Vihiga United FC: Champions of the Kenyan Premier League - Squad, Stats & Achievements

Overview of Vihiga United FC

Vihiga United FC is a prominent football club based in Kenya, competing in the Kenyan Premier League. Founded in 1970, the team has become a significant player in Kenyan football. Managed by [Current Manager], Vihiga United is known for its dynamic play and competitive spirit.

Team History and Achievements

Vihiga United has a rich history with notable achievements including multiple league titles and cup victories. The club’s most successful seasons have seen them dominate the league standings, establishing themselves as a powerhouse in Kenyan football.

Current Squad and Key Players

The current squad boasts several key players who are pivotal to the team’s performance. Star players like [Player Name] and [Player Name] have been instrumental in recent successes. Their roles and positions on the field are crucial for executing the team’s strategies.

Team Playing Style and Tactics

Vihiga United employs a versatile playing style, often utilizing formations such as 4-4-2 or 4-3-3. Their tactics focus on strong midfield control and quick counter-attacks, leveraging their strengths while addressing weaknesses like defensive vulnerabilities.

Interesting Facts and Unique Traits

The team is affectionately known as [Nickname], with a passionate fanbase that supports them through thick and thin. Rivalries with teams like [Rival Team] add excitement to their matches, while traditions such as pre-match rituals enhance the fan experience.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: [Player Name] – ✅
• Average Goals per Match: 1.5 – 🎰
• Defensive Record: Strongest in league – 💡

Comparisons with Other Teams in the League or Division

Vihiga United is often compared to top-tier teams like Gor Mahia and AFC Leopards. While they share similar ambitions, Vihiga’s unique tactical approach sets them apart in competitive scenarios.

Case Studies or Notable Matches

A breakthrough game was their victory against [Opponent Team] where they showcased exceptional teamwork and strategic prowess, leading to a memorable win that boosted their league position.

Tabs Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Statistic	Last Season	This Season (So Far)
Total Goals Scored	45	20
Total Wins	15	8

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

• Analyze recent form trends to predict future performance.
• Consider head-to-head records when placing bets against rivals.
• Maintain awareness of player injuries that could impact match outcomes.

Quotes or Expert Opinions about the Team (Quote Block)

“Vihiga United’s tactical flexibility makes them unpredictable opponents,” says football analyst [Analyst Name]. “Their ability to adapt mid-game is a key strength.”

Pros & Cons of the Team’s Current Form or Performance (✅❌ Lists)

• ✅ Pro: Strong offensive lineup capable of turning games around quickly.
• ✅ Pro: Experienced coach providing strategic depth.
• ❌ Con: Defensive lapses can be costly against top teams.
• ❌ Con: Injuries to key players have affected consistency.

</ul
<>: Hi there! I’m working with this code snippet that defines an `ArbitraryTree` class using nested `Node` classes within it. The `Node` class seems to represent individual nodes of an arbitrary tree structure.

Here’s the snippet:

python
class ArbitraryTree(object):
“””
Arbitrary tree node.g children ordered dict keyed by node label
“””
class Node(object):
def __init__(self, value=None):
# use dict instance to store children
self._children = {}
self._value = value
def __repr__(self):
return ” 0
def label(self):
return id(self)
def __nonzero__(self):
return True if self._value is not None else False
def __bool__(self):
return True if self._value is not None else False

I’m curious about how this structure could be used effectively. Could you help me understand how I might implement functionality to traverse this tree? I’m feeling a bit lost on where to start!
<>: python
Sure! To traverse an `ArbitraryTree`, you can implement different traversal methods such as depth-first search (DFS) or breadth-first search (BFS). Here’s a simple example of how you might implement these traversals:

### Depth-First Search (DFS)

You can use recursion for DFS:

python
def dfs(node):
print(node) # Process the current node
for child_label in node.labels():
dfs(node.get_child(child_label))

### Breadth-First Search (BFS)

For BFS, you can use a queue:

python
from collections import deque

def bfs(root_node):
queue = deque([root_node])

while queue:
current_node = queue.popleft()
print(current_node) # Process the current node

for child_label in current_node.labels():
queue.append(current_node.get_child(child_label))

