384 KiB
384 KiB
Imports
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV
from sklearn.metrics import confusion_matrix, mean_squared_error, mean_absolute_error, r2_score
import xgboost
import time
from joblib import dump, load
import os
Load the data
df = pd.read_csv('final_data_new.csv')
df.columns
Index(['minute', 'position_name', 'shot_body_part_name', 'shot_technique_name', 'shot_type_name', 'shot_first_time', 'shot_one_on_one', 'shot_aerial_won', 'shot_open_goal', 'shot_follows_dribble', 'shot_redirect', 'x1', 'y1', 'number_of_players_opponents', 'number_of_players_teammates', 'is_goal', 'angle', 'distance', 'x_player_opponent_Goalkeeper', 'x_player_opponent_8', 'x_player_opponent_1', 'x_player_opponent_2', 'x_player_opponent_3', 'x_player_teammate_1', 'x_player_opponent_4', 'x_player_opponent_5', 'x_player_opponent_6', 'x_player_teammate_2', 'x_player_opponent_9', 'x_player_opponent_10', 'x_player_opponent_11', 'x_player_teammate_3', 'x_player_teammate_4', 'x_player_teammate_5', 'x_player_teammate_6', 'x_player_teammate_7', 'x_player_teammate_8', 'x_player_teammate_9', 'x_player_teammate_10', 'y_player_opponent_Goalkeeper', 'y_player_opponent_8', 'y_player_opponent_1', 'y_player_opponent_2', 'y_player_opponent_3', 'y_player_teammate_1', 'y_player_opponent_4', 'y_player_opponent_5', 'y_player_opponent_6', 'y_player_teammate_2', 'y_player_opponent_9', 'y_player_opponent_10', 'y_player_opponent_11', 'y_player_teammate_3', 'y_player_teammate_4', 'y_player_teammate_5', 'y_player_teammate_6', 'y_player_teammate_7', 'y_player_teammate_8', 'y_player_teammate_9', 'y_player_teammate_10', 'x_player_opponent_7', 'y_player_opponent_7', 'x_player_teammate_Goalkeeper', 'y_player_teammate_Goalkeeper'], dtype='object')
df.head()
minute | position_name | shot_body_part_name | shot_technique_name | shot_type_name | shot_first_time | shot_one_on_one | shot_aerial_won | shot_open_goal | shot_follows_dribble | ... | y_player_teammate_5 | y_player_teammate_6 | y_player_teammate_7 | y_player_teammate_8 | y_player_teammate_9 | y_player_teammate_10 | x_player_opponent_7 | y_player_opponent_7 | x_player_teammate_Goalkeeper | y_player_teammate_Goalkeeper | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2 | Left Center Forward | Right Foot | Half Volley | Open Play | True | False | False | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | 26.6 | 53.1 | NaN | NaN |
1 | 5 | Left Back | Left Foot | Volley | Open Play | True | False | False | False | False | ... | 20.6 | 32.8 | NaN | NaN | NaN | NaN | 23.8 | 31.2 | NaN | NaN |
2 | 15 | Left Center Forward | Left Foot | Normal | Open Play | False | False | False | False | False | ... | 29.0 | NaN | NaN | NaN | NaN | NaN | 29.6 | 55.3 | NaN | NaN |
3 | 16 | Center Forward | Head | Normal | Open Play | False | False | True | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | 26.7 | 60.4 | NaN | NaN |
4 | 18 | Right Center Forward | Right Foot | Normal | Open Play | False | False | False | False | False | ... | 27.9 | 31.4 | 33.4 | NaN | NaN | NaN | 16.9 | 40.1 | NaN | NaN |
5 rows × 64 columns
Data preparation
# df[['minute',
# 'number_of_players_opponents',
# 'number_of_players_teammates']] = df[['minute',
# 'number_of_players_opponents',
# 'number_of_players_teammates']].astype(float)
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
df[['position_name',
'shot_technique_name',
'shot_type_name',
'shot_body_part_name']] = enc.fit_transform(df[['position_name',
'shot_technique_name',
'shot_type_name',
'shot_body_part_name']])
df[['minute',
'position_name',
'shot_technique_name',
'shot_type_name',
'shot_body_part_name']] = df[['minute',
'position_name',
'shot_technique_name',
'shot_type_name',
'shot_body_part_name']].astype(int)
df
minute | position_name | shot_body_part_name | shot_technique_name | shot_type_name | shot_first_time | shot_one_on_one | shot_aerial_won | shot_open_goal | shot_follows_dribble | ... | y_player_teammate_5 | y_player_teammate_6 | y_player_teammate_7 | y_player_teammate_8 | y_player_teammate_9 | y_player_teammate_10 | x_player_opponent_7 | y_player_opponent_7 | x_player_teammate_Goalkeeper | y_player_teammate_Goalkeeper | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 18 | 3 | 4 | 3 | False | False | False | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
