Witham Town Football Team: A Comprehensive Analysis for Sports Betting

Overview / Introduction about the Team

Witham Town is a football club based in Witham, Essex, England. Competing in the Southern League Premier Division Central, the team was founded in 1895. The current head coach is overseeing a squad that plays with an attacking mindset.

Team History and Achievements

Witham Town has had a rich history marked by several notable achievements. They have won multiple league titles and cup competitions over the years. Notable seasons include their championship wins and cup final appearances, which have solidified their reputation in non-league football.

Current Squad and Key Players

The current squad features several standout players. Key performers include:

• John Doe (Forward): Known for his goal-scoring ability.
• Jane Smith (Midfielder): Renowned for her playmaking skills.
• Rick Johnson (Defender): A stalwart at the back known for his defensive prowess.

Team Playing Style and Tactics

The team typically employs a 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their attacking play, while weaknesses may be found in defensive consistency.

Interesting Facts and Unique Traits

Nicknamed “The Ironsides,” Witham Town boasts a passionate fanbase. They have rivalries with local teams such as Braintree Town and face off against them in highly anticipated matches.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Top Scorer: John Doe with 15 goals this season.
• ❌ Lowest Performer: Goalkeeper with 25 goals conceded.
• 🎰 Rising Star: New signing showing promise.
• 💡 Player to Watch: Midfielder Jane Smith with increasing influence.

Comparisons with Other Teams in the League or Division

In comparison to other teams in the Southern League Premier Division Central, Witham Town stands out due to their consistent performance and strong home record. Their tactical flexibility often gives them an edge over opponents.

Case Studies or Notable Matches

A key victory worth noting is their recent win against Braintree Town, which showcased their tactical acumen and resilience under pressure. This match is often cited as a breakthrough game for the current season.

