API Request Example

This example demonstrates a simple request to the mloda API. In this case, mloda automatically determines that a ReadCsv feature group will fulfill the request and respond with the resulting DataFrame.

1. Import the Required Modules

We first need to import the necessary components to set up our request:

from mloda_core.api.request import mlodaAPI
from mloda_core.abstract_plugins.components.data_access_collection import DataAccessCollection

2. Define Data Sources

Next, we define the data source by specifying the file path and creating a DataAccessCollection object:

file_path = "tests/test_plugins/feature_group/src/dataset/creditcard_2023_short.csv"
data_access_collection = DataAccessCollection(files={file_path})

3. Define the features

We now specify the list of features we want to retrieve. The features are provided as a comma-separated string, which we split into a list:

feature_list = "id,V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,V21,V22,V23,V24,V25,V26,V27,V28,Amount,Class"
feature_list = feature_list.split(",")

4. Execute the Request and Retrieve Results

Finally, we send the request to the mloda API, specifying the compute framework and the data source. The result is returned as a DataFrame:

result = mlodaAPI.run_all(
    feature_list, 
    compute_frameworks=["PyarrowTable"],
    data_access_collection=data_access_collection)
result[0]

Expected output:

pyarrow.Table
V8: double
id: int64
...
V8: [[-0.13000604758867731,-0.13311827417649086,-0....]]
id: [[0,1,2,...]]
....

5. Summary

This example shows how easy it is to interact with the mloda API for data retrieval. By defining the data source and specifying the features, mloda handles the rest, ensuring you get the required data in an efficient and seamless manner.