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Add parameter count adjustment for functions that are class methods #92

Merged
merged 2 commits into from
Feb 20, 2023
Merged

Add parameter count adjustment for functions that are class methods #92

merged 2 commits into from
Feb 20, 2023

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richmr
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@richmr richmr commented Mar 9, 2022

Hi, I am using PyGAD.GA inside of another class. The various functions PyGAD will accept as parameters have strict parameter count checks. However, when you assign a class method to these functions they get an extra "self" parameter assigned to them. PyGAD then interprets these extra self parameters as improperly constructed functions.

Using the "inspect" module I have added a check to see if an assigned function is actually a method. If so, it then adds one to the expected parameter count. This allows these methods to be used as contributing functions. This, in turn, permits more well-formatted object oriented code to be used when desired. Users can still assign non-class functions to these parameters and the code will accept them.

I am using this in my current experiments and it is working well.

Thanks for your time!

Mike

@jack89roberts jack89roberts mentioned this pull request May 19, 2022
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@mschrader15
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Bumping this. Would be really nice to have!

@richmr
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richmr commented Oct 28, 2022

It does prevent PyGAD "save" function from working due to pickle issue, I discovered after submission. I did not correct that issue since I don't use "save". I see jack89roberts comment but I am not having any issues with a wide variety of fitness functions myself.

@mschrader15
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mschrader15 commented Oct 28, 2022 •
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We built a hacky solution for >2 arguments by saving the other arguments to a common temporary file and using a closure, but the class based method is ultimately what we want. I'll fork from your branch for the time being!

@richmr
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richmr commented Oct 28, 2022 via email

@ahmedfgad
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Thanks @richmr. This is a useful feature.

@ahmedfgad ahmedfgad merged commit fcf5d07 into ahmedfgad:master Feb 20, 2023
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richmr commented Feb 21, 2023 via email

ahmedfgad added a commit that referenced this pull request Feb 22, 2023
@ahmedfgad
PyGAD 2.19.0 Release
ed1199d 
PyGAD 2.19.0 Release Notes
1. A new `summary()` method is supported to return a Keras-like summary of the PyGAD lifecycle.
2. A new optional parameter called `fitness_batch_size` is supported to calculate the fitness function in batches. If it is assigned the value `1` or `None` (default), then the normal flow is used where the fitness function is called for each individual solution. If the `fitness_batch_size` parameter is assigned a value satisfying this condition `1 < fitness_batch_size <= sol_per_pop`, then the solutions are grouped into batches of size `fitness_batch_size` and the fitness function is called once for each batch. In this case, the fitness function must return a list/tuple/numpy.ndarray with a length equal to the number of solutions passed. #136.
3. The `cloudpickle` library (https://github.com/cloudpipe/cloudpickle) is used instead of the `pickle` library to pickle the `pygad.GA` objects. This solves the issue of having to redefine the functions (e.g. fitness function). The `cloudpickle` library is added as a dependancy in the `requirements.txt` file. #159
4. Support of assigning methods to these parameters: `fitness_func`, `crossover_type`, `mutation_type`, `parent_selection_type`, `on_start`, `on_fitness`, `on_parents`, `on_crossover`, `on_mutation`, `on_generation`, and `on_stop`. #92 #138
5. Validating the output of the parent selection, crossover, and mutation functions.
6. The built-in parent selection operators return the parent's indices as a NumPy array.
7. The outputs of the parent selection, crossover, and mutation operators must be NumPy arrays.
8. Fix an issue when `allow_duplicate_genes=True`. #39
9. Fix an issue creating scatter plots of the solutions' fitness.
10. Sampling from a `set()` is no longer supported in Python 3.11. Instead, sampling happens from a `list()`. Thanks `Marco Brenna` for pointing to this issue.
11. The lifecycle is updated to reflect that the new population's fitness is calculated at the end of the lifecycle not at the beginning. #154 (comment)
12. There was an issue when `save_solutions=True` that causes the fitness function to be called for solutions already explored and have their fitness pre-calculated. #160
13. A new instance attribute named `last_generation_elitism_indices` added to hold the indices of the selected elitism. This attribute helps to re-use the fitness of the elitism instead of calling the fitness function.
14. Fewer calls to the `best_solution()` method which in turns saves some calls to the fitness function.
15. Some updates in the documentation to give more details about the `cal_pop_fitness()` method. #79 (comment)
@whubaichuan
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@ahmedfgad hi, could you please share a code example for the 4th update in Pygad 2.19.0 Release? Is there any relationship to the class instance or class method? I am confused about this function but I am interested.

