Metal-Organic Frameworks Feature Regression

This project is about a regression model that predicts the properties of metal-organic frameworks (MOF).

The model uses geometric features of MOFs to predict the bulk and shear properties.

You can automatically tune the hyperparameters to get a better regression model by running the codes.

Dataset

The datasets are in the /data/ folder with form of csv file. The figure below is examples of 5 MOF datas.

Determining Regression Model

By running ipynb_files/MOF_lazy.ipynb with LazyPredict, you can determine the best regression model for the datasets. The figures below is top 3 models of the result for each of bulk and shear data.

• Best models for bulk data
• Best models for shear data

Functions

There are some useful functions in /util/ folder.

All of the examples can be found in /ipynb_files/MOF_regressor.ipynb file.

import_data.py/import_data(filename) function

Imports a dataset in the /data/filename route and splits it into geometric features, bulk data, and shear data.

• Arguments:

  • filename: Filename of target dataset.

• Returns:

  • Tuple: (geometric features, bulk data, shear data)

• Example:

  data_x, bulk_y, shear_y = import_data('toacco_geo_chem_mit_order.csv')

mof_util.py/test_regressor(model, data_x, bulk_y, shear_y, testsize=0.2, verbose=1) function

Tests a regression model with geometric feature data x and each of bulk/shear property data y.

It iterates with various testsizes. (e.g. testsize = [0.2, 0.4, 0.6, 0.8])

• Arguments:

  • model: Regression model input.
  • data_x: Geometric feature x data for the regression model test.
  • bulk_y: Target bulk property y data for the regression model test.
  • shear_y: Target shear property y data for the regression model test.
  • testsize: Minimum interval of the iteration. (Default = 0.2)
  • verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = plot only, 2 = full log for each iterations. (Default = 1)

• Returns:

  • Prints test $R^2$ plot and best result.

• Example:

  Input:

  from sklearn.ensemble import ExtraTreesRegressor
  extra_tree = ExtraTreesRegressor()
  test_regressor(extra_tree, data_x, bulk_y, shear_y, testsize=0.1, verbose=1)
  

  Output:

  ==============================
  ExtraTreesRegressor
  

  

  ------------------------------
  Best bulk test score: 85.48% at test_size = 0.4
  Best shear test score: 78.35% at test_size = 0.2
  ==============================
  

mof_util.py/tune_regressor(model, data_x, bulk_y, shear_y, testsize=0.2, verbose=1, param_grid=param_grid) function

Tunes model hyperparameters to get the best regression model by using GridSearch.

• Arguments:

  • model: Regression model input.

  • data_x: Geometric feature x data for the regression model test.

  • bulk_y: Target bulk property y data for the regression model test.

  • shear_y: Target shear property y data for the regression model test.

  • testsize: Minimum interval of the iteration. (Default = 0.2)

  • verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = plot only, 2 = full log for each iterations. (Default = 1)

  • param_grid: Hyperparameter grid for GridSearchCV.

    (Default = { 'n_estimators': [100, 150, 200, 250, 300], 'max_depth': [None, 6, 9, 12, 15, 18], 'min_samples_split': [0.01, 0.05, 0.1], 'max_features': ['sqrt', 'log2', None] })

• Returns:

  • Tuple: (Best model for bulk data, Best model for shear data)

• Example:

  Input:

  from sklearn.ensemble import ExtraTreesRegressor
  extra_tree = ExtraTreesRegressor()
  extra_bulk, extra_shear = tune_regressor(extra_tree, data_x, bulk_y, shear_y, testsize=0.1, verbose=1)

  Output:

  Bulk regression best parameters:
  {'max_depth': 18, 'max_features': None, 'min_samples_split': 0.01, 'n_estimators': 100}
  Shear regression best parameters:
  {'max_depth': 18, 'max_features': None, 'min_samples_split': 0.01, 'n_estimators': 300}
  
  ==============================
  ExtraTreesRegressor
  

  

  ------------------------------
  Best bulk test score: 79.16% at test_size = 0.6
  Best shear test score: 75.27% at test_size = 0.9
  ==============================
  

  ==============================
  ExtraTreesRegressor
  

  

  ------------------------------
  Best bulk test score: 79.1% at test_size = 0.5
  Best shear test score: 74.96% at test_size = 0.3
  ==============================