diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 0000000..e1a24b0 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "whole-auction", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/complete_notebook_final-OKE.ipynb b/complete_notebook_final-OKE.ipynb new file mode 100644 index 0000000..6ad012b --- /dev/null +++ b/complete_notebook_final-OKE.ipynb @@ -0,0 +1,3572 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "great-webmaster", + "metadata": {}, + "source": [ + "# Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "entitled-matter", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:37.411516Z", + "start_time": "2021-05-25T14:05:09.610332Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import warnings\n", + "%matplotlib inline\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "fallen-arrest", + "metadata": {}, + "source": [ + "load dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "resident-charles", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:37.813905Z", + "start_time": "2021-05-25T14:05:37.411516Z" + } + }, + "outputs": [], + "source": [ + "battles = pd.read_csv('dataset/battles.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "opened-drive", + "metadata": {}, + "source": [ + "## Preview Dataset\n", + "melakukan preview dataset untuk melihat sekilas gambaran kolom dan baris data yang ada" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "enhanced-tractor", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:38.161497Z", + "start_time": "2021-05-25T14:05:37.813905Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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nameyearbattle_numberattacker_kingdefender_kingattacker_1attacker_2attacker_3attacker_4defender_1defender_2defender_3defender_4attacker_outcomebattle_typemajor_deathmajor_captureattacker_sizedefender_sizeattacker_commanderdefender_commandersummerlocationregionnote
0Battle of the Golden Tooth2981Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNTullyNaNNaNNaNwinpitched battle1.00.015000.04000.0Jaime LannisterClement Piper, Vance1.0Golden ToothThe WesterlandsNaN
1Battle at the Mummer's Ford2982Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNBaratheonNaNNaNNaNwinambush1.00.0NaN120.0Gregor CleganeBeric Dondarrion1.0Mummer's FordThe RiverlandsNaN
2Battle of Riverrun2983Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNTullyNaNNaNNaNwinpitched battle0.01.015000.010000.0Jaime Lannister, Andros BraxEdmure Tully, Tytos Blackwood1.0RiverrunThe RiverlandsNaN
3Battle of the Green Fork2984Robb StarkJoffrey/Tommen BaratheonStarkNaNNaNNaNLannisterNaNNaNNaNlosspitched battle1.01.018000.020000.0Roose Bolton, Wylis Manderly, Medger Cerwyn, H...Tywin Lannister, Gregor Clegane, Kevan Lannist...1.0Green ForkThe RiverlandsNaN
4Battle of the Whispering Wood2985Robb StarkJoffrey/Tommen BaratheonStarkTullyNaNNaNLannisterNaNNaNNaNwinambush1.01.01875.06000.0Robb Stark, Brynden TullyJaime Lannister1.0Whispering WoodThe RiverlandsNaN
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" + ], + "text/plain": [ + " name year battle_number \\\n", + "0 Battle of the Golden Tooth 298 1 \n", + "1 Battle at the Mummer's Ford 298 2 \n", + "2 Battle of Riverrun 298 3 \n", + "3 Battle of the Green Fork 298 4 \n", + "4 Battle of the Whispering Wood 298 5 \n", + "\n", + " attacker_king defender_king attacker_1 attacker_2 \\\n", + "0 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", + "1 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", + "2 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", + "3 Robb Stark Joffrey/Tommen Baratheon Stark NaN \n", + "4 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n", + "\n", + " attacker_3 attacker_4 defender_1 defender_2 defender_3 defender_4 \\\n", + "0 NaN NaN Tully NaN NaN NaN \n", + "1 NaN NaN Baratheon NaN NaN NaN \n", + "2 NaN NaN Tully NaN NaN NaN \n", + "3 NaN NaN Lannister NaN NaN NaN \n", + "4 NaN NaN Lannister NaN NaN NaN \n", + "\n", + " attacker_outcome battle_type major_death major_capture attacker_size \\\n", + "0 win pitched battle 1.0 0.0 15000.0 \n", + "1 win ambush 1.0 0.0 NaN \n", + "2 win pitched battle 0.0 1.0 15000.0 \n", + "3 loss pitched battle 1.0 1.0 18000.0 \n", + "4 win ambush 1.0 1.0 1875.0 \n", + "\n", + " defender_size attacker_commander \\\n", + "0 4000.0 Jaime Lannister \n", + "1 120.0 Gregor Clegane \n", + "2 10000.0 Jaime Lannister, Andros Brax \n", + "3 20000.0 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... \n", + "4 6000.0 Robb Stark, Brynden Tully \n", + "\n", + " defender_commander summer location \\\n", + "0 Clement Piper, Vance 1.0 Golden Tooth \n", + "1 Beric Dondarrion 1.0 Mummer's Ford \n", + "2 Edmure Tully, Tytos Blackwood 1.0 Riverrun \n", + "3 Tywin Lannister, Gregor Clegane, Kevan Lannist... 1.0 Green Fork \n", + "4 Jaime Lannister 1.0 Whispering Wood \n", + "\n", + " region note \n", + "0 The Westerlands NaN \n", + "1 The Riverlands NaN \n", + "2 The Riverlands NaN \n", + "3 The Riverlands NaN \n", + "4 The Riverlands NaN " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.set_option('display.max_columns', None)\n", + "battles.head()" + ] + }, + { + "cell_type": "markdown", + "id": "plastic-coaching", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Informasi Dataset\n", + "\n", + "terdiri dari **38 baris** dan **25 kolom** \\\n", + "dengan tipe data _object_(16), _int64_(2), dan _float64_(7) \\\n", + "beberapa kolom memiliki nilai Null\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "equipped-imperial", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:38.415969Z", + "start_time": "2021-05-25T14:05:38.161497Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 38 entries, 0 to 37\n", + "Data columns (total 25 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 name 38 non-null object \n", + " 1 year 38 non-null int64 \n", + " 2 battle_number 38 non-null int64 \n", + " 3 attacker_king 36 non-null object \n", + " 4 defender_king 35 non-null object \n", + " 5 attacker_1 38 non-null object \n", + " 6 attacker_2 10 non-null object \n", + " 7 attacker_3 3 non-null object \n", + " 8 attacker_4 2 non-null object \n", + " 9 defender_1 37 non-null object \n", + " 10 defender_2 2 non-null object \n", + " 11 defender_3 0 non-null float64\n", + " 12 defender_4 0 non-null float64\n", + " 13 attacker_outcome 37 non-null object \n", + " 14 battle_type 37 non-null object \n", + " 15 major_death 37 non-null float64\n", + " 16 major_capture 37 non-null float64\n", + " 17 attacker_size 24 non-null float64\n", + " 18 defender_size 19 non-null float64\n", + " 19 attacker_commander 37 non-null object \n", + " 20 defender_commander 28 non-null object \n", + " 21 summer 37 non-null float64\n", + " 22 location 37 non-null object \n", + " 23 region 38 non-null object \n", + " 24 note 5 non-null object \n", + "dtypes: float64(7), int64(2), object(16)\n", + "memory usage: 7.5+ KB\n" + ] + } + ], + "source": [ + "battles.info()" + ] + }, + { + "cell_type": "markdown", + "id": "durable-karen", + "metadata": {}, + "source": [ + "## Persentase Missing Values\n", + "menghitung jumlah serta persentase nilai null yang terdapat pada setiap kolom" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "harmful-relevance", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:38.563712Z", + "start_time": "2021-05-25T14:05:38.415969Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Total Percentage Dtype\n", + "defender_4 38 100.000000 float64\n", + "defender_3 38 100.000000 float64\n", + "defender_2 36 94.736842 object\n", + "attacker_4 36 94.736842 object\n", + "attacker_3 35 92.105263 object\n", + "note 33 86.842105 object\n", + "attacker_2 28 73.684211 object\n", + "defender_size 19 50.000000 float64\n", + "attacker_size 14 36.842105 float64\n", + "defender_commander 10 26.315789 object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_values_total = battles.isnull().sum().sort_values(ascending=False)\n", + "missing_values_pct = (battles.isnull().mean()*100).sort_values(ascending=False)\n", + "\n", + "missing_values = pd.concat([missing_values_total, missing_values_pct, battles.dtypes],\n", + " keys=['Total','Percentage','Dtype'],\n", + " axis=1)\n", + "missing_values.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "senior-thanksgiving", + "metadata": {}, + "source": [ + "## Pemeriksaan Dataset\n", + "\n", + "setelah melakukan eksplorasi awal berupa preview data dan menghitung jumlah persentase nilai null serta membaca dari berbagai sumber yang ada di website kaggle dan wiki, ditemukan beberapa baris data yang dapat diperbaiki maupun ditambahkan untuk melengkapi dataset" + ] + }, + { + "cell_type": "markdown", + "id": "floppy-carter", + "metadata": {}, + "source": [ + "### Temuan 1\n", + "pada baris 37 terdapat _battle_type_ dengan nilai NaN yang bisa diisi sesuai dengan battle _name_ yaitu **siege**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "chief-natural", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:38.594924Z", + "start_time": "2021-05-25T14:05:38.563712Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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namebattle_type
37Siege of WinterfellNaN
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" + ], + "text/plain": [ + " name battle_type\n", + "37 Siege of Winterfell NaN" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "battles[['name','battle_type']].loc[battles['battle_type'].isnull()]" + ] + }, + { + "cell_type": "markdown", + "id": "present-snake", + "metadata": {}, + "source": [ + "### Temuan 2\n", + "pada baris 37 terdapat *attacker_outcome* dengan nilai NaN. \n", + "\n", + "dikutip dari [wiki March on Winterfell](https://awoiaf.westeros.org/index.php/March_on_Winterfell) (*link Siege_of_Winterfell di-alihkan ke March_on_Winterfell*),\\\n", + "pada Aftermath, Roose Bolton memulangkan pasukan dan Stannis Baratheon tetap tinggal disana bersama tuan rumah untuk selanjutnya bersiap perang melawan Bolton.\n", + "\n", + "dari kutipan Aftermath diatas,bisa disimpulkan *attacker_outcome* dapat di-isi dengan *win* " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dressed-referral", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:38.748647Z", + "start_time": "2021-05-25T14:05:38.594924Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " attacker_1 attacker_2 attacker_3 attacker_4 defender_1 defender_2 \\\n", + "0 Lannister NaN NaN NaN Tully NaN \n", + "1 Lannister NaN NaN NaN Baratheon NaN \n", + "2 Lannister NaN NaN NaN Tully NaN \n", + "3 Stark NaN NaN NaN Lannister NaN \n", + "4 Stark Tully NaN NaN Lannister NaN \n", + "\n", + " defender_3 defender_4 \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "battles[['attacker_1','attacker_2','attacker_3','attacker_4','defender_1','defender_2','defender_3','defender_4']].head()" + ] + }, + { + "cell_type": "markdown", + "id": "affected-technician", + "metadata": {}, + "source": [ + "### Temuan 5\n", + "pada *attacker_commander* dan *defender_commander* berisi nama commander yang dipisahkan oleh koma :\n", + "- bisa diekstraksi menjadi fitur baru berupa jumlah commander \n", + "- setelah itu bisa dihapus pada saat pemodelan" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "breeding-congo", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:39.196676Z", + "start_time": "2021-05-25T14:05:39.065148Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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4Robb Stark, Brynden TullyJaime Lannister
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" + ], + "text/plain": [ + " attacker_commander \\\n", + "0 Jaime Lannister \n", + "1 Gregor Clegane \n", + "2 Jaime Lannister, Andros Brax \n", + "3 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... \n", + "4 Robb Stark, Brynden Tully \n", + "\n", + " defender_commander \n", + "0 Clement Piper, Vance \n", + "1 Beric Dondarrion \n", + "2 Edmure Tully, Tytos Blackwood \n", + "3 Tywin Lannister, Gregor Clegane, Kevan Lannist... \n", + "4 Jaime Lannister " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "battles[['attacker_commander','defender_commander']].head()" + ] + }, + { + "cell_type": "markdown", + "id": "brutal-paste", + "metadata": {}, + "source": [ + "### Temuan 6\n", + "interval nilai dari jumlah pasukan sangat bervariasi, diperlukan scaling nilai agar interval nilai lebih ramping" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "numeric-option", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:39.366016Z", + "start_time": "2021-05-25T14:05:39.196676Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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attacker_sizedefender_size
count24.00000019.000000
mean9942.5416676428.157895
std20283.0920656225.182106
min20.000000100.000000
25%1375.0000001070.000000
50%4000.0000006000.000000
75%8250.00000010000.000000
max100000.00000020000.000000
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" + ], + "text/plain": [ + " attacker_size defender_size\n", + "count 24.000000 19.000000\n", + "mean 9942.541667 6428.157895\n", + "std 20283.092065 6225.182106\n", + "min 20.000000 100.000000\n", + "25% 1375.000000 1070.000000\n", + "50% 4000.000000 6000.000000\n", + "75% 8250.000000 10000.000000\n", + "max 100000.000000 20000.000000" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "battles[['attacker_size','defender_size']].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "norman-mapping", + "metadata": {}, + "source": [ + "### Temuan 7\n", + "Imbalanced Data, diperlukan teknik - teknik khusus pada saat membangun model, seperti :\n", + "- Menggunakan metrik evaluasi yang sesuai\n", + "- Melakukan resampling data\n", + "- Menggunakan cross-validation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "upset-shopping", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:40.743112Z", + "start_time": "2021-05-25T14:05:39.366016Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(data=battles,x=\"attacker_outcome\")\n", + "plt.title(\"Attacker Outcome Distribution\") \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "returning-match", + "metadata": {}, + "source": [ + "# Data Cleaning\n", + "\n", + "pada tahapan ini dilakukan pembersihan data yang telah didapatkan pada tahapan eksplorasi seperti memperbaiki baris data yang kurang tepat, mengisi baris data yang hilang, melakukan ekstraksi fitur/kolom baru serta melakukan penghapusan kolom yang memiliki nilai null diatas 60%" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "recent-liberia", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:40.773915Z", + "start_time": "2021-05-25T14:05:40.743112Z" + } + }, + "outputs": [], + "source": [ + "df = battles.