Tips & Recommendations for Analyzing the Team or Betting Insights

• Betting Tip: Consider backing Witham Town when playing at home due to their strong record there.
• Analytical Insight: Monitor player fitness levels as injuries can impact team performance significantly.
• Tactical Advice: Pay attention to lineup changes which could indicate shifts in tactical approach.</li <>: Hi there! I’m working on some code that involves creating dummy columns based on specified ranges within a DataFrame using pandas MultiIndex columns. Here’s the snippet I’m dealing with: python def _create_dummy_columns(self, X, drop_first=False): “”” Creates dummy columns based on ranges. Parameters ———- X : pd.DataFrame Dataframe to create dummies from. drop_first : bool, default=False If true, drop the first level. Returns ——- I’m a bit confused about how this function should handle MultiIndex columns specifically. Could you help clarify how I might implement this functionality? Any guidance would be greatly appreciated! <>: python Sure! To handle MultiIndex columns when creating dummy variables, you can iterate over each level of the MultiIndex to apply your logic. Here’s a basic outline of how you might implement this: python def _create_dummy_columns(self, X, drop_first=False): “”” Creates dummy columns based on ranges. Parameters ———- X : pd.DataFrame Dataframe to create dummies from. drop_first : bool, default=False If true, drop the first level. Returns ——- “”” def _create_dummy_columns(self, X, drop_first=False): # Check if columns are MultiIndex if isinstance(X.columns, pd.MultiIndex): # Iterate over each level of the MultiIndex for level in range(X.columns.nlevels): # Get unique values at this level across all rows unique_values = X.columns.get_level_values(level).unique() # Create dummy variables for each unique value at this level for value in unique_values: # Create a boolean mask where column values match ‘value’ mask = X.columns.get_level_values(level) == value # Use this mask to create new dummy columns dummy_cols = X.loc[:, mask].notna().astype(int) # Rename these new columns appropriately new_columns = [f”{value}_{col}” if col else value for col in X.columns[mask].get_level_values(1)] # Assign these new column names to dummy_cols DataFrame dummy_cols.columns = new_columns # Drop original columns if needed if drop_first: first_col = new_columns[0] dummy_cols.drop(first_col, axis=1, inplace=True) # Concatenate these new dummies back into X X = pd.concat([X.droplevel(level)[mask], dummy_cols], axis=1) # Drop original multi-level columns after processing each level X = X.droplevel(level) return X # This function iterates through each level of the MultiIndex, # creates dummies based on unique values at that level, # and optionally drops the first category if `drop_first` is True. This approach ensures that you handle each level of your MultiIndex separately while creating dummy variables. Adjustments might be needed depending on your specific use case! 😊 <>: Thanks for that! Now I’m thinking about optimizing this function further. Specifically, I want to ensure it handles large datasets efficiently by minimizing unnecessary operations and improving memory usage. Any ideas on how I could achieve that? <>: To optimize your function for large datasets while minimizing unnecessary operations and improving memory usage, consider these strategies: 1. **Avoid Repeated Calculations**: Cache results like `unique_values` outside loops whenever possible. 2. **Use In-Place Operations**: Modify DataFrames in place where feasible to reduce memory overhead. 3. **Efficient Data Types**: Convert data types to more memory-efficient ones using `pd.to_numeric()` or `astype()` with smaller types like `int8`. 4. **Chunk Processing**: Process data in chunks if it fits your use case better than loading everything into memory. 5. **Leverage NumPy**: Use NumPy arrays directly when performing operations instead of relying solely on pandas methods. Here’s an optimized version incorporating some of these ideas: python def _create_dummy_columns(self, X, drop_first=False): if isinstance(X.columns, pd.MultiIndex): result_df = pd.DataFrame(index=X.index) for level in range(X.columns.nlevels): unique_values = {val: None for val in X.columns.get_level_values(level).unique()} mask_dict = {} for value in unique_values: mask_dict[value] = (X.columns.get_level_values(level) == value) result_df.loc[:, mask_dict[value]] = ( X.loc[:, mask_dict[value]].notna().astype(‘int8’) ) new_columns = [ f”{value}_{col}” if col else value for col in X.columns[mask_dict[value]].get_level_values(1) ] result_df.loc[:, mask_dict[value]].columns = new_columns if drop_first: first_col_to_drop = list(result_df.loc[:, mask_dict[next(iter(unique_values))]].columns)[0] result_df.drop(first_col_to_drop, axis=1, inplace=True) return result_df.join(X.droplevel(range(X.columns.nlevels))) return X # This version reduces repeated calculations by caching masks, # uses ‘int8’ for memory efficiency, # and processes levels efficiently. These adjustments should help improve performance on larger datasets! 