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August 18, 2017 05:41
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| { | |
| "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 4.221376180648804\n", | |
| "TIME USED 4.383429050445557\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',) 0.148184714952\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'] = 15\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": [ | |
| "1690.53" | |
| ] | |
| }, | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "a" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "958.59400000000005" | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "b" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.3795368090846" | |
| ] | |
| }, | |
| "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 0x7f1312330a58>" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f131212bd30>" | |
| ] | |
| }, | |
| "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=\"1647319e-7193-43e4-b4ae-f3437522e0ce\">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(\"1647319e-7193-43e4-b4ae-f3437522e0ce\");\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(\"1647319e-7193-43e4-b4ae-f3437522e0ce\");\n", | |
| " if (element == null) {\n", | |
| " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1647319e-7193-43e4-b4ae-f3437522e0ce' 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(\"1647319e-7193-43e4-b4ae-f3437522e0ce\").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(\"1647319e-7193-43e4-b4ae-f3437522e0ce\")).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=\"751cb9c5-59be-4108-9c31-1e13e855604d\"></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(\"751cb9c5-59be-4108-9c31-1e13e855604d\");\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(\"0412114f-b474-4a8e-9084-d07dbab663a0\", 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(\"751cb9c5-59be-4108-9c31-1e13e855604d\");\n", | |
| " if (element == null) {\n", | |
| " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '751cb9c5-59be-4108-9c31-1e13e855604d' 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 = {\"b9048dd2-3a9f-4111-a0b9-7cfcaf8692f6\":{\"roots\":{\"references\":[{\"attributes\":{\"callback\":null,\"column_names\":[\"metric\",\"spend_prop\",\"overall_spend_prop\",\"denominator\",\"level_0\",\"precision\",\"recall\",\"level_2\",\"level_3\",\"idx\",\"level_1\",\"overall\",\"other_ga_fb_cpt\",\"levels\",\"size\",\"index\",\"imp_factor\",\"numerator\"],\"data\":{\"denominator\":{\"__ndarray__\":\"AAAAAADIiEA=\",\"dtype\":\"float64\",\"shape\":[1]},\"idx\":[0],\"imp_factor\":{\"__ndarray__\":\"ke18PzVe8j8=\",\"dtype\":\"float64\",\"shape\":[1]},\"index\":[0],\"level_0\":[\"Audience Types : DPA\"],\"level_1\":[\"- : -\"],\"level_2\":[\"- : -\"],\"level_3\":[\"- : -\"],\"levels\":[[{\"Audience 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by 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| "Under Valued\n", | |
| "Strategic Wins\n" | |
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| " return false;\n", | |
| " }\n", | |
| " \n", | |
| " var js_urls = [];\n", | |
| " \n", | |
| " var inline_js = [\n", | |
| " function(Bokeh) {\n", | |
| " (function() {\n", | |
| " var fn = function() {\n", | |
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| " var render_items = [{\"docid\":\"554a5452-ff9d-48ef-8914-5e194248d99b\",\"elementid\":\"14cdcb0e-eece-4e76-8121-bf65d1d3f57a\",\"modelid\":\"3f7d63a4-9551-4f3a-8cae-7ae81a20978f\",\"notebook_comms_target\":\"447bf3c3-66a4-499c-a416-91bfbabb152b\"}];\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(\"14cdcb0e-eece-4e76-8121-bf65d1d3f57a\")).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=\"db82c65a-430c-4f07-b655-3df362263d39\"></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(\"db82c65a-430c-4f07-b655-3df362263d39\");\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(\"82c4608e-75e8-4d41-91e3-2462129afff6\", 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(\"db82c65a-430c-4f07-b655-3df362263d39\");\n", | |
| " if (element == null) {\n", | |
| " console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'db82c65a-430c-4f07-b655-3df362263d39' 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 = {\"f2728329-3382-4a7a-a846-3b0916d52d7d\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"5cf0b4a5-00fb-4622-9093-4c23ead953a2\",\"type\":\"StringFormatter\"},{\"attributes\":{\"editor\":{\"id\":\"8033cf5b-abd4-4915-a2f8-13f83bcbd9dd\",\"type\":\"StringEditor\"},\"field\":\"level_2\",\"formatter\":{\"id\":\"a0f2868a-8dee-4956-b550-b6372db9f11a\",\"type\":\"StringFormatter\"},\"title\":\"level 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| " \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", | |
