| Nola Calcio |
<t[0]: # -*- coding: utf-8 -*-
[1]: """
[2]: Created on Mon Apr 16 20:53:12 2018
[3]: @author: mohammad
[4]: """
[5]: import os
[6]: import numpy as np
[7]: import pandas as pd
[8]: from sklearn.model_selection import train_test_split
[9]: from keras.models import Sequential
[10]: from keras.layers import Dense,LSTM,Dropout,Bidirectional,CuDNNLSTM,CuDNNRNN
[11]: from keras.callbacks import EarlyStopping,TensorBoard,LearningRateScheduler
[12]: def load_data(path):
[13]: data = []
[14]: for filename in os.listdir(path):
[15]: df = pd.read_csv(os.path.join(path,filename))
[16]: data.append(df)
***** Tag Data *****
ID: 4
description: Nested block structure with comments suggesting complex logic or processing,
potentially involving advanced algorithmic concepts.
start line: 55
end line: 78
dependencies:
– type: Other
name: Nested Block Structure with Comments
start line: 55
end line: 78
context description: This snippet contains nested blocks of comments that hint at
complex logic or processing steps which could involve sophisticated algorithms.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: N
************
## Challenging Aspects
### Challenging Aspects in Above Code:
The provided code snippet contains nested blocks of comments that suggest an intricate structure or logic flow. Here are some challenging aspects:
1. **Nested Comment Blocks**: The code consists of multiple layers of nested comments which obscure any underlying logic or instructions within them.
2. **Obscured Logic**: Since there's no actual executable code between these comments but rather placeholders (`pass` statements), it requires interpretation to understand what kind of logic might be hidden behind these comment blocks.
3. **Algorithmic Depth**: Understanding what kind of algorithms or processes could be embedded within such structured comments requires deep thought about potential use cases.
4. **Complexity through Indentation**: The indentation suggests hierarchical relationships between different parts of the code which can complicate understanding without additional context.
### Extension:
To extend this complexity further:
1. **Dynamic Processing**: Introduce elements where certain parts of this nested structure need to dynamically adapt based on input data characteristics.
2. **State Management**: Add requirements for maintaining state across different levels of nesting.
3. **Conditional Execution**: Incorporate conditional execution paths based on runtime conditions within these nested structures.
## Exercise:
### Problem Statement:
You are tasked with developing an advanced processing system that utilizes a complex nested structure similar to the one provided below ([SNIPPET]). Your system should handle dynamic input data and execute different algorithms based on specific conditions.
#### Requirements:
1. **Dynamic Nesting**:
– Implement a function `process_data(data)` where `data` is a dictionary containing various keys representing different stages or levels of processing.
– Each key may contain sub-dictionaries representing further nesting levels.
2. **Conditional Execution**:
– Depending on certain values within `data`, different algorithms should be executed at each level.
– Implement at least three distinct algorithms/functions that will be conditionally called during processing.
3. **State Management**:
– Maintain state information across different levels of nesting.
– Ensure that changes at deeper levels can influence higher levels if needed.
4. **Error Handling**:
– Implement robust error handling to manage unexpected inputs or states gracefully.
#### Example Input:
python
data = {
'level_1': {
'condition_A': {
'sub_level_1': {'value': 'execute_algorithm_1'},
'sub_level_2': {'value': 'execute_algorithm_2'}
},
'condition_B': {
'sub_level_1': {'value': 'execute_algorithm_3'}
}
}
}
### Full Exercise Implementation:
python
def algorithm_1(state):
# Example algorithm implementation A.
print("Executing Algorithm A")
state['processed'] = True
def algorithm_2(state):
# Example algorithm implementation B.
print("Executing Algorithm B")
state['processed'] = True
def algorithm_3(state):
# Example algorithm implementation C.
print("Executing Algorithm C")
state['processed'] = True
def process_data(data):
def recursive_process(level_data, state):
if isinstance(level_data, dict):
for key, value in level_data.items():
if isinstance(value, dict):
recursive_process(value, state)
else:
if value == 'execute_algorithm_1':
algorithm_1(state)
elif value == 'execute_algorithm_2':
algorithm_2(state)
elif value == 'execute_algorithm_3':
algorithm_3(state)
else:
print(f"Unknown command {value}")
else:
print(f"Invalid data format at {key}")
return state
initial_state = {'processed': False}
try:
final_state = recursive_process(data.get('level_1', {}), initial_state)
return final_state
except Exception as e:
print(f"An error occurred during processing: {e}")
return None
# Example usage with provided input structure.
data_example = {
'level_1': {
'condition_A': {
'sub_level_1': {'value': 'execute_algorithm_1'},
'sub_level_2': {'value': 'execute_algorithm_2'}
},
'condition_B': {
'sub_level_1': {'value': 'execute_algorithm_3'}
}
}
}
final_state = process_data(data_example)
print(final_state) # Expected output should show processed status after running all applicable algorithms.
## Follow-up Exercise:
### Problem Statement Extension:
Modify your `process_data` function to handle asynchronous execution where each level can potentially trigger concurrent tasks using Python’s `asyncio`. Ensure proper synchronization so that states are updated correctly even when tasks run concurrently.
#### Requirements:
– Convert your existing functions (`algorithm_x`) into asynchronous functions using `async def`.
– Modify `process_data` to handle asynchronous execution using `await`.
– Ensure proper synchronization mechanisms are implemented so shared states do not get corrupted due to concurrent modifications.
### Solution:
python
import asyncio
async def async_algorithm_1(state):
await asyncio.sleep(0) # Simulate async work here.
print("Executing Async Algorithm A")
state['processed'] = True
async def async_algorithm_2(state):
await asyncio.sleep(0) # Simulate async work here.
print("Executing Async Algorithm B")
state['processed'] = True
async def async_algorithm_3(state):
await asyncio.sleep(0) # Simulate async work here.
print("Executing Async Algorithm C")
state['processed'] = True
async def process_data_async(data):
async def recursive_process(level_data, state):
if isinstance(level_data, dict):
tasks = []
for key, value in level_data.items():
if isinstance(value, dict):
tasks.append(recursive_process(value,state))
else:
if value == "execute_algorithm_" + "1":
tasks.append(async_algorithm_1(state))
elif value == "execute_algorithm_" + "2":
tasks.append(async_algorithm_2(state))
elif value == "execute_algorithm_" + "3":
tasks.append(async_algorithm_3(state))
else:
print(f"Unknown command {value}")
await asyncio.gather(*tasks)
return state
initial_state = {"processed": False}
try:
final_state=await recursive_process(data.get('level_' + str(1), {}),initial_state)
return final_state
except Exception as e :
print(f"An error occurred during processing : {e}")
return None
# Run example asynchronously using event loop.
loop=asyncio.get_event_loop()
final_state=loop.run_until_complete(process_data_async(data_example))
print(final_state) # Expected output should show processed status after running all applicable algorithms asynchronously.
This follow-up exercise ensures students understand asynchronous programming concepts alongside maintaining complex nested structures efficiently.
userIf you were asked by an employee whether it was true that he had been fired because he failed his drug test (the employee had not been fired), what would you say?
