Pandas + scikit-learn K-means 无法正常工作 - 将所有数据帧行视为一个大的多维示例
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Pandas + scikit-learn K-means not working properly - treats all of dataframe rows as one big multi-dimensional example
提问by Maksim Khaitovich
I am currently trying to do some k-means clustering using my data which is stored in my pandas.dataframe (actually in one of its columns). Odd thing is that instead of treating each row as a separate example it threats all rows as one example but in very high dimension. So for example:
我目前正在尝试使用存储在我的 pandas.dataframe （实际上在其列之一中）中的数据进行一些 k-means 聚类。奇怪的是，它没有将每一行视为一个单独的示例，而是将所有行视为一个示例，但在非常高的维度上进行威胁。例如：
df = pd.read_csv('D:\Apps\DataSciense\Kaggle Challenges\Titanic\Source Data\train.csv', header = 0)
median_ages = np.zeros((2,3))
for i in range(0,2):
for j in range (0,3):
median_ages[i, j] =df[(df.Gender == i) &(df.Pclass == j+1)].Age.dropna().median()
df['AgeFill'] = df['Age']
for i in range(0, 2):
for j in range(0,3):
df.loc[ (df.Age.isnull()) & (df.Gender == i) & (df.Pclass == j+1), 'AgeFill'] = median_ages[i, j]
then I just check that it looks fine:
然后我只是检查它看起来不错：
df.AgeFill
Name: AgeFill, Length: 891, dtype: float64
Looks ok, 891 float64 number. I do custering:
看起来不错，891 float64 数字。我做 custering：
k_means = cluster.KMeans(n_clusters=1, init='random')
k_means.fit(df.AgeFill)
And I check for cluster centers:
我检查集群中心：
k_means.cluster_centers_
It returns me one giant array.
它返回给我一个巨大的数组。
Furthermore:
此外：
k_means.labels_
Gives me:
给我：
array([0])
What am I doing wrong? Why it thinks I have a one example with 891 dimensions, instead of having 891 example?
我究竟做错了什么？为什么它认为我有一个有 891 个维度的示例，而不是有 891 个示例？
Just to illustrate it better, if I try 2 clusters:
只是为了更好地说明它，如果我尝试 2 个集群：
k_means = cluster.KMeans(n_clusters=2, init='random')
k_means.fit(df.AgeFill)
Traceback (most recent call last): File "", line 1, in k_means.fit(df.AgeFill) File "D:\Apps\Python\lib\site-packages\sklearn\cluster\k_means_.py", line 724, in fit X = self._check_fit_data(X) File "D:\Apps\Python\lib\site-packages\sklearn\cluster\k_means_.py", line 693, in _check_fit_data X.shape[0], self.n_clusters)) ValueError: n_samples=1 should be >= n_clusters=2
回溯（最近一次调用）：文件“”，第 1 行，在 k_means.fit(df.AgeFill) 文件“D:\Apps\Python\lib\site-packages\sklearn\cluster\k_means_.py”，第 724 行, in fit X = self._check_fit_data(X) File "D:\Apps\Python\lib\site-packages\sklearn\cluster\k_means_.py", line 693, in _check_fit_data X.shape[0], self.n_clusters )) ValueError: n_samples=1 应该 >= n_clusters=2
So you could see that it REALLY thinks that it is just one giant sample.
所以你可以看到它真的认为它只是一个巨大的样本。
But:
但：
df.AgeFill.shape
(891,)
回答by elyase
You are passing a 1D array while scikit expects a 2D array with a samplesand a featuresaxis. This should do it:
您正在传递一个一维数组，而 scikit 需要一个带有样本和特征轴的二维数组。这应该这样做：
k_means.fit(df.AgeFill.reshape(-1, 1))
Before:
前：
>>> df.AgeFill.shape
(891,)
After:
后：
>>> df.AgeFill.reshape(-1, 1).shape
(891, 1)
