1354 строки
38 KiB
Plaintext
1354 строки
38 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"# Experiment 3: Football match prediction (GPU version)\n",
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"\n",
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"In this experiment we are going to use the [Kaggle football dataset](https://www.kaggle.com/hugomathien/soccer). The dataset has information from +25,000 matches, +10,000 players from 11 European Countries with their lead championship during seasons 2008 to 2016. It also contains players attributes sourced from EA Sports' FIFA video game series. The problem we address is to try to predict if a match is going to end as win, draw or defeat. \n",
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"\n",
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"Part of the code use in this notebook is this [kaggle kernel](https://www.kaggle.com/airback/match-outcome-prediction-in-football).\n",
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"\n",
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"The details of the machine we used and the version of the libraries can be found in [experiment 01](01_airline.ipynb)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"System version: 3.5.2 |Anaconda custom (64-bit)| (default, Jul 2 2016, 17:53:06) \n",
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"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
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"XGBoost version: 0.6\n",
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"LightGBM version: 0.2\n",
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"source": [
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"import os,sys\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import itertools\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.model_selection import train_test_split\n",
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"import xgboost as xgb\n",
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"import lightgbm as lgb\n",
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"from libs.loaders import load_football\n",
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"from libs.football import get_fifa_data, create_feables\n",
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"from libs.timer import Timer\n",
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"from libs.conversion import convert_cols_categorical_to_numeric\n",
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"from libs.metrics import classification_metrics_multilabel\n",
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"import pickle\n",
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"import pkg_resources\n",
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"import json\n",
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"\n",
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"\n",
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"print(\"System version: {}\".format(sys.version))\n",
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"print(\"XGBoost version: {}\".format(pkg_resources.get_distribution('xgboost').version))\n",
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"print(\"LightGBM version: {}\".format(pkg_resources.get_distribution('lightgbm').version))\n",
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"\n",
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"%matplotlib inline\n",
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"% load_ext autoreload\n",
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"% autoreload 2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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||
"deletable": true,
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"editable": true
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},
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"source": [
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"### Data loading and management\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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||
"metadata": {
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||
"collapsed": false,
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||
"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:libs.loaders:MOUNT_POINT not found in environment. Defaulting to /fileshare\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(11, 2)\n",
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"(25979, 115)\n",
