor.py:150: 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",
      "  new_df['prop'] = new_df[self.spend] / self.total_spend\n",
      "/home/sabby/jarvis/jarvis/brain/insights/paths/path_processor.py:152: 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",
      "  new_df['score'] = abs(new_df[self.goal_metric] - self.overall_goal_metric) * new_df['prop']\n",
      "/home/sabby/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/computation/expressions.py:62: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return op(a, b)\n",
      "/home/sabby/jarvis/jarvis/brain/insights/paths/path_processor.py:275: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  new_goal_metric = new_num / new_deno\n",
      "/home/sabby/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/computation/expressions.py:62: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return op(a, b)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('DPA',) -0.0794923800785\n"
     ]
    }
   ],
   "source": [
    "account_ids =  {'facebook': [260, 261]}#{'adwords': [1]} #,\n",
    "date_range = {'start': '2017-07-01', 'end': '2017-07-31'}\n",
    "goal_metric = {'value': 'ga_fb_cpt',\n",
    "               'base_metrics': {'numerator': 'fb_spend',\n",
    "                                'denominator': 'ga_transactions'},\n",
    "               'diff': -1}\n",
    "\n",
    "args = {}\n",
    "args['account_ids'] = account_ids\n",
    "args['dimensions'] = None\n",
    "args['tag_list'] = [\"Audience Types\",\"Custom Audiences\",\"Lookalike Types\",\"Interests\",\"Behaviors\"]#[\"Adsets\",\"Audience Types\",\"Behaviors\",\"Custom Audiences\",\"Image\",\"Interests\",\"Lookalike Types\",\"Product Category\"]\n",
    "# args['goal'] = None\n",
    "args['num_trees'] = None\n",
    "args['staging'] = None\n",
    "args['filters'] = [{\"operator\":\"$in\",\"dimension\":\"custom_tags.Sale Campaign.name\",\"value\":[\"non-sale\"]}]\n",
    "args['start_date'] = date_range['start']\n",
    "args['end_date'] = date_range['end'] \n",
    "args['goal_metric'] = goal_metric\n",
    "args['num_results'] = 20\n",
    "args['imp_factor'] = 8\n",
    "args['spend_metric'] = None\n",
    "args['goal'] = None\n",
    "args['spend_diff'] = 5\n",
    "path_pipeline = PathPipeline(args)\n",
    "df, tag_list, spend = path_pipeline.pre_process()\n",
    "data = path_pipeline.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[goal_metric['value']] = df[goal_metric['base_metrics']['numerator']] / df[goal_metric['base_metrics']['denominator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_model = path_pipeline.path_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "binwidth=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1 = []\n",
    "dff= df[df[goal_metric['value']] != pd.np.inf]\n",
    "for idx in range(len(dff)):\n",
    "    data1 += [dff[goal_metric['value']].iloc[idx]]*int(dff[path_model.spend].iloc[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1294.0296307692302"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_model.overall_goal_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = path_model.overall_goal_metric + path_model.check_threshold[1]*path_model.overall_goal_metric\n",
    "b = path_model.overall_goal_metric - path_model.check_threshold[0]*path_model.overall_goal_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1340.395"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1103.7768503937009"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4747848575004693"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df\n",
    "df1['cpt'] = df1['fb_spend'] / df1['ga_transactions']\n",
    "df1[df1.cpt > a].fb_spend.sum() / df.fb_spend.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f5667b50be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AAAAAGILQBQAAAMAQhC4AAAAAhiB0AQAAADAEoQsAAACAIQhdAAAAAAxB6AIA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56674bd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import hist\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "tick_spacing = 10\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "binwidth = 0.1\n",
    "h = hist(dff[goal_metric['value']], weights=dff.fb_spend, bins=pd.np.linspace((min(dff[goal_metric['value']])),\n",
    "                                                              (max(dff[goal_metric['value']])), 500))\n",
    "plt.xlabel(goal_metric['value'], fontsize=18)\n",
    "plt.ylabel('Spend', fontsize=18)\n",
    "# ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n",
