average_scores_and_best_scores = dict()
# best_model_name == 'Worst
average_scores_and_best_scores[best_model_name] \
= (best_score, best_score_dev, best_exec_time, best_model, {})
# Start evaluation process
print()
print("=== [task] Evaluation of DummyClassifier")
print()
wtr = eu.calculate_sample_weight(y_train)
evaluation_result = eu.single_classic_cv_evaluation(
X_train_transformed, y_train, 'DummyClf_2nd',
DummyClassifier(strategy='most_frequent'), wtr, scoring, outer_cv,
dict(), scores_of_best_model, results, names, seed)
average_scores_and_best_scores = evaluation_result[0]
scores_of_best_model = evaluation_result[1]
Dummy_scores.append(scores_of_best_model[0]) # Dummy score -- ROC_AUC
Dummy_scores.append(scores_of_best_model[1]) # Dummy score std
Dummy_scores.append(scores_of_best_model[2]) # Dummy cv results
Dummy_scores.append(scores_of_best_model[3]) # Dummy execution time
# Dummy model's name and estimator
Dummy_scores.append(scores_of_best_model[4])
names = []
results = []
scores_of_best_model = (best_score, best_score_dev, best_cv_results,
best_exec_time, best_model)
# Start evaluation process
print()
print("=== [task] Evaluation of DummyClassifier")
print()
wtr = eu.calculate_sample_weight(y_train)
strategy = 'stratified' # 'most_frequent'
average_scores_and_best_scores = eu.single_classic_cv_evaluation(
X_train_transformed, y_train, 'DummyClf_2nd',
DummyClassifier(strategy=strategy), wtr, scoring, outer_cv, dict(),
scores_of_best_model, results, names, seed)
scores_of_best_model = average_scores_and_best_scores[1]
Dummy_scores.append(scores_of_best_model[0]) # Dummy score -- ROC_AUC
Dummy_scores.append(scores_of_best_model[1]) # Dummy score std
Dummy_scores.append(scores_of_best_model[2]) # Dummy cv results
Dummy_scores.append(scores_of_best_model[3]) # Dummy execution time
# Dummy model's name and estimator
Dummy_scores.append(scores_of_best_model[4])
names = []
results = []
print()