boston_threads.py

# sklearn version 
from sklearn.ensemble import RandomForestRegressor as RFR
from multiprocessing.pool import ThreadPool

# allow up to 5 concurrent threads
pool = ThreadPool(5)

# hyperparameters to test out (n_trees)
parameters = [ 10, 20, 50]

# define a function to train a RF model and return metrics 
def sklearn_random_forest(trees, X_train, X_test, y_train, y_test):

    # train a random forest regressor with the specified number of trees
    rf= RFR(n_estimators = trees)
    model = rf.fit(X_train, y_train)

    # make predictions
    y_pred = model.predict(X_test)
    r = pearsonr(y_pred, y_test)

    # return the number of trees, and the R value 
    return [trees, r[0]**2]  

# run the tasks 
pool.map(lambda trees: sklearn_random_forest(trees, X_train,
                                           X_test, y_train, y_test), parameters)