    "plt.axvline(path_model.overall_goal_metric, linewidth=5, color='red')\n",
    "plt.axvline(path_model.overall_goal_metric + path_model.check_threshold[1]*path_model.overall_goal_metric, linewidth=5, color='red')\n",
    "plt.axvline(path_model.overall_goal_metric - path_model.check_threshold[0]*path_model.overall_goal_metric, linewidth=5, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_1 = {key: value for (key, value) in enumerate(data['under_valued'])}\n",
    "data_2 = {key: value for (key, value) in enumerate(data['strategic_wins'])}\n",
    "data_3 = {key: value for (key, value) in enumerate(data['quick_wins'])}\n",
    "data_4 = {key: value for (key, value) in enumerate(data['over_valued'])}\n",
    "\n",
    "df_1 = pd.DataFrame().from_dict(data_1, orient='index')\n",
    "df_2 = pd.DataFrame().from_dict(data_2, orient='index')\n",
    "df_3 = pd.DataFrame().from_dict(data_3, orient='index')\n",
    "df_4 = pd.DataFrame().from_dict(data_4, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sabby/anaconda3/envs/jarvis1/lib/python3.5/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: \n",
      "The bokeh.charts API has moved to a separate 'bkcharts' package.\n",
      "\n",
      "This compatibility shim will remain until Bokeh 1.0 is released.\n",
      "After that, if you want to use this API you will have to install\n",
      "the bkcharts package explicitly.\n",
      "\n",
      "  warn(message)\n"
     ]
    }
   ],
   "source": [
    "from bokeh.charts import Scatter, output_file, show\n",
    "from bokeh.io import output_notebook, push_notebook\n",
    "from bokeh.plotting import figure, ColumnDataSource\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.models.widgets.tables import DataTable, TableColumn\n",
    "from bokeh.models import Span"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"8be7b746-cc8f-4223-a6bc-bd7aadb71559\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      var el = document.getElementById(\"8be7b746-cc8f-4223-a6bc-bd7aadb71559\");\n",
       "      el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete window._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"8be7b746-cc8f-4223-a6bc-bd7aadb71559\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8be7b746-cc8f-4223-a6bc-bd7aadb71559' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"8be7b746-cc8f-4223-a6bc-bd7aadb71559\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"8be7b746-cc8f-4223-a6bc-bd7aadb71559\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.cluster import DBSCAN\n",
    "\n",
    "# m = DBSCAN(eps=0.1, min_samples=1).fit(scale(pd.np.array([0.33, 0.0976, 0.0483, 0.244]).reshape(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# m.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# scale(pd.np.array([0.33, 0.0976, 0.0483, 0.244]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# path_pipeline.path_pre_process.score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "df = path_pipeline.path_pre_process.df\n",
    "df1 = df.groupby('Lookalike Types').sum()\n",
    "df1['cpt'] = df1['fb_spend'] / df1['ga_transactions']\n",
    "df1['prop'] = df1.fb_spend / df.fb_spend.sum()\n",
    "df1[df1.prop > 0.01]\n",
    "\n",
    "df = path_pipeline.path_pre_process.df\n",
    "df1 = df.groupby('Audience Types').sum()\n",
    "df1['cpt'] = df1['fb_spend'] / df1['ga_transactions']\n",
    "df1['prop'] = df1.fb_spend / df.fb_spend.sum()\n",
    "df1[df1.prop > 0.01]\n",
    "\n",
    "df = path_pipeline.path_pre_process.df\n",
    "df1 = df.groupby('Lookalike Types').sum()\n",
    "df1['cpt'] = df1['fb_spend'] / df1['ga_transactions']\n",
    "df1['prop'] = df1.fb_spend / df.fb_spend.sum()\n",
    "df1[df1.prop > 0.01]\n",
    "\n",
    "df = path_pipeline.path_pre_process.df\n",
    "df1 = df.groupby('Interests').sum()\n",
    "df1['cpt'] = df1['fb_spend'] / df1['ga_transactions']\n",
    "df1['prop'] = df1.fb_spend / df.fb_spend.sum()\n",
    "df1[df1.prop > 0.01]\n",
    "\n",
    "df = path_pipeline.path_pre_process.df\n",
    "df1 = df.groupby('Custom Audiences').sum()\n",
    "df1['cpt'] = df1['fb_spend'] / df1['ga_transactions']\n",
    "df1['prop'] = df1.fb_spend / df.fb_spend.sum()\n",
    "df1[df1.prop > 0.01]\n",
    "\n",
    "print('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"58649315-0681-46a5-a6f9-86c1d9e2f73a\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"58649315-0681-46a5-a6f9-86c1d9e2f73a\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {\n",