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"(11, 3)\n",
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"(299, 5)\n",
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"(183978, 42)\n",
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"CPU times: user 4 s, sys: 864 ms, total: 4.86 s\n",
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"Wall time: 20.2 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"countries, matches, leagues, teams, players = load_football()\n",
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"print(countries.shape)\n",
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"print(matches.shape)\n",
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"print(leagues.shape)\n",
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"print(teams.shape)\n",
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"print(players.shape)"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 4,
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||
"metadata": {
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||
"collapsed": false,
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||
"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>country_id</th>\n",
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" <th>name</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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||
" <tr>\n",
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" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
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" <td>1</td>\n",
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" <td>Belgium Jupiler League</td>\n",
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" </tr>\n",
|
||
" <tr>\n",
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||
" <th>1</th>\n",
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||
" <td>1729</td>\n",
|
||
" <td>1729</td>\n",
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||
" <td>England Premier League</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>2</th>\n",
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||
" <td>4769</td>\n",
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||
" <td>4769</td>\n",
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||
" <td>France Ligue 1</td>\n",
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" </tr>\n",
|
||
" <tr>\n",
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" <th>3</th>\n",
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||
" <td>7809</td>\n",
|
||
" <td>7809</td>\n",
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||
" <td>Germany 1. Bundesliga</td>\n",
|
||
" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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||
" <td>10257</td>\n",
|
||
" <td>10257</td>\n",
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||
" <td>Italy Serie A</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>5</th>\n",
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||
" <td>13274</td>\n",
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||
" <td>13274</td>\n",
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" <td>Netherlands Eredivisie</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>6</th>\n",
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||
" <td>15722</td>\n",
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||
" <td>15722</td>\n",
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||
" <td>Poland Ekstraklasa</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>7</th>\n",
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||
" <td>17642</td>\n",
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||
" <td>17642</td>\n",
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||
" <td>Portugal Liga ZON Sagres</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>8</th>\n",
|
||
" <td>19694</td>\n",
|
||
" <td>19694</td>\n",
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||
" <td>Scotland Premier League</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>21518</td>\n",
|
||
" <td>21518</td>\n",
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||
" <td>Spain LIGA BBVA</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>24558</td>\n",
|
||
" <td>24558</td>\n",
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||
" <td>Switzerland Super League</td>\n",
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||
" </tr>\n",
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||
" </tbody>\n",
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||
