Regionalliga Middle stats & predictions

Team Preview: FC Stadlau

Famous for their disciplined defense and quick counterattacks, FC Stadlau looks poised to challenge SKU Amstetten head-on. Player Spotlight: Lukas Bauer

Lukas Bauer has been instrumental in orchestrating plays from midfield with his exceptional vision and passing accuracy. Player Stats:

• Average Pass Completion Rate: 85%
• Total Assists This Season: 12
• Awarded Man of the Match (x5) this season Player Quote:

  "I’m ready to lead my team with determination and skill," says Bauer. - Lukas Bauer Read More About Lukas Bauer & His Journey This Season >> Download Lukas Bauer's Player Profile >> Watch Lukas Bauer's Top Moments >> Explore Lukas Bauer's Career Stats >> Discover Lukas Bauer's Training Regimen >> View Fan Comments on Lukas Bauer >> Join Discussion on Lukas Bauer's Performance >> Submit Your Questions About Lukas Bauer >> Get Exclusive Content on Lukas Bauer >> Share Your Thoughts on Lukas Bauer >> Vote on Lukas Bauer as Player of the Season >> Learn About Upcoming Matches Featuring Lukas Bauer >>

  Team Preview: SKU Amstetten

  SU[0]: import logging [1]: import time [2]: from datetime import datetime [3]: import numpy as np [4]: import pandas as pd [5]: import torch [6]: from sklearn.metrics import roc_auc_score [7]: logger = logging.getLogger(__name__) [8]: device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") [9]: class EarlyStopping: [10]: """Early stops the training if validation loss doesn't improve after a given patience.""" [11]: def __init__(self, patience=7, verbose=False): [12]: """ [13]: Args: [14]: patience (int): How long to wait after last time validation loss improved. [15]: Default: 7 [16]: verbose (bool): If True, prints a message for each validation loss improvement. [17]: Default: False [18]: """ [19]: self.patience = patience [20]: self.verbose = verbose [21]: self.counter = 0 [22]: self.best_score = None [23]: self.val_loss_min = np.Inf [24]: self.best_model_wts = None [25]: def __call__(self, val_loss): [26]: score = -val_loss [27]: if self.best_score is None: [28]: self.best_score = score [29]: self.save_checkpoint(val_loss) [30]: elif score <= self.best_score: [31]: self.counter += 1 [32]: logger.info(f'EarlyStopping counter: {self.counter} out of {self.patience}') [33]: if self.counter >= self.patience: [34]: logger.info('Early stopping') [35]: return True [36]: else: [37]: self.best_score = score [38]: self.save_checkpoint(val_loss) [39]: self.counter = 0 # get validation predictions (need logits rather than labels) y_pred_val_logits = model.predict_proba(val_features) # get validation predictions (need logits rather than labels) # get validation predictions (need logits rather than labels) # get validation predictions (need logits rather than labels) y_pred_val_logits = model.predict_proba(val_features) # Calculate validation ROC AUC # Calculate validation ROC AUC # Calculate validation ROC AUC # Calculate validation ROC AUC # Calculate validation ROC AUC # Calculate validation ROC AUC # Calculate validation ROC AUC # Calculate validation ROC AUC # Calculate validation ROC AUC