1 | 5 | 18 | 1 | 4 | 3 | False | False | False | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 5 | 4 | 3 | 2 | 3 | True | False | False | False | False | ... | 48.9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 5 | 10 | 3 | 4 | 3 | False | False | False | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 5 | 17 | 1 | 4 | 3 | True | False | False | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
38337 | 61 | 3 | 3 | 4 | 3 | True | False | False | False | False | ... | 47.3 | 42.6 | 54.7 | NaN | NaN | NaN | 21.3 | 50.9 | NaN | NaN |
38338 | 66 | 0 | 3 | 4 | 3 | True | False | False | False | False | ... | 47.6 | 39.4 | 43.1 | NaN | NaN | NaN | 19.0 | 45.8 | NaN | NaN |
38339 | 73 | 3 | 1 | 6 | 3 | True | False | False | False | False | ... | 48.9 | 48.1 | 41.1 | NaN | NaN | NaN | 21.7 | 29.6 | NaN | NaN |
38340 | 75 | 13 | 1 | 4 | 3 | False | False | False | False | False | ... | 29.1 | 33.6 | 40.9 | NaN | NaN | NaN | 21.2 | 32.4 | NaN | NaN |
38341 | 90 | 3 | 3 | 4 | 3 | False | False | False | False | False | ... | 62.6 | 51.0 | 66.7 | NaN | NaN | NaN | 23.0 | 45.5 | NaN | NaN |
38342 rows × 64 columns
dump(enc,'labelEncoder.joblib')
['labelEncoder.joblib']
enc2 = load('labelEncoder.joblib')
# df[['position_name',
# 'shot_technique_name',
# 'shot_type_name',
# 'shot_body_part_name']] = enc2.inverse_transform(df[['position_name',
# 'shot_technique_name',
# 'shot_type_name',
# 'shot_body_part_name']])
# df[['position_name',
# 'shot_technique_name',
# 'shot_type_name',
# 'shot_body_part_name']] = enc2.transform(df[['position_name',
# 'shot_technique_name',
# 'shot_type_name',
# 'shot_body_part_name']])
# enc.inverse_transform(df[['position_name',
# 'shot_technique_name',
# 'shot_type_name',
# 'shot_body_part_name']])
# ############### NEW ################
# from sklearn.preprocessing import LabelEncoder
# le_posiotion_name = LabelEncoder()
# le_shot_technique_name = LabelEncoder()
# le_shot_type_name = LabelEncoder()
# le_shot_body_part_name = LabelEncoder()
# df['position_name'] = le_posiotion_name.fit_transform(df['position_name'])
# df['shot_technique_name'] = le_shot_technique_name.fit_transform(df['shot_technique_name'])
# df['shot_type_name'] = le_shot_type_name.fit_transform(df['shot_type_name'])
# df['shot_body_part_name'] = le_shot_body_part_name.fit_transform(df['shot_body_part_name'])
# Change the type of categorical features to 'category'
df[['minute',
'position_name',
'shot_technique_name',
'shot_type_name',
'number_of_players_opponents',
'number_of_players_teammates',
'shot_body_part_name']] = df[['minute',
'position_name',
'shot_technique_name',
'shot_type_name',
'number_of_players_opponents',
'number_of_players_teammates',
'shot_body_part_name']].astype('category')
# Splitting the dataset into features (X) and the target variable (y)
y = pd.DataFrame(df['is_goal'])
X = df.drop(['is_goal'], axis=1)
# Splitting the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
# Create cross-validation
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)
count_class_0, count_class_1 = y_train.value_counts()
# Display the count of shots attempted in the training set
print('Shots attempted in the training set:', count_class_0)
# Display the count of successful goals in the training set
print('Goals scored in the training set:', count_class_1)
Shots attempted in the training set: 58970 Goals scored in the training set: 7286
# Class imbalance in training data
scale_pos_weight = count_class_0 / count_class_1
print(f' Class imbalance in training data: {scale_pos_weight:.3f}')
Class imbalance in training data: 8.094
Training XGBoost model
# Define the xgboost model
xgb_model = xgboost.XGBClassifier(enable_categorical=True, tree_method='hist', objective='binary:logistic')
# Defining the hyper-parameter grid for XG Boost
param_grid_xgb = {'learning_rate': [0.01, 0.001, 0.0001],
'max_depth': [3, 5, 7, 8, 9],
'n_estimators': [100, 150, 200, 250, 300],
'scale_pos_weight': [1, scale_pos_weight]}
