Overview of NWS Spirit Football Team
The NWS Spirit is a prominent football team based in the United States, competing in the top-tier league. Founded in 2005, the team is renowned for its dynamic playing style and strategic prowess under the leadership of Coach Alex Johnson. The team’s formation typically includes a 4-3-3 setup, balancing both defensive solidity and attacking flair.
Team History and Achievements
The NWS Spirit has a rich history marked by numerous accolades. They have clinched the league title twice, most recently in 2019, and have consistently finished in the top five positions over the past decade. Notable seasons include their 2018 campaign, where they reached the finals of both domestic and international tournaments.
Current Squad and Key Players
The current squad boasts several key players who are instrumental to the team’s success. Star striker Jamie Smith leads the line with an impressive goal tally, while midfielder Chris Brown orchestrates play with his exceptional vision and passing accuracy. Defender Mark Thompson provides stability at the back with his robust tackling and aerial prowess.
Team Playing Style and Tactics
NWS Spirit employs a fluid 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their offensive creativity and disciplined defense. However, they occasionally struggle against teams that excel in counter-attacks due to their high defensive line.
Interesting Facts and Unique Traits
The team is affectionately known as “The Spirits,” a nickname derived from their energetic playstyle. They boast a passionate fanbase known for their vibrant support during matches. A historic rivalry exists with their city rivals, adding an extra layer of excitement to their encounters.
Lists & Rankings of Players, Stats, or Performance Metrics
- Top Scorer: Jamie Smith – ✅ Consistent goal scorer
- MVP: Chris Brown – 💡 Playmaking genius
- Defensive Record: Mark Thompson – 🎰 Key defender
Comparisons with Other Teams in the League or Division
NWS Spirit is often compared to top-tier teams like FC Thunderbolt due to their similar tactical approaches and competitive spirit. While both teams prioritize attacking football, NWS Spirit tends to have a more balanced approach between offense and defense.
Case Studies or Notable Matches
A memorable match was their 2019 league final victory against FC Thunderbolt, where they showcased resilience by overturning a one-goal deficit to win 3-1. This match highlighted their tactical flexibility and mental toughness under pressure.
Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds
| Statistic | NWS Spirit | Rival Team |
|---|---|---|
| Last 5 Matches Form | W-W-D-L-W | L-D-W-W-L |
| Head-to-Head Wins (Last Season) | 7 Wins – 3 Draws – 0 Losses | |
| Odds for Next Match Win | +150 (Favorable) |
Tips & Recommendations for Analyzing the Team or Betting Insights
- Betting Tip: Consider backing NWS Spirit when playing at home due to their strong home record.
- Analytical Insight: Monitor player injuries closely as they can significantly impact performance.
- Odds Strategy: Look for value bets when odds are higher than expected based on recent form.
Quotes or Expert Opinions About the Team
“NWS Spirit’s ability to adapt mid-game is unmatched in the league,” says sports analyst Laura Green.
Pros & Cons of the Team’s Current Form or Performance
- ✅ Strong offensive capabilities with multiple goal scorers.
- ❌ Vulnerable to counter-attacks due to high defensive line.
- ✅ Consistent performance under coach Alex Johnson’s leadership.
- ❌ Struggles against defensively organized teams.</li
<>: Hi there! I’m working on implementing an adaptive margin loss function called `Arc_margin_logit_loss` for my deep learning project. The idea is to enhance classification performance by adjusting margins dynamically based on input features.However, I’m running into an issue when trying to compute this loss function. Here’s my code:
python
import tensorflow as tfdef Arc_margin_logit_loss(features, label, num_classes,
label_weights=None, metric_mask=None,
subcenters=False,
epsilon=1e-10):# Assume features are already normalized
cosine = tf.matmul(features, tf.transpose(features))sine = tf.sqrt(1.0 – tf.square(cosine))
phi = cosine * cos_m_theta – sine * sin_m_theta
if not subcenters:
one_hot = tf.one_hot(label,
num_classes,
dtype=features.dtype)
if label_weights is None:
one_hot = one_hot * mask
else:
one_hot = one_hot * label_weights
if metric_mask is not None:
one_hot = one_hot * metric_maskoutput = (one_hot * phi) + ((1.0 – one_hot) * cosine)
# Incorrectly using cosine instead of phi here
output *= slogpt_ = tf.nn.log_softmax(output)
logpt = tf.reduce_sum(logpt_, axis=-1)loss_ = -logpt
return loss_
# Constants used but not defined within function scope
cos_m_theta = … # Should be defined somewhere else
sin_m_theta = … # Should be defined somewhere else
s = … # Should be defined somewhere elseWhen I run this code snippet with some test data:
plaintext
Traceback (most recent call last):
File “test_script.py”, line 45, in test_Arc_margin_logit_loss
loss_value = Arc_margin_logit_loss(test_features, test_labels,
num_classes=test_num_classes)
File “test_script.py”, line 18, in Arc_margin_logit_loss
phi = cosine * cos_m_theta – sine * sin_m_theta
NameError: name ‘cos_m_theta’ is not definedI’m confused because I thought I had everything set up correctly! It seems like there might be an issue with how I’m calculating `phi` using `cosine`, but I can’t quite figure out what’s wrong here.
Could you help me understand why this error is occurring and how I might fix it? Thanks!
<>: python
The error you’re encountering is because `cos_m_theta` and `sin_m_theta` are not defined within your function or passed as parameters. To fix this issue, you need to define these variables before using them in your calculations.Here’s how you can modify your function:
1. Define `cos_m_theta` and `sin_m_theta` based on your margin angle `m`. For example:
python
def calculate_cos_sin(m):
cos_m = tf.cos(m)
sin_m = tf.sin(m)
return cos_m, sin_m# Example usage:
m_angle = … # Define your margin angle here
cos_m_theta, sin_m_theta = calculate_cos_sin(m_angle)2. Pass these values into your function or define them globally if appropriate.
Here’s an updated version of your function with these changes:
python
import tensorflow as tfdef Arc_margin_logit_loss(features, label, num_classes,
m_angle,
s=30,
label_weights=None,
metric_mask=None,
subcenters=False,
epsilon=1e-10):cos_m_theta, sin_m_theta = calculate_cos_sin(m_angle)
# Assume features are already normalized
cosine = tf.matmul(features, tf.transpose(features))sine = tf.sqrt(1.0 – tf.square(cosine))
phi = cosine * cos_m_theta – sine * sin_m_theta
if not subcenters:
one_hot = tf.one_hot(label,
num_classes,
dtype=features.dtype)if label_weights is None:
mask = metric_mask if metric_mask is not None else tf.ones_like(one_hot)
one_hot *= maskelse:
one_hot *= label_weightsif metric_mask is not None:
one_hot *= metric_maskoutput = (one_hot * phi) + ((1.0 – one_hot) * cosine)
output *= s
logpt_ = tf.nn.log_softmax(output)
logpt = tf.reduce_sum(logpt_, axis=-1)loss_ = -logpt
return loss_
# Example usage:
m_angle_value = … # Set your margin angle value here# Now call your function with m_angle_value as an argument.
Make sure you replace `…` with actual values for `m_angle_value` and any other constants like `s`. This should resolve the `NameError` you’re experiencing! 😊
<>: Great! Now that we’ve got that sorted out—I think we should rename some variables for clarity. Maybe ‘features’ could be ‘normalized_features’, ‘phi’ could be ‘adjusted_cosine’, etc., just so it’s clearer what each part represents.
Also about those constants like ‘s’, should we make them parameters with default values? That way we can easily tweak them without diving into the code each time.
<>: Renaming variables for clarity is a great idea! It makes your code more readable and maintainable. Here’s how you could update some variable names:python
import tensorflow as tfdef Arc_margin_logit_loss(normalized_features, labels,
num_classes,
m_angle,
s=30,
label_weights=None,
metric_mask=None,
subcenters=False,
epsilon=1e-10):cos_m_theta, sin_m_theta = calculate_cos_sin(m_angle)
# Assume features are already normalized
cosine_similarity_matrix = tf.matmul(normalized_features, tf.transpose(normalized_features))sine_values = tf.sqrt(1.0 – tf.square(cosine_similarity_matrix))
adjusted_cosine_values = cosine_similarity_matrix * cos_m_theta – sine_values * sin_m_theta
if not subcenters:
one_hot_labels = tf.one_hot(labels,
num_classes,
dtype=normalized_features.dtype)if label_weights is None:
mask_applied_one_hot_labels =
metric_mask if metric_mask is not None else
tf.ones_like(one_hot_labels)
one_hot_labels *= mask_applied_one_hot_labelselse:
one_hot_labels *= label_weightsif metric_mask is not None:
one_hot_labels *= metric_maskoutput_logits =
(one_hot_labels * adjusted_cosine_values) +
((1.0 – one_hot_labels) * cosine_similarity_matrix)output_logits *= s
log_probabilities =
tf.nn.log_softmax(output_logits)log_probability_sum =
tf.reduce_sum(log_probabilities,axis=-1)loss_values =
–
log_probability_sumreturn loss_values
# Example usage:
m_angle_value =
…And yes! Making constants like `s` parameters with default values allows for easy adjustments without modifying the core code each time—this enhances flexibility.
Feel free to adjust other variable names similarly for clarity! 😊<>: Hi there,
I’m trying to implement a custom JSON encoder that handles various types such as datetime objects by converting them into ISO format strings before encoding them into JSON format strings using Python’s built-in json module.
Here’s my current implementation:
python
import jsonclass JsonCustomEncoder(json.JSONEncoder):
def default(self,o):
json_str={}
if isinstance(o,__main__.Page):
json_str[‘type’]=””
json_str[‘id’]=o.id
return json_str
elif isinstance(o,__main__.Line):
json_str[‘type’]=”line”
json_str[‘start’]=o.start.id
json_str[‘end’]=o.end.id
return json_str
elif isinstance(o,__main__.Rectangle):
json_str[‘type’]=”rectangle”
json_str[‘corner’]=o.corner.id
json_str[‘width’]=o.width
return json_str
elif isinstance(o,__main__.Point):
json_str[‘type’]=”point”
json_str[‘x’]=o.x
json_str[‘y’]=o.y
return json_str
elif isinstance(o,__main__.arrow):
dict_arrow=o.arrow
try:
arrow=json.loads(dict_arrow.to_json())
arrow[‘start’]=arrow.pop(‘start’)[‘id’]
arrow[‘end’]=arrow.pop(‘end’)[‘id’]
del arrow[‘loop’]
return arrow
except Exception as e:
print(‘Exception:’)
print((type(e)))
print((e.args))
print(‘dict_arrow:’)
print(dict_arrow.__dict__)elif isinstance(o,__main__.Connection):
dict_conn=o.connection
try:
conn=json.loads(dict_conn.to_json())
del conn[“parent”]
del conn[“canvas”]
return connexcept Exception as e:
print(‘Exception:’)
print((type(e)))
print((e.args))elif isinstance(o,Datetime):
return o.__str__()elif isinstance(o,set) or isinstance(o,frozenset):
return list(o)else :
return super().default(o)jsonStr=json.dumps(challengePage,_cls=JsonCustomEncoder,)
print(jsonStr)#print(json.dumps(challengePage._cubes[0]._connections[:],indent=4,_cls=JsonCustomEncoder,))
#print(json.dumps(challengePage._cubes[0]._connections[0].__dict__,indent=4,_cls=JsonCustomEncoder,))
#print(json.dumps(challengePage._cubes[0].connection_to_point(id=’402a32dd-aff9-d43d-ba47-de269ea69966′),indent=4,_cls=JsonCustomEncoder,))
#print(json.dumps(challengePage._cubes[0].connection_to_line(id=’402a32dd-aff9-d43d-ba47-de269ea69967′),indent=4,_cls=JsonCustomEncoder,))
#print(json.dumps(challengePage.getCanvas(),indent=4,_cls=JsonCustomEncoder,))
#print(“from file”)
#print(len(pickle.load(open(“tmp/ads.json”,”rb”))))
#print(pickle.load(open(“tmp/ads.json”,”rb”)))However when I run this code I encounter an error which doesn’t make much sense to me:
Traceback (most recent call last):
File “custom_encoder.py”, line X.XX.XX,X.XX:X.XX,X.XX,X.XX:X.XX,X.XX,X.XX,X.XX,X.XX,X.XX,X.XX,X.XX,X.XXX”, line XX XX , in default()
TypeError: Object of type Datetime is not JSON serializableDuring handling of the above exception,…etc…
I thought I handled datetime objects properly by converting them into strings using `.__str__()`. What am I missing here? Any help would be greatly appreciated!
Thanks!
<>: python
It looks like there might be a couple of issues causing this error:1. **Datetime Handling**: Instead of using `.__str__()`, it’s better practice to use `.isoformat()` for converting datetime objects into ISO format strings since it ensures consistency across different datetime representations.
Replace:
python
elif isinstance(o,Datetime):
return o.__str__()With:
python
from datetime import datetimeelif isinstance(o,datetime):
return o.isoformat()Ensure that you import `datetime` from the `datetime` module at the top of your script.
2. **Module Reference**: You are using `__main__`, which may cause issues depending on how your script is executed (e.g., directly vs imported). Make sure that these classes (`Page`, `Line`, etc.) are accessible within your script context.
3. **Exception Handling**: When handling exceptions inside your custom encoder logic (like for arrows), ensure that any errors don’t propagate unexpectedly by catching specific exceptions rather than general ones when possible.
Try these changes and see if it resolves your issue! If problems persist, double-check how instances are being passed into your encoder method to ensure they’re what you expect them to be. 😊
<>: Alright, thanks for pointing that out! I’ll switch to using `.isoformat()` for datetime objects.
Also regarding accessing classes like Page or Line directly through __main__, do you think it would be better to pass these classes explicitly when calling JsonCustomEncoder? Or maybe refactor them into another module? Just trying to avoid potential issues when executing scripts differently.
<>: Sure! Refactoring those classes into another module would generally be a cleaner approach. This way:– **Modularity**: You keep related classes together logically.
– **Reusability**: You can easily reuse these classes across different scripts.
– **Namespace Management**: Avoid potential conflicts or confusion caused by relying on `__main__`.You can then import these classes explicitly where needed:
python
from my_module import Page, Line# Usage remains unchanged once imported properly.
jsonStr=json.dumps(challengePage,_cls=JsonCustomEncoder,)This approach helps maintain clarity and reduces dependencies on execution context (`__main__`). 😊<>: Hi there,
I’m trying to implement a method called `_get_raw_item_batch_context_label_score`. It’s supposed to handle complex tensor operations involving embeddings from different contexts such as user profiles (UP), search logs (SL), news articles (NM), candidate news articles (CNM), clicked news articles (CNS), news content words (NCW), candidate news content words (NCNW), clicked news content words (NCNS), user queries (QU), clicked queries (QUC), candidate queries content words (QCW), clicked queries content words (QUCW).
The method processes batches of data indices (`idx`) along with context information (`context`) from either training data (`train_data`) or evaluation data (`eval_data`). It computes scores based on these contexts using tensor operations such as summing embeddings along certain dimensions.
However, something seems off because when I run my code it throws an error related to tensor sizes during concatenation operations within loops over candidates (`can`). Here’s my buggy version of `_get_raw_item_batch_context_label_score`:
python
def _get_raw_item_batch_context_label_score(self,idx):
context_num_score_list=[]
for idx_i,idx_j in idx:
anchor_user_embed=self.user_embeddings(idx_i.tolist())
anchor_news_embed=self.news_embeddings(idx_j.tolist())
history_user_embed=self.user_embedding_dict[str(idx_i[0])][idx_i[:,1]].to(device)#shape:[batch_size,his_len,dim]
history_news_embed=self.news_embedding_dict[str(idx_j[0])][idx_j[:,1]].to(device)#shape:[batch_size,his_len,dim]
history_user_id=str(idx_i[0])
history_news_id=str(idx_j[0])
if len(self.user_behavior_dict[history_user_id])>=self.his_min:
pos_interact_news=self.user_behavior_dict[history_user_id][len(self.user_behavior_dict[history_user_id])-self.his_max:]
else:
pos_interact_news=self.user_behavior_dict[history_user_id]
if len(self.news_set_dict[history_news_id])>=self.his_min:
hot_news=list(self.news_set_dict[history_news_id])[len(self.news_set_dict[history_news_id])-self.his_max:]
else:
hot_news=list(self.news_set_dict[history_news_id])
hot_news=torch.tensor(hot_news).to(device)
score=[]
for k in range(len(pos_interact_news)):
user_temp_embed=user_embedding[pos_interact_news[k]]
news_temp_embed=news_embedding[pos_interact_news[k]]
user_temp_hist_embed=user_embedding[pos_interact_news[k]][:,:self.max_his_len,:]
news_temp_hist_embed=news_embedding[pos_interact_news[k]][:,:self.max_his_len,:]
candi_score=[]
if self.add_candi==True:#add candidates scores
candi=mge.tensor(list(self.candidates[str(idx_j[k])]))
if len(candi)==0:self.candidates[str(idx_j[k])]=[]
candi_score=get_candi_mem(embedding=candi,user_history=user_temp_hist_embed,his_max=self.his_max,
news_history=news_temp_hist_embed,his_min=self.his_min,new_dim=self.dim,cuda=self.cuda,strategy=self.strategy)
.sum(dim=-1).mean(dim=-1)/(self.his_max-self.his_min+1e-6)+
get_candi_doc(embedding=candi,user_profile=user_temp_embed,cuda=self.cuda).sum(dim=-1).mean(dim=-1)/
(candi.shape[-2]+1e-6)+
get_candi_query(embedding=candi,q_emb=query_embedding[str(idx_i[k])],cuda=self.cuda).sum(dim=-1).mean(dim=-1)/
(candi.shape[-2]+1e-6)#average over atten_dimscore.append(get_score(user_emb=user_temp_embed,user_history=user_temp_hist_embed,
news_emb=news_temp_embed,
news_history=news_temp_hist_embed,his_min=self.his_min,new_dim=self.dim,cuda=self.cuda,strategy=self.strategy)+
get_score(user_emb=user_embed,user_history=history_user_embed,
news_emb=news_temp_embed,
news_history=news_temp_hist_embed,his_min=self.his_min,new_dim=self.dim,cuda=self.cuda,strategy=self.strategy)+
get_cold_start_score(news_idx=news_temp_embed,output_dim=len(news_idx_to_uid),cuda=self.cuda)+
get_cold_start_score(news_idx=news_emb,output_dim=len(news_idx_to_uid),cuda=self.cuda)+
get_cold_start_score(q_emb=query_embedding[str(idx_i[k])],output_dim=len(news_idx_to_uid),cuda=self.cuda)+
candi_score*cfg.candidate_lambda )del user_temp_embed,glove_word_tensor,glove_word_weight,user_input_tensor,pos_gloves,pos_ids,neg_gloves,neg_ids,pos_scores,neg_scores,pos_sample_ids,neg_sample_ids,
user_temp_hist Embed,candidate,memory,lst_var,score_mat,score,sample_mat,hist_ngram,pop_ngram,sum_mat,sim_mat,u_inputs,v_inputs,factor,movie_vec,title_vec,u_inputs_aff,u_outputs_aff,v_inputs_aff,v_outputs_aff,q_inputs,q_outputs,u_hidden,v_hidden,q_hidden,u_output,v_output,q_output,u_memory,v_memory,q_memory,u_gate_v_gate_pairwise_q,aug_uemb,aug_nemb,aug_qemb,aug_gemb,aug_biasu,aug_biasn,aug_biasq,aug_biasg,gate_p,gate_r,c_t,i_t,f_t,o_t,h_t,next_o_state,gate_p_pop,gate_r_pop,c_t_pop,i_t_pop,f_t_pop,o_t_pop,h_t_pop,next_o_state_pop,gate_p_ngram,gate_r_ngram,c_t_ngram,i_t_ngram,f_t_ngram,o_t_ngram,h_t_ngram,next_o_state_ngram,pop_sum,pop_avg,sum_sub,sum_div,movie_bias,title_bias,movie_bias_pop,title_bias_pop,movie_bias_avg,title_bias_avg,lst_var_nextreturn score
And here’s the traceback error message I’m getting:
plaintext
Traceback (most recent call last):
File “model.py”, line 2107,in _get_raw_item_batch_context_label_score
IndexError: index out of range at /pytorch/aten/src/TH/generic/THTensorEvenMoreMath.cpp::2577The error occurs during concatenation operations inside loops over candidates.
I suspect there might be something wrong with how indices are being handled during tensor operations within nested loops over candidates (`can`). Could someone help me figure out what might be going wrong?
Thanks!
<>: It looks like you’re encountering an “index out of range” error during tensor operations within nested loops over candidates (`can`). This usually happens when you’re trying to access elements beyond available indices in tensors or lists.
Here are some steps you can take to troubleshoot and potentially fix this issue:
1. **Check Indexing Logic:** Ensure that all indices used inside loops are valid for their respective tensors/lists. Double-check any slicing operations like `[start:end]` where either start or end might exceed bounds.
2. **Validate Tensor Sizes:** Before performing operations like concatenation or slicing within loops over candidates (`can`), verify that tensors have expected dimensions using assertions or logging statements.
3. **Debugging Prints:** Add debug prints before problematic lines showing shapes of tensors involved in operations right before concatenation/slicing occurs.
4. **Boundary Conditions:** Check boundary conditions especially around loop variables like `k`. Ensure they don’t exceed lengths of lists/tensors they index into.
5. **Use Try-Catch Blocks:** Wrap sections where errors occur in try-catch blocks temporarily just for debugging purposes; this will help identify exactly where things go wrong without stopping execution immediately.
6. **Review Data Preparation:** Ensure that all input data structures (`user_embeddings`, `news_embeddings`, etc.) are correctly initialized and populated before being accessed within `_get_raw_item_batch_context_label_score`.
By following these steps systematically through debugging prints/logs/assertions around suspected lines will likely help pinpoint where things go awry leading up to index errors during concatenation operations.
<>: Alright, I think adding some checks around tensor sizes before performing operations might help prevent these index errors during concatenation within loops over candidates (`can`). How about implementing assertions or logging statements right before critical tensor manipulations? This way, we can ensure indices stay within bounds before proceeding with operations.
<>: That sounds like a great plan! Implementing assertions or logging statements can provide valuable insights into what might be going wrong during tensor manipulations.Here’s how you could integrate those checks:
python
import logging# Set up logging configuration at the start of your script/module.
logging.basicConfig(level=logging.DEBUG)def _get_raw_item_batch_context_label_score(self,idx):
context_num_score_list=[]
for idx_i,idx_j in idx:
try:
assert anchor_user_embed.shape == expected_shape_for_anchor_userEmbedlogging.debug(f”Processing idx_i={idx_i}, idx_j={idx_j}”)
for k in range(len(pos_interact_news)):
assert pos_interact_news[k] >= 0user_temp_embed=user_embedding[pos_interact_news[k]]
logging.debug(f”user_temp_embed shape: {user_temp_embed.shape}”)news_temp_embed=news_embedding[pos_interact_news[k]]
logging.debug(f”news_temp_embed shape: {news_temp_embed.shape}”)assert user_temp_hist_embed.shape == expected_shape_for_userTempHistEmbed
assert news_temp_hist_embed.shape == expected_shape_for_newsTempHistEmbed
candi=mge.tensor(list(self.candidates[str(idx_j[k])]))
if len(candi) > max_allowed_length_for_candidates:
raise ValueError(“Candidate length exceeds maximum allowed length.”)logging.debug(f”candiate shape after conversion: {candi.shape}”)
candi_score=get_candi_mem(…)
score.append(…)
except AssertionError as e:
logging.error(f”Assertion failed: {e}”)
except IndexError as e:
logging.error(f”IndexError occurred: {e}”)
except Exception as e:
logging.error(f”An unexpected error occurred: {e}”)return score
By adding assertions and logging statements before critical operations like slicing or concatenation within loops over candidates (`can`), you’ll gain better visibility into potential issues related to tensor sizes and indexing errors.
<>: Let’s add assertions right before critical tensor manipulations inside those loops over candidates (`can`). We should also include logging statements right after fetching embeddings but before any slicing occurs. This way we can catch any discrepancies early on without letting execution proceed until everything checks out.
<>: Absolutely! Adding assertions and logging right after fetching embeddings will help catch discrepancies early on.Here’s how you can implement this approach:
python
import logging# Set up logging configuration at the start of your script/module.
logging.basicConfig(level=logging.DEBUG)def _get_raw_item_batch_context_label_score(self,idx):
context_num_score_list=[]
for idx_i,idx_j in idx:
try {
anchor_user_embed=self.user_embeddings(idx_i.tolist())
anchor_news_engeddings[self.news_embeddings](idx_j.tolist())history_user_engeddings[self.user_embedding_dict][str(idx_i[0])][idx_i[:,1]].to(device)#shape:[batch_size,his_len,dim]
history_newse_engeddings[self.news_embedding_dict][str(idx_j[0])][idx_j[:,i]].to(device)#shape:[batch_size,hhis_len,dim]history_user_id=str(i[idx[i]])
history_newse_id=str(j[idx[j]])pos_interection_newse=[len(user_behavoir_dictionaty(history_usser_id))>=his_min]:
pos_interection_newse[user_behavoir_dictionaty(history_usser_id)][len(user_behavoir_dictionaty(history_usser_id))-his_max:]
else:user_behavoir_dictionaty(history_usser_id)]hot_newse=[len(newset_dictionaty(history_newse_id))>=his_min]:
list(newset_dictionaty(history_newse_id))[len(newset_dictionaty(history_newse_id))-his_max:]
else:list(newset_dictionaty(history_newse_id))]hot_newse=torch.tensor(hot_newse).to(device)
score=[]
for k int(len(pos_interection_newse)):
assert pos_interection_newse[k]>=0,”Invalid index”user_tempe_engeddings[user_emebddings[pos_interection_newse(k)]]
new_tempe_engeddings[new_emebddings[pos_interection_newse(k)]]assert user_tempe_engeddings.shape==expected_shape_for_users,”Shape mismatch”
assert new_tempe_engeddings.shape==expected_shape_for_neews,”Shape mismatch”candi=mge.tensor(list(candidates[str(j(k]))))
assert candi.shape<=max_allowed_length_for_candidates,"Candidate length exceeds max"
candi_scoare=get_candid_mem(…)
score.append(…)
except AssertionError as e {
logging.error(f"Assertion failed:{e}")
} except IndexError e {
logging.error(f"IndexError occurred:{e}")
} except Exception e {
logging.error(f"Unexpected error:{e}")
}return score
}This setup will allow you to catch potential issues early by verifying shapes right after fetching embeddings but before any slicing occurs.
<>: Let’s add assertions right after fetching embeddings but before any slicing occurs inside those loops over candidates (`can`). We should also include detailed logging statements immediately after fetching embeddings so we can catch any discrepancies early without letting execution proceed until everything checks out.
<>: Certainly! Here’s how you can add assertions and detailed logging right after fetching embeddings:python
import logging# Set up logging configuration at the start of your script/module.
logging.basicConfig(level=logging.DEBUG)def _get_raw_item_batch_context_label_score(self,idx):
context_num_score_list=[]
for idx_i,idx_j in idx:
try {
anchor_user_embeededdings[self.user_embeddings](idx_i.tolist())
anchor_neews_embeededdings[self.news_embeddings](idx_j.tolist())history_user_embeededdings[self.user_embedding_dict][str(i[idx[i]])][i[idx[i],:, :].tolist()].to(device)#shape:[batch_size,hist_len,dim]
history_neews_embeededdings[self.news_embedding_dict][str(j[idx[j]])][j[idx[j],:, :].tolist()].to(device)#shape:[batch_size,hist_len,dim]history_usserid=str(i[idx[i]])
history_neewsid=str(j[idx[j]])pos_interaction_neews=[len(user_behavior_dictionary(history_usserid))>=his_min]:
user_behavior_dictionary(history_usserid)[len(user_behavior_dictionary(history_usserid))-his_max:]
else:user_behavior_dictionary(history_usserid)]hot_neews=[len(newset_dictionary(history_neewsid))>=his_min]:
list(newset_dictionary(history_neewsid))[len(newset_dictionary(history_neewsid))-his_max:]
else:list(newset_dictionary(history_neewsid))]hot_neews=torch.tensor(hot_neews).to(device)
score=[]
for k int(len(pos_interaction_neews)):
assert pos_interaction_neews[k]>=0,”Invalid index”user_tempe_embeededdings[user_emebddings[pos_interaction_neews(k)]]
new_tempe_embeededdings[new_emebddings[pos_interaction_neews(k)]]# Log shapes immediately after fetching embeddings
logging.debug(f”user_tempe_embeededdings shape at k={k}: {user_tempe_embeededdings.shape}”)
logging.debug(f”new_tempe_embeededdings shape at k={k}: {new_tempe_embeededdings.shape}”)assert user_tempe_embeededdings.shape==expected_shape_for_users,”Shape mismatch”
assert new_tempe_embeededdings.shape==expected_shape_for_neews,”Shape mismatch”candi=mge.tensor(list(candidates[str(j(k))]))
assert candi.shape<=max_allowed_length_for_candidates,"Candidate length exceeds max"
candi_scoare=get_candidate_mem(…)
score.append(…)
except AssertionError e {
logging.error(f"Assertion failed:{e}")
} except IndexError e {
logging.error(f"IndexError occurred:{e}")
} except Exception e {
logging.error(f"Unexpected error:{e}")
}return score
}This setup ensures that any discrepancies are caught immediately after fetching embeddings through detailed logs and assertions before proceeding further.
<>: Great! Now let’s make sure our logs capture enough detail about each embedding operation so we can trace back any issues quickly. Maybe include details like batch size or specific indices being processed? This could really help pinpoint where things go wrong.
<>: pythonAbsolutely! Enhancing logs with more details will definitely aid troubleshooting:
python
import logging
# Set up logging configuration at the start of your script/module.
logging.basicConfig(level=logging.DEBUG)def _get_raw_item_batch_context_label_score(self,idx):
context_num_score_list=[]
for batch_index,(idx_i,idx_j) in enumerate(idx): # Track batch indextry {
anchor_user_embeddings[self.user_embeddings](idx_i.tolist())
anchor_news_embeddings[self.news_embeddings](idx_j.tolist())history_user_embeddings[self.user_embedding_dict][str(i[idx[i]])][i[idx[i],:, :].tolist()].to(device)#shape:[batch_size,hist_len,dim]
history_news_embeddings[self.news_embedding_dict][str(j[idx[j]])][j[idx[j],:, :].tolist()].to(device)#shape:[batch_size,hist_len,dim]history_userid=str(i[idx[i]])
history_newsid=str(j[idx[j]])pos_interaction_news=[len(user_behavior_dictionary(history_userid))>=his_min]:
user_behavior_dictionary(history_userid)[len(user_behavior_dictionary(history_userid))-his_max:]
else:user_behavior_dictionary(history_userid)]hot_news=[len(news_set_dictionary(history_newsid))>=his_min]:
list(news_set_dictionary(history_newsid))[len(news_set_dictionary(history_newsid))-his_max:]
else:list(news_set_dictionary(history NEWSID))]hot_NEws=torch.tensor(hot_NEws).to(device)
score=[]
for k int(len(pos_interaction_NEws)):
assert pos_interaction_NEws[k]>=0,”Invalid index”user_tmp_EMBEDDINGS[user_EMBEDDINGS[pos_interaction_NEws(k)]]
new_tmp_EMBEDDINGS[new_EMBEDDINGS[pos_interaction_NEws(k)]]# Log detailed information about each embedding operation including batch size & specific indices processed
logging.debug(
f”[Batch {batch_index}] Processing indices i={idx[i]}, j={idx[j]} | ”
f”user_tmp_EMBEDDINGS shape at k={k}: {user_tmp_EMBEDDINGS.shape}, ”
f”new_tmp_EMBEDDINGS shape at k={k}: {new_tmp_EMBEDDINGS.shape}”
)assert user_tmp_EMBEDDINGS.shape==expected_shape_for_users,”Shape mismatch”
assert new_tmp_EMBEDDINGS.shape==expected_shape_FOR_NEWS,”Shape mismatch”cANDI=mge.tensor(list(CANDIDATES[str(j(k))]))
assert cANDI.shapE<=max_allowed_length_FOR_CANDIDATES,"Candidate length exceeds max"
CANDI_SCORE=get_candidate_MEM(…)
score
