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Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Imports"
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
trenowanie modelu xgboost dla finalnych danych
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"from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n",
"from sklearn.metrics import confusion_matrix, mean_squared_error, mean_absolute_error, r2_score\n",
"import xgboost\n",
"import time\n",
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"from joblib import dump, load\n",
"import os"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Load the data"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 2,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [],
"source": [
train model on new data
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"df = pd.read_csv('final_data_new.csv')"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 3,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
trenowanie modelu xgboost dla finalnych danych
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"outputs": [
{
"data": {
"text/plain": [
"Index(['minute', 'position_name', 'shot_body_part_name', 'shot_technique_name',\n",
" 'shot_type_name', 'shot_first_time', 'shot_one_on_one',\n",
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" 'shot_aerial_won', 'shot_open_goal', 'shot_follows_dribble',\n",
" 'shot_redirect', 'x1', 'y1', 'number_of_players_opponents',\n",
" 'number_of_players_teammates', 'is_goal', 'angle', 'distance',\n",
" 'x_player_opponent_Goalkeeper', 'x_player_opponent_8',\n",
" 'x_player_opponent_1', 'x_player_opponent_2', 'x_player_opponent_3',\n",
" 'x_player_teammate_1', 'x_player_opponent_4', 'x_player_opponent_5',\n",
" 'x_player_opponent_6', 'x_player_teammate_2', 'x_player_opponent_9',\n",
" 'x_player_opponent_10', 'x_player_opponent_11', 'x_player_teammate_3',\n",
" 'x_player_teammate_4', 'x_player_teammate_5', 'x_player_teammate_6',\n",
" 'x_player_teammate_7', 'x_player_teammate_8', 'x_player_teammate_9',\n",
" 'x_player_teammate_10', 'y_player_opponent_Goalkeeper',\n",
" 'y_player_opponent_8', 'y_player_opponent_1', 'y_player_opponent_2',\n",
" 'y_player_opponent_3', 'y_player_teammate_1', 'y_player_opponent_4',\n",
" 'y_player_opponent_5', 'y_player_opponent_6', 'y_player_teammate_2',\n",
" 'y_player_opponent_9', 'y_player_opponent_10', 'y_player_opponent_11',\n",
" 'y_player_teammate_3', 'y_player_teammate_4', 'y_player_teammate_5',\n",
" 'y_player_teammate_6', 'y_player_teammate_7', 'y_player_teammate_8',\n",
" 'y_player_teammate_9', 'y_player_teammate_10', 'x_player_opponent_7',\n",
" 'y_player_opponent_7', 'x_player_teammate_Goalkeeper',\n",
" 'y_player_teammate_Goalkeeper'],\n",
trenowanie modelu xgboost dla finalnych danych
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" dtype='object')"
]
},
train model on new data
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"execution_count": 3,
trenowanie modelu xgboost dla finalnych danych
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"metadata": {},
"output_type": "execute_result"
}
],
Dodanie wstepnej wersji modelu xgboost
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"source": [
trenowanie modelu xgboost dla finalnych danych
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"df.columns"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 4,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
trenowanie modelu xgboost dla finalnych danych
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" <th>minute</th>\n",
" <th>position_name</th>\n",
" <th>shot_body_part_name</th>\n",
" <th>shot_technique_name</th>\n",
" <th>shot_type_name</th>\n",
" <th>shot_first_time</th>\n",
" <th>shot_one_on_one</th>\n",
" <th>shot_aerial_won</th>\n",
" <th>shot_open_goal</th>\n",
small data and model improvement
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" <th>shot_follows_dribble</th>\n",
Dodanie wstepnej wersji modelu xgboost
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" <th>...</th>\n",
small data and model improvement
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" <th>y_player_teammate_5</th>\n",
trenowanie modelu xgboost dla finalnych danych
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" <th>y_player_teammate_6</th>\n",
" <th>y_player_teammate_7</th>\n",
" <th>y_player_teammate_8</th>\n",
" <th>y_player_teammate_9</th>\n",
" <th>y_player_teammate_10</th>\n",
" <th>x_player_opponent_7</th>\n",
" <th>y_player_opponent_7</th>\n",
" <th>x_player_teammate_Goalkeeper</th>\n",
" <th>y_player_teammate_Goalkeeper</th>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
train model on new data
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" <td>2</td>\n",
" <td>Left Center Forward</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>Right Foot</td>\n",
train model on new data
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" <td>Half Volley</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>Open Play</td>\n",
train model on new data
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" <td>True</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>...</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
train model on new data
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" <td>26.6</td>\n",
" <td>53.1</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
