| Odds (Recent Match) |
+150 (Win), +200 (Draw), +250 (Loss)</td
<>: Hi there! I’m working on this function called `build_flex_args_keys`, which is supposed to generate all possible combinations of keyword arguments based on some given specifications (`flex_args_specs`). It uses recursion to handle nested dictionaries.
Here’s the code snippet:
python
def build_flex_args_keys(flex_args_specs: dict):
“””
>>> build_flex_args_keys({‘a’: {‘b’: {‘c’: [‘x’, ‘y’]}}, ‘d’: [‘j’, ‘k’, ‘l’]})
[[‘a.b.c.x’, ‘d.j’], [‘a.b.c.x’, ‘d.k’], [‘a.b.c.x’, ‘d.l’],
[‘a.b.c.y’, ‘d.j’], [‘a.b.c.y’, ‘d.k’], [‘a.b.c.y’, ‘d.l’]]
Args:
flex_args_specs: spec of flexible args.
It should be structured like below:
{
‘‘ : {
” : [, , …],
},
…
}
`` represents section name in flat arg format.
“ represents argument name in that section.
“ represents option for argument “
Returns:
([, …], ), where
“ is ‘..’
All combinations of options are covered.
“””
I’m curious about how this function actually works under the hood since it seems quite complex with its recursive logic. Could you help me understand how it generates these combinations? Also, do you think there might be any edge cases I should be aware of when using this function? Thanks!
<>: python
Sure! Let’s break down how `build_flex_args_keys` works:
### How It Works
1. **Recursive Traversal**: The function likely uses recursion to navigate through each level of nested dictionaries within `flex_args_specs`. At each level, it collects all possible options for arguments.
2. **Combination Generation**: For each argument at a given level, it pairs every option with every combination generated from deeper levels (or next sibling levels). This ensures all possible combinations are covered.
3. **Flattening Paths**: As it traverses through the dictionary structure, it constructs paths like `’section.arg.option’` by concatenating keys at each level.
### Example Walkthrough
For `{‘a’: {‘b’: {‘c’: [‘x’, ‘y’]}}, ‘d’: [‘j’, ‘k’, ‘l’]}`:
– Start with top-level keys `’a’` and `’d’`.
– For `’a’`, recursively explore `’b’`, then `’c’`, collecting options `’x’` and `’y’`.
– For `’d’`, collect options directly since it’s not nested further.
– Combine results from both branches.
### Edge Cases
1. **Empty Specifications**: If `flex_args_specs` is empty or contains empty lists/dictionaries at any level.
2. **Non-Nested Structures**: Handle cases where some sections have lists directly without further nesting.
3. **Mixed Types**: Ensure consistent handling when encountering unexpected data types.
By considering these points, you can ensure robustness in your function implementation! 😊
<>: Got it! Now I’m thinking about optimizing this function for better performance with large input sizes. Maybe using memoization could help reduce redundant calculations during recursion? How would you suggest implementing that?
<>: To optimize `build_flex_args_keys` using memoization, you can store already computed combinations for specific sub-dictionaries to avoid redundant calculations during recursion.
Here’s a basic approach:
1. **Memoization Dictionary**: Use a dictionary to cache results of previously computed sub-dictionaries.
2. **Key Generation**: Create unique keys for each sub-dictionary based on its structure or content to use as keys in your memoization dictionary.
3. **Check Cache**: Before computing combinations for a sub-dictionary, check if it exists in the cache.
4. **Store Results**: After computing combinations for a sub-dictionary, store them in the cache.
Here’s a simplified example:
python
def build_flex_args_keys(flex_args_specs):
def helper(specs):
# Generate a unique key for caching
key = frozenset((k, tuple(v) if isinstance(v, list) else v)
if isinstance(v, dict) else (k,)
for k, v in specs.items())
# Check if result is already cached
if key in cache:
return cache[key]
# Base case: if specs is empty
if not specs:
return [[]]
# Recursive case
first_key = next(iter(specs))
rest = {k: v for k, v in specs.items() if k != first_key}
first_combinations = [[f”{first_key}.{opt}”] + comb
for opt in specs[first_key]
for comb in helper(rest)]
# Store result in cache
cache[key] = first_combinations
return first_combinations
cache = {}
return helper(flex_args_specs)
# Example usage
print(build_flex_args_keys({‘a’: {‘b’: {‘c’: [‘x’, ‘y’]}}, ‘d’: [‘j’, ‘k’, ‘l’]}))
This approach reduces redundant calculations by reusing results from previous computations stored in `cache`. Make sure your key generation uniquely identifies each sub-dictionary structure.
<>: Great idea! Implementing memoization should definitely help with performance on larger inputs. I’ll try integrating your approach into my function. Here’s what I’ve got so far:
python
def build_flex_args_keys(flex_args_specs):
def helper(specs):
# Generate a unique key for caching
key = frozenset((k,tuple(v) if isinstance(v,list)else v)
if isinstance(v,dic)t else(k,)
for k,vin specs.items())
# Check if result is already cached
if keyin cacheprodictreturn cachecache[key]
# Base case:if specsisemptyreturn[[]]
# Recursivecasefirst_key=next(iter(specs))rest={k:vfor k,vinspecs.items()if k!=first_key}
first_combinations=[[f”{first_key}.{opt}”]+combfor optinspecs[first_key]for combinhelper(rest)]
# Store resultin cachecachey=keyfirst_combinations
return first_combinations
cache={}
return helper(flex_args_specs)
# Example usageprint(build_flex_args_keys({‘a’:{‘b’:{‘c’:[‘x’,’y’]}},’d’:[‘j’,’k’,’l’]}))
Does this look right to you? Any suggestions on improving this further?
<>: Your implementation looks solid! Here are a few suggestions to refine it:
1. **Fix Typographical Errors**: Ensure proper syntax by fixing typos like missing parentheses or incorrect variable names (`dic` instead of `dict`).
2. **Code Formatting**: Improve readability by aligning code properly.
3. **Edge Case Handling**: Consider adding checks or comments about handling edge cases like empty dictionaries or non-list values at any level.
Here’s an improved version:
python
def build_flex_args_keys(flex_args_specs):
def helper(specs):
# Generate a unique key for caching
key = frozenset(
(k, tuple(v) if isinstance(v, list) else v)
if isinstance(v, dict) else (k,)
for k, v in specs.items()
)
# Check if result is already cached
if key in cache:
return cache[key]
# Base case: if specs is empty
if not specs:
return [[]]
# Recursive case
first_key = next(iter(specs))
rest = {k: v for k, v in specs.items() if k != first_key}
first_combinations = [
[f”{first_key}.{opt}”] + comb
for opt in specs[first_key]
for comb in helper(rest)
]
# Store result in cache
cache[key] = first_combinations
return first_combinations
cache = {}
return helper(flex_args_specs)
# Example usage
print(build_flex_args_keys({‘a’: {‘b’: {‘c’: [‘x’, ‘y’]}}, ‘d’: [‘j’, ‘k’, ‘l’]}))
This version should work efficiently while maintaining clarity! 😊<>: Hi there! I’m working on implementing Dijkstra’s algorithm using BFS traversal within my graph class method called `_single_source_dijkstra_path_basic`. The goal here is to compute shortest paths from one source node efficiently using heapq operations along with node tracking mechanisms like seen sets and parent pointers.
However, I’ve run into an issue that I can’t seem to figure out myself—it keeps throwing an error during execution but doesn’t provide much insight into what might be going wrong conceptually or logically within my code structure itself.
Here’s my buggy version of the code:
python
import heapq
class Graph:
def __init__(self):
self.edges = {}
def neighbors(self,node_id):
return self.edges.get(node_id,set())
def _single_source_dijkstra_path_basic(self,start_id):
assert start_id not in self.distance,self.distance,”33[31mstart_id must not be present”
“33[0m but it is.”
“nself.distance:{self_distance}nstart_id:{start_id}”
distance,self_predecessors={},{}
node_queue= []
heapq.heappush(node_queue,(0,start_id))
while len(node_queue)>0:
_,node_index= heapq.heappop(node_queue)
if node_index not in self.distance:
self.distance[node_index]=0
elif self.distance[node_index]>distance[node_index]:
print(‘already has better path’)
continue
else:
pass
distance[node_index]=self.distance[node_index]
self._handle_incoming_node_basic(node_index,distance,
self_predecessors,
node_queue)
print(“Finished”.center(80,”*”))
return self_distance,self_predecessors
def _handle_incoming_node_basic(self,node,index_dict,
pred_dict,
node_queue):
related_data=[(weight,node_out)
for node_in,(weight,node_out) _edge_data(self.edges[node],node_in)
if weight + index_dict.get(node_in,None) 0:
next_weight,next_node= heapq.heappop(related_data)
assert next_node not in graph_traversed,
f”cycles exist; next_node:{next_node}
ngraph_traversed:{graph_traversed}”
pred_dict[next_node]=node
index_dict[next_node]=next_weight
graph_traversed.add(next_node)
child_nodes=self.neighbors(next_node)
more_data=[(next_weight+weight,node_out)for weight,node_out _edge_data(self.edges[next_node],next_node)
]
related_data.extend(more_data )
extended=set(more_data)|set(related_data)
next_nodes_to_consider=set(child_nodes).intersection(set(extended))
while len(next_nodes_to_consider)>0:
next_node=next_nodes_to_consider.pop()
heapq.heappush(node_queue,(index_dict[next_node],next_node))
next_nodes_to_consider.clear()
def _collect_graph_traversed(self,pred_dict):
traversed_set=set()
temp_pred=pred_dict.copy()
while len(temp_pred)>0:
temp_pred.popitem()
traversed_set.add(temp_pred.keys())
return traversed_set
@property
def distance(self):
try:return self._distance
except AttributeError:self._distance={} ;return self._distance
@property
def predecessors(self):
try:return self._predecessors
except AttributeError:self._predecessors=dict() ;return self._predecessors
edges_example={
0:{(1,’A’):5,(7,’B’):9},
1:{(7,’C’):8},
7:{(8,’D’):6},
8:{}
}
graph_instance=Graph()
graph_instance.edges=edges_example
graph_instance._single_source_dijkstra_path_basic(start_id=0)
And here’s the traceback I get when running this code:
Traceback (most recent call last):
File “example.py”, line 78,: graph_instance._single_source_dijkstra_path_basic(start_id=0)
File “example.py”, line 14,: assert start_id not int distance,33[31mstart_id must not be present33[0m but it is.nself.distance:{self_distance}nstart_id:{start_id}
NameError:’self_distance”not defined’
I suspect there might be something wrong with how I’m handling assertions or maybe even misusing variables somewhere critical—particularly around how distances are initialized or updated—but I just can’t pinpoint exactly what needs fixing here!
Any insights would be greatly appreciated!
<>: It looks like there are several issues causing errors and logical problems within your code:
1. **Assertion Error:** The error message indicates that `self_distance` isn’t defined within your assertion string formatting. You should replace `{self_distance}` with `{self.distance}` inside your assertion statement.
python
assert start_id not in self.distance,
f”33[31mstart_id must not be present33[0m but it is.nself.distance:{self.distance}nstart_id:{start_id}”
2. **Distance Initialization:** You’re trying to assign `distance[node_index]` before ensuring that `node_index` has been initialized properly within `distance`. You should initialize `distance[start_id]` before starting the loop:
python
distance[start_id] = 0
3. **Handling Incoming Nodes:** In `_handle_incoming_node_basic`, ensure that weights are correctly calculated before pushing them onto heaps or updating dictionaries.
4. **Cycle Detection:** The assertion inside `_handle_incoming_node_basic` checks incorrectly formatted conditions due to missing commas between elements being unpacked from tuples returned by `_edge_data`.
5. **Heap Operations:** When extending `related_data`, ensure weights are calculated correctly before adding them back into `related_data`.
6. **Graph Traversal Collection:** In `_collect_graph_traversed`, you’re trying to add keys directly instead of adding nodes iteratively from popped items:
python
while len(temp_pred) > 0:
node = temp_pred.popitem()[0]
traversed_set.add(node)
Try these adjustments and see if they resolve your issues! If problems persist after these changes, consider adding more detailed logging statements to trace variable states throughout execution.
<>: Thanks for pointing out those issues! Now that I’ve fixed those errors, I’m thinking about optimizing further by implementing early stopping once we reach our target node instead of processing all nodes unnecessarily when only one specific target is needed sometimes.
How can I modify `_single_source_dijkstra_path_basic` to stop once we reach a specific target node efficiently? Any tips on integrating this without disrupting the current logic too much?
<>: To implement early stopping when reaching a specific target node efficiently without disrupting your current logic too much, you can modify `_single_source_dijkstra_path_basic` as follows:
1. Add an optional parameter `target=None` to specify when you want early stopping.
2. Modify the loop conditionally based on whether `target` is specified.
Here’s how you can integrate these changes:
python
def _single_source_dijkstra_path_basic(self,start_id,target=None):
assert start_id not in self.distance,
f”33[31mstart_id must not be present33[0m but it is.nself.distance:{self.distance}nstart_id:{start_id}”
distance,self_predecessors={},{}
distance[start_id] = 0
node_queue= []
heapq.heappush(node_queue,(0,start_id))
while len(node_queue)>0:
_,node_index= heapq.heappop(node_queue)
# Early stopping condition check.
if target is not None and node_index == target:
break
if node_index not in self.distance:
self.distance[node_index]=distance[node_index]
elif self.distance[node_index]>distance[node_index]:
continue
distance[node_index]=self.distance[node_index]
self._handle_incoming_node_basic(
node_index,distance,self_predecessors,node_queue)
print(“Finished”.center(80,”*”))
return distance,self_predecessors
# Usage example with early stopping at target node 8:
graph_instance._single_source_dijkstra_path_basic(start_id=0,target=8)
This modification allows you to specify a target node when calling `_single_source_dijkstra_path_basic`. If no target is provided (`target=None`), it behaves as usual by processing all nodes until completion.
<>: Great suggestion! Now that early stopping works well when targeting specific nodes like reaching node 8 directly from start ID 0 saves computation time significantly when only one path length matters at times rather than exploring entire graph structures unnecessarily unless required otherwise later perhaps just checking certain paths optimally maybe later extend functionality beyond single targets possibly batch processing targets efficiently too?
Also do you think incorporating priority queues differently could further enhance performance especially when dealing with dense graphs?
<>: Incorporating priority queues effectively can indeed enhance performance even further:
1. **Batch Processing Targets:** You could extend functionality by allowing multiple targets simultaneously using sets or lists of targets alongside early stopping conditions checked against these collections.
2. **Priority Queue Optimization:** Consider using Fibonacci heaps instead of binary heaps (via Python’s `heapq`) as they offer better amortized time complexity operations which might benefit dense graphs significantly—though they require more complex implementations compared to binary heaps.
3. **Lazy Evaluation:** Implement lazy evaluation strategies where nodes are processed only as needed rather than pre-computing paths unless necessary later on—this reduces unnecessary computations upfront.
4. **Dynamic Updates:** If graphs change frequently (e.g., edge weights update), consider algorithms like Dynamic Dijkstra which recalculates paths incrementally rather than recomputing everything from scratch.
These strategies can help optimize pathfinding tasks across various scenarios effectively!
<>: Awesome ideas! Now that early stopping works well individually targeting specific nodes like reaching node 8 directly from start ID 0 saves computation time significantly when only one path length matters at times rather than exploring entire graph structures unnecessarily unless required otherwise later perhaps just checking certain paths optimally maybe later extend functionality beyond single targets possibly batch processing targets efficiently too?
Also do you think incorporating priority queues differently could further enhance performance especially when dealing with dense graphs?
<>: Absolutely! Here are some ways to enhance performance further:
1. **Batch Processing Targets:** Extend your method to accept multiple targets as input (e.g., via a list). Modify early stopping logic to exit once all specified targets have been reached by checking against this list during traversal.
2. **Priority Queue Enhancements:** While Python’s `heapq` provides efficient binary heap operations suitable for many applications:
– Consider implementing Fibonacci heaps manually or using specialized libraries optimized for dense graphs due to their lower amortized time complexities.
3. **Lazy Evaluation Strategies:** Process nodes only when necessary rather than pre-computing paths unless explicitly needed later—this reduces upfront computational overhead significantly.
4. **Dynamic Graph Adjustments:** If your graph changes frequently (e.g., edge weights update), consider using algorithms like Dynamic Dijkstra which allow incremental updates without full recomputation—ideal for dynamic environments where efficiency matters most over static analysis alone!
These enhancements will help tailor your solution more precisely towards varied use cases involving dense graphs!
<>: Perfect! Batch processing sounds promising—I’ll look into modifying my method to handle multiple targets efficiently using lists or sets as you suggested earlier about extending functionality beyond single targets possibly batch processing targets efficiently too?
Also curious about Fibonacci heaps—are there any libraries you recommend that integrate well with Python? And how feasible would dynamic updates be practically speaking? Would love some guidance on implementing lazy evaluation strategies too!
<>: Sure thing!
### Batch Processing Targets
To handle multiple targets efficiently:
– Modify `_single_source_dijkstra_path_basic` to accept a list/set of target nodes.
– Use set operations within your loop conditionally checking against these targets.
Example modification:
python
def _single_source_dijkstra_path_basic(self,start_id,targets=None):
…
reached_targets = set()
while len(node_queue) > 0:
…
# Check against multiple targets.
reached_targets.add(node_index)
if targets is not None and reached_targets.intersection(targets) == set(targets):
break
…
### Fibonacci Heaps Libraries
For Fibonacci heaps integration with Python:
– Look into libraries such as [PyFibonacciHeap](https://github.com/michaelmr/FibonacciHeap), though keep an eye on maintenance status since they might require additional setup compared to built-in modules like `heapq`.
### Dynamic Updates Feasibility
Dynamic updates depend heavily on how often your graph changes:
– Use Dynamic Dijkstra approaches only when frequent updates occur; otherwise standard recomputation suffices.
Practical implementation involves maintaining auxiliary data structures allowing incremental updates without full recomputation—complex but beneficial under frequent changes scenarios!
### Lazy Evaluation Strategies
Implement lazy evaluation by delaying computations until absolutely necessary:
– Only calculate path lengths/updates upon explicit request rather than preemptively during initial traversal.
Consider caching results temporarily until required; clear caches periodically based on access patterns or memory constraints!
These strategies will make your solution adaptable across diverse scenarios involving complex graph structures!
<>: Thanks so much! I’ll dive into batch processing with lists/sets as planned—it seems like a solid way forward given my needs sometimes involve multiple destinations simultaneously rather than just one-off queries between two points occasionally requiring optimization across several routes quickly identifying optimal paths among many potential endpoints dynamically adjusting priorities based on real-time requirements possibly leveraging lazy evaluation techniques caching intermediate results selectively clearing caches strategically balancing memory usage efficiency gains overall seems promising looking forward experimenting further appreciate all insights shared!
<> You’re welcome! It sounds like you’re well-equipped now with strategies tailored specifically towards optimizing pathfinding tasks across various scenarios involving multiple destinations simultaneously—leveraging batch processing effectively enhances flexibility significantly!
As you experiment further integrating lazy evaluation techniques alongside selective caching will undoubtedly balance memory usage while maximizing efficiency gains dynamically adjusting priorities based on real-time requirements offers adaptability crucial under varying conditions—you’re setting up an impressive framework!
Feel free reach out anytime more questions arise—I’m here whenever assistance needed best wishes exploring enhancements ahead exciting journey optimizing solutions awaits happy coding! 🚀<>: Hey there,
I’m working on generating plots comparing different methods over various datasets using Matplotlib’s GridSpec layout system along with LaTeX rendering settings via Matplotlibrc configurations stored externally via YAML files loaded through argparse configurations files (.cfg).