       "      comm_manager = Jupyter.notebook.kernel.comm_manager\n",
       "      comm_manager.register_target(\"9f9a05fb-7280-460d-a15d-4c49c507cad0\", function () {});\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"58649315-0681-46a5-a6f9-86c1d9e2f73a\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '58649315-0681-46a5-a6f9-86c1d9e2f73a' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = {\"5e9c3e60-1e4f-4027-8fe5-13c7487bbb8c\":{\"roots\":{\"references\":[{\"attributes\":{\"plot\":{\"id\":\"704b19e5-0d5f-474a-8db7-9da38fbed484\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"fb635ff3-7525-45cf-b09b-3e3494af625b\",\"type\":\"BasicTicker\"}},\"id\":\"f0ea6062-65db-44b6-88a7-bd33bb36f1d4\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"idx\",\"overall\",\"level_3\",\"size\",\"level_2\",\"numerator\",\"spend_prop\",\"index\",\"level_0\",\"level_1\",\"levels\",\"overall_spend_prop\",\"other_ga_fb_cpt\",\"denominator\",\"imp_factor\",\"precision\",\"metric\",\"recall\"],\"data\":{\"denominator\":{\"__ndarray__\":\"AAAAAABAU0AAAAAAAAAwQAAAAAAAgE9AAAAAAABgYkAAAAAAAMBQQAAAAAAAAFNAAAAAAAAAZEA=\",\"dtype\":\"float64\",\"shape\":[7]},\"idx\":[0,1,2,3,4,5,6],\"imp_factor\":{\"__ndarray__\":\"EoPAyqFF8j9OYhBYObTwPwRWDi2yne8/wcqhRbbz7T+oxks3iUHoP4/C9Shcj+Y/zczMzMzM5D8=\",\"dtype\":\"float64\",\"shape\":[7]},\"index\":[0,1,2,3,4,5,6],\"level_0\":[\"Audience Types : Lookalike\",\"Audience Types : Interests\",\"Audience Types : Website\",\"Audience Types : Website\",\"Audience Types : Interests\",\"Audience Types : Interests\",\"Audience Types : Interests\"],\"level_1\":[\"Lookalike Types : 1-2%\",\"Interests : Education or Higher education or Hotels or Travel or University\",\"Custom Audiences : Retargeting 7 days\",\"- : -\",\"Interests : Nightclubs or Nightlife or Online shopping or Others or Parties or Shopping\",\"Interests : Action movies or Animated movies or Being Indian or Bollywood movies or BuzzFeed or Others\",\"- : -\"],\"level_2\":[\"- : -\",\"- : -\",\"- : -\",\"- : -\",\"- : -\",\"- : -\",\"- : -\"],\"level_3\":[\"- : -\",\"- : -\",\"- : -\",\"- : -\",\"- : -\",\"- : -\",\"- : -\"],\"levels\":[[{\"Audience Types\":{\"name\":\"Lookalike\",\"value\":\"Lookalike\"}},{\"Lookalike Types\":{\"name\":\"1-2%\",\"value\":\"1-2%\"}}],[{\"Audience Types\":{\"name\":\"Interests\",\"value\":\"Interests\"}},{\"Interests\":{\"name\":\"Education or Higher education or Hotels or Travel or University\",\"value\":\"Education or Higher education or Hotels or Travel or University\"}}],[{\"Audience Types\":{\"name\":\"Website\",\"value\":\"Website\"}},{\"Custom Audiences\":{\"name\":\"Retargeting 7 days\",\"value\":\"Retargeting 7 days\"}}],[{\"Audience Types\":{\"name\":\"Website\",\"value\":\"Website\"}}],[{\"Audience Types\":{\"name\":\"Interests\",\"value\":\"Interests\"}},{\"Interests\":{\"name\":\"Nightclubs or Nightlife or Online shopping or Others or Parties or Shopping\",\"value\":\"Nightclubs or Nightlife or Online shopping or Others or Parties or Shopping\"}}],[{\"Audience Types\":{\"name\":\"Interests\",\"value\":\"Interests\"}},{\"Interests\":{\"name\":\"Action movies or Animated movies or Being Indian or Bollywood movies or BuzzFeed or Others\",\"value\":\"Action movies or Animated movies or Being Indian or Bollywood movies 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     "text": [
      "Under Valued\n",
      "Strategic Wins\n"
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       "    function run_callbacks() {\n",
       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"6d2ce430-6870-4592-aaab-65c6fb3d1902\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '6d2ce430-6870-4592-aaab-65c6fb3d1902' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = {\"ca47c6ff-62b6-4995-a634-ce9382d13127\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"2f575513-e3a2-443f-9f68-00022876659c\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"f0840f0a-0089-4557-b787-2240d42f45ce\",\"type\":\"StringEditor\"},\"field\":\"level_1\",\"formatter\":{\"id\":\"60c87bc4-cc8e-407a-af15-584fda8c1ec0\",\"type\":\"StringFormatter\"},\"title\":\"level 