### Usage

To use these functions, create nodes and build your tree structure:

python
root = ArbitraryTree.Node(‘root’)
child1 = ArbitraryTree.Node(‘child1’)
child2 = ArbitraryTree.Node(‘child2’)

root.add_child(child1)
root.add_child(child2)

# Now you can traverse:
dfs(root)
bfs(root)

These methods will help you explore all nodes starting from the root. You can modify the `print` statements to perform other operations on each node as needed. 😊

<>: Great examples! Now, I want to add functionality to find all paths from the root to leaf nodes within this tree structure. How would I go about implementing this? Any tips on efficiently collecting these paths?
<>: python
To find all paths from the root to leaf nodes, you can use a recursive approach similar to DFS but keep track of the path as you traverse:

python
def find_paths(node, path=None):
if path is None:
path = []

# Add current node to path
path.append(node)

# If it’s a leaf node (no children), yield the path as it is complete
if not node.has_children():
yield list(path)

# Otherwise, continue exploring children recursively
else:
for child_label in node.labels():
child_node = node.get_child(child_label)
yield from find_paths(child_node, path)

# Backtrack by removing the current node from path before returning up call stack
path.pop()

# Usage example:
root = ArbitraryTree.Node(‘root’)
child1 = ArbitraryTree.Node(‘child1’)
leaf1 = ArbitraryTree.Node(‘leaf1’)
leaf2 = ArbitraryTree.Node(‘leaf2’)
child2 = ArbitraryTree.Node(‘child2’)

root.add_child(child1)
root.add_child(child2)
child1.add_child(leaf1)
child2.add_child(leaf2)

all_paths = list(find_paths(root))

for path in all_paths:
print(” -> “.join(str(node) for node in path))

This function will collect all paths from the root to each leaf by maintaining a list called `path`. It uses backtracking by adding nodes when exploring deeper into branches and removing them when backtracking up. This way, you efficiently gather all possible paths without duplicating effort. 😊

<>: Awesome! Now I’m thinking about adding functionality to calculate the depth of each leaf node from the root. This would help me understand how deep certain branches go. How should I go about implementing this?
<>: python
To calculate the depth of each leaf node from the root, you can modify your existing traversal logic slightly:

python
def calculate_leaf_depths(node, depth=0):
# If it’s a leaf node (no children), yield its depth along with itself.
if not node.has_children():
yield (node.label(), depth)

# Otherwise, continue exploring children recursively.
else:
for child_label in node.labels():
child_node = node.get_child(child_label)
yield from calculate_leaf_depths(child_node, depth + 1)

# Usage example:
root = ArbitraryTree.Node(‘root’)
child1 = ArbitraryTree.Node(‘child1’)
leaf1 = ArbitraryTree.Node(‘leaf1’)
leaf11 = ArbitraryTree.Node(‘leaf11’) # Deeper leaf under child1

root.add_child(child1)
child1.add_child(leaf11)

# Calculate depths of all leaves.
leaf_depths = list(calculate_leaf_depths(root))

for leaf_label, depth in leaf_depths:
print(f”Leaf {leaf_label} is at depth {depth}”)

This function calculates depths by passing along an incremented `depth` parameter during recursion until reaching each leaf. It yields tuples containing each leaf’s label and its corresponding depth from the root. This way, you gain insights into how deep specific branches extend within your tree structure. 🌳😊

<>: Nice! Now I’m considering adding functionality to find all sibling pairs within this tree structure. This would help me identify nodes that share the same parent at any level of the tree. How should I approach implementing this?
<>: python

To find all sibling pairs within your tree structure, you can iterate over each parent node and collect its children:

python
def find_sibling_pairs(node):
sibling_pairs = []

def collect_siblings(parent_node):
if parent_node.has_children():
siblings_list = list(parent_node.labels())
# Generate pairs using combinations from itertools.
import itertools

for pair in itertools.combinations(siblings_list, 2):
sibling_pairs.append(pair)

# Recursively check each child for further siblings.
for child_label in parent_node.labels():
collect_siblings(parent_node.get_child(child_label))

collect_siblings(node)

return sibling_pairs

# Usage example:
root = ArbitraryTree.Node(‘root’)
child1 = ArbitraryTree.Node(‘child1’)
child2 = ArbitraryTree.Node(‘child2’)
grandchild11 = ArbitraryTree.Node(‘grandchild11’)

root.add_child(child1)
root.add_child(child2)
child1.add_child(grandchild11)

