Automated Data Science and Machine Learning library to optimize workflow.
Fixed repr memory issue
Aethos 2.0 looks to address the intuitiveness and usability of the package and its API to make it easier to use and understand. It also addresses the ability to work with Pandas Dataframes side by side with Aethos.
Reduced import time of the package by simplifying and decoupling of the Aethos modules.
Only 1 object to analyze, visualize, transform, model and analyze results.
Can now specify the type of problem of either Classification, Regression or Unsupervised and only see the models specific to those problems.
Removed the complexity of adding data to the underlying dataframes through Aethos objects. You can access the underlying dataframes with the x_train
and x_test
properties.
Removed reporting feature.
Introduced new objects to support new cases:
Analysis: To analyze, visualize and run statistical analyis (t-test, anova, etc.) on your data.
Classification: To analyze, visualize, run statistical analysis, transform and impute your data to run classification models.
Regression: To analyze, visualize, run statistical analysis, transform and impute your data to run regression models.
Unsupervised: To analyze, visualize, run statistical analysis, transform and impute your data to run unsupervised models.
ClassificationModelAnalysis: Interpret, analyze and visualize classification model results.
RegressionModelAnalysis: Interpret, analyze and visualize regression model results.
UnsupervisedModelAnalysis: Interpret, analyze and visualize unsupervised model results.
TextModelAnalysis: Interpret, analyze and visualize text model results.
Removed dot notation when accessing DataFrame columns.
Can now chain methods together.
to_df
.remove
function calls to drop
to be more inline with PandasIntroducing interactive filtering and sorting with QGrid. You can now enable the option to use interactive DataFrames when working with your data. See usage on how to enable.
keep
is a list.x
and y
args for almost every plotAdded 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.
data
variable no longer existstrain_data
is now x_train to be more compliant with what you see in books/tutorialstest_data
is now x_test to be more compliant with what you see in books/tutorialsAdded crossvalidation
Added Gridsearch
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)