Dodanie nootbooka z modelem regresji logistycznej dla xG
This commit is contained in:
Maciej Chmielarz
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562e5e9a43
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{
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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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"source": [
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"# Importy"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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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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"source": [
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"# Wczytanie danych"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('data4.csv')"
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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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = pd.DataFrame(df['isGoal'])\n",
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"X = df.drop(['isGoal'], axis=1)"
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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": 21,
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"metadata": {},
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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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"d:\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
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" warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
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]
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}
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],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
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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": 22,
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"metadata": {},
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{
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" .dataframe tbody tr th {\n",
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" <th></th>\n",
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" <th>match_minute</th>\n",
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" <th>match_second</th>\n",
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" <th>position_x</th>\n",
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" <th>position_y</th>\n",
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" <th>play_type</th>\n",
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" <th>BodyPart</th>\n",
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" <th>Number_Intervening_Opponents</th>\n",
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" <th>Number_Intervening_Teammates</th>\n",
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" <th>Interference_on_Shooter</th>\n",
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" <th>outcome</th>\n",
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" <th>...</th>\n",
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" <th>Interference_on_Shooter_Code</th>\n",
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" <th>distance_to_goalM</th>\n",
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" <th>distance_to_centerM</th>\n",
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" <th>angle</th>\n",
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" <th>isFoot</th>\n",
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" <th>isHead</th>\n",
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" <th>header_distance_to_goalM</th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Medium</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>8767</th>\n",
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" <td>13</td>\n",
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" <td>28</td>\n",
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" <td>9.23</td>\n",
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" <td>-2.24</td>\n",
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" <td>Open Play</td>\n",
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" <td>Head</td>\n",
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" <td>3</td>\n",
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" <td>0</td>\n",
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" <td>Medium</td>\n",
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" <td>Goal</td>\n",
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" <td>...</td>\n",
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" <td>2</td>\n",
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" <td>2.245283</td>\n",
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" <td>13.672174</td>\n",
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" <tr>\n",
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" <th>5798</th>\n",
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" <td>78</td>\n",
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" <td>14.46</td>\n",
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" <td>12.72</td>\n",
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" <td>Open Play</td>\n",
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" <td>Left</td>\n",
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" <td>3</td>\n",
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" <td>0</td>\n",
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" <td>Low</td>\n",
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" <td>Saved</td>\n",
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" <td>...</td>\n",
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" <td>1</td>\n",
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" <td>19.278332</td>\n",
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" <td>12.750000</td>\n",
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" <tr>\n",
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" <th>6018</th>\n",
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" <td>78</td>\n",
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" <td>27</td>\n",
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" <td>9.73</td>\n",
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" <td>14.22</td>\n",
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" <td>Open Play</td>\n",
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" <td>Left</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>Low</td>\n",
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" <td>Missed</td>\n",
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" <td>...</td>\n",
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" <td>1</td>\n",
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" <td>17.257933</td>\n",
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" <td>14.253538</td>\n",
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" <td>55.681087</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4961</th>\n",
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" <td>34</td>\n",
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" <td>34</td>\n",
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" <td>34.91</td>\n",
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" <td>0.25</td>\n",
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" <td>Open Play</td>\n",
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" <td>Right</td>\n",
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" <td>4</td>\n",
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" <td>1</td>\n",
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" <td>Low</td>\n",
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" <td>Saved</td>\n",
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" <td>...</td>\n",
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" <td>1</td>\n",
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" <td>34.910899</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>447</th>\n",
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" <td>52</td>\n",
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" <td>57</td>\n",
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" <td>26.93</td>\n",
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" <td>1.00</td>\n",
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" <td>Open Play</td>\n",
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" <td>Left</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>Medium</td>\n",
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" <td>Saved</td>\n",
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" <td>...</td>\n",
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" <td>2</td>\n",
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" <td>26.948648</td>\n",
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" <td>1.002358</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 29 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" match_minute match_second position_x position_y play_type BodyPart \\\n",
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"8767 13 28 9.23 -2.24 Open Play Head \n",
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"5798 78 9 14.46 12.72 Open Play Left \n",
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"6018 78 27 9.73 14.22 Open Play Left \n",
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"4961 34 34 34.91 0.25 Open Play Right \n",
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"447 52 57 26.93 1.00 Open Play Left \n",
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"\n",
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" Number_Intervening_Opponents Number_Intervening_Teammates \\\n",
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"8767 3 0 \n",
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"5798 3 0 \n",
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"6018 2 0 \n",
