Metadata-Version: 2.1
Name: pyrasgo
Version: 0.1.13
Summary: Alpha version of the Rasgo Python interface.
Home-page: https://www.rasgoml.com/
Author: Patrick Dougherty
Author-email: patrick@rasgoml.com
License: MPL 2.0
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: df
Provides-Extra: snowflake
License-File: LICENSE

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
-------------------------------------------------------------------------------

- v0.1.13(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

- v0.1.12(June 11, 2021)
   - support fqtn in `publish.source_data(table)` parameter
   - trim timestamps in dataframe profiles to second grain

- 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

- v0.1.8(June 7, 2021)
   - `evaluate.train_test_split()` function supports non-timeseries dataframes
   - `evaluate.feature_importance()` function now runs on an 80% training set
   - adds `timeseries_index` parameter to `evaluate.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

- 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

