Add parameter count adjustment for functions that are class methods#92
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ahmedfgad merged 2 commits intoahmedfgad:masterfrom Feb 20, 2023
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Add parameter count adjustment for functions that are class methods#92ahmedfgad merged 2 commits intoahmedfgad:masterfrom
ahmedfgad merged 2 commits intoahmedfgad:masterfrom
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Bumping this. Would be really nice to have! |
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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. |
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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! |
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Good luck! Just be aware my fork is not up to date with the main. The new
keep_elitism function is not in my code. I probably need to do something
about that.
Mike
…On Fri, Oct 28, 2022 at 1:01 PM Max Schrader ***@***.***> wrote:
We built a hacky solution for >2 arguments by saving the other arguments
to a common temporary file and using a closure for the time being, but the
class based method is ultimately what we want. I'll fork from your branch
for the time being!
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Thanks @richmr. This is a useful feature. |
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Woohoo!
…On Mon, Feb 20, 2023 at 3:29 PM Ahmed Gad ***@***.***> wrote:
Merged #92 <#92>
into master.
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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)
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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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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. |
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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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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