Error AttributeError: 'implicit.evaluation._memoryviewslice' object has no attribute 'dtype' when calling mean_average_precision_at_k function

[MRossa157]

Hello! I encountered an issue when using the mean_average_precision_at_k function from the implicit library.

Problem Description:

When calling the mean_average_precision_at_k function, the following error occurs:

AttributeError: 'implicit.evaluation._memoryviewslice' object has no attribute 'dtype'

Context:

• Operating System: Windows 10
• Python: 3.10.7
• implicit library version: 0.7.2
• Installed dependencies:

  [tool.poetry.dependencies]
  python = "^3.10.7"
  pandas = "^2.2.2"
  implicit = "^0.7.2"

Steps to Reproduce:

1. Installed the implicit library version 0.7.2.
2. Called the mean_average_precision_at_k function with the following parameters:

   metric_map = mean_average_precision_at_k(
       model,
       csr_train,
       csr_test,
       K=6,
       show_progress=True,
   )

3. Encountered the above-mentioned error.

Expected Behavior:

The function should return the MAP@K metric value without errors.

Additional Information:

• Tried reinstalling the library and its dependencies, but the error persists.
• The code includes the following imports:

  import numpy as np
  import pandas as pd
  from implicit.cpu.als import AlternatingLeastSquares as ALScpu
  from implicit.evaluation import mean_average_precision_at_k
  from scipy.sparse import coo_matrix

I would appreciate any assistance in resolving this issue.

[fkurushin]

Hi @MRossa157 ! I have trained model with python implicit package and faced the same problem:

The minimum example to reproduce the error

import os
import random
import pandas as pd
from scipy.sparse import csr_matrix
from implicit.evaluation import train_test_split, ndcg_at_k, mean_average_precision_at_k
from implicit.gpu.als import AlternatingLeastSquares

os.environ['OPENBLAS_NUM_THREADS']="1"
os.environ['CUDA_VISIBLE_DEVICES']="0"

# init random data
n_actions = 100000
max_uid = 100000
max_action_id = 10000

df = pd.DataFrame(data={
    "user_id" : [random.randint(1, max_uid) for i in range(0, n_actions)],
    "action" : [random.randint(1, max_action_id) for i in range(0, n_actions)],
    "impression" : [1 for i in range(0, n_actions)]
})

# convert to sparse format
user_rows = [uid for uid in df.user_id.tolist()]
query_cols = [st for st in df.action.tolist()]
qvecs = csr_matrix((df.impression, (user_rows, query_cols)))

# train test split and model training
train_user_items, test_user_items = train_test_split(qvecs, train_percentage=0.9, random_state=19)

model = AlternatingLeastSquares(factors=130, regularization=0.05, alpha=1.0, calculate_training_loss=True)
model.fit(train_user_items)

# calculate ndcg
ndcg = ndcg_at_k(model, train_user_items, test_user_items, K=14, show_progress=True, num_threads=1)

packages version:
implicit-0.7.2 (built from source)
python-3.11.2
cuda-12.3

os:
Debian GNU/Linux 12

[sorlandet]

Updating to scipy 1.14.1 should resolve the issue.