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Python implementation of iterative-random-forests
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Yu-Group/iterative-Random-Forest
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The algorithm details are available at:
Sumanta Basu, Karl Kumbier, James B. Brown, Bin Yu, Iterative Random Forests to detect predictive and stable high-order interactions, PNAShttps://www.pnas.org/content/115/8/1943
The implementation is a joint effort of several people in UC Berkeley. See theAuthors.md for the complete list.The weighted random forest implementation is based on the random forest source code and API design fromscikit-learn, details can be found inAPI design for machine learning software: experiences from the scikit-learn project, Buitinck et al., 2013.. The setup file is based on the setup file fromskgarden.
Use conda to create a python3.6 environment. Run the following commands inside the package directory
pipinstallnumpycython==0.29packagingpipinstall-e .
(outdated) To install, simply runpip install irf. If you run into any issues, seeinstallation help.
In order to use irf, you need to import it in python.
importnumpyasnpfromirfimportirf_utilsfromirf.ensembleimportRandomForestClassifierWithWeights
Generate a simple data set with 2 features: 1st feature is a noise feature that has no power in predicting the labels, the 2nd feature determines the label perfectly:
n_samples=1000n_features=10X_train=np.random.uniform(low=0,high=1,size=(n_samples,n_features))y_train=np.random.choice([0,1],size=(n_samples,),p=[.5,.5])X_test=np.random.uniform(low=0,high=1,size=(n_samples,n_features))y_test=np.random.choice([0,1],size=(n_samples,),p=[.5,.5])# The second feature (which is indexed by 1) is very importantX_train[:,1]=X_train[:,1]+y_trainX_test[:,1]=X_test[:,1]+y_test
Then run irf
all_rf_weights, all_K_iter_rf_data, \ all_rf_bootstrap_output, all_rit_bootstrap_output, \ stability_score = irf_utils.run_iRF(X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test, K=5, # number of iteration rf = RandomForestClassifierWithWeights(n_estimators=20), B=30, random_state_classifier=2018, # random seed propn_n_samples=.2, bin_class_type=1, M=20, max_depth=5, noisy_split=False, num_splits=2, n_estimators_bootstrap=5)all_rf_weights stores all the weights for each iteration:
print(all_rf_weights['rf_weight5'])
The proposed feature combination and their scores:
print(stability_score)
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Python implementation of iterative-random-forests
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