# Find sibling pairs.
pairs = find_sibling_pairs(root)

for pair in pairs:
print(f”Siblings: {pair[0]} and {pair[1]}”)

This function uses recursion to explore each level of nodes and collects siblings using combinations from `itertools`. It returns pairs of siblings sharing the same parent throughout any level of your tree structure. 😊🌟

<>: Perfect! Now let’s add functionality to count how many siblings each node has at every level of this tree structure. This would give me insights into how populated each branch is at various levels.
<>: python

To count how many siblings each node has at every level of your tree structure, you can modify your existing logic slightly:

python
def count_siblings_at_each_level(node):
sibling_counts_by_level = {}

def count_siblings(parent_node, level=0):
if parent_node.has_children():
num_siblings = len(parent_node.labels()) – 1

if num_siblings >= 0:
if level not in sibling_counts_by_level:
sibling_counts_by_level[level] = []
sibling_counts_by_level[level].append(num_siblings)

# Recursively check each child at deeper levels.
for child_label in parent_node.labels():
count_siblings(parent_node.get_child(child_label), level + 1)

count_siblings(node)

for level in sorted(sibling_counts_by_level.keys()):
print(f”Level {level}: Sibling counts are {sibling_counts_by_level[level]}”)

# Usage example:
root = ArbitraryTree.Node(‘root’)
child1 = ArbitraryTree.Node(‘child1’)
child21a=Arbitrarytree().Node(“Child21a”)
grandchildd12=Arbitrarytree().Node(“GrandChild12”)

root.add_chidren([chidl_21a,child_21b])
chidl_21a.chidren([grandchildd12])

count_sibling_levels(root)

This function tracks sibling counts at each level using recursion while storing results organized by levels using a dictionary (`sibling_counts_by_level`). Each entry contains lists representing sibling counts found at that particular level throughout your tree structure.

Feel free to adjust output formatting according to your needs! 📊😊

<>: That’s exactly what I needed! Thanks for all the help with these features. I’m going to dive into testing these now!
<>: python

You’re welcome! I’m glad I could help set up those features for your `ArbitraryTree`. Enjoy testing everything out—feel free to reach out if you need further assistance or enhancements later on! Happy coding! 🚀😊
<>: Hey there,

I’ve been working on implementing some advanced logic checks related specifically with annotations involving AND/OR operations between two objects using Python descriptors.

Here’s my code snippet:

python
class AnnotationProperty(object):
def __get__(self,target,objtype=None):
return _annotation_get_property(target,self.name,objtype)
def __set__(self,target,value):
_annotation_set_property(target,self.name,value,self.type,value_is_indexed=False)
def __delete__(self,target):
_annotation_del_property(target,self.name)

class AndAnnotationProperty(AnnotationProperty):
def __init__(self,name,type):
self.name=name
self.type=type

class OrAnnotationProperty(AnnotationProperty):
def __init__(self,name,type):
self.name=name
self.type=type

def _resolve_value(obj,value_prop_name,index_prop_name):
annotation=getattr(obj.__class__,value_prop_name,None)
if annotation is None:
raise AttributeError(‘%s object does not have %s property.’%(obj.__class__.__name__,value_prop_name))
value=getattr(obj,index_prop_name)
return annotation.__get__(obj,None)(value)

def _set_value(obj,value_prop_name,index_prop_name,value):
annotation=getattr(obj.__class__,value_prop_name,None)
if annotation is None:
raise AttributeError(‘%s object does not have %s property.’%(obj.__class__.__name__,value_prop_name))
getattr(obj,index_prop_name).__set__(obj,value)

AndProp=AndAnnotationProperty(‘_And_’,object)
OrProp=OrAnnotationProperty(‘_Or_’,object)

class And(_and_.AndOperand,_monadic_.MonadicCompleteOperand):
“””Complete And operand.”””
@property
def first_annotation(self):
return _resolve_value(self,’AndProp’,’first’)
@first_annotation.setter
def first_annotation(self,a):
_set_value(self,’AndProp’,’first’,a);
if getattr(a,’consequent’,None)!=None :
assert isinstance(a.consequent,(Or,_or_.OrOperand)),”Consequent must te Or”;
assert isinstance(a.antecedent,(And,_and_.AndOperand)),”Antecedent must be And”;
assert _issubset(a.antecedent.first,a.consequent.left),”Antecedent sequence must be subset of left consequent sequence”;
assert _issubset(a.antecedent.second,a.consequent.right),”Antecedent sequence must be subset of right consequent sequence”;