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "differential-concept", + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-15T02:54:17.530075Z", + "start_time": "2021-03-15T02:54:17.514454Z" + } + }, + "source": [ + "## *battle_type* dan *attacker_outcome* pada baris ke-37\n", + "\n", + "melakukan input missing value pada kolom *battle_type* sesuai dengan battle *name* pada baris ke-37" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "separate-comparison", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:40.958481Z", + "start_time": "2021-05-25T14:05:40.773915Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "name Siege of Winterfell\n", + "battle_type NaN\n", + "attacker_outcome NaN\n", + "Name: 37, dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[37,['name','battle_type','attacker_outcome']]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "emerging-sacramento", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:41.174781Z", + "start_time": "2021-05-25T14:05:40.958481Z" + } + }, + "outputs": [], + "source": [ + "df.loc[37, 'battle_type'] = 'siege'" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "acoustic-patrol", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:41.306289Z", + "start_time": "2021-05-25T14:05:41.174781Z" + } + }, + "outputs": [], + "source": [ + "df.loc[37, 'attacker_outcome'] = 'win'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "frequent-premises", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:41.490385Z", + "start_time": "2021-05-25T14:05:41.306289Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "name Siege of Winterfell\n", + "battle_type siege\n", + "attacker_outcome win\n", + "Name: 37, dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[37,['name','battle_type','attacker_outcome']]" + ] + }, + { + "cell_type": "markdown", + "id": "binary-ecuador", + "metadata": {}, + "source": [ + "## *attacker_king* dan *defender_king* baris ke-27\n", + "melakukan penukaran nama *attacker_king* dan *defender_king* karena terjadi kesalahan pada dataset, hasil temuan dari eksplorasi dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "together-characterization", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:41.613355Z", + "start_time": "2021-05-25T14:05:41.490385Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "attacker_king Stannis Baratheon\n", + "defender_king Mance Rayder\n", + "Name: 27, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['attacker_king','defender_king']].loc[27]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "postal-country", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:41.744667Z", + "start_time": "2021-05-25T14:05:41.613355Z" + } + }, + "outputs": [], + "source": [ + "df.loc[27, 'attacker_king'] = 'Mance Rayder'\n", + "df.loc[27, 'defender_king'] = 'Stannis Baratheon'" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "premium-circuit", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:41.876272Z", + "start_time": "2021-05-25T14:05:41.744667Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "attacker_king Mance Rayder\n", + "defender_king Stannis Baratheon\n", + "Name: 27, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['attacker_king','defender_king']].loc[27]" + ] + }, + { + "cell_type": "markdown", + "id": "buried-isolation", + "metadata": {}, + "source": [ + "## Feature Extraction" + ] + }, + { + "cell_type": "markdown", + "id": "continuous-miller", + "metadata": {}, + "source": [ + "### *attacker_count* dan *defender_count*\n", + "pada kolom *attacker_1* s/d *attacker_4* dan *defender_1* s/d *defender_4* terdapat banyak sekali nilai null (60%-100%), \\\n", + "maka dari itu bisa dilakukan ekstraksi dengan cara menghitung jumlah attacker house dan defender house" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "hourly-relevance", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:42.029594Z", + "start_time": "2021-05-25T14:05:41.876272Z" + } + }, + "outputs": [], + "source": [ + "df['attacker_count'] = np.nan\n", + "df['defender_count'] = np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "amateur-youth", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:42.145500Z", + "start_time": "2021-05-25T14:05:42.029594Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "rubber-throw", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:47.783295Z", + "start_time": "2021-05-25T14:05:43.849683Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "le = LabelEncoder()\n", + "np.random.seed(1772023)\n", + "df[df.select_dtypes(['object']).columns] = df.select_dtypes(['object']).apply(lambda x: le.fit_transform(x.astype(str)))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "nonprofit-suffering", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:47.813877Z", + "start_time": "2021-05-25T14:05:47.783295Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " attacker_king defender_king attacker_1 defender_1 attacker_outcome \\\n", + "0 1 3 9 10 1 \n", + "1 1 3 9 0 1 \n", + "2 1 3 9 10 1 \n", + "3 3 1 10 6 0 \n", + "4 3 1 10 6 1 \n", + "\n", + " battle_type major_death major_capture attacker_size defender_size \\\n", + "0 1 1.0 0.0 15000.0 4000.0 \n", + "1 0 1.0 0.0 NaN 120.0 \n", + "2 1 0.0 1.0 15000.0 10000.0 \n", + "3 1 1.0 1.0 18000.0 20000.0 \n", + "4 0 1.0 1.0 1875.0 6000.0 \n", + "\n", + " summer location region attacker_count defender_count \\\n", + "0 1.0 6 6 1 1 \n", + "1 1.0 11 4 1 1 \n", + "2 1.0 15 4 1 1 \n", + "3 1.0 7 4 1 1 \n", + "4 1.0 25 4 2 1 \n", + "\n", + " attacker_commander_count defender_commander_count \n", + "0 1 2 \n", + "1 1 1 \n", + "2 2 2 \n", + "3 5 4 \n", + "4 2 1 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "annoying-gnome", + "metadata": {}, + "source": [ + "## Set Train Features and Target" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "norman-serbia", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:47.998907Z", + "start_time": "2021-05-25T14:05:47.813877Z" + } + }, + "outputs": [], + "source": [ + "y = df['attacker_outcome']\n", + "X = df.drop(columns='attacker_outcome')" + ] + }, + { + "cell_type": "markdown", + "id": "abroad-session", + "metadata": {}, + "source": [ + "\n", + "## Data Imputation" + ] + }, + { + "cell_type": "markdown", + "id": "honey-accused", + "metadata": {}, + "source": [ + "### Finding The Right Method for Imputation\n", + "\n", + "menggunakan metode `iterativeImputer` dan `SimpleImputer` untuk mencari metode yang cocok (nilai error terkecil pada saat prediksi) untuk melakukan imputasi data\n", + "\n", + "referensi: [imputing missing values with variants of iterativeimputer - scikit-learn](https://scikit-learn.org/stable/auto_examples/impute/plot_iterative_imputer_variants_comparison.html#imputing-missing-values-with-variants-of-iterativeimputer)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "thirty-elements", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:58.554660Z", + "start_time": "2021-05-25T14:05:47.998907Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.experimental import enable_iterative_imputer \n", + "from sklearn.impute import SimpleImputer, IterativeImputer\n", + "\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from sklearn.linear_model import BayesianRidge\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import ExtraTreesRegressor\n", + "from sklearn.neighbors import KNeighborsRegressor\n", + "\n", + "\n", + "df_y = df['attacker_outcome']\n", + "df_X = df.drop(columns='attacker_outcome')\n", + "\n", + "N_SPLITS = 5\n", + "br_estimator = BayesianRidge()\n", + "\n", + "np.random.seed(1772023)\n", + "\n", + "# Estimate the score after imputation (mean and median strategies)\n", + "score_simple_imputer = pd.DataFrame()\n", + "for strategy in ('mean', 'median'):\n", + " estimator = make_pipeline(\n", + " StandardScaler(),\n", + " SimpleImputer(missing_values=np.nan, strategy=strategy),\n", + " br_estimator\n", + " )\n", + " score_simple_imputer[strategy] = cross_val_score(\n", + " estimator, df_X, df_y, scoring='neg_mean_squared_error',\n", + " cv=N_SPLITS\n", + " )\n", + " \n", + "# Estimate the score after iterative imputation of the missing values\n", + "# with different estimators\n", + "estimators = [\n", + " BayesianRidge(),\n", + " DecisionTreeRegressor(max_features='sqrt', random_state=0),\n", + " ExtraTreesRegressor(n_estimators=10, random_state=0),\n", + " KNeighborsRegressor(n_neighbors=6)\n", + "]\n", + "\n", + "score_iterative_imputer = pd.DataFrame()\n", + "for impute_estimator in estimators:\n", + " estimator = make_pipeline(\n", + " StandardScaler(),\n", + " IterativeImputer(random_state=0, estimator=impute_estimator),\n", + " br_estimator\n", + " )\n", + " score_iterative_imputer[impute_estimator.__class__.__name__] = \\\n", + " cross_val_score(\n", + " estimator, df_X, df_y, scoring='neg_mean_squared_error',\n", + " cv=N_SPLITS\n", + " )\n", + "\n", + "scores = pd.concat(\n", + " [score_simple_imputer, score_iterative_imputer],\n", + " keys=['SimpleImputer', 'IterativeImputer'], axis=1\n", + ")\n", + "\n", + "# plot results\n", + "fig, ax = plt.subplots(figsize=(13, 6))\n", + "means = -scores.mean().sort_values()\n", + "errors = scores.std().sort_values()\n", + "means.plot.barh(xerr=errors, ax=ax)\n", + "ax.set_title('Imputation Method Comparison using Various Models')\n", + "ax.set_xlabel('MSE (smaller is better)')\n", + "ax.set_yticks(np.arange(means.shape[0]))\n", + "ax.set_yticklabels([\" - \".join(label) for label in means.index.tolist()])\n", + "plt.tight_layout(pad=1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "photographic-questionnaire", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:58.570245Z", + "start_time": "2021-05-25T14:05:58.554660Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "IterativeImputer DecisionTreeRegressor 0.072222\n", + " ExtraTreesRegressor 0.079369\n", + " BayesianRidge 0.079461\n", + "SimpleImputer mean 0.079595\n", + "IterativeImputer KNeighborsRegressor 0.080226\n", + "SimpleImputer median 0.084144\n", + "dtype: float64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "-scores.mean().sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "accepted-specific", + "metadata": {}, + "source": [ + "### Imputation\n", + "pengisian missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "occasional-execution", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:58.808867Z", + "start_time": "2021-05-25T14:05:58.570245Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.experimental import enable_iterative_imputer \n", + "from sklearn.impute import IterativeImputer\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "# y = df['attacker_outcome']\n", + "# X = df.drop(columns='attacker_outcome')\n", + "\n", + "impute_estimator = DecisionTreeRegressor(max_features='sqrt')\n", + "imputer = IterativeImputer(estimator=impute_estimator)\n", + "\n", + "np.random.seed(1772023)\n", + "X_imp = imputer.fit_transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "alert-retrieval", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:58.824494Z", + "start_time": "2021-05-25T14:05:58.808867Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.0e+00, 1.0e+00, 0.0e+00, 2.0e+00, 3.0e+00, 1.0e+00, 0.0e+00,\n", + " 5.0e+03, 8.0e+03, 0.0e+00, 2.6e+01, 2.0e+00, 4.0e+00, 2.0e+00,\n", + " 1.0e+00, 1.0e+00]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_imp[-1:]" + ] + }, + { + "cell_type": "markdown", + "id": "ambient-anchor", + "metadata": {}, + "source": [ + "## Data Scaling\n", + "dilakukan scaling pada dataset agar interval nilai pada dataset lebih ramping/tersebar dengan baik" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "still-scottish", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:58.971683Z", + "start_time": "2021-05-25T14:05:58.824494Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "np.random.seed(1772023)\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X_imp)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "behind-medline", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:05:59.094609Z", + "start_time": "2021-05-25T14:05:58.971683Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.40286125, -0.83265591, -1.73701782, -1.30480696, 1.33366267,\n", + " 1.30930734, -0.63828474, -0.12760986, 0.60453676, -1.5666989 ,\n", + " 1.45893777, -0.97930097, 3.34299247, 3.48066721, -0.79065761,\n", + " -0.5318713 ]])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_scaled[-1:]" + ] + }, + { + "cell_type": "markdown", + "id": "connected-devices", + "metadata": {}, + "source": [ + "## Oversampling using SMOTE\n", + "nilai *k_neighbors=2* merupakan nilai paling optimal dari segi akurasi setelah dilakukan pengujian (range 1-5) " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "exact-flavor", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:01.383293Z", + "start_time": "2021-05-25T14:05:59.094609Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ukuran data sebelum SMOTE: (38, 16)\n", + "Ukuran data sesudah SMOTE: (66, 16)\n", + "\n", + "Keseimbangan antar kelas (sebelum SMOTE):\n", + "1 86.842105\n", + "0 13.157895\n", + "Name: attacker_outcome, dtype: float64\n", + "\n", + "Keseimbangan antar kelas (sesudah SMOTE):\n" + ] + }, + { + "data": { + "text/plain": [ + "0 50.0\n", + "1 50.0\n", + "Name: attacker_outcome, dtype: float64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from imblearn.over_sampling import SMOTE \n", + "\n", + "smote = SMOTE(k_neighbors=2)\n", + "np.random.seed(1772023)\n", + "X_smote, y_smote = smote.fit_resample(X_scaled, y)\n", + "\n", + "print(f'''Ukuran data sebelum SMOTE: {X_scaled.shape}\n", + "Ukuran data sesudah SMOTE: {X_smote.shape}''')\n", + "\n", + "print('\\nKeseimbangan antar kelas (sebelum SMOTE):')\n", + "print(y.value_counts(normalize=True) * 100)\n", + "print('\\nKeseimbangan antar kelas (sesudah SMOTE):')\n", + "y_smote.value_counts(normalize=True) * 100" + ] + }, + { + "cell_type": "markdown", + "id": "surrounded-proposition", + "metadata": {}, + "source": [ + "## Split Dataset\n", + "setelah dilakukan perbandingan skor dari split dataset 70/30, 75/25, dan 80/20.\n", + "\n", + "split dataset 75/25 menghasilkan skor yg lebih bervariasi tanpa kehilangan akurasi yang besar dan bisa digunakan untuk menjelaskan perbandingan model dengan lebih baik" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "separated-relevance", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:01.405452Z", + "start_time": "2021-05-25T14:06:01.383293Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X_scaled, \n", + " y, \n", + " test_size=0.25, \n", + " random_state=1772023)\n", + "\n", + "X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_smote, \n", + " y_smote, \n", + " test_size=0.25, \n", + " random_state=1772023)" + ] + }, + { + "cell_type": "markdown", + "id": "broadband-going", + "metadata": {}, + "source": [ + "# Modelling" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "smoking-wealth", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:01.607259Z", + "start_time": "2021-05-25T14:06:01.405452Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "\n", + "cv = StratifiedKFold(n_splits=5, random_state=1772023, shuffle=True)\n", + "rf = RandomForestClassifier()\n", + "lr = LogisticRegression()" + ] + }, + { + "cell_type": "markdown", + "id": "incorporate-integral", + "metadata": {}, + "source": [ + "## Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "union-distinction", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:05.678651Z", + "start_time": "2021-05-25T14:06:01.607259Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "accuracy 0.926667\n", + "f1 0.977778\n", + "roc_auc 0.937500\n", + "dtype: float64" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = pd.DataFrame()\n", + "np.random.seed(1772023)\n", + "for scoring in ['accuracy','f1','roc_auc']:\n", + " scores[scoring] = cross_val_score(rf,\n", + " X_train, \n", + " y_train,\n", + " scoring=scoring,\n", + " cv=cv, n_jobs=-1)\n", + "scores.