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"4961 4 1 \n",
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||||
"447 2 0 \n",
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"\n",
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" Interference_on_Shooter outcome ... Interference_on_Shooter_Code \\\n",
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"8767 Medium Goal ... 2 \n",
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"5798 Low Saved ... 1 \n",
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"6018 Low Missed ... 1 \n",
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"4961 Low Saved ... 1 \n",
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"447 Medium Saved ... 2 \n",
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"\n",
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" distance_to_goalM distance_to_centerM angle isFoot isHead \\\n",
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"8767 9.499168 2.245283 13.672174 0 1 \n",
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||||
"5798 19.278332 12.750000 41.404002 1 0 \n",
|
||||
"6018 17.257933 14.253538 55.681087 1 0 \n",
|
||||
"4961 34.910899 0.250590 0.411271 1 0 \n",
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||||
"447 26.948648 1.002358 2.131616 1 0 \n",
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"\n",
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" header_distance_to_goalM High Low Medium \n",
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"8767 9.499168 0 0 1 \n",
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"5798 0.000000 0 1 0 \n",
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||||
"6018 0.000000 0 1 0 \n",
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||||
"4961 0.000000 0 1 0 \n",
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"447 0.000000 0 0 1 \n",
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"\n",
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"[5 rows x 29 columns]"
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]
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},
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"execution_count": 22,
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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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"X_train.head()"
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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": 23,
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"metadata": {},
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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",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>isGoal</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>8767</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5798</th>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6018</th>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4961</th>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>447</th>\n",
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" <td>0</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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" isGoal\n",
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"8767 1\n",
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"5798 0\n",
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"6018 0\n",
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"4961 0\n",
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"447 0"
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]
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},
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"execution_count": 23,
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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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"y_train.head()"
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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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"source": [
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"# Przygotowanie danych"
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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": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['match_minute', 'match_second', 'position_x', 'position_y', 'play_type',\n",
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" 'BodyPart', 'Number_Intervening_Opponents',\n",
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" 'Number_Intervening_Teammates', 'Interference_on_Shooter', 'outcome',\n",
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" 'position_xM', 'position_yM', 'position_xM_r', 'position_yM_r',\n",
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" 'position_xM_std', 'position_yM_std', 'position_xM_std_r',\n",
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" 'position_yM_std_r', 'BodyPartCode', 'Interference_on_Shooter_Code',\n",
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" 'distance_to_goalM', 'distance_to_centerM', 'angle', 'isFoot', 'isHead',\n",
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" 'header_distance_to_goalM', 'High', 'Low', 'Medium'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 24,
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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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"X_train.columns"
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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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"source": [
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"## Uwzględnienie wybranych cech: \n",
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"- Współrzędna x strzelającego,\n",
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"- Współrzędna y strzelającego,\n",
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"- Dystans do bramki,\n",
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"- Kąt do bramki,\n",
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"- Minuta meczu,\n",
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"- Liczba przeciwników przed piłką,\n",
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"- Liczba zawodników ze swojej drużyny przed piłką,\n",
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"- Część ciała."
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train_extracted = X_train[['position_x', 'position_y', 'distance_to_goalM', \n",
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" 'angle', 'match_minute', 'Number_Intervening_Opponents', \n",
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" 'Number_Intervening_Teammates', 'isFoot', 'isHead']]"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_test_extracted = X_test[['position_x', 'position_y', 'distance_to_goalM', \n",
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" 'angle', 'match_minute', 'Number_Intervening_Opponents', \n",
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" 'Number_Intervening_Teammates', 'isFoot', 'isHead']]"
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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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"source": [
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"# Trening danych"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LogisticRegression"
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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": 28,
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"metadata": {},
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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": [
|
||||
"d:\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
||||
" y = column_or_1d(y, warn=True)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500)</pre></div></div></div></div></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"LogisticRegression(max_iter=500)"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model = LogisticRegression(max_iter=500)\n",
|
||||
"model.fit(X_train_extracted, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Ewaluacja modelu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Zbiór danych testowych zawiera 2033 oddane strzały, gdzie 236 to strzały trafione.\n",
|
||||
"Dokładność klasyfikacji, czy strzał jest bramką, czy nie, wynosi 0.89%.\n",
|
||||
"klasyfikator uzyskał ROC-AUC na poziomie 0.76%.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.metrics import roc_auc_score\n",
|
||||
"\n",
|
||||
"print(f'Zbiór danych testowych zawiera {len(y_test)} oddane strzały, gdzie {y_test.sum()[\"isGoal\"]} to strzały trafione.')\n",
|
||||
"print(f'Dokładność klasyfikacji, czy strzał jest bramką, czy nie, wynosi {model.score(X_test_extracted, y_test):.2f}%.')\n",
|
||||
"print(f'klasyfikator uzyskał ROC-AUC na poziomie {roc_auc_score(y_test, model.predict_proba(X_test_extracted)[:, 1]):.2f}%.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" precision recall f1-score support\n",
|
||||
"\n",
|
||||
" 0 0.89 0.99 0.94 1797\n",
|
||||
" 1 0.59 0.09 0.16 236\n",
|
||||
"\n",
|
||||
" accuracy 0.89 2033\n",
|
||||
" macro avg 0.74 0.54 0.55 2033\n",
|
||||
"weighted avg 0.86 0.89 0.85 2033\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.metrics import classification_report\n",
|
||||
"\n",
|
||||
"print(classification_report(y_test,model.predict(X_test_extracted)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Zapisywanie modelu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['regresja_logistyczna.joblib']"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from joblib import dump\n",
|
||||
"dump(model, 'regresja_logistyczna.joblib') "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Wczytywanie modelu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from joblib import load\n",
|
||||
"\n",
|
||||
"model2 = load('regresja_logistyczna.joblib')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue