soft-search 0.6.0

searching for software promises in grant applications

soft-search

searching for software promises in grant applications

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Installation

Stable Release: pip install soft-search
Development Head: pip install git+https://github.com/si2-urssi/eager.git

Quickstart

1. Load our best model (the "TF-IDF Vectorizer Logistic Regression Model")
2. Pull award abstract texts from the NSF API
3. Predict if the award will produce software using the abstract text for each award

from soft_search.constants import NSFFields, NSFPrograms
from soft_search.label import load_soft_search_model
from soft_search.nsf import get_nsf_dataset

# Load the model
pipeline = load_soft_search_model()

# Pull data
data = get_nsf_dataset(
    start_date="2022-05-01",
    end_date="2022-07-01",
    program_name=NSFPrograms.Computer_and_Information_Science_and_Engineering,
    dataset_fields=[NSFFields.id_, NSFFields.abstractText],
    require_project_outcomes_doc=False,
)

# Predict
data["prediction"] = pipeline.predict(data[NSFFields.abstractText])
print(data)

#                                         abstractText       id              prediction
# 0  Human AI Teaming (HAT) is an emerging and rapi...  2213827  software-not-predicted
# 1  This project furthers progress in our understa...  2213756      software-predicted

Annotated Training Data

from soft_search.data import load_soft_search_2022

df = load_soft_search_2022()

Reproducible Models

To train and evaluate all of our models you can run the following:

pip install soft-search

fit-and-eval-all-models

Also available directly in Python

from soft_search.label.model_selection import fit_and_eval_all_models

results = fit_and_eval_all_models()

Documentation

For full package documentation please visit si2-urssi.github.io/eager.

Development

See CONTRIBUTING.md for information related to developing the code.

MIT License