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "organizational-cooper", + "metadata": {}, + "source": [ + "## Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "israeli-hormone", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:06.079667Z", + "start_time": "2021-05-25T14:06:05.678651Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "accuracy 0.926667\n", + "f1 0.959596\n", + "roc_auc 0.812500\n", + "dtype: float64" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = pd.DataFrame()\n", + "np.random.seed(1772023)\n", + "for scoring in ['accuracy','f1','roc_auc']:\n", + " scores[scoring] = cross_val_score(lr,\n", + " X_train,\n", + " y_train,\n", + " scoring=scoring,\n", + " cv=cv, n_jobs=-1)\n", + "scores.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "failing-david", + "metadata": {}, + "source": [ + "Hasil dari pelatihan RF dan LR diatas terlihat bahwa skor akurasi dan F1 sama persis bisa terjadi dikarenakan ukuran dataset yang kecil. \\\n", + "terlihat bahwa nilai ROC AUC pada model RF lebih besar daripada model LR, tetapi tidak dapat diandalkan karena pada kasus data imbalance kurang cocok untuk dijadikan sebagai metrik" + ] + }, + { + "cell_type": "markdown", + "id": "interim-violence", + "metadata": {}, + "source": [ + "## Random Forest with Oversampling" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "particular-nerve", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:07.335372Z", + "start_time": "2021-05-25T14:06:06.079667Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "accuracy 0.960000\n", + "f1 0.959596\n", + "roc_auc 1.000000\n", + "dtype: float64" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = pd.DataFrame()\n", + "np.random.seed(1772023)\n", + "for scoring in ['accuracy','f1','roc_auc']:\n", + " scores[scoring] = cross_val_score(rf, \n", + " X_train_sm, \n", + " y_train_sm, \n", + " scoring=scoring, \n", + " cv=cv, n_jobs=-1)\n", + "scores.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "treated-comparison", + "metadata": {}, + "source": [ + "## Logistic Regression with Oversampling" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "hundred-classification", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:07.435694Z", + "start_time": "2021-05-25T14:06:07.335372Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "accuracy 0.980000\n", + "f1 0.981818\n", + "roc_auc 1.000000\n", + "dtype: float64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = pd.DataFrame()\n", + "np.random.seed(1772023)\n", + "for scoring in ['accuracy','f1','roc_auc']:\n", + " scores[scoring] = cross_val_score(lr,\n", + " X_train_sm,\n", + " y_train_sm, \n", + " scoring=scoring, \n", + " cv=cv, n_jobs=-1)\n", + "scores.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "norman-young", + "metadata": {}, + "source": [ + "Pada hasil pemodelan antara Random Forest dan Logistic Regression menggunakan data yang telah dilakukan oversampling memperlihatkan bahwa secara akurasi dan skor f1 Logistic Regression lebih unggul dibandingkan Random Forest.\n", + "\n", + "Skor Akurasi lebih baik digunakan ketika kita ingin memfokuskan pemodelan dengan prediksi nilai True Positive dan True Negative maupun jika target kelas pada dataset seimbang, sedangkan F1-Score lebih baik digunakan untuk mengevaluasi model dengan target kelas dataset yang tidak seimbang" + ] + }, + { + "cell_type": "markdown", + "id": "suited-upset", + "metadata": {}, + "source": [ + "## Feature Importances & Coefficients " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "stable-oriental", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:07.836747Z", + "start_time": "2021-05-25T14:06:07.435694Z" + } + }, + "outputs": [], + "source": [ + "np.random.seed(1772023)\n", + "rf_model = rf.fit(X_train, y_train)\n", + "lr_model = lr.fit(X_train,y_train)\n", + "\n", + "# model with oversampled dataset\n", + "rf_sm_model = rf.fit(X_train_sm, y_train_sm)\n", + "lr_sm_model = lr.fit(X_train_sm, y_train_sm)" + ] + }, + { + "cell_type": "markdown", + "id": "refined-investigator", + "metadata": {}, + "source": [ + "### Random Forest Feature Importances\n", + "\n", + "Semakin besar nilai pada suatu fitur maka fitur tersebut semakin mempunyai pengaruh penting terhadap prediksi yang dilakukan model" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "touched-animal", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:08.200097Z", + "start_time": "2021-05-25T14:06:07.836747Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Random Forest Feature Importances')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
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FeatureScore
14attacker_commander_count0.389246
7attacker_size0.202693
15defender_commander_count0.132379
5major_death0.073968
10location0.065466
11region0.044452
8defender_size0.028504
0attacker_king0.017949
12attacker_count0.009969
4battle_type0.008906
\n", + "
" + ], + "text/plain": [ + " Feature Score\n", + "14 attacker_commander_count 0.389246\n", + "7 attacker_size 0.202693\n", + "15 defender_commander_count 0.132379\n", + "5 major_death 0.073968\n", + "10 location 0.065466\n", + "11 region 0.044452\n", + "8 defender_size 0.028504\n", + "0 attacker_king 0.017949\n", + "12 attacker_count 0.009969\n", + "4 battle_type 0.008906" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf_fi.sort_values(by=\"Score\",ascending=False).head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "composite-piano", + "metadata": {}, + "source": [ + "### Logistic Regression Coefficients \n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "focal-scientist", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:08.770323Z", + "start_time": "2021-05-25T14:06:08.215731Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Logistic Regression Coefficients')" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lr_fi = pd.DataFrame({\"Feature\":X.columns,\n", + " \"Score\":(lr_sm_model.coef_[0])})\n", + "\n", + "sns.barplot(data=lr_fi,\n", + " x=\"Score\",\n", + " y=\"Feature\",\n", + " order=lr_fi.sort_values(by=\"Score\",ascending=False).Feature)\n", + "plt.title(\"Logistic Regression Coefficients\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "searching-angola", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:54.983832Z", + "start_time": "2021-05-25T14:06:54.952573Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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FeatureScore
11region0.712785
10location0.615409
12attacker_count0.586209
6major_capture0.236514
1defender_king0.062163
4battle_type-0.050068
13defender_count-0.052609
3defender_1-0.056703
0attacker_king-0.221464
9summer-0.268198
8defender_size-0.312643
2attacker_1-0.314401
7attacker_size-0.462977
5major_death-0.468265
15defender_commander_count-0.719559
14attacker_commander_count-1.472369
\n", + "
" + ], + "text/plain": [ + " Feature Score\n", + "11 region 0.712785\n", + "10 location 0.615409\n", + "12 attacker_count 0.586209\n", + "6 major_capture 0.236514\n", + "1 defender_king 0.062163\n", + "4 battle_type -0.050068\n", + "13 defender_count -0.052609\n", + "3 defender_1 -0.056703\n", + "0 attacker_king -0.221464\n", + "9 summer -0.268198\n", + "8 defender_size -0.312643\n", + "2 attacker_1 -0.314401\n", + "7 attacker_size -0.462977\n", + "5 major_death -0.468265\n", + "15 defender_commander_count -0.719559\n", + "14 attacker_commander_count -1.472369" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr_fi.sort_values(by=\"Score\",ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "active-greek", + "metadata": {}, + "source": [ + "# Model Evaluation" + ] + }, + { + "cell_type": "markdown", + "id": "grave-canal", + "metadata": {}, + "source": [ + "## Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "administrative-syntax", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:08.917709Z", + "start_time": "2021-05-25T14:06:08.785902Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1\n", + " 1 1.00 1.00 1.00 9\n", + "\n", + " accuracy 1.00 10\n", + " macro avg 1.00 1.00 1.00 10\n", + "weighted avg 1.00 1.00 1.00 10\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "np.random.seed(1772023)\n", + "y_preds = rf_model.predict(X_test)\n", + "report = classification_report(y_test, y_preds)\n", + "\n", + "print(report)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "simple-imaging", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:09.340198Z", + "start_time": "2021-05-25T14:06:08.917709Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(rf_model,X_test,y_test) " + ] + }, + { + "cell_type": "markdown", + "id": "moderate-lithuania", + "metadata": {}, + "source": [ + "## Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "guided-dancing", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:09.371452Z", + "start_time": "2021-05-25T14:06:09.340198Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1\n", + " 1 1.00 1.00 1.00 9\n", + "\n", + " accuracy 1.00 10\n", + " macro avg 1.00 1.00 1.00 10\n", + "weighted avg 1.00 1.00 1.00 10\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "np.random.seed(1772023)\n", + "y_preds = lr_model.predict(X_test)\n", + "report = classification_report(y_test, y_preds)\n", + "\n", + "print(report)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "apparent-community", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:09.703595Z", + "start_time": "2021-05-25T14:06:09.371452Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(lr_model,X_test,y_test) " + ] + }, + { + "cell_type": "markdown", + "id": "honest-mattress", + "metadata": {}, + "source": [ + "## Random Forest with Oversampling" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "lesser-puppy", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:09.741229Z", + "start_time": "2021-05-25T14:06:09.703595Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 9\n", + " 1 1.00 1.00 1.00 8\n", + "\n", + " accuracy 1.00 17\n", + " macro avg 1.00 1.00 1.00 17\n", + "weighted avg 1.00 1.00 1.00 17\n", + "\n" + ] + } + ], + "source": [ + "np.random.seed(1772023)\n", + "y_preds = rf_sm_model.predict(X_test_sm)\n", + "report = classification_report(y_test_sm, y_preds)\n", + "\n", + "print(report)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "sustained-curve", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:10.088527Z", + "start_time": "2021-05-25T14:06:09.741229Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(rf_sm_model,X_test_sm,y_test_sm) " + ] + }, + { + "cell_type": "markdown", + "id": "affected-thousand", + "metadata": {}, + "source": [ + "## Logistic Regression with Oversampling" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "documented-grade", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:10.119776Z", + "start_time": "2021-05-25T14:06:10.088527Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 9\n", + " 1 1.00 1.00 1.00 8\n", + "\n", + " accuracy 1.00 17\n", + " macro avg 1.00 1.00 1.00 17\n", + "weighted avg 1.00 1.00 1.00 17\n", + "\n" + ] + } + ], + "source": [ + "np.random.seed(1772023)\n", + "y_preds = lr_sm_model.predict(X_test_sm)\n", + "report = classification_report(y_test_sm, y_preds)\n", + "\n", + "print(report)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "scientific-writing", + "metadata": { + "ExecuteTime": { + "end_time": "2021-05-25T14:06:10.420191Z", + "start_time": "2021-05-25T14:06:10.126289Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Rt0EM9EHfBrHjrn1lh1RJZSWstjUJI+I2SXu26/5VN+7Nvaxavt0r56tX9LDXAetLjMha2fHNffzpSc9x3qHTGDFygD0OWcceh6wrO6zqCUrrdC99eRlJcwdXI+xlY9nhFEZ643cl/Y4tow1/7OKxX+7E3//qUU7+3SP0ru/ioavcJBxKEcvLbI7SE1ZEzIuIGRExo4eRZYdTmNUrepiw+6ZXzsdP7OW5Z3tKjMhaeeq3OzDmLb2MHtdPdw+8/cgXWb5odNlhVVPBC/hlVXrC6lRLF49m0tRN7DZ5IyN6Bpg1Zy133Lhz2WFZip1272XF4u3pfVlEwFO/G83Yt3dOrb8oRS3gtzk8rKFNBvrFuV+exNkXP0ZXN9z407E8+fCossOyFBP328C02S9w0ZypdHUHE/beyLv+Zm3ZYVVPRCEL+EmaDPwYeDMwAMyLiO+kXdO2hCVpPjCLxq4Zy4AzI+L8dj2vihbcMoYFt7gPpE4OPmM1B5+xuuwwqq+Y2lMf8I8RsUjSTsDdkm6KiAeHu6CdbwmPb9e9zaxcRTT3ImIFsCL5/KKkh4BJwNZPWGbWoQLI3iQcL2lh0/m8iJj3+kLJEKj9Ae9LaGYFK2hfQgBJOwKXA2dExAtpZZ2wzCy3ot4ASuqhkawuiogrWpV3wjKz3Ap6SyjgfOChiPhWlms8DsvM8sk6aLR1Tnsv8DHgcEmLk+PotAtcwzKzXBoDR7e8hhURtye3y8wJy8zy85ruZlYXRdSwNocTlpnlU+KKo05YZpZTMXMJN4cTlpnl5yahmdWCN1I1s1pxDcvMasOd7mZWFxoop03ohGVm+QQeOGpm9SDCA0fNrEacsMysNpywzKwW3IdlZnVS1ltCL+BnZjlFo0mY5WhB0gWSVkpakuXJTlhmlk9QWMICLgRmZ320m4Rmll9BLcKIuC3Z4isTJywzy83jsMysPrInrEwbqWblhGVm+URAf+Y2YcuNVPNwwjKz/EpqEvotoZnlV9ywhvnA74HpkpZJ+kRaedewzCyfAApa0z0ijs9T3gnLzHIKCK+HZWZ1EOTpdC+UE5aZ5edxWGZWG05YZlYPmecJFs4Jy8zyCcCbUJhZbbiGZWb1kGtqTqGcsMwsn4DwOCwzq42CRrrn5YRlZvm5D8vMaiHCbwnNrEZcwzKzegiiv7+UJzthmVk+BS4vk5cTlpnlV9KwBq84ama5BBADkeloRdJsSUslPSrpi63KO2GZWT6RLOCX5UghqRs4FzgK2Bs4XtLeade4SWhmuRXU6X4g8GhEPAYg6afAHODB4S5QlPR6ciiSVgFPlh1HG4wHVpcdhOXSqb+zPSJiwpbcQNL1NP5+shgFbGg6f2VfQknHArMj4qTk/GPAQRFx2nA3q1QNa0v/IqtK0sIi92az9vPvbHgRMbugW2mo26dd4D4sMyvLMmBy0/lbgOVpFzhhmVlZFgDTJE2VtB1wHPCztAsq1STsYPPKDsBy8++szSKiT9JpwA1AN3BBRDyQdk2lOt3NzNK4SWhmteGEZWa14YTVRnmnHVj5JF0gaaWkJWXHYm/khNUmmzPtwCrhQqCocUZWMCes9nll2kFEbAIGpx1YhUXEbcCasuOwoTlhtc8k4Omm82XJd2a2mZyw2if3tAMzS+eE1T65px2YWTonrPbJPe3AzNI5YbVJRPQBg9MOHgIuaTXtwMonaT7we2C6pGWSPlF2TPYqT80xs9pwDcvMasMJy8xqwwnLzGrDCcvMasMJy8xqwwmrRiT1S1osaYmkSyWN3oJ7XZjsWoKk89ImZkuaJengzXjGE5LesLvKcN+/rsxLOZ/1NUmfzxuj1YsTVr28HBH7RcQ+wCbglOYfJitE5BYRJ0XEsHvBAbOA3AnLrGhOWPX1G+DtSe3nV5IuBu6X1C3p3yQtkHSfpE8CqOEcSQ9KugbYdfBGkm6VNCP5PFvSIkn3SrpZ0p40EuPnktrdIZImSLo8ecYCSe9Nrh0n6UZJ90j6IUPPp3wNSVdJulvSA5Lmvu5n30xiuVnShOS7t0m6PrnmN5L2KuRv0+ohInzU5ABeSv45Arga+BSN2s86YGrys7nAV5LPI4GFwFTgGOAmGov97w6sBY5Nyt0KzAAm0FhhYvBeY5N/fg34fFMcFwN/nnyeAjyUfP4u8NXk8wdoTPYeP8Sf44nB75uesT2wBBiXnAdwYvL5q8A5yeebgWnJ54OAW4aK0UdnHt41p162l7Q4+fwb4HwaTbW7IuLx5Pv3A+8e7J8CdgamAYcC8yOiH1gu6ZYh7v8e4LbBe0XEcOtCvQ/YW3qlAjVG0k7JM45Jrr1G0vMZ/kynS/pI8nlyEutzwADwP8n3PwGukLRj8ue9tOnZIzM8wzqEE1a9vBwR+zV/kfyLu675K+AzEXHD68odTevlbZShDDS6EmZGxMtDxJJ5rpekWTSS38yIWC/pVhpbmw8lkueuff3fgW073IfVeW4APiWpB0DSn0jaAbgNOC7p45oIHDbEtb8H/kLS1OTascn3LwI7NZW7kcbEbpJy+yUfbwNOTL47CnhTi1h3Bp5PktVeNGp4g7qAwVriCcDtEfEC8Likv0qeIUn7tniGdRAnrM5zHvAgsCjZSOGHNGrSVwKPAPcD3wd+/foLI2IVjT6wKyTdy6tNsp8DHxnsdAdOB2YknfoP8urbyn8GDpW0iEbT9KkWsV4PjJB0H/B14I6mn60D3inpbuBw4Kzk+xOBTyTxPYCXnd6meLUGM6sN17DMrDacsMysNpywzKw2nLDMrDacsMysNpywzKw2nLDMrDb+H9F5iaA73pLQAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(lr_sm_model,X_test_sm,y_test_sm) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "269px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/complete_notebook_final.ipynb b/complete_notebook_final.ipynb index 3584a19..41db344 100644 --- a/complete_notebook_final.ipynb +++ b/complete_notebook_final.ipynb @@ -10,12 +10,12 @@ }, { "cell_type": "code", - "execution_count": 391, + "execution_count": 1, "id": "entitled-matter", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:49.983625Z", - "start_time": "2021-04-22T09:25:49.968005Z" + "end_time": "2021-04-25T12:59:08.168588Z", + "start_time": "2021-04-25T12:58:48.025879Z" } }, "outputs": [], @@ -39,12 +39,12 @@ }, { "cell_type": "code", - "execution_count": 392, + "execution_count": 2, "id": "resident-charles", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.115108Z", - "start_time": "2021-04-22T09:25:49.983625Z" + "end_time": "2021-04-25T12:59:08.469730Z", + "start_time": "2021-04-25T12:59:08.168588Z" } }, "outputs": [], @@ -63,12 +63,12 @@ }, { "cell_type": "code", - "execution_count": 393, + "execution_count": 3, "id": "enhanced-tractor", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.237490Z", - "start_time": "2021-04-22T09:25:50.115108Z" + "end_time": "2021-04-25T12:59:08.708362Z", + "start_time": "2021-04-25T12:59:08.469730Z" }, "scrolled": false }, @@ -317,7 +317,7 @@ "4 