| import torch |
| import numpy as np |
|
|
| class ClinicalContextVectorizer: |
| """ |
| 将来自 clinical_latest.json 的 context_static 对象转换为一个扁平的数值向量。 |
| """ |
| def __init__(self, vector_dim: int, config: dict = None): |
| self.vector_dim = vector_dim |
| self.config = config or self._get_default_config() |
| |
| |
| self.feature_map = {} |
| self.vector_layout = {} |
| current_idx = 0 |
| |
| for feature, details in self.config.items(): |
| if details['type'] == 'categorical': |
| for value in details['values']: |
| self.feature_map[f"{feature}_{value}"] = current_idx |
| current_idx += 1 |
| self.vector_layout[feature] = {'start': self.feature_map[f"{feature}_{details['values'][0]}"] , 'end': current_idx} |
| elif details['type'] == 'numerical': |
| self.feature_map[feature] = current_idx |
| self.vector_layout[feature] = {'start': current_idx, 'end': current_idx + 1} |
| current_idx += 1 |
| elif details['type'] == 'binary_group': |
| for sub_feature in details['values']: |
| self.feature_map[sub_feature] = current_idx |
| current_idx += 1 |
| self.vector_layout[feature] = {'start': self.feature_map[details['values'][0]], 'end': current_idx} |
|
|
| |
| if current_idx != self.vector_dim: |
| raise ValueError(f"Configured vector dimension ({current_idx}) does not match expected dimension ({self.vector_dim})") |
|
|
| def _get_default_config(self): |
| |
| |
| return { |
| "sex_at_birth": {"type": "categorical", "values": ["male", "female", "unknown"]}, |
| "race": {"type": "categorical", "values": ["white", "black or african american", "asian", "unknown"]}, |
| "age_at_diagnosis_years": {"type": "numerical", "norm_mean": 60, "norm_std": 15}, |
| "who_grade": {"type": "numerical", "norm_mean": 3.5, "norm_std": 0.5}, |
| "genomics": { |
| "type": "binary_group", |
| "values": [ |
| "idh1", "idh2", "atrx", "mgmt_methylation", "braf_v600e", |
| "tert_promoter", "chr7_gain_chr10_loss", "h3_3a", "egfr_amp", |
| "pten", "cdkn2ab_deletion", "tp53_alteration" |
| ] |
| } |
| } |
|
|
| def vectorize(self, context_static: dict) -> torch.Tensor: |
| """将单个 context_static 字典转换为 Tensor""" |
| vec = torch.zeros(self.vector_dim) |
| |
| |
| for feature, details in self.config.items(): |
| if feature == "genomics": |
| continue |
| |
| value = context_static.get(feature) |
| |
| if details['type'] == 'categorical': |
| value = value if value in details['values'] else 'unknown' |
| key = f"{feature}_{value}" |
| if key in self.feature_map: |
| vec[self.feature_map[key]] = 1.0 |
| |
| elif details['type'] == 'numerical': |
| if value is not None: |
| |
| norm_val = (value - details['norm_mean']) / details['norm_std'] |
| vec[self.feature_map[feature]] = norm_val |
|
|
| |
| genomics_data = context_static.get("genomics", {}) |
| genomics_config = self.config.get("genomics", {}) |
| if genomics_config: |
| for sub_feature in genomics_config['values']: |
| value = genomics_data.get(sub_feature) |
| if value is not None and sub_feature in self.feature_map: |
| |
| if value == 0: |
| vec[self.feature_map[sub_feature]] = 0.0 |
| elif value == 1: |
| vec[self.feature_map[sub_feature]] = 1.0 |
| elif value == 2: |
| vec[self.feature_map[sub_feature]] = 0.5 |
| |
| return vec |
|
|
| if __name__ == '__main__': |
| |
| vectorizer = ClinicalContextVectorizer(vector_dim=20) |
| sample_context = {"sex_at_birth": "female", "race": "white", "age_at_diagnosis_years": 57.0, "who_grade": 4, "genomics": {"idh1": 0, "idh2": 0, "atrx": 2, "mgmt_methylation": 2, "braf_v600e": 2, "tert_promoter": 2, "chr7_gain_chr10_loss": 2, "h3_3a": 2, "egfr_amp": 2, "pten": 0, "cdkn2ab_deletion": 0, "tp53_alteration": 0}} |
| |
| tensor_vec = vectorizer.vectorize(sample_context) |
| print(tensor_vec) |
| print(tensor_vec.shape) |
