LycorisR is a lightweight recommendation algorithm framework based on LycorisNet.
Features
- Sequence-based recommendation.
- Discover correlations between items.
- Elegant and minimalist.
Installation
The project is based on LycorisNet, and the installation of LycorisNet can be found here.
pip install LycorisR
Documents
The APIs provided by Recommender (from LycorisR import Recommender):
| Function | Description | Inputs | Returns |
|---|---|---|---|
| Recommender(config) | Constructor. | config: The configuration information, including 11 configuration fields. | An object of the class Recommender. |
| embed(data) | Generate the mapping between one-hot encoding and embedding vectors. | data: Each dimension of data is spliced by several one-hot codes. For example, the encoding of "A B-> C:" can be expressed as: [0, 0, 1, 0, 1, 0, 1, 0, 0]. | |
| most_similarity(positive, negative=None, top_n=10) | Calculate and get the most similar top_n items. |
positive: List of items that contribute positively. negative: List of items that contribute negatively. top_n: Top N. |
The most similar top_n items and their similarities. |
| infer(items) | Inferring based on the trained model. | items: Each dimension of data is spliced by several one-hot codes. | One-hot codes of desired items. |
| get_vector(items) | Get the embedding vectors of items. | items: one-hot codes. | Embedding vectors of items. |
| save(path1, path2) | Save the model and related configurations. |
path1: The path to store the model. path2: The path to store the configurations. |
|
@staticmethodload(path1, path2) |
Import pre-trained models and related configurations. |
path1: The path to import the model. path2: The path to import the configurations. |
|
| set_config(config) | Set the configuration information of Recommender. | config: The configuration information, including 11 configuration fields. | |
| set_lr(learning_rate) | Set the learning rate of the neural network. | learning_rate: The learning rate of the neural network. | |
| set_workers(workers) | Set the number of worker threads to train the model. | workers: The number of worker threads. | |
@staticmethodversion() |
Returns the version information of Recommender. |
Configurable fields:
| Field | Description | Default |
|---|---|---|
| capacity | Capacity of LycorisNet. | |
| dimension | Dimension of each item. | |
| sequence | The number of items. | |
| nodes | The number of hidden nodes added for each neural network. | |
| connections | The number of connections added for each neural network. | |
| depths | Total layers of each neural network. | |
| batch_size | Batch size. | |
| epoch | Epoch. | |
| middle_layer | Select the number of layers as the embedding vector. It starts at index 0. |
|
| evolution | Number of LycorisNet evolutions. | 0 |
| verbose | Whether to output intermediate information. | False |
Usage
LycorisR is a sequence-based recommendation framework. The input data is a sequential list of items, and the framework automatically explores correlations between these items. Here is a simple example with 8 groups of sequences: A B -> C, A B -> D, B A -> C, B A -> D, C D -> A, C D -> B, D C -> A, D C -> B.
Import the dependent modules:
from LycorisR import RecommenderPrepare the data:
data = [[0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0]]Prepare the configuration information and instantiate the Recommender object:
conf = {"capacity": 64, "dimension": 4, "sequence": 3, "nodes": 1000, "connections": 30000, "depths": 8,
"batch_size": 8, "epoch": 16, "evolution": 8, "middle_layer": 4, "verbose": True}
lre = Recommender(conf)Embedding:
lre.embed(data)Inferring B C -> ?:
lre.infer([0, 0, 1, 0, 0, 1, 0, 0])Get the most similar items to "A":
lre.most_similarity([0, 0, 0, 1])Get the embedding vectors of "D":
lre.get_vector([1, 0, 0, 0])More examples will be released in the future.
License
LycorisR is released under the LGPL-3.0 license. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.
