(nested_dict, model_name, dataset_abbr, parallel=False)
| 130 | return ''.join(lines[start_index:-1]) |
| 131 | |
| 132 | def create_model_dataframe(nested_dict, model_name, dataset_abbr, parallel=False): |
| 133 | if model_name not in nested_dict: |
| 134 | print(f'Model {model_name} not found in the provided data.') |
| 135 | return pd.DataFrame() |
| 136 | |
| 137 | model_data = nested_dict[model_name] |
| 138 | data = [] |
| 139 | |
| 140 | for key, value in model_data.items(): |
| 141 | if parallel: |
| 142 | if dataset_abbr in key: |
| 143 | new_key_base = key.replace(dataset_abbr, '').strip('_') |
| 144 | for depth_key, score in value.items(): |
| 145 | new_key = f'{new_key_base}{depth_key}' |
| 146 | if 'average_score' not in new_key: |
| 147 | data.append([new_key, score]) |
| 148 | else: |
| 149 | if dataset_abbr in key: |
| 150 | score = value.get('score', None) |
| 151 | new_key = key.replace(dataset_abbr, '').strip('_') |
| 152 | data.append([new_key, score]) |
| 153 | |
| 154 | df = pd.DataFrame(data, columns=['dataset', model_name]) |
| 155 | return df |
| 156 | |
| 157 | def convert_to_k(value): |
| 158 | try: |
no test coverage detected