https://github.com/datamllab/AutoRec

https://github.com/datamllab/AutoRec

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• Host: GitHub
• URL: https://github.com/datamllab/AutoRec
• Owner: datamllab
• Created: 2019-10-10T02:42:13.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T22:54:23.000Z (over 1 year ago)
• Last Synced: 2024-05-21T15:33:44.461Z (7 months ago)
• Language: Python
• Size: 35.5 MB
• Stars: 49
• Watchers: 6
• Forks: 11
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• automl - Homepage - Automated Recommender System (Uncategorized / Uncategorized)

README

        
# AutoRec

AutoRec is a Keras-based implementation of automated recommendation algorithms for both rating prediction and Click Through Rate task. 

For more details, see the [Documentation](http://autorec.ai).

## Installation

Install from `pip`:

```

pip install autorec

```

## Quickstart

Build an rating prediction model which can search the model architecture automatically  on the MovieLens  dataset is very easy as follows:

```python

# -*- coding: utf-8 -*-

import tensorflow as tf

from autorecsys.auto_search import Search

from autorecsys.pipeline import Input, LatentFactorMapper, RatingPredictionOptimizer, ElementwiseInteraction

from autorecsys.pipeline.preprocessor import MovielensPreprocessor, NetflixPrizePreprocessor

from autorecsys.recommender import RPRecommender

# load dataset

#Movielens 1M Dataset

data = MovielensPreprocessor("./examples/datasets/ml-1m/ratings.dat")

data.preprocessing(val_test_size=0.1, random_state=1314)

train_X, train_y = data.train_X, data.train_y

val_X, val_y = data.val_X, data.val_y

test_X, test_y = data.test_X, data.test_y

user_num, item_num = data.user_num, data.item_num

# build the pipeline.

input = Input(shape=[2])

user_emb = LatentFactorMapper(column_id=0,

                              num_of_entities=user_num,

                              embedding_dim=64)(input)

item_emb = LatentFactorMapper(column_id=1,

                              num_of_entities=item_num,

                              embedding_dim=64)(input)

output = ElementwiseInteraction(elementwise_type="innerporduct")([user_emb, item_emb])

output = RatingPredictionOptimizer()(output)

model = RPRecommender(inputs=input, outputs=output)

# AutoML search and predict

searcher = Search(model=model,

                  tuner='greedy',  # hyperband, greedy, bayesian

                  tuner_params={"max_trials": 5}

                  )

searcher.search(x=train_X,

                y=train_y,

                x_val=val_X,

                y_val=val_y,

                objective='val_mse',

                batch_size=1024,

                epochs=10,

                callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=1)])

```