small data and model improvement
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" <td>NaN</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>5</td>\n",
train model on new data
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" <td>Left Back</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>Left Foot</td>\n",
train model on new data
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" <td>Volley</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>Open Play</td>\n",
train model on new data
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" <td>True</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>...</td>\n",
train model on new data
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" <td>20.6</td>\n",
" <td>32.8</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
train model on new data
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" <td>23.8</td>\n",
" <td>31.2</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
small data and model improvement
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" <td>NaN</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
train model on new data
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" <td>15</td>\n",
" <td>Left Center Forward</td>\n",
" <td>Left Foot</td>\n",
" <td>Normal</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>Open Play</td>\n",
train model on new data
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" <td>False</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>...</td>\n",
train model on new data
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" <td>29.0</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
train model on new data
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" <td>29.6</td>\n",
" <td>55.3</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
train model on new data
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" <td>16</td>\n",
" <td>Center Forward</td>\n",
" <td>Head</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>Normal</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>Open Play</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>False</td>\n",
" <td>False</td>\n",
train model on new data
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" <td>True</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>False</td>\n",
" <td>False</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>...</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
train model on new data
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" <td>26.7</td>\n",
" <td>60.4</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
small data and model improvement
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" <td>NaN</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
train model on new data
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" <td>18</td>\n",
" <td>Right Center Forward</td>\n",
" <td>Right Foot</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>Normal</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>Open Play</td>\n",
train model on new data
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" <td>False</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" <td>...</td>\n",
train model on new data
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" <td>27.9</td>\n",
" <td>31.4</td>\n",
" <td>33.4</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
train model on new data
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" <td>16.9</td>\n",
" <td>40.1</td>\n",
trenowanie modelu xgboost dla finalnych danych
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" <td>NaN</td>\n",
small data and model improvement
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" <td>NaN</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" </tbody>\n",
"</table>\n",
small data and model improvement
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"<p>5 rows × 64 columns</p>\n",
Dodanie wstepnej wersji modelu xgboost
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"</div>"
],
"text/plain": [
trenowanie modelu xgboost dla finalnych danych
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" minute position_name shot_body_part_name shot_technique_name \\\n",
train model on new data
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"0 2 Left Center Forward Right Foot Half Volley \n",
"1 5 Left Back Left Foot Volley \n",
"2 15 Left Center Forward Left Foot Normal \n",
"3 16 Center Forward Head Normal \n",
"4 18 Right Center Forward Right Foot Normal \n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
trenowanie modelu xgboost dla finalnych danych
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" shot_type_name shot_first_time shot_one_on_one shot_aerial_won \\\n",
train model on new data
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"0 Open Play True False False \n",
"1 Open Play True False False \n",
"2 Open Play False False False \n",
"3 Open Play False False True \n",
"4 Open Play False False False \n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
small data and model improvement
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" shot_open_goal shot_follows_dribble ... y_player_teammate_5 \\\n",
"0 False False ... NaN \n",
train model on new data
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"1 False False ... 20.6 \n",