"</table>\n",
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||
"</div>"
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||
],
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"text/plain": [
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||
" id country_id name\n",
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||
"0 1 1 Belgium Jupiler League\n",
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||
"1 1729 1729 England Premier League\n",
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||
"2 4769 4769 France Ligue 1\n",
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||
"3 7809 7809 Germany 1. Bundesliga\n",
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||
"4 10257 10257 Italy Serie A\n",
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||
"5 13274 13274 Netherlands Eredivisie\n",
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||
"6 15722 15722 Poland Ekstraklasa\n",
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||
"7 17642 17642 Portugal Liga ZON Sagres\n",
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||
"8 19694 19694 Scotland Premier League\n",
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||
"9 21518 21518 Spain LIGA BBVA\n",
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||
"10 24558 24558 Switzerland Super League"
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||
]
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||
},
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||
"execution_count": 4,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
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"leagues"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 5,
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||
"metadata": {
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||
"collapsed": false,
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||
"deletable": true,
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||
"editable": true
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||
},
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||
"outputs": [
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||
{
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||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>country_id</th>\n",
|
||
" <th>league_id</th>\n",
|
||
" <th>season</th>\n",
|
||
" <th>stage</th>\n",
|
||
" <th>date</th>\n",
|
||
" <th>match_api_id</th>\n",
|
||
" <th>home_team_api_id</th>\n",
|
||
" <th>away_team_api_id</th>\n",
|
||
" <th>home_team_goal</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>SJA</th>\n",
|
||
" <th>VCH</th>\n",
|
||
" <th>VCD</th>\n",
|
||
" <th>VCA</th>\n",
|
||
" <th>GBH</th>\n",
|
||
" <th>GBD</th>\n",
|
||
" <th>GBA</th>\n",
|
||
" <th>BSH</th>\n",
|
||
" <th>BSD</th>\n",
|
||
" <th>BSA</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008/2009</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008-08-17 00:00:00</td>\n",
|
||
" <td>492473</td>\n",
|
||
" <td>9987</td>\n",
|
||
" <td>9993</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.00</td>\n",
|
||
" <td>1.65</td>\n",
|
||
" <td>3.40</td>\n",
|
||
" <td>4.50</td>\n",
|
||
" <td>1.78</td>\n",
|
||
" <td>3.25</td>\n",
|
||
" <td>4.00</td>\n",
|
||
" <td>1.73</td>\n",
|
||
" <td>3.40</td>\n",
|
||
" <td>4.20</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008/2009</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008-08-16 00:00:00</td>\n",
|
||
" <td>492474</td>\n",
|
||
" <td>10000</td>\n",
|
||
" <td>9994</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>3.80</td>\n",
|
||
" <td>2.00</td>\n",
|
||
" <td>3.25</td>\n",
|
||
" <td>3.25</td>\n",
|
||
" <td>1.85</td>\n",
|
||
" <td>3.25</td>\n",
|
||
" <td>3.75</td>\n",
|
||
" <td>1.91</td>\n",
|
||
" <td>3.25</td>\n",
|
||
" <td>3.60</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008/2009</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008-08-16 00:00:00</td>\n",
|
||
" <td>492475</td>\n",
|
||
" <td>9984</td>\n",
|
||
" <td>8635</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>2.50</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" <td>3.25</td>\n",
|
||
" <td>2.65</td>\n",
|
||
" <td>2.50</td>\n",
|
||
" <td>3.20</td>\n",
|
||
" <td>2.50</td>\n",
|
||
" <td>2.30</td>\n",
|
||
" <td>3.20</td>\n",
|
||
" <td>2.75</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008/2009</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008-08-17 00:00:00</td>\n",
|
||
" <td>492476</td>\n",
|
||
" <td>9991</td>\n",
|
||
