diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..ce2e1f386 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Angelos Chatzimparmpas + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 000000000..5bdf4c873 --- /dev/null +++ b/README.md @@ -0,0 +1,71 @@ +# StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics + +This Git repository contains the code that accompanies the research paper "StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics". The details of the experiments and the research outcome are described in [the paper](https://doi.org/10.1109/TVCG.2020.3030352). + +**Note:** StackGenVis is optimized to work better for standard resolutions (such as 1440p/QHD (Quad High Definition)). Any other resolution might need manual adjustment of your browser's zoom level to work properly. + +**Note:** The tag `paper-version` matches the implementation at the time of the paper's publication. The current version might look significantly different depending on how much time has passed since then. + +**Note:** As any other software, the code is not bug free. There might be limitations in the views and functionalities of the tool that could be addressed in a future code update. + +# Data Sets # +All publicly available data sets used in the paper are in the `data` folder, formatted as comma separated values (csv). +Most of them are available online from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/index.php): Iris and Heart Disease. We also used a collection of data related to sentiment/stance detection in texts. This data set is not included due to permission issues, since it was parsed from well-known social media platforms by our group. + +# Requirements # +For the backend: +- [Python 3](https://www.python.org/downloads/) +- [Flask](https://palletsprojects.com/p/flask/) +- Other packages: `pymongo`, `numpy`, `scipy`, `scikit-learn`, `sk-dist`, `eli5`, and `pandas`. + +You can install all the backend requirements with the following command: +``` +pip install -r requirements.txt +``` + +For the frontend: +- [Node.js](https://nodejs.org/en/) +- [D3.js](https://d3js.org/) +- [Plotly.js](https://github.com/plotly/plotly.js/) + +There is no need to install anything for the frontend, since all modules are in the repository. + +# Usage # +Below is an example of how you can get StackGenVis running using Python for both frontend and backend. The frontend is written in JavaScript/HTML, so it could be hosted in any other web server of your preference. The only hard requirement (currently) is that both frontend and backend must be running on the same machine. +``` +# first terminal: hosting the visualization side (client) +# with Node.js +cd frontend +npm run dev +``` + +``` +# second terminal: hosting the computational side (server) +FLASK_APP=run.py flask run + +# (optional) recommendation: use insertMongo script to add a data set in Mongo database +# for Python3 +python3 insertMongo.py +``` + +Then, open your browser and point it to `localhost:8080`. We recommend using an up-to-date version of Google Chrome. + +# Hyper-Parameters per Algorithm # +**Base classifiers:** +- **K-Nearest Neighbor:** {'n_neighbors': list(range(1, 25)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']} +- **Support Vector Machine:** {'C': list(np.arange(0.1,4.43,0.11)), 'kernel': ['rbf','linear', 'poly', 'sigmoid']} +- **Gaussian Naive Bayes:** {'var_smoothing': list(np.arange(0.00000000001,0.0000001,0.0000000002))} +- **Multilayer Perceptron:** {'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0004)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']} +- **Logistic Regression:** {'C': list(np.arange(0.5,2,0.075)), 'max_iter': list(np.arange(50,250,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']} +- **Linear Discriminant Analysis:** {'shrinkage': list(np.arange(0,1,0.01)), 'solver': ['lsqr', 'eigen']} +- **Quadratic Discriminant Analysis:** {'reg_param': list(np.arange(0,1,0.02)), 'tol': list(np.arange(0.00001,0.001,0.0002))} +- **Random Forests:** {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']} +- **Extra Trees:** {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']} +- **Adaptive Boosting:** {'n_estimators': list(range(40, 80)), 'learning_rate': list(np.arange(0.1,2.3,1.1)), 'algorithm': ['SAMME.R', 'SAMME']} +- **Gradient Boosting:** {'n_estimators': list(range(85, 115)), 'learning_rate': list(np.arange(0.01,0.23,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']} + +**Meta-learner**: +- **Logistic Regression** with the default Sklearn hyper-parameters. By that time, the core hyper-parameter tuples were: C=1.0, max_iter=100, solver='lbfgs', and penalty='l2'. + +# Corresponding Author # +For any questions with regard to the implementation or the paper, feel free to contact [Angelos Chatzimparmpas](mailto:angelos.chatzimparmpas@lnu.se). diff --git a/__pycache__/run.cpython-38.pyc b/__pycache__/run.cpython-38.pyc new file mode 100644 index 000000000..85c0de263 Binary files /dev/null and b/__pycache__/run.cpython-38.pyc differ diff --git a/cachedir/joblib/run/GridSearchForModels/06d7aaef151021f9fba5a3513501f24e/output.pkl b/cachedir/joblib/run/GridSearchForModels/06d7aaef151021f9fba5a3513501f24e/output.pkl new file mode 100644 index 000000000..2192f1797 Binary files /dev/null and b/cachedir/joblib/run/GridSearchForModels/06d7aaef151021f9fba5a3513501f24e/output.pkl differ diff --git a/cachedir/joblib/run/GridSearchForModels/08e024f1191a69d535690e1cf01685ad/output.pkl 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