"2 False False ... 29.0 \n",
small data and model improvement
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"3 False False ... NaN \n",
train model on new data
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"4 False False ... 27.9 \n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
small data and model improvement
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" y_player_teammate_6 y_player_teammate_7 y_player_teammate_8 \\\n",
trenowanie modelu xgboost dla finalnych danych
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"0 NaN NaN NaN \n",
train model on new data
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"1 32.8 NaN NaN \n",
trenowanie modelu xgboost dla finalnych danych
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"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
train model on new data
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"4 31.4 33.4 NaN \n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
small data and model improvement
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" y_player_teammate_9 y_player_teammate_10 x_player_opponent_7 \\\n",
train model on new data
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"0 NaN NaN 26.6 \n",
"1 NaN NaN 23.8 \n",
"2 NaN NaN 29.6 \n",
"3 NaN NaN 26.7 \n",
"4 NaN NaN 16.9 \n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
small data and model improvement
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" y_player_opponent_7 x_player_teammate_Goalkeeper \\\n",
train model on new data
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"0 53.1 NaN \n",
"1 31.2 NaN \n",
"2 55.3 NaN \n",
"3 60.4 NaN \n",
"4 40.1 NaN \n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
small data and model improvement
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" y_player_teammate_Goalkeeper \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
"[5 rows x 64 columns]"
Dodanie wstepnej wersji modelu xgboost
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]
},
train model on new data
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"execution_count": 4,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
trenowanie modelu xgboost dla finalnych danych
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"df.head()"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Data preparation"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"execution_count": 43,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
trenowanie modelu xgboost dla finalnych danych
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"outputs": [],
Dodanie wstepnej wersji modelu xgboost
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"source": [
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"# df[['minute', \n",
"# 'number_of_players_opponents', \n",
"# 'number_of_players_teammates']] = df[['minute', \n",
"# 'number_of_players_opponents', \n",
"# 'number_of_players_teammates']].astype(float)"
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 5,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OrdinalEncoder\n",
"\n",
"enc = OrdinalEncoder()\n",
"\n",
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"df[['position_name', \n",
" 'shot_technique_name', \n",
" 'shot_type_name', \n",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
" 'shot_body_part_name']] = enc.fit_transform(df[['position_name', \n",
" 'shot_technique_name', \n",
" 'shot_type_name',\n",
" 'shot_body_part_name']])"
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 6,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"outputs": [],
"source": [
"df[['minute', \n",
" 'position_name', \n",
" 'shot_technique_name', \n",
" 'shot_type_name', \n",
" 'shot_body_part_name']] = df[['minute', \n",
" 'position_name', \n",
" 'shot_technique_name', \n",
" 'shot_type_name', \n",
" 'shot_body_part_name']].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
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" <th>y_player_teammate_10</th>\n",
" <th>x_player_opponent_7</th>\n",
" <th>y_player_opponent_7</th>\n",
" <th>x_player_teammate_Goalkeeper</th>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>47.6</td>\n",
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" <td>43.1</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>21.7</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>38340</th>\n",
" <td>75</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>29.1</td>\n",
" <td>33.6</td>\n",
" <td>40.9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>21.2</td>\n",
" <td>32.4</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38341</th>\n",
" <td>90</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>62.6</td>\n",
" <td>51.0</td>\n",
" <td>66.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" <td>45.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>38342 rows × 64 columns</p>\n",
"</div>"
],
"text/plain": [
" minute position_name shot_body_part_name shot_technique_name \\\n",
"0 0 18 3 4 \n",
"1 5 18 1 4 \n",
"2 5 4 3 2 \n",
"3 5 10 3 4 \n",
"4 5 17 1 4 \n",
"... ... ... ... ... \n",
"38337 61 3 3 4 \n",
"38338 66 0 3 4 \n",
"38339 73 3 1 6 \n",
"38340 75 13 1 4 \n",
"38341 90 3 3 4 \n",
"\n",
" shot_type_name shot_first_time shot_one_on_one shot_aerial_won \\\n",
"0 3 False False False \n",
"1 3 False False False \n",
