A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Related tags

Machine Learningpythondata-sciencemachine-learninginterpretable-machine-learninglucidmode
Overview

https://raw.githubusercontent.com/lucidmode/lucidmode/main/images/lucidmode_logo.png



Documentation Status Version License Version Visits

Currently a Beta-Version


lucidmode is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning models. It has built in machine learning methods optimized for visual interpretation of some of the most relevant calculations.

Documentation

Installation

  • With package manager (coming soon)

Install by using pip package manager:

pip install lucidmode
  • Cloning repository

Clone entire github project

[email protected]:lucidmode/lucidmode.git

and then install dependencies

pip install -r requirements.txt

Models

Artificial Neural Network

Feedforward Multilayer perceptron with backpropagation.

  • fit: Fit model to data
  • predict: Prediction according to model

Initialization, Activations, Cost functions, regularization, optimization

  • Weights Initialization: With 4 types of criterias (zeros, xavier, common, he)
  • Activation Functions: sigmoid, tanh, ReLU
  • Cost Functions: Sum of Squared Error, Binary Cross-Entropy, Multi-Class Cross-Entropy
  • Regularization: L1, L2, ElasticNet for weights in cost function and in gradient updating
  • Optimization: Weights optimization with Gradient Descent (GD, SGD, Batch) with learning rate
  • Execution: Callback (metric threshold), History (Cost and metrics)
  • Hyperparameter Optimization: Random Grid Search with Memory

Complementary

  • Metrics: Accuracy, Confusion Matrix (Binary and Multiclass), Confusion Tensor (Multiclass OvR)
  • Visualizations: Cost evolution
  • Public Datasets: MNIST, Fashion MNIST
  • Special Datasets: OHLCV + Symbolic Features of Cryptocurrencies (ETH, BTC)

Important Links

Author/Principal Maintainer

Francisco Munnoz (IFFranciscoME) Is an associate professor of financial engineering and financial machine learning ITESO (Western Institute of Technology and Higher Education)

License

GNU General Public License v3.0

Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.

Contact: For more information in reggards of this repo, please contact [email protected]

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Releases(v0.4-beta1.0)

  • v0.4-beta1.0(Apr 29, 2021)

    Metrics

    • Calculation of several metrics for classification sensitivity (TPR), specificity (TNR), accuracy (acc), likelihood ratio (positive), likelihood ratio (negative), confusion matrix (binary and multiclass) confusion tensor (binary for every class in multi-class)

    Sequential Class

    • Move the cost_f and cost_r parameters to be specified from the formation method, leave the class instantiation with just the model architecture

    • Move the init_weights method to be specified from the formation method

    Execution

    • Create formation method in the Sequential Class, with the following parameters init, cost, metrics, optimizer

    • Store selected metrics in Train and Validation History

    Visualizations

    • Select metrics for verbose output
    Source code(tar.gz)
    Source code(zip)

  • v0.3-beta1.0(Apr 27, 2021)

    Regularization:

    • On weights and biases, location: gradients

      • L1, L2 and ElasticNet

    • On weights and biases, location: cost function

      • L1, L2 and ElasticNet

    Numerical Stability:

    • in functions.py, in cost, added a 1e-25 value to A, to avoid a divide by zero and invalid multiply cases in computations of np.log(A)

    Data Handling:

    • train and validation cost

    Visualization:

    • print: verbose of cost evolution

    Documentation:

    • Improve README
    Source code(tar.gz)
    Source code(zip)

  • v0.2-beta1.0(Apr 27, 2021)

    Files:

    • complete data set: MNIST
    • complete data set: 'fashion-MNIST'

    Tests passed:

    • fashion MNIST
    • previous release tests

    Topology

    • single hidden layer (tested)
    • 1 - 2 hidden layers (tested)
    • different activation functions among hidden layer

    Activation functions:

    • For hidden -> Sigmoid, Tanh, ReLU (tested and not working)
    • For output -> Softmax

    Cost Functions:

    • 'binary-logloss' (Binary-class Cross-Entropy)
    • 'multi-logloss' (Multi-class Cross-Entropy)

    Metrics:

    • Confusion matrix (Multi-class)
    • Accuracy (Multi-class)
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    Source code(zip)

  • v0.1-beta1.0(Apr 26, 2021)

    First release!

    Tests passed:

    • Random XOR data classification

    Sequential model:

    • hidden_l: Number of neurons per hidden layer (list of int, with a length of l_hidden)
    • hidden_a: Activation of hidden layers (list of str, with length l_hidden)
    • output_n: Number of neurons in the output layer (1)
    • output_a: Activation of output layer (str)

    Layer transformations:

    • linear

    Activation functions:

    • For hidden -> Sigmoid, Tanh
    • For output -> Sigmoid (Binary)

    Weights Initialization:

    • Xavier normal, Xavier uniform, common uniform, according to [1]

    Training Schemes:

    • Gradient Descent

    Cost Functions:

    • Sum of Squared Error (SSE) or Residual Sum of Squares (RSS)

    Metrics:

    • Accuracy (Binary)
    Source code(tar.gz)
    Source code(zip)
    LucidNet_v0.1-beta1.0.zip(111.97 MB)
Owner
lucidmode
A lucid framework for interpretable machine learning models
lucidmode
GitHub Repository https://www.lucidmode.org
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