The Riverlands NaN " ] }, - "execution_count": 393, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -330,7 +330,11 @@ { "cell_type": "markdown", "id": "plastic-coaching", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## Informasi Dataset\n", "\n", @@ -342,12 +346,15 @@ }, { "cell_type": "code", - "execution_count": 394, + "execution_count": 4, "id": "equipped-imperial", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.353390Z", - "start_time": "2021-04-22T09:25:50.237490Z" + "end_time": "2021-04-25T12:59:08.940417Z", + "start_time": "2021-04-25T12:59:08.708362Z" + }, + "slideshow": { + "slide_type": "subslide" } }, "outputs": [ @@ -405,12 +412,12 @@ }, { "cell_type": "code", - "execution_count": 395, + "execution_count": 5, "id": "harmful-relevance", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.469292Z", - "start_time": "2021-04-22T09:25:50.353390Z" + "end_time": "2021-04-25T12:59:09.211602Z", + "start_time": "2021-04-25T12:59:08.940417Z" } }, "outputs": [ @@ -519,7 +526,7 @@ "defender_commander 10 26.315789 object" ] }, - "execution_count": 395, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -555,12 +562,12 @@ }, { "cell_type": "code", - "execution_count": 396, + "execution_count": 6, "id": "chief-natural", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.585233Z", - "start_time": "2021-04-22T09:25:50.469292Z" + "end_time": "2021-04-25T12:59:09.296221Z", + "start_time": "2021-04-25T12:59:09.211602Z" } }, "outputs": [ @@ -604,7 +611,7 @@ "37 Siege of Winterfell NaN" ] }, - "execution_count": 396, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -629,12 +636,12 @@ }, { "cell_type": "code", - "execution_count": 397, + "execution_count": 7, "id": "dressed-referral", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.701132Z", - "start_time": "2021-04-22T09:25:50.585233Z" + "end_time": "2021-04-25T12:59:09.544033Z", + "start_time": "2021-04-25T12:59:09.296221Z" } }, "outputs": [ @@ -678,7 +685,7 @@ "37 Siege of Winterfell NaN" ] }, - "execution_count": 397, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -690,7 +697,11 @@ { "cell_type": "markdown", "id": "personal-diabetes", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "### Temuan 3\n", "dikutip dari [notebook dianyuurl](https://www.kaggle.com/dianyuurl/game-of-thrones-battles-analysis), pada [wiki Game of Thrones](https://gameofthrones.fandom.com/wiki/Battle_of_Castle_Black) _attacker_king_ seharusnya Mance Rayder dan _defender_king_ seharusnya Stannis Baratheon" @@ -698,12 +709,12 @@ }, { "cell_type": "code", - "execution_count": 398, + "execution_count": 8, "id": "signed-briefs", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.801431Z", - "start_time": "2021-04-22T09:25:50.701132Z" + "end_time": "2021-04-25T12:59:09.745385Z", + "start_time": "2021-04-25T12:59:09.544033Z" } }, "outputs": [ @@ -717,7 +728,7 @@ "Name: 27, dtype: object" ] }, - "execution_count": 398, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -739,12 +750,12 @@ }, { "cell_type": "code", - "execution_count": 399, + "execution_count": 9, "id": "greater-senior", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:50.917250Z", - "start_time": "2021-04-22T09:25:50.801431Z" + "end_time": "2021-04-25T12:59:09.930449Z", + "start_time": "2021-04-25T12:59:09.745385Z" } }, "outputs": [ @@ -855,7 +866,7 @@ "4 NaN NaN " ] }, - "execution_count": 399, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -877,12 +888,12 @@ }, { "cell_type": "code", - "execution_count": 400, + "execution_count": 10, "id": "breeding-congo", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.017495Z", - "start_time": "2021-04-22T09:25:50.917250Z" + "end_time": "2021-04-25T12:59:10.130850Z", + "start_time": "2021-04-25T12:59:09.930449Z" } }, "outputs": [ @@ -957,7 +968,7 @@ "4 Jaime Lannister " ] }, - "execution_count": 400, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -977,12 +988,12 @@ }, { "cell_type": "code", - "execution_count": 401, + "execution_count": 11, "id": "numeric-option", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.139878Z", - "start_time": "2021-04-22T09:25:51.017495Z" + "end_time": "2021-04-25T12:59:10.499660Z", + "start_time": "2021-04-25T12:59:10.130850Z" }, "scrolled": true }, @@ -1069,7 +1080,7 @@ "max 100000.000000 20000.000000" ] }, - "execution_count": 401, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1092,18 +1103,18 @@ }, { "cell_type": "code", - "execution_count": 402, + "execution_count": 12, "id": "upset-shopping", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.402844Z", - "start_time": "2021-04-22T09:25:51.139878Z" + "end_time": "2021-04-25T12:59:12.118407Z", + "start_time": "2021-04-25T12:59:10.499660Z" } }, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -1115,7 +1126,7 @@ } ], "source": [ - "atk_size_plot = sns.countplot(data=battles,x=\"attacker_outcome\")\n", + "sns.countplot(data=battles,x=\"attacker_outcome\")\n", "plt.title(\"Attacker Outcome Distribution\") \n", "plt.show()" ] @@ -1132,12 +1143,12 @@ }, { "cell_type": "code", - "execution_count": 403, + "execution_count": 13, "id": "recent-liberia", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.418463Z", - "start_time": "2021-04-22T09:25:51.402844Z" + "end_time": "2021-04-25T12:59:12.149628Z", + "start_time": "2021-04-25T12:59:12.118407Z" } }, "outputs": [], @@ -1162,12 +1173,12 @@ }, { "cell_type": "code", - "execution_count": 404, + "execution_count": 14, "id": "separate-comparison", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.540838Z", - "start_time": "2021-04-22T09:25:51.418463Z" + "end_time": "2021-04-25T12:59:12.402139Z", + "start_time": "2021-04-25T12:59:12.149628Z" } }, "outputs": [ @@ -1180,7 +1191,7 @@ "Name: 37, dtype: object" ] }, - "execution_count": 404, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1191,12 +1202,12 @@ }, { "cell_type": "code", - "execution_count": 405, + "execution_count": 15, "id": "emerging-sacramento", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.641093Z", - "start_time": "2021-04-22T09:25:51.540838Z" + "end_time": "2021-04-25T12:59:12.617543Z", + "start_time": "2021-04-25T12:59:12.402139Z" } }, "outputs": [], @@ -1206,12 +1217,12 @@ }, { "cell_type": "code", - "execution_count": 406, + "execution_count": 16, "id": "acoustic-patrol", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.803824Z", - "start_time": "2021-04-22T09:25:51.641093Z" + "end_time": "2021-04-25T12:59:12.817872Z", + "start_time": "2021-04-25T12:59:12.617543Z" } }, "outputs": [], @@ -1221,12 +1232,12 @@ }, { "cell_type": "code", - "execution_count": 407, + "execution_count": 17, "id": "frequent-premises", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:51.957528Z", - "start_time": "2021-04-22T09:25:51.803824Z" + "end_time": "2021-04-25T12:59:12.980510Z", + "start_time": "2021-04-25T12:59:12.817872Z" } }, "outputs": [ @@ -1239,7 +1250,7 @@ "Name: 37, dtype: object" ] }, - "execution_count": 407, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1259,12 +1270,12 @@ }, { "cell_type": "code", - "execution_count": 408, + "execution_count": 18, "id": "together-characterization", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.057727Z", - "start_time": "2021-04-22T09:25:51.957528Z" + "end_time": "2021-04-25T12:59:13.181407Z", + "start_time": "2021-04-25T12:59:12.980510Z" } }, "outputs": [ @@ -1276,7 +1287,7 @@ "Name: 27, dtype: object" ] }, - "execution_count": 408, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1287,12 +1298,12 @@ }, { "cell_type": "code", - "execution_count": 409, + "execution_count": 19, "id": "postal-country", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.173589Z", - "start_time": "2021-04-22T09:25:52.057727Z" + "end_time": "2021-04-25T12:59:13.450278Z", + "start_time": "2021-04-25T12:59:13.181407Z" } }, "outputs": [], @@ -1303,12 +1314,12 @@ }, { "cell_type": "code", - "execution_count": 410, + "execution_count": 20, "id": "premium-circuit", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.289550Z", - "start_time": "2021-04-22T09:25:52.173589Z" + "end_time": "2021-04-25T12:59:13.634340Z", + "start_time": "2021-04-25T12:59:13.450278Z" } }, "outputs": [ @@ -1320,7 +1331,7 @@ "Name: 27, dtype: object" ] }, - "execution_count": 410, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1349,12 +1360,12 @@ }, { "cell_type": "code", - "execution_count": 411, + "execution_count": 21, "id": "hourly-relevance", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.374210Z", - "start_time": "2021-04-22T09:25:52.289550Z" + "end_time": "2021-04-25T12:59:13.819208Z", + "start_time": "2021-04-25T12:59:13.634340Z" } }, "outputs": [], @@ -1365,12 +1376,12 @@ }, { "cell_type": "code", - "execution_count": 412, + "execution_count": 22, "id": "amateur-youth", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.474493Z", - "start_time": "2021-04-22T09:25:52.374210Z" + "end_time": "2021-04-25T12:59:14.003003Z", + "start_time": "2021-04-25T12:59:13.819208Z" } }, "outputs": [ @@ -1438,7 +1449,7 @@ "4 NaN NaN" ] }, - "execution_count": 412, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1449,12 +1460,12 @@ }, { "cell_type": "code", - "execution_count": 413, + "execution_count": 23, "id": "exciting-circle", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.621604Z", - "start_time": "2021-04-22T09:25:52.474493Z" + "end_time": "2021-04-25T12:59:14.218990Z", + "start_time": "2021-04-25T12:59:14.003003Z" } }, "outputs": [], @@ -1472,12 +1483,12 @@ }, { "cell_type": "code", - "execution_count": 414, + "execution_count": 24, "id": "approximate-being", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.744063Z", - "start_time": "2021-04-22T09:25:52.621604Z" + "end_time": "2021-04-25T12:59:14.403572Z", + "start_time": "2021-04-25T12:59:14.218990Z" } }, "outputs": [ @@ -1545,7 +1556,7 @@ "4 2 1" ] }, - "execution_count": 414, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1567,12 +1578,12 @@ }, { "cell_type": "code", - "execution_count": 415, + "execution_count": 25, "id": "radical-overview", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.891169Z", - "start_time": "2021-04-22T09:25:52.744063Z" + "end_time": "2021-04-25T12:59:14.566653Z", + "start_time": "2021-04-25T12:59:14.403572Z" } }, "outputs": [], @@ -1583,12 +1594,12 @@ }, { "cell_type": "code", - "execution_count": 416, + "execution_count": 26, "id": "italian-wealth", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:52.991543Z", - "start_time": "2021-04-22T09:25:52.891169Z" + "end_time": "2021-04-25T12:59:14.682095Z", + "start_time": "2021-04-25T12:59:14.566653Z" } }, "outputs": [ @@ -1656,7 +1667,7 @@ "4 NaN NaN" ] }, - "execution_count": 416, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1667,12 +1678,12 @@ }, { "cell_type": "code", - "execution_count": 417, + "execution_count": 27, "id": "legislative-bedroom", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:53.123016Z", - "start_time": "2021-04-22T09:25:52.991543Z" + "end_time": "2021-04-25T12:59:14.804396Z", + "start_time": "2021-04-25T12:59:14.688601Z" } }, "outputs": [], @@ -1690,12 +1701,12 @@ }, { "cell_type": "code", - "execution_count": 418, + "execution_count": 28, "id": "appreciated-suite", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:53.276674Z", - "start_time": "2021-04-22T09:25:53.123016Z" + "end_time": "2021-04-25T12:59:14.936255Z", + "start_time": "2021-04-25T12:59:14.804396Z" } }, "outputs": [ @@ -1763,7 +1774,7 @@ "37 1 1" ] }, - "execution_count": 418, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1784,12 +1795,12 @@ }, { "cell_type": "code", - "execution_count": 419, + "execution_count": 29, "id": "gross-governor", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:53.361363Z", - "start_time": "2021-04-22T09:25:53.276674Z" + "end_time": "2021-04-25T12:59:15.067130Z", + "start_time": "2021-04-25T12:59:14.936255Z" } }, "outputs": [ @@ -1898,7 +1909,7 @@ "defender_commander 10 26.315789 object" ] }, - "execution_count": 419, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1909,12 +1920,12 @@ }, { "cell_type": "code", - "execution_count": 420, + "execution_count": 30, "id": "found-continent", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:53.477228Z", - "start_time": "2021-04-22T09:25:53.361363Z" + "end_time": "2021-04-25T12:59:15.204741Z", + "start_time": "2021-04-25T12:59:15.067130Z" } }, "outputs": [], @@ -1924,12 +1935,12 @@ }, { "cell_type": "code", - "execution_count": 421, + "execution_count": 31, "id": "recreational-regulation", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:53.608752Z", - "start_time": "2021-04-22T09:25:53.477228Z" + "end_time": "2021-04-25T12:59:15.304750Z", + "start_time": "2021-04-25T12:59:15.204741Z" } }, "outputs": [ @@ -1984,12 +1995,12 @@ }, { "cell_type": "code", - "execution_count": 422, + "execution_count": 32, "id": "configured-college", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:53.693412Z", - "start_time": "2021-04-22T09:25:53.608752Z" + "end_time": "2021-04-25T12:59:15.482856Z", + "start_time": "2021-04-25T12:59:15.304750Z" } }, "outputs": [], @@ -2011,12 +2022,12 @@ }, { "cell_type": "code", - "execution_count": 423, + "execution_count": 33, "id": "abandoned-south", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:53.793622Z", - "start_time": "2021-04-22T09:25:53.693412Z" + "end_time": "2021-04-25T12:59:15.567731Z", + "start_time": "2021-04-25T12:59:15.482856Z" } }, "outputs": [], @@ -2034,18 +2045,18 @@ }, { "cell_type": "code", - "execution_count": 424, + "execution_count": 34, "id": "streaming-piece", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:54.294934Z", - "start_time": "2021-04-22T09:25:53.793622Z" + "end_time": "2021-04-25T12:59:16.469091Z", + "start_time": "2021-04-25T12:59:15.567731Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2077,18 +2088,18 @@ }, { "cell_type": "code", - "execution_count": 425, + "execution_count": 35, "id": "molecular-holmes", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:54.749400Z", - "start_time": "2021-04-22T09:25:54.294934Z" + "end_time": "2021-04-25T12:59:16.738228Z", + "start_time": "2021-04-25T12:59:16.469091Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2120,18 +2131,18 @@ }, { "cell_type": "code", - "execution_count": 426, + "execution_count": 36, "id": "excessive-steam", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:54.949882Z", - "start_time": "2021-04-22T09:25:54.749400Z" + "end_time": "2021-04-25T12:59:16.907603Z", + "start_time": "2021-04-25T12:59:16.738228Z" } }, "outputs": [ { "data": { - "image/png": 