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BrightFuture

A first-in-class modeling engine that cleans and aggregates existing datasets and generates relevant forecasts of academic achievement.

Please look at src/Final Report Examples.ipynb for examples on how to use BrightFuture!

The first example shows how BrightFuture loads pre-cleaned datasets, merges them, and builds multiple models, as well as picks the best one, in just 4 lines of code. The second example demonstrates how in just 2 lines of code, BrightFuture loads a dataset and builds multiple models with them, this time reporting on the best one as we asked for in our display argument.

Directory Structure

The important files are organized as

├── data
│   ├── awards-data-messy.tsv
│   ├── awards-data.tsv
│   ├── csrankings
│   │   ├── area-counts.json
│   │   ├── area-counts-small.json
│   │   ├── authors.json
│   │   └── authors-small.json
│   ├── profs.html
│   ├── profs.tsv
│   └── uni_rankings.tsv
├── src
│   ├── bright_future_base.py
│   ├── Final Report Examples.ipynb
└── viz
    ├── data visualizations

Example Usage

Clone the repo with git clone [email protected]:n8kim1/bright-future.git and then try these in a Python file or Jupyter notebook within src/.

Start off with

import numpy as np
import pandas as pd
import json
from importlib import reload
import statsmodels.api as sm
import bright_future_base as bf
reload(bf)

Data Loading

Utilise load_df(dataset, grouped_by) to load a dataset (try "profs", "awards", or "works") and optionally a grouped_by argument (try "author"). Also try "uni_rankings" for the top 10 US universities for Computer Science.

Data Filtering

Utilise get_works_by_author(author) to load get an author's publications (try "Samuel Madden").

Data Aggregation

Utilise group_works_by_field() to load get publication counts by field.

Data Merging

Utilise merge_datasets(datasets) to cleanly merge datasets (try ["works", "awards"] or ["awards", "profs"] or ["profs", "awards"]).

Similarity Metric

Utilise similarity_by_author(author_1, author_2) to get a Cosine Similarity metric for the authors' publications. Try "Samuel Madden" and "Tim Kraska".

Automated Modeling

Utilise model_builder(data, responder, predictors, display="best", thresh=1.1) to automatically build a model and display either "all" models or the "best". Play around with the threshold for how stringent an R2 increase you want in your model. Example code is as below.

df_prof = bf.load_df("profs")
best_model = bf.model_builder(data=df_prof,
            responder="is_uni_top_10",
            predictors=["is_bachelors_top_10", "is_doctorate_top_10"],
            display="all", thresh = 1.0)