def build_pytorch_net(
feature_specs,
product_sets,
float_feature_order,
id_feature_order,
reward_type,
layers,
activations,
input_dim,
dropout_ratio=0.0,
):
"""Build PyTorch model that will be fed into skorch training."""
is_classification = reward_type == "binary"
output_dim = 2 if is_classification else 1
layers[0], layers[-1] = input_dim, output_dim
# handle changes of model architecture due to embeddings
first_layer_dim_increase, embedding_info = build_embedding_spec(
id_feature_order, feature_specs, product_sets)
layers[0] += first_layer_dim_increase
model_spec = {
"layers": layers,
"activations": activations,
"dropout_ratio": dropout_ratio,
"feature_specs": feature_specs,
"product_sets": product_sets,
"float_feature_order": float_feature_order,
"id_feature_order": id_feature_order,
"embedding_info": embedding_info,
"is_classification": is_classification,
}
return model_spec, EmbedDnn(**model_spec)
def build_pytorch_net(
feature_specs,
product_sets,
float_feature_order,
layers,
id_feature_order,
activations,
input_dim,
output_dim=1,
dropout_ratio=0.0,
):
"""Build PyTorch model that will be fed into skorch training."""
layers[0], layers[-1] = input_dim, output_dim
# handle changes of model architecture due to embeddings
first_layer_dim_increase, embedding_info = build_embedding_spec(
id_feature_order, feature_specs, product_sets)
layers[0] += first_layer_dim_increase
net_spec = {
"layers": layers,
"activations": activations,
"dropout_ratio": dropout_ratio,
"feature_specs": feature_specs,
"product_sets": product_sets,
"float_feature_order": float_feature_order,
"id_feature_order": id_feature_order,
"embedding_info": embedding_info,
}
return net_spec, EmbedDnn(**net_spec)