" <td>9998</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>7.50</td>\n",
|
||
" <td>1.45</td>\n",
|
||
" <td>3.75</td>\n",
|
||
" <td>6.50</td>\n",
|
||
" <td>1.50</td>\n",
|
||
" <td>3.75</td>\n",
|
||
" <td>5.50</td>\n",
|
||
" <td>1.44</td>\n",
|
||
" <td>3.75</td>\n",
|
||
" <td>6.50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008/2009</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2008-08-16 00:00:00</td>\n",
|
||
" <td>492477</td>\n",
|
||
" <td>7947</td>\n",
|
||
" <td>9985</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1.73</td>\n",
|
||
" <td>4.50</td>\n",
|
||
" <td>3.40</td>\n",
|
||
" <td>1.65</td>\n",
|
||
" <td>4.50</td>\n",
|
||
" <td>3.50</td>\n",
|
||
" <td>1.65</td>\n",
|
||
" <td>4.75</td>\n",
|
||
" <td>3.30</td>\n",
|
||
" <td>1.67</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 115 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" id country_id league_id season stage date \\\n",
|
||
"0 1 1 1 2008/2009 1 2008-08-17 00:00:00 \n",
|
||
"1 2 1 1 2008/2009 1 2008-08-16 00:00:00 \n",
|
||
"2 3 1 1 2008/2009 1 2008-08-16 00:00:00 \n",
|
||
"3 4 1 1 2008/2009 1 2008-08-17 00:00:00 \n",
|
||
"4 5 1 1 2008/2009 1 2008-08-16 00:00:00 \n",
|
||
"\n",
|
||
" match_api_id home_team_api_id away_team_api_id home_team_goal ... \\\n",
|
||
"0 492473 9987 9993 1 ... \n",
|
||
"1 492474 10000 9994 0 ... \n",
|
||
"2 492475 9984 8635 0 ... \n",
|
||
"3 492476 9991 9998 5 ... \n",
|
||
"4 492477 7947 9985 1 ... \n",
|
||
"\n",
|
||
" SJA VCH VCD VCA GBH GBD GBA BSH BSD BSA \n",
|
||
"0 4.00 1.65 3.40 4.50 1.78 3.25 4.00 1.73 3.40 4.20 \n",
|
||
"1 3.80 2.00 3.25 3.25 1.85 3.25 3.75 1.91 3.25 3.60 \n",
|
||
"2 2.50 2.35 3.25 2.65 2.50 3.20 2.50 2.30 3.20 2.75 \n",
|
||
"3 7.50 1.45 3.75 6.50 1.50 3.75 5.50 1.44 3.75 6.50 \n",
|
||
"4 1.73 4.50 3.40 1.65 4.50 3.50 1.65 4.75 3.30 1.67 \n",
|
||
"\n",
|
||
"[5 rows x 115 columns]"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"matches.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(21374, 115)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#Reduce match data to fulfill run time requirements\n",
|
||
"cols = [\"country_id\", \"league_id\", \"season\", \"stage\", \"date\", \"match_api_id\", \"home_team_api_id\", \n",
|
||
" \"away_team_api_id\", \"home_team_goal\", \"away_team_goal\", \"home_player_1\", \"home_player_2\",\n",
|
||
" \"home_player_3\", \"home_player_4\", \"home_player_5\", \"home_player_6\", \"home_player_7\", \n",
|
||
" \"home_player_8\", \"home_player_9\", \"home_player_10\", \"home_player_11\", \"away_player_1\",\n",
|
||
" \"away_player_2\", \"away_player_3\", \"away_player_4\", \"away_player_5\", \"away_player_6\",\n",
|
||
" \"away_player_7\", \"away_player_8\", \"away_player_9\", \"away_player_10\", \"away_player_11\"]\n",
|
||
"match_data = matches.dropna(subset = cols)\n",
|
||
"print(match_data.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Now, using the information from the matches and players, we are going to create features based on the FIFA attributes. This computation is heavy, so we are going to save it the first time we create it. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(21374, 23)\n",
|
||
"CPU times: user 29min 27s, sys: 1min 6s, total: 30min 33s\n",
|
||
"Wall time: 31min 14s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%time\n",
|
||
"fifa_data_filename = 'fifa_data.pk'\n",
|
||
"if os.path.isfile(fifa_data_filename):\n",
|
||
" fifa_data = pd.read_pickle(fifa_data_filename)\n",
|
||
"else:\n",
|
||
" fifa_data = get_fifa_data(match_data, players)\n",
|
||
" fifa_data.to_pickle(fifa_data_filename)\n",
|
||
"print(fifa_data.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Finally, we are going to compute the features and labels. The labels are related to the result of the team playing at home, they are: `Win`, `Draw`, `Defeat`. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Generating match features...\n",
|
||
"Generating match labels...\n",
|
||
"Generating bookkeeper data...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/anaconda/envs/strata/lib/python3.5/site-packages/pandas/core/indexing.py:297: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
|
||
" self.obj[key] = _infer_fill_value(value)\n",
|
||
"/anaconda/envs/strata/lib/python3.5/site-packages/pandas/core/indexing.py:477: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
|
||
" self.obj[item] = s\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(19673, 48)\n",
|
||
"CPU times: user 10min 44s, sys: 52.2 s, total: 11min 37s\n",
|
||
"Wall time: 11min 53s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%time\n",
|
||
"bk_cols = ['B365', 'BW', 'IW', 'LB', 'PS', 'WH', 'SJ', 'VC', 'GB', 'BS']\n",
|
||
"bk_cols_selected = ['B365', 'BW'] \n",
|
||
"feables = create_feables(match_data, fifa_data, bk_cols_selected, get_overall = True)\n",
|
||
"print(feables.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>match_api_id</th>\n",
|
||
" <th>home_team_goals_difference</th>\n",
|
||
" <th>away_team_goals_difference</th>\n",
|
||
" <th>games_won_home_team</th>\n",
|
||
" <th>games_won_away_team</th>\n",
|
||
" <th>games_against_won</th>\n",
|
||
" <th>games_against_lost</th>\n",
|
||
" <th>season</th>\n",
|
||
" <th>League_1.0</th>\n",
|
||
" <th>League_1729.0</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>away_player_9_overall_rating</th>\n",
|
||
" <th>away_player_10_overall_rating</th>\n",
|
||
" <th>away_player_11_overall_rating</th>\n",
|
||
" <th>B365_Win</th>\n",
|
||
" <th>B365_Draw</th>\n",
|
||
" <th>B365_Defeat</th>\n",
|
||
" <th>BW_Win</th>\n",
|
||
" <th>BW_Draw</th>\n",
|
||
" <th>BW_Defeat</th>\n",
|
||
" <th>label</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>493017.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2008.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>70.0</td>\n",
|
||
" <td>68.0</td>\n",
|
||
" <td>63.0</td>\n",
|
||
" <td>0.313804</td>\n",
|
||
" <td>0.276886</td>\n",
|
||
" <td>0.409310</td>\n",
|
||
" <td>0.307825</td>\n",
|
||
" <td>0.279410</td>\n",
|
||
" <td>0.412765</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>493025.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2008.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>67.0</td>\n",
|
||
" <td>73.0</td>\n",
|
||
" <td>68.0</td>\n",
|
||
" <td>0.327179</td>\n",
|
||
" <td>0.286281</td>\n",
|
||
" <td>0.386540</td>\n",
|
||
" <td>0.290493</td>\n",
|
||
" <td>0.300176</td>\n",
|
||
" <td>0.409331</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>493027.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2008.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>55.0</td>\n",
|
||
" <td>58.0</td>\n",
|
||
" <td>64.0</td>\n",
|
||
" <td>0.672897</td>\n",
|
||
" <td>0.209346</td>\n",
|
||
" <td>0.117757</td>\n",
|
||
" <td>0.672269</td>\n",
|
||
" <td>0.226891</td>\n",
|
||
" <td>0.100840</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>493034.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2008.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>74.0</td>\n",
|
||
" <td>70.0</td>\n",
|
||
" <td>69.0</td>\n",
|
||
" <td>0.207407</td>\n",
|
||
" <td>0.259259</td>\n",
|
||
" <td>0.533333</td>\n",
|
||
" <td>0.192717</td>\n",
|
||
" <td>0.274476</td>\n",
|
||
" <td>0.532807</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>493040.0</td>\n",
|
||
" <td>-2.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2008.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>60.0</td>\n",
|
||
" <td>63.0</td>\n",
|
||
" <td>65.0</td>\n",
|
||
" <td>0.535211</td>\n",
|
||
" <td>0.267606</td>\n",
|
||
" <td>0.197183</td>\n",
|
||
" <td>0.565759</td>\n",
|
||
" <td>0.254990</td>\n",
|
||
" <td>0.179250</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 48 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" match_api_id home_team_goals_difference away_team_goals_difference \\\n",
|
||
"0 493017.0 0.0 0.0 \n",
|
||
"1 493025.0 0.0 0.0 \n",
|
||
"2 493027.0 0.0 0.0 \n",
|
||
"3 493034.0 1.0 2.0 \n",
|
||
"4 493040.0 -2.0 0.0 \n",
|
||
"\n",
|
||
" games_won_home_team games_won_away_team games_against_won \\\n",
|
||
"0 0.0 0.0 0.0 \n",
|
||
"1 0.0 0.0 0.0 \n",
|
||
"2 0.0 0.0 0.0 \n",
|
||
"3 1.0 1.0 0.0 \n",
|
||
"4 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" games_against_lost season League_1.0 League_1729.0 ... \\\n",
|
||
"0 0.0 2008.0 1 0 ... \n",
|
||
"1 0.0 2008.0 1 0 ... \n",
|
||
"2 0.0 2008.0 1 0 ... \n",
|
||
"3 0.0 2008.0 1 0 ... \n",
|
||
"4 0.0 2008.0 1 0 ... \n",
|
||
"\n",
|
||
" away_player_9_overall_rating away_player_10_overall_rating \\\n",
|
||
"0 70.0 68.0 \n",
|
||
"1 67.0 73.0 \n",
|
||
"2 55.0 58.0 \n",
|
||
"3 74.0 70.0 \n",
|
||
"4 60.0 63.0 \n",
|
||
"\n",
|
||
" away_player_11_overall_rating B365_Win B365_Draw B365_Defeat BW_Win \\\n",
|
||
"0 63.0 0.313804 0.276886 0.409310 0.307825 \n",
|
||
"1 68.0 0.327179 0.286281 0.386540 0.290493 \n",
|
||
"2 64.0 0.672897 0.209346 0.117757 0.672269 \n",
|
||
"3 69.0 0.207407 0.259259 0.533333 0.192717 \n",
|
||
"4 65.0 0.535211 0.267606 0.197183 0.565759 \n",
|
||
"\n",
|
||
" BW_Draw BW_Defeat label \n",
|
||
"0 0.279410 0.412765 0 \n",
|
||
"1 0.300176 0.409331 1 \n",
|
||
"2 0.226891 0.100840 0 \n",
|
||
"3 0.274476 0.532807 0 \n",
|
||
"4 0.254990 0.179250 2 \n",
|
||
"\n",
|
||
"[5 rows x 48 columns]"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"feables = convert_cols_categorical_to_numeric(feables)\n",
|
||
"feables.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Let's now split features and labels."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(19673, 46)\n",
|
||
"(19673,)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"features = feables[feables.columns.difference(['match_api_id', 'label'])]\n",
|
||
"labs = feables['label']\n",
|
||
"print(features.shape)\n",
|
||
"print(labs.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Once we have the features and labels defined, let's create the train and test set."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"CPU times: user 16 ms, sys: 4 ms, total: 20 ms\n",
|
||
"Wall time: 17.8 ms\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%time\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(features, labs, test_size=0.2, random_state=42, stratify=labs)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"dtrain = xgb.DMatrix(data=X_train, label=y_train)\n",
|
||
"dtest = xgb.DMatrix(data=X_test, label=y_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"lgb_train = lgb.Dataset(X_train.values, y_train.values, free_raw_data=False)\n",
|
||
"lgb_test = lgb.Dataset(X_test.values, y_test, reference=lgb_train, free_raw_data=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### XGBoost analysis\n",
|
||
"Once we have done the feature engineering step, we can start to train with each of the libraries. We will start with XGBoost. \n",
|
||
"\n",
|
||
"We are going to save the training and test time, as well as some metrics. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 121,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"results_dict = dict()\n",
|
||
"num_rounds = 300\n",
|
||
"labels = [0,1,2]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 122,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"params = {'max_depth':3, \n",
|
||
" 'objective': 'multi:softprob', \n",
|
||
" 'num_class': len(labels),\n",
|
||
" 'min_child_weight':5, \n",
|
||
" 'learning_rate':0.1, \n",
|
||
" 'colsample_bytree':0.8, \n",
|
||
" 'scale_pos_weight':2, \n",
|
||
" 'gamma':0.1, \n",
|
||
" 'reg_lamda':1, \n",
|
||
" 'subsample':1,\n",
|
||
" 'tree_method':'exact', \n",
|
||
" 'updater':'grow_gpu'\n",
|
||
" }\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 123,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"with Timer() as t_train:\n",
|
||
" xgb_clf_pipeline = xgb.train(params, dtrain, num_boost_round=num_rounds)\n",
|
||
" \n",
|
||
"with Timer() as t_test:\n",
|
||
" y_prob_xgb = xgb_clf_pipeline.predict(dtest)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 124,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def quantitize_multilable_prediction(y_pred):\n",
|
||
" return np.argmax(y_pred, axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 125,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_pred_xgb = quantitize_multilable_prediction(y_prob_xgb)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 126,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"report_xgb = classification_metrics_multilabel(y_test, y_pred_xgb, labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 127,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"results_dict['xgb']={\n",
|
||
" 'train_time': t_train.interval,\n",
|
||
" 'test_time': t_test.interval,\n",
|
||
" 'performance': report_xgb \n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 128,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"del xgb_clf_pipeline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"\n",
|
||
"Now let's try with XGBoost histogram."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 129,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"params = {'max_depth':0, \n",
|
||
" 'max_leaves':2**3, \n",
|
||
" 'objective': 'multi:softprob', \n",
|
||
" 'num_class': len(labels),\n",
|
||
" 'min_child_weight':5, \n",
|
||
" 'learning_rate':0.1, \n",
|
||
" 'colsample_bytree':0.80, \n",
|
||
" 'scale_pos_weight':2, \n",
|
||
" 'gamma':0.1, \n",
|
||
" 'reg_lamda':1, \n",
|
||
" 'subsample':1,\n",
|
||
" 'tree_method':'hist', \n",
|
||
" 'grow_policy':'lossguide', \n",
|
||
" }\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 130,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"with Timer() as t_train:\n",