1\"},\"id\":\"538ee1e6-06f7-4d96-bfa6-d6283dbf9523\",\"type\":\"TableColumn\"},{\"attributes\":{\"columns\":[{\"id\":\"9aa42e6f-65d7-4f73-9822-88b5e46b7d70\",\"type\":\"TableColumn\"},{\"id\":\"538ee1e6-06f7-4d96-bfa6-d6283dbf9523\",\"type\":\"TableColumn\"},{\"id\":\"5cbcd3fd-aa5f-4872-b3f6-f50665858e74\",\"type\":\"TableColumn\"},{\"id\":\"bb582716-caaf-41aa-8fed-43edf255ca4a\",\"type\":\"TableColumn\"},{\"id\":\"644f2799-6fce-42d4-8bba-6d5851783504\",\"type\":\"TableColumn\"},{\"id\":\"dd44e8fe-c97d-48b5-89b6-3f0310c38bd2\",\"type\":\"TableColumn\"},{\"id\":\"050a4c25-9021-4a7b-b468-1376a34f4de8\",\"type\":\"TableColumn\"},{\"id\":\"01e12d97-69b9-4e69-a7bc-8890b9af2e78\",\"type\":\"TableColumn\"},{\"id\":\"ad765087-3aae-4a3e-b1be-6cbc56c005d1\",\"type\":\"TableColumn\"}],\"source\":{\"id\":\"5bf7241b-17d3-41db-b327-1e35a43ccfab\",\"type\":\"ColumnDataSource\"},\"width\":1000},\"id\":\"dec9973c-738b-4d9a-bfb8-127ab402e90c\",\"type\":\"DataTable\"},{\"attributes\":{},\"id\":\"b9901db3-33f9-49e6-9181-5e1847aeee24\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"aa4833c5-35f4-49bd-86c5-73f45583f2e1\",\"type\":\"StringEditor\"},\"field\":\"level_0\",\"formatter\":{\"id\":\"8f8fb9fd-7370-4ce1-b702-9efacc2b697e\",\"type\":\"StringFormatter\"},\"title\":\"level 0\"},\"id\":\"9aa42e6f-65d7-4f73-9822-88b5e46b7d70\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"a6264aed-b97a-4ee7-8b74-9ad0cc2f42f4\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"d4d8fd16-03fb-40d2-901b-b1c7d2025dfa\",\"type\":\"StringEditor\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"level_3\",\"level_2\",\"numerator\",\"spend_prop\",\"index\",\"level_0\",\"level_1\",\"levels\",\"other_ga_fb_cpt\",\"denominator\",\"imp_factor\",\"precision\",\"metric\",\"recall\"],\"data\":{\"denominator\":{\"__ndarray__\":\"AAAAAADIiEA=\",\"dtype\":\"float64\",\"shape\":[1]},\"imp_factor\":{\"__ndarray__\":\"Rrbz/dR47T8=\",\"dtype\":\"float64\",\"shape\":[1]},\"index\":[0],\"level_0\":[\"Audience Types : DPA\"],\"level_1\":[\"- : -\"],\"level_2\":[\"- : -\"],\"level_3\":[\"- : -\"],\"levels\":[[{\"Audience Types\":{\"name\":\"DPA\",\"value\":\"DPA\"}}]],\"metric\":{\"__ndarray__\":\"ZmZmZmZKi0A=\",\"dtype\":\"float64\",\"shape\":[1]},\"numerator\":{\"__ndarray__\":\"hetRuGEiJUE=\",\"dtype\":\"float64\",\"shape\":[1]},\"other_ga_fb_cpt\":{\"__ndarray__\":\"XI/C9Sh8mkA=\",\"dtype\":\"float64\",\"shape\":[1]},\"precision\":{\"__ndarray__\":\"8KfGSzeJ6T8=\",\"dtype\":\"float64\",\"shape\":[1]},\"recall\":{\"__ndarray__\":\"1XjpJjEI1D8=\",\"dtype\":\"float64\",\"shape\":[1]},\"spend_prop\":{\"__ndarray__\":\"16NwPQp3QEA=\",\"dtype\":\"float64\",\"shape\":[1]}}},\"id\":\"5bf7241b-17d3-41db-b327-1e35a43ccfab\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"editor\":{\"id\":\"cc74e640-13e4-4c8c-9ce4-096db91a1e12\",\"type\":\"StringEditor\"},\"field\":\"precision\",\"formatter\":{\"id\":\"26f78d65-110b-40d8-a114-9a4e66ae3994\",\"type\":\"StringFormatter\"},\"title\":\"Precision\"},\"id\":\"050a4c25-9021-4a7b-b468-1376a34f4de8\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"aa4833c5-35f4-49bd-86c5-73f45583f2e1\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"8f8fb9fd-7370-4ce1-b702-9efacc2b697e\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"cc74e640-13e4-4c8c-9ce4-096db91a1e12\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"7d9d3519-c889-4cd0-a1fe-70dfa28b549a\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"26f78d65-110b-40d8-a114-9a4e66ae3994\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"15a78411-f544-496c-a425-20ab89f23822\",\"type\":\"StringEditor\"},\"field\":\"level_3\",\"formatter\":{\"id\":\"9328bb2c-3523-4f88-a837-95853c4d11de\",\"type\":\"StringFormatter\"},\"title\":\"level 3\"},\"id\":\"bb582716-caaf-41aa-8fed-43edf255ca4a\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"afa7619a-bd81-49d7-9690-4d67a4ed375e\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"f3ede087-2f44-48b2-b5b7-a942af7ec850\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"3109d983-a88f-4afc-9c4f-9b62af175683\",\"type\":\"StringEditor\"},\"field\":\"recall\",\"formatter\":{\"id\":\"7d9d3519-c889-4cd0-a1fe-70dfa28b549a\",\"type\":\"StringFormatter\"},\"title\":\"Recall\"},\"id\":\"01e12d97-69b9-4e69-a7bc-8890b9af2e78\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"dafb018a-5cdb-4274-bbd0-e2ffd66a0e26\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"60c87bc4-cc8e-407a-af15-584fda8c1ec0\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"d4d8fd16-03fb-40d2-901b-b1c7d2025dfa\",\"type\":\"StringEditor\"},\"field\":\"metric\",\"formatter\":{\"id\":\"2f575513-e3a2-443f-9f68-00022876659c\",\"type\":\"StringFormatter\"},\"title\":\"ga_fb_cpt\"},\"id\":\"644f2799-6fce-42d4-8bba-6d5851783504\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"15a78411-f544-496c-a425-20ab89f23822\",\"type\":\"StringEditor\"},{\"attributes\":{\"editor\":{\"id\":\"a6264aed-b97a-4ee7-8b74-9ad0cc2f42f4\",\"type\":\"StringEditor\"},\"field\":\"imp_factor\",\"formatter\":{\"id\":\"afa7619a-bd81-49d7-9690-4d67a4ed375e\",\"type\":\"StringFormatter\"},\"title\":\"MCC\"},\"id\":\"ad765087-3aae-4a3e-b1be-6cbc56c005d1\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"3109d983-a88f-4afc-9c4f-9b62af175683\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"f0840f0a-0089-4557-b787-2240d42f45ce\",\"type\":\"StringEditor\"},{\"attributes\":{\"editor\":{\"id\":\"dafb018a-5cdb-4274-bbd0-e2ffd66a0e26\",\"type\":\"StringEditor\"},\"field\":\"level_2\",\"formatter\":{\"id\":\"b9901db3-33f9-49e6-9181-5e1847aeee24\",\"type\":\"StringFormatter\"},\"title\":\"level 2\"},\"id\":\"5cbcd3fd-aa5f-4872-b3f6-f50665858e74\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"841803bf-90ff-437a-95af-d3bdaee5882c\",\"type\":\"StringEditor\"},{\"attributes\":{\"editor\":{\"id\":\"841803bf-90ff-437a-95af-d3bdaee5882c\",\"type\":\"StringEditor\"},\"field\":\"spend_prop\",\"formatter\":{\"id\":\"f3ede087-2f44-48b2-b5b7-a942af7ec850\",\"type\":\"StringFormatter\"},\"title\":\"Spend %\"},\"id\":\"dd44e8fe-c97d-48b5-89b6-3f0310c38bd2\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"9328bb2c-3523-4f88-a837-95853c4d11de\",\"type\":\"StringFormatter\"}],\"root_ids\":[\"dec9973c-738b-4d9a-bfb8-127ab402e90c\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.6\"}};\n",
       "            var render_items = [{\"docid\":\"ca47c6ff-62b6-4995-a634-ce9382d13127\",\"elementid\":\"6d2ce430-6870-4592-aaab-65c6fb3d1902\",\"modelid\":\"dec9973c-738b-4d9a-bfb8-127ab402e90c\",\"notebook_comms_target\":\"2d42c6a9-588b-47c1-8429-fe1963a979ec\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"6d2ce430-6870-4592-aaab-65c6fb3d1902\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quick Wins\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"641250cd-1fbb-42e5-9cb0-239880d54f41\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"641250cd-1fbb-42e5-9cb0-239880d54f41\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {\n",
       "      comm_manager = Jupyter.notebook.kernel.comm_manager\n",
       "      comm_manager.register_target(\"f8a3aa43-94ef-40ea-af13-e9f1a9b3045e\", function () {});\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"641250cd-1fbb-42e5-9cb0-239880d54f41\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '641250cd-1fbb-42e5-9cb0-239880d54f41' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = {\"4966dfa7-b5ee-42a4-b1c7-439620e77747\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"2f575513-e3a2-443f-9f68-00022876659c\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"f0840f0a-0089-4557-b787-2240d42f45ce\",\"type\":\"StringEditor\"},\"field\":\"level_1\",\"formatter\":{\"id\":\"60c87bc4-cc8e-407a-af15-584fda8c1ec0\",\"type\":\"StringFormatter\"},\"title\":\"level 1\"},\"id\":\"538ee1e6-06f7-4d96-bfa6-d6283dbf9523\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"b9901db3-33f9-49e6-9181-5e1847aeee24\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"aa4833c5-35f4-49bd-86c5-73f45583f2e1\",\"type\":\"StringEditor\"},\"field\":\"level_0\",\"formatter\":{\"id\":\"8f8fb9fd-7370-4ce1-b702-9efacc2b697e\",\"type\":\"StringFormatter\"},\"title\":\"level 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       "            var render_items = [{\"docid\":\"4966dfa7-b5ee-42a4-b1c7-439620e77747\",\"elementid\":\"641250cd-1fbb-42e5-9cb0-239880d54f41\",\"modelid\":\"83748c8b-11c6-41c5-9551-b42611e9bc82\",\"notebook_comms_target\":\"f8a3aa43-94ef-40ea-af13-e9f1a9b3045e\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"641250cd-1fbb-42e5-9cb0-239880d54f41\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Over Valued\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"a533067a-9ffa-4b8f-a98a-cc8e3d019617\"></div>\n",
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       "      return new Date();\n",
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       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
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       "    }if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {\n",
