FutureWarning: The loss 'log' was deprecated in v1.1 and will be removed in version 1.3. Use loss='log_loss' which is equivalent. warnings.warn(

这个 `FutureWarning` 是因为在 scikit-learn v1.1 中，`loss='log'` 这个参数将被废弃，建议使用 `loss='log_loss'` 来代替。所以你可以将 `loss='log'` 修改为 `loss='log_loss'`，以避免这个警告。修改后的代码示例如下：

from sklearn.linear_model import SGDClassifier

# 建立多分类逻辑斯蒂回归模型
model = SGDClassifier(loss='log_loss', penalty='l2', alpha=0.0001, max_iter=1000, tol=1e-3)

# 模型训练
model.fit(merged_train_norm_vec, y_merged_train)

# 模型评估
y_pred = model.predict(valid_norm_vec)
print('Accuracy_score of initial model:', metrics.accuracy_score(y_valid, y_pred))

在 `SGDClassifier` 中，`loss='log_loss'` 表示使用对数损失函数来训练逻辑斯蒂回归模型，它是 `loss='log'` 的替代方案。

相关问题

FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.

Hello! It seems like you are encountering a FutureWarning related to the method `.ptp`. This warning is letting you know that the method `.ptp` will be removed in a future version of the software you are using, and you should instead use `numpy.ptp` for the same functionality.

To fix this warning, you can replace the usage of `.ptp` with `numpy.ptp`. Here's an example:

import numpy as np

# Assuming you have an array called 'data'
range_value = np.ptp(data)

By using `numpy.ptp`, you can avoid the warning and ensure compatibility with future versions of the software. Let me know if you have any further questions!

DeprecationWarning: The 'body' parameter is deprecated and will be removed in a future version. Instead use individual parameters. res = es.search(index=es_index, body=data)

这个警告说明在新版的 Elasticsearch 客户端库中，`body` 参数已经被弃用，建议使用单独的参数来代替。可以将代码修改如下：

from elasticsearch import Elasticsearch

es = Elasticsearch(
    hosts=['https://localhost:9200'],
    basic_auth=(es_user, es_pwd),
    timeout=15000
)

res = es.search(
    index=es_index,
    body={'query': query},
    size=size,
    from_=start
)

这里使用了单独的参数来代替 `body` 参数。例如，`query` 参数用于指定查询语句。同时，对于分页查询，也可以使用 `size` 和 `from_` 参数来指定返回结果的数量和起始位置。