"2 3 True False False \n",
"3 3 False False False \n",
"4 3 True False False \n",
"... ... ... ... ... \n",
"38337 3 True False False \n",
"38338 3 True False False \n",
"38339 3 True False False \n",
"38340 3 False False False \n",
"38341 3 False False False \n",
"\n",
" shot_open_goal shot_follows_dribble ... y_player_teammate_5 \\\n",
"0 False False ... NaN \n",
"1 False False ... NaN \n",
"2 False False ... 48.9 \n",
"3 False False ... NaN \n",
"4 False False ... NaN \n",
"... ... ... ... ... \n",
"38337 False False ... 47.3 \n",
"38338 False False ... 47.6 \n",
"38339 False False ... 48.9 \n",
"38340 False False ... 29.1 \n",
"38341 False False ... 62.6 \n",
"\n",
" y_player_teammate_6 y_player_teammate_7 y_player_teammate_8 \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"... ... ... ... \n",
"38337 42.6 54.7 NaN \n",
"38338 39.4 43.1 NaN \n",
"38339 48.1 41.1 NaN \n",
"38340 33.6 40.9 NaN \n",
"38341 51.0 66.7 NaN \n",
"\n",
" y_player_teammate_9 y_player_teammate_10 x_player_opponent_7 \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"... ... ... ... \n",
"38337 NaN NaN 21.3 \n",
"38338 NaN NaN 19.0 \n",
"38339 NaN NaN 21.7 \n",
"38340 NaN NaN 21.2 \n",
"38341 NaN NaN 23.0 \n",
"\n",
" y_player_opponent_7 x_player_teammate_Goalkeeper \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"... ... ... \n",
"38337 50.9 NaN \n",
"38338 45.8 NaN \n",
"38339 29.6 NaN \n",
"38340 32.4 NaN \n",
"38341 45.5 NaN \n",
"\n",
" y_player_teammate_Goalkeeper \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"38337 NaN \n",
"38338 NaN \n",
"38339 NaN \n",
"38340 NaN \n",
"38341 NaN \n",
"\n",
"[38342 rows x 64 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 7,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['labelEncoder.joblib']"
]
},
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 7,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dump(enc,'labelEncoder.joblib')"
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 8,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"outputs": [],
"source": [
"enc2 = load('labelEncoder.joblib')"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"# df[['position_name', \n",
"# 'shot_technique_name', \n",
"# 'shot_type_name', \n",
"# 'shot_body_part_name']] = enc2.inverse_transform(df[['position_name', \n",
"# 'shot_technique_name', \n",
"# 'shot_type_name',\n",
"# 'shot_body_part_name']])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"# df[['position_name', \n",
"# 'shot_technique_name', \n",
"# 'shot_type_name', \n",
"# 'shot_body_part_name']] = enc2.transform(df[['position_name', \n",
"# 'shot_technique_name', \n",
"# 'shot_type_name', \n",
"# 'shot_body_part_name']])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# enc.inverse_transform(df[['position_name', \n",
"# 'shot_technique_name', \n",
"# 'shot_type_name',\n",
"# 'shot_body_part_name']])"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# ############### NEW ################\n",
"# from sklearn.preprocessing import LabelEncoder\n",
"\n",
"# le_posiotion_name = LabelEncoder()\n",
"# le_shot_technique_name = LabelEncoder()\n",
"# le_shot_type_name = LabelEncoder()\n",
"# le_shot_body_part_name = LabelEncoder()\n",
"\n",
"# df['position_name'] = le_posiotion_name.fit_transform(df['position_name'])\n",
"# df['shot_technique_name'] = le_shot_technique_name.fit_transform(df['shot_technique_name'])\n",
"# df['shot_type_name'] = le_shot_type_name.fit_transform(df['shot_type_name'])\n",
"# df['shot_body_part_name'] = le_shot_body_part_name.fit_transform(df['shot_body_part_name'])"
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 9,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"outputs": [],
"source": [
"# Change the type of categorical features to 'category' \n",
"df[['minute',\n",
" 'position_name', \n",
" 'shot_technique_name', \n",
" 'shot_type_name', \n",
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
" 'number_of_players_opponents', \n",
" 'number_of_players_teammates', \n",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
" 'shot_body_part_name']] = df[['minute',\n",
" 'position_name', \n",
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
" 'shot_technique_name', \n",
" 'shot_type_name', \n",
" 'number_of_players_opponents', \n",
" 'number_of_players_teammates', \n",
" 'shot_body_part_name']].astype('category')"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 10,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [],
"source": [
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"# Splitting the dataset into features (X) and the target variable (y)\n",
"y = pd.DataFrame(df['is_goal'])\n",
"X = df.drop(['is_goal'], axis=1)\n",
"\n",
"# Splitting the data into a training set and a test set\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
"\n",
"# Create cross-validation \n",
"cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 11,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [
{
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"name": "stdout",
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"output_type": "stream",
"text": [
train model on new data
2024-01-20 09:26:51 +01:00
"Shots attempted in the training set: 58970\n",
"Goals scored in the training set: 7286\n"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
}
],
"source": [
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"count_class_0, count_class_1 = y_train.value_counts()\n",
"\n",
"# Display the count of shots attempted in the training set\n",
"print('Shots attempted in the training set:', count_class_0)\n",
"\n",
"# Display the count of successful goals in the training set\n",
"print('Goals scored in the training set:', count_class_1)"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 12,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
train model on new data
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" Class imbalance in training data: 8.094\n"
Dodanie wstepnej wersji modelu xgboost
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]
}
],
"source": [
"# Class imbalance in training data\n",
"scale_pos_weight = count_class_0 / count_class_1\n",
trenowanie modelu xgboost dla finalnych danych
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"print(f' Class imbalance in training data: {scale_pos_weight:.3f}')"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Training XGBoost model "
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 13,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [],
"source": [
"# Define the xgboost model\n",
trenowanie modelu xgboost dla finalnych danych
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"xgb_model = xgboost.XGBClassifier(enable_categorical=True, tree_method='hist', objective='binary:logistic')"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 14,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [],
"source": [
"# Defining the hyper-parameter grid for XG Boost\n",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"param_grid_xgb = {'learning_rate': [0.01],\n",
" 'max_depth': [3],\n",
" 'n_estimators': [300],\n",
Dodanie wstepnej wersji modelu xgboost
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" 'scale_pos_weight': [1, scale_pos_weight]}"
]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 15,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [],
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Starting the timer\n",
"start_time = time.time()\n",
"\n",
"# Perform grid search with cross-validation\n",
"grid_xg = GridSearchCV(xgb_model, param_grid=param_grid_xgb, cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
"# Fit the best model on the entire training set\n",
"grid_xg.fit(X_train, y_train)\n",
trenowanie modelu xgboost dla finalnych danych
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"\n",
"# Take the best parameters for xgboost model\n",
"best_xgb_model = grid_xg.best_estimator_\n",
"\n",
Dodanie wstepnej wersji modelu xgboost
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"# Stopping the timer\n",
"stop_time = time.time()\n",
"\n",
"# Training Time\n",
"xgb_training_time = stop_time - start_time"
]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 16,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
small data and model improvement
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"Best parameters: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 300, 'scale_pos_weight': 1}\n",
train model on new data
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"Model Training Time: 10.600 seconds\n"
Dodanie wstepnej wersji modelu xgboost
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]
}
],
"source": [
"# Print the best parameters and training time\n",
"print(\"Best parameters: \", grid_xg.best_params_)\n",
"print (f\"Model Training Time: {xgb_training_time:.3f} seconds\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Model evaluation"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"## Training set"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 17,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [
{
"data": {
train model on new data
2024-01-20 09:26:51 +01:00
"image/png": "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
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"# Evaluate the model on training set\n",
"y_pred_train = best_xgb_model.predict(X_train)\n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
trenowanie modelu xgboost dla finalnych danych
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"# Confusion Matrix for Training Data\n",
"cm_train_xg = confusion_matrix(y_train, y_pred_train)\n",
Dodanie wstepnej wersji modelu xgboost
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"ax = sns.heatmap(cm_train_xg, annot=True, cmap='BuPu', fmt='g', linewidth=1.5)\n",
"ax.set_xlabel('Predicted')\n",
"ax.set_ylabel('Actual')\n",
trenowanie modelu xgboost dla finalnych danych
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"ax.set_title('Confusion Matrix - Train Set')\n",
"plt.show()"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"## Test set"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 18,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [
{
"data": {
train model on new data
2024-01-20 09:26:51 +01:00
"image/png": "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
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"# Evaluate the model on test set\n",
"y_pred_test = best_xgb_model.predict(X_test)\n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
"# Confusion Matrix for Testig Data\n",
trenowanie modelu xgboost dla finalnych danych
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"cm_test_xgb = confusion_matrix(y_test, y_pred_test)\n",
