experiment_knn.py

import os
import argparse

parser = argparse.ArgumentParser()

parser.add_argument("--seed", default=42, type=int, help="Random seed")

parser.add_argument("--dataset", default="", type=str, help="dataset. For list of datasets leave this parameter blank.")
parser.add_argument("--validation", default="false", type=str, help="Use validation split: true/false")

parser.add_argument("--pu", default=1, type=int, help="User pruning aplied on training data.")
parser.add_argument("--pi", default=1, type=int, help="Item pruning aplied on training data.")

parser.add_argument("--neighbors", default=50, type=int, help="KNN N parameter")

parser.add_argument("--flag", default="none", type=str, help="flag for distinction of experiments, default none")

args = parser.parse_args([] if "__file__" not in globals() else None)

from datasets.utils import *
from baselines import KNNRecommender

from config import config

from time import time

if __name__ == "__main__":
    folder = f"results/{str(pd.Timestamp('today'))} {9*int(1e6)+np.random.randint(999999)}".replace(" ", "_")
    if not os.path.exists(folder):
        os.makedirs(folder)
    vargs = vars(args)
    pd.Series(vargs).to_csv(f"{folder}/setup.csv")
    print(folder)
    torch.manual_seed(args.seed)
    #keras.utils.set_random_seed(args.seed)
    np.random.seed(args.seed)
    print(args)
    try:
        assert args.dataset in config.keys()
    except:
        print(f"Dataset must be one of {list(config.keys())}.")
        raise
    
    dataset, params = config[args.dataset]
    params['random_state'] = args.seed
    print(f"Loding dataset {args.dataset} with params {params}")
    dataset.load_interactions(**params)
    print(dataset)
    
    if args.validation == "true":
        print("creating validation evaluator")
        val_evaluator = Evaluation(dataset, "validation")
        df = fast_pruning(dataset.train_interactions, args.pu,args.pi)
    else:
        df = fast_pruning(dataset.full_train_interactions, args.pu,args.pi)
    
    X = get_sparse_matrix_from_dataframe(df)
    
    print(f"Interaction matrix: {repr(X)}")
    
    print("creating test evaluator")
    test_evaluator = Evaluation(dataset, "test")
    
    print()
    
    
    model = KNNRecommender(X, item_idx=dataset.full_train_interactions.item_id.cat.categories, neighbors=args.neighbors)
    fits = []
    val_logs = []
    train_time=0
    if args.validation == "true":
        val_df_preds = model.predict_df(val_evaluator.test_src)
        val_results=val_evaluator(val_df_preds)
        dff = pd.DataFrame(val_logs)
        dff["epoch"] = np.arange(dff.shape[0])+1
        dff[list(dff.columns[-1:])+list(dff.columns[:-1])]
        dff.to_csv(f"{folder}/val_logs.csv")
        print("val_logs file written")

    df_preds = model.predict_df(test_evaluator.test_src)
    results=test_evaluator(df_preds)
    
    print(results)
    
    df = pd.DataFrame()
    
    df.to_csv(f"{folder}/history.csv")
    print("history file written")
    
    pd.Series(results).to_csv(f"{folder}/result.csv")
    print("results file written")
    
    pd.Series(train_time).to_csv(f"{folder}/timer.csv")
    print("timer written")