From 867b5c8bb00ebb43d8622e1eab984228a679b152 Mon Sep 17 00:00:00 2001 From: juan-cb Date: Wed, 11 Oct 2017 23:34:50 +0200 Subject: [PATCH] Adding notebooks used in the research --- machinelearning-results.ipynb | 36492 ++++++++++++++++++++++++++++++++ reinforcement-results.ipynb | 2436 +++ 2 files changed, 38928 insertions(+) create mode 100644 machinelearning-results.ipynb create mode 100644 reinforcement-results.ipynb diff --git a/machinelearning-results.ipynb b/machinelearning-results.ipynb new file mode 100644 index 0000000..694bc32 --- /dev/null +++ b/machinelearning-results.ipynb @@ -0,0 +1,36492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Importing required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests\n", + "from datetime import datetime\n", + "import pandas as pd\n", + "import numpy as np\n", + "import io\n", + "from packaging import version\n", + "\n", + "from scipy import stats\n", + "\n", + "from IPython.display import display\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('fivethirtyeight')\n", + "\n", + "\n", + "from sklearn import linear_model\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn import metrics\n", + "\n", + "#https://github.com/dianalam/yelp-classifier/blob/master/yelp-classification.ipynb\n", + "from sklearn.pipeline import Pipeline, make_pipeline\n", + "from collections import defaultdict\n", + "\n", + "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n", + "from scipy.spatial.distance import pdist, squareform\n", + "from sklearn.preprocessing import scale\n", + "from sklearn.preprocessing import normalize, scale, StandardScaler, Normalizer\n", + "from sklearn.grid_search import GridSearchCV\n", + "\n", + "\n", + "import seaborn as sns\n", + "\n", + "from sklearn.tree import DecisionTreeClassifier, export_graphviz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Connect to the OEEU's API and download data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with open(\"tokenOEEU.txt\") as file: \n", + " api_token = file.read() " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "getDataMaster=requests.get('https://datos.oeeu.org/api/master/estudiantes-titulaciones/',headers={'Authorization':api_token}).content\n", + "dataMaster=pd.read_csv(io.StringIO(getDataMaster.decode('utf-8')), low_memory=False, sep=\";\")\n", + "\n", + "getParadataEntornoMaster=requests.get('https://datos.oeeu.org/api/master/paradata_entorno/',headers={'Authorization':api_token}).content\n", + "paradataMaster=pd.read_csv(io.StringIO(getParadataEntornoMaster.decode('utf-8')), low_memory=False, sep=\";\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " estudiante_id browser_language browser_name browser_version \\\n", + "0 741300001 es Chrome 58.0.3029.81 \n", + "1 741300001 es Chrome 58.0.3029.81 \n", + "2 591300026 es-ES Firefox 52.0 \n", + "3 591300026 es-ES Firefox 52.0 \n", + "4 591300026 es-ES Firefox 52.0 \n", + "\n", + " device_pixel_ratio device_screen_height device_screen_width id \\\n", + "0 1 768 1366 28 \n", + "1 1 768 1366 29 \n", + "2 1 768 1366 30 \n", + "3 1 768 1366 31 \n", + "4 1 768 1366 32 \n", + "\n", + " landscape os os_version pantalla_cuestionario portrait \\\n", + "0 NaN Linux x86_64 0 NaN \n", + "1 NaN Linux x86_64 1 NaN \n", + "2 NaN Windows 7 0 NaN \n", + "3 NaN Windows 7 1 NaN \n", + "4 NaN Windows 7 2 NaN \n", + "\n", + " push_notification push_notification_id sesion_id tablet_or_mobile \\\n", + "0 NaN NaN 6 False \n", + "1 NaN NaN 6 False \n", + "2 NaN NaN 7 False \n", + "3 NaN NaN 7 False \n", + "4 NaN NaN 7 False \n", + "\n", + " userAgent viewport_height \\\n", + "0 Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... 678 \n", + "1 Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... 678 \n", + "2 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:52.0) G... 631 \n", + "3 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:52.0) G... 631 \n", + "4 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:52.0) G... 631 \n", + "\n", + " viewport_width \n", + "0 1319 \n", + "1 1319 \n", + "2 1366 \n", + "3 1366 \n", + "4 1366 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(paradataMaster.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data wrangling and cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dataMaster_work = dataMaster.filter(['estudiante_id', 'annoNacimiento','sexo_id','esEspannol', 'universidad_id', 'estudiosPadre_id', 'estudiosMadre_id', 'situacionLaboralPadre_id', 'situacionLaboralMadre_id','oficioProfesionPadre_id','oficioProfesionMadre_id','residenciaFamiliar_id','residencia_id','idMaster_id','especializacionMaster_id','masterHabilitante', 'titularidadMaster_id','modalidadMaster_id', 'cursoInicioMaster', 'cursoFinalizacionMaster','notaMedia_id','realizacionPracticasMaster', 'tiempoDuracionPracticasMaster','realizacionErasmusMaster','tiempoDuracionErasmus_id','paisErasmusMaster_id', 'viaAccesoMaster_id', 'verticalAsignado', 'cuestionarioFinalizado', 'numUniversidades', 'numUniversidadesEspannolas', 'ramaConocimiento_id', 'realDecreto'], axis=1)\n", + "paradataMaster_work = paradataMaster.filter(['estudiante_id', 'browser_language', 'browser_name', 'browser_version', 'device_pixel_ratio', 'device_screen_height', 'device_screen_width', 'landscape', 'os', 'os_version', 'portrait', 'push_notification', 'push_notification_id', 'tablet_or_mobile', 'userAgent', 'viewport_height', 'viewport_width'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "paradataMaster_work = paradataMaster_work.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " estudiante_id annoNacimiento sexo_id esEspannol universidad_id \\\n", + "0 11300021 1974.0 2 True 1 \n", + "1 11300023 1991.0 2 True 1 \n", + "2 11300040 1989.0 2 True 1 \n", + "3 11300040 1989.0 2 True 1 \n", + "4 11300044 1991.0 2 True 1 \n", + "\n", + " estudiosPadre_id estudiosMadre_id situacionLaboralPadre_id \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " situacionLaboralMadre_id oficioProfesionPadre_id oficioProfesionMadre_id \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " residenciaFamiliar_id residencia_id idMaster_id \\\n", + "0 10.0 10.0 4310483 \n", + "1 10.0 10.0 4310483 \n", + "2 10.0 10.0 4312625 \n", + "3 10.0 10.0 4312625 \n", + "4 10.0 10.0 4312840 \n", + "\n", + " especializacionMaster_id masterHabilitante titularidadMaster_id \\\n", + "0 3.0 True 1.0 \n", + "1 3.0 True 1.0 \n", + "2 2.0 False 1.0 \n", + "3 2.0 False 1.0 \n", + "4 2.0 False 1.0 \n", + "\n", + " modalidadMaster_id cursoInicioMaster cursoFinalizacionMaster \\\n", + "0 1.0 20132014.0 20132014 \n", + "1 1.0 20132014.0 20132014 \n", + "2 1.0 20132014.0 20132014 \n", + "3 1.0 20132014.0 20132014 \n", + "4 3.0 20132014.0 20132014 \n", + "\n", + " notaMedia_id realizacionPracticasMaster tiempoDuracionPracticasMaster \\\n", + "0 2.0 True 2.0 \n", + "1 3.0 True 2.0 \n", + "2 2.0 False NaN \n", + "3 2.0 False NaN \n", + "4 2.0 False NaN \n", + "\n", + " realizacionErasmusMaster tiempoDuracionErasmus_id paisErasmusMaster_id \\\n", + "0 False NaN NaN \n", + "1 False NaN NaN \n", + "2 False NaN NaN \n", + "3 False NaN NaN \n", + "4 False NaN NaN \n", + "\n", + " viaAccesoMaster_id verticalAsignado cuestionarioFinalizado \\\n", + "0 1.0 3 True \n", + "1 1.0 1 True \n", + "2 1.0 2 False \n", + "3 1.0 2 False \n", + "4 1.0 3 True \n", + "\n", + " numUniversidades numUniversidadesEspannolas ramaConocimiento_id \\\n", + "0 1 1 4.0 \n", + "1 1 1 4.0 \n", + "2 1 1 2.0 \n", + "3 1 1 2.0 \n", + "4 1 1 3.0 \n", + "\n", + " realDecreto browser_language browser_name browser_version \\\n", + "0 1393/2007 es Chrome 58.0.3029.110 \n", + "1 1393/2007 es Chrome 57.0.2987.133 \n", + "2 1393/2007 es-ES Mobile Safari 10.0 \n", + "3 1393/2007 es-ES Mobile Safari 10.0 \n", + "4 1393/2007 en-US MIUI Browser 8.8.6 \n", + "\n", + " device_pixel_ratio device_screen_height device_screen_width landscape \\\n", + "0 2 800 1280 NaN \n", + "1 1 1080 1920 NaN \n", + "2 2 667 375 NaN \n", + "3 2 667 375 NaN \n", + "4 3 640 360 NaN \n", + "\n", + " os os_version portrait push_notification push_notification_id \\\n", + "0 Mac OS 10.12.1 NaN NaN NaN \n", + "1 Windows 10 NaN NaN NaN \n", + "2 iOS 10.3.1 NaN NaN NaN \n", + "3 iOS 10.3.1 NaN NaN NaN \n", + "4 Android 5.1.1 NaN NaN NaN \n", + "\n", + " tablet_or_mobile userAgent \\\n", + "0 False Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1... \n", + "1 False Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... \n", + "2 True Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_1 like... \n", + "3 True Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_1 like... \n", + "4 True Mozilla/5.0 (Linux; U; Android 5.1.1; en-us; R... \n", + "\n", + " viewport_height viewport_width \n", + "0 628 1276 \n", + "1 974 1920 \n", + "2 559 375 \n", + "3 489 328 \n", + "4 514 360 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullMaster_work = pd.merge(dataMaster_work, paradataMaster_work, on='estudiante_id')\n", + "display(fullMaster_work.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Dropping all rows from the University = 6:

\n", + "

(users from that university had the same exactly paradata values which wasn't realistic and introduced noise)

" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With that university(8246, 49)\n", + "Without that university(7726, 49)\n" + ] + } + ], + "source": [ + "print \"With that university\" +str(fullMaster_work.shape)\n", + "\n", + "fullMaster_work = fullMaster_work[fullMaster_work['universidad_id'] != 6]\n", + "\n", + "print \"Without that university\" +str(fullMaster_work.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cleaning columns with too much NaN values\n", + "

Detecting the number of NaN values within each column of the dataframe.

\n", + "

Later, we will drop all the columns that have more than 10% of NaN values

" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Total rows: ', 7726)\n", + "\n", + "Total NaN values per column\n", + "----------\n", + "estudiante_id: 0\n", + "annoNacimiento: 440\n", + "sexo_id: 0\n", + "esEspannol: 437\n", + "universidad_id: 0\n", + "estudiosPadre_id: 6281\n", + "estudiosMadre_id: 6258\n", + "situacionLaboralPadre_id: 6471\n", + "situacionLaboralMadre_id: 6483\n", + "oficioProfesionPadre_id: 6702\n", + "oficioProfesionMadre_id: 6906\n", + "residenciaFamiliar_id: 1251\n", + "residencia_id: 1567\n", + "idMaster_id: 0\n", + "especializacionMaster_id: 3806\n", + "masterHabilitante: 2197\n", + "titularidadMaster_id: 1391\n", + "modalidadMaster_id: 2273\n", + "cursoInicioMaster: 567\n", + "cursoFinalizacionMaster: 0\n", + "notaMedia_id: 1546\n", + "realizacionPracticasMaster: 5155\n", + "tiempoDuracionPracticasMaster: 7089\n", + "realizacionErasmusMaster: 3871\n", + "tiempoDuracionErasmus_id: 7658\n", + "paisErasmusMaster_id: 7657\n", + "viaAccesoMaster_id: 2831\n", + "verticalAsignado: 0\n", + "cuestionarioFinalizado: 0\n", + "numUniversidades: 0\n", + "numUniversidadesEspannolas: 0\n", + "ramaConocimiento_id: 0\n", + "realDecreto: 0\n", + "browser_language: 0\n", + "browser_name: 0\n", + "browser_version: 0\n", + "device_pixel_ratio: 0\n", + "device_screen_height: 0\n", + "device_screen_width: 0\n", + "landscape: 7726\n", + "os: 0\n", + "os_version: 0\n", + "portrait: 7726\n", + "push_notification: 7726\n", + "push_notification_id: 7726\n", + "tablet_or_mobile: 0\n", + "userAgent: 0\n", + "viewport_height: 0\n", + "viewport_width: 0\n" + ] + } + ], + "source": [ + "NaNValues = fullMaster_work.isnull().sum().tolist()\n", + "columnas = fullMaster_work.columns.values.tolist()\n", + "\n", + "print(\"Total rows: \", fullMaster_work['estudiante_id'].count())\n", + "\n", + "print(\"\\nTotal NaN values per column\\n----------\")\n", + "i=0\n", + "for c in columnas:\n", + " print(str(columnas[i])+\": \"+str(NaNValues[i]))\n", + " i+=1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Drop column: ', 'annoNacimiento')\n", + "('Drop column: ', 'esEspannol')\n", + "('Drop column: ', 'estudiosPadre_id')\n", + "('Drop column: ', 'estudiosMadre_id')\n", + "('Drop column: ', 'situacionLaboralPadre_id')\n", + "('Drop column: ', 'situacionLaboralMadre_id')\n", + "('Drop column: ', 'oficioProfesionPadre_id')\n", + "('Drop column: ', 'oficioProfesionMadre_id')\n", + "('Drop column: ', 'residenciaFamiliar_id')\n", + "('Drop column: ', 'residencia_id')\n", + "('Drop column: ', 'especializacionMaster_id')\n", + "('Drop column: ', 'masterHabilitante')\n", + "('Drop column: ', 'titularidadMaster_id')\n", + "('Drop column: ', 'modalidadMaster_id')\n", + "('Drop column: ', 'cursoInicioMaster')\n", + "('Drop column: ', 'notaMedia_id')\n", + "('Drop column: ', 'realizacionPracticasMaster')\n", + "('Drop column: ', 'tiempoDuracionPracticasMaster')\n", + "('Drop column: ', 'realizacionErasmusMaster')\n", + "('Drop column: ', 'tiempoDuracionErasmus_id')\n", + "('Drop column: ', 'paisErasmusMaster_id')\n", + "('Drop column: ', 'viaAccesoMaster_id')\n", + "('Drop column: ', 'landscape')\n", + "('Drop column: ', 'portrait')\n", + "('Drop column: ', 'push_notification')\n", + "('Drop column: ', 'push_notification_id')\n" + ] + }, + { + "data": { + "text/html": [ + "
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411300044214312840201320143True113.01393/2007en-USMIUI Browser8.8.63640360Android5.1.1TrueMozilla/5.0 (Linux; U; Android 5.1.1; en-us; R...514360
\n", + "
" + ], + "text/plain": [ + " estudiante_id sexo_id universidad_id idMaster_id \\\n", + "0 11300021 2 1 4310483 \n", + "1 11300023 2 1 4310483 \n", + "2 11300040 2 1 4312625 \n", + "3 11300040 2 1 4312625 \n", + "4 11300044 2 1 4312840 \n", + "\n", + " cursoFinalizacionMaster verticalAsignado cuestionarioFinalizado \\\n", + "0 20132014 3 True \n", + "1 20132014 1 True \n", + "2 20132014 2 False \n", + "3 20132014 2 False \n", + "4 20132014 3 True \n", + "\n", + " numUniversidades numUniversidadesEspannolas ramaConocimiento_id \\\n", + "0 1 1 4.0 \n", + "1 1 1 4.0 \n", + "2 1 1 2.0 \n", + "3 1 1 2.0 \n", + "4 1 1 3.0 \n", + "\n", + " realDecreto browser_language browser_name browser_version \\\n", + "0 1393/2007 es Chrome 58.0.3029.110 \n", + "1 1393/2007 es Chrome 57.0.2987.133 \n", + "2 1393/2007 es-ES Mobile Safari 10.0 \n", + "3 1393/2007 es-ES Mobile Safari 10.0 \n", + "4 1393/2007 en-US MIUI Browser 8.8.6 \n", + "\n", + " device_pixel_ratio device_screen_height device_screen_width os \\\n", + "0 2 800 1280 Mac OS \n", + "1 1 1080 1920 Windows \n", + "2 2 667 375 iOS \n", + "3 2 667 375 iOS \n", + "4 3 640 360 Android \n", + "\n", + " os_version tablet_or_mobile \\\n", + "0 10.12.1 False \n", + "1 10 False \n", + "2 10.3.1 True \n", + "3 10.3.1 True \n", + "4 5.1.1 True \n", + "\n", + " userAgent viewport_height \\\n", + "0 Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1... 628 \n", + "1 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... 974 \n", + "2 Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_1 like... 559 \n", + "3 Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_1 like... 489 \n", + "4 Mozilla/5.0 (Linux; U; Android 5.1.1; en-us; R... 514 \n", + "\n", + " viewport_width \n", + "0 1276 \n", + "1 1920 \n", + "2 375 \n", + "3 328 \n", + "4 360 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullMaster_workClean = fullMaster_work\n", + "\n", + "totalRows = fullMaster_work['estudiante_id'].count()\n", + "\n", + "# max percentage of NaN values allowed in a column\n", + "NaNThreshold = 10\n", + "\n", + "NaNValues = fullMaster_work.isnull().sum().tolist()\n", + "columnas = fullMaster_work.columns.values.tolist()\n", + "\n", + "i=0\n", + "for c in columnas:\n", + " if fullMaster_work[columnas[i]].isnull().sum() > NaNThreshold:\n", + " print(\"Drop column: \",columnas[i])\n", + " fullMaster_workClean.drop([columnas[i]], axis=1, inplace=True)\n", + " i+=1\n", + "\n", + "display(fullMaster_workClean.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# replacing boolean values (True, False) with numeric values (1, 0)\n", + "fullMaster_workClean = fullMaster_workClean.replace(False,0)\n", + "fullMaster_workClean = fullMaster_workClean.replace(True,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of finalized questionnaires valuable for the model: index cuestionarioFinalizado\n", + "0 True 5969\n", + "1 False 1757\n" + ] + } + ], + "source": [ + "print \"Number of finalized questionnaires valuable for the model: \", fullMaster_workClean[\"cuestionarioFinalizado\"].value_counts().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fullMaster_workClean.drop_duplicates(subset=['estudiante_id'], keep=False, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of finalized questionnaires valuable for the model after cleaning users that used the questionnaire in several stages: index cuestionarioFinalizado\n", + "0 True 2379\n", + "1 False 1077\n" + ] + } + ], + "source": [ + "print \"Number of finalized questionnaires valuable for the model after cleaning users that used the questionnaire in several stages: \", fullMaster_workClean[\"cuestionarioFinalizado\"].value_counts().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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011300021214310483201320143True114.01393/2007esChrome58.0.3029.11028001280Mac OS10.12.1FalseMozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1...6281276
111300023214310483201320141True114.01393/2007esChrome57.0.2987.133110801920Windows10FalseMozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...9741920
711300049214310483201320143True114.01393/2007esChrome58.0.3029.11017681366Windows10FalseMozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...6621366
1111300063214313391201320142True114.01393/2007zh-CNEdge15.15063110801920Windows10FalseMozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...9701842
1211300071114312049201320141False114.01393/2007esChrome59.0.3071.8617681366Windows7FalseMozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...6621366
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" + ], + "text/plain": [ + " estudiante_id sexo_id universidad_id idMaster_id \\\n", + "0 11300021 2 1 4310483 \n", + "1 11300023 2 1 4310483 \n", + "7 11300049 2 1 4310483 \n", + "11 11300063 2 1 4313391 \n", + "12 11300071 1 1 4312049 \n", + "\n", + " cursoFinalizacionMaster verticalAsignado cuestionarioFinalizado \\\n", + "0 20132014 3 True \n", + "1 20132014 1 True \n", + "7 20132014 3 True \n", + "11 20132014 2 True \n", + "12 20132014 1 False \n", + "\n", + " numUniversidades numUniversidadesEspannolas ramaConocimiento_id \\\n", + "0 1 1 4.0 \n", + "1 1 1 4.0 \n", + "7 1 1 4.0 \n", + "11 1 1 4.0 \n", + "12 1 1 4.0 \n", + "\n", + " realDecreto browser_language browser_name browser_version \\\n", + "0 1393/2007 es Chrome 58.0.3029.110 \n", + "1 1393/2007 es Chrome 57.0.2987.133 \n", + "7 1393/2007 es Chrome 58.0.3029.110 \n", + "11 1393/2007 zh-CN Edge 15.15063 \n", + "12 1393/2007 es Chrome 59.0.3071.86 \n", + "\n", + " device_pixel_ratio device_screen_height device_screen_width os \\\n", + "0 2 800 1280 Mac OS \n", + "1 1 1080 1920 Windows \n", + "7 1 768 1366 Windows \n", + "11 1 1080 1920 Windows \n", + "12 1 768 1366 Windows \n", + "\n", + " os_version tablet_or_mobile \\\n", + "0 10.12.1 False \n", + "1 10 False \n", + "7 10 False \n", + "11 10 False \n", + "12 7 False \n", + "\n", + " userAgent viewport_height \\\n", + "0 Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1... 628 \n", + "1 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... 974 \n", + "7 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... 662 \n", + "11 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... 970 \n", + "12 Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple... 662 \n", + "\n", + " viewport_width \n", + "0 1276 \n", + "1 1920 \n", + "7 1366 \n", + "11 1842 \n", + "12 1366 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "backupFMasterClean = fullMaster_workClean.copy()\n", + "display(backupFMasterClean.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We clean columns that we don't need to generate the model:

\n", + "- browser_version and os_version: the version of the browser and operating system introduces a lot of combinations\n", + "- userAgent: this variable is a very complex string including a lot of different data\n", + "- estudiante_id: the id of the student is also unnecessary (it does not give any valuable information)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0214310483201320143True114.01393/2007esChrome28001280Mac OSFalse6281276
1214310483201320141True114.01393/2007esChrome110801920WindowsFalse9741920
7214310483201320143True114.01393/2007esChrome17681366WindowsFalse6621366
11214313391201320142True114.01393/2007zh-CNEdge110801920WindowsFalse9701842
12114312049201320141False114.01393/2007esChrome17681366WindowsFalse6621366
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" + ], + "text/plain": [ + " sexo_id universidad_id idMaster_id cursoFinalizacionMaster \\\n", + "0 2 1 4310483 20132014 \n", + "1 2 1 4310483 20132014 \n", + "7 2 1 4310483 20132014 \n", + "11 2 1 4313391 20132014 \n", + "12 1 1 4312049 20132014 \n", + "\n", + " verticalAsignado cuestionarioFinalizado numUniversidades \\\n", + "0 3 True 1 \n", + "1 1 True 1 \n", + "7 3 True 1 \n", + "11 2 True 1 \n", + "12 1 False 1 \n", + "\n", + " numUniversidadesEspannolas ramaConocimiento_id realDecreto \\\n", + "0 1 4.0 1393/2007 \n", + "1 1 4.0 1393/2007 \n", + "7 1 4.0 1393/2007 \n", + "11 1 4.0 1393/2007 \n", + "12 1 4.0 1393/2007 \n", + "\n", + " browser_language browser_name device_pixel_ratio device_screen_height \\\n", + "0 es Chrome 2 800 \n", + "1 es Chrome 1 1080 \n", + "7 es Chrome 1 768 \n", + "11 zh-CN Edge 1 1080 \n", + "12 es Chrome 1 768 \n", + "\n", + " device_screen_width os tablet_or_mobile viewport_height \\\n", + "0 1280 Mac OS False 628 \n", + "1 1920 Windows False 974 \n", + "7 1366 Windows False 662 \n", + "11 1920 Windows False 970 \n", + "12 1366 Windows False 662 \n", + "\n", + " viewport_width \n", + "0 1276 \n", + "1 1920 \n", + "7 1366 \n", + "11 1842 \n", + "12 1366 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullMaster_workClean.drop(['browser_version'], axis=1, inplace=True)\n", + "fullMaster_workClean.drop(['estudiante_id'], axis=1, inplace=True)\n", + "fullMaster_workClean.drop(['os_version'], axis=1, inplace=True)\n", + "fullMaster_workClean.drop(['userAgent'], axis=1, inplace=True)\n", + "\n", + "display(fullMaster_workClean.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We replace the assigned vertical of the questionnaire numeric values (verticalAsignado) with their assigned name (A, B or C)

" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sexo_iduniversidad_ididMaster_idcursoFinalizacionMasterverticalAsignadocuestionarioFinalizadonumUniversidadesnumUniversidadesEspannolasramaConocimiento_idrealDecretobrowser_languagebrowser_namedevice_pixel_ratiodevice_screen_heightdevice_screen_widthostablet_or_mobileviewport_heightviewport_width
021431048320132014CTrue114.01393/2007esChrome28001280Mac OSFalse6281276
121431048320132014ATrue114.01393/2007esChrome110801920WindowsFalse9741920
721431048320132014CTrue114.01393/2007esChrome17681366WindowsFalse6621366
1121431339120132014BTrue114.01393/2007zh-CNEdge110801920WindowsFalse9701842
1211431204920132014AFalse114.01393/2007esChrome17681366WindowsFalse6621366
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" + ], + "text/plain": [ + " sexo_id universidad_id idMaster_id cursoFinalizacionMaster \\\n", + "0 2 1 4310483 20132014 \n", + "1 2 1 4310483 20132014 \n", + "7 2 1 4310483 20132014 \n", + "11 2 1 4313391 20132014 \n", + "12 1 1 4312049 20132014 \n", + "\n", + " verticalAsignado cuestionarioFinalizado numUniversidades \\\n", + "0 C True 1 \n", + "1 A True 1 \n", + "7 C True 1 \n", + "11 B True 1 \n", + "12 A False 1 \n", + "\n", + " numUniversidadesEspannolas ramaConocimiento_id realDecreto \\\n", + "0 1 4.0 1393/2007 \n", + "1 1 4.0 1393/2007 \n", + "7 1 4.0 1393/2007 \n", + "11 1 4.0 1393/2007 \n", + "12 1 4.0 1393/2007 \n", + "\n", + " browser_language browser_name device_pixel_ratio device_screen_height \\\n", + "0 es Chrome 2 800 \n", + "1 es Chrome 1 1080 \n", + "7 es Chrome 1 768 \n", + "11 zh-CN Edge 1 1080 \n", + "12 es Chrome 1 768 \n", + "\n", + " device_screen_width os tablet_or_mobile viewport_height \\\n", + "0 1280 Mac OS False 628 \n", + "1 1920 Windows False 974 \n", + "7 1366 Windows False 662 \n", + "11 1920 Windows False 970 \n", + "12 1366 Windows False 662 \n", + "\n", + " viewport_width \n", + "0 1276 \n", + "1 1920 \n", + "7 1366 \n", + "11 1842 \n", + "12 1366 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullMaster_workClean[['verticalAsignado']] = fullMaster_workClean[['verticalAsignado']].replace([1, 2, 3], ['A', 'B', 'C'])\n", + "\n", + "display(fullMaster_workClean.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Performing one-hot encoding for categorial values on columns

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"\n", + " browser_language_mk-MK browser_language_nb-NO browser_language_nl \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_pl-PL browser_language_pt-BR browser_language_pt-PT \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_pt-br browser_language_ru browser_language_sv-se \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_zh-CN browser_language_zh-TW browser_language_zh-cn \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 1 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Android Browser browser_name_Chrome \\\n", + "0 0 1 \n", + "1 0 1 \n", + "7 0 1 \n", + "11 0 0 \n", + "12 0 1 \n", + "\n", + " browser_name_Chrome WebView browser_name_Edge browser_name_Firefox \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 1 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_IE browser_name_Iceweasel browser_name_MIUI Browser \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Mobile Safari browser_name_Opera browser_name_Safari \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Vivaldi browser_name_WebKit os_Android os_Chromium OS \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "7 0 0 0 0 \n", + "11 0 0 0 0 \n", + "12 0 0 0 0 \n", + "\n", + " os_Linux os_Mac OS os_Windows os_Windows Phone os_iOS \n", + "0 0 1 0 0 0 \n", + "1 0 0 1 0 0 \n", + "7 0 0 1 0 0 \n", + "11 0 0 1 0 0 \n", + "12 0 0 1 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullMaster_workDummies = pd.get_dummies(fullMaster_workClean)\n", + "display(fullMaster_workDummies.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ 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\n", + "
" + ], + "text/plain": [ + " sexo_id universidad_id idMaster_id cursoFinalizacionMaster \\\n", + "0 2 1 4310483 20132014 \n", + "1 2 1 4310483 20132014 \n", + "7 2 1 4310483 20132014 \n", + "11 2 1 4313391 20132014 \n", + "12 1 1 4312049 20132014 \n", + "\n", + " cuestionarioFinalizado numUniversidades numUniversidadesEspannolas \\\n", + "0 True 1 1 \n", + "1 True 1 1 \n", + "7 True 1 1 \n", + "11 True 1 1 \n", + "12 False 1 1 \n", + "\n", + " ramaConocimiento_id device_pixel_ratio device_screen_height \\\n", + "0 4.0 2 800 \n", + "1 4.0 1 1080 \n", + "7 4.0 1 768 \n", + "11 4.0 1 1080 \n", + "12 4.0 1 768 \n", + "\n", + " device_screen_width tablet_or_mobile viewport_height viewport_width \\\n", + "0 1280 False 628 1276 \n", + "1 1920 False 974 1920 \n", + "7 1366 False 662 1366 \n", + "11 1920 False 970 1842 \n", + "12 1366 False 662 1366 \n", + "\n", + " verticalAsignado_A verticalAsignado_B verticalAsignado_C \\\n", + "0 0 0 1 \n", + "1 1 0 0 \n", + "7 0 0 1 \n", + "11 0 1 0 \n", + "12 1 0 0 \n", + "\n", + " realDecreto_1393/2007 realDecreto_56/2005 browser_language_ast \\\n", + "0 1 0 0 \n", + "1 1 0 0 \n", + "7 1 0 0 \n", + "11 1 0 0 \n", + "12 1 0 0 \n", + "\n", + " browser_language_bg browser_language_ca browser_language_ca-ES \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_ca-es browser_language_cs browser_language_de \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_de-DE browser_language_en-AU browser_language_en-GB \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_en-US browser_language_en-ie browser_language_en-us \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_es browser_language_es-419 browser_language_es-AR \\\n", + "0 1 0 0 \n", + "1 1 0 0 \n", + "7 1 0 0 \n", + "11 0 0 0 \n", + "12 1 0 0 \n", + "\n", + " browser_language_es-CL browser_language_es-CO browser_language_es-EC \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_es-ES browser_language_es-ES_tradnl \\\n", + "0 0 0 \n", + "1 0 0 \n", + "7 0 0 \n", + "11 0 0 \n", + "12 0 0 \n", + "\n", + " browser_language_es-LA browser_language_es-MX browser_language_es-XL \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_es-es browser_language_es-xl browser_language_eu \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_fr browser_language_fr-FR browser_language_gl \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_gl-GL browser_language_it browser_language_it-IT \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_mk-MK browser_language_nb-NO browser_language_nl \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_pl-PL browser_language_pt-BR browser_language_pt-PT \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_pt-br browser_language_ru browser_language_sv-se \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_zh-CN browser_language_zh-TW browser_language_zh-cn \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 1 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Android Browser browser_name_Chrome \\\n", + "0 0 1 \n", + "1 0 1 \n", + "7 0 1 \n", + "11 0 0 \n", + "12 0 1 \n", + "\n", + " browser_name_Chrome WebView browser_name_Edge browser_name_Firefox \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 1 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_IE browser_name_Iceweasel browser_name_MIUI Browser \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Mobile Safari browser_name_Opera browser_name_Safari \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Vivaldi browser_name_WebKit os_Android os_Chromium OS \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "7 0 0 0 0 \n", + "11 0 0 0 0 \n", + "12 0 0 0 0 \n", + "\n", + " os_Linux os_Mac OS os_Windows os_Windows Phone os_iOS \n", + "0 0 1 0 0 0 \n", + "1 0 0 1 0 0 \n", + "7 0 0 1 0 0 \n", + "11 0 0 1 0 0 \n", + "12 0 0 1 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullMaster_workCleanDummies = fullMaster_workDummies.dropna()\n", + "\n", + "display(fullMaster_workCleanDummies.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of finalized questionnaires valuable for the model: index cuestionarioFinalizado\n", + "0 True 2379\n", + "1 False 1077\n" + ] + } + ], + "source": [ + "print \"Number of finalized questionnaires valuable for the model: \", fullMaster_workCleanDummies[\"cuestionarioFinalizado\"].value_counts().reset_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Algorithm execution\n", + "\n", + "

Preparing data: selecting data for training and testing from the dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fullMaster_workCleanDummies['is_train'] = np.random.uniform(0,1,len(fullMaster_workCleanDummies)) <= .66" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "train_gen, test_gen = fullMaster_workCleanDummies[fullMaster_workCleanDummies['is_train']==True], fullMaster_workCleanDummies[fullMaster_workCleanDummies['is_train']==False]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Defining function to search the best setup for Random forest

" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def grid_search_scores_rf(Xtrain, ytrain):\n", + " scores = ['accuracy', 'precision', 'roc_auc']\n", + " output = defaultdict(list)\n", + " pipeline = make_pipeline(StandardScaler(), RandomForestClassifier())\n", + " param = {\n", + " 'randomforestclassifier__max_features': ['auto', 'sqrt', 'log2'],\n", + " 'randomforestclassifier__min_samples_leaf': [1, 5, 10, 50, 100, 200, 500, 1000]}\n", + " for score in scores: \n", + " #print score, '\\n'\n", + " grid_search = GridSearchCV(pipeline, param_grid = param, verbose = 3, cv = 5, scoring = score)\n", + " grid_search.fit(Xtrain, ytrain)\n", + " output[score] = grid_search.best_score_, grid_search.best_params_\n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the best setup for the Random forest algorithm

" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "columnas_gen = fullMaster_workCleanDummies.columns.values.tolist()\n", + "#display(columnas)\n", + "\n", + "variable2predict_gen = 'cuestionarioFinalizado'\n", + "\n", + "columnas_gen.remove(variable2predict_gen)\n", + "#display(columnas)\n", + "\n", + "features_gen = pd.Index(columnas_gen)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 ..., 0 0 0]\n", + "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.774834 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.777042 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.721854 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.770419 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.631929 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto, score=0.774834 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto, score=0.801325 - 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0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt, score=0.642458 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt, score=0.565130 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt, score=0.609071 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=sqrt, score=0.603152 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=sqrt, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2, score=0.674635 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2, score=0.684619 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2, score=0.624715 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2, score=0.722068 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=log2, score=0.666770 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.650833 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.678617 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.670881 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.710942 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.722791 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.696178 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.706686 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.643382 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.717081 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.687350 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.678982 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.737916 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.663145 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.719010 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.715216 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.703651 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.696212 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.659220 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.723094 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.730089 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.659322 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.715187 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.704678 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.751381 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.644946 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.522353 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.640073 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.586319 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.645904 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.604893 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "defaultdict(, {'roc_auc': (0.71034409071717741, {'randomforestclassifier__min_samples_leaf': 100, 'randomforestclassifier__max_features': 'sqrt'}), 'precision': (0.92772666227138423, {'randomforestclassifier__min_samples_leaf': 200, 'randomforestclassifier__max_features': 'auto'}), 'accuracy': (0.790543526292532, {'randomforestclassifier__min_samples_leaf': 100, 'randomforestclassifier__max_features': 'sqrt'})})\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Done 120 out of 120 | elapsed: 6.8s finished\n" + ] + } + ], + "source": [ + "y_gen, _gen = pd.factorize(train_gen[variable2predict_gen])\n", + "print y_gen\n", + "scoresRF_results_gen = grid_search_scores_rf(train_gen[features_gen], y_gen)\n", + "print scoresRF_results_gen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Obtaining the best Random Forest configuration through the output of the benchmarks previously computed

" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('precision', (0.92772666227138423, {'randomforestclassifier__min_samples_leaf': 200, 'randomforestclassifier__max_features': 'auto'}))\n" + ] + } + ], + "source": [ + "best_RF_values_gen = max(scoresRF_results_gen.items(), key=lambda a: a[1])\n", + "if best_RF_values_gen[1][0] == 1.0:\n", + " best_RF_values_gen = sorted(scoresRF_results_gen.iteritems(),key=lambda (v): v[1],reverse=True)[1]\n", + "\n", + "print best_RF_values_gen\n", + " \n", + "#Accessing to the config values within the dict inside the list contained in the defaultdict\n", + "rfC__min_samples_leaf_gen = best_RF_values_gen[1][1]['randomforestclassifier__min_samples_leaf']\n", + "rfC__max_features_gen = best_RF_values_gen[1][1]['randomforestclassifier__max_features']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Run the Random Forest with the best setup found

" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "best_randomforest_gen = RandomForestClassifier(min_samples_leaf = rfC__min_samples_leaf_gen, max_features = str(rfC__max_features_gen))\n", + "best_randomforest_gen.fit(train_gen[features_gen], y_gen)\n", + "None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Test the trained Random Forest

" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "target_names_gen = np.array([True, False])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "preds_gen = target_names_gen[best_randomforest_gen.predict(test_gen[features_gen])]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "preds False True \n", + "actual \n", + "False 142 236\n", + "True 27 788" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(test_gen[variable2predict_gen], preds_gen, rownames=['actual'], colnames=['preds'])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " False 0.84 0.38 0.52 378\n", + " True 0.77 0.97 0.86 815\n", + "\n", + "avg / total 0.79 0.78 0.75 1193\n", + "\n" + ] + } + ], + "source": [ + "print metrics.classification_report(test_gen[variable2predict_gen], preds_gen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Identification of the most important features for the model

" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1) device_screen_width 0.297189\n", + " 2) viewport_width 0.292615\n", + " 3) browser_name_Firefox 0.100000\n", + " 4) device_pixel_ratio 0.098356\n", + " 5) viewport_height 0.096237\n", + " 6) device_screen_height 0.009573\n", + " 7) universidad_id 0.001965\n", + " 8) verticalAsignado_A 0.001841\n", + " 9) verticalAsignado_C 0.000894\n", + "10) idMaster_id 0.000869\n", + "11) browser_language_es 0.000257\n", + "12) browser_language_es-ES 0.000108\n", + "13) ramaConocimiento_id 0.000095\n", + "14) browser_language_en-US 0.000000\n", + "15) browser_language_es-419 0.000000\n", + "16) browser_language_en-GB 0.000000\n", + "17) browser_language_en-ie 0.000000\n", + "18) browser_language_en-us 0.000000\n", + "19) is_train 0.000000\n", + "20) browser_language_es-AR 0.000000\n", + "21) browser_language_es-CL 0.000000\n", + "22) browser_language_de-DE 0.000000\n", + "23) browser_language_es-CO 0.000000\n", + "24) browser_language_es-EC 0.000000\n", + "25) browser_language_es-ES_tradnl 0.000000\n", + "26) browser_language_en-AU 0.000000\n", + "27) browser_language_bg 0.000000\n", + "28) browser_language_de 0.000000\n", + "29) browser_language_cs 0.000000\n", + "30) browser_language_ca-es 0.000000\n", + "31) browser_language_ca-ES 0.000000\n", + "32) browser_language_ca 0.000000\n", + "33) browser_language_es-MX 0.000000\n", + "34) browser_language_ast 0.000000\n", + "35) realDecreto_56/2005 0.000000\n", + "36) realDecreto_1393/2007 0.000000\n", + "37) verticalAsignado_B 0.000000\n", + "38) tablet_or_mobile 0.000000\n", + "39) numUniversidadesEspannolas 0.000000\n", + "40) numUniversidades 0.000000\n", + "41) cursoFinalizacionMaster 0.000000\n", + "42) browser_language_es-LA 0.000000\n", + "43) browser_language_es-XL 0.000000\n", + "44) os_iOS 0.000000\n", + "45) browser_language_zh-cn 0.000000\n", + "46) browser_name_Chrome 0.000000\n", + "47) browser_name_Chrome WebView 0.000000\n", + "48) browser_name_Edge 0.000000\n", + "49) browser_name_IE 0.000000\n", + "50) browser_name_Iceweasel 0.000000\n", + "51) browser_name_MIUI Browser 0.000000\n", + "52) browser_name_Mobile Safari 0.000000\n", + "53) browser_name_Opera 0.000000\n", + "54) browser_name_Safari 0.000000\n", + "55) browser_name_Vivaldi 0.000000\n", + "56) browser_name_WebKit 0.000000\n", + "57) os_Android 0.000000\n", + "58) os_Chromium OS 0.000000\n", + "59) os_Linux 0.000000\n", + "60) os_Mac OS 0.000000\n", + "61) os_Windows 0.000000\n", + "62) os_Windows Phone 0.000000\n", + "63) browser_name_Android Browser 0.000000\n", + "64) browser_language_zh-TW 0.000000\n", + "65) browser_language_es-es 0.000000\n", + "66) browser_language_zh-CN 0.000000\n", + "67) browser_language_es-xl 0.000000\n", + "68) browser_language_eu 0.000000\n", + "69) browser_language_fr 0.000000\n", + "70) browser_language_fr-FR 0.000000\n", + "71) browser_language_gl 0.000000\n", + "72) browser_language_gl-GL 0.000000\n", + "73) browser_language_it 0.000000\n", + "74) browser_language_it-IT 0.000000\n", + "75) browser_language_mk-MK 0.000000\n", + "76) browser_language_nb-NO 0.000000\n", + "77) browser_language_nl 0.000000\n", + "78) browser_language_pl-PL 0.000000\n", + "79) browser_language_pt-BR 0.000000\n", + "80) browser_language_pt-PT 0.000000\n", + "81) browser_language_pt-br 0.000000\n", + "82) browser_language_ru 0.000000\n", + "83) browser_language_sv-se 0.000000\n", + "84) sexo_id 0.000000\n" + ] + } + ], + "source": [ + "importances_gen = best_randomforest_gen.feature_importances_\n", + "feat_labels_gen = fullMaster_workCleanDummies.columns[1:]\n", + "indices_gen = np.argsort(importances_gen)[::-1]\n", + "\n", + "importances_threshold_gen = 0.05\n", + "\n", + "listImportances_gen = []\n", + "listNonImportant_gen = []\n", + "\n", + "for f in range(train_gen[features_gen].shape[1]):\n", + " print(\"%2d) %-*s %f\" % (f + 1, 30, \n", + " features_gen[indices_gen[f]], \n", + " importances_gen[indices_gen[f]]))\n", + " if importances_gen[indices_gen[f]] > importances_threshold_gen:\n", + " listImportances_gen.append(features_gen[indices_gen[f]])\n", + " else:\n", + " listNonImportant_gen.append(features_gen[indices_gen[f]])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most important features for the model:\n" + ] + }, + { + "data": { + "text/plain": [ + "['device_screen_width',\n", + " 'viewport_width',\n", + " 'browser_name_Firefox',\n", + " 'device_pixel_ratio',\n", + " 'viewport_height']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Most important features for the model:\")\n", + "display(listImportances_gen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clusters\n", + "\n", + "

Selection of the important features identified in the previous step

" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cuestionarioFinalizadodevice_pixel_ratiodevice_screen_widthviewport_heightviewport_widthbrowser_name_Firefox
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" + ], + "text/plain": [ + " cuestionarioFinalizado device_pixel_ratio device_screen_width \\\n", + "0 True 2 1280 \n", + "1 True 1 1920 \n", + "7 True 1 1366 \n", + "11 True 1 1920 \n", + "12 False 1 1366 \n", + "\n", + " viewport_height viewport_width browser_name_Firefox \n", + "0 628 1276 0 \n", + "1 974 1920 0 \n", + "7 662 1366 0 \n", + "11 970 1842 0 \n", + "12 662 1366 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cluster_common = fullMaster_workCleanDummies\n", + "\n", + "for imp in listNonImportant_gen:\n", + " cluster_common.drop([imp], axis=1, inplace=True)\n", + "\n", + "display(cluster_common.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " device_pixel_ratio device_screen_width \\\n", + "cuestionarioFinalizado \n", + "True 1.369423 -0.082905 \n", + "True -0.427360 1.323852 \n", + "NaN -0.427360 0.106128 \n", + "NaN -0.427360 1.323852 \n", + "NaN -0.427360 0.106128 \n", + "\n", + " viewport_height viewport_width browser_name_Firefox \n", + "cuestionarioFinalizado \n", + "True -0.662002 -0.028499 -0.454184 \n", + "True 1.613499 1.453157 -0.454184 \n", + "NaN -0.438398 0.178565 -0.454184 \n", + "NaN 1.587193 1.273702 -0.454184 \n", + "NaN -0.438398 0.178565 -0.454184 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mysubset = [\"cuestionarioFinalizado\"]\n", + "cluster_common = cluster_common[sorted(fullMaster_workCleanDummies, key=lambda x: x not in mysubset)]\n", + "\n", + "columnas_hCluster = cluster_common.columns.values.tolist()\n", + "#display(columnas)\n", + "\n", + "variable2predict = 'cuestionarioFinalizado'\n", + "\n", + "columnas_hCluster.remove(variable2predict)\n", + "\n", + "#print(fullMaster_workCleanDummies.shape)\n", + "#print(len(columnas_hCluster))\n", + "\n", + "z_hCluster = scale(cluster_common.iloc[:,1:])\n", + "z_hCluster = pd.DataFrame(z_hCluster, columns=columnas_hCluster)\n", + "z_hCluster['cuestionarioFinalizado'] = cluster_common['cuestionarioFinalizado'] \n", + "z_hCluster = z_hCluster[sorted(z_hCluster, key=lambda x: x not in mysubset)]\n", + "z_hCluster = z_hCluster.set_index('cuestionarioFinalizado')\n", + "display(z_hCluster.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Fitting our model and obtaining the resulting dendograms

" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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f71d3d7ck6bLLLlNtba12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\nfve736GpqanDc40ZM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fitting our model.\n", + "cluster_tree = linkage(z_hCluster, 'ward')\n", + "\n", + "# Ploting our dendrogram.\n", + "colors = ['g', 'b', 'r', 'p', 'y', 'b', 'w']\n", + "plt.figure(figsize=(25, 10))\n", + "plt.title('Hierarchical Clustering Dendrogram')\n", + "plt.xlabel('Sources')\n", + "plt.ylabel('Distance')\n", + "\n", + "dendrogram(cluster_tree, leaf_rotation=80., leaf_font_size=14., color_threshold=4.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def fancy_dendrogram(*args, **kwargs):\n", + " max_d = kwargs.pop('max_d', None)\n", + " if max_d and 'color_threshold' not in kwargs:\n", + " kwargs['color_threshold'] = max_d\n", + " annotate_above = kwargs.pop('annotate_above', 0)\n", + "\n", + " ddata = dendrogram(*args, **kwargs)\n", + "\n", + " if not kwargs.get('no_plot', False):\n", + " plt.title('Hierarchical Clustering Dendrogram (truncated)')\n", + " plt.xlabel('sample index or (cluster size)')\n", + " plt.ylabel('distance')\n", + " for i, d, c in zip(ddata['icoord'], ddata['dcoord'], ddata['color_list']):\n", + " x = 0.5 * sum(i[1:3])\n", + " y = d[1]\n", + " if y > annotate_above:\n", + " plt.plot(x, y, 'o', c=c)\n", + " plt.annotate(\"%.3g\" % y, (x, y), xytext=(0, -5),\n", + " textcoords='offset points',\n", + " va='top', ha='center')\n", + " if max_d:\n", + " plt.axhline(y=max_d, c='k')\n", + " return ddata" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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pqQQHB1OvXj3MZjP/+te/mD17dp5lBgcHM27cOB555BGuueYa7rvvvkKtY/LkyUybNs31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\nYQELCwtu07RHb/YKhYK3ioqmpqbo0aMH9u3bh5iYGNTU1MDGxga2tra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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fancy_dendrogram(\n", + " cluster_tree,\n", + " truncate_mode='lastp',\n", + " p=12,\n", + " leaf_rotation=80.,\n", + " leaf_font_size=14.,\n", + " show_contracted=True,\n", + " annotate_above=5, # useful in small plots so annotations don't overlap\n", + " color_threshold=4.5, \n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Cutting the tree to obtain the clusters

" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([12, 8, 13, ..., 13, 6, 12], dtype=int32)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fullMaster_workCleanDummiesCluster = cluster_common\n", + "\n", + "max_distance = 15\n", + "clusters = fcluster(cluster_tree, max_distance, criterion='distance')\n", + "clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cuestionarioFinalizadodevice_pixel_ratiodevice_screen_widthviewport_heightviewport_widthbrowser_name_Firefoxcluster
0True212806281276012
1True11920974192008
7True113666621366013
11True11920970184208
12False113666621366013
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" + ], + "text/plain": [ + " cuestionarioFinalizado device_pixel_ratio device_screen_width \\\n", + "0 True 2 1280 \n", + "1 True 1 1920 \n", + "7 True 1 1366 \n", + "11 True 1 1920 \n", + "12 False 1 1366 \n", + "\n", + " viewport_height viewport_width browser_name_Firefox cluster \n", + "0 628 1276 0 12 \n", + "1 974 1920 0 8 \n", + "7 662 1366 0 13 \n", + "11 970 1842 0 8 \n", + "12 662 1366 0 13 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fullMaster_workCleanDummiesCluster['cluster'] = clusters\n", + "fullMaster_workCleanDummiesCluster.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters: 13\n" + ] + } + ], + "source": [ + "print \"Number of clusters: \"+ str(len(fullMaster_workCleanDummiesCluster.cluster.unique()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Number of users in each cluster

" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "13 1257\n", + "8 480\n", + "5 299\n", + "9 292\n", + "3 270\n", + "10 210\n", + "1 172\n", + "6 147\n", + "12 129\n", + "4 124\n", + "11 38\n", + "7 20\n", + "2 18\n", + "Name: cluster, dtype: int64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fullMaster_workCleanDummiesCluster['cluster'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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jsslDYSso9fZJjgbZus1t9Wkb4iaPaZNfoJ+HcLye+hOIx9PhmH6u2ep6ozeO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\n6j2UN8dQFD2ZRPVBW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h8Wy/tYLHsxmydiNs8DBrYfmxBi9OjL76oPH9ssJzloaaSoMJep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3g7EIrR1dsnl2TW9Zr1VjcmYS4i3Xw+FuxVFHPAxJy7DQIj9+Bxvb7OiE4v0Eu85Ix+j1juwK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\nq9KO0VjMvLw8zJo1C+np6VAoFFi9erX0pUFF8ZOSkrBo0SLY2dnB3d0d777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estudiante_idsexo_iduniversidad_ididMaster_idcursoFinalizacionMasterverticalAsignadocuestionarioFinalizadonumUniversidadesnumUniversidadesEspannolasramaConocimiento_idrealDecretobrowser_languagebrowser_namebrowser_versiondevice_pixel_ratiodevice_screen_heightdevice_screen_widthosos_versiontablet_or_mobileuserAgentviewport_heightviewport_width
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Parsing of the browser and OS version in order to know if they are modern versions or not

" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Chrome', 'Edge', 'Mobile Safari', 'IE', 'Safari', 'Firefox',\n", + " 'Vivaldi', 'Android Browser', 'Iceweasel', 'Chrome WebView',\n", + " 'Opera', 'WebKit', 'MIUI Browser'], dtype=object)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fullModelClean['browser_name'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Android Browser': {'max': '5.4', 'mean': '3.45', 'min': '1.5'},\n", + " 'Chrome': {'max': '60.0.3112.50', 'mean': '39.00', 'min': '18.0.1025.166'},\n", + " 'Chrome WebView': {'max': '58.0.3029.83',\n", + " 'mean': '51.00',\n", + " 'min': '44.0.2403.119'},\n", + " 'Edge': {'max': '15.15228', 'mean': '13.63', 'min': '12.10240'},\n", + " 'Firefox': {'max': '55.0', 'mean': '38.00', 'min': '21.0'},\n", + " 'IE': {'max': '11.0', 'mean': '11.00', 'min': '11.0'},\n", + " 'Iceweasel': {'max': '38.8.0', 'mean': '38.80', 'min': '38.8.0'},\n", + " 'MIUI Browser': {'max': '8.7.7', 'mean': '5.40', 'min': '2.1.1'},\n", + " 'Mobile Safari': {'max': '11.0', 'mean': '8.05', 'min': '5.1'},\n", + " 'Opera': {'max': '46.0.2597.39', 'mean': '41.00', 'min': '36.0.2130.80'},\n", + " 'Safari': {'max': '10.1.1', 'mean': '7.60', 'min': '5.1.7'},\n", + " 'Vivaldi': {'max': '1.10.829.3', 'mean': '1.50', 'min': '1.9.818.44'},\n", + " 'WebKit': {'max': '602.3.12', 'mean': '569.90', 'min': '537.51.2'}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "browserVersions_dict = {}\n", + "for browser in fullModelClean['browser_name'].unique():\n", + " maxV = None\n", + " minV = None\n", + " loop0 = True\n", + " for v in fullModelClean.loc[fullModelClean['browser_name'] == browser]['browser_version'].unique():\n", + " if loop0 == True:\n", + " maxV = v\n", + " minV = v\n", + " loop0 = False\n", + " else:\n", + " if version.parse(str(v)) > version.parse(str(maxV)):\n", + " maxV = v\n", + " if version.parse(str(v)) < version.parse(str(minV)):\n", + " minV = v\n", + " midDF = pd.DataFrame({\"v\" : [float(str((maxV.split('.',1)[0]+\".\"+maxV.split('.',1)[1].split('.',1)[0]))), float(str((minV.split('.',1)[0]+\".\"+minV.split('.',1)[1].split('.',1)[0])))]})\n", + " dictMinMax = {\"min\":minV, \"max\":maxV, \"mean\":\"%.2f\" % midDF['v'].mean()}\n", + " \n", + " browserVersions_dict[browser]= dictMinMax\n", + " \n", + "display(browserVersions_dict)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modern browser? index modern_browser\n", + "0 True 3405\n", + "1 False 51\n" + ] + } + ], + "source": [ + "fullModelCleanVersions = fullModelClean\n", + "\n", + "def browserFunc(c):\n", + " if version.parse(str(c.browser_version)) > version.parse(str(browserVersions_dict[c.browser_name][\"mean\"])):\n", + " return True\n", + " elif version.parse(str(c.browser_version)) == version.parse(str(browserVersions_dict[c.browser_name][\"mean\"])):\n", + " return True\n", + " elif version.parse(str(c.browser_version)) < version.parse(str(browserVersions_dict[c.browser_name][\"mean\"])):\n", + " return False\n", + "\n", + "fullModelCleanVersions['modern_browser'] = fullModelCleanVersions.apply(browserFunc, axis = 1) \n", + "\n", + "print(\"Modern browser?\" +str(fullModelCleanVersions[\"modern_browser\"].value_counts().reset_index()))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mac OS versions used by users in questionnaires: \n" + ] + }, + { + "data": { + "text/plain": [ + "array(['10.12.1', '10.9.5', '10.12.5', '10.10.5', '10.12', '10.12.3',\n", + " '10.12.4', '10.7.5', '10.11.6', '10.6', '10.6.8', '10.11.3',\n", + " '10.11.2', '10.11.4', '10.11.5', '10.8.5', '10.12.2', '10.10.3',\n", + " '10.10', '10.9.4', '10.6.1', '10.11', '10.9', '10.7', '10.10.4',\n", + " '10.12.0', '10.10.2', '10.8'], dtype=object)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Windows versions used by users in questionnaires: \n" + ] + }, + { + "data": { + "text/plain": [ + "array(['10', '7', '8.1', 'XP', '8', 'Vista'], dtype=object)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iOS versions used by users in questionnaires: \n" + ] + }, + { + "data": { + "text/plain": [ + "array(['10.2.1', '10.3.1', '10.3.2', '10.2', '9.3.5', '10.3', '10.1.1',\n", + " '10.0.2', '8.1', '8.4', '7.1.2', '8.0.2', '7.0.6', '9.0.2',\n", + " '10.0.1', '11.0', '9.2', '9.3.3', '9.1', '5.1.1'], dtype=object)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Android versions used by users in questionnaires: \n" + ] + }, + { + "data": { + "text/plain": [ + "array(['4.4.2', '6.0.1', '4.2.2', '6.0', '5.0.1', '5.1', '5.1.1', '7.0',\n", + " '5.0.2', '4.4.4', '4.1.2', '5.0', '7.1.1', '4.3', '4.0.4'], dtype=object)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linux versions used by users in questionnaires: \n" + ] + }, + { + "data": { + "text/plain": [ + "array(['i686', 'x86_64'], dtype=object)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Windows Phone versions used by users in questionnaires: \n" + ] + }, + { + "data": { + "text/plain": [ + "array(['10.0'], dtype=object)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chromium OS versions used by users in questionnaires: \n" + ] + }, + { + "data": { + "text/plain": [ + "array(['9202.64.0'], dtype=object)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for os in fullModelClean['os'].unique():\n", + " print str(os) + \" versions used by users in questionnaires: \"\n", + " display(fullModelClean.loc[fullModelClean['os'] == os]['os_version'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Android': {'max': '7.1.1', 'mean': '5.55', 'min': '4.0.4'},\n", + " 'Chromium OS': {'max': '9202.64.0', 'mean': '9202.64', 'min': '9202.64.0'},\n", + " 'Linux': {'max': '1.0', 'mean': '1.00', 'min': '1.0'},\n", + " 'Mac OS': {'max': '10.12.5', 'mean': '10.36', 'min': '10.6'},\n", + " 'Windows': {'max': '10', 'mean': '7.50', 'min': '5.0'},\n", + " 'Windows Phone': {'max': '10.0', 'mean': '10.00', 'min': '10.0'},\n", + " 'iOS': {'max': '11.0', 'mean': '8.05', 'min': '5.1.1'}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "XP = \"5.0\"\n", + "Vista = \"6.0\"\n", + "\n", + "i686 = \"1.0\"\n", + "x86_64 = \"1.0\"\n", + "\n", + "osVersions_dict = {}\n", + "for os in fullModelClean['os'].unique():\n", + " maxV = None\n", + " minV = None\n", + " loop0 = True\n", + " for v in fullModelClean.loc[fullModelClean['os'] == os]['os_version'].unique():\n", + " if v == 'XP':\n", + " v = XP\n", + " if v == 'Vista':\n", + " v = Vista\n", + " if v == 'i686':\n", + " v = i686\n", + " if v == 'x86_64':\n", + " v = x86_64\n", + " \n", + " if loop0 == True:\n", + " maxV = v\n", + " minV = v\n", + " loop0 = False\n", + " else:\n", + " if version.parse(str(v)) > version.parse(str(maxV)):\n", + " maxV = v\n", + " if version.parse(str(v)) < version.parse(str(minV)):\n", + " minV = v\n", + "\n", + " try:\n", + " midDF = pd.DataFrame({\"v\" : [float(str((maxV.split('.',1)[0]+\".\"+maxV.split('.',1)[1].split('.',1)[0]))), float(str((minV.split('.',1)[0]+\".\"+minV.split('.',1)[1].split('.',1)[0])))]})\n", + " except:\n", + " midDF = pd.DataFrame({\"v\" : [float(str(maxV.split('.',1)[0])), float(str(minV.split('.',1)[0]))]})\n", + " \n", + " dictMinMax = {\"min\":minV, \"max\":maxV, \"mean\":\"%.2f\" % midDF['v'].mean()}\n", + " \n", + " osVersions_dict[os]= dictMinMax\n", + " \n", + "display(osVersions_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modern os? index modern_os\n", + "0 True 1810\n", + "1 False 1646\n" + ] + } + ], + "source": [ + "XP = \"5.0\"\n", + "Vista = \"6.0\"\n", + "\n", + "i686 = \"1.0\"\n", + "x86_64 = \"1.0\"\n", + "\n", + "def osFunc(c):\n", + " v = c.os_version\n", + " \n", + " if v == 'XP':\n", + " v = XP\n", + " if v == 'Vista':\n", + " v = Vista\n", + " if v == 'i686':\n", + " v = i686\n", + " if v == 'x86_64':\n", + " v = x86_64\n", + " \n", + " if version.parse(str(v)) > version.parse(str(osVersions_dict[c.os][\"mean\"])):\n", + " return True\n", + " elif version.parse(str(v)) == version.parse(str(osVersions_dict[c.os][\"mean\"])):\n", + " return True\n", + " elif version.parse(str(v)) < version.parse(str(osVersions_dict[c.os][\"mean\"])):\n", + " return False\n", + "\n", + "fullModelCleanVersions['modern_os'] = fullModelCleanVersions.apply(osFunc, axis = 1) \n", + "\n", + "\n", + "print(\"Modern os?\" +str(fullModelCleanVersions[\"modern_os\"].value_counts().reset_index()))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fullModelCleanVersions = fullModelCleanVersions.replace(False,0)\n", + "fullModelCleanVersions = fullModelCleanVersions.replace(True,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fullModelCleanVersionsBackup = fullModelCleanVersions.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sexo_iduniversidad_ididMaster_idcursoFinalizacionMasterverticalAsignadocuestionarioFinalizadonumUniversidadesnumUniversidadesEspannolasramaConocimiento_idrealDecretobrowser_languagebrowser_namedevice_pixel_ratiodevice_screen_heightdevice_screen_widthostablet_or_mobileviewport_heightviewport_widthmodern_browsermodern_os
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sexo_iduniversidad_ididMaster_idcursoFinalizacionMasterverticalAsignadocuestionarioFinalizadonumUniversidadesnumUniversidadesEspannolasramaConocimiento_idrealDecretobrowser_languagebrowser_namedevice_pixel_ratiodevice_screen_heightdevice_screen_widthostablet_or_mobileviewport_heightviewport_widthmodern_browsermodern_os
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" + ], + "text/plain": [ + " sexo_id universidad_id idMaster_id cursoFinalizacionMaster \\\n", + "0 2 1 4310483 20132014 \n", + "1 2 1 4310483 20132014 \n", + "7 2 1 4310483 20132014 \n", + "11 2 1 4313391 20132014 \n", + "12 1 1 4312049 20132014 \n", + "\n", + " verticalAsignado cuestionarioFinalizado numUniversidades \\\n", + "0 C True 1 \n", + "1 A True 1 \n", + "7 C True 1 \n", + "11 B True 1 \n", + "12 A False 1 \n", + "\n", + " numUniversidadesEspannolas ramaConocimiento_id realDecreto \\\n", + "0 1 4.0 1393/2007 \n", + "1 1 4.0 1393/2007 \n", + "7 1 4.0 1393/2007 \n", + "11 1 4.0 1393/2007 \n", + "12 1 4.0 1393/2007 \n", + "\n", + " browser_language browser_name device_pixel_ratio device_screen_height \\\n", + "0 es Chrome 2 800 \n", + "1 es Chrome 1 1080 \n", + "7 es Chrome 1 768 \n", + "11 zh-CN Edge 1 1080 \n", + "12 es Chrome 1 768 \n", + "\n", + " device_screen_width os tablet_or_mobile viewport_height \\\n", + "0 1280 Mac OS False 628 \n", + "1 1920 Windows False 974 \n", + "7 1366 Windows False 662 \n", + "11 1920 Windows False 970 \n", + "12 1366 Windows False 662 \n", + "\n", + " viewport_width modern_browser modern_os \n", + "0 1276 True False \n", + "1 1920 True True \n", + "7 1366 True True \n", + "11 1842 True True \n", + "12 1366 True False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullModelCleanVersions[['verticalAsignado']] = fullModelCleanVersions[['verticalAsignado']].replace([1, 2, 3], ['A', 'B', 'C'])\n", + "\n", + "display(fullModelCleanVersions.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Performing one-hot encoding for categorial values on columns

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WebView browser_name_Edge browser_name_Firefox \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 1 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_IE browser_name_Iceweasel browser_name_MIUI Browser \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Mobile Safari browser_name_Opera browser_name_Safari \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Vivaldi browser_name_WebKit os_Android os_Chromium OS \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "7 0 0 0 0 \n", + "11 0 0 0 0 \n", + "12 0 0 0 0 \n", + "\n", + " os_Linux os_Mac OS os_Windows os_Windows Phone os_iOS \n", + "0 0 1 0 0 0 \n", + "1 0 0 1 0 0 \n", + "7 0 0 1 0 0 \n", + "11 0 0 1 0 0 \n", + "12 0 0 1 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullModelCleanVersionsDummies = pd.get_dummies(fullModelCleanVersions)\n", + 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" + ], + "text/plain": [ + " sexo_id universidad_id idMaster_id cursoFinalizacionMaster \\\n", + "0 2 1 4310483 20132014 \n", + "1 2 1 4310483 20132014 \n", + "7 2 1 4310483 20132014 \n", + "11 2 1 4313391 20132014 \n", + "12 1 1 4312049 20132014 \n", + "\n", + " cuestionarioFinalizado numUniversidades numUniversidadesEspannolas \\\n", + "0 True 1 1 \n", + "1 True 1 1 \n", + "7 True 1 1 \n", + "11 True 1 1 \n", + "12 False 1 1 \n", + "\n", + " ramaConocimiento_id device_pixel_ratio device_screen_height \\\n", + "0 4.0 2 800 \n", + "1 4.0 1 1080 \n", + "7 4.0 1 768 \n", + "11 4.0 1 1080 \n", + "12 4.0 1 768 \n", + "\n", + " device_screen_width tablet_or_mobile viewport_height viewport_width \\\n", + "0 1280 False 628 1276 \n", + "1 1920 False 974 1920 \n", + "7 1366 False 662 1366 \n", + "11 1920 False 970 1842 \n", + "12 1366 False 662 1366 \n", + "\n", + " modern_browser modern_os verticalAsignado_A verticalAsignado_B \\\n", + "0 True False 0 0 \n", + "1 True True 1 0 \n", + "7 True True 0 0 \n", + "11 True True 0 1 \n", + "12 True False 1 0 \n", + "\n", + " verticalAsignado_C realDecreto_1393/2007 realDecreto_56/2005 \\\n", + "0 1 1 0 \n", + "1 0 1 0 \n", + "7 1 1 0 \n", + "11 0 1 0 \n", + "12 0 1 0 \n", + "\n", + " browser_language_ast browser_language_bg browser_language_ca \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_ca-ES browser_language_ca-es browser_language_cs \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_de browser_language_de-DE browser_language_en-AU \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_en-GB browser_language_en-US browser_language_en-ie \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_en-us browser_language_es browser_language_es-419 \\\n", + "0 0 1 0 \n", + "1 0 1 0 \n", + "7 0 1 0 \n", + "11 0 0 0 \n", + "12 0 1 0 \n", + "\n", + " browser_language_es-AR browser_language_es-CL browser_language_es-CO \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_es-EC browser_language_es-ES \\\n", + "0 0 0 \n", + "1 0 0 \n", + "7 0 0 \n", + "11 0 0 \n", + "12 0 0 \n", + "\n", + " browser_language_es-ES_tradnl browser_language_es-LA \\\n", + "0 0 0 \n", + "1 0 0 \n", + "7 0 0 \n", + "11 0 0 \n", + "12 0 0 \n", + "\n", + " browser_language_es-MX browser_language_es-XL browser_language_es-es \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_es-xl browser_language_eu browser_language_fr \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_fr-FR browser_language_gl browser_language_gl-GL \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_it browser_language_it-IT browser_language_mk-MK \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_nb-NO browser_language_nl browser_language_pl-PL \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_pt-BR browser_language_pt-PT browser_language_pt-br \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_language_ru browser_language_sv-se browser_language_zh-CN \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 1 \n", + "12 0 0 0 \n", + "\n", + " browser_language_zh-TW browser_language_zh-cn \\\n", + "0 0 0 \n", + "1 0 0 \n", + "7 0 0 \n", + "11 0 0 \n", + "12 0 0 \n", + "\n", + " browser_name_Android Browser browser_name_Chrome \\\n", + "0 0 1 \n", + "1 0 1 \n", + "7 0 1 \n", + "11 0 0 \n", + "12 0 1 \n", + "\n", + " browser_name_Chrome WebView browser_name_Edge browser_name_Firefox \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 1 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_IE browser_name_Iceweasel browser_name_MIUI Browser \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Mobile Safari browser_name_Opera browser_name_Safari \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "7 0 0 0 \n", + "11 0 0 0 \n", + "12 0 0 0 \n", + "\n", + " browser_name_Vivaldi browser_name_WebKit os_Android os_Chromium OS \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "7 0 0 0 0 \n", + "11 0 0 0 0 \n", + "12 0 0 0 0 \n", + "\n", + " os_Linux os_Mac OS os_Windows os_Windows Phone os_iOS \n", + "0 0 1 0 0 0 \n", + "1 0 0 1 0 0 \n", + "7 0 0 1 0 0 \n", + "11 0 0 1 0 0 \n", + "12 0 0 1 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullModel_cleanVersionsDummies = fullModelCleanVersionsDummies.dropna()\n", + "\n", + "display(fullModel_cleanVersionsDummies.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of finalized questionnaires valuable for the model: index cuestionarioFinalizado\n", + "0 True 2379\n", + "1 False 1077\n" + ] + } + ], + "source": [ + "print \"Number of finalized questionnaires valuable for the model: \", fullModel_cleanVersionsDummies[\"cuestionarioFinalizado\"].value_counts().reset_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Algorithm execution (with browser and OS versions parsed)\n", + "\n", + "

Preparing data: selecting data for training and testing from the dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fullModel_cleanVersionsDummies['is_train'] = np.random.uniform(0,1,len(fullModel_cleanVersionsDummies)) <= .66" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "trainVersions, testVersions = fullModel_cleanVersionsDummies[fullModel_cleanVersionsDummies['is_train']==True], fullModel_cleanVersionsDummies[fullModel_cleanVersionsDummies['is_train']==False]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the best setup for the Random forest algorithm

" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "columnasVersions = fullModel_cleanVersionsDummies.columns.values.tolist()\n", + "\n", + "variable2predictVersions = 'cuestionarioFinalizado'\n", + "\n", + "columnasVersions.remove(variable2predictVersions)\n", + "\n", + "featuresVersions = pd.Index(columnasVersions)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 ..., 0 0 0]\n", + "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.797802 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.769231 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.770925 - 0.2s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.784141 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.423841 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto, score=0.793407 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=auto, score=0.778022 - 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0.0s\n", + "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.687698 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.676939 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Done 120 out of 120 | elapsed: 6.8s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.646804 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.693653 - 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0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.677714 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.670510 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.700816 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.674386 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.668790 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.658102 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.693991 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.645862 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.675455 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.678173 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.702241 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.705363 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.640227 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.690332 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.698237 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=100, randomforestclassifier__max_features=log2, score=0.699732 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.701610 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.692358 - 0.1s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.659031 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.719074 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=200, randomforestclassifier__max_features=log2, score=0.696799 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.576780 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.628549 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.571827 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.607530 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.601602 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "defaultdict(, {'roc_auc': (0.69377601431154989, {'randomforestclassifier__min_samples_leaf': 200, 'randomforestclassifier__max_features': 'log2'}), 'precision': (0.93522044903724033, {'randomforestclassifier__min_samples_leaf': 100, 'randomforestclassifier__max_features': 'auto'}), 'accuracy': (0.79568472038749449, {'randomforestclassifier__min_samples_leaf': 100, 'randomforestclassifier__max_features': 'log2'})})\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Done 120 out of 120 | elapsed: 6.8s finished\n" + ] + } + ], + "source": [ + "yVersions, _Versions = pd.factorize(trainVersions[variable2predictVersions])\n", + "\n", + "print yVersions\n", + "scoresRF_resultsVersions = grid_search_scores_rf(trainVersions[featuresVersions], yVersions)\n", + "print scoresRF_resultsVersions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Obtaining the best Random Forest configuration through the output of the benchmarks previously computed

" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('precision', (0.93522044903724033, {'randomforestclassifier__min_samples_leaf': 100, 'randomforestclassifier__max_features': 'auto'}))\n" + ] + } + ], + "source": [ + "best_RF_valuesVersions = max(scoresRF_resultsVersions.items(), key=lambda a: a[1])\n", + "if best_RF_valuesVersions[1][0] == 1.0:\n", + " best_RF_valuesVersions = sorted(scoresRF_resultsVersions.iteritems(),key=lambda (v): v[1],reverse=True)[1]\n", + "\n", + "print best_RF_valuesVersions\n", + " \n", + "#Accessing to the config values within the dict inside the list contained in the defaultdict\n", + "rfC__min_samples_leafVersions = best_RF_valuesVersions[1][1]['randomforestclassifier__min_samples_leaf']\n", + "rfC__max_featuresVersions = best_RF_valuesVersions[1][1]['randomforestclassifier__max_features']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Run the Random Forest with the best setup found

" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "best_randomforestVersions = RandomForestClassifier(min_samples_leaf = rfC__min_samples_leafVersions, max_features = str(rfC__max_featuresVersions))\n", + "best_randomforestVersions.fit(trainVersions[featuresVersions], yVersions)\n", + "None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Test the trained Random Forest

" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "target_namesVersions = np.array([True, False])" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predsVersions = target_namesVersions[best_randomforestVersions.predict(testVersions[featuresVersions])]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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predsFalseTrue
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" + ], + "text/plain": [ + "preds False True \n", + "actual \n", + "False 129 249\n", + "True 13 794" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(testVersions[variable2predictVersions], predsVersions, rownames=['actual'], colnames=['preds'])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " False 0.91 0.34 0.50 378\n", + " True 0.76 0.98 0.86 807\n", + "\n", + "avg / total 0.81 0.78 0.74 1185\n", + "\n" + ] + } + ], + "source": [ + "print metrics.classification_report(testVersions[variable2predictVersions], predsVersions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Identification of the most important features for the model

" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1) tablet_or_mobile 0.179032\n", + " 2) device_pixel_ratio 0.159406\n", + " 3) device_screen_height 0.097580\n", + " 4) device_screen_width 0.095784\n", + " 5) viewport_height 0.089050\n", + " 6) os_Android 0.063415\n", + " 7) os_Windows 0.045429\n", + " 8) os_iOS 0.025518\n", + " 9) modern_os 0.012174\n", + "10) browser_name_Firefox 0.006780\n", + "11) verticalAsignado_A 0.006510\n", + "12) verticalAsignado_C 0.004324\n", + "13) universidad_id 0.003989\n", + "14) idMaster_id 0.003260\n", + "15) os_Mac OS 0.002425\n", + "16) numUniversidadesEspannolas 0.001691\n", + "17) verticalAsignado_B 0.001435\n", + "18) viewport_width 0.001208\n", + "19) browser_language_es-ES 0.000577\n", + "20) browser_language_es 0.000246\n", + "21) browser_name_Chrome 0.000130\n", + "22) sexo_id 0.000035\n", + "23) modern_browser 0.000000\n", + "24) ramaConocimiento_id 0.000000\n", + "25) browser_language_es-CO 0.000000\n", + "26) cursoFinalizacionMaster 0.000000\n", + "27) browser_language_es-CL 0.000000\n", + "28) browser_language_es-AR 0.000000\n", + "29) browser_language_es-419 0.000000\n", + "30) numUniversidades 0.000000\n", + "31) browser_language_en-us 0.000000\n", + "32) browser_language_en-ie 0.000000\n", + "33) browser_language_en-US 0.000000\n", + "34) browser_language_en-GB 0.000000\n", + "35) browser_language_en-AU 0.000000\n", + "36) browser_language_de-DE 0.000000\n", + "37) browser_language_de 0.000000\n", + "38) browser_language_cs 0.000000\n", + "39) browser_language_ca-es 0.000000\n", + "40) browser_language_ca-ES 0.000000\n", + "41) browser_language_ca 0.000000\n", + "42) browser_language_bg 0.000000\n", + "43) browser_language_ast 0.000000\n", + "44) realDecreto_56/2005 0.000000\n", + "45) realDecreto_1393/2007 0.000000\n", + "46) browser_language_es-EC 0.000000\n", + "47) is_train 0.000000\n", + "48) browser_language_es-ES_tradnl 0.000000\n", + "49) browser_name_MIUI Browser 0.000000\n", + "50) browser_language_zh-TW 0.000000\n", + "51) browser_language_zh-cn 0.000000\n", + "52) browser_name_Android Browser 0.000000\n", + "53) browser_name_Chrome WebView 0.000000\n", + "54) browser_name_Edge 0.000000\n", + "55) browser_name_IE 0.000000\n", + "56) browser_name_Iceweasel 0.000000\n", + "57) browser_name_Mobile Safari 0.000000\n", + "58) browser_language_sv-se 0.000000\n", + "59) browser_name_Opera 0.000000\n", + "60) browser_name_Safari 0.000000\n", + "61) browser_name_Vivaldi 0.000000\n", + "62) browser_name_WebKit 0.000000\n", + "63) os_Chromium OS 0.000000\n", + "64) os_Linux 0.000000\n", + "65) os_Windows Phone 0.000000\n", + "66) browser_language_zh-CN 0.000000\n", + "67) browser_language_ru 0.000000\n", + "68) browser_language_es-LA 0.000000\n", + "69) browser_language_gl-GL 0.000000\n", + "70) browser_language_es-XL 0.000000\n", + "71) browser_language_es-es 0.000000\n", + "72) browser_language_es-xl 0.000000\n", + "73) browser_language_eu 0.000000\n", + "74) browser_language_fr 0.000000\n", + "75) browser_language_fr-FR 0.000000\n", + "76) browser_language_gl 0.000000\n", + "77) browser_language_it 0.000000\n", + "78) browser_language_pt-br 0.000000\n", + "79) browser_language_it-IT 0.000000\n", + "80) browser_language_mk-MK 0.000000\n", + "81) browser_language_nb-NO 0.000000\n", + "82) browser_language_nl 0.000000\n", + "83) browser_language_pl-PL 0.000000\n", + "84) browser_language_pt-BR 0.000000\n", + "85) browser_language_pt-PT 0.000000\n", + "86) browser_language_es-MX 0.000000\n" + ] + } + ], + "source": [ + "importancesVersions = best_randomforestVersions.feature_importances_\n", + "feat_labelsVersions = fullModel_cleanVersionsDummies.columns[1:]\n", + "indicesVersions = np.argsort(importancesVersions)[::-1]\n", + "\n", + "importancesVersions_threshold = 0.05\n", + "\n", + "listImportancesVersions = []\n", + "listNonImportantVersions = []\n", + "\n", + "for f in range(trainVersions[featuresVersions].shape[1]):\n", + " print(\"%2d) %-*s %f\" % (f + 1, 30, \n", + " featuresVersions[indicesVersions[f]], \n", + " importancesVersions[indicesVersions[f]]))\n", + " if importancesVersions[indicesVersions[f]] > importancesVersions_threshold:\n", + " listImportancesVersions.append(featuresVersions[indicesVersions[f]])\n", + " else:\n", + " listNonImportantVersions.append(featuresVersions[indicesVersions[f]])" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most important features for the model:\n" + ] + }, + { + "data": { + "text/plain": [ + "['tablet_or_mobile',\n", + " 'device_pixel_ratio',\n", + " 'device_screen_height',\n", + " 'device_screen_width',\n", + " 'viewport_height',\n", + " 'os_Android']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Most important features for the model:\")\n", + "display(listImportancesVersions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clusters\n", + "\n", + "

Selection of the important features identified in the previous step

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Fitting our model and obtaining the resulting dendograms

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qqanR5s2b1dXVpXXr1qm4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6ioKDgcDlit1n75lGNaWpp8j6WF5HtKEASkpaWhvLw86BsOHX2apr02jSAIANDrb6a2rmMDbfO+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\nLw8A2o03K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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fitting our model.\n", + "cluster_treeVersions = linkage(z_vCluster, 'ward')\n", + "\n", + "# Ploting our dendrogram.\n", + "colors = ['g', 'b', 'r', 'p', 'y', 'b', 'w']\n", + "plt.figure(figsize=(25, 10))\n", + "plt.title('Hierarchical Clustering Dendrogram')\n", + "plt.xlabel('Sources')\n", + "plt.ylabel('Distance')\n", + "\n", + "dendrogram(cluster_treeVersions, leaf_rotation=80., leaf_font_size=14., color_threshold=4.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3bhxpaWmEhoZSo0YNzGYzf/vb35gxY0aeNkNDQxk5ciQvvPACN910E61atSrUOsaPH8/E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\nogOWWq3m4cOHNGzYsIonVXFxMXl5eeTn51OvXj1dPBil8jQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fancy_dendrogram(\n", + " cluster_treeVersions,\n", + " truncate_mode='lastp',\n", + " p=12,\n", + " leaf_rotation=80.,\n", + " leaf_font_size=14.,\n", + " show_contracted=True,\n", + " annotate_above=5, # useful in small plots so annotations don't overlap\n", + " color_threshold=4.5, \n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Cutting the tree to obtain the clusters

" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([11, 7, 5, ..., 5, 8, 11], dtype=int32)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_versionsN = cluster_versions\n", + "\n", + "max_distance = 16\n", + "clustersV = fcluster(cluster_treeVersions, max_distance, criterion='distance')\n", + "clustersV" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cuestionarioFinalizadodevice_pixel_ratiodevice_screen_heightdevice_screen_widthtablet_or_mobileviewport_heightos_Androidcluster
0True28001280False628011
1True110801920False97407
7True17681366False66205
11True110801920False97007
12False17681366False66205
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" + ], + "text/plain": [ + " cuestionarioFinalizado device_pixel_ratio device_screen_height \\\n", + "0 True 2 800 \n", + "1 True 1 1080 \n", + "7 True 1 768 \n", + "11 True 1 1080 \n", + "12 False 1 768 \n", + "\n", + " device_screen_width tablet_or_mobile viewport_height os_Android \\\n", + "0 1280 False 628 0 \n", + "1 1920 False 974 0 \n", + "7 1366 False 662 0 \n", + "11 1920 False 970 0 \n", + "12 1366 False 662 0 \n", + "\n", + " cluster \n", + "0 11 \n", + "1 7 \n", + "7 5 \n", + "11 7 \n", + "12 5 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cluster_versionsN['cluster'] = clustersV\n", + "display(cluster_versionsN.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters: 12\n" + ] + } + ], + "source": [ + "print \"Number of clusters: \"+ str(len(cluster_versionsN.cluster.unique()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Number of users in each cluster

" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5 1502\n", + "7 625\n", + "9 388\n", + "8 268\n", + "2 164\n", + "4 130\n", + "3 114\n", + "11 96\n", + "12 76\n", + "1 46\n", + "10 26\n", + "6 21\n", + "Name: cluster, dtype: int64" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_versionsN['cluster'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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FCM3MRPT4uRCEoJ6f2FiL89+Wo+5c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\nY9OmTYwYM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UDaXOEd1157LUuWLOHjjz9Gp9O5kgJPmzaNO++8k02bNpGYmAjAwIEDuffe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\nl4Oa715vsjg6nOe0fglmGozun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AgNIUD8OSa9CI8RC+rkHi1NHAmb4jWF0+rzgXVTuq4LQ7oRsdj/qva9Bc0QiLxQLNxo/hs7cD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estudiante_idsexo_iduniversidad_ididMaster_idcursoFinalizacionMasterverticalAsignadocuestionarioFinalizadonumUniversidadesnumUniversidadesEspannolasramaConocimiento_idrealDecretobrowser_languagebrowser_namebrowser_versiondevice_pixel_ratiodevice_screen_heightdevice_screen_widthosos_versiontablet_or_mobileuserAgentviewport_heightviewport_widthmodern_browsermodern_os
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111300023214310483201320141True114.01393/2007esChrome57.0.2987.133110801920Windows10FalseMozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...9741920TrueTrue
711300049214310483201320143True114.01393/2007esChrome58.0.3029.11017681366Windows10FalseMozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...6621366TrueTrue
1111300063214313391201320142True114.01393/2007zh-CNEdge15.15063110801920Windows10FalseMozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...9701842TrueTrue
1211300071114312049201320141False114.01393/2007esChrome59.0.3071.8617681366Windows7FalseMozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...6621366TrueFalse
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Data cleaning and wrangling

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True True 1 1 \n", + "27 True True 1 1 \n", + "28 True True 1 1 \n", + "\n", + " realDecreto_56/2005 browser_language_ast browser_language_ca \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_ca-ES browser_language_ca-es browser_language_cs \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_de browser_language_de-DE browser_language_en-GB \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_en-US browser_language_en-ie browser_language_es \\\n", + "0 0 0 1 \n", + "7 0 0 1 \n", + "22 0 0 1 \n", + "27 0 0 0 \n", + "28 0 0 1 \n", + "\n", + " browser_language_es-419 browser_language_es-AR browser_language_es-ES \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_es-LA browser_language_es-MX browser_language_es-XL \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_es-es browser_language_fr browser_language_fr-FR \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 1 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_it browser_language_nb-NO browser_language_pt-BR \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_pt-PT browser_language_ru browser_language_sv-se \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_language_zh-TW browser_language_zh-cn \\\n", + "0 0 0 \n", + "7 0 0 \n", + "22 0 0 \n", + "27 0 0 \n", + "28 0 0 \n", + "\n", + " browser_name_Android Browser browser_name_Chrome \\\n", + "0 0 1 \n", + "7 0 1 \n", + "22 0 1 \n", + "27 0 0 \n", + "28 0 1 \n", + "\n", + " browser_name_Chrome WebView browser_name_Edge browser_name_Firefox \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_name_IE browser_name_MIUI Browser browser_name_Mobile Safari \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 1 \n", + "28 0 0 0 \n", + "\n", + " browser_name_Opera browser_name_Safari browser_name_Vivaldi \\\n", + "0 0 0 0 \n", + "7 0 0 0 \n", + "22 0 0 0 \n", + "27 0 0 0 \n", + "28 0 0 0 \n", + "\n", + " browser_name_WebKit os_Android os_Chromium OS os_Linux os_Mac OS \\\n", + "0 0 0 0 0 1 \n", + "7 0 0 0 0 0 \n", + "22 0 0 0 0 0 \n", + "27 0 0 0 0 0 \n", + "28 0 0 0 0 0 \n", + "\n", + " os_Windows os_Windows Phone os_iOS \n", + "0 0 0 0 \n", + "7 1 0 0 \n", + "22 1 0 0 \n", + "27 0 0 1 \n", + "28 1 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fullModelClean_dummiesA = fullModelClean_dummiesA.dropna()\n", + "display(fullModelClean_dummiesA.head())\n", + "\n", + "fullModelClean_dummiesB = fullModelClean_dummiesB.dropna()\n", + "display(fullModelClean_dummiesB.head())\n", + "\n", + "fullModelClean_dummiesC = fullModelClean_dummiesC.dropna()\n", + "display(fullModelClean_dummiesC.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of finalized questionnaires valuable for the model A : index cuestionarioFinalizado\n", + "0 True 757\n", + "1 False 236\n", + "Number of finalized questionnaires valuable for the model B: index cuestionarioFinalizado\n", + "0 True 933\n", + "1 False 470\n", + "Number of finalized questionnaires valuable for the model C: index cuestionarioFinalizado\n", + "0 True 689\n", + "1 False 371\n" + ] + } + ], + "source": [ + "print \"Number of finalized questionnaires valuable for the model A : \", fullModelClean_dummiesA[\"cuestionarioFinalizado\"].value_counts().reset_index()\n", + "\n", + "print \"Number of finalized questionnaires valuable for the model B: \", fullModelClean_dummiesB[\"cuestionarioFinalizado\"].value_counts().reset_index()\n", + "\n", + "print \"Number of finalized questionnaires valuable for the model C: \", fullModelClean_dummiesC[\"cuestionarioFinalizado\"].value_counts().reset_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Algorithm execution (for every vertical)\n", + "\n", + "

Preparing data: selecting data for training and testing from the datasets

" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fullModelClean_dummiesA['is_train'] = np.random.uniform(0,1,len(fullModelClean_dummiesA)) <= .66\n", + "\n", + "fullModelClean_dummiesB['is_train'] = np.random.uniform(0,1,len(fullModelClean_dummiesB)) <= .66\n", + "\n", + "fullModelClean_dummiesC['is_train'] = np.random.uniform(0,1,len(fullModelClean_dummiesC)) <= .66" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "trainA, testA = fullModelClean_dummiesA[fullModelClean_dummiesA['is_train']==True], fullModelClean_dummiesA[fullModelClean_dummiesA['is_train']==False]\n", + "\n", + "trainB, testB = fullModelClean_dummiesB[fullModelClean_dummiesB['is_train']==True], fullModelClean_dummiesB[fullModelClean_dummiesB['is_train']==False]\n", + "\n", + "trainC, testC = fullModelClean_dummiesC[fullModelClean_dummiesC['is_train']==True], fullModelClean_dummiesC[fullModelClean_dummiesC['is_train']==False]" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "columnasvA = fullModelClean_dummiesA.columns.values.tolist()\n", + "variable2predictA = 'cuestionarioFinalizado'\n", + "columnasvA.remove(variable2predictA)\n", + "featuresvA = pd.Index(columnasvA)\n", + "\n", + "\n", + "columnasvB = fullModelClean_dummiesB.columns.values.tolist()\n", + "variable2predictB = 'cuestionarioFinalizado'\n", + "columnasvB.remove(variable2predictB)\n", + "featuresvB = pd.Index(columnasvB)\n", + "\n", + "\n", + "columnasvC = fullModelClean_dummiesC.columns.values.tolist()\n", + "variable2predictC = 'cuestionarioFinalizado'\n", + "columnasvC.remove(variable2predictC)\n", + "featuresvC = pd.Index(columnasvC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the best setup for the Random forest algorithm

" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0\n", + " 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0\n", + " 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1\n", + " 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0\n", + " 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0\n", + " 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0\n", + " 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 0 0 0 0 1\n", + " 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 0 1 0 1 0 0 1\n", + " 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0\n", + " 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0\n", + " 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0\n", + " 1 0 0 1 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 0 1\n", + " 0 0 0 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0\n", + " 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 1 1 0 1 0\n", + " 0 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0\n", + " 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 0]\n", + "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.849624 - 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0.0s\n", + "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=1.000000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.678571 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.833333 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Done 120 out of 120 | elapsed: 4.6s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] randomforestclassifier__min_samples_leaf=1, randomforestclassifier__max_features=auto, score=0.585366 - 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0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.696759 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.715394 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.687037 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=5, randomforestclassifier__max_features=log2, score=0.720804 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.680517 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.677199 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.757523 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.722917 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=10, randomforestclassifier__max_features=log2, score=0.798463 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=50, randomforestclassifier__max_features=log2, score=0.690247 - 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0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=500, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2 \n", + "[CV] randomforestclassifier__min_samples_leaf=1000, randomforestclassifier__max_features=log2, score=0.500000 - 0.0s\n", + "defaultdict(, {'roc_auc': (0.72715299285546742, {'randomforestclassifier__min_samples_leaf': 10, 'randomforestclassifier__max_features': 'log2'}), 'precision': (0.91692410373760491, {'randomforestclassifier__min_samples_leaf': 50, 'randomforestclassifier__max_features': 'log2'}), 'accuracy': (0.77246376811594208, {'randomforestclassifier__min_samples_leaf': 5, 'randomforestclassifier__max_features': 'auto'})})\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Done 120 out of 120 | elapsed: 4.7s finished\n" + ] + } + ], + "source": [ + "y_vA, _vA = pd.factorize(trainA[variable2predictA])\n", + "print y_vA\n", + "scoresRF_resultsA = grid_search_scores_rf(trainA[featuresvA], y_vA)\n", + "print scoresRF_resultsA\n", + "\n", + "y_vB, _vB = pd.factorize(trainB[variable2predictB])\n", + "print y_vB\n", + "scoresRF_resultsB = grid_search_scores_rf(trainB[featuresvB], y_vB)\n", + "print scoresRF_resultsB\n", + "\n", + "y_vC, _vC = pd.factorize(trainC[variable2predictC])\n", + "print y_vC\n", + "scoresRF_resultsC = grid_search_scores_rf(trainC[featuresvC], y_vC)\n", + "print scoresRF_resultsC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Obtaining the best Random Forest configurations through the output of the benchmarks previously computed

" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Scores A: [('roc_auc', (0.68038217900280729, {'randomforestclassifier__min_samples_leaf': 50, 'randomforestclassifier__max_features': 'sqrt'})), ('precision', (0.94174116352633752, {'randomforestclassifier__min_samples_leaf': 10, 'randomforestclassifier__max_features': 'log2'})), ('accuracy', (0.82299546142208779, {'randomforestclassifier__min_samples_leaf': 10, 'randomforestclassifier__max_features': 'log2'}))]\n", + "('precision', (0.94174116352633752, {'randomforestclassifier__min_samples_leaf': 10, 'randomforestclassifier__max_features': 'log2'}))\n", + "\n", + "Scores B: [('roc_auc', (0.70379237030606367, {'randomforestclassifier__min_samples_leaf': 1, 'randomforestclassifier__max_features': 'sqrt'})), ('precision', (0.94076407074882329, {'randomforestclassifier__min_samples_leaf': 50, 'randomforestclassifier__max_features': 'auto'})), ('accuracy', (0.78002125398512223, {'randomforestclassifier__min_samples_leaf': 50, 'randomforestclassifier__max_features': 'sqrt'}))]\n", + "('precision', (0.94076407074882329, {'randomforestclassifier__min_samples_leaf': 50, 'randomforestclassifier__max_features': 'auto'}))\n", + "\n", + "Scores C: [('roc_auc', (0.72715299285546742, {'randomforestclassifier__min_samples_leaf': 10, 'randomforestclassifier__max_features': 'log2'})), ('precision', (0.91692410373760491, {'randomforestclassifier__min_samples_leaf': 50, 'randomforestclassifier__max_features': 'log2'})), ('accuracy', (0.77246376811594208, {'randomforestclassifier__min_samples_leaf': 5, 'randomforestclassifier__max_features': 'auto'}))]\n", + "('precision', (0.91692410373760491, {'randomforestclassifier__min_samples_leaf': 50, 'randomforestclassifier__max_features': 'log2'}))\n" + ] + } + ], + "source": [ + "print \"\\nScores A: \"+str(scoresRF_resultsA.items())\n", + "\n", + "best_RF_valuesA = max(scoresRF_resultsA.items(), key=lambda a: a[1])\n", + "if best_RF_valuesA[1][0] == 1.0:\n", + " best_RF_valuesA = sorted(scoresRF_resultsA.iteritems(),key=lambda (v): v[1],reverse=True)[1]\n", + "\n", + "print best_RF_valuesA\n", + " \n", + "#Accessing to the config values within the dict inside the list contained in the defaultdict\n", + "rfC__min_samples_leafA = best_RF_valuesA[1][1]['randomforestclassifier__min_samples_leaf']\n", + "rfC__max_featuresA = best_RF_valuesA[1][1]['randomforestclassifier__max_features']\n", + "\n", + "\n", + "print \"\\nScores B: \"+str(scoresRF_resultsB.items())\n", + "\n", + "best_RF_valuesB = max(scoresRF_resultsB.items(), key=lambda a: a[1])\n", + "if best_RF_valuesB[1][0] == 1.0:\n", + " best_RF_valuesB = sorted(scoresRF_resultsB.iteritems(),key=lambda (v): v[1],reverse=True)[1]\n", + "\n", + "print best_RF_valuesB\n", + " \n", + "#Accessing to the config values within the dict inside the list contained in the defaultdict\n", + "rfC__min_samples_leafB = best_RF_valuesB[1][1]['randomforestclassifier__min_samples_leaf']\n", + "rfC__max_featuresB = best_RF_valuesB[1][1]['randomforestclassifier__max_features']\n", + "\n", + "\n", + "print \"\\nScores C: \"+str(scoresRF_resultsC.items())\n", + "\n", + "best_RF_valuesC = max(scoresRF_resultsC.items(), key=lambda a: a[1])\n", + "if best_RF_valuesC[1][0] == 1.0:\n", + " best_RF_valuesC = sorted(scoresRF_resultsC.iteritems(),key=lambda (v): v[1],reverse=True)[1]\n", + "\n", + "print best_RF_valuesC\n", + " \n", + "#Accessing to the config values within the dict inside the list contained in the defaultdict\n", + "rfC__min_samples_leafC = best_RF_valuesC[1][1]['randomforestclassifier__min_samples_leaf']\n", + "rfC__max_featuresC = best_RF_valuesC[1][1]['randomforestclassifier__max_features']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Run the Random Forests with the best setup found

" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "best_randomforestA = RandomForestClassifier(min_samples_leaf = rfC__min_samples_leafA, max_features = str(rfC__max_featuresA))\n", + "best_randomforestA.fit(trainA[featuresvA], y_vA)\n", + "None\n", + "\n", + "\n", + "best_randomforestB = RandomForestClassifier(min_samples_leaf = rfC__min_samples_leafB, max_features = str(rfC__max_featuresB))\n", + "best_randomforestB.fit(trainB[featuresvB], y_vB)\n", + "None\n", + "\n", + "\n", + "best_randomforestC = RandomForestClassifier(min_samples_leaf = rfC__min_samples_leafC, max_features = str(rfC__max_featuresC))\n", + "best_randomforestC.fit(trainC[featuresvC], y_vC)\n", + "None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Test the trained Random Forests for every questionnaire vertical

" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "target_namesA = np.array([True, False])\n", + "target_namesB = np.array([True, False])\n", + "target_namesC = np.array([True, False])" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predsA = target_namesA[best_randomforestA.predict(testA[featuresvA])]\n", + "predsB = target_namesB[best_randomforestB.predict(testB[featuresvB])]\n", + "predsC = target_namesC[best_randomforestC.predict(testC[featuresvC])]" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Identification of the most important features for the models

" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1) viewport_width 0.267931\n", + " 2) tablet_or_mobile 0.139438\n", + " 3) os_iOS 0.132425\n", + " 4) device_screen_height 0.118814\n", + " 5) device_screen_width 0.067581\n", + " 6) device_pixel_ratio 0.066577\n", + " 7) os_Android 0.054088\n", + " 8) browser_name_Mobile Safari 0.039764\n", + " 9) universidad_id 0.024741\n", + "10) idMaster_id 0.023637\n", + "11) viewport_height 0.019682\n", + "12) browser_name_Chrome 0.010694\n", + "13) os_Mac OS 0.008293\n", + "14) ramaConocimiento_id 0.007058\n", + "15) sexo_id 0.004604\n", + "16) browser_language_es-es 0.003941\n", + "17) browser_name_Firefox 0.003088\n", + "18) browser_name_Safari 0.002913\n", + "19) browser_name_IE 0.001133\n", + "20) browser_language_es 0.000993\n", + "21) browser_language_es-ES 0.000979\n", + "22) modern_os 0.000671\n", + "23) numUniversidades 0.000576\n", + "24) numUniversidadesEspannolas 0.000207\n", + "25) os_Windows 0.000128\n", + "26) browser_language_en-US 0.000043\n", + "27) browser_language_en-GB 0.000000\n", + "28) browser_language_de 0.000000\n", + "29) browser_language_ca-es 0.000000\n", + "30) realDecreto_56/2005 0.000000\n", + "31) browser_language_ca-ES 0.000000\n", + "32) browser_language_ca 0.000000\n", + "33) realDecreto_1393/2007 0.000000\n", + "34) verticalAsignado_A 0.000000\n", + "35) modern_browser 0.000000\n", + "36) cursoFinalizacionMaster 0.000000\n", + "37) browser_language_en-us 0.000000\n", + "38) is_train 0.000000\n", + "39) browser_language_es-419 0.000000\n", + "40) browser_language_es-AR 0.000000\n", + "41) os_Linux 0.000000\n", + "42) os_Chromium OS 0.000000\n", + "43) browser_name_Vivaldi 0.000000\n", + "44) browser_name_Opera 0.000000\n", + "45) browser_name_Iceweasel 0.000000\n", + "46) browser_name_Edge 0.000000\n", + "47) browser_name_Android Browser 0.000000\n", + "48) browser_language_zh-cn 0.000000\n", + "49) browser_language_zh-CN 0.000000\n", + "50) browser_language_ru 0.000000\n", + "51) browser_language_pt-PT 0.000000\n", + "52) browser_language_pl-PL 0.000000\n", + "53) browser_language_nl 0.000000\n", + "54) browser_language_it-IT 0.000000\n", + "55) browser_language_it 0.000000\n", + "56) browser_language_gl-GL 0.000000\n", + "57) browser_language_gl 0.000000\n", + "58) browser_language_fr-FR 0.000000\n", + "59) browser_language_fr 0.000000\n", + "60) browser_language_es-XL 0.000000\n", + "61) browser_language_es-MX 0.000000\n", + "62) browser_language_es-EC 0.000000\n", + "63) browser_language_es-CO 0.000000\n", + "64) browser_language_es-ES_tradnl 0.000000\n", + " 1) viewport_height 0.294176\n", + " 2) viewport_width 0.167701\n", + " 3) device_screen_height 0.102463\n", + " 4) device_pixel_ratio 0.085122\n", + " 5) os_Android 0.076196\n", + " 6) os_Windows 0.040090\n", + " 7) idMaster_id 0.011246\n", + " 8) universidad_id 0.008421\n", + " 9) browser_language_es 0.002908\n", + "10) os_Mac OS 0.002408\n", + "11) browser_name_Firefox 0.002283\n", + "12) sexo_id 0.002246\n", + "13) os_iOS 0.001867\n", + "14) modern_os 0.001497\n", + "15) ramaConocimiento_id 0.000751\n", + "16) browser_name_Chrome 0.000625\n", + "17) browser_language_ca 0.000000\n", + "18) browser_language_en-US 0.000000\n", + "19) browser_language_en-GB 0.000000\n", + "20) browser_language_en-us 0.000000\n", + "21) browser_language_en-AU 0.000000\n", + "22) browser_language_de-DE 0.000000\n", + "23) browser_language_de 0.000000\n", + "24) browser_language_cs 0.000000\n", + "25) browser_language_ca-es 0.000000\n", + "26) browser_language_ca-ES 0.000000\n", + "27) modern_browser 0.000000\n", + "28) browser_language_bg 0.000000\n", + "29) browser_language_ast 0.000000\n", + "30) realDecreto_56/2005 0.000000\n", + "31) realDecreto_1393/2007 0.000000\n", + "32) verticalAsignado_B 0.000000\n", + "33) browser_language_es-CL 0.000000\n", + "34) tablet_or_mobile 0.000000\n", + "35) device_screen_width 0.000000\n", + "36) numUniversidadesEspannolas 0.000000\n", + "37) numUniversidades 0.000000\n", + "38) cursoFinalizacionMaster 0.000000\n", + "39) browser_language_es-419 0.000000\n", + "40) is_train 0.000000\n", + "41) browser_language_es-CO 0.000000\n", + "42) browser_language_es-EC 0.000000\n", + "43) os_Linux 0.000000\n", + "44) browser_name_WebKit 0.000000\n", + "45) browser_name_Vivaldi 0.000000\n", + "46) browser_name_Safari 0.000000\n", + "47) browser_name_Opera 0.000000\n", + "48) browser_name_Mobile Safari 0.000000\n", + "49) browser_name_MIUI Browser 0.000000\n", + "50) browser_name_IE 0.000000\n", + "51) browser_name_Edge 0.000000\n", + "52) browser_name_Chrome WebView 0.000000\n", + "53) browser_name_Android Browser 0.000000\n", + "54) browser_language_zh-cn 0.000000\n", + "55) browser_language_zh-CN 0.000000\n", + "56) browser_language_ru 0.000000\n", + "57) browser_language_pt-br 0.000000\n", + "58) browser_language_pt-PT 0.000000\n", + "59) browser_language_pt-BR 0.000000\n", + "60) browser_language_mk-MK 0.000000\n", + "61) browser_language_it 0.000000\n", + "62) browser_language_gl-GL 0.000000\n", + "63) browser_language_gl 0.000000\n", + "64) browser_language_fr-FR 0.000000\n", + "65) browser_language_fr 0.000000\n", + "66) browser_language_eu 0.000000\n", + "67) browser_language_es-xl 0.000000\n", + "68) browser_language_es-es 0.000000\n", + "69) browser_language_es-XL 0.000000\n", + "70) browser_language_es-MX 0.000000\n", + "71) browser_language_es-ES_tradnl 0.000000\n", + "72) browser_language_es-ES 0.000000\n", + " 1) device_screen_width 0.193903\n", + " 2) viewport_height 0.143456\n", + " 3) device_screen_height 0.108721\n", + " 4) tablet_or_mobile 0.100000\n", + " 5) viewport_width 0.093584\n", + " 6) device_pixel_ratio 0.088479\n", + " 7) os_Windows 0.055153\n", + " 8) browser_name_Firefox 0.009283\n", + " 9) modern_os 0.003147\n", + "10) idMaster_id 0.001418\n", + "11) sexo_id 0.001056\n", + "12) ramaConocimiento_id 0.000691\n", + "13) browser_language_es-ES 0.000594\n", + "14) browser_language_es 0.000515\n", + "15) browser_language_ca-ES 0.000000\n", + "16) browser_language_es-419 0.000000\n", + "17) browser_language_en-ie 0.000000\n", + "18) browser_language_en-US 0.000000\n", + "19) browser_language_en-GB 0.000000\n", + "20) browser_language_de-DE 0.000000\n", + "21) browser_language_de 0.000000\n", + "22) browser_language_cs 0.000000\n", + "23) browser_language_ca-es 0.000000\n", + "24) browser_language_ca 0.000000\n", + "25) numUniversidadesEspannolas 0.000000\n", + "26) browser_language_ast 0.000000\n", + "27) realDecreto_56/2005 0.000000\n", + "28) realDecreto_1393/2007 0.000000\n", + "29) verticalAsignado_C 0.000000\n", + "30) modern_browser 0.000000\n", + "31) universidad_id 0.000000\n", + "32) cursoFinalizacionMaster 0.000000\n", + "33) numUniversidades 0.000000\n", + "34) browser_language_es-AR 0.000000\n", + "35) is_train 0.000000\n", + "36) os_iOS 0.000000\n", + "37) browser_name_Chrome WebView 0.000000\n", + "38) os_Windows Phone 0.000000\n", + "39) os_Mac OS 0.000000\n", + "40) os_Linux 0.000000\n", + "41) os_Chromium OS 0.000000\n", + "42) os_Android 0.000000\n", + "43) browser_name_WebKit 0.000000\n", + "44) browser_name_Vivaldi 0.000000\n", + "45) browser_name_Safari 0.000000\n", + "46) browser_name_Opera 0.000000\n", + "47) browser_name_Mobile Safari 0.000000\n", + "48) browser_name_MIUI Browser 0.000000\n", + "49) browser_name_IE 0.000000\n", + "50) browser_name_Edge 0.000000\n", + "51) browser_name_Chrome 0.000000\n", + "52) browser_language_es-MX 0.000000\n", + "53) browser_name_Android Browser 0.000000\n", + "54) browser_language_zh-cn 0.000000\n", + "55) browser_language_zh-TW 0.000000\n", + "56) browser_language_sv-se 0.000000\n", + "57) browser_language_ru 0.000000\n", + "58) browser_language_pt-PT 0.000000\n", + "59) browser_language_pt-BR 0.000000\n", + "60) browser_language_nb-NO 0.000000\n", + "61) browser_language_it 0.000000\n", + "62) browser_language_fr-FR 0.000000\n", + "63) browser_language_fr 0.000000\n", + "64) browser_language_es-es 0.000000\n", + "65) browser_language_es-XL 0.000000\n", + "66) browser_language_es-LA 0.000000\n" + ] + } + ], + "source": [ + "importancesA = best_randomforestA.feature_importances_\n", + "feat_labelsA = fullModelClean_dummiesA.columns[1:]\n", + "indicesA = np.argsort(importancesA)[::-1]\n", + "\n", + "importancesA_threshold = 0.05\n", + "\n", + "listImportancesA = []\n", + "listNonImportantA = []\n", + "\n", + "for f in range(trainA[featuresvA].shape[1]):\n", + " print(\"%2d) %-*s %f\" % (f + 1, 30, \n", + " featuresvA[indicesA[f]], \n", + " importancesA[indicesA[f]]))\n", + " if importancesA[indicesA[f]] > importancesA_threshold:\n", + " listImportancesA.append(featuresvA[indicesA[f]])\n", + " else:\n", + " listNonImportantA.append(featuresvA[indicesA[f]])\n", + " \n", + " \n", + "importancesB = best_randomforestB.feature_importances_\n", + "feat_labelsB = fullModelClean_dummiesB.columns[1:]\n", + "indicesB = np.argsort(importancesB)[::-1]\n", + "\n", + "importancesB_threshold = 0.05\n", + "\n", + "listImportancesB = []\n", + "listNonImportantB = []\n", + "\n", + "for f in range(trainB[featuresvB].shape[1]):\n", + " print(\"%2d) %-*s %f\" % (f + 1, 30, \n", + " featuresvB[indicesB[f]], \n", + " importancesB[indicesB[f]]))\n", + " if importancesB[indicesB[f]] > importancesB_threshold:\n", + " listImportancesB.append(featuresvB[indicesB[f]])\n", + " else:\n", + " listNonImportantB.append(featuresvB[indicesB[f]])\n", + " \n", + " \n", + "importancesC = best_randomforestC.feature_importances_\n", + "feat_labelsC = fullModelClean_dummiesC.columns[1:]\n", + "indicesC = np.argsort(importancesC)[::-1]\n", + "\n", + "importancesC_threshold = 0.05\n", + "\n", + "listImportancesC = []\n", + "listNonImportantC = []\n", + "\n", + "for f in range(trainC[featuresvC].shape[1]):\n", + " print(\"%2d) %-*s %f\" % (f + 1, 30, \n", + " featuresvC[indicesC[f]], \n", + " importancesC[indicesC[f]]))\n", + " if importancesC[indicesC[f]] > importancesC_threshold:\n", + " listImportancesC.append(featuresvC[indicesC[f]])\n", + " else:\n", + " listNonImportantC.append(featuresvC[indicesC[f]])" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Most important features for the model in A vertical:\n" + ] + }, + { + "data": { + "text/plain": [ + "['viewport_width',\n", + " 'tablet_or_mobile',\n", + " 'os_iOS',\n", + " 'device_screen_height',\n", + " 'device_screen_width',\n", + " 'device_pixel_ratio',\n", + " 'os_Android']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Most important features for the model in B vertical:\n" + ] + }, + { + "data": { + "text/plain": [ + "['viewport_height',\n", + " 'viewport_width',\n", + " 'device_screen_height',\n", + " 'device_pixel_ratio',\n", + " 'os_Android']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Most important features for the model in C vertical:\n" + ] + }, + { + "data": { + "text/plain": [ + "['device_screen_width',\n", + " 'viewport_height',\n", + " 'device_screen_height',\n", + " 'tablet_or_mobile',\n", + " 'viewport_width',\n", + " 'device_pixel_ratio',\n", + " 'os_Windows']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\nMost important features for the model in A vertical:\")\n", + "display(listImportancesA)\n", + "\n", + "print(\"\\nMost important features for the model in B vertical:\")\n", + "display(listImportancesB)\n", + "\n", + "print(\"\\nMost important features for the model in C vertical:\")\n", + "display(listImportancesC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clusters within every questionnaire vertical\n", + "\n", + "

