# sarplus
pronounced sUrplus as it's simply better if not best!

Features
* Scalable PySpark based implementation
* Fast C++ based prediction: 
* Reduced memory consumption: similarity matrix cached in-memory once per worker, shared accross python executors 
* Easy setup using [Spark Packages](https://spark-packages.org/package/eisber/sarplus)

# Benchmarks

| # Users | # Items | # Ratings | Runtime | Environment | Dataset | 
|---------|---------|-----------|---------|-------------|---------|
| 2.5mio  | 35k     | 100mio    | 1.3h    | Databricks, 8 workers, [Azure Standard DS3 v2](https://azure.microsoft.com/en-us/pricing/details/virtual-machines/linux/) | |

# Usage

```python
import pandas as pd
from pysarplus import SARPlus

# spark dataframe with user/item/rating/optional timestamp tuples
train_df = spark.createDataFrame(
      pd.DataFrame({
        'user_id': [1, 1, 2, 3, 3],
        'item_id': [1, 2, 1, 1, 3],
        'rating':  [1, 1, 1, 1, 1],
    }))
   
# spark dataframe with user/item tuples
test_df = spark.createDataFrame(
      pd.DataFrame({
        'user_id': [1, 3],
        'item_id': [1, 3],
        'rating':  [1, 1],
    }))
    
model = SARPlus(spark, col_user='user_id', col_item='item_id', col_rating='rating', col_timestamp='timestamp')
model.fit(train_df, similarity_type='jaccard')

model.recommend_k_items(test_df, 'sarplus_cache', top_k=3).show()
```

# Jupyter Notebook

# PySpark Shell

```bash
pip install pysarplus
pyspark --packages eisber:sarplus:0.2.1 --conf spark.sql.crossJoin.enabled=true
```

# Databricks

One must set the crossJoin property to enable calculation of the similarity matrix (Clusters / <Cluster> / Configuration / Spark Config)

```
spark.sql.crossJoin.enabled true
```

1. Navigate to your workspace 
2. Create library
3. Under 'Source' select 'Maven Coordinate'
4. Enter eisber:sarplus:0.2.1
5. Hit 'Create Libbrary'

This will install C++, Python and Scala code on your cluster.

# Packaging

For [databricks](https://databricks.com/) to properly install a [C++ extension](https://docs.python.org/3/extending/building.html), one must take a detour through [pypi](https://pypi.org/).
Use [twine](https://github.com/pypa/twine) to upload the package to [pypi](https://pypi.org/).

```bash
cd python

python setup.py sdist

twine upload dist/pysarplus-*.tar.gz
```

On [Spark](https://spark.apache.org/) one can install all 3 components (C++, Python, Scala) in one pass by creating a [Spark Package](https://spark-packages.org/). Documentation is rather sparse. Steps to install

1. Package and publish the [pip package](python/setup.py) (see above)
2. Package the [Spark package](scala/build.sbt), which includes the [Scala formatter](scala/src/main/scala/eisber/sarplus) and references the [pip package](scala/python/requirements.txt) (see below)
3. Upload the zipped Scala package to [Spark Package](https://spark-packages.org/) through a browser. [sbt spPublish](https://github.com/databricks/sbt-spark-package) has a few [issues](https://github.com/databricks/sbt-spark-package/issues/31) so it always fails for me. Don't use spPublishLocal as the packages are not created properly (names don't match up, [issue](https://github.com/databricks/sbt-spark-package/issues/17)) and furthermore fail to install if published to [Spark-Packages.org](https://spark-packages.org/).  

```bash
cd scala
sbt spPublish
```

# Testing

To test the python UDF + C++ backend

```bash
cd python 
python setup.py install && pytest -s tests/
```

To test the Scala formatter

```bash
cd scala
sbt test
```

(use ~test and it will automatically check for changes in source files, but not build.sbt)