# aws_feature_store
It is a simplified implementation of SageMaker Feature Store approach.
## Installation
---------------
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.
```bash
pip install aws_feature_store
```
## Initialize feature group
---------------------------
```python
from aws_feature_store import FeatureGroup,FeatureDefinition,FeatureTypeEnum
bucket_name = '{bucket_for_feature_store}'
s3_folder = '{folder_for_feature_store}'
my_feature_name = '{your_feature_name}'
feature_group_name = f'{my_feature_name}/commit_id={my_feature_name}_{commit_id}'
feature_group = FeatureGroup(
name=feature_group_name,
boto3_session = boto3_session,
s3_uri=f"s3://{bucket_name}/{s3_folder}"
)
```
## Create feature group
--------------------
```python
def create_feature_group(feature_group):
description="What is my feature group about"
feature_script_repo="{repo_link_to_script}"
data_source="{what data are used}"
record_identifier_feature_name = "column name to store id"
event_time_feature_name = "{column name to store timestamp}"
partition_columns=['biz_id','customer_id']
feature_definitions=[
FeatureDefinition(feature_name="column_name1", feature_type=FeatureTypeEnum.INTEGRAL),
FeatureDefinition(feature_name="column_name2", feature_type=FeatureTypeEnum.STRING),
]
feature_group.create(
record_identifier_name=record_identifier_feature_name,
event_time_feature_name=event_time_feature_name,
feature_script_repo=feature_script_repo,
partition_columns=partition_columns,
data_source=data_source,
description=description,
file_format='parquet/json',
feature_definitions=feature_definitions
)
return feature_group
if feature_group.exists() is None:
feature_group = create_feature_group(feature_group)
```
## Ingest data
--------------
```python
import pandas as pd
data = pd.read_json('data.json')
feature_group.ingest_data_frame(data,f"mlfow_parent_run_id={parent_run_id}/{filename_without_extention}")
```
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