Skip to main content

Can you efficiently analyse the type of method parameters in Python

I am making a PyCharm plugin that will generate code examples based on a Python class. The user will be able to select a class and the plugin will generate code examples that utilise the attributes and methods of the class.

Basic Class

class Rectangle:
    
    # Hardcoded values
    width = 5
    length = 10
    
    def calculate_area(self):
        return self.width*self.length

Basic Example

r = Rectangle()
r.calculate_area()

The problem arises when the class contains methods that take parameters. For example, if the class contains a method that takes a string parameter, then I want the generated examples to pass either an empty or a random string to the method. Is there a good way to analyse the potential type of the parameters (when not strongly defined) to generate code examples?

Potential Solutions

So far I have come up with 2 applicable solutions (passing None parameters when calling the method is not applicable):

  1. A potential solution would be to keep track of the parameters and analyse the operations that they use/are used in to determine their type. In the code below num will most likely be an integer, as determined by the method body:
def calculate_even(num):
    return num % 2 == 0
  1. I know that you can execute a string containing Python code and check if it compiles correctly (source). A possible solution (one that I would love to avoid) is to generate code examples, passing different types as method parameters and determining which ones work after executing said code examples.

I can see huge downsides with both solutions, as the first one will require the handling of lots of operations for the different types and will still most likely be inaccurate, and the second one becomes incredibly slow for methods that take multiple parameters. I am really hoping that there's a more elegant solution that I have missed.



source https://stackoverflow.com/questions/75440251/can-you-efficiently-analyse-the-type-of-method-parameters-in-python

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...