| import torch | |
| import torch.nn as nn | |
| import os | |
| import numpy as np | |
| from fastshap import FastSHAP | |
| from opendataval.dataloader import DataFetcher | |
| Array = np.ndarray | |
| def load_model(dataset_name: str) -> FastSHAP: | |
| explainer = torch.load(os.path.join("fastshap", dataset_name, "explainer.pt")) | |
| imputer = torch.load(os.path.join("fastshap", dataset_name, "imputer.pt")) | |
| shap = FastSHAP(explainer, imputer, link=nn.Softmax(dim=-1)) | |
| return shap | |
| def onehot_to_location(y: Array) -> Array: | |
| res = np.zeros((y.shape[0])) | |
| for i in range(y.shape[0]): | |
| for j in range(len(y[i])): | |
| if y[i, j] > 0: | |
| res[i] = j | |
| break | |
| return res | |
| def load_dataset(dataset_name: str, train_size: int, valid_size: int) -> tuple[Array, Array, Array, Array]: | |
| fetcher = DataFetcher(dataset_name=dataset_name).split_dataset_by_count( | |
| train_size, valid_size | |
| ) | |
| x_train, y_train, x_val, y_val, _, _ = fetcher.datapoints | |
| x_train = np.array(x_train.tolist()) | |
| y_train = onehot_to_location(np.array(y_train.tolist())) | |
| x_val = np.array(x_val.tolist()) | |
| y_val = onehot_to_location(np.array(y_val.tolist())) | |
| return x_train, y_train, x_val, y_val | |
| fastshap = load_model("electricity") | |
| x_train, y_train, *_ = load_dataset("electricity", 100, 0) | |
| print(np.sum(fastshap.shap_values(np.array([x_train[24]]))[:, int(y_train[24])])) |