def main(loader, name):
scores = []<EOL>raw_scores_ds = {}<EOL>X, y, mapping = loader()<EOL>clf = linear_model.LogisticRegression(solver='<STR_LIT>', multi_class='<STR_LIT>', max_iter=<NUM_LIT:200>, random_state=<NUM_LIT:0>)<EOL>encoders = (set(category_encoders.__all__) - {'<STR_LIT>'})  <EOL>for encoder_name in encoders:<EOL><INDENT>encoder = getattr(category_encoders, encoder_name)<EOL>start_time = time.time()<EOL>score, stds, raw_scores, dim = score_models(clf, X, y, encoder)<EOL>scores.append([encoder_name, name, dim, score, stds, time.time() - start_time])<EOL>raw_scores_ds[encoder_name] = raw_scores<EOL>gc.collect()<EOL><DEDENT>results = pd.DataFrame(scores, columns=['<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>'])<EOL>raw = pd.DataFrame.from_dict(raw_scores_ds)<EOL>ax = raw.plot(kind='<STR_LIT>', return_type='<STR_LIT>')<EOL>plt.title('<STR_LIT>' % (name,))<EOL>plt.ylabel('<STR_LIT>')<EOL>for tick in ax.get_xticklabels():<EOL><INDENT>tick.set_rotation(<NUM_LIT>)<EOL><DEDENT>plt.grid()<EOL>plt.tight_layout()<EOL>plt.show()<EOL>return results, raw<EOL>
Here we iterate through the datasets and score them with a classifier using different encodings.