Liga Primera U20 Apertura stats & predictions

Fan Reactions and Social Media Buzz

The anticipation surrounding tomorrow’s matches is palpable across social media platforms:

• Fans are speculating about potential upsets and standout performances using hashtags like #LigaPrimeraU20AperturaNicaragua2023.
Sports analysts are sharing their insights on Twitter, offering predictions based on statistical analyses and historical data.

The excitement is not just limited to Nicaragua; international supporters are also engaging in discussions about these young talents shaping the future of Nicaraguan football.

Economic Impact on Local Communities

The Liga Primera U20 Apertura is more than just a sporting event; it has significant economic implications for local communities:

= 0 [44]: if threshold is not None: [45]: output = output > threshold output = output.float() target = target.float() tp = (output * target).sum(dim=(0,)) tn = ((1 - output) * (1 - target)).sum(dim=(0,)) fp = (output * (1 - target)).sum(dim=(0,)) fn = ((1 - output) * target).sum(dim=(0,)) precision = tp / (tp + fp + eps) recall = tp / (tp + fn + eps) if reduce: precision = precision.mean() recall = recall.mean() f_score = ( 1 + beta ** 2 ) * precision * recall / ( beta ** 2 * precision + recall + eps ) return f_score else: _f_score= ( 1 + beta ** 2 ) * precision * recall / ( beta ** 2 * precision + recall + eps ) return _f_score num_classes = output.shape[ 1 if output.dim() == target.dim() else 0 ] dims = tuple( range(2 if output.dim() == target.dim() else 1, output.dim())) if threshold is None: _target_max= target.max(dim=dims)[0] _target= _target_max.gt(0).float() threshold= _target_max[_target_max.nonzero()].min() else: _threshold= torch.empty_like(output) _threshold.fill_(threshold) _output= output.gt(_threshold).float() _target= target.float() else: _target= ( target == target.argmax(dim=dim)[..., None] ).float() dim= ( 1 if dim is None else dim ) _output= _output.float() else: _output= ( output == output.argmax(dim=dim)[..., None] ).float() tp = (_output * _target).sum(dim=dims) tn = ((1 - _output) * (1 - _target)).sum(dim=dims) fp = (_output * (1 - _target)).sum(dim=dims) fn = ((1 - _output) * _target).sum(dim=dims) if reduce: tp=torch.mean(tp) tn=torch.mean(tn) fp=torch.mean(fp) fn=torch.mean(fn) else: precision= tp / (tp + fp + eps) recall= tp / (tp + fn + eps) f_score=( 1 + beta ** 2 ) * precision * recall / ( beta ** 2 * precision + recall + eps ) return f_score ***** Tag Data ***** ID: 2 description: Calculation of F-score involving handling tensor shapes dynamically, applying thresholds conditionally based on tensor content. start line: 26 end line: 55 dependencies: - type: Function name: f_score start line: 26 end line: 55 context description: This snippet is part of a function `f_score` which computes F-score. It involves complex tensor operations including conditional logic based on input shapes. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 5 interesting for students: 5 self contained: Y ************* ## Suggestions for complexity 1. **Weighted F-Score Calculation:** Modify `f_score` to compute a weighted F-score where different classes can have different weights. 2. **Class-wise Thresholding:** Introduce class-specific thresholds instead of a single global threshold. 3. **Multi-label Output Handling:** Adapt `f_score` to handle multi-label classification where each instance can belong to multiple classes simultaneously. 4. **Batch-wise Reduction:** Implement reduction logic at batch level rather than across all samples. 5. **Custom Aggregation Functions:** Allow users to pass custom aggregation functions instead of using simple mean. ## Conversation <|user|># Need help understanding [SNIPPET] specifically how it handles tensor shapes<|end|> <|assistant|># Sure! The function `f_score` takes two tensors `output` and `target`, both having shape `[N,C,H,W]`. Here's how it handles tensor shapes: python if threshold is not None: If a threshold is provided: python