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@@ -85,7 +85,7 @@ def precompute_normlime_weights(list_data_, predict_fn, num_samples=3000, batch_
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precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, save_dir)
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# load precomputed results, compute normlime weights and save.
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- fname_list = glob.glob(os.path.join(save_dir, 'lime_weights_s{}.npy'.format(num_samples)))
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+ fname_list = glob.glob(os.path.join(save_dir, 'lime_weights_s{}*.npy'.format(num_samples)))
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return compute_normlime_weights(fname_list, save_dir, num_samples)
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@@ -174,6 +174,7 @@ def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, sav
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def compute_normlime_weights(a_list_lime_fnames, save_dir, lime_num_samples):
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normlime_weights_all_labels = {}
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+
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for f in a_list_lime_fnames:
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try:
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lime_weights_and_cluster = np.load(f, allow_pickle=True).item()
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@@ -183,7 +184,6 @@ def compute_normlime_weights(a_list_lime_fnames, save_dir, lime_num_samples):
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logging.info('When loading precomputed LIME result, skipping' + str(f))
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continue
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logging.info('Loading precomputed LIME result,' + str(f))
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-
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pred_labels = lime_weights.keys()
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for y in pred_labels:
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normlime_weights = normlime_weights_all_labels.get(y, {})
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sunyanfang01