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richmr commented Feb 25, 2023

If you are referring to this pull, all that was added was the ability to use class methods ("def method(self):" in a class) to be the assigned callback functions for fitness, generation, etc. You can still assign flat functions (not defined in a class) as well. I use class methods for all my pygad callbacks.

@ahmedfgad ahmedfgad added the enhancement New feature or request label Feb 25, 2023
ahmedfgad added a commit that referenced this pull request Apr 8, 2023
@ahmedfgad
PyGAD 3.0.0 Release
6659d5d 
PyGAD 3.0.0 Release Notes
1. The structure of the library is changed and some methods defined in the `pygad.py` module are moved to the `pygad.utils`, `pygad.helper`, and `pygad.visualize` submodules.
  2. The `pygad.utils.parent_selection` module has a class named `ParentSelection` where all the parent selection operators exist. The `pygad.GA` class extends this class.
  3. The `pygad.utils.crossover` module has a class named `Crossover` where all the crossover operators exist. The `pygad.GA` class extends this class.
  4. The `pygad.utils.mutation` module has a class named `Mutation` where all the mutation operators exist. The `pygad.GA` class extends this class.
  5. The `pygad.helper.unique` module has a class named `Unique` some helper methods exist to solve duplicate genes and make sure every gene is unique. The `pygad.GA` class extends this class.
  6. The `pygad.visualize.plot` module has a class named `Plot` where all the methods that create plots exist. The `pygad.GA` class extends this class.

```python
...
class GA(utils.parent_selection.ParentSelection,
         utils.crossover.Crossover,
         utils.mutation.Mutation,
         helper.unique.Unique,
         visualize.plot.Plot):
...
```

2. Support of using the `logging` module to log the outputs to both the console and text file instead of using the `print()` function. This is by assigning the `logging.Logger` to the new `logger` parameter. Check the [Logging Outputs](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#logging-outputs) for more information.
3. A new instance attribute called `logger` to save the logger.
4. The function/method passed to the `fitness_func` parameter accepts a new parameter that refers to the instance of the `pygad.GA` class. Check this for an example: [Use Functions and Methods to Build Fitness Function and Callbacks](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks). #163
5. Update the documentation to include an example of using functions and methods to calculate the fitness and build callbacks. Check this for more details: [Use Functions and Methods to Build Fitness Function and Callbacks](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks). #92 (comment)
6. Validate the value passed to the `initial_population` parameter.
7. Validate the type and length of the `pop_fitness` parameter of the `best_solution()` method.
8. Some edits in the documentation. #106
9. Fix an issue when building the initial population as (some) genes have their value taken from the mutation range (defined by the parameters `random_mutation_min_val` and `random_mutation_max_val`) instead of using the parameters `init_range_low` and `init_range_high`.
10. The `summary()` method returns the summary as a single-line string. Just log/print the returned string it to see it properly.
11. The `callback_generation` parameter is removed. Use the `on_generation` parameter instead.
12. There was an issue when using the `parallel_processing` parameter with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the `predict()` method gives incorrect and weird results when more than 1 thread is used. #145 ahmedfgad/TorchGA#5 ahmedfgad/KerasGA#6. Thanks to this [StackOverflow answer](https://stackoverflow.com/a/75606666/5426539).
13. Replace `numpy.float` by `float` in the 2 parent selection operators roulette wheel and stochastic universal. #168
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@richmr @mschrader15 @ahmedfgad @whubaichuan