| import pandas as pd |
|
|
| def load_user_metadata(): |
| |
| user_metadata_path = "public_dataset/user_metadata_raw.csv" |
| user_metadata = pd.read_csv(user_metadata_path) |
| return user_metadata |
|
|
| |
| remove_row_dict = { |
| "user_id": ["ensemble_all","ed52331460c0479cb52d92bb6feb4bf7"], |
| } |
|
|
| remove_columns = [ |
| "is_ensemble_user", "user_group", "event_count", "images_annotated_real", "annotated_pixels_count_union", "gt_pixels_count_union", "annotated_pixels_count_sum_objects", "gt_pixels_count_sum_objects", "avg_polygon_confidence", "greedy_bbox_match_precision", "greedy_bbox_match_recall", "greedy_bbox_match_f1" |
| ] |
| remove_columns += "greedy_bbox_match_mean_iou,coco_bbox_detection_ap,coco_bbox_detection_ap50,coco_bbox_detection_ap75,coco_bbox_detection_ar,coco_instance_segmentation_ap,coco_instance_segmentation_ap50,coco_instance_segmentation_ap75,coco_instance_segmentation_ar,semantic_segmentation_precision,semantic_segmentation_recall,semantic_segmentation_f1,semantic_segmentation_miou".split(",") |
|
|
| |
| user_metadata = load_user_metadata() |
| for column, values in remove_row_dict.items(): |
| user_metadata = user_metadata[~user_metadata[column].isin(values)] |
| |
| user_metadata = user_metadata.drop(columns=remove_columns) |
|
|
| |
| cleaned_user_metadata_path = "public_dataset/user_metadata.csv" |
| user_metadata.to_csv(cleaned_user_metadata_path, index=False) |
|
|
| |
| num_unique_users = user_metadata["user_id"].nunique() |
| print(f"Number of unique users in cleaned user metadata: {num_unique_users}") |
|
|
| |
| num_unique_images = user_metadata["image_name"].nunique() |
| print(f"Number of unique images in cleaned user metadata: {num_unique_images}") |