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9WWAtYGVgY+A2SRPytheTzvus1MvMzDpJVZJ9RGwUEX3y1wZSd25zx2qg7VsJk/Kf3YHXc+IbSGpxbpjXXQ9sDmwP3ClpMinRjmxlv23FM7Vs+bRmyjYtQ1mCLekO/LUs5rUpa+nmOCF1hTe0EmvJpaSLhZ4RcS7pNsibpCQ+vmwfk8q2+SuwLan7//hW9l0eeyme7sD5ZfGvA5zG/567//p7bEe9zMysiqrVst+H1L0NsBopSX8KvB4R2wFExLqkLt8xFe7zVWDxiNio7Bh/AZD0Baklfjozu/BbvV/fAfFUYiSwbUR8LSIagD+Q7t+316bA25LGky5uzpZ0Pak3YmlScm5qc+AkSbeQzgkR0Z2U3Nu60HqQ1GPSIyLmAm4mXVCNBLaJiFKrfX9gxGzUy8zMqqhayf4MYM+IeAVYhZTkAHYDDomIF4HfAdtJmtrCPv5L7l7eATg3Il4gDULbt6zItcCnkkrd/muSup9b0+54Koz5eeCXpKT5EikZ/3oWd3NnRIyOiGeBY4Af5eVnAFdFxBhSb8EoYLlmtj8ZeDQiXgY2Asbmck8B60ZEi/FIug24kXQrZQwwGrhS0gv5+A9FxKukQYInzGK9zMyskzQ0NjZ2dQyzLbdUTwM+kHReV8dj7dIXeOPFYUcz9dMJbZU1K6Q1j7qUceM+6+owOl3v3gvVZb1LZqf+3bo10LNnD0iNuLEtlauV5+xn1yjSPettujoQMzOzWlOIZJ8f8zIzM7Nm+N34ZmZmBedkb2ZmVnBO9mZmZgVX0T37PDlNuUbSbGxjJN3T4VGZmZlZh6m0Zb8qadKZnqRnqvcBtgZOiYgTqxOamZmZdYRKk/0SwJqSDpV0OOkd6Y2kl7TsWK3gzMzMbPZVmux7Snq/9CVPgNIzv23uy6pEZmZmZh2i0ufsX4+IM0izmzWQXlP7r4hYB5hereDMzMxs9lXast+b9DrT50jvVF+aNE/6Gsycn93MzMxqUCHejW+F0Bd4o6uDMOtK06ZO4aNPOmwurjmG341fI+/Gj4j1SNPHLk7Z/OSSVmtXdGYtmDBhIjNm1OcFqP/Dq9/613PdrXNUes/+j6R54p8ljcI3MzOzOUSlyX6ap441MzObM1U6QG9MRKxa1UjMzMysKipt2S8PPBMRbwKTSgt9z97MzKz2VZrsj69qFGZmZlY1rXbjR8QK+eNnLfyYmZlZjWurZX8OacKbG5tZ10jq3jczM7Ma1mqyl7R1/nO5pusiYtEqxWR1LL8com717r1QV4fQpcrrP2XqVD79ZEoXRmNWHJW+VOcMSceWfd8MuBxYplqBWX0aev0vGT/xw64Ow2rA8L0vAJzszTpCpQP01o+IocBvgbOBHYCfVC0qMzMz6zCVPme/FfADQKS57VeRdFO1gjIzM7OO09Zo/DUiYg0ggJOAuYERwLJ5uZmZmdW4trrxm47CnwocnT97NL6ZmdkcoK3R+P8zCr+piDhC0rkdF5KZmZl1pErv2bdm1w7Yh5mZmVVJRyT7hraLmJmZWVfpiGTv+e3NzMxqWEckezMzM6thTvZmZmYF53v2ZmZmBVdRso+IP7Wy+uwOisXMzMyqoNJ34w+MiAZJ/zMYT9JfOjimuhQRfYF/AC/nRd2AhYErJf2ije1GSurbzLqxwGBJY1vZfl7gPGBjYAbwMXCEpKcjYhFguKRtZ6Eeg4GTJQ2udBszM6uuSpP9u8BLEfEEMLG0UNIhVYmqfr0raWDpS0T0Af4ZEddKeqVKxzyMdGGxqqTGiNgAuDUilgEWA1av0nHNzKyTVJrsH88/1rmWIo2J+AwgIo4DdgOmA/cCR+Vy80XEX0lzGPwL2FfSR3ndyRExAJgMHCDphSbHWBKYhzTvwVRJf4+IvYHuwIVAn4i4SdK2EXEasCmwOOkCcCdJ/4mIccCoHO+RpR1HxKHAtsBWkr7osLNiZmazpKJ79pJ+CZwF/A34FXB2XmYdq09EjI6IVyNiPHAqsK2kdyJiCLANsBaptd0PODBv9zXgt5IGkJL9SWX7/Kek1Ul/b1c2c8wLgHWBcRFxS0QcAjwuaTJwCKm3YduI6AesAKwvqT/wFunCA6AXcGbulfgSICL2An4IbO1Eb2bWtSodoLcOKYncAfQB3o6I9asZWJ0qdeOvBFxF+vu5L6/bFLhG0heSpgGX52UAkvRI/nwVMLhsn5fmAneSZitctPyA+X7+KsDmwJPAHsDoZsq9BhwB7BcR5wLrAT3KijxZ9nkV4BLgAkkTMTOzLlXpo3fnAJsBEyS9A+xOahFaFUiaQeoOXxoYmhc3/btqYOZtmGlly7uRW9fNrGtoso6IOB3oI+kpSadLWovURb95k3Jrkm4ddANuAG6i7LFLSZPKin8GbAecHRELtlpZMzOrukqT/QKSSqPES63ESu/3Wzvk1vtQ4MSIWBJ4ENg5IuaPiLmAvYERufiKEVEaSLc3cH/ZrnYFiIhtgVckfd7kUEvnY8yTyy1Jui3wIulCofT3vDFp1P8w0lMDW5Pu6zfnTUm3ASOBU2a17mZm1rEqTfZfRsRi5PfgR0RULyQrkXQ3aWDkryTdDtxOGgj3Eume+W9z0deAkyLiRaA3cHrZbvpHxGjgcGDPZg5zMOn34B8R8RJwF3C0pFeB/wBvRcQI4DpgQD7GyBxHW1MgHwnsGhFrzEq9zcysYzU0NrY9j01EfA84jTRy+wFgC+DHkm6sbnhWR/oCbwy9/peMn/hhV8diNWD43hcwbtxnXR1Gp+jde6G6qWtzXP/2179btwZ69uwBqfE1tqVyFXXFS7otIl4h3cftDpxSxee+zczMrANVlOwjYl1JT5C6i4mI+SLiIkk/rWp0ZmZmNtsqvWd/XUSsDBARg4DnSQO7zMzMrMZVOqJ+V+DmiLgJ2AsYKqm1yXHMzMysRlT6Br1Hgf2Bg4BtnOjNzMzmHK227PNjVuXD9aeRJkl5H0DSalWMzczMzDpAW934B3dKFGZmZlY1rXbjS3pI0kOk9+LvlD+/D/wcUCfEZ2ZmZrOp0tH4w4FX8+c3SW9Qu7wK8ZiZmVkHqzTZ95J0IYCkyZLOJ81dbmZmZjWu0mQ/V0T0KX2JiCUom/HMzMzMalelz9mfR5rj/G7S6PzNSJOcmJmZWY2raCIcgIgYAHyb9PjdCEljqhmY1Z2+wBtdHYTVjilTp/LpJ1O6OoxO4YlgXP+amAgnexu4gdR93z0iNpd0X7uiM2vBhAkTmTGjsgvQovF/ePVdf7NqqnQinFOAY/PXL4F5gZeBVasUl5mZmXWQSgfo7QEsQ2rZ9ye9H/+lKsVkZmZmHajSZP+BpPeAV4ABkq7CrXozM7M5QqXJ/suI+CbprXkbRcRcwHzVC8vMzMw6SqXJ/gzgYuB2YDvSYL0HqxWUmZmZdZxKR+M/J2lTgIgYCHwLmF6toMzMzKzjtDXF7eL5450RMZiZb817H3gIWKF6oZmZmVlHaKtlfw2wef48oWz5NNLIfLMOlV8OUbd6916oq0OYLV9OnsLHn03t6jDMrIlWk72kLQEi4nJJ+3ROSFbPRhw+lEnjJ7Rd0GrSVn+6ApzszWpOpQP0/ueVZhHhlr2ZmdkcoK179n8AliY9bte7bNU8wPLVDMzMzMw6Rlv37C8DVgEGMPO9+JBemftUFeMyMzOzDtLWPftRwKiIeB0YCixInggH+DXpFbpmZmZWwyq9Z/8H4O/AwsDVwKfAjdUKyszMzDpOxQP0JJ0JjAReBXYEtqhWUGZmZtZxKk32pUmm/wWsImkSfoOemZnZHKHS1+U+GRHXAScCd0REf9KLdczMzKzGVdqy/znwG0n/AA7L2+1craDMzMys41TUspfUCDyRP98B3FHNoMzMzKzjVNqyNzMzszlUpffsCysitgeOJZ2LbsCfJJ2d1/0SuF/SIx14vG2AtSSdVEHZk4EDSbMMNuSfQyWN6KBYRkjaJH9ulNTQ1jZmZjbnqeuWfUQsDZwLbCFpALAe8KOckAE2Jr1AqMNIurWSRF9mmKSBOb49gGs7MJzBHbgvMzOrUfXesu8FzA0sAEyQNDEi9gQmR8QewFrApRGxLbA4cFouuyjwc0m3RMRw4BNgTdI8AqdIuiK3ypcGvgUsC1wq6bSI2AsYLGmviDiHNIXwDOBmSb9sI95FgA9KXyLiNGDTHNu7wE6S/hMR44BRwFLAIOD3pNceLwG8QBpceWbex5OS1smfh5EueAB+KOm1iBgE/CbXezxwgKQ38hMZF+djfw4cIunpls5HG/UyM7MqquuWvaTngVuA1yPiqYg4E+gu6TVJfyIlzP0kvQj8LH9eA9gPOLVsV98ANgK2Ac4pW74a6eVD6wDHRMSipRURsSwwJLfYNwBWioj5mgnzwIgYHRGvAA+QEi8R0Q9YAVhfUn/gLWC3vE0v4ExJA0nJe6qk9YB+pAuVrSQdks/BOmXHuj/Hcx9wQETMA1wK7JLrfS5wSS77Z+BCSauRnta4ISLmbeN8mJlZF6jrZA8g6SCgL+mVwMsCT0TEds0U3Q1YJSJOBI4AepStuzc/sTCG1NItGSFpqqQPgA9JLfOSfwOTIuLvwKHA0ZImN3PcUjf+isDKwK8jYgNJr+U49ouIc0lJvTymJ3P9HgZ+HxE/BS4g9TT0oHk35z9fIl0w9Ae+CdwaEaNJvQHLR0QPoJ+kv+VjPJHrF22cDzMz6wJ1newj4rsRsZOkf0u6QtKPgEOAfZsp/giwNvAMqTu/fDDbZPjqEUWaLs8ay7eRNI3U4j8R6Ak8nrvGWyRJpDkK1ouINYF7SX+HNwA3Ndn/pFzHbUjzGXwBXAE83CT28v2XXpRUirU78Hq+2BhI6prfkOZ/bxqYeVuopfNhZmZdoK6TPSkBnhERfQEiogEYCDyX108D5oqIxUmt3JOAu4DvM5sD9yJideAh4GFJQ4GXmdkybmmbRYE1gGdJgwdHShoG/APYuoWYNgP+mu+bfwxsUlZuekS0Nm7jVWDxiNgof98H+IukT0m3PrbLca0LLElqyZuZWY2p62SfH2H7JXB7RIiU3KYDv8pF7gaGke6NX0bq3n4FWAhYICIWnI1jPwc8DoyJiGdJyf6uZoqW7tk/CzwGXCLpQeA6YEBEvEiaoGgUsFwz218C7JzLXU/qGSiVuwV4voWxAkiaAuwAnBsRLwB7MrPXYzfgkLzf3wHbSZo6K+fAzMw6R0Njo3tarSb0Bd4YcfhQJo2f0NWxWDtt9acrGDfus7YLNqN374Xave2crp7rDq7/7NS/W7cGevbsAakRN7bFcu3au5mZmc0xnOzNzMwKzsnezMys4JzszczMCs7J3szMrOCc7M3MzArOyd7MzKzgnOzNzMwKzsnezMys4JzszczMCs7J3szMrOCc7M3MzAqutelNzTrdJued09Uh2Gz4cvKUrg7BzJrhZG81ZcKEicyYUZ8zMdb7zF9mVj3uxjczMys4J3szM7OCc7I3MzMrOCd7MzOzgnOyNzMzKzgnezMzs4JzsjczMys4P2dvNaVnzx5ffZ46ZRqffDqpC6MxMysGJ3urKRedfSeffPwFAMedtn0XR2NmVgzuxjczMys4J3szM7OCc7I3MzMrOCd7MzOzgnOyNzMzKzgnezMzs4JzsjczMys4J3szM7OCc7I3MzMrOCd7MzOzgnOyNzMzKzgnezMzs4Ir3EQ4ETEYuB14DWgA5gGGSbqgje1GAidLGtnO414GnAvcCXwBTC1b/Zykvduz33bGshxwJrAmMA0YBxwt6e8dsO9TgFGSbp3dfZmZWecoXLLPRkkaDBARCwEvR8R9kl6u4jFXkvRyRABsJWlsFY/VoojoCTwKnCRpx7xsPeDGiFhd0n9mZ/+STuqAMM3MrBMVNdmXmx+YDnwCEBE7AEfk5fMC+0h6rHyDiDgO2C1vdy9wFPAN4CZgDLA68B9gB0kfRsQA4Pm2AinvPYiIvsBISX0jYjjQE+iXjzUOuACYDxgPHCDptbz9U8BGQG/gZ5LuanKYA4C/S7qstEDS4xFxBLBgjmMcMApYChiUz8eOQHfgHuBo4BSgm6Tj8zbDgbuAITnu4RGxd962EXgGOBjYCfi2pF3zdicDkySd2db5MTOz6ijqPfu1ImJ0RLwAjAVGAu9GRDfgQGBrSQOAs4BjyzeMiCHANsBapKTeL28DMAA4T9IqwMfArnn5EFIiLLkzH7/0U0kX/gRJK5KS7bXAwTnGYcA1ZeXmkbQe8HPg1Gb2sy7wcNOFkq6R9Hr+2gs4U9JAYFNSd/+gXN+lc72uAHaJiIaIWAD4NnBLaX8RsSpwPLCxpFWBz4FfANcBm+UeFYCdgasqqL+ZmVVJUZP9KEkDJa0GLAn0B46RNAPYFtgy33veC+jRZNtNgWskfSFpGnB5XgbwgaTn8ucxwOJl2zxYto+t8vFLP1dUEPOT+c/+wEeSngaQdD3QLyIWyevvbub4TTWWPkTEn/IFx2sRMbSZ420GrENqmT9LushZOV8YjAX+D/ghcIekyWXbbwzcJmlC/n4xsKmkiaRxC9tFxEbA65LeraD+ZmZWJUVN9l+R9CmptblBRPQgdYMvR2r9XkgaxFeu6TlpYObtjvJk1wg0RMTCwHRJn1UQTmPZ8eZusm5SC8cvxdC9SQzl+yr3NLBB6YukPXIL/s+UXdhIKh2vO3B+6cKElPhPy+suB3bJP8ObHKe189TadmZm1skKn+wjojswmNRq7U9KkqcDI4DtmJlESx4Edo6I+SNiLmDvXLYlmwP3VxjOeGDl/PkHLZQR0DMiBuX4dwTelPRhhcf4A7BhROwVEQ15H0sA65HGIDT1ILB7RPTI9b0Z2D6vu4HUa7GkpCebbDcS2CYiSr0L+5PPk6RHgK8Dm+T9mZlZFypqsi/ds3+ONHDuC9KjaM8Do4FXgZdIA+GWLd9Q0u2kR/dG5TJvAb9t5VhDmNm1XtL0nn1pAOBZwE8i4lnSAMH/IWkKaZDb7yJiDDMHvVVE0nhgfVKSHhMRrwL3AQ8AZzdT/jbgRlK3/hjS+bkyr5sEPM5/jxkobfcCcAbwUD7GosAJZUX+BjyY62NmZl2oobGxse1SVndyr8BCpGS/qaT38/KbgKsl3dDKdvOQLjAOk/RshYfsC7xx0dl38snHXwBw3GnbM25cJXdHiqF374Xqqr5N1XP967nu4PrPTv27dWugZ88ekG5Pj22xXLv2bvVgEOkX5+KyRH89EMBDrWy3JPA+8MQsJHozM6uienjO3tpB0lM0Ge0vaYcKtnsPWKxacZmZ2axzy97MzKzgnOzNzMwKzsnezMys4JzszczMCs7J3szMrOCc7M3MzArOyd7MzKzgnOzNzMwKzsnezMys4JzszczMCs6vy7Wa8tMjt/rq89Qp07owEjOz4nCyt5oyYcJEZszwTIxmZh3J3fhmZmYF55a91YrukOZmrmeuf/3Wv57rDq5/e+tftl331so1NDa6y9RqwobAI10dhJnZHGoj4NGWVjrZW62YFxgEvAdM7+JYzMzmFN2BpYCngSktFXKyNzMzKzgP0DMzMys4J3szM7OCc7I3MzMrOCd7MzOzgnOyNzMzKzgnezMzs4JzsjczMys4vy7XakJE7AKcAMwNnC/poi4Oqd0i4hfAjvnrHZKOiojNgPOA+YHrJJ2Qyw4ELgUWBh4GDpQ0LSKWAf4MfA0QsKukiRGxKHA1sDwwDthR0vudVrlZEBHnAL0k7VUv9Y+I7wG/ABYE7pV0aL3UHSAidgOOzV/vkjS0HuofEQsDjwFbSxpb7TpHxDzAZcBawCRgF0mvthajW/bW5SJiaeA00itzBwI/joiVujSodsr/yLcAVifVZc2I2Bm4HPg+sCIwKCKG5E3+DBwsqT/QAOyfl/8e+L2kFYBRwIl5+anAI5JWBC4BLqh6pdohIjYF9syf56cO6h8RywPDgB8AqwFr5HoWvu4AEbEAcCGwMTAA2Chf/BS6/hGxDuk1tf3z9874fT8E+DwvPwwY3lacTvZWCzYDHpT0oaTPgRuA7bs4pvZ6DzhC0lRJXwKvkP4T+KekNyRNI/2D3yEilgXml/RE3nZ4Xj438H+k8/DV8vz5u6QrfYBrgCG5fM2IiMVJF2+n50VrUx/135bUinsn/93vBHxBfdQd0mtbu5F6NebOP59S/PrvD/wUeDd/74zf96+WS3oY6J17B1rkZG+1oA8pSZa8B3y9i2KZLZJeKv1jjohvkbrzZ9B8/Vqqdy/g0/wfRflyyrfJ6z8FelelMu33R+B44KP8vaV6Fq3+/YDuEXFrRIwGfkL91B1Jn5FapK8C7wBjqYP6S9pPUvkkXp1R51n+P9PJ3mpBN6B8koYGUoKcY0XEysB9wJHA6zRfv5bq3XQ5zDwfTefBrKlzFRH7AW9LeqBscaX1nNPrPxepl2pfYD1gHdK91nqoOxGxGrAPsCwpGU0n9WrVRf3LdMbv+yz/n+lkb7XgHdKsTSVLMrNLbI4TERsADwDHSLqSluvX0vIPgEUiojQ/9VLMPB//zuWIiLmAhYAJ1alJu+wEbJFbtqcA2wD7UR/1fx+4X9I4SZOAm0jJvx7qDrAl8ICkDyRNIXVHD6Z+6l/SGf/eZ/n/TCd7qwX3A5tGRO88yOeHwN1dHFO7RMQ3gJtJo2OvzYufTKuiX/4HvQtppPKbwOR8cQCwe17+JfAIKXEC7AHclT/fmb+T1z+Sy9cESZtLWkXSQOAk4FZgCPVR/9uBLSNi0VzPIaT7sPVQd4Dngc0iYsGIaAC+Rx397pfpjDp/tTwiNgQmS3qrtaD86J11OUn/jojjgRHAPMClkp7q4rDaaygwH3BeRJSWDQP2Am7M6+5k5mCcXYFL8qM7z5JGM0O633tlRJwAvAXsnJefCAyPiJeAj/P2NU3S5IjYi4LXX9KTEXEWaWT23KTbOH8g3cMudN0BJN0bEasDzwBfAk8BJ5POQ+HrX9JJv++/Bf6Yl08hXTi0yvPZm5mZFZy78c3MzArOyd7MzKzgnOzNzMwKzsnezMys4JzszczMCs7J3szMrOCc7M1sjhMRgyJiWNn3eyOiVzv3NTgixnRcdBUf9845dXZHm/P4pTpmNidamf+e+GPzrgqkvSRt1dUxWP1wsjezmhUR3YDfAOuS3gveABxIeu/+IhFxRVnxERGxFWku9eNIb2P8GnClpBPz/vYBjiBN0jIe2LPJ8TYE/gL8SNJjeT72E/K+vgCGSno8Ik4mTXbTB3he0m6t1OGgHPNUYDJwgKSXI2IsaSrndZg5rznASsCZkk5s6fgVnj6zr/gNemZWsyJiPeBwYCdJMyLiGGAD0qtIt5e0dS7XSJr6cwLwIPBjSf+MiD6k148uCSxNmodhDUlvR8RhwArAtcDvgJ8BlwDbSXohT1H8N2CwpAl5JsP7SVPZHgn8CFilbGrS5uLvTkrSfSW9FxG7k+Y0v7iU7CWNKit/EGnWvI1JFxLNHl/S57NzXq3++J69mdWs3Io9ATggIs4htYR7tFK+kTQBy5oR8QvgPFJvwILApsA9kt7OZc+XdGDe9OukiWxulvRCXrY5aWaxB/IsfleTphHtl9c/0Vqiz8eYDlwPPBYRvyO93/yy5spGxLakuRW2zsm8reObVczJ3sxqVkR8F7gjf72FNKlQ0zm+y8svCDwHrEGaaORI0qQsDcA0yuYAj4j5I2KF/HUaKbnuGRHr5GXdSVO2Diz9kG4nlAbzTaykDrmL/3vAa8AxwDXNxL0BcBHwPUnvV3h8s4o52ZtZLdscuE3SH4BRwA9ISXAaaWa5kun5+7eAhYETJN1Gmk993rzNCNIUrKV5wA8Azsqf35f0GKllfVWeavkBYIvSBUEeD/ACMH+lwUdEr4h4G5gg6XxSL8WgJmVWJLX+d5H0ctmq2T6+WYmTvZnVsmHA4Ih4kdRS/xewHGn61OUj4m+53PXAQ6Ru7tuBVyPiFVKL+mXSfe4XSS39uyPieeA7pIFzX5F0JWlK2nNz4v0xcG0u/ytgG0kVtejz/sYDp5K64p8Bfs1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\n", 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" ] @@ -2166,18 +2177,18 @@ }, { "cell_type": "code", - "execution_count": 427, + "execution_count": 37, "id": "herbal-strap", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:55.181716Z", - "start_time": "2021-04-22T09:25:54.949882Z" + "end_time": "2021-04-25T12:59:17.108060Z", + "start_time": "2021-04-25T12:59:16.907603Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2207,18 +2218,18 @@ }, { "cell_type": "code", - "execution_count": 428, + "execution_count": 38, "id": "static-memorabilia", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:55.397797Z", - "start_time": "2021-04-22T09:25:55.181716Z" + "end_time": "2021-04-25T12:59:17.308525Z", + "start_time": "2021-04-25T12:59:17.108060Z" } }, "outputs": [ { "data": { - "image/png": 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Blc5rZmZV5iRf374INAAfAETEycA+wMfAfcDxudzSEfEXIIB/AAdLejdvOzUi1gdmAYdJer7JOVYBlgQ+A8yR9GhEHAh0B34DrBoRN0vaOSJOB7YGVgLeAPaU9J+ImAKMy/EeVzpwRBwN7AzsIOmjDrsqZmZWET+Try+rRsSEiHglIqYCvwJ2lvTviNge2AnYkNS67gMcnvf7PHCRpPVJSX5Y2TH/LmkD4JfAFc2c80JgE2BKRNwaEUcBj0uaBRxF6l3YOSL6AGsDm0nqC/yTdMMBsDJwdu6FmAsQEQcAuwI7OsGbmdWGk3x9KXXXfw24ivT93J+3bQ38WdJHkuYBl+V1AJL0SP58FTCk7JiX5AJ3AWtGxArlJ8zP69cFtgWeBPYDJjRT7lXgGOCQiDgP2BToUVbkybLP6wJ/BC6UNKPi2puZWYdykq9DkuaTur1XA47Nq5t+Vw0seNwyr2x9N3JrupltDU22ERFnAKtKekrSGZI2JHXFb9uk3CDSI4JuwA3Azfl4pZhnlhX/ANgFGBERy7VaWTMzqxon+TqVW+vHAj+PiFWA0cBeEbFMRCwBHAiMycXXiYjSALkDgQfKDjUUICJ2Bl6W9GGTU62Wz7FkLrcKqfv/BdINQulGYkvSKP6RpF8B7Eh6bt+c1yXdDowFhi9s3c3MrGM4ydcxSfcAjwO/lHQHcAdpgNuLpGfiF+WirwLDIuIFoBdwRtlh+kbEBOCnwP7NnOZI0r+Dv0XEi8DdwAmSXgH+A/wzIsYA1wHr53OMzXGs1UYVjgOGRsTAham3mZl1jIbGxsZax2AG0BuY9MLIE5gzfVqtYzGzLmjQ8ZcwZcoHFZfv1Wv5hSpfj7p1a6Bnzx6QGl2TP7W9swMyMzOzzuEkb2ZmVlBO8mZmZgXlJG9mZlZQTvJmZmYF5SRvZmZWUBW9oCYihjVZ1Qh8BEyUdG+HR2VmZmbtVmlLvj9wGNATWAE4iDTj2fCI+Hl1QjMzM7P2qDTJfwEYJOloST8lvQmtEdgC2KNawZmZmdmiqzTJ95T0VmlB0rS8bg5NXnhiZmZm9aGiZ/LAaxFxJvAH0pvHDgb+EREbAx9XKzgzMzNbdJW25A8kzS3+LPAU6c1lhwADWfAqVDMzM6sjfkGN1YvewKRaB2FmXde8ObN59/05FZdfHF5QU+lP6DYlvb50JVJ3PQCS1uuQKM2yadNmMH9+cW88i/A/lba4jsWwONRxcVDpM/nfA6OA8aRR9WZmZlbnKk3y8ySdX9VIzMzMrENVOvBuYkT0r2okZmZm1qEqbcl/GXgmIl4HZpZW+pm8mZlZ/ao0yZ9S1SjMzMysw7XaXR8Ra+ePH7Twx8zMzOpUWy35c0kvormxmW2NpG58MzMzq0OtJnlJO+a/12q6LSJWqFJMthjLkzoUWq9ey9c6hKqrtI6z58xh+vuzqxyN2eKr0slwzpR0UtnyNsBlwBrVCswWT8defxpTZ7xT6zCsk4w68ELASd6sWiodeLdZRBwLXASMAHYHflS1qMzMzKzdKv2d/A7A9wGR3i2/rqSbqxWUmZmZtV9bo+sHRsRAIIBhwGeAMcCaeb2ZmZnVqba665uOqp8DnJA/e3S9mZlZHWtrdP2nRtU3FRHHSDqv40IyMzOzjlDpM/nWDO2AY5iZmVkH64gk39B2ETMzM+tsHZHk/X55MzOzOtQRSd7MzMzqkJO8mZlZQfmZvJmZWUFVlOQj4spWNo/ooFjMzMysA1U6d/2AiGiQ9KlBdpKu6eCYOlVE7AacRLoW3YArJY3I204DHpD0SAeebydgQ0nDKih7KnA48Bapx6QBOFrSmA6KZYykrfLnRknulTEzK5BKk/wbwIsR8QQwo7RS0lFViaqTRMRqwHnAQEnTIqIH8FBESNJtwJakaXw7TD7ubQuxy0hJp+Z4BwD3kt4f0BGGdNBxzMysDlWa5B/Pf4pmZdJ8/MsC0yTNiIj9gVkRsR+wIXBJROwMrAScnsuuAPxE0q0RMQp4HxgErAYMl3R5boWvBnwVWBO4RNLpEXEAMETSARFxLrAtMB+4RdJpbcT7OeDt0kJEnA5snWN7A9hT0n8iYgowDvgiMBj4f8C6pJuD54G9gLPzMZ6UtHH+PBLYNB9+V0mvRsRg4Ne53lOBwyRNioi+wB/yuT8EjpL0dEvXo416mZlZFVT0TD4nn3OAm4BfAiMqSEh1T9JzwK3AaxHxVEScDXSX9KqkK0mJ8hBJLwD/mz8PBA4BflV2qNWBLYCdgHPL1q8HbAdsDJwYESuUNkTEmsD2ktYHvg58LSKWbibMwyNiQkS8DDxISrhERB9gbWAzSX2BfwL75H1WBs6WNICUtOdI2hToQ7pB2aHUC1NK8NkDOZ77gcMiYkngEmDvXO/zgD/msn8CfiNpPeAnwA0RsVQb18PMzDpRpQPvNgb+AdwJrAr8KyI2q2ZgnUXS/wC9gYtJLe4nImKXZoruA6wbET8HjgF6lG27L49XmEhq2ZaMkTRH0tvAO6SWeMn/ATMj4lHgaOAESbOaOe9ISQMkrQP0A86KiK9LejXHcUhEnEdK5uUxPZnr9zDw/yLiCOBCUs9CD5p3S/77RdKNQl/gK8BtETGB1Pr/cn6s0UfSTfkcT+T6RRvXw8zMOlGlP6E7F9iG1KX9b2BfUsLo0iLiOxGxp6T/k3S5pB8ARwEHN1P8EWAj4BlSt335ILVZAM0MTCxP2o3l+0iaR2rh/xzoCTyeu8BbJEnAo8CmETEIuI/0Hd4A3Nzk+DNzHXcCrgY+Ai4HHqaFnz3mmMpj7Q68lm8yBpC64Den+X83DSx4/NPS9TAzs05UaZJfVtJLpQVJd1H58/x69hFwZkT0BoiIBmAA8GzePg9YIiJWIrVqhwF3A98jJcBFFhEbAA8BD0s6FniJBS3hlvZZARgIjCcNChwraSTwN2DHFmLaBvhLfi7+HrBVWbmPI6K17/EVYKWI2CIvHwRcI2k66RHHLjmuTYBVSC13MzOrE5Um+bkRsSJ5nvqIaDUZdRX5p2inAXdEhEhJ7WPSuAOAe4CRpGffl5K6sV8GlgeWjYjl2nHuZ0mDGSdGxHhSkr+7maKlZ/LjgceAP0oaDVwHrB8RLwBjSeMHmns18B+BvXK560k9AaVytwLPtTAWAEmzgd2B8yLieWB/FvRy7AMclY/7W2AXSXMW5hqYmVl1NTQ2tt2jGhHfJXVRr0Ia/LUd8ENJN1Y3PFuM9AYmHXv9aUyd8U6tY7FOMurAC5ky5YNah7HQevVavkvGvTBcx66hW7cGevbsAanxNrnp9oq63CXdnkd3b0vq6h0u6eUOjNPMzMw6WEVJPiI2ySOoX83LS0fE7yQdUdXozMzMbJFV+kz+uojoB5AnR3mONNGJmZmZ1alKR8gPBW6JiJuBA4Bj82QxZmZmVqcqnfHur8ChwP8AOznBm5mZ1b9WW/L551Hlw+/nkWY/ewsgT2lqZmZmdait7vojOyUKMzMz63CtdtdLekjSQ6R56/fMn98ivZBEnRCfmZmZLaJKR9ePIs0GB/A6aYa1y6oQj5mZmXWQSpP8ypJ+AyBplqQLSO8qNzMzszpVaZJfIiJWLS1ExBdo4U1mZmZmVh8q/Z38+cCEiLiHNNp+G+C4qkVlZmZm7VbRC2oAImJ94Jukn9GNkeTXilpH6g1MqnUQ1rlmz5nD9Pdn1zqMhVaEF5u0xXXsGjrkBTXZv4AbSN303SNiW0n3d0SQZiXTps1g/vzKbjy7oiL8T6Uti0MdzbqKSl9QMxw4KS/OBZYivf+8f5XiMjMzs3aqdODdfsAapJZ8X9L89S9WKSYzMzPrAJUm+bclvQm8DKwv6SrcijczM6trlSb5uRHxFdIsd1tExBLA0tULy8zMzNqr0iR/JvAH4A5gF9IgvNHVCsrMzMzar9LR9c9K2hogIgYAXwU+rlZQZmZm1n5tvWp2pfzxrogYwoJZ7t4CHgLWrl5oZmZm1h5tteT/DGybP08rWz+PNNLerEPlSR0KrVev5WsdwkKbO2s2730wp9ZhmNlCajXJS/oWQERcJumgzgnJFmdjfnosM6dOa7ugdaodrrwcnOTNupxKB959agqyiHBL3szMrI619Uz+YmA10s/mepVtWhL4cjUDMzMzs/Zp65n8pcC6wPosmLce0tS2T1UxLjMzM2untp7JjwPGRcRrwLHAcuQX1ABnkaa6NTMzszpU6TP5i4FHgc8CVwPTgRurFZSZmZm1X8UD7ySdDYwFXgH2ALarVlBmZmbWfpUm+dLLof8BrCtpJp7xzszMrK5VOq3tkxFxHfBz4M6I6EuaEMfMzMzqVKUt+Z8Av5b0N+DHeb+9qhWUmZmZtV9FLXlJjcAT+fOdwJ3VDMrMzMzar9KWvJmZmXUxlT6T7zLy2/LuAF4l/aZ/SWCkpAvb2G8scKqksYt43kuB84C7gI+A8om+n5V04KIcdxFjWQs4GxhEGjsxBThB0qMdcOzhwDhJt7X3WGZmVl2FS/LZOElDACJieeCliLhf0ktVPOfXJL0UEQA7SJpcxXO1KCJ6An8FhknaI6/bFLgxIjaQ9J/2HF/SsA4I08zMOkFRk3y5ZUg/93sfICJ2B47J65cCDpL0WPkOEXEysE/e7z7geGB14GZgIrAB8B9gd0nvRMT6wHNtBVLeWxARvYGxknpHxCigJ9Ann2sKcCGwNDAVOEzSq3n/p4AtgF7A/0q6u8lpDgMelXRpaYWkxyPiGNKMhUTEFGAc8EVgcL4ee5BmMrwXOAEYDnSTdEreZxRwN7B9jntURByY920EngGOBPYEvilpaN7vVGBmnmfBzMw6UVGfyW8YERMi4nlgMmkSnzciohtwOLCjpPWBc4CTyneMiO2BnYANScm8T94H0hz+50taF3gPGJrXb09KgCV35fOX/lTSVT9N0jqkJHstcGSOcSTw57JyS0ralPSLh181c5xNgIebrpT0Z0mv5cWVgbMlDQC2JnXrD871XS3X63Jg74hoiIhlgW8Ct5aOFxH9gVOALSX1Bz4EfgFcB2yTe1Ag/Qrjqgrqb2ZmHayoSX6cpAGS1gNWAfoCJ0qaD+wMfCs/Wz4A6NFk362BP0v6SNI84LK8DuBtSc/mzxOBlcr2GV12jB3y+Ut/Lq8g5ifz332BdyU9DSDpeqBPRHwub7+nmfM39d9XA0fElflG49WIOLaZ820DbExqiY8n3dz0yzcEk4FvALsCd0qaVbb/lsDtkkovf/8DsLWkGaRxCbtExBbAa5LeqKD+ZmbWwYqa5P9L0nRS6/LrEdGD1N29Fqm1+xsWvFmvpOk1aWDBY43yJNcINETEZ4GPJX1A2xrLzveZJttmtnD+Ugzdm8RQfqxyTwNfLy1I2i+32P9E2Q1NnrWQfNwLSjckpIR/et52GbB3/jOqyXlau