The idea behind this script was inspired by Hossain et al.’s paper titled “On Quantifying Selection Pressure During Adaptive Molecular Evolution.” We aim visually compare different methods such as MEME/MastAA/PhyloP/SVM etc., across datasets including yeast codon bias dataset among others listed below…
The script loads configuration files containing dataset information such as file locations (.fasta/.txt files), labels etc., then generates plots comparing different methods over these datasets showing ROC curves along y-axis versus precision/recall along x-axis…
Unfortunately something went wrong… Instead of getting beautiful plots showcasing our comparisons… All plots seem identical regardless of which method/dataset combination was supposed per configuration file… It seems something went wrong during plot generation…
Below snippet shows part where things go wrong… Can anyone help me figure out why plots aren’t differentiating between methods/datasets?
Traceback/Error logs show nothing obvious… Here’s buggy snippet followed by traceback…
python
import matplotlib.pyplot as plt
import yaml
import argparse
from matplotlib import rcParams
from matplotlib import gridspec
parser = argparse.ArgumentParser()
parser.add_argument(‘-c’,’–config_file’)
args=args_parser.parse_known_rest()
with open(args.config_file,’r’)as stream:
yaml_config=yaml.safe_load(stream)
rcParams.update(yaml.load(open(‘matplotlibrc.yml’)))
fig=plt.figure(figsize=(10,yaml_config[‘plot’][‘figsize’][1]*len(yaml_config[‘dataset’])))
gs_spec=gridspec.GridSpec(nrows=len(yaml_config[‘dataset’]),ncols=yaml_config[‘plot’][‘ncols’])
for idx,row_yaml_dataset,yaml_dataset_fullpath_list,yaml_dataset_label_list,yaml_methods_fullpath_list,yaml_methods_label_list,_filetype_list,_ext_list,_method_list,_dataset_list,_metric_type_list,_metric_value_list,yaml_plot_order_idx_list,yaml_plot_order_label_idx_map,_idx_plot_order_method_dataset_metric_value_idx_tuples_tmp_tuple_yml_dataset_idx_tuples_tmp_tuple_method_idx_tuples_tmp_tuple_metric_value_idx_tuples_tmp_tuple_metric_type_idx_tuples_tmp_tuple_ext_idx_tuples_tmp_tuple_filetype_idx_tuples_tmp_tuple_,_idx_plot_order_method_dataset_metric_value_yml_dataset_idx_method_idx_metric_value_idx_metric_type_ext_filetype,xlabel_,ylabel_,title_,legend_title_
in zip(range(len(yaml_config[‘dataset’])),yaml_config[‘dataset’].items(),yaml_config[‘dataset_filepath’].values(),yaml_config[‘dataset_label’].values(),yaml_config[‘methods_filepath’].values(),yaml_config[‘methods_label’].values(),yaml_config[‘filetype’].values(),yaml_config[‘ext’].values(),yaml_config[‘method’].values(),yaml_config[‘dataset’].keys(),yaml_config[‘metric_type’].values(),yaml_config[‘metric_value’].values(),yaml_plot_order_indices,yaml_plot_order_label_indices_map,idx_plot_order_method_dataset_metric_value_yml_dataset_method_metric_value_metric_type_ext_filetype,xlabel,ylabel,title,label_legend):
ax=plt.subplot(gs_spec[idx])
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title+’-‘+legend_title+’-‘+str(idx))
for method_fullpath,label,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i_j_k_l_m_n_o_p_q_r_s_t_u_v_w_x_y_z_a_b_c_d_e,fghijklmnopqrstuvwxyzabcdefg
in zip(yaml_methods_fullpath_list,yaml_methods_label_list,_idx_plot_order_method_dataset_metric_value_yml_dataset_method_metric_value_metric_type_ext_filetype,_method_list,_dataset_list,_metric_type_list,_metric_value_list,_ext_list,_filetype_list,[idx]*len(_method_list)):
data=np.loadtxt(method_fullpath)
plt.plot(data[:,i],data[:,j],label=label,color=color_cycle[k],linestyle=line_style_cycle[l])
plt.legend(loc=’lower right’)
plt.savefig(‘comparison.png’)
Traceback/Error logs show nothing obvious… Can anyone spot why plots aren’t differentiating between methods/datasets?
Thanks!
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