Dodanie wstepnej wersji modelu xgboost
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"ax = sns.heatmap(cm_test_xgb, annot=True, cmap='Blues', fmt='g', linewidth=1.5)\n",
"ax.set_xlabel('Predicted')\n",
"ax.set_ylabel('Actual')\n",
trenowanie modelu xgboost dla finalnych danych
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"ax.set_title('Confusion Matrix - Test Set')\n",
"plt.show()"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 19,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
train model on new data
2024-01-20 09:26:51 +01:00
"The test dataset contains 16565 shots, with 1891 of them being goals.\n"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
}
],
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Number of goals in test set\n",
"print(f'The test dataset contains {len(y_test)} shots, with {y_test.sum()[\"is_goal\"]} of them being goals.')"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"cell_type": "markdown",
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"## Feature importance"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 20,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [
{
"data": {
train model on new data
2024-01-20 09:26:51 +01:00
"image/png": "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
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
trenowanie modelu xgboost dla finalnych danych
2023-12-28 23:45:45 +01:00
"# Plot feature importance with Gain\n",
"xgboost.plot_importance(best_xgb_model, importance_type='gain', xlabel='Gain', max_num_features=20)\n",
Dodanie wstepnej wersji modelu xgboost
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"plt.show()"
]
},
{
"cell_type": "code",
train model on new data
2024-01-20 09:26:51 +01:00
"execution_count": 21,
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
"metadata": {},
"outputs": [
{
"data": {
train model on new data
2024-01-20 09:26:51 +01:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAHHCAYAAAAoOjd/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxO6f/48dfdqkVREpnIErIlOxkyUjRj7NnJkjE0JjuDlK3sjWWYjaxjNozPyBJjX0KWMZYQyaCxRRKt9++Pfp2vW4sQqvv9fDx66FznnOtc73Pfut/3Ode5LpVarVYjhBBCCCGEKPJ03ncDhBBCCCGEEO+GJP9CCCGEEEJoCUn+hRBCCCGE0BKS/AshhBBCCKElJPkXQgghhBBCS0jyL4QQQgghhJaQ5F8IIYQQQggtIcm/EEIIIYQQWkKSfyGEEEIIIbSEJP9CCCFEIRISEoJKpSI6Ovp9N0UIUQhJ8i+EEKJAy0x2s/uZMGHCWznm4cOH8ff35+HDh2+lfm2WmJiIv78/e/fufd9NEUIr6b3vBgghhBB5MW3aNCpWrKhRVqtWrbdyrMOHDxMQEICXlxclSpR4K8d4XX379qVHjx4YGhq+76a8lsTERAICAgBwcXF5v40RQgtJ8i+EEKJQaNeuHQ0aNHjfzXgjT548wcTE5I3q0NXVRVdXN59a9O6kp6eTnJz8vpshhNaTbj9CCCGKhG3btvHhhx9iYmJC8eLF+fjjjzl37pzGNn///TdeXl5UqlSJYsWKUaZMGQYOHMj9+/eVbfz9/Rk7diwAFStWVLoYRUdHEx0djUqlIiQkJMvxVSoV/v7+GvWoVCrOnz9Pr169KFmyJM2bN1fWr127lvr162NkZISFhQU9evTgxo0bL40zuz7/dnZ2fPLJJ+zdu5cGDRpgZGRE7dq1la41GzdupHbt2hQrVoz69etz6tQpjTq9vLwwNTXl6tWruLu7Y2Jigo2NDdOmTUOtVmts++TJE0aPHo2trS2GhoZUq1aNefPmZdlOpVLh4+PDunXrqFmzJoaGhixfvhwrKysAAgIClHObed7y8vo8f26vXLmi3J0xNzdnwIABJCYmZjlna9eupVGjRhgbG1OyZElatGjBzp07NbbJy/tHiKJArvwLIYQoFB49esS9e/c0ykqVKgXAmjVr6N+/P+7u7syePZvExESWLVtG8+bNOXXqFHZ2dgCEhYVx9epVBgwYQJkyZTh37hzfffcd586d4+jRo6hUKjp37sylS5f46aefWLhwoXIMKysr7t69+8rt7tatG/b29syaNUtJkGfOnMmUKVPw9PRk8ODB3L17l8WLF9OiRQtOnTr1Wl2Nrly5Qq9evfjss8/o06cP8+bNo3379ixfvpyvvvqKYcOGARAYGIinpyeRkZHo6PzfNcC0tDTatm1LkyZNmDNnDtu3b2fq1KmkpqYybdo0ANRqNZ9++il79uxh0KBB1K1blx07djB27Fhu3rzJwoULNdr0119/8csvv+Dj40OpUqVwdHRk2bJlfP7553Tq1InOnTsDUKdOHSBvr8/zPD09qVixIoGBgZw8eZIffviB0qVLM3v2bGWbgIAA/P39adasGdOmTcPAwIDw8HD++usv3NzcgLy/f4QoEtRCCCFEAbZy5Uo1kO2PWq1WP378WF2iRAm1t7e3xn6xsbFqc3NzjfLExMQs9f/0009qQL1//36lbO7cuWpAfe3aNY1tr127pgbUK1euzFIPoJ46daqyPHXqVDWg7tmzp8Z20dHRal1dXfXMmTM1ys+ePavW09PLUp7T+Xi+bRUqVFAD6sOHDytlO3bsUANqIyMj9fXr15Xyb7/9Vg2o9+zZo5T1799fDai/+OILpSw9PV398ccfqw0MDNR3795Vq9Vq9ebNm9WAesaMGRpt6tq1q1qlUqmvXLmicT50dHTU586d09j27t27Wc5Vpry+PpnnduDAgRrbdurUSW1paaksX758Wa2jo6Pu1KmTOi0tTWPb9PR0tVr9au8fIYoC6fYjhBCiUFi6dClhYWEaP5Bxtfjhw4f07NmTe/fuKT+6uro0btyYPXv2KHUYGRkpvz979ox79+7RpEkTAE6ePPlW2j106FCN5Y0bN5Keno6np6dGe8uUKYO9vb1Ge19FjRo1aNq0qbLcuHFjAD766CPKly+fpfzq1atZ6vDx8VF+z+y2k5yczK5duwAIDQ1FV1eXESNGaOw3evRo1Go127Zt0yhv2bIlNWrUyHMMr/r6vHhuP/zwQ+7fv098fDwAmzdvJj09HT8/P427HJnxwau9f4QoCqTbjxBCiEKhUaNG2T7we/nyZSAjyc2OmZmZ8vuDBw8ICAhgw4YN3LlzR2O7R48e5WNr/8+LIxRdvnwZtVqNvb19ttvr6+u/1nGeT/ABzM3NAbC1tc22PC4uTqNcR0eHSpUqaZRVrVoVQHm+4Pr169jY2FC8eHGN7RwcHJT1z3sx9pd51dfnxZhLliwJZMRmZmZGVFQUOjo6uX4BeZX3jxBFgST/QgghCrX09HQgo992mTJlsqzX0/u/jzpPT08OHz7M2LFjqVu3LqampqSnp9O2bVulnty82Oc8U1paWo77PH81O7O9KpWKbdu2ZTtqj6mp6UvbkZ2cRgDKqVz9wgO6b8OLsb/Mq74++RHbq7x/hCgK5B0thBCiUKtcuTIApUuXxtXVNcft4uLi2L17NwEBAfj5+SnlmVd+n5dTkp95ZfnFyb9evOL9svaq1WoqVqyoXFkvCNLT07l69apGmy5dugSgPPBaoUIFdu3axePHjzWu/l+8eFFZ/zI5ndtXeX3yqnLlyqSnp3P+/Hnq1q2b4zbw8vePEEWF9PkXQghRqLm7u2NmZsasWbNISUnJsj5zhJ7Mq8QvXhUODg7Osk/mWPwvJvlmZmaUKlWK/fv3a5R/8803eW5v586d0dXVJSAgIEtb1Gp1lmEt36UlS5ZotGXJkiXo6+vTunVrADw8PEhLS9PYDmDhwoWoVCratWv30mMYGxsDWc/tq7w+edWxY0d0dHSYNm1aljsHmcfJ6/tHiKJCrvwLIYQo1MzMzFi2bBl9+/alXr169OjRAysrK2JiYti6dSvOzs4sWbIEMzMzWrRowZw5c0hJSaFcuXLs3LmTa9euZamzfv36AEyaNIkePXqgr69P+/btMTExYfDgwQQFBTF48GAaNGjA/v37lSvkeVG5cmVmzJjBxIkTiY6OpmPHjhQvXpxr166xadMmhgwZwpgxY/Lt/ORVsWLF2L59O/3796dx48Zs27aNrVu38tVXXylj87dv355WrVoxadIkoqOjcXR0ZOfOnfzxxx/4+voqV9FzY2RkRI0aNfj555+pWrUqFhYW1KpVi1q1auX59cmrKlWqMGnSJKZPn86HH35I586dMTQ05Pjx49jY2BAYGJjn948QRcZ7GmVICCGEyJPMoS2PHz+e63Z79uxRu7u7q83NzdXFihVTV65cWe3l5aU+ceKEss2///6r7tSpk7pEiRJqc3Nzdbdu3dS3bt3KdujJ6dOnq8uVK6fW0dHRGFozMTFRPWjQILW5ubm6ePHiak9PT/WdO3dyHOozc5jMF/3+++/q5s2bq01MTNQmJibq6tWrq4cPH66OjIzM0/l4cajPjz/+OMu2gHr48OEaZZnDlc6dO1cp69+/v9rExEQdFRWldnNzUxsbG6utra3VU6dOzTJE5uPHj9UjR45U29jYqPX19dX29vbquXPnKkNn5nbsTIcPH1bXr19fbWBgoHHe8vr65HRuszs3arVavWLFCrWTk5Pa0NBQXbJkSXXLli3VYWFhGtvk5f0jRFGgUqvfwRM/QgghhCiwvLy8+O2330hISHjfTRFCvGXS518IIYQQQggtIcm/EEIIIYQQWkKSfyGEEEIIIbSE9PkXQgghhBBCS8iVfyGEEEIIIbSEJP9CCCGEEEJoCZnkSwgtl56ezq1btyhevDgqlep9N0cIIYQQeaBWq3n8+DE2Njbo6OT9er4k/0JouVu3bmFra/u+myGEEEKI13Djxg0++OCDPG8vyb8QWq548eIAXLt2DQsLi/f
Dodanie wstepnej wersji modelu xgboost
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Plot feature importance with Weight\n",
"xgboost.plot_importance(best_xgb_model, importance_type='weight', xlabel='Weight', max_num_features=30)\n",
Dodanie wstepnej wersji modelu xgboost
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"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"## Summary"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 22,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [],
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Calculating MAE, RMSE and R2 for training and test sets \n",
"mae_train = mean_absolute_error(y_train, y_pred_train)\n",
"rmse_train = mean_squared_error(y_train, y_pred_train, squared=False)\n",
"r2_train = r2_score(y_train, y_pred_train)\n",
"\n",
"mae_test = mean_absolute_error(y_test, y_pred_test)\n",
"rmse_test = mean_squared_error(y_test, y_pred_test, squared=False)\n",
"r2_test = r2_score(y_test, y_pred_test)"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 23,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
train model on new data
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"#T_64bf4_row0_col0, #T_64bf4_row0_col1, #T_64bf4_row0_col2, #T_64bf4_row0_col3, #T_64bf4_row0_col4, #T_64bf4_row0_col5, #T_64bf4_row0_col6 {\n",
Dodanie wstepnej wersji modelu xgboost
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" font-weight: bold;\n",
" border: 2.0px solid grey;\n",
" color: white;\n",
"}\n",
"</style>\n",
train model on new data
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"<table id=\"T_64bf4\">\n",
Dodanie wstepnej wersji modelu xgboost
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" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
train model on new data
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" <th id=\"T_64bf4_level0_col0\" class=\"col_heading level0 col0\" >Training MAE</th>\n",
" <th id=\"T_64bf4_level0_col1\" class=\"col_heading level0 col1\" >Training RMSE</th>\n",
" <th id=\"T_64bf4_level0_col2\" class=\"col_heading level0 col2\" >Training R2</th>\n",
" <th id=\"T_64bf4_level0_col3\" class=\"col_heading level0 col3\" >Testing MAE</th>\n",
" <th id=\"T_64bf4_level0_col4\" class=\"col_heading level0 col4\" >Testing RMSE</th>\n",
" <th id=\"T_64bf4_level0_col5\" class=\"col_heading level0 col5\" >Testing R2</th>\n",
" <th id=\"T_64bf4_level0_col6\" class=\"col_heading level0 col6\" >Training Time (mins)</th>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" <tr>\n",
" <th class=\"index_name level0\" >Model Name</th>\n",
" <th class=\"blank col0\" >&nbsp;</th>\n",
" <th class=\"blank col1\" >&nbsp;</th>\n",
" <th class=\"blank col2\" >&nbsp;</th>\n",
" <th class=\"blank col3\" >&nbsp;</th>\n",
" <th class=\"blank col4\" >&nbsp;</th>\n",
" <th class=\"blank col5\" >&nbsp;</th>\n",
" <th class=\"blank col6\" >&nbsp;</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
train model on new data
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" <th id=\"T_64bf4_level0_row0\" class=\"row_heading level0 row0\" >XG Boost</th>\n",
" <td id=\"T_64bf4_row0_col0\" class=\"data row0 col0\" >0.09726</td>\n",
" <td id=\"T_64bf4_row0_col1\" class=\"data row0 col1\" >0.31186</td>\n",
" <td id=\"T_64bf4_row0_col2\" class=\"data row0 col2\" >0.00629</td>\n",
" <td id=\"T_64bf4_row0_col3\" class=\"data row0 col3\" >0.09955</td>\n",
" <td id=\"T_64bf4_row0_col4\" class=\"data row0 col4\" >0.31551</td>\n",
" <td id=\"T_64bf4_row0_col5\" class=\"data row0 col5\" >0.01560</td>\n",
" <td id=\"T_64bf4_row0_col6\" class=\"data row0 col6\" >0.17666</td>\n",
Dodanie wstepnej wersji modelu xgboost
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" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
train model on new data
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"<pandas.io.formats.style.Styler at 0x138923a50>"
Dodanie wstepnej wersji modelu xgboost
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]
},
train model on new data
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"execution_count": 23,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating of dataframe of summary results\n",
"summary_df = pd.DataFrame({'Model Name':['XG Boost'],\n",
trenowanie modelu xgboost dla finalnych danych
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" 'Training MAE': mae_train, \n",
" 'Training RMSE': rmse_train,\n",
" 'Training R2':r2_train,\n",
" 'Testing MAE': mae_test, \n",
" 'Testing RMSE': rmse_test,\n",
" 'Testing R2':r2_test,\n",
" 'Training Time (mins)': xgb_training_time/60})\n",
Dodanie wstepnej wersji modelu xgboost
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"summary_df.set_index('Model Name', inplace=True)\n",
trenowanie modelu xgboost dla finalnych danych
