n_components=10,
n_tastes=3,
user_repr_graph=NormalizedLinearRepresentationGraph(),
attention_graph=None,
loss_graph=BalancedWMRBLossGraph(),
)
model_with_attention = TensorRec(
n_components=10,
n_tastes=3,
user_repr_graph=NormalizedLinearRepresentationGraph(),
attention_graph=LinearRepresentationGraph(),
loss_graph=BalancedWMRBLossGraph(),
)
results_without_attention = fit_and_eval(model=model_without_attention,
user_features=user_features,
item_features=item_features,
train_interactions=train_interactions,
test_interactions=test_interactions,
fit_kwargs=fit_kwargs)
results_with_attention = fit_and_eval(model=model_with_attention,
user_features=user_features,
item_features=item_features,
train_interactions=train_interactions,
test_interactions=test_interactions,
fit_kwargs=fit_kwargs)
logging.info("Results without attention: {}".format(results_without_attention))
logging.info("Results with attention: {}".format(results_with_attention))
CosineSimilarityPredictionGraph,
EuclidianSimilarityPredictionGraph):
for repr_graph in (LinearRepresentationGraph, ReLURepresentationGraph):
for n_tastes in (1, 3):
# Build the model, fit, and get a result packet
model = TensorRec(
n_components=n_components,
n_tastes=n_tastes,
biased=biased,
loss_graph=loss_graph(),
prediction_graph=pred_graph(),
user_repr_graph=NormalizedLinearRepresentationGraph(),
item_repr_graph=repr_graph())
result = fit_and_eval(model, user_features, item_features,
train_interactions, test_interactions,
fit_kwargs)
# Build results row for this configuration
res_string = "{}".format(loss_graph.__name__)
res_string = append_to_string_at_point(res_string,
pred_graph.__name__, 30)
res_string = append_to_string_at_point(res_string,
repr_graph.__name__, 66)
res_string = append_to_string_at_point(res_string, biased, 98)
res_string = append_to_string_at_point(res_string, n_tastes,
108)
res_string = append_to_string_at_point(
res_string, ": {}".format(result[0]), 118)
res_string = append_to_string_at_point(res_string, result[1],
141)
user_repr_graph=user_repr(),
loss_graph=BalancedWMRBLossGraph(),
biased=False)
# Fit the model and get a result packet
fit_kwargs = {
'epochs': 500,
'learning_rate': .01,
'n_sampled_items': 100,
'verbose': True
}
result = fit_and_eval(model,
user_features,
item_features,
train_interactions,
test_interactions,
fit_kwargs,
recall_k=100,
precision_k=100,
ndcg_k=100)
# Build results row for this configuration
res_string = "{}".format(user_repr.__name__)
res_string = append_to_string_at_point(res_string, item_repr.__name__,
40)
res_string = append_to_string_at_point(res_string,
": {:0.4f}".format(result[3]),
68)
res_string = append_to_string_at_point(res_string,
"{:0.4f}".format(result[0]), 90)
res_string = append_to_string_at_point(res_string,
]
# Try different configurations using DeepRepresentationGraph for both item_repr and user_repr. If
# DeepRepresentationGraph is used, a deep neural network will learn to represent the users or items.
for user_repr in (NormalizedLinearRepresentationGraph, DeepRepresentationGraph):
for item_repr in (LinearRepresentationGraph, DeepRepresentationGraph):
model = TensorRec(n_components=20,
item_repr_graph=item_repr(),
user_repr_graph=user_repr(),
loss_graph=BalancedWMRBLossGraph(),
biased=False)
# Fit the model and get a result packet
fit_kwargs = {'epochs': 500, 'learning_rate': .01, 'n_sampled_items': 100, 'verbose': True}
result = fit_and_eval(model, user_features, item_features, train_interactions, test_interactions, fit_kwargs,
recall_k=100, precision_k=100, ndcg_k=100)
# Build results row for this configuration
res_string = "{}".format(user_repr.__name__)
res_string = append_to_string_at_point(res_string, item_repr.__name__, 40)
res_string = append_to_string_at_point(res_string, ": {:0.4f}".format(result[3]), 68)
res_string = append_to_string_at_point(res_string, "{:0.4f}".format(result[0]), 90)
res_string = append_to_string_at_point(res_string, "{:0.4f}".format(result[4]), 110)
res_string = append_to_string_at_point(res_string, "{:0.4f}".format(result[1]), 130)
res_string = append_to_string_at_point(res_string, "{:0.4f}".format(result[5]), 150)
res_string = append_to_string_at_point(res_string, "{:0.4f}".format(result[2]), 170)
logging.info(header)
logging.info(res_string)
result_strings.append(res_string)
# Log the final results of all models
n_components=10,
n_tastes=3,
user_repr_graph=NormalizedLinearRepresentationGraph(),
attention_graph=None,
loss_graph=BalancedWMRBLossGraph(),
)
model_with_attention = TensorRec(
n_components=10,
n_tastes=3,
user_repr_graph=NormalizedLinearRepresentationGraph(),
attention_graph=LinearRepresentationGraph(),
loss_graph=BalancedWMRBLossGraph(),
)
results_without_attention = fit_and_eval(model=model_without_attention,
user_features=user_features,
item_features=item_features,
train_interactions=train_interactions,
test_interactions=test_interactions,
fit_kwargs=fit_kwargs)
results_with_attention = fit_and_eval(model=model_with_attention,
user_features=user_features,
item_features=item_features,
train_interactions=train_interactions,
test_interactions=test_interactions,
fit_kwargs=fit_kwargs)
logging.info("Results without attention: {}".format(results_without_attention))
logging.info("Results with attention: {}".format(results_with_attention))
# Iterate through many possibilities for model configuration
for loss_graph in (WMRBLossGraph, BalancedWMRBLossGraph):
for pred_graph in (DotProductPredictionGraph, CosineSimilarityPredictionGraph,
EuclidianSimilarityPredictionGraph):
for repr_graph in (LinearRepresentationGraph, ReLURepresentationGraph):
for n_tastes in (1, 3):
# Build the model, fit, and get a result packet
model = TensorRec(n_components=n_components,
n_tastes=n_tastes,
biased=biased,
loss_graph=loss_graph(),
prediction_graph=pred_graph(),
user_repr_graph=NormalizedLinearRepresentationGraph(),
item_repr_graph=repr_graph())
result = fit_and_eval(model, user_features, item_features, train_interactions, test_interactions,
fit_kwargs)
# Build results row for this configuration
res_string = "{}".format(loss_graph.__name__)
res_string = append_to_string_at_point(res_string, pred_graph.__name__, 30)
res_string = append_to_string_at_point(res_string, repr_graph.__name__, 66)
res_string = append_to_string_at_point(res_string, biased, 98)
res_string = append_to_string_at_point(res_string, n_tastes, 108)
res_string = append_to_string_at_point(res_string, ": {}".format(result[0]), 118)
res_string = append_to_string_at_point(res_string, result[1], 141)
res_string = append_to_string_at_point(res_string, result[2], 164)
res_strings.append(res_string)
print(res_string)
print('--------------------------------------------------')
for res_string in res_strings:
