item_graph_module = GraphModule(data=contexts)
ratio_split = RatioSplit(data=ratings,
test_size=0.2,
rating_threshold=3.5,
shuffle=True,
exclude_unknowns=True,
verbose=True,
item_graph=item_graph_module)
pcrl = PCRL(k=100, z_dims=[300], max_iter=300, learning_rate=0.001)
# Evaluation metrics
nDgc = metrics.NDCG(k=-1)
rec = metrics.Recall(k=20)
pre = metrics.Precision(k=20)
# Instantiate and run your experiment
exp = Experiment(eval_method=ratio_split,
models=[pcrl],
metrics=[nDgc, rec, pre])
exp.run()
"""
Output:
| NDCG@-1 | Recall@20 | Precision@20 | Train (s) | Test (s)
---- + ------- + --------- + ------------ + --------- + --------
pcrl | 0.1922 | 0.0862 | 0.0148 | 2591.4878 | 4.0957
*Results may change slightly from one run to another due to different random initial parameters
"""
lambda_d=0.1,
min_user_freq=2,
max_iter=1000,
trainable=True,
verbose=True,
init_params=params,
)
n_items = eval_method.train_set.num_items
k_1 = int(n_items / 100)
k_5 = int(n_items * 5 / 100)
k_10 = int(n_items * 10 / 100)
Experiment(
eval_method,
models=[model],
metrics=[
AUC(),
Recall(k=k_1),
Recall(k=k_5),
Recall(k=k_10),
NDCG(k=k_1),
NDCG(k=k_5),
NDCG(k=k_10),
],
show_validation=True,
save_dir="dist/toy/result",
verbose=True,
).run()
ratio_split = RatioSplit(data=ratings,
test_size=0.2,
rating_threshold=3.5,
shuffle=True,
exclude_unknowns=True,
verbose=True,
item_graph=item_graph_module)
mcf = MCF(k=10, max_iter=40, learning_rate=0.001, verbose=True)
# Evaluation metrics
ndcg = metrics.NDCG(k=-1)
rmse = metrics.RMSE()
rec = metrics.Recall(k=20)
pre = metrics.Precision(k=20)
# Instantiate and run your experiment
exp = Experiment(eval_method=ratio_split,
models=[mcf],
metrics=[rmse, ndcg, rec, pre])
exp.run()
"""
Output:
| RMSE | NDCG@-1 | Recall@20 | Precision@20 | Train (s) | Test (s)
--- + ------ + ------- + --------- + ------------ + --------- + --------
MCF | 1.0854 | 0.1598 | 0.0348 | 0.0057 | 7.4057 | 4.1801
*Results may change from one run to another due to different random initial parameters
"""
exclude_unknowns=True,
verbose=True,
user_graph=user_graph_modality,
seed=123,
)
# Instantiate CVAECF model
cvaecf = CVAECF(z_dim=20,
h_dim=20,
autoencoder_structure=[40],
learning_rate=0.001,
n_epochs=70,
batch_size=128,
verbose=True,
seed=123)
# Evaluation metrics
ndcg = metrics.NDCG(k=50)
rec = metrics.Recall(k=50)
pre = metrics.Precision(k=50)
# Put everything together into an experiment and run it
Experiment(eval_method=ratio_split, models=[cvaecf], metrics=[ndcg, pre,
rec]).run()
"""
Output:
| NDCG@50 | Precision@50 | Recall@50 | Train (s) | Test (s)
------ + ------- + ------------ + --------- + --------- + --------
CVAECF | 0.4171 | 0.0781 | 0.8689 | 13.0752 | 1.4574
"""
# Define an evaluation method to split feedback into train and test sets
ratio_split = RatioSplit(data=ratings,
test_size=0.2,
rating_threshold=2.5,
exclude_unknowns=True,
verbose=True,
user_graph=user_graph_modality,
seed=123)
# Instantiate SoRec
sorec = SoRec(k=10, max_iter=50, learning_rate=0.001, verbose=False, seed=123)
# Evaluation metrics
ndcg = metrics.NDCG(k=-1)
rmse = metrics.RMSE()
rec = metrics.Recall(k=20)
pre = metrics.Precision(k=20)
# Put everything together into an experiment and run it
exp = Experiment(eval_method=ratio_split,
models=[sorec],
metrics=[rmse, ndcg, pre, rec])
exp.run()
"""
Output:
| RMSE | NDCG@-1 | Precision@20 | Recall@20 | Train (s) | Test (s)
----- + ------ + ------- + ------------ + --------- + --------- + --------
SoRec | 0.7574 | 0.3707 | 0.0756 | 0.3736 | 0.8198 | 0.8037
"""
# load office rating data in triplet format (uid, iid, rating)
office_ratings = np.loadtxt("path to office ratings")
# load office item context information in triplet format (item_id, context_iterm_id, value), see C2PF paper for details
office_context = np.loadtxt("path to office content data")
item_graph_module = GraphModule(data=office_context)
ratio_split = RatioSplit(data=office_ratings,
test_size=0.2,
rating_threshold=3.5,
shuffle=True,
exclude_unknowns=True,
verbose=True,
item_graph=item_graph_module)
rec_c2pf = C2PF(k=100, max_iter=80, variant='c2pf')
# Evaluation metrics
nDgc = metrics.NDCG(k=-1)
mrr = metrics.MRR()
rec = metrics.Recall(k=20)
pre = metrics.Precision(k=20)
# Instantiate and run your experiment
exp = Experiment(eval_method=ratio_split,
models=[rec_c2pf],
metrics=[nDgc, mrr, rec, pre])
exp.run()
ratings = office.load_feedback()
contexts = office.load_graph()
# Instantiate a GraphModality, it makes it convenient to work with graph (network) auxiliary information
# For more details, please refer to the tutorial on how to work with auxiliary data
item_graph_modality = GraphModality(data=contexts)
# Define an evaluation method to split feedback into train and test sets
ratio_split = RatioSplit(
data=ratings,
test_size=0.2,
rating_threshold=3.5,
exclude_unknowns=True,
verbose=True,
item_graph=item_graph_modality,
)
# Instantiate C2PF
c2pf = C2PF(k=100, max_iter=80, variant="c2pf")
# Evaluation metrics
ndcg = metrics.NDCG(k=-1)
mrr = metrics.MRR()
rec = metrics.Recall(k=20)
pre = metrics.Precision(k=20)
# Put everything together into an experiment and run it
Experiment(eval_method=ratio_split,
models=[c2pf],
metrics=[ndcg, mrr, rec, pre]).run()
from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP
from cornac.eval_methods import PropensityStratifiedEvaluation
from cornac.experiment import Experiment
# Load the MovieLens 1M dataset
ml_dataset = cornac.datasets.movielens.load_feedback(variant="1M")
# Instantiate an instance of PropensityStratifiedEvaluation method
stra_eval_method = PropensityStratifiedEvaluation(
data=ml_dataset,
n_strata=2, # number of strata
rating_threshold=4.0,
verbose=True)
# define the examined models
models = [
WMF(k=10, seed=123),
BPR(k=10, seed=123),
]
# define the metrics
metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(), AUC(), MAP()]
# run an experiment
exp_stra = Experiment(eval_method=stra_eval_method,
models=models,
metrics=metrics)
exp_stra.run()