# Starting the timer
start_time = time.time()
# Perform grid search with cross-validation
grid_xg = GridSearchCV(xgb_model, param_grid=param_grid_xgb, cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)
# Fit the best model on the entire training set
grid_xg.fit(X_train, y_train)
# Take the best parameters for xgboost model
best_xgb_model = grid_xg.best_estimator_
# Stopping the timer
stop_time = time.time()
# Training Time
xgb_training_time = stop_time - start_time
# Print the best parameters and training time
print("Best parameters: ", grid_xg.best_params_)
print (f"Model Training Time: {xgb_training_time:.3f} seconds")
Best parameters: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 250, 'scale_pos_weight': 1} Model Training Time: 1550.563 seconds
Model evaluation
Training set
# Evaluate the model on training set
y_pred_train = best_xgb_model.predict(X_train)
# Confusion Matrix for Training Data
cm_train_xg = confusion_matrix(y_train, y_pred_train)
ax = sns.heatmap(cm_train_xg, annot=True, cmap='BuPu', fmt='g', linewidth=1.5)
ax.set_xlabel('Predicted')
ax.set_ylabel('Actual')
ax.set_title('Confusion Matrix - Train Set')
plt.show()
Test set
# Evaluate the model on test set
y_pred_test = best_xgb_model.predict(X_test)
# Confusion Matrix for Testig Data
cm_test_xgb = confusion_matrix(y_test, y_pred_test)
ax = sns.heatmap(cm_test_xgb, annot=True, cmap='Blues', fmt='g', linewidth=1.5)
ax.set_xlabel('Predicted')
ax.set_ylabel('Actual')
ax.set_title('Confusion Matrix - Test Set')
plt.show()
# Number of goals in test set
print(f'The test dataset contains {len(y_test)} shots, with {y_test.sum()["is_goal"]} of them being goals.')
The test dataset contains 16565 shots, with 1891 of them being goals.
Feature importance
# Plot feature importance with Gain
xgboost.plot_importance(best_xgb_model, importance_type='gain', xlabel='Gain', max_num_features=20)
plt.show()
# Plot feature importance with Weight
xgboost.plot_importance(best_xgb_model, importance_type='weight', xlabel='Weight', max_num_features=30)
plt.show()
Summary
# Calculating MAE, RMSE and R2 for training and test sets
mae_train = mean_absolute_error(y_train, y_pred_train)
rmse_train = mean_squared_error(y_train, y_pred_train, squared=False)
r2_train = r2_score(y_train, y_pred_train)
mae_test = mean_absolute_error(y_test, y_pred_test)
rmse_test = mean_squared_error(y_test, y_pred_test, squared=False)
r2_test = r2_score(y_test, y_pred_test)
# Creating of dataframe of summary results
summary_df = pd.DataFrame({'Model Name':['XG Boost'],
'Training MAE': mae_train,
'Training RMSE': rmse_train,
'Training R2':r2_train,
'Testing MAE': mae_test,
'Testing RMSE': rmse_test,
'Testing R2':r2_test,
'Training Time (mins)': xgb_training_time/60})
summary_df.set_index('Model Name', inplace=True)
# Displaying summary of results
summary_df.style.format(precision =5).set_properties(**{'font-weight': 'bold',
'border': '2.0px solid grey','color': 'white'})
Training MAE | Training RMSE | Training R2 | Testing MAE | Testing RMSE | Testing R2 | Training Time (mins) | |
---|---|---|---|---|---|---|---|
Model Name | |||||||
XG Boost | 0.09622 | 0.31019 | 0.01693 | 0.09949 | 0.31542 | 0.01620 | 25.84271 |
Keeping the xgboost model
# Save the model
best_xgb_model.save_model('xgboost.json')
best_xgb_model.get_params()
{'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 250, 'n_jobs': None, 'num_parallel_tree': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': 1, 'subsample': None, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
# Save the model
# dump(best_xgb_model, 'xgboost.joblib')
# # Load the model
# model = load('xgboost.joblib')
best_xgb_model.predict_proba(X)
array([[0.9005616 , 0.09943844], [0.938443 , 0.06155698], [0.96434194, 0.03565805], ..., [0.8334278 , 0.1665722 ], [0.8697108 , 0.13028917], [0.96131223, 0.03868778]], dtype=float32)
new_xgb_model = xgboost.XGBClassifier()
new_xgb_model.load_model('xgboost.json')
new_xgb_model.predict_proba(X)
array([[0.9005616 , 0.09943844], [0.938443 , 0.06155698], [0.96434194, 0.03565805], ..., [0.8334278 , 0.1665722 ], [0.8697108 , 0.13028917], [0.96131223, 0.03868778]], dtype=float32)