Aethos

Release Notes
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General
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- Python 3.5 is no longer supported
- Fixed bug where the run parameter for running models was not being taken into account
- Added visualizing clusters in 3d
- Added properties for y_train and y_test

Models
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- Added Gridsearch support for unsupervised models
- Added 2d and 3d Scatterplot for visualizing clusters

Release Notes
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Added Cleaning, Preprocessing and Feature Engineering techniques.

Added Regression, Classification, Text and Clustering models.

Some models include Agglomerative Hierarchical Clujstering, doc2vec, word2vec, XGBoost Classification and Regression, etc. There are now over 35+ automated and implemented models.

Can now views metrics and compare Classification and Regression models.

Can access model methods from the model name variable. For example: model.log_reg.get_params(), etc.

Release Notes
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General

- Can now write to csv
- Refactored variables
- `data` variable no longer exists
- `train_data` is now x_train to be more compliant with what you see in books/tutorials
- `test_data` is now x_test to be more compliant with what you see in books/tutorials
- Environment is now automatically for reporting

Modelling

- Added crossvalidation
- Stratified Kfold
- KFold
- Learning curve

- Added Gridsearch
- GridsearchCV

- Can now queue multiple models and run them in parallel on a local machine or one after the other if there are limited resources

- Can now compare models across all metrics for a given problem (classification vs. regression)

Model Results

- Can now understand model results with both SHAP and Microsoft Interpret
- Can now view all metrics for classification problem
- Added ROC Curve and Confusion Matrix

Release Notes
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Bug Fixes

- Fixed data type of PoS tagging and split sentences when stored in Dataframe, they are now correctly lists

General

- Added the ability to filter, and do group by analysis and gain descriptive statistical insights into grouped dataframes.
- Added scatterplot
- Added encoding label functionality
- Added ability to search dataframe
- Added pandas summary data report
- Added word split

Modelling

- Introduced automated modelling for text models, classification models and clustering models
- Added confusion matrix, and evaluation metrics

Pypi Sync issue resolved.

Release Notes
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Bug Fixes
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- Fixed error where apply tfidf on test set would fail

General
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- No longer need to specify data_properties when transferring between steps, can now just pass the step object, example `Feature(Clean)`
- Transformational Feature algorithms now have an option to drop the column being transformed, default is set to keep the field
- Can now specify new column name with text data

Features
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- Added stemming as a technique
- Columns can now be dropped through a regex expression
- Can now drop all columns except ones specified by the user
- Added a filtering function to drill down into the data