06t7WdmZp2k8Ek+IroDQ0it1L6k5HgGMAbYhQXJs2Q0sFdELBMRSwAH5rIt2RZ4oMJwpgL98ufvt1BGQM+IGJzj3wN4XdI7FZ7jYmDziDggIhryMb4AbErzUxGPBvaNiB65vrcAu+VtN5B6KVaR9GST/cYCO0VEqTfhUPJ1kvQI8CVgq3w8MzOrgaIm+dIz+WdJA+I+Iv2k7DlgAuklOy+SBritWb6jpDtIP8Ebl8v8E7iolXNtz4Iu9JKmz+RLA/vOAX4UEeNJA/8+RdJs0uC130bERBYMZquIpKnAZqTkPDEiXgHuBx4ERjRT/nbSGwWfJD0CmABckbfNBB7nk2MCSvs9D5wJPJTPsQLws7IiNwGjc33MzKwGGhobG9suZYud3AuwPCnJby3prbz+ZuBqSTe0st+SpBuLH0saX+EpewOTxvz0WGZOndZWWetkO1x5OVOmVPJECnr1Wr7isl2V61gMRahjt24N9OzZA9Jj6Mmf2t7ZAVmXMZj0D+YPZQn+eiCAh1rZbxXgLeCJhUjwZmZWBYvD7+RtEUh6iiaj9yXtXsF+bwIrVisuMzOrnFvyZmZmBeUkb2ZmVlBO8mZmZgXlJG9mZlZQTvJmZmYF5SRvZmZWUE7yZmZmBeUkb2ZmVlBO8mZmZgXlJG9mZlZQntbW6spW559b6xCsGXNn+WWCZl2Rk7zVlWnTZjB/fnHfjFiEt16ZWdfh7nozM7OCcpI3MzMrKCd5MzOzgnKSNzMzKygneTMzs4JykjczMysoJ3kzM7OC8u/kra707Nmj1iFU1by5H9c6BDNbjDjJW1353Yi7eP+9j2odRtWcfPputQ7BzBYj7q43MzMrKCd5MzOzgnKSNzMzKygneTMzs4JykjczMysoJ3kzM7OCcpI3MzMrKCd5MzOzgnKSNzMzKygneTMzs4JykjczMysoJ3kzM7OC8gtqOkhE9AYmAX+QdFjZ+gHAs8CBkkZ1ckyNwHN5sQFYAbgH+JGkdr0OLSImA0MkTW7PcczMrHqc5DvWNODbEdG9LInuCUypVUCSBpQ+R8RngYnAdsDdtYrJzMw6h5N8x5oBTAC+AYzJ67YDHigViIgjgX2B5YA5wF6SlFvGVwHfytv2k/RM7gn4PbAs8A4wVNK/I+JEYA+gO3AvcIKkxjbiW7nsOETE6cDWwErAG6Qbkh2Bb0oamsucCswE/gj8CVgdeAlYOm/vDowAhuRYRkn6dUQMAc7J6yZK2r+iK2hmZh3Gz+Q73l+A3QAiYjDwPCmZl1rS3yd1c68L3AEcWbbvNEkbASOBk/O6q4FfSuoPXAscHRHfBgYBg4ENgNWAoc0FExETIuLFiJgCjAKOkvRkRPQB1gY2k9QX+CewD3AdsE1ELJ8PsRfp5mM4MD7H8TvgC3n7oQCSBgIbAd+LiC3ytr6kGwYneDOzGnCS73i3AdtHRDdSy/i60gZJ04G9gR9ExJnAd4EeZfvek/+eCKwUESsDX5R0R97/YknHAdsAGwPPAOOBDYF+zQUjaYCkfsAZwOeAW/P6V4FjgEMi4jxgU6CHpBnAXcAuOVm/JukNUkv9urzvw8Br+RTbADtFxATgSeBLQP8Fp9f7lV86MzPrSE7yHSwnyeeAzYFv8smu+tWBx0kD4O4mtawbynaflf9uzOvn5s+l/ZeOiC+TusAvyAl8ACnhn95GXL8G3iJ1rRMRg4D7SP8GbgBuLovlMtLNyN45xvKYSublv7sDx5fFskneH1I3v5mZ1YiTfHX8BTgLGCdpXtn6wcCrOeE+DexMSpLNyq3gf0fEdnnVvqRu89HAvhHRIyKWAG4hPyJow0+BgyNiPWBLYKykkcDfSM/iu+fzPkJqkW+Vjw3pZmVf+O9jiD55/Wjg0Ij4TET0AP5KSvRmZlZjTvLVcTswgLKu+uw+oFtEvETqZn8FWKuNY+0DDMvd4XsCx0m6HbiR1D0+kTTY74q2gpL0InAlcF6Obf2IeAEYC4xrEstNwGhJs/PyL4CvRMSLwIks6K4fCfyd9DPBccDlksa2FYuZmVVfQ2NjWwOybXESEQ3AksD9wI8lje+kU/cGJv1uxF28/95HnXTKznfy6bsxZcoHtQ6jqnr1Wt51LADXsWvo1q2Bnj17QGqkTf7U9s4OyOreKqRn9090YoI3M7Mq8O/k7RMkvQmsWOs4zMys/dySNzMzKygneTMzs4JykjczMysoJ3kzM7OCcpI3MzMrKCd5MzOzgnKSNzMzKygneTMzs4JykjczMysoJ3kzM7OC8rS2VleOOG6HWodQVfPmflzrEMxsMeIkb3Vl2rQZzJ9f3Dcj9uq1fK1DMLPFiLvrzczMCsoteasX3SG9G7noXMdicB2LoavXsSz+7s1tb2hsLG7XqHUpmwOP1DoIM7Muagvgr01XOslbvVgKGAy8CXh0mplZZboDXwSeBmY33egkb2ZmVlAeeGdmZlZQTvJmZmYF5SRvZmZWUE7yZmZmBeUkb2ZmVlBO8mZmZgXlJG9mZlZQntbW6kJE7A38DPgMcIGk39U4pA4VEWOAzwNz86rDJD1Zw5A6TER8FngM2FHS5IjYBjgfWAa4TtLPahpgB2imjpeTZmn8MBc5TdLNNQuwnSLiF8AeefFOSccX7XtsoY6F+h6b48lwrOYiYjXSdIyDSDM2PQbsJemlmgbWQSKiAfg3sKakebWOpyNFxMbAH4G1gb7AfwABWwL/Au4k3bTdXbMg26lpHXOSfwHYTtKbtY2u/XIyPw3YCmgE7gEuAc6mIN9jC3X8LTCcgnyPLXF3vdWDbYDRkt6R9CFwA7BbjWPqSJH/vi8inouII2saTcc6FDgCeCMvbwT8XdKkfEPzJ2D3WgXXQT5Rx4hYFlgDuCwino+I0yKiK/+/9E3gGElzJM0FXibdsBXpe2yujmtQrO+xWe6ut3qwKuk/wpI3ScmiKFYEHgT+l/Q4YmxESNL9tQ2r/SQdAhBRuo9p9rv8UieH1aGaqeMqwGjgR8D7wB3AwaTWfpcj6cXS54j4KqlL+yIK9D22UMctgCEU5HtsiZO81YNupC60kgZgfo1i6XCSHgceLy1HxKXADkCXT/LNKPR3CSDpNWDn0nJEXATsRxdPDhHRj9Qtfxwwj9SaLynE91heR0migN9jU4XrmrAu6d+ktyiVrMKC7t8uLyI2j4ity1Y1sGAAXtEU+rsEiIj+EbFr2aou/31GxNdJvU0nSrqCAn6PTetYxO+xOW7JWz14ADg1InqRRrnuCvywtiF1qBWA4RGxGam7fn/g8JpGVD1PAhERfYBJwN7AZbUNqcM1ABdExGhgBunf6hW1DWnRRcTqwC3AnpJG59WF+h5bqGOhvseWuCVvNSfp/4BTgDHABOAaSU/VNKgOJOkOUhfhs8AzwGW5C79wJM0CDgBuBF4CXiENpCwMSc8DZwKPkuo4QdKfaxtVuxwLLA2cHxETImIC6Ts8gOJ8j83VcTOK9T02yz+hMzMzKyi35M3MzArKSd7MzKygnOTNzMwKyknezMysoJzkzczMCspJ3szMrKCc5M2sWRExOCJGli3fFxErL+KxhkTExI6LrngiYuWI6JK/aY6IYRHxvVrHYZ/mJG9mLenHJ19Ksm2tArG6903SbI5WZzytrdliLr9e89fAJsDypOk+Dye9a/tzEXF5WfExEbEDsD5wMrAk8HngCkk/z8c7CDgG+BiYSprGt/x8mwPXAD+Q9FhEfBf4WT7WR8Cxkh6PiFOBTUlvtntO0j6t1KEv8Pscy3zgV5Kuyy8k+S3Qk/TinPMkXRkRQ0iznf2T9CrgD4GzgKPy8o2SfrIQ5Zq7hodIejQiRgHTgf7A6sDzwH6SZkTELsDpud5PN6nTwaQ3pHUDpgFHSnolH28l4CvAHZJOaOW6bAz8BlgOmJOv7eiI2AIYASyb1/9M0j0RcQCwm6Qd8/7/XW6pHqTvd0NgRER8LOnmluKxzueWvJltTEqkm0r6Gmn+7mOBYcAjkg6UdGAuuxXp5SXHAPtL2pCU2E7K3c3rA2cD35a0HnAbacpiACJiK2AUsGNO8F8FzgB2kLQBaf7wmyJiubzLmsAGrSX47Frgekn9SG/4OyMiPpvPf1GOZfu8ftO8z2DgLEkDSMnrJOA7wEDgiIhYdSHKNXcNTyyLbxDwbWAdoDewe0R8gTQf/K6SBgGvl12nLUnJc4t8Xc4BypPnspL6tZHgP0Oar324pHWBQ4ELI6InaYrao/N12R/4U0Ss1cr1bbEekn4HjCO92c0Jvs44yZst5vI8+j8DDouIc4HdgB6tlG8EvgsMiohfAOeTWq7LAVsD90r6Vy57gaTSy3i+RHpn9y15/ndIjwC+CDyY5xO/mtQS75O3PyFpXmvxR8RKpJ6FS/I5/yXpK/l8S0u6Ka9/gzQX+7fzrpMkPZs//wMYI2mOpKmkZL5SpeUquIb3SJotaS7wQj725sALkl7KZX5fVv47+Ro8lq/LOcCKua4Af23tmmT9gY8l3Znr/4yk/qQbklclPZnXv0iav31IBcdsrh5Wx5zkzRZzEfEd0gt0AG4FRpKSdkvllyO9bGcgMJ70/vG5eZ95lL1PPiKWiYi18+I8UlLfP3cjA3QHHpQ0oPSH1DNQGqQ3o4IqlG4Cys8b+dhNB7J1Y8Gz49lNtrX0mtE2y1VwDWeWfW4s21ZepvxmpjtwVdk1GUjqEn83b6/0unyi/hGxLq1fl/LYID1CKddSPaxOOcmb2bbA7ZIuJnW7fp+UCObxycFUH+flrwKfJT3HvZ3UAlwq7zMG2CYiSu8iP4zUCgV4S9JjpEcBV0XEsqT3e29XuhHIz/ufB5apNHhJ00lv99s/H2N1Usv0PWBufu5N7lbfFbi/0mMvhJauYWseBvrlRxyQ3vpWci+wV9l1PJx0rRaGgMaI2BYgIgYCo0mvkV07IjbK6/sB3wDGAlOAdSNi6dzdv1uF52r6b8XqhJO8mY0EhkTEC6SW+T+AtYCngC9HxE253PXAQ6Tu9DuAVyLiZVLX/UtAH0kvkFr290TEc6Su8cPLTybpCtKrS8/LXdU/BK7N5X8J7CSpkpZqub2BPfIxbicNevsXKdkeHRHPAw+Qnk+PWchjV6LZa5gH5DVL0pQc99URMZ50zUvb7iONbbg/x743sEt+VFIRSbOBXYBf5C7/kfkYbwO7AxfleK8BDpT0N+A+0nf8CukmZFyFp7sNODMi9m+zpHUqv2rWzMysoPwTOjOrexExlNRD0JyrJY3ozHjqRUQcBwxtYfMISVd3ZjxWf9ySNzMzKyg/kzczMysoJ3kzM7OCcpI3MzMrKCd5MzOzgnKSNzMzK6j/D7d2K21GE2H+AAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -2248,20 +2259,20 @@ }, { "cell_type": "code", - "execution_count": 429, + "execution_count": 39, "id": "short-shell", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:55.899130Z", - "start_time": "2021-04-22T09:25:55.397797Z" + "end_time": "2021-04-25T12:59:17.710117Z", + "start_time": "2021-04-25T12:59:17.308525Z" } }, "outputs": [ { "data": { - "image/png": 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dbtGI+BkQwD3AXpn5SH3ssIhYC3ga2Cczb+5zjuWA0cAoYEpmXhERewIjgOOB5SPil5m5Y0QcBWwGLAU8AOycmf+KiEnAdTXvQb0HjoiPATsC22Tmk4P2qkiSJOkFx+LSLstHxI3AosAywLXAjpn594jYGtgeWBeYAvwC2Bc4H3gZ8K3M/H1EfAU4FPhEPeafM3OPiNgGOIXnj6B8sx5jUkRMBC6hTE97OiL2p0xD2zEiVgVeC2yYmTMi4lRKiTquZj0mMyfWqWJExB7AO5jD0nL8p3fodtMXnBnTpjJ85KimY0gLnSlTpzcdQZLUBYtLuzyQmeMiYjilEKwO/LY+thnw494SEBEnAbtTSkdm5u/rdqdRCkqvEykbXBARp0fEkpn5n94HM/P+iHgDsB6wOfB+4BMRMVPBycy7I+IAYO+ICGADyuhOr6s7br8B+AHwnsx8fE5egFtOOIQpj06ek11eMNY5+EQmTXqs6RjPGjt2TGvytCkLmGcgbcwjSWo/ryrWQpk5gzLlagXgwHp33z+rYTxXPKd13D8cmNrxdedjw/o8RkR8CVg+M6/JzC9l5rqUaWBb9NluHcr0tOHAmcAv6/F6Mz/VsfljwE7AVyJi8dk+WUmSJKkLFpeWysxplNLy+YhYDrgU2CUiFouIkcCewGV189d1jJDsCVzccahdASJiR+COzHyiz6lWqOcYXbdbjjL17BZK6ektR5tQpo2dQLn62baUdTCz8pfM/BUwEThiTp+7JEmS1JfFpcUy8zfAlcAXM/M84DzKIvjbgL8C36qb3g0cGhG3AGOBL3UcZrW6buaTlKllfe1H+XtwV0TcBvwaOCQz7wT+Bfw1Ii4DfgqsVc8xseZYeYCncBCwa0S8cU6etyRJktSXa1xaon7WykqzuH/LjttHAkf22eR+yqL5WR3zecebxTaPAR/q57GpwIYdd63fz3adU8YmAuPr7cmUq5ZJkiRJ88QRF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1Hojmw4gdVpj32OajtBaM6ZNbTqCJElSYywuapXJkx9nxoyepmMAMHbsGCZNeqzpGM8aO3YM8HTTMSRJkhrhVDFJkiRJrWdxkSRJktR6FhdJkiRJrdfVGpeIOLTPXT3Ak8CtmXnhoKeSJEmSpA7djrisAewDLA0sCXwA2BY4IiI+PzTRJEmSJKnotrgsC6yTmR/LzE8C61JGXd4CvHuowkmSJEkSdF9cls7MB3u/yMzJ9b4pgB8uIUmSJGlIdfs5LvdGxNHA94FhwF7APRGxPjB9qMJJkiRJEnQ/4rInsBJwA3ANsAKwN/BG4MAhSSZJkiRJVVcjLpn5MLDLLB767uDGkSRJkqTn6/ZyyBsAXwKWokwVAyAz1xyiXHqBWnrpJZqOMJOxY8c0HWEm5ulfm7KAeWZnylRnGEuS5ly3a1y+B0wArqdcTUwaEvsffTYPP/JE0zEkDaEzjt216QiSpAVQt8VlWmZ+bUiTSJIkSVI/ul2cf2tErDGkSSRJkiSpH92OuKwC/Cki/gI81Xuna1wkSZIkzQ/dFpfPDmkKSZIkSZqN2U4Vi4jX1puP9fNLkiRJkobcQCMuXwW2BX4xi8d6KFPIJEmSJGlIzba4ZOa29feV+z4WEUsOUSZJkiRJmklXVxWLiKP7fL05cPOQJJIkSZKkPrpdnL9hRBwIfAv4CvAu4CNDlkqSJEmSOnT7OS7bADsACSwLvCEzfzlUoSRJkiSp00BXFXtjRLwRCOBQYBRwGfCqer8kSZIkDbmBpor1vZrYFOCQeturikmSJEmaLwa6qtjzribWV0QckJnHDV4kSZIkSZpZt2tcZmfXQTiGJEmSJPVrMIrLsEE4hiRJkiT1azCKS88gHEOSJElqnYhYLyJO6Pj6oohYZi6PNT4ibh28dPMuIlaOiL7r2ltpMIqLJEmStLB6PfCKjq+3aCrIEHkV5QrCrdftB1BKkiRJC62IGA58HXgzMIayHGJf4AjgJRFxcsfml0XENsBawGeA0cDLgFMy8/P1eB8ADgCmAw8Du/c538bAGcB7MvOPEbEd8Ll6rCeBAzPzyog4DNgAWB64KTN3m81zeD3wbWBpyqyo4zLz1IgYD3w7M99Qtxtft1sLOBFYISIuzMy3R8S2wJGUAY4ngH0z86aI2AH4Qr3/MeCTmXlNzfdqYAXg5cCfKB+fsjuwMnBwZv64nvezwDvqMe4HPpKZD/T3fPpyjYskSZIE61PKwQaZuTpwCnAg5bMMf5+Ze2bmnnXbTYG/U4rJ7pm5LqXwfDoilomItYBjgK0yc03gXOCzvSeKiE2BCcC2tbS8BvgSsE1mrg18CDgrIhavu7wKWHuA0jKynudb9ZxbA1+KiA362yczpwN7A/fU0rIscDqwZz3GV4AvR8RrgROAd2TmWvU1OSciXlwPtTGwI/BGygfXr56ZbwX2Aw6v+d4PrAG8KTPHARdQSlPXuiouEXHqbB7+ypycUJIkSWqbzLySMuKxT0R8FXgnsMRstu8BtgPWiYgvAF+j/EB/cWAz4MLM/Fvd9huZuW/d9RXAecDZmXlzvW8LymjFJRFxI/AjYAawan38qsycNsBTWA1YNDPPqud8gPKZjFt19woAsBFwa2beUI9xVmZuDbwNuCQz7633Xwo8BKxT97s4M/+bmU8BDwC/qfffAyxVb29LKXfX1ef4UeZwilq3Iy7jImKWIyuZecacnFCSJElqm4j4H+D8+uU5lBGGfmcW1dGQGyijDNcDBwFT6z7T6LiAVUQsVkctqI9tAeweEevX+0ZQisG43l+UN/m9C/kf7+IpjOD5F80aDoyq93c+l9H9HKNv7mERseYAxwZ4ps9jU/vJd0zH81uXUpS61u0alweA2yLiKjpeuMzcf05ONj/UOXvnAXdT/oBGAydk5jcH2G8icFhmTpzL8/4QOI4y7PUkMKXj4Rs6hhaHXESsTBmeXIfyF3AScEhmXjEIxz4CuC4zz53XY0mSJLXIFsCvMvO7EbEYcAjlzfY0nnuDDmXNyijKyMmLgc9l5pSIeB+wSN3nMsq0sZdn5j+BfSijFl8DHqzTww4ETouIccAlwBER8drMvLOun/kR8Mo5yH8nMDUidsrMsyJiecp6kl0p7wVXjIiX1dvv6div8/ldDbwuIl6fmbcB/0tZ4/Me4NCIWCUz742It9VsVwMbdpnvQspo1o8y89F63DcyBxc76HbE5Urgp8BfgMkdv9rqutrm1gLeBBwYEasP8TlXz8zb6+1tOhvzfC4tSwN/oAxPvjozg/ITgJ/XeYvzJDMPtbRIkqSF0AnA+Ii4hTKCcg9lcfk1wCoRcVbd7ufA5ZSpXOcBd0bEHZRpY7cDq2bmLZT3X7+JiJso07X27TxZZp5CKRvH1feQHwJ+Urf/IrB9ZnYz0tJ7vKnADsDHIuJm4GLgiMy8rB7/e8B1wFXAfR273g48HRHXUKZ/7QqcUqdzfZJy8YDbgY9Q1t3cCnwZ2C4z/9ttPsp6lvOAqyLiNmBNYI852J9hPT3dfQxLbZ6rArdR5s89OScnml/qiMthmTm+fv0yyl+4jTLzHxHxLspCqsUorfgDtfVOrPtNjIjPALtRGvVFwMGUVvlLypDd2sC/gHdl5r/rAqwPZ+a+EXE/MD4z759Fts5zrARMzMyVImIC5eoPq9ZzTQK+CSxKuQrFPpl5d93/GuAtwFjgo5n56z7n+AwwLjPf3ef+XYCra0ueRPmL+3Jgvfp6vJvyE4ILKT9hOAIYnpmfrftPAH5NWeg1MTMnRMSedd8eyhUk9gN2Bt6WmbvW/Q4DnsrMY/r5I+u1EnDf/kefzcOPPDHAppIWZGccuyuTJj3WdIxnjR07pjV52pQFzDOQNuVpUxZoZR4vJrUQ6GqqWJ1/90vKUNKGwE0RsV1m/nEow82DdWtLHE4pAz8DHqiXuduXcgWHh+tl6j5NacgARMTWwPaUeXdTKIua9qXMeVyLUnRuqB/UsyvwLcqb+c4CcUFEdE4V+2Zmdl5Cb1YmZ+Z2ETEauItSiq6tRevHlIIBMDozN6iXzDuyz3mhzIe8qO/Bey9DVy1DmWM4MSK2okwpW49SQE6rz+tkygKxz1FK3tvq67B1fZ3WoFwdY/3MnBwR36FcIu9wyhUsxmTmY8AulCtvdOX4T+/Q7aaSFlBTpk5vOoIkLZAiYlfKSM6s/CgzF+qLZnW7xuWrwOaUF+TvdQ7fN3nuzXTbXNcx4vJiypUNPpWZR0fEjsB2ERHAeMqoSqfNgB/3jihFxEmU61CfDzzUe5UFysjLUh37fKfjGNvMasRlAFfX31cDHsnMawEy8+cR8f2IeEl9vPcqDZ3n76tzUdWplKG4JShrfb7a53ybUy7/96f69WLAXzPz9Dp69FZgReD8zHy6vGwAbEKZB9o7ZfD7wMmZeVBEXADsFBH3AvfOyfW5J09+nBkzuhsFHGot/GmRefrRpixgnoGMHTum6QiStEDKzB9R1r68IHW7xuVFHes3yMwLWEA+vLIu/vkpsFFELEGZarUy8DvgeJ5/tYi+r8kwnnuuT3fc3wMMq8Voeh1dGEjnFR1G9XnsqX7O35thRJ8Mfa8O0etaOq7QkJnvr1duOJ2OS/rVy9VRj/uNjis8rA8cVR87CXhv/TWhz3lm9zrNbj9JkiRpjnVbXKZGxEupP8mPjh+7t11EjKCMrFxPGc3ooXzAz2XATjxXCHpdCuxSL1s3EtizbtufLSiLn7rxMPD6enuHfrZJYOmIWK/mfzfwl8z8d5fn+C6wcUTs0XsJ67oofwOeP7oE5fm+LyKWqM/3bMp1ywHOpIwmLZeZV/fZbyKwfUT0jvp8kPo6ZebvKVfa2LQeT5IkSZon3RaXoyhXT3hFRPwY+CNlfUVbrRsRN0bEDcBNlMsTH1Nv30i5gsNtlEXwr+rcMTPPo1zx4Lq6zV8p61j6szXPTd/qdUE9f++v3rVAxwIfiYjrKVOyniczn6EscP92vWpD74L3rmTmw5R1SJsBt0bEncBvKZfZe968x8z8FWUdz9WU6Wc3Uj4ptndU5krKGpu++90MHA1cXs+xJOVDm3qdBVxan48kSZI0T+bkqmKrUkYXej8g546hDKZm1dGaMZTisllmPljv/yVlrdOZs9lvNKUsfTwzr+/ylCsB97nGpX/m6V+bsoB5BmKe/rUpC5hnIG3K06Ys0Mo8jV5VbMrUaQ+OHjVynj+WYhbH/dfoUSOXm5t9I2JdYN/M3HuQYw2Zbq8q9ubMvIryoY5ExKIR8Z3M/L8hTacmrUcZSTq8o7T8HAjK6Ft/lqNcD/wHc1BaJEmSFlqjR41c9r0HD/6a+jOO3XWuy1BmXgcsMKUFul9g/9OI2CYzb6trL04HHHFZiGXmNfS5allmvquL/f4JvHSockmSJKk79cM0352Zd0TEGcB/M/PDEbEBZY32tZk5vpvPCmyDbte47AqcHRHHUi4LfFRm7jBkqSRJkiTNq/Mp654B1gA2rre3Ag7ss+3ozNwA+AQtXcveVXHJzD9Qrhr1YWD7zDx1SFNJkiRJmlcXAJtFxOqUi05Nj4iXUS4u9USfbbv5rMBGzXaqWB1e6lwpPQ04NyIeBMjMNYcwmyRJkqS590fKZ+ptTvkoi39RPvZiFOXKuZ0G+qzAxg20xmW/+ZJCkiRJ0qDKzGkRcQ2wP7At8E/g/7GAfkD4bKeKZeblmXk5cA+wc739IGXuW86HfJIkSZLm3vnA4pl5J+XKsMtSPrNwgdPtVcUmAOfW23+hDDWdBGwz+JEkSZKkhceUqdP+NS+XLp7dcUePmv3b+cw8DTit3v4PM7//H1/vH9+x/f2Uz9drnW6vKrZMZh4PkJlPZ+Y3gJcPWSpJkiRpIVE/JHLYYP+a2w+fXFB1W1xGRsTyvV9ExLK0dNGOJEmSpIVPt1PFvgbcGBG/oVxpYHPgoCFLJUmSJEkduv0cl5OALYAbgOuAt2fmGUMZTJIkSZJ6dTtVDOBvwJnAOcATEbHF0ESSJEmSpJl1NVUsIo4APl2/nAosAtwOrDFEuSRJkiTpWd2OuLwfWJEy4rIasAdw2xBlkiRJkhYaM6ZOeZCyTnxQf9XjvmB0uzj/ocz8Z0TcAayVmadFxKeGMpgkSZK0MBg+avSyfzp270E/7joHnzjgZ8NExHjgsM7PallQdTviMjUiXg0k8JaIGAksOnSxJEmSJOk53Y64HA18H9ge+CKwO3DeUIWSJEmSNHgiYjXK+/mlgCeA/TPz2oh4L3AwMB24D9gNWAb4EbA4MKNue1UjwTt0O+JyQ2ZulplPAOOArYBvDlkqSZIkSYPpdOD4zFwT+ARwZkQsAhwJbJmZ61CKy2uBvYDzMnNd4FBg44Yyz2S2Iy4RsVS9eUGdHzesfv0gcDnliUmSJElqryWAVTLzLIDMvCoi/g0E8Cvgioj4JfCLzLwxIhYHzoqItYHzgW83FbzTQCMuPwYeplz2eHK9/TDlM12uH9pokiRJkgbBrN7zDwNGZubHgHcAjwCnR8RumXkFsDpwIbAzpdw0brYjLpn5doCIOCkzPzB/IkmSJEkaRI8C90bETpl5VkS8GVgOuDUi/gxskplHR8QoYO2IWBP4R2Z+MyIuA25oMPuzul2c39P3jog4MzPfOch5JEmSpIXKjKlT/tXNpYvn5rjDR43udvPdgBMi4nDgGWCnzJwSEYcCv42Ip4CHKJ/XuAhwRkTsSVm0//7Bzj43Blrj8l1gBcolkMd2PDQaWGUog0mSJEkLg+GjRi83RMcdcJvMnAiMr1+On8XjP6YsD+nrLXOfbGgMNOLyQ+ANwFrAmTy3OH8qcM0Q5pIkSZKkZw20xuU64LqIuBc4kHIt52HACODLwIpDnlCSJEnSC163n+PyXeAK4MWUD6N5FPjFUIWSJEmSpE7dFpeezDwGmAjcCbwb2HKoQkmSJElSp26Ly2P193uAN2TmU5QrDEiSJEnSkOv2cshXR8RPgc8D50fEasC0oYslSZIkSc/pdsTlE8DXM/Mu4ON1v12GKpQkSZIkdepqxCUze4Cr6u3zgfOHMpQkSZIkdep2qpg0Xyy99BJNR5jJ2LFjmo4wkzblmTLVZW6SJGn+sbioVfY/+mwefuSJpmOoC2ccu2vTESRJ0gtIt2tcJEmSJKkxFhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrWdxkSRJktR6FhdJkiRJrTey6QBNi4h3Ap+mvBbDgVMz8yv1scOBizPz94N4vu2BdTPz0C62PQzYF3gQGFZ/fSwzLxukLJdl5qb1dk9mDhuM40qSJEmD7QU94hIRKwDHAVtm5lrABsB7arkA2AQYMZjnzMxzuyktHU7IzHE13/uBnwxinPGDeCxJkiRpyLzQR1yWAUYBLwImZ+bjEbE78HREvB9YFzgxInYElgKOqtsuCXwiM8+JiAnAf4F1gBWAIzLz5DpasgLwGuBVwImZeVRE7AGMz8w9IuKrwBbADODszDx8gLwvAR7q/SIijgI2q9keAHbOzH9FxCTgOuDlwHrA/wPeACwL3AzsAhxTj3F1Zq5fb59AKW8A78jMuyNiPeDr9Xk/DOyTmfdFxGrA9+u5nwD2z8xr+3s9BnhekiRJ0my9oEdcMvMm4Bzg3oi4JiKOAUZk5t2ZeSrlzf/emXkL8NF6+43A3sCRHYd6JfAWYHvgqx33rwlsCawPfCoilux9ICJeBWxdR1I2AlaPiEVnEXPfiLgxIu4ALqGUCCJiVeC1wIaZuRrwV2C3us8ywDGZOY5SRKZk5gbAqpTStU1m7l9fg/U7znVxzfNbYJ+IGA2cCLy3Pu/jgB/UbU8Hjs/MNYFPAGdGxCIDvB6SJEnSXHmhj7iQmR+OiCMpBePtwFURsWtmntVn092AbSPiXcCbgSU6HrsoM3si4lbKCESvyzJzCvBQRPybMmLS6x/AUxFxBXAecEhmPj2LiCdk5mEAERHA7yMiM/OKiDgA2LvevwFwT8d+V9fn97uImBwR/0cpOq/pk73T2fX324C3AqsBrwbOLacA4MURsQSwau9rlJlX1efXu1F/r8eAjv/0DnOy+ZCaMW0qw0eOajpGa02ZOr3pCJIk6QXkBV1cIuJ/gCUy86fAycDJEfFBYC+gb3H5PXAZMJEy8nFGx2NPA9Q36/S9v+qhLK6nbjstItanrKPZBrgyIjbJzLv6y5uZWYvOBhHxNPBj4GvAmcD0Psd/qj7H7YEjgG/W57hM53Z9jj+tT9YRwL115IaIGEGZbjarkbphPPf3qb/XY0C3nHAIUx6dPEf7DJV1Dj6RSZMeazrGs8aOHdO6PJIkSfPLC3qqGPAkcHRErAQQEcOAccAN9fFpwMiIWIoy+nAo8Gvgf5nHRfsRsTZwOfC7zDwQuJ3nRiz622dJ4I3A9ZTCMzEzTwDuArbtJ9PmwM/qOpP/AJt2bDc9ImZXXu8EloqIt9SvPwCckZmPUqbX7VRzvRlYDrh1dvklSZKkufWCLi71ssKHA+dFRFLeqE8Hvlg3+Q1wAmWK1Q8pU6juAMYAL4qIxefh3DcAVwK3RsT1lOLy61ls2rvG5Xrgj8APMvNS4KfAWhFxC2UU6Dpg5Vns/wNgl7rdz4ErOrY7B7ipn7U1ZOYzwLuA4yLiZmB3ymgUlKlz+9fjfhvYqU6LkyRJkgbdsJ6enqYzSAArAfc5Vax/bZwq1pY8bcoC5hmIefrXpixgnoG0KU+bskAr8/hZdQuBF/SIiyRJkqQFg8VFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUutZXCRJkiS1nsVFkiRJUuuNbDqA1GmNfY9pOsKzZkyb2nQESZIkVRYXtcrkyY8zY0ZP0zEAGDt2DPB00zEkSZKEU8UkSZIkLQAsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfVGNh1gYRERKwH3Ad/PzH067h8H3ADsmZkT5nOmHuCm+uUwYEngN8BHMnP6PB77fmB8Zt4/L8eRJEmSumFxGVyTga0iYkRHMdgZmNRUoMwc13s7Il4M3ApsCfy6qUySJEnSnLK4DK7HgRuBtwKX1fu2BC7u3SAi9gPeBywOTAF2ycysIxinAW+vj70/M/9UR2y+B7wI+Dewa2b+PSI+BbwbGAFcCBySmT0D5Fum4zhExFHAZsBSwAOUkrUt8LbM3LVucxjwFPAD4HTglcDtwKL18RHAV4DxNcuEzPx6RIwHjq333ZqZu3f1CkqSJEmzYHEZfD8D3glcFhHrATdTpmn1jnjsQJli9VREHAHsB3y07js5M98UER8FPgO8A/gRpZScFxEfBj4WEZcA6wDrAT2UwrMrpVjMJCJuBEYBLwPuAPbPzKsjYlXgtcCGmTkjIk4FdqOUpC9FxJjMfAzYBdgUOAK4PjO3iYi3UkoTwAcBMvONEbEIcGFEXFcfWw14VWb+t9sXb+mll+h20yE3Y9rUpiNIkiSpsrgMvnOBIyNiOGUE46fAewAy89GIeC/wnohYDdiKMkLT6zf191uBnSJiGeDlmXle3f+7ABHxVWB94E91+8WAv84qTO9UsYj4BLAHcE69/