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"\n",
Dodanie wstepnej wersji modelu xgboost
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"# Displaying summary of results\n",
trenowanie modelu xgboost dla finalnych danych
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"summary_df.style.format(precision =5).set_properties(**{'font-weight': 'bold',\n",
Dodanie wstepnej wersji modelu xgboost
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" 'border': '2.0px solid grey','color': 'white'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Keeping the xgboost model"
Dodanie wstepnej wersji modelu xgboost
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]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 24,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'objective': 'binary:logistic',\n",
" 'base_score': None,\n",
" 'booster': None,\n",
" 'callbacks': None,\n",
" 'colsample_bylevel': None,\n",
" 'colsample_bynode': None,\n",
" 'colsample_bytree': None,\n",
" 'device': None,\n",
" 'early_stopping_rounds': None,\n",
" 'enable_categorical': True,\n",
" 'eval_metric': None,\n",
" 'feature_types': None,\n",
" 'gamma': None,\n",
" 'grow_policy': None,\n",
" 'importance_type': None,\n",
" 'interaction_constraints': None,\n",
" 'learning_rate': 0.01,\n",
" 'max_bin': None,\n",
" 'max_cat_threshold': None,\n",
" 'max_cat_to_onehot': None,\n",
" 'max_delta_step': None,\n",
" 'max_depth': 3,\n",
" 'max_leaves': None,\n",
" 'min_child_weight': None,\n",
" 'missing': nan,\n",
" 'monotone_constraints': None,\n",
" 'multi_strategy': None,\n",
" 'n_estimators': 300,\n",
" 'n_jobs': None,\n",
" 'num_parallel_tree': None,\n",
" 'random_state': None,\n",
" 'reg_alpha': None,\n",
" 'reg_lambda': None,\n",
" 'sampling_method': None,\n",
" 'scale_pos_weight': 1,\n",
" 'subsample': None,\n",
" 'tree_method': 'hist',\n",
" 'validate_parameters': None,\n",
" 'verbosity': None}"
]
},
train model on new data
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"execution_count": 24,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Save the model\n",
"best_xgb_model.save_model('xgboost.json')\n",
"\n",
"best_xgb_model.get_params()"
]
},
{
"cell_type": "code",
"execution_count": 69,
Dodanie wstepnej wersji modelu xgboost
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"metadata": {},
"outputs": [],
"source": [
trenowanie modelu xgboost dla finalnych danych
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"# Save the model\n",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"# dump(best_xgb_model, 'xgboost.joblib') \n",
Dodanie wstepnej wersji modelu xgboost
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"\n",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"# # Load the model\n",
"# model = load('xgboost.joblib')"
]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 25,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
2024-01-19 15:25:32 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
train model on new data
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"array([[0.88809854, 0.11190146],\n",
" [0.94282126, 0.05717874],\n",
" [0.9659079 , 0.03409215],\n",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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" ...,\n",
train model on new data
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" [0.8352859 , 0.16471413],\n",
" [0.87101626, 0.12898372],\n",
" [0.95800334, 0.04199664]], dtype=float32)"
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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]
},
train model on new data
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"execution_count": 25,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_xgb_model.predict_proba(X)"
]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 26,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"metadata": {},
"outputs": [],
"source": [
"new_xgb_model = xgboost.XGBClassifier()\n",
"new_xgb_model.load_model('xgboost.json')"
]
},
{
"cell_type": "code",
train model on new data
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"execution_count": 27,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
train model on new data
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"array([[0.88809854, 0.11190146],\n",
" [0.94282126, 0.05717874],\n",
" [0.9659079 , 0.03409215],\n",
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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" ...,\n",
train model on new data
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" [0.8352859 , 0.16471413],\n",
" [0.87101626, 0.12898372],\n",
" [0.95800334, 0.04199664]], dtype=float32)"
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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]
},
train model on new data
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"execution_count": 27,
Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_xgb_model.predict_proba(X)"
Dodanie wstepnej wersji modelu xgboost
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]
}
],
"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",
train model on new data
2024-01-20 09:26:51 +01:00
"version": "3.11.6"
Dodanie wstepnej wersji modelu xgboost
2023-12-12 15:22:01 +01:00
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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