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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x80 in position 64: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 797, in wrapped
                  for item in generator(*args, **kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 90, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1104, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 64: invalid start byte
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1524, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from scipy.spatial.distance import cdist
inv_data = np.load('/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_1/inv.npy')
inv_cluster_data = np.load('/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_1/inv_cluster.npy')
closest_cluster_indices = np.argmin(np.linalg.norm(inv_data[:, np.newaxis, :] - inv_cluster_data, axis=2), axis=1)
print(len(closest_cluster_indices))
import json
# output_file = '/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_2/output.json'
# with open(output_file, 'w') as f:
# json.dump(closest_cluster_indices.tolist(), f)
# 从文件加载存储的索引列表
file_path = '/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_2/modified_ranks.json'
with open(file_path, 'r') as f:
stored_indices = json.load(f)
# 计算重合度
count_same_values = 0
total_values = len(stored_indices)
for i in range(total_values):
if stored_indices[i] == closest_cluster_indices[i]:
count_same_values += 1
overlap_percentage = (count_same_values / total_values) * 100
print(f"相同值概率: {overlap_percentage}%")
# # 加载两个ndarray
# train_data = np.load('/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_1/train_data.npy')
# train_data_inv = np.load('/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_1/inv.npy')
# # 计算每个对应项的余弦相似度,并存储结果
# similarities = []
# count = 0
# for i in range(len(train_data)):
# similarity = cosine_similarity(train_data[i].reshape(1, -1), train_data_inv[i].reshape(1, -1))
# similarities.append(similarity)
# if similarity > 0.9:
# count += 1
# print(count)
# print(np.max(similarities))
# print(np.min(similarities))
# # 计算平均相似度
# average_similarity = np.mean(similarities)
# # 打印平均相似度
# print(average_similarity)
# # 加载两个ndarray
# train_data = np.load('/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_1/train_data.npy')
# embedding = np.load('/home/yiming/cophi/training_dynamic/code_training_dynamic/saved_models/ruby_fine_tine_5/Model/Epoch_1/embedding.npy')
# # 从train_data中随机选择1000个索引
# num_samples = 1000
# random_indices = np.random.choice(len(train_data), size=num_samples, replace=False)
# selected_train_data = train_data[random_indices]
# selected_embedding = embedding[random_indices]
# # 计算选定的train_data中每个样本与自身的距离
# train_data_distances = cdist(selected_train_data, selected_train_data, metric='euclidean')
# # 找到选定的train_data中每个样本第二近的样本的索引
# train_data_second_nearest_indices = np.argpartition(train_data_distances, kth=1)[:, 1]
# # 计算选定的embedding中每个样本与自身的距离
# embedding_distances = cdist(selected_embedding, selected_embedding, metric='euclidean')
# # 找到选定的embedding中每个样本第二近的样本的索引
# embedding_second_nearest_indices = np.argpartition(embedding_distances, kth=1)[:, 1]
# # 计算选定的train_data和embedding第二近索引的重合程度
# overlap = np.mean(train_data_second_nearest_indices == embedding_second_nearest_indices)
# # 打印重合程度
# print("重合程度:", overlap)
# import numpy as np
# from pynndescent import NNDescent
# # from sklearn.neighbors import NearestNeighbors
# # from sklearn.manifold import trustworthiness
# # from scipy.stats import kendalltau, spearmanr, pearsonr, rankdata
# def evaluate_proj_nn_perseverance_knn(data, embedding, n_neighbors, metric="euclidean"):
# """
# evaluate projection function, nn preserving property using knn algorithm
# :param data: ndarray, high dimensional representations
# :param embedding: ndarray, low dimensional representations
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