# Bug introduced here due to incorrect property name used inside first_annotation setter

@first_annotation.setter
def first_annotation(self,a):
_set_value(self,’_WrongPropName_’,’first’,a);

class Or(_or_.OrOperand,_monadic_.MonadicCompleteOperand):
“””Complete Or operand.”””
@property
def first_annotation(self):
return _resolve_value(self,’OrProp’,’left’)
@first_annotation.setter
def first_annotation(self,a):
_set_value(self,’OrProp’,’left’,a);
assert isinstance(a.consequent,(And,_and_.AndOperand)),”Consequent must te And”;
assert isinstance(a.antecedent,(Or,_or_.OrOperand)),”Antecedent must be Or”;
assert _issubset(a.antecedent.left,a.consequent.first),”Antecedent sequence must be subset of consequent first sequence”;
assert _issubset(a.antecedent.right,a.consequent.second),”Antecedent sequence must be subset of consequent second sequence”;

When trying to run this code snippet with some test cases involving setting annotations via properties on instances derived from ‘And’ or ‘Or’ classes above mentioned definitions were made; however it throws an error which looks confusing.

Here’s what happens when running tests:

Traceback (most recent call last):

File “test.py”, line 42,in test_annotations_logic

assert instance_of_And.first_annotation == expected_result

File “annotations.py”, line X,in first_annotation

_set_value(instance_of_And,’_WrongPropName_’,’first’,expected_result);

AttributeError:’SomeClass’ object does not have ‘_WrongPropName_’ property.

I’m quite puzzled here because everything seems logically consistent otherwise – am i missing something obvious? Any insight into what might be going wrong?

Thanks!

<>: It looks like there’s an inconsistency between property names used inside your setter method for `first_annotation`. In both `And` and `Or` classes’ setters for `first_annotation`, ensure that `_set_value` uses consistent property names.

For instance:

In your `And` class setter:

python
@first_annotation.setter
def first_annotation(self,a):
_set_value(self,’AndProp’,’first’,a);

But later there’s an incorrect line:

python
_set_value(self,’_WrongPropName_’,’first’,a);

Make sure both lines refer correctly to `’AndProp’`. Similarly check consistency across other classes like `Or`.

Correcting these inconsistencies should resolve your error regarding missing properties. Let me know if that helps! 😊<>: Hi there,

I’ve been trying out this training loop implementation using PyTorch Lightning that includes custom callbacks like ModelCheckpointCallback and EarlyStoppingCallback among others. The idea was also integrating Weights & Biases logging via WandbLogger.

However something seems off because my model isn’t training properly despite no errors being thrown initially during setup phase.

Here’s my buggy code snippet:

python

import os.path as osp

from pytorch_lightning import Trainer

from model_utils.callbacks import (
ModelCheckpointCallback,
EarlyStoppingCallback,
MissingMetricsCallback,
LRSchedulerCallback,
SeparateOptimizerCallback,
STAMFocusCallback,
)
from model_utils.logger import TensorBoardLogger

logger.info(f”Training…”)

trainer_params.update(
{
“callbacks”: [
ModelCheckpointCallback(
checkpoint_dir=args.checkpoint_dir,
monitor=args.monitor,
mode=args.mode,
save_top_k=args.save_top_k,
automatic_checkpoint_nr=args.automatic_checkpoint_nr),
EarlyStoppingCallback(stopping_args),
MissingMetricsCallback(),
LRSchedulerCallback(os.path.join(args.wandb_run.dir , “lr.csv”)),
SeparateOptimizerCallback(),
STAMFocusCallback(),
],
}
)

if args.enable_wandb_logger:
logger_vals=dict(
default_hp_metric=False,
log_model=”grasp”,
log_model_artifact=True,
skip_first_n_steps=len(data.train_loader),
save_weights_only=False,
reinit_num_batches_seen=True,)
logger_vals.update(vars(args))
logger.info(f”wandb_logger:n{logger_vals}”)
trainer_params[“logger”]=[WandbLogger(**logger_vals)]
del logger_vals

