Metadata-Version: 2.1
Name: cost2fitness
Version: 2.0.7
Summary: PyPI package for 1) conversion cost values (less is better) to fitness values (more is better) and vice versa, 2) using fast neural networks for forward propagation
Home-page: https://github.com/PasaOpasen/cost2fitness
Author: Demetry Pascal
Author-email: qtckpuhdsa@gmail.com
Maintainer: ['Demetry Pascal']
License: UNKNOWN
Description: [![PyPI
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        # cost2fitness
        
        PyPI package for conversion cost values (less is better) to fitness values (more is better) and vice versa
        
        ```
        pip install cost2fitness
        ```
        
        - [cost2fitness](#cost2fitness)
          - [About](#about)
          - [Transformers](#transformers)
          - [Pipeline of transformers](#pipeline-of-transformers)
          - [How to plot](#how-to-plot)
          - [Examples with plotting](#examples-with-plotting)
            - [Each transformer](#each-transformer)
            - [Pipeline](#pipeline)
          - [Neural net tools](#neural-net-tools)
            - [Layers](#layers)
            - [Helpers](#helpers)
            - [Examples](#examples)
        
        ## About
        
        This is the package containing several methods for transformation numpy arrays depended on scales, averages and so on. But the primary way to use it is the conversion from cost values (less is better) to fitness values (more is better) and vice versa. It can be highly helpful when u r using 
        
        * evolutionary algorithms depended on numeric differences: so, it's important to set good representation of samples scores for better selection
        * sampling methods with probabilities depended on samples scores 
        
        ## Transformers
        
        There are several simple transformers. Each transformer is the subclass of `BaseTransformer` class containing `name` field and `transform(array)` method which transforms input array to new representation. 
        
        Checklist:
        
        * `ReverseByAverage`, 
        * `AntiMax`, 
        * `AntiMaxPercent(percent)`, 
        * `Min2Zero`, 
        * `Min2Value(value)`, 
        * `ProbabilityView` (converts data to probabilities), 
        * `SimplestReverse`, 
        * `AlwaysOnes` (returns array of ones), 
        * `NewAvgByMult(new_average)`,
        * `NewAvgByShift(new_average)`
        * `Divider(divider_number_or_array)` (divides array on number or array, useful for fixed start normalization)
        * `Argmax` (returns position of maximum element in array)
        * `Prob2Class(threshold = 0.5)` (to convert probabilities to classes 0/1)
        * `ToNumber` (converts array to one number by getting first element)
        
        U can create your transformer using simple logic from [file](cost2fitness/transformers.py).
        
        ```python
        import numpy as np 
        
        from cost2fitness import Min2Zero
        
        tf = Min2Zero()
        
        arr_of_scores = np.array([10, 8, 7, 5, 8, 9, 20, 12, 6, 18])
        
        tf.transform(arr_of_scores)
        # array([ 5,  3,  2,  0,  3,  4, 15,  7,  1, 13])
        ```
        
        ## Pipeline of transformers
        
        U also can combine these transformers using `Pl` pipeline. For example:
        
        ```python
        import numpy as np 
        
        from cost2fitness import ReverseByAverage, AntiMax, Min2Zero, Pl
        
        pipe = Pl([
                Min2Zero(),
                ReverseByAverage(),
                AntiMax()        
                ])
        
        
        arr_of_scores = np.array([10, 8, 7, 5, 8, 9])
        
        
        # return each result of pipeline transformation (with input)
        pipe.transform(arr_of_scores, return_all_steps= True)
        #array([[10.        ,  8.        ,  7.        ,  5.        ,  8.        ,
        #         9.        ],
        #       [ 5.        ,  3.        ,  2.        ,  0.        ,  3.        ,
        #         4.        ],
        #       [ 0.66666667,  2.66666667,  3.66666667,  5.66666667,  2.66666667,
        #         1.66666667],
        #       [ 5.        ,  3.        ,  2.        ,  0.        ,  3.        ,
        #         4.        ]])
        
        # return only result of transformation
        pipe.transform(arr_of_scores, return_all_steps= False)
        #array([5., 3., 2., 0., 3., 4.])
        
