Alpha version of the Rasgo Python interface.
Project description
pyRasgo is a python SDK to interact with the Rasgo API. Rasgo accelerates feature engineering for Data Scientists.
Visit us at https://www.rasgoml.com/ to turn your data into Features in minutes!
Documentation is available at: https://docs.rasgoml.com/rasgo-docs/pyrasgo/
Package Dependencies
- idna>=2.5,<3
- more-itertools
- pandas
- pyarrow>=3.0
- pydantic
- pyyaml
- requests
- snowflake-connector-python>=2.4.0
- tqdm
Release Notes
-
v0.2.01(July 01, 2021)
- expand
evaluate.feature_importance()to support calculating importance for collections
- expand
-
v0.2.0(June 24, 2021)
- introduce
publish.experiment()method to fast track dataframes to Rasgo objects - fix register bug
- introduce
-
v0.1.14(June 17, 2021)
- improve new user signup experience in
register()method - fix dataframe bug when experiment wasn't set
- improve new user signup experience in
-
v0.1.13(June 16, 2021)
- intelligently run Regressor or Classifier model in
evaluate.feature_importance() - improve model performance statistics in
evaluate.feature_importance(): include AUC, Logloss, precision, recall for classification
- intelligently run Regressor or Classifier model in
-
v0.1.12(June 11, 2021)
- support fqtn in
publish.source_data(table)parameter - trim timestamps in dataframe profiles to second grain
- support fqtn in
-
v0.1.11(June 9, 2021)
- hotfix for unexpected histogram output
-
v0.1.10(June 8, 2021)
- pin pyarrow dependency to < version 4.0 to prevent segmentation fault errors
-
v0.1.9(June 8, 2021)
- improve model performance in
evaluate.feature_importance()by adding test set to catboost eval
- improve model performance in
-
v0.1.8(June 7, 2021)
evaluate.train_test_split()function supports non-timeseries dataframesevaluate.feature_importance()function now runs on an 80% training set- adds
timeseries_indexparameter toevaluate.feature_importance()&prune.features()functions
-
v0.1.7(June 2, 2021)
- expands dataframe series type recognition for profiling
-
v0.1.6(June 2, 2021)
- cleans up dataframe profiles to enhance stats and visualization for non-numeric data
-
v0.1.5(June 2, 2021)
- introduces
pip install "pyrasgo[df]"option which will install: shap, catboost, & scikit-learn
- introduces
-
v0.1.4(June 2, 2021)
- various improvements to dataframe profiles & feature_importance
-
v0.1.3(May 27, 2021)
- introduces experiment tracking on dataframes
- fixes errors when running feature_importance on dataframes with NaN values
-
v0.1.2(May 26, 2021)
- generates column profile automatically when running feature_importance
-
v0.1.1(May 24, 2021)
- supports sharing public dataframe profiles
- enforces assignment of granularity to dimensions in Publish methods based on list ordering
-
v0.1.0(May 17, 2021)
- introduces dataframe methods: evaluate, prune, transform
- supports free pyrago trial registration
-
v0.0.79(April 19, 2021)
- support additional datetime data types on Features
- resolve import errors
-
v0.0.78(April 5, 2021)
- adds include_shared param to get_collections() method
-
v0.0.77(April 5, 2021)
- adds convenience method to rename a Feature’s displayName
- adds convenience method to promote a Feature from Sandbox to Production status
- fixes permissions bug when trying to read Community data sources from a public org
-
v0.0.76(April 5, 2021)
- adds columns to DataSource primitive
- adds verbose error message to inform users when a Feature name conflict is preventing creation
-
v0.0.75(April 5, 2021)
- introduce interactive Rasgo primitives
-
v0.0.74(March 25, 2021)
- upgrade Snowflake python connector dependency to 2.4.0
- upgrade pyarrow dependency to 3.0
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