""" Remove the meta-features which are not in the meta-dataset (i.e. the features which have not been selected in the feature selection process) """ metafeatures_to_be_removed = [] for metafeature in meta_features.keys(): if metafeature in metadataset_feature_selected.columns: pass else: metafeatures_to_be_removed.append(metafeature) [meta_features.pop(key) for key in metafeatures_to_be_removed] #========================META-LEARNING: RANKING================================ #KNN Ranking Method top1, top2, top3 = KNN_ranking(metadataset_feature_selected, meta_features, nested_results_f1) print("==========================================") print(" f1 ") print("==========================================") print("Top 1 predicted model: " + top1) print("Top 2 predicted model: " + top2) print("Top 3 predicted model: " + top3) #Actual results with open(str(p.parents[5])+'/actual/wine_top_3_f1.pickle', 'rb') as handle: actual_results = pickle.load(handle) print("==========================================") print("Top 1 ACTUAL model: " + actual_results[0]) print("Top 2 ACTUAL model: " + actual_results[1]) print("Top 3 ACTUAL model: " + actual_results[2])
import pandas as pd #=====================META-FEATURE EXTRACTION================================== with open(str(p.parents[5])+'/actual/sensor_metafeatures_202.pickle', 'rb') as handle: meta_features = pickle.load(handle) #============Load the meta-dataset and model performance of each dataset======= df_results = pd.read_pickle(str(p.parents[7])+'/test/df_results.plk') #nested_results is a nested dictionary with all the AUC-ROC performances for each dataset and all models with open(str(p.parents[6])+'/nested_results_f1.pickle', 'rb') as handle: nested_results_f1 = pickle.load(handle) #========================META-LEARNING: RANKING================================ #KNN Ranking Method top1, top2, top3 = KNN_ranking(df_results, meta_features, nested_results_f1) print("==========================================") print(" F1 metric ") print("==========================================") print("Top 1 predicted model: " + top1) print("Top 2 predicted model: " + top2) print("Top 3 predicted model: " + top3) #Actual results with open(str(p.parents[5])+'/actual/sensor_top_3_f1.pickle', 'rb') as handle: actual_results = pickle.load(handle) print("==========================================") print("Top 1 ACTUAL model: " + actual_results[0]) print("Top 2 ACTUAL model: " + actual_results[1]) print("Top 3 ACTUAL model: " + actual_results[2])