PySpark ML中用MinHash LSH实现Jaccard相似度时遇导入错误

Hey there, that import error usually pops up because of one of two common issues: either you're using the wrong library, or your import statement doesn't match the library's structure. Let's walk through how to fix this step by step.

Step 1: Make sure you're using the right library #

The most popular Python library for MinHash LSH (especially for shopping basket data like yours) is datasketch. If you haven't installed it yet, run this command in your terminal:

pip install datasketch

Step 2: Use the correct import statement #

In the latest versions of datasketch, MinHashLSH lives directly under the datasketch module. Your import should look like this:

from datasketch import MinHash, MinHashLSH

Avoid trying to import it from submodules (like datasketch.lsh), as that's deprecated in newer releases.

Step 3: Upgrade if you have an older version #

If you already have datasketch installed but still get the error, your version might be out of date. Upgrade it with:

pip install --upgrade datasketch

Quick Example with Your Shopping Basket Data #

Here's a tiny snippet to test things out with your Cust_ID/Item_id dataset structure:

from datasketch import MinHash, MinHashLSH
import pandas as pd

# Sample shopping basket data
data = pd.DataFrame({
    'Cust_ID': ['C1', 'C1', 'C2', 'C2', 'C3'],
    'Item_id': ['I1', 'I2', 'I2', 'I3', 'I1']
})

# Create MinHash objects for each customer
lsh = MinHashLSH(threshold=0.5, num_perm=128)
customer_hashes = {}

for cust_id, group in data.groupby('Cust_ID'):
    mh = MinHash(num_perm=128)
    for item in group['Item_id']:
        mh.update(item.encode('utf8'))
    customer_hashes[cust_id] = mh
    lsh.insert(cust_id, mh)

# Query similar customers for C1
result = lsh.query(customer_hashes['C1'])
print(f"Customers similar to C1: {result}")

This should work without the import error once you've set up the library correctly.

内容的提问来源于stack exchange，提问作者Sai Kiran Kodukula