zombak79/recsysdata

Recommender System Datasets (and Baselines)

1. In the datasets/amazon directory run download_amazon, unpack dups.zip and run preprocess.py, merge_ratings.py and merge_items.py.
2. Use experiment_*.py to run experiments, for example python experiment_knn.py --dataset amazon will run knn on amazon dataset.
3. You can also setup experiments in json config file and run it in batches: python run.py --config experiments/test.json
4. Than you can see the results by running analyze_results.py:

$ python analyze_results.py -f none test
        dataset  flag  recall@20  recall@50  ndcg@100  training_time  std_recall@20  std_recall@50  std_ndcg@100  std_time  n_experiments
0        amazon  none   0.095107   0.110732  0.087584            0.0            NaN            NaN           NaN       NaN              1
1  amazon-small  test   0.026373   0.052412  0.025858            0.0            NaN            NaN           NaN       NaN              1

To use data in your own script please import datasets.utils and config.config. Then you can get the data like this:

from datasets.utils import *
from config import config

dataset, params = config["amazon"]
dataset.load_interactions(**params)

X = get_sparse_matrix_from_dataframe(dataset.train_interactions)

Evaluation is done through recpack, models should have the predict_df method:

class SomeModel:
  ...
  def predict_df(self, df: pd.DataFrame, n:int, batch_size:int = 1000):
    """
    This method recieves a dataframe with interactions and returns top_n predictions for every user as a dataframe
    (note that model can work with different number of items than evaluator)
    """

To evaluate a model you should do something like this:

test_evaluator = Evaluation(dataset, "test")
test_df_preds = model.predict_df(test_evaluator.test_src)
test_results=test_evaluator(test_df_preds)

test_results is dictionary of {'metric_name':metric_value}