Description:
PyGRF 0.0.9
PyGRF
PyGRF: An improved Python Geographical Random Forest (GRF) model.
Installation:
PyGRF can be installed from PyPI:
$ pip install PyGRF
Example:
Below shows an example on how to fit a PyGRF model and use it to make predictions.
from PyGRF import PyGRF
from sklearn.model_selection import train_test_split
# Split your data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
#Create a PyGRF model by specifying hyperparameters
pygrf_example = PyGRF.PyGRFBuilder(n_estimators=60, max_features=1, band_width=39, train_weighted=True, predict_weighted=True, bootstrap=False,
resampled=True, random_state=42)
#Fit the created PyGRF model based on training data and their spatial coordinates
pygrf_example.fit(X_train, y_train, xy_coord)
#Make predictions for testing data using the fitted PyGRF model and you specified local model weight
predict_combined, predict_global, predict_local = pygrf_example.predict(X_test, coords_test, local_weight=0.46)
Parameters:
If you want to learn more about the major parameters in this package, please refer to the Description of Parameters.
Reference:
Kai Sun, Ryan Zhenqi Zhou, Jiyeon Kim, and Yingjie Hu. 2024. PyGRF: An improved Python Geographical Random Forest model and case studies in public health and natural disasters. Transactions in GIS.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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