def feature_Kmeans(data,label): mms = MinMaxScaler() feats = [f for f in data.columns if f not in ['loan_id', 'user_id', 'isDefault']] data = data[feats] mmsModel = mms.fit_transform(data.loc[data['class'] == label]) clf = KMeans(5, random_state=2021) pre = clf.fit(mmsModel) test = pre.labels_ final_data = pd.Series(test, index=data.loc[data['class'] == label].index) if label == 1: /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:74: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:75: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:76: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
时间: 2024-04-02 19:37:29 浏览: 15
这段代码中出现了 `SettingWithCopyWarning` 警告,警告的原因是在对 DataFrame 进行切片操作之后,对切片对象进行了赋值操作,这可能会导致原 DataFrame 的值也被改变,进而导致错误的结果。建议使用 `.loc` 或 `.iloc` 进行切片并进行赋值操作,以避免这个警告的出现。你可以使用如下代码来避免这个警告:
```python
if label == 1:
final_data = pd.DataFrame({'class': final_data})
data.loc[data['class'] == label, 'class'] = final_data['class']
```
相关问题
import itertools from sklearn.metrics import silhouette_score from sklearn.cluster import KMeans k_list = range(2,10,1) for k in itertools.product(k_list): km = KMeans(n_clusters=k) labels = km.fit(data).labels_迭代器为什么用不了
`itertools.product`是返回一个迭代器,它可以生成指定的多个可迭代对象的笛卡尔积。
在上述代码中,使用`for k in itertools.product(k_list)`是错误的,因为`product`返回的是一个迭代器对象,而不是一个可迭代对象(如列表、元组等)。因此,应该使用`itertools.combinations`来生成所有可能的聚类数量k的组合,代码如下:
```python
import itertools
from sklearn.metrics import silhouette_score
from sklearn.cluster import KMeans
k_list = range(2, 10, 1)
for k in itertools.combinations(k_list, 2):
km = KMeans(n_clusters=k)
labels = km.fit_predict(data)
score = silhouette_score(data, labels)
print(f"k={k}, score={score}")
```
在上述代码中,我们使用`itertools.combinations(k_list, 2)`来生成所有可能的聚类数量k的组合,然后对每个组合进行KMeans聚类,并计算聚类结果的轮廓系数。
import numpy as np from sklearn.cluster import KMeans from sklearn import metrics from sklearn.metrics import silhouette_score from sklearn.datasets import load_iris X=load_iris().data kmeans_model = KMeans(n_clusters=3,random_state=1).fit(X) labels=kmeans_model.labels_ print("=======K均值聚类的ARI和轮廓系数分别是:========") dbscan= db_labels print("=======DBSCAN均值聚类的ARI和轮廓系数分别是:========")
这段代码存在一个问题,就是 dbscan 变量没有定义或赋值,因此会出现 NameError 错误。如果您想要计算 DBSCAN 聚类的 ARI 和轮廓系数,可以按照以下步骤进行:
```python
from sklearn.cluster import DBSCAN
dbscan_model = DBSCAN(eps=0.5, min_samples=5).fit(X)
dbscan_labels = dbscan_model.labels_
print("=======K均值聚类的ARI和轮廓系数分别是:========")
kmeans_ari = metrics.adjusted_rand_score(load_iris().target, labels)
kmeans_silhouette = silhouette_score(X, labels)
print("K均值聚类 ARI: ", kmeans_ari)
print("K均值聚类轮廓系数: ", kmeans_silhouette)
print("=======DBSCAN均值聚类的ARI和轮廓系数分别是:========")
dbscan_ari = metrics.adjusted_rand_score(load_iris().target, dbscan_labels)
dbscan_silhouette = silhouette_score(X, dbscan_labels)
print("DBSCAN聚类 ARI: ", dbscan_ari)
print("DBSCAN聚类轮廓系数: ", dbscan_silhouette)
```
这样,就可以计算出 K 均值聚类和 DBSCAN 聚类的 ARI 和轮廓系数,并打印输出。请注意,DBSCAN 的聚类参数 eps 和 min_samples 可以根据具体数据集进行调整。