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
Oficial Website: https://www.lucidmode.org
Documentation: https://lucidmode.readthedocs.io
Python Package Index (PyPI) repository: https://pypi.org/project/lucidmode/
Github repository: https://github.com/lucidmode/lucidmode
Installation
With package manager (coming soon)
Install by using pip package manager:
pip install lucidmode
Cloning repository
Clone entire github project
git@github.com: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
Release notes: https://github.com/lucidmode/lucidmode/releases
Example Notebooks: https://github.com/lucidmode/lucidmode/tree/main/notebooks
Documentation: https://lucidmode.readthedocs.io
Python Package Index (PyPI) repository: https://pypi.org/project/lucidmode/
Current Contributors
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 francisco.me@iteso.mx
1 maintainer
Release files for lucidmode 0.4.3.12
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lucidmode-0.4.3.12.tar.gz | 26.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lucidmode-0.4.3.12-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 69.5 kB
Release files / lucidmode-0.4.3.12.tar.gz
| Download URL | lucidmode-0.4.3.12.tar.gz |
|---|---|
| Size | 26.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.5
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Release files / lucidmode-0.4.3.12-py2.py3-none-any.whl
| Download URL | lucidmode-0.4.3.12-py2.py3-none-any.whl |
|---|---|
| Size | 43.5 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.5
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