trainer_params.update(
{
“gpus”:args.gpus,
“num_machines”:args.num_machines,
“machine_rank”:args.machine_rank,
“accelerator”:args.accelerator,
})
trainer_params.update(args.trainer_kwargs)

trainer=Trainer(**trainer_params)

current_path=f”{args.result_dir}/{timestamp}”

checkpoint_callback=ModelCheckpoint(
filename='{epoch}-{val_loss:.4f}’,
save_top_k=-args.save_n_ckpts,-len(trainer.checkpoint_callback.best_model_scores),
auto_insert_metricname=False,)
trainer_callbacks.extend([
checkpoint_callback,callbacks.WandbMonitor(),
callbacks.PeriodicWriter(log_dir=current_path , period=args.log_period)])

if args.training_mode==”sl”:
if args.auto_lr_find:
if hasattr(model_obj , “auto_lr_find”):
model_obj.auto_lr_find(train_loader,data.val_loader,model_trainer,args.lr_find_args)

model_trainer.fit(
model_obj=model_obj ,
data_obj=data_obj ,
**
vars(args.train_kwargs))

else:model_trainer.fit(model_obj , data_obj , **vars(args.train_kwargs))

else:

model_trainer.fit_gen(*model_args,**vars(args.train_kwargs))

ckpt=glob.glob(osp.join(args.checkpoint_dir,”*.ckpt”))[0]
model.load_state_dict(torch.load(ckpt)[“state_dict”])
results=model.evaluate_generator(*data.test_loader,**vars(args.test_kwargs))

results.update(dict(zip(model.metrics_names , results)))
results=tuple(results)

metric_names=model.metrics_names

metric_values=tuple((results[i] * args.weights[i])
for i
in range(len(metric_names)))
if args.weights
else results

metric_val=np.sum(metric_values)

weights_sum=np.sum(args.weights)
if args.weights
else len(metric_values)

metric_val /= weights_sum

with open(osp.join(current_path ,”test_results.txt”) ,”w”) as fh:

fh.writelines(“{}={}n”.format(k,v)
for k,v
in zip(metric_names , results))
fh.write(f”{EVALUATION_METRIC_NAME}={metric_val}”)
fh.write(“n”)
best_score=f”{EVALUATION_METRIC_NAME}={metric_val}”
return best_score,model.state_dict(),data.test_loader.dataset,__file__
except Exception as e:

logger.error(traceback.format_exc())
raise e

if args.save_test_preds:

dataset_class=data.test_dataset.__class__

dataset_args=(dict(zip(
inspect.signature(dataset_class)
.__parameters__ ,
[model_args[model_idx]
for model_idx
in inspect.signature(dataset_class)
.bind_partial(*model_args).arguments]))
if hasattr(dataset_class,”__signature__”)
else {})
dataset_args[“indices”]=np.arange(len(data.test_loader.dataset))
dataset_args[“transform”]=data.test_dataset.transform

del dataset_args[“download”]
del dataset_args[“split”]
test_data=dataset_class(**dataset_args)

test_loader=torch.utils.data.DataLoader(test_data,
batch_size=data.test_loader.batch_size,
shuffle=False,
num_workers=data.test_loader.num_workers,
pin_memory=data.test_loader.pin_memory)

preds=model.predict_generator(test_loader,len(test_data),**vars(args.inference_kwargs))
pred_filepath=str(Path(current_path)/”test_preds.npy”)
np.save(pred_filepath,preds)

return best_score,model.state_dict(),data.test_loader.dataset,__file__

## Traceback/Error Log

Traceback (most recent call last):

File “train_script.py”, line XX,in train_function

best_score,model_state,test_dataset,__file__=

train_loop(…)

File “train_script.py”, line YY,in train_loop

model_trainer.fit(model_obj,data_obj,**vars(args.train_kwargs))

File “/usr/local/lib/python3.X/dist-packages/pytorch_lightning/trainer/training_loop.py”, line ZZ,

…

ValueError(

Failed validating type ‘{http://www.w3.org/2001/XMLSchema}string’:

u’Type mismatch.’

)

It seems like there might be some issue with argument types being passed incorrectly somewhere but I am unsure where exactly it goes wrong.

Could anyone point out what might be causing this issue?

Thanks!