Our Run Models CoLab provides Logistic Regression, Support Vector Machines (SVM), MLP, XGBoost and two Random Forests.

Paths for features and targets are joined based on location IDs (state or county FIPS, etc.) or brain regions for eye blink predictions.

For Features, the UI picks features.dcid if it exists otherwise features.data otherwise the filename from features.path.
For Targets, it picks targets.dcid if it exists otherwise targets.data otherwise the filename from targets.path.

Our main input is currently industry features by county ID (FIPS) for exploring environmental impact targets like bee data and tree canopy.
We are also preping data for International Trade Flow.

Run-Models-bkup.ipynb is a backup of the Run Models CoLab that we run locally. We append "-bkup" to indicate it is not the primary source.

Learn about our cuML GPU speed enhancements - and SMOTE balancing of our classes We're using SHAP to explain our model predictions

Design your Stream

Bee YAML Updated - Changed bee data target to bees-targets-top-20-percent.csv in parameters yaml. This new "colony density" target uses the top 20% of counties with the highest bee population density (rather than top colony growth between years, as was used in bees-targets.csv).

In the CoLab, a select menu allows you to choose default parameter yaml paths set in parameter-paths.csv. parameters-simple.yaml - Bee density for 2020 for Maine and New York by top industries (2-digits) parameters.yaml - Predicts bee density by industry (6-digit NAICS) for New England states 2017 to 2021 parameters-gdc.yaml - Google Data Commons - Predicting states using demographics parameters-zip.yaml - Needs zip code target. Uses bee population density.
parameters-blinks.yaml - Uses only features dataset (which contains the target column).

TO DO: Top ten counties in each state likely to have declining tree canopy

TO DO: Within Run Models, add python pull from Google Data Commons API for population, education levels, income/poverty levels to use as both features and targets. - IN PROGRESS

TO DO: Use Tensorflow.org for Neural Network predictions with our training data.

Running RealityStream Locally (CLI)

You can run the RealityStream model pipeline locally using the CLI.

Run with default parameters file

python run_models.py parameters/parameters.yaml

Run with other parameter files

python run_models.py parameters/parameters-years.yaml

Expected Output

You should see:

[INFO] RealityStream local runner
[INFO] Using parameters file: ...

This confirms the CLI is working correctly.