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| " }\n", | |
| " \n", | |
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| " \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", | |
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| ] | |
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| "metadata": {}, | |
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| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Over Valued\n" | |
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| "data": { | |
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| " <div class=\"bk-root\">\n", | |
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| " \n", | |
| " (function(global) {\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", | |
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| " \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", | |
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| " delete window._bokeh_onload_callbacks\n", | |
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| " 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", | |
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| " if (js_urls == null || js_urls.length === 0) {\n", | |
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| " return null;\n", | |
| " }\n", | |
| " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", | |
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| " if (window._bokeh_is_loading === 0) {\n", | |
| " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", | |
| " run_callbacks()\n", | |
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| " };\n", | |
| " s.onerror = function() {\n", | |
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| " };\n", | |
| " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", | |
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| " }\n", | |
| " };var element = document.getElementById(\"df81f9d1-fa14-4394-8254-f1916c3e386c\");\n", | |
| " if (element == null) {\n", | |
| " console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'df81f9d1-fa14-4394-8254-f1916c3e386c' 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", | |
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3\"},\"id\":\"6d82e5a2-6aa3-4f9b-8230-45ef598383c9\",\"type\":\"TableColumn\"},{\"attributes\":{\"editor\":{\"id\":\"22737714-896c-4c94-86f2-517ac7fae621\",\"type\":\"StringEditor\"},\"field\":\"imp_factor\",\"formatter\":{\"id\":\"b7d10ff3-a16c-4310-bf66-b99a6e5ed4a4\",\"type\":\"StringFormatter\"},\"title\":\"MCC\"},\"id\":\"257546c3-1b23-4956-9548-f3edc6f2305a\",\"type\":\"TableColumn\"},{\"attributes\":{\"columns\":[{\"id\":\"538911dc-3889-44c6-9251-6fbd3388ba7d\",\"type\":\"TableColumn\"},{\"id\":\"fd22727a-ed98-4571-8d43-e174f081fe8d\",\"type\":\"TableColumn\"},{\"id\":\"3b71b9ea-dbd6-4814-94b2-f1ceba99a4e8\",\"type\":\"TableColumn\"},{\"id\":\"6d82e5a2-6aa3-4f9b-8230-45ef598383c9\",\"type\":\"TableColumn\"},{\"id\":\"489a426f-324f-4318-9b09-beee5df91d7a\",\"type\":\"TableColumn\"},{\"id\":\"106fa0a8-6dd8-4552-bf92-986e7998c252\",\"type\":\"TableColumn\"},{\"id\":\"22c56289-f3a8-43e0-a662-2d71bcd0add6\",\"type\":\"TableColumn\"},{\"id\":\"78882748-7490-44af-9479-07c11d7ee7f6\",\"type\":\"TableColumn\"},{\"id\":\"257546c3-1b23-4956-9548-f3edc6f2305a\",\"type\":\"TableColumn\"}],\"source\":{\"id\":\"a0dd39df-95e1-4c5f-9c33-363c9c5829d8\",\"type\":\"ColumnDataSource\"},\"width\":1000},\"id\":\"45c3d012-cb4b-48a6-baef-a028047ab88b\",\"type\":\"DataTable\"}],\"root_ids\":[\"45c3d012-cb4b-48a6-baef-a028047ab88b\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.6\"}};\n", | |
| " var render_items = [{\"docid\":\"430f768f-2bd3-482c-a92d-763fb45a90b9\",\"elementid\":\"df81f9d1-fa14-4394-8254-f1916c3e386c\",\"modelid\":\"45c3d012-cb4b-48a6-baef-a028047ab88b\",\"notebook_comms_target\":\"cf5c9de4-65c6-49da-a607-d8e939baa53d\"}];\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(\"df81f9d1-fa14-4394-8254-f1916c3e386c\")).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": [] | |
| } | |
| ], | |
| "metadata": { | |
| "anaconda-cloud": {}, | |
| "kernelspec": { | |
| "display_name": "Python [jarvis1]", | |
| "language": "python", | |
| "name": "Python [jarvis1]" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.5.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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