Selection of the important features identified in the previous step

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Fitting our models and obtaining the resulting dendograms

" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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1tbXpK1/5itrb2/Xcc89p\ncHBQixcv1oIFCySlG/eKigrdcccd2r17t86ePatUKqXOzk719fUpkUjI7/erqqpKZ86cUWdnpzwe\nj3XyymxP0WhUJ06cUDKZ1D333KOVK1fqjjvu0KxZs/Tcc8/pt7/9rUpKSrRo0SLNmjVL5eXlampq\n0rlz53TixAlJUlVVldXxa29vl8vl0rRp07Rw4UJ5PB4dO3bMOkHn9XrV3Nys6upq3X///frd736n\nr33ta+ro6NDcuXN12223yefz6YUXXpDX69Vzzz2nU6dOWYnrvr4+rVy5csQJirfffts6AZxIJLRs\n2TJ961vfUiKR0EsvvaS2tjZt2LBBCxcutNq6n/zkJzp+/LjeeOMNqyO6du1abdy4UZs2bdKf/dmf\nad68eXr33Xd18OBBTZ8+XevWrdP8+fP1ve99TytXrtRHPvIR3XnnnRoeHtavfvUr3XjjjZo3b57+\n53/+R/F4XF1dXdYJmSVLlujOO+/UW2+9JZ/Pp/nz5+vkyZNqbm7WO++8o8HBQatT7ff7FQgE1N7e\nrpaWFvl8Pn3gAx+w2vWOjg653W599rOf1b59+9TR0aHu7m65XC59+ctfVnV1tYaGhvTjH//Y2u+Y\ng21zML9mzRotXbpUTU1Nmj59urU8T58+ba1Xd9xxhzo6OvTv//7veuihh1RZWamDBw+qtbVVs2bN\n0urVq9XY2KiWlhb19/dbJ8IffvhhHTlyRN3d3RoYGFBLS4u6u7slSRUVFVq6dKk6OjoUiURGJBlM\nh3HlypVauHChhoeHdfjwYZ08edKK3XSIV61apdWrV2vhwoX61a9+ZZ04O3PmjOLxuAYGBrR48WLN\nnz/fOsmSSqXvCCspKdGSJUv0uc99Tj/72c+0b98+VVdX6+TJk/rkJz+pc+fOqaOjw+rUHTp0SGvW\nrFF1dbUqKyvldrvV2Nio48ePq7e3VwsWLJDP57OSQ5J0/vx5q/1bsGCB2trarO1y+fLl+shHPqKj\nR4+qv79fL774oj7+8Y/r3nvv1Re+8AV9+MMf1q233qodO3aoo6ND06dP19KlS5VIJBSNRtXR0aFE\nIqGqqiqVl5erqqpKhw4dUnNzs4aGhpRKpazOqyQtXLhQLpdL5eXlKisrs5IvkhSJRDR//nzrpM+s\nWbM0NDRkdYxNHUybNk3V1dXy+Xzq7+9XcXGx5syZoxkzZmju3LmqqKjQoUOHdOTIEZ09e1YdHR2a\nNWuWbr75Zg0NDVkn5vr7+/WJT3xCy5Yt07Jly7R8+XL19/frhRdeUHl5uf7whz/o3Llzam1t1RNP\nPKGXX35ZP/vZz3T69GlNmzZN4XBYbrdbFRUV8ng8mjVrls6fP6+SkhItXbpUN9xwgyorK/U3f/M3\neuihh/Tss8/qi1/8or70pS/pd7/7nX76059aScbZs2frb//2b7VixQolk0kdPnxYP/7xj9XW1qay\nsjK99tpr2rBhg+677z7NmDFD06dP189+9jPt379/xJVR58+fV2VlpXVxhrmQ5/bbb1dHR4fefPNN\nfexjH1NfX59eeuklLViwQLfffruWLVumeDyu9vZ2tba2amhoSNOnT9fq1atVXV2t//zP/9SBAweU\nSCSs+ezt7dXQ0JA8Ho/Kysp09uxZeTweK+leWVmpQCBgnSQ8evSolegzy2TVqlUaHBzUokWLFI1G\nNW/ePN1777165plndOTIEetgraioSCUlJZo5c6aqq6sVjUbV1NSkcDisadOmWQcvn//857Vq1Sq9\n+OKLeuONNzRz5kyVlJSorKxMJ06c0MmTJ7Vs2TKtXr1ap0+f1uLFi60E4ltvvaXi4mKtWLFCc+fO\n1d69e/Wnf/qn+uIXv6hTp04pFovp9ddf12uvvaaenh6dPXtWM2fO1OLFi1VcXKwFCxaoqalJLS0t\nisVi+vSnP63Ozk6dO3dOXV1d8vl8Kikp0fnz5zU0NKRjx45pxowZWrp0qU6cOKH169fr5ptvlpQ+\nCG1ra1M4HJbH49HQ0JDOnj2raDRqXXh14403KpFIaPr06aqsrLTWg3A4bNXLokWLFAgElEgkdODA\nAZ0+fVo33nijZs6cqXnz5ikSiai1tXXEhT2mj7xo0SINDQ1p+fLl+v+Z++7ouKpr/W961XSNNOpW\ntWUJJNuS3LCxMcVgTIkxJDjG1JBQHuXxQmgvCUneC+ERMC2FhBYSEiAxEAwkMQFsbGzHRbJldVl1\npFGf0VRpyu8Prb197kiyRZL3ftlrecnS3Ln33HP22eXb5VRVVeHBBx/kBKqGhgZYrVZUVlbivPPO\nw0MPPYQtW7bgtttuQ39/P375y1+ivr4e8Xgc7733Ht555x38/ve/h8/nQ2pqKqLRKDo7OzE+Po7l\ny5fD7/dDo9FgYGAAkUiEbZqcnByMjY0hMzMTCxcuxE9+8hOUlpbCaDSyTT06Oor09HSUlZWhp6cH\nHo8H0WiUA0nBYBBOpxNLlixBXl4e6uvrkZubiwsuuABHjhxBS0sL9u/fj1gshrS0NKSnp2P+/Pkw\nm83YuXMnjh49CgDIy8tDeXk5PB4PsrKysGPHDtxwww3YvHkzDh06hNraWthsNlx00UV4+eWX8dZb\nb8FsNkOlUnGQMjU1leViRkYGy7ju7m6Ew2EUFBRgcnISY2NjsNls+Otf/4qSkhKsXr0au3btQjQa\nhclkQjweR1paGhoaGljOE3hPYGVGRgYcDgdGR0eRmpoKg8GAvr4++Hw+FBcXIxqNsi/ndDqhUqmw\ncuVKBINB3HnnnUhPT0c0GsXatWuRnp4Oq9WKrq4uNDc3Y8+ePbBYLCgpKUFLSws2btwIi8WCQ4cO\ncUB3dHQUdXV1KCkpQWVlJfR6PTo7O9HS0oLh4WEEAgGYTCbk5+djbGwMgUAAg4ODAIBNmzahqKgI\n+/fvx/z587Fy5Urs2LEDbrcbLS0teOSRR5CRkYHnnnsObW1tCAQCCIVCSCQS2LRpEw4cOID6+nre\nzxaLBampqZDL5fD7/airq0MwGOTvkP+Ynp6O8fFx+Hw+6PV6OJ1OGI1GfPTRRygqKmK7fGRkBD6f\nD6FQCHa7HTk5OWhvb8eiRYtw3nnn4fDhw+js7GTMwGazYWxsDD09PfD7/bjyyitRXFyM3Nxc9PX1\nobe3F3v37kVhYSEqKirwzDPP4LrrroPJZILT6cTevXvxxhtvwOVyITMzEzfffDOMRiOi0Si2b9+O\nlpYWqNVqZGZmwul04uTJk/D7/XA6nTCZTKzfKVFs8eLFqKqqQn5+Pn7961+zXZORkYHy8nJotVoc\nO3YMExMTcLvduOyyy7B582bs2rULJ06cYFnc29uL4eFhtLa2AgBGRkaQkZGBvLw8uN1uaDQaNDc3\n45JLLkFWVhbcbjcnZ+Tl5aGnpwexWAzRaBSRSAShUAitra2wWCw499xzMW/ePPT19aG5uRm9vb3w\neDwoLCyETqfD2NgYbr75ZlitVshkMrz55ptwu91obm5Gfn4+vvSlL+HYsWPo6urCiRMnsGjRIqSn\npyORSPC47733Xrz22mtoa2tDRUUFLrroIqSkpMDv9+OTTz7Bzp07cf7552PVqlX47LPP0NXVhSVL\nluC8887DH/7wB+zYsQMpKSnQaDRIT09Heno6FixYgE2bNqGpqYkTiFJTU5Gfnw+j0cggINlG3d3d\nqKurw4UXXsgAIABOKDSZTFCpVPB4PFCpVLDZbBI8amhoCCaTCWq1GsPDw9BqtZz0SpR8L7qOkm9l\nMhnLAxFEBYDu7m588sknuOKKK6DRaBAKhTjYEI/HMTw8DJ1OB7/fj+9///s4cOAAA8WUrCtWC8hk\nMuTl5SEYDLKNTETAp0aj4WBzVlYW24EEuk9MTCA/Px+dnZ1IJBJIS0uD3W5HR0cH/H6/JEhBoK0I\nIZpMJthsNrhcLgwODsLj8bA9D5yqQk9JSUF2djZKSkqQnZ2N3Nxc/OAHP4DH48FDDz2Ew4cP48MP\nP4RcLodWq0VJSQmOHj2KaDSKefPm4c4778Szzz6LQCCAvr4+XHjhhSgoKOA9PzY2hvHxcTz55JPI\nzc3FiRMn8M1vfhODg4OMERIGJQYECwoKsGLFCvT29uLIkSMwmUx44IEHoNfr8frrryMajeLkyZNo\nbW1FOBwGAKxduxYulwuffPIJenp64HK5sH37dlx99dVYunQpbDYbsrOzee8vWLAAFRUVCIVC2LVr\nFz7//HMEg0GW2wSok7972WWXYd++ffjss89gsVigVCpht9tRWlqKHTt24Prrr0dRURH27duHxsZG\ntn+USiW0Wi3y8vJw6623oqioCOFwGJFIBAcPHkRraysOHjyIQCAArVYLj8eDYDDISW+UoEtBYbvd\nDp/Px0EHt9sNi8WCyspKxtt6enrYRgAAnU4Hh8OBUCjE/gQlwLW2tiIWi2HhwoUIBAK8ZrFYDMFg\nEGlpaXC5XPj888+xdu1a6PV6RKNReL1eTlAbHR1lPef3+zE6Ogq/349oNAqdTgeTyQSDwcDJXjqd\njpNgIpEI1q9fD6fTiczMTOzfvx87duzA4sWLYTAYMDIygsHBQaSmpmL+/PnYtm0b8vPz0d3djddf\nfx1NTU3w+/3s40YiETz22GNYvHgxGhsbUVJSgv379+ONN95Af38/lixZApVKhebmZni9XkxOTiI3\nNxdlZWVYvnw5B/U8Hg927NiBoaEh9Pf3IxwOQ61WIzU1FWlpaSguLsbx48exZ88eTE5Osu5LJBIS\nXDQcDkOr1cLv97N9m56eDmAqueSRRx6BRqPBe++9h2PHjqG0tBSLFi3CiRMn+NkajQaZmZmor69n\nPDORSMBoNMLtdmNychI2m42fQxgBBZouueQSXHXVVXjiiSc4+Llx40Z8/etfRzAYRG1tLUKhEBoa\nGtDT04Pf//73yMrKwmOPPYaf/vSnWLFiBQ4ePMh+UTwe50BheXk5wuEwGhsbOeHH6XRicHAQubm5\nKCwsRHl5Od59910MDAygpKSE7SGNRoPbb78df/zjH9Hd3Y2zzjoLW7duRSKR4AB1fn4+lixZwgHc\nudK/TFCltrYW27dvR1tbG0KhEAPOyWVSpyMCB2YiUSGOjo7OmJFwuu//I6RQKCTK70x/T/6cMqXE\n8qbTjTX5MzGTKJnsdjs77RQlnY2So6RilgVwqqT0i5AYJZ5t/Kf7O70bIM06Od06UoYhzYn4XmK2\nEjlsFPlWqVRwOp1ITU3FoUOHYDabOSNCrVYz4HImHlIqlRKDh96DflJQRqfTMYhHFRSk6AicJGE7\nNDTEjo24JiaTiZ1DMmr/0b10ps8pGyG5BHEu+0utVjNQSPcqLS1FbW2t5DqtVssgPH2PFCatw1yI\ngNiUlBQO2IljnonE90jOMki+N4E9xEcKhQKFhYWor6+Hw+HAZZddhg8++ABDQ0OcfSLSTLzyRUgm\nk7FhRGNKSUlhkEe8jviG9oBGo/lCc3k6ksvlMBgMsFgsrOBnyvgSM7jErFfiJVFmGgwGBINBDlYC\nUzyuVquhVqs5MCyeKUHvRYqfMl3Gx8c5o4dAZXo2MMWHGo0GVVVVbMxQVj85cnR/QFpePRsvUaCB\nPidjWsxyS94PRDqdDqFQaMb7UkXnXInmV8zKjsfjnMU10/MJ0P9HZImYcXWmsZ3pXsB0fSryVWpq\nKgYGBqDVajnb6fzzz0c0GsXHH3/M70jg9dDQ0GnfTXwW8Qhw+vOuiF9Jz3xRXTkTkaw1Go3wer38\nN3IUk0l8Lv1f1MGi008BWTG77HT6NVkuyuVyWK1WWCwWdHd3c3sEYGqeNBoN0tLS0NHRMed3Bab2\nEzlUZrMZo6OjHLjxer0Ih8OSSo5oNIr8/HxkZ2dzQIH2HclFSmagTDwKAJ6JZtIFyZ8bDAaYTCa4\n3e4ZbTOlUgmNRsPgCzkuMxEBvE6nU1JdNNt8zVUOiHwxk07T6XScVTgbWSwWyGQyrjQ+HdG99Xo9\nJicnJXtpLvpuNjt4pt/pZ7Kd+c8gyiIFpuSZuE9ORzKZDCaTCZFIhG2O5HegsSuVSrhcLgSDQYyN\njTFAd9555+H9998/7diSgTeR5HI5LBYLJwPR34DprY6+iG9E/BwIBOZ0Pd0fmLLrIpEIdDodV/3R\nXjUajQCkZ0Sd7l7JPJzsr8z0vS/i/4kZoWf6HtmmYstUk8nESThUbZ5MlIgm2tPiZykpKQyaz/Ud\nxExPSmAjPUw2ayQS4QSHM1Gy7ADO7AOJ46TMcACz8irptGQQN/l+er0e4XBY4isD4E4O9D4ymYwz\npZPHQ+9PlGyHJvMUZZfPZKOeiSwWC7xe7zS+nE2XzORj/D12BNmVM/n05GOqVCpOokl+BvnQp1vn\n5O8l+/oz2fgzzR3pSALAyS5JSUlBQUEBli5divLycrzzzjsYHBxEV1cXJy41NTVh6dKliMViWLFi\nBeLxOJqbmxGJRDghoKurC4lEAosWLUJWVhZ8Ph/a2to4WK/X6+HxeODxeGA2mzlAq1KpMDAwwD7V\n0NAQhoaGIJPJoNVqkZubC7vdjgMHDkChUODGG29EaWkp3G43XnrpJU5co+owo9GIG264AUajEQcO\nHMCJEyfYX+nt7YXdbmewVNQ5ydjQXCiZl2bj2dPJzJn4kfbybPcS7QmZ7FQlbTweZ9lIIDzJeYfD\nAb1ez1XqVHkwGzYo6s+qqir09PSgv78fwCn7/HR+dfJ7iXb7bJS8j/Ly8tDR0TFNNtK9zWYzJwmf\niWaT6eIeFudDrIyi9ySMw2KxcJI4VQEkEgnGKcheoL1KvrXIX1qtdprNMhdKxrjEOZlJ9hFfzNUO\nmQv/i/Y26UyqoAEwoz4Qv5ufn8+Je5QkmTx2k8mEUCgEi8WCgYEB/kyj0cBgMGB0dHQaf8lkMtjt\ndqxduxY7d+7kpEoAEgyJ7E2yz+i7Il4xkx5Kfh+1Wo38/Hy0tbVJePCfjYHPdr/58+djaGgIer0e\ng4ODHHQjfk5PT4fH4+G9d6YqxGSaCTcR9yhhu9FolDEgtVqNcDjMfG80GrFkyRL8+7//OzIyMub+\nzv8qQZX7779fUpJEQNehQ4fg9/tZictkMokwTAZXT0cUqacsB2I4EaA6kzFzJpBTZAwCM8m5p9+T\nFY4Igswl0EKgDwnE2YwREVSg35OZUjQgSZCJAM3/FRH4QG02aLw0Z2Qwy2QydrbovRwOB0dIaeOQ\n8Enml5me+0VA/9mIxpPMH6KxT0EPotTUVM4g+2cQKdeZjNLk9yKFRWDJXIF7MUgz1zGZTKZ/Cj8p\nFAoYDIY5PZ+ChKKBMZMBSCWFOp2OK56AKZlgNBp5fcT9NRenaa6GB4EGyWcdzMXYEt8RmB2AT/7O\nXB2+ZMfzTCQCgzPd6/+XqhHXP9nIJeMxGo1y5dFs453JASWay7uJIDaNgQxbyhAEAKPRiImJCYke\noTmd6xympqZiZGRkRsBnLmuh1+vPGFCbSZ+cib8MBgO3ohTvA2CaA5587+TrKEgpAiCzGdazvTPt\nIQp6kuNuNpt5Pb4olZeX4/jx43MOOFVXV2P//v1zvv9c9jnZOdSWSRzLTHNkNBoRDodZD4hG599D\n/6yg0Rd5HjDFGw6HA1qtFgMDA0hLS0N3d7fk2rnKIhE0pWeI8lZ8tvg3hUIBk8nElVsUvI3H44hE\nInN+9sKFC3H8+HHJ32fSa6KMpjESqEABZJEo4120rWZ6PoFXYtsIYOaALgWEaf//I8ELutdc7Ji5\n0Pz589HY2HjG6yiz83S+xGyBKpPJxMG82cZLmdKnCyRTawTxPmdyJmf7nAAE0Z6YTYclO9yTk5Mw\nm80wGAzo7e2V+DOi/JnLPp9L8Dz5nb4IabVaDsylpaXB4/F84Wcl63KZTIZ58+ZxwHemxDHaa2Ji\nhVwuh9FonNVGTbbRRf1OfgJVwQFTZ+8lBylns03o73a7nTNhRXkgEiVinQkUNxqN8Pv9p7WjRaLA\nIgFVc7WByS6iRA6yI2muaRw0rplkMM3lXO3VfxZRdahKpWL7kYj20pkA2X/k8+TrTrcnU1JSMD4+\nLvlbMlA310Qup9OJYDDI6yKTybhKdqbfT0cVFRWora2VyDSlUslrqdPpJEm2tOeS/bLTgbDEZ2JL\nIrpGpNn0IX2W/H9RN55u7ilRcjb9QvMlfi6OpbKyErW1tYjH47yOM+kLIhoLyd/T2eYGg2FaEDwr\nKws9PT2S8Z2JD5PXTfwerZk4RyaTiVtWE1mtVq46pXvS/v6iADDpawJKdTodV0OIc0TPJZvt7yVx\nHlUqFcxmM4aHh+c0b/TT6XRyt5QvQqTrlUolSkpK0NPTw9015hoEycjIgNvtBnB6PROPx+fkh8w0\nxtPJFuLVmZKtZ7sfgGlVWTPpvL+H/tGk1tloLkHp/01SKpUwmUyc3DoXO24m+TETD5jNZvh8vmlB\n23nz5qGtrY3/bjQaGeefKdhJ2KFWq8Xg4OBpx0c4irhnkmXp6Uh8/8rKSthsNvztb3/jSqoXXngB\narV6bvea01X/B+R2u1FWVoa+vj5UVFTA4/HgrLPOQiwWg8lkgsViwTXXXMMlXAA4i+DRRx/lFxbP\nMxD7cgNThnEoFGKAV6vVwm63S5SrCLTZbDYGO6nPNXAqI098BpWii0JfZKrs7GzE43F2VIgoKEQG\nsDhepVLJ2VlEsVgMg4ODEuPDYDBMAwqpJzUFdSgbAphiQDIsyFARDQSfz4eSkhIu66aKCZqXZCNE\nJMp2tlqtvGmSSafTcWmxeIaHmGFE7yKeu0Fl5Xq9nrOtVCoVhoaGMDk5yddSJkBxcTFHJZNLd5PX\nWlwvMcPW5XJJDPbZiAJ+drud50G8J60lOdYKhYJBy4svvpjnhXgMmDJqiNeTiZSvSIlEApdeeinz\nLF2nUqm4rFi8loKMaWlpUCqV0Ov1kufpdDpJr0EAkpYIK1euRGpqKs8lHSQn3iORSPB+IwCR1pOC\nZPQZEc019U8U39nn80neWzw7Ivn9RF4SQSYaLykI6q9Pe4We73A4YLVaYbPZ2KkksIna7QDgvW8y\nmfj5omJwOBzT1k+8joxiciZJhhAvUDBJ7P8pVhTQ7yTPKDBN1yfPSzJRm62ZKNk4VqlU3NJBJApM\nUCDTZrOx/KDn0veoF3cyzba/qPySeDh5T5C8y83NnfH7ooElKmbiEVqrs88+G2azme9ZVlYmmRfx\nPkajkQ9Ao31E8yDOCf0kQ1SUn+QkUCkyrRdVk5GMNhqNyM7Ohkwmw5e+9KVp703tBsS1pmAg/Y0C\nEHK5HBdffDHrAJKJtI+IxIAKld8vX75cck8yrIGpvfzVr34V2dnZ/LlarYZKpUJFRQXfSyy5F+fG\nYDDA6XTy/aqrqyVzKd5T3L/RaFRyrtCKFStQXV2NmpqaaTxA8yAS7SEReJTL5QxqpaenY9WqVQCm\nqjoLCwu5vSddS/KCkgAaGxtZ3ohrYjAYWP7QuycSCezfv19yVpvFYsE3v/nNafKdvvPlL3+Z90Sy\nfKR7RCIRCQglvrfNZsMDDzzA95PL5dzaVFynRCKBvLw8yf1VKhWf+SCuS/JYiK8oK0zU6clE8pvG\nSM4o3Z/sPPFshGQSM7mHhobg8/lQU1OD7u7uM8rA2e6ZSCQklWRKpRJf+9rXmEc1Gg10Op0EaAGm\neGp0dJTlHFV7mc1mmEwmaDQatusMBgPMZjOWLFky7fliQIXeQQQaybbR6/XIzc1l20ccC1W4Aqfk\nt0ajQVFREWKxmMQuoDPt6FoK6oqJO3K5nPc18QMR2b9i9S/dl/4RibKHvkvPnclxo/dnMgM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i1bUFdXx1lhJNOqqqqwePFiNDQ0IDs7W9KiI5FITCu7f++992AwGHDJJZegrq5OEiDu7+/Hzp07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/qK2tRTQaxeDg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KAAAg\nAElEQVRgPhh5qYp2IqqqqkJ9fb3IWTcZCYKAH/zgB/jd7353Se/7uu7tZIHsRA4hCtolMkC+KID8\nZcZL6B0+JdtiscDj8Vx0bcjI4YNEdI75szyZA5IfPyFLyXH6VYPul0oKhQIulwtdXV1f6u8uFoCQ\nyWTIzMxkJd6+iEdcjKTflSr6TqcTAwMDExx/X5akd1O6VxfTib7MO6RnVavVTgDaJCKpo3WyMUnf\np9VqRWfoi3jUpYCneFKpVEhJSWHlMmj9DQYD0tLScO7cuUnHKnWM0d7m5ubi7Nmzou8RSvfLktFo\nRFFREesn8O/u4cWID1STg4HQnF/0d4n0Q/5zKt8iJeJZUp2W3v9Fjt7JiLI6wuEwnE4nK9MDADab\njZWxuhh9HWsrDWRbrVZW6vY/yKz+0nQpQJREAQvp/Uykoyf6zlc99zzwi3fq0d6rVCrccsst2LNn\nj0hPvBgQ4lJ1YKl9IrUJL4VHA3EdnMr3TEaXoqfyui+9j/irQqFASkoKy0T/usB5PHhDGsy61HtN\nDjN+bQiISmPln61UKmG329HT0zPpM0kvAr5e3fz/VprM4cn/O5EeSz4jAtNe6lon0qOloLFLIbqr\nxIOlZ5t8DlKwGmUES3nX123zS8eqVqthMpnQ29srGqtMJmMZ92NjYyyD+es+t1+HT+fL6pSJnsnz\nh6ysLKhUKrS2tk7QpaVnMC0tDZ2dnReVHUDcZm5qavrK+vbF5kJjJ9mf6F4k4v0U+PkyRIBcvjXD\nvzN+OvuCIGBoaGjC53SHtFptQlA0/7MgCAwccLE95itiGAwG0RpMZqdJ9e4v45NOBELU6/WiUtIK\nhQJGo5HJCNJJ9Ho9AoEAQqEQy/rx+XyYOXMmnnnmmYusrmQMl/zN/w/o3LlzWL58OTIyMjBz5kyo\nVCpUVVWxUgvAhQWVLmokEoHb7UYgEGDRbgAoLi5mPxPzBsCYqjTIQReRdzQKggAAWLZsGVMUpYoX\nORYBsO9LSTpuEkZlZWXQ6XTs94Rko7GOj4+jo6ODPZui2uScp8ghX2KpqKhING+eHA7HhLElYkBV\nVVUTGBu/tvR3qampqKioYO8hQyzR2vA/6/V6rF69WvRMOvB0udRqtegzuqQmk4n9ni6RIAgoKCiA\nXq8HEI/+KhQKXHHFFWytgPiZMJlMbM1pneVyOZYtWzZh3cgpAkCkPKvVaqhUKlEtWiJpyhgJM6VS\nCYvFAqfTCY1Gwz4PBALsHTJZvCQDMS4irVaLJUuWiNaSX08ik8k0Yc+BeB8BekcsFmNIlEgkwiKx\ntMeUxcLPidLJEzE0HjVPa0YOJZo/Py4ah91uFz2D1oR/RyAQgNlsZv+mplWJziJvLBkMBtxyyy1I\nT08XrQf/czgcRltbGxsP/w5aW/5u0jrQfAHxvicimSxeG5SvzRmNRpGUlMS+MzAwIMowACAqg8MT\npVdKSavVivgTT7SX/HnhFTeFQiGKxstkMlitVvZvs9kMhULBeBv/fDKuCfkUjUZZCZpEGWaJDC4e\nRe3xeBg/ojHTf/Te0dFRJhQBMFRHdnY2c4BJ9zQrK4vNX8q/+blTeQyaN72Tv9P8OhsMBvZvh8Mx\noS4npeDzdywRSWUblSEgBUin07GUaCJCctDPkxHxn5ycHOTk5ACIB/L4O8TzH54P0Hhyc3NRWFgo\nciKpVCrRuaNxkFyS0r59+9Df3y+SkXS/+P0F4nLvlVdeYe8nHqBSqTB16tQJz+bfqdPpoNVqRec9\n0XgS/S4ajU5AMRGvtFqtbM0IGU7lN6kME82N7gWVayLig5CJiNdT+DH5fD5YLBa2N06nExs2bBDN\nnx8fAFEtbvo3oX35dX/88cfZuGl8crkcubm5jNdR6QLac15uSvcOAMtMks7bZDJBr9dPkF10b/mx\nUg8gAEhOTha9c7J7lGhd9Xo9Qw92d3czQINUHykuLsbNN98skmXJyckifYOI15loPDz/njdvHpsP\nlYoislgsTI7we83PXyaLl4/ldSCj0Qir1Sp6VmZmJvs+zYMyCYiorGMiksniZVaSk5MnzI/Wx2Aw\nMF7Iv1smu9Cvj0ovAUB+fr7ojhMwgv+70dFRuFwu9jur1Sqaq3SMiXSPRDyPfhcOhzE4OMh6WPDl\ncHi5On369AnPiMViSElJYeOJxeJ9b6QAh1gsJgqoGI1GLFq0iP1M68HrekTUB9Dj8UxAwqnVarZ/\nvMwCgNTUVFZCgojOjVQnIqIgKm/kzp49m72DP+96vZ79nEhW0zkhQJj0nTx/o54hOp1OZGstXLiQ\nyc65c+eycfHjkBLNkfrjGQwGkT5M76TzT6TX69keSx399D0+ozGRTp1oTLxtSPouPXPjxo3seySX\n5HL5hHuQ6Jm8vsmPZcaMGWwcVPaWiJp7KxQKpKWlJXy2Xq9nJXcns880Gg1+/vOfi+ZN85TKFR64\nRGvA319BECYAdghgNdl8SX+UyeKggYsRnyHIj4tIp9PhwIEDzHamsUajUREP43kwBd5pTLyuRePl\nS04DmOCoJL7OryuV6CEiG5l0WDojvLxUqVRMT6V1TWSD0xh4eRSLxSacTbpvt9xyC4D4/vD7lejc\nS+cu1ftpvaT3Y+bMmex3giBg9uzZCZ/Z29vLniGlSCSC1NRUJqc1Gg1+/OMfY3R0lJVXTTRmPlAs\n9V8Q8fyYsjL5tSAfg8ViEdmaWq0Wd911F3vvxe4y+R/S0tJEfgUinn/Qv4ELQD86Y+RnSET5+fkT\n5kd2mJSSk5MxZ86cCc8iZ/FkNNn+SIGqPNiHKJE8l+pnSUlJsNvtCefI87hEz+FtWGnmO8lI0slo\nTZ566imRbjR37lym0/J/z/MIIrpDl0L82SSbgsBk/BrEYjGcOnWKgU75tRYEAQ8++CD7N2+X8nvG\n/yzl74m+I6VEPh2bzZZQb8nIyGDzu/LKKyd95mS8ha9IQGW0YrEYs+lUKhVKSkomjK+jo0Pkg6DM\nKSnV19cDuLCOvJ+F/z0R+VuJEtkmtD40dvJXaDSaCbpbIp1pfHycfc/pdIp8BJPdbarqAYjXj+bD\nyykAkz6Hxh+NxisNUdKB1Gan8oc+nw8ul0uUBEDnjmQS3R8pgIJ/H/2fMk6DwaDoTExW3UEaUJls\nLxLxftL/eBtgyZIlIn5IZT55/53NZsPIyAhKS0tx4403skwrKg124MCByZZ2Av1HBVXmzJmDrVu3\nYurUqax3xenTp1lNUSJKm+OdD0qlEpmZmax+MC1ia2sr+zulUonLL78cQHxjpBeXFvmGG24QKVnk\nYHG73bjxxhvZs8j5L5PJWPCGSg0QSTfeYrEw4UOXoLOzU2SMX3755YhGo6KAECmQ4XAYVquVMTte\ncXU6naxO4YoVK9i7HQ4H+1mr1eKee+6ZIOwIeURBgh//+Md48MEHReOcDIEyNjbGSpXQ/vBMmjec\neYdUQUEBtm/fztAOQJwhVVdXMybAl+qIRCKi+UrJ4/GIjHlq2rh3714AYEKSmijRGubl5QGI7392\ndrZI+JCSS0KMdz4Hg0E4nU62JlOmTGFn8tvf/jabE/88anBI6Yrk4KQ1AS4wIqrXT3s1NjbGBAaN\n12AwYP369fjGN77Bfk+ln4joHFksFlFTemJysVgMFRUV7N00lkgkgsLCQtH7pIgimh+dn0RCmFC6\nfIomOUj5c0+pneXl5ex9QPzM8MgkCryRE52njIwMkeN93759Exg+OQXonNCdpH2NxeLNlonpU116\nCoRRKigJtLlz57I56fV6UQCIBJNUQTOZTCgvL2drSA3lyDgGxAKcXw+FQsGErUajYXtHabv0HenZ\nIqcK7cn4+Djy8/Oh1WpZrxx6F50JOnvUf4pPf6YxlZSUMGOVR6LSWKSOBN5JxPNHMmB5dDQ59UmB\n5h0G/Nkh5ZrQeNT4mT+PXV1dIqcKGWUKhULEszMyMpjywJdn4o21a665ht0HPqWUT6cnoywUCmHq\n1KlsLNOmTUsY5DObzbj22mvZ78n5RyiL4uJixGIx3HvvvezsRiIRlimRm5srcojxvNbhcECpVLKe\nUQBY7yf6Dl/aZcGCBRMyNRoaGtg6RaNRlJSUIBwOo7CwUOR8oPNEhg3vpJe+CwDS09PhcrnYnvDO\nh7KyMjYfcl5Go1FRQA2Il3iivUpLS2PlhHijjPaFgrIymQzTpk1jn5vNZhQUFLB/82MkZLfH48GM\nGTPY2lM9ehof7RVf7xkQK528MllVVQUpEf+n9aIeLXy5sFAohIaGBhQUFLB9DgaDGBsbwx133CF6\n76JFixhvs9ls7Ll0L6PRKM6cOcPuEwW6CahCdzAlJQXj4+Pw+/0YGBgQ8QqtVjvB+Kba/8AFpB6h\nBqWZDfy5JRIEAfPmzWO8Mzc3V8QfgcSGYllZmej3VMebLxFD/IsfAwX+d+/eLTIo+LR34MK9omAG\nrSUfoAaADz74gGXqUVNXIuqvJXXI0RrS+zMzMyc4QKkuOv87+j/poyqVCmvXrmXfkcpu+pvc3Fzm\nHJMaOfz9540s0nPoOXT3BwYG2Pfnzp0rGiPV/af1ox5WPE8l+cMHfvn15nVPQRDw3e9+F5dddplo\nzWlOZWVlbF40bt7paLfb2fN4pysPJOnu7mZgASC+x6Q78E4ZnoLBIANJjY2NsfWorKxkzpjs7Gw2\nJ3KkkDwnnYJq+gNxfvzDH/6Q7R/1leJluUqlQnFxsWiNyEkhk8mYg4lkdDgcxsmTJxkinCdqQEuk\nUqmYHqBQKBiPo3dNmzZNpN+Q04UoFotNALt99tlnzDF/4sQJtlckP0k2k6FPZV14GhsbQ1JSEpYv\nXw4gfhYDgQDrDUVjHh0dxfnz50UOaYPBAJvNxsZNc6E7KgiCaA2kQAYgfi55+UH3LhgMMj2denXR\n/ZKeU94hTShL/k6sWLGC/VxTUyMC/82aNQvABectzUPKE3mdJxG6lGQhANasnXf2kg08a9asCTw6\nPz9fJK/5PU5OThYBYuidPACP+v7wgRn6mecf/M80X2p2X1RUlNAmHB4eZn1dpLYV30iZ3xfaJ7q3\nfCCLxks8kEhqE5eWlooC0hRQjEQiTB8huwu4gJ6ndSb7hwL/CoVChFZWKBQiGwO4kBXG+wz4Mk3U\nnNnj8eD48eMsGC7NVuWBDzxYkPiH1HlL8+TXn/Qi8iXEYjFs2rQJMplMBPbkAYn83/PA1v7+fmZX\nBoNBDA4Osj4FgiAwwCO/RwSeogAiEe8M59eP1pa3J2KxGOshBFwIwlDpZFonCn4ncvDRnlOPMZ5K\nSkoQi8VEti49IxwOw+Vy4Zvf/CZbT41Gg+9+97ts3Wn9yP6lMwTE7wqvV5Ks6unpYf0DpU5a6fel\njmjeiUxrXVZWxsZLJAgCbrrppoR6mVKphE6nQ1pamkiPGxwchNfrZTKfAo5qtRp33nkn+96UKVOY\n/47mPBlFIhE0NDSIfudwOBCLxXDXXXeJqtlUVlbCarVi3bp1onl89tlnuOqqq0T8g5elRFTa2mq1\nss+o7w9wQdfKz89nPReVSiWysrLYM6jMKgVR6T4EAgH87//+L/serdHY2BjmzZvHfk9+Klo3kqF0\npxUKBbZs2YJNmzaxvSAiMIv0vgwPD4vuxMKFC2G1WvGd73wHQJxvDg4OTnDO0zik1Sn4dSFfYigU\nYuWuaL9CodCk2elUBovW6bbbbhOdffI9ms1mprPzZ91sNov47ooVK1BRUYH58+ez7/BZTHK5nNlD\nUj2JZJAUgEfnl/aKdBv6HlUPICosLGQ8jH5P46b5pKenw2azQSaTsT4isViM8RW5XC7S9wCIzik9\nl/pnkf3D8xwqTQlc0BP5Up5kmxDwQS6Xo6ysLGESAQ+40Gq18Hq9GB8fF8knAMzelvoIaH78/6U0\nWcCc98HIZDIcP35cVLLZ6/XizJkzEwJl1LerqKgIMpkMAwMDKC8vx+Dg4EUDVlJSPPLII49c8rf/\nX6acnBzs2rUL2f9PEy4qH7Rz505s3LgRb731FgssUNqoTqdjBrpMJmPKAwU3pNHrK664AidPnoRc\nHm9iL90wmUzGerrQZ1arFePj4+ju7sbZs2fZgXM6nczZSCgluTxe45ivl8wTz2hoo6mkBh3E8+fP\nA8CE8ZHCTv/JZDKRgkxNwxQKBQ4dOsQEIdWhIzp48CDMZjMr6wKArSMQvxC9vb2sybfFYmGZFFIl\nSiaLIw6pkS3Niyceoc87YqhWJzl8iVpaWtgzaC/pc/r9ZCW4mpqaRPPy+XyixsJWqxW9vb3MiQ3E\nnT8UvDly5AhWrlyJ06dPs/FGIhFm6BCjIZQGj1KkxstqtRo1NTVsP2ks1BuGDHyVSiUqg2O320X9\nCKjuLD9XPm0vFoujJ8ngnEwQ9ff3M0EWi8VYzWK/38+EZldXF2OqdJdIqSVK5ADind8k2Pg95v/P\nE51TufxC6Qg6X2lpaay0FZ0N/hmBQICdKQpwENF4yPAKBoMYHR0V3RNyltPfGY3GCcYs/77e3l6R\nQ4jWjMbd1tbGeFYoFMKCBQvQ1NSEaDQKs9mMcDjMmnjS+k+dOpU13uWzNKi3Cn/GiOjveZRgJBJv\nwNzR0SFynFHQLBQKYfbs2WhvbxetD/3f4/Ek7D1D8w4GgzCZTBNKt/H39cYbb4TFYmElFkj48gKa\nX1M+cMcb0dJSJInQDzwv49/B8y5+bFJEEP+ZWq1GQUGBKKMKACvFQplTdP/oO0ajEXV1dbBarSy9\nnJ7LB1ioSTKV76JeMpT+TQZhLBZDamoqcnJyUFpaisOHDyMRkcPniiuuAHBBTtjtdvj9fgwNDSEY\nDLJn8+tOQcJIJILOzk4A4j3knY+UuZaoRBcfzKCmcn19fSJFlBz/0WgU06ZNQ29vL2bOnMn4Ez82\nmUzGlBbKoEhNTWUBWLp7/NmOxWIiZKZcLmd1g4E4z6fMkWeffRbbt29ne0FKOcl3MtqpJjdffo/W\nEbjgBAfA1o+IX0cqXSGTyRjvpIa1ACY4AMhxazAY8MILL+CDDz5gz9NoNAiHw6ymLt+rimjnzp3Q\narWsVFMkEq/BS3tnsVhY8CwQCDCjhowBWjNq6kprQL+XZs7xZaxIngAX7p9MFq8vLOUXtGfEe/kS\nLQSe4PeX0MCtra0MJNDZ2cmQzfwe0hngg2YkJxUKxYSAFPF9nrdSVlZfX9+E8kEjIyOie84brDRP\nylaSIid5Zx6tD38/ed4BTOSV3d3d7PzTOCORCDvjQPxOTpkyhRmnpMuMjY0xAAnJA54oQEMlTV0u\n14SyAETUSyQSiaC9vV1UAkHaIwuI63AVFRWicg1EpMM7HA7YbDYm64n3ZGVliYBGiUoEhMNhdHZ2\norW1dYJeMjg4iKGhIchkMqSmprKAGskbAKJ7RA3D+fNEhjGvN9De8n3U+O/Td06ePMn4Le211+tl\nZXIGBwdRXl7OgCL8e6Voc3pmZ2enSPaRbqBUKlkD3f7+ftbDBIgHwnp6etjZlu4/yVu+BjcZpPye\nhcNhVFdXo7m5mRn7tBZ0LzMzMxnPkYJ6LBYLO398jxWaBw9coP0hYz41NRX9/f1YvHgxOjo6Jhj/\nR44cYY6GWCyefU29FcgGJBAKySSj0YhAIACr1Yquri7RWGlOlGVHGcE8P+PvNwXk6Cykp6djeHgY\nzc3NAOJ8gWrxS/mc2WwWyVO73S4qZwWAlZpLTk6ekJ1MTiip450vdcE/S1peRTpn4qEdHR1wu90M\nNep0OjE6OspkFZFKpUJ/f7+otCRf/tDj8TAQGu944tec7FgekUs8Tq1Wi/gr/RyJxBsx9/X1IRKJ\niPoUJnK08neY+DHZdUQ8jyU+JJPJ0NzcLJItY2NjUKlUEARhgkORqL+/f4LOolQqMTIyIgJd0uck\nk2KxGOu7mMjW4sdqMBhEyHaZLI5alp7nRM8gnYd+J73TtC80nilTpqC5uVlUvhoAk8O0XzyR3ka8\n3ul04syZM/B4PCL5azAYEAgEkJ6eDq/XC5PJJLIDIpGIyAY+deoUQqEQtFqtKGg0d+5cZuOQ/s7r\nmGRzE/EyWGoXEPHP4fWeI0eOiOzeUCjEdCee6DuhUAinTp0S6QCkk/t8PpGco7PmdrvZ3SeZc/bs\n2QnVBcgGIMQ8+TLIx8HvMVUQoL2lktQjIyPw+/3MD7V06VJ4PJ4JvSEUCnFfRTp//FmNRCI4ffo0\ns82kug1ltEp9KpRVSuOl9+3Zs0cELvzBD36ATz75JOEZpz0lX5b0bhL/lMq3o0ePQqPRoKamRuRr\nsVqtqK2tRW9vL6ZPn876svF91Wj85JOj80AyVcp7CBCen5+PxsbGhH09+PmT7kD/Hh4eZu8mGUNr\nY7FYWACO5B1fSmzPnj3o7OycoN+SD7OkpAQ9PT3M38mPBYjfaZPJhF27drH1JXCHlJ/wf8ffnWg0\nCpvNxoL79DckG4n4gDJ/b6RjamxsZOcnFoshOTkZPp8Po6OjInuUStVJ+zw2NTWhvb0d3d3dk7ZA\nGB8fF/kaaCwmk2mCXgkAy5cvR0NDA0wmE5PF/OcjIyOiOzM8PIxQKIT09HSmD1D/RjqrXq8XNptt\ngv1HZ1qhUMDtdovGQjootUWgtaD38XJfJpOJSknT3Zbq3uR7CgQCUKvV6O7unqCT8WQymRAOh9ln\nZI8Q8b45hULB7i6Nw2KxsHtNfnWSd/QdQRCg1WrZHPmy8XK5nN1Negcv64EL/hGZTIahoSF0dHTA\n5/PBYDAgPz8fNTU1uPPOOyfNopfSf1RQxWg0YsaMGcjLy8Pll18OnU6HNWvWoLu7G7/73e8wPj7O\nyiiQgq/X63HNNdegublZpADzzIqiZUlJSTh58iRmzZqFrq4utrnUhJ3KhUgv1vj4OBMEvPPA5/OJ\nHMh6vR6xWBy5TLXpeMePlNkAF+oDSw8vMVS1Ws0UD6kzgBimVHhIhRlPJACI6ZAzgFf0gPhlpTXy\n+XxwOp1wOp0IBoPs0PLre7E+I9I5S8vMkHOSnknzNBgMzCGQSIgqFApkZGRgZGSEKSR8gEaKoAES\nB2NCoRCroS+Xy1lAhdaSnsMbQ06nE7fddhtOnz49oeahdO1JMPDninfOk0JMgbnMzEzk5uZiYGBg\ngnOKUpX5c9XR0THB0cdTcnIyi74SEnPVqlXM0UtjkDpZiSi6L1WwpPOkAAk5DvkzTWtIzgdSsEjg\nm81mPPvsszhx4gSrda5SqfA///M/KC8vR1dXF0ZHR6HT6SYEJmk8KpWKCSxCAYfDYZHThzfUiBwO\nB7tH69atQ319vegdVIaNAgDkCKRnVlVVQS6Xo7u7GzKZDG63Gz/+8Y9x2223AYgbkbyjkpD2Q0ND\nouwAClDSd3hKSkpiay79bGBgAKWlpQzBwDsq6W/pztLv0tLSmAEjNT74taVzEY1GEwrOVatW4Y03\n3sD58+dhtVrZWvOKkCAIE/YNgEigk7Gr1+ths9lYKTeeyMnMoyeACxkGXq+XnSfe0QyI68kSn1Gr\n1aKgEpW4oO/xShGRTCbDM888g5qaGng8HnaPNRoN5s2bh7a2NtH8eOWUVzyJX1ksFuj1evz617/G\nwoULEQqFMDAwgJ6eHshkF1LNeWfrJ598whwqS5YsQW9vL8bGxnDZZZcxg5nnW4nkDk8qlYoBBIiH\n9vb2QqFQMNk42TNoXjQ+CqxSGTSPx4NnnnkGmzZtgslkwpkzZ0RGs91uRywWY45NOrfEKyeTY3SO\nU1JS2Dzz8/NFimFSUhI++ugjDA0NiZxO2dnZzPGnUsUb07/88ss4dOgQ+vv7kZaWBq1Wy8bEAx6k\nZ4GInOzklOQz6/x+PwRBQE5ODqLRKFauXInGxkZRtoZCocCZM2dEBg+veJP84O+qWq1m52hkZAQm\nkwnBYJDxgZycHKxbtw4nTpyA3+9HSkoKCgoKMDQ0JNoDCm7TXeHvrvQMSVGJ9G9BELBp0ybU1dVh\nbGwMkUhEFJSitUrkIE90TqdPnz7BiATid9LpdDKnOZUwueGGG3D+/HmWCUp7kpeXB7vdzlDv2dnZ\nyM/PR15eHnPcmUwmrFmzBqOjo+jq6hK9jzfqSB8jZBwBU6LRKHPe0xpSCSLeYUzP42XtZOd7MuKd\nBTT2UCgkyjoD4neanNBOp1MkAwltaTKZ4Pf7GZ8np4bL5RIZQqSnkYy8GGhCrVYjNzeXla6UrqFM\nJsP3v/99nD59Gj09Pejs7GTzIP15YGCAGaZVVVXw+XyMz8pkMjzyyCMIhUJob29n4Azam8WLF6Ox\nsZE59LxeL0KhEKtPPRkfJNlC5yY5OZndf5J7vJ7LO0pIttntduh0OianSd+hBveRSARr165Fd3c3\nOjo6kJOTI3IIA2JwE0/SQAUR8bWcnBzmuKW7RDwoGo0j8Xm9kT8ncrkcJSUlGB4eZo59epdKpYLZ\nbEZzczO0Wi0LmFI5B5LbUkctPxeSj2T4SoEOiYh4C/WbIP2Pz3IkXZ/4HXAhABoIBCY4hiij55ln\nnkEgEEBtbe2E+0fgBpKp5NSlMfM2Bf2e+AEQDyQQev2KK65Ad3c3BgcHodPpMG/ePHR1dU0AYEyf\nPh0+n29C9iW/FsR7eR6pVquxZs0adHZ2snOUyMk/2bile0DnhsAAVFOczgO/V1OnTkVBQQEUCgWb\nR1JSkghxTXrBZM1kydnD64X8WEhPT01NFe0xABHggw9US5+fnJzMbCbKRJTL5UzXueyyy2A0GkXn\nV0p0j8kJbbfbUVFRgYaGBhiNxgl3SnoPeF2cHHTk1JY6XifLXuSJAuYE+iE9ke7Dd77zHYyOjk7g\nLVJSKpWw2WxYsmQJFi9ejGPHjk2wBaxWK3PWU3CS5C5vq/BBf7PZjGAwCIfDgXA4DL1ej9raWjZu\nej5wwR4n244HXND3eFuSPuNr86vValaRxOVywWAwsEyEWCyeVcSX0qH3JtLp+PfyNFl/BtLB+Czf\njRs34ty5c2yPEvkvCKRKa0hVENRqNaqqqtDa2iqSMdKABmXuEEUiEZEPh767amkZH4YAACAASURB\nVNUqjI2NsWwa3o6w2+0MBCQFFNKYyUFvsVjwxhtvYNu2bSKZSzKAdGnykcVi8UbkicA1gUDgkvpL\n8H4sygo4cuQIC1rwNg4gDmKR/00K2KX1IR6oVqshl8uZz5B4iVwux+7du9HZ2cn0peLiYrS2toqq\nREj5KPE0aooNxG1tArH6/X60t7ejpaUF4+PjLGteGvDbsGEDFAoFC2TzwRGdTicCppnNZpYxT/1I\naZ68Duf3+9Hf38/uVEFBAWw2G7OZPB6PCOTA81Rqtk76FBGdKdLx5HI5srOzEYvFmNynvnY0N+L5\nGo0GDoeD+UZIN+ZlDWVi8DYmvTcWi2F0dJSVW9TpdMzfwxNliqalpcFms8Hr9bLzywMleVAhnTm1\nWo3Zs2ejsLAQbW1tIj1AWhmEygSeP38eWq2WBd8oQEJEOiX5GihoSzreNddcI/JR8wGJycjlcjH/\nFxE997777kNdXV1C0DuB1qX+OiL+MylPJF8M+fF4e5DmSetEJC3rTn7zYDCIrKwstqdKpRJz5sxh\n/S8pu27x4sUYHBxkwRIADJxF+jqv5yuVShYsTlSdgCd6ntfrZTbAnj17sG7dOpYVfCn0H9WoHohH\nQ+vr61laMJXZeu6559DQ0IDVq1dDEAQ89thj6Ovrw4IFC/DEE0/g8OHDePjhhxGNRpGXlwe32422\ntjYYDAZoNBrce++9ePfdd3Hw4EFWk3N8fByVlZVITk7Ge++9ByDOAKnBsiAIWLJkCZqamjB//nyU\nlpaioaEBbW1tcLlceOWVVzA+Po4nn3wSVqsVTz/9NKLRKBobG5kxVVRUhNOnT2NoaAhmsxkzZsxA\ndXU1gHg5l+bmZvz617/GtGnT4HQ6ceLECSxcuBBdXV1ISkrCbbfdBr/fjyeeeALp6eno6+tDT08P\nc3jNnTsXq1evRl9fH1566SV0dnbCarWiqKiIObXKy8uhUqkYQlomk+G9996DTCbDo48+Cq/XizVr\n1gAAVq9ejQ8//BDt7e2iC0rOC95pNWfOHFitVnzve9/Dgw8+iLa2NkybNg1FRUX49NNPoVDE6/0n\nJSXh7NmzCAQCmD9/PqZPn86UIELX+Hw+LFiwAF6vF8899xyUSiXWrFmDQCCAuro6zJkzBw0NDdi6\ndSsUCgVuv/12RKNRvPbaa6zMl1wuh8vlQnFxMQ4ePMgilED8EmdmZuKee+7BP/7xD+zYsQNA3Pmx\nYcMGLFu2DLt27cJf/vIXZvhkZWVh6tSpKC8vR2lpKR544AHIZDL88pe/xJQpUyCXy/HXv/4Vhw8f\nhkKhYM74yspKRCIR5lTo6OiAIAhwuVzIzMyEWq3G+fPn0d/fj5kzZyI1NRX79u1DS0sL9Ho9/vjH\nP+LEiRN4++230d/fjyuvvBIHDhxALBavRe1wOPDxxx8zxl1aWooPP/wQ3//+9yEIAk6cOIHm5mbm\n5A+FQigoKMBPfvIT+P1+5Ofno62tDZFIBE899RR8Ph/uv/9+PPvss/D5fPjlL3+Jp59+Gl6vFxkZ\nGairq2ONwQVBQEdHB775zW+iqKgIO3fuhN/vh16vZ+WOZs+ejfLycqxcuRIej4fNPRAI4IEHHsDm\nzZtZ4IoUnGAwKArU6HQ63HzzzYhGo3jggQfwz3/+ExUVFdDpdHj55ZfxzjvvIBwOQ61Wo6ysDI89\n9hieffZZVFZWwmg0YmBgAOfPn4dKpYLNZkNdXR1MJhMqKytx6NAhaDQa/OhHP4Lb7caZM2dgMBhQ\nUlKC3NxcHD16FOvXr4dCocCKFSuwYMECHD9+HA0NDYhGo7j88svR3NyMsbExOJ1OOBwO7Ny5E4cP\nH0YkEsG0adNw//33o6ioCP39/WhsbERtbS1eeuklqFQqXHXVVdi7dy+6u7sRi8VQVlYGn8+Hzs5O\nliVwww034L333mOBNqr7HIlEmDNMrVYjPz8ffr+fOaVmzJiBoaEh6PV6bNmyBV6vF1OmTMEvfvEL\nvPDCC/D5fEhKSsL111+P4uJi1NfX45FHHoFKpcKcOXPwzjvvQKlUorq6GsnJyejr64NMJkNpaSlM\nJhNefPFFeL1exmMrKysxMDDA7t2MGTPQ09OD7u5uOJ1OnD17Flu2bEF9fT1MJhPuuOMObN68mZVJ\nGRwcxP3334/du3ezc9Pc3IyOjg4UFxfj0UcfRSQSgcvlYkrF448/jsbGRtx+++2wWq3YvHkzRkdH\ncfLkSRw4cAD5+flYuHAh1Go17r33XpjNZjzwwAO47777YDKZsGDBAlRXV2PKlCm4//77sWfPHgSD\nQWRnZ6O0tBStra1oa2sTOUlJEZXL5bj//vtx9uxZVsIkGAzCbrdj+fLl2LlzJ8sMmzNnDkZGRtDZ\n2YmcnBx0dnbCaDRi+vTpqKmpQVVVFRwOB5KTk9HT04MdO3bg4MGD0Ol0mDp1KlatWoWmpiZ88MEH\nCIfDuP7667Fv3z5s27YNQLzMxKZNm3D48GFEo1FUVVVBpVLho48+AhBHuR8/fhy5ubno6elBX1+f\nCBRQVlaGe+65B21tbTh58iSGhobw0EMPYdeuXejp6cGcOXOwc+dOLF68GDk5Ofj000/ZfKiMREdH\nB0OXKhQKXHnllVi5ciXef/99dHd3s1JlFPwYGhpCKBTCxo0bIZPJsH79eoTDYfT09ODYsWNobGzE\nsmXLUFVVhUAggIcffhhKpRL33Xcfzpw5g8OHD2NwcBB+vx8FBQW45pprUFBQgLy8PDz++OPYtm0b\notEorr76amg0Gnz88ccoKChAdXU1/vKXv0CpVOKNN97AW2+9xRxggiBg/fr12L59O4qLi3HixAl8\n/PHHcLvd0Ol0WL16NcxmMx599FEEg0Hcd999OHToEHPQlJSUoLe3F2azGcuWLcPhw4fx5ptvMmXe\nZrPhwIEDDPVKQWo+C5DOllKpZAY3GVDXX389c+rOmjULSUlJaGpqYkpiTU0N63ezbds2vP766wDi\nGalNTU144403MGPGDOzfvx/79++Hw+FAbm4urrrqKkSjURw6dAj79u1DeXk5rFYr4zMUjFm3bh2W\nL1+O3bt3w2q1or+/Hy+99BJaW1tRWFiI9evXIzc3F+np6Th27BjuvvtukfNo3bp1SElJgcViwTvv\nvIPx8XE4HA6YzWZkZGRgeHgYFosFvb296O7uRlZWFhYsWACPx4NPPvkEXq8Xx48fR1lZGe677z78\n4Q9/gMfjQXV1Ndrb29HY2MjuK60p7xCQBm6pZN1TTz2FwsJChMNh9vePP/44hoaG4PV6cdlllyEl\nJQVbt27F2NgYUlJSWHkmn88Hn8+HwcFB5OfnIz09HcePH2eG6ebNm9HU1IQXX3yRZR8Gg0HodDoY\nDAZUV1cjPT0dXV1deO2111BZWYn09HS43W6MjIwgNTUVw8PDsNlsuPLKK6HVavHKK6/AZDLBbDaj\nsrISy5cvh9FoxLZt2/Czn/0MPp8PN954I2bMmIGWlhY0NTWhqqoKqamp6O7uxpNPPolwOIx169bh\nyJEjLA1+6dKlSElJwdtvv80cGQqFAj6fj/W9s9vtOHDgAGpqakRBZ6VSieHhYeh0OqxcuRKzZ89G\nMBjEn/70J9hsNrhcLjQ2NmL+/Pmoq6tDS0sLBgcHEQqFWOCY5Hg0GkV+fj5GRkbQ3t6OadOm4e67\n78bDDz+M1tZWpKSk4Prrr8dtt92G5uZmvP/++zhz5gzq6+uZI43WH4iXVUhJScHJkyfh8/mg0+nw\n2GOPYdq0aXjyySfx8ccfIyUlBWNjYxgaGkJ+fj7KysrY3WtpaYFOp8OpU6eQnZ2N3t5ejIyMIBAI\nsMw4lUqF9PR09PT0wGazwWAwQK1WY+HChairq0NaWhpWr16Nn//85xgbG8Nzzz0Ht9uN73//+xMy\nAdVqNcsis9vtDIl/qcQ7mCgDShAE5tyaPn06SktL8eabb0KhUOCWW25hpbfKysqQl5eHTz/9FPn5\n+diyZQs6Ozuh0+lQXl7OmklTr4R//etfSEtLw/DwMHp6ekToQCrBSqUDaT/IsZ2RkYHbb78d7e3t\n6OvrQ1ZWFgKBAI4cOQK3241ly5ZBrVbj5ZdfZutN502v16O/v5/t5dGjR9Hc3MwyUyhrmPSc8fFx\ndHV1IRgMQq/Xo6qqCmvXroVKpcJf//pXeDwezJs3D7m5ufjkk0/g8/mwYcMGXH/99czR+8orr+D1\n119He3s71qxZg7///e+sJvfx48fhcrlw55134vPPP4fX64XdbkdOTg7Onz+PmpoalJSUYPr06ait\nrUVtbS0CgQC6urowODgIl8uF119/HYODg7j11lvh9/tRVVUFj8cDj8eDa6+9FtFoFD09PVCpVKiu\nrkZNTQ3q6+uxadMmnD9/Hu+88w5sNhtMJhOSkpLQ2tqK9957Dw6HAzfeeCNCoRBqamqY08Zms6Gy\nspLpTDU1NVAqlfjhD3+IXbt24ciRIzAajbjjjjtQXl6Ou+++G4ODg6iurobRaITJZMLKlSshl8tR\nW1uLnTt3slJOer0ehYWFWL58Ofx+Pw4cOICPP/4YOTk5yMjIQHt7O/7+97/DZDJh8+bN+N3vfgdB\nEJCVlYXe3l58/vnnaG9vZxkQZrMZGo0mYcbbZETOXXIEabVazJw5E+FwmGXkDw0NoaqqCikpKdi1\naxcGBweh0Wgwf/58VhGDysA6nU5MmzYN2dnZSElJwb333ouWlhZoNBpkZ2fju9/9Lv785z+js7MT\nTqcT3/zmNzE+Po7t27cze6+3t5cFjXt7exkISxpAIceVXq/Hvffeiz//+c/o6+tDZmYmK6m4b98+\n+P1+3HrrrWhvb8fixYuRnZ2N559/Hj6fD7/4xS/Q0dGB66+/ngE6KECoUCgwb948+Hw+NDQ0MMcv\n9aAjR9mUKVNgMBhQVFSE8fFxmM1m1NXVYXR0FHPnzkVqaioeffRRDA8PY86cOYhGo7jpppswdepU\n1NTUIBgM4v3338dVV12Fs2fPIj09HU1NTXj77beZg5RHJDscDhac7O/vh8FgwIYNG2C321nZ0BMn\nTqCnp4c5TufOnQu73Y76+nq89dZbsNvt+M1vfoMXX3wR9fX1+OlPf4p9+/ahsbERXV1dOHfuHJPf\nkUiEgcAIgBCJRHDNNdcgNzcXzc3N+MY3vgGbzYbXXnsNqampmDVrFtrb2/Hqq68iEAhg3rx5uOuu\nu7Bt2zbs378fsVgM99xzDywWC/bv34/c3FzWH/Kdd96Bw+FAamoqTpw4gWAwiJSUFBw7dgw6nQ4z\nZsxAUVERlixZAplMhueffx7Hjh2DIAhYuHAhRkZGMDAwgOPHjyMzM5NllhK/vummmxAOhzEyMoID\nBw4gEomXxt6wYQNOnTqFuro69PT0YNasWejp6WH8kYB9JPu//e1vw+VyISMjA0VFRdi/fz9+//vf\ns/JjO3bsQGVlJes3Vl5ejjfeeAOLFi2CRqPBm2++CbfbjeTkZGg0GrS2trJy58QTrFYrysvLkZKS\nguPHj+O3v/0tNBoNnn76abzyyisoKirCyMgI2traYDKZmE/ugw8+YCAWYGLFg69CJLMUinjvm5/+\n9Keor6/H3//+d3R0dDBnvNlsZsEnpVLJ7kAsFu/V+8EHH6CzsxM+nw/p6enYsGED6urqWLnln/70\npzh37hy2b9+Of/3rX9DpdLDb7bj77rtRU1OD3//+94jFYigtLcVDDz2EkpIS7Nu3D5s3b4Zer0dp\naSnefvttyOVyZGRkMPAPlWrz+XwoLS2Fz+dDXV0dtFotSktLmT7v8XjQ39+P6667DsPDwzhz5gwc\nDge+973v4dprr4XX64XRaITNZkNnZycsFgtuvvlmeDweJCUl4b/+679w9OhR/OEPf4BarUZmZiZ2\n7NjBbCTKSHvyySeRmpqK0dFRPPTQQ9BoNMjJycG5c+eYPRMIBHDXXXdh4cKFWL9+Pa699lpMnz4d\nf/vb3/D222+zqjcEzKLnkb1WWVmJyy+/HAcPHsTQ0BALFFFmGV+FBACTIaFQCIFAAEVFRVizZg3S\n09Px9NNPo6GhAffddx9CoRBOnDgBm82G/v5+tLa2sn58drsd7e3t6OnpQVpaGmbNmgWDwYDTp0+j\nsLAQJSUlrBTo2rVrMTg4yPitUqlkfazlcjm+853vwOVyYevWrWhpacHMmTPhdDpx8uRJ2O12rF27\nFiUlJQiFQnj//fexZ88eZueQX1oQBCxatAiHDh0CADzxxBN4++23me9neHiY+c4fe+wxyGQy1NTU\n4M0330R3dzdMJhMKCwtx2WWXwel04ic/+Qn0ej1WrVqFHTt2oLq6GmazGQMDA8weJtD5j370I7z5\n5pvMh2exWFBdXY2cnBzk5ubiyJEj+Oijj5CWloaHHnoIe/fuhdFoxK5du/DZZ58xHZl0ObIV6T5q\ntVr88Ic/xPe+9z0cO3YMLpcLXV1daGpqwmuvvcb0J14fMJlMyM3NxdKlS9HQ0IDt27dDqVTiW9/6\nFhYuXIiZM2fiN7/5DbZs2SIKvPGl/YgHzJgxA+vWrcOWLVtw1VVXobKy8svxlP+UoIrf78cvf/lL\n7Nq1Cx6PRxRhBeJRYXKs2e12ZGdn4+zZs2hvb2eNcgkFpdPpREgFnijFiKKlBoOBbSpF50nYksJh\nMpmwevVq7N69G+fOnZugCCUiHp3Ho6sAsLqKFIk0GAwYHh4WBSzo72i8ZJRPtl1SBJNer2flz6gE\njVwuZ6nvFLXk0QTEwKxWK/bt24eKigqUlpbi5MmTDClGB5IXZCqVikXc/X4/NBoNysrKcO7cOcYc\nCYGSaPyEulWr1SguLobb7WblWqjUBPUDGR8fZynUlGlDTmVCqFEk2Ol0oq2tTYScczgcGB0dhVar\nRXt7O6xWK4twJkIJqdVqzJw5Ex0dHYxRqlQqlJeXY9GiRXjhhRfYOeM/p/RGg8EgypYwGo1QqVRw\nOBwwGAysBJQUCUYBIrvdziLfkUi8tAtFj6XnQRAEhpT54x//iM7OTuTl5aGsrAxtbW0Ih8O48847\nsXXrVuzduxezZs3Cgw8+CLfbjV/96lcslTg7OxslJSUYHx/HZ599xvrPEBqTzgE5+kgBfuutt9DU\n1MQyyAhprFQqkZaWxpqWDgwMoKCggKU7NjY2sjJYNB+FIt6QbGRkhDVHJoQylUWjcQDxDB6KeEci\nESQnJyMSicBoNKKgoADvv/++KMOK0CeCIMDtdiMSiTBkld/vZ2gGajxNqF/eyaHVahnyorCwEHV1\ndZg3bx7OnTvHkDiEpklLS4PD4YDL5YLf78eZM2cwMDCAjRs3YunSpQgGg3j88ceZkMzKysLtt9+O\n3bt347333kNhYSEGBgYYEpDQAeQwpH8D8Qwvi8WCvr4+UWkGQp/wRhbdY1LQKGVULpcjMzMT1157\nLT766CM0NTUxBNCsWbOQnZ2Nbdu2wePxIDk5GQUFBXj66aeh1+uxadMmuFwupKWl4f3338fw8DBD\nIMhkMrY2Uh7Cz0HK1wgFwWebELKSsgnIQccjUAiJG4tdSP8n5zUpYAaDAX6/H1lZWRgaGhKt8cWI\nUE6J0JhUk12aaUfnurS0FA6HA83NzWhqappQf1apVCIjIwMWiwVdXV1wu90M3ejz+UQpvSqVit3P\nRJl4Wq0WOp1uAsqIX2NCdKWkpKC4uJiVcsvLy8PChQvx6quvoq2tTWTU0Fip6bbH42FGB+1BcnIy\n3G43U2r5vaL91mq1DG0VjcabGxOSlCeSoxkZGdBqtQxBSwY6yXzitTxqhUfP8gEMHm3Ho2ul66NW\nq2Gz2TB//nwcPnyYySV+bPTOiooKOJ1OyOXxOucGgwFbtmzBsmXLkJubi08//RRHjx4FAFa+RyaL\nl80qLCzEgQMHmHN1bGyMKdCjo6M4ffo046OhUIihpQhxlJGRgXA4jN7eXpblSo5myugkR2s0GmWB\nXV6xdDgcGBkZQTAYRHFxMbRaLSsFqNfrsWLFCuzevRtHjhxh6G/KUCSnhcfjYftAPNdms0EQBPYZ\n3QvK1OTL7NFzCTlIqNsVK1agra0N7e3t7Oy53W6sXbsWL774IpMN4+PjqKioEJVy5RGNer0eTqcT\n8+fPx4cffgiv14vU1FSMjY0hGIz3Guvv7xfpX0AcUUuNwgkMYrPZ0NzcDKUy3gx+cHAQeXl5rAkn\nT4Twnwy9TXyaz4pMS0vDHXfcgZqaGlZ+lkAqhFamWu8pKSmIRCJMh6bn8GnvdHcuxt8IjEBZHllZ\nWejr64Pb7WZ3nC8xQfckUfkEns+TbmC329HW1gan0wm1Wo1ly5Zh//79rDwJlXiRy+WizAm6Mzz/\nyMzMhEqlYkFLqQ6Xl5fHGsyTHkEI7ZaWFuzevZutB8k+aSlKei/tEV9GiM4L6StkTBsMBtjtdsyY\nMQOVlZXIzc1FUlISnnrqKezZs4fJmURIdKvVitHRUVbOIzs7G+fPn5+Q6UHj4rM3pCVjSS+iNV21\nahU8Hg8OHz7Mslmk4yAEvDTDyWKxwO/3o6SkBGlpaYjFYsjLy8O7776L0tJS1NXVMRlsMpnQ09Mj\nAmcRT5LJZGz+drsdCxYswO7du9HW1gaz2Yw777wTt9xyC7Zt24aXX36ZoROdTif0ej3279+PKVOm\nMCAIn8nw7LPPorm5GVu3bkVHRwei0XjpCKvViqGhIQYCcjqdMJvNaGtrY2WJeF4ok8VLfNhsNqhU\nKrjdbmY30r2ivSA+zpduJf7PB5yk8pPWhO6T2WyGzWZDW1sbq3FP9gHxxNzcXEyZMgWnTp3CwMAA\nbr75ZtTW1uLQoUPsXeQsIGQ42bl8GQzivzwfpvnL5XJWBoZKNJMORtlR0r5YPGm1WrhcLhbo5LPb\n+Ey77OxsBAIBjI6OwmAwIDc3F1dffTUyMzNZiV+1Wo277roLycnJaGpqwujoKG699Va8+uqrCd/9\nRURNwEnX4889BYcEQcD111+Pw4cPo6qqCiMjIzh06BAbU3t7O5NPxF8zMzOxZs0aPPfccxgcHGS2\n3/DwMORyOa6++mosXrwYXV1dDJxENjPtCfUpITltsVhYqceysjKMj4+joaFB5BSy2+0QBAF9fX0M\n/U4lD41GIxYtWoT333+fveu6667D7t27WXnxRDa52Wxm/JX0btq7i8kNKrtMCGutVsucbeFwGB98\n8AEWLVqEAwcOsIoeRqMR4+PjrGpGX18fkpKSRGc/HA6zYHckEkF6ejrC4TBuuOEGfOtb30JtbS3+\n9Kc/IRKJ911MSkrC4sWLcfr0abz77rtwu90YGBiYUP4HiKPUySdEJZwBsP4fvb29jBcDF/pupaWl\n4ZFHHsG7776LnTt3YnR0VGST2+12UVkoQJxFIiW73Y6SkhJmf1IZOQpQOBwODAwMoKGhgWVtxmIx\nOJ1OFBQUoKurCzk5ObDZbOju7mYl2yKRiEi+kU5FmUOJ9p/X0Sfbb4Xi/3D33tFxnmX68DV9NJoq\naUa9W5bkJlu2lbgkdmycYKcXpwfbKQ4s4IQsBBI4bODAJsAhbBbYZQkLCWxgWZawyRKHDaSQ5jhx\nbCfuTbYlWVYd9RlpNOX7Q3vdet533pFkw+7y++5zfDSeectT7ufuxYJgMAi/348zZ86IHY0ZdaSD\nRvP1eDxwOBwik3J92FuPvIY2sMsvvxyrVq3Cz372M4yMjIhj6dlnn0U8HsemTZvw7//+7+jt7U3L\n/GD5xUyQlZWFQCCA4eFh5OXlITc3F729vejt7RU9RJXjcnNzpRqG2WyWfkiRSCSt4guQ3vuhuroa\nl1xyCZ566imh65QXSVdZ3o/7w3XgWSD4fD6sW7cOv//976VkMsu7U95jBYahoSHk5eWhuLhYnDLx\neFxooMrDaPhmpiiz/dxuN5xOpzg+x8bGcPbsWQkg454ya57yEO2yACS7xGKxIBKJwG63w+/3S/sC\nAvkd9Qrqj52dnWhqasIPfvADvPbaa3j88cdlPqxOMXfuXPT09CAcDme0m9rtdumrzZJPF154IS67\n7DK88cYb2LFjBwYGBlBWVqYpWxgIBOD3+yUzXi2dmJWVJf1+e3t7Rfa12WxSBjUajabJl8Qjt9uN\nhQsXoqioCB988AFOnz4Nn8+H4eFhKdvF/VT7RHK9VB6ulh1VbdEcK6tg0D5JubWoqEgy/FOpFP7r\nv/4Ll1xyCcLhMA4ePCj2nExAuYnBLY2NjSgtLcVvfvMbnDp1CjabDXPnzkVbWxui0ShGRkZEHyeu\ncA7Z2dmorKxEVlYW9uzZI2XzvF6vlG1ntp1aUYLjCwaDaGxsxJIlS/D9739fKjecPn0azc3NSCQS\nmD9/PhKJBDo7OxGLxdDY2Ij169fjsssuM+wRPR38xThVPv/5z2PHjh0YGhqC3W4XQSKTEX66YVN5\n09ehVSPjjYBplf8by6IKtqrBBzBO+WU0sL602f/UWEnEmUaojtfv92dMVweMlbtzBR4yfaoenT9q\nqR0SLzW1GoAIE+o6UbkCJhiq3vAxU+BeVVZWore3V5jaTCMnyHioqKh9a84H9IbnYDCoqZXKMZFh\nBwIB1NfX48033zznSA+j8l/nco+aLk5jPT3hgHGZGEYypVITqcX6Ui0884xIUCM3MzFVIyFbfd5M\n6MxURiS+R01lVuf60Y9+VCIUmpubNWnRNDaS6ejTefnec8EZfemsTMCG0plAP0fSJqfTqSnXohoJ\njUC/flPRv5nQOUbDngs+TzdXYEJ45fkmGOGXOk4276Yxiw5nGj6IB0b3ApAymBS+/zeBUaI0yOt5\nlBHOZ1LQZwLnysdU4ZFOCRo46dQG0usnT/Ue9bfpaLgR3hqVGqDzTn9u+dfhcODhhx+WjEGbzQaP\nxyPNyMmbzjcaj3SfDlM6Y9RnGtHcTOuk7vP/pOxhBGoDTtV4yIh8veJMGWTr1q148skn4fF4EI1G\nRSac6R7r50mHMx07ND6zHITKz/6cwH2iwy0T3cg0j3MFlf8YPetcnqvK7dKD8QAAIABJREFUH9wz\nrlFJSQl6e3s1vZj4fCBziaiZAveZ/N7IMEgjE88GS7sxoMRIRnS5XJK9Tb2Ba6Ifs9Vq1QQa6cem\nB/0686/L5TLUaVQnsv5+/f9ZzlRvoDkfmGr87MVo5KCaDpjpz0hv9Xu1tGCmd3OvKftXVVXh1KlT\n5yyz/jlpHB1VNHROZcxUSyDre2JyXJQJc3JyJJMoE9BRUlRUhPb29in3Xe0jlwn4fn1J2pmulUpb\nCwsLxbmvgppxAmidh3r9g4ZQdb52ux2BQEB60c0U9PNnlHEkEpkRLtPJojo67XY7gsEgUqmUZPdy\n/pRd9PPVw1RnTX2fCmqfADoveJ7+FF2T7wWm3nPivErz7Ha7yNNjY2NStueSSy7BK6+8YjgmVVbh\nGtC5wRIveh49HV3NBDPRB/TXm81mqRwxk3dMJXsw8I08XsVzOtkcDocGF8+HTjmdTjFmzqQc1/kA\nx5WTk5PW+yjTeDOtDXsKskya0+nEddddh6eeekoM9EbPIN2g7a+8vDytib0qA9POQFsb8U6VWTLB\nTOR0vU5MnZUlSqeyR+mfr3+WkQwwlSxAvsC+eipvzaTP6enjueKe3lY31XUANOee39Hw//rrrwMw\npkEq7TuXMVJ2YR9NI1lOpe1T7fdM8CFTYM1U9uA/Fc4Xjzmu6XiHOm4G8+tLqavAoOje3t6MjnvV\nZkIdSJV9p7IzkW7m5eUhEAjg5MmTGBsbkwALZjkzOLC8vByVlZV46KGHUFhYmHHcRmCe/pL/HfjD\nH/4g9cR/8pOfSFRCTk5O2rUqsumBRsNUKoX6+vq0xrAkFEuWLEm7l17O6RB41apVGqMwMEkACF6v\nV67xeDyGz2Gtb46L3sovfOELcDqdaXNm3URgZgLNTBrr8Dn68QPp0eMkhAA0kTIsQ6JCMpnMKJjr\n14MebD2QIOqNpPpoSEYJqfMYGRkRj3kgEEBBQYH8rj5rKgaWnZ2d8TdgEpeam5s13lujtVSfR0Ga\nQqUaqXHxxRfL9RR6pgPioNp3B5ioO6w2d+R4h4aGEI9PNOV9/fXXZT1CoRCWL18+7fv4PArzMwEK\nDASOhYSZ486kKJNgRqNROBwOhMNhQ+GCPVXmzJkjZyrTGaERx+/3G9ITPY4xqiHTNQT9+PX9QdS5\nbt++Ha2trdi7d68mg4PjpkFLPYd8L8/NuShGrPs5HfA8qaCuEedITz6jHdgDgUrJVEoi56FCeXm5\n4JSK+5n2kGU4CLFYDAsWLJhybnrQK1CFhYWaNWKkUm5uruY6jlMdm/o5Go1KGr3X6wUwsa6q4Y3X\nFxYWoqSkRPP85uZm/PKXv8w4dyO+wvcAk8Ylgv75ANLmRIjH4+jt7dUYf4zGofZ8KS8vN3zWVED8\n0T87Pz9/SvrL65PJpETTch9pTEsmk2m4PlXkiTqGuXPnan7LysrKeC0wgQuBQCBtzPrsNv1assSZ\nOnYqnypvYhQWgLQ5mUymjDIG94+ZgBRIVcH1O9/5Ttr9Rk4zVRkyuuZ8QL+uQPr8CIziU/tacG0z\nNfxMJpP47W9/C6/Xi56eHtxzzz0yn+mUHf1eqUoeDbRqTwA1cowlrQjk+VOB+jtLCXDd+Tt5YCaa\nowfK0QSVX/v9fvlM44p+/qpDRf+uTO+tqalJm48qf/A+8kFGrOl5hXqtXg+gPAeky2lUvrhulKUz\nNb7nWFQ+ypJmqVRK8y4VWFYDmMyOC4VCSKVSGvmfz//www/T8JSGAr0MrV9n4rLqUCFOqVkzBD2d\nU/WJ0dFRVFRUGDoYjHA003nkmI1oaiqVkt5NwWDQ8P6pIJFIaMpV8l0cs1HghMqP1Z4JbrdbMpUA\nYNasWVO+W38ujGA63cDoPlYIyORQYe8eVYYfHh42pJGkfcxKpKybCahjMGPdCDhvZvtPBXw/M8NV\n+jYTvdNisUgZTDpU8vPzNdfoM2HUcevXT9803mq1orCwEMXFxQC0eM2ynQT9XjY0NGj+39PTg56e\nHkOHipHuqzrFgMk+pq2trWhra5P580yTp06V+ePxeDL+NpVzVDUKplIpjX6p2lH4N9O+G/Ev1TGU\nSf5Qq2Lw/lgspsmiZ4bHyZMnJRJbD5x7MplEaWmpGI+ZhaHHh0xrVVVVJTQzk7GM2eszhVgshtHR\nUQnUTKVSKCoqmvKeqZ7PMl61tbVpNJpZXyouMlv1XGF0dFQyBQiUOwAtbt94442yf+eyNtwX9Z6c\nnBxD+nfZZZcBmOwBpqdnLP/M7Iru7m786Ec/MnTcG9GNVColwRJ6nq7aHVKpiYxCZlmocuZ0QQiZ\n8E5dy0QiIWtOwz95u5EcS4ed0fP1MhN7kBAq/ru/CUFvwCdfUDMoE4kELrjggozynVFvVHV+Kg3R\n21/VOajPp25qMplw9dVXp41VPf/J5EQ5zNdff10M6vr3AhBdBwAqKytljOzXnQkou2RyrOqzqggq\nn6bM5HQ6DfVuFdRxq/Zr/j0fPWs6u6GKx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SOOYLPZMDQ0hI0bN2oCqvRZG+SXpM2XXnqp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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fitting our model.\n", + "cluster_treeA = linkage(z_vClusterA, 'ward')\n", + "\n", + "# Ploting our dendrogram.\n", + "colors = ['g', 'b', 'r', 'p', 'y', 'b', 'w']\n", + "plt.figure(figsize=(25, 10))\n", + "plt.title('Hierarchical Clustering Dendrogram A')\n", + "plt.xlabel('Sources')\n", + "plt.ylabel('Distance')\n", + "dendrogram(cluster_treeA, leaf_rotation=80., leaf_font_size=14., color_threshold=4.5)\n", + "plt.show()\n", + "\n", + "\n", + "####\n", + "\n", + "# Fitting our model.\n", + "cluster_treeB = linkage(z_vClusterB, 'ward')\n", + "\n", + "# Ploting our dendrogram.\n", + "colors = ['g', 'b', 'r', 'p', 'y', 'b', 'w']\n", + "plt.figure(figsize=(25, 10))\n", + "plt.title('Hierarchical Clustering Dendrogram B')\n", + "plt.xlabel('Sources')\n", + "plt.ylabel('Distance')\n", + "dendrogram(cluster_treeB, leaf_rotation=80., leaf_font_size=14., color_threshold=4.5)\n", + "plt.show()\n", + "\n", + "\n", + "####\n", + "\n", + "# Fitting our model.\n", + "cluster_treeC = linkage(z_vClusterC, 'ward')\n", + "\n", + "# Ploting our dendrogram.\n", + "colors = ['g', 'b', 'r', 'p', 'y', 'b', 'w']\n", + "plt.figure(figsize=(25, 10))\n", + "plt.title('Hierarchical Clustering Dendrogram C')\n", + "plt.xlabel('Sources')\n", + "plt.ylabel('Distance')\n", + "dendrogram(cluster_treeC, leaf_rotation=80., leaf_font_size=14., color_threshold=4.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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pUAWJyWTi119/dd7+888/tbtGRERKrNhYC8OGlcNqNQEm9uzxYdiwcipK3KhQ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\nkVJTUykgIIAqKiqoqqqKhg8fTlu2bCGRSETDhg2jsrIyIiI6ePAgLV6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JSSEgIIC6detiNpt5+OGHmTVrVo42AwICeOqpp3jssce44YYb6NixY76WMXnyZKZMmeL8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\nf39Z2tJWrnkZWq2WoKAgSkpKaNy4MUajUbrZV69enYCAAFq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Qodq/TSTQFamQpKWlMWXKFDp06EDnzp2ZO3cuO3bs8HU2EZECYmMdxMfnEBOT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\nwsJK577oQp3pog3p1jrwH0TF5aQGDRrg6uqKq6ur9JouGFF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fancy_dendrogram(\n", + " cluster_treeA,\n", + " truncate_mode='lastp',\n", + " p=12,\n", + " leaf_rotation=80.,\n", + " leaf_font_size=14.,\n", + " show_contracted=True,\n", + " annotate_above=5, # useful in small plots so annotations don't overlap\n", + " color_threshold=4.5, \n", + ")\n", + "\n", + "plt.show()\n", + "\n", + "###\n", + "\n", + "fancy_dendrogram(\n", + " cluster_treeB,\n", + " truncate_mode='lastp',\n", + " p=12,\n", + " leaf_rotation=80.,\n", + " leaf_font_size=14.,\n", + " show_contracted=True,\n", + " annotate_above=5, # useful in small plots so annotations don't overlap\n", + " color_threshold=4.5, \n", + ")\n", + "\n", + "plt.show()\n", + "\n", + "###\n", + "\n", + "fancy_dendrogram(\n", + " cluster_treeC,\n", + " truncate_mode='lastp',\n", + " p=12,\n", + " leaf_rotation=80.,\n", + " leaf_font_size=14.,\n", + " show_contracted=True,\n", + " annotate_above=5, # useful in small plots so annotations don't overlap\n", + " color_threshold=4.5, \n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Cutting the trees to obtain the clusters within every questionnaire vertical

" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([12, 8, 5, ..., 8, 4, 3], dtype=int32)" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_versionsA = cluster_A\n", + "\n", + "max_distanceA = 9\n", + "clustersVA = fcluster(cluster_treeA, max_distanceA, criterion='distance')\n", + "clustersVA\n", + "\n", + "###\n", + "\n", + "cluster_versionsB = cluster_B\n", + "\n", + "max_distanceB = 9\n", + "clustersVB = fcluster(cluster_treeB, max_distanceB, criterion='distance')\n", + "clustersVB\n", + "\n", + "###\n", + "\n", + "cluster_versionsC = cluster_C\n", + "\n", + "max_distanceC = 11\n", + "clustersVC = fcluster(cluster_treeC, max_distanceC, criterion='distance')\n", + "clustersVC" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cuestionarioFinalizadodevice_pixel_ratiodevice_screen_heightviewport_heightviewport_widthos_Androidcluster
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cuestionarioFinalizadodevice_pixel_ratiodevice_screen_heightdevice_screen_widthtablet_or_mobileviewport_heightviewport_widthos_Windowscluster
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" + ], + "text/plain": [ + " cuestionarioFinalizado device_pixel_ratio device_screen_height \\\n", + "0 True 2 800 \n", + "7 True 1 768 \n", + "22 True 1 1080 \n", + "27 False 2 667 \n", + "28 False 1 1080 \n", + "\n", + " device_screen_width tablet_or_mobile viewport_height viewport_width \\\n", + "0 1280 False 628 1276 \n", + "7 1366 False 662 1366 \n", + "22 1920 False 950 1920 \n", + "27 375 True 559 375 \n", + "28 1920 False 950 1920 \n", + "\n", + " os_Windows cluster \n", + "0 0 12 \n", + "7 1 8 \n", + "22 1 5 \n", + "27 0 1 \n", + "28 1 5 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cluster_versionsA['cluster'] = clustersVA\n", + "display(cluster_versionsA.head())\n", + "\n", + "cluster_versionsB['cluster'] = clustersVB\n", + "display(cluster_versionsB.head())\n", + "\n", + "cluster_versionsC['cluster'] = clustersVC\n", + "display(cluster_versionsC.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters in vertical A: 13\n", + "Number of clusters in vertical B: 12\n", + "Number of clusters in vertical C: 12\n" + ] + } + ], + "source": [ + "print \"Number of clusters in vertical A: \"+ str(len(cluster_versionsA.cluster.unique()))\n", + "\n", + "print \"Number of clusters in vertical B: \"+ str(len(cluster_versionsB.cluster.unique()))\n", + "\n", + "print \"Number of clusters in vertical C: \"+ str(len(cluster_versionsC.cluster.unique()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Number of users in each cluster

" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9 434\n", + "2 154\n", + "3 107\n", + "4 79\n", + "12 38\n", + "8 35\n", + "7 35\n", + "11 26\n", + "5 26\n", + "10 21\n", + "13 20\n", + "6 11\n", + "1 7\n", + "Name: cluster, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "5 591\n", + "6 231\n", + "10 159\n", + "11 130\n", + "2 65\n", + "4 62\n", + "8 58\n", + "3 56\n", + "1 22\n", + "9 17\n", + "7 7\n", + "12 5\n", + "Name: cluster, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "8 395\n", + "5 159\n", + "1 102\n", + "9 87\n", + "4 80\n", + "3 80\n", + "2 67\n", + "12 33\n", + "7 33\n", + "11 15\n", + "6 7\n", + "10 2\n", + "Name: cluster, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(cluster_versionsA['cluster'].value_counts())\n", + "display(cluster_versionsB['cluster'].value_counts())\n", + "display(cluster_versionsC['cluster'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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dnwsTRA+e0WhwSz3urIsxebaejro8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h+/bt6O7uxqxZs7B69WokJSWFIr6Q02Zl92tnSRNIH9+e7sA9OwwADEA1sDK+A0W8+QRJqP3r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\n1kRERERENAoJiE1NHXK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colorsA = [\"#3366cc\", \"#dc3912\", \"#ff9900\", \"#109618\", \"#990099\", \"#0099c6\", \"#dd4477\", \"#66aa00\", \"#b82e2e\", \"#316395\", \"#994499\", \"#22aa99\", \"#aaaa11\", \"#6633cc\", \"#e67300\", \"#8b0707\", \"#651067\", \"#329262\", \"#5574a6\", \"#3b3eac\"]\n", + "\n", + "figA = sns.pairplot(cluster_versionsA, hue='cluster', palette=colorsA, vars=columnas_vCA)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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jsQZxMSqHMQOjlQ45+FqN+FocMWP5es0601kt9zR2+1pVB6ntP8dpByFIGmSvXQAYFK0E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\n4f7776ekpITFixdjt7dMEG6z2YiIiMBoNGK1Wtstd0dsrKn7jTqRf8T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VQDuOPa2n6enE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88fVOOGK5kdTgjRVxIvhBBiaEmc9Q55H8WgZd7S0mW9vgBiL4Sx3x10lW+99Rbl5eXcfvvtaDQa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\nUUxzTTUOqxWcTpqbbH6/Bxlsb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4+OLki5BVBQaSkgY9SFZMZ8d9B1NjQ0oNfrATAYDDQ3\nN+N0DqxjvCS1HeirtqHd0TLNvhYg501MUX7u8qiLwHHpj4kJ4H72InC1P6dV0PmcPnt+KQEHECnn\nmRBCAKBv2IXmyKqWQhm4YlMw6WQYhBiG5FoyoAVk/tFBqDqCS8bf49U6V6xYwZo1a1i2bBkOh4Of\n//znaLXa3nfsgiS1HQTV5XUuB9hJJUR/yDkthBADI/FTCOEL52qsCQsL44UXXvBKXZLUdtBsnEa1\n4jbM5Q70scGEhU/zd5Pci79XFuUTlDoBR841oPTeWopiZHOETaL9XHKO8IndbjusOJ1Yv/6ShsJj\nhKSlo5t5HigCq6uOEGJ4G1bxU2Ki6InNRuP7W8gvKiY0NRnt966GIEkDAoXDOLlDrJnkt7YMV3I2\nd1BZqOPQ81+4yxPGLkTn58kGgz75BzXPrnaXI1wuHLlL/NgiMZxYD5mxK29Apa3H3hhG80ELXOjv\nVg2e9esvObT6Xnd5wuNPoss+v+uNz17sFZwoQpWUIhd7ogcu+rqubduatp23t9vt4LFghgJvrYEr\nfGc4xc+OMXHcqlU01dRKgisAaPzgLQ7+rm1Fk4ko0V7q4xVGRLfMkZdAzptoLYdoDJ2AOUqWLuwv\nSWo7MO/b16msy57rp9a0sBcd6lRW5vqpMWLYsR3dT/W/3nGX9VdEobzwGj+2yDsaCo91KneX1PYr\nARbnvG//tIuGqoYetwmJCmH6ref1afv224rhZTjFz44xseaLL6jcvh2QmCfAVHyyQ7mEgY1cFEPB\nhRJT1AK0mYswyZjnAZGktgN1hLHHsj+oUj27O6lSJuDwU1vE8DNSz5+QNM/lCUJS07vZsn8JsBCV\n+ZWYy8w9bqMfo+/z9u23FcPLcIqfHWNikE7n/r/EPKGOGtWhLGsei5FFktoO1MmpxC5YgMNqJUin\nQ52U2vtOQ8yRc01Ll+OifIJSMnFcJN1FRN+1nj/2okMtF2Qj5PzRzTyPCY8/2TJ+LDUdXVb3d8L6\nkwALIUSr4RQ/28dETYSRwvXr3c9JzBPq9HGe17fp4/zdJCG8SpLaDnTTZ4HDif1EEarEFHQzZvm7\nSaAMwpG7hOhrZRp2MQBnzx9lLgF7h2FAFEp02ef36e5D68We+3PdQwIshBBuwyl+to+JLicZEaP6\n9KOfODfoZswGp6vd9a2cE2JkkaS2o7NfComXfk8SSCFGCvlciyHjQj+65+7FLc+7kImihM/040c/\ncY6Q70ERgOx2Ow888ADHjx9HpVKxdu1aMjMzB1SXJLWi35ppZmvpZg4fPEKmcTxXxF2FElliyJda\n/wb5NYeZEJHJFXFX+btJQpyzQm85hdJe1+3zOlW4D1sjAklXsVq+L4UQHQ2Ha2uX00nVl19iLS0l\nfOJEwiZMGHSdb775JhqNhk2bNlFUVMTPf/5zNm/ePKC6JKkV/ba1dDOrP3/AXXZd4OKquGv92KJz\nT1d/g9ujV/ixRUL4Uudld0wmE55L6LQa+ruj/1v1XSqszd0+P1oXzNToIW+GCEDyfSmE6IvhECtO\nb9vGvtWrweUiOCyMWS+9NOjE9tixY+Tk5ACQkpJCeXk5ZrMZvb7/EyxKUiv6Lb/mcOdynJ8ac47q\n8m8gxDmkZnc5joaeRzkGhQQRMSt2iFuiYGuJiQKzrdst0vRq1mQOdTtEIJLvSyFEXwyHWFGzZw+4\nWn5Qbq6vp+7gwUEntRMmTODjjz/m4osv5ttvv6WmpoaGhgZJaoVvTIjw7OueGTHeTy05d8nfQJzb\nXOQ/nY/5eC/L7iTpOX9TDDKWVfiLxGohRF8Mh1ihS0hoKygU6MaMGXSdV199NQUFBVx33XXMmDGD\n5ORkjMaBLacqSa3otyvirsJ1gYvDtUcYbxzHwrir/d2kc07r3yC/5jCZEePlbyDOOf973zbKGk73\nuM2YkFjO56J+1tz7xE8gkz+JvpFYLYToi+FwbT32iisAsJaUEJGVRdScOYOuc9++fcyZM4c1a9Zw\n4MAB9u3bh1qtHlBdktSKflMSxFVx1xI9TZYY8pfWv0GgdU0RwjcU7K7cQ5GpuMetUgzJDCTp/HKc\nmsrR2h63iTaqkXllRW8kVgsh+mI4XFurw8NJueEGr9aZkpLC3XffzYsvvohGo+HRRx8dcF2S1Aoh\nhBBuCvYXVnLyTM8XFfGjDMhdWiGEEGLgjEYjr776qlfqUnqlFiGEEEIIIYQQwg8kqRVCCCGEEEII\nMWxJ92MhhBDDjIsEfXyvW7VsI5M5CSGEEP+fvTuPi+q89wf+mWFmmGFWlmFHEAQXxA1cUhoiNiba\nNCZpahON2pjUxt7YJvXXxLhkMZs2jU3TRltTm/RGvS69N4tJvK3XNtEsJlqSasQdRBCUdRiYYTaY\n8/sDGRhwGJDZwM/79fIlz5lznvPM8JzD853zLEMdg1oiIhp0divDIRYpet3HGRGOpgCVh4iIiIKH\nQS0REQ0yIkhqv0CYqaTXvdpUGeBTWiIioqGPY2qJiIiIiIho0GJQS0RERERERAF39OhRLFy4EABQ\nXl6O+fPnY8GCBVi7dm2/8mFQS0RERERERB45nQIOFZfj7YPFOFVe65M8t2zZgjVr1sDhcAAA1q1b\nh+XLl2Pbtm1wOp3Yv39/n/PimFoiIhpkBDhVqV73at+Hsx8TEREN1P5/ncPKP/0fBAFQR8iw+Zd3\nYNQw/YDyTE1NxcaNG/H4448DAIqLi5GXlwcAKCgowOeff46bb765T3kxqCUiokGnumgE2urVve4T\nFh2H8BsCVCAiIqIhrOhsFQSh/efmFjtOlNUOOKidOXMmKisrXWmh4wQAlEolmpub+5wXg1oiIhpk\nRHCcKIKz8nyvezmThiOcT2mJiIgGLFmvdf0sEgGJ0Sqfn0Ms7hwZazabodFo+nwsg1oiIiIXAQl9\n+EPdvg+7NhMR0fVhTv4oiABU1BoxcUQipoxO8fk5xowZgyNHjmDy5Mk4ePAgpk2b1udjQzqoFQQB\nzzzzDE6fPg2ZTIYXXngBKSm+/wCJiGgoE6BK7T1QbX+9vdvT7beeRou9odf9I2RRAGb4qHxERESh\nTauUY8EtE/x6jhUrVuDJJ5+Ew+FARkYGZs2a1edjQzqo3b9/P+x2O3bu3ImjR49i3bp12LRpU7CL\nRUREg4z5ifEwW5weXxcpOrs8/dP4fdRYWnvNL1YhwdQEnxWPiIjoupSUlISdO3cCANLS0rB169Zr\nyiekg9qioiLceOONAIDx48fj+PHjQS4RERH5g9NuhqQPi8w5JQoAQFhcstd92/dp7yK862MjLtY5\nPO6bHCPFzRMiAQg4arDigsnzvgCQqpJ6L+xVCeh4Ity7jm7Nfd1X5DVvs9kMwNll//7oT7nZJZuI\niAIrpINak8kEtbpzdkuJRAKn0+k2iJiIiAY/i8OEMFmYl70EyNEe1L5d8BsYTL0/TY1USXDflZ/j\no3oPQru+vvbTJlhrrb3uL9fLgSszK3sLcLu/XtRogqXV81NjhUSMXF373z7hZAkEq+cAWySXQjR6\nhCvd2/6mHvt7LoM78TWVhYiIKFBEQte5k0PM+vXrMWHCBFd/6unTp+Pjjz8ObqGIiIiIiIgoZIT0\nI89JkybhwIEDAIB///vfyMrKCnKJiIiIiIiIKJSE9JParrMfA8C6deswfPjwIJeKiIiIiIiIQkVI\nB7VEREREREREvQnp7sdEREREREQ0NB09ehQLFy5027Zu3Trs2rWrX/mE9OzHREREREREFFyCU0Dl\n15VovtwM/Ug9YkbEDDjPLVu24L333oNSqQQANDQ0YMWKFbhw4QLS09P7lRef1BIREREREZFHpQdL\nsXflXnzy20/wwWMfoO5s3YDzTE1NxcaNG13plpYW/OxnP8OcOXP6nReDWiIiIiIiIvLo0jeXgCsz\nMdlNdtSeqR1wnjNnzkRYWOca9cnJyRg3btw15cWgloiIiIiIiDxSJ6g7EyJAHaf2vHMQcEwtERER\nEREReTTq1lEQQYSmS02IHxuPpElJPsvbF4vxMKglIiIiIiIij8LV4Rj3g2vrGuyNSCQaeB5cp5aI\niIiIiIgGK46pJSIiIiIiokGLQS0RERERERENWgxqiYiIiIiIaNBiUEtERERERESDFoNaIiIiIiIi\nGrT8uqSP0+nEmjVrcP78eYjFYqxduxYymQxPPPEExGIxMjMz8fTTTwMAdu/ejV27dkEqlWLp0qWY\nPn06bDYbHnvsMdTX10OlUmH9+vWIjIz0Z5GJiIiIiIhoEPHrk9p//vOfEIlE2LFjBx555BH85je/\nwbp167B8+XJs27YNTqcT+/fvR11dHbZu3Ypdu3Zhy5Yt2LBhAxwOB3bs2IGsrCxs374dd9xxBzZt\n2uTP4hIREREREVGAHD16FAsXLgQAnDx5Evfddx8WLVqEH//4x2hoaOhzPn4Nam+++WY899xzAICq\nqipotVqcOHECeXl5AICCggJ8/vnnOHbsGHJzcyGRSKBSqZCWloZTp06hqKgIBQUFrn0PHTrkz+IS\nERERERFRN05BwKeXL2JnyUkUG+p8kueWLVuwZs0aOBwOAMCLL76Ip556Cm+99RZmzpyJ119/vc95\n+X1MrVgsxsqVK/H888/je9/7HgRBcL2mVCphMplgNpuhVqtd2yMiIlzbVSqV275EREREREQUOHsr\nSnH/J3ux+qtPsODABz4JbFNTU7Fx40ZX+pVXXsHIkSMBAK2trQgPD+9zXgGZKGrdunX4+9//jjVr\n1sBms7m2m81maDQaqFQqt4C163az2eza1jXwJSIiIiIiIv87UncJHY8mmxx2fGOoHXCeM2fORFhY\nmCsdExMDAPjqq6/wX//1X7j//vv7nJdfg9p3330XmzdvBgCEh4dDLBZj7NixOHz4MADg4MGDyM3N\nRU5ODoqKimC329Hc3IzS0lJkZmZi4sSJOHDgAADgwIEDrm7Lven6JJhoMGCdpcGGdZYGG9ZZGoxY\nbymUDFN2PlwUAUiO8M/Dxr1792Lt2rV4/fXX+zVBsF9nP541axaeeOIJLFiwAK2trVizZg3S09Nd\nfaczMjIwa9YsiEQiLFy4EPPnz4cgCFi+fDlkMhnmzZuHFStWYP78+ZDJZNiwYYPXc4pEItTWNg+4\n7Hq9OqTy8WVeQzUfX+al1weuVwDr7PWbjy/zGox11pd8+TvxhVArDxB6ZRpqddbfn+9gzz8Q5whE\n/oHkj3rr68/IH5858/R9nr5w9/BRAEQoNzchLyYe34pL8km+Xb333nvYvXs3tm7dCo1G069j/RrU\nyuVy/Pa3v+2xfevWrT22zZ07F3Pnzu1x/Kuvvuq38hEREREREVHvdLJwPDhynN/ydzqdePHFF5GY\nmIiHH34YIpEIU6ZMwbJly/p0vF+DWiIiIiIiIqKrSUpKws6dOwEAX3755TXnE5CJooiIiIiIiIj8\ngUEtERERERERDVoMaomIiIiIiGjQYlBLREREREREgxaDWiIiIiIiIhq0GNQSERERERHRoMWgloiI\niIiIiAYtBrVEREREREQ0aDGoJSIiIiIiokGLQS0RERERERENWgxqiYiIiIiIaNBiUEtERERERESD\nFoNaIiIiIiIiGrQY1BIREREREdGgxaCWiIiIiIiIBi0GtURERERERDRoSYJdgJDT1grJwWOoKa+F\ndJgerQUTgDDG/kR0FVfuFw7eL8gfWL+IyFfYvqUhzq9BbWtrK1atWoXKyko4HA4sXboUCQkJeOih\nh5CWlgYAmDdvHmbPno3du3dj165dkEqlWLp0KaZPnw6bzYbHHnsM9fX1UKlUWL9+PSIjI/1ZZEgO\nHoPhT//rSkcCaC2c5NdzEtHgxPsF+RPrFxH5Cu8nNNT5Najds2cPIiMj8dJLL8FoNOLOO+/Eww8/\njAceeAD333+/a7+6ujps3boV77zzDqxWK+bNm4f8/Hzs2LEDWVlZWLZsGfbu3YtNmzZh9erV/iwy\nHOW1PdIiv56RiAYr3i/In1i/iMhXeD+hoc6v/Q5mz56NRx55BADgdDohkUhQXFyMjz76CAsWLMCa\nNWtgNptx7Ngx5ObmQiKRQKVSIS0tDadOnUJRUREKCgoAAAUFBTh06JA/iwsAkA7T95omIurA+wX5\nE+sXEfkK7yc01Pn1Sa1CoQAAmEwmPPLII3j00Udht9sxd+5cjBkzBps3b8Zrr72G0aNHQ61Wu46L\niIiAyWSC2WyGSqUCACiVSphMJn8WFwDQWjABkYD7GCYioqvg/YL8ifWLiHyF9xMa6kSCIAj+PMGl\nS5ewbNkyLFiwAHfddReam5tdAWxJSQmef/55LFq0CAcPHsTTTz8NAFi2bBl++tOfYvPmzViyZAly\ncnJgMpkwb948vP/++/4sLhEREREREQ0ifn1SW1dXhwcffBBPPfUUpk2bBgD48Y9/jDVr1iAnJweH\nDh1CdnY2cnJy8Morr8But8Nms6G0tBSZmZmYOHEiDhw4gJycHBw4cAB5eXl9Om9tbfOAy67Xq0Mq\nH1/mNVTz8WVeer3a+04+FEqfY6j+PoZiPr7MazDWWV/y5e/EF0KtPEDolWmo1Vl/f76DPf9AnCMQ\n+Qear9+Prz8jf3zmzNP3eV4P/BrUbt68GU1NTdi0aRM2btwIkUiEVatW4cUXX4RUKoVer8ezzz4L\npVKJhQsXYv78+RAEAcuXL4dMJsO8efOwYsUKzJ8/HzKZDBs2bPBncYmIiIiIiGiQ8WtQu3r16qvO\nVrxjx44e2+bOnYu5c+e6bZPL5Xj11Vf9Vj4iIiIiIiIa3LjqMhEREREREQ1aDGqJiIiIiIho0GJQ\nS0RERERERIOWX8fUDkqCE+LjZTBU1UKcqIdz7HBAJAp2qYgoFF25X7RdqEZYahzvFxR4rINE1Bds\n39IQx6C2G/HxMhjXdU5kpV05D86c9CCWiIhCFe8XFGysg0TUF7xX0FDH7sfdtF2o7jVNRNSB9wsK\nNtZBIuoL3itoqGNQ201YalyvaSKiDrxfULCxDhJRX/BeQUMdux934xw7HNqV8yCuqoWzY8wBEdFV\ndNwv3MYzEgUQ6yAR9QXbtzTUMajtTiSCMycd0TPGo7a2OdilIaJQduV+IcpJhzPYZaHrE+sgEfUF\n27c0xLH7MREREREREQ1aDGqJiIiIiIho0GJQS0RERERERIOW1zG1RqMRH374IQwGAwRBcG1ftmyZ\nXwtGRERERERE5I3XoPbhhx9GVFQUMjMzIRKJAlEmIiIiIiIioj7p05Pabdu2BaIsRERERERERP3i\ndUxtVlYWjh8/HoiyEBEREREREfWLxye1M2bMgEgkgtVqxd69exEXF4ewsDAIggCRSIR//OMfgSwn\nERERERERUQ8eg9qtW7cOOPPW1lasWrUKlZWVcDgcWLp0KUaMGIEnnngCYrEYmZmZePrppwEAu3fv\nxq5duyCVSrF06VJMnz4dNpsNjz32GOrr66FSqbB+/XpERkYOuFxEREREREQ0NHjsfpyUlISkpCSs\nX7/e9XPHv1WrVvUp8z179iAyMhLbt2/Hli1b8Nxzz2HdunVYvnw5tm3bBqfTif3796Ourg5bt27F\nrl27sGXLFmzYsAEOhwM7duxAVlYWtm/fjjvuuAObNm3y2RsnIiIiIiKiwc/jk9qHH34YJ0+eRE1N\nDb7zne+4tre1tSE+Pr5Pmc+ePRuzZs1yHRcWFoYTJ04gLy8PAFBQUIDPPvsMYrEYubm5kEgkUKlU\nSEtLw6lTp1BUVIQlS5a49mVQS0RERERERF15DGp/9atfobGxES+88ALWrFnTeYBEgujo6D5lrlAo\nAAAmkwmPPPIIfvGLX+BXv/qV63WlUgmTyQSz2Qy1Wu3aHhER4dquUqnc9iUiIiIiIiLq4DGoPXny\nJADggQceQFVVldtr5eXlmDx5cp9OcOnSJSxbtgwLFizAbbfdhl//+teu18xmMzQaDVQqlVvA2nW7\n2Wx2besa+PZGr+/bfoMtH1/mNVTz8XVegRJqn2Mo/j6Gaj6+zitQQrHMoVamUCsPEJplCpRAvHd/\nn2Ow5x+Icwy1Ou6P9+PrPAdDGa/3PK8HHoPaP//5zwCA2tpanD9/HjfccAPCwsLw5ZdfYuTIkXjr\nrbe8Zl5XV4cHH3wQTz31FKZNmwYAGD16NI4cOYLJkyfj4MGDmDZtGnJycvDKK6/AbrfDZrOhtLQU\nmZmZmDhxIg4cOICcnBwcOHDA1W3Zm9ra5j7tdzV2OLG9shmnjFaM0SqwIEmFMO8rH3mk16sHVB5/\n5DVU8/FlXoG+oYTS5xiqv4/u+bTCib9eMqG40YqxOgV+mKCE2MO16oSAj+stOG12YKRKisIoBUQQ\n+bQ8wc5rMNZZX/Ll76QvOupUsdGKbJ0cYQCONbb/XBilQKxeg9ra5h77DbTuDUSgPyNvhlqd9ffn\nO9jzD8Q5ApF/oPnq/fi6fdvBH5858/R9ntcDj0HtH//4RwDAgw89GxRXAAAgAElEQVQ+iD179iA5\nORkAUFNTg8cff7xPmW/evBlNTU3YtGkTNm7cCJFIhNWrV+P555+Hw+FARkYGZs2aBZFIhIULF2L+\n/PkQBAHLly+HTCbDvHnzsGLFCsyfPx8ymQwbNmzwwVvu3XlLM1oFAYIgoFUQUGtpRrxC6/fz9sbV\nIC83+qRBTjQUlFhMMLU5YXc6YW5zosZiQrxCc9V9P2tswSmzA+ebrBBBQLhYQL5OGeAS02DVcQ8+\n22xFuFSCsmYrRurC0dIqoNbeilPNNmikYtTaW3HMaMVFqwMJdU2oMjvRGibGmcYW6CPCsfuCAedb\nHLhstkMdLkGSQoK74jx/GUNE5Cuh2L4l8iWPQW2HS5cuuQJaANDr9aiuru5T5qtXr8bq1at7bL/a\nckFz587F3Llz3bbJ5XK8+uqrfTqXrxysF7D6yEVX2jk5GUuSezkgAD6ut+Deg6Wu9M6CdMyIjghi\niYiC72C9s8/X6slmB54p6tz3+bxk5Ov8XUIaKjruwfdmRGNnSftwnMfHJ+Clo5cA4Mr2etf+92ZE\nIydKAYSJsPpIxZV92497u8yAezOi8eqJatybEQ27U8C8hKt/GUNE5Cuh2L4l8iWvQe24cePwy1/+\nErfddhsEQcB7772HKVOmBKJsQXHOaOuZDvJFX2y09kgzqKXrXX+u1ZImW69pot503INNDqdrW5XZ\n4fq56/aO9Nku9+2u+3bd3+RworjRCjCoJSI/C8X2LZEveQ1qn332WWzfvh27du0CAOTn52PevHl+\nL1iwZOnkbulMrdzDnoGT3a1M2SFQJqJg68+1GorXNQ0OTgiIUUjwvWGRyI5U4INyAwAgSSlz7aOW\nhrkdo5KKkamVuzoVJymlPV7v+L/7/Z2IyB/4d5CGOo9BbW1tLfR6Perq6nDrrbfi1ltvdb1WU1OD\nxMTEgBQw0G6MAl6YnIyzRisytXIU9G31Ir8qjFJgZ0F6+yQ3SikKoxXBLhJR0H2727V6Yy/X6vwk\nNYTJKa5970u+PiZNoIH7uN6CR74oBwB8crkJ6yanoMJkQ0JEGNZPTkaZyY4EhRS/mZqMM012RMok\niJSLITicEIeJ8eLkZFjtrfjVlBSUNdswTBWOy2Y71kxMQqI8DN+PVwX5HRLR9SAU27dEvuQxqF2z\nZg02b96MBQsWQCQSQRAEt///8Y9/BLKcAfPGeSveOF3rSj8wUo/1Y4I7kF4EEWZER+CeUaE1cyVR\nML3Zj2tVATF+nKyFfmIyryHql67DP4z2NrTY2/DMqNge+z13uhaWVif+eLJzCbzFWXr8KjsuIOUk\nIupNKLZviXzJY1C7efNmAMBf//pXREdfP1/nZGrcu2OM0LB7BlEo4rVKgdDX4R8pqnB8Xd/itm0U\nuxYTUYjg30wa6ryOqV20aBFUKhVuuukmFBYWYvTo0YEoV/A4nXh8fAKqzA4kKmUQO53ejyGigGtt\nbcO9GdEwOZxQScVoa23zuC+XxaJr1TH8o9hoRbZW7nH4R7PNgb0VBizMjIEY7WNu0yMkECCwrhFR\n8LF9S0Oc16D2ww8/xMWLF3Hw4EH87ne/Q1lZGaZMmYK1a9cGonwBFyENw8qiSlf61WnDglgaIvIk\nUyPHk193dvXcWZDucV8ui0XXqmP4h7f6kq1TwGhvg8MpuC3vw7pGRKGA7Vsa6ryu+O50OmEwGGCx\nWCAIAhwOBwwGQyDKFhR1FkevaSIKDR1P0J4cn4idBem9TqB2tWWxiHypoz5qus2EzLpGRKGA7Vsa\n6rw+qc3Ly0NERATuu+8+PProoxg1alQgyhU02Tr3hnG2ljMNE4Wivj5BA7gsFvlfR32ECHj9VI1r\nO+saEYUCtm9pqPMa1P7+97/HoUOHcPDgQXz66afIy8vDlClTkJ+fH4jyBdxNUXK8Om0YThitGKOV\nY3o0GyREoahjnGyx0YpsnbzXcbJcFot8zVP9Y10jolDE9i0NdV6D2vz8fOTn56OpqQn/93//h82b\nN+Ott97C119/HYjyBdwZiwWmNidsbU60tAmosFiQqlAGtUyc5Iao02U48cHFZpw1WpGllWOcVoST\nJjsUYuAG3dWf2nJZLLoWHffes802aMLDUGNxQBMugbXViWZ7K6IUMpja2lBhdeC3pVYY7K0YoQlH\nbUsrNOEShHkd4ENEFBhnu7VvL1osGBbk9i2RL3kNal9++WV88cUXaG5uxo033ognn3wSU6dODUTZ\nguKzegdWH7noSguTk/Hj5CAWCJzkhqirDy42Y9WRClf6hcnJONlohVziOagluhYd9957M6LdJn9a\nmBmDhAgpVh2pwOPjE1BU1+L2+uPjE/HSsSrMTtGhzQner4ko6D4NwfYtkS95DWqjo6Px0ksvIT29\n58yiu3btwj333OOXggXLWaOtZzrIF/3VJrlhI4muV2e7XQ9njVbsKKlDmioxSCWioarj3mtyuC99\nIQZwwtD+WpXZ0eP1EwYLZqfoYHI4eb8mopAQiu1bIl/y2jlq8eLFVw1oAWDnzp0+L1CwZXab1GOE\nNjxIJenESW6IOmV1q/8ZVxaQN9hbg1EcGsI67r3qbjMaR8ulrm1JSlmP11VSsWv9ZN6viSgUhGL7\nlsiXvD6p7Y0gCL4qR8j4XrgIsUkyVFQ3Ylh8JCZHB3/sKiceoaHIaLHi4DflKL9yrd2ckwGF3Psg\nxImRErwwORnnjDaka8Lx/vn2bp9jdQweqP+cghP/Lq1GjdEMTUQ4Gs02tNjscDoBo8mKN5LV0Eps\n+M6wcLRIpGjVR8ImCPjz6RrcmxGN2hYbJsUokKZKRJ2tFW1OAf9T1oDHxyciK0KKgijer4ko+EKx\nfUvkSwMKakWioXdB/PtCDVrPNiC9uRXW5gb8WyzCrFxtUMvk9tXB0PvI6Tp18JtyvPbOl50bBAG3\nT8nyetxEZQQarRbI2gRkX2rGmJoWqFIjMT5O5dqn+8y0N0XJcaDeysnWqIevS6px4JsypMVHwt7q\nxLnK9i9J/n74nGufh+ZMRphYhDf/+xM8unA6zskj8OzERCSXNUJebUL6iGhEjY3FRw1WFBut2Dwt\nFd/P1OPts7V4rdTgdXZuIiJ/C8X2LZEvDSioHYpiDa248OF5dIzai40N/gXPiaJoKCqvbuw17UnH\nTMb1lUYc/O2nAIA6AMOemI7ocQkAel4zf7ghGfYvvkBmdTlq4ofh02/fiBujOevjkOZ0wvLVl2gp\nPYeIjBFQTJoKiHr2BCi91ACLzYGquiaIAFhsjh77dLwGAJ+UVGNLqxy/U8lQ92YRAOAigIInpmPG\nuATXvfnDCgPv20QUMkKxfUvkS35fcODo0aNYuHAhAODkyZMoKCjAokWLsGjRIvzv//4vAGD37t24\n++67ce+99+Ljjz8GANhsNvz85z/Hfffdh4ceeggGg8HfRQUAKGrdJ6FR1Fg97Bk4V5soimiwGxYf\n6Z6O0/Xr+NoTNR7T3a+RlBNfY+RrzyPqr28h6/fPQ/vNkX6WlgYby1df4uQTj+HC65txcsVjsBR9\nedX90hOjEBEuQ5Jeg2HxOkSEyxARLnPbJzFGg5Q4HVQKGcI07T0Csmta3PbpXh+P1Vvc0rxvE1Ew\nhWL7lsiXrulJrd1uh0wmg1qt7nW/LVu24L333oNS2f5E5Pjx43jggQdw//33u/apq6vD1q1b8c47\n78BqtWLevHnIz8/Hjh07kJWVhWXLlmHv3r3YtGkTVq9efS3F7Re5xn3gvFwd/IH0nCiKhqIJo2Lw\n8F1TUVHdiJQ4HSaM1vfr+HClrFta6vq5+zUTWXUBXVenVVde6Hd5aXBpKT3XI63Iu6HHE9xJE6dC\naG6EwRkGrVoOiTgGLTY7HrwtF0aTFfFRKoRLxNi2/xgW356HX1S2zyCq6Pa3ont9HNdt7gPet4ko\nmEKxfUvkS16D2nvuuQe7du1ypZ1OJ+6++268//77eOutt3o9NjU1FRs3bsTjjz8OACguLkZZWRn2\n79+PtLQ0rFy5EseOHUNubi4kEglUKhXS0tJw6tQpFBUVYcmSJQCAgoICbNq0aSDvs8/EWhnG/3A8\nzPVmKGOUEOuk3g/yM04URUPRv45fxvnLjbDYHLBXNsDZ1oaUGyK9H3iFQi1yu1YV6s6OJx3XTLHR\nimytHMllzTjZ5dj4kZk+fCcUiiIyRrin09vTHU9wO4xe/2sMLz0H8eubAQAJajVafv4UquU6RGnk\nOFZ6GRHhMpitDlhMVmy+YTiKjVZILzchozADDosDcq0cMnU4Sj84CV1qJKLGxmFOapRbHeR9mwJL\nQLcZOa5CBE7Ucf0IxfYtkS95DGoXLVqEw4cPAwBGjx7t2h4WFoYZM2b0KfOZM2eisrLSlR4/fjx+\n+MMfYsyYMdi8eTNee+01jB492u2Jb0REBEwmE8xmM1Sq9m5eSqUSJpOpf+/sGtnEAqy1JjgsDghO\nAXJ9VEDO25uOMYT3jFKjtrbZ+wFEg4A6ItxtMp7H7snv24GCE/XHa2CsMsNmbkPF4QrYzXZ8a3Hn\nJFMd14xrDGPUVIxe/2s4ys9DOmw4FLlTfflWKAQpJrX/zltKzyEifYTrd361J7hdA2BnczMmqoET\nOh1W/Xk/AEAtl+H/TcmGutqJJF0TZoyNA9J0uOQEGssM0CRpcPj1zu7NBSsLoZ+hca+DRAH27z8d\nRkt9y1Vfi4iOwIQlvA9eT0KxfUvkSx6D2o6nsGvXrsXTTz/tk5PdfPPNrgD25ptvxvPPP48pU6a4\nBaxmsxkajQYqlQpms9m1zVtX5670+r7v2925RjtKPipxpXMSVQPKb6Dl8VdeQzUfX+cVKKH2OQbi\n99FssbunrfZez9vxWtlnZTi47iPX9ozCDJR8VAKTUYSxvZV79i39KLV3rLOhV+YeZZp9CwD337tz\nzGhcACBRqxGTnw+0mBEuk2DCq6/CdPo0VJmZiJ0+HX/7sMh1zPwRqaje+g2qAZwDcMsztyAtPw3R\nc7JR/kU5Ko5UuJ3DXNV09fKEgFAsU6AE4r37+xx9zb+trQ21p2phunT1BwKqBBWiopQIC3NfY/l6\n+owGC1+9H3+0bzv44zNnntRfXrsff/HFFz472Y9//GOsWbMGOTk5OHToELKzs5GTk4NXXnkFdrsd\nNpsNpaWlyMzMxMSJE3HgwAHk5OTgwIEDyMvL6/N5BvI0s7XJ3iM9kPz0+oE/XbXAgOLa7agxnUWc\nOgvjYhZDimv/9t8XZQrFfHyZV6BvKKH0OQbq9zEyQecaUzssXofMFJ3HfbvmU33GfUIeh6V9tlpF\nlMpruX39GXVfOuhalm25nuusL/X1cxRnT8Lo9S/BdKEMFX/YBLFag1OaJBhTR6FtWB6cFgHWd79E\nRLgMc/JHtU8gVdaCrqOwL5w8h5rEr2A/IcOJ31YhozDD7RzKRA2AwfsZBcpQq7P+/nz7l7+3rsdA\nQ4MZXbsfB6J+hNZndG35B5qv3o+v27cd/PGZM0/f53k98BrUjho1Cu+++y7GjRsHubxzoovExMR+\nn2zt2rVYu3YtpFIp9Ho9nn32WSiVSixcuBDz58+HIAhYvnw5ZDIZ5s2bhxUrVmD+/PmQyWTYsGFD\nv893LWSR7uOeZLrgT+5RXLsd7xavcqWFbAF5+mVBLBHRwJVUN2Fjl3Vql901FaPiY70ep0t1H3er\nTdYiozADltoGn5fRGy63NQiJxDg0fAKcRccRBaDue/PRlJCJsvI6xEep8J9/+zcemjPZbQ3l9TOn\nuGVRoypCS80ZJJ27CwBQcbgCGYUZkCqkiB2fgOixcYF8R0REXoVi+5bIl7wGtUePHsXRo0fdtolE\nIvzjH//o0wmSkpKwc+dOAO0B8o4dO3rsM3fuXMydO9dtm1wux6uvvtqnc/iSWCntHEgfrYRYJfN+\nkJ/VmM72TPdvoliikHOt69QiTISMwgyEycKg0CrQVN0EsVgMpdbvK5T1cLXlthjUhr5ioxX6+GGI\nAlAj1cBQ3wyLzYHaxvbxh1V1TW77H7Y2Y/yicWgqb8Tw4TGoNRxDqvRbECJkGDZNAqlCiorDFZj2\n83xE58QH4R0REfUuFNu3RL7kNaj95z//GYhyhAyh2Y6juzuD+AkLJgaxNO3i1Flu6VgVZ26lEHS5\nFRc/KIPxrBG6LB2S5g/vdfdrXae2sbQBJR+VIPPmTLdrNWZJbv/LPEBcbitEtbahdt8ltBla4TA7\nYCo3QRGrgCyyvRGXNiIC/y8sBS/+4hmoo5PR1GBCemLnpClJeo1bdmplOErhhFwZhuNvfgUAiP2B\nBN/8d2f9y1ucxye0RBSyQrF9S+RLXoPahoYGPPvsszh06BDa2towbdo0PPPMM4iJiQlE+QLOarD0\nmg4GjSQed2S/gFrTWehVmdBKE4JdJCJ3TicuflCGI6uOuDZNFgToV3r+ozkqNapzTG2cDqOGR/fp\nVB3dj20mm9t2Y6UZ/R8UMTA3Rcnx6rRhKG60YqxOgenRDGqDzulE5dYytFna0NrSiqMvdTbicp/J\nReOpRuC5Cqx9KgeijGE4VnYZmgg5mkxWyGVSLPv+VNhsdiy7ayrKa4xIilFDExGO6oZmDJfKUHYl\nL2u3p/SWRiucIuDj+hYUG62YZLbjW0ppv8dYExH5Qyi2b4l8yWtQ+9RTT2HixIl4/vnn4XQ6sWvX\nLqxevRqbN28ORPkCTp7o/g19eGLwB1dXmI7in+d+60rPGPEIMnV3B7FERO7qP66B8azRbVv3dHfF\npbX4w3udQfBP75iMTL33wDZqbBwKVhbCdNGI8i/KXdt1aX1f49ZXDtRb8UiXMsRyTG3Q1X9cg8uf\nXEZ4dDiENvfJcprON8FhcqC10Y6IagM2FZ9xvXbrlBF4+5P21Yz/4+5RqGk+A5sjAcVlNYgIl2F4\nvBapo2NR9u7pq543elQMx1gTUcgKxfYtkS95DWorKirw2muvudJLlizBnj17/FqoYDomsSJ3cR6a\nK5ugTtLga5kN6UEuU5ruRkxOuQxbqwnhEjWG6wqCXCIid8ZiA7RZWrdt2hFaD3u3q6p3n92vqq6P\ns/2JRIjOiUf0yBiEScNgrDRCm6RFwtRh/SqzL3BMbegxFhsgVUuhydCg1dwKAJBpJci6pw3KuK8h\nzkrE5U+kqJS5zwRqsTlcPxuMJsQq07F73zeubY/dk+/6QsV4wYDIEdGIHhEDQ1kD1PFqhMnCUNLk\n3nuA9YGIQsUxaei1b4l8yWtQKxKJcOnSJSQktHd5raqqgkTi9bBBK8cejqI3O58ejX9wchBL004Q\n2nCkYqcrPTb2riCWhqgnXXYkFEnhmPzCZBjPGqHN1CK6oPenrimx7t8aJ8f271vjqk9LceSNzrVE\nxWInEmdk9XKE73FMbejRZUfi5O9OICItAqp4FfJeyINMchaV219Ex9cmU9auQbEyBqgqcx2nCJcC\nANRyGcaL9HCW2bFs/Cj85XQpTFY7DM2Wzi9UcuJR/81lfPGHQ67jMwoz8O1JSW5lYX0golCRYwu9\n9i2RL3mNTh955BHcc889GD9+PARBwNGjR/Hcc88FomxBoVLLMHnx5PanP8layGKCPztcs/US5mQ/\nhzpTCfSqTJis1QB7jVAwWVtxcXsZjGeM0I3UIeOOOpzYoQasgLPVCclUO4qjtuOjw1fWVlYugFTh\n/uRWo3TioTmTUVXXhMQYDXQqz+sqGsoNuPhZGYyVRuiSdIgaGwVDhfuT3caKZs9jap1OWL76EiXl\n5yFNHQ7FpKmAaOCzJRdGynEiRYa2C9UIS4tHdFQ/ghjBCfHxMhiqaiFO1MM5djgg4vhLAK7fV0vp\nOURkjOjX7yuqMA43bPk2WkqbIBGfgb3hHKQZKkjUarQ2N0OiVgOySmTYS7B6zgScN7ViuDgO1riL\niFCkYKo0HmVbvnblt+TukciI1kBhBU7vOgqVXoXmy81QRCkwZckUNJY3IiIqAmHyMJhP1eDwMB0O\npuiQoVfiW0qpbz6PK3Wl7UI1wlLj+lZXrnYMEV23QrF9S53EaIX20jbgVDEitWNhTFgAJ8KCXaxB\nxWtQW1hYiPHjx+PYsWMQBAFr165FdHTfJnQZjOxNdhzp8k3W5AeC/01WK8zYU/ykK31n9otBLA0R\ncHF7GY6s7LxOBOckyORhOPJs+zbbyvqeaysr3NdWrjM68Pqezkl8fjJnvMfzlX1WhiNvdLkuF0+G\nJEKC8T8cj8aLjZCES6BJ1ng83vLVlzj5xGOu9Oj1v4Yi74Y+vNPehR0vQ/hv/tqZXjkPzpy+degS\nHy+DcV3nEmfafhw71A3o9yUSQRA74VR/ifI//hEAUPd/QPysWbj8t78hJj8fFX98EwCgwg6MXj4N\n4rzhMDQfg0Z5Py4X1wEAZEoZUqakQGoRQW4XcPSvx5B6QyoOv3fYdaqMwgyUfFQCABj/w/E49eEp\nAMDM/7gB4yYPQ21tH7vUe3EtdeVqx2CG52uMhhoBqliVx1fbXxMATmR23QjF9i110l7aBskXPwfQ\nHpxppwkwJNwf1DINNl6D2qamJvzhD3/AF198AYlEgoKCAvz0pz+FXD40u1UZK4090slBKksHrlNL\nocZ4pvukUM0QdXmS1pc629hs6TXtlv9Vrss2RxtOfHTCtS0mw/NMyy2l53qkfRHUtl2o7pEW9TEw\nHcixQ91Af1+VEZ9CaCxy29aqEUO98Ga02tyf+IZXtaG6pQJNtZPQYpZAoWl/PWVKiitgBdoD2O7t\nf4elcxxuS0MLMgoz4LA4YG20QHB67nnQX9dSV652DF1fvsySoTb26m01vU6Ggd8BaTAJxfYtdRI3\nFvdMc7GTfvEa1D722GNIT0/Hyy+/DEEQ8D//8z9YvXo1NmzYEIjyBZwmodv6hPHB7+cbo0zvlmY3\nMgou3Uj3NWW1mWqIRJ3dZPpSZxOi3fOIj/a8Tq0uqVvX5UQNak7VuG1rrvEcFEdkjHBPp4/wsGf/\nhKXG9Ug7A3DsUBeRMQIStRox+flos1gQHhkJCM4+d0E2KEsQkSRx67gVlhCJc9mnEXNa6rbdlhgG\nkfm72PbeBfzgRjHCxEDSrZkIE7ufy2FxQJvsXg+lis7uxZoEDYq2tgfS5V+UIzYrFsos3/Rqupa6\ncrVj6PpSZ7TgcoP5qq+JONThuhOK7Vvq5NSNhdgtnR20sgxWXoPayspKt+V7Vq9ejdtuu82vhQom\nqUqK8T8cD3O9GcpoJWSq4I85iFWOxi0jH4fRUgWtIhGxyjHBLhJd55IWDcdkQYDxjBHaLC0y76xB\nwzmNa6IouVPjVmejhZ5BpMlkd42pTYrRoMVkv8qZ2g3PT4cAuGY6Lv20FLok9yBYrvZ8rSomTcXo\n9b+Go/w8pMOGQ5E79Zrfe1fOscOhXTnvmsYtdhwrrqqFs2NMLQFo/32l/8d/4MyvfgUAqD1wAKMj\no/v8tDY5cQJ21i3Bjx5ZBeHfFxGmUKDujT0Y/Ys70ZgHaJ5Kh6SiBUKKBoYsERqPaXHbtCyMC1Og\nas9RlOPKk9ku4sbEoa2tzfU0NlwVjqjhUe1d35M0sHTraVB1rAqZmVE+GSd9LfVsIHXzml3L2F/y\nm+nTP4HJVnPV11ThsQBmBLZAFFSh2L6lTsaEBdBOEyAxFqNVmw1jwsJgF2nQ8RrUpqSk4Ouvv8bE\nie1d+86ePYthwwK/dEagOKytvaaDQRBEMLSUw9ZqglNoRYraNw1yomsmESP5wRGurksWjEDt22dR\ntLp9vM6oRQoYFJ11Vm8ZC3R7UKRQhOO1d750pZfd5blea4dpkaxoP1/Z309Dl6SDNELqNqa261Oz\nHrr2BPVlG1skgjMnHaKc9P4/Zb1ybPSM8T4bezlkiMSwGRrdNvWnC3K65hbcrvgNHKdL0fzVV4jJ\nz0fkpElQVqpg3J0Ow9zzeCdpBeAEcAr4lvZzfPjRGWSmdgZ+FYcrkPODHBgvGiFVSGHXSJE+OQP1\nx6thvGCANjUS0WPjkHglaGv45rJbGSwGC+qPVyM6J35gnwVwbfVsIHXzGnGceGipaj6KBsuFq74W\npUgNcGko2EKxfUudnAiDIeF+6MepYWCb4Jp4DWovX76M+fPnY+TIkRCLxThz5gyioqIwe/ZsiEQi\n7N27NxDlDBhnqxOmWhMcFgcEpwBNhOcukYFS1XTMbUmfGMVIDFfNDGKJiHpqqWxx/Vxl+QZH6jrr\nrDYmDaNxJ1qdbfj42AWUXGqAuFtX0sqaJo95t9kdqDlwHI0XGqFKjkXJRyWuiXxkShmcrU40V1+9\nmx3gv4miyH8G0mVcBDGSjTfC7oxAeL4Dl//2NwDtT3yT7lsFi60Fc8sfhD1Jgn3qnbhsbH+aZdV0\nfjFiN9thbbRCqpDCYXFA0tjek6BjSZ/uosbGIfdHuag9XQupQoqKwxVQJah9E9QOEhwnHlq+/fmL\nsDXYrvpaeFQ4kBPgAlFQhWL7lsiXvAa1r732WiDKETLC1eHQJevQVN0ETbwGMm14sIuEePXYbmn2\ns6fQE5EQ0fnzpRSgy/CdlJRJAICPj13Ar3d9BgCYNSXT7fgRyVEe8z774Vf4bPM3AACZshpTH5yE\n5lorFFoFTHUmRERFQBUX4fF4f00URf7T0WW8pfQcItJH9LvLeFRhHAzhebAfO++2XWgpRcNL7V+4\nhAGYufweWNN1ACqx7WwZ7rttOOKdEsQn6WCpMKJkf/sXKFKFFC27jiF2TBwgEaGxtAG61EhEjY1r\n72IrEkGVqEHRf3ZOUKVNjRzoxzCocJx4KBGh5C8lMJWYrvqqKkOF9IdGB7hMFEyh2L4l8iWvQe3S\npUtx0003Yfr06cjNzR3ykwvYm2yuyT4AIPdHuUEsTbt09Uwszt2NOutJxMhHI119S7CLRNSDepQG\nGfdmwGFyIK42FrcYX0ZTdBk09WlQ1o8C7gZKLjW49v/0mwtYPHsi6owtSIzRIC7K8/ITDecNrp/t\nZjtMlbVQpSTg8J86l1eZsmSKx+P9NVEU+ZFIDEXeDdf+5V/6YwUAACAASURBVINIhMj8OBiN7l8C\nyiLdn07EXRqOan0UFs+eiJKqBpSFC9j+TQlui8/C+Cv91rvOhHxyzwm3pXwKVha6nsZGjY1DwcpC\nGC8YEJcVC2Wm5y9qhqKgjOMloj4JxfYtkS95DWrfeOMNfPLJJ9i2bRtWrVqFcePGYcaMGfjud78b\niPIFXPPl5l7TwSCCGBnqWZiWPpdj7yhkRX5bD2uVBY3FBjSdbUb9G2oAOagHYPiJAfF3pyEjsXM2\nWJPFDqcg4P3PTwMAfnrHZOQMi71q3lHDowCUu9K6VB1qy9zHXDaWNyLJQ9n8NVEUhT7DxQSopy2D\nqK0aQlgcHE73QPN8dB7Wvf0J7p89EQePdo4/jNZGoLy2vY51Xbqne9p4wdDZxVgkcnVP1uvV19/9\nOgjjeImob0KxfUvkS16DWr1ej7vuuguZmZk4dOgQtm3bhs8//3zIBrW61EjX7JZShRTatOB3HxPg\nRGnzPnxZexIx8jFIV8+ECH1b2oIoUBo+rUXtoRo4TA7E3qhH9Ibm9ie1DcOhE7cvhTIjJxUQBJRc\nakB8lAr/c6BznVmjyeox76zbc9HW1obGC43QpeoQe+MYtIkuuF2rUem9PBW78tRv2Oxbrr9A4zqn\nGaHDgTUCgFgAAkb9JAYpD66BxXwKzhQVjtraxxz+7xdn8KNZE2CxOHBDhAbOixZckIVh2H3jobC5\nh2hdJyVz62IsOFF/vAbGCwaYO57UDvHeTUQ0OIRi+5bIl7wGtUuWLEFpaSlGjRqFKVOm4PXXX8eo\nUaMCUbagaANc3coAQDfCN+sMDkRp8z68WfRDV3px7m5kqGcFsUREPZlOGVGys/3asdxyEvvif9n+\nQgSg0/wZCUiHWCzGzRPScfOEdBSVXEK1oXNyp7FpntfRFEvCEHvTWHR9jiuSiN2u1dhsrsNJVxEm\nQsa9GRCHi+G0OeF0CDjZWo59I54CAGRq1gHQoNpgxn/+7d/YePuN+Oa1Q53HL50MsV6K3B/lwtpk\nQ/TIGIgkYqgS1K4ZkDvUH6/BwXUfudJduyYTEQVTm9jp3r4deX0Nj6Chz2tQO2bMGLS0tKCxsRH1\n9fWoq6uD1WqFXC7v0wmOHj2Kl19+GVu3bkV5eTmeeOIJiMViZGZm4umnnwYA7N69G7t27YJUKsXS\npUsxffp02Gw2PPbYY6ivr4dKpcL69esRGen/b5Vs9ebOdbxilLA1tHg/yM8EQYQ7s19AjeksYtUj\nIRH4lJb8wOlE/cc1KD99BqqRGkQVxvXrKVN4fLhrndqa0X8DOofBoj7sTI/9J6XH48UHb0bppQak\nJ0RhUobnxn9V9VnU/KMBxkoTtMkqxN6UAENpg9s+htIGxH+Ly1RcV67UWWOxAeZJsVB+q8uTUacT\nDQcvo7X8KGJHVUIWmwa7fBjCJUcRXWLEuMY/wpakwIWUCiy7NwoNBjWyk0dAdMrodgplkwPpN6aj\n/ngNnIIR9iYbLI0W90mirjBeMLgd69Y1mYgoiHq0b+uD376lTiI4oarfB5SfhFo1BqaomRDYK7Nf\nvAa1v/jFLwAAZrMZ+/btw7PPPouqqiocP37ca+ZbtmzBe++9B6VSCQBYt24dli9fjry8PDz99NPY\nv38/JkyYgK1bt+Kdd96B1WrFvHnzkJ+fjx07diArKwvLli3D3r17sWnTJqxevXqAb9c7hUaOI2/+\ny5WevDjP7+f0ptF2Fu8Wd773O7NfRCo4WRT5Vv3HNTh4b5enTDsLET2jW4O8SxChy450C3wdtQ4c\nubJObfS4WLgWsQUQr3KfwRsAIBKgizuKxIhi6NRjAVEcPC0iW/OPBhx+86grPUUAtIlKt320CZ5n\nP6ahyWOddTpxaVc5BMM3uPz2r1yvZzyyDCUvd87on3b//UBtOb6MWAMAmBD1O0SmuveCccTWoPxr\nG4pePuk2QRTQ80msrttsx9fb7MdEFLoUqoiQa99SJ1X9PsgPtvfKlANAwW40R7NXZn94DWo/+eQT\nHDp0CIcOHYLT6cStt96Km266qU+Zp6amYuPGjXj88ccBAMXFxcjLa7+ICgoK8Nlnn0EsFiM3NxcS\niQQqlQppaWk4deoUioqKsGTJEte+mzZtutb32C9NVY29poOhxnS2Z1ofpMLQkGUsNvRIdw9qewt8\njWc7n3A1PavD3W9sRqOqFPGqbIxKv73H+frTrd5YaeqRToi5iEk/SIepwQlVlBgKSzGAkX17szQk\neKqz9R/X4ItHDiHnwRq31y1lFW5pW20twq1twJXJsC81F2P82B+hYGUhqksvoEZVhP+2r8CNJesA\niHtMGFV/qra9+/GVL3au99mPiSh0hWL7ljqFGYt7phnU9ovXoHb79u0oLCzEokWLEB/fv25UM2fO\nRGVlpSstCILrZ6VSCZPJBLPZDLVa7doeERHh2q5Sqdz2DQRtkvtyD5pEbUDO25tYdZZ7WsXlSMj3\ndNndnjJl93zK1Fvgq8vqvHYcja3Qn5qMW1b+xOPETJebi3ukPQW12uRu12WSDvIYoP7TcxDscrRa\nrZDn87q43nTUWZlWhpTZKWhtakXDPy931lOJ+98sxfBhbulwvR62qHpXOkGd7ZrB+FT0duw/sxZo\nBYS4JgA6twmiAMDaaEH98csARDBeMEB3ZYztdTv7MRGFrFBs31KnNt1YdP0L06bN9rgvXZ3XoPaP\nf/wj9uzZgx07duChhx7Cvn37cOedd17TycTizr7hZrMZGo0GKpXKLWDtut1sNru2dQ18vdHr+75v\ndyUt9s4xB9FK2CyOAeU30PIAQJplaueYWlUm0lQ3BL1MoZqPr/MKlFD4HGPmqhAeLkX9sXpEj4tG\n6pxUiMTu3YHNk9yX3ImbFOs6Z+MopWtMrTZTC9VoVa9lSrNPALoMtU2NmeBx3wtip9t12RbmhFOZ\ngRY54BAccIZL4VRl9On9h8Jn7c+8AiUUytxRZxuKG/CvNVe61b0K5L+WDwA4vTMMWfcsgzrRBKl+\nGKziDKT/8hFYS8ogT0qELVEB7fBp+HYzkByVgxszlkIibv+z2LV+fipbhx+teB+oUyIqLQo1p2sg\nCZeg4nAFJHIJWq2tqDhcAbvZjlueuQVp+WkAQuMz6i4UyxQogXjv/j5HX/Nva2vzuk9UlBJhYWHX\nlP9AhMpnNFj46v2UWG3u7VubPST/Hl63ecZ8Hwh/B6g/BkSPgzx1DuQijqntD69B7csvv4zLly+j\nuLgYDzzwAN5++22cOnUKTzzxRL9PNmbMGBw5cgSTJ0/GwYMHMW3aNOTk5OCVV16B3W6HzWZDaWkp\nMjMzMXHiRBw4cAA5OTk4cOCAq9tyXwzk23FZuAz/+s/OMQe5i3IHlJ8vvq2PVkyCsbUObWhFpDwT\n0YpJQS9TKObjy7wC/UcxVD5HZX400u5MQ21tM+rqe/aOUH4rCgU7C2EsNkCbHQnlt6Jc56w9UIvj\nv+0ca5/96Fhk3pnpsUyJskIszt2Ny83FiFdnI0lW6HHfmGQ99j2zz5UuWFmI+vP17jM5DtNBmdn7\nbOWss74TKk8hlfnRqP7KvZtxS6PFrZ6GycQo310Gh+kypKpUJN9dAN1N7bMWxwEYqf8BAMBQb3Hl\n0aN+qnMgghgN31zGhTePuPZrtbai5KMS13jb6jM1UGZFh+ST2lAr01Crs/7+fPuXv+B1j4YGM7rO\nYxCI+hFan9G15R9ovno/Mmk4/vVfvmvfdvDHZ37d5qn8DvRpd7bnWWf2vn8fDbUvejzxGtR++umn\neOedd3DXXXdBq9XijTfewJw5c64pqF2xYgWefPJJOBwOZGRkYNasWRCJRFi4cCHmz58PQRCwfPly\nyGQyzJs3DytWrMD8+fMhk8mwYcOGa3qD/WVptrqt42U12QJy3t6IIEaGehampc8NqQYJXYdEIkTP\niO85gRSA6GnuA72jp8b0ntWVet2X5alSb0h1jVXsWEal/lSt2z7WpuBfqxQc3bvOq0do3eupIKDN\n7nQFuVEFsVfJxZ2n+tkxbrbm6CU4LA5UHG4fp9sx3paTQxFRKArF9i2RL3kNaju6DIuuTIRht9vd\nuhF7k5SUhJ07dwIA0tLSsHXr1h77zJ07F3PnznXbJpfL8eqrr/b5PL4Sk6XHgfVdJsJ5YnrAy0A0\nGEUVxrk9HYsu9N26sSJx+zjHrjPNxozqFkSP6j2IpqErqjAOt7xzC6q/qrl63evly5h+uzLmVgTg\nQJc1afUj9UgrzHBbt5aIKFSwfUtDndegdtasWXj00UdhNBrxl7/8BXv27MH3vve9QJQtKKJy2r+F\nN1c1QZmoYQOFqK98GTj0QdeZZjue3tJ1SiRC2p1pUOb33v3cl65a//qxrjMRUSCxfUtDndeg9ic/\n+Qk++eQTJCYm4tKlS/jZz36GwsLCQJQtOK58Cz9qhuexgEQUAkQ9n94SBQzrHxENJmzf0hDnsR9x\ncXH7chtHjhyBXC7HjBkzcPPNN0OlUuHIkSOeDiMiIiIiIiIKGI9Panfu3InnnnsOS5cuRXZ2+1pJ\nHevMikQivPXWW4EpYaA5naj/uAblp89ANVKDqEJ2KSPyiyvXmrHYAF12JK81Ch7WRSIa6ti+pSHO\nY1D73HPPAQBSU1PR0NCAOXPm4Pbbb0dCQkLAChcMdUfq8WlqE0qTm5AhAW48HIaoqXrvBxJRvzR8\nUovKd8vhMDlgOtcMhIkQdVNwxvi0og1/b6jAuWYDMtWRmBU1DGLPHVloiKn/uAZfPPQZUmanwPB1\nA2w1ViTck+r3Bp8TThxursHZK/VOLBLhdFMDstSRmKKOgwhscBKRb3T9m9t4qjGof3OpJ1c7pLwR\nmSod2yHXwOuY2rfffhsXLlzABx98gJ/85CfQ6XSYM2dOj9mKh4qvU80wCK2wWQQ0KFpRlGrGTAQ3\nqLXAgOLa7ag5fxZx6iyMi1kMKSKCWiaigTKdMqJkZ5d1ZsfoPP6BrTZU492aKpSajchQajE7OhHy\nMN9dA39vqMBzxw650sI44LaoNJ/lT/4jwInS5n34svYkYuRjkK6eCVE/GwLGYgNSZqe46mP5B+Uo\niJV7nPTMKbTh04vlON1ch5ER0WiUtqLE1ow0tQwjpWcwSndnn857uLkGjxZ1zkZ6e0oG3q9oL8Nv\ncwsxTc3xukTkG21aCyLv0KLpYjM0KWqERdmDXSTq4uPGStTYWtDS6kC1rQUfNV7Ed3TDgl2sQcVr\nUAu0P61dvHgxhg0bhjfffBN/+tOfhmxQWy+2Y/OJo670o2Nyg1iadsW12/Fu8SpXWsgWkKdfFsQS\nEQ2ctc7Wa7qrd6uq8NKJzrH8wpjJ+H7sCJ+V5VyzoWeaQe2gUNq8D28W/dCVXpy7u09rH3ely46E\n4esGt23GYoPHoPbTixV4vOQLAMDtKQLeL+v8cubnI1PgcG5FnP5nXs97tlu9M7c63F5jUEtEvmKt\nt+Fff/7Klc57cBK0QSwPuauzW7H5dGjFH4ON16+z9+3bh5///Of47ne/i6KiIqxZswb79u0LRNmC\n4mKLqdd0MNSYzvaaJhqMYm7ots7sNM/rzJaajb2mBypTHemWHtEtTaHrcnNxr+m+iCqMQ+ItSW7b\ntNme68CZLsFo10AUAC5aRLjUxzJkdatnSonU9XP3OklENBBNF5t7TVNwhWL8Mdh4fVL7/vvv4447\n7sCGDRsglUq97T7oZUS4f2+VHqEJUkk6xamz3NKxqswglYTId6IK41CwsxDGYgO02ZGILvQ8tidD\n2e26VPr2++VZUcMgjGt/QjtCHYnZUezyM1jEq8d2S2f3PxORCAn3pKIgVt6n+jhSHQnUtP/cNRAF\ngGSFgITwvpVhijoOv80tvDKmVgexSIxhCjUy1ZGYquZYNyLyHU2K2j2drPawJwXDCLWu1zR55zWo\n/f3vfx+IcoSMOdHDIYwRUGo2Il2pxR3R6cEuEsbFLIaQLaDGdBaxqkyM1z8Q7CIRDZxIhOgZ8R67\neHZ1d/JwCIDruvxudKJPiyKGuH0MLbscDzrp6plYnLsbddaTiJGPRrr6lmvLqB/18dvJw/ASgNPN\ntRgZFoWcTJ1rTG2W9DRG6xb27ZQQYZo63q2b8RQVg1ki8r34m1KQJ7Q/odUkqxE/PSXYRaIubo9O\ng5AjoKTZiAy1FrdHpwW7SINOn8bUXk/CwsT4fuwI6PXqkFmcWooI5OmXQT8mdMpEFEjRWh2+b/fd\nGFoaOkQQI0M9C9PS5wbs/igSifH/2bv/uKjqfH/gr/nJADPMAIKKIiKKv1JMy7p2RTQpWrVUtLDC\nfnjbbH/c3dV+mLmpWy5Yu3fbrvmtbnfzZm30Y9WKLNMELcty2bRAJUUU/JEiwjAz8mOY+Xz/QAYG\nGJgZ5ie+no9Hjzznc3if95zznsN5c86ZSY0filQM7WJ0nE9yICJyhUIWhvibRwbU+S21kUGKedFJ\niBnF/eMuNrUd8Xu8iHyD3w1K3sC6IiLqjOe31Mexqe2guvAC9ma1fcVCat50p25Hu5o0oxkfnt2C\n0sM/YpRuJO6Imw8pZP5O66rSug+O1pRidOQo3BE3398puYzvNQK6ruXeHE9YV0REnfHYGNh4bt17\nbGo70JfUdJrmm97eh2e3YMVXq2zTYorA/Lg7u/kJ8rSu9sHDMUv8mJHr+F4jwPPHE9YVEVFnPDYG\nNp5b955r31B/FdB1+BqH7r7W4Wp1tKa022nyvr6wD/heI8Dztcy6IiLqjMfGwNYXzuv8jVdqO2j9\nmhFTaR3CR0Z0+7UOV6vRkaPspkdFjvRTJlevvrAPXPlKH+q7PF3LrCsios54fhvY+sJ5nb+xqe3o\nytc6jLprBD99zIE74uZDTBEorf0RI3XJmBuX6e+Urjqt++BoTSlGRY4Mzn3gwleoUN/l8VpmXRER\ndcbz24DGc+ve80tTO3/+fKjVagDA4MGDsXTpUqxYsQJSqRQjRozA6tWrAQDvvvsu3nnnHSgUCixd\nuhRpaWn+SJc6kEKG+XF3IiaFHzvuL637AJ79ulYin2MtExHR1Y7n1r3n86a2qakJAPDGG2/Y5j3y\nyCNYtmwZrrvuOqxevRq7du3ChAkTsHnzZmzduhUNDQ1YtGgRbrrpJigUCl+nTERERERERAHK503t\n0aNHcfnyZSxZsgQWiwW/+93vcPjwYVx33XUAgNTUVOzbtw9SqRSTJk2CXC6HWq3G0KFDUVpaimuu\nucbXKRMREREREVGA8nlTq1KpsGTJEixcuBAnT57EQw89BCGEbTw8PBxGoxEmkwkajcY2PywsDAYD\nL8cTERERERFRG583tUOHDkVCQoLt3zqdDocPH7aNm0wmREREQK1Ww2g0dprvjJgYTc8LBWEcT8bq\nq3E8HctXAm07BuL+6KtxPB3LVwIx50DLKdDyAQIzJ1/xxWv39jqcjW+xWHpcJioqHDKZzK34vREo\n2yhYeOP1eDpmMOR4tce8Gvi8qd2yZQtKS0uxevVqnD9/HkajETfddBO+/fZbTJ48GXv37sWNN96I\ncePG4S9/+QuamprQ2NiIEydOYMSIEU6twxMPWMfEeOZBbU/F8WSsvhrHk7F8fUAJpO0YqPujL8bx\nZKxgrFlP8uQ+8YRAywcIvJz6Ws16e/u6Fl/0uMSlSyYAEjfjuyewtpF78X3N06/H09vIG9ucMT0f\n82rg86Z2wYIFWLlyJe655x5IJBLk5uZCp9Nh1apVMJvNSEpKQkZGBiQSCbKzs3H33XdDCIFly5ZB\nqVT6Ol3qgqHeisKSy6i8cBEJ/UMxY3woQhVSf6dFftBWCw2sBQpY7et0SKwK0Rozak1K3JISCpmM\n9UpEfR/P3aiv83lTK5fL8dxzz3Wav3nz5k7zFi5ciIULF/oirYAmgRXq6s+AiiPQqMfAGJUOAf8d\niApLLmPjtrO2aauIw5zr1H7Lh7yjte5k+hJYdNd0WXesBQpEHWt3x6kp2LjtnG38F3PjsHHbGUDE\n4bZJrFdyhoBFchmQSBwu8VO1CTKE+zAnIufx93VgC7Rz/WDkl++pJdeoL2yFat8DAAAVANz0Fgyx\nc/yWT+WFhi6meWDsa9TVn0G1904AgAKAJHk9avdVQBE/HM1p8yEJjWQtUMCx1p+E7sQnCPnxCQAt\ntXtb8ov4X9VNMDS03JJZcaVuT55nvZLzHv1mJT6u+MTh+H0j78FT41b7MCMi580cfBFzp+yF+XQZ\nFPHDYRx8B3j8Cxztz7lUAJD6LgzRGX7NKdiwqQ0Ccs1w4Pp1gP4YoB0JuWacX/O5eboR6mv24YT+\nBIbrkjCz3x1+zQcAjKjF1oqtKPuhJacF8XdBhVB/pxVUOv6V8FjYQOyc9ALK9OUYrktCZr80mFem\nAQAiIWC57T+Q0N9+G8fHqvyQOV3tzuMkNhftRpm+HNdEjYFFq8GR4b/BcF0SBoUOwi2wYEvGGzhg\nSkHOlykYcqVOh/ZnvRJ5h0BXz/W2fACotd0cCdo/10veo/5uF2peedY2Hbm05fc4BYZO51xho3gW\n6yI2tUFAWv0FcOCptunrrcDgX/stn2+rCvDstzm2aTFZIHvIA37LBwC2VmzFM+1zEv7PKdh0/Cvh\nzkkv4Jlvc23jYrIVrX++MFeWQQpgxvhQWEUcKi80ID5WhZkpYT7Pm+izit12tZo5fC7+cXwbAGDV\n5BX4G4BllX/FLQCG3/U2ii398J/z4nDLBNYrUWsDajabYd9wtud687nnYh1MzY7iAeFyKab107oU\nk9xnrizrNM2bWwPHzqqiTudc2UMe9GNGwYdNbQeBeE+7RH+sw/RxYLCfkgFQpj/R7bQ/BGJOgULS\n3ATNsU2Q1h2BVTsGhuQHIKTt3vr1JyEr3AVZZIndz5Xpyx1OK+KTYAEQqpBeeSaHtzCRhzU3Q/bF\n+zCXH4EyeTxCR4dBpj/c8nx36EhIC3fBXFkGRXwSygafsvtRk9lk+/eJDnUcLz0KzfBZPnkJRMFB\noHTLD2jQN3Q5qtKqMHL+OLja1K747gLKjE0Ox5PUSnydzqbWVxSJY6BOnw/rZROkYWooEkej5y9+\nIl/p7pyLnMOmtoNAvKddaJPtfpUIrXNfbeQtw3VJdtNJ2mF+yqRNIOYUKDTHNiHk6KMtE2cBCIG6\n0T+3jcsKd6Hm5WeheHAOFO1+rvM2TYR61iIo4pPQnJbJG8bIq2RfvI+aF1YAAGIfnA3V3jcAtD3f\nffrl9bZlh29YY/ez4Yq2D+sZpk20q1WLdqy3UiYKWp9IjKjC5S7HYiTNGOnjfMgLhAXGnVtsk5HJ\n/n2Ujex1dc5FrmFT24FMX9J52s9NrSF6FjTXWyHRH4PQjoAherZf88mMvwNCCJTpTyBJOwwLhsz1\naz4AsCD+LrucFg7J8ndKAUNad6Tb6dZbki6+WwjcuRiq/gpYRtyKzOgbIIQVZfpyJGkTsWDIPEh/\nHgkL+AQUeZ+5vK1OFao6tD/flhp+tFs2s18axOSWWh0bNQZWYUGILATDtIkYHDYYt8iVMKc8DYv2\nGhijb/HVS6A+RWDWgJuQoo53uERSpH//4Ow+CX44UYXTF7v+bszB/TTgUT/4mU+Wdprm7ceBo/3v\nsSRtIhbEzPB3SkGHTW0HFt01dlerAuGv+pbCz1H+8p9t05FLQwE/PtwfikhkD3nAJ1/U7iwVQgMu\np0Bh1Y5puULbOh0x2m5cET+8Zb7RgAt/y0fU0lVojs5AKMDnOchvFMPG2P5tbtCi/Uc6WTXJdsuq\nd32CO15bj8jf5sIy7softNo9otEMoFaX5rVc6Wogwfzz2xFyeovDJczJD6F2oP8/OJGoK62/69um\nk3j7cQCJrS/FsqLf2qYbUt+FIXSo/xIKQmxqOzBGpQOp70JlOoKG8NEB8Vd9S9p8RELYnh+zpGX6\nOyUKIobkBwAhWp6pjRgNw0j7RrVjfUlZXxQALKkLECkEzOVH0Bw1Dg1jftbyTK12LIxhNyByqcJW\ns+amJvRbvh5N/77A32kTEQUknksGtkDsP4INm9oOBKQwRGdANWohDIFyxS80Epbb/gP9eRWS3CCk\ncrtnaDu5Ul9SABYAUawzCgRSGSzTsyCd3nKl1QAA0bfZhtvXrBSAlnVLROQYzyUDWkD2H0GGt9MT\nERERERFR0OKVWiIiIqIAJ4SA4++RbY8f6kREVx82tURERETdErgcPhumAdc5XEIWMsSrGVitVhz8\nn29xubrrr94BgLDoMEx46Aav5kFEFIjY1BIRERF1SwLDJwUw7/3Y4RKhty+GYvjtXstACIH6S/Uw\nVZkcLiORSAAI8GotEV1t2NQSERERBTghBJJHXYB5oN7hMgqt1ocZEREFDja1RERERAFOIpHgi9GF\n0DecdbiMVhWHuVjoxSzElf+6w6vEROR7bGqJiIiIgkBd4znUNFQ4HG+5/di79lysg6m56w+sCpdL\nMa0frxYTke+xqSUiIiIKcEIILDr2Gwi94w+KkmjDgHHezWPFdxdQZmzqcixJrcTX6Wxqicj3Arqp\nFUJgzZo1KC0thVKpxLp16xAfH+/vtIiIiIg8oKdbedtIJBKUlUai/oLK4TKhsaEY5Ym0iIiCTEA3\ntbt27UJTUxPy8vJw6NAh5OTkYOPGjf5Oi4iIiK4qAufTHoJhwj0Ol4iKicAgN+I+f/yfuNDg+Opr\nrCoMjw2/DhKJBJUfVsJYZnS4rDpJjVFPpricw8BotcPRljF+ojIRBbaAbmqLioowdepUAEBKSgqK\ni4v9nBERERFdfSTIK4vFnu8dN5Rzb9Lh4fGAcx+m1BITAG6PPodGs8HhUiEKjUuZthBQJzhuVFvG\n2hrVObeW4nLTpS6XDVNGAZjhRg7e0P22NZlMAKxoeV1swomuJgHd1BqNRmg0bQdzuVwOq9UKqVTq\nx6yIiIjoajP1GjVGDApxOJ4UF2r79wnjj2iydv3cKQAopUoMU48EAIR/PhYKfTfLapXAgpZ/d9eo\ndhyfmXMYksauG1UREgUTptmmixsW4lKjpctlo6wy1wTX0wAAIABJREFU3NBuOkGtcLj+9mPdLdd5\nvOsPnrLXcu4njpRBNJi7XMIIQKJSQDJ6uBPxiKgvkQghnH+gw8dyc3MxYcIEZGRkAADS0tJQWFjo\n36SIiIiIiIgoYAT0Jc+JEydiz549AICDBw8iOTnZzxkRERERERFRIAnoK7XtP/0YAHJycpCYmOjn\nrIiIiIiIiChQBHRTS0RERERERNSdgL79mIiIiIiIiKg7bGqJiIiIiIgoaLGpJSIiIiIioqDFppaI\niIiIiIiCFptaIiIiIiIiClpsaomIiIiIiChosaklIiIiIiKioMWmloiIiIiIiIIWm1oiIiIiIiIK\nWmxqiYiIiIiIKGixqSUiIiIiIqKgxaaWiIiIiIiIghabWiIiIiIiIgpabGqJiIiIiIgoaAVEU3vo\n0CFkZ2d3mp+fn48777wTd999N9asWeP7xIiIiIiIiCig+b2pfe2117Bq1SqYzWa7+Y2NjXjxxRfx\n5ptv4u9//zsMBgMKCgr8lCUREREREREFIr83tQkJCXjppZc6zVcqlcjLy4NSqQQANDc3IyQkxNfp\nERERERERUQDze1Obnp4OmUzWab5EIkFUVBQAYPPmzaivr8eUKVN8nR4REREREREFMLm/E+iOEALP\nPfccTp06hQ0bNjj9MxKJxMuZEXkOa5aCDWuWgg1rloIR65bIeQHT1AohOs37/e9/D5VKhY0bNzod\nRyKRoKrK0Ot8YmI0ARXHk7H6ahxPxoqJ0XggG+ewZq/eOJ6MFYw160me3CeeEGj5AIGXU1+rWW9v\n32CP74t1+CK+L3mjbj29jbyxzRnT8zGvBgHT1Lb+JSo/Px/19fUYO3YstmzZgkmTJiE7OxsSiQSL\nFy/GzJkz/ZwpERERERERBYqAaGoHDRqEvLw8AMDs2bNt8w8fPuyvlIiIiIiIiCgI+P2DooiIiIiI\niIjcxaaWiIiIiIiIghabWiIiIiIiIgpabGqJiIiIiIgoaLGpJSIiIiIioqDFppaIiIiIiIiCFpta\nIiIiIiIiClpsaomIiIiIiChoyf2dABEREREReV/VySpcqqx1OC4LkUGb2A+AxHdJEXkAm1oiIiIi\noqtA3ek67PlDgcPx/hMGYMrjab5LiMhDePsxERERERERBS02tURERERERBS02NQSERERERFR0AqI\npvbQoUPIzs7uNH/37t1YsGABsrKy8N577/khMyIiIiIiIgpkfv+gqNdeew0ffPABwsPD7eY3Nzcj\nNzcXW7ZsQUhICBYtWoSbb74ZUVFRfsqUiIiIiIiIAo3fm9qEhAS89NJLePzxx+3ml5WVISEhAWq1\nGgAwadIkHDhwALfeeqtX82lEEz6qqsDJcj0SNVrM6zcUUj9vJiVOQnM6Hyg5hmhdMgyD7kYTIv2W\nz084iZ0VBSj74QSG65KQGX8HQv2YDwAYUYutFVttOS2IvwsqhPo1J29qRjM+PLsFR2tKMTpyFO6I\nm+9wWQmsUFd/Bpm+BBbdNTBGpUN0cZNG63KoOAKNegyORY3EzordKNOXd7ufbfENxyAJ0UDUX4Ql\nMgWwWmyxHK3TWfWWJmytLkNZXS2SInS4PToRMg/eaNIMC3ZcqsRxQw1GaCKRETUE0sC4kaXP66qW\npZDZLdNTDZ/HSWwuaqnVa6LGwCIsOFJTiuG6JAwKHYR0uQLqS/+EJLQfhLkREqUKkrrjkKhi0KQZ\nA2PkDJfq0worvjVcwDFDDUZGROGiuR7H6lyvnfZxRmgiIZVIUFp3CcmaSEzW9IeEX6nhd621dyxs\nIHZWFbUdD/ulIba+tMdjm6P6bkIztldX4LihFvFhGsQoQ5CmG+ywdiSwAie3QXf+YLfH8fakaIb2\n3JuQ1pbAqrsG+oH3wtrhvUVE1Ff4valNT0/HmTNnOs03Go3QaDS26fDwcBgMBq/n81FVBf5UcsA2\nLcYCC2KGe3293dGczof0wEoALfeLa64XqB78K7/ls7OiAM98m2ObFkIge8gDfssHALZWbA24nLzp\nw7NbsOKrVbZpMUXg4ZglXS6rrv4Mqr13AgAUAJD6LgzRGd0upwKwc9ILeObb3LZ1CCuyhzzo+OeS\nsoCyvJb1tPu3qpt1OuvNY0ew/odv22aMA+ZFJ7kdr6MdlyrxzPdf26bFeGBW1FCPxSfHuqrl+XF3\n2i3TUw1/VrHbrlYzh8/FP45vAwCsmrwCrwNYdugPLYMpjwNFz9mWVSVlAdZml+rzW8MF/Lao5Ssx\n5sQn4aPKsrb8Xaid9nE6xnph0nTcqBngdE7kHa211+l4ONmKZUW/7fHY5qi+P64+ZXdMe3hkCj6x\nVjisHXX1Z8DeO6FA98fx9rTn3oR8/38CaDl30N4oUDPw/h5eMRFRcPJ7U+uIWq2G0Wi0TZtMJkRE\nRDj1szExmp4XcuBkud5+2qhHzBj34/U2HwBAyTG7San+GGKu9V9OZT+csJ/Wn+j1a+ztz3sjJ19y\nNdfSwz/aT9f+6DhOxRG7SZXpCFSjFva4XJm+vNN0t/HNbe9Xu393t04nHT9p/0XxZQY9Yka5t3+7\neg3HK+zjHzfWImZk9/E9WV/BVKutPJVzV7Uck9Ihdg81XPaDfa2azCbbv090qGOYztpPm40u1+fJ\nqracTc1mu7H2tdPTNmofp2Oskw11mDNshNM5OSsYa81T3HrtV2qvq+Mh0LkWO67DUX2XnbQ/1/ip\n3oQ6c5Pj446zx/H2jpbYTcr1JYgZ77vjmr/W0ddq3NOvp670QrfjUokE0dFqSKXO373ijW3OmOSq\ngGlqhRB200lJSTh16hTq6uqgUqlw4MABLFnS9ZWojqqq3L+im6jR2k0PVWt7FS8mRtOrnweAaF2y\n3U1GVu0IVPsxp+E6+ytkSdphft9G3sjJl1zNdZRupN30SF2ywzga9ZiWq6VXNISPhsGJ5Tpv08Tu\n4yvabTOF/fZztE5njYjQ2eeice996ajWRqjt4w9X67qN74ma9XSsQK9ZR7qq5Y6xe6rhjrUarmj7\njIZh2kT7m3jDB9knoFC7XJ+JqrY/sIbLFXZjrbXjzH5tH6djrKGqCI9t41aerFtPCIaaba29ro6H\ngH0tdrV9HdV3UodzjQGh4YgNCXOYo7PH8fYitdfYneQ1a8eixkfHNX+twxfxfc3X71mrEKiuNgJO\nPv7gjW3OmJ6PeTUImKZWIml58+Tn56O+vh4LFy7Ek08+iQcffBBCCCxcuBCxsbFez2Nev6EQY1uu\n0A5VazE/ZqjX19kTw6C7obleQKo/Bqt2BAyD7/FrPpnxd0AIgTL9CSRph2HBkLl+zQcAFsTfZZfT\nwiFZ/k7Jq+6Imw8xReBoTSlGRY7E3LhMh8sao9KB1HdbnkfUjoUx+pZul1OZjqAhfDQyo2+AEFaU\n6cuRpE3EgiHzuo9vOA7Jjf/W8kytLgUYNM8Wy9E6nbV4+DWwCmH3TK0nZUQNgRgPHDfUYLgmErdF\nDfFofHLMmVruqYZbjkkttTo2agyswoIQWQiGaRMxOGwwbpErYU55uu2Z2hv/GxLDcUhU/dCkHgNj\n1M0u5TxZ0x8vTJp+5ZnaSFwbFYtjda7XTvs4IzQ6SCVSDAnVYIQmEjdo+ruUE3lHa+1lho2CmNzu\neBgzAw2p7/Z4bHNU37dHJ0IyToLjhloMDlMjRqnCdN3gbvNQ3bIV5vMHuz2Ot6cfeC+0N4orz9SO\nhX5g52+ZICLqKySi4yXSPsBTVz0CKY4nY/XVOJ6MFQxXEDrq6/ujL8bxZKxgrFlPCsSrkIGUDxB4\nOfW1mu0LVyG5jXqO72uefj11pRfw+drPHY73nzAAUx5PA6/U9q2YVwN+vCcREREREREFrYC5/ZiI\niIiIiLynIVyG+PvHORwPi1ABEHD2Si1RoGBTS0RERER0FThvbcZTBd85HL8+eSCevdFzX5lH5Cu8\n/ZiIiIiIiIiCFptaIiIiIiIiClpsaomIiIiIiChosaklIiIiIiKioMWmloiIiIiIiIIWm1oiIiIi\nIiIKWmxqiYiIiIiIKGixqSUiIiIiIqKgxaaWiIiIiIiIghabWiIiIiIiIgpafm1qhRBYvXo1srKy\nsHjxYlRWVtqN79y5E5mZmVi4cCHefvttP2VJREREREREgUruz5Xv2rULTU1NyMvLw6FDh5CTk4ON\nGzfaxnNycvDBBx9ApVJh1qxZmD17NjQajR8zJiIiIiIiokDi16a2qKgIU6dOBQCkpKSguLjYblyh\nUECv10MikQCA7f9EREREREREgJ+bWqPRaHflVS6Xw2q1QiptuSv6wQcfRGZmJsLCwpCeng61Wu2v\nVImIiIiIiCgA+fWZWrVaDZPJZJtu39CeO3cOb775Jnbv3o3du3ejuroaO3bs8FeqREREREREFID8\neqV24sSJKCgoQEZGBg4ePIjk5GTbWGNjI2QyGZRKJSQSCaKiolBXV+dU3JgYzzx3G2hxPBmrr8bx\ndCxfCbTtGIj7o6/G8XQsXwnEnAMtp0DLBwjMnHzFF6/d2+sI9vi+WEdfq3FPv57YygqsmJnkcFwT\nEY7oaLXtIpMzvLHNGZNc5demNj09Hfv27UNWVhaAlg+Gys/PR319PRYuXIi5c+ciKysLKpUKQ4YM\nwbx585yKW1Vl6HVuMTGagIrjyVh9NY4nY/n6gBJI2zFQ90dfjOPJWMFYs57kyX3iCYGWDxB4OfW1\nmvX29g32+L5Yhy/i+5qnX09U7Xlocx53OK6dPBnV148H4Nzn2HhjmzOm52NeDfza1EokEqxdu9Zu\nXmJiou3f999/P+6//34fZ0VERERE1PdcjpMhNOc+h+NyVTQAAWebWqJA4demloiIiIiIfKMurAZv\nmZY7HE8OuRmL4dydkUSBxK8fFEVERERERETUG2xqiYiIiIiIKGixqSUiIiIiIqKg5dQztddeey0k\nEgmEEGhoaIBarYZMJoNer0d0dDS+/PJLb+dJRERERERE1IlTTe13330HAFi5ciWmTZuGW2+9FQDw\nxRdfID8/33vZERERERGRR+gaY5DZ/zmH42GhUeCnH1MwcunTjw8fPow//vGPtumpU6fiT3/6k8eT\nIiIiIiIiz6qzJMBwOdThuBxKH2ZD5DkuNbXh4eF477338LOf/QxCCGzZsgWRkZHeyo2IiIiIiDzk\npEKJBRWNDsdv7i/H27xKS0HIpQ+Kev755/H555/j3//935Gamop//vOfeP75572VGxEREREREVG3\nXLpSGxcXh5dfftlbuRARERERERG5xKmm9uGHH8Yrr7yCGTNmQCLpfEvC559/7vHEiIiIiIiIiHri\nVFP7zDPPAAA2b97s1WSIiIiIiIiIXOFUUxsbGwug5fbjt99+G/v370dzczNuvPFG3HvvvV5NkIiI\niIiIiMgRl56pfe6553Dq1ClkZmbaPv349OnTWLlypbfyIyIiIiIiInLIpaZ237592LZtG6TSlg9N\nTktLw5w5c9xeuRACa9asQWlpKZRKJdatW4f4+Hjb+Pfff4/169cDAPr374/169dDoVC4vT4iIiIi\nIiLqW1z6Sh+LxYLm5ma7aZlM5vbKd+3ahaamJuTl5WH58uXIycmxG3/66aeRm5uLt956C//2b/+G\n06dPu70uIiIiIiIi6ntculI7Z84cLF68GLNmzQIAfPzxx5g9e7bbKy8qKsLUqVMBACkpKSguLraN\nlZeXQ6fT4fXXX8exY8eQlpaGxMREt9dFREREREREfY9LTe1DDz2E0aNHY//+/RBCYOnSpUhLS3N7\n5UajERqNpi0ZuRxWqxVSqRQ1NTU4ePAgVq9ejfj4eDz88MO45pprcMMNN7i9PiIiIiIiIupbXGpq\nFyxYgK1bt2LatGkeWblarYbJZLJNtza0AKDT6TBkyBDb1dmpU6eiuLjYqaY2JkbT4zLOCLQ4nozV\nV+N4OpavBNp2DMT90VfjeDqWrwRizoGWU6DlAwRmTr7ii9fu7XUEe3xfrKOv1bjHX09dfbfDEqkE\n0dFq2/m4M7yxzRmTXOVSUxsdHY1//vOfGD9+PJRKZa9XPnHiRBQUFCAjIwMHDx5EcnKybSw+Ph6X\nL19GZWUl4uPjUVRUhAULFjgVt6rK0OvcYmI0ARXHk7H6ahxPxvL1ASWQtmOg7o++GMeTsYKxZj3J\nk/vEEwItHyDwcuprNevt7Rvs8X2xDl/E9zVfv2eFVaC62ghA4tTy3tjmjOn5mFcDl5ra4uLiTt9L\nK5FIcOTIEbdWnp6ejn379iErKwsAkJOTg/z8fNTX12PhwoVYt24dli1bBgC49tprPXaFmIiIiIiI\niPoGl5ra/fv3e3TlEokEa9eutZvX/sOgbrjhBrz33nseXScRERERERH1HU41tRs2bOh2/Fe/+pVH\nkiEiIiIiIiJyhVNPgYeFhSEsLAyHDx9GYWEh1Go1dDodvvnmG5SXl3s7RyIiIiIiIqIuOXWl9sEH\nHwQA7NixA2+99RZCQkIAAHfeeSfuuece72VHRERERERE1A3nP68bQG1tLaxWq226qakJdXV1Hk+K\niIiIiIiIyBkufVDUXXfdhfnz5yMtLQ1WqxUFBQV44IEHvJUbERERERERUbdcamoffPBBTJ48Gd9+\n+y0kEglefPFFjBo1ylu5EREREREREXXLpduPm5ubcfHiRURFRSEyMhJHjx7Ftm3bvJUbERERERER\nUbdculK7fPlynD17FklJSZBIJLb5c+fO9XhiRERERERERD1xqaktLS3FJ598YtfQEhERERFR4JMC\nGBjq+PQ/OkQOQADguT4FF5ea2qSkJFRVVSE2NtZb+RARERERkRdcU34Zr3xW43BcO9oKTPJhQkQe\n4lJT29DQgIyMDCQnJ0OpVNrmv/HGGx5PjIiIiIiIPEcYLTj35kmH49abBwAPjfZdQkQe4lJT+/DD\nD3eax1uRiYiIiIiIyF9camonT55s+3dTUxM+/vhjvPPOO8jLy/N4YkREREREREQ9campBYCysjK8\n8847+OCDD6DVarF48WK3Vy6EwJo1a1BaWgqlUol169YhPj6+03JPP/00dDodli1b5va6iIiIiIiI\nqO9xqqk1m8349NNP8c477+Do0aNIS0uDQqHAjh07enX78a5du9DU1IS8vDwcOnQIOTk52Lhxo90y\neXl5+PHHH+2uEhMREREREREBTja1qampmDhxIu677z6kpqYiJCQEN998c6+fpy0qKsLUqVMBACkp\nKSguLrYb/+677/DDDz8gKysLJ06c6NW6iIiIiIiIqO9xqqmdO3cuPv30UxgMBlRXV+PWW2/1yMqN\nRiM0Gk1bMnI5rFYrpFIpqqqqsGHDBmzcuBHbt2/3yPqIiIiIiIiob5EIIYQzC1osFuzZswdbtmzB\nl19+CQDIzc1Feno6ZDKZWyvPzc3FhAkTkJGRAQBIS0tDYWEhAGDz5s3Ytm0bwsPDUVVVhcbGRvzn\nf/4n5s6d69a6iIiIiIiuZmXbyvD5vM8djg+cORCzdsyCVCr1YVZEvef0B0XJZDLMmDEDM2bMwKVL\nl/Dhhx9i48aNWLduHb744gu3Vj5x4kQUFBQgIyMDBw8eRHJysm0sOzsb2dnZAICtW7eivLzc6Ya2\nqsrgVj7txcRoAiqOJ2P11TiejBUTo+l5IQ8KpO0YqPujL8bxZKxgrFlP8uQ+8YRAywcIvJz6Ws16\ne/sGe3xfrMMX8X3N1+9ZqxCorjYCcO4RQ29sc8b0fMyrgVt/homKisL999+PDz/8EC+//DIA4Pe/\n/73LcdLT06FUKpGVlYXc3Fw8+eSTyM/Px3vvvedOWkRERERERHSVcfkrfToaO3YsAHT6kCdnSCQS\nrF271m5eYmJip+XmzZvnXnJERERERETUp/GGeSIiIiIiIgpabGqJiIiIiIgoaLGpJSIiIiIioqDl\nsabWyW8GIiIiIiIiIvKYXjW1RqPR9u8pU6b0OhkiIiIiIiIiV7jU1BYUFOD555+HyWTCbbfdhptv\nvhlvvfUWAODxxx/3SoJEREREREREjrjU1G7YsAHz58/H9u3bMX78eOzevRv/+Mc/vJUbERERERER\nUbdcvv04KSkJhYWFmDFjBsLDw2E2m72RFxEREREREVGPXGpq+/Xrh2eeeQbFxcWYOnUqcnNzERcX\n563ciIiIiIiIiLrlUlP75z//GZGRkRg3bhweffRRREVF4b/+67+8lRsRERERERFRt+SuLPzSSy/h\n1KlTyMzMhBACW7ZswcWLF7Fy5Upv5UdERERERETkkEtN7b59+7Bt2zZIpS0XeNPS0jBnzhyvJEZE\nRERERETUE5duP7ZYLGhubrablslkHk+KiIiIiIiIyBkuXamdM2cOFi9ejFmzZgEAPv74Y8yePdsr\niRERERERERH1xKWmdunSpRg9ejT2798PIQSWLl2KtLQ0t1cuhMCaNWtQWloKpVKJdevWIT4+3jae\nn5+PN954A3K5HMnJyVizZo3b6yIiIiIiIqK+x6WmFgCmTZuGadOmeWTlu3btQlNTE/Ly8nDo0CHk\n5ORg48aNAIDGxka8+OKLyM/Ph1KpxPLly1FQUIDp06d7ZN1EREREREQU/Fx6ptbTioqKMHXqVABA\nSkoKiouLbWNKpRJ5eXlQKpUAgObmZoSEhPglTyIiIiIiIgpMfm1qjUYjNBqNbVoul8NqtQIAJBIJ\noqKiAACbN29GfX09pkyZ4pc8iYiIiIiIKDBJhBDCXyvPzc3FhAkTkJGRAaDlK4IKCwtt40IIPPfc\nczh16hReeOEF21VbIiIiIiJyTdm2Mnw+73OH4wNnDsSsHbNsX99JFCxcfqbWkyZOnIiCggJkZGTg\n4MGDSE5Othv//e9/D5VKZXvO1llVVYZe5xYTowmoOJ6M1VfjeDJWTIym54U8KJC2Y6Duj74Yx5Ox\ngrFmPcmT+8QTAi0fIPBy6ms16+3tG+zxfbEOX8T3NV+/Z61CoLraCEDi1PLe2OaM6fmYVwO/NrXp\n6enYt28fsrKyAAA5OTnIz89HfX09xo4diy1btmDSpEnIzs6GRCLB4sWLMXPmTH+mTERERERERAHE\nr02tRCLB2rVr7eYlJiba/n348GFfp0RERERERERBhDfMExERERERUdBiU0tERERERERBi00tERER\nERERBS02tURERERERBS02NQSERERERFR0GJTS0REREREREGLTS0REREREREFLTa1REREREREFLTY\n1BIREREREVHQYlNLREREREREQYtNLREREREREQUtNrVEREREREQUtNjUEhERERERUdDya1MrhMDq\n1auRlZWFxYsXo7Ky0m589+7dWLBgAbKysvDee+/5KUsiIiIiIiIKVH5tanft2oWmpibk5eVh+fLl\nyMnJsY01NzcjNzcXmzZtwubNm/HOO+/g0qVLfsyWiIiIiIiIAo3cnysvKirC1KlTAQApKSkoLi62\njZWVlSEhIQFqtRoAMGnSJBw4cAC33nqrV3Oqv1SD6n9Vo+SMHtpBWkTfFI3Q0EivrrPHnOpPosSY\njwvlxxCrSUZK+N1Q+DEnQ70VhSWXUXnhIhL6h2LG+FCEKngne8Bqbobsi/dhLj8CxbAxsKQuAKSy\ntvH6k5AV7oL5p7OoVc5E+cUIaMf1Q9T0/oBEAgCw1p+Eot0y+jodFAol6o7XIWKEFt+PjsL0aKB6\nbzX0x/WIfliH05pPcKH8GPprkjH+Ss02Wy0o/P4Uys5dwh3XJuFARRUqztdiyAAdZo6LRahK1+VL\nqKioQPWX1dCf0UM3WAdltBKXSi8hvF846s7VQTswAlE3DURoaHhLvhAorK5Hib4BY3UqXK+WonlH\nPs6dLEdo4jCE3HY7QmWK3m9acyNC9vyA5jMXIR/UD+ZpEyF19r0grJAWn0TN2SpI42JgvSbRtr2v\nelYr6r/7Bk1lP6LpUjU0Q+OhSp8HyK78ympfs5p0nB5Shkuqk4hqHol40/VoOt+A6qFHMaCqAc2V\n56GKiUWjvhay/lGQWaVoqDiNkIFxOBo7HIf1FkTqIhCuUqCqxoiJcjVw2oCwqFDIFDIYzhuhjYsA\nABgvGhGmC0PdmTpoBmkgk8tQe6oW2sFaSJQSNOob0VjXCFVUKE5FhODQMB2KapsQH66AkEhwxtiE\nBE0IzhqboAmRY1CoHPP6h0Pq6O/LlmbI934Pc0UVFIP7wSyXQRYVYV8rV+rIcuo8ZAn928a6mu9t\njnIJZq21VlkGxdBRgETSciyNHw7jtXdg95kefgdaraguvAB9SQ10YyNtx9V6Sw0uFl5A3WkDIuI1\niIgJg/WSEpGpsW3bzGrBuX2VqDutR3j/cJQb9FCEhqPuJyO0g3Qw1ZkQpgmHqcYEZVgI5Co5JAAa\nLjdCFRqC+rp6hEaEQn/lfCYiMQL7q2sw3CBDw08GhEWFQRoRgiZ9A8y1DTgRqUKzVoGQZgGr3owG\nQyMaYlU4H6XAhNH98FG1wHF9I5J1KsyMkmNwaCgKqxuwv/oy+qnk0ColuNBgxcUGMzITQnCgWuDY\nleXvHqRBqLvXUVytq/bvmyExaE6dAMh4jkLU1/m1qTUajdBoNLZpuVwOq9UKqVTaaSw8PBwGg8Hr\nOVX/qxoH/nbANn09rsfgmf5takuM+dhWsrJtxliB60J/5bd8CksuY+O2s7Zpq4jDnOvUfsuHuif7\n4n3UvLDCNh0pBCzTs9rGC3eh5uVnUT9xFb566ZRtfmredETPGAAAUHRYJilLjrK8Mtuy1627HtUA\nDjzV8t6Jf7TarmbFlZot/P4Unn9nHwBgYHQEXtr6TVui4gbMmdx1U1v9ZTUOvN72vrzu/uvQaGjE\n4Q8P2+ZdL67H4PThAIDC6npk7T1hGyuVH0X5X1+wTScKgdA5C7rZas4J2fMDav+2wzatA2CZeZ1T\nPystPgl9ztu2ae2Ti2AdN6zXOfUF9f/6BjWFn+OnTz+1zRtjtUL1s7sA2Nds6bAf8NnlR4HLLcvN\nk7wMU70Rw85ZcPKvb9l+fkBGBmRyBco2bQIg9FdjAAAgAElEQVQAXFq0FM9/01Y/t04ejqGXJTj6\nccu8pOlJKCtoq/Gk6UlQx6jtfj+0X2ZS9iQc/PtBu7HhDRb8h74eWUnRyCurto1lJUXjr4fPIysp\nGk1WgUUDI7rcDvK936Pmfz6xTeuyZ6L2lbftasVRHXU1HzNSulyPp/TFmm6ttVbq9Pkw7twCAIh8\nWGDDV6nd/g6sLryAvVkFtunW4+rFwgv45//+yzb/uiUTUfPBOVgtwnbcPfdVJfb/v6+RND0Jhz86\njJQ7U3Bg03e2n0m5MwUH/nagZXxry/ihdw9hUvYkHPjbAdt4q+sfuB6jIcM/2x1LU+5MwQ/vHrJN\nJ01PgjxGjUPt5olZifi22YKnzjTZ5lmvHwy1zILf7K+wzXs8ZSCeO3QOABCvHoynDpxui3F9PP5j\nsNbhdu6Oq3XV8X0TCaB5+kS31k1EwcOvTa1arYbJZLJNtza0rWNGo9E2ZjKZEBHR9S/+jmJiND0v\n5EDJGb3dtP6MHtf2Il5v8wGAC+XH7KeNxxAzxn85VV642GG6ATExA/2Wjzdj+Yqncu4qTlX5Ubtp\nS/lRxNzZttz5ypaT8rpqDQCrbb6ptA6j7hrR5TJmo9kuZt1xPSDapi8Yu67ZEz/V2OZVnq+1W6bi\nfK3D7dDxfVl3tg7mevsc2r9XSyvsl284Wd5purfbPCZGgwtn7N8LzWcuItbJuDVnq+ympWerEO3l\npsOTvPk+K6soh6W+3m6eqfwk4q+ss3091l1/yG65avmPaIpugigPt5tvqa9HY1XbNj8jQmHrhAHU\nN5oRapCi4cp0x/oy15thqjZ1mteq7nxdpzHraT2gUcJottqNtU4bzVYc1jcgZvygTtsAAC5U2NdI\n8/mWR3Da14qjOupqPuDd/RboNe3Oa2+ttVbWy201YD5dBiDV7ndgx3VUlP5oN916XC05bf9H+rrT\nBpiNZrvj7tFTLcfI1jrrWH+t0x3HW2ux4/L6DsfRrpbpqs5DDc04cb4WQJht3jF9A0I6XP08azLb\njbd3TN+AmGsHA3B9P7haV+YO7xtzRZXTx+VA5On3bB0udDsulUgQHa22nY87wxvHFcYkV/m1qZ04\ncSIKCgqQkZGBgwcPIjk52TaWlJSEU6dOoa6uDiqVCgcOHMCSJUuciltV5f4VXe0gbafp3sSLidH0\n6ucBIFaTbD+tHuHXnBL6h9pNx8eq/L6NPB3L1wcUT+XcVRzZsNH204mj7JZTxLdc3YzoZwDQ1giE\nj4ywLddxGYXG/tbdiOFaSNr9/nNUs8MGRtnmDRlgf1V2SH+dw+2g6/AX/oi4CFjMFrt57d+rI9X2\n+YUm2v9VXzU00SM1Kx/Uz26+fFA/p+NK42Lspq1xMb3OyZc89Z7tiiIhEfJy+z9EhCcO7bIeIy4l\ntj/XRnRzMkyXTJAm2NeHLDQUqv79bdODJPYn3aEhCjREtBWxItS+hhShCoT3C+80r1VE/4hOY/LB\nWkBfD41CZjemvnKrqlohxRit4+OnYoh9jcj7t7x/2teKozrqaj7g3f3mak0HQ8221loraVhbDSgG\nJwEVbb8DuzoGq0fa10XrcTUi3v61RwzWwKK22h13tQktx8jWOutYf+HR4V2OR1y58t9xee0gbfu/\nPXa5jCJUYYvbql4jR3x/HdDuSu0IrQoamX1dDwpvez+M1NqfJ4zQOt5GPXGlrmJiNJ3eN4ohvTu2\ndozva958z3bFKgSqq40AnHt0wJPncIzpvZhXA4kQouMxzmeEEFizZg1KS0sBADk5OSgpKUF9fT0W\nLlyIwsJCbNiwAUIILFiwAIsWLXIqbm+Kob6+BtX7qm3PoPT2mVpPFKe5vgaHjG/hgvEYYtUjkKK+\np1fP1PY2p3qzFbsOXUblhQbEx6owMyWsV8/Usqn1blMLqwWyPe+1PAeWOBqWaQs7PFNbA1nhP9qe\nl70YgYhx/RDd7plaUV8DeftlDDoo5C3P1GqGR+CHMdGYPgyo/qjlmdrEh6NxWJPfqWatVit2f3+y\n5ZnatCQc+OHKM7X9dZg53vEzteb6Gpy/8kytdrAWIdEhLc/URoej7qc6RAyIQPS/tz1TKyBQ0PpM\nrVaFf9Mp0LD9AzScLIdqaCJUP7ujV8/Utm5rq9kKxZ5/uflMrYC0uBzSs1WweuCZ2mCsWYfElWdq\nj/+Ipuorz9Te0v6Z2raa1WtuQWXCcVwKOYko80jEX74R5vOXcXFoKQZcrEdzxXmE9OuHpjo9ZAOi\nIbNI0VBRCeWAgSiNHYHDdW3P1F6sNeJaeThQaURoVCjkChkMF648Uys6PFMbp4FM0fJMbcTgCEiV\n0rZnaiNVQIQK3yddeaY27MoztaYmDFGH4Jyp5ZnaOJUM8weou3mm1gr53oM9PFPbUkedn6ntPD8m\nNsLL+81BLg4ERc221lplGRRDRwIS6ZVnapNgvHYudp1u+x3Y5TFYCFQXnIe+pAbasZG246rZchk/\nFVa2PFM7WIOI2DBYLikRZfdMrRXnvqpAXaUe4bHhMBvbnqmNGKTD5ToTwiLCYbpkgjI0BLIwOaRW\noKG+3TO1mlDoz+qhjdMiYlgE9l+68kztuZbnxqVaFZpqW56pDdGp0KyTI8QCWGtbnqmtj1XhQqQc\nE8bE2J6pHaENQXq0AvGhoSho90xthFKCqivP1N6bEIK9V56pHaFV4Z7BLc/UuvU72oW6ionRoOon\nfdv7xsPP1PaFprZu3wV8Pu9zh+P9bx6AKW+ngU1t34p5NfBrU+stXm0Q/BTHk7H6ahxPxgqKk60O\n+vr+6ItxPBkrGGvWk7xxItAbgZYPEHg59bWa9fb2Dfb4vliHL+L7GptaxvREzKsBPw6OiIiIiIiI\nghabWiIiIiIiIgpabGqJiIiIiIgoaLGpJSIiIiIioqDl16/0ISIiIiIi6rvElf964v43IBCbWiIi\nIiIiIq85Wvc96i2NDsdDZSEYFZHiw4z6Hja1REREREREXvKbrx9HueGkw/FEzVDsuHWH7xLqg/hM\nLREREREREQUtXqklIiIiIroKhMY0YMorYx2OyyNC0PL8J5/vpODCppaIiIiI6CoQ3nwS9ZsWOxxX\nTEoFbv4/H2ZE5Bm8/ZiIiIiIiIiCFq/UEhEREREReYVAvHpwt0u0jDvztT/kCJtaIiIiIiIiL3k3\nPARSSajDcWtYCOp8mE9fxKaWiIiIiIjIKySQV+2HzFjmcAmLOgn8cK7e8VtT29jYiMceewzV1dVQ\nq9XIzc1FZGSk3TKbNm3C9u3bIZFIkJqail/+8pd+ypaIiIiIiIgCkd+a2rfffhvJycn41a9+he3b\nt2Pjxo146qmnbOOVlZXIz8/H+++/DwBYtGgR0tPTkZyc7K+UiYiIiIiC1kXNUFT94hWH4ypNOAbx\nK30oCPmtqS0qKsJDDz0EAEhNTcXGjRvtxuPi4vDaa6/ZppubmxESEuLTHImIiIiI+ooK9MdjXzQ4\nHL8uORTPTGFDS8HHJ03t+++/j//7P/vvvOrXrx/UajUAIDw8HEaj0W5cJpNBp9MBANavX48xY8Yg\nISHBF+kSEREREfU5kRo57poW6XA8Lrp9a2DtIVrrN4P2tJwry/a1mC3LWdXd9zA9jVPPJEIIv3x+\n9K9//Wv8/Oc/x7hx42A0GrFo0SJ89NFHdss0NTXhySefhEajwerVqyGR8C9HRERERERE1Eba8yLe\nMXHiROzZswcAsGfPHlx33XWdlnnkkUcwevRorFmzhg0tERERERERdeK3K7UNDQ144oknUFVVBaVS\niT//+c+Ijo7Gpk2bkJCQAIvFguXLlyMlJQVCCEgkEts0EREREREREeDHppaIiIiIiIiot/x2+zER\nERERERFRb7GpJSIiIiIioqDFppaIiIiIiIiClk++p9bThBBYs2YNSktLoVQqsW7dOsTHx9vGN23a\nhPfffx9RUVEAgD/84Q8YOnSow3iHDh3Cn/70J2zevNlu/u7du7Fx40bI5XJkZmZi4cKF3eblKI4r\n+TQ3N2PlypU4c+YMzGYzli5dihkzZricU09xnM3JarVi1apVKC8vh1Qqxdq1azF8+HC3tlFPsVzd\nb9XV1cjMzMTrr7+OxMREt3LqLo6r+TjS1b4YPnw4VqxYAalUihEjRmD16tUAgHfffRfvvPMOFAoF\nli5dirS0tG7zlclkbsV59dVXsXv3bjQ3N+Pee+/FxIkT3YojhMBTTz2F8vJyyGQyPPPMMy7n1P59\nU1FR4fTPNjY24rHHHkN1dTXUajXuvfdevPrqq9i8eTOOHDmCZ599FjKZDEqlEs899xyioqJcjtPq\no48+wltvvYW8vDyn88nNzUVFRYXttV26dAmrVq2CwWCAEALr16/HoEGDXM7pxIkTWLVqFSQSCYYO\nHYp169a5lFNkpOPvJ+xJT8deb5s/f77t+80HDx6MpUuXuv0+6g1P1Wxv94ejnI4cOYKHH37Ydrxa\ntGgRbrvtNp/k1NvjXaDXrLdq0Ns15c368PY+7yr+wIEDPfoaujo/USqVPq1bZ2Js2rQJ27dvh0Qi\nQWpqKn75y192Gaununf1PMmZmPn5+XjjjTcgl8uRnJyMNWvW9Dpmq6effho6nQ7Lli3rVbzvv/8e\n69evBwD0798f69evh0Kh6FXMnTt34uWXX4ZUKsX8+fOxaNGiHl93K0/1H87EdGf/BB0RhD777DOx\nYsUKIYQQBw8eFI888ojd+KOPPipKSkqcivU///M/Yvbs2eKuu+6ym282m0V6erowGAyiqalJZGZm\niurqapfjuJrPP/7xD/HHP/5RCCFEbW2tSEtLcyun7uK4ktPOnTvFypUrhRBCfPPNN3bb2tVt1F0s\nV3JqXfcvf/lLceutt4oTJ064nZOjOK7m0532+0Kv14u0tDSxdOlSceDAASGEEE8//bTYuXOnqKqq\nErNnzxZms1kYDAYxe/Zs0dTU1G2+7sT55ptvxNKlS4UQQphMJvHXv/7V7Xz27t0rfvvb3wohhNi3\nb5/49a9/7VKsju8bV3729ddfF//93/8thBBi+fLlYvLkybY49957rzh69KgQQoi8vDyRm5vrVhwh\nhCgpKRH33XefbZ4zcT7++GORlZVl99pWrFghPvnkEyGEEPv37xcFBQVu5fS73/1O7N271zbmbJyP\nP/5YPPvssz1Ua/d6OvZ6U2Njo5g3b57dPHfrtjc8VbOe2B+Ocnr33XfF66+/breMr3Lq7fEukGvW\nWzXo7Zrydn14e593dT7z3nvvefQ1dHV+4uu67SlGRUWFyMzMtE1nZWWJ0tLSLmN1V/eunic5E7Oh\noUGkp6eLxsZGIYQQy5YtE7t37+5VzFZvv/22uOuuu8Sf//znXse74447REVFhRCi5X3Q8bzPnZjT\np08XdXV1oqmpSaSnp4u6uroeYwrhuf7DmZju7p9gE5S3HxcVFWHq1KkAgJSUFBQXF9uNl5SU4JVX\nXsHdd9+NV199tdtYCQkJeOmllzrNLysrQ0JCAtRqNRQKBSZNmoQDBw64HMfVfG677Tb85je/AdDy\nl0O5vO1iuis5dRfHlZxmzpyJZ555BgBw5swZaLVat/LpKZYrOQHA+vXrsWjRIsTGxtrNdzUnR3Fc\nzac77feFxWKBTCbD4cOHbd/NnJqaiq+++grff/89Jk2aBLlcDrVajaFDh6K0tNRhvkIIt+J8+eWX\nSE5Oxi9+8Qs88sgjmDFjhtv5hISE2K48GgwGyOVyl2J1fN+UlJQ49bNHjx5FUVERUlNTbcu2r6e/\n/OUvGDlyJICWv/IrlUq34tTU1OCFF17AU089ZZvnbJxz587ZvbZ//etf+Omnn/DAAw8gPz8fN954\no1s5hYSEoLa2FkIImEwmyOVyp+N8/fXXDqrUOT0de73p6NGjuHz5MpYsWYL7778fhw4dcrtue8OT\nNdvb/dFdToWFhbj33nuxatUqmEwmn+XUm+NdoNest2rQ2zXl7frw9j7v6nympKQEBQUFHnsN7c9P\nzp49C61W6/O67SlGXFwcXnvtNdt0c3MzQkJCHMZyVPeunic5E1OpVCIvLw9KpbLH3JyNCQDfffcd\nfvjhB2RlZfUYq6d45eXl0Ol0eP3115GdnY26ujq7u/PczVGhUECv16OxsREAIJFInMrVU/2HMzHd\n3T/BJiibWqPRCI1GY5uWy+WwWq226VmzZmHt2rV44403UFRUhD179jiMlZ6eDplM1uM6wsPDYTAY\nXI7jaj6hoaEICwuD0WjEb37zG/zud79zK6fu4riak1QqxZNPPol169Zhzpw5buXTUyxXctqyZQui\no6Nx0003QXT4RipXcuoujiv59KSrfdF+feHh4TAajTCZTHa5h4WF2eXeVb7t697ZODU1NSguLsaL\nL76INWvW4NFHH3UrDgBMmjQJjY2NyMjIwNNPP43s7GyXXlvH942zP9s6v/U2wDlz5uDy5cu2Zfr1\n6wegpZH8+9//jvvvv79TbfQUp/V2tBUrViA0NNT2c87ECQ8PBwC713bmzBnbL9QBAwbg1VdfdTkn\nAMjOzsazzz6LWbNm4dKlS5g8ebLTORmNRvRGT8deb1KpVFiyZAn+93//11a37ryPestTNeuJ/eEo\np5SUFDz++ON48803ER8fjw0bNvisRnpzvAv0mvVWDXq7prxdH97e5x3j//a3v8X48ePxxBNPeLTG\nW89Pnn32WcyePdurdfv+++9jzpw5dv8ZjcZuY8hkMuh0OgAtf+AeM2YMEhISuozfXd27c+7WU0yJ\nRGJ7VGvz5s2or6/HlClTehWzqqoKGzZswNNPP93lOZqr8WpqanDw4EFkZ2fj9ddfx1dffYVvvvmm\nVzEB4MEHH0RmZibmzJmDtLQ02z7siaf6D2diurt/gk1QNrVqtRomk8k2bbVaIZW2vZT77rsPOp0O\ncrkc06ZNw+HDh91aR/sDislkQkREhFv5uprPuXPncN9992HevHn42c9+5nZOjuK4k1NOTg527NiB\nVatWoaGhwa18uovlSk5btmzBvn37kJ2djaNHj+KJJ55AdXW1yzl1F8eVfJzRfl/MmjXLrl5bc+wp\n9/b5lpaW4oknnkBNTY3LcXQ6HaZOnQq5XI7ExESEhIR0ubwz2/K1117DxIkTsWPHDnz44Yd44okn\nYDab3YoFwKXt0v440P6EotX27duxdu1avPrqq4iMjHQ5TklJCSoqKrBmzRosX74cx48fR05OjtNx\n2v9Sat3u06dPBwDMmDEDxcXF0Gg0Lr+2xx57DH//+9+xfft23H777cjNzXU6TsecXNXTsdebhg4d\nittvv932b51OZ/d+dbXWPKU3Ndvb/eHIzJkzMWbMGNu/jx496rMaAXp3vAvkmvVVDXp7e3mjPry9\nzzvG91aNtz8/ab3y5qnX0N6CBQvw0Ucf2f3nTIympiYsX74c9fX13T4T2V3du1ufPb2XxJXPivj6\n66+xYcOGHuP1FPPTTz9FbW0tHnroIbz66qvIz8/Htm3b3I6n0+kwZMgQJCYmQi6XY+rUqU7dudFd\nzHPnzuHNN9/E7t27sXv3blRXV2PHjh1Ovfbu1ueN32Hu7J9gE5RN7cSJE21XzQ4ePIjk5GTbmNFo\nxJw5c1BfXw8hBPbv34+xY8f2GLPjX4GSkpJw6tQp1NXVoampCQcOHMCECRNcjuNqPhcvXsSSJUvw\n2GOPYd68eW7n1F0cV3Latm0bXnnlFQAttz1KpVLbm9nVbdRdLFdyevPNN7F582Zs3rwZo0aNwvr1\n6xEdHe1yTt3FcbeOutLVvhg9erTtdpK9e/di0qRJGDduHIqKitDU1ASD4f+3d+9xNedpHMA/pwux\nKJdiSuYlWlFKskiiWjssopkSRrLrZRm62HHSMKqhYiXFyCWZrXFpRrNTMW4NI5dGdJgdK5cQ05Sa\nlEjqdFHn2T/Oq9906HJOis7M8/4rOr/n95zf9znf8/3+Lt+e4f79+zA1NW0y302bNsHe3l7lODY2\nNkhLSwMAPHz4EJWVlRg7diwkEolKcQBAKpUKE67u3bujtrYWw4YNa1UsABg2bJjS78fa2lroB86d\nOwdLS0shzuHDhxEfH4/9+/fDyMgIAGBpaal0HCLC8OHDceTIEezbtw+RkZEYPHgwVq9erXSc+tvW\nGh73+t9fvnwZpqamrXpvlZWVwjHv27cvysrKlI7zYk6qaq7vbW9JSUnYuHEjAHndlpeXw87OrtW1\n1lZepWZftT2asmjRImRmZgIALl68CHNz89eW06v2dx25Zl9XDbZ3TbV1fbR3mzcWv63fQ2PjEwsL\nC6Xbti3qtmGtNhVj6dKlGDp0KNauXdvsba7N1X1rx7ctfZYCAwPx/Plz7Ny5U7jN9VVizp8/H4mJ\nidi3bx8WL16M6dOnw8XFpdXxjI2NIZVKkZeXB0B+W3HDxUpbE7O6ulpYkLL+amhZWZlS771eW80/\nmosJtK591I2IlL2m34FQg5XIAPmZtRs3bqCyshKzZs3CsWPHEBcXh86dO8PW1hbe3t7NxsvPz4dY\nLMbBgwdx9OhRIc7Zs2exfft2EBHc3NxaXNGsqTiq5LN+/XqcOHECJiYmICKIRCK4u7urnFNLcZTN\nqaqqCqtWrcKjR49QW1uLxYsXQyqVtuoYtRRL1XYDAE9PT6xbt06h/VVtt6bitCafxjTWFmvWrEFo\naCieP3+OQYMGITQ0FCKRCP/5z3+QkJAAIsLSpUsxadKkZvMViURCR6VKnM2bN+PSpUsgIojFYhgZ\nGSEgIEDlOGVlZVi9ejWePHmCuro6LFiwAObm5irFavi5ycnJUfr9VFVV4aOPPkJxcTE6deoEPz8/\nhIaG4osvvoCtrS0MDQ3RrVs3iEQijB49Gt7e3irFqV/p+MUcASgVJyIiAlVVVcJ2BQUFCAgIQGVl\nJbp3746IiAh0795d5ZzS09OxdetWdO7cGZ06dUJISAgMDQ2Vzqn+xE1rNNb3KvNMUltouAKqSCTC\nypUroaen16q6fVVtVbOv2h5N5ZSVlYV169ZBW1sb+vr6CA4Oxh/+8IfXktOr9ncduWbbswbbu6ba\nsz7au80biy8Wi7Fx48Y2ew8vjk+WLFkCExMTpdu2Leq2qRiff/453n77bdTV1UEsFsPKykrhOFhZ\nWb0Uq6VxcmvGSc3FNDc3h5ubG2xsbADIb3f19PRsse5byrNecnIyfvrpJ5VWP24sXkZGBjZv3gwA\nsLa2xscff/xK73vWrFn4/PPPceTIEejo6GDAgAEICQl5aR2bprTV/KOlmK1tH3WjlpNaxhhjjDHG\nGGMMUNPbjxljjDHGGGOMMYAntYwxxhhjjDHG1BhPahljjDHGGGOMqS2e1DLGGGOMMcYYU1s8qWWM\nMcYYY4wxprZ4UssYY4wxxhhjTG3xpLYdhISE4NChQyptc/DgQSQkJLRTRr8qKirCkiVLWrWtmZlZ\nm+Rw7do14e+EpaamIioqqk3issZ15HrsCPLz8+Hk5KTSNkuWLEFxcXGTv5dIJJg/f36jv/P09FRp\nX0x1gYGBuHHjxhvNQZl2dnJyQkFBgdIxt23bhjNnzjT7mqb66aioKPzwww9K74u9GepSuy1pqo/8\n4IMPcPnyZZSXl8PLywtA6/pg9vsQFhYGW1tbPH/+XOVtY2NjsX37dqVf39z42NraWuX9s9dPub8O\nzNrdnDlzXst+DAwMsHv37lZtKxKJ2iSHe/fuoaSkBIB8UMdfZh3P66rHjkLV2lbmM9RUTIlEotK+\nmOpCQkLedApKtbOqdefr69vqmBKJBGPHjlVpf+z1U5fabUlLfWRpaSlu3bol/Lutxhfst6Ourg4p\nKSkYOXIkUlJS4Ozs3K77a258zPWpHnhS20bCwsKQmpoKfX19aGlpYfjw4Th06BD27dsHIoK5uTmC\ngoKQkJCAnJwcBAYGCtv17dsX5eXlAABvb28cOXIE0dHR0NDQgIWFBUJDQ1FdXY3g4GDcvXsXMpkM\n//jHPzB16tQm85FIJNi1axeICIWFhbCyskJoaCiKioowf/58nD59Gu7u7pgzZw5cXV0RGBgIXV1d\n+Pn5NZp3p06dWjwGycnJSE5ORmlpKRwdHTF9+nSEhISgsrISJSUlWLhwIWbOnIlt27ZBKpVi9+7d\nMDAwgEQiwb/+9S9cvXoVGzZsQE1NDXr27Il169ZhwIABbdNAvzMdrR4fPnwIPz8/VFZWQkNDAwEB\nAbC0tER6ejrCwsJARDA0NMTmzZtx6tQphTry9PREUFAQCgsLoaGhgRUrVsDW1hZSqbTRHJKTk5GW\nloanT58iLy8PdnZ2+OSTT5o9XlVVVRCLxbhz5w50dXWxY8cO6OrqIi0tDdu2bUNdXR369++PkJAQ\n6OrqwsnJCQcOHICBgQGCgoLw448/wsDAACKRSLj68PjxYyxevBi5ubkwMTHB1q1bsWnTJgDA7Nmz\nfzdXwtubj48PnJ2d8c477wAA3nvvPfz888+Ijo7Gn/70J8TExCAlJQUymQzjx4+Hn58fPvjgA8yb\nNw/29vbYsmULbt68iT179qC4uBgLFy5EdHQ0vL290a9fP+Tl5cHIyAjh4eHo0aMHzpw5g08//RRE\nBGNjYwQHB6NXr15wcnKClZUVsrKyhMljS+1MRNi+fTtu3bqFqqoqhIWFwdLSErm5uVi7di1KS0vR\npUsXBAYGwszMDKtXr8aYMWPg4uKCffv2IT4+Hj169MDAgQMxYMAAeHt7g4iwdu1a/PjjjxCJRMIV\n2uvXryMgIADbt2+Hqanpa2kb1jx1rd24uDiUlJTAz88PFy5cgI+PD65cuQINDQ1MmzYN+/btw6xZ\ns3DgwAH06dMHQUFBuHbtGt566y2UlpYCANavX4+ioiL4+Phg1apVTfbBTD1FR0fjyJEj0NTUhJ2d\nHby8vCAWi/Ho0SMA8rGFo6NjszHOnTsHY2NjuLi4YO/evcKkViKRYPfu3dDR0cG9e/cwZMgQRERE\nQEtLC7GxsUhISICenh569+6NYcOGAY1EXaQAAA5sSURBVADGjh0LCwsLlJSU4Ouvv8aePXsU8vP3\n90dBQQHmz5+P1NRUFBQUYOXKlaioqMCwYcNARO17wFjbIPbKvv32W/Lw8KC6ujoqLS0lR0dHio+P\np/fff5+qq6uJiCgiIoJ27dpFJSUlNGHCBJLJZERE5OjoSMXFxRQVFUVRUVFUWFhI48aNo4cPHxIR\nkb+/P3333Xe0efNm2r9/PxERPXv2jKZPn055eXlN5pSRkUEjRoyg3NxcIiLy9fWluLg4evDgATk5\nORERUXZ2Nk2YMIGOHj1KLi4u9Pz5c7p7926jeRMRDRkypNnjkJSURO+8847w3jZs2EAXL14kIqLc\n3FyytrYWXrdq1SqFn2tqasjR0ZGuX79OREQnTpwgV1dXpduA/aoj1mNUVBT9+9//JiJ5bcbGxlJ1\ndTWNGzeOsrKyiIgoMjKSDhw48FIdffjhh5SamkpEREVFRTRp0iSqqKhoMoekpCRydHQkqVRKlZWV\nNHHiRLpz506TuT148IDMzMwoMzOTiIh8fHwoPj6eSkpKaObMmVRWVkZERAcPHqQ1a9YQEZGTkxPl\n5+fT/v37acWKFURElJ+fTzY2NiSRSCgjI4NGjhxJ+fn5RETk5uZGZ8+eJaKWP0dMNadOnSIfHx8i\nIsrJyaFp06bR/PnzSSKR0Pnz58nX15dkMhnJZDISi8V0+PBhOnjwIIWFhRER0fvvv09OTk4kk8ko\nMTGRwsPDhZr473//S0REGzdupNDQUCopKSF7e3sqKCggIqLPPvuMli9fTkTyz05ycrKQl5mZWYu5\nOzo6UlxcHBERHThwQIg1Z84cunXrFhHJ++nJkycTEdGqVasoOTmZsrKyaMqUKVRRUUHV1dXk7u5O\nUVFRRCSvr5MnTwp5b9q0iYiIPDw86PLly608yqw9qGvt3rt3T/h+Dg8PJzs7O7p27Rrl5eWRu7s7\nEf3aR8bGxpJYLCYiory8PLK2tiaJRKIwFmmqD2bq6ezZszR79myqrq6muro6Wrp0KUVFRVFwcDAR\nyfu0+n6pOV5eXvTll19SVVUV2djYUHZ2NhHJxxDW1tb08OFDkslk5ObmRmfOnKHMzEyaMmUKSaVS\nqqqqIldXV4V+sb7/ayy/+Ph4hZpcsmQJffXVV0RElJKSolR/zt48vlLbBjIyMjB58mRoaGhAV1cX\nkyZNAhHh559/xuzZs0FEqK2thbm5OXr16oWhQ4fi0qVL0NbWxsCBA9GnTx8h1tWrV2FjYwMDAwMA\n8itnALBz505UV1fj66+/BgBUVlYiOzsb/fv3bzKvcePGwdjYGAAwc+ZMfPXVV/jLX/4i/H7QoEHw\n8PCAv78/Dh8+DC0tLWRkZDSat7LMzc2F2zQ++ugjpKW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colorsB = [\"#3366cc\", \"#dc3912\", \"#ff9900\", \"#109618\", \"#990099\", \"#0099c6\", \"#dd4477\", \"#66aa00\", \"#b82e2e\", \"#316395\", \"#994499\", \"#22aa99\", \"#aaaa11\", \"#6633cc\", \"#e67300\", \"#8b0707\", \"#651067\", \"#329262\", \"#5574a6\", \"#3b3eac\"]\n", + "\n", + "figB = sns.pairplot(cluster_versionsB, hue='cluster', palette=colorsB, vars=columnas_vCB)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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q6oz+jYXIiYDy5s9Q33ay35pYVRYA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\n1DcQYmJQ+tvf2teNmT0bFz/4w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E3P3NwAOBI7Fq1Sr87Gc/g9PpREFBARYvXhzwZyMWeHQ4HHjooYdgMpmg1WrxzDPPIDExUbLN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\nAxERhUd3cDJvdhXUlY+g5xE2obu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jX5N+twsXH/mZoxz1yMuwTZkdwBpRoEm/24XyC58A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g/2L3NVN6AqzBr38edHhXlHFJhCNMyPlDojpo7B9SPzwfPJ+n0enpomVKmxhJ9fMOL2ruoG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\n5nR0x3NhYvodz1p+Ox7XrVvH9773P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vb2+NnjNmzBikpKQovT8uLg7+/v4K7xsxYoRGbTHO4fIqa+xv164dAPmxOTIyErNnz66w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\nVJTS50ml0lJFmWy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+ "data": { + "text/plain": [ + "count 20\n", + "unique 2\n", + "top True\n", + "freq 17\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.84999999999999998" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 6\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11\n", + "unique 2\n", + "top True\n", + "freq 9\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.81818181818181823" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 4\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79\n", + "unique 2\n", + "top True\n", + "freq 63\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.79746835443037978" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35\n", + "unique 2\n", + "top True\n", + "freq 29\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.82857142857142863" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 11\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26\n", + "unique 2\n", + "top False\n", + "freq 22\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.15384615384615385" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 8\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35\n", + "unique 2\n", + "top True\n", + "freq 28\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.80000000000000004" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 5\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26\n", + "unique 2\n", + "top True\n", + "freq 21\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.80769230769230771" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 12\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38\n", + "unique 2\n", + "top False\n", + "freq 37\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.026315789473684209" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 10\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21\n", + "unique 2\n", + "top False\n", + "freq 19\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.095238095238095233" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 1\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7\n", + "unique 2\n", + "top True\n", + "freq 5\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7142857142857143" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for cluster in cluster_versionsA['cluster'].unique():\n", + " print \"Cluster: \"+str(cluster)\n", + " display((cluster_versionsA.loc[cluster_versionsA['cluster'] == cluster]['cuestionarioFinalizado']).describe())\n", + " display((cluster_versionsA.loc[cluster_versionsA['cluster'] == cluster]['cuestionarioFinalizado']).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Descriptive statistics of the features within every cluster\n", + "& plotting the distribution of observations of every feature

" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.000000\n", + "mean 360.923077\n", + "std 4.706787\n", + "min 360.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 360.000000\n", + "max 384.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 360.923076923\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26\n", + "unique 1\n", + "top True\n", + "freq 26\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.000000\n", + "mean 684.153846\n", + "std 176.001748\n", + "min 592.000000\n", + "25% 640.000000\n", + "50% 640.000000\n", + "75% 640.000000\n", + "max 1280.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 684.153846154\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.000000\n", + "mean 388.615385\n", + "std 97.670088\n", + "min 360.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 360.000000\n", + "max 720.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 388.615384615\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.0\n", + "mean 2.0\n", + "std 0.0\n", + "min 2.0\n", + "25% 2.0\n", + "50% 2.0\n", + "75% 2.0\n", + "max 2.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 2.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 11\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38.000000\n", + "mean 379.973684\n", + "std 52.243446\n", + "min 320.000000\n", + "25% 375.000000\n", + "50% 375.000000\n", + "75% 375.000000\n", + "max 667.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 379.973684211\n" + ] + }, + { + "data": { + "image/png": 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aQNfAPR5AFzdixAjV1NRo+vTpkr69p2DMmDHas2ePbr31VjU1NSkjI0OTJk2S\ndPwf/eLiYqWlpSkhIUENDQ2n/DjqJZdcoscee0xffvmlBg0apAceeEAJCQl6+OGHNWfOHDU0NOj7\n3/++lixZcso1Txg7dqwmTpyoF1988TvfIvv6669XRUWFrrvuOknSkCFDdPHFFzd//sT3nD59uj7/\n/HPdeOON6tmzpwYOHChJ6tGjh773ve/p5z//uZYuXcpPtQBdhOPyfxMAnGT//v2688479dprr3X0\nKAAiEPd4ADhDe917MHPmTAUCgeaPXdeV4zj62c9+1uKP2wKIXNzjAQAAzPDkUgAAYIbwAAAAZggP\nAABghvAAAABmCA8AAGCG8AAAAGb+D+ma5kI3HvlAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38\n", + "unique 1\n", + "top True\n", + "freq 38\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38.000000\n", + "mean 662.026316\n", + "std 37.685388\n", + "min 568.000000\n", + "25% 667.000000\n", + "50% 667.000000\n", + "75% 667.000000\n", + "max 736.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 662.026315789\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38.000000\n", + "mean 372.289474\n", + "std 21.042952\n", + "min 320.000000\n", + "25% 375.000000\n", + "50% 375.000000\n", + "75% 375.000000\n", + "max 414.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 372.289473684\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38.000000\n", + "mean 2.105263\n", + "std 0.311012\n", + "min 2.000000\n", + "25% 2.000000\n", + "50% 2.000000\n", + "75% 2.000000\n", + "max 3.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 2.10526315789\n" + ] + }, + { + "data": { + "image/png": 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rrZo7d66WL1+uX/7yl8edu6u/pTJjxgxNnjxZgUBAI0aM0ODBg/Xpp5/qggsu\nUGlpqR577DENHjxYktSrVy899thjWrBggQ4fPqzU1FQ9/vjjXbo/AN8dH08PoEuqqqr07LPP6tln\nn430KACiCFc4AHzLP//5TxUXFx9z5cE5J5/Pp9tuu83kPtevX69nnnnmuPe5Zs0ak/sEcPJwhQMA\nAJjjRaMAAMAcwQEAAMwRHAAAwBzBAQAAzBEcAADAHMEBAADM/T+p6VTexnBk+gAAAABJRU5ErkJg\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 38.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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KAADMERwAAMAcwQEAAMwRHAAAwBzBAQAAzBEcAADA3P8DI7PxcOgyEBYAAAAA\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 12\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21.000000\n", + "mean 342.857143\n", + "std 78.558440\n", + "min 0.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 360.000000\n", + "max 360.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 342.857142857\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21\n", + "unique 1\n", + "top True\n", + "freq 21\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21.000000\n", + "mean 637.714286\n", + "std 10.474459\n", + "min 592.000000\n", + "25% 640.000000\n", + "50% 640.000000\n", + "75% 640.000000\n", + "max 640.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 637.714285714\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21.0\n", + "mean 360.0\n", + "std 0.0\n", + "min 360.0\n", + "25% 360.0\n", + "50% 360.0\n", + "75% 360.0\n", + "max 360.0\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 360.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21.000000\n", + "mean 3.285714\n", + "std 0.462910\n", + "min 3.000000\n", + "25% 3.000000\n", + "50% 3.000000\n", + "75% 4.000000\n", + "max 4.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 3.28571428571\n" + ] + }, + { + "data": { + "image/png": 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nPOfq1atNzgng+8MVDgAAYI5vGgUAAOYIDgAAYI7gAAAA5ggOAABgjuAAAADm\nCA4AAGDu/wFTezIxlrVEaAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 21.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 1.0\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhwAAAF6CAYAAABSu4GmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 10\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79.000000\n", + "mean 1264.886076\n", + "std 53.787566\n", + "min 982.000000\n", + "25% 1280.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1281.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1264.88607595\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79\n", + "unique 1\n", + "top False\n", + "freq 79\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79.0\n", + "mean 1024.0\n", + "std 0.0\n", + "min 1024.0\n", + "25% 1024.0\n", + "50% 1024.0\n", + "75% 1024.0\n", + "max 1024.0\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1024.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79.0\n", + "mean 1280.0\n", + "std 0.0\n", + "min 1280.0\n", + "25% 1280.0\n", + "50% 1280.0\n", + "75% 1280.0\n", + "max 1280.0\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1280.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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ff/65Nm/eLElav369TjjhBKWlpfXomACODGc4gK+wCRMmaPPmzbrssst0/PHH\na8SIEUpLS9MPf/hDXX/99XLOadSoUbrpppskfXEWIDMzU+3t7TrmmGMOuq8hQ4Zo7ty5mjFjhiKR\niM455xxNnjxZLS0tWrBggS677DJFIhH95Cc/ib5c0pkhQ4Zo9uzZ+vTTT3X++edrypQp+uijj+Tz\n+RQKhXTPPfeooqJCQ4cO1fXXX6/S0lItW7ZMt9xyy2Hn7um/Urn11lt1zTXXKBgMKisrSyNGjNCH\nH36ob3/726qoqNDSpUs1YsQISVL//v21dOlS3XPPPWptbVV6eroeeuihHh0PwJHj4+kB9EhdXZ0e\nf/xxPf744/EeBUAC4QwHgEO88sorWrRo0UFnHpxz8vl8+v73v29yzPXr12vFihWHPea6detMjgng\n6OEMBwAAMMcvjQIAAHMEBwAAMEdwAAAAcwQHAAAwR3AAAABzBAcAADD3/wDrxVeJA4vvwQAAAABJ\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 79.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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xx/H7TJ8+XVOnTtVzzz2n8847T7179/6qawFwjPH19AAAwBxXOABIkubOnavX\nX3/9C59xMWTIED300EMpmgpApuAKBwAAMMcvjQIAAHMEBwAAMEdwAAAAcwQHAAAwR3AAAABzBAcA\nADD3/wGeqjEnN0lfXwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 4\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.000000\n", + "mean 1020.771429\n", + "std 8.815380\n", + "min 987.000000\n", + "25% 1024.000000\n", + "50% 1024.000000\n", + "75% 1024.000000\n", + "max 1024.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1020.77142857\n" + ] + }, + { + "data": { + "image/png": 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w+MUvfqGXX35Zzjl9/fXXev/99yVJUVFRp0QQ0QG0PZwJAdq4/v37q6KiQtnZ\n2ZL+ewZh8ODB2r17t26++WbV1dUpIyNDWVlZkr4PgJUrVyotLU1xcXGqqak55Vdczz//fD322GPa\nv3+/evTooRkzZiguLk7z58/XlClTVFNTo4svvliFhYWn3OcJQ4YM0ahRo7R27VrFxMScdu6rr75a\npaWlGjBggCSpV69e6tSpU/DjJ24zOztbn332ma677jp16dJF3bt3lyRdcMEF+slPfqLf/OY3Wrhw\nIb8dA7RBHsc/HwCcpKSkRHfccYfeeOMN61EAhDnOhAD4kVCdVRg/frwCgUDwbeecPB6Pbr311np/\nhRdAZOBMCAAAMMETUwEAgAkiBAAAmCBCAACACSIEAACYIEIAAIAJIgQAAJj4P/vFdPbuVkn9AAAA\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35\n", + "unique 1\n", + "top False\n", + "freq 35\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.000000\n", + "mean 751.314286\n", + "std 48.907940\n", + "min 600.000000\n", + "25% 768.000000\n", + "50% 768.000000\n", + "75% 768.000000\n", + "max 768.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 751.314285714\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.000000\n", + "mean 1023.542857\n", + "std 2.704494\n", + "min 1008.000000\n", + "25% 1024.000000\n", + "50% 1024.000000\n", + "75% 1024.000000\n", + "max 1024.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1023.54285714\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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06VItXLhQBw8eVM+ePfXggw+26XgAjh1vTw+gTSoqKvTEE0/oiSeeSPYoADyE\nMxwAjvCPf/xDJSUlXznz4JyTz+fTTTfdZHLMDRs26PHHHz/qMdetW2dyTADthzMcAADAHE8aBQAA\n5ggOAABgjuAAAADmCA4AAGCO4AAAAOYIDgAAYO7/APg+7x4u+UaYAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 8\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.000000\n", + "mean 1249.115385\n", + "std 180.239025\n", + "min 950.000000\n", + "25% 1080.500000\n", + "50% 1272.500000\n", + "75% 1385.250000\n", + "max 1600.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1249.11538462\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26\n", + "unique 1\n", + "top False\n", + "freq 26\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.000000\n", + "mean 1098.461538\n", + "std 51.667577\n", + "min 1050.000000\n", + "25% 1080.000000\n", + "50% 1080.000000\n", + "75% 1080.000000\n", + "max 1200.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1098.46153846\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.000000\n", + "mean 1870.769231\n", + "std 188.062183\n", + "min 1600.000000\n", + "25% 1740.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 2560.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1870.76923077\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 26.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 5\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1585.000000\n", + "std 577.403672\n", + "min 1137.000000\n", + "25% 1208.500000\n", + "50% 1317.000000\n", + "75% 1832.000000\n", + "max 2560.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1585.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7\n", + "unique 1\n", + "top False\n", + "freq 7\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 1440.0\n", + "std 0.0\n", + "min 1440.0\n", + "25% 1440.0\n", + "50% 1440.0\n", + "75% 1440.0\n", + "max 1440.0\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1440.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 2560.0\n", + "std 0.0\n", + "min 2560.0\n", + "25% 2560.0\n", + "50% 2560.0\n", + "75% 2560.0\n", + "max 2560.0\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 2560.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1.428571\n", + "std 0.534522\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 1.000000\n", + "75% 2.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.42857142857\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 1\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1865.720779\n", + "std 107.827173\n", + "min 1346.000000\n", + "25% 1920.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 1920.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1865.72077922\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154\n", + "unique 1\n", + "top False\n", + "freq 154\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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Jzc1VbW2tbr/9dn388cc6cuSI8vPz9eabb+qqq67S1q1blZ+f36djNTdz5sQtwWCAfbqE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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1081.441558\n", + "std 28.738621\n", + "min 1050.000000\n", + "25% 1080.000000\n", + "50% 1080.000000\n", + "75% 1080.000000\n", + "max 1200.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1081.44155844\n" + ] + }, + { + "data": { + "image/png": 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NnDlTktS/f3/HnJECKoUBYI23337bDB482JSUlJiTJ0+abt26maVLl5r09HRT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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1884.987013\n", + "std 86.787020\n", + "min 1680.000000\n", + "25% 1920.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 2048.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1884.98701299\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1.012987\n", + "std 0.113588\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.01298701299\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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wYYMGDx6s0047Td27d9eNN9541DEvuuii75zvvffeO+q569ev1/XXXy9Juu66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\nxSAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 2\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1865.720779\n", + "std 107.827173\n", + "min 1346.000000\n", + "25% 1920.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 1920.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1865.72077922\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154\n", + "unique 1\n", + "top False\n", + "freq 154\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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Jzc1VbW2tbr/9dn388cc6cuSI8vPz9eabb+qqq67S1q1blZ+f36djNTdz5sQtwWCAfbqE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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1081.441558\n", + "std 28.738621\n", + "min 1050.000000\n", + "25% 1080.000000\n", + "50% 1080.000000\n", + "75% 1080.000000\n", + "max 1200.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1081.44155844\n" + ] + }, + { + "data": { + "image/png": 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NnDlTktS/f3/HnJECKoUBYI23337bDB482JSUlJiTJ0+abt26maVLl5r09HRT\nWFhojDFmzpw55o9//KM5fvy46dKliwmFQsYYY7p162aOHTtmXnjhBfPCCy+Yr776ynTq1MkcPXrU\nGGPM6NGjTW5urpk9e7bJyckxxhjj9/vNvffeaw4dOnTJNb3wwgvmpZdeMsYYs3XrVvOnP/3JFBYW\nmk6dOpmPP/7YGGPM7373O7NkyRKzatUqc+edd4bXNGLECLN27VpjjDFff/216dGjhwkGg5dcw6pV\nq0y3bt1MQUGBOX36tPnhD39o9u/ff8m1ffnll+amm24y//rXv4wxxjz55JNm6dKl5vjx46ZPnz7m\n1KlTxhhjli9fbsaNG2eMMaZ79+7m8OHDJicnx2RmZhpjjDl8+LBJTU0127ZtM1u3bjXt27c3hw8f\nNsYY88ADD5j169cbY4xp0aJFOV9JAJHijAlgka1bt+rHP/6x4uLiVLt2bfXo0UPGGP373/9W//79\nZYxRcXGxWrdurXr16qlly5b65z//qWrVqun6668PX45ckj744AOlpqaqfv36ks6dUZGk+fPnq7Cw\nUH/5y18kSadPn9ann36qxo0bl7qmTp066amnntKePXvUtWtXDRo0SPv371eDBg3UokULSdKIESMk\nSatXr1br1q3Db4Vs3rxZn3/+uX7/+99LkkpKSnTw4EFt3rz5gjWcOXNGn376qSSpXbt2qlmzpiTp\nuuuu0zfffHPZ71mDBg108803S5JSUlJ04sQJ7d69W0eOHNGQIUNkjFEoFFKdOnUk/fcibJs2bVL/\n/v0lSQ0bNtTtt98e3udNN92khg0bSpKaNWumEydOXHYNAKKHMAEs4nK5Lrh6qdvtVklJie666y6N\nGzdO0rkrmJaUlEiSevfurbfeekvVqlVT7969L9iXx+O5YF/5+fmSzv3DPGvWLLVs2VKSdOzYMdWt\nW/eSa2rfvr3efPNNrVu3Tn/729+0evVqjR49+oJtAoFA+NLm1atXD99ujNHLL7+sxMRESefeFkpO\nTlYoFLpoDXXq1NEbb7yh+Pj4C/Ztyricl9vtDv/5/PevpKREqampmj9/viTp7Nmz4be4zkeT2+1W\nKBQq9Xn+d58Arh1+KwewSKdOnfTmm2+G/yFdt26dpHNX6M3Pz5cxRpMnT9bLL78sSfrRj36k7du3\na9OmTbrzzjsv2FebNm20e/duHT9+XJI0bdo0rV27VrfddpuWLVsm6VwQ3H///Tpy5Mgl1zRnzhy9\n9tpr+slJBsoxAAACFklEQVRPfqLx48dr7969uuGGG3TixAl99tlnkqRFixZp+fLlFz32tttu09Kl\nSyVJn376qXr37q0zZ86oY8eOF63hq6++iuh7Vlq43HLLLfrggw/0xRdfSJKysrI0a9asC7Y//72W\nzgXTtm3byowQj8dzQcwAiD7OmAAW6d69uz788EPdd999qlevnm644QYlJiZq2LBh+tnPfiZjjFq2\nbKnHHntM0rmzE6mpqTp79mz47Y/z6tevr3Hjxunhhx9WKBRSu3bt1LdvXwWDQU2ZMkX33XefQqGQ\nnnnmGV133XWXXNOgQYM0cuRIrV69Wm63W1OmTFF8fLxmzZql0aNHq7i4WP/3f/+nmTNn6u9///sF\njx0/frwmTpwYPpszZ84c1apVS8OGDSt1DTt27Ljg8eU5W1HaNklJSZo2bZp+9atfKRQK6bvf/a5m\nz559wfYPPvigPv74Y913332qX7++GjVqpOrVq+v06dOXfK7u3burT58+evXVVy86swMgOlymrPOk\nABCDNmzYIGOMunbtqkAgoPvvv1+vvvpq+G0nAJWDMAGgHTt2aOrUqRecfTDGyOVyaeHChUpOTq60\ntR06dEhPPvlkqWubOnWqWrduHdF+v/zyS40ePVoFBQVyuVwaOnSo7r333mgtG0CECBMAAGANfvgV\nAABYgzABAADWIEwAAIA1CBMAAGANwgQAAFiDMAEAANb4f3/ceyU6iIcRAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1884.987013\n", + "std 86.787020\n", + "min 1680.000000\n", + "25% 1920.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 2048.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1884.98701299\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.000000\n", + "mean 1.012987\n", + "std 0.113588\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.01298701299\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 154.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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wYYMGDx6s0047Td27d9eNN9541DEvuuii75zvvffeO+q569ev1/XXXy9Juu66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\nxSAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 2\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 107.000000\n", + "mean 1459.336449\n", + "std 164.907003\n", + "min 784.000000\n", + "25% 1440.000000\n", + "50% 1440.000000\n", + "75% 1600.000000\n", + "max 1920.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1459.3364486\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 107\n", + "unique 1\n", + "top False\n", + "freq 107\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 107.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 107.000000\n", + "mean 899.663551\n", + "std 3.480251\n", + "min 864.000000\n", + "25% 900.000000\n", + "50% 900.000000\n", + "75% 900.000000\n", + "max 900.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 899.663551402\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 107.000000\n", + "mean 1500.710280\n", + "std 77.632541\n", + "min 1440.000000\n", + "25% 1440.000000\n", + "50% 1440.000000\n", + "75% 1600.000000\n", + "max 1600.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1500.71028037\n" + ] + }, + { + "data": { + "image/png": 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BQeD0+nhLjzZ+2hbKEx3Jfs2oujJMBw96JTU+QmJjPSoudu6jeezXEEalvnJVVZv6P7WZ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\npLO9+OKLZu7cucY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 107.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 107.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 3\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11.000000\n", + "mean 1093.818182\n", + "std 231.560713\n", + "min 768.000000\n", + "25% 896.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1280.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1093.81818182\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11\n", + "unique 1\n", + "top False\n", + "freq 11\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11.000000\n", + "mean 855.272727\n", + "std 109.073453\n", + "min 768.000000\n", + "25% 800.000000\n", + "50% 800.000000\n", + "75% 912.000000\n", + "max 1024.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 855.272727273\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11.000000\n", + "mean 1093.818182\n", + "std 231.560713\n", + "min 768.000000\n", + "25% 896.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1280.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1093.81818182\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11.000000\n", + "mean 1.090909\n", + "std 0.301511\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.09090909091\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 11.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 6\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.000000\n", + "mean 1293.342857\n", + "std 100.697550\n", + "min 905.000000\n", + "25% 1264.500000\n", + "50% 1280.000000\n", + "75% 1336.000000\n", + "max 1440.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1293.34285714\n" + ] + }, + { + "data": { + "image/png": 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ATPw/WlFcqRs8EJMAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35\n", + "unique 1\n", + "top False\n", + "freq 35\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.000000\n", + "mean 842.857143\n", + "std 50.209645\n", + "min 800.000000\n", + "25% 800.000000\n", + "50% 800.000000\n", + "75% 900.000000\n", + "max 900.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 842.857142857\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.000000\n", + "mean 1348.571429\n", + "std 80.335431\n", + "min 1280.000000\n", + "25% 1280.000000\n", + "50% 1280.000000\n", + "75% 1440.000000\n", + "max 1440.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1348.57142857\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.0\n", + "mean 2.0\n", + "std 0.0\n", + "min 2.0\n", + "25% 2.0\n", + "50% 2.0\n", + "75% 2.0\n", + "max 2.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 2.0\n" + ] + }, + { + "data": { + "image/png": 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PfnTcudv6r1Rmz56tG264QeFwWMOGDdPAgQP1ySef6OKLL1ZJSYmWLVumgQMH\nSpK6dOmiZcuWacmSJTp8+LB69eqlRx55pE3HA3Dy+Hh6AG1SUVGhp59+Wk8//XSiRwHgIZzhAPA1\nf//731VUVHTMmQfnnHw+n2655RaTY27atElPPfXUcY+5fv16k2MCOHU4wwEAAMzxolEAAGCO4AAA\nAOYIDgAAYI7gAAAA5ggOAABgjuAAAADm/g9ZFAVciZfkBgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 35.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 7\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 434.000000\n", + "mean 1350.048387\n", + "std 47.408158\n", + "min 983.000000\n", + "25% 1366.000000\n", + "50% 1366.000000\n", + "75% 1366.000000\n", + "max 1366.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1350.0483871\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 434\n", + "unique 1\n", + "top False\n", + "freq 434\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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GEAEAAMYQIgAAwBhCBAAAGEOIAAAAYwgRAABgjLuzO9vb2zV//nwdOHBAbW1tKi4u1qWX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\n/4ffnVlu2LBBn33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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 434.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 0.0\n" + ] + }, + { + "data": { + "image/png": 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ECAAAMIYQAQAAxhAiAADAGEIEAAAYQ4gAAABjXN3d2dnZqYULF+rYsWMKh8OaO3eurrzy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\n9Nlnn6mkpETHjh1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 434.000000\n", + "mean 771.633641\n", + "std 14.179218\n", + "min 720.000000\n", + "25% 768.000000\n", + "50% 768.000000\n", + "75% 768.000000\n", + "max 921.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 771.633640553\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 434.000000\n", + "mean 1355.096774\n", + "std 31.648106\n", + "min 1152.000000\n", + "25% 1366.000000\n", + "50% 1366.000000\n", + "75% 1366.000000\n", + "max 1366.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1355.09677419\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 434.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 434.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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Cjhw5otjYWH33u9/ldEQdm6ckNTU1afbs2fL5fDwf/xXa+ogHr9d7VtyFQiEl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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 9\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 13 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 20.000000\n", + "mean 944.000000\n", + "std 126.091028\n", + "min 704.000000\n", + "25% 768.000000\n", + "50% 1024.000000\n", + "75% 1024.000000\n", + "max 1024.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 944.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 13 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 20\n", + "unique 1\n", + "top False\n", + "freq 20\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 13 || feature: os_iOS\n" + ] + }, + { + "data": { + "text/plain": [ + "count 20.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_iOS, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_iOS: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 13 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 20.0\n", + "mean 1024.0\n", + "std 0.0\n", + "min 1024.0\n", + "25% 1024.0\n", + "50% 1024.0\n", + "75% 1024.0\n", + "max 1024.0\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1024.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 13 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 20.0\n", + "mean 768.0\n", + "std 0.0\n", + "min 768.0\n", + "25% 768.0\n", + "50% 768.0\n", + "75% 768.0\n", + "max 768.0\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 768.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 13 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 20.000000\n", + "mean 1.700000\n", + "std 0.470162\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 2.000000\n", + "75% 2.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.7\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 13 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 20.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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fffbZ6plnntl83/r166tDhw6trly5srpu3brqsGHDvLdb0dI+16xZUx06dGh17dq11Wq1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\nDgCguP8PhA/8RsWBh4YAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 13\n", + "for column in listImportancesA:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsA.loc[cluster_versionsA['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 6\n" + ] + }, + { + "data": { + "text/plain": [ + "count 231\n", + "unique 2\n", + "top True\n", + "freq 170\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.73593073593073588" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 5\n" + ] + }, + { + "data": { + "text/plain": [ + "count 591\n", + "unique 2\n", + "top True\n", + "freq 448\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7580372250423012" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 2\n" + ] + }, + { + "data": { + "text/plain": [ + "count 65\n", + "unique 2\n", + "top False\n", + "freq 64\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.015384615384615385" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 1\n" + ] + }, + { + "data": { + "text/plain": [ + "count 22\n", + "unique 2\n", + "top True\n", + "freq 16\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.72727272727272729" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 11\n" + ] + }, + { + "data": { + "text/plain": [ + "count 130\n", + "unique 2\n", + "top True\n", + "freq 97\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.74615384615384617" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 4\n" + ] + }, + { + "data": { + "text/plain": [ + "count 62\n", + "unique 2\n", + "top False\n", + "freq 55\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.11290322580645161" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 12\n" + ] + }, + { + "data": { + "text/plain": [ + "count 5\n", + "unique 2\n", + "top True\n", + "freq 4\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.80000000000000004" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 8\n" + ] + }, + { + "data": { + "text/plain": [ + "count 58\n", + "unique 2\n", + "top True\n", + "freq 45\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.77586206896551724" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 3\n" + ] + }, + { + "data": { + "text/plain": [ + "count 56\n", + "unique 2\n", + "top False\n", + "freq 51\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.089285714285714288" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 10\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159\n", + "unique 2\n", + "top True\n", + "freq 122\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.76729559748427678" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 9\n" + ] + }, + { + "data": { + "text/plain": [ + "count 17\n", + "unique 2\n", + "top True\n", + "freq 13\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.76470588235294112" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7\n", + "unique 2\n", + "top True\n", + "freq 5\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7142857142857143" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for cluster in cluster_versionsB['cluster'].unique():\n", + " print \"Cluster: \"+str(cluster)\n", + " display((cluster_versionsB.loc[cluster_versionsB['cluster'] == cluster]['cuestionarioFinalizado']).describe())\n", + " display((cluster_versionsB.loc[cluster_versionsB['cluster'] == cluster]['cuestionarioFinalizado']).mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 231.000000\n", + "mean 955.731602\n", + "std 43.977927\n", + "min 683.000000\n", + "25% 944.000000\n", + "50% 956.000000\n", + "75% 974.000000\n", + "max 1110.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 955.731601732\n" + ] + }, + { + "data": { + "image/png": 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BCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABW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\nPffcc3rttdc0ZMg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 231.000000\n", + "mean 1863.385281\n", + "std 108.209794\n", + "min 1359.000000\n", + "25% 1917.500000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 1924.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1863.38528139\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 231.000000\n", + "mean 1084.727273\n", + "std 45.022787\n", + "min 1050.000000\n", + "25% 1080.000000\n", + "50% 1080.000000\n", + "75% 1080.000000\n", + "max 1440.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1084.72727273\n" + ] + }, + { + "data": { + "image/png": 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P5RxrypQpmjZtmlpaWlRQUKCSkhJ5PB5dffXV+va3v60uXbroG9/4hgoKCtSr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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 231.000000\n", + "mean 1.012987\n", + "std 0.113464\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.01298701299\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 231.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 6\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 65.000000\n", + "mean 513.600000\n", + "std 54.627946\n", + "min 372.000000\n", + "25% 460.000000\n", + "50% 559.000000\n", + "75% 559.000000\n", + "max 591.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 513.6\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 65.000000\n", + "mean 350.461538\n", + "std 27.552834\n", + "min 320.000000\n", + "25% 320.000000\n", + "50% 375.000000\n", + "75% 375.000000\n", + "max 375.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 350.461538462\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 65.000000\n", + "mean 620.123077\n", + "std 54.733875\n", + "min 480.000000\n", + "25% 568.000000\n", + "50% 667.000000\n", + "75% 667.000000\n", + "max 667.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 620.123076923\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 65.0\n", + "mean 2.0\n", + "std 0.0\n", + "min 2.0\n", + "25% 2.0\n", + "50% 2.0\n", + "75% 2.0\n", + "max 2.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 2.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 65.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 2\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 22.000000\n", + "mean 629.227273\n", + "std 19.272314\n", + "min 559.000000\n", + "25% 628.000000\n", + "50% 628.000000\n", + "75% 628.000000\n", + "max 660.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 629.227272727\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 22.000000\n", + "mean 412.227273\n", + "std 8.314828\n", + "min 375.000000\n", + "25% 414.000000\n", + "50% 414.000000\n", + "75% 414.000000\n", + "max 414.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 412.227272727\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 22.000000\n", + "mean 732.863636\n", + "std 14.710849\n", + "min 667.000000\n", + "25% 736.000000\n", + "50% 736.000000\n", + "75% 736.000000\n", + "max 736.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 732.863636364\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 22.0\n", + "mean 3.0\n", + "std 0.0\n", + "min 3.0\n", + "25% 3.0\n", + "50% 3.0\n", + "75% 3.0\n", + "max 3.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 3.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 22.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 1\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 62.000000\n", + "mean 537.241935\n", + "std 52.320790\n", + "min 279.000000\n", + "25% 524.000000\n", + "50% 560.000000\n", + "75% 560.000000\n", + "max 572.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 537.241935484\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 62.000000\n", + "mean 369.032258\n", + "std 49.875572\n", + "min 360.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 360.000000\n", + "max 640.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 369.032258065\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 62.000000\n", + "mean 630.967742\n", + "std 49.875572\n", + "min 360.000000\n", + "25% 640.000000\n", + "50% 640.000000\n", + "75% 640.000000\n", + "max 640.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 630.967741935\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 62.000000\n", + "mean 3.129032\n", + "std 0.337972\n", + "min 3.000000\n", + "25% 3.000000\n", + "50% 3.000000\n", + "75% 3.000000\n", + "max 4.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 3.12903225806\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 62.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 4\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 56.000000\n", + "mean 547.589286\n", + "std 41.169617\n", + "min 407.000000\n", + "25% 513.500000\n", + "50% 559.000000\n", + "75% 560.000000\n", + "max 680.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 547.589285714\n" + ] + }, + { + "data": { + "image/png": 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92OMBQFLTz7FxKRMmTJDf7w9ddhxHHo9HDz30UNhft72U6667Tvv379f9998v\nj8eju+++m+gAWin2eAAAADMcXAoAAMwQHgAAwAzhAQAAzBAeAADADOEBAADMEB4AAMDM/wMIZDUi\nNCU5hgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 56.000000\n", + "mean 404.714286\n", + "std 191.877396\n", + "min 320.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 360.000000\n", + "max 1280.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 404.714285714\n" + ] + }, + { + "data": { + "image/png": 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uQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 56.000000\n", + "mean 643.428571\n", + "std 38.558978\n", + "min 480.000000\n", + "25% 640.000000\n", + "50% 640.000000\n", + "75% 640.000000\n", + "max 800.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 643.428571429\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 56.000000\n", + "mean 1.946429\n", + "std 0.227208\n", + "min 1.000000\n", + "25% 2.000000\n", + "50% 2.000000\n", + "75% 2.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.94642857143\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 56.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 3\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1396.428571\n", + "std 221.104694\n", + "min 1251.000000\n", + "25% 1262.000000\n", + "50% 1290.000000\n", + "75% 1422.500000\n", + "max 1865.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 1396.42857143\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1962.857143\n", + "std 675.445144\n", + "min 1200.000000\n", + "25% 1270.500000\n", + "50% 2319.000000\n", + "75% 2560.000000\n", + "max 2560.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1962.85714286\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1411.428571\n", + "std 157.842598\n", + "min 1080.000000\n", + "25% 1440.000000\n", + "50% 1440.000000\n", + "75% 1440.000000\n", + "max 1600.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1411.42857143\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 7\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 591.000000\n", + "mean 649.049069\n", + "std 27.129931\n", + "min 494.000000\n", + "25% 638.000000\n", + "50% 653.000000\n", + "75% 662.000000\n", + "max 767.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 649.049069374\n" + ] + }, + { + "data": { + "image/png": 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PH9eXX34ZOKampkb5+flKTU1VcnKybrrpppBtZmdn6+jRo4HHuQoG0L9wzggQ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\nAFOEEQAAYIowAgAATP0fgBMxNEll3wQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 591.000000\n", + "mean 1331.795262\n", + "std 90.724861\n", + "min 657.000000\n", + "25% 1366.000000\n", + "50% 1366.000000\n", + "75% 1366.000000\n", + "max 1371.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1331.79526227\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 591.000000\n", + "mean 771.600677\n", + "std 17.701947\n", + "min 600.000000\n", + "25% 768.000000\n", + "50% 768.000000\n", + "75% 768.000000\n", + "max 900.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 771.600676819\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 591.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 591.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 5\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 58.000000\n", + "mean 727.241379\n", + "std 105.962806\n", + "min 504.000000\n", + "25% 662.250000\n", + "50% 674.000000\n", + "75% 781.500000\n", + "max 960.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 727.24137931\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 58.000000\n", + "mean 1134.775862\n", + "std 219.779319\n", + "min 768.000000\n", + "25% 1024.000000\n", + "50% 1047.000000\n", + "75% 1280.000000\n", + "max 1440.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1134.77586207\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 58.000000\n", + "mean 932.275862\n", + "std 96.437198\n", + "min 800.000000\n", + "25% 800.000000\n", + "50% 900.000000\n", + "75% 1024.000000\n", + "max 1024.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 932.275862069\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 58.0\n", + "mean 2.0\n", + "std 0.0\n", + "min 2.0\n", + "25% 2.0\n", + "50% 2.0\n", + "75% 2.0\n", + "max 2.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 2.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 58.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 8\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 17.000000\n", + "mean 619.176471\n", + "std 110.465625\n", + "min 355.000000\n", + "25% 597.000000\n", + "50% 653.000000\n", + "75% 669.000000\n", + "max 783.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 619.176470588\n" + ] + }, + { + "data": { + "image/png": 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6eZ6nH/3oR8QFkII4gwEAAMzxIk8AAGCOwAAAAOYIDAAAYI7AAAAA5ggMAABg\njsAAAADm/g/vyjxNgOD3fgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 17.000000\n", + "mean 1261.470588\n", + "std 184.900689\n", + "min 1024.000000\n", + "25% 1146.000000\n", + "50% 1264.000000\n", + "75% 1440.000000\n", + "max 1552.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1261.47058824\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 17.000000\n", + "mean 1084.705882\n", + "std 60.468757\n", + "min 1024.000000\n", + "25% 1024.000000\n", + "50% 1080.000000\n", + "75% 1080.000000\n", + "max 1200.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1084.70588235\n" + ] + }, + { + "data": { + "image/png": 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oyJAhGj9+vGpqajR37lxddNFFikQi+vWvf62+ffsecqYpU6Zo5syZWrVqlbxe\nr+bOnauUlBTdf//9mjVrlsLhsI4//ngtXLhQr776aqvHzp49W4WFhS1XXRYtWqRjjjlG06ZNO+gM\nmzZtavX4WK4qHOyYHj16aMGCBfrVr36lSCSiH/zgB3rggQdaHX/55ZerpKREF110kTIzM9W7d2+l\npqaqrq7ukM+Vk5Ojiy++WC+88MJ3rsAAiA/eYh6Aa7z55psyxmjEiBEKhUIaN26cXnjhhZaXfAAk\nB7EBuNSmTZs0f/78VlcJjDFyHEdLly5VIBBI2my7du3SjTfeeNDZ5s+fr4EDBx7Reb/88kvNmjVL\ntbW1chxHU6dO1ZgxY+I1NoAjRGwAAACr+AeiAADAKmIDAABYRWwAAACriA0AAGAVsQEAAKwiNgAA\ngFX/C8PC4M5MquEoAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 17.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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XXmlyznXr1mnBggUHPeeqVatMzgng8OEKBwAAMMc3jQIAAHMEBwAAMEdwAAAA\ncwQHAAAwR3AAAABzBAcAADD3v8tR5qoPzOPmAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 17.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 9\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.000000\n", + "mean 779.113208\n", + "std 24.470334\n", + "min 699.000000\n", + "25% 770.000000\n", + "50% 784.000000\n", + "75% 794.000000\n", + "max 839.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 779.113207547\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.000000\n", + "mean 1498.314465\n", + "std 114.548046\n", + "min 1114.000000\n", + "25% 1440.000000\n", + "50% 1440.000000\n", + "75% 1600.000000\n", + "max 1600.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1498.31446541\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.000000\n", + "mean 899.547170\n", + "std 4.024762\n", + "min 864.000000\n", + "25% 900.000000\n", + "50% 900.000000\n", + "75% 900.000000\n", + "max 900.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 899.547169811\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 10\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 130.000000\n", + "mean 894.184615\n", + "std 36.691630\n", + "min 767.000000\n", + "25% 881.750000\n", + "50% 894.000000\n", + "75% 918.000000\n", + "max 985.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 894.184615385\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 130.000000\n", + "mean 1261.023077\n", + "std 115.436043\n", + "min 768.000000\n", + "25% 1280.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1564.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1261.02307692\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 130.000000\n", + "mean 1034.046154\n", + "std 20.856788\n", + "min 1024.000000\n", + "25% 1024.000000\n", + "50% 1024.000000\n", + "75% 1024.000000\n", + "max 1080.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1034.04615385\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 130.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 130.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 0.0\n" + ] + }, + { + "data": { + "image/png": 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GqLS0VNdee626deum3//+98d8uWPHjh2qra3V8OHD47d981H0S5Ys0aRJk476\nfeeee65uvvlmFRUVKSsrS2eeeWaL729tvrFjx+rzzz+PP2b69OmaPHmyli1bpnPPPVc9e/Zs71oA\nGHBcftQcAAAY4YoIgGOaM2eO3nzzze+8R8fAgQP1wAMPGE0FoCvhiggAADDDD6sCAAAzhAgAADBD\niAAAADOECAAAMEOIAAAAM4QIAAAw8/8Bk5m/+bUCR9gAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 11\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 5.000000\n", + "mean 1022.200000\n", + "std 134.313812\n", + "min 904.000000\n", + "25% 936.000000\n", + "50% 936.000000\n", + "75% 1144.000000\n", + "max 1191.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 1022.2\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 5.000000\n", + "mean 780.800000\n", + "std 17.527122\n", + "min 768.000000\n", + "25% 768.000000\n", + "50% 768.000000\n", + "75% 800.000000\n", + "max 800.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 780.8\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 5.000000\n", + "mean 1126.400000\n", + "std 140.216975\n", + "min 1024.000000\n", + "25% 1024.000000\n", + "50% 1024.000000\n", + "75% 1280.000000\n", + "max 1280.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1126.4\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 5.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: os_Android\n" + ] + }, + { + "data": { + "text/plain": [ + "count 5.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Android, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Android: 1.0\n" + ] + }, + { + "data": { + "image/png": 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1KS72d0vwiIi2tra4+eabY968eVH4Kdde6Wmfr732WqxZsyZaWlrijjvuiMcffzyefPLJ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\nSlwAAKnEBQCQ6v8DTOQ5fNl/mNgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 12\n", + "for column in listImportancesB:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsB.loc[cluster_versionsB['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 12\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33\n", + "unique 2\n", + "top True\n", + "freq 23\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.69696969696969702" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 8\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395\n", + "unique 2\n", + "top True\n", + "freq 304\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.76962025316455696" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 5\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159\n", + "unique 2\n", + "top True\n", + "freq 116\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.72955974842767291" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 1\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102\n", + "unique 2\n", + "top False\n", + "freq 91\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.10784313725490197" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 4\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80\n", + "unique 2\n", + "top True\n", + "freq 62\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.77500000000000002" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 9\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87\n", + "unique 2\n", + "top True\n", + "freq 62\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.71264367816091956" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 3\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80\n", + "unique 2\n", + "top True\n", + "freq 57\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.71250000000000002" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 2\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67\n", + "unique 2\n", + "top False\n", + "freq 53\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.20895522388059701" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33\n", + "unique 2\n", + "top True\n", + "freq 24\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.72727272727272729" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 11\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15\n", + "unique 2\n", + "top True\n", + "freq 10\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.66666666666666663" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 10\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2\n", + "unique 2\n", + "top True\n", + "freq 1\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster: 6\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7\n", + "unique 2\n", + "top True\n", + "freq 5\n", + "Name: cuestionarioFinalizado, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7142857142857143" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for cluster in cluster_versionsC['cluster'].unique():\n", + " print \"Cluster: \"+str(cluster)\n", + " display((cluster_versionsC.loc[cluster_versionsC['cluster'] == cluster]['cuestionarioFinalizado']).describe())\n", + " display((cluster_versionsC.loc[cluster_versionsC['cluster'] == cluster]['cuestionarioFinalizado']).mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 1148.121212\n", + "std 280.314974\n", + "min 768.000000\n", + "25% 768.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1440.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1148.12121212\n" + ] + }, + { + "data": { + "image/png": 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EAACYIEIAAIAJIgQAAJggQgAAgAkiBAAAmCBCAACACSIEAACYIEIAAIAJIgQAAJggQgAA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\nFAqF1KNHD918882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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 678.575758\n", + "std 49.965007\n", + "min 504.000000\n", + "25% 655.000000\n", + "50% 672.000000\n", + "75% 713.000000\n", + "max 787.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 678.575757576\n" + ] + }, + { + "data": { + "image/png": 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XVVRUFPkxSjgc1rRp0zR9+nRJ0vHjx9WjRw/17NlT4XBYr7zyijIzM3XOOeeo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\nAGAN4QEAAKwhPAAAgDX/H9+tFwFtB7cOAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 898.909091\n", + "std 98.281599\n", + "min 800.000000\n", + "25% 800.000000\n", + "50% 900.000000\n", + "75% 1024.000000\n", + "max 1024.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 898.909090909\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33\n", + "unique 1\n", + "top False\n", + "freq 33\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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ss85SSUmJmpqaNGHCBPXq1Uv19fXH3H6kxwkgOTg9PQAAMMcRDgBHKSsrUygU\navvadV05jqORI0dG/bXZRFwfQMfEEQ4AAGCON40CAABzBAcAADBHcAAAAHMEBwAAMEdwAAAAcwQH\nAAAw9//IkTrf1T1WWAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 1201.575758\n", + "std 163.283884\n", + "min 768.000000\n", + "25% 1024.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1440.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1201.57575758\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.0\n", + "mean 2.0\n", + "std 0.0\n", + "min 2.0\n", + "25% 2.0\n", + "50% 2.0\n", + "75% 2.0\n", + "max 2.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 2.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 12 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.0\n" + ] + }, + { + "data": { + "image/png": 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qDRkyRJJ01113ac2aNbrttttUVFSkOXPmRLUnAK2Ly9MDAABzyfEeAEB8FBUV\n6Z133vnekYk+ffqc87k1ACASjnAAAABzfIYDAACYIzgAAIA5ggMAAJgjOAAAgDmCAwAAmCM4AACA\nuf8DQtsFJRpZBGAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 12\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.000000\n", + "mean 1883.773585\n", + "std 86.189952\n", + "min 1680.000000\n", + "25% 1920.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 1920.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1883.77358491\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.000000\n", + "mean 953.169811\n", + "std 30.055267\n", + "min 874.000000\n", + "25% 944.000000\n", + "50% 950.000000\n", + "75% 974.000000\n", + "max 1083.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 953.169811321\n" + ] + }, + { + "data": { + "image/png": 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opPT0dK1duzZ8n/3796t///7hj91ut0KhUKdjnpj7iTEBnB44EgKchm688Uat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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.000000\n", + "mean 1077.735849\n", + "std 20.123811\n", + "min 1050.000000\n", + "25% 1080.000000\n", + "50% 1080.000000\n", + "75% 1080.000000\n", + "max 1200.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1077.73584906\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159\n", + "unique 1\n", + "top False\n", + "freq 159\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.000000\n", + "mean 1842.327044\n", + "std 178.769334\n", + "min 944.000000\n", + "25% 1919.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 1920.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1842.32704403\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 5 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 159.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 1.0\n" + ] + }, + { + "data": { + "image/png": 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64oortGHDhk5D5FRGjhypNWvWSJLeeeedyJmSzMxMvfrqq5KkhoYGVVRUKCsr\nS+eff7769eunt956S+np6crMzNTzzz+v2267TZL0yCOPaM+ePbrrrrv00EMPad++fWe8IwBfP0IE\nOEfk5OQoJydHeXl5GjdunC655BJNmTJFBw4c0Lhx4xQMBvWLX/wi8hZJZ4YPH65//vOfysrKityX\nnZ2t1tZWXXzxxSc9vrOfhpk3b542btyoCRMmaMOGDRo4cKAk6YEHHtCRI0c0btw4TZs2TdOnT9fV\nV18tSRo1apSSkpJ03nnnKSsrS3V1dZFfM37ffffphRde0A9/+EMtWLBAs2fPjmpPAL5eHufL50EB\nAAC+Jj7rAQCcXRYsWKCtW7eedCbj29/+9hlfGwQAusIZEQAAYIbPiAAAADOECAAAMEOIAAAAM4QI\nAAAwQ4gAAAAzhAgAADDzf+nKBXCLXnLXAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 5\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102.000000\n", + "mean 356.078431\n", + "std 21.670661\n", + "min 320.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 375.000000\n", + "max 375.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 356.078431373\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102.000000\n", + "mean 525.686275\n", + "std 55.285932\n", + "min 320.000000\n", + "25% 460.000000\n", + "50% 559.000000\n", + "75% 559.000000\n", + "max 616.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 525.68627451\n" + ] + }, + { + "data": { + "image/png": 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J8uabb3b4hM24uLiQcdCRpKQkXXnlldq4caOkE2dtZs2addZtxo0bp5qaGvl8\nPrW0tGjTpk2dPlHV4/HI7/d3ex4AkcUZDyAKjR07Vk1NTZo5c6ak03/Zceutt6qxsVH33HOPAoGA\ncnJylJ+fL+nEQzSrV69WVlaWEhIS5Pf7NWnSpOAxL7roIj3//PPas2ePRo8erccff1wJCQlaunSp\nHnroIfn9fl122WV69tlnz7rPUyZNmqSpU6fq7bff7vBtsTuKheeee05lZWV6/fXXFR8frxdeeOGs\n6zMyMlRQUKB7771XQ4YM0YUXXhg829LRMSdMmKAlS5Zo+fLluvHGG0OuJwA7LofzksCAt3fvXv3q\nV7/Se++919ejtOurr77Sli1bgg8hzZ8/X/fcc0/w+SkAogdnPABI6v5rbHRk9uzZ8nq9wc8dx5HL\n5dK9997b6Z/bduSyyy7TF198oTvvvFMul0s/+clPiA4gSnHGAwAAmOHJpQAAwAzhAQAAzBAeAADA\nDOEBAADMEB4AAMAM4QEAAMz8H+dUwmh3i0QzAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102.000000\n", + "mean 631.196078\n", + "std 41.665580\n", + "min 480.000000\n", + "25% 593.500000\n", + "50% 640.000000\n", + "75% 667.000000\n", + "max 667.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 631.196078431\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102\n", + "unique 1\n", + "top True\n", + "freq 102\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102.000000\n", + "mean 364.235294\n", + "std 52.358471\n", + "min 320.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 375.000000\n", + "max 667.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 364.235294118\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102.0\n", + "mean 2.0\n", + "std 0.0\n", + "min 2.0\n", + "25% 2.0\n", + "50% 2.0\n", + "75% 2.0\n", + "max 2.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 2.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 1 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 102.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 1\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87.000000\n", + "mean 1356.022989\n", + "std 105.046831\n", + "min 768.000000\n", + "25% 1280.000000\n", + "50% 1366.000000\n", + "75% 1440.000000\n", + "max 1600.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1356.02298851\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87.000000\n", + "mean 687.137931\n", + "std 71.380274\n", + "min 506.000000\n", + "25% 634.000000\n", + "50% 676.000000\n", + "75% 730.500000\n", + "max 839.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 687.137931034\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87.000000\n", + "mean 835.402299\n", + "std 64.568777\n", + "min 600.000000\n", + "25% 800.000000\n", + "50% 800.000000\n", + "75% 900.000000\n", + "max 1024.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 835.402298851\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87\n", + "unique 1\n", + "top False\n", + "freq 87\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87.000000\n", + "mean 1308.137931\n", + "std 128.195542\n", + "min 729.000000\n", + "25% 1266.500000\n", + "50% 1280.000000\n", + "75% 1413.500000\n", + "max 1535.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1308.13793103\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 9 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 87.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 9\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 1281.500000\n", + "std 13.416408\n", + "min 1280.000000\n", + "25% 1280.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1400.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1281.5\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 895.675000\n", + "std 23.430898\n", + "min 820.000000\n", + "25% 893.250000\n", + "50% 894.000000\n", + "75% 910.250000\n", + "max 953.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 895.675\n" + ] + }, + { + "data": { + "image/png": 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TU6O6ujpt2LCh1TeqJiYmqqGhod37ARBdvOIBdEADBgxQdXW1brvtNknf/s2O\nIUOGaPfu3brlllsUCASUk5Oj3NxcSUd/RPPiiy9q4MCBSklJUUNDg4YOHRp8zLPOOktPPfWU9u7d\nqz59+mjq1KlKSUnRvHnzdN9996mhoUHnnXeeFixYcMKaxwwdOlSjR4/WK6+8ouTk5Gb33VIsPPnk\nkyouLtbSpUuVnJysp59++oTbe/Xqpby8PN16661KTU3VmWeeGXy1paXHvO666zR79mw9/vjjuuKK\nK0LOE4Adj+N1SSDulZWV6c4779T69etP9Vaa9dlnn2nz5s3BHyHde++9uuWWW4LvTwHQcfCKBwBJ\n7f+MjZYUFBTI7/cHv3bOyePx6NZbb231r9u25LzzztOOHTt04403yuPx6NprryU6gA6KVzwAAIAZ\n3lwKAADMEB4AAMAM4QEAAMwQHgAAwAzhAQAAzBAeAADAzP8BTqaUZlXeD4AAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 1022.350000\n", + "std 10.994936\n", + "min 960.000000\n", + "25% 1024.000000\n", + "50% 1024.000000\n", + "75% 1024.000000\n", + "max 1050.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1022.35\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80\n", + "unique 1\n", + "top False\n", + "freq 80\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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ypfr376/c3FxPP2dhYaGeeeYZSdJHH32kNWvWJC5x/9JLL6mrq0s7d+7Uq6++\nqgsuuOArX+Pzn/Omm25KfE4AqUHyA2nipJNOUv/+/fXXv/5Ve/fu1bhx43TiiSfq//7v//Tvf/9b\nkpSfn68//vGPmjNnjk499VRVV1dL+t/RizFjxmjjxo268sorFY/HVVRUpHHjxkk68AOdY8eO1fLl\ny5WZmXnI+2dmZuqhhx7Sb37zG+3Zs0d5eXl6+OGHD3r9ZHryuDvuuEN33nmnli9fLp/Pp3vuuUf5\n+fmSpFNOOUWlpaXq7OzU5MmTNWTIELW1tR3y+t19TgCpweXpAQCAOY5wADjIpEmTFI1GE7dd15Xj\nOLrqqquS/tqsF88H0DdxhAMAAJjjh0YBAIA5ggMAAJgjOAAAgDmCAwAAmCM4AACAOYIDAACY+/9a\nZZr+RyuzGwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 1279.775000\n", + "std 17.540532\n", + "min 1185.000000\n", + "25% 1280.000000\n", + "50% 1280.000000\n", + "75% 1280.000000\n", + "max 1400.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1279.775\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 3 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 1.0\n" + ] + }, + { + "data": { + "image/png": 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HELvcKk+U0xcAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 3\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67.000000\n", + "mean 375.358209\n", + "std 26.379343\n", + "min 360.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 394.500000\n", + "max 480.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 375.358208955\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67.000000\n", + "mean 567.611940\n", + "std 46.997729\n", + "min 511.000000\n", + "25% 524.000000\n", + "50% 560.000000\n", + "75% 598.000000\n", + "max 757.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 567.611940299\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67.000000\n", + "mean 667.313433\n", + "std 46.876436\n", + "min 640.000000\n", + "25% 640.000000\n", + "50% 640.000000\n", + "75% 701.500000\n", + "max 853.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 667.313432836\n" + ] + }, + { + "data": { + "image/png": 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79mj69OltzrZgwQKlp6eHtN29e/dq5syZqqurk8fj0ZQpUzR+/PhwjQ2gCwgP\nAABghheXAgAAM4QHAAAwQ3gAAAAzhAcAADBDeAAAADOEBwAAMPP/AJ843xuE+G69AAAAAElFTkSu\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67\n", + "unique 1\n", + "top True\n", + "freq 67\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67.000000\n", + "mean 375.358209\n", + "std 26.379343\n", + "min 360.000000\n", + "25% 360.000000\n", + "50% 360.000000\n", + "75% 394.500000\n", + "max 480.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 375.358208955\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67.000000\n", + "mean 3.119403\n", + "std 0.326709\n", + "min 3.000000\n", + "25% 3.000000\n", + "50% 3.000000\n", + "75% 3.000000\n", + "max 4.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 3.11940298507\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 2 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 67.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 2\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 1830.303030\n", + "std 172.924927\n", + "min 1280.000000\n", + "25% 1680.000000\n", + "50% 1920.000000\n", + "75% 1920.000000\n", + "max 1920.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1830.3030303\n" + ] + }, + { + "data": { + "image/png": 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eJ8ywb9++On3PEhIS9PTTT+u6667T9ddfrxUrVqhbt24nfLLzjTfeqFWrVklS\n8HUn0nehB8AWR0IAY3379tWOHTt0++2367zzztNll12m5s2ba8KECRoxYoScc+rcubN+9atfSao8\n6hAfH6/S0tLgUxjHtGnTRmlpaRo1apQCgYCuvvpq3XnnnfL7/crIyNDtt9+uQCCgqVOnVvko+uMl\nJSVpypQpWr16tSIjI5WRkaHo6GjNnz9f06ZNU3l5uS655BI99thjWrduXZXrpqena/bs2cGjNAsW\nLFCzZs00YcKEk86wbdu2Ktc/PiRO5uabb9bSpUvVo0cPNWnSROXl5erbt+8JX/fggw9q6tSpevXV\nV9WpUye1atVKktStWzdlZ2friSee0GWXXVbr+wdQPzyO444AAMAAR0KAMLVt2zZlZmZW+c3fOSeP\nx6NnnnlGPp/PbLYvv/xS999//0lny8zMVFxcnNlsAOoPR0IAAIAJXpgKAABMECEAAMAEEQIAAEwQ\nIQAAwAQRAgAATBAhAADAxP8Ba2DmLSlrWC8AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 928.272727\n", + "std 67.027640\n", + "min 809.000000\n", + "25% 881.000000\n", + "50% 927.000000\n", + "75% 979.000000\n", + "max 1080.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 928.272727273\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 1081.151515\n", + "std 41.731374\n", + "min 1024.000000\n", + "25% 1050.000000\n", + "50% 1080.000000\n", + "75% 1080.000000\n", + "max 1200.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1081.15151515\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33\n", + "unique 1\n", + "top False\n", + "freq 33\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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ss85SSUmJmpqaNGHCBPXq1Uv19fXH3H6kxwkgOTg9PQAAMMcRDgBHKSsrUygU\navvadV05jqORI0dG/bXZRFwfQMfEEQ4AAGCON40CAABzBAcAADBHcAAAAHMEBwAAMEdwAAAAcwQH\nAAAw9//IkTrf1T1WWAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 1697.484848\n", + "std 267.393568\n", + "min 1105.000000\n", + "25% 1523.000000\n", + "50% 1842.000000\n", + "75% 1920.000000\n", + "max 1920.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1697.48484848\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.000000\n", + "mean 1.060606\n", + "std 0.242306\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.06060606061\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 7 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 33.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.0\n" + ] + }, + { + "data": { + "image/png": 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qDRkyRJJ01113ac2aNbrttttUVFSkOXPmRLUnAK2Ly9MDAABzyfEeAEB8FBUV\n6Z133vnekYk+ffqc87k1ACASjnAAAABzfIYDAACYIzgAAIA5ggMAAJgjOAAAgDmCAwAAmCM4AACA\nuf8DQtsFJRpZBGAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 7\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15.000000\n", + "mean 772.266667\n", + "std 11.259705\n", + "min 768.000000\n", + "25% 768.000000\n", + "50% 768.000000\n", + "75% 768.000000\n", + "max 800.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 772.266666667\n" + ] + }, + { + "data": { + "image/png": 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NmzZNN910k0KhkCZMmHDEy5x/14gRIzR+/HitXLlS8fHxmjZtmpo1a6a5c+dq\n4sSJCgaDOvvss/XQQw9p9erVRxyblZWl7Ozs2rsxOTk5atGihe65555jrmHz5s1HHP/deDiWa6+9\nVk888YR69uyp5ORkBYNBDRw48KjPu//++zVhwgQtX75cF154odq2bStJ6tGjhxYsWKBHHnlE55xz\nTr3PDyB8Psc9RQAAYIQ7HkCM2rx5s2bMmHHE/8N3zsnn8ykvL0+pqakNtrbdu3dr7Nixx1zbjBkz\n1K1btwZbG4BTwx0PAABghl8uBQAAZggPAABghvAAAABmCA8AAGCG8AAAAGYIDwAAYOb/AbeV0A0q\nUMAsAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15.000000\n", + "mean 956.466667\n", + "std 77.090732\n", + "min 904.000000\n", + "25% 927.000000\n", + "50% 928.000000\n", + "75% 932.500000\n", + "max 1144.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 956.466666667\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15.000000\n", + "mean 1058.133333\n", + "std 90.077638\n", + "min 1024.000000\n", + "25% 1024.000000\n", + "50% 1024.000000\n", + "75% 1024.000000\n", + "max 1280.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1058.13333333\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15\n", + "unique 1\n", + "top False\n", + "freq 15\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15.000000\n", + "mean 772.266667\n", + "std 11.259705\n", + "min 768.000000\n", + "25% 768.000000\n", + "50% 768.000000\n", + "75% 768.000000\n", + "max 800.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 772.266666667\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15.000000\n", + "mean 1.666667\n", + "std 0.487950\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 2.000000\n", + "75% 2.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.66666666667\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 11 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 15.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 11\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 10 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2.0\n", + "mean 1080.0\n", + "std 0.0\n", + "min 1080.0\n", + "25% 1080.0\n", + "50% 1080.0\n", + "75% 1080.0\n", + "max 1080.0\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1080.0\n", + "\n", + "\n", + "Cluster 10 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2.000000\n", + "mean 499.500000\n", + "std 24.748737\n", + "min 482.000000\n", + "25% 490.750000\n", + "50% 499.500000\n", + "75% 508.250000\n", + "max 517.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 499.5\n", + "\n", + "\n", + "Cluster 10 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2.000000\n", + "mean 1848.000000\n", + "std 101.823376\n", + "min 1776.000000\n", + "25% 1812.000000\n", + "50% 1848.000000\n", + "75% 1884.000000\n", + "max 1920.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1848.0\n", + "\n", + "\n", + "Cluster 10 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2\n", + "unique 2\n", + "top True\n", + "freq 1\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.5\n", + "\n", + "\n", + "Cluster 10 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2.0\n", + "mean 360.0\n", + "std 0.0\n", + "min 360.0\n", + "25% 360.0\n", + "50% 360.0\n", + "75% 360.0\n", + "max 360.0\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 360.0\n", + "\n", + "\n", + "Cluster 10 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2.0\n", + "mean 3.0\n", + "std 0.0\n", + "min 3.0\n", + "25% 3.0\n", + "50% 3.0\n", + "75% 3.0\n", + "max 3.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 3.0\n", + "\n", + "\n", + "Cluster 10 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 2.0\n", + "mean 0.0\n", + "std 0.0\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 0.0\n", + "max 0.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.0\n" + ] + } + ], + "source": [ + "selected_cluster = 10\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " #sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 2560.0\n", + "std 0.0\n", + "min 2560.0\n", + "25% 2560.0\n", + "50% 2560.0\n", + "75% 2560.0\n", + "max 2560.0\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 2560.0\n" + ] + }, + { + "data": { + "image/png": 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II4+ob9++qqysVHp6ulq2bFntTCkpKRozZoyysrLk8Xj0yCOPKC4uTrNmzdK4\nceNUUVGhVq1aaebMmYG3rB03adIkZWRkBI6qzJ49W2effbbuu+++U86wcePGE+7/w0A4lW7dumnR\nokW6+uqrFR8fr4qKCvXs2fOk2z300ENKT0/X8uXLddlll6l58+aSpCuvvFLz5s3TnDlz1LZt2x/9\n/QGEhneLAAAAUxy5ABq4jRs3aurUqSf8pO44jlwul5555hn5/f6IzbZnzx794Q9/OOVsU6dOVWJi\nYsRmAxA6jlwAAABTvKATAACYIi4AAIAp4gIAAJgiLgAAgCniAgAAmCIuAACAqf8HBh926rOq/BIA\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1242.714286\n", + "std 83.637540\n", + "min 1117.000000\n", + "25% 1185.000000\n", + "50% 1263.000000\n", + "75% 1303.000000\n", + "max 1343.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 1242.71428571\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.0\n", + "mean 1440.0\n", + "std 0.0\n", + "min 1440.0\n", + "25% 1440.0\n", + "50% 1440.0\n", + "75% 1440.0\n", + "max 1440.0\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 1440.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7\n", + "unique 1\n", + "top False\n", + "freq 7\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1892.428571\n", + "std 558.290802\n", + "min 1148.000000\n", + "25% 1549.500000\n", + "50% 1638.000000\n", + "75% 2401.000000\n", + "max 2560.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1892.42857143\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 1.142857\n", + "std 0.377964\n", + "min 1.000000\n", + "25% 1.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 2.000000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.14285714286\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 6 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 7.000000\n", + "mean 0.142857\n", + "std 0.377964\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 0.142857142857\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 6\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 1573.600000\n", + "std 164.609057\n", + "min 1152.000000\n", + "25% 1440.000000\n", + "50% 1600.000000\n", + "75% 1600.000000\n", + "max 1920.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1573.6\n" + ] + }, + { + "data": { + "image/png": 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hAADACiIEAABYQYQAAAAriBAAAGAFEQIAAKwgQgAAgBVECAAAsIIIAQAAVhAhAADACiIE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\niBAAAGAFEQIAAKw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 759.262500\n", + "std 48.571686\n", + "min 507.000000\n", + "25% 751.750000\n", + "50% 770.000000\n", + "75% 789.000000\n", + "max 836.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 759.2625\n" + ] + }, + { + "data": { + "image/png": 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Rm7FjxyoQCEQ+dhxHHo9HDz744BX/uW1tbrzxRu3du1f33XefPB6P7rrrLqID\naKY44wEAAMzw5lIAAGCG8AAAAGYIDwAAYIbwAAAAZggPAABghvAAAABm/h+sTjYMEtps5gAAAABJ\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 932.575000\n", + "std 70.848881\n", + "min 864.000000\n", + "25% 900.000000\n", + "50% 900.000000\n", + "75% 900.000000\n", + "max 1200.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 932.575\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80\n", + "unique 1\n", + "top False\n", + "freq 80\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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ypfr376/c3FxPP2dhYaGeeeYZSdJHH32kNWvWJC5x/9JLL6mrq0s7d+7Uq6++\nqgsuuOArX+Pzn/Omm25KfE4AqUHyA2nipJNOUv/+/fXXv/5Ve/fu1bhx43TiiSfq//7v//Tvf/9b\nkpSfn68//vGPmjNnjk499VRVV1dL+t/RizFjxmjjxo268sorFY/HVVRUpHHjxkk68AOdY8eO1fLl\ny5WZmXnI+2dmZuqhhx7Sb37zG+3Zs0d5eXl6+OGHD3r9ZHryuDvuuEN33nmnli9fLp/Pp3vuuUf5\n+fmSpFNOOUWlpaXq7OzU5MmTNWTIELW1tR3y+t19TgCpweXpAQCAOY5wADjIpEmTFI1GE7dd15Xj\nOLrqqquS/tqsF88H0DdxhAMAAJjjh0YBAIA5ggMAAJgjOAAAgDmCAwAAmCM4AACAOYIDAACY+/9a\nZZr+RyuzGwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.000000\n", + "mean 1468.500000\n", + "std 187.647124\n", + "min 816.000000\n", + "25% 1440.000000\n", + "50% 1554.000000\n", + "75% 1600.000000\n", + "max 1849.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1468.5\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.00000\n", + "mean 1.02500\n", + "std 0.15711\n", + "min 1.00000\n", + "25% 1.00000\n", + "50% 1.00000\n", + "75% 1.00000\n", + "max 2.00000\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.025\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 4 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 80.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 1.0\n" + ] + }, + { + "data": { + "image/png": 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HELvcKk+U0xcAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 4\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_screen_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395.000000\n", + "mean 1340.177215\n", + "std 82.564720\n", + "min 1024.000000\n", + "25% 1366.000000\n", + "50% 1366.000000\n", + "75% 1366.000000\n", + "max 1680.000000\n", + "Name: device_screen_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_width: 1340.17721519\n" + ] + }, + { + "data": { + "image/png": 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hiMr5eeyEx3zCYz7R9ZPDZNy4cfJ6vcrIyFBVVZXS09OVkZGhOXPmqLGxUQ0NDdq4caPS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\nJsBh4u6779ayZct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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: viewport_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395.000000\n", + "mean 645.270886\n", + "std 34.903421\n", + "min 396.000000\n", + "25% 638.000000\n", + "50% 657.000000\n", + "75% 662.000000\n", + "max 710.000000\n", + "Name: viewport_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_height: 645.270886076\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_screen_height\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395.000000\n", + "mean 770.820253\n", + "std 27.551071\n", + "min 600.000000\n", + "25% 768.000000\n", + "50% 768.000000\n", + "75% 768.000000\n", + "max 1050.000000\n", + "Name: device_screen_height, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_screen_height: 770.820253165\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: tablet_or_mobile\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395\n", + "unique 1\n", + "top False\n", + "freq 395\n", + "Name: tablet_or_mobile, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :tablet_or_mobile: 0.0\n" + ] + }, + { + "data": { + "image/png": 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PCxYsUHNzswYMGKC8vDxZlqXCwkIVFBTItm2VlJTI5/N113MAAABxyrJtO7Zrq12ES2Lu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\noUGzZ89WXV2dfD6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: viewport_width\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395.000000\n", + "mean 1328.427848\n", + "std 104.404751\n", + "min 510.000000\n", + "25% 1366.000000\n", + "50% 1366.000000\n", + "75% 1366.000000\n", + "max 1366.000000\n", + "Name: viewport_width, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :viewport_width: 1328.4278481\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: device_pixel_ratio\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: device_pixel_ratio, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :device_pixel_ratio: 1.0\n" + ] + }, + { + "data": { + "image/png": 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cevDBBzV79mzV1taqW7duysrKksvlUl5ennJycmSMUX5+vpKSks7V1wAAAOKUyxgT27nV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\nbhfA2SNEgDg1d+5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cluster 8 || feature: os_Windows\n" + ] + }, + { + "data": { + "text/plain": [ + "count 395.0\n", + "mean 1.0\n", + "std 0.0\n", + "min 1.0\n", + "25% 1.0\n", + "50% 1.0\n", + "75% 1.0\n", + "max 1.0\n", + "Name: os_Windows, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of feature :os_Windows: 1.0\n" + ] + }, + { + "data": { + "image/png": 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eeeQRLV68WG1tbRo0aJAKCwvlcDjk9/tVWloqy7JUXl6u5OTkC/U1AACABOWwLCu2s9U4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\nGEOIAAAAYwgRAABgzP8C7+gscTdD0ogAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_cluster = 8\n", + "for column in listImportancesC:\n", + " print \"\\n\\nCluster \"+str(selected_cluster)+\" || feature: \"+str(column) \n", + " display(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].describe())\n", + " print(\"Mean of feature :\"+str(column)+\": \"+str(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column].mean()))\n", + " sns.distplot(cluster_versionsC.loc[cluster_versionsC['cluster'] == selected_cluster][column], kde=False, rug=True);\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Results regarding the finalization rate of the users

" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total users in A vertical: 993\n", + "Finalized/not Finalized: index cuestionarioFinalizado\n", + "0 True 757\n", + "1 False 236\n", + "Completion rate: 0.762336354481\n", + "\n", + "Total users in B vertical: 1403\n", + "Finalized/not Finalized: index cuestionarioFinalizado\n", + "0 True 933\n", + "1 False 470\n", + "Completion rate: 0.665003563792\n", + "\n", + "Total users in C vertical: 1060\n", + "Finalized/not Finalized: index cuestionarioFinalizado\n", + "0 True 689\n", + "1 False 371\n", + "Completion rate: 0.65\n" + ] + } + ], + "source": [ + "totalA = fullModelClean_vA.shape[0]\n", + "print (\"Total users in A vertical: \" +str(totalA))\n", + "finNoFinA = fullModelClean_vA[\"cuestionarioFinalizado\"].value_counts().reset_index()\n", + "print \"Finalized/not Finalized: \",str(finNoFinA) \n", + "print \"Completion rate: \"+str(float(757)/float(fullModelClean_vA.shape[0]))\n", + "\n", + "totalB = fullModelClean_vB.shape[0]\n", + "print (\"Total users in B vertical: \" +str(totalB))\n", + "finNoFinB = fullModelClean_vB[\"cuestionarioFinalizado\"].value_counts().reset_index()\n", + "print \"Finalized/not Finalized: \",str(finNoFinB) \n", + "print \"Completion rate: \"+str(float(933)/float(fullModelClean_vB.shape[0]))\n", + "\n", + "\n", + "totalC = fullModelClean_vC.shape[0]\n", + "print (\"Total users in C vertical: \" +str(totalC))\n", + "finNoFinC = fullModelClean_vC[\"cuestionarioFinalizado\"].value_counts().reset_index()\n", + "print \"Finalized/not Finalized: \",str(finNoFinC) \n", + "print \"Completion rate: \"+str(float(689)/float(fullModelClean_vC.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/reinforcement-results.ipynb b/reinforcement-results.ipynb new file mode 100644 index 0000000..7203207 --- /dev/null +++ b/reinforcement-results.ipynb @@ -0,0 +1,2436 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reinforcement and device-based questionnaire adaptativity results