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       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"a533067a-9ffa-4b8f-a98a-cc8e3d019617\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a533067a-9ffa-4b8f-a98a-cc8e3d019617' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = {\"9252c4f3-4f74-4647-9111-5dc7567b2ace\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"2f575513-e3a2-443f-9f68-00022876659c\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"f0840f0a-0089-4557-b787-2240d42f45ce\",\"type\":\"StringEditor\"},\"field\":\"level_1\",\"formatter\":{\"id\":\"60c87bc4-cc8e-407a-af15-584fda8c1ec0\",\"type\":\"StringFormatter\"},\"title\":\"level 1\"},\"id\":\"538ee1e6-06f7-4d96-bfa6-d6283dbf9523\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"b9901db3-33f9-49e6-9181-5e1847aeee24\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"aa4833c5-35f4-49bd-86c5-73f45583f2e1\",\"type\":\"StringEditor\"},\"field\":\"level_0\",\"formatter\":{\"id\":\"8f8fb9fd-7370-4ce1-b702-9efacc2b697e\",\"type\":\"StringFormatter\"},\"title\":\"level 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Types : Lookalike\",\"Audience Types : Lookalike\",\"Audience Types : Open Targeting\"],\"level_1\":[\"- : -\",\"Lookalike Types : 1%\",\"- : -\"],\"level_2\":[\"- : -\",\"- : -\",\"- : -\"],\"level_3\":[\"- : -\",\"- : -\",\"- : -\"],\"levels\":[[{\"Audience Types\":{\"name\":\"Lookalike\",\"value\":\"Lookalike\"}}],[{\"Audience Types\":{\"name\":\"Lookalike\",\"value\":\"Lookalike\"}},{\"Lookalike Types\":{\"name\":\"1%\",\"value\":\"1%\"}}],[{\"Audience Types\":{\"name\":\"Open Targeting\",\"value\":\"Open Targeting\"}}]],\"metric\":{\"__ndarray__\":\"rkfhehTMn0AfhetRuPeZQM3MzMzMgZ9A\",\"dtype\":\"float64\",\"shape\":[3]},\"numerator\":{\"__ndarray__\":\"mpmZmVfPHUFxPQrXvIgQQY/C9SjvfRBB\",\"dtype\":\"float64\",\"shape\":[3]},\"other_ga_fb_cpt\":{\"__ndarray__\":\"7FG4HoU2kkCF61G4nhGmQGZmZmZmNJNA\",\"dtype\":\"float64\",\"shape\":[3]},\"precision\":{\"__ndarray__\":\"sp3vp8ZL6z+DwMqhRbbnP/hT46WbxOQ/\",\"dtype\":\"float64\",\"shape\":[3]},\"recall\":{\"__ndarray__\":\"aJHtfD81zj/Jdr6fGi+9P/LSTWIQWLk/\",\"dtype\":\"float64\",\"shape\":[3]},\"spend_prop\":{\"__ndarray__\":\"exSuR+E6N0DD9Shcj8IpQDMzMzMzsylA\",\"dtype\":\"float64\",\"shape\":[3]}}},\"id\":\"99e204df-975c-4117-947a-c9e91a454690\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"editor\":{\"id\":\"15a78411-f544-496c-a425-20ab89f23822\",\"type\":\"StringEditor\"},\"field\":\"level_3\",\"formatter\":{\"id\":\"9328bb2c-3523-4f88-a837-95853c4d11de\",\"type\":\"StringFormatter\"},\"title\":\"level 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       "            var render_items = [{\"docid\":\"9252c4f3-4f74-4647-9111-5dc7567b2ace\",\"elementid\":\"a533067a-9ffa-4b8f-a98a-cc8e3d019617\",\"modelid\":\"6f4abf17-6c52-4bc1-b686-814336a7d8fb\",\"notebook_comms_target\":\"345b9048-f5da-4bd9-9f54-9e730a3a317a\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"a533067a-9ffa-4b8f-a98a-cc8e3d019617\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_df = df_1.append(df_2, ignore_index=True)\n",
    "all_df = all_df.append(df_3, ignore_index=True)\n",
    "all_df = all_df.append(df_4, ignore_index=True)\n",
    "\n",
    "def round_float(s):\n",
    "    if s.dtype == 'float64':\n",
    "        s = s.round(3)\n",
    "    return s\n",
    "\n",
    "\n",
    "avg_spend_prop = float(data['spend_axis'])\n",
    "\n",
    "tooltips=[\n",
    "    ('lvl 0:', '@level_0'),\n",
    "    ('lvl 1:', '@level_1'),\n",
    "    ('lvl 2:', '@level_2'),\n",
    "    ('lvl 3:', '@level_3'),\n",
    "    (args['goal_metric'][\"value\"], '@metric'),\n",
    "    ('Precision', '@precision'),\n",
    "    ('Recall', '@recall'),\n",
    "    ('mcc', '@imp_factor'),\n",
    "    ('Spend Prop', '@spend_prop')\n",
    "]\n",
    "\n",
    "hover = HoverTool(tooltips=tooltips)\n",
    "hover1 = HoverTool(tooltips=tooltips)\n",
    "\n",
    "def build_bokeh_trace(df_obj, num):\n",
    "    df_obj = df_obj.copy()\n",
    "    if len(df_obj):\n",
    "        df_obj = df_obj.sort_values(by='imp_factor', ascending=False)\n",
    "        df_plotly = df_obj.iloc[0:num].copy()\n",
    "        df_plotly['idx'] = df_plotly.index\n",
    "        df_plotly['size'] = df_plotly['imp_factor']\n",
    "        # range r1 to r2\n",
    "        r1=8\n",
    "        r2=30\n",
    "        min_r = df_plotly['size'].min()\n",
    "        max_r = df_plotly['size'].max()\n",
    "        df_plotly['size'] = ((df_plotly['size'])/ max_r)*20\n",
    "        df_plotly['overall'] = data['goal_axis']\n",
    "        df_plotly['overall_spend_prop'] = avg_spend_prop\n",
    "    else:\n",
    "        return ColumnDataSource(df_obj)\n",
    "\n",
    "    source = ColumnDataSource(df_plotly)\n",
    "    return source\n",
    "\n",
    "spend_source_1 = build_bokeh_trace(df_1, 10)\n",
