def cv_modeling(estimator, data, y_name, candidates_location, n_trees, w_name): from sklearn.ensemble import estimator from sklearn.grid_search import GridSearchCV temp = data.copy() candidates = get_variables("./%s" % candidates_location) model = estimator() parameters = {} clf = GridSearchCV(model, parameters) res = clf.fit(temp[candidates], temp[y_name]) return res
def cv_modeling(estimator,data,y_name,candidates_location,n_trees,w_name): from sklearn.ensemble import estimator from sklearn.grid_search import GridSearchCV temp=data.copy() print("made temp copy") candidates=get_variables("./%s"%candidates_location) print("got candidates for regressors") # temp=rf_trim(temp,y_name,candidates) # print("trimmed dataset") model=estimator() print("assigned model") parameters={} clf=GridSearchCV(model,parameters) res=clf.fit(temp[candidates],temp[y_name]) # print("fit model") # joblib.dump(res,"./%sgb_model%s.pkl"%(y_name,datetime.datetime.today())) # print("saved model") return res