        ```
        
        ## How to plot
        
        There is `plot_scores` function for plotting transformation process results. It has arguments:
        
        * `scores` : 2D numpy array
               2D numpy array with structure `[start_values, first_transform(start_values), second_transform(first_transform), ...]`, where each object is 1D-array of scores (values/costs/fitnesses).
        * `names` : `None`/string list, optional
                Names for each step for plot labels. The default is `None`.
        * `kind` : str, optional
                for **'beside'** each new column will be beside previous; for **'under'** there will be new plot under previous. The default is 'beside'.
        * `save_as` : `None`/str, optional
                File path to save the plot. The default is `None`.
        
        ## Examples with plotting
        
        ### Each transformer
        
        [Code](tests/tf_for_readme.py)
        
        ![](tests/Simplest%20reverse%20example.png)
        ![](tests/Shifted%20new%20average%20=%205%20example.png)
        ![](tests/Reverse%20by%20average%20example.png)
        ![](tests/Prob.%20view%20example.png)
        ![](tests/Multiple%20new%20average%20=%205%20example.png)
        ![](tests/Min%20to%20zero%20example.png)
        ![](tests/Min%20to%20value%20=%202%20example.png)
        ![](tests/AntiMax%20with%20percent%20=%200.5%20example.png)
        ![](tests/AntiMax%20example.png)
        ![](tests/Always%20ones%20example.png)
        
        ### Pipeline
        [Code](tests/simple_pipe.py)
        
        ![](tests/pipe_example_beside.png)
        ![](tests/pipe_example_under.png)
        
        
        ## Neural net tools
        
        I have made basic neural network tools here because it's very necessary to use simple networks with some **reinforcement learning** tasks, but common packages like [Keras](https://keras.io) work extremely slow if u need just prediction (forward propagation) only for 1 sample but many times. So it will be faster to use simple numpy-based packages for these cases.
        
        
        ### Layers
        
        It was not so difficult to use this transformers logic for creating neural networks. So this package has next neural network layers as transformers:
        
        * **Activations**:
          * `Softmax`
          * `Relu`
          * `LeakyRelu(alpha = 0.01)`
          * `Sigmoid`
          * `Tanh`
          * `ArcTan`
          * `Swish(beta = 0.25)`
          * `Softplus`
          * `Softsign`
          * `Elu(alpha)`
          * `Selu(alpha, scale)`
        
        * **Dense layers tools**:
          * `Bias(bias_len, bias_array = None)` -- to add bias with length `bias_len`. If `bias_array` is `None`, uses random bias
          * `MatrixDot(from_size, to_size, matrix_array = None)`
          * `NNStep(from_size, to_size, matrix_array = None, bias_array = None)` -- it's `MatrixDot` and `Bias` together, if u wanna create them faster
        
        ### Helpers
        
        And there are several helpers methods for using `pipeline` object like neural network (only for **forward propagation** of course):
        
        * **`pipeline` objects methods**:
          * `get_shapes()` -- to get list of shapes of needed array for NN
          * `total_weights()` -- get count of weights for overall NN
          * `set_weights(weights)` - set weights (as list of arrays with needed shapes) for NN
        
        * **Alone functions**:
          * `arr_to_weigths(arr, shapes)` -- converts 1D-array `arr` to list of arrays with shapes `shapes` to put it in `set_weights` method
        
        ### Examples
        
        See [simplest example](tests/compare_NN.py)
        
        
        
        
        
        
        
Keywords: optimization,evolutionary algorithms,fast,easy,evolution,generator,simple,converter,min2max,max2min,barplots,reinforcement learning,neural networks,layers
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