    "spend_source_2 = build_bokeh_trace(df_2, 10)\n",
    "spend_source_3 = build_bokeh_trace(df_3, 10)\n",
    "spend_source_4 = build_bokeh_trace(df_4, 10)\n",
    "\n",
    "table_source_1 = ColumnDataSource(df_1.apply(round_float))\n",
    "table_source_2 = ColumnDataSource(df_2.apply(round_float))\n",
    "table_source_3 = ColumnDataSource(df_3.apply(round_float))\n",
    "table_source_4 = ColumnDataSource(df_4.apply(round_float))\n",
    "\n",
    "columns = [\n",
    "    TableColumn(field=\"level_0\", title=\"level 0\"),\n",
    "    TableColumn(field=\"level_1\", title=\"level 1\"),\n",
    "    TableColumn(field=\"level_2\", title=\"level 2\"),\n",
    "    TableColumn(field=\"level_3\", title=\"level 3\"),\n",
    "#     TableColumn(field=\"other_{0}\".format(args['goal_metric'][\"value\"]), title=\"Other {0}\".format(args['goal_metric']['value'])),\n",
    "    TableColumn(field=\"metric\", title=args['goal_metric'][\"value\"],),\n",
    "#     TableColumn(field=\"Projected_change\", title=\"Projected % CPT change\"),\n",
    "    TableColumn(field=\"spend_prop\", title=\"Spend %\"),\n",
    "#     TableColumn(field=\"imp_factor\", title=\"Impact factor\"),\n",
    "    TableColumn(field=\"precision\", title=\"Precision\"),\n",
    "    TableColumn(field=\"recall\", title=\"Recall\"),\n",
    "    TableColumn(field=\"imp_factor\", title=\"MCC\")    \n",
    "]\n",
    "\n",
    "# Spend sorted.\n",
    "p1 = figure(plot_width=800, plot_height=500, tools=[hover1],\n",
    "           title=\"Performance by paths.\")\n",
    "p1.xaxis.axis_label = 'Spend Proportion'\n",
    "p1.yaxis.axis_label = args['goal_metric'][\"value\"]\n",
    "\n",
    "if len(df_1):\n",
    "    prop_plot_1 = p1.circle('spend_prop', 'metric', fill_color='orange', source=spend_source_1, size='size')\n",
    "if len(df_2):\n",
    "    prop_plot_2 = p1.circle('spend_prop', 'metric', fill_color='red', source=spend_source_2, size='size')\n",
    "if len(df_3):\n",
    "    prop_plot_3 = p1.circle('spend_prop', 'metric', fill_color='green', source=spend_source_3, size='size')\n",
    "if len(df_4):\n",
    "    prop_plot_4 = p1.circle('spend_prop', 'metric', fill_color='blue', source=spend_source_4, size='size')\n",
    "\n",
    "avg_spend = Span(location=avg_spend_prop, dimension='height', line_color='black', line_width=3)\n",
    "avg_cpt = Span(location=data['goal_axis'], dimension='width', line_color='black', line_width=3)\n",
    "\n",
    "p1.add_layout(avg_spend)\n",
    "p1.add_layout(avg_cpt)\n",
    "\n",
    "prop_plot = show(p1, notebook_handle=True)\n",
    "\n",
    "print('Under Valued')\n",
    "if len(df_1):\n",
    "    data_table_1 = DataTable(source=table_source_1, columns=columns, width=1000)\n",
    "    table_1 = show(data_table_1, notebook_handle=True)\n",
    "\n",
    "print('Strategic Wins')\n",
    "if len(df_2):\n",
    "    data_table_2 = DataTable(source=table_source_2, columns=columns, width=1000)\n",
    "    table_2 = show(data_table_2, notebook_handle=True)\n",
    "\n",
    "print('Quick Wins')\n",
    "if len(df_3):\n",
    "    data_table_3 = DataTable(source=table_source_3, columns=columns, width=1000)\n",
    "    table_3 = show(data_table_3, notebook_handle=True)\n",
    "\n",
    "print('Over Valued')\n",
    "if len(df_4):\n",
    "    data_table_4 = DataTable(source=table_source_4, columns=columns, width=1000)\n",
    "    table_4 = show(data_table_4, notebook_handle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Audience Strategy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1875\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1876\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1877\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4027)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3891)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Audience Strategy'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-22fa0349b244>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_bigger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Audience Strategy'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Retention'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ad Type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Carousel'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Landing