|
||
" xgb_hist_clf_pipeline = xgb.train(params, dtrain, num_boost_round=num_rounds)\n",
|
||
" \n",
|
||
"with Timer() as t_test:\n",
|
||
" y_prob_xgb_hist = xgb_hist_clf_pipeline.predict(dtest)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 131,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_pred_xgb_hist = quantitize_multilable_prediction(y_prob_xgb_hist)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 132,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"report_xgb_hist = classification_metrics_multilabel(y_test, y_pred_xgb_hist, labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 133,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"results_dict['xgb_hist']={\n",
|
||
" 'train_time': t_train.interval,\n",
|
||
" 'test_time': t_test.interval,\n",
|
||
" 'performance': report_xgb_hist\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 134,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"del xgb_hist_clf_pipeline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### LightGBM analysis\n",
|
||
"\n",
|
||
"Now let's compare with LightGBM."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 135,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"params = {'num_leaves': 2**3,\n",
|
||
" 'learning_rate': 0.1,\n",
|
||
" 'colsample_bytree': 0.80,\n",
|
||
" 'scale_pos_weight': 2,\n",
|
||
" 'min_split_gain': 0.1,\n",
|
||
" 'min_child_weight': 5,\n",
|
||
" 'reg_lambda': 1,\n",
|
||
" 'subsample': 1,\n",
|
||
" 'objective':'multiclass',\n",
|
||
" 'num_class': len(labels),\n",
|
||
" 'task': 'train'\n",
|
||
" }"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 136,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"with Timer() as t_train:\n",
|
||
" lgbm_clf_pipeline = lgb.train(params, lgb_train, num_boost_round=num_rounds)\n",
|
||
" \n",
|
||
"with Timer() as t_test:\n",
|
||
" y_prob_lgbm = lgbm_clf_pipeline.predict(X_test.values)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 137,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_pred_lgbm = quantitize_multilable_prediction(y_prob_lgbm)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 138,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"report_lgbm = classification_metrics_multilabel(y_test, y_pred_lgbm, labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 139,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"results_dict['lgbm']={\n",
|
||
" 'train_time': t_train.interval,\n",
|
||
" 'test_time': t_test.interval,\n",
|
||
" 'performance': report_lgbm \n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 140,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"del lgbm_clf_pipeline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Finally, the results."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 141,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{\n",
|
||
" \"lgbm\": {\n",
|
||
" \"performance\": {\n",
|
||
" \"Accuracy\": 0.5344345616264294,\n",
|
||
" \"F1\": 0.4704311590503636,\n",
|
||
" \"Precision\": 0.48847893806298454,\n",
|
||
" \"Recall\": 0.5344345616264294\n",
|
||
" },\n",
|
||
" \"test_time\": 0.029374134999670787,\n",
|
||
" \"train_time\": 0.976751588001207\n",
|
||
" },\n",
|
||
" \"xgb\": {\n",
|
||
" \"performance\": {\n",
|
||
" \"Accuracy\": 0.5359593392630242,\n",
|
||
" \"F1\": 0.4704659043141339,\n",
|
||
" \"Precision\": 0.4825747269523364,\n",
|
||
" \"Recall\": 0.5359593392630242\n",
|
||
" },\n",
|
||
" \"test_time\": 0.006567717999132583,\n",
|
||
" \"train_time\": 7.09927419500309\n",
|
||
" },\n",
|
||
" \"xgb_hist\": {\n",
|
||
" \"performance\": {\n",
|
||
" \"Accuracy\": 0.537992376111817,\n",
|
||
" \"F1\": 0.4723094570741036,\n",
|
||
" \"Precision\": 0.4944404394401915,\n",
|
||
" \"Recall\": 0.537992376111817\n",
|
||
" },\n",
|
||
" \"test_time\": 0.007724854996922659,\n",
|
||
" \"train_time\": 4.588017762001982\n",
|
||
" }\n",
|
||
"}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Results\n",
|
||
"print(json.dumps(results_dict, indent=4, sort_keys=True))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"As it can be seen, in the case of multilabel LightGBM is faster than XGBoost in both versions. The performance metrics are really poor, so we wouldn't recommend to bet based on this algorithm :-)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"deletable": true,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3.5",
|
||
"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": "3.5.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|