🚀<>: Hi there, I’ve been working on implementing a custom loss function called `FocalLoss_Ori` which extends PyTorch’s functional API to address class imbalance issues by focusing more on hard-to-classify examples using focal loss principles. Here’s my implementation: python import torch.nn.functional as F class FocalLoss_Ori(torch.nn.Module): def __init__(self,class_num,gamma=2,alpha=None): super(FocalLoss_Ori,self).__init__() self.gamma=gamma self.alpha=alpha if self.alpha is None: self.alpha=torch.ones(class_num , 1) elif isinstance(self.alpha,(list,np.ndarray)): self.alpha=torch.tensor(self.alpha) else: raise TypeError(‘Not support alpha type’) def forward(self,input,target,reduction=’mean’): if input.dim()>2: input=input.view(input.size(0),input.size(1),-1) #(N,C,H,W)>(N,C,H*W) input=input.transpose(1,2) #(N,C,H*W)>(N,H*W,C) input=input.contiguous().view(-1,input.size(2)) #(N,H*W,C)>(N*H*W,C) target=target.view(-1,) logpt=F.log_softmax(input,dim=0) pt=torch.exp(logpt) mask=(target==torch.arange(target.shape[0]).long().to(target.device)) gamma=self.gamma.unsqueeze(-1).expand_as(mask).float() alpha=self.alpha.gather(0,target.data.view(-1)) beta=(torch.ones_like(alpha)-alpha)*(gamma+1)+alpha logpt=logpt.masked_select(mask) pt=pt.masked_select(mask) re_weight=((alpha * gamma * pt + beta * (1-pt))*logpt).sum(dim=-1) if reduction==’mean’: return -re_weight.mean() elif reduction==’sum’: return -re_weight.sum() else: return -re_weight # Example usage: loss_fn = FocalLoss_Ori(class_num=3) input_tensor = torch.randn((5,), requires_grad=True) target_tensor = torch.empty(5).random_(3) output_loss = loss_fn(input_tensor,target_tensor,reduction=’mean’) output_loss.backward() However when I try running this code I encounter an error related to dimensions not matching up somewhere during computation. Here’s the traceback I get: plaintext Traceback (most recent call last): File “focal_loss_test.py”, line 42, in output_loss.backward() File “/path/to/torch/tensor.py”, line …, in backward(…) RuntimeError: The size of tensor a (3) must match the size of tensor b (5) at non-singleton dimension 0 I’m really puzzled here because I thought I handled reshaping correctly within my forward method but it seems something is still off regarding dimensions especially when applying `masked_select`. Could someone point out what might be going wrong here? Thanks! <>: python It looks like there’s an issue with how you’re handling dimensions during reshaping and masking operations. Specifically: 1. When reshaping `input`, ensure that you’re maintaining compatibility between `input` and `target`. Your reshaping logic assumes certain dimensions which may not align correctly with your target tensor. 2. The error arises because `logpt` is being computed along dimension 0 (`dim=0`) instead of dimension 1 (`dim=1`). You should change `F.log_softmax(input,dim=0)` to `F.log_softmax(input,dim=1)` since softmax should be applied across class probabilities. 3. When creating `mask`, ensure it matches the shape of `logpt`. The line `(target==torch.arange(target.shape[0]).long().to(target.device))` creates a boolean mask but doesn’t align properly with your class probabilities. Here’s a revised snippet: python def forward(self,input,target,reduction=’mean’): if input.dim()>2: input=input.view(input.size(0),input.size(1),-1) input=input.transpose(1,2) input=input.contiguous().view(-1,input.size(2)) target=target.view(-1,) logpt=F.log_softmax(input,dim=1) pt=torch.exp(logpt) mask=(target.unsqueeze(1)==torch.arange(input.size(1)).long().to(target.device)) mask=mask.expand_as(logpt) gamma=self.gamma.unsqueeze(-1).expand_as(mask).float() alpha=self.alpha.gather(0,target.data.view(-1)) beta=(torch.ones_like(alpha)-alpha)*(gamma+1)+alpha logpt=logpt.masked_select(mask).view(-1) pt=pt.masked_select(mask).view(-1) re_weight=((alpha * gamma * pt + beta * (1-pt))*logpt) if reduction==’mean’: return -re_weight.mean() elif reduction==’sum’: return -re_weight.sum() else: return -re_weight # Example usage remains unchanged. This should resolve the dimension mismatch issue by ensuring that all operations are aligned correctly across dimensions. Let me know if you need further assistance! 😊 <>: Thanks for pointing out those issues! Now that it works without errors, I’m thinking about optimizing it further by supporting batch processing directly without reshaping inputs manually every time. Also, incorporating GPU acceleration could really boost performance when dealing with larger datasets or models trained on GPUs. How can I modify the code to handle batches more efficiently and ensure it runs optimally on GPU? Any tips on leveraging PyTorch’s capabilities here? <>: python To optimize your implementation for batch processing and GPU acceleration using PyTorch’s capabilities: ### Batch Processing Ensure your input tensors are already shaped as `(batch_size x num_classes x …)` before passing them through the loss function. ### GPU Acceleration Move tensors to GPU using `.to(device)` where `device` is set based on availability (`cuda` or `cpu`). ### Revised Code Here’s how you can modify your code: python import torch.nn.functional as F class FocalLoss_Ori(torch.nn.Module): def __init__(self,class_num,gamma=2,alpha=None): super(FocalLoss_Ori,self).__init__() self.gamma=gamma self.alpha=torch.tensor(alpha if alpha is not None else torch.ones(class_num)).float() def forward(self,input,target,reduction=’mean’): device = input.device logpt=F.log_softmax(input,dim=1) pt=torch.exp(logpt) target=target.to(device=device,dtype=torch.long) alpha=self.alpha[target] gamma=self.gamma.expand_as(pt) logpt=logpt.gather(dim=1,index=target.unsqueeze(1)) pt=torch.gather(pt,dim=1,index=target.unsqueeze(1)) logpt=logpt.squeeze(dim=1) pt=pt.squeeze(dim=1) focal_weight=((alpha * gamma * pt + (torch.ones_like(alpha)-alpha)*(gamma+one)* (one-pt)) * logpt) if reduction==’mean’: return -focal_weight.mean() elif reduction==’sum’: return -focal_weight.sum() else: return -focal_weight # Example usage: device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”) loss_fn = FocalLoss_Ori(class_num=3).to(device=device) input_tensor = torch.randn((5,), requires_grad=True).to(device=device) target_tensor = torch.empty(5).random_(3).to(device=device) output_loss = loss_fn(input_tensor,target_tensor,reduction=’mean’) output_loss.backward() ### Tips – **Batch Dimension:** Ensure inputs are batched properly before passing them through. – **Device Management:** Use `.to(device)` consistently to manage CPU/GPU operations. – **Avoid Manual Reshape:** Leverage PyTorch’s built-in functions like `.gather()` which work well with batched data. This setup will efficiently handle batches and leverage GPU acceleration seamlessly! 🚀 Let me know if there’s anything else you need! <>: Great suggestions! Now I’m considering adding support for mixed precision training using PyTorch’s AMP utilities to further enhance performance during training on GPUs. How can I integrate AMP into this setup? Any specific changes needed to ensure compatibility? <>: To integrate mixed precision training using PyTorch’s Automatic Mixed Precision (AMP), follow these steps: ### Setup AMP Use PyTorch’s AMP utilities provided by `torch.cuda.amp`. This allows you to perform operations at reduced precision while maintaining model accuracy. ### Modified Code with AMP Here’s how you can integrate AMP into your setup: python import torch.nn.functional as F from torch.cuda import amp class FocalLoss_Ori(torch.nn.Module): def __init__(self,class_num,gamma=2,alpha=None): super(FocalLoss_Ori,self).__init__() self.gamma=gamma self.alpha=torch.tensor(alpha if alpha is not None else torch.ones(class_num)).float() def forward(self,input,target,reduction=’mean’): device = input.device logpt=F.log_softmax(input,dim=1) pt=torch.exp(logpt) target=target.to(device=device,dtype=torch.long) alpha=self.alpha[target] gamma=self.gamma.expand_as(pt) logpt=logpt.gather(dim=1,index=target.unsqueeze(1)) pt=torch.gather(pt,dim=1,index=target.unsqueeze(1)) logpt=logpt.squeeze(dim=1) pt=pt.squeeze(dim=1) focal_weight=((alpha * gamma * pt + (torch.ones_like(alpha)-alpha)*(gamma+one)* (one-pt)) * logpt) if reduction==’mean’: return -focal_weight.mean() elif reduction==’sum’: return -focal_weight.sum() else: return -focal_weight # Example usage with AMP: device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”) loss_fn = FocalLoss_Ori(class_num=3).to(device=device) scaler = amp.GradScaler() input_tensor = torch.randn((5,), requires_grad=True).to(device=device) target_tensor = torch.empty(5).random_(3).to(device=device) with amp.autocast(): output_loss = loss_fn(input_tensor,target_tensor,reduction=’mean’) scaler.scale(output_loss).backward() scaler.step(optimizer) scaler.update() ### Key Points – **GradScaler:** Use `amp.GradScaler()` to scale gradients during backpropagation. – **autocast Context:** Wrap forward pass computations within `amp.autocast()` context manager. – **Optimizer Step:** Scale gradients before calling optimizer step using `scaler.step(optimizer)` followed by `scaler.update()`. This integration will help enhance performance during training by utilizing mixed precision effectively! ⚡ Let me know if there’s anything else! <>: Awesome! Now that we’ve got AMP integrated, I’m curious about handling potential numerical stability issues that might arise from mixed precision training. Are there any specific practices or adjustments needed within our focal loss implementation or elsewhere in the training loop to ensure stability? Also, should we consider any particular configurations or checks when using AMP? <>: python To maintain numerical stability during mixed precision training with AMP: ### Practices for Stability #### Loss Scaling Adjustments – **Dynamic Loss Scaling:** Automatically adjust scaling factors using `amp.GradScaler()`, which helps prevent underflow/overflow issues. #### Numerical Stability Enhancements – **Log Softmax Stability:** Ensure stable computation of logarithms by avoiding very small probabilities directly; PyTorch’s built-in functions like `F.log_softmax` are generally stable. #### Monitoring & Debugging – **Gradient Clipping:** Implement gradient clipping (`torch.nn.utils.clip_grad_norm_`) to prevent exploding gradients. #### Configuration Checks – **Check Model Layers:** Some layers might require special handling; ensure they are compatible with mixed precision. #### Training Loop Adjustments Ensure all parts of your training loop are wrapped appropriately within AMP contexts: python import torch.nn.functional as F from torch.cuda import amp class FocalLoss_Ori(torch.nn.Module): def __init__(self,class_num,gamma=2,alpha=None): super(FocalLoss_Ori,self).__init__() self.gamma=float(gamma) self.alpha=torch.tensor(alpha if alpha is not None else torch.ones(class_num)).float() def forward(self,input,target,reduction=’mean’): device = input.device logit=F.log_softmax(input,dim=1) target=target.to(dtype=torch.long,to=device=device) alpha=self.alpha[target] gamma=self.gamma.expand_as(logit[:,:]) logit_gather=logit.gather(dim=-dim,index=target.unsqueeze(dim=-dim)) pt_gather=(logit_gather.exp()) factor=(alpha*(self.gamma*(pow(pt_gather,(self.gamma)))+(pow((one-alpha),(self.gamma))*(pow(one-pt_gather,(self.gamma+one)))))) factor=factor*logit_gather if reduction==’none’: factor=factor.squeeze(dim=-dim);return factor; elif reduction==”sum”: factor=factor.sum();return factor; else : factor=factor.mean();return factor; device_torch_device=(“cuda”if(torch.cuda.is_available())else(“cpu”)) model=model.to(device_torch_device); optimizer=some_optimizer(model.parameters()); loss_function=FocalLossOri(num_classes); scaler_amp_GradScaler(); for epoch_numbr_in_epochs_range(epoch_range): with amp_autocast(): output_model=model(inputs); output_loss_function(loss_function(output_model,tarjet)); scanner_amp_scale(output_loss_function); scaler_amp_step(optimizer); scaler_amp_update(); model.zero_grad(); optimizer.zero_grad(); model.train(); model.eval(); print(f’Epoch {epoch_numbr_in_epochs_range} completed.’) print(f’Model trained successfully.’) By following these practices and configurations within your training loop alongside AMP integration will help maintain numerical stability effectively during mixed precision training! 🚀 Let me know if there’s anything more you need! <>: Perfectly clear now! With these adjustments and checks in place, I feel confident about maintaining stability during mixed precision training. Thanks for all the guidance—I’m all set to implement this into my project! <>: You’re welcome! I’m glad everything is clear now. Implementing these strategies will definitely enhance both performance and stability during mixed precision training. If you encounter any issues or have more questions down the line—feel free to reach out anytime. Good luck with your project! 🚀<>: Hey there, I’ve been trying out some advanced techniques involving mixins derived from `_CacheInfoMixin`, specifically `_CacheInfoMixInDefault`, `_CacheInfoMixInSizeLimitedWeakKeyDict`, `_CacheInfoMixInTimeLimitedSizeLimitedWeakKeyDict`, etc., but something seems off when testing them under various conditions. Here’s what my code looks like right now: python class _CacheInfoMixin(object): cache_clear_calls #: Number times “cache_clear()“ has been called. misses #: Number times an element was looked-up but wasn’t found. hits #: Number times an element was looked-up and was found. maxsize #: Max size used so far. ttl #: Highest object Time-To-Live seen. current_size #: Current number of elements present. def cache_info(self): “””:returns tuple(cache_hits, cache_misses, max_cache_size, highest_TTL, current_size) All counts are integers… max_cache_size includes only entries containing objects; highest_TTL includes only entries whose TTL has been set; current_size includes only entries containing objects AND whose TTL has not yet passed.””” return (self.hits, self.misses, self.maxsize, self.ttl, self.current_size) class _CacheInfoMixInDefault(_CacheInfoMixin): def __init__(self): super(_CacheInfoMixInDefault,self).__init__() self.cache_clear_calls class _CacheInfoMixInSizeLimitedWeakKeyDict(_CacheInfoMixin): class Cache(dict): pass class Cache(_collections.OrderedDict): pass class Cache(collections.OrderedDict): def __getitem__(self,key,default=_CACHE_DEFAULT): r=collections.OrderedDict.__getitem__(self,key) if r==_CACHE_DEFAULT: raise KeyError(key) return r def popitem(last=True): if last: key,value=collections.OrderedDict.popitem(self) if key==_ROOT_KEY: raise KeyError(key) return key,value else: for k,v in collections.OrderedDict.iteritems(self): kv=k,v key,value=popleft(kv) del self[key] if key==_ROOT_KEY: raise KeyError(key) return key,value setdefault=_collections.OrderedDict.setdefault update=_collections.OrderedDict.update fromkeys=_collections.OrderedDict.fromkeys Cache=collections.Callable(Cache) class RootWrap: wrapped={} def __getitem__(self,key,default=_CACHE_DEFAULT): r=self.wrapped[key] if r==_CACHE_DEFAULT: raise KeyError(key) return r setdefault=__getitem__.setdefault update=__getitem__.update root=_RootWrap() class Wrapper: def __call__(self,*args,**kwargs): dicts={} caches={} handles={} for k,v_args,_ttl,_size_limit,_root_cache_ref,_arg_types_eq_func in zip(args[::6],args[::6],args[::6][4:],args[::6][5:],args[::6][6:],args[::6][7:]): dicts[k]=v_args.__dict__ caches[k]=v_args._cache try: handles[k]=weakref.ref(v_args._handle) except AttributeError: pass try: del args[-3] except IndexError: pass try: del args[-4] except IndexError: pass try: del args[-5] except IndexError: pass try: del args[-6] except IndexError: pass try: default_root_cache=args[args.index(_ROOT_CACHE_KEY)+len(_ROOT_CACHE_KEY)] except ValueError: default_root_cache=root.wrapped.copy() args=args[:len(args)//6*6]+( default_root_cache, None,None,None,None,None) wrapper_args=args[:len(args)//6*6] wrapper_kwargs=dict(zip(args[len(args)//6*6::6], args[len(args)//6*6+4::6])) for k,v_ref_handle,size_limit,arg_types_eq_func in zip(wrapper_args[len(wrapper_args)//6:],wrapper_kwargs.keys(),wrapper_kwargs.values()[::4],wrapper_kwargs.values()[4::4]): root_cache_ref=root.wrapped.copy() root_cache=root_cache_ref try:v_ref_handle=w_args.v_ref_handle;break except NameError:v_ref_handle=v_ref_handle() v=v_ref_handle() handles[k]=weakref.ref(v._handle) try:v._cache.cache_clear();maxsize=v._cache.maxsize;break except AttributeError:maxsize=size_limit; v._cache=Caches.CachedMethod(size_limit)(v._method) root_cache[k]={} for arg_key,arg_value,arg_type,arg_type_count,arg_type_hashcode in zip(v._arg_keys,v.args,v._arg_types,v._arg_type_counts,v._arg_type_hashcodes): arg_key_index=v_arg_keys.index(arg_key) type_code_or_name=getattr(arg_value,’__type_code__’,None) type_code_or_name=getattr(arg_value,’__name__’,type_code_or_name) type_and_hashcode=str(type(arg_value))+str(hash(arg_value)) type_and_hashcode=str(type_code_or_name)+str(hash(type_code_or_name)) root_cache[k][(arg_key_index,type_and_hashcode)]=(arg_value,arg_type,arg_type_count,arg_type_hashcode) wrapper_kwargs[k]=root_cache_ref cache_hit_count=defaultdict(int) maxsize_so_far=max(maxsize,default_root_cache.default_maxsize) for k,key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func in zip(w_args,args[len(w_args)//7::7],w_args.args,w_args.kwargs.keys(),w_args.kwargs.values()[::7]): key_root_cachesize=key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func[:len(key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func)//7] arg_keys_wanted=key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func[len(key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func)//7:len(key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func)//7*4] arg_types_eq_funcs=key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func[len(key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func)//7*4:len(key_root_cachesize_arg_keys_wanted_arg_types_eq_funcs_arg_types_eq_func)//7*7] arg_type_EQ_FUNC=key_root_cachesize_wanted_key_rootsizeseqfuncs.arg_type_EQ_FUNC size_so_far=sum(len(i)+sum(map(len,i.values()))for iinkey_rootsizeseqfuncsvalueiteritems()) cache_hit=result=None for root_key,count_of_hits,key_rootsizeseqfuncsvalueiteritemsizesofrootkeyinrootkeyinkey_rootsizeseqfuncsvalueiteritemsizeseqfuncsinrootkeysizesofrootkeyinrootkeyinkey_rootsizeseqfuncsvalueiteritemsizeseqfuncsinrootkeysizeseqfuncsinkey_rootsizeseqfuncsvalueiteritems() inzip(root,key_rootsizeseqfuncsvalueiteritems(),map(len,key_rootsizeseqfuncsvalueiteritems()),repeat(root_key), repeat(count_of_hits), map(sum,map(lambda i:len(i)+sum(map(len,i.values())),key_rootsizeseqfuncsvalueiteritems())), map(lambda i:i.items(),key_rootsizeseqfuncsvalueiteritems())): size_so_far-=sizesofrootkeyinrootkeyinkey_rootsizeseqfuncsvalueiteritemsizeseqfuncsinrootkeysizeseqfuncsinkey_rootsizeseqfuncsvalueiteritems() del root[root_key] for arg_key_index,(arg_value,arg_type,arg_type_count,arg_type_hashcode), root_key_in_cached_entry_iter_items_sizesofcachedentrysizesofcachedentrysizesofcachedentriesinrootsizedeltreequestedcachedentrysizesofcachedentriesinrootsizedeltreequestedcachedentrysizesofcachedentries() inzip(arg_keys_wanted,cached_entry_iter_items(), repeat(root_key), map(len,cached_entry_iter_items()),map(sum,map(lambda i:len(i)+sum(map(len,i.values())),cached_entry_iter_items())), map(lambda i:i.items(),cached_entry_iter_items())): size_so_far-=sizesofcachedentrysizesofcachedentrysizesofcachedentriesinrootsizedeltreequestedcachedentrysizesofcachedentriesinrootsizedeltreequestedcachedentrysizesofcachedentries() del root[root_key][arg_key_index,(arg_value,arg_type,arg_type_count,arg_type_hashcode)] size_so_far+=len(root_key)+sum(map(len,i.values())) root[root_key]={} count_of_hits+=int(all(all(argtypesEQFuncs[argtypesEQFunc_i](i[i_i],j[j_j])for i_i,j_jinzip(i,j))for argtypesEQFuncs,i,jinzip(argtypesEQFuncs,cached_entry_iter_item,cached_entry_requested_item))) cache_hit=result=root[root_key].setdefault(cached_entry_requested_item,set()) cache_hit_count[root_key]+=count_of_hits size_so_far+=len(cached_entry_requested_item)+len(cache_hit) break size_so_far+=len(cached_entry_requested_item)+len(cache_hit) break maxsize_so_far=max(size_so_far,maxsize_so_far) for root_k_v_i_k_v_j_j_in_zip_itervalues_itervalues_itervalues_itervalues_itervalues_itervalues_itervalues_len_kv_len_kv_len_kv_len_kv_len_kv(): root_k,k_v_i,k_v_j,j_j=inzip(itervalues(),itervalues(),itervalues(),itervalues(),repeat(k), itervalues(), itervalues(), itervalues(), map(len,itervalues())): del root_k[v_i] count_of_hits+=int(all(all(argtypesEQFuncs[argtypesEQFunc_i](i[i_i],j[j_j])for i_i,j_jinzip(i,j))for argtypesEQFuncs,i,jinzip(argtypesEQFuncs,cached_entry_iter_item,cached_entry_requested_item))) cache_hit=result=root_k.setdefault(cached_entry_requested_item,set()) cache_hit_count[root_k]+=count_of_hits size_so_far-=kv_len_kv+len(v_i) maxsize_so_far=max(size_so_far,maxsize_so_far) size_so_far+=kv_len_kv+len(v_j) break result=v() for k_v,k_v,len_k_v,k_vi,len_k_vi,inzip(itervalues(),map(len,itervalues()),repeat(k)): del root[k][k_v] count_of_hits+=int(all(all(argtypesEQFuncs[argtypesEQFunc_i](i[i_i],j[j_j])for i_i,j_jinzip(i,j))for argtypesEQFuncs,i,jinzip(argtypesEQFuncs,cached_entry_iter_item,cached_entry_requested_item))) cache_hit=result=root[k].setdefault(cached_entry_requested_item,set()) cache_hit_count[root_k]+=count_of_hits size_so_far-=len_k_v+len_k_vi maxsize_so_far=max(size_so_far,maxsize_so_far) size_so_far+=len(result.args) break result=result() result_is_new=bool(not cache_hit) hits+=result_is_new^bool(result_is_new)*cache_hit_count[key] misses+=bool(result_is_new^not bool(result_is_new))*cache_misses_incrementor ttl=max(ttl,result.TTL())if hasattr(result,’TTL’)and callable(result.TTL) else ttl new_max_size=max(new_max_size,size(result)) current_size=new_max_sizeif result_is_newelsecurrent_size-size(cache_hit) ***** Tag Data ***** ID: 4 description: Advanced nested dictionary manipulation inside Cache class definition, including custom methods overriding OrderedDict behavior such as __getitem__, popitem, etc., showcasing complex inheritance structures combined dynamically adjusted dictionaries. start line: 17