Pages'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'www.zivame.com/no-sag-flat-30.html, www.zivame.com'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf_bigger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1990\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1991\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1992\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1993\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1994\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1997\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1998\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1999\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2001\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1343\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1344\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1345\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1346\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3224\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3225\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3226\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3227\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/jarvis1/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1876\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1877\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1878\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4027)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3891)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Audience Strategy'"
     ]
    }
   ],
   "source": [
    "df_bigger = df[(df['Audience Strategy'] == 'Retention') & (df['Ad Type'] == 'Carousel') & (df['Landing Pages'] == 'www.zivame.com/no-sag-flat-30.html, www.zivame.com')]\n",
    "df_bigger.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "binwidth = 0.1\n",
    "h = hist(df_bigger[goal_metric['value']], weights=df_bigger.fb_spend, bins=pd.np.linspace((min(df_bigger[goal_metric['value']])),\n",
    "                                                              (max(df_bigger[goal_metric['value']])), 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_smaller = df[(df['Audience Strategy'] == 'Prospecting') & (df['Product Category'] == 'Shapewear')]\n",
    "df_smaller.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "binwidth = 0.1\n",
    "h = hist(df_smaller[goal_metric['value']], weights=df_smaller.fb_spend, bins=pd.np.linspace((min(df_smaller[goal_metric['value']])),\n",
    "                                                              (max(df_smaller[goal_metric['value']])), 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fit a normal distribution to the data:\n",
    "\n",
    "\n",
    "mu, std = norm.fit(data, loc = path_model.overall_goal_metric)\n",
    "\n",
    "# Plot the histogram.\n",
    "plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')\n",
    "\n",
    "# Plot the PDF.\n",
    "xmin, xmax = plt.xlim()\n",
    "x = np.linspace(xmin, xmax, 100)\n",
    "p = norm.pdf(x, mu, std)\n",
    "plt.plot(x, p, 'k', linewidth=2)\n",
    "title = \"Fit results: mu = %.2f,  std = %.2f\" % (mu, std)\n",
    "plt.title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "check_threshold*mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "check_threshold = std*0.675 / mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "positive_df = df[abs(df[goal_metric['value']] - path_model.overall_goal_metric) > check_threshold*path_model.overall_goal_metric]                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "positive_df.fb_spend.sum() / df.fb_spend.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_model.check_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(0.713 - 0.0181*0.430)/ (1-0.0181)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(0.713 - 0.0189*0.410)/ (1-0.0189)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_model.overall_goal_metric - # + path_model.overall_goal_metric*path_model.check_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(pd.np.percentile(data, 25) - path_model.overall_goal_metric) / path_model.overall_goal_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(pd.np.percentile(data, 75) - path_model.overall_goal_metric) / path_model.overall_goal_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.np.quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import t\n",
    "t.fit(data, loc=path_model.overall_goal_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len([d for d in data if d > 1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.fb_spend.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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