First example

This example will show you how to run your very first experiment using Cornac.

import cornac as cn

# Load MovieLens 100K dataset
ml_100k = cn.datasets.movielens.load_100k()

# Split data based on ratio
ratio_split = cn.eval_methods.RatioSplit(data=ml_100k,
                                         test_size=0.2,
                                         rating_threshold=4.0,
                                         seed=123)

# Here we are comparing: Biased MF, PMF, and BPR
mf = cn.models.MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True)
pmf = cn.models.PMF(k=10, max_iter=100, learning_rate=0.001, lamda=0.001)
bpr = cn.models.BPR(k=10, max_iter=200, learning_rate=0.01, lambda_reg=0.01)

# Define metrics used to evaluate the models
mae = cn.metrics.MAE()
rmse = cn.metrics.RMSE()
rec_20 = cn.metrics.Recall(k=20)
ndcg_20 = cn.metrics.NDCG(k=20)
auc = cn.metrics.AUC()

# Put it together into an experiment and run
exp = cn.Experiment(eval_method=ratio_split,
                    models=[mf, pmf, bpr],
                    metrics=[mae, rmse, rec_20, ndcg_20, auc],
                    user_based=True)
exp.run()

Output:

	MAE	RMSE	Recall@20	NDCG@20	AUC	Train (s)	Test (s)
MF	0.7441	0.9007	0.0622	0.0534	0.2952	0.0736	8.1187
PMF	0.7493	0.9084	0.0835	0.0673	0.4749	358.7642	8.4184
BPR	1.5595	1.8864	0.0744	0.0657	0.5932	2.5395	8.5734