+6IOADYOyIC2AC4JzMfj4gL6vnvBe7NzAfqCMoudd/f1ccANgfGRcTb6tdLAGtQRmVyTkoLwC0nHMKURyfPyS5DZp2DTwSebjqGJEmS8Kpigy4zH6csiN8YeBszTxN7JXAlZZH8r4EJ1NGYqvddck+9f2q93bv/ohGxCmX61Tcyc1wtJusDRw2Q6+vAg5RpXUTEOsBFlL8DZwK/7MhyEvDe+mtCn0y9ptXfRwAHd2R5c90fyhQzSZIkaZ5ZXIbGz4AvA9dl5rSO+9cD7q4l4lpgR8ob/1mqoxV/j4gt613vo0zZuhR4X0QsEREjgbMp09MG8klgr4hYE9gEmJiZJwB3Uda2jKjn/T3wCsoUsbPrvhfX81OnwK1a778U+GBEjIqIJYA/UMqLJEmSNGgsLkPjV8A4yjSxThcBwyPiduB64E5g5QGOtRtwaF2rsjNwUGb+CvgFcDVlWtmNwCkDhcrM24BTgeNqtrUi4hZgInBdnyxnAZdm5jP16y8Ar46I24BPAb1TxU4A/ky55PN1wMmZOXGgLJIkSdKcGNbTM9CFqPRCEhHDgNHAb4GPZ+b18+nUKwH3tW2Ny6RJjzUd41ljx44xTz/alAXMMxDz9K9NWcA8A2lTnjZlgVbmGTbwVmo7R1zU13KUtTBXzcfSIkmSJM2WVxXTTDLzn8BLm84hSZIkdXLERZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktZ7FRZIkSVLrWVwkSZIktd7IpgNIndbY95imIzxrxrSpTUeQJElSZXFRq0ye/DgzZvQ0HQOAsWPHAE83HUOSJEk4VUySJEnSAsDiIkmSJKn1LC6SJEmSWs81LmqLEQDDhw9rOsdMzDN7bcrTpixgnoGYp39tygLmGUib8rQpC7Quz0rA34FpDefQPBjW09OOhdB6wdsY+H3TISRJ0kJrZeD+pkNo7llc1BaLAOsB/wSmN5xFkiQtfBxxWcBZXCRJkiS1novzJUmSJLWexUWSJElS61lcJEmSJLWexUWSJElS61lcJEmSJLWexUWSJElS61lcJEmSJLWexUWSJElS641sOoAEEBHvBT4HjAK+kZnfaTjPi4E/Attm5v0NZ/kC8O765fmZeXDDeY4A3gn0AD/MzK81mQcgIr4KLJOZezSc4zLgZcDUetc+mXl1g3m2A74ALA5clJkfazDL3sB+HXetDJyWmfv1s8uQi4jdgE/XL3+dmQc2laXm+RSwJ/AM8NPMPKqBDDN974uIzYGvAYvVTJ9rMk+971Tg0sycMD+zzCpPRHwI2J/y/fA6yr/5KQ3m+TDl39kw4Hzg4MycL5803t//mxGxH/DOzBw/P3L0lyciTgY2Bp6omxyemb+cn5k07xxxUeMiYgXgKMo3lHHAhyJi9QbzrA/8AVitqQwdWTYHtgTWprw260TEjg3m2QR4G7AmsC7w0YiIpvLUTJsBuzeZoeYYRvk7s1Zmjqu/miwtqwAnADtQ/rzeGBFbN5UnM0/sfV2AXYGHgMOayhMRLwKOBzYB1gLeUv+9NZVnc+C9wHqUf+/rR8RO8znDTN/7ImIx4CTgf4HXAevNz79Ds8izfET8ivKDk/luFnlWAw4CNqT8GxsO/F+DeVYGPgm8CVij5tqiiSwd968OfGp+ZOgiz7rAWzu+P1taFkAWF7XB5pSfnv07M58AzqSh/5iqD1L+83mgwQy9/gkckJlTMnMqcAewYlNhMvNyYNPMnEYZWRjJcz+9mu8iYilK6f1SUxk69Ba4iyLipvpTxibtSPkJ+d/r352dgcaKVB/fBT6TmQ83mGEE5f/AxSkjvaOApxrMszZwYWY+mpnTgd9QSuf81Pd735uAP2fmffXf/OnAuxrMsytwDvCz+ZhhdnmeAT5S/8x6gFuYv9+fZ8qTmfcBq9f/R5cEXgL8p4ksABGxCPA94ND5lKHfPPUHFSsCJ0XEzRFxeET4HngB5FQxtcHylDfovf5J+Q+zEZm5N0DDAwkAZOZtvbcj4jWUKWMbNZcIMnNqRBwOHAj8HPhHg3G+B3wWeGWDGXq9FLgE+CjlTfDEiMjM/G1DeVYFpkTEuZT/sM8DPt9QlmfVkYXFMvPnTebIzMci4vPAncCTwOWUaSVNuR74ekQcXfNsz3z+4eIsvvfN6nvzK5rKk5lfqV9vPL8yDJDnL8Bf6n1jKVO09mgqT71vakR8EPgqcA1wY1NZgKMpI3b3zY8MA+RZDrgU+AjwX8r3w72AH8zvbJo3tk21wXDK/OBew4AZDWVppYh4PfBb4KDM/HPTeTLzC8BYSmH4YBMZ6pqJv2XmJU2cv6/MvDIz35+Z/60jCT8Etmkw0kjKaOZewAbA+rRgSh2wD2XNRKMiYk3gA8CrKG/Qp1PKeCPq3+MJwETKaMsfgPm2VqIffm/uQp3ufAllzd/EhuOQmT8AlgYepKHpmBGxBbBiZp7cxPn7ysx7M3PHzPxnZj4JfItmvz9rLllc1AZ/B17e8fVytGOaVitExEaU/xQ/lZmnNJzltRExDqB+8z+LMre7CTsDW0bEjcARwPYR8fWGshARG9f1Nr2G8dwi/SY8CFycmZMy8ynglzQ4kgkQEaMpa0rObTJH9Xbgksx8KDOfoZSG8U2FiYgxwC8yc826iPkZ4J6m8lR+bx5ARLyWMlJ3SmZ+seEsr6z/X1Cn9v2E5r4/7wK8vn5/PhFYNyJ+2lAWImKNiHhHx11Nf3/WXHKqmNrgYuCwOtT+BPAO4EPNRmqHiHglcDawc2Ze2nAcgFWAw+tUjR7Kot2TmgiSmc8uOo2IPYDxmfmJJrJUSwJHRMSGlKliuwP7NpjnPOCUiFgSeAzYmvJ3qUlrAnfVOfhNuwk4NiIWp0zN2g64tsE8KwOnRsS6lHU3e9VfTboaiIhYlTLd57009O+9jWrZvAj4bGae1nQeypqWH9UfLv2Xslb0D00EycwP9N6OiPHAYZm5cxNZqmHANyLiUuBxynuMRn8QqLnjiIsal5n/oKxTuIwyH/eMzLym0VDtcSCwKPC1iLix/mrszXBmXkC5xOYNwJ+AP2bmT5rK0yaZeR4zvzYnZeaVDea5GjiW8sbldspc/KanbaxC+Sl+4zLzIuDHlD+rmyll88sN5rkZ+EXNcg3lsvBXNJWnZnqasmbjF5S/Q3dSLp6iYm9gWeCAju/PRzQVJjNvpawr+SOlmD8JHNdUnjap/76OBq6g/F2+MTN/3GwqzY1hPT3z5fLekiRJkjTXHHGRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0mSJEmtZ3GRJEmS1HoWF0nSQi8iLoqIZZrOIUmaexYXSdILwRYDbyJJajM/x0WS1KiI+ABwADAdeBjYHdga2L/e9y9gv8y8KyImALdm5lfrvs9+HRH3AxOAzYAVgVMz8/MRcTLlgxRvBbbJzL/NtycnSRo0jrhIkhoTEWsBxwBbZeaawLnAJcDBwKaZuRZwBnB2RAzr4pBLZOZbgA2BAyNi5czcsz62qaVFkhZcFhdJUpM2Ay7sLRSZ+Q3gbOCnmTmp3jcBWAFYqYvjnVP3+QfwELDUYAeWJDXD4iJJatI04Nk5yxGxGPDqzvuqYcCoen/nyMvoPts91XG777aSpAWYxUWS1KTLgM0j4uX1632AbYD3RMRYgIjYE5gM3A1MAtat9y8PbNLleaZTio8kaQFlcZEkNSYzbwEOAn4TETcBW1FGXL4OXBoRt1EW62+bmTOAbwEvj4gETgYu7fJUPwcuj4g3DPZzkCTNH15VTJIkSVLrOeIiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfUsLpIkSZJaz+IiSZIkqfX+P7YkLdb7KGheAAAAAElFTkSuQmCC\n", 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\n", 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" + "
" ] }, "metadata": { @@ -2292,20 +2303,20 @@ }, { "cell_type": "code", - "execution_count": 430, + "execution_count": 40, "id": "colonial-maximum", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:56.431657Z", - "start_time": "2021-04-22T09:25:55.899130Z" + "end_time": "2021-04-25T12:59:18.126284Z", + "start_time": "2021-04-25T12:59:17.710117Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { @@ -2337,19 +2348,19 @@ }, { "cell_type": "code", - "execution_count": 431, + "execution_count": 41, "id": "foster-photography", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:56.770180Z", - "start_time": "2021-04-22T09:25:56.431657Z" + "end_time": "2021-04-25T12:59:18.775852Z", + "start_time": "2021-04-25T12:59:18.126284Z" }, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -2376,12 +2387,12 @@ }, { "cell_type": "code", - "execution_count": 432, + "execution_count": 42, "id": "chemical-program", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:56.785802Z", - "start_time": "2021-04-22T09:25:56.770180Z" + "end_time": "2021-04-25T12:59:18.791464Z", + "start_time": "2021-04-25T12:59:18.775852Z" } }, "outputs": [], @@ -2405,27 +2416,31 @@ }, { "cell_type": "code", - "execution_count": 433, + "execution_count": 43, "id": "applied-humidity", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:56.901772Z", - "start_time": "2021-04-22T09:25:56.785802Z" + "end_time": "2021-04-25T12:59:18.928655Z", + "start_time": "2021-04-25T12:59:18.791464Z" } }, "outputs": [], "source": [ - "df.drop(columns=['battle_number','name','year','attacker_commander','defender_commander'],inplace=True)" + "df.drop(columns=['battle_number',\n", + " 'name',\n", + " 'year',\n", + " 'attacker_commander',\n", + " 'defender_commander'],inplace=True)" ] }, { "cell_type": "code", - "execution_count": 434, + "execution_count": 44, "id": "banner-pregnancy", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:57.155867Z", - "start_time": "2021-04-22T09:25:56.901772Z" + "end_time": "2021-04-25T12:59:19.029126Z", + "start_time": "2021-04-25T12:59:18.928655Z" } }, "outputs": [ @@ -2477,12 +2492,12 @@ }, { "cell_type": "code", - "execution_count": 435, + "execution_count": 45, "id": "rubber-throw", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:57.356395Z", - "start_time": "2021-04-22T09:25:57.155867Z" + "end_time": "2021-04-25T12:59:21.555590Z", + "start_time": "2021-04-25T12:59:19.029126Z" } }, "outputs": [], @@ -2496,12 +2511,12 @@ }, { "cell_type": "code", - "execution_count": 436, + "execution_count": 46, "id": "nonprofit-suffering", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:57.456276Z", - "start_time": "2021-04-22T09:25:57.356395Z" + "end_time": "2021-04-25T12:59:21.586875Z", + "start_time": "2021-04-25T12:59:21.555590Z" } }, "outputs": [ @@ -2680,7 +2695,7 @@ "4 2 1 " ] }, - "execution_count": 436, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2699,12 +2714,12 @@ }, { "cell_type": "code", - "execution_count": 437, + "execution_count": 47, "id": "norman-serbia", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:25:57.557108Z", - "start_time": "2021-04-22T09:25:57.456276Z" + "end_time": "2021-04-25T12:59:21.702994Z", + "start_time": "2021-04-25T12:59:21.586875Z" } }, "outputs": [], @@ -2736,18 +2751,18 @@ }, { "cell_type": "code", - "execution_count": 438, + "execution_count": 48, "id": "thirty-elements", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.081283Z", - "start_time": "2021-04-22T09:25:57.557108Z" + "end_time": "2021-04-25T12:59:30.740739Z", + "start_time": "2021-04-25T12:59:21.702994Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2825,22 +2840,22 @@ "means = -scores.mean().sort_values()\n", "errors = scores.std().sort_values()\n", "means.plot.barh(xerr=errors, ax=ax)\n", - "ax.set_title('Perbandingan Imputasi Menggunakan berbagai Model')\n", + "ax.set_title('Imputation Method Comparison using Various Models')\n", "ax.set_xlabel('MSE (smaller is better)')\n", "ax.set_yticks(np.arange(means.shape[0]))\n", - "ax.set_yticklabels([\" w/ \".join(label) for label in means.index.tolist()])\n", + "ax.set_yticklabels([\" - \".join(label) for label in means.index.tolist()])\n", "plt.tight_layout(pad=1)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 439, + "execution_count": 49, "id": "photographic-questionnaire", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.096840Z", - "start_time": "2021-04-22T09:26:03.081283Z" + "end_time": "2021-04-25T12:59:30.756343Z", + "start_time": "2021-04-25T12:59:30.740739Z" } }, "outputs": [ @@ -2856,7 +2871,7 @@ "dtype: float64" ] }, - "execution_count": 439, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -2875,12 +2890,12 @@ }, { "cell_type": "code", - "execution_count": 440, + "execution_count": 50, "id": "occasional-execution", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.397887Z", - "start_time": "2021-04-22T09:26:03.096840Z" + "end_time": "2021-04-25T12:59:30.972702Z", + "start_time": "2021-04-25T12:59:30.756343Z" } }, "outputs": [], @@ -2889,6 +2904,9 @@ "from sklearn.impute import IterativeImputer\n", "from sklearn.tree import DecisionTreeRegressor\n", "\n", + "y = df['attacker_outcome']\n", + "X = df.drop(columns='attacker_outcome')\n", + "\n", "impute_estimator = DecisionTreeRegressor(max_features='sqrt')\n", "imputer = IterativeImputer(estimator=impute_estimator)\n", "\n", @@ -2898,12 +2916,12 @@ }, { "cell_type": "code", - "execution_count": 441, + "execution_count": 51, "id": "alert-retrieval", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.413238Z", - "start_time": "2021-04-22T09:26:03.397887Z" + "end_time": "2021-04-25T12:59:30.988321Z", + "start_time": "2021-04-25T12:59:30.972702Z" } }, "outputs": [ @@ -2915,7 +2933,7 @@ " 1.0e+00, 1.0e+00]])" ] }, - "execution_count": 441, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -2929,18 +2947,18 @@ "id": "ambient-anchor", "metadata": {}, "source": [ - "## Standarization\n", + "## Data Scaling\n", "dilakukan standarisasi pada dataset agar interval nilai pada dataset lebih ramping/tersebar dengan baik" ] }, { "cell_type": "code", - "execution_count": 442, + "execution_count": 52, "id": "still-scottish", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.582452Z", - "start_time": "2021-04-22T09:26:03.413238Z" + "end_time": "2021-04-25T12:59:31.143252Z", + "start_time": "2021-04-25T12:59:30.988321Z" } }, "outputs": [], @@ -2953,12 +2971,12 @@ }, { "cell_type": "code", - "execution_count": 443, + "execution_count": 53, "id": "behind-medline", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.660959Z", - "start_time": "2021-04-22T09:26:03.582452Z" + "end_time": "2021-04-25T12:59:31.259651Z", + "start_time": "2021-04-25T12:59:31.143252Z" } }, "outputs": [ @@ -2971,7 +2989,7 @@ " -0.5318713 ]])" ] }, - "execution_count": 443, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -2991,12 +3009,12 @@ }, { "cell_type": "code", - "execution_count": 444, + "execution_count": 54, "id": "exact-flavor", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.782832Z", - "start_time": "2021-04-22T09:26:03.660959Z" + "end_time": "2021-04-25T12:59:33.167958Z", + "start_time": "2021-04-25T12:59:31.259651Z" } }, "outputs": [ @@ -3023,7 +3041,7 @@ "Name: attacker_outcome, dtype: float64" ] }, - "execution_count": 444, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -3057,20 +3075,27 @@ }, { "cell_type": "code", - "execution_count": 445, + "execution_count": 55, "id": "separated-relevance", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.883336Z", - "start_time": "2021-04-22T09:26:03.782832Z" + "end_time": "2021-04-25T12:59:33.183816Z", + "start_time": "2021-04-25T12:59:33.167958Z" } }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", - "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.25, random_state=1772023)\n", - "X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_smote, y_smote, test_size=0.25, random_state=1772023)" + "X_train, X_test, y_train, y_test = train_test_split(X_scaled, \n", + " y, \n", + " test_size=0.25, \n", + " random_state=1772023)\n", + "\n", + "X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_smote, \n", + " y_smote, \n", + " test_size=0.25, \n", + " random_state=1772023)" ] }, { @@ -3083,12 +3108,12 @@ }, { "cell_type": "code", - "execution_count": 446, + "execution_count": 56, "id": "smoking-wealth", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:03.983579Z", - "start_time": "2021-04-22T09:26:03.883336Z" + "end_time": "2021-04-25T12:59:33.300366Z", + "start_time": "2021-04-25T12:59:33.183816Z" } }, "outputs": [], @@ -3114,25 +3139,25 @@ }, { "cell_type": "code", - "execution_count": 447, + "execution_count": 57, "id": "union-distinction", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:07.014438Z", - "start_time": "2021-04-22T09:26:03.983579Z" + "end_time": "2021-04-25T12:59:36.335331Z", + "start_time": "2021-04-25T12:59:33.300366Z" } }, "outputs": [ { "data": { "text/plain": [ - "accuracy 0.960000\n", + "accuracy 0.926667\n", "f1 0.959596\n", - "roc_auc 0.906250\n", + "roc_auc 0.937500\n", "dtype: float64" ] }, - "execution_count": 447, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -3159,12 +3184,12 @@ }, { "cell_type": "code", - "execution_count": 448, + "execution_count": 58, "id": "israeli-hormone", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:07.108172Z", - "start_time": "2021-04-22T09:26:07.014438Z" + "end_time": "2021-04-25T12:59:36.671555Z", + "start_time": "2021-04-25T12:59:36.335331Z" } }, "outputs": [ @@ -3177,7 +3202,7 @@ "dtype: float64" ] }, - "execution_count": 448, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -3213,12 +3238,12 @@ }, { "cell_type": "code", - "execution_count": 449, + "execution_count": 59, "id": "particular-nerve", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:08.431891Z", - "start_time": "2021-04-22T09:26:07.108172Z" + "end_time": "2021-04-25T12:59:37.864140Z", + "start_time": "2021-04-25T12:59:36.671555Z" } }, "outputs": [ @@ -3226,12 +3251,12 @@ "data": { "text/plain": [ "accuracy 0.960000\n", - "f1 0.959596\n", - "roc_auc 1.000000\n", + "f1 0.981818\n", + "roc_auc 0.992000\n", "dtype: float64" ] }, - "execution_count": 449, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -3258,12 +3283,12 @@ }, { "cell_type": "code", - "execution_count": 450, + "execution_count": 60, "id": "hundred-classification", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:08.547769Z", - "start_time": "2021-04-22T09:26:08.431891Z" + "end_time": "2021-04-25T12:59:37.942514Z", + "start_time": "2021-04-25T12:59:37.864140Z" } }, "outputs": [ @@ -3276,7 +3301,7 @@ "dtype: float64" ] }, - "execution_count": 450, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -3305,30 +3330,51 @@ }, { "cell_type": "markdown", - "id": "comprehensive-trick", + "id": "suited-upset", "metadata": {}, "source": [ - "## Feature Importances\n", + "## Feature Importances & Coefficients " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "stable-oriental", + "metadata": { + "ExecuteTime": { + "end_time": "2021-04-25T12:59:38.276879Z", + "start_time": "2021-04-25T12:59:37.942514Z" + } + }, + "outputs": [], + "source": [ + "np.random.seed(1772023)\n", + "rf_model = rf.fit(X_train, y_train)\n", + "lr_model = lr.fit(X_train,y_train)\n", "\n", - "Semakin besar nilai pada suatu fitur pada Feature Importances maka fitur tersebut semakin berpengaruh (penting) terhadap model" + "# model with oversampled