def feature_selection(trainX, trainY, method='rfe'):
if method == 'mutual_info':
mi = mutual_info_regression(trainX, trainY, n_neighbors = 10, random_state=1)
mi = mi/max(mi)
mask = mi >= 0.2
if method == 'forest':
importances = RandomForestRegressor(n_estimators=10, n_jobs=-1).fit(trainX,trainY).feature_importances_
mask = importances.argsort()[-144:][::-1]
if method == 'rfe':
#estimator = RandomForestRegressor(n_estimators=10, n_jobs=-1)
estimator = LinearRegression(fit_intercept=True)
selector = RFE(estimator, 6*5, 50, verbose=2).fit(trainX, trainY)
mask = selector.support_
return mask
