Skip to content

Latest commit

 

History

History
79 lines (58 loc) · 3.76 KB

README.md

File metadata and controls

79 lines (58 loc) · 3.76 KB
Raw

ISI Datamart

This project provides an implementation of the D3M's Datamart API.

For REST service access to the ISI Datamart, use this ISI Datmart Link Panel.

Using ISI Datamart

Here is a Jupyter notebook that shows how to search the datamart.

Below are the key steps to to query for datamart datasets using a supplied D3M dataset, and to augment this supplied dataset with datamart datasets.

First, load in the D3M dataset, and denormalized it:

dataset_uri = 'uri path to DA_poverty_estimation datasetDoc.json'
dataset = D3MDatasetLoader().load(dataset_uri=dataset_uri)
denormalize_primitive = DenormalizePrimitive(hyperparams=hyper_denormalize.defaults())
dataset = denormalize_primitive.produce(inputs=dataset)

Create an ISI datamart instance, and call its search_with_data method:

from datamart_isi.entries import Datamart

connection_url = "http://dsbox02.isi.edu:9001/blazegraph/namespace/datamart3/sparql"
datamart = Datamart(connection_url=connection_url)
search_cursor = search_result.search_with_data(query=None, supplied_data=dataset)
page = search_cursor.get_next_page()

In this case page should contain only two datasets. Now, augment the supplied dataset with these two datasets.

augmented_dataset_1 = page[0].augment(supplied_data=search_cursor.supplied_data)
augmented_dataset_2 = page[1].augment(supplied_data=augmented_dataset_1)

Using the Search Results in Pipelines

To use the search results in pipelines, the search results have to serialized and passed in as hyperparameters to the Datamart primitives in the common primitives repository.

from common_primitives.datamart_augment import Hyperparams as hyper_augment

result0 = pickle.dumps(page[0])

hyper = hyper_augment.defaults()
hyper = hyper.replace({"search_result": result0})
augment_primitive = DataMartAugmentPrimitive(hyperparams=hyper)
augment_result = augment_primitive.produce(inputs=dataset).value

Here is a sample pipeline.

Uploading Dataset

To upload datasets into the ISI Datamart, use the python class datamart_isi.upload.Datamart_isi_upload, here.

Here is a sample Jupyter notebook that shows how to upload datasets into the ISI Datamart.

First, create an uploader instance, and call its load_and_preprocess method with a URL pointing to the CSV file. The load_and_preprocess method returns two lists: a list of dataframes and a list of metadata describing those dataframes. In this case, the length of each list is one since the input URL references a single CSV file. At this point the dataset has not yet been uploaded to the datamart.

from datamart_isi.upload.store import Datamart_isi_upload

uploader = Datamart_isi_upload()
url_to_csv_file = 'https://raw.githubusercontent.com/usc-isi-i2/datamart-userend/master/example_datasets/List_of_United_States_counties_by_per_capita_income.csv'
dataframes, metadata = uploader.load_and_preprocess(input_dir=url_to_csv_file, file_type='online_csv')

The metadata contains information deduced by the data profiler. To add additional metadata information to the dataset do:

metadata[0]['title'] = "County Income"
metadata[0]['description'] = "Rank of counties by income"
metadata[0]['keywords'] = ["Per capita income", "Median household income", "Median family income"]

Finally, upload the dataset with the updated metadata into the datmart:

uploader.model_data(dataframes, metadata, 0)
uploader.upload()