From 4cc9789f09cd45c05b6a35c9bdbd16ec0f404ba3 Mon Sep 17 00:00:00 2001 From: LemonadeXyz Date: Thu, 17 Mar 2022 14:19:43 +0800 Subject: [PATCH] update stepwise --- stepwise_selection.py | 44 +++++++++++++++++++++++++------------------ 1 file changed, 26 insertions(+), 18 deletions(-) diff --git a/stepwise_selection.py b/stepwise_selection.py index 01cc2a1..a8f43cd 100644 --- a/stepwise_selection.py +++ b/stepwise_selection.py @@ -3,11 +3,12 @@ from statsmodels.formula.api import ols -def stepwiseSelection(data, tag, - initial_list=list(), - threshold_in=0.05, - threshold_out=0.10, - verbose=True): +def stepwiseSelection(data, + tag, + initial_list=None, + threshold_in=0.05, + threshold_out=0.10, + verbose=True): ''' data: contaning indep/dep variables tag: name of target @@ -25,17 +26,17 @@ def stepwiseSelection(data, tag, if mark in col: ab_dic[col.replace(mark, '')] = col data.rename(columns={col: col.replace(mark, '')}, inplace=True) - - included = initial_list + + included = initial_list if initial_list else [] + excluded = data.columns.tolist() + excluded.remove(tag) formula = f'{tag}~1' # set a constant model as initial reduced_model best_r2_dif = .0 while True: # end loop when no variable gets in/out - excluded = list(set(data.columns) - set(included)) - excluded.remove(tag) changed = False - + full_model = ols(formula=formula, data=data).fit() last_adj_r2 = full_model.rsquared_adj @@ -43,45 +44,52 @@ def stepwiseSelection(data, tag, for new_feature in excluded: # Note here the test_model has more variable than full_model - test_model = ols(formula=formula+f'+{new_feature}', data=data).fit() + test_model = ols( + formula=formula+f'+{new_feature}', data=data).fit() # find feature whose contribution to adj_r2 largest if test_model.rsquared_adj - last_adj_r2 > best_r2_dif: best_r2_dif = test_model.rsquared_adj - last_adj_r2 last_adj_r2 = test_model.rsquared_adj best_feature = new_feature - + # Partial F-test # Note that in anova_lm models with few variables are put forward - full_model_pro = ols(formula=formula+f'+{best_feature}', data=data).fit() + full_model_pro = ols( + formula=formula+f'+{best_feature}', data=data).fit() anova_tbl = sm.stats.anova_lm(full_model, full_model_pro) criterion = anova_tbl['Pr(>F)'][1] if criterion <= threshold_in: included.append(best_feature) + excluded.remove(best_feature) formula += f'+{best_feature}' full_model = full_model_pro changed = True best_r2_dif = .0 if verbose: - print('Add {:25} with f_pvalue {:.6}'.format(best_feature, criterion)) + print('Add {:25} with f_pvalue {:.6}'.format( + best_feature, criterion)) # backward step for old_feature in included: - test_model = ols(formula=formula.replace(f'+{old_feature}', ''), data=data).fit() + test_model = ols(formula=formula.replace( + f'+{old_feature}', ''), data=data).fit() # Note here the test_model has less variable than full_model anova_tbl = sm.stats.anova_lm(test_model, full_model) criterion = anova_tbl['Pr(>F)'][1] if criterion >= threshold_out: included.remove(old_feature) + excluded.append(old_feature) formula = formula.replace(f'+{old_feature}', '') changed = True best_r2_dif = .0 if verbose: - print('Drop {:25} with f_pvalue {:.6}'.format(old_feature, criterion)) - + print('Drop {:25} with f_pvalue {:.6}'.format( + old_feature, criterion)) + if not changed: - break + break return [ab_dic[x] if x in ab_dic.keys() else x for x in included]