semvec/pypi-packages huggingface dataset:
aws-feature-store 0.0.17
Simplified SageMaker Feature Store
License: Apache License 2.0
Requires Python: >= 3.6
Keywords: Simplified SageMaker Feature Store

# 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}")
```
fcm-mp 0.1.0
Fuzzy Cognitive Map with Moore-Penrose inverse learning
License: Apache License 2.0

The package implements a learning method based on the Moore-Penrose inverse for hybrid Fuzzy Cognitive Maps. In this hybrid model, the user can specify how the problem features interact or let the algorithm compute that matrix from the data using unsupervised learning. The supervised learning step focuses on computing the relationships between the last hidden state of the Fuzzy Cognitive Maps and the outputs. Therefore, the model is devoted to solving multi-output regression problems where problem features are connected in non-trivial ways.