dataset\n", + "rf_sm_model = rf.fit(X_train_sm, y_train_sm)\n", + "lr_sm_model = lr.fit(X_train_sm, y_train_sm)" ] }, { "cell_type": "markdown", - "id": "surgical-masters", + "id": "refined-investigator", "metadata": {}, "source": [ - "### Random Forest Feature Importances" + "### Random Forest Feature Importances\n", + "\n", + "Semakin besar nilai pada suatu fitur maka fitur tersebut semakin mempunyai pengaruh penting terhadap prediksi yang dilakukan model" ] }, { "cell_type": "code", - "execution_count": 451, - "id": "mineral-raising", + "execution_count": 62, + "id": "touched-animal", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:09.027003Z", - "start_time": "2021-04-22T09:26:08.547769Z" + "end_time": "2021-04-25T12:59:38.544616Z", + "start_time": "2021-04-25T12:59:38.278901Z" } }, "outputs": [ @@ -3338,13 +3384,13 @@ "Text(0.5, 1.0, 'Random Forest Feature Importances')" ] }, - "execution_count": 451, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -3356,9 +3402,8 @@ } ], "source": [ - "rf.fit(X_train_sm, y_train_sm)\n", "rf_fi = pd.DataFrame({\"Feature\":X.columns,\n", - " \"Score\":rf.feature_importances_})\n", + " \"Score\":rf_sm_model.feature_importances_})\n", "\n", "sns.barplot(data=rf_fi,\n", " x=\"Score\",\n", @@ -3369,12 +3414,12 @@ }, { "cell_type": "code", - "execution_count": 452, - "id": "hidden-insulation", + "execution_count": 63, + "id": "fitted-mechanics", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:09.049138Z", - "start_time": "2021-04-22T09:26:09.027003Z" + "end_time": "2021-04-25T12:59:38.559981Z", + "start_time": "2021-04-25T12:59:38.544616Z" } }, "outputs": [ @@ -3405,54 +3450,54 @@ " \n", " \n", " \n", - " 7\n", - " attacker_size\n", - " 0.221837\n", - " \n", - " \n", " 14\n", " attacker_commander_count\n", - " 0.211951\n", + " 0.389246\n", + " \n", + " \n", + " 7\n", + " attacker_size\n", + " 0.202693\n", " \n", " \n", " 15\n", " defender_commander_count\n", - " 0.174623\n", + " 0.132379\n", " \n", " \n", " 5\n", " major_death\n", - " 0.119852\n", + " 0.073968\n", " \n", " \n", " 10\n", " location\n", - " 0.081136\n", + " 0.065466\n", " \n", " \n", - " 0\n", - " attacker_king\n", - " 0.042444\n", - " \n", - " \n", - " 4\n", - " battle_type\n", - " 0.034728\n", + " 11\n", + " region\n", + " 0.044452\n", " \n", " \n", " 8\n", " defender_size\n", - " 0.028142\n", + " 0.028504\n", " \n", " \n", - " 11\n", - " region\n", - " 0.024071\n", + " 0\n", + " attacker_king\n", + " 0.017949\n", " \n", " \n", - " 2\n", - " attacker_1\n", - " 0.020294\n", + " 12\n", + " attacker_count\n", + " 0.009969\n", + " \n", + " \n", + " 4\n", + " battle_type\n", + " 0.008906\n", " \n", " \n", "\n", @@ -3460,19 +3505,19 @@ ], "text/plain": [ " Feature Score\n", - "7 attacker_size 0.221837\n", - "14 attacker_commander_count 0.211951\n", - "15 defender_commander_count 0.174623\n", - "5 major_death 0.119852\n", - "10 location 0.081136\n", - "0 attacker_king 0.042444\n", - "4 battle_type 0.034728\n", - "8 defender_size 0.028142\n", - "11 region 0.024071\n", - "2 attacker_1 0.020294" + "14 attacker_commander_count 0.389246\n", + "7 attacker_size 0.202693\n", + "15 defender_commander_count 0.132379\n", + "5 major_death 0.073968\n", + "10 location 0.065466\n", + "11 region 0.044452\n", + "8 defender_size 0.028504\n", + "0 attacker_king 0.017949\n", + "12 attacker_count 0.009969\n", + "4 battle_type 0.008906" ] }, - "execution_count": 452, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -3483,36 +3528,41 @@ }, { "cell_type": "markdown", - "id": "juvenile-incentive", + "id": "composite-piano", "metadata": {}, "source": [ - "### Logistic Regression Feature Importances" + "### Logistic Regression Coefficients \n", + "\n", + "peluang yang diinterpretasikan harus bertipe data numerik agar hasil intepretasi lebih jelas dipahami. \n", + "\n", + "contoh intepretasi: \n", + "* penambahan dari setiap 1 aliansi dari penyerang (attacker_count) meningkatkan kemungkinan menang sebesar 1.79" ] }, { "cell_type": "code", - "execution_count": 453, - "id": "mysterious-alcohol", + "execution_count": 64, + "id": "focal-scientist", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:09.598052Z", - "start_time": "2021-04-22T09:26:09.049138Z" + "end_time": "2021-04-25T12:59:39.015046Z", + "start_time": "2021-04-25T12:59:38.559981Z" } }, "outputs": [ { "data": { "text/plain": [ - "Text(0.5, 1.0, 'Logistic Regression Feature Importances')" + "Text(0.5, 1.0, 'Logistic Regression Coefficients')" ] }, - "execution_count": 453, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3524,25 +3574,24 @@ } ], "source": [ - "lr.fit(X_train_sm, y_train_sm)\n", "lr_fi = pd.DataFrame({\"Feature\":X.columns,\n", - " \"Score\":abs(lr.coef_[0])})\n", + " \"Score\":(lr_sm_model.coef_[0])})\n", "\n", "sns.barplot(data=lr_fi,\n", " x=\"Score\",\n", " y=\"Feature\",\n", " order=lr_fi.sort_values(by=\"Score\",ascending=False).Feature)\n", - "plt.title(\"Logistic Regression Feature Importances\")" + "plt.title(\"Logistic Regression Coefficients\")" ] }, { "cell_type": "code", - "execution_count": 454, - "id": "vertical-queens", + "execution_count": 65, + "id": "searching-angola", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:09.613583Z", - "start_time": "2021-04-22T09:26:09.598052Z" + "end_time": "2021-04-25T12:59:39.030486Z", + "start_time": "2021-04-25T12:59:39.015046Z" }, "scrolled": true }, @@ -3574,16 +3623,6 @@ " \n", " \n", " \n", - " 14\n", - " attacker_commander_count\n", - " 1.472369\n", - " \n", - " \n", - " 15\n", - " defender_commander_count\n", - " 0.719559\n", - " \n", - " \n", " 11\n", " region\n", " 0.712785\n", @@ -3599,49 +3638,59 @@ " 0.586209\n", " \n", " \n", - " 5\n", - " major_death\n", - " 0.468265\n", + " 6\n", + " major_capture\n", + " 0.236514\n", " \n", " \n", - " 7\n", - " attacker_size\n", - " 0.462977\n", + " 1\n", + " defender_king\n", + " 0.062163\n", " \n", " \n", - " 2\n", - " attacker_1\n", - " 0.314401\n", + " 4\n", + " battle_type\n", + " -0.050068\n", " \n", " \n", - " 8\n", - " defender_size\n", - " 0.312643\n", + " 13\n", + " defender_count\n", + " -0.052609\n", + " \n", + " \n", + " 3\n", + " defender_1\n", + " -0.056703\n", + " \n", + " \n", + " 0\n", + " attacker_king\n", + " -0.221464\n", " \n", " \n", " 9\n", " summer\n", - " 0.268198\n", + " -0.268198\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Feature Score\n", - "14 attacker_commander_count 1.472369\n", - "15 defender_commander_count 0.719559\n", - "11 region 0.712785\n", - "10 location 0.615409\n", - "12 attacker_count 0.586209\n", - "5 major_death 0.468265\n", - "7 attacker_size 0.462977\n", - "2 attacker_1 0.314401\n", - "8 defender_size 0.312643\n", - "9 summer 0.268198" + " Feature Score\n", + "11 region 0.712785\n", + "10 location 0.615409\n", + "12 attacker_count 0.586209\n", + "6 major_capture 0.236514\n", + "1 defender_king 0.062163\n", + "4 battle_type -0.050068\n", + "13 defender_count -0.052609\n", + "3 defender_1 -0.056703\n", + "0 attacker_king -0.221464\n", + "9 summer -0.268198" ] }, - "execution_count": 454, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -3668,12 +3717,12 @@ }, { "cell_type": "code", - "execution_count": 455, + "execution_count": 66, "id": "administrative-syntax", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:09.899493Z", - "start_time": "2021-04-22T09:26:09.613583Z" + "end_time": "2021-04-25T12:59:39.162347Z", + "start_time": "2021-04-25T12:59:39.030486Z" } }, "outputs": [ @@ -3696,16 +3745,54 @@ "source": [ "from sklearn.metrics import classification_report\n", "np.random.seed(1772023)\n", - "rf.fit(X_train, y_train)\n", - "y_preds = rf.predict(X_test)\n", + "y_preds = rf_model.predict(X_test)\n", "report = classification_report(y_test, y_preds)\n", "\n", "print(report)" ] }, + { + "cell_type": "code", + "execution_count": 76, + "id": "simple-imaging", + "metadata": { + "ExecuteTime": { + "end_time": "2021-04-25T13:08:15.184340Z", + "start_time": "2021-04-25T13:08:14.968204Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(rf_model,X_test,y_test) " + ] + }, { "cell_type": "markdown", - "id": "blond-cutting", + "id": "moderate-lithuania", "metadata": {}, "source": [ "## Logistic Regression" @@ -3713,12 +3800,12 @@ }, { "cell_type": "code", - "execution_count": 456, - "id": "hungarian-viking", + "execution_count": 68, + "id": "guided-dancing", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:09.952119Z", - "start_time": "2021-04-22T09:26:09.899493Z" + "end_time": "2021-04-25T12:59:39.564659Z", + "start_time": "2021-04-25T12:59:39.533437Z" } }, "outputs": [ @@ -3741,13 +3828,51 @@ "source": [ "from sklearn.metrics import classification_report\n", "np.random.seed(1772023)\n", - "lr.fit(X_train, y_train)\n", - "y_preds = rf.predict(X_test)\n", + "y_preds = lr_model.predict(X_test)\n", "report = classification_report(y_test, y_preds)\n", "\n", "print(report)" ] }, + { + "cell_type": "code", + "execution_count": 77, + "id": "apparent-community", + "metadata": { + "ExecuteTime": { + "end_time": "2021-04-25T13:08:20.465329Z", + "start_time": "2021-04-25T13:08:20.248922Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(lr_model,X_test,y_test) " + ] + }, { "cell_type": "markdown", "id": "honest-mattress", @@ -3758,12 +3883,12 @@ }, { "cell_type": "code", - "execution_count": 457, + "execution_count": 70, "id": "lesser-puppy", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:10.168368Z", - "start_time": "2021-04-22T09:26:09.952119Z" + "end_time": "2021-04-25T12:59:39.934229Z", + "start_time": "2021-04-25T12:59:39.896332Z" } }, "outputs": [ @@ -3785,13 +3910,51 @@ ], "source": [ "np.random.seed(1772023)\n", - "rf.fit(X_train_sm, y_train_sm)\n", - "y_preds = rf.predict(X_test_sm)\n", + "y_preds = rf_sm_model.predict(X_test_sm)\n", "report = classification_report(y_test_sm, y_preds)\n", "\n", "print(report)" ] }, + { + "cell_type": "code", + "execution_count": 78, + "id": "sustained-curve", + "metadata": { + "ExecuteTime": { + "end_time": "2021-04-25T13:08:25.631547Z", + "start_time": "2021-04-25T13:08:25.415442Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(rf_sm_model,X_test_sm,y_test_sm) " + ] + }, { "cell_type": "markdown", "id": "affected-thousand", @@ -3802,12 +3965,12 @@ }, { "cell_type": "code", - "execution_count": 458, + "execution_count": 72, "id": "documented-grade", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:10.199598Z", - "start_time": "2021-04-22T09:26:10.168368Z" + "end_time": "2021-04-25T12:59:40.220292Z", + "start_time": "2021-04-25T12:59:40.194398Z" } }, "outputs": [ @@ -3829,8 +3992,7 @@ ], "source": [ "np.random.seed(1772023)\n", - "lr.fit(X_train_sm, y_train_sm)\n", - "y_preds = lr.predict(X_test_sm)\n", + "y_preds = lr_sm_model.predict(X_test_sm)\n", "report = classification_report(y_test_sm, y_preds)\n", "\n", "print(report)" @@ -3838,50 +4000,30 @@ }, { "cell_type": "code", - "execution_count": 459, + "execution_count": 79, "id": "scientific-writing", "metadata": { "ExecuteTime": { - "end_time": "2021-04-22T09:26:10.485043Z", - "start_time": "2021-04-22T09:26:10.199598Z" + "end_time": "2021-04-25T13:08:35.174939Z", + "start_time": "2021-04-25T13:08:34.959148Z" } }, "outputs": [ { "data": { - "image/png": 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uAO6+EvgB0XhYS+pCikhuWXajSJMXkmibrnPNbH+i0HckMLddYbXARCS/QX4L6e4/Am4Dfgf8FrjP3a9vV14tMBHJrRaG1MLOESrpen/uPhOYmaasApiI5KeZ+CJSVdqRVUQqqxamWEqkACYipaQupIhUlbqQIlJdaoGJSFWpBSYi1RWE1IKECJV0fQAUwEQkv6HchTSz0cB9wOHuvqDIZ5VZrRZyypcXMX7XV1j9Wo05M8axeMHwbldL2njt1RqzT9+OZ55+AyM3qnPylxYydofXul2tUur2NIrC1kKa2d7APcDORT2jKvZ73zKGDQ84/YidmPulrZl2zuJuV0k6uP3aLdhwVJ1Lbp3Piecv4rKztu12lcprkNdCZlXkYu7jgZOA9f6/1gl7reChn28MwBMPj2Kn3VZ2uUbSydPzR7DnIS8BMG7HV3l6/ogu16i8suwHVoTCApi7T3X3XxV1/yoZuXHAiuW9a86DoEZPb4H/VGVA3jLhFX7z09GEITz+25E8/8ww6vVu16qkwjDdURAN4q8DK1/qYeRGawcKajUI6kVmy5OBeO/k53l6/nA+c9SO7Lrny+y420p6e5O/tz4qIitRFtoPbB147MFR7HnIcgB2mbiCBU+oS1JmPm8kE/ZawVdv+jPvev8ytt5OA/jtdLsLqRbYOnDv7Zsw8YCXufiW+QBcNH1cl2sknYwd/yrXzNqamy7fklGj60yf/XS3q1RiabqI2SKYmZ0D/Ht8epu7f6ZdWQWwdSAMa1x6pt5kVcUmW9S58Ht/6XY1KmGwZ+Kb2aHAYcAeRJHvx2b2YXe/uVX5wgOYu29f9DNEpEsGfyLrEuAMd38NwMweB7ZrV1gtMBHJLUsLLE1eSHf/Y+PPZrYTUVfyXe3urQAmIvnVw+hIKsOavJD9nUuL/e/NbAJRco9Pu/v8drfWW0gRyS3LW8g0eSEBzOxdwF3Ame5+TafnqwUmIgOQ/i1kmryQZjYO+CFwtLvfnfR0BTARyS/NPK9sg/gzgBHARWbW+Oxyd7+8VWEFMBHJb5DfQrr7qcCpacsrgIlIbrU61BIG8WsFriNVABOR3IrIzJ2FApiI5DeUd2QVkaFu8NdCZqEAJiK5KSuRiFRXmg0LNQYmImVUq4cp3kIqgIlIGWkQX0SqStMoRKTC9BZSRKoqiI+kMgVRABOR3NSFFJHqCkIIEppYgQKYiJSRupAiUlU1UnQhNYgvIqVU0Ex8MxsN3Acc7u4L2pXTnvgikl8jgCUdGZjZ3sA9wM5JZRXARCS/RlaipCOb44GTgMVJBdWFFJH8UkyjaLTA0uSFBHD3qQBNe+K3pQAmIvllGAPLkhcyLXUhRSS/kHguWIcjY17ILNQCE5H8MrTA0uSFzEoBTETy04aGIlJZ9SA6ksrk4O7bJ5VRABOR/MIgOpLKFEQBTEQGQPuBiUhVBSTvNqHF3CJSShrEF5HKUgATkcqq16MjqUxBFMBEZAA0iC8iVaUupIhUlt5CikhlhQGhJrKKSCUVuJQoDQUwEckvDJLTqqkFJiKlpEF8EamqMAgJE1pgoRLbikgpqQUmIpXV2DY6qUxBFMBEJLcwqBMmLBUKg2xLiczsGOALwDBgjrtf1q6sknqISH5huHZTw7ZH+haYmY0FLgD2B3YHppnZru3Kl7EF1gvwxrGbd7sekkXv2G7XQNLq2arxp96B3mqLbTZLHKTfYpvNgNR5IQ8F7nb3pQBmdiNwFHBeq3uXMYBtDTDnnvO7XQ+RoW5r4C85v7sceGHOL8/bLE3hVatWrbryyivT5IXcBljSdL4E2KvdfcsYwB4EJhFVvLh9OETWX71EwevBAdxjKbAjMDpN4WuvvZZly5a1uvRiv/MeXr99RY0OqynLGMBeBe7pdiVEhri8La9mS+Mj0ZQpU5gyZUqaoguJGjANWwGL2xUuYwATkfXXncBMMxsDrAA+CkxrV1hvIUWkNNx9EXAW8DNgHnCduz/QrnwtLHCWrIhIkdQCE5HKUgATkcpSABORylIAE5HK0jSKdSTLAlUpBzMbDdwHHO7uC7pcHWlBLbB1IOsCVek+M9ubaEL1zt2ui7SnALZurFmg6u4rgMYCVSmv44GT6DALXLpPXch1I9MCVek+d58KYGbdrop0oBbYupFpgaqIpKMAtm4sJN4mKNZxgaqIpKMu5LqRaYGqiKSjFtg6kHWBqoiko8XcIlJZaoGJSGUpgIlIZSmAiUhlKYCJSGUpgIlIZWkeWEWY2fZEmWT+0PRxDbjE3ecO8N63Aje6+9VmNg84qF+y0eaymwA3u/shGZ9xFHCyux/U7/ODgK+7+1sTvh8CY9z9uQzPvBp41N2/lqWuUh0KYNXyirvv3jiJd7l41MwecvdHBuMBzfdvYzO0jlNKQgGswtx9kZnNB3Y2s4nAFGAUsMzdDzazKcCJREMFzxO1gJ4ws22Aa4gWmT8FbNm4Z3NLx8w+B3wC6APmA8cC3wY2jFtq7yDabuYSYAuihKmXNlqEZnYe8LH42fOTfo+Z7QxcBmxMtPRqHnC0u6+Ki1xgZnvGv+cL7n5r/L2WvzPDX6VUlMbAKszM9iXKjvyb+KMJRN2/g83sQKLgM8nd9wBmATfH5S4Dfu3uE4BPAbu0uPcRRAFr37h79yRwMnAca1uCNaKtgc5093cABwIzzGwfMzuSaMnU7sB+wCYpftLxwDXuvk/8u8YDH2i6/ld3nwh8HLjGzMYk/E4Z4tQCq5ZGyweif3bPAR9z97/F27484u7L4+sfIAoC9zVtCbOZmW1OtD/ZDAB3/7OZ3d3iWYcC33f3F+Jy02HNWFzDzsBbgLlNz9gQ2APYFfiBu78Uf28uUbDs5LPAe8zsM/G9twE2arp+eVyXR83sMWBfok0i2/1OGeIUwKrllYQxqpeb/twLfMfdPwtgZj1EAeEFoq19ak1l+1rcq4+mLYDMbFNg035leom6q7s3lXsTsAz4aopn9Pddon8nvwfcBmzX7x71pj/3AKvp/DtliFMXcuj6CfAfZtbYxueTwF3xn39MvBuGmW0HHNzi+3cCH4n3hQeYCUwnCkS9ZlYDHHjFzD4e32sc8CjR2NjtwL+Z2aZxUPnPFHV+L3Ceu98Qn+9NFKAajo2fM5G1XedOv1OGOLXAhih3v8PMLgR+amYBsBz4iLuHZnYS8G0ze5xor7J5Lb7/o3jf/nvjrtkficaoVgIPxOeTgCOBS+Ju3zDgbHe/F8DM3gY8RNQa+j0wJqHanwduNrMVRK24XxAFqoYdzOx3RC3Dye6+FOj0OzP8jUkVaTcKEaksdSFFpLIUwESkshTARKSyFMBEpLIUwESkshTARKSyFMBEpLIUwESksv4foLei6pLw870AAAAASUVORK5CYII=\n", "text/plain": [ - "
" + "" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", - "plot_confusion_matrix(rf,X_test,y_test) \n", - "plt.grid(False)" - ] - }, - { - "cell_type": "code", - "execution_count": 460, - "id": "surprised-break", - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-22T09:26:10.700893Z", - "start_time": "2021-04-22T09:26:10.485043Z" - } - }, - "outputs": [ + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, "metadata": { @@ -3891,13 +4033,13 @@ } ], "source": [ - "from sklearn.metrics import plot_roc_curve\n", - "plot_roc_curve(rf,X_train,y_train)\n", - "plt.show()" + "from sklearn.metrics import plot_confusion_matrix,roc_curve\n", + "plot_confusion_matrix(lr_sm_model,X_test_sm,y_test_sm) " ] } ], "metadata": { + "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python",