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import requests\n", + "import io\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "from IPython.display import display\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

General results (BEFORE reinforcement)

\n", + "\n", + "

General results before the rules were applied.

" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data_before_reinf = pd.read_csv(\"dataBeforeReinforcement.csv\", low_memory=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Overall results taking into account only the users that have started the questionnaire

" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of students that have STARTED the questionnaire: 5768\n", + "Number of students that have NOT finished the questionnaire: 1358\n", + "Number of students that have finished the questionnaire: 4410\n", + "\n", + "Completion rate BEFORE reinforcement: 76.46%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_before_reinf = data_before_reinf[data_before_reinf[\"cuestionarioIniciado\"] == True].shape[0]\n", + "print \"Number of students that have STARTED the questionnaire: \" + str(total_started_questionnaires_before_reinf)\n", + "\n", + "uncompleted_questionnaires_before_reinf = data_before_reinf[(data_before_reinf[\"cuestionarioIniciado\"] == True) & (data_before_reinf[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of students that have NOT finished the questionnaire: \" + str(uncompleted_questionnaires_before_reinf)\n", + "\n", + "completed_questionnaires_before_reinf = data_before_reinf[(data_before_reinf[\"cuestionarioIniciado\"] == True) & (data_before_reinf[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of students that have finished the questionnaire: \" + str(completed_questionnaires_before_reinf)\n", + "\n", + "completion_rate_before_reinf = 100 * float(completed_questionnaires_before_reinf) / total_started_questionnaires_before_reinf\n", + "print \"\\nCompletion rate BEFORE reinforcement: \" + '% .2f' % completion_rate_before_reinf + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Overall results taking into account all the users that have clicked the questionnaire link (started or not)

" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of students that have ENTERED the questionnaire: 6360\n", + "Number of students that have NOT finished the questionnaire: 1950\n", + "Number of students that have finished the questionnaire: 4410\n", + "\n", + "Completion rate BEFORE reinforcement: 69.34%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_before_reinf_2 = data_before_reinf[(data_before_reinf[\"verticalAsignado\"] != 0)].shape[0]\n", + "print \"Number of students that have ENTERED the questionnaire: \" + str(total_started_questionnaires_before_reinf_2)\n", + "\n", + "uncompleted_questionnaires_before_reinf_2 = data_before_reinf[(data_before_reinf[\"verticalAsignado\"] != 0) & (data_before_reinf[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of students that have NOT finished the questionnaire: \" + str(uncompleted_questionnaires_before_reinf_2)\n", + "\n", + "completed_questionnaires_before_reinf_2 = data_before_reinf[(data_before_reinf[\"verticalAsignado\"] != 0) & (data_before_reinf[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of students that have finished the questionnaire: \" + str(completed_questionnaires_before_reinf_2)\n", + "\n", + "completion_rate_before_reinf_2 = 100 * float(completed_questionnaires_before_reinf_2) / total_started_questionnaires_before_reinf_2\n", + "print \"\\nCompletion rate BEFORE reinforcement: \" + '% .2f' % completion_rate_before_reinf_2 + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

General results (AFTER reinforcement)

\n", + "\n", + "

General results after the rules were applied.

" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with open(\"tokenOEEU.txt\") as file: \n", + " api_token = file.read() " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data_after_reinf = requests.get('https://datos.oeeu.org/api/master/estudiantes/',headers={'Authorization': api_token}).content\n", + "data_after_reinf = pd.read_csv(io.StringIO(data_after_reinf.decode('utf-8')), low_memory=False, sep=\";\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Overall results taking into account only the users that have started the questionnaire

" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of students that have STARTED the questionnaire: 6738\n", + "Number of students that have NOT finished the questionnaire: 1524\n", + "Number of students that have finished the questionnaire: 5214\n", + "\n", + "Completion rate AFTER reinforcement: 77.38%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_after_reinf = data_after_reinf[data_after_reinf[\"cuestionarioIniciado\"] == True].shape[0]\n", + "print \"Number of students that have STARTED the questionnaire: \" + str(total_started_questionnaires_after_reinf)\n", + "\n", + "uncompleted_questionnaires_after_reinf = data_after_reinf[(data_after_reinf[\"cuestionarioIniciado\"] == True) & (data_after_reinf[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of students that have NOT finished the questionnaire: \" + str(uncompleted_questionnaires_after_reinf)\n", + "\n", + "completed_questionnaires_after_reinf = data_after_reinf[(data_after_reinf[\"cuestionarioIniciado\"] == True) & (data_after_reinf[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of students that have finished the questionnaire: \" + str(completed_questionnaires_after_reinf)\n", + "\n", + "completion_rate_after_reinf = 100 * float(completed_questionnaires_after_reinf) / total_started_questionnaires_after_reinf\n", + "print \"\\nCompletion rate AFTER reinforcement: \" + '% .2f' % completion_rate_after_reinf + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Overall results taking into account all the users that have clicked the questionnaire link (started or not)

" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of students that have ENTERED the questionnaire: 7349\n", + "Number of students that have NOT finished the questionnaire: 2135\n", + "Number of students that have finished the questionnaire: 5214\n", + "\n", + "Completion rate AFTER reinforcement: 70.95%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_after_reinf_2 = data_after_reinf[data_after_reinf[\"verticalAsignado\"] != 0].shape[0]\n", + "print \"Number of students that have ENTERED the questionnaire: \" + str(total_started_questionnaires_after_reinf_2)\n", + "\n", + "uncompleted_questionnaires_after_reinf_2 = data_after_reinf[(data_after_reinf[\"verticalAsignado\"] != 0) & (data_after_reinf[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of students that have NOT finished the questionnaire: \" + str(uncompleted_questionnaires_after_reinf_2)\n", + "\n", + "completed_questionnaires_after_reinf_2 = data_after_reinf[(data_after_reinf[\"verticalAsignado\"] != 0) & (data_after_reinf[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of students that have finished the questionnaire: \" + str(completed_questionnaires_after_reinf_2)\n", + "\n", + "completion_rate_after_reinf_2 = 100 * float(completed_questionnaires_after_reinf_2) / total_started_questionnaires_after_reinf_2\n", + "print \"\\nCompletion rate AFTER reinforcement: \" + '% .2f' % completion_rate_after_reinf_2 + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Device-based questionnaire variant selection

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Device data gathering (after and before the reinforcement)

" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "env_paradata_before_reinf = pd.read_csv(\"deviceDataBeforeReinforcement.csv\", low_memory=False)\n", + "\n", + "'''\n", + "We get the device data from the most recent access of the paradata BEFORE before reinforcement.\n", + "The reason is because in case the user used different devices, we take into account the one with which the user\n", + "abandoned the questionnaire (and, therefore, the device which the user made the last access with)\n", + "'''\n", + "env_paradata_before_reinf = env_paradata_before_reinf.drop_duplicates(subset=[\"estudiante_id\"], keep=\"last\")\n", + "env_paradata_before_reinf = env_paradata_before_reinf[[\"estudiante_id\", \"device_pixel_ratio\", \"device_screen_height\", \"device_screen_width\", \"tablet_or_mobile\", \"viewport_width\", \"viewport_height\", \"os\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "env_paradata_after_reinf = requests.get('https://datos.oeeu.org/api/master/paradata_entorno/',headers={'Authorization': api_token}).content\n", + "env_paradata_after_reinf = pd.read_csv(io.StringIO(env_paradata_after_reinf.decode('utf-8')), low_memory=False, sep=\";\")\n", + "\n", + "'''\n", + "We get the device data from the first access of the paradata dataset AFTER the reinforcement date (2017-07-16),\n", + "because the redirection rules are applied based on the device with which the user returned to the questionnaire.\n", + "'''\n", + "env_paradata_after_reinf = env_paradata_after_reinf[env_paradata_after_reinf[\"sesion__tiempoInicio_sesion\"] > \"2017-07-16\"]\n", + "env_paradata_after_reinf = env_paradata_after_reinf.drop_duplicates(subset=[\"estudiante_id\"], keep=\"first\")\n", + "env_paradata_after_reinf = env_paradata_after_reinf[[\"estudiante_id\", \"device_pixel_ratio\", \"device_screen_height\", \"device_screen_width\", \"tablet_or_mobile\", \"viewport_width\", \"viewport_height\", \"os\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before merging the paradata and students' datastets, we first clean the students' datasets in order to keep only the variables that are\n", + "necessary to obtain the completion rate results:\n", + "- The vertical assigned (verticalAsignado)\n", + "- The boolean variables about the questionnaires' state: started (cuestionarioIniciado) and finalized (cuestionarioFinalizado)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_before_reinf = data_before_reinf[['estudiante_id', 'verticalAsignado', 'cuestionarioFinalizado', 'cuestionarioIniciado']]\n", + "data_after_reinf = data_after_reinf[['estudiante_id', 'verticalAsignado', 'cuestionarioFinalizado', 'cuestionarioIniciado']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we merge the datasets to obtain a single dataset containing the device and questionnaires' variables\n", + "before and after the reinforcement took place, in order to facilitate the comparison of these variables" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results_before_reinf = data_before_reinf.merge(env_paradata_before_reinf, how='left', on='estudiante_id')\n", + "results_after_reinf = data_after_reinf.merge(env_paradata_after_reinf, how='left', on='estudiante_id')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "full_dataset = results_before_reinf.merge(results_after_reinf, how='left', on='estudiante_id', suffixes=(\"_before_reinf\", \"_after_reinf\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Identification of target users (users that entered the questionnaire after the reinforcement date, 07/16/2017)

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We keep only the sessions started after the reinforcement date in order to identify all the users to which the\n", + "experiment was applied" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "session_paradata = requests.get('https://datos.oeeu.org/api/master/sesiones-estudiantes/',headers={'Authorization': api_token}).content\n", + "session_paradata = pd.read_csv(io.StringIO(session_paradata.decode('utf-8')), low_memory=False, sep=\";\")\n", + "\n", + "new_sessions_after_reinf = session_paradata[(session_paradata[\"tiempoInicio_sesion\"] > \"2017-07-16\")].drop_duplicates(subset=[\"estudiante_id\"], keep=\"first\").copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We keep only the users that had not finished the questionnaire before the reinforcement took place (target users)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "full_dataset = full_dataset.merge(new_sessions_after_reinf, on=\"estudiante_id\")\n", + "full_dataset = full_dataset[full_dataset[\"cuestionarioFinalizado_before_reinf\"] == False]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Identification of the different types of users affected by the reinforcement and redirection rules

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Users that had started the questionnaire after the reinforcement message.\n", + "We identify them through the following characteristics:\n", + "- They had not started the questionnaire before reinforcement (cuestionarioIniciado_before_reinf == False)\n", + "- They didn't have a questionnaire vertical assigned (verticalAsignado_before_reinf == 0)\n", + "- They started the questionnaire after reinforcement (cuestionarioIniciado_after_reinf == True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "new_users = full_dataset[(full_dataset[\"cuestionarioIniciado_before_reinf\"] == False) & (full_dataset[\"verticalAsignado_before_reinf\"] == 0)].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Users that resumed the questionnaire and had been redirected to a different questionnaire vertical.\n", + "We identify them through the following characteristics:\n", + "- They did have a questionnaire vertical assigned before reinforcement (verticalAsignado_before_reinf != 0)\n", + "- Their questionnaire vertical assigned after reinforcement differs from the previous one, and therefore, they\n", + "have been redirected to another questionnaire vertical (verticalAsignado_before_reinf != verticalAsignado_after_reinf)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "redirected_users = full_dataset[(full_dataset[\"verticalAsignado_before_reinf\"] != 0) & (full_dataset[\"verticalAsignado_before_reinf\"] != full_dataset[\"verticalAsignado_after_reinf\"])].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Users that resumed the questionnaire and had NOT been redirected to a different questionnaire vertical.\n", + "We identify them through the following characteristics:\n", + "- They did have a questionnaire vertical assigned before reinforcement (verticalAsignado_before_reinf != 0)\n", + "- Their questionnaire vertical assigned after reinforcement is the same as the previous one, and therefore, they\n", + "have NOT been redirected to another questionnaire vertical (verticalAsignado_before_reinf == verticalAsignado_after_reinf)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "not_redirected_users = full_dataset[(full_dataset[\"verticalAsignado_before_reinf\"] != 0) & (full_dataset[\"verticalAsignado_before_reinf\"] == full_dataset[\"verticalAsignado_after_reinf\"])].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

General numbers after reinforcement and rules' application

" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of users that started the questionnaire after reinforcement: 1003\n", + "Number of users that resumed the questionnaire after reinforcement and had been redirected: 110\n", + "Number of users that resumed the questionnaire after reinforcement and had NOT been redirected: 52\n" + ] + } + ], + "source": [ + "new_users_number = new_users.shape[0]\n", + "print \"Number of users that started the questionnaire after reinforcement: \" + str(new_users_number)\n", + "\n", + "redirected_users_number = redirected_users.shape[0]\n", + "print \"Number of users that resumed the questionnaire after reinforcement and had been redirected: \" + str(redirected_users_number)\n", + "\n", + "not_redirected_users_number = not_redirected_users.shape[0]\n", + "print \"Number of users that resumed the questionnaire after reinforcement and had NOT been redirected: \" + str(not_redirected_users_number)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

General results after reinforcement and rules' application

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

New users' completion rate

" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of new users that have started the questionnaire: 1003\n", + "Number of new users that have NOT finished the questionnaire: 285\n", + "Number of new users that have finished the questionnaire: 718\n", + "\n", + "Completion rate of new users: 71.59%\n" + ] + } + ], + "source": [ + "print \"Number of new users that have started the questionnaire: \" + str(new_users_number)\n", + "\n", + "new_users_uncompleted_questionnaires = new_users[(new_users[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of new users that have NOT finished the questionnaire: \" + str(new_users_uncompleted_questionnaires)\n", + "\n", + "new_users_completed_questionnaires = new_users[(new_users[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of new users that have finished the questionnaire: \" + str(new_users_completed_questionnaires)\n", + "\n", + "new_users_completion_rate = 100 * float(new_users_completed_questionnaires) / new_users_number\n", + "print \"\\nCompletion rate of new users: \" + '% .2f' % new_users_completion_rate + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Redirected users' completion rate

" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of redirected users that have started the questionnaire: 110\n", + "Number of redirected users that have NOT finished the questionnaire: 49\n", + "Number of redirected users that have finished the questionnaire: 61\n", + "\n", + "Completion rate of redirected users: 55.45%\n" + ] + } + ], + "source": [ + "print \"Number of redirected users that have started the questionnaire: \" + str(redirected_users_number)\n", + "\n", + "redirected_users_uncompleted_questionnaires = redirected_users[(redirected_users[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of redirected users that have NOT finished the questionnaire: \" + str(redirected_users_uncompleted_questionnaires)\n", + "\n", + "redirected_users_completed_questionnaires = redirected_users[(redirected_users[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of redirected users that have finished the questionnaire: \" + str(redirected_users_completed_questionnaires)\n", + "\n", + "redirected_users_completion_rate = 100 * float(redirected_users_completed_questionnaires) / redirected_users_number\n", + "print \"\\nCompletion rate of redirected users: \" + '% .2f' % redirected_users_completion_rate + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Not redirected users' completion rate

" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of not redirected users that have started the questionnaire: 52\n", + "Number of not redirected users that have NOT finished the questionnaire: 27\n", + "Number of not redirected users that have finished the questionnaire: 25\n", + "\n", + "Completion rate of not redirected users: 48.08%\n" + ] + } + ], + "source": [ + "print \"Number of not redirected users that have started the questionnaire: \" + str(not_redirected_users_number)\n", + "\n", + "not_redirected_users_uncompleted_questionnaires = not_redirected_users[(not_redirected_users[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of not redirected users that have NOT finished the questionnaire: \" + str(not_redirected_users_uncompleted_questionnaires)\n", + "\n", + "not_redirected_users_completed_questionnaires = not_redirected_users[(not_redirected_users[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of not redirected users that have finished the questionnaire: \" + str(not_redirected_users_completed_questionnaires)\n", + "\n", + "not_redirected_users_completion_rate = 100 * float(not_redirected_users_completed_questionnaires) / not_redirected_users_number\n", + "print \"\\nCompletion rate of not redirected users: \" + '% .2f' % not_redirected_users_completion_rate + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "

Analysis of the rules application impact

" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "

Identification of the rule that would have be applied to pre-reinforcement users

" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "pre_reinforcement_users = results_before_reinf[results_before_reinf[\"verticalAsignado\"] != 0].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def rule_identification_pre(x):\n", + " if np.isnan(x.tablet_or_mobile) or np.isnan(x.device_pixel_ratio) or np.isnan(x.device_screen_height) or np.isnan(x.device_screen_width):\n", + " return 9\n", + " elif x.os == \"Mac OS\":\n", + " return 5\n", + " elif x.os != \"iOS\" and x.os != \"Android\" and x.os != \"Mac OS\":\n", + " return 4\n", + " elif x.os == \"Android\" and x.tablet_or_mobile == False:\n", + " return 8\n", + " elif x.os == \"Android\" and x.device_pixel_ratio == 2:\n", + " return 1\n", + " elif x.os == \"Android\" and (x.device_pixel_ratio == 3 or x.device_pixel_ratio == 4):\n", + " return 2\n", + " elif x.os == \"iOS\" and x.device_pixel_ratio == 3:\n", + " return 3\n", + " elif x.os == \"Android\" and x.device_pixel_ratio > 1000:\n", + " return 6\n", + " elif x.os == \"iOS\" and x.device_screen_height == 1024 and x.device_screen_width == 768 and (x.device_pixel_ratio == 1 or x.device_pixel_ratio == 2):\n", + " return 7\n", + " else:\n", + " return 9" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "pre_reinforcement_users[\"applied_rule\"] = pre_reinforcement_users.apply(rule_identification_pre, axis = 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "

After identifying the rule, we compare it to the vertical that was randomly assigned to obtain the G1 and G2 users' groups

" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def assigned_vertical_eval(x):\n", + " if x.applied_rule == 1:\n", + " right_vertical = 1\n", + " elif x.applied_rule == 2:\n", + " right_vertical = 2\n", + " elif x.applied_rule == 3:\n", + " right_vertical = 2\n", + " elif x.applied_rule == 4:\n", + " right_vertical = 1\n", + " elif x.applied_rule == 5:\n", + " right_vertical = 1\n", + " elif x.applied_rule == 6:\n", + " right_vertical = 2\n", + " elif x.applied_rule == 7:\n", + " right_vertical = 1\n", + " elif x.applied_rule == 8:\n", + " right_vertical = 1\n", + " elif x.applied_rule == 9:\n", + " if x.verticalAsignado == 3:\n", + " return 0\n", + " else:\n", + " # lost values\n", + " return -99\n", + " \n", + " if right_vertical == x.verticalAsignado:\n", + " return 1\n", + " else:\n", + " return 0" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pre_reinforcement_users[\"rightVertical\"] = pre_reinforcement_users.apply(assigned_vertical_eval, axis = 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "

Group 1 users finalization rate

" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "g1 = pre_reinforcement_users[pre_reinforcement_users[\"rightVertical\"] == 0].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users that ENTERED the questionnaire: 3833\n", + "Number of G1 users that have NOT finished the questionnaire: 1232\n", + "Number of G1 users that have finished the questionnaire: 2601\n", + "\n", + "Completion rate of G1 users: 67.86%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1 = g1.shape[0]\n", + "print \"Number of G1 users that ENTERED the questionnaire: \" + str(total_started_questionnaires_g1)\n", + "\n", + "uncompleted_questionnaires_g1 = g1[(g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire: \" + str(uncompleted_questionnaires_g1)\n", + "\n", + "completed_questionnaires_g1 = g1[(g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire: \" + str(completed_questionnaires_g1)\n", + "\n", + "completion_rate_g1 = 100 * float(completed_questionnaires_g1) / total_started_questionnaires_g1\n", + "print \"\\nCompletion rate of G1 users: \" + '% .2f' % completion_rate_g1 + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Group 2 users finalization rate

" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "g2 = pre_reinforcement_users[pre_reinforcement_users[\"rightVertical\"] == 1].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G2 users that ENTERED the questionnaire: 1542\n", + "Number of G2 users that have NOT finished the questionnaire: 387\n", + "Number of G2 users that have finished the questionnaire: 1155\n", + "\n", + "Completion rate of G2 users: 74.90%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g2 = g2.shape[0]\n", + "print \"Number of G2 users that ENTERED the questionnaire: \" + str(total_started_questionnaires_g2)\n", + "\n", + "uncompleted_questionnaires_g2 = g2[(g2[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G2 users that have NOT finished the questionnaire: \" + str(uncompleted_questionnaires_g2)\n", + "\n", + "completed_questionnaires_g2 = g2[(g2[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G2 users that have finished the questionnaire: \" + str(completed_questionnaires_g2)\n", + "\n", + "completion_rate_g2 = 100 * float(completed_questionnaires_g2) / total_started_questionnaires_g2\n", + "print \"\\nCompletion rate of G2 users: \" + '% .2f' % completion_rate_g2 + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Group 3 users finalization rate

" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "g3 = new_users.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def rule_identification_post(x):\n", + " if np.isnan(x.tablet_or_mobile_after_reinf) or np.isnan(x.device_pixel_ratio_after_reinf) or np.isnan(x.device_screen_height_after_reinf) or np.isnan(x.device_screen_width_after_reinf):\n", + " return 9\n", + " elif x.os_after_reinf == \"Mac OS\":\n", + " return 5\n", + " elif x.os_after_reinf != \"iOS\" and x.os_after_reinf != \"Android\" and x.os_after_reinf != \"Mac OS\":\n", + " return 4\n", + " elif x.os_after_reinf == \"Android\" and x.tablet_or_mobile_after_reinf == False:\n", + " return 8\n", + " elif x.os_after_reinf == \"Android\" and x.device_pixel_ratio_after_reinf == 2:\n", + " return 1\n", + " elif x.os_after_reinf == \"Android\" and (x.device_pixel_ratio_after_reinf == 3 or x.device_pixel_ratio_after_reinf == 4):\n", + " return 2\n", + " elif x.os_after_reinf == \"iOS\" and x.device_pixel_ratio_after_reinf == 3:\n", + " return 3\n", + " elif x.os_after_reinf == \"Android\" and x.device_pixel_ratio_after_reinf > 1000:\n", + " return 6\n", + " elif x.os_after_reinf == \"iOS\" and x.device_screen_height_after_reinf == 1024 and x.device_screen_width_after_reinf == 768 and (x.device_pixel_ratio_after_reinf == 1 or x.device_pixel_ratio_after_reinf == 2):\n", + " return 7\n", + " else:\n", + " return 9" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "g3[\"applied_rule\"] = g3.apply(rule_identification_post, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users that ENTERED the questionnaire: 1003\n", + "Number of G3 users that have NOT finished the questionnaire: 285\n", + "Number of G3 users that have finished the questionnaire: 718\n", + "\n", + "Completion rate of G3 users: 71.59%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3 = g3.shape[0]\n", + "print \"Number of G3 users that ENTERED the questionnaire: \" + str(total_started_questionnaires_g3)\n", + "\n", + "uncompleted_questionnaires_g3 = g3[(g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire: \" + str(uncompleted_questionnaires_g3)\n", + "\n", + "completed_questionnaires_g3 = g3[(g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire: \" + str(completed_questionnaires_g3)\n", + "\n", + "completion_rate_g3 = 100 * float(completed_questionnaires_g3) / total_started_questionnaires_g3\n", + "print \"\\nCompletion rate of G3 users: \" + '% .2f' % completion_rate_g3 + \"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Correlation between the questionnaire vertical assignation and the finalization rate

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

G1-G2 correlation

" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G2-G3 correlation

" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " G2 G3\n", + "Finished 1155 718\n", + "Not finished 387 285" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g2, completed_questionnaires_g3], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g2, uncompleted_questionnaires_g3], name='Not finished')\n", + "confMatrix2 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrix2.columns = ['G2', 'G3']\n", + "confMatrix2" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 3.44177543362\n", + "Significance 0.0635673487709\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrix2, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

G1-G3 correlation

" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " G1 G3\n", + "Finished 2601 718\n", + "Not finished 1232 285" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1, completed_questionnaires_g3], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1, uncompleted_questionnaires_g3], name='Not finished')\n", + "confMatrix3 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrix3.columns = ['G1', 'G3']\n", + "confMatrix3" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 5.12969488294\n", + "Significance 0.023519861893\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrix3, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Correlation between the rules application and the finalization rate

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 1 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 1 applied): 374\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 1 applied): 123\n", + "Number of G1 users that have finished the questionnaire (with rule 1 applied): 251\n", + "\n", + "Completion rate of G1 users (with rule 1 applied): 67.11%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r1 = g1[g1[\"applied_rule\"] == 1].shape[0]\n", + "print \"Number of G1 users (with rule 1 applied): \" + str(total_started_questionnaires_g1_r1)\n", + "\n", + "uncompleted_questionnaires_g1_r1 = g1[(g1[\"applied_rule\"] == 1) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 1 applied): \" + str(uncompleted_questionnaires_g1_r1)\n", + "\n", + "completed_questionnaires_g1_r1 = g1[(g1[\"applied_rule\"] == 1) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 1 applied): \" + str(completed_questionnaires_g1_r1)\n", + "\n", + "completion_rate_g1_r1 = 100 * float(completed_questionnaires_g1_r1) / total_started_questionnaires_g1_r1\n", + "print \"\\nCompletion rate of G1 users (with rule 1 applied): \" + '% .2f' % completion_rate_g1_r1 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 1 applied): 106\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 1 applied): 29\n", + "Number of G3 users that have finished the questionnaire (with rule 1 applied): 77\n", + "\n", + "Completion rate of G3 users (with rule 1 applied): 72.64%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r1 = g3[g3[\"applied_rule\"] == 1].shape[0]\n", + "print \"Number of G3 users (with rule 1 applied): \" + str(total_started_questionnaires_g3_r1)\n", + "\n", + "uncompleted_questionnaires_g3_r1 = g3[(g3[\"applied_rule\"] == 1) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 1 applied): \" + str(uncompleted_questionnaires_g3_r1)\n", + "\n", + "completed_questionnaires_g3_r1 = g3[(g3[\"applied_rule\"] == 1) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 1 applied): \" + str(completed_questionnaires_g3_r1)\n", + "\n", + "completion_rate_g3_r1 = 100 * float(completed_questionnaires_g3_r1) / total_started_questionnaires_g3_r1\n", + "print \"\\nCompletion rate of G3 users (with rule 1 applied): \" + '% .2f' % completion_rate_g3_r1 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule1G3_Rule1
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" + ], + "text/plain": [ + " G1_Rule1 G3_Rule1\n", + "Finished 251 77\n", + "Not finished 123 29" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r1, completed_questionnaires_g3_r1], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r1, uncompleted_questionnaires_g3_r1], name='Not finished')\n", + "confMatrixR1 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR1.columns = ['G1_Rule1', 'G3_Rule1']\n", + "confMatrixR1" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 1.16688333615\n", + "Significance 0.280042557643\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrixR1, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 2 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 2 applied): 362\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 2 applied): 105\n", + "Number of G1 users that have finished the questionnaire (with rule 2 applied): 257\n", + "\n", + "Completion rate of G1 users (with rule 2 applied): 70.99%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r2 = g1[g1[\"applied_rule\"] == 2].shape[0]\n", + "print \"Number of G1 users (with rule 2 applied): \" + str(total_started_questionnaires_g1_r2)\n", + "\n", + "uncompleted_questionnaires_g1_r2 = g1[(g1[\"applied_rule\"] == 2) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 2 applied): \" + str(uncompleted_questionnaires_g1_r2)\n", + "\n", + "completed_questionnaires_g1_r2 = g1[(g1[\"applied_rule\"] == 2) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 2 applied): \" + str(completed_questionnaires_g1_r2)\n", + "\n", + "completion_rate_g1_r2 = 100 * float(completed_questionnaires_g1_r2) / total_started_questionnaires_g1_r2\n", + "print \"\\nCompletion rate of G1 users (with rule 2 applied): \" + '% .2f' % completion_rate_g1_r2 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 2 applied): 126\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 2 applied): 35\n", + "Number of G3 users that have finished the questionnaire (with rule 2 applied): 91\n", + "\n", + "Completion rate of G3 users (with rule 2 applied): 72.22%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r2 = g3[g3[\"applied_rule\"] == 2].shape[0]\n", + "print \"Number of G3 users (with rule 2 applied): \" + str(total_started_questionnaires_g3_r2)\n", + "\n", + "uncompleted_questionnaires_g3_r2 = g3[(g3[\"applied_rule\"] == 2) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 2 applied): \" + str(uncompleted_questionnaires_g3_r2)\n", + "\n", + "completed_questionnaires_g3_r2 = g3[(g3[\"applied_rule\"] == 2) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 2 applied): \" + str(completed_questionnaires_g3_r2)\n", + "\n", + "completion_rate_g3_r2 = 100 * float(completed_questionnaires_g3_r2) / total_started_questionnaires_g3_r2\n", + "print \"\\nCompletion rate of G3 users (with rule 2 applied): \" + '% .2f' % completion_rate_g3_r2 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule2G3_Rule2
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" + ], + "text/plain": [ + " G1_Rule2 G3_Rule2\n", + "Finished 257 91\n", + "Not finished 105 35" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r2, completed_questionnaires_g3_r2], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r2, uncompleted_questionnaires_g3_r2], name='Not finished')\n", + "confMatrixR2 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR2.columns = ['G1_Rule2', 'G3_Rule2']\n", + "confMatrixR2" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 0.0688667329932\n", + "Significance 0.792994017496\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrixR2, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 3 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 3 applied): 51\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 3 applied): 19\n", + "Number of G1 users that have finished the questionnaire (with rule 3 applied): 32\n", + "\n", + "Completion rate of G1 users (with rule 3 applied): 62.75%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r3 = g1[g1[\"applied_rule\"] == 3].shape[0]\n", + "print \"Number of G1 users (with rule 3 applied): \" + str(total_started_questionnaires_g1_r3)\n", + "\n", + "uncompleted_questionnaires_g1_r3 = g1[(g1[\"applied_rule\"] == 3) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 3 applied): \" + str(uncompleted_questionnaires_g1_r3)\n", + "\n", + "completed_questionnaires_g1_r3 = g1[(g1[\"applied_rule\"] == 3) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 3 applied): \" + str(completed_questionnaires_g1_r3)\n", + "\n", + "completion_rate_g1_r3 = 100 * float(completed_questionnaires_g1_r3) / total_started_questionnaires_g1_r3\n", + "print \"\\nCompletion rate of G1 users (with rule 3 applied): \" + '% .2f' % completion_rate_g1_r3 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 3 applied): 23\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 3 applied): 10\n", + "Number of G3 users that have finished the questionnaire (with rule 3 applied): 13\n", + "\n", + "Completion rate of G3 users (with rule 3 applied): 56.52%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r3 = g3[g3[\"applied_rule\"] == 3].shape[0]\n", + "print \"Number of G3 users (with rule 3 applied): \" + str(total_started_questionnaires_g3_r3)\n", + "\n", + "uncompleted_questionnaires_g3_r3 = g3[(g3[\"applied_rule\"] == 3) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 3 applied): \" + str(uncompleted_questionnaires_g3_r3)\n", + "\n", + "completed_questionnaires_g3_r3 = g3[(g3[\"applied_rule\"] == 3) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 3 applied): \" + str(completed_questionnaires_g3_r3)\n", + "\n", + "completion_rate_g3_r3 = 100 * float(completed_questionnaires_g3_r3) / total_started_questionnaires_g3_r3\n", + "print \"\\nCompletion rate of G3 users (with rule 3 applied): \" + '% .2f' % completion_rate_g3_r3 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule3G3_Rule3
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Not finished1910
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" + ], + "text/plain": [ + " G1_Rule3 G3_Rule3\n", + "Finished 32 13\n", + "Not finished 19 10" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r3, completed_questionnaires_g3_r3], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r3, uncompleted_questionnaires_g3_r3], name='Not finished')\n", + "confMatrixR3 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR3.columns = ['G1_Rule3', 'G3_Rule3']\n", + "confMatrixR3" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 0.257613676822\n", + "Significance 0.611764350995\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrixR3, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 4 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 4 applied): 2196\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 4 applied): 702\n", + "Number of G1 users that have finished the questionnaire (with rule 4 applied): 1494\n", + "\n", + "Completion rate of G1 users (with rule 4 applied): 68.03%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r4 = g1[g1[\"applied_rule\"] == 4].shape[0]\n", + "print \"Number of G1 users (with rule 4 applied): \" + str(total_started_questionnaires_g1_r4)\n", + "\n", + "uncompleted_questionnaires_g1_r4 = g1[(g1[\"applied_rule\"] == 4) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 4 applied): \" + str(uncompleted_questionnaires_g1_r4)\n", + "\n", + "completed_questionnaires_g1_r4 = g1[(g1[\"applied_rule\"] == 4) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 4 applied): \" + str(completed_questionnaires_g1_r4)\n", + "\n", + "completion_rate_g1_r4 = 100 * float(completed_questionnaires_g1_r4) / total_started_questionnaires_g1_r4\n", + "print \"\\nCompletion rate of G1 users (with rule 4 applied): \" + '% .2f' % completion_rate_g1_r4 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 4 applied): 421\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 4 applied): 100\n", + "Number of G3 users that have finished the questionnaire (with rule 3 applied): 321\n", + "\n", + "Completion rate of G3 users (with rule 4 applied): 76.25%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r4 = g3[g3[\"applied_rule\"] == 4].shape[0]\n", + "print \"Number of G3 users (with rule 4 applied): \" + str(total_started_questionnaires_g3_r4)\n", + "\n", + "uncompleted_questionnaires_g3_r4 = g3[(g3[\"applied_rule\"] == 4) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 4 applied): \" + str(uncompleted_questionnaires_g3_r4)\n", + "\n", + "completed_questionnaires_g3_r4 = g3[(g3[\"applied_rule\"] == 4) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 3 applied): \" + str(completed_questionnaires_g3_r4)\n", + "\n", + "completion_rate_g3_r4 = 100 * float(completed_questionnaires_g3_r4) / total_started_questionnaires_g3_r4\n", + "print \"\\nCompletion rate of G3 users (with rule 4 applied): \" + '% .2f' % completion_rate_g3_r4 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule4G3_Rule4
Finished1494321
Not finished702100
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" + ], + "text/plain": [ + " G1_Rule4 G3_Rule4\n", + "Finished 1494 321\n", + "Not finished 702 100" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r4, completed_questionnaires_g3_r4], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r4, uncompleted_questionnaires_g3_r4], name='Not finished')\n", + "confMatrixR4 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR4.columns = ['G1_Rule4', 'G3_Rule4']\n", + "confMatrixR4" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 11.2150794442\n", + "Significance 0.000811353363034\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrixR4, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 5 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 5 applied): 325\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 5 applied): 92\n", + "Number of G1 users that have finished the questionnaire (with rule 5 applied): 233\n", + "\n", + "Completion rate of G1 users (with rule 5 applied): 71.69%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r5 = g1[g1[\"applied_rule\"] == 5].shape[0]\n", + "print \"Number of G1 users (with rule 5 applied): \" + str(total_started_questionnaires_g1_r5)\n", + "\n", + "uncompleted_questionnaires_g1_r5 = g1[(g1[\"applied_rule\"] == 5) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 5 applied): \" + str(uncompleted_questionnaires_g1_r5)\n", + "\n", + "completed_questionnaires_g1_r5 = g1[(g1[\"applied_rule\"] == 5) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 5 applied): \" + str(completed_questionnaires_g1_r5)\n", + "\n", + "completion_rate_g1_r5 = 100 * float(completed_questionnaires_g1_r5) / total_started_questionnaires_g1_r5\n", + "print \"\\nCompletion rate of G1 users (with rule 5 applied): \" + '% .2f' % completion_rate_g1_r5 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 5 applied): 49\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 5 applied): 13\n", + "Number of G3 users that have finished the questionnaire (with rule 5 applied): 36\n", + "\n", + "Completion rate of G3 users (with rule 5 applied): 73.47%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r5 = g3[g3[\"applied_rule\"] == 5].shape[0]\n", + "print \"Number of G3 users (with rule 5 applied): \" + str(total_started_questionnaires_g3_r5)\n", + "\n", + "uncompleted_questionnaires_g3_r5 = g3[(g3[\"applied_rule\"] == 5) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 5 applied): \" + str(uncompleted_questionnaires_g3_r5)\n", + "\n", + "completed_questionnaires_g3_r5 = g3[(g3[\"applied_rule\"] == 5) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 5 applied): \" + str(completed_questionnaires_g3_r5)\n", + "\n", + "completion_rate_g3_r5 = 100 * float(completed_questionnaires_g3_r5) / total_started_questionnaires_g3_r5\n", + "print \"\\nCompletion rate of G3 users (with rule 5 applied): \" + '% .2f' % completion_rate_g3_r5 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule5G3_Rule5
Finished23336
Not finished9213
\n", + "
" + ], + "text/plain": [ + " G1_Rule5 G3_Rule5\n", + "Finished 233 36\n", + "Not finished 92 13" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r5, completed_questionnaires_g3_r5], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r5, uncompleted_questionnaires_g3_r5], name='Not finished')\n", + "confMatrixR5 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR5.columns = ['G1_Rule5', 'G3_Rule5']\n", + "confMatrixR5" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 0.0665922138454\n", + "Significance 0.796364713353\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrixR5, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 6 (G1-G3)


\n", + "There are no users with rule 6 applied in G1 and G2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 7 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 7 applied): 73\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 7 applied): 24\n", + "Number of G1 users that have finished the questionnaire (with rule 7 applied): 49\n", + "\n", + "Completion rate of G1 users (with rule 7 applied): 67.12%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r7 = g1[g1[\"applied_rule\"] == 7].shape[0]\n", + "print \"Number of G1 users (with rule 7 applied): \" + str(total_started_questionnaires_g1_r7)\n", + "\n", + "uncompleted_questionnaires_g1_r7 = g1[(g1[\"applied_rule\"] == 7) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 7 applied): \" + str(uncompleted_questionnaires_g1_r7)\n", + "\n", + "completed_questionnaires_g1_r7 = g1[(g1[\"applied_rule\"] == 7) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 7 applied): \" + str(completed_questionnaires_g1_r7)\n", + "\n", + "completion_rate_g1_r7 = 100 * float(completed_questionnaires_g1_r7) / total_started_questionnaires_g1_r7\n", + "print \"\\nCompletion rate of G1 users (with rule 7 applied): \" + '% .2f' % completion_rate_g1_r7 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 7 applied): 27\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 7 applied): 7\n", + "Number of G3 users that have finished the questionnaire (with rule 7 applied): 20\n", + "\n", + "Completion rate of G3 users (with rule 7 applied): 74.07%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r7 = g3[g3[\"applied_rule\"] == 7].shape[0]\n", + "print \"Number of G3 users (with rule 7 applied): \" + str(total_started_questionnaires_g3_r7)\n", + "\n", + "uncompleted_questionnaires_g3_r7 = g3[(g3[\"applied_rule\"] == 7) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 7 applied): \" + str(uncompleted_questionnaires_g3_r7)\n", + "\n", + "completed_questionnaires_g3_r7 = g3[(g3[\"applied_rule\"] == 7) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 7 applied): \" + str(completed_questionnaires_g3_r7)\n", + "\n", + "completion_rate_g3_r7 = 100 * float(completed_questionnaires_g3_r7) / total_started_questionnaires_g3_r7\n", + "print \"\\nCompletion rate of G3 users (with rule 7 applied): \" + '% .2f' % completion_rate_g3_r7 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule7G3_Rule7
Finished4920
Not finished247
\n", + "
" + ], + "text/plain": [ + " G1_Rule7 G3_Rule7\n", + "Finished 49 20\n", + "Not finished 24 7" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r7, completed_questionnaires_g3_r7], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r7, uncompleted_questionnaires_g3_r7], name='Not finished')\n", + "confMatrixR7 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR7.columns = ['G1_Rule7', 'G3_Rule7']\n", + "confMatrixR7" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 0.445188282931\n", + "Significance 0.504628863756\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrixR7, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 8 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 8 applied): 25\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 8 applied): 7\n", + "Number of G1 users that have finished the questionnaire (with rule 8 applied): 18\n", + "\n", + "Completion rate of G1 users (with rule 8 applied): 72.00%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r8 = g1[g1[\"applied_rule\"] == 8].shape[0]\n", + "print \"Number of G1 users (with rule 8 applied): \" + str(total_started_questionnaires_g1_r8)\n", + "\n", + "uncompleted_questionnaires_g1_r8 = g1[(g1[\"applied_rule\"] == 8) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 8 applied): \" + str(uncompleted_questionnaires_g1_r8)\n", + "\n", + "completed_questionnaires_g1_r8 = g1[(g1[\"applied_rule\"] == 8) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 8 applied): \" + str(completed_questionnaires_g1_r8)\n", + "\n", + "completion_rate_g1_r8 = 100 * float(completed_questionnaires_g1_r8) / total_started_questionnaires_g1_r8\n", + "print \"\\nCompletion rate of G1 users (with rule 8 applied): \" + '% .2f' % completion_rate_g1_r8 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 8 applied): 10\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 8 applied): 2\n", + "Number of G3 users that have finished the questionnaire (with rule 8 applied): 8\n", + "\n", + "Completion rate of G3 users (with rule 8 applied): 80.00%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r8 = g3[g3[\"applied_rule\"] == 8].shape[0]\n", + "print \"Number of G3 users (with rule 8 applied): \" + str(total_started_questionnaires_g3_r8)\n", + "\n", + "uncompleted_questionnaires_g3_r8 = g3[(g3[\"applied_rule\"] == 8) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 8 applied): \" + str(uncompleted_questionnaires_g3_r8)\n", + "\n", + "completed_questionnaires_g3_r8 = g3[(g3[\"applied_rule\"] == 8) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 8 applied): \" + str(completed_questionnaires_g3_r8)\n", + "\n", + "completion_rate_g3_r8 = 100 * float(completed_questionnaires_g3_r8) / total_started_questionnaires_g3_r8\n", + "print \"\\nCompletion rate of G3 users (with rule 8 applied): \" + '% .2f' % completion_rate_g3_r8 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule8G3_Rule8
Finished188
Not finished72
\n", + "
" + ], + "text/plain": [ + " G1_Rule8 G3_Rule8\n", + "Finished 18 8\n", + "Not finished 7 2" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r8, completed_questionnaires_g3_r8], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r8, uncompleted_questionnaires_g3_r8], name='Not finished')\n", + "confMatrixR8 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR8.columns = ['G1_Rule8', 'G3_Rule8']\n", + "confMatrixR8" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Odds ratio: 0.642857142857\n", + "Fisher's exact test significance: 0.488478483718\n" + ] + } + ], + "source": [ + "oddsratio, p_value = stats.fisher_exact(confMatrixR8, alternative=\"less\")\n", + "print \"Odds ratio: \" + str(oddsratio)\n", + "print \"Fisher's exact test significance: \" + str(p_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Rule number 9 (G1-G3)

" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G1 users (with rule 9 applied): 427\n", + "Number of G1 users that have NOT finished the questionnaire (with rule 9 applied): 160\n", + "Number of G1 users that have finished the questionnaire (with rule 9 applied): 267\n", + "\n", + "Completion rate of G1 users (with rule 9 applied): 62.53%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g1_r9 = g1[g1[\"applied_rule\"] == 9].shape[0]\n", + "print \"Number of G1 users (with rule 9 applied): \" + str(total_started_questionnaires_g1_r9)\n", + "\n", + "uncompleted_questionnaires_g1_r9 = g1[(g1[\"applied_rule\"] == 9) & (g1[\"cuestionarioFinalizado\"] == False)].shape[0]\n", + "print \"Number of G1 users that have NOT finished the questionnaire (with rule 9 applied): \" + str(uncompleted_questionnaires_g1_r9)\n", + "\n", + "completed_questionnaires_g1_r9 = g1[(g1[\"applied_rule\"] == 9) & (g1[\"cuestionarioFinalizado\"] == True)].shape[0]\n", + "print \"Number of G1 users that have finished the questionnaire (with rule 9 applied): \" + str(completed_questionnaires_g1_r9)\n", + "\n", + "completion_rate_g1_r9 = 100 * float(completed_questionnaires_g1_r9) / total_started_questionnaires_g1_r9\n", + "print \"\\nCompletion rate of G1 users (with rule 9 applied): \" + '% .2f' % completion_rate_g1_r9 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of G3 users (with rule 9 applied): 241\n", + "Number of G3 users that have NOT finished the questionnaire (with rule 9 applied): 89\n", + "Number of G3 users that have finished the questionnaire (with rule 9 applied): 152\n", + "\n", + "Completion rate of G3 users (with rule 9 applied): 63.07%\n" + ] + } + ], + "source": [ + "total_started_questionnaires_g3_r9 = g3[g3[\"applied_rule\"] == 9].shape[0]\n", + "print \"Number of G3 users (with rule 9 applied): \" + str(total_started_questionnaires_g3_r9)\n", + "\n", + "uncompleted_questionnaires_g3_r9 = g3[(g3[\"applied_rule\"] == 9) & (g3[\"cuestionarioFinalizado_after_reinf\"] == False)].shape[0]\n", + "print \"Number of G3 users that have NOT finished the questionnaire (with rule 9 applied): \" + str(uncompleted_questionnaires_g3_r9)\n", + "\n", + "completed_questionnaires_g3_r9 = g3[(g3[\"applied_rule\"] == 9) & (g3[\"cuestionarioFinalizado_after_reinf\"] == True)].shape[0]\n", + "print \"Number of G3 users that have finished the questionnaire (with rule 9 applied): \" + str(completed_questionnaires_g3_r9)\n", + "\n", + "completion_rate_g3_r9 = 100 * float(completed_questionnaires_g3_r9) / total_started_questionnaires_g3_r9\n", + "print \"\\nCompletion rate of G3 users (with rule 9 applied): \" + '% .2f' % completion_rate_g3_r9 + \"%\"" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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G1_Rule9G3_Rule9
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" + ], + "text/plain": [ + " G1_Rule9 G3_Rule9\n", + "Finished 267 152\n", + "Not finished 160 89" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nFinalized = pd.Series([completed_questionnaires_g1_r9, completed_questionnaires_g3_r9], name='Finished')\n", + "nNotFinalized = pd.Series([uncompleted_questionnaires_g1_r9, uncompleted_questionnaires_g3_r9], name='Not finished')\n", + "confMatrixR9 = pd.concat([nFinalized, nNotFinalized], axis=1).T\n", + "confMatrixR9.columns = ['G1_Rule9', 'G3_Rule9']\n", + "confMatrixR9" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson's Chi-square: 0.0193031668756\n", + "Significance 0.88950085155\n" + ] + } + ], + "source": [ + "chi2, p_value, dof, ex = stats.chi2_contingency(confMatrixR9, correction=False)\n", + "print \"Pearson's Chi-square: \" + str(chi2)\n", + "print \"Significance \" + str(p_value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}